University of Southern California Dissertations and Theses

Germanium and silicon isotope geochemistry in terrestrial and marine low-temperature environments

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Germanium and silicon isotope geochemistry in terrestrial and marine low-temperature environments

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Content GERMANIUM AND SILICON ISOTOPE GEOCHEMISTRY IN
TERRESTRIAL AND MARINE LOW-TEMPERATURE ENVIRONMENTS
by
Jok¯ ubas Jotautas Baronas
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(EARTH SCIENCES)
August 2017
Copyright 2017 Jok¯ ubas Jotautas Baronas
Nature is always more subtle, more intricate, more elegant than what
we are able to imagine.
-Carl Sagan, The Demon-Haunted World: Science as a Candle in the Dark
I have measured out my life with coffee spoons
-T.S. Eliot, The Love Song of J. Alfred Prufrock
Where’d you get your PhD? Trump University?
-Bernie Sanders to Lamar Smith, Twitter
ii
Acknowledgments
First and foremost, I am extremely grateful to Doug Hammond for teaching
me to think slowly and carefully, for the freedom to pursue my interests, for the
sustained funding while I stumbled around towards becoming a scientist, and for
thelackofpressurethroughoutitall. IwouldliketothankJoshWestforeffectively
becoming my co-advisor on all things weathering related (whether he wanted to
or not!), letting me in on all the cool projects in his group, and the always incisive
feedback on ideas and manuscripts. In the end, the corestone of an enjoyable
gradschool experience is a great advisor-advisee relationship and I was blessed
with not just one but two.
I am very grateful to Olivier Rouxel for inviting me to France, helping to
secure the funding, kindly hosting me at Ifremer, and helping with all the Ge
isotope analyses. This dissertation would have been very different if not for the
huge amount of samples we managed to blaze through in that month. Also, thanks
to Stefan Lalonde and Maxence Guillermic for their hospitality in Brest. Similarly,
I would like to thank Bastian Georg for hosting me at Trent University in Canada
and helping generate a large amount of Si isotope data in a few short weeks (not
all of which, unfortunately, even made it into this dissertation).
Mark Torres – thanks for the endless conversations about geochemistry, the
jam sessions at Bedrock, the reading groups, the camping trips, and many other
iii
great times. Adam Holt and Hannah Liddy – for all the nights out in Echo Park
and Silverlake, the brunches, hikes, raves, and all the other shenanigans. Gen Li
– for being the nicest person I have met and my best and only office mate for six
years. Mia – for surviving two long years of my company. Esther, Danie, Sylvia,
Joyce, Paulina, Willie, Audra, Kirstin, Nick, and many others in the department –
for serving as a wonderful extended USC family. Will, Frank, and Sarah – for the
positive feedback and encouragement. Joyce, Liana, Sydney, and many others –
for their time and effort with the science policy group. Cindy, John, Deb, Miguel,
and the rest of staff – for taking such good care of us all. My housemates Dave,
Dan, Diana, and Boo! – for all the beach trips, croq and guac, and saving me
from multiple expensive parking tickets. California and Los Angeles in particular
– for being such a welcoming island of acceptance and friendliness in this conflicted
world, and an absolutely amazing place to live in.
Galu ˛ gale, Mama ir Augustai – ači¯ u už j¯ usu ˛ besąlygišką meilę ir pasitik˙ ejimą,
kuriuos visad jaučiu net kitoj Atlanto pus˙ ej, ir kantrybę, kol aš baladojuos po
pasauli ˛.
I’m not good at expressing it but I love you all.
iv
Contents
ii
Acknowledgments iii
List of Tables ix
List of Figures xi
Abstract xv
1 Introduction 1
2 Global Ge isotope budget 8
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.1.1 Ge biogeochemistry and Ge/Si . . . . . . . . . . . . . . . . . 9
2.1.2 Ge isotopes . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
2.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.1 Sample collection . . . . . . . . . . . . . . . . . . . . . . . . 13
2.2.2 Elemental concentration analysis . . . . . . . . . . . . . . . 15
2.2.3 Ge isotope analysis . . . . . . . . . . . . . . . . . . . . . . . 16
2.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.1 Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.2 Seawater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
2.3.3 Hydrothermal fluids . . . . . . . . . . . . . . . . . . . . . . 20
2.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.1 Rivers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
2.4.2 Seawater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
2.4.3 Hydrothermal fluids . . . . . . . . . . . . . . . . . . . . . . 34
2.4.4 Global budget . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
v
3 Contrasting Ge and Si isotope dynamics in marine sediments 49
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
3.2 Study sites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.3 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3.1 Sample collection . . . . . . . . . . . . . . . . . . . . . . . . 55
3.3.2 Core incubations . . . . . . . . . . . . . . . . . . . . . . . . 56
3.3.3 Element concentration analyses . . . . . . . . . . . . . . . . 56
3.3.4 Ge isotope analyses . . . . . . . . . . . . . . . . . . . . . . . 57
3.3.5 Si isotope analyses . . . . . . . . . . . . . . . . . . . . . . . 60
3.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61
3.4.1 Seawater . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
3.4.2 Pore waters . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.4.3 Core incubations . . . . . . . . . . . . . . . . . . . . . . . . 65
3.5 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.5.1 Pore waters . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.5.2 Core incubations . . . . . . . . . . . . . . . . . . . . . . . . 71
3.5.3 Ge isotope dynamics during marine sediment authigenesis:
a summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
3.5.4 Effect of authigenesis on Si isotope composition . . . . . . . 79
3.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
4 Ge and Si behavior during tropical weathering: La Selva, Costa
Rica 84
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
4.1.1 Ge/Si in the critical zone . . . . . . . . . . . . . . . . . . . . 88
4.1.2 Si isotopes in the critical zone . . . . . . . . . . . . . . . . . 89
4.1.3 Ge isotopes in the critical zone . . . . . . . . . . . . . . . . 91
4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.2.1 Site description . . . . . . . . . . . . . . . . . . . . . . . . . 92
4.2.2 Sample collection . . . . . . . . . . . . . . . . . . . . . . . . 93
4.2.3 Major and trace element analyses . . . . . . . . . . . . . . . 95
4.2.4 Germanium concentration analysis . . . . . . . . . . . . . . 96
4.2.5 Silicon isotope analysis . . . . . . . . . . . . . . . . . . . . . 96
4.2.6 Germanium isotope analysis . . . . . . . . . . . . . . . . . . 98
4.3 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.3.1 Rocks and soils . . . . . . . . . . . . . . . . . . . . . . . . . 101
4.3.2 Streams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.4.1 Interbasin groundwater . . . . . . . . . . . . . . . . . . . . . 112
4.4.2 Lowland critical zone processes . . . . . . . . . . . . . . . . 116
4.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125
vi
5 Ge/Si and Ge isotope behavior during glacial weathering: field
and experimental data from West Greenland 129
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130
5.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132
5.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . 133
5.3.1 Glacial stream chemistry . . . . . . . . . . . . . . . . . . . . 133
5.3.2 Sediment weathering experiment . . . . . . . . . . . . . . . 139
5.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148
6 Ge and Si isotope geochemistry in global rivers 150
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151
6.2 Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154
6.2.1 Sample collection . . . . . . . . . . . . . . . . . . . . . . . . 154
6.2.2 Element concentration analyses . . . . . . . . . . . . . . . . 156
6.2.3 Si isotope analyses . . . . . . . . . . . . . . . . . . . . . . . 157
6.2.4 Ge isotope analyses . . . . . . . . . . . . . . . . . . . . . . . 161
6.2.5 Inter-laboratory calibration of Ge and Si isotope analyses . . 164
6.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . 166
6.3.1 Ge and Si isotope composition of river-transported solids . . 166
6.3.2 Ge isotope composition of global rivers . . . . . . . . . . . . 172
6.3.3 ComparisonofGe,Si,andtheirisotopecompositioninglobal
rivers: biotic vs. abiotic fractionation . . . . . . . . . . . . . 174
6.3.4 Riverine Ge isotope composition relationship with weather-
ing intensity . . . . . . . . . . . . . . . . . . . . . . . . . . . 179
6.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7 Interpreting seawater Ge isotope variations during the penulti-
mate deglaciation 187
7.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188
7.1.1 Ge/Si as paleoceanographic tracer . . . . . . . . . . . . . . . 189
7.1.2 Ge isotopic composition as paleoceanographic tracer . . . . . 190
7.1.3 Ge isotope and Ge/Si paleorecord in the Southern Ocean . . 192
7.2 Modeling approach . . . . . . . . . . . . . . . . . . . . . . . . . . . 194
7.2.1 Steady state calculations . . . . . . . . . . . . . . . . . . . . 194
7.2.2 Temporal modeling . . . . . . . . . . . . . . . . . . . . . . . 199
7.2.3 Modeling scenarios . . . . . . . . . . . . . . . . . . . . . . . 201
7.3 Results and discussion . . . . . . . . . . . . . . . . . . . . . . . . . 207
7.3.1 Steady state calculations: modern Ge isotope budget . . . . 207
7.3.2 Glacial-interglacial seawater Ge isotope dynamics . . . . . . 208
7.3.3 Implications for the interpretation of other paleorecords . . . 213
7.4 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216
vii
8 Conclusions 218
Reference List 222
A A global Ge isotope budget: Supplementary material 244
A.1 Extended methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 244
A.1.1 Ge concentration analysis . . . . . . . . . . . . . . . . . . . 244
A.1.2 Ge isotopic analysis . . . . . . . . . . . . . . . . . . . . . . . 245
A.2 Supplementary data for river samples . . . . . . . . . . . . . . . . . 252
A.3 Low-temperature hydrothermal fluid fractionation model . . . . . . 255
A.4 Global marine Ge cycle model . . . . . . . . . . . . . . . . . . . . . 259
A.4.1 Model setup . . . . . . . . . . . . . . . . . . . . . . . . . . . 259
A.4.2 Reasonable boundary conditions . . . . . . . . . . . . . . . . 260
B Contrasting Ge and Si isotope dynamics in marine sediments:
Supplementary data 264
C La Selva interbasin groundwater reactive transport model 270
C.1 Model description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 270
C.2 Model application . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274
D La Selva lowlands isotopic mass balance model 278
D.1 Model description . . . . . . . . . . . . . . . . . . . . . . . . . . . . 278
D.2 Model results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 282
E Ge and Si isotope geochemistry in global rivers: Supplementary
data 285
viii
List of Tables
2.1 Summary of measured riverine d
74
Ge and sample details. . . . . . . 23
2.2 Summary of measured seawater d
74
Ge and sample details. . . . . . 24
2.3 Summary of measured hydrothermal fluid d
74
Ge and sample details. 25
2.4 Summary of global marine Si and Ge fluxes, along with their esti-
mated d
74
Ge signatures, assuming steady state. . . . . . . . . . . . 41
3.1 Study site details and seawater chemistry. . . . . . . . . . . . . . . 62
3.2 Ge and Si chemistry of pore waters. . . . . . . . . . . . . . . . . . . 63
3.3 Summary of core incubation data. . . . . . . . . . . . . . . . . . . . 66
4.1 La Selva solids chemistry. . . . . . . . . . . . . . . . . . . . . . . . 103
4.2 Chemical alteration of La Selva solids. . . . . . . . . . . . . . . . . 105
4.3 La Selva fluids chemistry. . . . . . . . . . . . . . . . . . . . . . . . . 110
5.1 Greenland river field data. . . . . . . . . . . . . . . . . . . . . . . . 137
5.2 Greenland river sediment dissolution data. . . . . . . . . . . . . . . 141
5.3 Greenland river sediment dissolution data (continued). . . . . . . . 142
6.1 River water sample details. . . . . . . . . . . . . . . . . . . . . . . . 155
6.2 Ge and Si concentration and isotope analyses of solids compared to
reference values. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
ix
6.3 Comparison of analytical d
74
Ge methods. . . . . . . . . . . . . . . . 165
6.4 Ge and Si concentrations and isotope composition of rocks and river
sediments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 168
6.5 Ge and Si concentrations and isotopic compositions in world’s rivers.173
6.6 Chemical weathering and erosion fluxes of global rivers. . . . . . . . 181
7.1 Global Si, Ge, and d
74
Ge budget (updated from Chapter 2). . . . . 197
7.2 Changes in the global Si and Ge budget during glaciation. . . . . . 204
A.1 Determined mean isotopic composition of standard materials. . . . 246
A.2 Supplementary river chemistry data. . . . . . . . . . . . . . . . . . 253
A.3 Isabella Dam reservoir mass balance (Kern river) . . . . . . . . . . 254
A.4 Summary of measured and calculated hydrothermal fluid parameters 258
A.5 Global Ge isotope mass budget model results. . . . . . . . . . . . . 263
B.1 Si and trace metal concentrations in pore waters. . . . . . . . . . . 265
B.2 Ammonia concentrations in pore waters. . . . . . . . . . . . . . . . 266
B.3 Sulfate concentrations in seawater and pore water. . . . . . . . . . . 267
B.4 Ge and Si concentrations during San Pedro Basin core incubations. 268
B.5 Ge and Si concentrations during Santa Monica Basin core incubations.269
C.1 La Selva interbasin groundwater model input parameters. . . . . . . 277
D.1 La Selva lowlands steady-state model input values. . . . . . . . . . 282
D.2 La Selva lowlands steady-state model results. . . . . . . . . . . . . . 283
E.1 Additional global river data. . . . . . . . . . . . . . . . . . . . . . . 285
E.2 Major ion chemistry for global river samples. . . . . . . . . . . . . . 286
x
List of Figures
2.1 The relationship between dissolved d
74
Ge and Ge concentration in
rivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.2 Variation in seawater d
74
Ge with depth. . . . . . . . . . . . . . . . 21
2.3 Summary of hydrothermal fluid d
74
Ge signatures. . . . . . . . . . . 27
2.4 Riverine d
74
Ge composition as a function of Ge/Na and Ge/S
+
ratios. 28
2.5 Low-temperature hydrothermal fluid d
74
Ge as a function of Ge pre-
cipitation from solution. . . . . . . . . . . . . . . . . . . . . . . . . 38
2.6 A summary of the oceanic Ge sources and sinks and their estimated
d
74
Ge signatures. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
3.1 Pore water profiles of San Pedro Basin (SPOT) sediments. . . . . . 64
3.2 Pore water profiles of Gulf of Mexico (GoMex) sediments. . . . . . . 65
3.3 San Pedro and Santa Monica basins core incubation results. . . . . 68
3.4 The effect of opal and non-opal benthic fluxes ond
74
Ge composition
of the overlying water. . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.5 The summary of dissolved d
74
Ge and Ge/Si in SPOT, SMB, and
GoMex seawater and sediments. . . . . . . . . . . . . . . . . . . . . 77
3.6 The summary of dissolved d
30
Si signatures in San Pedro seawater
and sediments. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80
xi
4.1 Elemental composition of La Selva solids. . . . . . . . . . . . . . . . 102
4.2 Immobile element concentrations in La Selva solids. . . . . . . . . . 104
4.3 Chemical alteration of La Selva bulk soils. . . . . . . . . . . . . . . 106
4.4 Silicon and germanium isotope composition of La Selva solids. . . . 107
4.5 The range of chemical and d
30
Si compositions in La Selva streams. . 109
4.6 Themodeledevolutionoffluidchemistryalongtheinterbasinground-
water (IBGW) flowpath. . . . . . . . . . . . . . . . . . . . . . . . . 117
4.7 Modeledd
30
Siandd
74
Gecompositioninthefluidsandtertiaryclays
of La Selva lowlands. . . . . . . . . . . . . . . . . . . . . . . . . . . 122
4.8 Summary of observed Ge/Si, d
30
Si, and d
74
Ge composition in La
Selva. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126
5.1 Map of West Greenland sample locations. . . . . . . . . . . . . . . . 134
5.2 Dissolved Ge dynamics in Greenland streams. . . . . . . . . . . . . 135
5.3 Evolution of solution chemistry in the Greenland sediment dissolu-
tion experiment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143
5.4 RelationshipbetweenfinalSiconcentrationandwater/sedimentratio
in the dissolution experiment. . . . . . . . . . . . . . . . . . . . . . 144
5.5 Modelingd
74
Gefractionationduringthesedimentweatheringexper-
iment. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
6.1 A map of river d
74
Ge sample locations. . . . . . . . . . . . . . . . . 156
6.2 The relationship between Ge/Si vs. d
30
Si and d
74
Ge of solids in the
Andes-Amazon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
6.3 Effects of sediment sorting ond
74
Ge,d
30
Si, and Ge/Si in the Andes-
Amazon. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171
xii
6.4 The relationship between d
74
Ge vs. 1/Ge, Ge/Si, and lithology in
global rivers. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
6.5 The relationship between dissolved Ge/Si and d
30
Si in global rivers. 178
6.6 Thr relationship between dissolved d
74
Ge and d
30
Si in global rivers. 180
6.7 The relationship between weathering intensity and dissolved d
74
Ge,
d
30
Si, and d
7
Li. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
7.1 Diatom Ge/Si paleorecord (Mortlock et al., 1991). . . . . . . . . . . 190
7.2 Diatom d
74
Ge, Ge/Si, and d
30
Si paleorecord (Mantoura, 2006). . . . 195
7.3 Modeled glacial-interglacial changes in continental inputs. . . . . . . 205
7.4 Distribution of calculated authigenic Ge burial parameters. . . . . . 208
7.5 Modeling d
74
Ge
sw
response to glacial-interglacial changes in conti-
nental inputs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210
7.6 Modeling d
74
Ge
sw
response to glacial-interglacial changes in authi-
genic Ge sink. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
7.7 Modeling d
74
Ge
sw
response to glacial-interglacial changes in both
continental inputs and authigenic Ge sink. . . . . . . . . . . . . . . 214
A.1 Standard reproducibility over time. . . . . . . . . . . . . . . . . . . 246
A.2 The effect of signal intensity on the measured Ge isotope ratios. . . 248
A.3 Typical range of measured signal intensities for a given sample. . . . 250
A.4 An example of a linear regression that was used to obtain the
interference-free isotopic ratios. . . . . . . . . . . . . . . . . . . . . 251
A.5 Historgrams of randomly generated input parameters of Ge isotope
mass balance model. . . . . . . . . . . . . . . . . . . . . . . . . . . 261
A.6 Historgrams of Ge isotope mass balance model results. . . . . . . . 262
C.1 Modeled Si reaction rates along interbasin groundwater flowpath. . 276
xiii
D.1 The steady state Ge and Si isotope mass balance. . . . . . . . . . . 280
D.2 ThedistributionofcalculatedGeandSiisotopefractionationfactors
and Ge/Si ratios for La Selva lowlands. . . . . . . . . . . . . . . . . 284
xiv
Abstract
The conditions on the surface of planet Earth, including climate and its ability
to support life, are controlled by a complex interaction of physical and chemical
processes, taking place over a range of spatial and temporal scales. Due to the
ubiquity of silicon (Si) in the solid Earth, a number of these processes involve the
transformation and translocation of silica-containing compounds (i.e., the global
Si cycle). The rates of the various chemical reactions and mass fluxes within
the Si cycle are often difficult to assess directly, especially in the geological past.
Therefore, geochemical tracers can help evaluate their importance in controlling
the evolution of Earth’s climate, landscape, and life.
In this PhD dissertation, I investigate the use of germanium (Ge) and Si isotope
composition (d
74
Ge and d
30
Si, respectively) in natural fluids and solids to trace
various Earth surface processes, including silicate rock weathering and marine
sediment authigenesis. Ge, like Si, is primarily supplied by the weathering of
silicate rocks on continents, and consumed during biogenic silica production in the
oceans, making it a reliable tracer of the Si cycle. In particular, different chapters
of this dissertation are focused on establishing Ge isotope systematics in various
different Earth surface environments, as there is very little prior data available.
A second objective is to demonstrate how the combined use of d
74
Ge, d
30
Si, and
xv
Ge/Si signatures can yield insights otherwise unobtainable if either proxy was
applied in isolation.
Ge and Si are primarily supplied to the ocean via rivers and hydrothermal
fluids, and removed via burial of biogenic silica and authigenic phases that pre-
cipitate within marine sediments. In Chapter 2 I presented the first d
74
Ge data
from a number of analytically challenging low temperature fluid samples, including
seawater and river water. The dissolved d
74
Ge composition of rivers and seawa-
ter was significantly heavier (2.0-5.6 % and 3.2±0.4 %, respectively) than the
source silicate rocks (0.4-0.8 %), indicating significant Ge isotope fractionation
during low temperature processes. High temperature ridge-axis hydrothermal flu-
ids exhibited d
74
Ge of 0.7-1.6 % and are likely controlled by isotopic equilibration
with hydrothermal quartz. Low temperature ridge-flank hydrothermal fluids had
d
74
Ge of 2.9-4.1 %, consistent with isotopic fractionation during Ge adsorption or
co-precipitation with Fe oxyhydroxides. A steady state isotopic mass budget for
the ocean was used to predict that Ge sequestration during sediment authigenesis
(i.e. the non-opal Ge sink) must involve a D
74
Ge
authigenic–seawater
fractionation
factor of -0.6±1.8 %.
In Chapter 3 I sought to directly observe and quantify the d
74
Ge fractiona-
tion associated with marine sediment authigenesis, using Ge/Si and d
30
Si data to
provide additional constraints. The same set of sedimentary processes appeared
to control dissolved Ge dynamics in all three studied continental margin sites
(San Pedro Basin, Santa Monica Basin, and Gulf of Mexico continental shelf).
First, biogenic Si (bSi) dissolution supplies dissolved Ge to the pore waters with
d
74
Ge =∼ 3 %, identical to seawater composition. Second, reductive dissolution
of Fe oxides (FeOx) in the subsurface sediments results in pore water d
74
Ge as low
as 1.3-1.9 %, coinciding with a Ge/Si maximum of up to 3 mmol/mol. It is unclear
xvi
whethertheFeoxidesareoflithogenicorauthigenicorigin(i.e., suppliedassettling
detrital particles or previously formed in-situ). However, variations in the benthic
dissolved Ge flux and its isotopic composition suggest sensitivity to redox condi-
tions, indicating -1.2 % difference in d
74
Ge between FeOx and bSi. Third, d
30
Si
fractionation indicates that authigenic aluminosilicates precipitate throughout the
sediments. The latter process results in significant dissolved Ge draw down (Ge/Si
ratio decreases to 0.3 mmol/mol) without any significant Ge isotope fractionation
(pore water d
74
Ge∼ 2 %). Overall, authigenically buried Ge is∼1 % lighter
than seawater, close to the value calculated in Chapter 2.
Chapters 4-6 focus on Ge and Si isotope systematics during continental weath-
ering. In Chapter 4, a detailed study of soils, streams, and ground water in
the tropical, supply-limited weathering environment of La Selva, Costa Rica was
used to elucidate the competing controls of secondary mineral precipitation and
vegetation uptake on d
74
Ge, d
30
Si, and Ge/Si signatures. The soils of La Selva
lowlands are strongly weathered and composed almost exclusively of secondary
minerals. The streams, however, reflect a mixture of lowland soil waters and
interbasin groundwater, the latter representing volcanic rock weathering at higher
elevations. Similar degrees of isotopic fractionation were observed during weath-
ering of fresh volcanic rock by groundwater (D
30
Si
clay–fluid
= –1.2± 0.1 % and
D
74
Ge
clay–fluid
= –2.6±0.2 %) and of chemically depleted lowland soils by rain-
water (D
30
Si
clay–fluid
= –2.4±0.6 % and D
74
Ge
clay–fluid
= –3.0±0.5 %). The
observed Ge/Si and Ge and Si isotope signatures are best explained by the precip-
itation of secondary and tertiary clays, with vegetation playing a negligible role.
The magnitude of fractionation observed in the fluids and the solids was shown
to depend on the chemical mobility of each element. Due to the high degree of
Ge retention in secondary products, weathering fluid d
74
Ge signatures were more
xvii
strongly fractionated than d
30
Si. The opposite was true for the d
74
Ge and d
30
Si
composition of the residual soils.
Chapter 5 focuses on the other climatic extreme, investigating Ge/Si and
d
74
Ge behavior during glacial weathering processes in West Greenland. The field
study was coupled with a long-term river water and sediment incubation experi-
ment to provide a direct observation of Ge/Si and d
74
Ge evolution with continued
weathering. The dissolved Ge/Si ratios in periglacial streams, the Watson River,
and its tributaries ranged from 0.9 to 2.2 mmol/mol, higher than most non-glacial
rivers around the world, and likely reflecting preferential dissolution of Ge-rich
biotite during subglacial weathering. Dissolved d
74
Ge of the Watson River was
0.86±0.24 %, only slightly heavier than the river suspended load (0.48±0.23 %),
indicating limited precipitation of secondary weathering products during the short
rock-water contact times associated with glacial weathering. Incubating unfil-
tered river water with its suspended sediment in the laboratory for 1.5-2 years has
resulted in the reduction of dissolved Ge/Si to∼0.5 mmol/mol, indicating signifi-
cant Ge removal from solution with increased rock-water contact time, most likely
duetoadsorptiontoFeoxyhydroxides. Atthesametime,dissolvedd
74
Geincreased
to 1.9-2.2 %. The D
74
Ge
sec–diss
fractionation factor was calculated as -2.1±1.4
%, in good agreement with previously determined values for Ge adsorption onto
Fe oxide particles and similar to the values determined for tropical weathering in
Chapter 4.
Chapter6presentsanoverviewofd
74
Ge,d
30
Si, andGe/Sidatainanumberof
world’s rivers, spanning different climatic and geomorphic regimes. Co-variation of
all three proxy signatures confirmed that fluid composition is primarily determined
by the precipitation of secondary weathering products. Ge/Si and d
30
Si signatures
deviated from this trend only where biological uptake of Si is thought to have a
xviii
major effect on the river chemistry. In contrast, d
74
Ge appeared unaffected by
the limited uptake of Ge by diatoms or vegetation. Despite the broad similarity
betweentheriverined
74
Ge,d
30
Si, andd
7
Libehavior, eachproxydisplayedaunique
global relationship with silicate chemical weathering intensity. These differences
likely stem from variable affinity of each element to different secondary weathering
phases (Fe and Al oxides, various aluminosilicates).
Finally,Chapter 7 presents and updated global Ge isotope budget, taking into
account the revised global riverine d
74
Ge composition from Chapter 6. The knowl-
edge of d
74
Ge dynamics gained from the preceding chapters is used to investigate
the glacial-interglacial changes in Ge and Si cycles, using the paleorecord previ-
ously presented by Mantoura (2006). Since diatom d
74
Ge composition appears
insensitive to vital effects, sedimentary diatom d
74
Ge paleorecords can be used
to track secular changes in the seawater signature that result from the shifting
balance between the different Ge sources and sinks. However, similar d
74
Ge com-
position of various input fluxes and limited isotopic fractionation during authigenic
Ge removal from pore water (∼-1 % relative to seawater), coupled with short Ge
residence time in the ocean, results in only small d
74
Ge
SW
variations over glacial-
interglacial cycles. More work is needed to confirm and explain the small∼0.5 %
positive excursion during glacial termination T2 at 130 ka. In summary, d
74
Ge
paleorecords should be insensitive to local biological overprinting and could help
identify large magnitude perturbations in the global Ge and Si cycles.
xix
Chapter 1
Introduction
The habitability and surface conditions of planet Earth are strongly depen-
dent the chemical composition of the geosphere, hydrosphere, atmosphere, and
biosphere. Investigation of the cycling of chemical elements between these differ-
ent reservoirs is therefore a crucial step towards fully understanding the processes
controlling climate and habitability of Earth and other planets. On continents,
the physical and chemical weathering of rocks supplies essential nutrients to liv-
ing organisms, shapes the landscape, and affects the chemical composition of the
atmosphere. In the ocean, nutrients delivered by rivers and dust are efficiently
utilized by the marine food web and ultimately buried in the sediments. In turn,
continuously accumulating sediments provide a valuable record of Earth surface
conditionsinthepast. Studiesofsuchpaleorecordshaverevealedlargefluctuations
in the atmospheric composition and climate over Earth’s history (e.g., Lyons et al.,
2014; Lea, 2013). Geochemical and isotopic signatures of various elements (also
called tracers or proxies) recorded in sediments can serve as tools to elucidate
the driving forces responsible for these fluctuations. However, geochemical and
isotopic proxies are only useful for paleoreconstruction if their signatures can be
interpreted in the context of earth surface processes they reflect. Such knowledge
can only be obtained by the careful study of the distribution and transformation
of geochemical signatures across a range of different earth surface conditions and
reservoirs in the contemporary environment. The aim of this PhD dissertation is
to develop the germanium (Ge) stable isotope composition of natural materials as
1
a novel tool for tracing Earth’s surface processes. To aid in this effort, Ge isotope
variations in various low temperature settings are combined with already estab-
lished and closely related proxies, such as stable silicon (Si) isotopes and Ge/Si
ratios.
Silicon is the second most abundant crustal element after oxygen, comprising
about 28 % of Earth’s crust by mass. As a result, the biogeochemical cycling of Si
is coupled to various Earth surface processes. For example, chemical weathering
of silicate rocks consumes atmospheric CO
2
, potentially driving long-term changes
to global climate (Walker et al., 1981; Berner et al., 1983), while dissolved Si is
utilized as a nutrient by various marine organisms and land plants (Conley, 2002;
Tréguer & De La Rocha, 2013). Germanium is trace element about a million times
less abundant than Si. Due to similar atomic properties (Bernstein, 1985), both
elements participate in many of the same chemical reactions, which has led to
the development of Ge/Si ratio as a proxy of the Si cycle (Froelich et al., 1985;
Mortlock & Froelich, 1987; Froelich et al., 1992; Hammond et al., 2000, 2004).
In the geosphere, both Ge and Si are primarily contained in silicate rocks (Bur-
ton et al., 1959). During chemical weathering, they are released into soil-, ground-,
and river water where a range of biotic and abiotic processes modulate the concen-
tration and isotopic composition of both elements. For example, the precipitation
of secondary weathering products, such as oxides and aluminosilicate clays, has
been shown to preferentially incorporate Ge over Si (Murnane & Stallard, 1990;
Froelich et al., 1992; Kurtz et al., 2002) and light Si isotopes over heavy ones
(Ziegler et al., 2005a; Opfergelt et al., 2009, 2011), modifying the dissolved compo-
sition. Once delivered to the ocean, biological uptake and marine sedimentary pro-
cesses can further overprint the isotopic signatures of Ge and Si (e.g., De La Rocha
et al., 2011; Ehlert et al., 2016). The oceanic budget and isotopic composition of
2
both elements is also affected by the discharge of hydrothermal fluids (Mortlock
et al., 1993). Thorough reviews of the Si cycle (Tréguer & De La Rocha, 2013),
global Si isotope dynamics (Hendry & Brzezinski, 2014; Frings et al., 2016) and
previous work on stable Ge isotopes (Rouxel & Luais, 2017) have been recently
published and will not be recounted in detail here. Additional reviews of the
relevant literature are given in the introductory sections of each chapter below.
The development of a geochemical proxy usually starts with a broad survey of
signatures in different earth surface reservoirs: various rocks, fluids, and sediments.
In the case of Ge isotope composition (reported hereafter as the
74
Ge/
70
Ge ratio,
expressedasd
74
Gein%),previousresearchershavediscoveredonlyanarrowrange
of values in silicate rocks (Escoube et al., 2012; Rouxel & Luais, 2017). Heavier
and more variable values were reported for hydrothermal fluids, which were the
result of the precipitation of isotopically light hydrothermal deposits (Escoube et
al., 2015).
Measuring Ge isotope composition of fluids in the low temperature environ-
ment, namely river water and seawater, had proved analytically challenging. Due
to its relatively low concentration in silicate rocks (about 1-5 ppm) and its low
chemical mobility, Ge concentration in rivers is in the pmol/L range (e.g., Froelich
et al., 1985, 1992). In the ocean, biosilicifying organisms (primarily diatoms)
deplete Ge concentrations in the surface seawater as they unintentionally take up
Ge along with Si due to the chemical similarity between the two elements (Froelich
et al., 1985; Sutton et al., 2010). As a result, precise Ge concentration analyses
require hydride generation techniques that offer a much higher sample transfer
efficiency into the mass spectrometer (Mortlock & Froelich, 1996). Ge isotope
analyses are even more challenging because counting statistics required for reliable
3
measurements of isotopic ratios necessitate up to about 100 times more Ge com-
pared to concentration measurements (i.e., 1-10 ng vs. 10-100 pg). Nevertheless,
analyses of marine diatoms recovered from top sediments, coupled with culture
experiments, have shown that both biogenic silica and seawater should be isotopi-
cally heavy (d
74
Ge of about 3 %) relative to silicate rocks and hydrothermal fluids
Mantoura (2006).
InChapter2, Ireportthefirstd
74
Gemeasurementsofseawaterandriverwater,
along with low- and high-temperature hydrothermal fluids. Although based on a
relativelysmallnumberofsamples, thisstudyprovidesthebasicconstraintsneeded
to establish a global Ge isotope budget, which is necessary for the interpretation
of any d
74
Ge paleorecords that may be published in the future. Having d
74
Ge
estimates of all the major oceanic Ge inputs has allowed me to make a prediction
of the isotopic composition of the outputs that is required to balance the global
budget. Chapter 3 focuses directly on one of the two major marine Ge sinks –
precipitation of authigenic minerals from sediment pore waters. This process is
important to investigate because both empirical Ge flux observations (Baronas
et al., 2016) and the d
74
Ge steady state mass budget (Chapter 2) suggest that
more Ge is buried in authigenic phases than as biogenic silica. An independent
constraint on any isotopic fractionation during sediment authigenesis is therefore
crucial to testing the steady state mass budget assumption made in Chapter 2.
One of the outstanding questions in geological sciences is the mechanistic rela-
tionship between Earth’s climate, tectonics, and rock weathering. As carbonic acid
derived from atmospheric CO
2
dissolves silicate rocks, Ca
2+
, Mg
2+
, and HCO
–
3
ions
are released into solution, and eventually re-precipitated as carbonate minerals and
buried in marine sediments (Walker et al., 1981). Because volcanism continuously
releases CO
2
to the atmosphere, silicate weathering is thought to prevent the
4
continuous accumulation of atmospheric CO
2
and maintain a habitable climate
throughout most of the planet’s history (Berner et al., 1983). On the other hand,
as atmospheric CO
2
drops, chemical weathering must slow down as well, if an
ice-house planet is to be avoided. While this climate stabilization mechanism is
well accepted within the community, it lacks direct geological evidence and the
details of the relationship between climate, tectonics, and silicate weathering are
still debated (Raymo & Ruddiman, 1992; West et al., 2005; Maher & Chamberlain,
2014; Caves et al., 2016).
A geological test of the relationship between climate and weathering in the
past requires the ability to reconstruct silicate weathering fluxes, or at least the
intensity of chemical weathering. Multiple geochemical (isotopic) proxies have
been investigated for this purpose (Froelich et al., 1992; Oxburgh, 1998; Edmond,
1992; Georg et al., 2007; Misra & Froelich, 2012). However, the interpretation of
these proxy signatures is often hampered by uncertainties in their source rocks
(Goddéris & François, 1995; Derry & France-Lanord, 1996; Georg et al., 2013),
global isotopic budgets (Li & West, 2014; Frings et al., 2016), or diagenetic effects
(Hammond et al., 2000). A robust reconstruction of past silicate weathering will
therefore require the study of multiple proxy systems that, despite their individual
flaws, can support a single mechanistic hypothesis relating weathering, tectonics,
and climate. With this in mind, Chapters 4-6 investigate the coupled d
74
Ge, d
30
Si,
and Ge/Si dynamics during continental silicate weathering.
Chapter 2 showed that river composition exhibits d
74
Ge values significantly
heavier than the silicate rocks that supply the dissolved Ge. For the unequivocal
interpretation of riverine and paleorecord signatures, however, it is important to
understand the details of the reactions during which d
74
Ge signatures are frac-
tionated. During weathering, soil water and groundwater initially start out as
5
dilute rain water, over time acquiring solutes and evolving over a range of ther-
modynamic states with regards to various primary and secondary minerals (e.g.,
Brantley, 2008). A large range of secondary minerals precipitate during different
phases of this process. It can therefore be difficult to track at which stage isotopic
fractionation of solutes occurs. Chapters 4 and 5 present studies of fluid evolution
in contrasting (i.e. supply- and kinetic-limited) weathering regimes and associated
d
74
Ge, d
30
Si, and Ge/Si fractionation. The simultaneous investigation of all three
proxy signatures offers an opportunity to assess the interplay of thermodynamic,
kinetic, and mass balance effects on the evolution of Ge and Si isotope signatures
during progressively more intense weathering.
On a global scale, rock weathering rates and the chemical composition of river
waters are strongly dependent on the tectonic regime and the supply of fresh
rock to the weathering environment (Riebe et al., 2004; West et al., 2005). The
weathering rates are highest in steep, uplifting, and rapidly eroding areas, where
chemical weathering intensity, i.e. the efficiency of primary mineral dissolution, is
low (Bouchez et al., 2014). A useful weathering proxy must therefore be sensitive
to the different weathering intensity regimes represented by global rivers. Previ-
ous studies have indicated a somewhat complex relationship between weathering
intensity and the Si and Li isotopic composition of global rivers (Dellinger et al.,
2015; Frings et al., 2016). The relationship between riverine d
74
Ge and weathering
intensity is similarly assessed in Chapter 6 and compared the above mentioned
proxies. In addition, the effects of mineral sorting and vegetation uptake on the
d
74
Ge, d
30
Si, and Ge/Si signatures are investigated, highlighting the individual
sensitivity of each tracer to these processes.
Having gained a basic understanding of Ge isotope behavior during continental
weathering and marine sediment diagenesis, the last chapter of this dissertation
6
explores the potential of coupled d
74
Ge-Ge/Si paleorecords to trace past changes
in these processes, with a focus on the penultimate deglaciation, for which a sedi-
mentary diatom d
74
Ge record is available.
7
Chapter 2
Global Ge isotope budget
Author contributions
The study design was conceived together with Doug Hammond. Ge isotope
analyses were carried out by myself and Doug Hammond at Oregon State Uni-
versity. I carried out all other analyses at USC. This manuscript has been peer-
reviewed and published as: Baronas, J.J., Hammond, D.E., McManus, J., Wheat,
C.G., and Siebert, C., 2017, A global Ge isotope budget: Geochimica et Cos-
mochimica Acta, v. 203, p. 265–283, doi: 10.1016/j.gca.2017.01.008.
Abstract
We present measurements of Ge isotope composition and ancillary data for
samples of river water, low- and high-temperature hydrothermal fluids, and seawa-
ter. The dissolved d
74
Ge composition of analyzed rivers ranges from 2.0 to 5.6 %,
which is significantly heavier than previously determined values for silicate rocks
(d
74
Ge = 0.4 - 0.7 %, Escoube et al., GGR, 36(2), 2012) from which dissolved
Ge is primarily derived. An observed negative correlation between riverine Ge/Si
and d
74
Ge signatures suggests that the primary d
74
Ge fractionation mechanism
during rock weathering is the preferential incorporation of light isotopes into sec-
ondary weathering products. High temperature (>150
◦
C) hydrothermal fluids
analyzed in this study have d
74
Ge of 0.7 - 1.6 %, most likely fractionated during
8
fluid equilibration with quartz in the reaction zone. Low temperature (25 - 63
◦
C)
hydrothermal fluids are heavier (d
74
Ge between 2.9 and 4.1 %) and most likely
fractionated during Ge precipitation with hydrothermal clays. Seawater from the
open ocean has a d
74
Ge
sw
value of 3.2± 0.4 %, and is indistinguishable among
the different ocean basins at the current level of precision. This value should
be regulated over time by the isotopic balance of Ge sources and sinks, and a
new compilation of these fluxes is presented, along with their estimated isotopic
compositions. Assuming steady-state, non-opal Ge sequestration during sediment
authigenesis likely involves isotopic fractionation D
74
Ge
solid–solution
that is -0.6±
1.8 %.
2.1 Introduction
2.1.1 Ge biogeochemistry and Ge/Si
Germanium (Ge) is a trace element with electronic structure and chemical
behavior similar to silicon (Si), allowing Ge to substitute for Si in mineral and
amorphous silicates at parts-per-million (ppm) levels (Burton et al., 1959). Dif-
ferences in the chemical behavior of Ge and Si are primarily caused by the larger
atomic radius of Ge, which allows it to form longer bonds with oxygen and sulfur
(Bernstein, 1985). As a result, Ge preferentially partitions into poorly-interlinked
phyllosilicates rather than highly-interlinked tectosilicates, and is also able to enter
octahedralcoordinationsitesinFeoxidesandsulfides, enablingtheuseoftheGeto
Si ratio (Ge/Si) as a geochemical tracer. For example, during silicate rock weather-
ing the formation of secondary products, such as metal oxides and aluminosilicate
clays, incorporates Ge either structurally or through adsorption (Pokrovsky et al.,
2006) with a higher Ge/Si (commonly 4-6 mmol/mol but sometimes in excess of
9
100 mmol/mol)(Kurtz et al., 2002; Derry et al., 2006; Scribner et al., 2006; Kurtz
et al., 2011) relative to the initial bedrock (typically 1-3 mmol/mol)(Mortlock &
Froelich, 1987; Murnane & Stallard, 1990; Kurtz et al., 2002). As a result, the dis-
solved species (germanic and silicic acid, or Ge(OH)
4
and Si(OH)
4
) in river waters
occur at a lower ratio, with Ge/Si
riv
ranging from 0.1 to 3 mmol/mol (Murnane
& Stallard, 1990; Froelich et al., 1992; Chillrud et al., 1994; Filippelli et al., 2000;
Anders et al., 2003; Meek et al., 2016). In hydrothermal fluids, the Ge/Si ratio is
largely controlled by the difference in thermodynamic properties of aqueous and
solid Ge and Si compounds between which the equilibrium is established at high
temperatures (Pokrovski & Schott, 1998; Evans & Derry, 2002). Due to generally
higher solubility of Ge minerals (e.g., GeO
2
vs. SiO
2
), as well as the reluctance
of Ge to enter the highly-interlinked lattice of quartz precipitated during cool-
ing, hydrothermal fluids tend have much higher Ge/Si, commonly 4-25 mmol/mol
but reaching up to 1000 mmol/mol in some terrestrial hot springs (Arnorsson,
1984; Mortlock et al., 1993; Evans & Derry, 2002; Wheat & McManus, 2005, 2008;
Escoube et al., 2015).
In the ocean, Ge is taken up and incorporated into biogenic silica (bSi) by
diatoms, sponges, and other biosilicifiers, leading to a tight coupling between Ge
and Si concentrations, nutrient-like Ge profiles, and a relatively constant deep
water Ge/Si of 0.76 mmol/mol (Froelich et al., 1985, 1989; Ellwood et al., 2006;
Sutton et al., 2010). Ge removal from the ocean occurs through bSi burial and
through Ge sequestration from marine porewaters into certain authigenic miner-
als (termed "non-opal burial"). This latter process has been observed to occur in
rapidly accumulating shallow-redox marine sediments, most commonly in conti-
nental margin areas and suboxic basins (Hammond et al., 2000; King et al., 2000;
McManus et al., 2003; Baronas et al., 2016). Although the exact Ge-incorporating
10
phases have not been identified, similarly to continental weathering, both iron
oxyhydroxides and aluminosilicates have been implicated.
Germanium residence time in seawater is similar to that of Si, that is, <10 ky
(Hammond et al., 2004). Diatom Ge/Si paleorecords indicate large shifts in the
oceanic Ge/Si signature over glacial-interglacial cycles (Froelich et al., 1989; Mort-
lock et al., 1991) and throughout the Cenozoic (Shemesh et al., 1989). These shifts
could reflect variations in the non-opal Ge burial flux, driven by changes in ocean
water temperature (Hammond et al., 2004) and detrital inputs or sediment redox
conditions (Baronas et al., 2016), which could shed light on changes in biogenic
silica cycling and the role that diatom productivity plays in glacial-interglacial cli-
mate variability. Another possibility is that the oceanic Ge/Si is primarily reflect-
ing the riverine Ge/Si signature and flux, which would imply large changes in the
silicate weathering flux over glacial-interglacial timescales (Froelich et al., 1992),
suggesting a high sensitivity of silicate weathering to climate (Vance et al., 2009),
in contrast to many current models (Foster & Vance, 2006; Opfergelt et al., 2013a;
von Blanckenburg et al., 2015; Frings et al., 2016). In addition, better constraints
on how weathering processes influence the elemental and isotopic composition of
Ge and other trace elements would allow for a more robust interpretation of paleo-
records spanning million-year and longer timescales, providing further clues on the
relationship between climate and the intensity of silicate weathering (e.g., Raymo
& Ruddiman, 1992; Misra & Froelich, 2012; Li & Elderfield, 2013). Variations in
the Ge isotope composition of seawater may help resolve these questions, if the
various Ge sources and sinks have distinct isotopic signatures.
11
2.1.2 Ge isotopes
Germanium has five naturally occurring isotopes: the stable isotopes are
70
Ge
(20.4% abundance),
72
Ge (27.3%),
73
Ge (7.8%), and
74
Ge (36.7%), while
76
Ge
(7.8%) is slightly radioactive (half life of 1.55×10
21
years)(Klapdor-Kleingrothaus
et al., 2001; de Laeter et al., 2003). Ge isotopic composition is discussed and
reported here as d
74
Ge, where
d
74
Ge =
(
74
Ge/
70
Ge)
sample
(
74
Ge/
70
Ge)
NIST3120a
–1 (2.1)
expressed in %, consistent with the recent literature (Escoube et al., 2012;
Pokrovsky et al., 2014; Belissont et al., 2014; Escoube et al., 2015). Previous
work has shown that different types of silicate rocks have similar d
74
Ge compo-
sition of 0.40-0.68 %(Rouxel et al., 2006; Escoube et al., 2012). Theoretical and
experimental studies have demonstrated that in almost all cases the light Ge iso-
topes are preferentially incorporated into mineral phases (Li et al., 2009; Li & Liu,
2010; Pokrovsky et al., 2014). Furthermore, hydrothermal fluids from the East
Pacific Rise and the Loihi seamount (0.61 to 2.20 %)(Escoube et al., 2015) and
terrrestrial hotsprings in the Cascade mountain range (1.65 to 3.29 %, recalcu-
lated here relative to NIST 3120a)(Siebert et al., 2006, 2011) were also shown to
be isotopically heavy relative to the oceanic crust, indicating the incorporation of
light Ge in hydrothermal Fe oxide and sulfide minerals, which have been observed
to have d
74
Ge as low as -4.71± 0.18 % (Escoube et al., 2015). In contrast, var-
ious natural phases formed from seawater are all significantly heavier, e.g. iron
formations (1.09± 0.09 %), authigenic glauconite (2.44± 0.15 %), siliceous
sponge spicules (1.56-2.60 %) (all reported by Rouxel et al., 2006; Escoube et al.,
2012), and diatom frustules (3.10-3.63 %) (Mantoura, 2006), suggesting either an
12
additional isotopically heavy Ge source, or strong fractionation during Ge removal
from the ocean. Therefore, the isotopic characterization of oceanic Ge fluxes other
than the high-temperature hydrothermal input is needed if the global Ge isotope
budget is to be constrained and seawaterd
74
Ge composition established as a useful
proxy.
With this in mind, we have developed methods to analyze the isotope com-
position of Ge in water samples with very low Ge concentrations (as low as∼20
pM). We measured the dissolved Ge isotopic composition of several rivers, sea-
water from different ocean basins, and hydrothermal fluids from both high- and
low-temperature systems, focusing on the previously uncharacterized sediment-
covered Juan de Fuca ridge. These data, along with previously published data,
has been used to construct a preliminary Ge isotope budget for the global ocean
and predict the isotopic signature associated with non-opal Ge burial.
2.2 Methods
2.2.1 Sample collection
For river water analysis, 2-4L samples were collected between 2008 and 2013
during field expeditions in California, Hawaii, and Peru (Table 2.1). Peruvian
river samples were collected in polyethylene bags, filtered on-site through 0.2 mm
polyethersulfone (PES) membrane and acidified, spiked, and co-precipitated in the
field (see section 2.2.3). The other samples were filtered either on-site or back in
the laboratory within 48h of collection through 0.2 or 0.4 mm PES or polyethylene
(PE) membrane.
13
For seawater analysis, the Atlantic Ocean samples were collected at the
Bermuda Atlantic Ocean Time Series station (BATS) during the June 2008 GEO-
TRACES intercalibration cruise aboard the R/V Knorr. 250L of the GDI sample
was collected into multiple Niskin bottles on a trace metal-clean rosette. The GPrI
samplewascollectedintoa12LGO-Flobottle. Sampleswerefilteredinacleanvan
using0.2mmPallAcropakcapsuleandacidifiedimmediatelywithtracemetal-clean
HCl. Additional details and data are given in Boyle et al. (2012). The Cascadia
basin samples were collected into 10L Niskin bottles in August 2006 during a cruise
aboard R/V Thomas G. Thompson, filtered using 0.45 mm Whatman-Nuclepore
Track-Etch membrane and acidified back in the lab. Additional details and data
are given in Esther et al. (2010).
The Gulf of Mexico samples were collected aboard R/V Endeavor in August
2011. The Ge and Si cycling in the water column and the sediments have been pre-
viously described in Baronas et al. (2016). Briefly, seawater samples were collected
into 10L Niskin bottles and filtered on-deck through AcroPak 200 Capsule filters
with a 0.8/0.2 mm Supor membrane. Sediment cores were collected using a multi-
corer, and pore waters were sampled using 0.15 mm pore size Rhizons (Rhizosphere
Research Products, Wageningen, The Netherlands) inside a cold van. Whole cores
were incubated as previously described by Hammond et al. (2004). Briefly, sed-
iment cores with∼1 L of overlying water were sealed off from the atmosphere,
and the overlying water was stirred using a magnetic stir bar. After 5 days, the
overlying water was collected and immediately filtered through a 0.4mm PES filter.
All river and seawater samples were stored at 3-4
◦
C shortly after collection.
Some samples were acidified immediately after filtration with trace metal-clean
HCl or HNO
3
and others only prior to adding double spike (see below). Tests
showed that acidification was unnecessary to keep Ge and Si in solution, based on
14
the stable Ge and Si concentrations of unacidified samples stored refrigerated in
the dark for several years.
The hydrothermal fluid samples were collected and processed during four sepa-
rate expeditions. All samples were collected with Walden-Weiss titanium samplers
deployed from a submersible. Juan de Fuca Ridge (JdFR) flank samples were
collected from sites of natural focused venting at the Baby Bare outcrop (Wheat
& Mottl, 2000), and boreholes drilled through sediment into the basalt basement
rock tens of kilometers away during ODP Leg 168 (Elderfield et al., 1999; Wheat
et al., 2004). JdFR axial fluids (Middle Valley) were collected from vent chimneys
exhibiting focused flow (Butterfield et al., 1994), and Loihi fluids were collected
in 1996 from buoyant jets in the Pele’s Pit (Wheat et al., 2000). All samples
were filtered though a 0.4 mm membrane immediately upon recovery to remove
any entrained rust in the case of borehole samples, sediment and microbial mats
in the case of low temperature vents, and vent chimney flakes in the case of high-
temperature fluids. Since all the fluids in this study were cooler than 200
◦
C and
did not contain high concentrations of dissolved sulfide, Ge is unlikely to be scav-
enged during cooling of the fluids prior to filtration. All samples were acidified
with trace metal-clean HCl prior to storage and analysis and the high-temperature
(Loihi and Middle Valley) fluids were additionally diluted 1:20 with 10% HCl to
prevent amorphous Si precipitation.
2.2.2 Elemental concentration analysis
Si concentrations were measured using molybdate blue colorimetry (Mullin &
Riley, 1955) in seawater and river water samples and with ICP-AES in hydrother-
mal fluids, with a typical 2 S.D. uncertainty of <5%. Ge concentrations were mea-
sured using isotope dilution hydride generation inductively coupled plasma mass
15
spectrometry (ID-HG-IPC-MS) on a Thermo Element 2, as described in Mortlock
& Froelich (1996) and modified by Hammond et al. (2000) and Baronas et al.
(2016), with a typical 2 S.D. uncertainty of <5%. Accuracy and precision were
assessed by measurements of NIST 3120a (n = 26), as well as internal seawater
(n = 36) and river water (n = 17) standards analyzed alongside the samples. The
procedural blank was variable but typically around 1-2 pg (15-30 fmol) Ge.
River water major cation concentration measurements were done on a
Microwave Plasma Atomic Emission Spectrometer (MP-AES; Agilent, USA).
Accuracy and precision (2 S.D. better than 10% for all analytes, n = 4) were
assessedbymeasuringION-915certifiedreferencematerial(EnvironmentCanada).
2.2.3 Ge isotope analysis
Ge isotope composition was analyzed using a previously described double-spike
technique (Siebert et al., 2006, 2011). Briefly, a double isotope spike (
73
Ge/
70
Ge
at a ratio of 1) was added to each sample to obtain a spike/sample ratio between
1 and 3 (total Ge mass). Volumes of sample used ranged from 0.5 to 5 L and
containedbetween3and40ng(40-550pmol)ofGe. Thedoublespikeusedherewas
calibrated previously as described in Siebert et al. (2006, 2011). All samples were
allowed to equilibrate with the spike for 24 hours or longer. At this stage samples
werealsoacidifiedtopH2(wherethiswasnotdonepreviously)andstirredtodegas
CO
2
while equilibrating with the spike. Due to low natural Ge concentrations in
low-temperaturefluids,pre-concentrationwasrequiredpriortoGeisotopeanalysis.
In the case of freshwater samples, a purified MgCl
2
or Mg(NO
3
)
2
solution was
added to produce a final Mg concentration of 20-120 mM. Ge in seawater and
freshwater samples was then co-precipitated with Mg(OH)
2
by raising the pH to 9-
10 with a stream of NH
3
vapor or by adding trace-grade NaOH when precipitating
16
in the field. The precipitate was collected by decanting and centrifuging, and
redissolved using teflon-distilled HNO
3
.
The hydrothermal samples were analyzed without co-precipitation, using the
method described in Siebert et al. (2006). Briefly, the hydride generation was done
online using a continuous hydride generator, where NaBH
4
and sample are mixed
at a constant rate and the resulting hydride is carried by a Helium gas stream into
the instrument. Each sample was bracketed with a liquid in-house standard. The
isotope composition of this standard and the samples was related to NIST 3120a
through measurements of the BCR rock reference material, determined to be 1.20
± 0.18 % (2 S.D., n = 9) relative to the in-house OSU standard. The isotope
composition of all bracketing standards used is given in Appendix A Table A.1
and Fig. A.1.
MethylatedGespeciesareunreactiveanddonotparticipateintheinorganicGe
cycling to any appreciable degree but are present in relatively high concentrations
in seawater (Lewis et al., 1989). Therefore, it is necessary to ensure the removal
of methylated Ge prior to isotope Ge analysis of seawater. To achieve this, the
hydride generation of river and seawater samples was done offline by using a He
stream to capture the evolved GeH
4
in a liquid N
2
cold trap, based on the method
of Mortlock & Froelich (1996). When the cold trap was allowed to warm to room
temperature, inorganic and methylated Ge hydrides present in seawater eluted
separately, allowing the collection of the inorganic fraction in a Tedlar bag. The
separation was ensured by monitoring the elution of the methylated Ge hydrides
using ICP-MS. The collected inorganic GeH
4
was kept in Tedlar bags for several
days until isotope analysis. Tests showed that GeH
4
diffusion from Tedlar bags
was negligible over the course of several weeks. Prior to isotope analysis, the GeH
4
was diluted with He and injected into the instrument by applying steady pressure
17
to the bag. Additional details on sample preparation and hydride generation are
given in Appendix A.
River water and seawater analyses were interspersed with similarly prepared
NIST 3120a standard, as well as two other Ge isotope standards to monitor accu-
racy and instrument drift (Table A.1, Fig.A.1). In the case of seawater and river
samples, a small (0.4 mV) interference at m/z 72 was observed, most likely from
40
Ar
16
O
+
2
or
36
Ar
+
2
. A similar effect but for m/z 70 was observed previously by
Rouxel et al. (2006). To correct for this interference, each sample was measured
at a range of signal intensities by adjusting the gas flow out of the Tedlar bag
(Appendix A Fig A.3). The
72
Ge signal was plotted against the other isotope
signals (Appendix A Fig. A.4) and a linear regression was used to calculate the
interference-free 72/70, 72/73, and 72/74 ratios for the analyte (Appendix A Fig.
A.2). The isotope composition of each sample and standard was then calculated
using the double-spike data reduction scheme outlined previously by Siebert et al.
(2006, 2011).
AllmeasurementsweremadeonaNuInstrumentsHRMC-ICP-MS(Wrexham,
North Wales, UK) in the W.M. Keck Collaboratory at Oregon State University.
The voltage was measured in static mode and at low resolution in cups L2, C,
H2, and H4 for
70
Ge,
72
Ge,
73
Ge, and
74
Ge, respectively. The hydrothermal fluid
samples were analyzed in July 2006 and the seawater and river samples over three
analytical sessions in 2013-2014. The uncertainty of all samples is reported either
as 2 S.D. reproducibility of the bracketing standard during the given analytical ses-
sion, or 2 S.D. of 2-3 sample replicate measurements (some hydrothermal samples
only), whichever is greater.
18
2.3 Results
2.3.1 Rivers
The d
74
Ge composition of rivers and streams sampled for this study (n = 16)
ranged between 2.0 and 5.6 % (Table 2.1), significantly heavier than the 0.4 - 0.7
of silicate rocks % (Rouxel et al., 2006; Escoube et al., 2012), indicating that river
water d
74
Ge is strongly fractionated relative to the bedrock source. The overlap in
d
74
Ge measured in rivers draining basalts, meta-sediments, and granites suggests
that watershed lithology is not the primary factor governing the isotopic compo-
sition of dissolved Ge in rivers (Fig. 2.1). Samples taken from a single watershed
along a steep morphological gradient from the Peruvian Andes to the Amazon
foreland floodplain (see Ponton et al. (2014) for a detailed description of the study
site), span most of the observed riverine d
74
Ge range (Table 2.1). The d
74
Ge
composition of river water correlates well with dissolved Ge concentration and
Ge/Si ratio, especially when samples affected by human activities are excluded.
The Lower Kern river sample exhibiting very high Ge/Si ratio was collected down-
stream of a dam reservoir (fed by both North and South forks of the Kern river),
suggesting that Ge/Si is most likely affected by diatom growth in the reservoir.
2.3.2 Seawater
We have measured the isotopic composition of multiple seawater samples from
various locations (total measurements n = 15, unique samples n = 9). In addition,
pore water (n = 1), and water from core incubations containing a mix of Ge from
bottom water and Ge diffusing from sediments (n=3) were analyzed. The isotope
data, along with Ge/Si ratios and sample details are summarized in Table 2.2 and
Fig. 2.2. Measured seawater d
74
Ge values range from 2.18± 0.50 to 3.48± 0.35
19
Figure 2.1: A: Riverine Ge isotope composition as a function of Ge concentration
reciprocal. Symbols reflect the dominant lithology of each watershed. Grey sym-
bols are samples affected by anthropogenic activities (Table 2.1) and are excluded
from the linear regression in both panels. B: Riverine Ge isotope composition as
a function of Ge/Si. Dashed box shows the range of values observed in mafic, fel-
sic, and sedimentary silicate rocks (Mortlock & Froelich, 1987; Kurtz et al., 2002;
Rouxel et al., 2006; Escoube et al., 2012).
%. The lightest values were found in the bottom waters in the northern Gulf
of Mexico (GoMex) shelf, both in-situ and after core incubations. These values
are similar to the Mississippi river signature (Table 2.1, Fig. 2.2), which is a
major source of dissolved Ge and Si to the area. Atlantic and Pacific ocean deep
water samples were in the narrow 3.07-3.48 % range, indistinguishable within
uncertainty. The simple average± 2 S.D. of all measured deep water values is
3.24±0.44 %, which we propose as a preliminary average seawater composition.
Finally, pore water from Gulf of Mexico continental slope sediments (90 m water
depth) was indistinguishable from seawater.
2.3.3 Hydrothermal fluids
The hydrothermal fluids are enriched in Ge relative to ambient seawater, reach-
ing concentrations of 10-500 nM, which are 100-5000 times higher than seawater.
20
Figure 2.2: Seawater d
74
Ge measured in different locations plotted against sam-
pling depth. The dashed black line and the light blue area indicate 3.24± 0.44
%, the mean value of all deep ocean samples (Table 2.2).
Ourmeasuredhydrothermalfluidd
74
Gevaluesvaryfrom0.69±0.29to4.09±1.27
% (Table 2.3), expanding the range of previously determined values (Siebert et
al., 2006, 2011; Escoube et al., 2015). Loihi seamount fluids reported in this study
were collected only 3 months after a large eruption in 1996 that destroyed multiple
previous vents and created what is now known as the Pele’s Pit. These samples
represent hot fluids (160 - 200
◦
C) that are extremely concentrated in Ge (200-500
nM) and isotopically relatively close to basalt (d
74
Ge = 0.69 - 1.42 %). These
values are at the lower range of those reported for Loihi fluids collected in 2006-
2007 (Escoube et al., 2015) but exhibit generally higher Ge/Si ratios (Fig. 2.3).
21
Juan de Fuca ridge (JdFR) fluids sampled from a borehole at the Middle Valley
site (276
◦
C) exhibit similar Ge concentration (250 - 260 nM) and isotopic compo-
sition (d
74
Ge = 1.5 %) to fluids venting in the East Pacific Rise (EPR) (Escoube
et al., 2015), despite exhibiting higher Ge/Si ratios than previously sampled high-
temperature mid-ocean ridge fluids at EPR and JdFR (Mortlock & Froelich, 1986;
Mortlock et al., 1993; Escoube et al., 2015).
22
Table 2.1: Summary of measured riverine d
74
Ge and sample details. Asterisk
indicates possible anthropogenic influence.
Sample Lat.,
◦
Long.,
◦
Lithology
a
[Ge], pM [Si], mM
Ge/Si,
mmol/mol
d
74
Ge %
b
California
Kern River (North fork) 35.8 -118.4 gr 614 327 1.88 2.75
Kern River (South fork) 35.8 -118.2 mixed
c
97 361 0.27 5.37
Kern River (Lower)*
d
35.6 -118.5 mixed
e
299 36 8.28 3.04
San Gabriel River (North fork) 34.3 -117.7 gr 68 256 0.26 5.61
San Gabriel River (West fork) 34.3 -117.7 gr 81 331 0.24 4.67
San Gabriel River (Lower)*
f
33.8 -118.1 gr 422 230 1.84 3.91
Hondo River 34.1 -118.0 gr 172 290 0.59 5.50
Andes-Amazon (Peru)
Kosnipata Stream (MMD-02)
g
-13.06 -71.54 sh 90 236 0.38 4.96
Carbon Stream (MMD-06)
g
-12.89 -71.36 sh + gr 258 188 1.37 3.30
Madre de Dios River (MMD-28)
h
-12.58 -70.10 sh + gr 107 128 0.84 3.24
Inambari River (MMD-29)
h
-12.72 -69.75 sh + gr 211 160 1.32 3.17
Madre de Dios River (MMD-32)
h
-12.56 -69.18 sh + gr 147 163 0.90 3.72
Piedras River (MMD-34)
i
-12.52 -69.25 mixed
j
113 393 0.29 4.72
Hawai’i
Molokai Spring 21.2 -157.0 bas 809 823 0.98 2.33
Iao Valley River 20.9 -156.6 bas 120 354 0.34 3.63
Other
Mississippi River*
k
29.95 -90.06 mixed
l
266 166 1.60 2.01
a
sh = shale; gr = granitic; bas = basaltic.
b
n = 1 for all samples, external 2 S.D. error± 0.22 %.
c
Shale, siltstone and sandstone with some granitic outcrops (Bartow & Pittman, 1983).
d
Sampled downstream of a reservoir, Ge/Si very likely affected by diatom growth.
e
Downstream of North and South fork confluence.
f
Affected by urban runoff.
g
Small Andean catchment.
h
Large catchment integrating a large range of elevations from Andes to Amazon foreland flood-
plain.
i
Amazon foreland floodplain river, headwaters draining actively uplifted area.
j
Primarily Quaternary with some Miocene deposits.
k
Affected by urban runoff and coal power plant waste effluents (Froelich & Lesley, 2001).
l
Alargevarietyofsiliclasticandcarbonatesedimentarydepositsofvariousages(TomMcGlothlin,
1944).
23
Table 2.2: Summary of measured seawater d
74
Ge and sample details.
Sample Station n = Lat.,
◦
Long.,
◦
Depth, m [Ge], pM [Si], mM Ge/Si, mmol/mol d
74
Ge, %
a
PACIFIC OCEAN
Cascadia Basin 56-2 3 48.42 -127.50 2590 134 182 0.74 3.40± 0.27
San Pedro Basin SPOT 3 33.55 -118.40 500 55 74 0.74 3.48± 0.35
ATLANTIC OCEAN
Geotraces (GDI-30,31) BATS 1 31.67 -64.17 2000 24 17 1.36 3.46± 0.22
Geotraces (#2672) BATS 1 31.67 -64.17 3500 27 28 0.97 3.03± 0.50
GULF OF MEXICO
Deep offshore
CTD-33-1 Sta. G 1 26.28 -92.02 749.5 22 25 0.86 2.63± 0.22
CTD-32-3 Sta. G 1 26.28 -92.02 2120.6 17 25 0.70 3.07± 0.22
Shelf surface
CTD-45-7 Sta. 9 1 28.97 -90.40 2 43 10 4.28 2.79± 0.50
CTD-45-8 Sta. 9 2 28.97 -90.40 3 21 9 2.20 3.23± 0.20
Shelf bottom
CTD-6 Sta. 1 2 28.59 -90.54 30 45 25 1.84 2.21± 0.37
Core incubations
MC-2 Core A Sta. 1 1 28.59 -90.54 30 111 70 1.59 2.71± 0.50
MC-3 Core A Sta. 1 1 28.59 -90.54 30 113 83 1.35 2.36± 0.50
MC-3 Core B Sta. 1 1 28.59 -90.54 30 113 89 1.27 2.18± 0.50
Pore waters
MC-13 0-5 cm depth Sta. 5 1 28.14 -92.16 90 104 127 0.82 3.05± 0.50
DEEP OCEAN AVERAGE 3.24± 0.44
b
a
Uncertaintygivenas2S.D.ofstandardreproducibilitywheren=1and2S.D.ofsamplereplicates
where n > 1.
b
Simple average (± 2 S.D.) of all samples from>2000 m depth.
24
Table 2.3: Summary of measured hydrothermal fluid d
74
Ge and sample details.
Sample Temp,
◦
C n = Lat.,
◦
Long.,
◦
[Ge],
nmol/kg
[Si],
mmol/kg
Ge/Si,
mmol/mol
d
74
Ge,
%
a
Comments
HIGH TEMPERATURE
Loihi seamount: Pele’s Pit 18.92 -155.27
Bouyant jets, sampled in Oct 1996. No overlying
sediment.
340 Green 194 2 512 9.91 52 0.69± 0.29
340 Black 194 2 202 3.89 52 0.74± 0.29
393 Black 161 2 328 n/m
b
- 0.83± 0.29
341 Black 193 2 472 9.58 49 0.83± 0.29
398 Silver 166 2 263 n/m - 1.42± 0.35
Juan de Fuca Ridge axis: Middle Valley 48.45 -128.70
Juan de Fuca axial rift valley. Some fluid
interaction with 2 km of overlying sediment.
MV Red Maj 276 1 254 8.50 30 1.56± 0.62
MV White Maj 276 1 263 8.61 31 1.50± 0.62
LOW TEMPERATURE
Juan de Fuca Ridge flank: Baby Bare 47.71 -127.79
Basement outcrop. Bore hole upwelling fluids
focused by an inverted funnel (overlying sediment
removed). Reaction at 63
◦
C, exit at 25
◦
C.
Major CF 2972-13 25 2 9.7 0.319 30 3.55± 1.83
Major AF 2972-11 25 2 9.2 0.326 28 3.42± 0.52
Major AF 2974-14 25 2 9.4 0.330 28 2.94± 1.69
Major CF 2974-13 25 1 10.0 0.324 31 3.95± 0.91
Juan de Fuca Ridge flank: ODP
Near-basement pore waters sampled from bore
holes, prior to any fluid interaction with 100-200
m overlaying sediment).
ODP 1025 3608 Red 38.6 2 47.89 -128.65 9.6 0.567 17 2.85± 1.21
ODP 1026 3466 Red 61.7 2 47.76 -127.76 22 0.730 30 3.82± 0.80
3476 Red 61.7 2 21 0.745 28 4.09± 1.27
TERRESTRIAL [Ge],
nmol/L
[Si],
mmol/L Cascade Hot Springs
Bigelow 59 3 44.24 -122.06 77 1.23 63 3.29± 0.62
Previously reported by Siebert et al. (2011).
Natural hot springs fed by topographically
controlled groundwater discharge.
Terrwilliger 46 3 44.08 -122.23 26 0.78 33 2.88± 0.21
McCredie 74 3 43.70 -122.29 94 1.28 74 2.68± 0.21
Kahneeta 83 3 44.86 -121.20 71 1.11 64 2.47± 0.21
Paulina Lake 52 3 43.73 -121.25 20 3.11 6.4 1.85± 0.21
East Lake 57 3 43.72 -121.20 26 3.47 7.5 1.65± 0.21
25
Baby Bare is a basement rock outcrop on the eastern flank of the Juan de
Fuca ridge, within what is otherwise a sediment-blanketed Cascadia basin. Here,
typically diffuse low-temperature fluids are focused and discharge through a small,
thermally extinct volcano (Mottl et al., 1998). Several boreholes have also been
drilled through the sediment blanket during the ODP Leg 168 and upwelling fluids
sampledfromjustwithinthebasementrock(Elderfieldetal.,1999). Thebasement
temperature at which crust alteration occurs has been determined to be 43
◦
C for
the ODP 1025 site and 63
◦
C for all the fluids presented here (Elderfield et al.,
1999; Wheat & Mottl, 2000). Both the vent and the borehole fluids had Ge/Si
similar to the Middle Valley fluids but much lower Ge concentrations (9 - 22 nM)
and some of the highestd
74
Ge of all hydrothermal fluids analyzed to date, between
2.94 and 4.09 % (Fig 2.3).
2.4 Discussion
2.4.1 Rivers
Fractionation during weathering
Considering that silicate rocks have d
74
Ge of∼0.6 %, the riverine data pre-
sented here (Table 2.1) imply an apparent D
74
Ge
solid–liquid
fractionation factor
that spans around -5 to -2 %. However, it is more likely that the observed range
ofriverined
74
Gevaluesreflectsthevaryingdegreesofisotopefractionationasfluids
in contact with rocks evolve, rather than a large range of actual D
74
Ge
solid–liquid
fractionation factors. It has been shown that riverine Ge/Si ratios are relatively
insensitive to bedrock composition and are primarily controlled by weathering
intensity, i.e. the fraction of material removed from a watershed in the dissolved
26
-2
0
2
4
6
1 10 100 1000
δ
74
Ge, ‰
Ge/Si, µmol/mol
Loihi (hot jet uids)
1
Loihi (warm diuse uids)
2
Loihi Fe-rich deposits
2
JdFR ank: Baby Bare
1
JdFR ank: ODP 1025 & 1026
1
JdFR axis: Middle Valley
1
East Pacic Rise axis
2
Terrestrial: Cascade hotsprings
3
Oceanic crust
4
Figure 2.3: Summary of hydrothermal fluidd
74
Ge values measured to date, plotted
against fluid Ge/Si. Also shown are the average oceanic basalt composition and
the hydrothermal Fe-rich deposits previously measured at Loihi (open circles).
References: 1) this study; 2) Escoube et al. (2015); 3) Siebert et al. (2006, 2011);
4) Escoube et al. (2012).
phase (chemical weathering) relative to total denudation (the sum of chemical
and physical weathering) (Mortlock & Froelich, 1987; Murnane & Stallard, 1990;
Froelich et al., 1992). In low weathering intensity, fast eroding watersheds, the
high rate of fresh mineral supply results in high solute concentrations, and the
chemical composition of the streams is thought to be largely limited by weathering
reaction kinetics (West et al., 2005). In this case, the dissolved Ge/Si and, we pro-
pose, d
74
Ge composition should be strongly fractionated due to rapid precipitation
of secondary weathering products, such as Fe and Al oxides and aluminosilicates.
27
Figure 2.4: A: Riverine Ge isotope composition as a function of Ge/Na ratio.
Symbolsreflectthesamplingregion. TheCalifornianstreamsdraintheSanGabriel
and Sierra Nevada mountains. Peruvian streams are all part of the Madre de Dios
watershed draining the Peruvian Andes and Amazon foreland-floodplain. Grey
triangle is the Mississippi sample, which was excluded from the "All" fit. Lines
show the logarithm fit to each dataset and D
74
Ge is defined as the slope of each
fit. B: Riverine Ge isotope composition as a function of Ge/S
+
, where S
+
=
Na
+
+Ca
2+
+Mg
2+
+K
+
in meq/L. All major cation concentrations are given in the
Appendix A Table A.2.
In slowly eroding watersheds fresh primary minerals become depleted, limiting
solute concentrations and resulting in limited precipitation (or even dissolution)
of secondary weathering products. In the latter case, riverine Ge/Si and d
74
Ge
composition should more closely resemble the initial bedrock values.
Unfortunately, there is currently no d
74
Ge data of secondary weathering prod-
ucts available to directly test the above hypothesis. However, the degree to which
Ge has been removed from solution through secondary phase precipitation can
be assessed by normalizing Ge concentration to a conservative solute that is not
incorporated into secondary products, such as Na or other major cations. This
approach has been previously employed to investigate Si and Li isotope behav-
ior during weathering (Hughes et al., 2013; Dellinger et al., 2015). Fig. 2.4a
demonstrates that rivers exhibiting lower Ge/Na ratios are also isotopically the
28
heaviest. Assuming relatively invariant silicate rock Ge/Na ratios and a Rayleigh
type behavior, the relationship in Fig. 2.4a can be used to estimate the Ge isotope
fractionation factor a
solid–liquid
of 0.9992 (D
74
Ge
solid–liquid
= -0.8 %). To mini-
mize the potential effect of lithologic variability, d
74
Ge of the Californian and the
Peruvian streams can be fit separately against Ge/Na or Ge/S
+
(where S
+
is the
sum of major cations), which yield similar D
solid–liquid
between -1.1 and -0.8 %
(Fig. 2.4).
Pokrovsky et al. (2014) have conducted laboratory experiments showing that
Ge removal from solution during Fe-(oxy)hydroxide precipitation results in prefer-
entialincorporationoflightGeisotopes, withfractionationfactorsD
74
Ge
solid–liquid
between -1.7 % (for adsorption) and -4.4 % (for co-precipitation). Ge incorpo-
ration into the mineral lattice often results in a change in Ge coordination with
longer and weaker bonds compared to those in dissolved germanic acid (Pokrovsky
et al., 2006). At equilibrium, these weaker mineral bonds should preferentially
incorporate light Ge isotopes (Li et al., 2009; Li & Liu, 2010), resulting in isotopic
fractionation that is consistent with the riverine d
74
Ge signatures presented here.
Fractionation during biological uptake
Biological uptake of Ge by vegetation and freshwater diatoms may potentially
exhibit additional control on riverine d
74
Ge composition. Plants are known to
discriminate against Ge during nutrient uptake, in some cases raising Ge/Si of the
soil pore water (e.g., Derry et al., 2005; Lugolobi et al., 2010; Meek et al., 2016).
However, this process can only have a limited effect due to low Ge uptake rates
(Blecker et al., 2007; Delvigne et al., 2009). In addition, bulk river chemistry can
only be affected transiently with changing vegetation biomass, typically only over
seasonal timescales. Finally, any fractionation by vegetation would likely result
29
in a positive d
74
Ge vs. Ge/Si, in contrast to the negative relationship observed
(Fig. 2.1b). It may be responsible for some of the scatter observed but it does not
appear to be a major driver of Ge chemistry in the watersheds investigated here.
Marine diatoms are also known to discriminate against Ge, but only when
stressed for Si (Sutton et al., 2010). The Lower Kern river is strongly affected
by diatom growth in a dam reservoir upstream of the sampling point (Table 2.1).
Mass balance calculations show that up to 89% Si and 44% Ge has been removed
by diatom growth in the reservoir (Appendix A Table A.3). While Ge/Si has
been strongly fractionated, d
74
Ge composition remains unchanged within analyt-
ical uncertainty relative to that expected from conservative mixing of the feeding
rivers. This provides evidence against significant d
74
Ge fractionation by fresh-
water diatoms, consistent with findings in the marine environment (see Section
2.4.2). The data presented here therefore suggests that riverine d
74
Ge is primarily
controlled by weathering reactions.
Comparison to other isotopic weathering proxies
Considering all of the above, riverine d
74
Ge appears to be a promising weath-
ering tracer that could complement already established proxies, such as d
30
Si and
d
7
Li. The sense of fractionation, where the dissolved phase is heavy and secondary
minerals are light, is in agreement with d
30
Si (e.g., De La Rocha et al. (2000);
Georg et al. (2007); Hughes et al. (2013)). However, whereas d
30
Si can be signif-
icantly affected by vegetation cycling (e.g., Ziegler et al. (2005b); Opfergelt et al.
(2006a); Cornelis et al. (2010)), as we argue above, d
74
Ge is unlikely to be strongly
influenced by this process. If true, d
74
Ge could be a powerful tool to disentangle
the biological influence on the riverine Ge/Si and d
30
Si signatures.
30
d
74
Ge could also provide a useful complement to d
7
Li, which is fractionated
in the same direction during secondary clay precipitation (e.g., Huh et al. (2001);
Bagard et al. (2015); Pogge von Strandmann & Henderson (2015)). However, Ge
is typically incorporated into both tetrahedral and octahedral sites in secondary
minerals(Bernstein,1985;Pokrovskyetal.,2006), whereasLitypicallyentersocta-
hedral and interlayer cation sites (Vigier et al., 2008; Wimpenny et al., 2015), often
replacing Mg (Wimpenny et al., 2010a; Pogge von Strandmann et al., 2012). The
careful investigation of d
74
Ge and d
7
Li together could therefore help disentangle
different secondary weathering reactions and their spatial variability on the soil or
the catchment scale.
Anthropogenic influence and the global riverine d
74
Ge signature
Qi et al. (2011) have demonstrated that Ge-rich coals in the Lincang deposit in
China are isotopically variable, with d
74
Ge ranging from -2.59 to 4.72 % and ash
produced during coal combustion further enriched in light isotopes by up to 2.25
%. Asaresult,careneedstobeexercisedwhentryingtoassessthenaturalriverine
d
74
Ge composition in areas with known coal deposits and coal power generation. It
has been shown that several of the world’s major rivers, including the Mississippi,
are enriched in Ge due to coal ash contamination (Mortlock & Froelich, 1987;
Froelich & Lesley, 2001), which can explain why the Mississippi does not fit the
fractionation pattern observed in the rest of the rivers (Fig. 2.4). The other rivers
presented here are most likely free of coal ash contamination, with the possible
exception of the Lower Kern, where a coal power plant is present about 60 km
downstream of the sampling point.
Although the dataset presented here lacks many of the world’s major rivers,
it can be used to determine a preliminary value for the global d
74
Ge
riv
signature.
31
Using the relationship in Fig. 2.1a and the average (uncontaminated) riverine
Ge concentration of∼100 pM based on the global compilation of Froelich et al.
(1992) yields a global d
74
Ge
riv
of∼4.5 %. This estimate, however, does not
account for the non-linear averaging effect, which should result in a lower global
d
74
Ge
riv
estimate, due to the negative correlation between Ge concentration and
d
74
Ge composition of individual rivers (Fig. 2.1a). Additionally, a true global
mean value would have to be discharge weighted. In the dataset presented here,
the Mississippi river has an annual discharge of about 580 km
3
/y, while the next
largest river in our data set -Madrede Dios - is about ten times smaller (50 km
3
/y).
TherestoftheriversinTable2.1aremuchsmallerstill, whichmeansthatweighing
the current dataset by discharge would result in a mean estimate that is heavily
dominated by the anthropogenically contaminated Mississippi. Considering all of
this, we select a very preliminary global mean value of 3.5± 1.5 % since the
true value could lie anywhere in the 2-5 % range. Analysis of the world’s major
rivers, especially those unlikely to be contaminated by coal combustion, such as
the Amazon, is needed to obtain a more robust estimate.
Finally, the potential alteration of the riverined
74
Ge signature during estuarine
processes is currently unknown. A previous study has shown that some Ge may
be released from the riverine suspended load upon mixing with seawater (Froelich
et al., 1985) and this source is taken into account in the global budget presented
below (see Section 2.4.4). In addition, non-conservative loss of Ge due to organic
matter flocculation, although not observed to date, is theoretically possible and
should be addressed in future studies.
32
2.4.2 Seawater
The seawater d
74
Ge composition is about 2.6 % higher than the continental
or oceanic crust (Table 2.2, Fig. 2.2). This observation is not surprising, given
a blend of hydrothermal and weathering inputs that are both enriched in heavy
Ge isotopes (see Sections 2.3.1 and 2.3.3). However, seawater has to reflect the
isotopic composition of both sources and sinks, which is discussed in Section 2.4.4.
The deep water d
74
Ge
sw
in all three basins is identical within uncertainty (Fig.
2.2). Differences might be expected based on the relatively short (3-6 ky) residence
time of Ge in the ocean (see Section 2.4.4) but are below the level of precision
obtained here. There is one exception - bottom seawater and core incubations at
one continental shelf site in the Gulf of Mexico are∼1 % lighter than the rest
of the seawater samples. These samples most likely reflect the strong influence
of the Mississippi River, which has a very similar d
74
Ge signature (Table 2.1),
since they are situated within 100 km of the river delta and within the river plume
(Baronas et al., 2016). Alternatively, these lowerd
74
Ge values may partially reflect
the dissolution of terrigenous particles delivered by the Mississippi or previously
formed authigenic (and isotopically fractionated, see Section 2.4.4) phases. One
possible scenario is that seasonal hypoxia had caused shoaling of the pore water
Fe redox boundary, resulting in the release of reduced Fe, along with isotopically
light Ge that had previously been adsorbed on Fe oxides.
ThesurfaceseawaterattheGulfofMexicoexhibitselevatedGe/Siratios(Table
2.2), due to either biological fractionation by diatoms or the influence of the Missis-
sippiriver(Baronasetal.,2016). Inthelattercase, thesewaterswouldbeexpected
to have lower d
74
Ge
sw
(Fig. 2.2), whereas the observed values are identical within
uncertainty to the deep ocean composition. Our preferred interpretation is there-
fore that surface Ge/Si
sw
is fractionated during diatom uptake, which implies that
33
this process does not result in significant d
74
Ge fractionation. This hypothesis is
supported by the results of Mantoura (2006), who showed no detectable diatom
d
74
Ge fractionation during diatom culture experiments in Ge-enriched seawater,
2.4.3 Hydrothermal fluids
High temperature systems
In high temperature hydrothermal systems, the silica chemistry is primar-
ily controlled by equilibration of fluid with quartz (e.g., Mottl & Holland, 1978;
Von Damm et al., 1991). Experimental data have shown that elevated hydrother-
mal fluid Ge/Si could therefore result from thermodynamic differences between
Ge(OH)
4(aq)
and Si(OH)
4(aq)
species and preferential solubilization of Ge relative
to Si during water-rock reactions at high temperature (Pokrovski & Schott, 1998;
Evans & Derry, 2002; Pokrovski et al., 2005). Using this framework and measured
Ge/Si
fluid
and d
74
Ge
fluid
values in hydrothermal discharge from the East Pacific
Rise (340-380
◦
C fluids) and Loihi seamount (21-54
◦
C fluids), Escoube et al.
(2015) obtained an average d
74
Ge
quartz
of∼ -2.4 % and equilibrium fractionation
factor D
74
Ge
quartz–fluid
of∼ -4.1 % (ranging between -0.3 and -7.7 %) at these
sites.
Applying this model to the Middle Valley fluids analyzed in this study, we
obtain d
74
Ge
quartz
of -3.0± 0.3 and D
74
Ge
quartz–fluid
of -4.5± 0.4 %, in good
agreementwithEscoubeetal.(2015). MiddleValleyfluidsinteractwithathick(up
to 2km) sediment blanket before discharging, which can affect the fluid chemistry.
For example, alkali metals and calcium are especially affected, although water-rock
interaction in the high-temperature reaction zone is still the dominant process
setting the fluid chemistry, as indicated by conservative Si concentrations and
87
Sr/
86
Sr ratios (Butterfield et al., 1994). The good agreement between the Middle
34
Valley fluids and other high temperature systems investigated by Escoube et al.
(2015) indicates that in this and perhaps other sedimented hydrothermal systems
d
74
Ge
fluid
is not strongly affected by fluid interaction with the overlying sediment.
In contrast, applying the quartz equilibrium model of Escoube et al. (2015)
to the Loihi fluids presented here yields d
74
Ge
quartz
between -0.4 and 0.1 % and
D
74
Ge
quartz–fluid
between -1.2 and -0.6 %, significantly smaller than the values
obtained in the previous study. The Loihi samples presented here were collected
as high-temperature (∼200
◦
C) jet fluids that were observed and sampled only
in October 1996, a few months after an explosive eruption of the seamount. By
2006-2007, the system had returned to a "steady state" venting of diffuse, low
temperature (20-60
◦
C) fluids (Glazer & Rouxel, 2009), which were sampled and
analyzed by Escoube et al. (2015). The simplest explanation for the discrepancy
betweenourd
74
Ge
fluid
dataandthatofEscoubeetal.(2015)isthereforeadecrease
in water-rock interaction temperature from 1996 to 2006, which would result in
an increase of the D
74
Ge
quartz–fluid
magnitude over time, as predicted by quantum
chemistry calculations (Li et al., 2009).
A change in temperature should also affect the fluid Ge/Si ratio, which appears
to have decreased from 49-52 mmol/mol in 1996 (Table 2.3) to around 30 mmol/mol
in 2006-2007 (Escoube et al., 2015). There is a disagreement in the literature on
how a decrease in temperature should affect the Ge/Si ratio of equilibrated fluid.
Using a hexagonal quartz-like GeO
2
structure, Evans & Derry (2002) predicted
that Ge/Si ratio in fluids should increase with decreasing temperature, consis-
tent with observations at various high temperature systems (Mortlock et al., 1993;
Escoube et al., 2015). In contrast, using a tetragonal rutile-like GeO
2
structure,
35
Pokrovski et al. (2005) predicted a decrease in Ge/Si ratio with decreasing temper-
ature, in agreement with Icelandic and Cascade hydrothermal fluids (Arnorsson,
1984; Siebert et al., 2011).
In addition, Li et al. (2009) have demonstrated that D
74
Ge
solid–fluid
values for
a range of minerals are strongly dependent on the length of the Ge-O bond in the
mineral structure. Furthermore, they used a tetrahedral GeO
2
structure to simu-
late quartz binding environment, which has Ge-O bond length of 1.753 Å, similar
but slightly shorter than Ge(OH)
4(aq)
(1.77 Å). As a result, the calculations predict
a small but positive D
74
Ge
quartz–fluid
value, ranging from 1.1 % at 25
◦
C to 0-0.15
% at 300-400
◦
C (the estimated water-rock interaction temperature at Loihi; Sed-
wick et al. (1992)), in direct disagreement with the field data presented here and
by Escoube et al. (2015). The field and theoretical values of D
74
Ge
quartz–fluid
can
be brought in agreement if the mineral Ge-O bond length is instead set to 1.88 Å,
as measured in tetragonal GeO
2
(Hazen & Finger, 1981). Using the relationship
between Ge-O and the reduced isotope partitioning function ratio (RPFR) given
by Li et al. (2009), we obtain D
74
Ge
rutile–fluid
of -7.2 % at 25
◦
C. While currently
no calculations are available to predict this value for temperatures above 200
◦
C,
it is expected to be much lower, likely in the -1 to -3 % range, which would be
consistent with the field-derived values.
The temporal evolution of Loihi d
74
Ge
fluid
data could also be explained by
increasing kinetic isotope effects, due to Rayleigh-type depletion of (preferentially
light) Ge in the fluids, and/or larger kinetic fractionation factors associated with
the precipitation of amorphous silica or other hydrothermal minerals at lower fluid
temperatures. The importance of kinetic fractionation during similar processes has
been recently demonstrated for the d
30
Si system (Oelze et al., 2014; Geilert et al.,
2014, 2015).
36
There are other possible explanations for the evolution of the Loihi fluids from
1996 to 2006, such as an increase in pH-controlled congruency of basalt weathering
(Wheat et al., 2000), or perhaps transient dissolution of Ge-enriched and isotopi-
cally light deposits (Escoube et al., 2015) in the months after the eruption. A full
exploration of these possibilities is outside of the scope of this study, however. In
the end, these data demonstrate that there is not a simple relationship between
Ge/Si and d
74
Ge in high temperature fluids and further studies of both the fluids
and the solids in diverse hydrothermal systems are needed.
Low temperature systems
Ourresultsshowthatlow-temperaturefluidsoftheJuandeFucaRidge(JdFR)
exhibit higher d
74
Ge
fluid
relative to high temperature systems (Fig. 2.3). To
some degree, this might be the result of larger equilibrium or kinetic fractionation
factors at the lower reaction temperature (Li et al., 2009). The heavy isotopic
composition of these fluids would once again require a much larger and negative
D
74
Ge
mineral–fluid
thanthatpredictedbyLietal.(2009)forfluidequilibrationwith
quartz (see the discussion above). However, unlike in high temperature hydrother-
malsystems,fluidsinlowtemperaturesystemsareunlikelytoachievetruechemical
equilibrium with most of the mineral phases that they circulate through. Instead,
their chemistry is strongly controlled by the degree of crust alteration, that is, the
partial dissolution of basalt and the precipitation of various secondary minerals
(e.g., Wilkens et al., 1991).
The JdFR flank fluids analyzed had low Mg concentrations, which varied
depending on the degree of dilution with seawater during sampling but extrap-
olate to formation fluids with Mg concentrations of 1 mmol/kg for Baby Bare
and 2-28 mmol/kg for ODP sites (Wheat et al., 2004; Wheat & McManus, 2005).
37
Figure 2.5: Low-temperature hydrothermal fluid d
74
Ge plotted against fGe
diss
,
the estimated fraction of Ge remaining in solution (see Appendix A for details).
Black star marks the isotopic composition of basalt. The dashed lines show the
evolution of d
74
Ge
fluid
composition calculated using a Batch model for Ge removal
with Fe-(oxy)hydroxides at basement temperatures in the area (a = 0.9958 or
D
74
Ge
FeOx–fluid
= -4.2 % for co-precipitation at 63
◦
C and a = 0.9984 or -1.6 %
for adsorption at 40
◦
C). The black lines show d
74
Ge
fluid
evolution for adsorption
at 40 and 63
◦
C in a Rayleigh model (D
74
Ge
FeOx–fluid
= -1.6 and -1.4 %, respec-
tively). References: 1) this study; 2) Escoube et al. (2012); 3) Pokrovsky et al.
(2014); 4) Li & Liu (2010).
Mg depletion in hydrothermal fluids is caused by the precipitation of secondary
Mg-rich clay minerals during basalt alteration. Since these clays also incorpo-
rate Si and Ge, the measured Mg anomaly can be used to estimate the minimum
amount of Si and Ge that had been initially released and then removed during
secondary clay precipitation. Wheat & McManus (2008) have calculated these
38
values to be 50-100 mmol/kg Si and 120-250 nmol/kg Ge assuming the precipi-
tation of chlorite/scectite with Si/Mg stoichiometry of 2 (Seyfried, 1987). A four
times higher estimate is obtained by assuming the precipitation of celadonite with
Si/Mg stoichiometry of 8 (Wheat & McManus, 2008). Such high concentrations
were probably never achieved in the fluids, since both the release of elements from
basalt dissolution and the uptake into secondary minerals are likely to take place
simultaneously. Considering that Ge concentrations measured in the fluids are
9-22 nmol/kg, this implies that only 1-8 % of the originally released Ge remains in
solution by the time the fluids upwell into the water column (Appendix A Table
A.4; see Appendix A text for detailed calculations).
Assuming that Ge is leached from basalt without isotopic fractionation, the
d
74
Ge
fluid
should then primarily be controlled by the the degree of Ge removal into
secondary minerals and the D
74
Ge
mineral–fluid
associated with this reaction (Fig.
2.5), similar to the low-temperature weathering reactions (see Section 2.4.1). This
calculation does not require that Ge removal be ascribed to a specific secondary
mineral. Although it is highly likely to be the same secondary silicate that removes
SiandMg, dissolvedFeisalsoknowntodiffusefromtheoverlyingsedimentsandto
precipitate as oxides and sulfides within the basement reaction zone (Wheat et al.,
2002), providing an alternative pathway for Ge removal from solution. While there
arenoD
74
Ge
mineral–fluid
valuesyetdeterminedformosttypicalhydrothermalclays,
thevaluesdeterminedforFeoxidesagreewellbetweentheoreticalandexperimental
studies (Li & Liu, 2010; Pokrovsky et al., 2014), enabling us to adapt them to
different reaction temperatures, and therefore serve as a good starting point.
The isotopic fractionation associated with a reaction involving a phase change
can be estimated by assuming either a Batch system, where the two phases are
allowed to isotopically equilibrate, or a Rayleigh type system, where they are
39
separated (see Appendix A for equations). Due to the long (5-10 ky; Elderfield et
al. (1999)) residence time of these fluids in the basement, it is possible for either of
these two models to be applicable, depending on the secondary minerals involved
and the spatial locus of the reactions involving Ge.
Fig. 2.5showsthatthemeasuredJdFRflankd
74
Ge
fluid
compositionagreeswell
with the values predicted by the Ge depletion model, considering the uncertainties
and the number of assumptions made. The measured values fall in the middle
between those predicted by the Batch and the Rayleigh variants of the model.
While these calculations were done assuming no isotopic fractionation during the
initial basalt dissolution, the model is relatively insensitive to this assumption and
is primarily governed by the fractionation factor associated with the secondary
mineral precipitation. For example, even if the starting "basalt" isotopic composi-
tion is offset by 2 % in either direction, the data is still fit well with at least one
of the model scenarios shown in Fig. 2.5.
2.4.4 Global budget
ThemajorsourcesofGetotheoceanareriversandhydrothermalfluids,andthe
main sinks are the burial of opal (bSi) and non-opal authigenic minerals in marine
sediments (Table 2.4, Fig. 2.6). The first attempt to constrain the global Ge
isotope budget was by Escoube et al. (2015). By assuming a riverine and seawater
d
74
Ge composition of 3.0 % (quite close to the actual values, as determined in this
study), they estimated a fractionation of about 2 % , associated with Ge removal
into authigenic marine phases (the non-opal sink). Here, we provide an updated
global d
74
Ge budget, utilizing data-based estimates of the riverine and seawater
isotopic composition as given above and a more thorough treatment of the less well
constrained fluxes (Table 2.4).
40
Table 2.4: Summary of global marine Si and Ge fluxes, along with their estimated
d
74
Ge signatures, assuming steady state.
Flux Si flux (FSi)
a
, Ge/Si, Ge flux (FGe)
b
, d
74
Ge,
Tmol/y mmol/mol Mmol/y %
INPUTS
Rivers, dissolved Si 5.7± 0.4 0.57± 0.20
c
3.2± 1.2 3.5± 1.5
d
Rivers, amorphous Si 1.1± 1.0 0.57± 0.30
e
0.6± 0.6 3.5± 1.5
f
Groundwater 0.65± 0.54 0.30± 0.20
g
0.2± 0.2 4.5± 1.5
h
Detrital (river susp. load and aeolian) 1.1± 0.7 1.4± 0.3
i
1.6± 1.5 0.56± 0.10
j
Ridge-axis hydrothermal fluids 0.5± 0.3 9± 6
k
4.5± 4.2 1.5± 0.4
l
Ridge-flank hydrothermal fluids 0.5± 0.4 25± 24
m
9.2± 9.2 3.5± 0.5
n
Total 9.5± 1.4 2.0± 1.1 19.2± 11.2 2.8± 0.7
OUTPUTS
Diatom bSi 6.3± 2.8 0.76± 0.04
o
5.0± 2.1 3.2± 0.4
p
Sponge bSi 3.2± 3.2 0.23± 0.12
q
0.7± 0.7 2.2± 1.0
r
Non-opal (authigenesis) - - 13.3± 10.6
s
2.7± 1.7
t
Total 9.5± 3.0 2.0± 1.1 19.2± 11.2 2.8± 0.7
a
All Si flux values and uncertainties from Tréguer & De La Rocha (2013); Frings et al. (2016).
b
Calculated as FGe
i
= FSi
i
×Ge/Si
i
, where i is the appropriate flux, except where noted otherwise.
c
Based on global riverine Ge vs. Si slope from Froelich et al. (1992), updated to global average
riverine Si concentration from Dürr et al. (2011).
d
Estimated using the data presented in this study (see Section 2.4.1).
e
Assumed to be equal to dissolved riverine signature but with a higher uncertainty assigned to
account for potential biological fractionation.
f
Assumed equal to riverine composition, based on the lack of evidence for biological d
74
Ge frac-
tionation (see Sections 2.4.1 and 2.4.2).
g
Based on data from Kurtz et al. (2011).
h
Based on the d
74
Ge vs. Ge/Si relationship in Fig. 2.1.
i
Assumed congruent weathering: equal to average continental crust (Froelich et al., 1992).
j
Assumed congruent weathering: equal to average continental crust (Escoube et al., 2012).
k
Mortlock et al. (1993); Wheat & McManus (2005).
l
Average of axial fluids from EPR (Escoube et al., 2015) and JdFR (this study). This value is
identical to the previous estimate of Escoube et al. (2015).
m
Warm (10-60
◦
C) fluids typically have avg. Ge/Si of 25 mmol/mol, whereas the effect of basalt
alteration at <10
◦
C is very poorly constrained, and may be dominated by marine porewater
signature of∼1 mmol/mol (Wheat & McManus, 2005). We therefore assign a large uncertainty,
encompassing a range of possible fluid Ge/Si signatures.
n
Average of ridge flank fluids (Baby Bare and ODP data presented here).
o
Froelich et al. (1985); Sutton et al. (2010)
p
Assumed to be equal to average seawater reported in this study (Table 2.2, Fig. 2.2).
q
Ellwood et al. (2006)
r
Rouxel et al. (2006)
s
Calculated using Eq. A.7 to achieve a steady state in the Ge mass budget (see Appendix
A),median± 2 S.D. based on Monte Carlo modeling that takes into account uncertainty of
all parameters in this table.
t
Calculated using Eq. A.8 (see Appendix A), median± 2 S.D. based on Monte Carlo modeling
that takes into account uncertainty of all parameters in this table.
41
A number of assumptions had to be made for the fluxes where little to no data
exist. Forexample, weestimatedagroundwaterd
74
Gesignatureassumingthatthe
riverine d
74
Ge vs. Ge/Si applies to these fluids as well, since their composition is
largely controlled by the same set of weathering reactions. In contrast to previous
Ge budget estimates (e.g., King et al., 2000; Hammond et al., 2004; Escoube et
al., 2015), we have also assumed that there is no Ge/Si (and by extension no
d
74
Ge) fractionation associated with the dissolution of aeolian dust and riverine
detrital material in seawater, as secondary mineral formation is unlikely to happen
in the water column and any such process within the pore waters is accounted
for by the non-opal flux. Due to the relatively small detrital Ge input, this latter
assumption does not strongly influence the overall global budget or the model
results discussed below, as confirmed by sensitivity tests. Finally, we treat low
temperature hydrothermal fluids without separating the effect of the "warm" (>10
◦
C)fluidsfromthebasaltalteration(sometimestermedthelow-temperaturebasalt
weathering) associated with "cool" (<10
◦
C) fluids but allow for a large Ge/Si
uncertainty due to poor constraints on the latter process (Wheat & McManus,
2008). These and other assumptions, along with the literature data sources, are
listed in the footnotes of Table 2.4.
In the end, the two least well constrained fluxes in the Ge cycle are the ridge-
flank (low temperature) hydrothermal input and the non-opal burial output (Table
2.4). While the Si flux associated with low-temperature (<63
◦
C) alteration
of oceanic crust is estimated to be low (Wheat & McManus, 2005; Tréguer &
De La Rocha, 2013), the wide range of Ge/Si values observed in low-temperature
hydrothermal fluids results in a high uncertainty of the Ge flux associated with
this process. However, the constraints on the non-opal Ge burial flux are arguably
poorerstill(Hammondetal.,2000;McManusetal.,2003;Baronasetal.,2016)and
42
there are currently no published data regarding its d
74
Ge composition. Assuming
a steady state in the global ocean Ge cycle, inputs must balance outputs:
S(F
input
×d
74
Ge
input
) = S(F
output
×d
74
Ge
output
) (2.2)
where F represents the flux of Ge into or out of the ocean, as shown in Table 2.4. A
versionofthisequationwiththeexpandedformulationofalltheindividualtermsis
presented in the Appendix A (Eq. A.8). The steady state assumption is reasonable
given the relatively short residence time of Ge in the ocean (see discussion below).
However, it can only truly be tested once more accurate estimates are obtained for
all the fluxes in the marine Ge cycle.
Using Eq. 7.2 we can estimate the flux and the d
74
Ge composition of the non-
opal Ge burial flux. This calculation was done 100,000 times, each time randomly
selecting a value of each parameter in Table 2.4 from within its associated uncer-
tainty (i.e., the Monte Carlo approach). Additional details of the model used are
given in the Appendix A. The median non-opal Ge burial flux value is calculated
as 13.4± 10.7 Mmol/y. Although this range encompasses previous estimates, the
median value is 2-3 times larger compared to previous budgets, which typically
did not include a number of the secondary Ge fluxes, such as groundwater or sus-
pended river load inputs (Hammond et al., 2000; King et al., 2000; Escoube et al.,
2015) and which did not account for the possibility of significant Ge inputs during
basalt alteration at temperatures at or below 20
◦
C (Wheat & McManus (2005);
but see also Wheat & McManus (2008)). This new larger estimate, however, is in
agreement with the recent value of 18± 14 Mmol/y that was calculated based on
field measurements of Ge non-opal burial in marine sediments around the globe
(Baronas et al., 2016).
43
Figure 2.6: A summary of the oceanic Ge sources and sinks and their estimated
d
74
Ge signatures. The non-opal burial isotopic signature (in italics) was calculated
assuming a steady-state Ge cycle (see Table 2.4 and Appendix A). Crust values
from Escoube et al. (2012), sponge bSi value from Rouxel et al. (2006), diatom bSi
value based on work by Mantoura (2006). High temperature hydrothermal value
from data reported here and in Escoube et al. (2015). All other values from data
reported in this study.
44
Using Eq. 7.2, the Ge isotope composition associated with non-opal burial was
calculated to be 2.7± 1.7 %, or about 0.5 % lower than seawater (Table 2.4),
indicating possible preferential incorporation of light Ge isotopes into authigenic
marine phases, most likely Fe oxides or aluminosilicate clays (Baronas et al., 2016).
This implied fractionation is lower but within uncertainty of the -2 % previously
estimated by Escoube et al. (2015) and also similar to the D
74
Ge
mineral–fluid
calcu-
lated from riverine composition (Fig. 2.4) and Fe oxide precipitation experiments
(Pokrovsky et al., 2014). Studies of marine sediments and pore waters affected by
this process are needed to confirm this value and further put better constraints on
the Ge isotope budget.
Another caveat of the current marine Si and Ge budget is the highly uncertain
flux of sponge bSi burial. The relatively large value used here (Table 2.4) was
first proposed by Tréguer & De La Rocha (2013) to close the modern Si budget,
despite the lack of direct evidence for such large significance of sponges globally. It
is possible that Si burial during reverse weathering (i.e., clay authigenesis, which
may also be driving non-opal Ge burial) is instead more important than previously
thought (e.g., Michalopoulos & Aller, 2004), as demonstrated by a recent study
utilizing cosmogenic
32
Si (Rahman et al., 2016). In this case, a lower sponge Si
burialfluxwouldbeneededtobalancetheSibudget, resultinginalowerGesponge
burial flux, and therefore requiring a lighter d
74
Ge
non–opal
value to balance the
modern Ge isotope budget. This would imply a largerD
74
Ge
mineral–fluid
associated
with marine sediment authigenesis.
Finally, the Ge mass budget presented here (Table 2.4) can be used to update
the estimate of the residence time of the inorganic Ge species in the ocean. Using
the global ocean Si inventory of 97,000 Tmol (Tréguer & De La Rocha, 2013), and
Ge/Si of 0.76 mmol/mol (Froelich et al., 1985; Sutton et al., 2010), yields a Ge
45
inventory of 73,700 Mmol. Depending on which set of fluxes is used, Ge residence
time in the oceans is calculated as 4300± 1500 (median± 1 S.D.). These values
are shorter than previous estimates of∼10,000 y (Hammond et al., 2000), due to
the recognition of likely additional Ge sources to the ocean (see discussion above).
A similar correction was recently calculated for the Si cycle, resulting in a residence
time that has been revised from∼15,000 - 20,000 y (Treguer et al., 1995) to 10,000
- 12,000 y (Tréguer & De La Rocha, 2013; Frings et al., 2016).
The global Ge isotope budget presented here sets up a basic framework for a
possible interpretation of future d
74
Ge
sw
records. The one pore water d
74
Ge mea-
surement was identical to seawater (Table 2.2, Fig. 2.2), suggesting that there is
little if any d
74
Ge fractionation associated with bSi dissolution and early diagene-
sis in the sediments. Combined with the lack of observed biological fractionation
by diatoms (see Section 2.4.1), diatom d
74
Ge promises to be a powerful proxy for
tracing secular changes in the isotopic composition of the seawater. When com-
bined with the available Ge/Si
sw
records (Shemesh et al., 1989; Mortlock et al.,
1991), it is bound to yield new insights into past continental silicate weathering
and/or cycling of biogenic silica in the ocean.
2.5 Conclusions
We present measurements of Ge isotopic composition (d
74
Ge) in seawater, sev-
eral rivers, and low and high temperature hydrothermal fluids. All these fluids
are significantly fractionated towards heavy d
74
Ge composition relative to silicate
rocks from which Ge is leached, most likely due to the precipitation of secondary
phases that are isotopically light. River waters show the highest variability, span-
ninga∼4%rangeofd
74
Gevalues. Thedeepocean, ontheotherhand, appearsto
46
be mostly homogeneous within the uncertainty of our measurements. High tem-
perature hydrothermal system fluids are less fractionated than low-temperature
ones. It therefore appears that the reaction zone temperature and the degree of
Ge removal from solution into hydrothermal minerals could be the main controls
of d
74
Ge in hydrothermal fluids.
The data presented here, along with that published previously, characterizes
the d
74
Ge composition of all the major oceanic Ge sinks and sources, except for
non-opal burial (i.e., authigenesis in marine sediments). Assuming that the con-
temporary Ge cycle is at steady state, we have calculated that there should be an
isotopic fractionation of -0.6± 1.8 % (solid-solution) associated with non-opal Ge
burial in marine sediments. The large uncertainty (including the sign) of this value
is primarily due to poor constraints on Ge input from ridge-flank hydrothermal flu-
ids. A better estimate of this value (ideally from direct measurements) is needed
to assess the sensitivity of d
74
Ge
sw
to this process and therefore its potential to
trace input (weathering vs. hydrothermal) or output (opal vs. non-opal burial)
dynamics in the past.
Despite the large remaining uncertainties, this study marks an important early
step towards applying d
74
Ge as a paleoceanographic proxy. Considering the short
residence time of Ge in the ocean (4300± 1500 yr, based on our budget), paleo-
records of d
74
Ge
sw
may help test important hypotheses on the causes and conse-
quences of glacial-interglacial cycles, such as changes in rates of silicate weathering
(e.g., Vance et al., 2009; von Blanckenburg et al., 2015) and volcanic degassing
(e.g., Lund et al., 2016; Huybers & Langmuir, 2017), as well as the causes of cool-
ing over the Cenozoic, which may have been driven by short-term bursts of silicate
weathering (Caves et al., 2016) that may be undetectable by proxies with long
oceanic residence times.
47
Acknowledgments
This study was funded by National Science Foundation grant OCE-1061700 to
DEH. Some of the hydrothermal sample analyses were supported by NSF grants
OCE-0327016 and OCE-0326574 to JM, and CS was also supported in part by
Swiss NSF grant PBBE2-102997 and ACS PRF# 40264-AC2.
We appreciate the assistance of a number of colleagues in sample collection: Ed
Boyle collected and filtered the Atlantic Water on the Geotraces Intercalibration
cruise, and Tabitha Esther and Rick Schwartz assisted with collection of the Pacific
samples and early protocol development. Peruvian river samples were collected by
Mark Torres, A. Joshua West, Camilo Ponton, and Adan Ccahuana. Other river
water samples were obtained with assistance from Paulina Pinedo, Harris Talsky,
Gina Erazo, Jane Hammond, Doug Barna, and Rick Schwartz. Gulf of Mexico
samples were collected during R/V Endeavor cruise EN-497 with the help from
William Berelson, Silke Severmann, Nick Rollins, Jesse Muratli, April Abbott, Joe
Jennings, and Kanchan Maiti. Olivier Rouxel has provided valuable discussion,
advice, and an initial aliquot of what is now the NIST 3120a standard. Jim Moffett
and Sergio Sañudo-Wilhelmy have allowed us to use their ICP-MS, and A. Joshua
West – his MP-AES. Andy Ross, Andy Ungerer and Brian Haley have assisted
with the work at OSU.
Thoughtful reviews by Olivier Rouxel, Philip Pogge von Strandmann, and two
anonymous reviewers have greatly improved this manuscript. We thank Horst
Marschall for editorial handling. Gen Li and Mark Torres are thanked for fruitful
discussions prior to submission.
48
Chapter 3
Contrasting Ge and Si isotope
dynamics in marine sediments
Author contributions
I designed the study with help from Doug Hammond. The sampling campaigns
inSanPedroandSantaMonicabasinswereorganizedandcarriedouttogetherwith
Danielle Monteverde, with help from Nick Rollins, Doug Hammond, Will Berelson,
Elias Karkabi, and Brian Seegers. I carried out all sample analyses. The Ge
isotope analyses were carried out at Ifremer in collaboration with Olivier Rouxel.
The Si isotope analyses were carried out at Trent University in collaboration with
Bastian Georg. This study will be published in a peer-reviewed journal with the
following preliminary list of co-authors: Danielle Monteverde, Doug Hammond,
Olivier Rouxel, Bastian Georg, and Nick Rollins.
Abstract
Theremovalofchemicalspeciesfromseawaterduringtheprecipitationofauthi-
genic minerals is difficult to constrain but may play a major role in the global bio-
geochemical cycles of some elements, including silicon (Si) and germanium (Ge).
Here, we present Ge/Si, d
74
Ge, and d
30
Si data of pore waters and core incuba-
tions at three continental margin sites. Benthic Ge fluxes appear to be strongly
49
controlled by Fe redox conditions at the sediment-water interface. Fe oxide precip-
itation reduces the benthic Ge flux and results in d
74
Ge fractionation of about -1.2
%. Pore water signatures are distinct from the overlying seawater and biogenic
silica (the latter typically having Ge/Si = 0.7 mmol/mol, d
74
Ge = 3.0-3.5 %, and
d
30
Si = 0.7-0.9 %). Pore water signatures are controlled by 1) Fe oxide dissolution
in the subsurface sediments, resulting in high Ge/Si (up to 3 mmol/mol) and low
d
74
Ge (1.3-1.9 %); and 2) the precipitation of authigenic aluminosilicate miner-
als, which preferentially incorporates Ge over Si and light over heavy Si isotopes,
resulting in low Ge/Si (down to 0.3 mmol/mol) and high pore water d
30
Si (0.8-
1.6 %). Authigenic aluminosilicate precipitation, however, has little to no effect
on pore water d
74
Ge (2.0-2.1 %), indicating negligible isotopic Ge fractionation.
Nevertheless, as a result of fractionation by Fe oxides, authigenic aluminosilicate
precipitation in marine sediments results in the burial of Ge that is up to 1 %
lighter than seawater.
3.1 Introduction
Understanding the operation of Earth’s surface processes over geological
timescales is only possible via interrogation of sedimentary records of past con-
ditions. To date, the role that many of the different biogoechemical cycles play in
controlling Earth’s climate and surface conditions over thousand- and million-year
times spans is still unclear. Silicon (Si) is a major constituent of Earth’s silicate
crust. Chemical weathering of continental silicate rocks consumes atmospheric
CO
2
and is therefore believed to play a major role in controlling Earth’s climate
over million year timescales (Walker et al., 1981; Berner et al., 1983). One of the
products - dissolved Si - is carried by rivers to the ocean where it is utilized as
50
an essential nutrient by diatoms and other biosilicifying organisms (e.g., Tréguer
& De La Rocha, 2013). The rate of marine bioproductivity and the resulting
burial of organic matter in marine sediments may be responsible for some of the
climate variability observed over glacial-interglacial (1000-10,000 year) timescales
(e.g., Kohfeld et al., 2005; Martinez-Garcia et al., 2014; Cartapanis et al., 2016).
Although both silicate rock weathering and marine organic matter burial are
well established as likely climate-control mechanisms, the actual variability and
importance of these and other Earth surface processes is still poorly constrained.
These uncertainties are largely due to the difficulty associated with constraining
the rates of chemical reactions and the fluxes of matter between the different mass
reservoirs (rocks, soils, oceans, atmosphere, biosphere, and sediments) in the past.
Such reconstructions often require the use of indirect biogeochemical proxies, for
example, elemental or isotopic ratios of different chemical species. Such methods
rely on the fact that different reservoirs contain unique chemical signatures or that
such signatures are affected (fractionated) during chemical reactions and transfer
from one reservoir to another. Ultimately, a robust interpretation of marine pale-
orecords requires the understanding of all of the major input and output fluxes
controlling the seawater signature.
Silicon isotope ratio (
30
Si/
28
Si, expressed as d
30
Si) and the germanium-to-
silicon (Ge/Si) ratio have been developed as proxies tracing the biogeochemical
cycling of Si (e.g., Froelich et al., 1985; Murnane & Stallard, 1990; De La Rocha
et al., 2000; Ziegler et al., 2005a). Recently, the stable isotope composition of
Ge (
74
Ge/
70
Ge ratio, expressed as d
74
Ge) has been measured in the major Earth
surface reservoirs and proposed as an additional proxy helping to constrain the
global biogeochemical cycle of Ge and Si (Rouxel et al., 2006; Escoube et al., 2012,
2015; Rouxel & Luais, 2017; Baronas et al., 2017a). Germanium is a useful tracer
51
of Si cycle due to it mainly being concentrated in silicate rocks and the similar
atomic properties and chemical behavior of the two elements (e.g., Burton et al.,
1959; Mortlock & Froelich, 1987). The ranges of Ge/Si, d
30
Si, and d
74
Ge signa-
tures in silicate rocks are relatively narrow (Mortlock & Froelich, 1987; Frings et
al., 2016; Escoube et al., 2012), allowing these proxies to primarily trace chemical
transformation processes rather than source mixing. During continental weather-
ing processes, the precipitation of secondary weathering products such as Al- and
Fe-oxides and aluminosilicate clays preferentially incorporates Ge relative to Si
and light Ge and Si isotopes preferentially relative to heavy ones, resulting in dis-
solved river composition that has lower Ge/Si but higher d
30
Si and d
74
Ge relative
to silicate rocks (e.g., Froelich et al., 1992; Kurtz et al., 2002; Ziegler et al., 2005a;
Georg et al., 2006a; Baronas et al., 2017a, also see Chapters 4 and 6). Additional
(usually small) Ge/Si and d
30
Si fractionation may be in some cases induced by
vegetation uptake of Si and Ge (Cornelis et al., 2011).
To a first order, the dissolved seawater Ge/Si, d
30
Si, and d
74
Ge composition
is controlled by a balance of riverine and hydrothermal inputs (Hammond et al.,
2000; Frings et al., 2016; Baronas et al., 2017a). However, intense biological uptake
of Si preferentially removes light isotopes, overprinting the input d
30
Si signature,
which results in a large range of observed seawater d
30
Si values (e.g., De La Rocha
et al., 2000; Cardinal et al., 2005; Fripiat et al., 2011). In contrast, Ge/Si ratios
are affected by biological fractionation only in extreme cases. Diatoms have been
shown to discriminate against Ge only when dissolved Si is depleted below∼10
mmol/L (Sutton et al., 2010). Ge isotopic composition appears to be unfraction-
ated during diatom uptake (Mantoura, 2006; Rouxel & Luais, 2017; Baronas et al.,
2017a). In contrast, all sponge spicules analyzed to date exhibit Ge/Si ratios sig-
nificantly lower than seawater, indicating strong biological fractionation (Ellwood
52
et al., 2006). Similarly to Si, sponges appear to also preferentially incorporate light
Ge isotopes (Guillermic et al., 2017). However, sponges likely account only for a
smallfractionofGeandSiburialintheoceanandthereforeshouldhavearelatively
small influence on the global seawater Ge/Si, d
30
Si, and d
74
Ge composition.
While the burial of diatom biogenic silica (bSi) has no effect on the seawa-
ter d
74
Ge composition, a large portion of bSi reaching the seafloor dissolves at or
just below the water-sediment interface, releasing dissolved Ge and Si into marine
porewaters. A range of complex diagenetic reactions take place in marine sedi-
ments, including the precipitation of various authigenic clay minerals (e.g., Aller,
2013). In some cases, these authigenic minerals incorporate Ge and Si, affecting
pore water Ge/Si (Hammond et al., 2000; King et al., 2000; Baronas et al., 2016),
d
30
Si (Ehlert et al., 2016), and possibly d
74
Ge signatures. Recent studies have
suggested that authigenic ("non-opal") burial plays a major in the global marine
cycles of Ge and Si (Baronas et al., 2016; Rahman et al., 2016). Indeed, variations
in authigenic Ge burial fluxes are likely responsible for the large seawater Ge/Si
fluctuations over glacial-interglacial cycles (Mortlock et al., 1991; Hammond et al.,
2004; Baronas et al., 2016).
Here, we present dissolved Ge/Si, d
74
Ge, and d
30
Si data from continental mar-
gin sediments at three different study sites. Pore water signatures were used to
track the potential isotopic fractionation with progressing sediment diagenesis and
authigenesis, while sediment core incubation data were used to constrain the net
effect of these processes on seawater d
74
Ge composition.
53
3.2 Study sites
San Pedro and Santa Monica basins are located in the Southern California
continental margin,∼20 and∼40 km offshore from Los Angeles, respectively. A
detailed description of the geological and oceanographic setting of the Southern
California Bight can be found elsewhere (Gorsline, 1992; Hickey, 1992). San Pedro
basin has an area of 819 km
2
and is 900 m deep. The Santa Monica basin is 2225
km
2
large and 925 m deep. Both basins are silled below 725 m depth, restrict-
ing the circulation of bottom water. Organic matter remineralization renders the
restricted bottom water suboxic (typically <9mmol/L O
2
) in the San Pedro basin
and completely anoxic in the Santa Monica basin (typically <4mmol/L O
2
), result-
ing in reducing sediment pore waters and limiting macrofaunal activity in the ben-
thos (Gorsline, 1992). In San Pedro basin, sediment irrigation and bioturbation is
prevalent enough to prevent stratification, whereas Santa Monica basin sediments
are finely laminated, indicating lack of macrofaunal activity. Material is supplied
to both basins primarily through particle infall from the overlying water column
and through nepheloid plume transport. The sediments are comprised of 8-10 %
CaCO
3
and∼4 % organic matter.
AdditionaldataarepresentedfromthecontinentalGulfofMexicoshelf, closeto
the Mississippi River delta. The processes governing Ge/Si ratios in the area have
been described in detail by Baronas et al. (2016). In this study, new d
74
Ge data
from two sites are presented and analyzed in the context of previously published
pore water concentrations and core incubation results.
54
3.3 Methods
3.3.1 Sample collection
San Pedro basin sediment cores were collected on 2014-09-04 aboard
the R/V Yellowfin at the San Pedro Ocean Timeseries (SPOT) study site.
SPOT is the site of a multi-year monthly water column sampling campaign
(https://dornsife.usc.edu/spot/). Santa Monica basin (SMB) cores were collected
on 2012-03-08 aboard the R/V Yellowfin in the central part of the basin (Table
3.1). The cores were kept on ice before being placed in a 5
◦
C cold room within
8 hours from retrieval. For half of the cores, the overlying water was siphoned
off while avoiding the disturbance of the sediment interface. Then, pore waters
were sampled using Rhizons (0.2 mm membrane; Rhizosphere Research Products,
The Netherlands). The suction was applied at all depth horizons simultaneously
to minimize vertical pore water advection during sampling. The pore waters were
collected for up to 24h. All samples were acidified to 0.1 % with Teflon-distilled
HNO
3
. Another set of cores with overlying water were incubated for several days
as described below.
Additional water column samples were collected at SPOT in 2014 and prior
(Table 3.1). These samples were collected in Niskin bottles on a CTD rosette
aboard R/V Yellowfin. They were filtered through a 0.2 mm pore size filter within
10 hours from retrieval and acidified prior to analysis.
The Gulf of Mexico (GoMex) samples were collected in August 2011 during
cruise EN-494 aboard the R/V Endeavor. Sediment cores were collected using a
multi-corer and seawater samples using Niskin bottles. Pore waters were collected
via sectioning under inert atmosphere and centrifugation. All samples were filtered
55
through 0.2 mm membrane and acidified prior to analysis. A detailed description
of the GoMex methods is given in Baronas et al. (2016).
3.3.2 Core incubations
Core incubations were carried out using the method described by Hammond
et al. (2004). Briefly, sediment cores with 1-1.5 L of overlying water were capped,
placed in a 5
◦
C cold room and the water slowly stirred (20-30 rpm) using a sus-
pended magnetic stir bar. Care was taken to minimize the introduction of air
bubbles between the cap and the water. 10-20 mL of overlying water was period-
ically sampled for Ge and Si concentration analyses using a plastic syringe, while
the piston was advanced to keep air out. The samples were immediately filtered
through a 0.2 mm membrane and acidified prior to analysis. After the incubation
was completed, the remaining overlying water was collected via siphoning, filtered
through a 0.2 mm membrane and acidified prior to d
74
Ge and d
30
Si analyses.
3.3.3 Element concentration analyses
Silicic acid and ammonia concentrations were measured standard colorimet-
ric techniques (Mullin & Riley, 1955; Bower & Holm-Hansen, 1980) with precision
betterthan5%. Ammoniaanalyseswerecarriedoutwithin24hoursofsamplecol-
lection to minimize degassing. Iron and manganese concentrations were analyzed
by ICP–MS on a Thermo Scientific Element2 with a precision of∼10 %. Sulfate
concentrations were measured on a Metrohm Ion Chromatograph. Germanium
concentrations were measured using isotope-dilution-hydride-generation-ICP-MS
on a Thermo Scientific Element2, using the method developed by (Mortlock &
Froelich, 1996) and modified by Baronas et al. (2016).
56
3.3.4 Ge isotope analyses
Ge co-precipitation. Filtered and acidified samples of pore water from sim-
ilar depths of multiple cores were combined to obtain larger composite samples
required for d
74
Ge analyses. Ge concentrations of individual aliquots were ana-
lyzed beforehand to ensure that all cores had similar Ge and Si concentration pro-
files. Pore water and seawater samples ranging from 100 mL to 9 L and containing
4-13 ng of Ge were then spiked with a Ge isotope double spike (
73
Ge/
70
Ge≈ 1,
previously calibrated and used by Escoube et al. (2012, 2015)) in a spike/sample
Ge mass ratio of 1-2 and a purified FeCl
3
solution to obtain a Fe concentration of
∼0.2 mmol/L. The samples were well mixed, and allowed to equilibrate for at least
16h. Next, Fe(OH)
3
flock was precipitated by bubbling pure NH
3
gas through the
sample until the solution reached a pH of 8-10. The flock was collected by settling
and centrifugation, redissolved in 2 mL concentrated Teflon-distilled HNO
3
and
diluted to 10 mL with Milli-Q. The samples were then dried down, redissolved in
1 mL concentrated Optima-grade HF and diluted to 30 mL with Milli-Q to obtain
a final 1M HF solution. They were then purified through anion exchange columns
as described below. The procedural blank was determined by processing spiked
Milli-Q and ranged from 0.01 to 0.3 ng Ge.
Anion-exchangechromatographicseparation. Aprocedureadaptedfrom
Rouxel et al. (2006) and described in detail by Rouxel & Luais (2017) was used.
All reagents used were either in-house Teflon-distilled or Optima-grade. A 10 mL
column was loaded with 1.8 mL (wet volume) of BioRad AG1-X8 resin, washed
with 10 mL of 3M HNO
3
, 0.28M HNO
3
, and Milli-Q in sequence and conditioned
with 5 mL 1M HF. Samples in 1M HF solution as prepared above were centrifuged
to separate insoluble fluorides and 10-29 mL of the solution was carefully added
to columns. The presence or amount of insoluble fluorides at this stage did not
57
appear to affect the final Ge recovery. The remaining matrix was eluted with 5
mL of 1M HF followed by 3 mL of Milli-Q, leaving fluorinated Ge retained on the
column. Ge was then eluted with 10 mL 0.28M HNO
3
. If required, the solution
was dried down and redissolved in a smaller volume of 0.28M HNO
3
to obtain
the 0.5-10 ppb Ge concentration required for isotope measurements. Each column
was reused 4-5 times, except when retention of DOC from the previous sample
was observed based on the color, in which case the resin was replaced. Ge blanks
from reused resin were below detection limit. Ge recovery ranged from 20 to 90%,
with one sample being as low as 8%. Incomplete recovery was most likely due to
variable Ge co-precipitation efficiency with Fe(OH)
3
, due to variable precipitation
rates, final pH and variable sample matrices (especially DOC concentrations in
pore waters), as well as some loss during co-precipitate recovery from the solution.
Importantly, incomplete recovery does not affect the measured d
74
Ge values, as all
samples were double-spiked prior to sample preparation.
Seawater exhibits relatively high concentrations of methylated Ge (which does
not participate in the inorganic Ge cycle (Froelich et al., 1985)). It is therefore
important to separate the inorganic and the methylated species prior to d
74
Ge
analysis. Baronasetal.(2017a)achievedthisviachromatographicseparationofthe
the methylated and inorganic Ge hydrides. In this study, separation was achieved
during anion column chromatography, and is confirmed by the good agreement of
seawater d
74
Ge determined via both methods (Table 3.1).
HG-MC-ICP-MS. Ge isotope analyses were performed on a Thermo Nep-
tune multi-collector ICP-MS at Ifremer using a method adapted from Rouxel et
al. (2006) and Escoube et al. (2015) and described in detail by Rouxel & Luais
(2017). Sample solutions of 0.5-10 ppb natural Ge in 0.28M HNO
3
were intro-
duced into an online hydride generation system (CETAC HGX-200) at a rate of
58
150 mL/min where they were mixed with 0.25 M NaBH
4
solution (in 1.5 M NaOH)
introduced at an equal rate. The dissolved Ge(OH)
4
species were reduced to
gaseous GeH
4
and transported into the ICP-MS torch using Ar carrier gas. The
Neptune MC-ICP-MS was operated in low mass resolution mode, measuring
70
Ge,
72
Ge,
73
Ge, and
74
Ge in L2, C, H1 and H2 cups, respectively. In addition, L4, L3,
L1 and H4 cups were also monitored for
68
Zn (possible interference as
70
Zn),
69
Ga,
71
Ga (possible interferences at m/z 70), and
77
Se (possible interference as
74
Se),
respectively. No interferences were detected in any of the runs. The samples were
bracketed using a NIST-3120a standard solution that had a total Ge concentration
generally within∼20 % of the bracketed sample, and was double-spiked to have a
spike/sample ratio within∼20 % of the bracketed sample. Each sample or stan-
dard run consisted of 6 measurement blocks each lasting 2 min (30 cycles of 4 s
each), and in most cases 4-5 blocks displaying the most stable signal were retained.
Therefore, each measurement represents 8-10 min of counting statistics at signal
intensities ranging from 0.4 to 6 V at
74
Ge (depending on Ge concentration in
sample solution, instrument tuning, and the age of NaBH
4
solution). The d
74
Ge
values are calculated for each block using the double-spike data reduction routine
of Siebert et al. (2001) and are reported in % as
74
Ge/
70
Ge sample ratio normal-
ized to the average
74
Ge/
70
Ge ratio of bracketing NIST 3120a measurements. This
method also yields Ge concentration values based on the measured spike/sample
ratio. The measurement uncertainty is reported as the internal 2s standard error
of the used sample blocks, or 2s standard deviation of all NIST 3120a bracketing
standard measurements within a given analytical session, whichever is higher.
59
3.3.5 Si isotope analyses
Cation-exchange chromatographic separation. A procedure adapted
from Georg et al. (2006b) was used. All reagents used were either in-house Teflon-
distilled or Optima-grade. A 10 mL column was filled with 1.8 mL (wet volume in
0.5M HNO
3
) BioRad AG50W-X12 (200-400 mesh) resin. The column was washed
with 15 mL Milli-Q, 3.6 mL 0.15M HF (new resin only), 10 mL 3M HCl, 5 mL
6M HCl, 4 mL 11M HCl, 5 mL 6M HCl, 5 mL 3M HCl, and 2x10 mL Milli-Q.
Between 0.5 and 2 mL of sample was then loaded onto the column and eluted with
8-10 mL Milli-Q to obtain a final solution of 1.5 ppm Si. Larger batches of several
standards were prepared by eluting with up to 40 mL Milli-Q to obtain 3 ppm Si
solutions that were then further diluted to 1.5 ppm. All bracketing standard and
reference material aliquots were purified using the same cation-exchange method
as the samples. The columns were reused up to a maximum of four times, at which
point the resin was replaced.
MC-ICP-MS. Si isotope measurements were performed on a Thermo Nep-
tune instrument at the Water Quality Centre at Trent University. An Elemental
Scientific ApexQ was used for sample introduction at a rate of 300 mL/min. The
Neptune was operated in low mass resolution.
28
Si,
29
Si, and
30
Si were measured
in cups C, H2, and H3, respectively. The measurements were done at the flat part
of the low mass peak shoulder that is free of the major interference from
14
N
16
O
+
.
The measurement window was manually re-positioned at least once within each
analytical session but the drift was never more than±0.0002 mass units. Sample
runs were bracketed with concentration-matched NBS-28 standard solution pre-
pared using the same fusion and cation exchange methods described above. Each
sample was measured as 2-4 bracketed replicate runs consisting of 30 cycles of 8s
each. This resulted in 8-16 min of counting statistics for each sample at typical
60
signal intensities of 8-18 V
28
Si. The d
30
Si values are reported as the sample
30
Si/
28
Si ratio normalized to the bracketing NBS-28 standard. Mass dependence
was ensured by comparing the d
30
Si and d
29
Si values of each run, which were
within 0.2 % (in terms of d
30
Si) of the mass-dependent fractionation line for the
vast majority of the measurements. A number of different reference materials were
analyzed multiple times interspersed with the samples, all of which agreed well
with previously reported values (see Chapter 6). The measurement uncertainty is
reportedastheinternal2sstandarderrorofsamplereplicates, or2sstandarddevi-
ation of all NBS-28 bracketing standard measurements within a given analytical
session, whichever is higher.
Using repeated analyses at different sample dilutions, a systematic offset of
the measured d
30
Si values was observed for samples that were diluted less than
five-fold during the sample preparation. This matrix effect most likely arises due
to the presence of mmol/L levels of sulfate, as the cation exchange column does
not remove anions. No effect was observed when the same samples were diluted
by more than 5 times during or after the chromatographic separation. Therefore,
all data reported here was obtained after 5-15 fold dilution.
3.4 Results
All of the Gulf of Mexico (GoMex) data, with the exception of d
74
Ge, was
previously reported and discussed by Baronas et al. (2016). This section therefore
focuses on the newly acquired data from San Pedro and Santa Monica basins.
61
Table 3.1: Study site locations and details of seawater samples. No measurements
were made where data are not available.
Gulf of Mexico
this study
Baronas et
al. (2017)
GOM CTD-45-7 Sta. 9 8/15/2011 28.97 -90.40 2.3 43 10 4.28 -- 2.79 ± 0.25 -- --
GOM CTD-6 (30m) Sta. 1 8/1/2011 28.59 -90.54 30 45 25 1.84 2.13 ± 0.15 2.21 ± 0.18 -- --
GOM-CTD-32-3 Sta. G 8/9/2011 26.28 -92.02 2121 17 25 0.70 3.13 ± 0.28 3.07 ± 0.22 -- --
Santa Monica Basin SMB 3/8/2012 33.81 -118.78 910 -- -- -- -- -- -- --
San Pedro Basin
SSW SPOT 9/10/2014 33.55 -118.40 0 1.6 1.1 1.43 -- -- -- --
UP-18 SPOT 4/23/2014 33.55 -118.40 50 7.0 8.0 0.88 3.41 ± 0.25 -- -- --
SPOT 500m SPOT -- 33.55 -118.40 500 55 74 0.74 -- 3.44 ± 0.15 -- --
SPOT 885m SPOT 9/10/2014 33.55 -118.40 885 78 105 0.74 2.97 ± 0.2 -- -- --
MC-2D OLW SPOT 9/4/2014 33.55 -118.40 885 91 114 0.79 3.06 ± 0.22 -- 1.15 75
MC-3D OLW SPOT 9/4/2014 33.55 -118.40 885 82 111 0.74 3.23 ± 0.28 -- 0.69 42
MC-5C OLW SPOT 9/4/2014 33.55 -118.40 885 109 125 0.87 2.9 ± 0.32 -- 0.11 46
MC-1D OLW SPOT 9/4/2014 33.55 -118.40 885 92 118 0.78 -- -- 1.71 105
MC-2C OLW SPOT 9/4/2014 33.55 -118.40 885 103 120 0.85 -- -- -- --
MC-2A OLW SPOT 9/4/2014 33.55 -118.40 885 89 116 0.77 -- -- -- --
Atlantic
GPrI-19 BATS 6/8/2011 31.67 -64.17 1000 12 13 0.90 3.04 ± 0.28 -- -- --
GDI-30,31 / 32 BATS 6/8/2011 31.67 -64.17 2000 24 17 1.36 3.68 ± 0.28 3.46 ± 0.11 -- --
GPrI-3 BATS 6/8/2011 31.67 -64.17 3500 27 28 0.97 -- 3.03 ± 0.25 -- --
Fe,
µmol/L
Mn,
nmol/L
Depth,
m
Ge/Si,
µmol/mol
Sample Station Date Lat., ° Long., °
Ge,
pmol/L
Si,
µmol/L
δ
74
Ge, ‰
3.4.1 Seawater
Seawater data are reported in Table 3.1. A number of the samples were previ-
ously analyzed for d
74
Ge by Baronas et al. (2017a) and re-analyzed in this study.
Considering the significant differences between the two methods, the good agree-
ment of the inter-calibration measurements serves to illustrate the robustness of
our d
74
Ge analyses. A more detailed comparison of the two methods and inter-
calibration of river samples is given in Chapter 6.
The Ge/Si values determined for SPOT bottom seawater range from 0.74 to
0.87 mmol/mol, close to the global ocean value of 0.76 mmol/mol (Froelich et al.,
1985; Sutton et al., 2010). SPOT bottom seawater d
74
Ge ranged from 2.9 to 3.2
%, slightly lighter than the 3.4-3.5 % determined higher in the water column
(Table 3.1; Baronas et al. (2017a)).
62
3.4.2 Pore waters
Table 3.2: Ge and Si chemistry of composite pore waters. Individual high resolu-
tion sample measurements are given in Appendix B Tables B.1 - B.3.
San Pedro Basin
MC-2A, MC-2C, MC-2D SPOT 1-5 568 347 1.64 1.33 ± 0.15 1.12 ± 0.15
MC-1D, MC-3D SPOT 1-5 595 298 1.99 1.58 ± 0.16 0.82 ± 0.15
MC-5C (C-1, C-2) SPOT 1-5 595 460 1.29 1.94 ± 0.31 0.98 ± 0.15
MC-1D − MC-5C (all cores) SPOT 6-10 346 391 0.89 2.12 ± 0.13 1.20 ± 0.42
MC-1D − MC-5C (all cores) SPOT 11-20 251 430 0.58 2.00 ± 0.22 1.17 ± 0.22
MC-1D − MC-5C (all cores) SPOT 21-37 147 455 0.32 2.11 ± 0.21 1.15 ± 0.15
MC-2A SPOT 0 531 297 1.78 -- 1.58 ± 0.15
MC-2A SPOT 8 380 356 1.07 -- 1.22 ± 0.15
MC-2A SPOT 15 214 440 0.49 -- 1.32 ± 0.15
MC-2A SPOT 33 111 525 0.21 -- 1.07 ± 0.17
Santa Monica Basin
D1-S2, D1-S4, D3-S1, D3-S3 SMB 4-6 379 366 1.03 2.30 ± 0.28 --
Gulf of Mexico *
MC-6C, MC6-D, MC6-E Sta. 2 0-3 386 313 1.23 2.29 ± 0.42 --
MC-6C, MC6-D, MC6-E Sta. 2 3-6 639 462 1.38 2.44 ± 0.16 --
MC-6C, MC6-D, MC6-E Sta. 2 6-10 517 521 0.99 2.12 ± 0.32 --
MC-6C, MC6-D, MC6-E Sta. 2 10-15 326 520 0.63 2.03 ± 0.32 --
MC-6C, MC6-D, MC6-E Sta. 2 15-20 385 435 0.88 1.94 ± 0.32 --
* All data except δ
74
Ge were previously published in Baronas et al. (2016).
Cores Station
Depth,
cm
δ
30
Si, ‰
Ge,
pmol/L
Si,
µmol/L
Ge/Si,
µmol/mol
δ
74
Ge, ‰
The pore water solute concentrations were similar in each individual core. Ge
concentrationsrangedfrom90to1200pmol/L(AppendixBTableB.1)andshowed
a maximum at the 2-3 cm horizon, decreasing monotonically with depth (Fig.
3.1). In contrast, Si concentrations were lowest at the sediment-water interface
and increased from∼250 mmol/L to∼400 mmol/L within the top 10 cm, with a
continued slow increase to∼500 mmol/L by 35 cm depth (Fig. 3.1). Ge/Si ranged
from a high of 2-3 mmol/mol at 2-3 cm depth to a low of 0.2-0.4 mmol/mol at the
bottom of the cores. d
74
Ge values ranged from 1.3 to 2.3 % in SPOT and SMB
and from 1.9 to 2.4 % in GoMex sediments (Table 3.2) and showed little variation
63
with depth. Pore water d
30
Si ranged from 0.8 to 1.6 %, with the heaviest value
observed at the sediment-water interface.
Fe, Mn, NH
3
, and SO
4
concentrations in SPOT pore waters are reported in
Figs. 3.1 and Appendix B Tables B.1-B.3. Fe concentrations range from 1 to 300
mmol/L, with a maximum at 2-3 cm depth. Mn and NH
3
concentrations range
from 0 to 600 nmol/L and from 20 to 400 mmol/L, respectively, and both increase
monotonically with depth. Sulfate concentrations are in 24-26 mmol/L range and
appear invariable with depth within analytical uncertainty.
0 1 2 3 4
δ
74
0
10
20
30
40
Depth, cm
a
0.2 0.5 1 2
Ge/Si, µmol/mol
b
50 100 200 400 1000
Concentration
c
Ge, pM
Si, µM
0.5 5 50 500
Concentration
d
20 22 24 26 28 30
SO
4
, mM
e
NH
3
, µM
Fe, µM
Mn, nM
Figure 3.1: Pore water profiles of San Pedro Basin (SPOT) sediments collected
from five cores. Bottom seawater data is plotted at an arbitrary depth above
the sediment-water interface. a) Ge isotope composition of composite samples,
verticalbarsshowthedepthrangeofcombinedsamples. Threedifferentcomposites
were measured at the surface horizon (see Table 3.2); b) Ge/Si composition of
composite samples as in (a); also shown are individual sample Ge/Si ratios in
gray. Note the inverse log scale of x-axis. The green band shows the estimated
d
74
GeandthemeasuredGe/SicompositionofbSi(Baronasetal.,2016), equivalent
to seawater. The gray bands show the typical composition of lithogenic silicates
(Escoube et al., 2012; Rouxel & Luais, 2017); c) Ge and Si concentrations; d)
NH
3
, Fe, and Mn concentrations; e) Sulfate concentrations from cores MC-1D and
MC-2A (Appendix B Table B.3), uncorrected for potential variations in salinity.
64
0 1 2 3 4
δ
74
0
10
20
30
40
Depth, cm
a
0.5 1 2 5
Ge/Si, µmol/mol
b
0 200 400 600
Concentration
c
0.5 5 50 500
Concentration
d
NH
3
, µM
Fe, µM
Mn, nM
25 26 27 28 29 30
SO
4
, mM
e
Ge, pM
Si, µM
Figure 3.2: Pore water profiles of Gulf of Mexico (GoMex) sediments collected
from three cores at Sta. 2 (Table 3.2; Baronas et al. (2016)). GoMex seawater
data is plotted at an arbitrary depth above the sediment-water interface. a) Ge
isotope composition of composite samples, vertical bars show the depth range of
combined samples; b) Ge/Si composition of composite samples as in (a). Note
the inverse log scale of x-axis. The green band shows the estimated d
74
Ge and
Ge/Si composition of bSi in GoMex shelf area affected by the Missisippi River
(pink dotted line; see text). The gray bands show the typical composition of
lithogenic silicates (Escoube et al., 2012; Rouxel & Luais, 2017); c) Ge and Si
concentrations, including additional Si data from a separate core MC-5; d) NH
3
,
Fe, and Mn concentrations in MC-5; e) Sulfate concentrations in MC-5, corrected
for salinity. All the data except for d
74
Ge were previously published in Baronas et
al. (2016).
3.4.3 Core incubations
Overlying sediment water incubations were performed for up to six days, to
constrain the benthic Ge and Si fluxes out of the sediment, and the net effect of
sediment diagenetic processes on the d
74
Ge and d
30
Si composition of the benthic
flux. Throughout the incubation, Ge concentrations increased from 70-80 to 80-
120 pmol/L in SPOT overlying water and from 90-100 to 120-150 pmol/L in SMB
overlying water. Si concentrations increased from∼100 to 130-150 mmol/L in
SPOT and from∼130 to 150-190 mmol/L in SMB (Fig. 3.3; Appendix B Tables
B.4 and B.5)). Table 3.3 summarizes the chemical and isotopic composition of
65
Table 3.3: Summary of core incubation data, showing the final post-incubation
composition of the overlying water, as well as the calculated Ge and Si fluxes and
the fraction of Ge added or removed by dissolution or precipitation of authigenic
non-opal. Individual time-series data for San Pedro and Santa Monica basins are
given in Appendix B Tables B.4 and B.5. Gulf of Mexico data (except for d
74
Ge)
was previously reported by Baronas et al. (2016).
San Pedro Basin
MC-3A SPOT 118 142 0.83 2.07 ± 0.71 0.71 ± 0.15 1.78 1.59 1.12 0.12 ± 0.12
MC-4C SPOT 110 149 0.74 2.93 ± 0.28 -- 0.50 0.75 0.67 -0.02 ± 0.05
MC-5A SPOT 90 156 0.58 3.53 ± 0.28 0.92 ± 0.32 0.02 0.73 0.03 -0.30 ± 0.09
MC-5B SPOT 80 144 0.55 3.14 ± 0.28 -- 0.43 0.77 0.56 -0.06 ± 0.07
MC-5D SPOT 98 129 0.76 3.38 ± 0.18 -- 0.41 0.98 0.41 -0.07 ± 0.09
Santa Monica Basin
D3-S2 SMB 145 154 0.946 3.26 ± 0.28 -- 0.91 0.50 1.81 0.19 ± 0.08
D4-S1 SMB 147 165 0.894 -- -- 1.12 0.75 1.49 0.18 ± 0.10
D4-S4 SMB 147 170 0.864 3.42 ± 0.28 -- 0.87 0.70 1.24 0.14 ± 0.11
D5-S1 SMB 151 189 0.799 2.95 ± 0.28 -- 1.48 1.43 1.04 0.12 ± 0.14
D5-S4 SMB 116 163 0.711 -- -- 0.90 1.00 0.90 0.05 ± 0.10
Gulf of Mexico
MC-2A * Sta. 1 111 70 1.59 2.71 ± 0.25 -- 2.17 2.09 1.04 -0.28 ± 0.65
MC-2B Sta. 1 99 58 1.70 2.43 ± 0.28 -- 2.28 1.65 1.38 -0.06 ± 0.50
MC-3A * Sta. 1 113 83 1.35 2.36 ± 0.25 -- 4.09 2.65 1.54 0.00 ± 0.16
MC-3B * Sta. 1 113 89 1.27 2.18 ± 0.25 -- 3.55 2.43 1.46 -0.03 ± 0.14
MC-6A, MC-6B Sta. 2 58 73 0.79 3.44 ± 0.24 -- -- 1.97 -- --
* Previously published in Baronas et al. (2017). Water depth is 30-38 m at Sta. 1 and 22-24 m at Sta. 2.
†
Uncertainty reported as ± 2σ (sample replicate or bracketing standard reproducibility, whichever is higher).
** Fraction of Ge in the overlying water at the end of the incubation that added via dissolution (positive values) or removed via precipitation (negative
values) of non-opal (authigenic or lithogenic) phases (Eq. 3.5). Calculated assuming a ±20% uncertainty on the measured Ge and Si fluxes.
δ
30
Si, ‰
†
final composition after incubation
Cores Station fGe
non-opal
**
Ge/Si flux,
µmol/mol
δ
74
Ge, ‰
†
Si flux,
mmol m
-2
d
-1
Ge flux,
nmol m
-2
d
-1
Ge/Si,
µmol/mol
Ge,
pmol/L
Si,
µmol/L
the post-incubation overlying water and the calculated benthic fluxes. The Ge
benthic fluxes were calculated to range∼0-1.8 nmol m
–2
d
–1
at SPOT and 0.9-1.5
nmol m
–2
d
–1
at SMB. The Si benthic fluxes ranged from 0.7 to 1.6 mmol m
–2
d
–1
at SPOT and from 0.5 to 1.4 mmol m
–2
d
–1
at SMB. As a result, the benthic flux
ratios exhibited a wide range at both sites (0.03-1.1mmol/mol at SPOT and 0.9-1.8
mmol/mol at SMB). The post-incubationd
74
Ge was in the 2.9-3.5 % range, similar
to the initial overlying composition (2.9-3.2 %), with the exception of one SPOT
incubation (2.07± 0.71 %). d
30
Si composition of two samples post-incubation
overlying water samples was determined as 0.7 and 0.9 % (Table 3.3).
66
In the Gulf of Mexico, the d
74
Ge composition of two post-incubation overlying
water samples was determined in addition to previous data reported by Baronas
et al. (2017a) (Table 3.3). In Sta. 1, which is located close to the Mississippi River
delta, d
74
Ge was determined to be 2.7 %, in agreement with the data of Baronas
et al. (2017a). In Sta. 2, which was located several hundred kilometers away from
the river delta, d
74
Ge was determined to be 3.4 %, similar to the deep seawater
in the Gulf of Mexico and other oceanic basins (Table 3.1).
3.5 Discussion
3.5.1 Pore waters
Broadly, the d
74
Ge, d
30
Si, and Ge/Si composition of sediment overlying and
pore water could be controlled by 1) mixing of various solute sources (dissolution of
bSi vs. lithogenic particles); and 2) solute removal via precipitation of authigenic
phases, and any isotopic or elemental fractionation associated with this process.
Pore water d
74
Ge measured in SPOT and SMB sediments was 1-2 % lighter than
the expected composition of the dissolving diatom bSi, which is likely to be similar
to the overlying seawater (Fig. 3.1a). Therefore, the light d
74
Ge
pw
composition
must reflect either a significant contribution from an isotopically lighter source, or
fractionation during Ge incorporation into precipitating authigenic phases. Rouxel
et al. (2006) and Escoube et al. (2012) have shown that various lithogenic silicates,
including marine sediments exhibit a narrow d
74
Ge range of 0.4-0.7 %. However,
secondary continental clays, for example Fe oxides, are known to be enriched in
Ge (Kurtz et al., 2002) and to preferentially incorporate light Ge isotopes during
formation (Pokrovsky et al., 2014). In Chapter 4, we show how continued intense
continental chemical weathering can produce lithogenic particles as light as -0.3
67
0 50 100 150
Time, h
60
80
100
120
140
160
Concentration
San Pedro Basin (SPOT)
a
0 50 100 150
Time, h
0.5
0.6
0.7
0.8
0.9
1
Ge/Si, µmol/mol
c
100 120 140 160
Si, µmol/L
60
80
100
120
Ge, pmol/L
e
0 50 100 150
Time, h
80
100
120
140
160
180
200
Concentration
Santa Monica Basin (SMB)
b
0 50 100 150
Time, h
0.5
0.6
0.7
0.8
0.9
1
Ge/Si, µmol/mol
d
120 140 160 180
Si, µmol/L
80
100
120
140
160
Ge, pmol/L
f
D3-S2
D4-S1
D4-S4
D5-S1
D5-S4
MC-3A
MC-4C
MC-5A
MC-5B
MC-5D
Ge
(pM)
SMB SPOT
Si
(µM)
Figure 3.3: Incubation results of cores from San Pedro and Santa Monica basins
(SPOT and SMB, respectively): a & b) The increase of Ge and Si concentrations
in the incubated overlying water. These slopes, once corrected for water loss via
sampling (see Tables B.4 and B.5), were used to determine the benthic Ge and Si
fluxes reported in Table 3.3; c & d) the change in the Ge/Si ratio of the overlying
water. The thick dashed line shows the Ge/Si of biogenic silica in the SPOT and
SMB sediments (0.7 mmol/mol; Baronas et al. (2016)); e & f) plots showing Ge vs.
Si concentration over the course of the incubations. The slopes are reflective of
the benthic flux Ge/Si ratio (Table 3.3). The thick dashed line shows the Ge/Si
of biogenic silica (0.7 mmol/mol).
%. In SPOT pore waters, the lowest d
74
Ge
pw
values are found in the 1-5 cm
depth horizon, coinciding with the highest Ge/Si ratios and Fe concentrations in
pore waters (Fig. 3.1). Therefore, the simplest interpretation of these data is that
surface d
74
Ge
pw
are primarily controlled by a mixing of two dissolved Ge sources:
68
bSi (Ge/Si = 0.7 mmol/mol; d
74
Ge = 3-3.5 %) and lithogenic particles (Ge/Si >
1.5 mmol/mol; d
74
Ge < 0.6 %).
Effect of authigenic clay precipitation on Ge isotope composition
Belowabout15cmdepth, porewaterGe/Sivaluesdecreasebelow0.7mmol/mol
(Fig. 3.1b), indicating that pore water Ge is being removed via the precipita-
tion of authigenic phases. This phenomenon, sometimes termed the "non-opal"
Ge sink, has previously been described in various sedimentary settings, including
the San Pedro basin (Hammond et al., 2000; King et al., 2000; McManus et al.,
2003) and occurs most rapidly in continental margin settings, where the supply
of both bSi and lithogenic particulates to the sediments is high (Baronas et al.,
2016). Although the stoichiometry and the mineralogy of the precipitating phases
is poorly constrained, a similar decrease in both Ge and Fe concentrations (Fig.
3.1c-d) suggests it could be Fe-rich aluminosilicate clays. The formation of silicate
clays is also indicated by the asymptotic Si concentrations of∼400-500 mmol/L
(Table 3.2, Figs. 3.1 and 3.2), which is significantly lower than the solubility of
bSi, which can range from 600 to 1000 mmol/L, depending on bSi age and the
degree of surface inactivation via coating (e.g., Dixit et al., 2001).
The minimum estimate of dissolved Ge removed via authigenesis can be cal-
culated from the change in pore water Ge/Si composition. Assuming that all of
pore water Ge below 30 cm depth is derived from bSi dissolution (Ge/Si = 0.7
mmol/mol), at least∼50-70 % of dissolved Ge has to be precipitated to achieve
the measured Ge/Si of 0.2-0.3 mmol/mol. At the same time, the d
74
Ge
pw
composi-
tion remains constant with depth, indicating that no isotopic fractionation occurs
during this process.
69
Although the geochemical setting is more complicated and there are less data
available, the same conclusion can be drawn from the GoMex pore waters (Fig.
3.2). Here, d
74
Ge
pw
are also relatively constant in the 1.9-2.4 % range, whereas
Ge/Si varies between 1.4 and 0.6 mmol/mol, generally decreasing with depth.
The Mississippi River supplies a large amount of Ge and Si to the studied area,
partly due to contamination by anthropogenic activity (Mississippi Ge/Si = 1.6
mmol/mol; d
74
Ge = 2.0 %). Its discharge and possibly chemical and isotopic
composition varies temporally, as a result variably affecting the elemental and iso-
topic composition of diatom bSi that is supplied to and dissolves in the sediments
(Baronas et al., 2016). Despite the uncertainty of the bSi end-member, the large
decrease in pore water Ge/Si can only be explained by authigenic Ge precipitation.
Yet, similarly to SPOT sediments, no variation of d
74
Ge
pw
with depth is observed
at GoMex (Fig. 3.2a).
Large changes in Ge bonding environment (coordination number, bond lengths
and angles) have been shown to result in large isotopic fractionation (Li et al.,
2009; Pokrovsky et al., 2014; Rouxel & Luais, 2017). The lack of d
74
Ge fractiona-
tion during Ge removal from pore waters therefore supports a similar tetrahedral
Ge bonding environment in the aqueous and the precipitating phases. Ge is often
tetrahedrally bound in silicate minerals, whereas octahedral coordination is com-
mon in Fe oxides (Bernstein, 1985; Pokrovsky et al., 2006). Our data therefore
suggest that Ge is removed from deeper (> 10 cm depth) pore waters via the
precipitation of authigenic aluminosilicate phases rather than Fe oxides.
70
3.5.2 Core incubations
Ge and Si benthic fluxes
Pore water composition enables one to observe the in-situ effects of sediment
authigenesis on the geochemical and isotopic composition of solutes. However,
large differences between the chemical composition of the pore waters and the
overlying seawater often result in steep (sub-cm) chemical gradients just below the
water-sediment interface. Although difficult to resolve, rapid chemical reactions
(such as oxidation of reduced Fe) can take place at this boundary, significantly
affecting chemical exchange between the seawater and the pore waters. Measuring
the geochemical and isotopic composition of the benthic solute flux allows the net
effect of all sedimentary processes to be assessed.
The benthic Si fluxes were similar at SPOT and SMB sites and individual core
experiments agreed well with each other at a given site (relative standard deviation
of∼40 %; Table 3.3 and Fig. 3.3). In contrast, Ge fluxes differed significantly
between the two sites and were especially variable within SPOT, resulting in a
wide range of observed benthic flux Ge/Si ratios. We propose that Ge fluxes in
these experiments are affected by variable redox conditions in the sediments that
wereperturbedduringretrieval, transportation, andsampling. SantaMonicabasin
bottom waters and sediments are known to be consistently anoxic, exhibiting high
benthic Fe flux and reduced pore water Fe up to (or nearly up to) the sediment-
water interface (McManus et al., 1997; Elrod, 2004; Severmann et al., 2010). As
a result, the Ge/Si flux at SMB was significantly higher than the 0.7 mmol/mol
of dissolving bSi in all cases (Fig. 3.3, Table 3.3). These results suggest the
dissolution of either lithogenic or previously formed authigenic phases with higher
Ge/Si ratios.
71
AtSPOT,benthicGefluxesandfluxGe/Siratiosvarywidely,thelatterranging
from∼0 to 1.1 mmol/mol (Fig. 3.3, Table 3.3). Considering that all cores were
collected in close proximity (cores MC-5A, -5B, and -5D were collected during a
single multi-corer deployment), this variability is unlikely to be caused by spatial
heterogeneity in surface sediment composition. A much more likely possibility is
that the range of observed fluxes is the result of the perturbation of bottom redox
conditionsduringcorerecoveryandincubation. Itisunavoidablethatsomeoxygen
is introduced during sampling. As a result, any reduced Fe in the overlying water
and the surficial pore water can get oxidized, capturing a portion (or all, in the
case of MC-5A) of the potential Ge benthic flux. Germanium is well known to
adsorb or co-precipitate with Fe oxides (e.g., Pokrovsky et al., 2006). Indeed, Fe
oxide (FeOx) co-precipitation is used to pre-concentrate Ge from dissolved samples
during sample preparation (see Section 6.2). Orange, most likely FeOx, flock was
observed on sediment surface of most cores. In addition, core MC-5A, which
had negligible benthic Ge flux, also had a large burrow at the sediment water
interface and seemed the most disturbed during recovery, including air bubbles
trapped in the core liner. In support of this hypothesis, the benthic dissolved Fe
flux at SPOT was previously determined by in-situ incubations and water column
measurements to often be 1-2 orders of magnitude lower than that based on pore
water Fe gradients, suggesting that a large portion of Fe precipitates at the water-
sediment interface (Elrod, 2004; Severmann et al., 2010; John et al., 2012).
In summary, the core incubations performed on SPOT and SMB sediments
together represent a range of bottom redox conditions. This variety provides an
independent test of any d
74
Ge fractionation potentially associated with Fe oxide
precipitation in marine sediments.
72
Effect of Fe oxide precipitation on Ge isotope composition
The large amount of seawater needed for d
74
Ge analyses prevents the collec-
tion of d
74
Ge time-series data during core incubations. However, the final post-
incubation d
74
Ge signature of the overlying water (d
74
Ge
olw
) can be used to assess
the degree of isotopic composition associated with the non-opal Ge fluxes. As
discussed above, Ge/Si flux values lower than the 0.7 mmol/mol of dissolving bSi
indicate Ge retention in the sediments, through precipitation of either Fe oxides
or aluminosilicate clays. Ge/Si flux values above 0.7 mmol/mol indicate additional
Ge release, from dissolution of either lithogenic particles or previously formed and
destabilized authigenic phases.
Figure 3.4a shows that there does not appear to be a strong relationship
between the benthic flux Ge/Si ratio and the final d
74
Ge
olw
composition. Espe-
cially illustrative is the d
74
Ge
olw
similarity between the two incubations with the
most extreme flux Ge/Si ratios (∼0 mmol/mol at SPOT MC-5A and 1.8 mmol/mol
at SMB D3-S2). Figure 3.4b shows the same data in a three end-member mixing
space (deep seawater, bSi, and lithogenic clays). All data points but one plot on
a mixing line between isotopically identical bSi and seawater. Linear regression
through the core incubation data also yields a statistically insignificant slope.
To quantitatively assess the possible range of fractionation factor D
74
Ge
ppt–sw
values, the amount of Ge added from or removed to non-opal phases must first be
quantified. The overlying water of each core incubation had an initial amount of
dissolved Ge, to which additional Ge was supplied from opal and non-opal sources:
n
olw
+n
opal
+n
non–opal
= n
total
(3.1)
73
-0.5 -0.25 0 0.25 0.5
fGe
no
(fraction Ge supplied from non-opal)
0
1
2
3
4
5
δ
74
Adj. R
2
= 0.02
p = 0.32
c
0 0.005 0.01 0.015 0.02
1/Ge, µmol
-1
L
0
1
2
3
4
5
δ
74
Adj. R
2
= -0.13
p = 0.69
b
removal addition
012 3
Benthic flux Ge/Si, µmol/mol
0
1
2
3
4
5
δ
74
a
SPOT core inc.
SMB core inc.
Deep seawater
Estimated bSi
Lithogenic silicates
Figure 3.4: The effect of opal and non-opal benthic fluxes on d
74
Ge composition of
the overlying water in San Pedro and Santa Monica basins. a) The post-incubation
overlying d
74
Ge plotted against the benthic flux Ge/Si (Table 3.3). The composi-
tionoftwomainGeandSisources, biogenicsilica(Ge/Si=0.7mmol/mol, reported
by Baronas et al. (2016); d
74
Ge = 3.2 %, estimated equal to the mean San Pedro
seawater value (Table 3.1)) and lithogenic silicates (Escoube et al., 2012) are also
shown; b) The post-incubation overlying d
74
Ge plotted against the inverse of the
post-incubation Ge concentration. The gray areas show the range of possible mix-
ing curves between bSi, lithogenic silicates, and San Pedro seawater (500 m depth),
given the uncertainties of the end-member compositions. c) The post-incubation
overlying d
74
Ge plotted against fGe
no
, the fraction of Ge supplied (positive num-
bers) or removed (negative numbers) via dissolution or precipitation of non-opal
phases (Table 3.3). In panels b and c, the thick black line shows a linear regression
through the core incubation data only, and the dotted black lines show the 95%
C.I. of the fit. The p-value indicates the probability that the slope is statistically
significant.
74
where n is the amount of Ge in mol. n
olw
is known from Ge
olw
concentration
and the water volume at the start of the incubation. The amount added from bSi
dissolution is then
n
opal
= FGe
opal
×A
surf
×t
total
(3.2)
and
FGe
opal
= FSi
opal
×Ge/Si
opal
(3.3)
whereFSi
opal
is the measured Si flux in mmol m
–2
d
–1
, Ge/Si
opal
= 0.7mmol/mol,
A
surf
is sediment surface area in m, and t
total
is the total incubation time (corrected
for volume change caused by sampling; see Section 6.2) in days. The amount of
Ge added from or removed to non-opal phases is then
n
non–opal
= (FGe
total
–FGe
opal
)×A
surf
×t
total
(3.4)
The fraction of Ge supplied to or removed from the total Ge inventory in the
overlying water via non-opal phases is then simply
fGe
non–opal
=
n
non–opal
n
total
(3.5)
The calculated fGe
non–opal
fraction for all the incubated cores is given in Table
3.3. The d
74
Ge
olw
dependence on fGe
non–opal
is shown in Fig. 3.4c with a lin-
ear regression through the core incubation data. Here, the slope is equal to
D
74
Ge
ppt–sw
and the intercept at fGe
non–opal
= 0 is equal to d
74
Ge
sw
. Assum-
ing that non-opal Ge addition to the overlying water results from dissolution of
authigenic FeOx rather than lithogenic particles, the fit to all the core incubation
datayieldsD
74
Ge
ppt–sw
= –1.2±2.6%. Takinganalyticald
74
Geuncertaintiesinto
75
account, the fit residuals are minimized with D
74
Ge
ppt–sw
= –0.6 %. Goodness-
of-fit statistics indicate that this fit is not statistically significant (see Fig. 3.5c).
Nevertheless, it is interesting to note that a non-opal fractionation of –0.6±1.8 %
was calculated by Baronas et al. (2017a) based on a steady-state global ocean mass
budget. Despite the large uncertainties associated with the above calculations, this
apparent fractionation factor is similar to the experimentally determined value of
–1.7±0.1 % for Ge adsorption to FeOx (Pokrovsky et al., 2014).
The potential effect of this process on the d
74
Ge
olw
value is however self-
limiting. If only a small fraction of Ge is removed via FeOx precipitation, there
will be little fractionation of the benthic flux d
74
Ge (d
74
Ge
benthic
) composition. If,
on the other hand, the majority of Ge is precipitated at the sediment-water inter-
face, the benthic Ge flux itself will approach zero, and therefore will have little
effect on the d
74
Ge
olw
composition, even if d
74
Ge
benthic
is strongly fractionated.
The maximum isotopic forcing is achieved when exactly half of Ge is precipitated
at the sediment-water interface, and therefore the maximum possible difference
between d
74
Ge
olw
and d
74
Ge
benthic
is half of the actual D
74
Ge
ppt–sw
value, i.e.
between -0.3 and -0.6 %, assuming the values determined above are correct. This
self-limitation explains why there is so little isotopic difference observed before and
after the incubation experiments (Fig. 3.4c).
3.5.3 Ge isotope dynamics during marine sediment authi-
genesis: a summary
Theuseofbothd
74
GeandGe/Sisignaturesallowsavisualizationofthevarious
sources and processes controlling Ge dynamics in marine sediments. Figure 3.5
shows that the majority of pore water and core incubation data at all three study
sites can be explained by simple mixing between bSi and lithogenic sources of Ge.
76
The observed d
74
Ge
pw
of about 2 % could therefore reflect a mixture of biogenic
(3.2 %) and lithogenic (0.6 %) Ge in a ratio of∼1:1. Another possibility is that
the low d
74
Ge is supplied to pore waters via reduction and dissolution of Fe oxides
precipitated near the sediment-water interface, which may be up to 1.7 % lighter
than the dissolving bSi (Pokrovsky et al., 2014).
0.2 0.5 1 2 3
Ge/Si, µmol/mol
0
1
2
3
4
δ
74
SPOT & SMB
a
Seawater
Pore waters
Core incubations
Lith. silicates
Estim. bSi
0.5 1 2 3 5
Ge/Si, µmol/mol
0
1
2
3
4
δ
74
Gulf of Mexico
b
Seawater
Pore waters
Core incubations
Lith. silicates
Mississippi
Surf.
Bott.
SPOT
SMB
SPOT
SMB
Surf.
Bott.
Sta. 2
Sta. 2
Sta. 1
FeOx diss.?
clay ppt. clay ppt.
biofractionation
Figure 3.5: The summary of processes controlling the dissolved d
74
Ge and Ge/Si
composition in the seawater and sediments of a) San Pedro and Santa Monica
basins; and b) the Gulf of Mexico. Note the reverse log scale of the x-axis. The
grayareashowstherangeofpossiblemixingslopesbetweenlithogenicandbiogenic
silica, given the uncertainty of the end-member compositions. The arrows show
the possible fractionating processes. In panel b, the composition of the Mississippi
River, a major Ge and Si source to the study area, is also shown (reported pre-
viously by Baronas et al. (2017a)). Clay precipitation and biofractionation seem
to affect only the Ge/Si ratio without apparent effect on d
74
Ge, whereas FeOx
dissolution affects both.
Additional authigenic Ge precipitation at depth is clearly illustrated by the
decrease of pore water Ge/Si values at SPOT (Figs. 3.1, 3.5a). As argued above,
the lack of a corresponding change in d
74
Ge indicates negligible isotopic fractiona-
tion during thisprocess. Inthis case, Ge is most likelyincorporated into authigenic
77
Fe-rich aluminosilicate clays, which have been shown to form rapidly in continental
margin environments (Michalopoulos & Aller, 1995; Michalopoulos et al., 2000),
includingGoMexsediments(Presti&Michalopoulos,2008). IntheGulfofMexico,
an additional complication arises due to the supply of isotopically distinct Ge by
the Mississippi River, and the fractionation of Ge/Si during extreme Si depletion in
surface waters (Baronas et al., 2016, 2017a). The latter process is likely responsible
for the Ge/Si enrichment in the surface waters of the GoMex shelf (Fig. 3.5b).
As discussed by Baronas et al. (2017a), the similarity between d
74
Ge of surface
and bottom waters, despite the strong Ge/Si fractionation, suggests that there is
little to no d
74
Ge fractionation during diatom growth, in agreement with diatom
culture experiments (Mantoura, 2006; Rouxel & Luais, 2017). Despite these com-
plications, a similar authigenic Ge precipitation is observed in the GoMex pore
waters, again without any detectable d
74
Ge
pw
fractionation (Fig. 3.5b).
Although the exact source of the isotopically light Ge to pore waters is
unknown, the consistency between the d
74
Ge
pw
values at the three sites suggests
that the global non-opal Ge sink could be close to 2 %. This value is within
uncertainty of the 2.7±1.7 % previously proposed by Baronas et al. (2017a) based
on a global steady state Ge mass budget. However, as discussed in Chapter 6,
additional major river d
74
Ge data has resulted in a revision of the global mean
riverine input from 3.5± 1.5 % used in Baronas et al. (2017a) to 2.6± 0.5 %.
A revised global d
74
Ge budget is presented in Chapter 7. Based on a steady state
assumption, the updated budget requires a non-opal d
74
Ge of about 2.4 %, indi-
cating a fractionation factor D
74
Ge
sw–no
of -0.9 %, in excellent agreement with
the data presented in this chapter. Overall, the good agreement suggests that the
contemporary Ge isotope mass budget should be close to steady state.
78
Finally, the above interpretation of pore water d
74
Ge data is supported by a
previous study of an authigenic glauconite mineral by Rouxel et al. (2006) (d
74
Ge
values normalized to the accepted standard NIST 3120a given in Escoube et al.
(2012)). The iron-rich glauconite clay analyzed (reference material GL-O) was
enriched in Ge, with Ge/Si of 7.3 mmol/mol, supporting the role of authigenic
aluminosilicate clays as the non-opal Ge sink in marine sediments. Despite this
enrichment, it exhibited d
74
Ge of 2.44± 0.15 %, very close to the pore water
values presented here. We argue that this value may therefore represent a mixture
of biogenic (d
74
Ge≈ 3 %) and lithogenic or FeOx-fractionated isotopically lighter
Ge, with little to no fractionation during precipitation of the glauconite itself, in
a fashion analogous to modern sediments. It must be noted that the age of this
glauconite was estimated to be 95 Ma (Kapusta et al., 1997), and d
74
Ge
sw
at
that time may have been significantly different from the current value, potentially
invalidating the above interpretation.
3.5.4 Effect of authigenesis on Si isotope composition
Comparison of Ge and Si isotope signatures in pore waters can yield further
insight into the diagenetic processes taking place. Lacking direct biogenic silica
d
30
Si measurements, it is difficult to quantitatively assess the degree of Si isotope
fractionation during clay or FeOx precipitation in the sediments. However, the bSi
d
30
Si can be approximated based on measurements of post-incubation overlying
water (Table 3.3, Fig. 3.6a). Given that the two samples had very similar d
30
Si
of 0.7-0.9 % despite showing very different Si and Ge fluxes, suggests that authi-
genic processes have little effect on the d
30
Si composition of the benthic flux and
the latter should therefore primarily reflect the signature of dissolving bSi. Most
d
30
Si
pw
values are significantly heavier, suggesting preferential removal of light
79
isotopes during authigenic aluminosilicate precipitation. Our data are consistent
with the recent study ofd
30
Si signatures in the Peru upwelling margin Ehlert et al.
(2016), where bSi d
30
Si ranged from 0.3 to 1.2 % and d
30
Si
pw
were significantly
heavier at 1.1-1.9 %. Ehlert et al. calculated a fractionation factor of -2 % for
authigenic aluminosilicate precipitation in Peruvian margin sediments. Although
a quantitative comparison is not possible, our data suggest the same direction of
fractionation in the California margin sediments.
01 2
δ
30
0
10
20
30
40
Depth, cm
a
0.2 0.5 1 2 3
Ge/Si, µmol/mol
-0.5
0
0.5
1
1.5
2
δ
30
b
0123 4
δ
74
-0.5
0
0.5
1
1.5
2
δ
30
c
Pore waters
Core incubations
Lith. silicates
Estim. bSi
Composite
MC-2A
clay ppt.
FeOx diss. FeOx diss.
clay ppt.
Figure 3.6: Dissolved d
30
Si dynamics in San Pedro basin sediments. a) Pore
water d
30
Si variation with depth. Shown are data from both composite pore water
samples and from individual high resolution samples in core MC-2A (Table 3.2).
Coreincubationdataareplottedabovethesediment-waterinterfaceatanarbitrary
depth. The green band shows the estimated d
30
Si of bSi (assumed to be equivalent
to post-incubation seawater). The gray bands show the typical composition of
lithogenic silicates (Frings et al., 2016, and references therein); b) d
30
Si vs. Ge/Si.
Note the reverse log-scale x-axis; c) d
30
Si vs. d
74
Ge. The gray area in panels b
and c shows the range of possible mixing slopes between lithogenic and biogenic
silica, given the uncertainty of the end-member compositions. The arrows show
possible fractionating processes.
In addition to the composite pore water samples, d
30
Si was also analyzed on
a set of high resolution pore water samples from one of the cores (Fig. 3.6a).
While the composite and the high resolution samples agreed well in deeper pore
80
waters, the heaviest d
30
Si
pw
value (1.58± 0.15 %) was observed very close to
the sediment-water interface. This observation suggests that authigenic clay pre-
cipitation may be the most intense in the surface sediments. In contrast to the
deeper pore waters, surfaced
30
Si
pw
signatures are expected to be more sensitive to
aluminosilicate precipitation due to lower Si concentrations, whereas surface pore
water Ge/Si is less sensitive due to the release of additional Ge from the reduction
of Fe oxides. Alternatively, it is possible that shallow d
30
Si
pw
is fractionated dur-
ing pore water Si adsorption with FeOx, in agreement with experimental data of
Delstanche et al. (2009).
In addition, the high d
30
Si
pw
values imply that very little, if any pore water Si
is derived from dissolution of lithogenic silicates which typically have d
30
Si values
ranging from -0.5 to 0 %(e.g., Frings et al., 2016, and references therein). This
suggests that the source of isotopically light Ge to the sediments is the reduction
and dissolution of Fe oxides (Fig. 3.6b,c).
Finally, theaboveanalysisreliesontheassumptionthatpost-incubationoverly-
ing water is representative of thed
30
Si
bSi
signature. However, the above discussion
has shown that Si isotope fractionation takes place in the sediments and therefore
the benthic flux d
30
Si composition should be to some degree offset from the ini-
tial bSi value. Unfortunately, no direct d
30
Si
bSi
data is available to confirm this.
Therefore, the offset between d
30
Si
pw
and d
30
Si
bSi
estimated from core incuba-
tion data should be taken as a minimum estimate of d
30
Si fractionation in these
sediments.
81
3.6 Conclusions
We have presented Ge/Si, d
74
Ge, and d
30
Si data from seawater, pore waters,
and core incubations at three continental margin sites. The combined use of all
three proxies has helped identify the dominant authigenic processes controlling
the Ge and Si dynamics in these sediments. The release of dissolved Ge from
sediments is strongly controlled by Fe oxide precipitation at the sediment-water
interface. FeoxideprecipitationpreferentiallyincorporateslightGeisotopeswitha
fractionation factor of -1.2± 2.6 %, which, although highly uncertain and difficult
to quantify based on our data, is similar to the experimentally determined value
of -1.7± 0.1 % (Pokrovsky et al., 2014).
Below the very shallow redox boundary, pore water d
74
Ge is a mixture of heav-
ier Ge released via bSi dissolution and the lighter Ge released via reduction and
dissolution of Fe oxides that were either precipitated in-situ or delivered with detri-
tal particles. With depth, the precipitation of authigenic Fe-rich aluminosilicate
clays with a high Ge/Si ratio, coupled with continued dissolution of bSi, results
in a plateau of Si (McManus et al., 1995) and the depletion of both Fe and Ge
concentrations in the pore waters. Authigenic clays are enriched in Ge and light
Si isotopes, removing up to 50% of dissolved Ge and maintaining pore water d
30
Si
at heavier values. In contrast, d
74
Ge signatures are not fractionated during clay
precipitation. However, sinceisotopicallylightGeisreleasedduringFeoxidedisso-
lution, aluminosilicate clay precipitation still results in the removal of isotopically
light Ge (d
74
Ge≈ 2 %) relative to seawater (d
74
Ge≈ 3 %). This value is close
to that required to keep the global marine Ge isotope budget at steady state.
82
Acknowledgements
Financial support for the SPOT cruise was provided by NSF grant OCE-
1260692toDEH.SMBcruisewassupportedbyNSFgrantsOCE-0962209toSergio
Sañudo-Wilhelmy, OCE-0934073toDouglasCapone, andPLR-1029878toWilliam
Berelson. Additional support was provided by NSF grant OCE-1061700 to DEH.
JJB was also supported by a CUAHSI Pathfinder graduate student fellowship, an
InterRidge research fellowship, and a John Montagne Award from GSA Quater-
nary Geology and Geomorphology Division. We thank the crew of R/V Yellowfin,
Elias Karkabi, and Will Berelson for their assistance with sample collection and
processing. We also thank Emmanuel Ponzevera for assistance with Ge isotope
measurements at Ifremer.
83
Chapter 4
Ge and Si behavior during
tropical weathering: La Selva,
Costa Rica
Author contributions
The original design of the study was conceived and the sampling campaign was
carried out by Josh West, Sophie Opfergelt, Rachael James, Kevin Burton, and
Philip Pogge von Strandmann. Major and trace element and Si isotope analyses
were carried out by Josh West, Kevin Burton, and Sophie Opfergelt. I performed
additionalmajorelementandGeconcentrationanalysesattheUniversityofSouth-
ern California and Ge isotope measurements in collaboration with Olivier Rouxel
at Ifremer in Brest, France. The methods section of this chapter was written by
Josh West, except for the Ge isotope subsection. This study will be submitted
as a manuscript to a peer reviewed journal with the following preliminary list of
co-authors: Josh West, Olivier Rouxel, Sophie Opfergelt, Kevin Burton, Doug
Hammond, Rachael James, and Philip Pogge von Strandmann.
84
Abstract
In this study, we have measured silicon (Si) and germanium (Ge) isotope ratios
in the soils, clays, and various fluids in the intensely weathered environment of
Costa Rican tropical lowland rainforest. The bulk soils are almost exclusively
composed of secondary silicates, and as a result are depleted in Si, and isotopically
light(mean±1sofallsamples: Ge/Si = 6.3±0.2mmol/mol,d
30
Si = –2.07±0.25%,
d
74
Ge = –0.13±0.12 %) relative to the parent bedrock (Ge/Si = 2.2±0.2mmol/mol,
d
30
Si = –0.11±0.05 %, d
74
Ge = 0.59±0.07 %). The formation of tertiary clays
(Ge/Si = 7.4± 0.5 mmol/mol, d
30
Si = –2.5± 0.2 %, d
74
Ge = –0.16± 0.09 %)
results in isotopically heavier fluids (Ge/Si = 1–2 mmol/mol , d
30
Si = –1.3 to 0.4
%, d
74
Ge = 2.3 – 2.6 %). A number of lowland streams are also affected by
discharge of solute-rich interbasin groundwater derived from weathering of fresh
volcanic rock at higher elevations (Ge/Si = 0.3 mmol/mol , d
30
Si = 1.0± 0.2 %,
d
74
Ge = 4.0±0.1 %).
We argue that Ge/Si, d
30
Si, and d
74
Ge are all primarily fractionated by the
precipitation of secondary and tertiary clays, while vegetation plays a negligible
role. The estimated fractionation factors for tertiary clay precipitation in lowland
soils are D
30
Si
clay–fluid
= –2.4±0.6 % and D
74
Ge
clay–fluid
= –3.0±0.5 %. The
estimated fractionation factors for secondary clay precipitation during weathering
of volcanic rock by interbasin groundwater are D
30
Si
clay–fluid
= –1.2±0.1 % and
D
74
Ge
clay–fluid
= –2.6± 0.2 %. These fractionation factors are consistent with
previous studies of Si and Ge incorporation into Al and Fe oxides and aluminosil-
icate clays. We propose that the difference in the degree of fractionation of d
30
Si
and d
74
Ge is controlled by the relative mobility of each element during chemical
weathering. More specifically, solid and fluid weathering products have heavier
d
74
Ge composition relative to d
30
Si due to the high degree of Ge retention in the
85
secondary and tertiary clays. A multi-proxy combination of d
74
Ge–d
30
Si–Ge/Si
is therefore expected to be sensitive to a wider range of weathering regimes and
over a wider range of timescales, compared to each individual proxy by itself.
4.1 Introduction
Physical and chemical interaction of water with rocks shapes the land surface,
supplies dissolved nutrients and trace elements to terrestrial and marine ecosys-
tems, and modulates climate via the consumption of atmospheric CO
2
. The latter
process, which relies on the chemical weathering of silicate rocks, has been pro-
posed as a major mechanism stabilizing Earth’s climate over geological timescales
(Walker et al., 1981; Berner et al., 1983; Caves et al., 2016), and perhaps even driv-
ing long-term cooling over the Cenozoic (Raymo & Ruddiman, 1992). A true test
of these hypotheses therefore requires the reconstruction of silicate weathering over
geological timescales. Although there are currently no methods that could reliably
trace the absolute chemical weathering fluxes in the past, a number of goechemical
proxies, such as elemental or isotopic ratios preserved in the sedimentary record
(e.g., Raymo et al., 1988; Froelich et al., 1992; Misra & Froelich, 2012), have been
proposed as tracers of the relative rates of chemical and physical weathering (the
fraction of material removed via chemical weathering relative to total denudation
is often termed "weathering intensity", (e.g., Bouchez et al., 2014)).
The chemical weathering of silicate rocks results in the release of silicon (Si),
aluminum (Al), iron (Fe), and other elements from minerals to the solution. A
portion of these solutes is then re-precipitated as secondary oxides and clays. With
limited erosion and continued intense chemical weathering (e.g., in humid tropical
environments), the secondary clays are dissolved and tertiary oxides and clays are
86
precipitated, and so on. The precipitation of secondary, tertiary, etc. phases often
results in the fractionation of element ratios due to differing stoichiometry and
thermodynamic properties of the dissolved and precipitating phases, and in the
fractionation of isotope ratios due to quantum atomic effects (e.g., Blanchard et
al., 2017). The elemental and isotopic signatures of the liquid and solid weathering
products in nature therefore reflect the rate of supply of fresh minerals to the
weathering zone and the degree of secondary (or tertiary) phase precipitation, i.e.,
silicate weathering intensity (e.g., Murnane & Stallard, 1990; Kurtz et al., 2002;
Bouchez et al., 2013). Over thousand year and longer timescales, this fractionation
results in preferential loss of certain elements and isotopes from the critical zone,
providing a record of the long-term weathering intensity.
Theanalysisofisotopicandelementalsignatures(orproxies)inthesedimentary
record can thus potentially be used to reconstruct silicate weathering intensity in
the past. Multiple proxies have been investigated with this aim in mind, most
notably germanium to silicon (Ge/Si) ratios (Froelich et al., 1992), lithium isotope
ratios (Misra & Froelich, 2012), and silicon isotope ratios (e.g., Ziegler et al.,
2005a). However, each elemental or isotopic proxy reflects a multitude of processes
in the critical zone, and – if reconstructions are based on marine records – in the
ocean, resulting in a range of possible interpretations for each proxy record (e.g.,
Hammond et al., 2000; Li & West, 2014; Frings et al., 2016). Several approaches
can be usedto address thisproblem: 1) further studiesof the processes thatcontrol
the signatures of already "established" weathering proxies; 2) development of novel
proxies; 3) simultaneous investigation of multiple proxies, to evaluate their relative
sensitivities to different critical zone processes and therefore reduce the uncertainty
of interpretations, when proxy signatures are considered together.
87
Inthisstudy, wehaveattemptedtoemployallthreeoftheaboveapproaches, by
simultaneously investigating the distribution and fractionation of two well estab-
lished proxies (Ge/Si and Si isotope ratios) and a novel proxy (Ge isotope ratios)
during chemical weathering in the humid, intensely weathered tropical rainforest
of Costa Rica lowlands. We briefly review the relevant literature for each of these
systems first.
4.1.1 Ge/Si in the critical zone
GermaniumisatraceelementthatischemicallysimilartoSi, andinthecritical
zone, both elements are primarily sourced from silicate rocks that exhibit Ge/Si
ratios in the range of 1-3 mmol/mol (De Argollo & Schilling, 1978; Bernstein, 1985;
Mortlock & Froelich, 1987). During the precipitation of secondary weathering
phases, Ge is removed from solution preferentially over Si, resulting in natural
waters with Ge/Si ratios of 0.1-3 mmol/mol (Froelich et al., 1985; Mortlock &
Froelich, 1987; Murnane & Stallard, 1990; Froelich et al., 1992; Anders et al.,
2003; Lugolobi et al., 2010; Meek et al., 2016; Baronas et al., 2017a). Continued
chemical weathering and loss of Si therefore results in progressively higher Ge/Si
of the residual soils (Kurtz et al., 2002). Murnane & Stallard (1990) showed that
the dissolved Ge/Si signature of rivers in the Orinoco basin depends on the ratio of
chemicaltophysicaldenudationofsilicates,i.e. weatheringintensity. Froelichetal.
(1992) further showed that this relationship is true for many different (unpolluted)
streams globally. Some recent studies have also shown stream Ge/Si signature
to be a useful hydrological tracer over storm-event timescales (Derry et al., 2006;
Kurtz et al., 2011). Finally, a number of studies have shown that the combination
of Ge/Si and Si isotope ratio distributions in soils and soil waters may be used
to delineate the role that vegetation plays in the cycling of Si in the critical zone
88
(Delvigne et al., 2009; Cornelis et al., 2010, 2011; Opfergelt et al., 2010; White et
al., 2012; Cornelis et al., 2014).
4.1.2 Si isotopes in the critical zone
The current knowledge of Si isotope variability in the critical zone has
been recently summarized in a pair of thorough reviews (Opfergelt & Delmelle,
2012; Frings et al., 2016). Si isotope ratios are reported as d
30
Si =
(
30
Si/
28
Si)
sample
/(
30
Si/
28
Si)
NBS–28
– 1, expressed in %. The majority of primary
silicate minerals fall in the narrow range of d
30
Si = –0.5 to 0.5 % (Frings et al.,
2016). A large number of studies have documented consistent d
30
Si fractionation
during critical zone processes. The primary mechanism of fractionation, similarly
to Ge/Si, is the precipitation of secondary phases, to which Si is either adsorbed
(e.g., Al and Fe oxides; Delstanche et al. (2009); Oelze et al. (2014)) or incor-
porated structurally (aluminosilicate clays, such as kaolinite; e.g., Ziegler et al.
(2005a); Georg et al. (2007); Opfergelt et al. (2012)). The reported fractionation
factors (D
30
Si
clay–fluid
, where clay refers to the precipitating solid) range from 0 to
-2 % (Opfergelt & Delmelle, 2012; Frings et al., 2016). As a result, the secondary,
tertiary, etc. weathering products are isotopically light (d
30
Si = –3 to 0 %), and
the corresponding pore waters and streams are isotopically heavy (d
30
Si = 0 to 3
%; Opfergelt & Delmelle (2012); Frings et al. (2016)). In a steady state system
(i.e. over geological timescales), the isotopic fractionation between the fluids and
the bedrock can only be maintained if the secondary products are continuously
eroded from the catchment (Bouchez et al., 2013). In constrast, continued chemi-
cal weathering of soils in erosionally limited regimes results in progressive depletion
of
30
Si, driving all soil materials, as well as the corresponding weathering fluids
towards lower d
30
Si (Ziegler et al., 2005a; Opfergelt et al., 2012).
89
The major second critical zone process potentially affecting d
30
Si distribution
is Si cycling by vegetation. Depending on the species, plants can contain up to
10% Si of dry weight (Cornelis et al., 2011). Root uptake of Si from soil solution
often results in preferential uptake of
28
Si (D
30
Si
veg–fluid
= –2 to 0 %; Opfergelt
& Delmelle (2012); Frings et al. (2016)). During evapotranspiration and transport
of xylem water from roots to leaves, phytoliths of amorphous Si are precipitated,
which further fractionates d
30
Si and produces a range of phytolith d
30
Si values
(e.g., Opfergelt et al., 2006b; Ding et al., 2008; Sun et al., 2016). As plant litter
decays in the surface soils, phytoliths are redissolved, returning the light Si to
pore solutions. If this process dominates over inorganic Si cycling, a d
30
Si-depth
gradient is expected in the bulk soil material and pore waters, with surface soils
and pore waters displaying lower d
30
Si values. In rapidly eroding environments,
the phytoliths may be eroded before having a chance to dissolve, which would
result in a net loss of light Si from the system.
Thephytolithandsecondaryclaysignaturesareoftenindistinguishableind
30
Si
space, hindering data interpretation. A multi-proxy approach combining d
30
Si
and Ge/Si measurements makes it possible to distinguish between these two d
30
Si-
fractionating processes above. Most plants discriminate against Ge and as a result,
the phytoliths exhibit low Ge/Si values, in contrast to Ge-rich clays (Derry et
al., 2005; Blecker et al., 2007; Lugolobi et al., 2010; Sparks et al., 2010; Meek et
al., 2016). Therefore, pore waters and streams strongly influenced by vegetation
Si cycling will plot on a positive d
30
Si-Ge/Si trend, whereas systems primarily
controlled by secondary clay precipitation will plot on a negatived
30
Si-Ge/Si trend
(Delvigne et al., 2009; Cornelis et al., 2011). To date, this framework has been
successfully used to distinguish biological from inorganic Si cycling in multiple soil
90
studies (Cornelis et al., 2010; Opfergelt et al., 2010; White et al., 2012; Cornelis et
al., 2014).
4.1.3 Ge isotopes in the critical zone
The precise determination of Ge isotope composition has only recently
been achieved for rocks and sediments (Galy et al., 2003; Rouxel et al.,
2006), hydrorthermal fluids (Escoube et al., 2015), and low temperature
fluids (Baronas et al., 2017a). Ge signatures are reported as d
74
Ge =
(
74
Ge/
70
Ge)
sample
/(
74
Ge/
70
Ge)
NIST–3120a
– 1, expressed in %. Silicate rocks dis-
play a very narrow range of values (d
74
Ge = 0.4 – 0.7 %; Rouxel et al. (2006);
Escoube et al. (2012). Chapter 2 has presented the first dissolved riverine d
74
Ge
values, showing that they span a relatively large range (2.0–5.6 %) and, similarly
to d
30
Si, are all heavy relative to the source rocks (Baronas et al., 2017a). A broad
correlation between d
74
Ge and Ge/Si signatures in rivers was used to suggest that
light Ge isotopes are retained in secondary weathering products, in a manner sim-
ilar to other isotope systems such as d
30
Si (see above) and d
7
Li (e.g., Pistiner &
Henderson,2003;Huhetal.,2004;Vigieretal.,2008). Theseresultsagreewithfirst
principles quantum modeling that has suggested an equilibrium D
74
Ge
solid–fluid
of
-1.7 % when Ge is adsorbed onto Fe oxides (Li & Liu, 2010). An experimen-
tal study by Pokrovsky et al. (2014) later confirmed this value and additionally
determined D
74
Ge
solid–fluid
fractionation up to -4.4 % when Ge is irreversibly co-
precipitated with Fe oxides. Similar fractionation factors were observed during Ge
precipitation from hydrothermal fluids in the marine environment (Escoube et al.,
2015).
Here, we show that secondary weathering products in Costa Rican soils indeed
exhibit low d
74
Ge values, whereas corresponding fluids are heavy, consistent with
91
the theoretical and experimental studies above. By simultaneously analyzing
d
74
Ge, d
30
Si, and Ge/Si on this set of samples we also show that all three proxies
are primarily governed by secondary phase precipitation and that the degree of
isotopic fractionation depends on the mobility of each element.
4.2 Methods
4.2.1 Site description
The study site spans the La Selva Biological Station of the Organization for
Tropical Studies (OTS) in the Atlantic lowlands of Costa Rica. The elevation
ranges from 20 to 140 m above sea level. The study site lies 35 km northeast
of Volcan Barva volcano. Rainfall averages 4 m/y, relatively constant in Spring
through Fall (300-500 mm/month), with a minimum of 150-160 mm in January-
February (Genereux & Jordan, 2006). The mean annual temperature is 24-26
◦
C,
relatively constant all year round (Sanford et al., 1994). The warm and wet climate
supports a tropical wet forest ecosystem.
The bedrock primarily consists of several >1.2 My old andesitic plagioclase-
rich lava flows (Alvarado Induni, 1990; McDade et al., 1994). The study area is
underlain by >20 m thick, strongly weathered soils, developed on top of the lava
flows, as well as alluvial Holocene-Pleistocene deposits at the lowest elevations
along the Rio Sarapiqui and Rio Puerto Viejo rivers. The mineralogy of bulk
soils has been described in detail previously by Kleber et al. (2007). Briefly, the
soils are primarily composed of gibbsite, and various forms of halloysite-kaolinite,
with smaller amounts of goethite and hematite. Trace amounts of quartz and
magnetite are also present. There is only weak relationship of mineralogy and
bulk soil chemistry with depth.
92
4.2.2 Sample collection
Samples were collected from the La Selva Biological Station and neighboring
areas between 14th and 20th May 2010. Most samples were collected from streams
within the Biological Station, accessed by foot using the station trail network.
Additionalsamples(prefixCR)werecollectedfromriversdrainingthewiderregion,
accessed by road. Guacimo Spring, thought to reflect concentrated discharge of
groundwater draining Volcan Barva (Genereux et al., 2009), was also sampled.
Stream and river water samples were collected in pre-rinsed 15 L collapsible
plastic containers and transported directly back to the laboratory at the La Selva
Biological Station. Temperature, pH, and conductivity were measured in situ at
the sampling site for each stream using a handheld meter calibrated daily with pH
buffers. Samples were immediately (typically within 1 hour) filtered through 142
mm diameter, 0.2 mm polyethersulfone (PES) membrane filters using a peristaltic
pump and an inline Teflon (PTFE) filter holder. The filtration apparatus was
cleaned between each sample and pre-rinsed with sample before collecting filtrate.
For the analyses reported in this study, two aliquots of filtrate were collected into
separate polypropylene (PP) bottles. One bottle was filled completely and left
unpreserved, for analysis of anion concentrations. The other bottle was acidified
with quartz-distilled HNO
3
to pH < 2, for cation, Ge concentration, and Si isotope
ratio analyses conducted in this study. A third larger bottle was filled for sample
used for Ge isotope analysis. Gran titrations were completed on an aliquot of each
sample immediately following filtration. Filter papers were retained in plastic
bags and returned to the University of Oxford, where suspended sediment was
removedwithaspatulaanddriedat60
◦
C.Asmallamountoffine-grainedmaterial
could not be removed from the filter papers, but this small proportion is not
expected to bias analysis of sediment composition. Sediment from the stream bed
93
was collected at selected sites in plastic bags and dried at 60
◦
C (referred to as
“bedload” samples). Since bedrock exposures are not present at La Selva or in the
immediately surrounding area, visibly well-preserved clasts of rock were collected
from selected stream sites to provide a sampling of rock from the region. Ground
waterswerecollectedfromexistingwellsat LaSelva (Genereux etal.,2005)usinga
plastic bailer and were filtered following the same procedure as used for the stream
waters.
Soils were collected from two deep (originally 3-4m depth) soil pits dug in 1997
at the La Selva “Carbono” plots (Clark & Clark, 2000; Veldkamp et al., 2003;
Kleber et al., 2007). Soil pit IDs were adopted from the previous plot naming
conventions, with samples collected from the same two pits (A4 and L6) that were
studied in detail for mineralogical composition byKleber et al. (2007). Pit A4
reflects “old alluvial” soil developed on a river terrace probably of Pleistocene age,
while L6 is “residual” soil developed on∼1.2 Ma lava flows (Veldkamp et al., 2003).
Both are deeply weathered Oxisols, with similar parent material but differing in
age (Kleber et al., 2007). Soils were collected from the exposed pit walls. Since
some additional weathering had occurred since the pits were dug,∼50 cm was
excavated into the side of the pit wall before collecting samples into plastic bags
with a hand trowel. To collect soil pore waters, rhizon samplers (10 cm length,
2.0 mm diameter, composed of porous PES with 0.15 mm pores) were inserted
horizontally into the excavated surface, with suction applied using a syringe held
open with a wooden popsicle stick. Syringes were left to fill for∼24 hours before
dispensing the collected pore waters into pre-washed 30 mL PP bottles. Vegetation
samples were collected from a single species of understory vegetation at each plot
site.
94
All water, sediment, and soil samples were refrigerated following collection,
except during the airline transport (estimated around 24 hours) from Costa Rica
to Oxford.
4.2.3 Major and trace element analyses
The untreated aliquot of each water sample was analyzed for major anions (Cl
–
,
SO
2–
4
, and NO
–
3
) by Ion Chromatography at the University of Southern California
using a Metrohm IC 861 equipped with an A4/150 anion exchange column with
conductivity suppression, using a 3.2 mM Na
2
CO
3
/ 1.0 mM NaHCO
3
eluent with
a flow rate of 0.7 mL/min. Conductivity signals were calibrated against a series of
standards prior to the analytical session. A certified reference material (ION-915,
Environment Canada) was run after every 10-15 samples. All analyses were within
the range of certified values, and replicate analyses were within 5% (1 s).
Other major and trace elements, including Ca, Mg, Na, K, Sr, Li, and U,
were measured on the acidified portion of each sample by ICP-MS using a Thermo
Element2 at the University of Oxford. All elements were analyzed in low resolution
mode. Intensities for trace elements were calibrated using multiple dilutions of
an in-house standard solution with a similar composition to average river water.
Major elements were calibrated using dilutions of the TROIS-94 certified reference
material. Indium was used as an internal standard to correct for signal drift during
the analytical session. Charge balance was within 20% for all but one sample
(LS06), and was within 5% for most samples.
Si concentrations were measured by spectrophotometry using the molybdate
blue method with citric and amino acid reagents.
95
Solid samples were powdered using a mortar and pestle and dissolved in a
HF/HNO
3
mixture in Teflon vials at elevated temperature. Solutions were evapo-
rated to dryness, and residual salts dissolved in 2% HNO
3
for analysis by ICP-MS
at the Open University, UK, for major and trace elements.
4.2.4 Germanium concentration analysis
Dissolved Ge concentrations were measured using isotope-dilution-hydride-
generation-ICP-MS based on the method by Mortlock & Froelich (1996), as mod-
ified by Baronas et al. (2016). Briefly, a mono-isotopic
70
Ge spike was added to
5 mL of sample gravimetrically, and the sample diluted to 20 mL with 0.01 M
distilled HCl. Dissolved Ge was converted to hydride by reacting with NaBH
4
and
the resulting GeH
4
gas pre-concentrated on a liquid N
2
trap prior to being injected
into a Thermo Element 2 ICP-MS instrument at the University of Southern Cal-
ifornia. Dissolved Ge concentration in the sample was then determined based on
the measured
70
Ge/
74
Ge ratio.
4.2.5 Silicon isotope analysis
Cation-exchange chromatographic separation. Si isotope analyses fol-
lowedtheapproachofGeorgetal.(2006b). Siwasseparatedfromsamplematrixby
ionexchangechromatography. A10mLcolumnwasfilledwith1.8mL(wetvolume
BioRad AG50W-X12 (200-400 mesh) resin. The column was washed a sequence
of full column volumes of Milli-Q quality water (MQ) followed by a sequence of
3M-6M-10M-6M-3M HCl, and then preconditioning with three column volumes
of MQ H
2
O. Samples (volumes ranging from 0.05 to∼3 mL) were then loaded
onto the column in an HNO
3
matrix and eluted with 2-5 mL Milli-Q to obtain
a final solution of 0.5 ppm Si (yielding about∼8V for
28
Si during analysis). All
96
bracketing standard and reference material aliquots were purified using the same
cation-exchange method as the samples. Reference materials (Diatomite, BHVO-
2, and Merck Quartz) were processed with the same method. Larger batches of
NBS-28 were prepared by eluting with up to 20 mL MQ H
2
O.
Fusion of solids. Prior to loading on columns, solid samples were digested
using a fusion procedure following Georg et al. (2006b). 20 mg of finely powdered
sample was fused with∼200 mg trace-grade NaOH in silver crucibles at 720
◦
C for
10 minutes. The crucibles with fusion cakes were sonicated for 30 min in 20 mL
MQ H
2
O and left to dissolve for 24 hours. The solution was then transferred into
250 mL polyethylene bottles, carefully rinsing to ensure complete recovery, diluted
to 200-250 mL with MQ H
2
O, and acidified with 3 mL trace grade concentrated
HNO
3
. Vegetation samples were ashed in Pt crucibles at 720
◦
C prior to fusion.
MC-ICP-MS. FollowingchromatographicseparationofSi, isotoperatioswere
measured using a Nu Plasma II HR-MC-ICP-MS at the University of Oxford in
medium resolution mode, on the flat part of the peak shoulder to avoid
14
N
16
O
+
interference. For each sample, ratios were measured over 25 integrations of 10
seconds, repeated in 5 blocks. For each block, the sample analysis was brack-
eted by measurement of concentration-matched NBS-28 standard solution which
had passed through the same column chromatography procedure as the samples.
d
30
Si values are reported in % as
30
Si/
28
Si sample ratio normalized to the average
30
Si/
28
Si ratio of bracketing NBS-28 measurements. The measurement uncertainty
is reported as the internal 2s standard error of sample replicates, or 2s standard
deviation of all NBS-28 bracketing standard measurements within a given analyt-
ical session, whichever is higher. Reference materials (diatomite, BHVO-2, and
Merck Quartz) were analyzed during sample runs and were all within the ranges
reported in the prior studies (Georg et al., 2009).
97
4.2.6 Germanium isotope analysis
River water. 0.5 to 1.0 L of filtered river water containing 6-18 ng of Ge
was acidified with trace clean HNO
3
, and spiked with a Ge isotope double spike
(
73
Ge/
70
Ge≈ 1, previously calibrated and used by Escoube et al. (2012, 2015))
in a spike/sample Ge mass ratio of 1-2 and a purified FeCl
3
solution to obtain
a Fe concentration of∼0.2 mmol/L. The samples were well mixed, and allowed
to equilibrate for at least 16h. Next, Fe(OH)
3
flock was precipitated by adding
Optima-grade NH
4
OH until the solution reached a pH of 8-10. The flock was
collected by settling and centrifugation, redissolved in 2 mL concentrated Teflon-
distilled HNO
3
and diluted to 10 mL with Milli-Q. The samples were then dried,
redissolved in 1 mL concentrated Optima-grade HF and diluted to 30 mL with
Milli-Q to obtain a final 1M HF solution. They were then purified through anion
exchange columns as described below. The full procedural blank was determined
by processing spiked Milli-Q and ranged from 0.01 to 0.3 ng Ge.
Rocks and sediments. A method adapted from Rouxel et al. (2006) was
used for solid sample digestion. 10-130 mg of dried ground sample containing
30-300 ng of Ge was weighed into Teflon digestion vessels and spiked with a Ge
isotope double spike (
73
Ge/
70
Ge≈ 1) in a spike/sample Ge mass ratio of 1-2. The
samples were digested by adding 10 mL concentrated Teflon-distilled HNO
3
and
heating in a pressurized Teflon vessel at 90
◦
C for 48h. Then, 1mL of Optima-
grade H
2
O
2
was added and heated uncapped at 70
◦
C until dry. After cooling
down 4 mL Milli-Q and 1mL concentrated trace-metal grade HF were added and
samples were digested at 70
◦
C for 48 h. Replicate digestions done without H
2
O
2
yielded the same results, indicating that this step was not necessary (see Chapter
6 for a detailed comparison of the methods). After cooling down, the samples were
diluted to 30 mL with Milli-Q to obtain a final 1M HF solution that was purified
98
through anion exchange columns as described below. The full procedural blank
was determined by processing only Ge double spike and was below 1.7 ng Ge.
Anion-exchangechromatographicseparation. Aprocedureadaptedfrom
Rouxel et al. (2006) was used. All reagents used were either in-house Teflon-
distilled or Optima-grade. A 10 mL column was loaded with 1.8 mL (wet volume)
of BioRad AG1-X8 resin, washed with 10 mL aliquots of 3M HNO
3
, 0.28M HNO
3
,
and Milli-Q each, then conditioned with 5 mL 1M HF. Samples in 1M HF solution
as prepared above were centrifuged to separate insoluble fluorides and 10-29 mL of
thesolutionwascarefullyaddedtocolumns. Theremainingmatrixwaselutedwith
5 mL of 1M HF followed by 3 mL of Milli-Q, while fluorinated Ge was retained on
the column. Ge was then eluted with 10 mL 0.28M HNO
3
. If required, the solution
was dried down and redissolved in a smaller volume of 0.28M HNO
3
to obtain the
2-10 ppb Ge concentration required for isotope measurements. Each column was
reused 4-5 times, except when retention of DOC from the previous sample was
observed based on the color, in which case the resin was replaced. Ge blanks from
reused resin were below detection limit. Ge recovery ranged from 20 to 100%, and
was in the 70-90% range for most river samples and in the 100±7% range for solid
samples. Incomplete recovery of river samples was most likely due to variable Ge
co-precipitation efficiency with Fe(OH)
3
, due to variable precipitation rates, final
pH and natural sample matrices, as well as some loss during co-precipitate recovery
from the solution. Incomplete recovery does not affect the measured d
74
Ge values,
as all samples were double-spiked prior to sample preparation.
HG-MC-ICP-MS.GeisotopeanalyseswereperformedonaThermoNeptune
multi-collector ICP-MS at Ifremer using a method adapted from Rouxel et al.
(2006) and Escoube et al. (2015). Sample solutions of 2-10 ppb natural Ge in
0.28M HNO
3
were introduced into an online hydride generation system (CETAC
99
HGX-200) at a rate of 150 mL/min were they were mixed with NaBH
4
solution
introduced at an equal rate. The dissolved Ge(OH)
4
species were reduced to
gaseous GeH
4
and transported into the ICP-MS torch using Ar carrier gas. The
Neptune MC-ICP-MS was operated in low mass resolution mode, measuring
70
Ge,
72
Ge,
73
Ge, and
74
Ge in L2, C, H1 and H2 cups, respectively. In addition, L4, L3,
L1 and H4 cups were also monitored for
68
Zn (possible interference as
70
Zn),
69
Ga,
71
Ga (possible interferences at m/z 70), and
77
Se (possible interference as
74
Se),
respectively. No interferences were detected in any of the runs. The samples were
bracketed using a NIST-3120a standard solution that had a total Ge concentration
generally within∼20 % of the bracketed sample, and was double-spiked to have
a spike/sample ratio within∼20 % of the bracketed sample. Each sample or
standard run consisted of 6 measurement blocks each lasting 2 min (30 cycles of 4
seach),andinmostcases4-5blocksdisplayingthemoststablesignalwereretained.
Therefore, each measurement represents 8-10 min of counting statistics at signal
intensities ranging from 0.4 to 6 V at
74
Ge (depending on Ge concentration in
sample solution, instrument tuning, and the age of NaBH
4
solution). The d
74
Ge
valuesarecalculatedforeachblockusingthedouble-spikedatareductionroutineof
Siebert et al. (2001) and are reported in % as
74
Ge/
70
Ge sample ratio normalized
to the average
74
Ge/
70
Ge ratio of bracketing NIST 3120a measurements. This
method also yields Ge concentration values based on the measured spike/sample
ratio. The measurement uncertainty is reported as the internal 2s standard error
of the used sample blocks, or 2s standard deviation of all NIST 3120a bracketing
standard measurements within a given analytical session, whichever is higher.
100
4.3 Results
4.3.1 Rocks and soils
General chemistry
The basaltic bedrock samples were found to contain∼50-60 % SiO
2
, 15 %
Al
2
O
3
, and 6-10 % Fe
2
O
3
, with smaller amounts of other major elements (Table
4.1, Fig. 4.1). Relative to this parent bedrock, the bulk soils and separated clays
sampled at two soil pits are strongly desilicified (20-28 % SiO
2
), enriched in Al
2
O
3
(27-35 %) and Fe
2
O
3
(15-18 %), and strongly depleted in all major cations (< 1 %
of Na
2
O, K
2
O, CaO, and MgO). There are no significant differences between the
two soil pits (A4 and L6). These results are consistent with previous studies, which
identified the strongly weathered character of these soils (Kautz & Ryan, 2003;
Kleber et al., 2007). In terms of major oxide chemistry, the separated clays are
chemically indistinguishable from bulk soils (Fig. 4.1). Using qualitative powder
XRD (Proto AXRD at the Natural History Museum of Los Angeles), the major
mineral phases were found to be gibbsite, kaolinite/halloysite, and goethite, as
well as traces of magnetite (data not shown). These results are consistent with
previous studies of the A4 and L6 pit mineralogy (Kleber et al., 2007). There were
nodistinguishabledifferencesinmineralogybetweenseparatedclaysandbulksoils,
except that a small amount of residual quartz was present in the bulk soil samples.
Chemical depletion
The degree of weathering can be further assessed by considering the concen-
trations of typically immobile elements. As other more mobile elements are lost
throughchemicalweathering,theconcentrationsofimmobileelementsareexpected
to increase. Indeed, the soils of La Selva are enriched by about a factor of 2 in the
101
0 200 400 600 800 1000
Concentration, g/kg
Bedrock
SiO
2
Al
2
O
3
Fe
2
O
3
Na
2
O
K
2
O
CaO
MgO
0 200 400 600 800
Concentration, g/kg
0
50
100
150
200
Depth, cm
Soils
6 L6 L
0 200 400 600 800
Concentration, g/kg
0
50
100
150
200
Depth, cm
A4
0 200 400 600 800
Concentration, g/kg
Clays
L6
0 200 400 600 800
Concentration, g/kg
A4
Figure 4.1: Elemental composition of bedrock, bulk soils, and separated clays in
La Selva.
102
Table 4.1: La Selva solids chemistry. n/m = not measured
Sample Depth Si Ge Al Fe Th Ge/Si
δ
30
Si
± 2σ
δ
74
Ge
± 2σ
g/g µg/g g/g g/g µg/g µmol/mol ‰ ‰
Bedrock
Rock 1 -- 0.29 1.54 0.075 0.040 16.6 2.07 -0.13 0.18 0.64 0.08
Rock 2 -- 0.25 1.52 0.075 0.065 4.8 2.34 -0.09 0.25 0.54 0.02
Average -- 0.27 1.53 0.075 0.052 10.7 2.20 -0.11 0.05 0.59 0.14
Soils
Soil pit A4 0 cm 0.10 1.55 0.143 0.106 13.2 6.16 -1.74 0.21 -0.22 0.15
Soil pit A4 50 cm 0.13 n/m 0.186 0.119 16.6 n/m -2.11 0.23 n/m n/m
Soil pit A4 100 cm 0.12 1.87 n/m n/m n/m 6.00 n/m n/m -0.16 0.08
Soil pit A4 200 cm 0.13 2.22 0.181 0.128 17.5 6.48 -1.88 0.16 -0.08 0.08
Soil pit L6 0 cm 0.10 1.63 0.165 0.121 13.6 6.33 -2.05 0.19 -0.28 0.27
Soil pit L6 50 cm 0.12 1.95 0.184 0.125 15.0 6.30 -2.19 0.18 -0.08 0.08
Soil pit L6 200 cm 0.12 2.08 0.165 0.110 15.3 6.63 -2.47 0.25 0.07 0.04
Clays
Soil pit A4 0 cm 0.10 1.95 0.174 0.110 14.7 7.21 -2.60 0.23 -0.27 0.05
Soil pit A4 200 cm 0.13 2.56 0.184 0.116 19.0 7.76 -2.44 0.07 -0.06 0.08
Soil pit L6 0 cm 0.11 2.16 0.174 0.120 15.0 7.69 -2.69 0.15 -0.20 0.13
Soil pit L6 200 cm 0.13 2.27 0.185 0.119 17.9 6.74 -2.37 0.09 -0.12 0.08
Vegetation
Above A4 -- 0.005 n/m 0.004 0.003 0.5 n/m -1.90 0.11 n/m n/m
Above L6 -- 0.11 n/m 0.003 0.002 0.3 n/m -1.78 0.19 n/m n/m
Bedload
LS01 (bulk) -- 0.13 n/m 0.119 0.068 15.3 n/m -1.44 0.09 n/m n/m
LS01 (clay) -- n/m n/m n/m n/m n/m n/m -2.52 0.13 n/m n/m
LS02 -- 0.17 n/m 0.122 0.069 15.2 n/m -1.06 0.07 n/m n/m
Suspended load
LS01 0 m 0.14 n/m 0.147 0.087 19.9 n/m -1.91 0.17 n/m n/m
LS02 0 m 0.16 n/m 0.146 0.107 21.4 n/m -1.34 0.17 n/m n/m
LS04 0 m 0.02 n/m 0.150 0.122 21.2 n/m -2.37 0.12 n/m n/m
LS15 0 m 0.06 n/m 0.175 0.106 30.6 n/m -2.59 0.14 n/m n/m
typically immobile Ti, Zr, and Th, relative to the parent bedrock (Fig. 4.2). There
ishowever, alargedegreeofheterogeneityintheZrandThconcentrationsbetween
the two bedrock samples, whereas Ti concentrations appear homogeneous. It is
therefore chosen as the immobile element for the normalization described below.
The degree of gain or loss of element X during chemical weathering can be
assessed using the method of Brimhall & Dietrich (1987) and Chadwick et al.
(1990), by normalizing to the X/Ti ratio of the bedrock composition:
103
0.5 1 1.5 2
Ti, µg/g
×10
4
0
50
100
150
200
Depth, cm
0 200 400 600
Zr, µg/g
0 10 20
Th, µg/g
Bulk soil (A4)
Bulk soil (L6)
Bedrock
Figure 4.2: Concentrations of typically immobile elements in the bedrock, bulk
soils, and separated clays of La Selva. The depth scale (y-axis) applies only to soil
and clay samples and not the bedrock.
t
X
=
(X/Ti)
sample
(X/Ti)
bedrock
–1 (4.1)
t
X
equals zero when there is no loss or gain of X, whereas positive values indi-
cateenrichmentandnegativevaluesindicatedepletion, with-1indicatingcomplete
loss of X through chemical weathering. Since the immobile element (in this case
Ti) has extremely low chemical mobility, it is primarily lost only through erosion.
As other more mobile elements are chemically leached, the absolute concentrations
of the less mobile elements may increase simply because they are lost at a slower
rate. This normalization therefore allows to assess the relative chemical loss of
different elements.
The results show that∼80 % of Si and∼100 % of alkali and alkaline earth
elements have been lost through chemical weathering of La Selva soils (Table 4.2,
Fig. 4.3). In contrast, the more refractory Fe and Al have been sightly enriched by
2-30 %. Ge shows a lower degree of depletion (∼20-40 %) relative to Si, consistent
with previously documented enrichment of Ge in secondary weathering products,
such as aluminosilicates and Fe oxides (Murnane & Stallard, 1990; Kurtz et al.,
104
2002). Finally, there does not appear to be any significant variation in chemical
depletion with depth (Fig. 4.3), indicating that these soils have been extensively
chemicallyweatheredandthattheactiveweatheringfrontandtheregolith-bedrock
interface is likely to be found at much higher depths.
Table 4.2: Chemical alteration of soils and river sediments relative to parent
bedrock, calculated relative to Ti using Eq. 4.1.
Sample Depth τ
Si
τ
Al
τ
Fe
τ
Na
τ
K
τ
Ca
τ
Mg
τ
Ge
τ
Li
τ
Mn
Bulk soils
Soil pit A4 0 cm -0.81 0.02 0.08 -0.99 -0.94 -0.98 -0.95 -0.46 -0.71 -0.58
Soil pit A4 50 cm -0.77 0.22 0.12 -0.99 -0.95 -1.00 -0.96 n/m -0.61 -0.37
Soil pit A4 200 cm -0.78 0.09 0.10 -1.00 -0.98 -1.00 -0.97 -0.34 -0.76 -0.76
Soil pit L6 0 cm -0.80 0.19 0.25 -0.99 -0.96 -1.00 -0.95 -0.43 -0.64 -0.86
Soil pit L6 50 cm -0.77 0.30 0.25 -0.99 -0.97 -1.00 -0.96 -0.33 -0.53 -0.84
Soil pit L6 200 cm -0.73 0.31 0.24 -1.00 -0.97 -1.00 -0.97 -0.20 -0.57 -0.87
Bedload
LS01 (bulk) -- -0.64 0.15 -0.07 -0.85 -0.81 -0.80 -0.84 n/m -0.34 -0.41
LS01 (clay) -- n/m 0.60 -0.18 -0.88 -0.95 -0.95 -0.96 n/m -0.06 -0.83
LS02 -- -0.52 0.24 0.00 -0.70 -0.66 -0.63 -0.71 n/m 0.30 -0.58
Suspended load
LS01 0 m -0.66 0.31 0.10 -0.94 -0.89 -0.94 -0.94 n/m -0.35 -0.68
LS02 0 m -0.61 0.31 0.37 -0.78 -0.72 -0.76 -0.82 n/m 0.18 -0.62
LS04 0 m -0.96 0.35 0.56 -0.93 -0.95 -0.94 -0.96 n/m -0.31 -0.14
LS15 0 m -0.89 0.20 0.03 -0.99 -0.96 -0.99 -0.98 n/m -0.65 -0.80
Ge and Si element and isotope distribution
The Ge and Si isotope composition and the Ge/Si ratios of various sample
types in the La Selva soils are summarized in Fig. 4.4 and Tables 4.1 and 4.3. The
bedrockexhibitsGe/Siof2.2±0.1mmol/mol, typicalofbasalticrocks, whereasbulk
soils show strongly elevated Ge/Si ratios of 6-6.5 mmol/mol, typically associated
with secondary minerals such as kaolinite and Fe oxides (Mortlock & Froelich,
1987; Kurtz et al., 2002). The in-situ neo-formed clays are further enriched in
Ge/Si, reaching values of 6.7-7.8 mmol/mol, consistent with higher degree of Ge
105
-1 -0.5 0 0.5
τ
Si
0
50
100
150
200
Depth, cm
a
-1 -0.5 0 0.5
τ
Al
b
-1 -0.5 0 0.5
τ
Fe
d
-1 -0.5 0 0.5
τ
Ge
0
50
100
150
200
Depth, cm
c
Bulk soil (A4)
Bulk soil (L6)
Figure 4.3: Chemical alteration of bulk soils relative to parent bedrock, calculated
relative to Ti using Eq. 4.1.
retention relative to Si during chemical weathering (see Section 4.3.1). There is
no clear relationship of Ge/Si with depth, either in bulk soils or in separated clays
(Fig. 4.4a).
Isotopically, the bulk soils, clays, vegetation, and most pore waters are all sig-
nificantly fractionated relative to the starting bedrock. The bulk soils are strongly
enriched in light Si isotopes (d
30
Si
soil
= -1.7 to -2.5 %) and the in-situ neo-formed
clays are enriched further still (d
30
Si
clay
= -2.4 to -2.7 %), relative to the parent
bedrock (d
30
Si
bedrock
= -0.1 %). Pore water composition is heavier than the bulk
106
soils but still lighter than the parent bedrock, with the exception of two samples
(d
30
Si
pw
= 0 to -1.3 %; Fig. 4.4c). Two different unidentified plant species that
exhibit very different degrees of Si uptake (Si weight fraction of 0.5 and 11 %;
Table 4.1) are isotopically indistinguishable (d
30
Si
veg
= -1.8 to -1.9 %).
Similarly to Si, light Ge isotopes are enriched in soils and clays (d
74
Ge = -0.1 to
-0.3 %) relative to parent bedrock (d
74
Ge
bedrock
= 0.6 %; Fig. 4.4b). However,
whereas d
30
Si does not show any depth dependence either in bulk soils or clays,
there is a small but statistically robust depth gradient in d
74
Ge, with surface soils
and clays∼0.2 % lighter than those collected at 2m depth (Student’s test p-value
= 0.01).
2.3‰
246 8
Ge/Si, µmol/mol
0
50
100
150
200
Depth, cm
a
-0.3 0 0.3 0.6
δ
74
b
-3 -2 -1 0
δ
30
c
Pore water
Bulk soil
Clay
Vegetation
Bedrock
L6 A4
Lowland groundwater
Figure 4.4: Silicon and germanium isotope composition of various solids in La
Selva. The depth scale (y-axis) does not apply to vegetation, bedrock, and ground-
water samples. The individual sample depths are slightly offset for clarity, the
actual sampling depths were identical for all samples, and the sampling horizons
were< 5 cm thick. The lowland groundwater d
74
Ge value is outside of the x-axis
range in panel b.
107
4.3.2 Streams
Major and trace element concentrations in La Selva streams span a wide range
of values (Table 4.3). It is well established that the first order control on stream
chemistry in La Selva is the mixing of dilute lowland surface water with solute-
rich interbasin groundwater (IBGW) sourced from higher elevation on the slopes
of Volcan Barva, some 10km south of the study site (e.g., Genereux & Jordan,
2006; Genereux et al., 2009). The IBGW input affects each stream to a different
degree and its influence can also vary within a watershed. This heterogeneity is
well exemplified by comparing two streams where samples were taken at different
points along each stream (Table 4.3). In the Taconazo watershed, the upstream
chemistry (LS14) is very similar to the outlet (LS01/LS12), consistent with negli-
gible contribution from IBGW, whereas in the Arboleda watershed, the upstream
fluid (LS15) is initially very similar to Taconazo, but the stream water sampled at
the outlet (LS02) is highly enriched in solutes and closely resembles the chemistry
of pure IBGW end-member.
The first order variability of the whole stream chemistry dataset can therefore
be described in terms of a mixing relationship (Fig. 4.5a) between the IBGW
end-member (best represented by the Guacimo groundwater spring, sample CR05
in Table 4.3) and lowland water end-member (represented by pore water samples).
Similar correlations to that in Fig. 4.5a are observed for all major cations and
anions.
Besides having distinct solute concentrations, the two end-members also differ
in their d
30
Si, d
74
Ge, and Ge/Si signatures (Table 4.3). IBGW has Ge/Si of 0.3
mmol/mol, d
30
Si of 1.0 %, and d
74
Ge of 4.0 %, whereas locally sourced dissolved
Si in soil pore waters and lowland groundwater has Ge/Si of 1.3-2.0 mmol/mol and
is significantly lighter isotopically (d
30
Si -1.3 to 0.2 %, d
74
Ge∼2.3 %; Fig. 4.4).
108
0 500 1000 1500
Na, µM
10
-3
10
-2
10
-1
1/Si, µM
-1
a
Rain
Local streams
Large rivers
Pore waters
Local groundwater
Interbasin groundwater
0 0.01 0.02 0.03 0.04
1/Si, µM
-1
-1.5
-1
-0.5
0
0.5
1
1.5
2
δ
30
b
Figure 4.5: The range of chemical and d
30
Si compositions in La Selva streams.
In each panel, the dashed line shows the mixing relationship between interbasin
groundwater (sample CR05) and lowland pore waters.
As result, the mixing of interbasin and lowland waters is also the major control
on the Ge and Si signatures in streams of La Selva – Fig. 4.5b shows an example
in the case of d
30
Si. For this reason, we will not be discussing the intermediate
values but will focus the discussion on how the two distinct end-member signatures
originate.
109
Table 4.3: La Selva fluids chemistry.
Sample Details
Sample
ID
Tempe-
rature
pH
Conduc-
tivity
Alk. Si Na Mg K Ca Cl SO
4
Al Fe Ge Ge/Si δ
30
Si ± 2σ δ
74
Ge ± 2σ
°C µS meq/L µM µM µM µM µM µM µM nM nM pM µmol/mol ‰ ‰
Rainwater LS16 n/m n/m n/m n/m 4 60 8 4 5 60 13.4 399 194 9 2.34 n/m n/m
Streams & groundwater seeps
Taconazo at weir LS01 25.1 5.6 9 0.06 120 60 12 9 14 53 6.5 644 689 107 0.89 0.29 ± 0.13 2.58 ± 0.12
Arboleda at weir LS02 24.9 6.5 276 2.49 1066 768 533 125 400 355 46.1 431 649 423 0.40 1.03 ± 0.12 3.25 ± 0.12
El Salto east trib. LS04 24.4 6.5 16 0.15 232 93 22 n/m 29 51 5.8 409 449 88 0.38 0.46 ± 0.18 3.05 ± 0.12
El Salto gw. seep LS05 25.3 6.7 424 4.07 1264 1247 877 173 529 619 76.8 843 164 498 0.39 1.27 ± 0.21 n/m
El Salto tributary LS06 25.8 6.6 233 4.68 1293 1342 1085 236 624 678 86.3 265 225 505 0.39 1.12 ± 0.14 3.61 ± 0.12
El Salto main stem LS07 25.2 7.1 157 1.22 550 464 327 74 209 237 28.5 643 832 231 0.42 1.03 ± 0.10 3.35 ± 0.12
El Sura main stem LS08 25.5 6.6 10 0.09 167 72 18 16 19 46 5.2 274 380 90 0.54 0.57 ± 0.15 n/m
Piper main stem LS09 25.4 6.0 13 0.12 151 67 24 9 28 50 5.7 266 478 89 0.59 0.70 ± 0.14 n/m
Quebrada Esquina LS10 25.1 6.8 21 0.20 284 96 31 31 42 52 5.1 1192 511 104 0.37 0.92 ± 0.26 3.02 ± 0.12
El Saltito gw. seep LS11 25.1 6.0 6 0.04 196 64 8 16 8 49 5.2 475 502 131 0.67 0.67 ± 0.27 2.63 ± 0.12
Taconazo at weir LS12 n/m n/m 10 0.06 143 60 13 11 15 50 5.2 319 299 103 0.72 0.64 ± 0.20 n/m
Arboleda at weir LS13 n/m n/m 142 3.10 1142 839 583 139 439 379 49.0 638 569 455 0.40 1.23 ± 0.15 n/m
Taconazo upstream LS14 25.7 5.4 10 0.02 77 60 13 15 12 59 9.5 907 549 97 1.26 0.38 ± 0.15 2.39 ± 0.12
Arboleda upstream LS15 26.2 5.4 14 0.02 88 65 16 22 12 62 14.2 781 278 101 1.14 0.19 ± 0.09 2.60 ± 0.12
El Saltito main stem LS17 n/m n/m 117 0.94 423 307 209 45 144 167 21.5 661 739 188 0.44 1.03 ± 0.13 n/m
Large rivers
Rio Sarapiqui at Puerto Viejo CR01 27.2 7.2 50 0.43 279 n/m n/m n/m n/m 58 34.2 1682 847 136 0.49 1.32 ± 0.14 n/m
Rio Sarapiqui at Sardinal CR02 25.8 7.1 27 0.39 295 n/m n/m n/m n/m 114 180.0 565 168 120 0.41 1.34 ± 0.26 n/m
San Miguel main stem CR03 22 8.0 117 0.87 684 n/m n/m n/m n/m 94 158.4 2048 544 177 0.26 1.52 ± 0.21 n/m
Interbasin groundwater
Guacimo spring CR04 25.3 5.6 25 0.19 286 n/m n/m n/m n/m 50 7.9 279 <21 156 0.54 -0.03 ± 0.24 n/m
Guacimo spring CR05 25.4 6.4 590 n/m 1297 1342 1491 n/m 757 733 125.8 308 366 405 0.31 1.02 ± 0.24 4.02 ± 0.12
Local groundwater (wells)
Farm n/m LS03 25.1 5.2 20 0.04 51 70 8 n/m 23 60 18.2 324 472 97 1.91 0.06 ± 0.13 2.24 ± 0.12
Taconazo n/m LS18 n/m n/m n/m n/m 74 56 11 3 13 53 9.5 1922 119 99 1.35 0.17 ± 0.14 n/m
Saltito 60 cm LS19 n/m n/m n/m n/m 53 71 22 7 25 49 7.5 5917 735 92 1.75 0.21 ± 0.18 n/m
Salto 60 cm LS20 n/m n/m n/m n/m 69 60 16 6 16 53 7.6 2068 587 135 1.97 -0.67 ± 0.07 n/m
Salto 100 cm LS21 n/m n/m n/m n/m 87 84 17 n/m 17 52 8.5 17606 3981 n/m n/m -0.40 ± 0.26 n/m
Soil pore water
Soil pit A4 0 cm n/m n/m n/m n/m 72 122 62 n/m 175 63 44.8 12036 n/m n/m n/m -1.25 ± 0.08 n/m
Soil pit A4 25 cm n/m n/m n/m n/m 52 87 18 n/m 59 66 22.5 294 n/m n/m n/m -1.02 ± 0.10 n/m
Soil pit A4 50 cm n/m n/m n/m n/m 53 101 22 n/m 62 56 26.3 295 n/m n/m n/m -1.21 ± 0.08 n/m
Soil pit A4 200 cm n/m n/m n/m n/m 45 67 23 n/m 52 73 12.6 915 n/m n/m n/m -0.96 ± 0.09 n/m
Soil pit L6 0 cm n/m n/m n/m n/m 139 120 50 n/m 79 103 35.1 23918 n/m n/m n/m -0.03 ± 0.11 n/m
Soil pit L6 50 cm n/m n/m n/m n/m 43 81 13 n/m 32 69 17.5 331 n/m n/m n/m -1.04 ± 0.08 n/m
Soil pit L6 200 cm n/m n/m n/m n/m 33 54 10 n/m 36 40 18.8 61 n/m n/m n/m 0.02 ± 0.10 n/m
110
4.4 Discussion
ThebroadsystematicsofGe/Siandd
30
Sifractionationduringlow-temperature
rock weathering are well established (e.g., Cornelis et al., 2011; Frings et al., 2016),
even though particular details can be complex and variable in natural settings
(see Section 6.1). Both proxies have been shown to be primarily affected by the
weathering intensity of a given environment. Low Ge/Si and high d
30
Si values are
observed in high-erosion regimes with an abundant supply of primary minerals and
a high degree of fractionation during formation of secondary phases. In systems
where erosion is low and primary silicates have been depleted, secondary phases
become destabilized, supplying high Ge/Si and low d
30
Si solutes, driving fluid
composition back towards the initial bedrock signature. These two extreme end-
member regimes are represented in our field study site at La Selva. Interbasin
groundwater has obtained a highly fractionated signature from the weathering of
fresh basalt on the slopes of Vulcan Barva, whereas the intensely weathered soils
of La Selva supply solutes from dissolution of previously formed secondary clays,
with a small amount of tertiary clays forming during this process (referred to as
lowland weathering below).
A key observation of this study is the correlation of d
74
Ge and d
30
Si between
these different weathering environments and the documentation of light d
74
Ge in
soils and clays, providing direct evidence that Ge isotopes are fractionated by the
same set of processes as d
30
Si and Ge/Si during continental weathering of silicate
rocks, asinferredinChapter2. Below, wediscuss1)theimportanceofhydrologyin
determining the distribution of chemical and isotopic signatures in this study area;
and 2) the details of weathering processes and associated isotopic fractionation
that lead to the two distinct signatures observed in interbasin groundwater and
lowland weathering fluids.
111
4.4.1 Interbasin groundwater
The interbasin groundwater (IBGW) d
30
Si signature in La Selva is similar to
that observed in other weathering environments where secondary clay precipita-
tion during silicate weathering results in isotopically heavy river or ground water
(e.g., Ziegler et al., 2005a; Georg et al., 2006a, 2009; Hughes et al., 2013). Low
Ge/Si ratio of 0.3 mmol/mol in the groundwater is also consistent with significant
secondary mineral precipitation (Murnane & Stallard, 1990; Froelich et al., 1992).
While there are no other groundwater d
74
Ge data available, the heavy signature
of IBGW is consistent with the negative correlation between Ge/Si and d
74
Ge
observed in Chapter 2 (Baronas et al., 2017a).
Reactive transport modeling of isotope fractionation in interbasin
groundwater
Having the data on all three proxy signatures (d
30
Si, d
74
Ge, and Ge/Si) offers
a unique opportunity to test whether a single weathering process can be used to
explain the observed interbasin groundwater (IBGW) composition. To do this, we
utilize a simple analytical reactive transport model developed to investigate U-Th
series isotopes in groundwater by Tricca et al. (2000) and Porcelli (2008) and more
recently applied to trace Li isotopes by Pogge von Strandmann et al. (2014). The
details of the model are described and the derivation of key equations is given in
Appendix C.
The model can be used to predict how fluid chemistry would evolve along its
pathway and to test whether a steady state assumption is reasonable. Using a
range of silicate dissolution rates reported in the literature, IBGW fluids reach
steady state within 100-1000 meters of subsurface flow (Fig. 4.6), whereas the
minimum distance of the subsurface flowpath is estimated to be∼9 km, ignoring
112
tortuosity effects, and the mean age of the discharging fluid∼3 ky (Genereux et
al., 2009). The fluid "equilibration" lengthscale (final plateau in Fig. 4.6) should
be seen as an upper estimate, given that the dissolution rates used were in the
lower range of literature values (see Appendix C). This suggests that the fluids
do indeed reach a steady state (SS) chemical composition long before discharging
in the lowlands of La Selva. The SS assumption supported by the fact that the
dissolved Si concentration (CR05; Table 4.3) is very close to saturation with amor-
phous Si, and therefore unlikely to increase any further (Table 4.3). Dissolved Ge
in natural (uncontaminated) waters is derived exclusively from the dissolution of
silicateminerals; itisthusreasonabletoassumethatGeconcentrationandisotopic
composition has also achieved steady state. Additional samples of fluid chemistry
along the flowpath would make it possible to directly test this assumption and
would yield better constraints on the reaction rates by revealing the actual "equili-
bration"lengthscale. However, suchadditionalconstraintsareunlikelytoaffectthe
calculated fractionation factors, as long as the IBGW fluid sampled at the end of
the flowpath (i.e., discharging in the lowlands) has reached a chemical steady state.
The concentration of a given isotope in the fluid will then depend on the ratio of
the dissolution and removal (co-precipitation or net adsorption) rate constants:
[
28
Si]
ss
fluid
=

r
rock
(1–f)
r
fluid
f
!
[
28
Si]
rock
28
k
diss
28
k
rem
(4.2)
where r [g cm
–1
] is density, f is porosity, and k [s
–1
] are first-order rate con-
stants for dissolution (diss) and removal from solution (rem) via co-precipitation
with secondary phases. Similar equations can be written for
30
Si,
70
Ge, and
74
Ge.
Assuming fully congruent dissolution, i.e no elemental or isotope fractionation,
all dissolution rate constants will be equal (
28
k
diss
=
30
k
diss
=
70
k
diss
=
74
k
diss
)
113
and the isotopic and elemental fractionation of the fluids will occur during sec-
ondary phase precipitation and will depend solely on the differences between
28
k
rem
,
30
k
rem
,
70
k
rem
, and
74
k
rem
. The isotope fractionation factors are then
defined as:
30/28
a
rem
=
30
k
rem
28
k
rem
(4.3a)
74/70
a
rem
=
74
k
rem
70
k
rem
(4.3b)
The Si isotope composition of the IBGW fluid will be:

30
Si
28
Si
!
ss
fluid
=
1
30/28
a
rem

30
Si
28
Si
!
rock
(4.4)
An equivalent expression can be written for Ge isotopes. It must be noted
here that no assumptions are necessary about the nature or the stoichiometry of
the precipitating secondary phases, nor the mechanism of Si and Ge removal from
solution (i.e., precipitation or net adsorption).
Using the mean isotopic composition of the groundwater-rich fluids (i.e. sam-
ples LS02, LS05, LS06, LS13, and CR05; Table 4.3), the application of the above
model yields the following isotope fractionation factors between the precipitating
solids (subscript clay) and fluids:
30/28
a
rem
=
30/28
a
clay–fluid
= 0.9988± .0001
and
74/70
a
rem
=
74/70
a
clay–fluid
= 0.9970± .0005, or expressed in D notation,
D
30
Si
clay–fluid
= –1.2± 0.1 % and D
74
Ge
clay–fluid
= –3.0± 0.5 % (Table C.1).
Both are within the range of values determined previously for adsorption or co-
precipitation with either Al or Fe oxides in the case of Si (Delstanche et al., 2009;
Oelze et al., 2014, 2015) and with Fe oxides in the case of Ge (Pokrovsky et al.,
2014). Secondary aluminosilicate clays, however, show similar fractionation factors
114
for d
30
Si (Frings et al. (2016) and references therein), which may also be the case
for d
74
Ge (no data available).
The model also simulates the evolution of the Ge/Si ratio in the fluids, as well
as in the precipitated material (Fig. 4.6). The partitioning of Ge/Si between the
fluids and the precipitating solids during low temperature weathering has been
thoroughly investigated in the past (Mortlock & Froelich, 1986; Murnane & Stal-
lard,1990;Froelichetal.,1992;Kurtzetal.,2002), whichallowsadditionalground-
truthing of the model parameters used here. Given the parameters in Table C.1,
the Ge/Si of the accumulated precipitate is calculated to be 3.9–4.1 mmol/mol
(depending on the dissolution and reaction rates and the resulting equilibration
timescale). TheGe/Sipartitioningcoefficientbetweenthedissolvingprimaryrocks
and the precipitating secondary solids (sec) is simply defined as
K
w
=
(Ge/Si)
clay
(Ge/Si)
rock
(4.5)
Given that Ge/Si of primary dissolving minerals was set to 3 mmol/mol, Eq.
4.5 yields a K
w
value of 1.3–1.4. This result is somewhat sensitive to the various
parameters used (Table C.1) – especially the dissolution rate (the higher the disso-
lution rate, the lower the K
w
value) – but agrees with values of 1.3–2.5 previously
predicted from various river datasets (Murnane & Stallard, 1990; Froelich et al.,
1992).
The above model can therefore be used to simultaneously model and reproduce
the concentrations of Ge and Si (yielding the Ge/Si ratio), as well as the d
30
Si
and d
74
Ge signatures, of the IBGW fluid and the associated precipitating sec-
ondary phases. The consistency of results between the three ratios, with respect
to independently inferred fractionation factors, is encouraging evidence that the
model is capturing the geochemical dynamics of the IBGW system. This approach
115
could be especially powerful when applied on aquifers where multiple groundwater
(and host rock) samples are available along the flowpath, to model not only the
final concentrations but also test the accuracy of the time- and distance-resolved
evolution of these chemical signatures.
4.4.2 Lowland critical zone processes
The watersheds in the La Selva lowlands represent a weathering regime that
stands in stark contrast to the one which determines the composition of IBGW.
Due to the warm and humid tropical climate combined with low denudation rates,
the soils of La Selva are entirely depleted of primary minerals (with the exception
of some refractory quartz), and therefore represent a supply-limited weathering
regime (see Section 4.3.1). Progressive weathering has resulted in the formation
of thick oxisols, where secondary silicate minerals are likely destabilized and are
in the process of being converted into refractory Al- and Fe-oxides (Kleber et al.,
2007), whichresultsinfurtherfractionationofd
30
Si,d
74
Ge, andGe/Siasdiscussed
below.
Besides mineralogy (see Section 4.3.1), the lack of primary silicates in La Selva
soils is well demonstrated by the fact that all the fluid and solid d
30
Si values
measured in this environment (except for two pore water samples) were isotopi-
cally lighter than the parent bedrock composition (Fig. 4.4c). Low temperature
weathering and Si uptake by vegetation typically drives fluids to heavier isotope
composition, and only rarely have fluids lighter than the parent bedrock been
observed in soil waters or rivers (Frings et al., 2016). In La Selva, the dissolved Si
and Ge is primarily sourced from the dissolution of secondary clays that already
have low d
30
Si and d
74
Ge and high Ge/Si compared to the parent bedrock (Table
4.1, Fig. 4.4). Our hypothesis is that the in-situ neo-formation of tertiary clays
116
0
200
400
600
800
1000
1200
1400
0.01 0.1 1 10 100 1000 10000
Concentration
Distance, m
Si, µmol/L
Ge, pmol/L
-3
-2
-1
0
1
2
3
4
5
0.01 0.1 1 10 100 1000 10000
δ, ‰
Distance, m
δ
30
Si fluid
δ
74
Ge fluid
δ
30
Si acc. ppt
δ
74
Ge acc. ppt.
0
2
4
6
8
10
12
0.01 0.1 1 10 100 1000 10000
Ge/Si, µmol/mol
Distance, m
Fluid
Acc. ppt.
a
b
c
Bedrock
IBGW
Bedrock
IBGW
Bedrock
IBGW
Figure 4.6: The modeled evolution of fluid chemistry along the interbasin ground-
water (IBGW) flowpath. Panel (a) shows dissolved Si and Ge concentration, panel
(b) shows the isotopic composition of the fluids and the accumulated secondary
precipitates, and panel (c) shows the Ge/Si ratio in the fluid and the accumulated
precipitates. The model assumes first order reaction kinetics and constant solid
reactivity along the flow path length. Fluid velocity set to 3 m/y (Genereux et al.,
2005).
117
is the main mechanism controlling Si and Ge isotope distribution in these soils,
whereas vegetation effects play a negligible role. We explore this scenario below,
before coming back and considering the potential effects of vegetation.
Weathering and formation of tertiary clays
To determine the isotopic fractionation associated with soil processes, the iso-
topic composition of the Si and Ge that is supplied to the solution needs to be
determined first. Due to the lack of any identifiable primary minerals in these
soils, as a first approximation, we take the composition of the bulk soil (mean±1
S.D. d
30
Si
soil
= –2.1±0.3 %, d
74
Ge
soil
= –0.13±0.12) to represent the original
signature of solutes released during weathering of soil materials. Previous research
has shown that desilicification of kaolinite and precipitation of mostly amorphous
Al-Si oxides (some combination of allophane, halloysite, and/or gibbsite) appears
to be the major weathering process taking place in these soils (Sollins et al., 1994;
Kautz & Ryan, 2003; Kleber et al., 2007). We refer to the neo-forming phases
as tertiary clays. During this process, a portion of the released Si and Ge co-
precipitates with or adsorbs to the forming Al oxides (or Al and Fe oxides already
present in the soil). Light Si isotopes are then preferentially incorporated in the
tertiary clays (d
30
Si
clay
= –2.5±0.2 %), which drives the lowland weathering flu-
ids (pore waters, lowland groundwater, and local streams not impacted by IBGW)
back towards the heavier values measured (d
30
Si
LW
= –1.3 to 0.2 %). This frame-
work is consistent with the negative correlation between d
30
Si
pw
and [Si]
pw
of
individual samples (excluding one outlier; Fig. 4.5b). These observations in La
Selva are fully consistent with a number of previous studies that showed a trend
towards overall lighter d
30
Si signatures in progressively weathered volcanic soils
118
(Ziegler et al., 2005a; Georg et al., 2007; Opfergelt et al., 2012). The La Selva data
represents the extreme end of this trend.
ThisstudyprovidesthefirstfieldevidencethatlightGeisotopesarealsoprefer-
entially incorporated into secondary and tertiary weatheringproducts, as indicated
by the offset between fresh bedrock and the lowland soils (Fig. 4.4). The soils,
however, appear less fractionated when compared to d
30
Si and there appears to
be no further fractionation during the formation of tertiary clays. Although no
d
74
Ge
pw
data is available, lowland groundwater and streams unaffected by IBGW
appear more fractionated (i.e. still significantly heavier than the parent bedrock)
in d
74
Ge space relative to d
30
Si (Fig 4.4). Below, we estimate the isotope fraction-
ation factors for both systems and show that differences in the relative mobility of
eachelementduringweatheringcanreadilyexplainthedivergentisotopicbehavior.
Isotope fractionation modeling in lowlands
Modeling isotope fractionation in the lowland soils requires a different approach
from that employed for IBGW fluids. On the scale of a soil profile or a hillslope,
the water inputs can be highly spatially variable (i.e., both vertical and horizontal
flow) and the fluid flowpaths from the topsoil to the river are relatively short, on
the order of meters. As such, the evolution of fluid composition along a single
flowpath is unlikely to be a good approximation of the system. Instead, we employ
a simple steady state mass balance model, which is described in detail in Appendix
D.
The assumption of steady state is justified by the lack of systematic trends in
pore water composition with depth, both in terms of d
30
Si (Fig. 4.4) and solute
concentrations(Table4.3). Theconcentrationsofsomeelementsareenrichedinthe
surface horizon, likely from the decomposition of organic plant matter, however,
119
the pore water d
30
Si composition is similar to deeper horizons, suggesting that
plant cycling of Si is not a major control on d
30
Si systematics in these soils. This
point is discussed in more detail in Section 4.4.2.
Given a steady state Si isotopic mass balance, the observed isotopic difference
between the separated tertiary clays and the lowland fluids (represented either
by pore waters, well-sampled groundwaters, or IBGW-unaffected rivers like the
Taconazo) is equal to the equilibrium fractionation factor, i.e. D
30
clay–fluid
= d
30
clay
–
d
30
fluid
. The isotopic difference between the dissolving minerals (i.e., bulk soil) and
the fluids will then simply depend of the fraction of Si that has been removed from
solution (Eq. 4.6a). Although there is no pore water d
74
Ge data available, the Ge
isotopic system can also be assumed to be at steady state (Eq. 4.6b), given that
Ge likely exhibits faster secondary precipitation/adsorption kinetics (see IBGW
discussion above). Indeed, the three lowland fluid samples analyzed (LS01 and
LS12 - Taconazo River, and LS03 - groundwater well) all show a very narrow
range of d
74
Ge values (Table 4.3).
d
30
Si
fluid
≈ d
30
Si
soil
–D
30
Si
clay–fluid
×f
Si
rem
(4.6a)
d
74
Ge
fluid
≈ d
74
Ge
soil
–D
74
Ge
clay–fluid
×f
Ge
rem
(4.6b)
This model has been employed in multiple previous studies of isotope fraction-
ation during weathering and has been alternatively referred to as either "closed
system equilibrium", "steady state equilibrium", or "batch" model (e.g., Johnson et
al., 2004; Georg et al., 2007; Tipper et al., 2012; Bouchez et al., 2013; Hughes et
al., 2013; Dellinger et al., 2015). There is no simple way to independently estimate
f
rem
values for individual pore water or stream samples in La Selva. An often
120
used approach is to normalize the element of interest to Na, which is derived from
silicate weathering but not incorporated into secondary phases (Gislason et al.,
1996). This is impossible to implement at our study site, since the parent material
(i.e., bulk soil) is already completely depleted of Na and other cations (Fig. 4.1),
and the majority of Na in La Selva waters is derived from cyclic salts. However,
since the isotopic composition of all three exchanging mass reservoirs (soils, clays,
and fluids) has been determined, Eqs. 4.6 can be employed to calculate f
Ge
rem
and
f
Si
rem
. To do this, we use the means of the bulk soil, clay, and lowland fluid d
30
Si
andd
74
Ge measurements, assuming that the measurements represent a normal dis-
tribution The uncertainty is then assessed using a Monte Carlo approach (details
in Appendix D). The calculated values are f
Si
rem
= 0.76± 0.15 (mean±1s) and
f
Ge
rem
= 0.95±0.04, indicating that Ge has a higher affinity during clay formation,
consistent with multiple previous studies (e.g., Murnane & Stallard, 1990; Froelich
et al., 1992; Kurtz et al., 2002).
Again, the unique advantage of investigating both Ge and Si isotopic systems
together is that the Ge/Si ratios of the different mass reservoirs (soils, clays, and
fluids) can be used to place additional constraints on the system. Ge/Si frac-
tionation between the dissolving (bulk soil) and the precipitating (tertiary clays)
material is simply:
Ge/Si
clay
=
f
Ge
rem
f
Si
rem
×Ge/Si
soil
(4.7)
where the f
Ge
rem
/f
Si
rem
is equivalent to the partitioning coefficient K
w
as discussed
above(Murnane&Stallard,1990;Froelichetal.,1992). Thevalidityofthef
rem
val-
uesobtainedfromEqs. 4.6abovecanthereforebeconfirmedutilizingthemeasured
soil and clay Ge/Si ratios. A similar expression can be derived for the relationship
between fluid and clay Ge/Si (Appendix D Eq. D.8). Therefore, a comparison of
121
the calculated and measured Ge/Si ratios can be used to refine the predictions of
D
clay–fluid
values. Using this approach, we determine D
30
Si
clay–fluid
= –2.4±0.6
% (mean±1s) and D
74
Ge
clay–fluid
= –2.6± 0.2 %. The optimization routine
further narrows down the calculated f
Si
rem
to 0.80± 0.07 and f
Ge
rem
to 0.96± 0.03,
which translates to a K
w
partitioning coefficient of 1.21± 0.08, similar to the
1.3–1.4 value estimated for IBGW weathering (see Section 4.4.1).
-5
-4
-3
-2
-1
0
1
2
3
0 0.2 0.4 0.6 0.8 1
δ
30
Si, ‰
f
rem
Si
-5
-4
-3
-2
-1
0
1
2
3
0 0.2 0.4 0.6 0.8 1
δ
74
Ge, ‰
f
rem
Ge
Δ
74
Ge
sol-fluid
= -2.6±0.2 ‰
Δ
30
Si
sol-fluid
= -2.4±0.6 ‰
a b
Modeled lowland fluids
Modeled lowland clays
Measured lowland fluids
Measured lowland clays
Measured lowland soils
Figure 4.7: Modeled composition of d
30
Si (a) and d
74
Ge (b) in the fluids and ter-
tiary clays of La Selva lowlands, as function of fraction of the element removed
from solution (Eqs. 4.6). The fractionation factors D
clay–fluid
were determined
assuming a steady state equilibrium isotope mass balance (see text and Appendix
D for model description). Bulk soil values were used as the initial fluid compo-
sition, assuming no fractionation during dissolution. The horizontal color bars
show the range of measured fluid (teal) and clay (yellow) values. The black solid
and dashed lines indicate the calculated isotopic composition of the fluids and the
clays, respectively. The grey area is the 1s of the mean measured values. The grey
vertical dotted lines indicate the region of f
rem
values that are consistent with the
measured d
30
Si, d
74
Ge, and Ge/Si values in soils, clays, and fluids.
Figure 4.7 shows the dependence of d
30
Si and d
74
Ge of fluids and tertiary clays
on f
rem
, calculated using Eqs. 4.6. It also demonstrates the range of f
rem
values
(optimized via Ge/Si modeling) for which the modeled and observed d
30
Si and
d
74
Ge agree. In the case of d
30
Si, the fractionation during lowland weathering
is about twice as high as that calculated for IBGW weathering (–2.4± 0.6 vs.
122
–1.2± 0.1 %, respectively). In the case of d
74
Ge, the fractionation factors are
indistinguishable within uncertainty (–3.0± 0.5 vs. –2.6± 0.2 %for IBGW and
lowlands, respectively).
First principles quantum modeling predicts D
74
Ge
solid–fluid
< 0 % for a num-
ber of silicates, as well as for Ge incorporation into Fe oxides (Li et al., 2009; Li
& Liu, 2010). Pokrovsky et al. (2014) have experimentally determined Ge iso-
tope fractionation to range from -1.7 % during reversible Ge chemisorption to Fe
oxides, up to -4.4 % when Ge is irreversibly co-precipitated. The field-derived
fractionation factors in this study could therefore conceivably represent a mixture
of Ge adsorption and co-precipitation during Fe redox cycling. It may also reflect
d
74
Ge fractionation via kaolinite precipitation, for which no theoretical or exper-
imental estimates are available. The relatively large and constant D
74
Ge
clay–fluid
value is also consistent with the low chemical mobility of Ge during weathering
processes, which in turn is indicative of fast kinetics of Ge removal from solution by
secondary (or tertiary) weathering products. There is currently not enough exper-
imental data to assess whether Ge isotope fractionation is dominated by kinetic or
equilibrium fractionation.
As discussed above, a large number of previous field, experimental, and theo-
retical studies have shown Si isotope fractionation to range from 0 to -5 % (Frings
et al. (2016) and references therein). First principles quantum calculations have
estimated D
30
Si
kaolinite–quartz
= –1.6 % (Méheut et al., 2007, 2009), indicating a
thermodynamic preference for light isotopes in secondary silicates relative to pri-
mary silicates, generally consistent with the data in this study. Finally, Oelze et al.
(2014, 2015) have shown that non-steady state Si fractionation during adsorption
onto Al oxides can range from 0 to -5 %, depending on solution composition and
approach to equilibrium. Therefore, the increase in D
30
Si
clay–fluid
from –1.2±0.1
123
%during volcanic rock IBGW weathering to –2.4±0.6 during lowland soil weath-
ering could reflect a shift from more equilibrium-like fractionation at the high Si
concentrations of IBGW to a more kinetically controlled fractionation in the dilute
pore waters. An alternative explanation is a shift in the dominant precipitating
phase, perhaps from primarily Fe and Al oxides during IBGW weathering to kaoli-
nite and other silicate clays during lowland soil weathering. The d
74
Ge and d
30
Si
fractionation factors proposed for the two weathering environments (i.e., IBGW
and lowlands) are summarized in Fig. 4.8b.
The minor role of vegetation in Ge and Si cycling
Above, we have shown that the distribution of Si and Ge isotopes in the solids
and fluids of La Selva lowlands can be readily explained by invoking only inor-
ganic fractionation during the formation of secondary and tertiary clays. How-
ever, vegetation uptake of Si is known to consistently fractionate both d
30
Si (e.g.,
De La Rocha et al., 1997) and Ge/Si (e.g., Derry et al., 2005) that enters the plant
and forms amorphous silica phytoliths. Cornelis et al. (2011) have summarized
the vegetation effects on both proxies. There is currently no data available on any
potential vegetation fractionation of d
74
Ge, although, based on indirect evidence
of riverine compositions, Baronas et al. (2017a) argue that this effect is unlikely
to be significant. Nevertheless, vegetation cycling effects have the potential to be
amplified in extremely solute depleted environments such as La Selva and therefore
must be considered.
Mostpreviousstudiesofd
30
Sifractionationbyplantshavedeterminedfraction-
ation factors D
30
Si
veg–fluid
in the range of -2 to 0 % (Ding et al., 2008; Opfergelt
et al., 2006a,b; Sun et al., 2016; Frings et al., 2016). Consistent with these previous
studies, the measured vegetation samples at La Selva (d
30
Si
veg
= –1.8± 0.1 %)
124
are lighter by∼1 % relative to the soil pore waters (Fig. 4.4). Active and rapid Si
cycling by vegetation is expected to create a vertical gradient in d
30
Si and Ge/Si
signatures in the soils and pore waters, since plants act as an "biolift" (e.g., Brant-
ley & Lebedeva, 2011), preferentially taking up light Si isotopes and Si relative to
Ge from the pore waters at the main rooting depth, driving the solution (and over
time, the surrounding solids) towards heavier d
30
Si and higher Ge/Si values. The
solids of the surface soils then should be enriched in light d
30
Si and low Ge/Si from
the accumulation and decay of plant litter. The lack of d
30
Si or Ge/Si gradients
in La Selva soil profiles (Fig. 4.4) suggests that this process does not occur to
a significant degree in this environment. Although unlikely in a humid tropical
climate, it could be argued that
28
Si-rich phytoliths are exported from the topsoils
and watersheds before they have a chance to accumulate and/or decay, without
imparting their light d
30
Si signature on the topsoils. If this were the case, it would
be expected that the loss of d
30
Si-light and Ge/Si-low material would drive the
local streams and groundwaters in the opposite direction, i.e. towards heavy d
30
Si
and high Ge/Si values in this two-proxy space (Cornelis et al., 2010, 2011). Figure
4.8a shows no such indication, once again suggesting a negligible role of vegetation
in the Si cycle of La Selva lowlands.
4.5 Conclusions
In this study we have investigated the processes controlling the fractionation of
Si and Ge elemental and isotope ratios in the volcanic soils of a tropical rainforest
in Costa Rica. The lowland soils are extremely chemically weathered and as a
result exhibit high Ge/Si, low d
30
Si, and low d
74
Ge values relative to the parent
bedrock. The formation of tertiary clays results in further preferential removal of
125
Δ
30
Si
sol-fluid
= –1.2‰
Δ
74
Ge
sol-fluid
= –2.6‰
Δ
74
Ge
sol-fluid
= –3.0‰
Δ
30
Si
sol-fluid
= –2.4‰
Lowlands
IBGW
0.3 0.5 1 2 4 8
Ge/Si, µmol/mol
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
δ
30
a
0 1 23 4
δ
74
-3
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
δ
30
b
Bedrock
Soils
Clays
Local streams
Local groundwater
Interbasin groundwater
Figure 4.8: The relationship between Si isotopes and Ge/Si ratios (a) and Ge iso-
topes (b), in solids and fluids of La Selva. The dashed lines show an expected
mixing relationship between lowland groundwater (sample LS03) and interbasin
groundwater (sample CR05). The arrows in panel (b) show the determined frac-
tionation factors for the two different weathering environments represented at this
study site. Note the reversed direction and the log scale of Ge/Si axis in panel (a).
light Si isotopes, driving lowland pore waters and streams back towards heavier
d
30
Si values. In contrast, d
74
Ge signatures of tertiary clays are identical to the
bulk soils, whereas lowland fluids are strongly enriched in heavy Ge isotopes.
A number of streams in the lowlands are affected by discharge of solute-rich
interbasin groundwater (IBGW) derived from higher elevations and reflecting the
weathering of fresh volcanic rock, as observed by previous researchers (Genereux
& Pringle, 1997; Genereux & Jordan, 2006). The IBGW exhibits low Ge/Si, high
d
30
Si, and high d
74
Ge values. Based on previous estimates of IBGW transit time
and predicted reaction kinetics, the IBGW fluids are expected to have reached a
chemical and isotopic steady state. Similarly, the lack of concentration or isotopic
gradientsinlowlandsoilporewaters, suggeststhattheyareatsteadystatewiththe
soil column. If the steady state assumption is correct, then the observed isotopic
126
composition of the dissolving and precipitating phases and the fluids can be used
to determine the fractionation factors, which are similar to previous experimental
and field estimates (-1.2 to -2.4 % for d
30
Si and -2.6 to -3.0 % for d
74
Ge). It
is however, unclear whether the observed isotope fractionation reflects primarily
kinetic or equilibrium effects. We show how Ge/Si fractionation can provide a
useful additional constraint on the Ge-Si system, validating the modeled isotope
fractionation of both elements.
Overall, we argue that the observed Ge/Si, d
30
Si, and d
74
Ge signatures depend
on the relative mobility of each element during chemical weathering. The majority
of dissolved Ge is reincorporated into secondary and tertiary weathering products,
which results in relatively less
74
Ge-depleted clays but highly
74
Ge-enriched fluids.
AcomparativelysmallerfractionofSiisreincorporatedintosecondaryandtertiary
phases, resulting in highly
30
Si-depleted clays but relatively less
30
Si-enriched flu-
ids. This hypothesis is consistent with the signatures observed both in the IBGW
and the lowland streams.
Future studies may reveal additional complexity in the critical zone controls of
Ge/Si,d
30
Si, andespeciallytherecentlydevelopedd
74
Ge. Ourworkhere, however,
shows that a simple framework can be used to explain the signatures of all three
proxies in two contrasting weathering environments. The use of such a multi-proxy
approach increases the confidence of interpretations. The dependence of isotopic
fractionation on the relative mobility of each element suggests that a d
74
Ge-d
30
Si
multi-proxy system would be sensitive to a wider range of weathering conditions
and timescales than each isotopic system in isolation.
127
Acknowledgements
The Costa Rica work was financially supported by the UK Natural Environ-
ment Research Council (NERC Grant NE/H012656/1 to AJW and KB). JJB was
supported by NSF grant OCE-1260692 to DEH, the John Montagne Award (Qua-
ternary Geology & Geomorphology division, GSA), and the InterRidge Research
Fellowship. Field work was hosted by the Institute of Tropical Studies, La Selva
Research Station. We thank Emmanuel Ponzavera for assisting with Ge isotope
analyses at Ifremer.
128
Chapter 5
Ge/Si and Ge isotope behavior
during glacial weathering: field
and experimental data from West
Greenland
Author contributions
I conceived the design of this study and performed the sediment weathering
experiment. The samples were collected by Mia Bennett and Lincoln Pitcher
(University of California, Los Angeles). Ge isotope analyses were done in collabo-
ration with Olivier Rouxel at Ifremer in Brest, France. I also performed the major
and trace element concentration analyses at the University of Southern California,
with the help of Yi Hou. This manuscript will be submitted to a peer-reviewed
journal with the following preliminary list of co-authors: Doug Hammond, Olivier
Rouxel, Mia Bennett, Lincoln Pitcher.
Abstract
Glacial environments offer the opportunity to study weathering processes in
the presence of large amounts of freshly ground bedrock and the absence of
129
any significant vegetation. This study investigates germanium (Ge) geochem-
istry during glacial weathering in West Greenland. The dissolved Ge/Si ratios
in periglacial streams and in the Watson River and its tributaries ranged from 0.9
to 2.2 mmol/mol, higher than most non-glacial rivers around the world but simi-
lar to other glacial streams reported previously. The high dissolved Ge/Si likely
results from preferential dissolution of Ge-rich biotite during subglacial weather-
ing, coupled with limited secondary phase precipitation. Dissolved d
74
Ge of the
Watson River was 0.86±0.24 %, only slightly heavier than the river suspended
load(0.48±0.23%). Incubatingunfilteredriverwaterwithitssuspendedsediment
in the laboratory for 1.5-2 years has resulted in the reduction of dissolved Ge/Si
to∼0.5 mmol/mol, indicating significant Ge removal from solution with increased
rock-water contact time, most likely due to adsorption to Fe oxyhydroxides. At the
same time, dissolved d
74
Ge increased to 1.9-2.2 %. Assuming closed system equi-
librium ("batch") fractionation, D
74
Ge
sec–diss
fractionation factor is calculated as
-2.1±1.4 %, in good agreement with previously determined values for Ge adsorp-
tion onto Fe oxide particles. This study therefore provides direct evidence of d
74
Ge
fractionation during weathering.
5.1 Introduction
Glacial rivers are often dilute, especially in terms of silicate-derived elements,
reflecting the limited degree of silicate mineral dissolution due to low temperatures
and short water-rock interaction times (Anderson, 2007; Tranter & Wadham, 2014,
and references therein). They therefore offer an opportunity to study the incipient
stages of weathering, in an environment where secondary aluminosilicate clay pre-
cipitation is limited. Furthermore, the presence of vegetation is naturally limited
130
in glaciated environments, allowing to study weathering processes in isolation,
especially useful for elements which might otherwise be fractionated by vegeta-
tion, such as Si and Ge (e.g., Derry et al., 2005; Street-Perrott & Barker, 2008).
Globally, dissolved riverine Ge/Si ratios have been observed to vary from 0.1 to 3
mmol/mol, often significantly lower than silicate rock ratios of 1-2 mmol/mol, which
are the primary source of Ge and Si to solution (Froelich et al., 1985; Mortlock
& Froelich, 1987; Murnane & Stallard, 1990; Froelich et al., 1992; Baronas et al.,
2017a). In contrast, secondary clay minerals (such as Fe oxides and aluminosili-
cates like kaolinite) exhibit Ge/Si ratios commonly above 5 mmol/mol (Mortlock
& Froelich, 1987; Murnane & Stallard, 1990; Kurtz et al., 2002). These obser-
vations have been used to develop a conceptual model for Ge/Si behavior during
weathering, which assumes that 1) initial dissolution of silicate rocks takes place
without Ge/Si fractionation; and 2) dissolved Ge/Si ratios are fractionated during
subsequent precipitation of secondary phases. To date, no observations directly
confirming these assumptions have been available. Dissolved Ge/Si fractionation
appears to take place rapidly, and most natural weathering solutions (e.g. soil
pore water, river water, or groundwater) exhibit lower Ge/Si ratios relative to
the source silicate rocks (see references above). Glacial streams are a potential
exception as they have been shown to consistently exhibit Ge/Si ratios in the 1-3
mmol/mol range, similar to most bulk silicate rocks (Mortlock & Froelich, 1987;
Chillrud et al., 1994; Anders et al., 2003). However, it is unclear whether glacial
river Ge/Si ratios reflect the congruent dissolution of silicates and limited precipi-
tation of secondary phases or rather the preferential dissolution of Ge-rich primary
minerals (e.g. biotite) coupled with precipitation of secondary phases, as both pro-
cesses could result in the solution Ge/Si being close to the bulk primary silicate
signature.
131
The riverine Ge isotope (d
74
Ge) data presented in the other chapters of this
dissertation similarly indicate Ge isotope fractionation during critical zone pro-
cesses and are primarily interpreted as reflecting fractionation during secondary
phase precipitation in a manner similar to Ge/Si and other isotopic weathering
proxies such as Si and Li isotopes (d
30
Si and d
7
Li). In this study, the evolution
of dissolved Ge/Si and d
74
Ge signatures during continued water-rock interaction
in glacial river samples was directly observed. The data provide experimental
evidence of Ge/Si and d
74
Ge fractionation during weathering reactions, adding
support to the interpretation of riverine chemistry data in the other chapters of
this dissertation. Furthermore, the dissolved d
74
Ge signature helps to test the
congruency of silicate dissolution during glacial weathering.
5.2 Methods
The study area is located in southwest Greenland, close to the town of Kanger-
lussuaq (Fig. 5.1). The bedrock is primarily composed of Archean gneiss metamor-
phosed to amphibolite (Wimpenny et al., 2010b). The area lies at the boundary
of the Arctic circle. The mean annual temperature is -5
◦
C and varies between -50
and 20
◦
C over the course of the year (Russell, 2007). Locally, evaporation exceeds
precipitation, resulting in salinization and high solute concentration of local lakes
(Higgins & Secher, 2001), which were not sampled in this study.
A number of stream and river samples were collected in West Greenland in
August 2014. One set of samples was collected from small streams next to the
Russell Glacier which feeds all rivers in the area, referred to here as "periglacial"
(Fig. 5.1). Anothersetofsampleswascollectedfromthelargestriverintheregion,
the Watson River, and the two main tributaries near the town of Kangerlussuaq,
132
which integrate both periglacial and some non-glacial streams, referred to as "large
rivers".
For a number of samples, an aliquot was filtered in the field using 0.45 mm pore
size polyethersulfone membrane. The remaining unfiltered stream water (volume
ranging from 60 to 500 mL) was set on a shaker table and left to incubate at 25
◦
C
in the dark for up to∼800 days. The water was periodically sampled and filtered
using 0.45 mm pore size polyethersulfone membrane. Both field and incubated
samples were acidified with trace grade HNO
3
several weeks prior to analysis.
Detailed analytical methods are given in Chapters 3 and 6. Briefly, major
cation and Si concentrations were measured using Agilent 4100 MP-AES. Addi-
tional Si measurements were done using molybdate blue colorimetry (Mullin &
Riley, 1955). Ge concentrations were measured using isotope dilution-hydride
generation-ICP-MS on a Thermo Element 2, following the method developed by
Mortlock & Froelich (1996), as modified by Baronas et al. (2016). Trace metal con-
centrations were measured using ICP-MS (Thermo Element 2). Ge isotope com-
position was measured using hydride generation-multi-collector-ICP-MS (Thermo
Neptune) as described in Chapter 6.
5.3 Results and discussion
5.3.1 Glacial stream chemistry
Themeasuredsoluteconcentrationswerelowinallsampledstreams(Table5.1),
in agreement with previous studies in the area (Wimpenny et al., 2010b, 2011; Yde
et al., 2014; Hindshaw et al., 2014) and are considerably lower than most rivers in
other settings globally (for example, see Chapter 6). The low concentrations result
133
Watson Riv.
Russell
Glacier
5 km
50 km
Labrador
Sea
66.5
67.0
66.0
54.0 52.0 50.0
Periglacial streams
Watson & tribs.
JO-1/13
JO-2
JO-3/12
JO-4/7
JO-10/11
JO-14
JO-5/6
JO-8
JO-9
Figure 5.1: Map showing the river sample locations in West Greenland. The inset
shows the larger area, including the Russell Glacier whose melt water feeds the
sampled rivers. Source: Google Earth.
from the relatively unreactive gneissic lithology and the short amount of time that
water and rock interact in this periglacial weathering environment.
Dissolved Ge/Si ratios ranged from 0.9 to 2.2mmol/mol, similar to most silicate
rocks (e.g., Murnane & Stallard, 1990; Froelich et al., 1992, also see Chapters 4 and
6). The lack of Ge/Si depletion and the lack of Ge/Si correlation with dissolved Ge
concentrations (Fig. 5.2a) suggest limited secondary mineral precipitation, con-
sistent with the dilute chemistry and the presumed short water-rock interaction
times. Interestingly, the analysis of suspended sediment from the Watson River
yielded a Ge/Si ratio of 1.2 mmol/mol (Chapter 6). It therefore appears that these
glacial streams have elevated Ge/Si relative to bulk rock. The local bedrock is
134
0 20 40 60
Ge, pmol/L
0.5
1
1.5
2
2.5
Ge/Si, µmol/mol
a
0.5 1 1.5 2
K/Na, mol/mol
0
1
2
3
4
5
Ge/Na, µmol/mol
b
JO-10
JO-12
JO-13
JO-14
JO-5
JO-6
JO-7
JO-8
JO-9
Large rivers
Periglacial
streams
Figure 5.2: Dissolved Ge/Si as a function of Ge concentration (panel a) and dis-
solved Ge/Na as a function of K/Na (panel b) in West Greenland streams. The
dashed line in panel a shows Ge/Si ratio of the suspended load in the Watson
River.
essentially monolithological, and the Watson suspended load should therefore pro-
vide a reasonable estimate of its chemical composition. It is possible that the
sediment sample is not representative of the dissolving phases, due to hydrody-
namic sorting and presence of secondary clays. However, that should only serve to
increase Ge/Si of the suspended material (as demonstrated in Chapter 6).
Glacial grinding action is thought to result in preferential weathering of biotite,
and especially in leaching of K and other cations from biotite interlayer sites,
resulting in non-stoichiometric release of these elements(e.g., Blum & Erel, 1995).
Glacial comminution may therefore also result in relatively higher rates of Ge and
Si release from biotite. Indeed, this mechanism has been proposed to explain the
relatively high Ge/Si in other rivers draining glaciated regions (Anders et al., 2003)
and appears to also apply to the streams studied here. Different primary minerals
can have variable Ge/Si ratios, with biotite often exhibiting higher Ge/Si up to 9
mmol/mol (Mortlock & Froelich, 1987). This notion is supported by the correla-
tion of Ge/Na and K/Na ratios in the studied area, with both ratios considerably
135
elevated in the periglacial streams (Fig. 5.2b). Both Ge and Si are primarily
located in the tetrahedral sheet layers within silicates (Bernstein, 1985) and there-
fore are more likely to be released in a stoichiometric ratio, in contrast to interlayer
cations. Unfortunately, this prevents the use of Ge/Na or other Ge/cation ratios
to be used to quantitatively assess the degree of Ge removal into secondary weath-
ering phases. Nevertheless, the similarity of dissolved Ge/Si (discussed above)
and d
74
Ge (discussed below) composition to the bedrock values is consistent with
limited secondary phase formation.
136
Table 5.1: Greenland river field data.
Na, K, Ca, Mg, Ge, Si, Ge/Si,
δ
74
Ge
µmol/L µmol/L µmol/L µmol/L pmol/L µmol/L µmol/mol ‰
Watson (North trib.) JO-1 field 2014-08-19 14:04 67.021 -50.645 -- -- -- -- -- -- -- --
Watson (Erkendalen trib.) JO-2 field 2014-08-19 14:50 67.014 -50.643 -- -- -- -- -- -- -- --
Watson (main stem) JO-3a field 2014-08-19 17:19 67.020 -50.669 -- -- -- -- -- -- -- --
Watson (main stem) JO-3b field 2014-08-19 17:19 67.020 -50.669 -- -- -- -- -- -- -- --
Periglacial stream JO-4 field 2014-08-20 13:37 67.147 -50.107 -- -- -- -- -- -- -- --
Periglacial stream JO-5 field 2014-08-20 14:36 67.135 -50.117 3.9 4.6 13.4 5.7 16 7.3 2.17 --
Periglacial stream JO-6 field 2014-08-22 15:42 67.135 -50.117 7.3 12.8 27.4 12.8 23 18.2 1.29 --
Periglacial stream JO-7 field 2014-08-22 16:27 67.147 -50.107 5.1 4.8 13.1 5.4 12 6.6 1.85 --
Periglacial stream JO-8 field 2014-08-22 17:08 67.143 -50.123 6.1 6.8 17.2 7.9 14 16.0 0.89 --
Periglacial stream JO-9 field 2014-08-22 18:30 67.133 -50.136 3.7 4.8 14.9 6.5 14 9.5 1.50 --
Watson (main stem) JO-10 field 2014-08-23 17:24 67.005 -50.689 23.3 18.6 26.7 8.6 24 16.4 1.46 --
Watson (main stem) JO-11 field 2014-08-24 10:06 67.005 -50.689 28.2 21.3 30.0 10.4 46 24.3 1.88 --
Watson (main stem) JO-12 field 2014-08-24 14:15 67.020 -50.669 35.8 23.0 34.2 10.5 39 23.6 1.63 --
Watson (North trib.) JO-13 field 2014-08-24 14:45 67.021 -50.645 36.0 22.8 34.5 10.8 39 24.1 1.62 --
Watson (main stem) JO-14 field 2014-08-27 9:01 67.010 -50.682 -- -- -- -- 55 33.3 1.66 0.86±0.24
Lat., ° Long., ° River
Sample
ID
Date Hour
137
The Watson River exhibits the lowest dissolved d
74
Ge composition (0.86±0.24
%) of all rivers measured to date (Chapter 6). This value is only slightly higher
than the suspended load (0.48±0.23 %; see Chapter 6), which can be taken to
represent the fresh bedrock, due to the limited chemical weathering taking place in
thisenvironment. Thelackofd
74
GefractionationintheWatsonRiverisconsistent
with the dissolved Ge/Si signatures, as well as previous studies indicating little
secondary clay formation at this site (Wimpenny et al., 2010b). Furthermore, if
most of dissolved Ge is indeed derived from preferential weathering of biotite, this
datum suggests that the d
74
Ge composition of biotite (and likely other primary
minerals) is similar to that of bulk rock.
Thelackofdissolvedd
74
Gefractionationstandsinstarkcontrastofd
7
Libehav-
ior in this area. Wimpenny et al. (2010b) have shown that the dissolved d
7
Li
composition of the Watson and other glacial and non-glacial streams in the area
is significantly heavier than the bedrock. They also showed that secondary alu-
minosilicate clays are strongly undersaturated in the studied streams and that
d
7
Li fractionation is primarily the result of Li adsorption to Fe oxides formed in
the subglacial environment. The relatively high SO
4
concentration in the Watson
and adjacent streams indicates high rates of pyrite dissolution under the ice sheet
(Wimpenny et al., 2010b). However, Fe concentrations in these streams are much
lower, indicating that most Fe precipitates prior to discharge. It has been exper-
imentally shown that Ge is readily removed from solution via Fe oxyhydroxide
(FeOx) precipitation (Anders et al., 2003; Pokrovsky et al., 2006). Pokrovsky et
al. (2014) further showed that this process results in Ge isotopic fractionation, with
lighter isotopes preferentially removed with the FeOx precipitate. Pyrite weather-
ing and subsequent FeOx precipitation taking place in this glacial weathering envi-
ronment is thus expected to fractionate the dissolved Ge/Si and d
74
Ge signatures,
138
in a fashion similar to d
7
Li. It is therefore somewhat surprising that very little (if
any) Ge/Si and d
74
Ge fractionation is observed in the current study. The most
likely explanation is that due to the extremely low dissolved Ge concentration in
thesubglacialweatheringenvironment, onlyasmallfractionofGeisreversibly(i.e.
via adsorption) or irreversibly (i.e. via co-precipitation) removed from solution.
An additional explanation could be exchange and isotopic equilibration between
dissolved and FeOx-adsorbed Ge occurs more rapidly, whereas Li exchange is lim-
ited (Wimpenny et al., 2010b). Such difference in removal mechanisms could cause
a Rayleigh type fractionation of Li isotopes, driving dissolved d
7
Li composition to
much heavier values. In contrast, reversible isotopic exchange between dissolved
and adsorbed Ge would result in a smaller degree of d
74
Ge fractionation.
5.3.2 Sediment weathering experiment
To monitor Ge/Si and d
74
Ge fractionation with increasing rock-water contact
time, unfiltered stream samples were incubated, allowing the suspended to interact
with the dissolved load for up to 2 years. This setup did not aim to represent the
subglacial or even pro-glacial weathering environment, as most of the reactive
mineral phases such as pyrite and carbonates are likely already depleted to some
degree at the start of the experiment, and the water/rock ratio was very different
from that in the subglacial environment. Nevertheless, it offered a way to directly
observe Ge/Si and d
74
Ge behavior during water-rock interaction.
The changes in solution chemistry over the duration of the experiment are sum-
marized in Tables 5.2 and 5.3 and Fig. 5.3. There were clear differences between
the two sample groups: the small periglacial streams sampled adjacent to the
glacier and the large rivers further away that incorporate subglacial, supraglacial,
and non-glacial waters. While all samples started out with similarly low Si and Ge
139
concentrations (Table 5.1), the large river samples (warm colored symbols in Fig.
5.3) showed a much larger increase in dissolved Si and Ge throughout the course of
the experiment, relative to the periglacial streams (cool colors in Fig. 5.3). These
differences were likely controlled by the suspended sediment concentration – the
Watson and its tributaries carried significantly larger suspended sediment loads
relative to the small periglacial streams. Fig. 5.4 shows that the final Si con-
centration is strongly correlated with the water/sediment ratio for samples where
enough sediment was available to quantify sediment concentrations.
Ge/Si fractionation
While dissolved Ge concentration also increased (especially in the large river
samples), it reached a plateau sooner and remained relatively constant for most of
the experiment (Fig. 5.3b). As a result, Ge/Si ratios decreased significantly with
time (Fig. 5.3e), achieving values typical of rivers in non-glaciated settings and
indicating increasingly less congruent weathering with time.
Wimpenny et al. (2010b) have shown that almost all of total dissolved (i.e.,
species smaller than 0.2 mm) Fe and Al are present in colloidal form in these rivers.
Their concentrations decreased with time during the experiment (Fig. 5.3c-d),
most likely indicating the coagulation of colloids and increase in the FeOx and
AlOx particle size with time. Another possibility is that, with the significant
increase in dissolved Si, colloidal Fe and Al were transformed and incorporated
into precipitating aluminosilicate clays.
The stability of Ge concentrations throughout the course of the experiment
suggests a buffering mechanism that is independent of the total dissolved Si, Fe,
or Al concentrations. I propose that Ge concentrations in these solutions were
controlled primarily by adsorption to Fe oxides in the suspended sediments, rather
140
Table 5.2: Greenland river sediment dissolution data.
Incub. time Ge Si Ge/Si Al Fe Mn δ
74
Ge
days pmol/L µmol/L µmol/mol nmol/L nmol/L nmol/L ‰
JO-1 Field 2014-08-19 0 -- -- -- -- -- -- --
T-1 2014-10-10 52 124 141 0.88 -- -- -- --
T-2 2014-11-21 94 -- 117 -- -- -- -- --
T-3 2015-01-07 141 -- 126 -- -- -- -- --
T-4 2015-02-25 190 -- 131 -- -- -- -- --
T-5 2015-06-01 286 68 142 0.48 -- -- -- --
Final 2016-10-12 785 -- 168 -- -- -- -- --
JO-2 Field 2014-08-19 0 -- -- -- -- -- -- --
T-1 2014-10-10 52 80 92 0.86 5087 1015 126 --
T-2 2014-11-21 94 66 106 0.62 -- -- -- --
T-3 2015-01-07 141 76 118 0.64 2631 389 73 --
T-4 2015-02-25 190 76 130 0.59 -- -- -- --
T-5 2015-06-01 286 70 138 0.51 890 51 53 --
Final 2015-12-01 469 -- 152 -- 540 75 59 1.86±0.28
JO-3A Field 2014-08-19 0 -- -- -- -- -- -- --
T-1 2014-10-10 54 59 80 0.74 -- -- -- --
T-2 2014-11-21 96 -- 97 -- -- -- -- --
T-3 2015-01-07 143 -- 112 -- -- -- -- --
T-4 2015-02-25 192 -- 121 -- -- -- -- --
T-5 2015-06-01 288 -- 129 -- -- -- -- --
Final 2015-12-01 471 69 139 0.50 -- -- -- 2.24±0.35
JO-3B Field 2014-08-19 0 -- -- -- -- -- -- --
T-1 2014-10-10 52 55 82 0.66 1748 305 107 --
T-2 2014-11-21 94 -- 101 -- -- -- -- --
T-3 2015-01-07 141 -- 119 -- 1422 149 91 --
T-4 2015-02-25 190 -- 127 -- -- -- -- --
T-5 2015-06-01 286 -- 137 -- 84 0 73 --
Final 2015-12-01 469 -- 147 -- 316 41 75 --
JO-12 Field 2014-08-24 0 39 24 1.63 1869 903 87 --
T-1 2014-10-10 52 64 101 0.63 -- -- -- --
T-2 2015-02-25 190 70 168 0.42 -- -- -- --
T-3 2015-06-01 286 69 178 0.39 -- -- -- --
Final 2016-10-12 785 -- 203 -- -- -- -- --
Bottle Sample Date
141
Table 5.3: Greenland river sediment dissolution data (continued).
Incub. time Ge Si Ge/Si
days pmol/L µmol/L µmol/mol
JO-4 Field 2014-08-20 0 -- -- --
T-1 2014-10-10 52 18 14 1.27
T-2 2014-11-21 94 14 19 0.75
T-3 2015-01-07 141 18 19 0.97
T-4 2015-02-25 190 18 19 0.94
T-5 2015-06-01 286 25 21 1.21
Final 2016-10-12 785 -- 28 --
JO-5 Field 2014-08-20 0 16 7 2.17
T-1 2014-10-10 52 21 22 0.95
T-2 2015-02-25 190 29 28 1.04
T-3 2015-06-01 286 29 30 0.98
Final 2016-10-12 785 -- 37 --
JO-6 Field 2014-08-22 0 23 18 1.29
T-1 2014-10-10 52 29 22 1.33
T-2 2015-02-25 190 -- 28 --
T-3 2015-06-01 286 26 29 0.90
Final 2016-10-12 785 -- 32 --
JO-7 Field 2014-08-22 0 12 7 1.85
T-1 2014-10-10 52 -- 13 --
T-2 2015-02-25 190 -- 13 --
T-3 2015-06-01 286 -- 18 --
Final 2016-10-12 785 -- 26 --
JO-8 Field 2014-08-22 0 14 16 0.89
T-1 2014-10-10 52 18 20 0.91
T-2 2015-02-25 190 -- 24 --
T-3 2015-06-01 286 -- 23 --
Final 2016-10-12 785 -- 28 --
JO-9 Field 2014-08-22 0 14 10 1.50
T-1 2014-10-10 52 -- 15 --
T-2 2015-02-25 190 -- 20 --
T-3 2015-06-01 286 -- 20 --
Final 2016-10-12 785 -- 27 --
JO-10 Field 2014-08-23 0 24 16 1.46
T-1 2014-10-10 52 -- 72 --
T-2 2015-02-25 190 -- 108 --
T-3 2015-06-01 286 -- 115 --
Final 2016-10-12 785 -- 130 --
JO-11 Field 2014-08-24 -- 46 24 1.88
JO-13 Field 2014-08-24 0 39 24 1.62
T-1 2014-10-10 52 -- 122 --
T-2 2015-02-25 190 -- 198 --
T-3 2015-06-01 286 -- 210 --
Final 2016-10-12 785 -- 235 --
Bottle Sample Date
142
0 500 1000
0
50
100
150
200
250
Si, µmol/L
0 200 400 600
0
20
40
60
80
100
120
140
Ge, pmol/L
0 200 400 600
0
1
2
3
4
5
6
Al, µmol/L
0 200 400 600
0
0.2
0.4
0.6
0.8
1
1.2
Fe, µmol/L
0 200 400 600
Time, days
0
0.5
1
1.5
2
2.5
Ge/Si, µmol/mol
JO-1
JO-2
JO-3a
JO-3b
JO-10
JO-12
JO-13
JO-14
JO-4
JO-5
JO-6
JO-7
JO-8
0 200 400 600
Time, days
0
0.5
1
1.5
2
2.5
3
δ
74
Large rivers
Periglacial
streams
a
c
e
b
d
f
Figure 5.3: Evolution of solution chemistry in the Greenland sediment dissolution
experiment.
than precipitation of secondary aluminosilicates, which may dominate in other
weathering environments (e.g., Kurtz et al., 2002). The dissolved Ge concen-
tration is unlikely to be limited by available suspended sediment surface area,
since the periglacial stream samples had both lower sediment concentrations (data
not shown) and lower dissolved Ge concentrations (Fig. 5.3b). Furthermore, the
release of Ge via the dissolution of primary minerals (as indicated by increasing
143
Si concentrations) was not reflected in the dissolved Ge concentrations, suggest-
ing an independent buffering mechanism. It is therefore reasonable to speculate
that the dissolved Ge concentration in these samples was primarily regulated by
Ge adsorption, i.e. described by a partitioning coefficient, most likely onto Fe
oxides. Such a mechanism has been previously proposed by Mortlock & Froelich
(1987) and Anders et al. (2003) to explain Ge behavior and Ge/Si fractionation in
Alaskan periglacial streams. They showed that temporal and spatial Ge/Si vari-
ation in stream water was caused primarily by changing Si concentrations, while
Ge concentrations remained relatively constant, and thus attributed this behavior
to the buffering of dissolved Ge by adsorption onto Fe oxides. The operation of
this mechanism is also fully consistent with the thermodynamic calculations of
Wimpenny et al. (2010b), who showed that secondary aluminosilicate clays are
strongly undersaturated in West Greenland streams, whereas goethite is in most
cases saturated or supersaturated.
0.5 1 1.5 2
Sediment/water ratio, mg/g
160
180
200
220
240
Final Si, μmol/L
JO-1 final
JO-12 final
JO-13 final
Figure 5.4: Relationship between final Si concentration and water/sediment ratio
in the dissolution experiment
144
Ge isotope fractionation
Dissolved d
74
Ge increased significantly by the end of the experiment, from
0.86±0.24 % to 1.9-2.2 % (Table 5.2, Fig. 5.3f). It must be noted that the field
(pre-experiment) and the post-experiment analyses were done on samples of the
Watson river collected at different times, due to the large amount of water required
for d
74
Ge analysis (see methods in Chapter 6). Nevertheless, these results are
consistent with the broad negative correlation between Ge/Si and d
74
Ge in global
rivers (Chapter 6). This study provides direct evidence that d
74
Ge fractionation
occurs during extended interaction between rocks and water, and is likely caused
by the presence of secondary weathering phases.
The following observations and assumptions were used to quantitatively con-
strain d
74
Ge fractionation during the sediment weathering experiment:
1) The initial Ge and Si concentrations in the solution (i.e. in melt water
before rock-water contact) were assumed to be negligible. The dissolved Ge and Si
in the initial unfiltered river water samples (at t = 0) used in the experiment were
therefore taken to be derived from rock-water interaction that took place before
sample collection.
2) The measured increase in dissolved Si reflected primary silicate dissolution,
and it was assumed that secondary aluminosilicate precipitation had a negligible
effect on dissolved Si. This is reasonable, given the low dissolved Al concentrations
(Fig. 5.3c).
3) Primary mineral dissolution released Ge into solution with a Ge/Si
primary
ratio of 2.2mmol/mol, which was the highest dissolvedGe/Si value observed among
all the field samples (Table 5.1) and was higher than the 1.2 mmol/mol of bulk sed-
iment due to preferential dissolution of Ge-rich biotite (see discussion above). The
145
assumed Ge/Si
primary
value had no effect on the calculated Ge isotope fractionation
factor, as discussed below.
4) The measured Ge concentrations during the experiment reflected the differ-
ence between the Ge initially released from primary silicate dissolution and the Ge
removed via adsorption onto FeOx.
Given all of the above, the fraction of Ge remaining in solution after adsorption
can be calculated as:
f
diss
Ge =
Ge
measured
Ge/Si
primary
×Si
measured
(5.1)
This calculation provides the upper limit estimate of f
diss
Ge, since preferential
biotite dissolution could result in even higher Ge/Si signature released to solution
(see discussion above) and the used field value of 2.2 mmol/mol may already be
affected by Ge removal from solution.
The increase in d
74
Ge with time (coupled with relatively constant Ge concen-
trations) is most simply explained by a loss of isotopically light Ge from solution,
while dissolution of silicates continues. Assuming that Ge removed from solution
is able to isotopically equilibrate with dissolved Ge (e.g., if the removal mechanism
is reversible adsorption to FeOx), then:
d
74
Ge
diss
= d
74
Ge
primary
–D
74
Ge
sec–diss
×(1–f
diss
Ge) (5.2)
where d
74
Ge
primary
is the composition of the dissolving primary minerals
(assumed to be equal to the Watson suspended load value of 0.48±0.23 %) and
D
74
Ge
sec–diss
is the isotopic fractionation factor associated with Ge removal from
solution. Using Eq. A.6 on the sediment weathering experimental data yields
D
74
Ge
sec–diss
= –2.1±1.4 % (Fig. 5.5). Despite the relatively large uncertainty,
146
this value is similar to the -1.7±0.1 % determined for experimental Ge adsorption
onto pure FeOx (geothite) (Pokrovsky et al., 2014) and supports the hypothesis
that this is the main mechanism controlling Ge solution chemistry in glacial rivers.
The calculated D
74
Ge
sec–diss
value is insensitive to the uncertainty in absolute
f
diss
Ge values that results from having to estimate Ge/Si
primary
, since the slope in
Fig. 5.5 is not affected. Instead, some uncertainty may arise if the different sam-
ples had slightly different suspended sediment assemblages, and therefore different
dissolving Ge/Si
primary
signatures. This is highly unlikely, however, considering
that samples of suspended load, JO-3a, and JO-14 were all collected at the same
location, within 8 days of each other (Table 5.1) and the lithological homogeneity
of the area. Additional confidence is supplied by the similarity of the final d
74
Ge
signatures of the two different tributary samples (JO-2 and JO-3a in Fig. 5.5).
0 0.2 0.4 0.6 0.8 1
f
diss
Ge
-0.5
0
0.5
1
1.5
2
2.5
3
δ
74
JO-2 final
JO-3a final
JO-14 field
Watson susp. load
Figure 5.5: Dissolved d
74
Ge as a function of dissolved Ge fraction remaining in
solution (f
diss
Ge). The gray line shows a linear regression fit to the data, which
has a slope equivalent to the fractionation factor D
74
Ge
sec–diss
(see Eq. A.6).
The dashed lines show the 95 % confidence interval of the fit, which represents
the uncertainty of D
74
Ge
sec–diss
, as reported in the text. The regression slope is
statistically significant (Adj. R
2
= 0.93, p = 0.02).
147
5.4 Conclusions
This study investigated Ge/Si andd
74
Ge signatures in West Greenland streams
draining the Greenland ice sheet. The dissolved Ge/Si ratios were higher than
most other non-glacial rivers around the world, which likely reflects preferential
weathering of Ge-rich minerals, such as biotite, due to glacial grinding action.
Furthermore, dissolved d
74
Ge of the Watson River was found to be only slightly
heavier than the river suspended load, indicating limited Ge isotope fractionation
during subglacial weathering, despite extensive Fe oxide precipitation.
Sediment weathering experiments, where unfiltered river water was incubated
for up to 2 years, resulted in large increase in dissolved Si, a smaller increase in
dissolved Ge, a decrease in dissolved Ge/Si and an increase in d
74
Ge. Despite
continued release from primary mineral dissolution, dissolved Ge concentrations
appeared strongly buffered and limited by adsorption to Fe oxyhydroxides. The
change in dissolved d
74
Ge composition during incubation correlated with the frac-
tion of Ge removed from solution. Applying a simple batch (closed system equi-
librium) model to the experimental data has yielded a D
74
Ge
sec–diss
fractionation
factor of -2.1± 1.4 %, consistent with previously determined values for experi-
mental Fe oxyhydroxide precipitation, further supporting the hypothesis that Ge
adsorption onto Fe oxides is the major mechanism controlling dissolved Ge chem-
istry, including d
74
Ge composition, in glacial streams.
Acknowledgements
Financial support was provided by NSF grants OCE 1061700 and 1260692 to
DEH. JJB was additionally supported by the John Montagne Award from GSA
148
Quaternary Geology and Geomorphology Division. Josh West is thanked for allow-
ing to continuously run and take up space in his incubator for 2 years.
149
Chapter 6
Ge and Si isotope geochemistry in
global rivers
Author contributions
I conceived the design of this study and performed Ge isotope analyses in col-
laboration with Olivier Rouxel at Ifremer in Brest, France, and Si isotope analyses
in collaboration with Bastian Georg at Trent University in Peterborough, Canada.
I also performed the element concentration analyses at the University of South-
ern California, except where noted otherwise in the text. Colleagues who have
assisted with measurements and especially with the collection of a large number of
the samples are all listed in the acknowledgements. This manuscript will be sub-
mitted to a peer-reviewed journal with the following preliminary list of co-authors:
Doug Hammond, Josh West, Olivier Rouxel, Bastian Georg, Mark Torres, Julien
Bouchez, and Jerome Gaillardet.
Abstract
Reliable geochemical (elemental and isotope ratio) proxies are necessary to
trace weathering and other critical zone processes both in the present and in the
past. Development of novel proxies and the combined use of isotopic signatures
of multiple elements can improve our understanding of how element and isotope
150
ratios are fractionated in nature, and ultimately address long-standing questions
about the link between weathering and climate.
Here we present a global overview of a novel tracer – Ge isotope signatures
(d
74
Ge) – in a number of large and small rivers around the world. The dissolved
d
74
Ge values range from 0.9 to 5.5 % with a global discharge-weighted average of
2.6±0.5 %. Riverine suspended and bedload sediments range from 0.5 to 0.7 %,
indistinguishable from silicate source rocks. A direct comparison of d
74
Ge, d
30
Si,
and Ge/Si signatures analyzed on the same set of samples shows that they are
all primarily fractionated by secondary phase precipitation. In some cases, d
30
Si
and Ge/Si can be strongly modified by biological uptake (vegetation, diatoms) but
d
74
Ge appears unaffected. The global dataset shows a good correlation between
dissolved riverine d
74
Ge signatures and weathering intensity (the ratio of chemical
to total denudation). We propose that Ge isotopes could be a useful complement
to already established weathering proxies.
6.1 Introduction
Chemical weathering of rocks is an important process that determines nutrient
supply to ecosystems, landscape evolution, and climate over geological timescales.
Measuring weathering rates is challenging in the contemporary environment and
even more so in the past. Elemental and isotopic ratios of weathering-derived
chemical species in waters and sediments offer a way to indirectly trace the variety
of water-rock-life interactions that have taken place in Earth’s critical zone. The
signatures of these proxies preserved in the sedimentary record offer the potential
toreconstructkeyaspectsoftheweatheringsysteminthepastandtotestavariety
of hypotheses linking weathering and climate (Walker et al., 1981; Berner et al.,
151
1983; Raymo & Ruddiman, 1992; Li & Elderfield, 2013; Maher & Chamberlain,
2014; Caves et al., 2016).
Germanium(Ge)tosilicon(Si)ratios(i.e., Ge/Si), aswellassiliconandlithium
isotopic composition (i.e., d
30
Si and d
7
Li) in dissolved and solid river loads have
all been shown to primarily reflect the weathering intensity of a given catch-
ment (Murnane & Stallard, 1990; Froelich et al., 1992; Dellinger et al., 2015;
Frings et al., 2016). The weathering intensity concept was introduced by Stal-
lard & Edmond (1983) who demonstrated that the weathering fluxes and riverine
chemistry differed greatly between uplifting, actively eroding Andean catchments
("weathering-limited" or low weathering intensity) and tectonically quiescent low-
lands ("transport-limited" or high weathering intensity). This dominant control of
erosion over chemical weathering was later confirmed on a global scale (Riebe et
al., 2004; West et al., 2005). More recently, Bouchez et al. (2013) have developed
a simple mass balance model demonstrating that the degree of isotopic fractiona-
tion observed in the dissolved phase is controlled by how much of the secondary
material is eroded from the catchment rather than re-dissolved in situ.
Despite the similar interpretation of Ge/Si, d
30
Si, andd
7
Li signatures in rivers,
there have been very few studies directly comparing the behavior of these proxies
(see below for examples), and the exact mechanisms and conditions required for
extensive fractionation and the type of weathering environment they reflect are
poorly understood. An additional complication is that both Ge/Si and d
30
Si can
be affected by biological uptake (and subsequent release upon remineralization)
of these species by either standing vegetation or freshwater and marine diatoms
(De La Rocha et al., 2000; Derry et al., 2005; Sutton et al., 2010), potentially
overprinting the weathering signal. In fact, the combination of Ge/Si and d
30
Si
has been used to trace the importance of vegetation vs. secondary mineral cycling
152
in several soil studies (Cornelis et al., 2010; Opfergelt et al., 2010; White et al.,
2012).
In the ocean, fractionation during biological uptake and sediment authigenesis
further overprints the river-borne weathering signal of Ge/Si and d
30
Si (e.g., Ham-
mond et al., 2000, 2004; Baronas et al., 2016; De La Rocha et al., 1998; Ehlert et
al., 2016), complicating the interpretation of marine paleorecords. Foraminifera-
recorded d
7
Li has so far yielded the most promising record of potential changes
in continental weathering over the Cenozoic, suggesting a a significant decrease in
weathering intensity since∼60 Ma (Misra & Froelich, 2012). However, the original
interpretation has been challenged to various degrees (Li & West, 2014; Vigier &
Goddéris, 2015; Dellinger et al., 2015; Pogge von Strandmann & Henderson, 2015;
Coogan et al., 2017). Furthermore, the >1 My residence time of Li in the ocean
(Huh et al., 1998) prevents this proxy from recording any potential shorter-term
variations in weathering, such as transient climatic optima or glacial-interglacial
cycles. As it stands, there is a need to continue the search for complementary prox-
ies, andtostudythevariousweatheringproxiessimultaneously. Suchamulti-proxy
approach can help to deconvolve complications associated with each individual
proxy through identifying the first-order processes governing isotopic signatures of
rivers (e.g., Opfergelt et al. (2013b); Pogge von Strandmann & Henderson (2015);
Sullivan et al. (2016); Pogge von Strandmann et al. (2017); also see Chapter 4).
Here, we explore the range of Ge isotope (d
74
Ge) and Ge/Si signatures in a
range of global rivers, along with complementary d
30
Si data in some cases. A pre-
vious study of d
74
Ge in terrestrial and marine low-temperature settings has shown
that dissolved riverine signatures are up to 5 % heavier (Baronas et al., 2017a)
than the isotopically homogeneous source silicate rocks (0.4-0.8 % range; Escoube
et al. (2012); Rouxel & Luais (2017)). Furthermore, the heaviest signatures were
153
associated with lowest Ge concentrations and potentially the highest degree of Ge
removal into precipitating secondary phases. This study expands on the previous
dataset, and now includes more of the world’s major rivers, allowing us to calculate
the discharge-weighted riverine d
74
Ge input to the ocean. We also present the first
d
74
Ge data from river suspended and bedload sediments. Finally, we explore the
global relationship between d
74
Ge, Ge/Si, and d
30
Si, as well as the potential for
d
74
Ge to trace weathering intensity.
6.2 Methods
6.2.1 Sample collection
Due to low dissolved Ge concentrations, anywhere from 0.5 to 3 L of river water
is needed to obtain the >2 ng of Ge required for a d
74
Ge measurement. The data
set presented in this study consists of samples collected over several years (Table
6.1). All samples were collected in plastic bottles or bags, filtered through 0.2-0.45
mm pore size membrane generally within∼24 h or less and usually acidified with
trace clean HCl or HNO
3
shortly thereafter. Our testing showed that acidification
is not required to keep Ge and Si in solution (except possibly in very Fe-rich
samples). The collection of bedload samples in Peru is described in Torres et al.
(2016). For suspended load samples, the sediment was either rinsed off filters with
deionized water, centrifuged, and dried, or gently scraped off after drying the filter
at 50-60
◦
C, and stored in plastic vials.
The sample set covers a wide range of terrestrial environments, from a
periglacial river in Greenland, to rivers draining the tropical floodplain of the
Amazon basin (Fig. 6.1). In a few cases, bedload and/or suspended load material
was collected along with the dissolved sample, and also analyzed (Section 6.3.1).
154
Table 6.1: River water sample details.
River Sample ID Sampling location
Time
sampled
Lat., ° Long., °
Dominant
lithology
Mean
discharge,
km
3
/y
†
North America
Mississippi* (Aug 2011) MI11 New Orleans, USA 2011-08-20 29.954 -90.063 mixed 580
Mississippi* (Jul 2015) GRO001622 New Orleans, USA 2015-07-07 29.920 -90.140 mixed 580
Kaweah KW13 Sierra Nevada mts., USA 2013-08-31 34.680 -118.840 gran. --
Lone Pine Creek LPC13 Sierra Nevada mts., USA 2013-08-17 36.595 -118.140 gran. --
Kern (North Fork) KN09 Sierra Nevada mts., USA 2009-03-21 35.916 -118.444 mixed --
Kern* (Lower) KL09 Sierra Nevada mts., USA 2009-03-21 35.916 -118.444 gran. --
San Gabriel (North Fork) SGN13 San Gabriel mts., USA 2013-07-28 34.343 -117.725 gran. --
Santa Clara SC13 San Gabriel mts., USA 2013-12-19 34.348 -119.052 sed. --
Hondo* RH08 Los Angeles, USA 2008-12-20 34.098 -118.021 urban --
Los Angeles River* LAR13 Los Angeles, USA 2013-11-21 33.804 -118.205 urban --
Mackenzie CAN10-(11+14) Tsiigehtchic, Canada 2010-09-07 67.458 -133.727 mixed 308
Liard CAN10-(46+48) Fort Simpson, Canada 2010-09-13 61.823 -121.298 mixed --
Peel CAN10-(03+05) Fort McPherson, Canada 2010-09-07 67.430 -134.906 sed. --
Fraser FR15 Fort Langley, Canada 2013-09-30 49.180 -122.567 gran. 112
Greenland
Watson JO-14 Kangerlussuaq, Greenland 2014-08-27 67.010 -50.682 gneiss --
Volcanic islands
Waimano OA14-1 Oahu, Hawai'i 2014-03-01 21.433 -157.922 bas. --
Uhva OA14-2 Oahu, Hawai'i 2014-03-01 21.485 -157.871 bas. --
Kahaua OA14-3 Oahu, Hawai'i 2014-03-01 21.548 -157.877 bas. --
Molokai ML09 Molokai, Hawai'i 2009-03-29 21.141 -157.015 bas. --
Iao Valley MA09 Maui, Hawai'i 2009-03-31 20.876 -156.557 bas. --
Ytra Ranga YR15 Hella, Iceland 2015-08-05 63.846 -20.394 bas. --
Grande Riviere de Goyave AN-14-(42-44) Basse-Terre, Guadeloupe 2014-06-14 16.205 -61.654 bas. --
Asia & Oceania
Changjiang* (Nov 2014) CJ14 Nanjing, China 2014-11-12 32.016 118.676 mixed 928
Changjiang* (Jan 2015) CJ15 Nanjing, China ; 32.016 118.676 mixed 928
Mekong ME14 Luang Prabang, Laos 2014-03-29 19.896 102.136 sed. 467
Nam Xong NX14 Vang Vieng, Laos 2014-04-01 18.922 102.445 sed. --
Otira OT14 Southern Alps, New Zealand 2015-01-31 -42.851 171.560 sed. --
Central & South America
Taconazo LS01 La Selva, Costa Rica 2010-05-14 10.432 -84.013 bas. --
Kosñipata (Aug 2013) MMD-02 Andes mts., San Pedro, Peru 2013-08-11 -13.058 -71.545 sed. --
Kosñipata (Oct 2015) KOS15 Andes mts., San Pedro, Peru 2015-10-25 -13.058 -71.545 sed. --
Carbon MMD-05 Andes mts., San Pedro, Peru 2013-08-12 -12.889 -71.355 sed. --
Madre de Dios MMD-28 CICRA, Peru 2013-08-16 -12.580 -70.096 sed. --
Madre de Dios MMD-32 Puerto Maldonado, Peru 2013-08-18 -12.563 -69.176 sed. --
Inambari MMD-29 Puerto Maldonado, Peru 2013-08-16 -12.719 -69.752 mixed --
Piedras MMD-34 Puerto Maldonado, Peru 2013-08-19 -12.519 -69.248 sed. --
Solimões (Jun 2005) AM-05-(05-08) Manacapuru, Brazil 2005-06-04 -3.325 -60.549 sed. --
Solimões (Dec 2014) AM14-36 Manacapuru, Brazil 2014-12-10 -3.317 -60.550 sed. --
Negro AM14-40 Manaus, Brazil 2014-12-11 -3.167 -60.004 sed. --
Madeira AM06-(34-43) Foz Madeira, Brazil 2006-03-19 -3.408 -58.791 sed. --
Amazon AM06-(23-30) Iracema, Brazil 2006-03-18 -3.318 -58.828 sed. 6590
† Values from Gaillardet et al. (1999), shown for large rivers only.
*Affected by anthropogenic activity. The Mississippi, Hondo, and Los Angeles rivers are incorporate significant industrial and urban runoff. Changjiang
chemistry is affected by extensive irrigation and farming of rice paddies in the floodplain (Ding et al., 2004). Lower Kern river was sampled downstream
of a dam reservoir in which water chemistry is strongly modified by diatom growth (Baronas et al., 2017a).
All samples were taken at the surface (0m), with the following exceptions: AM-05-(05-08) composite of 0-21m; AM06-(23-30) composite of 0-45m (two
depth profiles); AM06-(34-43) composite of 0-15m; AN-14-(42-44) composite of surf. samples taken at Duclas and at the mouth; CAN-10-(03+05)
composite of 2.5 and 8.5m; CAN-10-(11+14) composite of 5 and 19.4m; CAN-10-(46+48) composite of 1.5 and 4.8m.
155
Ytra Ranga
(Iceland)
Watson
(Greenland)
Andes-Amazon streams
(Peru)
Amazon
Taconazo
(Costa Rica)
Negro,
Solimões,
Madeira
Changjiang
Mekong,
Nam Xong
(Laos)
Otira
(New Zealand)
Mackenzie
Hawaiian
streams
Californian
streams
Mississippi
Peel, Liard
Fraser
Grande Rivière à Goyaves
(Guadeloupe)
Figure6.1: Amapofmajorriverbasins, showingthelocationsofriversandstreams
for which d
74
Ge data is now available. Major river basins are shown in dark blue.
Smaller rivers and streams are shown as circles. Light blue color indicates newly
collected d
74
Ge data, whereas orange color indicates locations for which d
74
Ge
data was reported previously (Chapters 2 and 4). Map based on WRIBASIN data
(http://www.fao.org/ geonetwork/srv/en/metadata.show?id=30914).
6.2.2 Element concentration analyses
Major cation (Na, K, Ca, Mg) and Si concentrations in river water were mea-
sured using an Agilent 4100 Microwave Plasma-Optical Emission Spectrometer
(MP-OES) at the University of Southern California. Additional Si analyses were
performed using molybdate blue colorimetry (Mullin & Riley, 1955). The values
obtained via both methods agreed within uncertainty. Precision and accuracy were
assessed by analyzing ION-915 (Environment Canada) certified reference material
interspersed with samples. Reproducibility of replicate analyses was better than
5% (1s) for all analytes.
Si concentrations in solid samples were determined in one of two ways: 1)
quadrupole ICP-MS at Ifremer on aliquots from HNO
3
+HF digestions described
below; or 2) after NaOH fusion and dissolution in Teflon-distilled HNO
3
using the
method described below, followed by molybdate blue colorimetry. A number of
156
samples were analyzed using both methods, and some of the Andes-Amazon rock
and bedload samples have also been previously analyzed using XRF (Torres et al.,
2016). The Si concentrations agree within 10% using all three methods.
Ge concentrations in river water were measured using isotope dilution hydride
generation inductively coupled plasma mass spectrometry (ID-HG-IPC-MS) on a
Thermo Element 2, as described in Mortlock & Froelich (1996) and modified by
Hammond et al. (2000) and Baronas et al. (2016), with a typical 2 S.D. uncer-
tainty of <5%. Accuracy and precision were assessed by measurements of NIST
3120a and internal river water standards analyzed alongside the samples at similar
concentrations. The procedural blank was variable but typically around 1-2 pg
(15-30 fmol) Ge. Ge concentrations in solid samples were determined during Ge
isotope analyses, as described below.
6.2.3 Si isotope analyses
Fusion. Siisotopecompositioninsolidsampleswasanalyzedusingthemethod
adapted from Georg et al. (2006b). Briefly, 10-20 mg of sample was fused with
∼200 mg trace-grade NaOH in silver crucibles at 710
◦
C. The crucibles with fusion
cakes were sonicated for 30 min in 20 mL Milli-Q and left to dissolve for 24h. The
solution was then transferred into 500 mL polyethylene bottles, carefully rinsing
to ensure complete recovery, diluted to 400-500 mL with Milli-Q, and acidified
with 5 mL trace grade conc. HNO
3
. This method is noted as "reg. fus." in Table
6.4. A number of organic-rich shale samples were ashed in graphite crucibles at
550
◦
C for 2h prior to NaOH fusion ("ash. fus." in Table 6.4). In both cases, the
fused samples were then processed in the same way as river water samples. The
Si concentrations and d
30
Si values determined using both fusion methods were
identical within uncertainty.
157
Cation-exchange chromatographic separation. A procedure adapted
from Georg et al. (2006b) was used for dissolved samples and for the solutions
obtained from fusion. All reagents used were either in-house Teflon-distilled or
Optima-grade. A 10 mL column was filled with 1.8 mL (wet volume in 0.5M
HNO
3
) BioRad AG50W-X12 (200-400 mesh) resin. The column was washed with
15 mL Milli-Q, 3.6 mL 0.15M HF (new resin only), 10 mL 3M HCl, 5 mL 6M HCl,
4 mL 11M HCl, 5 mL 6M HCl, 5 mL 3M HCl, and 2x10 mL Milli-Q. Between
0.3 and 8 mL sample was then loaded onto the column and eluted with 4-10 mL
Milli-Q to obtain a final solution of 1.5 ppm Si. Larger batches of several standards
were prepared by eluting with up to 40 mL Milli-Q to obtain 3 ppm Si solutions
that were then further diluted to 1.5 ppm. All bracketing standard and reference
material aliquots were purified using the same cation-exchange method as the sam-
ples. The columns were reused up to a maximum of four times, at which point the
resin was replaced.
MC-ICP-MS. Si isotope measurements were performed on a Thermo Nep-
tune instrument at the Water Quality Centre at Trent University. An Elemental
Scientific ApexQ was used for sample introduction at a rate of 300 mL/min. The
Neptune was operated in low mass resolution.
28
Si,
29
Si, and
30
Si were measured
in cups C, H2, and H3, respectively. The measurements were done at the flat part
of the low mass peak shoulder that is free of the major interference from
14
N
16
O
+
.
The measurement window was manually re-positioned at least once within each
analytical session but the drift was never more than±0.0002 mass units. Sample
runs were bracketed with concentration-matched NBS-28 standard solution pre-
pared using the same fusion and cation exchange methods described above. Each
sample was measured as 2-4 bracketed replicate runs consisting of 30 cycles of 8s
each. This resulted in 8-16 min of counting statistics for each sample at typical
158
signal intensities of 8-18 V
28
Si. The d
30
Si values are reported as the sample
30
Si/
28
Si ratio normalized to the bracketing NBS-28 standard. Mass dependence
was ensured by comparing the d
30
Si and d
29
Si values of each run, which were
within 0.2 % (in terms of d
30
Si) of the mass-dependent fractionation line for the
vast majority of the measurements. A number of different reference materials were
analyzedmultipletimesinterspersedwiththesamples, allofwhichagreedwellwith
previously reported values (Table 6.2). The measurement uncertainty is reported
as the internal 2s standard error of sample replicates, or 2s standard deviation of
all NBS-28 bracketing standard measurements within a given analytical session,
whichever is higher.
159
Table 6.2: Ge and Si concentration and isotope analyses of solids compared to reference values.
Ge/Si,
µmol/mol
this study prev.
a
this study cert.
b
this study prev.
a
this study prev.
c
USGS Dunite DTS-1 reg. (DB-1) 0.79 0.197 1.55 0.63 ± 0.23
USGS Dunite DTS-1 ox., pres. (DB-2) 0.81 0.191 1.65 0.58 ± 0.06
USGS Icelandic Basalt BIR-1 reg. (DB-1) 1.55 0.235 2.55 0.70 ± 0.08
USGS Icelandic Basalt BIR-1 ox., pres. (DB-2) 1.55 0.216 2.78 0.53 ± 0.08
USGS Hawaiian Basalt BHVO-1 NaOH fusion (B3) -- 1.55, 1.64 0.253 0.233 (2.44) -- 0.55 ± 0.15 -0.37 ± 0.26 -0.29 ± 0.16
USGS Hawaiian Basalt BHVO-2 NaOH fusion (Trent) -- 1.53 -- 0.233 (2.54) -- 0.51 ± 0.10 -0.32 ± 0.09 -0.29 ± 0.11
USGS Devonian Ohio Shale SDO-1 ox., pres. (DB-2) 0.81 -- 0.203 1.55 0.79 ± 0.08 -- -- --
USGS Devonian Ohio Shale SDO-1 ashed ox., pres. (DB-2)
2.03
d
-- 0.256 3.06 0.81 ± 0.08 -- -- --
USGS Cody Shale SCO-1 NaOH fusion (B3) -- -- 0.253 0.294 -- -- -- -0.26 ± 0.15 --
Quartz IRMM-018 -- -- -- -- -- -- -- -- -1.57 ± 0.08 -1.63 ± 0.15
Diatomite standard Diatomite -- -- -- -- -- -- -- -- 1.22 ± 0.14 1.23 ± 0.16
a
Escoube et al. (2012)
b
Certified USGS value (crustal.usgs.gov)
c
Georg et al. (2009)
d
Uncorrected for mass loss during ashing.
Digestion
method
ID Reference material
0.189
0.224
Si, g/g δ
74
Ge, ‰ δ
30
Si, ‰
0.230
0.75 - 0.88
1.40 - 1.53
0.64 ± 0.26
0.62 ± 0.13
-- --
-- --
Ge, µg/g
160
6.2.4 Ge isotope analyses
River water. 0.2 to 4.7 L of filtered river water containing 2-100 ng of Ge
was acidified with trace clean HNO
3
, and spiked with a Ge isotope double spike
(
73
Ge/
70
Ge≈ 1, previously calibrated and used by Escoube et al. (2012, 2015))
in a spike/sample Ge mass ratio of 1-2 and a purified FeCl
3
solution to obtain a
Fe concentration of∼0.2 mmol/L. The samples were well mixed, and allowed to
equilibrate for at least 16h. Next, Fe(OH)
3
flock was precipitated either by adding
Optima-grade NH
4
OH solution or bubbling pure NH
3
gas through the sample
until the solution reached a pH of 8-10. The flock was collected by settling and
centrifugation, redissolved in 2 mL concentrated Teflon-distilled HNO
3
and diluted
to 10 mL with Milli-Q. The samples were then dried down, redissolved in 1 mL
concentrated Optima-grade HF and diluted to 30 mL with Milli-Q to obtain a
final 1M HF solution. They were then purified through anion exchange columns
as described below. The procedural blank was determined by processing spiked
Milli-Q and ranged from 0.01 to 0.3 ng Ge.
Rocks and sediments. A method adapted from Rouxel et al. (2006) was used
for solid sample digestion. 10-130 mg of dried ground sample containing 30-300
ng of Ge was weighed into Teflon digestion vessels and spiked with a Ge isotope
double spike (
73
Ge/
70
Ge≈ 1) in a spike/sample Ge mass ratio of 1-2. Two slightly
different digestion methods were tested to ensure that organic-rich shale samples
were fully dissolved. Regular digestion (sample batch DB-1; Tables 6.2 and 6.4)
was done by adding 5 mL of concentrated Teflon-distilled HNO
3
and heating to
90
◦
C for 16h. The samples were then dried down and 2-3 mL of MQ (to ensure
all of sample was fully wetted) and 1m L concentrated trace metal-grade HF were
added. The vessels were tightly capped and left on a 70
◦
C hotplate for 20-24h.
After cooling, the samples were diluted to 30 mL with Milli-Q to obtain a final
161
1M HF solution that was purified through column anion exchange columns as
described below. A second batch of samples including replicates (DB-2; Tables 6.2
and 6.4) was digested by adding 10 mL concentrated Teflon-distilled HNO
3
and
heatinginapressurizedTeflonvesselat90
◦
Cfor48h. Then, 1mLofOptima-grade
H
2
O
2
was added and heated uncapped at 70
◦
C until dry. After cooling, 4 mL
Milli-Q and 1mL concentrated trace-metal grade HF were added and samples were
digested at 70
◦
C for 48 h. After cooling, the samples were diluted to 30 mL with
Milli-Q to obtain a final 1M HF solution that was purified through column anion
exchange columns as described below. The full procedural blank was determined
by processing only Ge double spike and was below detection for DB-1 and 1.7 ng
Ge for DB-2. The d
74
Ge values determined using both digestion methods were
identical within uncertainty, indicating that ashing is not necessary to extract all
Ge contained in shales.
Anion-exchangechromatographicseparation. Aprocedureadaptedfrom
Rouxel et al. (2006) was used. All reagents used were either in-house Teflon-
distilled or Optima-grade. A 10 mL column was loaded with 1.8 mL (wet vol-
ume) of BioRad AG1-X8 resin, washed with 10 mL of 3M HNO
3
, 0.28M HNO
3
,
and Milli-Q in sequence, and conditioned with 5 mL 1M HF. Samples in 1M HF
solution as prepared above were centrifuged to separate insoluble fluorides. The
presence or amount of insoluble fluorides at this stage did not appear to affect the
final Ge recovery. After centrifugation, 10-29 mL of solution was carefully added to
columns. The remaining matrix was eluted with 5 mL of 1M HF followed by 3 mL
of Milli-Q, leaving fluorinated Ge retained on the column. Ge was then eluted with
10 mL 0.28M HNO
3
. If required, the solution was dried down and redissolved in a
smaller volume of 0.28M HNO
3
to obtain the 2-10 ppb Ge concentration required
162
for isotope measurements. Each column was reused 4-5 times, except when reten-
tion of DOC from the previous sample was observed based on the color, in which
case the resin was replaced. Ge blanks from reused resin were below detection
limit. Ge recovery ranged from 20 to 100%, and was in the 70-90% range for most
river samples and in the 100±7% range for solid samples. Incomplete recovery of
river samples was most likely due to variable Ge co-precipitation efficiency with
Fe(OH)
3
, due to variable precipitation rates, final pH and natural sample matrices,
as well as some loss during co-precipitate recovery from the solution. Importantly,
incomplete recovery does not affect the measured d
74
Ge values, as all samples were
double-spiked prior to sample preparation.
HG-MC-ICP-MS. Ge isotope analyses were performed on a Thermo Nep-
tune multi-collector ICP-MS at Ifremer using a method adapted from Rouxel et
al. (2006) and Escoube et al. (2015). Sample solutions of 2-10 ppb natural Ge in
0.28M HNO
3
were introduced into an online hydride generation system (CETAC
HGX-200) at a rate of 150 mL/min where they were mixed with 0.25 M NaBH
4
solution (in 1.5 M NaOH) introduced at an equal rate. The dissolved Ge(OH)
4
species was reduced to gaseous GeH
4
and transported into the ICP-MS torch
using Ar carrier gas. The Neptune MC-ICP-MS was operated in low mass reso-
lution mode, measuring
70
Ge,
72
Ge,
73
Ge, and
74
Ge in L2, C, H1 and H2 cups,
respectively. In addition, L4, L3, L1 and H4 cups were also monitored for
68
Zn
(possible interference as
70
Zn),
69
Ga,
71
Ga (possible interferences at m/z 70), and
77
Se (possible interference as
74
Se), respectively. No interferences were detected in
any of the runs. The samples were bracketed using a NIST-3120a standard solu-
tion that had a total Ge concentration generally within∼20 % of the bracketed
sample, and was double-spiked to have a spike/sample ratio within∼20 % of the
bracketed sample. Each sample or standard run consisted of 6 measurement blocks
163
each lasting 2 min (30 cycles of 4 s each), and in most cases 4-5 blocks displaying
the most stable signal were retained. Therefore, each measurement represents 8-10
min of counting statistics at signal intensities ranging from 0.4 to 6 V at
74
Ge
(depending on Ge concentration in sample solution, instrument tuning, and the
age of NaBH
4
solution). The d
74
Ge values are calculated for each block using
the double-spike data reduction routine of Siebert et al. (2001) and are reported
in % as
74
Ge/
70
Ge sample ratio normalized to the average
74
Ge/
70
Ge ratio of
bracketing NIST 3120a measurements. This method also yields Ge concentration
values based on the measured spike/sample ratio. The measurement uncertainty
is reported as the internal 2s standard error of the used sample blocks, or 2s
standard deviation of all NIST 3120a bracketing standard measurements within a
given analytical session, whichever is higher.
6.2.5 Inter-laboratory calibration of Ge and Si isotope
analyses
The accuracy of Ge and Si concentration and isotope analyses was confirmed by
re-analyzingseveralreferencematerialsandsamplesthathavebeenpreviouslyana-
lyzed in other studies. Regardless of the digestion method (see above for details),
the Ge and Si concentrations, d
74
Ge, and d
30
Si compositions of solids reported in
this study agree within uncertainty with previously published values (Table 6.2).
Eight of the dissolved river samples previously analyzed for d
74
Ge by Baronas
et al. (2017a) (Chapter 2) have also been re-analyzed using the method described
above. The method used by Baronas et al. (2017a) relied on offline hydride gen-
eration, where methylated and inorganic Ge hydride species were separated chro-
matographically and the inorganic species were collected in Tedlar bags for later
164
Table 6.3: Comparison of analytical d
74
Ge methods.
Study this study Baronas et al. (2017a)
Double-spike
Ifremer aliquot;
73
Ge/
70
Ge ≈ 1;
calibrated and used by
Escoube et al. (2012, 2015)
OSU aliquot;
73
Ge/
70
Ge ≈ 1;
calibrated and used by
Siebert et al. (2006, 2011)
Ge co-precipitation
method
Fe(OH)
3
Mg(OH)
2
Purification
AG1-X8 anion exchange
column (Rouxel et al., 2006)
Offline hydride generation
and GeH
4
capture in Tedlar
gas bags
Sample introduction
Online hydride generation
(constant rate) coupled to MC-
ICP-MS
Pressure-controlled GeH
4
injection at variable rate
MC-ICP-MS
Thermo Neptune (Ifremer,
France)
Nu Instruments Plasma
(Oregon State University,
USA)
Range of
74
Ge signal
intensities
(among all samples)
0.4 - 6.0 V 0.02 - 1.5 V
analysis on a Nu Plasma MC-ICP-MS at Oregon State University. The differ-
ences in the methodology are summarized in Table 6.3. All sample replicates agree
within∼0.5 % and fall within the reported external 2 S.D. reproducibility, with
the exception of the Lower Kern river for which the two reported values are very
slightly outside of their 2 S.D. ranges (Table 6.5). Overall, this good agreement
is notable, considering that the two methods employed in these studies use very
different approaches to purify and analyze the samples. The high degree of consis-
tency between these techniques most likely stems from the use of a double isotope
spike that corrects for any mass-dependent fractionation that occurs during sample
preparation and measurement.
165
Additional comparison of seawater d
74
Ge analyses in Chapter 3 demonstrated
a similarly good agreement between the two methods. It must be noted that
the method of Baronas et al. (2017a) has not been employed for the analysis
solid samples. Since it does not include anion exchange purification, the high
concentration of matrix elements would likely interfere with the efficiency of Ge
hydride generation.
6.3 Results and discussion
6.3.1 Ge and Si isotope composition of river-transported
solids
The Ge isotope composition of rock, bedload, and suspended load samples falls
in the narrow 0.5-0.7 % range (Table 6.4, Fig. 6.2) and is indistinguishable from
various igneous rocks analyzed previously (Rouxel et al., 2006; Escoube et al.,
2012; Rouxel & Luais, 2017). The sample set presented here includes shale rock
and bedload samples from the Peruvian Andes, as well as surface suspended load
samples from an Andean headwater river and the Mekong. All of these samples
are composed predominantly of phyllosilicates formed during chemical weathering
in the geological past or more recently (e.g., Gaillardet et al., 1999). Indeed,
the Andean shales and bedloads, relative to igneous rocks, are enriched in light Si
isotopes and exhibit elevated Ge/Si ratios (Fig. 6.2a), indicative of secondary clays
(e.g., Murnane & Stallard, 1990; Froelich et al., 1992; Ding et al., 2004; Ziegler et
al., 2005b). It may therefore seem surprising that they retain the original igneous
d
74
Ge composition, given that fractionation occurs during weathering, as indicated
by heavy riverine dissolved d
74
Ge composition (Baronas et al. (2017a); see also
Section 6.3.2). As shown in Chapter 4, only extremely weathered tropical soils
166
in Costa Rica are enriched in light Ge isotopes to a detectable level, and even
then the enrichment is less than 1 %. As discussed in Chapter 4, the weak Ge
isotopic fractionation of the solids is the result of its low chemical mobility (i.e.,
strong retention during secondary, tertiary, etc. clay formation). The undetectable
d
74
Gefractionationofshalesandriver-transportedsedimentsisconsistentwiththis
interpretation, and contrasts with the d
30
Si system.
167
Table 6.4: Ge and Si concentrations and isotope composition of rocks and river sediments.
Ge, δ
74
Ge, δ
30
Si, Ge/Si,
µg/g ‰ ‰ µmol/mol
Andean rocks
reg. (DB-1) 2.16 0.46 ± 0.23
ox., pres. (DB-2) 2.19 0.54 ± 0.08
reg. (DB-1) 1.96 0.66 ± 0.23 reg. fus. 0.243 -0.62 ± 0.15 2.86
ox., pres. (DB-2) 1.81 0.62 ± 0.08 ash. fus. 0.266 -0.56 ± 0.16 2.63
reg. (DB-1) 1.99 0.61 ± 0.23
ox., pres. (DB-2) 1.90 0.54 ± 0.08
reg. (DB-1) 2.64 0.58 ± 0.23
ox., pres. (DB-2) 2.63 0.60 ± 0.08
Andes-Amazon river loads
reg. fus. 0.258 -0.55 ± 0.15
ash. fus. 0.285 -0.59 ± 0.15
Kosñipata susp. load KOS15 San Pedro, Peru 1360 reg. (DB-1) 2.04 0.57 ± 0.08 -- -- -- --
Kosñipata bedload S200511 San Pedro, Peru 1360 ox., pres. (DB-2) 2.17 0.50 ± 0.08 reg. fus. 0.284 -0.53 ± 0.17 2.96
reg. fus. 0.284 -0.48 ± 0.15
ash. fus. 0.298 -0.47 ± 0.15
Madre de Dios susp. load CMD-29_3m CICRA, Peru 212 -- -- -- reg. fus. 0.324 -0.37 ± 0.15 --
Madre de Dios susp. load CMD-29_6m CICRA, Peru 212 -- -- -- reg. fus. 0.344 -0.35 ± 0.15 --
Madre de Dios susp. load CMD-29_8m CICRA, Peru 212 -- -- -- reg. fus. 0.313 -0.30 ± 0.15 --
Madre de Dios bedload CMD-29_10m CICRA, Peru 212 -- -- -- reg. fus. 0.313 -0.21 ± 0.15 --
reg. fus. 0.368 -0.24 ± 0.15
ash. fus. 0.380 -0.26 ± 0.15
Colorado bedload RCOL_0240 Boca Colorado, Peru 240 -- -- -- reg. fus. 0.309 -0.22 ± 0.15 --
Amigos bedload RAMI_0225 CICRA, Peru 225 -- -- -- reg. fus. 0.317 -0.18 ± 0.15 --
Torre susp. load RT15 Tambopata, Peru 192 reg. (DB-1) 1.74 0.70 ± 0.24 HF digest. 0.408 -- 1.65
Other
Watson susp. load JO-14 Kangerlussuaq, Greenland -- reg. (DB-1) 1.09 0.48 ± 0.23 HF digest. 0.355 -- 1.18
Mekong susp. load ME14 Luang Prabang, Laos -- reg. (DB-1) 2.00 0.49 ± 0.24 HF digest. 0.349 -- 2.21
Sample ID Sample
Digestion
method (Ge)
Digestion
method (Si)
Si, g/g
Elevation,
m
Location
3.23
1.55 0.52 ± 0.08 1.50 Madre de Dios bedload
550
212
2250
Alto Madre de Dios bedload
ox., pres. (DB-2) CICRA, Peru RPtMA-0177
0.56 ± 0.08 2.05 ox., pres. (DB-2) MLC, Peru RKOS-0550 2.79
ROCK-4 Shale
0.59 ± 0.08 2.26 ox., pres. (DB-2) Wayqecha, Peru R2250_21_03 Kosñipata bedload
3472 Wayqecha, Peru ROCK-1 Shale
4.01 -0.67 ± 0.15 0.254 reg. fus. 3472 Wayqecha, Peru
3472 Wayqecha, Peru ROCK-2 Shale 2.91 -0.60 ± 0.15 0.259 reg. fus.
2.26 -0.25 ± 0.15 0.373 Igneous WP-13 San Pedro, Peru ~1500 reg. fus.
168
123 4
Ge/Si, µmol/mol
-0.9
-0.8
-0.7
-0.6
-0.5
-0.4
-0.3
-0.2
-0.1
δ
30
a
123 4
Ge/Si, µmol/mol
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
δ
74
b
W S M
Bedload
C
Igneous
Shale
Susp. load
Figure 6.2: The relationship between Ge/Si and a) d
30
Si, b) d
74
Ge of solids in the
Andes-Amazon. W=Kosñipatariv. atWayqecha(Mountain-1); S=Kosñipataat
Sen Pedro (Mountain-2); M = Alto Madre de Dios riv. at MLC (Mountain-front);
C = Madre de Dios river at CICRA (Foreland-floodplain).
Another important observation is that hydrodynamic sorting of sediments is
expressed in the d
30
Si depth profile of the Madre de Dios river in the Amazon
foreland-floodplain (Fig. 6.3a). At high elevation, the bedload composition of shal-
low, turbulent headwater streams is indistinguishable from the shale-dominated
bedrock (Fig. 6.3b-d). However, with decreasing elevation along the Andes-
Amazon transect, the riverine bedload d
30
Si increases and Ge/Si decreases (Fig.
6.3b,c) - the opposite from an expected weathering signal. As river channel width
and depth increase, the flow velocity at the bottom decreases, and heavier min-
erals (likely quartz and other primary minerals) are preferentially settled from
suspended load to bedload. Indeed, the igneous Andean rock sample had higher
d
30
Si and lower Ge/Si than shales, and quartz, although not measured in this
location, typically has the lowest Ge/Si ratios of all silicate minerals (Murnane
& Stallard, 1990; Kurtz et al., 2002; Derry et al., 2006). In contrast, the surface
suspended load is dominated smaller clay particles, which have lower d
30
Si and
169
higher Ge/Si. In the Amazon foreland floodplain, it is difficult to tell whether the
clays are secondary weathering products formed within the watershed, or derived
primarily from the Andean shales. Hydrodynamic sorting of Ge/Si signatures has
been observed on a depth profile scale in the suspended sediments of Madre de
Dios and other large Amazon rivers (J. Bouchez, unpublished data), supporting
the interpretation of Fig. 6.3c presented here.
In contrast tod
30
Si and Ge/Si, the sorting effect is not reflected ind
74
Ge values
of the bedloads in the Andes-Amazon (Fig 6.3d), which implies that quartz and
other heavier minerals have d
74
Ge values similar to the clay-rich suspended load.
Again, this observation is consistent with little recorded Ge isotope fractionation
in solid samples.
170
-0.8
-0.6
-0.4
-0.2
0
δ
30
b
0
1
2
3
4
Ge/Si, µmol/mol
c
0 1 2 3 4
Elevation, km
0
0.2
0.4
0.6
0.8
δ
74
d
-0.5 0
δ
30
2
4
6
8
10
Depth, m
a
quartz
W S M
Bedload
C
Igneous
Shale
Susp. load
Figure 6.3: Effects of sediment sorting on d
74
Ge, d
30
Si, and Ge/Si of solids in the
Andes-Amazon. a) Suspended load d
30
Si as a function of depth in Madre de Dios
at CICRA (Foreland-floodplain). Also shown is the composition of shales (top)
and igneous rocks (bottom) in the study area (Table 6.4). b) d
30
Si, c) Ge/Si, and
d) d
74
Ge relationship with elevation in the Andes-Amazon transect in Madre de
Dios watershed (Table 6.4). W = Kosñipata riv. at Wayqecha (Mountain-1); S
= Kosñipata at Sen Pedro (Mountain-2); M = Alto Madre de Dios riv. at MLC
(Mountain-front); C = Madre de Dios river at CICRA (Foreland-floodplain). The
black rectangle in panel c shows the range of previously measured quartz separates
(Murnane & Stallard, 1990; Kurtz et al., 2002; Derry et al., 2006).
171
6.3.2 Ge isotope composition of global rivers
The riverine dissolved d
74
Ge
riv
composition of the dataset presented in this
study ranges from 0.86±0.24 to 5.20±0.24 % (Table 6.5), expanding the lower
end of the previously reported range (Baronas et al., 2017a). Nevertheless, all
rivers measured to date are isotopically heavier relative to the silicate rocks from
which the dissolved Ge is sourced (d
74
Ge
rock
= 0.4–0.7 %; Escoube et al. (2012)).
Consistent with the smaller dataset previously reported by Baronas et al. (2017a),
there is no identifiable relationship between the riverine d
74
Ge composition and
the dominant watershed lithology (Table 6.1, Fig. 6.4). Major ion concentrations
are given in Appendix E Table E.2.
Using the signatures of the major rivers in this dataset (Amazon, Changjiang,
Mackenzie, Mekong, and Fraser), the flux-weighted global d
74
Ge
riv
value can be
estimated. The Mississippi is excluded due to likely anthropogenic contamination
by coal combustion (Froelich & Lesley, 2001; Baronas et al., 2016). The mean Ge
flux-weighted (calculated from Ge concentration and mean river discharge) value
is calculated to be 2.64±0.15 %, significantly lower than the previous preliminary
estimate of 3.5±1.5 % (Baronas et al., 2017a). Similarly, the discharge weighted
riverine Ge/Si ratio is calculated to be 0.90 mmol/mol, significantly higher than
the previous estimate of 0.57 mmol/mol that was based on the global Ge/Si and Si
concentration correlation (Froelich et al., 1992). This difference most likely stems
from the inclusion of the Changjiang and Mekong rivers, which exhibit elevated
Ge/Si due to hydrothermal or agricultural impacts. Excluding the Changjiang and
the Mekong yields global Ge/Si of 0.68 mmol/mol, much closer to the estimate of
Froelich et al. (1992), whereas d
74
Ge
riv
is effectively unchanged (2.56±0.15 %).
The above values represent∼23 % of global river water discharge (Gaillardet
et al., 1999) and∼19 % of the estimated riverine Ge flux to the ocean and are
172
Table 6.5: Ge and Si concentrations and isotopic compositions in world’s rivers
studied here. Sample locations, dates, and other details are given in Table 6.1.
Ge, Si, Ge/Si, δ
30
Si,
pmol/L µmol/L µmol/mol ‰
North America this study
Baronas et
al. (2017a)
Mississippi* (Aug 2011) MI11 266 166 1.60 2.17 ± 0.24 2.01 ± 0.22 --
Mississippi* (Jul 2015) GRO001622 215 147 1.46 2.60 ± 0.30 -- 1.70 ± 0.15
Kaweah KW13 690 210 3.29 2.32 ± 0.07 -- --
Lone Pine Creek LPC13 94 164 0.57 2.46 ± 0.24 -- --
Kern (North Fork) KN09 614 327 1.88 2.70 ± 0.25 2.75 ± 0.22 --
Kern* (Lower) KL09 299 36 8.31 2.56 ± 0.24 3.04 ± 0.22 4.23 ± 0.20
San Gabriel (North Fork) SGN13 68 256 0.27 5.20 ± 0.24 5.61 ± 0.22 2.13 ± 0.15
Santa Clara SC13 205 414 0.50 3.38 ± 0.08 -- --
Hondo* RH08 172 290 0.59 5.17 ± 0.24 5.50 ± 0.22 --
Los Angeles River* LAR13 4011 155 25.9 0.93 ± 0.43 -- --
Mackenzie CAN10-(11+14) 64 65 0.98 2.57 ± 0.09 -- 1.15 ± 0.15
Liard CAN10-(46+48) 55 89 0.62 2.76 ± 0.22 -- 0.96 ± 0.22
Peel CAN10-(03+05) 76 62 1.22 3.30 ± 0.09 -- 0.96 ± 0.15
Fraser FR15 39 77 0.51 1.67 ± 0.28 -- 1.12 ± 0.15
Greenland
Watson JO-14 55 33 1.66 0.86 ± 0.24 -- -0.12 ± 0.15
Volcanic islands
Waimano (Hawai'i) OA14-1 47 201 0.24 2.05 ± 0.28 -- --
Uhva (Hawai'i) OA14-2 91 492 0.19 3.53 ± 0.28 -- 0.86 ± 0.15
Kahaua (Hawai'i) OA14-3 198 399 0.50 2.54 ± 0.28 -- --
Molokai (Hawai'i) ML09 809 823 0.98 2.61 ± 0.24 2.33 ± 0.22 --
Iao Valley (Hawai'i) MA09 120 354 0.34 3.17 ± 0.24 3.63 ± 0.22 --
Ytra Ranga (Iceland) YR15 273 364 0.75 3.97 ± 0.23 -- 1.13 ± 0.15
Grande Riviere de Goyave AN-14-(42-44) 77 366 0.21 3.59 ± 0.23 -- 1.22 ± 0.15
Asia & Oceania
Changjiang* (Nov 2014) CJ14 262 131 2.00 2.72 ± 0.24 -- --
Changjiang* (Jan 2015) CJ15 264 121 2.18 2.50 ± 0.24 -- --
Mekong ME14 240 171 1.41 2.91 ± 0.24 -- --
Nam Xong NX14 84 305 0.28 3.90 ± 0.11 -- --
Otira OT14 233 66 3.54 1.61 ± 0.24 -- --
Central & South America
Taconazo LS01 107 120 0.89 2.58 ± 0.12 -- 0.29 ± 0.13
Kosñipata (Aug 2013) MMD-02 90 236 0.38 -- 4.96 ± 0.22 0.94 ± 0.15
Kosñipata (Oct 2015) KOS15 76 192 0.40 3.19 ± 0.28 -- --
Carbon MMD-05 258 188 1.37 -- 3.30 ± 0.22 --
Madre de Dios MMD-28 107 128 0.84 -- 3.24 ± 0.22 0.82 ± 0.15
Madre de Dios MMD-32 147 163 0.90 -- 3.72 ± 0.22 1.08 ± 0.15
Inambari MMD-29 211 160 1.32 -- 3.17 ± 0.22 0.74 ± 0.15
Piedras MMD-34 113 393 0.29 -- 4.72 ± 0.22 1.53 ± 0.15
Solimões (Jun 2005) AM-05-(05-08) 106 158 0.67 2.43 ± 0.09 -- --
Solimões (Dec 2014) AM14-36 134 155 0.87 2.01 ± 0.24 -- 1.02 ± 0.15
Negro AM14-40 70 90 0.78 2.00 ± 0.28 -- 0.56 ± 0.15
Madeira AM06-(34-43) -- 153 -- 2.50 ± 0.09 -- --
Amazon AM06-(23-30) 119 145 0.82 2.54 ± 0.09 -- 0.88 ± 0.15
*Affected by anthropogenic activity. The Mississippi, Hondo, and Los Angeles rivers are incorporate significant industrial and urban
runoff. Changjiang chemistry is affected by extensive irrigation and farming of rice paddies in the floodplain (Ding et al., 2004). The Lower
Kern River was sampled downstream of a dammed reservoir in which water chemistry is strongly modified by diatom growth (Baronas et
al., 2017a).
Sample ID River
δ
74
Ge,
‰
173
dominated by the composition of the Amazon river. For example, Ge/Si of the
Amazon river at Obidos has been measured to vary between 0.57 and 0.79 (n
= 3; Appendix E Table E.1; Gaillardet et al. (1999, 2014)). Furthermore, this
signature does not reflect the contribution of two other major Amazon tributaries
– the Tapajos and the Trombetas, which were measured to have Ge/Si of 0.41 and
0.56 mmol/mol, respectively (Appendix E Table E.1). The Ge/Si of Amazon at
the mouth is therefore likely to be close to the previous global estimate of 0.57
mmol/mol (Froelich et al., 1992). Unfortunately, there is currently no d
74
Ge data
available for the Tapajos and Trombetas, or any possible temporal variation of
the Amazon at its mouth. However, measurements of the three major tributaries
(Solimões, Madeira, and Negro) all show a remarkably narrow d
74
Ge range of 2.0-
2.5 % (Table 6.5). Furthermore, although only two data points are available,
there does not appear to be a large seasonal d
74
Ge variation in the Solimões (2.0-
2.4 %). Considering all of the above, we propose a conservative mean (pristine)
globald
74
Ge
riv
of2.6±0.5%, whereasforGe/Si, theFroelichetal.(1992)estimate
of 0.57±0.10 mmol/mol is retained (Fig. 6.4). Time-series measurements of the
Amazon and other major rivers of the world are needed to refine these values
further.
6.3.3 Comparison of Ge, Si, and their isotope composition
in global rivers: biotic vs. abiotic fractionation
For a number of rivers, both Ge and Si isotope compositions were measured
on the same sample, allowing direct comparison of d
74
Ge, d
30
Si, and Ge/Si proxy
behavioronabroadscale. Usingasmallerdataset, Baronasetal.(2017a)identified
a negative correlation between d
74
Ge and Ge/Si (See Chapter 2). The expanded
dataset presented here is only broadly consistent with that trend (Fig. 6.4b), i.e.
174
01 2 3 4
Ge/Si, µmol mol
-1
0
1
2
3
4
5
6
δ
74
Granitic
Basaltic
Sedimentary
Mixed
Dominant watershed lithology
0 0.005 0.01 0.015 0.02 0.025 0.03
1/Ge, pmol
-1
L
0
1
2
3
4
5
6
δ
74
a b
Figure 6.4: Summary of dissolved d
74
Ge composition in rivers draining various
lithologies (as designated in Table 6.1). Small rivers draining primarily urban areas
(namely, Hondo and Los Angeles rivers) are shown as gray crosses. a) d
74
Ge
riv
as
a function of Ge concentration reciprocal. b) d
74
Ge
riv
as a function of Ge/Si ratio.
The blue star shows a global discharge-weighted average, calculated from the data
in Tables 6.1 and 6.5 (see Section 6.3.2). Los Angeles river is not shown due to its
very high Ge/Si.
all rivers that have d
74
Ge > 4 % also have Ge/Si < 1 mmol/mol and rivers with
Ge/Si> 2 mmol/mol all have d
74
Ge< 3 %. A very similar pattern is observed if
d
74
Ge is instead plotted against Ge/Na or Ge/[sum of major cations] (not shown).
As discussed by Baronas et al. (2017a), as well as in Section 6.3.1, given the
uniform d
74
Ge and Ge/Si composition of silicate rocks, this variability in dissolved
signatures is primarily the result of fractionation during critical zone processes,
such as secondary mineral precipitation and biological uptake.
It has been suggested that up to 27 % of the global continental dissolved Si flux
is cycled through vegetation first (Cornelis et al., 2011), which could potentially
haveamajoreffectondissolvedd
30
Si
riv
signatures. Arequirementforanyobserved
fractionation on a watershed scale, however, is that a portion of the solid secondary
phases (whether neoformed clays or biogenic silica) is exported (eroded) from the
catchment without redissolving (Bouchez et al., 2013). Biogenic silica phytoliths
175
are in general much more soluble than aluminosilicate clays and therefore may
degrade within soils and not exert any effect on d
30
Si
riv
over annual timescales,
barring any non-steady state processes, such as deforestation or re-vegetation.
Nevertheless, without additional tracers, it is not possible to tell whether the
commonly heavy riverined
30
Si signatures on a global scale are primarily controlled
by secondary mineral precipitation or biological Si uptake.
The coupled use of d
30
Si and Ge/Si can deconvolve biological fractionation
from that associated with secondary mineral precipitation (Delvigne et al., 2009;
Lugolobi et al., 2010; Opfergelt et al., 2011; Cornelis et al., 2011). This approach
can also be applied on a global scale by comparing the d
30
Si and Ge/Si signatures
in rivers. Although global compilations of both d
30
Si (Frings et al., 2016) and
Ge/Si (Froelich et al., 1992) exist, they rely on separate sets of samples, and the
sometimeslarge(orpoorlyconstrained)temporal-variabilityofthesesignatureshas
until now prevented the direct comparison of these proxies in riverine signatures
on a global scale.
In the case of secondary mineral precipitation, an inverse relationship between
Ge/Si and d
30
Si is expected in the soils and the associated weathering fluids (see
Section 6.1). Indeed, using the global river data presented here (Table 6.5), a
statistically robust negative correlation is observed, despite significant scatter (Fig.
6.5). Any significant Si and Ge fractionation via formation of biogenic Si (whether
by plants or by diatoms) is expected to produce a positive correlation between
Ge/Si and d
30
Si. An extreme example of this scenario is represented by the Lower
Kern river (data point in the upper right corner of Fig. 6.5). There, diatom growth
in a dam reservoir has consumed∼89 % of incoming Si and∼44 % of Ge, strongly
fractionatingtheGe/Siratio(Baronasetal.,2017a). Thepresentstudyshowsthat
this also significantly affects the d
30
Si composition of the water, with the Lower
176
Kern value of 4.23± 0.20 % falling at the upper limit of riverine d
30
Si signatures
reported to date (Frings et al., 2016). This situation is unusual and should only
affect rivers flowing through reservoirs with clear water and long residence times,
allowing significant Si removal by diatom growth.
Overall, the predominantly negative correlation between d
30
Si and Ge/Si in
global rivers suggests that the heavy d
30
Si of many large rivers is primarily caused
by abiotic weathering fractionation, rather than biological Si uptake. A notable
exception is the Changjiang river, which is strongly impacted by rice paddy agri-
culture, and which has been separately shown to have very high d
30
Si in the range
of 2.3-3.0 % (Ding et al., 2004) and high Ge/Si of∼2 mmol/mol (Table 6.5).
Although measured on separate sets of samples, these signatures are fully consis-
tent with the biological-fractionation vector suggested by the Lower Kern river in
Fig. 6.5.
The dissolved riverine d
74
Ge and d
30
Si signatures exhibit a robust positive
correlation (Fig. 6.6), again consistent with a predominantly weathering-induced
isotopic fractionation of both elements, as previously suggested by Baronas et al.
(2017a) (Chapter 2) and explored in detail in Chapter 4. The same two anthro-
pogenically affected samples as in Fig. 6.5 (the Missisippi and the Lower Kern)
were excluded from the fit in Fig. 6.6. Importantly, there is no detectable d
74
Ge
fractionation of the Lower Kern signature, in strong contrast to d
30
Si and Ge/Si
(the mass balance comparing d
74
Ge inputs into and outputs from the reservoir
is presented in Appendix A). These results suggest negligible d
74
Ge fractionation
during freshwater diatom uptake, which is consistent with data obtained from
preliminary marine diatom culture experiments (Mantoura, 2006).
Although there is currently no data available regarding vegetation d
74
Ge sig-
natures, on a watershed scale, the effect is expected to be even smaller than that
177
0 1 2 3 4
δ
30
0.2
0.5
1
2
5
10
Ge/Si, μmol/mol
Adj. R
2
= 0.52
p << 0.001
(n = 34)
California, Mississippi *
Changjiang **
Mackenzie basin
Fraser
Andes-Amazon
Major Amazon trib.
Hawaii
La Selva (Costa Rica)
Greenland
Iceland
Guadeloupe
Figure6.5: RelationshipbetweendissolvedGe/Siandd
30
Si. Notethelogscaleofy-
axis. The La Selva samples shown are the Taconazo and Arboleda rivers discussed
in Chapter 4. Los Angeles river is not shown due to its very high Ge/Si. Statistical
parameters refer to the linear regression fit (dashed line). The gray points are
samples affected by anthropogenic disturbance and not used in the regression: *
The Mississippi and Lower Kern rivers, as discussed above; ** Changjiang river,
affected by extensive rice paddy agriculture in the floodplain. d
30
Si and error bars
show the range of values determined by Ding et al. (2004), Ge/Si data from this
study (Table 6.5).
observed for d
30
Si. Multiple studies have shown that plants discriminate against
Ge relative to Si, with a partitioning coefficient K
D
ranging between 0.01 and 0.5,
i.e. Ge uptake being lower than Si by anywhere from 50 to 99 %, depending on
plant species and organ (Blecker et al., 2007; Delvigne et al., 2009; Lugolobi et al.,
2010; White et al., 2012). Therefore, only 0.3-13 % of the continental Ge flux may
178
be recycled through vegetation, limiting any potential isotopic fractionation that
the biological cycle could impose on the dissolved signature.
Finally, a detailed study of d
74
Ge, d
30
Si, and Ge/Si in the tropical, Si-depleted
soilsofCostaRicaindicatednegligiblevegetationsignalonallthreeproxies,despite
some local plant species containing up to∼10 % Si (dry weight) (Chapter 4),
indicating that much of the plant-assimilated Si must be rapidly re-dissolved.
6.3.4 Riverine Ge isotope composition relationship with
weathering intensity
The isotopic composition of weathering tracers affected by secondary precip-
itation (e.g., d
7
Li, d
30
Si, and d
74
Ge) will be controlled by 1) the initial isotopic
signature of source silicate rocks; 2) the isotopic fractionation factor associated
with the formation of secondary authigenic materials, such as clays or plant phy-
toliths; and 3) the fraction of the initial solute (in this case dissolved Ge or Si)
that is precipitated with or adsorbed onto these authigenic materials and eroded
in this solid form. The last factor is simply a mass balance constraint that reflects
"congruency" of chemical weathering in a given catchment, i.e. the portion of an
element released during weathering that is exported in the dissolved form. In most
cases, the first two factors, that is the source rock composition and the fractiona-
tion factor, are not expected to change very strongly, especially over geologically
short (<1 My) timescales. The fractionation factor might vary if secondary phase
mineralogy changes, and it may also be climate-dependent to some degree but
these effects are still poorly constrained for most isotopic systems. Nevertheless,
the major factor responsible for changes in the isotopic composition of weathering-
derived solutes is thought to be the degree of secondary material erosion, also
179
0 1 2 3 4
δ
30
1
2
3
4
5
6
δ
74
Adj. R
2
= 0.48
p < 0.001
(n = 19)
California, Mississippi *
Changjiang **
Mackenzie basin
Fraser
Andes-Amazon
Major Amazon trib.
Hawaii
La Selva (Costa Rica)
Greenland
Iceland
Guadeloupe
Figure 6.6: Relationship between dissolved d
74
Ge and d
30
Si in global rivers. The
LaSelvasamplesshownaretheTaconazoandArboledariversdiscussedinChapter
4. Statistical parameters refer to the linear regression fit (dashed line). The
gray points are samples affected by anthropogenic disturbance and not used in
the regression: * The Mississippi and Lower Kern rivers, as discussed above; **
Changjiang river, affected by extensive rice paddy agriculture in the floodplain.
d
30
Si and error bars show the range of values determined by Ding et al. (2004),
d
74
Ge data from this study (Table 6.5).
termed weathering intensity (Bouchez et al., 2013), which regulates weathering
"congruency".
The chemical weathering flux (W) and the erosion flux of the solids (E) can be
used to estimate the weathering intensity (W/D) of a given catchment, where D
is total denudation (D = W + E). W/D in different catchments depends largely
180
Table 6.6: Chemical weathering and erosion fluxes of global rivers, where data
is available. The chemical weathering rates (W) were calculated from silicate
rock-derived dissolved loads and the denudation rates (D) were calculated from
suspended sediment loads (data sources given in the footnote).
Weathering (W) Erosion Denudation (D)
t/km
2
/y t/km
2
/y t/km
2
/y
Mississippi 3.8 20.3 24.2 0.158
Mackenzie 1.0 63.7 64.7 0.016
Liard 1.2 167.3 168.5 0.007
Peel 1.7 294.6 296.3 0.006
Fraser 5.0 22.3 27.3 0.183
Changjiang 5.3 60.0 65.3 0.081
Mekong 14.3 76.2 90.6 0.158
Kosñipata (Peru) 102.5 3500 3602.5 0.028
Taconazo (La Selva) 2.1 5.5 7.6 0.275
Madre de Dios 20.7 570.0 590.7 0.035
Solimões 19.8 187.5 207.3 0.096
Negro 5.7 13.9 19.6 0.291
Madeira 9.5 184.4 193.9 0.049
Amazon 14.2 231.0 245.2 0.058
W/D River
Kosnipata erosion flux from Clark et al. (2017) and dissolved flux calculated from data in
Torres et al. (2015). Taconazo data from J. West, unpublished. Other data from
Gaillardet et al. (1999); compil. of Dellinger et al. (2015) and references therein.
on the rate of supply of fresh source rock (dependent on E) and on climate (which
affects both W and E) (Bouchez et al., 2014). The value of isotopic weathering
tracers, such as d
7
Li, d
30
Si, and potentially d
74
Ge therefore to a large degree lies
in their ability to trace W/D, such that paleorecords of these proxies could be
used to reconstruct past erosional and climatic conditions (e.g., Misra & Froelich,
2012). To establish this ability, each proxy needs to be calibrated by assessing the
dissolved d-values across a range of W/D conditions in the modern environment.
Such work has been recently done for both d
7
Li (Dellinger et al., 2015) and d
30
Si
181
(Frings et al., 2016) and is reproduced in Fig. 6.7. The results show that both
of these tracers exhibit a "humped" distribution across the range of W/D values.
The W/D estimates for some of the d
30
Si data are especially uncertain (P. Frings,
personal comm.), likely adding noise to the relationship. Nevertheless, for both
d
7
Li and d
30
Si, the highest degree of fractionation occurs at intermediate W/D
values. In cases where W/D approaches 1, a majority of the material is exported
indissolvedform, andtherefored
riv
approachesd
rock
(ord
riv
–d
rock
= D
riv–rock
→ 0)
if a mass balance is to be maintained (Bouchez et al., 2013). The reason why there
is little fractionation of d
7
Li and d
30
Si in very low-W/D environments, where the
majority of the material is exported in solid phase via erosion, is less clear but
may be related to soil residence times. From a geomorphic perspective, higher
erosion rates result in thinner regolith (Heimsath et al., 1997) with shorter mineral
residence times and therefore less chemically weathered minerals (Riebe et al.,
2001). If the rate of secondary mineral precipitation is linearly scaled to soil
thickness (which is debatable), then high erosion should result in low secondary
precipitation rates (Ferrier & Kirchner, 2008) and very little isotopic fractionation,
i.e. D
riv–rock
approaches 0 again (Bouchez et al., 2013).
The conceptual framework of Bouchez et al. (2013) and Ferrier & Kirchner
(2008) can successfully explain the observations of riverine d
7
Li and d
30
Si sig-
natures, likely because both of these elements are primarily fractionated by the
kinetically sluggish precipitation of kaolinite. On the other hand, Fe- and Al-oxide
precipitation occurs rapidly in most natural weathering fluids (i.e., well oxygenated
and circum-neutral pH), which results in the consistent depletion of Fe and Al in
the dissolved phase (Gaillardet et al., 2014), even in rapidly eroding, thinly soil-
mantled catchments (Baronas et al., 2017b). Therefore, a similar kinetic limit on
isotopic fractionation of the dissolved phase is not necessarily expected for solutes
182
that primarily adsorb or co-precipitate with Al- and Fe-oxides. Germanium has
a high affinity to Fe, co-precipitating or chemically adsorbing to Fe-oxides both
in natural and laboratory settings (Kurtz et al., 2002; Pokrovsky et al., 2006).
As a result, a high degree of d
74
Ge
riv
fractionation could occur even in very low-
W/D environments, where kaolinite precipitation rate is limited but FeOx and
AlOx precipitation is still rapid. The relationship between d
74
Ge
riv
and W/D is
shown in Fig. 6.7, primarily for large rivers where chemical and physical denuda-
tion estimates are available (Table 6.6). Although the data density is much lower
than in the d
7
Li and d
30
Si compilations of Dellinger et al. (2015) and Frings et al.
(2016),theexpectedtrendemerges,withlowerdegreeoffractionation,i.e.,d
74
Ge
riv
approaching d
74
Ge
rock
, as W/D approaches 1. At the low-W/D end, d
74
Ge
riv
composition is more scattered but overall heavier without a clear "hump" as that
observed for d
7
Li
riv
and d
30
Si
riv
, which would be consistent with predominantly
FeOx-driven fractionation. This interpretation is also supported by the extremely
heavy composition (d
74
Ge = 4.96 %) of the Kosñipata river, where high rates of
pyrite weathering and resulting Fe oxide precipitation have been documented (Tor-
res et al., 2016; Baronas et al., 2017b). While more data is needed at the extreme
low-W/D end to test the robustness of this relationship, this unique behavior sug-
gests that d
74
Ge
riv
(and therefore possibly paleorecords of d
74
Ge
seawater
) could be
a powerful supplement to other already established weathering intensity proxies.
183
1
2
3
4
5
δ
74
0
0.5
1
1.5
2
2.5
δ
30
10
-2
10
-1
10
0
W/D
0
10
20
30
40
δ
7
silicates
Changjiang, Mekong
Mississippi
Mackenzie basin
Fraser
Taconazo (Costa Rica)
Andes-Amazon
Major Amazon trib.
Other rivers
a
b
c
Figure6.7: Relationshipbetweenweatheringintensityanddissolvedrivera)d
74
Ge;
b) d
30
Si; and c) d
7
Li. The weathering intensity of each watershed was calculated
as a ratio of chemical weathering (W) to total denudation (D) (Table 6.6). The
ranges of typical silicate rock composition are shown as black squares (Escoube
et al., 2012; Dellinger et al., 2014; Frings et al., 2016). The W/D uncertainty
is difficult to estimate and is not shown. The Taconazo river data is presented
and discussed in 4. d
30
Si data for other rivers (grey circles in panel b) is from the
compilation by Frings et al. (2016). Alld
7
Li data in panel c is from the compilation
by Dellinger et al. (2015).
184
6.4 Conclusions
We have analyzed the dissolved d
74
Ge and Ge/Si composition of 28 rivers
worldwide, including the Amazon, Changjiang, Mekong, and Mackenzie rivers.
Combined with the previous dataset of Baronas et al. (2017a), the d
74
Ge values
range from 0.9 to 5.5 %. The global discharge-weighted average is 2.6±0.5 %,
whether anthropogenically affected rivers (Changjiang, Mississippi, and Mekong)
are included or not. Several samples of river suspended and bedload sediments
exhibit d
74
Ge that is indistinguishable from silicate bedrock (0.4 - 0.7 %), consis-
tent with low Ge solubility and transport predominantly in the solid phase.
We showed that the dissolved signatures of Ge/Si, d
74
Ge, andd
30
Si are to some
extent coupled, indicating that the dominant process controlling all three proxies is
the precipitation of secondary phases, such as Fe oxides and aluminosilicate clays.
The only strong deviations from this trend are rivers strongly affected by biological
uptake of Si and Ge, either by vegetation (Changjiang) or diatoms (Lower Kern
river downstream of a dammed reservoir), which affects both Ge/Si and d
30
Si
signatures. No detectable effect on d
74
Ge was observed, suggesting that it may be
much less sensitive to biological processes than Si-based proxies.
Finally, we showed that dissolved riverine d
74
Ge signature correlates with the
weathering intensity (the ratio of chemical to total denudation) of a given catch-
ment. Higher weathering intensity results in less fractionated (lighter) d
74
Ge sig-
natures that approach silicate rock composition, in a manner similar to that pre-
viously observed for d
30
Si (Frings et al., 2016) and d
7
Li (Dellinger et al., 2015).
In contrast to these proxies, however, there does not appear to be a strong kinetic
limitation of d
74
Ge fractionation at very low weathering intensity, although more
data is required to confirm this preliminary observation. We propose that the dis-
tinct behavior of of d
74
Ge may reflect different secondary phases that each element
185
is primarily incorporated into - Si and Li are thought to be primarily incorporated
into secondary aluminosilicate clays (e.g., kaolinite) whereas a large portion of Ge
may be adsorbed or co-precipitated with rapidly forming Fe- (and perhaps Al-)
oxides.
Acknowledgements
Financial support was provided by NSF grants OCE 1061700 and 1260692 to
DEH. JJB was additionally supported by a CUAHSI Pathfinder graduate student
fellowship, an InterRidge research fellowship, and a John Montagne Award from
GSA Quaternary Geology and Geomorphology Division. We thank Emmanuel
Ponzevera for analytical assistance with Ge isotope measurements at Ifremer and
Yi Hou for help with Si concentration analyses at USC. We are extremely grateful
to the following people who helped collect the global river samples used in this
study: ValierGaly,SarahRosengard, PaulinaPiñedo-Gonzalez,BernhardPeucker-
Ehrenbrink, Mathieu Dellinger, Mia Bennett, Lincoln Pitcher, Gen Li, Shilei Li,
Jane Hammond, Emily Burt, and Camilo Ponton. Sample collection in Peru was
accomplished with help from Kathryn Clark, Sarah Feakins, Valier Galy, Camilo
Ponton, a number of field assistants over the years, and field support from ACCA
Peru, Manu Learning Centre, and Incaterra.
186
Chapter 7
Interpreting seawater Ge isotope
variations during the penultimate
deglaciation
Author contributions
I designed the study and performed all the modeling. A modified version of
this manuscript may be submitted to a peer reviewed journal.
Abstract
The global biogeochemical cycles of silicon (Si) and germanium (Ge) are inti-
mately linked with each other and are controlled by a range of important processes,
such as silicate rock weathering, hydrothermal activity, marine productivity, and
sediment diagenesis. Paleorecords of oceanic Ge and Si isotope and Ge/Si ratios
can therefore yield insights into the coupling between tectonics, weathering, cli-
mate, and silicifying biota. Here, I use an updated global Ge isotope (d
74
Ge) bud-
get to investigate the processes controlling seawater Ge/Si and d
74
Ge signatures
over 80-170 ka, spanning the penultimate glacial-interglacial transition (Mantoura,
2006). Relatively small changes in authigenic (non-opal) Ge burial can explain the
0.2 mmol/mol change in Ge/Si
SW
at 130 ka. Whereas Si isotopes are strongly
187
affected by biological fractionation and turnover, the lack of Ge isotope fraction-
ation during diatom biosilicification allows sedimentary d
74
Ge to track secular
changes in seawater composition that result from the shifting balance between the
different Ge sources and sinks. However, similar d
74
Ge composition of various
input fluxes and limited isotopic fractionation during authigenic Ge removal from
pore water (∼-1 % relative to seawater), coupled with short Ge residence time in
the ocean, results in only small d
74
Ge
SW
variations over glacial-interglacial cycles.
Therefore, d
74
Ge paleorecords may help identify large magnitude perturbations in
the global Ge and Si cycles.
7.1 Background
The global biogeochemical cycles of various elements are inextricably linked
to Earth’s climate and its variations over time. Over its geological history, the
planet has undergone multiple warming and cooling cycles (Zachos et al., 2001),
both driving and driven by a complex array of biological and chemical processes in
the atmosphere, the ocean, and the continents. Sedimentary paleorecords can help
reveal the intensity and the timing of geochemical perturbations that accompany
past climate change (e.g., Broecker, 1982; Archer et al., 2000; McClymont et al.,
2013; Li & Elderfield, 2013). In particular, silicon (Si) is a major constituent of the
Earth’s crust and its chemistry plays a major role in stabilizing climate via silicate
rock weathering (Walker et al., 1981; Berner et al., 1983) and marine biosilicifier
productivity (e.g., Cermeño et al., 2015). Germanium (Ge) is a trace element
whose cycle is closely coupled to that of Si (Froelich & Andreae, 1981). As a
result, Ge/Si ratios, in combination with Ge and Si isotopes, can potentially be
188
used to trace past changes in silicate rock weathering, diatom productivity in the
ocean, and marine sediment authigenesis, among other processes.
7.1.1 Ge/Si as paleoceanographic tracer
The initial interest in Ge/Si as a paleoceanographic tracer arose when the ratio
recorded in diatoms was shown to closely track the glacial-interglacial cycles over
the past 500 ky (Fig 7.1; Froelich et al. (1989); Shemesh et al. (1989); Mortlock
et al. (1991). Initially, the oceanic Ge/Si ratio (Ge/Si
SW
) was thought to be gov-
erned solely by the ratio of weathering to hydrothermal fluid inputs. Therefore, the
glacial-interglacial variation was interpreted as a weathering signal, which would
have suggested significantly higher weathering fluxes of Si during glacial periods
(Froelich et al., 1992), which are known to have been much drier (e.g., Petit et al.,
1999; Muhs, 2013). Chemical weathering is strongly dependent on runoff (West
et al., 2005), suggesting that dry periods should have instead resulted in lower
weathering fluxes. However, multiple subsequent studies documented significant
removal of dissolved Ge from marine pore waters via the precipitation of authigenic
minerals, likely Fe oxides or authigenic aluminosilicate clays, i.e. the non-opal Ge
sink (Murnane et al., 1989; Hammond et al., 2000; King et al., 2000; McManus et
al., 2003). Modeling done by Hammond et al. (2004) further showed that glacial-
interglacial Ge/Si
SW
variations can be fully explained if the effect of ocean tem-
perature on bSi rain reaching the sediments (and supplying Ge to pore waters) is
taken into account. Furthermore, Baronas et al. (2016) have showed that authi-
genic Ge burial rates are significantly higher in continental margins, suggesting
that detrital material plays an important role in this process by supplying Al and
Fe to the sediments. An increase in dust supply to the ocean during glacial periods
189
could therefore drive additional Ge removal via precipitation of authigenic Fe- and
Al-rich minerals, resulting in lower Ge/Si
SW
.
0 100 200 300 400 500
0.4
0.5
0.6
0.7
Age, ka
Seawater Ge/Si, µmol/mol
1 2-3 5 6 7 8 9 10 11 12 4
Figure 7.1: Diatom Ge/Si paleorecord in the Atlantic sector of the Southern Ocean
(piston core RC13-259), modified from Mortlock et al. (1991). The numbers indi-
cate Marine Isotope Stages as defined by the LR04 d
18
O stack of Lisiecki & Raymo
(2005). Blue color indicates glacial, and pink color – interglacial periods.
7.1.2 Ge isotopic composition as paleoceanographic tracer
Despite the progress on the global Ge cycle, the interpretation of Ge/Si paleo-
records remains ambiguous. More broadly, the response of continental weathering
to glacial-interglacial cycles is still poorly understood as well. Here, I propose that
paleorecords of Ge stable isotope composition of seawater (d
74
Ge
SW
) can help
address these questions. d
74
Ge
SW
has been recently measured for the first time
(Baronas et al., 2017a; Guillermic et al., 2017) and a global marine Ge isotope
budget established (Baronas et al., 2017a). The average deep seawater d
74
Ge
SW
is 3.14± 0.38 % (Guillermic et al., 2017). About 1 %of variation is observed in
190
the upper water column, which is possibly related to slight isotopic fractionation
the organic cellular material of diatoms (Mantoura, 2006; Guillermic et al., 2017).
However, good agreement of modern seawater with core top diatom bSi suggests
that there is little to no d
74
Ge fractionation during diatom biosilicification (Man-
toura, 2006).
More broadly, the Ge isotope composition of the ocean is controlled by the
balance of Ge inputs and outputs. The global marine d
74
Ge budget is presented
in Table 7.1. The major sources of Ge to the ocean are hydrothermal fluids and
rivers. The global riverine value was recently updated (Chapter 6). Overall, the
continental (i.e. riverine, groundwater, and detrital inputs) and the hydrothermal
fluid (combined ridge- axis and flank) d
74
Ge signatures are similar in the 2-3 %
range. Ge removal from seawater occurs via burial of bSi (opal) and authigenic
minerals (non-opal) in marine sediments. In contrast with diatoms, sponge bSi is
consistently offset from seawater with a fractionation factor D
74
Ge
S–SW
of -0.87
± 0.37 % (Guillermic et al., 2017) but likely accounts for only a small portion
of total Ge burial (Table 7.1). Assuming the contemporary marine Ge budget is
close to steady state, the majority of Ge must be removed via precipitation of
authigenic minerals within marine sediments. A recent study of pore waters in
continental margins has shown that this process results in a fractionation factor of
D
74
Ge
NO–SW
of∼-1 % (Chapter 3).
ASouthernOceandiatompaleorecordofGe/Siandd
74
Gespanningthepenulti-
matedeglaciationhasbeencompiledbyMantoura(2006)andwasrecentlyincluded
in the extensive review of Ge isotopes by Rouxel & Luais (2017). In this study, we
used the updated global d
74
Ge budget to analyze this paleorecord and to test sev-
eral different scenarios that could explain the observed Ge/Si and d
74
Ge variations
over glacial-interglacial cycles.
191
7.1.3 Ge isotope and Ge/Si paleorecord in the Southern
Ocean
The sedimentary diatom record considered here was first analyzed and pre-
sented by Mantoura (2006). The methods and major observations are briefly
recounted here. The sediment core was collected at ODP site 1094 (located at
-53.18
◦
N and 5.13
◦
E) at 2807 m water depth. The age model is based on correlat-
ing diatom-derived summer surface seawater temperature with the Vostok ice core
record of Petit et al. (1999) (Schneider-Mor, 2005). 10-64 mm size fraction bSi was
separated and purified using the method described by Singer & Shemesh (1995).
Extensive investigation of trace element concentrations and ratios was undertaken
to ensure that the separated bSi was clean of detrital contaminants. The cleaned
bSi was dissolved in Na
2
CO
3
and Si concentrations were measured using ICP-AES.
Ge was purified using the column chromatography method adapted from Rouxel
et al. (2006) and Ge concentrations and isotope ratios were measured on a Nu
Instruments MC-ICP-MS coupled to hydride generation, using a sample-standard
bracketing method adapted from Galy et al. (2003) and Rouxel et al. (2006). All
d
74
Ge values reported in this study are normalized to the NIST 3120a Ge isotope
standard. Si isotope composition was measured using a method adapted from
Georg et al. (2006b) and Reynolds et al. (2007) and is reported relative to the
NBS-28 Si isotope standard.
The ODP 1094 Ge/Si record (Fig. 7.2a) closely resembles the previously pub-
lished records from other parts of the Southern Ocean (e.g., Fig. 7.1), with a
gradual decrease during the penultimate glacial period (MIS-6) and a rapid 0.2
mmol/mol increase during the deglaciation at 130 ka (T2). The absolute Ge/Si
ratios of ODP 1094 are∼0.15 mmol/mol lower than the records of Froelich et al.
(1989) and Mortlock et al. (1991). The most likely explanation for this discrepancy
192
is that the different records are composed of different bSi assemblages, as a result
of either natural differences between the sites or differences in methodology. Core
top measurements of certain large diatom species and radiolarian tests (> 38 mm)
have been shown to exhibit Ge/Si lower than contemporary seawater, indicating
some biological fractionation (Froelich et al., 1989; Shemesh et al., 1989). The
records of Froelich et al. (1989) and Mortlock et al. (1991) were therefore based
on the <38 mm size fraction alone. The ODP 1094 record discussed here was
measured on the 10-64 mm size fraction, which likely contains a higher proportion
of large diatom frustules and radiolarian tests, therefore yielding generally lower
Ge/Si than previous records (Fig. 7.1) for the same time period. Nevertheless, the
relative difference between glacial and interglacial Ge/Si values, and the timing
and shape of the ODP 1094 Ge/Si curve is in excellent agreement with previous
records, indicating that it reflects secular Ge/Si
SW
variations.
The ODP 1094 Si isotope record shows a similar pattern to Ge/Si, i.e. a
rapid∼1.5 % increase during the deglaciation at 130 ka and sustained higher
d
30
Si values throughout the interglacial MIS-5 (Fig. 7.2c). This difference is
similar to the 1.2 % shift observed by Brzezinski et al. (2002) (coincidentally,
measured on the same RC13-259 core on which the Ge/Si record of Mortlock et
al. (1991) shown in Fig. 7.1 is based). The T2 d
30
Si shift is also similar to the
most recent deglaciation (T1), which ranges from 0.4 to 1.5 %, depending on
the specific (diatom-based) record (Brzezinski et al., 2002; Beucher et al., 2007;
Horn et al., 2011; Hendry et al., 2014). Due to the fact that diatoms significantly
fractionate d
30
Si (e.g., De La Rocha et al., 1997; Sutton et al., 2013), most of this
variation has been interpreted to reflect the variability in nutrient supply, diatom
productivity, and dissolved Si utilization in the surface ocean (Brzezinski et al.,
2002; Beucher et al., 2007; Horn et al., 2011; Hendry et al., 2014). However, more
193
recently the possibility that a significant fraction of this variability may reflect
secular changes in continental d
30
Si inputs has been raised (Frings et al., 2016),
using a modeling approach that was utilized in the present study to investigate the
Ge isotope dynamics. Below, I test the response of Ge/Si and d
74
Ge to the same
potential continental input changes that were used by Frings et al. (2016) to test
the d
30
Si response.
7.2 Modeling approach
7.2.1 Steady state calculations
A simple one-box ocean model was built to explore the possible interpretations
of the coupled Ge/Si-d
74
Ge record of Mantoura (2006). The box model utilizes the
most up-to-date contemporary global Ge isotope budget (Table 7.1) as a starting
point for the interglacial MIS-5e period. It is based on the global Si budget as
reported by Tréguer & De La Rocha (2013) and Frings et al. (2016). Ge fluxes
are calculated using the best estimates of Ge/Si ratios for the various sources and
sinks. Ge isotopic composition of the fluxes is based on previous studies, including
the other chapters of this dissertation, as cited in Table 7.1. Initially, the global
budget is assumed to be in steady state, and mass balance is closed using the
diatom Si burial flux:
FSi
D
= S(FSi
input
)–FSi
S
(7.1)
where FSi is Si flux in Tmol/y, and subscripts D and S refer to diatom and
sponge bSi burial, respectively. All the Si and Ge input and output flux estimates
194
0.3
0.4
0.5
0.6
Ge/Si, µmol/mol
3
3.5
4
δ
74
Ge, ‰
0.7
a
b
c
δ
30
Si, ‰
0
0.5
1
1.5
2
2.5
50 100 150
Age, ka
MIS-6 MIS-5 MIS-3
Figure 7.2: Diatom d
74
Ge, Ge/Si, and d
30
Si paleorecord in the Atlantic sector of
the Southern Ocean (ODP Leg 177, Core 1094) measured by Mantoura (2006) and
published in Rouxel & Luais (2017).
are summarized in Table 7.1. In turn, the Ge mass budget is closed using the
authigenic burial (non-opal) sink:
195
FGe
NO
= S(FGe
input
)–(FGe
D
+FGe
S
) (7.2)
where Ge fluxes are in Mmol/y. The Ge/Si ratio of sponge burial depends
on seawater Ge concentration and was calculated using the empirical relationship
established by Ellwood et al. (2006):
Ge/Si
S
= 0.0031×[Ge]
SW
+0.0818 (7.3)
196
Table 7.1: Global Si, Ge, and d
74
Ge budget (updated from Chapter 2).
Subscript Source Source Source
Inputs
Rivers (dissolved load) RD 5.7 ±0.4 1 0.57 ±0.10 3 3.3 ±0.8 2.6 ±0.5 12
Rivers (susp. amorphous silica) RB 1.1 ±1.0 2 0.57 ±0.30 4 0.6 (0-1.8) 2.6 ±0.5 13
Groundwater GW 0.65 ±0.54 1 0.30 ±0.20 5 0.2 (0-0.6) 4.0 ±2.0 14
Detrital (susp. river load) RS 0.8 ±0.5 1 1.4 ±0.5 6 1.1 (0.3-2.5) 0.56 ±0.10 12
Detrital (aeolian dust) A 0.3 ±0.2 1 1.4 ±0.5 6 0.4 (0.1-1.0) 0.56 ±0.10 15
Hydrothermal (ridge axis) HH 0.5 ±0.3 1 11 ±3 7 5.5 (1.6-11.2) 1.5 ±0.5 16
Hydrothermal (ridge flank) HC 0.5 ±0.4 1 25 ±24 8 12.5 (0.1-44.1) 3.5 ±0.5 17
Total inputs 9.6 (6.3-12.9) 23.6 (4.6-65.2)
Outputs
Diatom bSi D 8.6 (3.6-12.9) m.b.* 0.76 ±0.04 10 6.5 (2.6-10.3) 19
Sponge bSi S 1.0 (0-2.7) 2** 0.21 -- 11 0.2 (0-0.6) 20
Authigenesis (non-opal) NO -- -- -- -- -- -- m.b.* (2.1-54.4) m.b.*
* m.b. = mass balance
†
calculated as the product of Si flux and its respective Ge/Si ratio, except for FGe
NO
Sources:
(1) Frings et al. (2016)
(2) Treguer & De La Rocha (2013); **reduced from the original estimate
(3) RIverine Ge vs Si from Froelich et al. (1992), avg. global river [Si] from Durr et al. (2011)
(4) assumed equal to rivers
(5) Kurtz et al. (2011), Chapter 4.
(6) crustal avg., Froelich et al. (1992)
(7) Mortlock et al. (1993), Wheat & McManus (2005), Escoube et al. (2015)
(8) Wheat & McManus (2005)
(9) assumed equal to basalt
(10) Diatom Ge/Si as measured at 160ka (Mantoura (2006), Rouxel & Luais (2017))
(11) Calculated from sponge Ge/Si relationship with [Ge]
SW
, as reported by Ellwood et al. (2006)
(12) Chapter 6.
(13) assumed identical to dissolved δ
74
Ge (see Chapter 6)
(14) Chapter 4.
(15) assumed equal to average continental crust (Escoube et al. (2012), Rouxel & Luais (2017))
(16) Avg. of all high temp systems (normalized to no. of samples per system), Baronas et al. (2017a) and Escoube et al. (2015)
(17) Baronas et al. (2017a)
(18) assumed equal to average seafloor basalt (Escoube et al. (2012), Rouxel & Luais (2017))
(19) No δ
74
Ge fractionation by diatoms (Mantoura (2006), Rouxel & Luais (2017))
(20) Rouxel et al. (2006), Guillermic et al. (2017)
FSi, Tmol/yr FGe, Mmol/yr
†
Ge/Si, µmol/mol δ
74
Ge, ‰
Δ
D-SW
= 0
Δ
S-SW
= -1 ±0.2
Δ
NO-SW
= -0.9 (-2.7 to 0)
197
Due to the constraints of a 1-box model, the average seawater Ge concentration
was used to calculate sponge Ge/Si. This approximation has little influence on the
total budget due to the small role that sponges play in the Ge cycle.
Ge isotopic composition of the sinks (bSi and authigenic minerals) depends on
the composition of the seawater (3.2 % during interglacials, including the modern
ocean), with a constant fractionation factor:
d
74
Ge
i
= d
74
Ge
SW
+D
74
Ge
i–SW
(7.4)
where i refers to the burial phase of interest (bSi or non-opal) and D
74
Ge
i–SW
is the fractionation factor relative to seawater. D
74
Ge
bSi–SW
is set to 0 % and
-1.0±0.2 % for diatoms and sponges, respectively, as indicated in studies by Man-
toura (2006), Rouxel et al. (2006), and Guillermic et al. (2017). The d
74
Ge compo-
sition of the authigenic sink is calculated by assuming a steady state mass balance:
d
74
Ge
NO
=

S(FGe
input
×d
74
Ge
input
)–(FGe
D
d
74
Ge
D
+FGe
S
d
74
Ge
S
)

1
FGe
NO
(7.5)
which is in turn used to calculate the authigenic fractionation factor based on
present day seawater composition:
D
74
Ge
NO–SW
= d
74
Ge
NO
–d
74
Ge
SW
(7.6)
The steady state assumption for the contemporary Ge isotope budget is rea-
sonable given the short residence time of Ge in the ocean of∼4300 y (Baronas et
al., 2017a).
198
7.2.2 Temporal modeling
The temporal variations in the oceanic Si budget are modeled assuming a first-
order kinetic response to perturbations. This can be expressed in terms of seawater
Si concentration as:
C
t
= C
0
+(C
ss
–C
0
)(1–e
–kdt
) (7.7)
where C
0
is the initial seawater Si concentration, C
t
is Si concentration at time
=t,C
ss
isthenewequilibriumconcentrationtowardswhichthesystemisadjusting
and k is the first-order rate constant (units of y
–1
) defined as:
k =
FSi
out
n
Si
(7.8)
wheren
Si
is the total oceanic Si inventory in TMol. Given that n
Si
= C×V
ocean
:
FSi
out
)
0
= kC
0
V
ocean
(7.9)
FSi
out
)
t
= kC
t
V
ocean
(7.10)
and at the new equilibrium steady state:
FSi
out
)
t
= FSi
in
)
t
= kC
ss
V
ocean
(7.11)
Equations 7.9 - 7.11 can be plugged into Eq. 7.7 and rearranged to yield:
FSi
net
)
t
= FSi
in
)
t
–FSi
out
)
t
=

FSi
in
)
t
–FSi
out
)
0

e
–kdt
(7.12)
199
The advantage of Eq. 7.12 is that it can be applied on step-by-step basis,
without the need to assign the equilibrium state to which the system is responding.
In this and the following equations, t refers to the current time-step,
0 refers to the preceding timestep, and dt is the size of the individual
time-steps. In truth, the value of k in Eqs. 7.9 - 7.11 is different but given small
enough time-steps (200 y time-steps were used here), this results in negligible error
in the calculated net Si flux.
The sponge bSi burial flux is kept constant, while the diatom burial flux rep-
resents the dynamic portion of the total output:
FSi
D
= FSi
out
–FSi
S
= (FSi
in
–FSi
net
)–FSi
S
(7.13)
The oceanic Si inventory is recalculated at each time step as:
n
Si
)
t
= n
Si
)
0
+FSi
net
)
0
×dt (7.14)
The initial oceanic Si inventory is set equal to the modern, i.e. 97,000 Tmol
(Tréguer & De La Rocha, 2013) and the Ge inventory is calculated from the sea-
water Ge/Si ratio. In the modeling scenarios described below, certain input or
output fluxes are allowed to vary over time, which affects the total marine Si and
Ge inventories, as well as its d
74
Ge composition.
The initial Ge oceanic inventory is calculated as:
n
Ge
)
0
= Ge/Si
SW
)
0
×n
Si
)
0
(7.15)
and subsequently recalculated for each time step (see different modeling sce-
narios below).
200
The initial Ge isotope inventory is calculated from the composition of seawater
(initally set to 3.2 % based on modern observations and paleorecord data; see
below) and the Ge inventory:
n
d
74
Ge
)
0
= d
74
Ge
SW
)
0
×n
Ge
)
0
(7.16)
To temporally model the evolution of d
74
Ge
SW
, the total Ge isotope inventory
is recalculated at pre-defined time steps based on Ge input and output fluxes and
their isotopic composition:
n
d
74
Ge
)
t
= n
d
74
Ge
)
0
+

S(FGe
in
×d
74
Ge
in
)
t
–S(FGe
out
×d
74
Ge
out
)
0

×dt (7.17)
The seawater isotope composition at any given time step is then:
d
74
Ge
SW
)
t
=
n
d
74
Ge
)
t
n
Ge
)
t
(7.18)
Testsshowedthattheuseofd
74
Gevaluesratherthanabsolute
74
Ge/
70
Geratios
in the above calculations results in a negligible approximation error on the final
d
74
Ge
SW
values.
7.2.3 Modeling scenarios
Threedifferentmodelingscenarioswereexploredtoassessthepotentialchanges
in global Ge isotope cycle that can best explain the glacial-interglacial paleorecord
of Mantoura (2006) described above. In each case, the model is initiated by ran-
domly selecting input parameter values (fluxes and their isotopic compositions)
201
from within the uncertainty in Table 7.1 (assuming a uniform distribution of val-
ues within the uncertainty of each parameter) and performing the steady state
calculations described above, at t = 0 y, corresponding to an age of 175 ka. At
this stage, the various D
74
Ge fractionation factors are assigned (Eqs. 7.4 and 7.6)
and kept constant throughout the modeled time period. A 5 ky model spin-up
(from 175 to 170 ka) is performed to ensure that the initial steady state condition
is satisfied. Different scenarios are explored by inducing a change in one or more of
the Si and Ge fluxes over time, as described below, and recalculating d
74
Ge
SW
for
each time step (set to 200 y) using Eq. 7.18. Utilizing a Monte Carlo approach,
each scenario described below is run 500 times to get a representative range of
d
74
Ge
SW
evolution curves, accounting for the uncertainty in both the steady state
Ge isotope budget and the possible glacial-interglacial changes to the budget.
Scenario 1: continental input changes only
Scenario1 exploresthepotential effectofglacial-interglacialchanges inthecon-
tinental Ge input (riverine, groundwater, and aeolian) on d
74
Ge
SW
and Ge/Si
SW
.
The most likely changes to Si fluxes have been estimated by Frings et al. (2016)
and shown to reproduce the observed change in surface seawater Si isotopic com-
position from the Last Glacial Maximum to present. Here, the same magnitude of
Si flux changes is invoked for the penultimate glacial-interglacial transition. The
potential changes in Ge/Si and d
74
Ge of the respective fluxes are also summarized
in Table 7.2. The timing of the changes is based on Vostok Antarctic ice core tem-
perature and dust concentration records (Petit et al., 1999). A summer surface
seawater temperature reconstruction was done by Schneider-Mor (2005) based on
diatom assemblage paleorecords from core ODP 1094, i.e. the same core on which
the d
74
Ge record of Mantoura (2006) is based. The SSST changes reconstructed
202
by Schneider-Mor (2005) closely resemble the Vostok DT (temperature relative to
Holocene) record of Petit et al. (1999), which was in turn used to establish the age
model of ODP 1094. Therefore only the Vostok data are plotted for reference in
Figs. 7.3-7.7). The estimated 2-10 fold increase in aeolian dust flux was applied
starting at 170 ka, reaching the maximum values at 160 ka and then decreasing
back to interglacial values during 140-130 ka (Fig. 7.3a). This timing is set to
correlate with the increased dust concentration observed in the Vostok ice core
(Petit et al., 1999). The dissolved and suspended river flux is set to glacial values
at the start of the model run at 170 ka and the change towards interglacial values
is timed to coincide with the positive temperature excursion, reaching the maxi-
mum difference at 130 ka and then decreasing back to glacial values by 110 ka.
The associated change in riverine Ge/Si and d
74
Ge has the same timing. Glacial
riverine Ge/Si is allowed to potentially go up as high as 1 mmol/mol (relative to
the modern interglacial value of 0.57 mmol/mol) and d
74
Ge to decrease by up to
1 % (Table 7.2). These changes are based on the observations in rivers drain-
ing glaciated regions, which exhibit high Ge/Si and low d
74
Ge values relative to
temperate and tropical rivers (see Chapters 5 and 6).
As Si input fluxes vary over time, the diatom burial flux responds as outlined
in Eqs. 7.1 and 7.12. Ge output flux is comprised of both diatom and sponge bSi
and authigenic burial. As the diatom burial flux varies, the authigenic burial flux
varies by the same magnitude, such that the diatom/authigenic burial ratio of Ge
remains constant throughout the model simulation, based on the ratio calculated
from the initial steady state budget (at t = 0). This assumption is reasonable
since both the bSi and the authigenic burial fluxes are heavily dependent on bSi
delivery to the seafloor and therefore should be coupled (Hammond et al., 2004).
Therefore, the Ge mass imbalance is calculated for each time step as:
203
Table 7.2: Changes in the global Si and Ge budget during glaciation, relative to
interglacial (contemporary) budget. Ge/Si and d
74
Ge changes justified in the text.
Used in modeling scenarios 1 and 3 (the total change in Ge flux and the timing of
the modeled changes is shown in Fig. 7.3b).
FSi,
§
Ge/Si, δ
74
Ge,
Tmol/yr µmol/mol ‰
Inputs
Rivers (dissolved load) RD ×0.8-1.3 0.57-1.0 1.6-2.6
Rivers (susp. amorphous silica) RB no change 0.57-1.0 1.6-2.6
Groundwater GW ×1-2 no change no change
Rivers (susp. lithogenic silicates) RS ×1-2 no change no change
Dust (aeolian) A ×2-10 no change no change
Hydrothermal (high temperature) HH no change no change no change
Hydrothermal (low temperature) HC no change no change no change
Outputs
Diatom bSi D m.b.* dynamic** Δ
D-SW
= 0
¥
Sponge bSi S no change
dynamic
†
Δ
S-SW
= -1 ±0.2
¥
Authigenesis (non-opal) NO -- m.b.* Δ
NO-SW
= -0.9 (-2.7 to 0)
¥
§
Si flux changes estimated by Frings et al. (2016)
* m.b. = mass balance
†
Calculated from sponge Ge/Si relationship with [Ge]
SW
, as reported by Ellwood et al. (2006)
Subscript
** During Scenario 1 (forward model) responds dynamically to inputs and depends on the Ge and Si mass
budgets. During Scenarios 2 and 3 (inverse model) is forced to match the paleorecord of Mantoura (2006).
See text for details.
¥
Unchanged from the contemporary (interglacial) values.
FGe
net
= FGe
in
–(FGe
D
+FGe
S
+FGe
NO
) (7.19)
where FGe
D
= Ge/Si
SW
×FSi
D
, FGe
S
= Ge/Si
S
×FSi
S
, and FGe
NO
= FGe
D
×
(FGe
NO
/FGe
D
)
ss
, and ss refers to the fluxes calculated at the initial steady state
(Eq. 7.2). The oceanic Ge mass inventory is recalculated for each time step as:
n
Ge
)
t
= n
Ge
)
0
+FGe
net
)
0
×dt (7.20)
204
Equations 7.16-7.18 are then used to calculate the seawater Ge isotope compo-
sition over time.
0
0.5
1
1.5
Dust, ppm
-10
-5
0
5
Δ T, °C
a
Δ T, °C
Dust, ppm
80 100 120 140 160
Age, ka
0
5
10
15
Ge flux, Mmol/y
b
MIS-5 MIS-6
Riv. (FGe
RD
)
Dust (FGe
A
)
Total continental
Figure 7.3: a) The relative Antarctic temperature (DT) reconstruction (red line)
and relative dust concentration in Antarctic ice (purple line, no scale shown) of
Petit et al. (1999); b) Continental Ge input variation in Scenarios 1 and 3. Shown
are the median (thick lines) and the 5-95th percentiles (thin lines) from a Monte
Carlo simulation (500 model runs). The increase in dust flux (Table 7.2) is timed
to coincide with the elevated dust concentration in Antarctic ice. The maximum
change in dissolved fluxes is timed to coincide with maximum temperature. The
vertical dashed line denotes the glacial-interglacial boundary between MIS-5 and
MIS-6.
205
Scenario 2: authigenic burial changes only
Scenario 2 explores the hypothesis that the observed Ge/Si
SW
and d
74
Ge
SW
variations are driven exclusively by changes in the authigenic Ge burial in marine
sediments, without any changes in the continental weathering Si or Ge fluxes, or
bSi burial. In this case, the observed Ge/Si
SW
record (Fig. 7.2a) is inversely
modeled to calculate the variation in authigenic Ge burial flux and its effect on
d
74
Ge
SW
. The Ge/Si record of Mantoura (2006) is fit with a local regression, using
weighted linear least squares and a second degree polynomial model (MATLAB
function smooth, method ’loess’) and additionally smoothed to reduce sharp slope
changes. The fractionation factor D
74
Ge
NO–SW
is calculated for the initial steady
state budget (Eq. 7.6) and assumed to stay constant with time. In this case, the
Ge mass inventory is calculated for each time step as:
n
Ge
)
t
= Ge/Si
SW
)
t
×n
Si
)
t
(7.21)
and the authigenic burial flux as:
FGe
NO
)
t
= FGe
in
)
t
–(FGe
D
+FGe
S
)
t
–
n
Ge
)
t
–n
Ge
)
0
dt
(7.22)
Then, d
74
Ge
SW
is calculated using Eqs. 7.17-7.18.
Scenario 3: continental and authigenic changes
Modeling scenario 3 assesses the combined effect of both the likely changes in
continental weathering inputs and the marine authigenic Ge sink on the evolution
of d
74
Ge
SW
across the penultimate deglaciation. The change in continental Si and
Ge inputs is the same as in Scenario 1 (Table 7.2). The authigenic burial flux
FGe
NO
is then calculated from inverse modeling of Ge/Si
SW
as in Scenario 2 (Eqs.
206
7.21 and 7.22). Therefore, d
74
Ge
SW
is affected by both changing continental input
and authigenic output.
7.3 Results and discussion
7.3.1 Steady state calculations: modern Ge isotope budget
The Ge isotope budget of the global ocean presented in Table 7.1 is very similar
to that previously published by Baronas et al. (2017a) but has been updated with
regards to the Ge isotopic composition of global riverine input (including reduced
uncertainty) based on the compilation presented in Chapter 6. This updated bud-
get enables re-calculation of the fractionation factor D
74
Ge
NO–SW
associated with
the authigenic (non-opal) Ge sink in marine sediments, assuming that the contem-
porary ocean is in steady state (Eq. 7.6). The distribution of calculated values
is shown in Fig. 7.4a. The median calculated value was -0.9 % with a 95 %
confidence interval of -2.7 to 0 %. This value is slightly lower than the -0.6±
1.8 % previously calculated by Baronas et al. (2017a) (Chapter 2). The new
estimate has >95 % probability of having a negative D
74
Ge
NO–SW
value, with
important implications for the interpretation of d
74
Ge
SW
paleorecords, discussed
below. Importantly, the median -0.9 % estimate presented here is in excellent
agreement with the fractionation observed in marine sediment pore waters (Chap-
ter 3), adding confidence to the best-estimate FGe and d
74
Ge values presented in
Table 7.1.
The estimated authigenic fraction of total Ge burial ranges between 60-88%
(95% C.I.) with a median value of 77%, indicating the dominance of this process
in the removal of dissolved Ge from seawater.
207
-4 -2 0
Δ
74
Ge
NO-SW
0
20
40
60
80
100
120
Probability density
a
0.4 0.6 0.8 1
FGe
NO
/FGe
out
0
5
10
15
20
25
30
35
40
45
50
Probability density
b
Figure 7.4: Distribution of calculated a) authigenic fractionation factor
D
74
Ge
NO–SW
(Eq. 7.6); and b) authigenic fraction of total Ge burial, taking
into account the uncertainties in the contemporary Ge isotope budget (Table 7.1)
using a Monte Carlo approach (2000 runs).
7.3.2 Glacial-interglacial seawater Ge isotope dynamics
Scenario 1: continental input changes only
The Scenario 1 model results are shown in Fig. 7.5. Overall, the modeled
changes in weathering inputs have a relatively small effect on Ge/Si
SW
with a
slight minimum during the glacial-interglacial transition (Fig. 7.5b). Modeled
d
74
Ge
SW
shows a negative excursion during the dusty part of the MIS-6 glacial
period (160-140 ka), and a positive excursion during the deglaciation at∼130 ka
(Fig. 7.5c). The few data points available during the early glacial do not indicate a
negative excursion, perhaps even the opposite. The timing of the modeled positive
excursion during the deglaciation, however, quite closely matches the paleorecord
data. The magnitude of the modeled excursion is also similar to the paleorecord
208
data. However, the median absolute d
74
Ge
SW
maximum (thick line in Fig. 7.5c)
is well below the data, due to the more negative baseline during MIS-6.
Overall, Scenario 1 demonstrates the limited impact of changing continental
fluxes on d
74
Ge
SW
, which could partly explain the relatively invariant d
74
Ge
SW
values observed in the paleorecord. However, this scenario fails to reproduce the
observed range and timing of Ge/Si
SW
variations (Fig. 7.5b), and therefore addi-
tional processes need to be invoked to explain both the Ge/Si and delta
74
Ge sig-
natures recorded in the sediments.
Scenario 2: authigenic burial changes only
The Scenario 2 model results are shown in Fig. 7.6. The model is forced
to faithfully reproduce the Ge/Si
SW
paleorecord by adjusting the authigenic (non-
opal) Ge burial flux (FGe
NO
). Due to the large absolute FGe
NO
values, dominating
the total Ge removal from seawater (see discussion above), only a small perturba-
tionof∼2Mmol/yisrequiredtoreproducethelargeGe/Si
SW
changes. TheFGe
NO
minimum coincides closely with the maximum temperature anomaly at the begin-
ning of MIS-5 (Fig. 7.6), supporting the negative temperature-FGe
NO
relationship
proposed by Hammond et al. (2004) as an explanation for the glacial-interglacial
Ge/Si
SW
variations (Fig. 7.1).
Because of its importance as a Ge sink, authigenic burial could potentially
have a much larger influence on d
74
Ge
SW
. However, due to the fact that d
74
Ge
NO
depends directly on d
74
Ge
SW
(Eq. 7.6), authigenic burial can only affect d
74
Ge
SW
transiently, as long as there is an imbalance between Ge inputs and outputs (indi-
cated by changing Ge/Si
SW
). Therefore, the size of any authigenic burial-driven
209
0
5
10
15
20
Continental Ge input
(FGe
cont
), Mmol/y
-10
-5
0
5
ΔT, °C
a
MIS-5 MIS-6
FGe
cont
Δ T
Dust conc.
0.3
0.4
0.5
0.6
0.7
0.8
Ge/Si, μmol/mol
b
80 100 120 140 160
Age, ka
2.5
3
3.5
4
δ
74
c
Figure 7.5: Model Scenario 1. Shown are the median (thick lines) and the 5-95th
percentiles (thin lines) from a Monte Carlo simulation (500 model runs). a) The
continental (riverine, groundwater, and dust) Ge input (orange lines), as described
in Table 7.2. The relative Antarctic temperature reconstruction (red line) and
relative dust concentration in Antarctic ice (purple line, no scale shown) of Petit
et al. (1999) are also plotted for reference; b) The Ge/Si of seawater is forward-
modeled and depends on the inputs, green lines show multiple model runs; c) The
calculated seawater d
74
Ge for multiple model runs (green lines). The paleorecords
of Mantoura (2006) are also plotted as black circles in panels b and c. The vertical
dashed line denotes the glacial-interglacial boundary between MIS-5 and MIS-6.
d
74
Ge
SW
excursion is strongly dependent on the rapidity of the Ge budget pertur-
bation and the D
74
Ge
NO–SW
value. Within the 95% C.I., Scenario 2 can repro-
duce the size of the positive d
74
Ge
SW
excursion (Fig. 7.6c). However, the modeled
210
excursion is very gradual, which is largely due to the linear interpolation in the
data-poor section of the record between 160 ka and 140 ka. Nevertheless, such
gradual Ge/Si decrease matches the pattern observed in other Ge/Si records (see
Fig. 7.1, for example). Independent of the pattern at 160-140 ka, the d
74
Ge
SW
maximum always occurs about 10 ky too early, suggesting that the oceanic Ge
isotope composition is unlikely to be driven solely by variations in FGe
NO
.
Scenario 3: continental and authigenic changes
The Scenario 3 model results are shown in Fig. 7.7. In this case, both continen-
tal input and authigenic burial fluxes are varied to the same extent as described in
Scenarios 1 and 2 above. Expectedly, the resultingd
74
Ge
SW
curve is a combination
of those shown in Figs. 7.5 and 7.6 and does a slightly better job at reproducing
the paleorecord than either of the previous scenarios. During MIS-6, increasing
authigenic burial counteracts the increasing dust flux, resulting in a smaller neg-
ative d
74
Ge
SW
excursion, in better agreement with the paleorecord. During the
deglaciation, a small positive excursion is still observed, although its magnitude is
underpredictedwithin the95% C.I.In termsof timing, the excursionmedian shows
a much better agreement with the paleorecord, relative to Scenario 2. It is, how-
ever, notable that as small as the d
74
Ge
SW
excursion measured in the paleorecord
is, all of the tested scenarios struggle to reproduce its magnitude. This could sug-
gest either that the timing and more importantly the magnitude of changes in the
continental Si and Ge inputs is underestimated. Recently, glacial-interglacial vari-
ations in hydrothermal fluid fluxes have been proposed (Lund et al., 2016; Huybers
& Langmuir, 2017). Given the potentially large Ge inputs from ridge-flank fluid
circulation (Table 7.1), such variations may have an outsize influence the oceanic
Ge isotope budget.
211
-4
-2
0
2
4
6
8
Change in authigenic Ge
burial (Δ FGe
no
), Mmol/y
-10
-5
0
5
ΔT, °C
a
MIS-5 MIS-6
Δ FGe
no
Δ T
0.3
0.4
0.5
0.6
0.7
0.8
Ge/Si, μmol/mol
b
80 100 120 140 160
Age, ka
2.5
3
3.5
4
δ
74
c
Figure 7.6: Model Scenario 2. Shown are the median (thick line) and the 5-95th
percentiles (thin lines) from a Monte Carlo simulation (500 model runs). a) The
global Ge isotope budget is driven by changes in the authigenetic (non-opal) Ge
sink (green lines). The relative Antarctic temperature reconstruction of Petit et
al. (1999) is also plotted for reference (red line); b) The non-opal flux is calculated
by inverse modeling the Ge/Si paleorecord, i.e. forcing Ge/Si
SW
to always follow
the orange line; c) The calculated seawater d
74
Ge (green lines). The paleorecords
of Mantoura (2006) are also plotted as black circles in panels b and c. The vertical
dashed line denotes the glacial-interglacial boundary between MIS-5 and MIS-6.
To summarize, the most robust interpretation of the paleorecords of Mantoura
(2006) is as follows. Ge/Si
SW
is primarily driven by changes in the ratio of authi-
genic to biogenic Ge burial fluxes. Warmer ocean water results in faster bSi rem-
ineralization in the water column and a reduced supply of dissolved Ge to pore
212
waters, where authigenic minerals precipitate (Hammond et al., 2004). Therefore,
during the glacial termination the relative proportion of bSi that dissolves within
the marine sediments decreases, increasing the biogenic to authigenic Ge burial
ratio and resulting in a Ge/Si
SW
rise. The effect of this on d
74
Ge
SW
is limited to
transient timescales over which the authigenic/biogenic burial ratio is changing.
While shifts in continental Ge inputs (or their d
74
Ge) can have a some influ-
ence on d
74
Ge
SW
, this influence is limited by the relatively small difference (0.6
%) between riverine d
74
Ge and d
74
Ge
SW
. Furthermore, Baronas et al. (2016)
previously proposed that the authigenic Ge burial flux is coupled to the supply
of detrital particulates to the ocean, as authigenic mineral precipitation is often
limited by dissolved Fe or Al which are primarily supplied by continental detrital
material (e.g., Aller, 2013). This mechanism is fully consistent with the observed
Ge/Si
SW
decrease during MIS-6 (160-140 ka) when dust supply to the ocean was
significantly higher (Fig. 7.3a). As a result, the enhanced supply of low d
74
Ge
by dust is counteracted by the enhanced authigenic burial of isotopically light Ge,
explaining the remarkably stable d
74
Ge
SW
composition over glacial-interglacial
cycles. The∼0.5 %/ positive excursion measured at T2, if representative of the
global ocean composition, remains to be fully explained.
7.3.3 Implications for the interpretation of other paleo-
records
As argued above, in the context of our current understanding of the global
Ge isotope budget, observable d
74
Ge
SW
variations are only expected for rapid
and large scale perturbations of the global Ge cycle. This behavior is distinct
from that of d
30
Si recorded by diatoms, which shows consistent and sustained
differences between glacial and interglacial cycles (Fig. 7.2c; see also Section
213
-4
-2
0
2
4
6
8
Change in authigenic Ge
burial (Δ FGe
no
), Mmol/y
0
5
10
15
20
Continental Ge input
(FGe
cont
), Mmol/y
a
MIS-5 MIS-6
FGe
cont
FGe
no
0.3
0.4
0.5
0.6
0.7
0.8
Ge/Si, μmol/mol
b
80 100 120 140 160
Age, ka
2.5
3
3.5
4
δ
74
c
Figure 7.7: Model Scenario 3. Shown are the median (thick line) and the 5-95th
percentiles (thin lines) from a Monte Carlo simulation (500 model runs). a) The
global Ge isotope budget is driven by changes in both the continental inputs and
the authigenetic (non-opal) Ge sink; b) Same as Scenario 2, the non-opal flux
is calculated by inverse modeling the Ge/Si paleorecord, i.e. forcing Ge/Si
SW
to
always follow the orange line; c) The calculated seawater d
74
Ge (green lines). The
paleorecords of Mantoura (2006) are also plotted as black circles in panels b and c.
The vertical dashed line denotes the glacial-interglacial boundary between MIS-5
and MIS-6.
7.1). Many studies have demonstrated that diatom-recorded d
30
Si composition
is strongly influenced by dissolved Si depletion in the surface ocean due to bio-
logical d
30
Si fractionation by diatoms (e.g., De La Rocha et al., 1998; Cardinal et
214
al., 2005; Reynolds et al., 2006). Therefore, d
30
Si paleorecords, including the one
shown in Fig. 7.2c, are strongly influenced by a local biological signal, potentially
overprinting any secular changes in whole ocean d
30
Si. However, the continental
input changes in Si and Ge tested in Scenarios 1 and 3 are very similar to that
tested by Frings et al. (2016) for d
30
Si shift during the most recent deglaciation
(T1). The fact that Scenario 3 is struggled to reproduce the d
74
Ge record of
ODP 1094 suggests that the glacial-interglacial variability in continental Si inputs
prescribed by Frings et al. (2016) (Table 7.2) may be underestimated, or that
weathering system experienced a larger perturbation during glacial termination
T2 relative to T1. Future studies of d
74
Ge paleorecords during T1, as well as
a fully coupled d
74
Ge-d
30
Si ocean model would help further constrain the likely
boundaries of glacial-interglacial variations in continental and weathering fluxes.
Over million year timescales, long-term changes of d
74
Ge
SW
could take
place as a result of either shifting continental/hydrothermal input ratio, bio-
genic/authigenic burial ratio, sponge/diatom bSi burial ratio, or a combination
of the above. A change in the fractionation factors, either during weathering-
associated secondary mineral precipitation, or authigenic mineral precipitation in
marine sediments, may also play a role, although the former is likely more sensi-
tive to climatic changes than the latter. Despite the large number of unknowns,
a consistent interpretation of a combined Ge/Si, d
74
Ge, and d
30
Si record would
greatly reduce the number of possible scenarios. For example, a recent study by
Fontorbe et al. (2016) has shown that d
30
Si
SW
has changed very little since 60 Ma,
suggesting that diatom expansion and therefore diatom/sponge ratio in bSi burial
has also remained relatively constant throughout the Cenozoic. In turn, this would
indicate that the million-year variations in Ge/Si over the late Cenozoic (ranging
from 0.5 to 1.0 mmol/mol; Shemesh et al. (1989)) reflect abiotic processes, such as
215
shifting proportions of continental/hydrothermal inputs or biogenic/authigenic Ge
burial. Due to the short Ge residence time in the ocean (Baronas et al., 2017a)
and the relatively small isotopic contrast between the different input and out-
put fluxes (Table 7.1), detectable variations in d
74
Ge
SW
over the Cenozoic would
indicate significant large scale perturbations to the global Ge cycle. A Cenozoic
record of d
74
Ge
SW
might therefore provide an upper limit estimate on changes
in weathering, hydrothermal, and authigenic fluxes associated with the tectonic
rearrangement and long-term cooling that took place over this period.
7.4 Conclusions
In this study, an updated global Ge isotope budget was used to model seawater
Ge/Siandd
74
Geresponsetopotentialglacial-interglacialchangesintheinputsand
outputs of Ge and Si. Assuming that the contemporary ocean is at steady state,
Ge isotope fractionation during authigenic mineral precipitation was calculated as
-0.9 % (95% confidence interval of -2.7 to 0 %), in excellent agreement with a
recent marine sediment pore water study (Chapter 3).
Several different modeling scenarios were tested for the interpretation of Ge/Si
andd
74
Gepaleorecordsacrossthepenultimatedeglaciation(Mantoura,2006). The
observed 0.2 mmol/mol increase in Ge/Si requires a decrease in the authigenic
(non-opal) Ge burial in marine sediments. However, neither continental input
nor authigenic output variations, nor combination of the two, could reproduce
both the magnitude and the timing of positive∼0.5 % d
74
Ge
SW
excursion at 130
ka. This may suggest that glacial-interglacial weathering variations are larger than
estimated previously (e.g., Frings et al., 2016) or that other fluxes (e.g., ridge-flank
hydrothermal inputs) vary over the same timescales.
216
The calculated variations in the authigenic Ge sink are consistent with changes
inglobaltemperature(whichcontrolstheamountofbiogenicsilicareachingmarine
sediments and therefore Ge supply to pore waters) as proposed previously by Ham-
mond et al. (2004), as well as changes in detrital inputs (which controls Al and Fe
supply to marine pore waters), as proposed by Baronas et al. (2016).
Overall, temporal d
74
Ge
SW
variability is muted due to the short Ge residence
time in the ocean and the isotopic similarity between different Ge sources and
sinks. Ge isotope paleorecords should therefore be relatively insensitive to small
scale perturbations and may help constrain the upper limits of climatic or tectonic
effects on the global Ge and Si cycle. Finally, due to the relative lack of bio-
logical fractionation, d
74
Ge
SW
should primarily track secular changes in seawater
composition, providing a valuable counterpart to d
30
Si records which are strongly
influenced by biological productivity.
Acknowledgements
Financial support was provided by NSF grants OCE 1061700 and 1260692 to
DEH. JJB was additionally supported by the John Montagne Award from GSA
Quaternary Geology and Geomorphology Division. We thank Samia Mantoura
and Albert Galy for sharing unpublished data and manuscripts.
217
Chapter 8
Conclusions
The aim of this PhD dissertation was twofold: 1) to establish the global sys-
tematics of Ge isotope behavior in terrestrial and marine low temperature envi-
ronments, laying the foundations for its use as a paleoproxy for the global Si cycle;
and 2) to demonstrate the utility of a coupled Ge-Si isotope multiproxy approach
in unraveling the complex range of processes affecting Ge and Si cycling in nature.
To this end, Chapter 2 presented the first d
74
Ge data from a number of
analytically challenging low temperature fluid samples, including seawater and
river water. These data allowed me to estimate the isotopic composition of all the
major oceanic Ge sources (rivers and hydrothermal fluids) and one of the two sinks
(biogenicopal), establishingtheglobalmarineGeisotopebudget. Itpredictedthat
the other major process removing Ge from seawater – authigenic Ge precipitation
in marine sediments – must preferentially remove light Ge isotopes, if the global
isotopic balance is to be maintained.
InChapter 3, I sought to test this prediction by investigating d
74
Ge dynamics
in marine pore waters and benthic fluxes in the continental margins in Southern
California and the Gulf of Mexico. In these sediments, reductive dissolution of Fe
oxides releases isotopically light Ge to the pore waters. In deeper sediments, Ge
ultimately ends up being removed from pore waters via the precipitation of authi-
genic aluminosilicate clays. The latter process is indicated by the fractionation of
pore water d
30
Si and Ge/Si composition but has no effect on d
74
Ge. As the result
of these two processes, authigenically buried Ge ends up being isotopically lighter
218
than seawater, in good agreement with the global budget presented in Chapter 2
and later updated in Chapter 7.
Chapters 4, 5, and 6 focused on the d
74
Ge, d
30
Si, and Ge/Si dynamics during
continental weathering. In Chapter 4, a detailed study of soils and streams
in the tropical, supply-limited weathering environment of La Selva, Costa Rica
was presented. The stream d
74
Ge, d
30
Si, and Ge/Si composition at this site is
controlled by the mixing of two types of distinct fluids. The first type represents
the weathering of the chemically depleted lowland soils that exhibit low d
74
Ge and
d
30
Si signatures. The second type is isotopically heavy interbasin groundwater,
derived from the weathering of fresh volcanic rock. I used different modeling
approachestocalculatetheisotopicfractionationfactorsassociatedwithsecondary
weathering reactions in both environments. Modeling results showed that the
precipitation of Fe oxides and/or aluminosilicate clays preferentially removes light
Ge and Si isotopes from solution. The magnitude of fractionation observed in the
fluids and the solids, however, depends on the chemical mobility of each element.
Due to the high degree of Ge retention in secondary products, weathering fluid
d
74
Ge signatures are more strongly fractionated than d
30
Si.
Chapter 5 describes a study of Ge behavior at the other extreme of climatic
conditions, during glacial weathering in West Greenland. The streams were shown
to exhibit elevated Ge/Si, in agreement with previous studies in glaciated environ-
ments, likelyduetobothenhancedbiotitedissolutionandlimitedsecondaryweath-
ering product precipitation. The Watson river in Greenland exhibited the lowest
d
74
Ge signature of all global rivers, once again consistent with limited secondary
phase precipitation and associated isotope fractionation. A long-term river sedi-
ment incubation experiment allowed me to directly observe the increasing Ge/Si
and d
74
Ge fractionation during continued water-rock interaction. The Ge isotope
219
fractionation factor determined from the incubation experiment agrees well with
thevaluemeasuredduringexperimentalFeoxideprecipitationbyotherresearchers,
and is similar to that calculated based on the field data presented in Chapter 4.
These results further support the importance of Fe redox chemistry in affecting Ge
isotope distribution in nature.
Chapter 6 presented an overview of d
74
Ge signatures in a large number of
world’s major and minor rivers. An updated estimate of the global riverine flux
d
74
Ge was given. A direct comparison was made with Ge/Si and d
30
Si signa-
tures of the same samples. Co-variation of all three proxy signatures confirmed
the dominant role of secondary weathering product precipitation in determining
riverine composition. Notable exceptions were observed where biological uptake
strongly fractionated d
30
Si and Ge/Si but not d
74
Ge. Finally, a promising corre-
lation was demonstrated between d
74
Ge composition and the chemical weathering
intensity of a given watershed, demonstrating the potential for d
74
Ge to be used
as a weathering regime tracer.
Finally, in Chapter 7, I used an updated version of the global Ge isotope
budget to investigate the potential drivers of seawater Ge/Si and d
74
Ge compo-
sition over glacial-interglacial cycles, as evidenced in previously presented paleo-
records. To this end, I used a simple ocean box model, testing how changes to
various oceanic Ge input and output fluxes might affect the seawater composi-
tion. I show that the remarkably stable seawater d
74
Ge composition across the
penultimate glacial-interglacial cycle results from the similar d
74
Ge composition
of various input fluxes and limited Ge isotope fractionation in the ocean, coupled
with short Ge residence time in the ocean. In contrast, seawater Ge/Si is sensi-
tive to relatively small changes in the authigenic Ge burial flux, explaining the
large systematic variations over the past several glacial-interglacial cycles. Based
220
on the good agreement between seawater and sediment diatom signatures, d
74
Ge
paleorecords should be insensitive to local biological overprinting and could help
identify large magnitude secular changes in the global Ge and Si cycles.
In summary, this dissertation presents the first in-depth exploration of Ge iso-
tope systematics during low temperature Earth surface processes. Through several
field and experimental studies in terrestrial and marine environments, this work
has now established the basics of d
74
Ge behavior during rock weathering on the
continents and sediment diagenesis in the oceans. I showed how the multi-tracer
approach of simultaneously utilizing d
74
Ge, d
30
Si, and Ge/Si data can yield a
deeper understanding of the various biogeochemical processes in play. Finally, the
global Ge isotope budget was established and used for a preliminary exploration
of the paleo-proxy potential of coupled d
74
Ge and Ge/Si signatures recorded in
marine sediments.
The exploration of Si isotopes and especially Ge isotopes as natural process
tracers has begun only recently, and my hope is that this work will serve as a
useful stepping stone for continued development of these biogeochemical tools.
221
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Appendix A
A global Ge isotope budget:
Supplementary material
A.1 Extended methods
A.1.1 Ge concentration analysis
Ge concentration in samples was analyzed using a previously described isotope
dilution hydride generation-ICP-MS method (Mortlock & Froelich, 1996; Ham-
mond et al., 2000). Briefly, each sample was spiked with a known amount
70
Ge to
obtain a final
70
Ge/
74
Ge ratio of 2-10 and allowed to equilibrate overnight. The
samples were degassed with He for 2 min and 0.5 mL of 10 mM NaBH
4
in 0.04
M NaOH was injected to convert dissolved Ge(OH)
4
species to gaseous hydride
(GeH
4
). The hydride was carried with a He gas stream and collected in a liquid
N
2
trap for 3 min. The trap was then removed and the He stream carried GeH
4
,
through a NaOH pellet-filled moisture trap, into the ICP-MS. Liquid N
2
trapping
allows sample pre-concentration, reducing detection limit, and also achieves chro-
matographic separation of the inorganic and the methylated Ge species. The Ge
concentration was calculated from the measured
70
Ge/
74
Ge ratio.
244
A.1.2 Ge isotopic analysis
Hydrothermal fluids
Hydride generation and mass spectrometry. Ge isotopic composition was
analyzed using a previously described double-spike technique (Siebert et al., 2006,
2011). Briefly, a double isotopic spike (
70
Ge/
73
Ge = 1) was added to each sample
to obtain a final
70
Ge/
72
Ge ratio in the range of 2-5 (2-8 for standards). The dou-
ble spike used here was calibrated previously as described in Siebert et al. (2006,
2011). Allsampleswereallowedtoequilibratewiththespikefor24hoursorlonger.
Dissolved Ge was converted to GeH
4
using an online hydride generation method
and transported using He as a carrier gas into a Nu Instruments multi-collector
inductively coupled plasma mass spectrometer (MC-ICP-MS) in the W.M. Keck
Collaboratory at Oregon State University. The absence of methylated Ge species
was confirmed during offline hydride generation and Ge concentration measure-
ments. Each sample was bracketed with a similarly double spiked BCR-3 standard
prior to and after analysis to correct for instrument drift (Fig. A.1). The isotopic
composition of each sample and standard was calculated using the double-spike
data reduction scheme outlined previously by Siebert et al. (2006, 2011) and nor-
malized to NIST 3120a Ge standard by applying a 0.6 % conversion factor from
the OSU standard (Table A.1).
River and seawater samples
Sample preparation and hydride generation. After the Mg precipitate had
been dissolved in 1-10 mL conc. teflon-distilled HNO3, the solution was diluted to
20-25 mL with Milli-Q water, adding 5 mL of 1.9 M TRIS-HCl and 0.5mL of 0.2 M
245
-2.5
-2.0
-1.5
-1.0
-0.5
0.0
0.5
1.0
0 10 20 30 40 50 60 70 80 90
δ
74
Ge, ‰
Measurement
OSU
NIST 3120a
"Spex"
AlfaAesar
Hydrothermal fluid measurements
River and seawater measurements
Figure A.1: Standard reproducibility over time. Error bars represent 2 S.D. uncer-
tainty, determined from standard replicates within each analytical session. The
hydrothermal fluid samples were measured over one session and bracketed using
BCR-3 as the standard. The river and seawater samples were measured during
three separate analytical sessions and bracketed using NIST 3120a as the stan-
dard. Two more standards were measured to determine accuracy. Solid line indi-
cates mean isotopic composition for each standard (NIST 3120a by definition as 0
%) and the dashed lines indicate long-term 2 S.D. uncertainty for each standard.
The gray dashed symbols were treated as outliers and not used in sample bracket-
ing. However, the two NIST 3120a outliers were used in calculating reproducibility,
resulting in an increased uncertainty for all samples measured during that given
analytical session.
Table A.1: Determined mean isotopic composition of standard materials.
Standard d
74
Ge
NIST
± 2 S.D. n Previously reported
a
NIST 3120a 0.00 0.22 / 0.50
b
15
OSU -0.59 0.29 21 n/a
AlfaAesar -1.60 0.24 5 n/a
"Spex" -1.04 0.45 4 -0.71± 0.21
a
Escoube et al. (2012)
b
Depending on the analytical session.
246
EDTA, and neutralized to pH 6-7 with clean NH
4
OH, which was prepared by bub-
bling analytical grade NH
3
vapor through Milli-Q water. The total procedural Ge
blank was less than 20 pg, typically <0.1% of sample. Each sample was converted
to hydride using a procedure similar to that described above for Ge concentration
measurements, with increased He purging time of 3 min, injection of 1.5mL of
10 mM NaBH
4
in 0.04 M NaOH and hydride collection on a liquid N
2
trap for 7
min. The trap allowed chromatographic separation of organic (monomethyl- and
dimethyl-) and inorganic Ge hydrides with a separation of at least 10 s. Using
He as carrier gas, the inorganic GeH
4
was collected into a 1L Tedlar gas bag and
the organic peak was directed to an ICP-MS to confirm separation and that no
spike equilibration occurred with the organic species, which was the case for all
the samples. Samples were converted into hydride 1-3 days prior to analysis. Over
this time, most of the He contained in the Tedlar bags had diffused out but we
found that the GeH
4
concentrations remained stable for at least 2 weeks. Tedlar
bags were re-filled with 0.5-1 L He prior to analysis.
Multi-collector mass spectrometry. Ge hydride was introduced into the
plasma by applying pressure to Tedlar bags and mixing the expelled sample gas
with Ar carrier gas prior to entering the ICP, without a nebulizer. The rate of sam-
ple introduction was controlled by adjusting the pressure (weight) on the Tedlar
bag.
Prior to the measurement, the sample inlet system was flushed with He for
5-10 min, until the memory effect from the previous sample was less than 1-2 mV.
This on-peak background was measured every few samples and subtracted from
the total signal. Analysis was done in 40-80 blocks of 2 s each, resulting in 80-160
s measurement time per sample. Typical background for all cups was less than 1-2
247
1.360
1.364
1.368
1.372
1.376
0 0.2 0.4 0.6 0.8 1
Measured
74
Ge/
72
Ge ratio
72
Ge signal, V
3.95
3.96
3.97
3.98
3.99
4.00
4.01
0 0.2 0.4 0.6 0.8 1
Measured
73
Ge/
72
Ge ratio
72
Ge signal, V
4.06
4.07
4.08
4.09
4.10
0 0.2 0.4 0.6 0.8 1
Measured
70
Ge/
72
Ge ratio
72
Ge signal, V
Figure A.2: The effect of signal intensity on the measured Ge isotope ratios. The
dashed lines show the ratio determined from the linear fit to the two isotope signal
intensities plotted against each other (see Fig. A.4).
248
mV (~0.1 - 1 % of sample signal), mostly due to memory effect from the previous
sample. On-peak electronic background was measured for 1 s before each block
deflecting the ESA. The strength of the interference at m/z 72 was dependent on
the Ar gas flow and the type of cones. The additional Ar gas flow was optimized
to reduce this interference to 0.4 mV while minimizing loss of sensitivity. Due to
the small amount of Ge in each sample, the signal strength could not be increased
enough to render the interference negligible throughout each run. We therefore
employed an alternative approach, utilizing the fact that the interference effect
on the measured isotope ratios depended on the signal strength (Fig. A.2). We
measured a large range of signal intensities for each sample (Fig. A.3), which
allowed for the full correction of the interference at m/z = 72 (see below).
249
0 10 20 30 40 50 60
0
0.2
0.4
0.6
0.8
1
Block #
Ge signal, V
Figure A.3: Typical range of measured signal intensities for a given sample. Inten-
sity jumps represent deliberate changes in sample delivery rate.
Data workup. Variations in Ge hydride flow and signal perturbations unavoid-
ably result in higher isotopic errors, especially at the points where the signal was
changed rapidly (Fig. A.3). Any such data blocks that were clear outliers relative
to the surrounding data blocks were therefore removed and the linear regression
(Fig. A.4) repeated with the remaining data. The isotopic ratios obtained this
way are independent of signal intensity and the interference at m/z = 72. Finally,
the d
74
Ge for each sample was calculated from these ratios by normalizing to the
mean isotope ratios determined for the NIST 3120a standard during the same
analytical session. During seawater and river water measurements, bracketing was
done only every few samples but instrument drift was monitored using additional
isotopic standards and no corrections were deemed necessary over the three days
of analyses (Fig. A.1).
250
0 0.2 0.4 0.6 0.8
0
0.5
1
1.5
2
72
Ge signal, V
74
Ge signal, V
0 0.2 0.4 0.6 0.8
0
1
2
3
72
Ge signal, V
73
Ge signal, V
0 0.2 0.4 0.6 0.8
0
1
2
3
72
Ge signal, V
70
Ge signal, V
Figure A.4: An example of a linear regression that was used to obtain the
interference-free isotopic ratios. Gaps in groups of data points reflect abrupt
changes in pressure applied to expel sample gas from the bag, with dense data
clusters reflecting a slow decline in gas delivery rate as bag deflates.
251
A.2 Supplementary data for river samples
252
Table A.2: Supplementary river chemistry data.
Collection date Na, mM Mg, mM Ca, mM K, mM pH Susp. load, g/L
California
Kern River (North fork) 2009-03-21 384 79 343 38 n/m n/m
Kern River (South fork) 2009-03-21 n/m 202 674 75 7.7 n/m
Kern River (Lower) 2009-03-21 596 105 430 44 8.0 n/m
San Gabriel River (North fork) 2013-07-28 475 484 1063 112 n/m n/m
San Gabriel River (West fork) 2008-12-20 584 566 1041 69 n/m n/m
San Gabriel River (Lower) 2013-11-21 n/m n/m n/m n/m n/m n/m
Hondo River 2008-12-20 693 606 1141 75 n/m n/m
Andes-Amazon (Peru)
Kosnipata Stream (MMD-02) 2013-08-11 162 189 247 7 n/m 0.03
Carbon Stream (MMD-06) 2013-08-12 150 93 217 28 7.6 n/m
Madre de Dios River (MMD-28) 2013-08-15 92 86 344 26 8.0 1.07
Inambari River (MMD-29) 2013-08-17 94 51 116 17 7.2 0.29
Madre de Dios River (MMD-32) 2013-08-18 112 81 270 26 n/m 0.30
Piedras River (MMD-34) 2013-08-19 318 182 427 59 n/m 0.13
Hawai’i
Molokai Spring 2009-03-31 n/m 610 377 184 n/m n/m
Iao River 2009-03-31 247 130 188 15 7.9 n/m
Other
Mississippi Rivier 2011-11-08 1369 884 1382 137 n/m n/m
253
Table A.3: Isabella Dam reservoir mass balance (Kern river)
Site Discharge, m
3
/s
a
Ge, pM Si, mM Ge/Si, mmol/mol d
74
Ge, %
North Fork, measured 8.64
b
614 326 1.88 2.75± 0.22
South Fork, measured 1.62
c
97 360 0.27 5.36± 0.38
North + South Forks, calculated
d
10.26 532 332 1.60 2.82± 0.44
Lower Kern (dam outlet), measured 10.90
e
298 36 8.28 3.04± 0.22
a
Avg. for Oct 2008 - Sep 2009.
b
http://waterdata.usgs.gov/nwis/inventory/?site_no=11186000
c
http://waterdata.usgs.gov/nwis/inventory/?site_no=11189500
d
Calculated concentrations and isotopic composition are discharge-weighted.
e
http://waterdata.usgs.gov/nwis/nwisman/?site_no=11192500
254
A.3 Low-temperature hydrothermal fluid frac-
tionation model
The amount of Ge originally released from basalt alteration and then precip-
itated with secondary hydrothermal minerals can be estimated from Si and Mg
concentrations, assuming that these three elements are removed from solution by
secondary silicates. The calculation also assumes that a negligible amount of the
Mg precipitated comes from the rock, and that dissolution of the rock yields Ge/Si
equal to that of the bulk rock. Using an approach previously outlined in Wheat &
McManus (2008), the amount of Si originally released from basalt alteration can
be estimated as follows:
Si
released
= (Si/Mg)
mineral
×([Mg]
SW
–[Mg]
fluid
)+[Si]
fluid
(A.1)
where[Mg]
SW
=54mM,"fluid"referstothehydrothermalformationfluids,and
"mineral"referstoasecondaryhydrothermalMg-silicateresponsibleforMg,Si,and
Ge sequestration. Although the actual Si/Mg ratio of minerals forming at each site
is unknown, the reasonable range can be captured by assuming the stoichiometry
of either saponite (Si/Mg = ~2) or celadonite (Si/Mg = ~8) (Schramm et al.,
2005). The amount of Ge released is then:
Ge
released
= Si
released
×Ge/Si
basalt
(A.2)
where Ge/Si
basalt
= 2.4 mmol/mol (Mortlock & Froelich, 1987). The fraction of
Ge remaining in hydrothermal fluids is calculated as:
fGe
diss
=
[Ge]
fluid
Ge
released
(A.3)
255
The original Ge concentration in seawater is negligible compared to that
released from basalt alteration. Combining Equations A.1-A.3 yields:
fGe
diss
=
[Ge]
fluid
Ge/Si
basalt
×((Si/Mg)
mineral
×([Mg]
SW
–[Mg]
fluid
)+[Si]
fluid
)
(A.4)
Thisapproachcanbefurthertestedbycalculatingd
74
Ge
fluid
thatisobtainedby
applying an independently determined fractionation factor. The isotopic composi-
tion of the fluids can be predicted based on the starting composition (d
74
Ge
basalt
= 0.56 %; Escoube et al. (2012)) and fGe
diss
. An assumption needs to be made
whether the system behaves like a Rayleigh distillation (Eq. A.5), where the solid
products do not re-equilibrate with the liquid reactants, or as a batch reactor (Eq.
A.6), where the solids and liquids are allowed to exchange (e.g., Johnson et al.,
2004).
d
74
Ge
fluid
≈ d
74
Ge
basalt
+D
74
Ge
mineral–fluid
×ln(fGe
diss
) (A.5)
d
74
Ge
fluid
≈ d
74
Ge
basalt
–D
74
Ge
mineral–fluid
×(1–fGe
diss
) (A.6)
Several isotopic fractionation factors for Ge incorporation into secondary min-
eral phases have been determined previously. Based on reduced partition function
ratios, Li et al. (2009) and Li & Liu (2010) estimated D
74
Ge
mineral–fluid
values for
solutions at equilibrium with several different Ge-bearing minerals. Of the min-
erals applicable to hydrothermal systems, quartz and albite are predicted to have
D
74
Ge
mineral–fluid
= 1.1 and 0.4 %at 25
◦
C, respectively. Their precipitation in
the reaction zone would therefore drive the fluids towardsd
74
Ge values lighter than
basalt, which is the opposite of observations presented here and previously. On the
256
other hand, the precipitation of sphalerite (D
74
Ge
sphalerite–fluid
= -11.4 to -12.2 %
at 25
◦
C) and FeOx (D
74
Ge
FeOx–fluid
= -1.7 % at 25
◦
C) show the appropriate
fractionation direction. The FeOx value has also been confirmed experimentally
(Pokrovsky et al., 2014).
In addition to secondary silicates, various geochemical and observational evi-
dence points to significant FeOx precipitation at the Baby Bare sites (Mottl et
al., 1998; Wheat et al., 2002). Using D
74
Ge
FeOx–fluid
, adjusted to the appropriate
reaction temperature at each hydrothermal system, yielded d
74
Ge
fluid
values that
encompass the measured values at most sites (Table A.4). On the other hand,
using the sphalerite fractionation factor resulted in d
74
Ge
fluid
values much heavier
than measured, e.g. up to 50 % at Baby Bare (not shown).
257
Table A.4: Summary of measured and calculated hydrothermal fluid parameters
Sample pH
Reaction
temp.,
◦
C
Mg,
mmol/kg
Mg,
mmol/kg
Si,
mmol/kg
Ge,
nmol/kg
Si
removed,
mmol/kg
Ge
removed,
nmol/kg
fGe
diss
D
74
Ge
FeOx–fluid
,
%
Calculated d
74
Ge
fluid
, %
Measured
d
74
Ge
fluid
,
%
measured formation fluids
a
Ads.
b
Coppt.
b
Rayleigh
c
Batch
d
Juan de Fuca Ridge flank: Baby Bare
e f
Major CF 2972-13 (Marker 17) 8.3 63 8.45 1 0.36 11.3 103-414 249-995 1-5 % -1.4 -4.2 4.9 - 6.8 1.9 - 4.5 3.55± 1.83
Major AF 2972-11 (Marker 17) 8.3 63 8.26 1 0.36 11.3 103-414 249-995 1-5 % -1.4 -4.2 4.9 - 6.8 1.9 - 4.5 3.42± 0.52
Major AF 2974-14 (Marker 17) 8.3 63 7.56 1 0.36 11.3 103-414 249-995 1-5 % -1.4 -4.2 4.9 - 6.8 1.9 - 4.5 2.94± 1.69
Major CF 2974-13 (Marker 17) 8.3 63 5.49 1 0.36 11.3 103-414 249-995 1-5 % -1.4 -4.2 4.9 - 6.8 1.9 - 4.5 3.95± 0.91
Juan de Fuca Ridge flank: ODP 1025 & 1026
g
1025 3608 Red 7.7 40 27.6 27.6 0.58 8.7 50-201 121-484 2-7 % -1.6 -4.3 4.6 - 6.8 2.0 - 4.6 2.85± 1.21
1026 3466 Red 8.0 63 5.1 2 0.71 19 101-406 244-975 2-7 % -1.4 -4.2 4.1 - 6.1 1.9 - 4.4 3.82± 0.80
1026 3476 Red - 63 3.4 2 0.71 19 101-406 244-975 2-8 % -1.4 -4.2 4.1 - 6.1 1.9 - 4.4 4.09± 1.27
a
From Wheat & McManus (2008).
b
Based on results of Pokrovsky et al. (2014), and adjusted for reaction temperature based on relationship from Li &
Liu (2010).
c
Adsorption only, calculated using Eq. A.5 and temperature-adjusted D
74
Ge
FeOx–fluid
.
d
Adsorption and co-precipitation, calculated using Eq. A.6 and temperature-adjusted D
74
Ge
FeOx–fluid
.
e
pH and reaction temperature from Mottl et al. (1998).
f
Defined as the fraction of originally released Ge that remains in solution by the time fluids are vented. Calculated
using Eq. A.4.
g
pH and reaction temperature from Elderfield et al. (1999).
258
A.4 Global marine Ge cycle model
A.4.1 Model setup
A global box model was constructed to assess the Ge mass and isotopic budget
of the ocean. The model is based on the recently revised marine Si budget (Tréguer
& De La Rocha, 2013; Frings et al., 2016) and the estimated Ge/Si ratios are used
to calculate Ge fluxes. The d
74
Ge composition of seawater, river water, and low
temperature hydrothermal fluids, combined with high temperature hydrothermal
fluid data reported here and previously (Escoube et al., 2015) allowed us to con-
struct an isotopic mass budget for Ge. The Ge mass balance is defined as follows:
FGe
rd
+FGe
gw
+FGe
ra
+FGe
lt
+FGe
hh
+FGe
hl
= FGe
d
+FGe
s
+FGe
no
(A.7)
The subscripts are: rd = riverine dissolved; gw = groundwater; ra = river-
ine amorphous Si; lt = riverine lithogenic and aeolian particulates; hh = high-
temperature hydrothermal fluids; hl = low-temperature hydrothermal fluids; d =
diatom bSi burial; s = sponge bSi burial. This equation is used to calculate the
non-opal burial flux FGe
no
. The isotopic composition of the non-opal burial flux
is then calculated as:
d
74
Ge
no
=
1
FGe
no
(d
74
Ge
rd
FGe
rd
+d
74
Ge
gw
FGe
gw
+d
74
Ge
ra
FGe
ra
+d
74
Ge
lt
FGe
lt
+
+d
74
Ge
hh
FGe
hh
+d
74
Ge
hl
FGe
hl
–(d
74
Ge
d
FGe
d
+d
74
Ge
s
FGe
s
)) (A.8)
259
Non-opal Ge burial is a large but highly uncertain flux in the Ge cycle (account-
ing for up to 50% or perhaps even more of the total Ge burial in the oceans (Ham-
mond et al., 2000; King et al., 2000; Baronas et al., 2016). This flux is difficult
to measure directly and its d
74
Ge signature is currently unknown, although it is
essential for a robust and meaningful interpretation of d
74
Ge paleorecords.
We have employed a number of strategies to fully assess and incorporate the
appropriate degree of uncertainty associated with each parameter. The Si fluxes
summarized by Tréguer & De La Rocha (2013) and Frings et al. (2016) have asso-
ciated uncertainties, as well as the Ge/Si ratios used to convert these Si fluxes to
Ge fluxes (Table 2.4 of main text). These uncertainties, as well as those associated
withd
74
Ge estimates for each flux, propagate through the mass balance equations.
Simple linear error propagation is complicated by the covariance of uncertainties
in Eqs. A.7 and A.8. We therefore chose a Monte Carlo simulation approach,
where a random value of each parameter was selected from within its respective
uncertainty range (treated either as 2s with a normal distribution around the best-
estimate value or a simple uniform distribution within the uncertainty range (Fig.
A.5). The randomly selected values are then plugged into equations A.7 and A.8
to calculate d
74
Ge
no
and FGe
no
as the mass-balancing flux. This was done 100,000
times to ensure a good representation of the full uncertainty range. An example of
model output is given in Fig. A.6. This approach also allows imposition of certain
boundary conditions, as discussed below.
A.4.2 Reasonable boundary conditions
Randomizing the input parameters (especially with normal distribution) can
result in physically unreasonable scenarios. These model runs can be eliminated
by applying certain boundary conditions, such as:
260
1) Limiting net oceanic Si (non-steady state) flux to±3 Tmol/yr, which is
~3%/kyr of the global ocean Si inventory (97000 Tmol; Tréguer & De La Rocha
(2013)), as the modern ocean is unlikely to be much further out of steady state
(Tréguer & De La Rocha, 2013).
12345 6
FGe
RD
, Mmol/y
0
50
100
150
200
250
300
350
No. of model runs
0246 8
δ
74
Ge
RD
0
50
100
150
200
250
300
No. of model runs
024 6
FGe
RD
, Mmol/y
0
100
200
300
400
500
600
700
800
900
No. of model runs
0246 8
δ
74
Ge
RD
0
100
200
300
400
500
600
700
800
900
No. of model runs
Figure A.5: A histogram of randomly generated normally distributed input param-
eters (top) and uniformly distributed input parameters (bottom) for the dissolved
riverine Ge flux (FGe
rd
) and the isotopic composition of this flux (d
74
Ge
rd
) for
100,000 model runs. The black lines show normal distribution fits.
261
-10 0 10 20 30
FGe
non-opal
, Mmol/y
0
50
100
150
200
250
300
350
400
450
No. of model runs
-1 01234 5
δ
74
Ge
non-opal
0
100
200
300
400
500
600
700
800
900
No. of model runs
5 10 15 20 25
Ge non-opal burial flux, Mmol/y
-0.5
0
0.5
1
1.5
2
2.5
3
3.5
δ
74
Ge
non-opal
5 10 15 20 25
Ge non-opal burial flux, Mmol/y
0
0.5
1
1.5
2
2.5
3
3.5
δ
74
Ge
non-opal
-10 0 10 20 30
FGe
non-opal
, Mmol/y
0
50
100
150
200
250
300
350
400
450
No. of model runs
-1 01234 5
δ
74
Ge
non-opal
0
100
200
300
400
500
600
No. of model runs
normally distributed
inputs
uniformly distributed
inputs
Figure A.6: Histograms of Monte Carlo model results for 100,000 runs, enforcing
the reasonable boundary conditions, as described in the text. The left-side panels
represent the results from model runs where input uncertainty was treated as
a 2 S.D. normal distribution around the best estimate. The right-side panels
show the results from model runs where input uncertainty was treated as a simple
uniform range. The points in bottom panels represent individual model runs. The
statistical values obtained from these runs are given in Table A.5).
2)LimitingoceanicGeresidencetimeto>2000years. Verylargeinput/output
fluxesofGecouldtheoreticallyresultinoceanicGeresidencetimesthatareshorter
than the ocean mixing time. It is, however, known that dissolved Ge/Si is well
262
mixed in the global ocean, and variations in Ge concentrations simply reflect bio-
logical bSi cycling (Froelich et al., 1985; Sutton et al., 2010).
3) Limiting the fraction of Ge buried in the non-opal sink to 10-80%, based on
mass balance considerations and an empirical flux estimate (Baronas et al., 2016).
The global budget model described above was run with and without apply-
ing these boundary conditions, which showed that they had a small effect on the
output value medians but significantly reduced the 2s uncertainty. Furthermore,
using either the normally or uniformly distributed inputs also resulted in negli-
gible differences in the model output. In the main text (Table 2.4) we therefore
report the model results of the normally distributed input run, with the boundary
conditions applied (bolded values in Table A.5).
Table A.5: Global Ge isotope mass budget model results. Comparison of different
settings. Bolded values are considered the most representative and are reported in
the main text (Table 2.4).
Input
dis-
trib.
Boundary
condi-
tions
Parameter Mean Median 2 S.D. 5th perc. 95th perc.
Normal
Enforced
FGe
no
(Mmol/y) 13.4 13.3 10.6 4.8 22.4
d
74
Ge
no
(%) 2.5 2.7 1.7 1.4 3.4
D
74
Ge
no–sw
(%) -0.7 -0.6 1.8 -2.0 0.3
Unenforced
FGe
no
(Mmol/y) 17.1 16.1 17.1 5.1 32.6
d
74
Ge
no
(%) 2.3 2.8 194.8 1.5 3.5
D
74
Ge
no–sw
(%) -1.4 -0.4 288.8 -1.8 0.4
Uniform
Enforced
FGe
no
(Mmol/y) 12.1 11.4 11.3 4.0 22.6
d
74
Ge
no
(%) 2.4 2.6 1.6 1.1 3.5
D
74
Ge
no–sw
(%) -0.8 -0.7 1.8 -2.3 0.4
Unenforced
FGe
no
(Mmol/y) 17.1 15.0 20.1 4.2 36.8
d
74
Ge
no
(%) 2.6 2.8 50.6 1.3 3.6
D
74
Ge
no–sw
(%) -0.5 -0.5 19.2 -2.1 0.5
263
Appendix B
Contrasting Ge and Si isotope
dynamics in marine sediments:
Supplementary data
264
TableB.1: Siandtracemetalconcentrationsinhighresolutionporewatersamples.
No measurements were made where data are not available.
Core
Depth,
cm
Ge,
pmol/L
Si,
µmol/L
Ge/Si,
µmol/mol
Fe,
µmol/L
Mn,
nmol/L
MC-1D 1 380 249 1.53 63 57
MC-1D 3 696 324 2.15 106 90
MC-1D 3 707 308 2.30 128 100
MC-1D 5 471 352 1.34 118 124
MC-1D 7 356 367 0.97 40 161
MC-1D 9 379 391 0.97 87 183
MC-1D 13 203 390 0.52 55 202
MC-1D 13 249 422 0.59 60 228
MC-1D 16 262 452 0.58 59 265
MC-1D 24 199 462 0.43 53 375
MC-1D 29 180 492 0.37 39 321
MC-1D 33 181 517 0.35 39 398
MC-2A 0 531 297 1.78 88 36
MC-2A 1 730 340 2.15 156 54
MC-2A 2 1177 363 3.24 159 75
MC-2A 4 432 353 1.22 118 226
MC-2A 8 380 356 1.07 83 123
MC-2A 15 214 440 0.49 72 217
MC-2A 29 117 517 0.23 43 354
MC-2A 33 111 525 0.21 -- --
MC-2D 0 336 230 1.46 18 3
MC-2D 3 614 338 1.82 137 72
MC-2D 5 351 370 0.95 113 113
MC-2D 10 286 395 0.72 84 174
MC-2D 16 266 468 0.57 94 223
MC-2D 20 171 457 0.37 63 266
MC-3D 1 859 273 3.15 128 66
MC-3D 2 695 320 2.17 117 83
MC-3D 2 556 282 1.97 97 102
MC-3D 15 540 451 1.20 77 259
MC-3D 22 196 447 0.44 38 250
MC-3D 30 -- 464 -- 46 619
MC-3D 35 139 508 0.27 29 374
MC-5C-1 1 518 309 1.67 1 0
MC-5C-1 2 531 514 1.03 261 175
MC-5C-1 4 522 546 0.96 272 352
MC-5C-1 8 446 463 0.96 306 321
MC-5C-1 13 315 426 0.74 79 218
MC-5C-1 19 252 447 0.56 67 258
MC-5C-1 23 177 460 0.38 51 290
MC-5C-1 27 120 439 0.27 30 278
MC-5C-2 1.5 413 -- -- -- --
MC-5C-2 6.5 535 -- -- -- --
MC-5C-2 14.5 241 -- -- -- --
MC-5C-2 25.5 130 -- -- -- --
MC-5C-2 34.5 89 -- -- -- --
265
Table B.2: Ammonia concentrations in high resolution pore water samples and
overlying water. Cores MC-1B, MC-2B, and MC-4B are distinct from cores ana-
lyzed for Ge and Si, but were collected alongside the cores discussed above.
Core
Depth,
cm
NH
3
,
µmol/L
Core
Depth,
cm
NH
3
,
µmol/L
MC-1B 0.5 24 MC-1B OLW 8
MC-1B 2.5 51 MC-2B OLW 8
MC-1B 4.5 78 MC-4B OLW 24
MC-1B 6.5 93
MC-1B 8.5 120 MC-2A 0 44
MC-1B 12.5 158 MC-2A 1 37
MC-1B 16 187 MC-2A 2 33
MC-1B 20.5 217 MC-2A 3 59
MC-1B 27 276 MC-2A 4 62
MC-2A 5 83
MC-2B 0.5 29 MC-2A 7 102
MC-2B 3.5 63 MC-2A 8 97
MC-2B 5.5 116 MC-2A 9 127
MC-2B 10.5 136 MC-2A 10 123
MC-2B 15.5 176 MC-2A 11 160
MC-2B 20.5 203 MC-2A 12 137
MC-2B 25.5 238 MC-2A 14 169
MC-2B 30.5 263 MC-2A 15 172
MC-2B 36 302 MC-2A 17 209
MC-2B 42.5 337 MC-2A 20 234
MC-2A 24 261
MC-4B 0.5 17 MC-2A 27 264
MC-4B 3.5 57 MC-2A 29 274
MC-4B 6.5 86 MC-2A 30 294
MC-4B 9.5 139 MC-2A 33 297
MC-4B 15.5 201
MC-4B 18.5 222 MC-4B 34 339
MC-4B 22.5 273 MC-4B 40 379
MC-4B 28.5 299 MC-4B 43 430
266
Table B.3: Sulfate concentrations in seawater, high resolution pore water samples,
and overlying water. Measurement uncertainty is 4 %.
Sample
Depth,
m
SO
4
,
mmol/L
SPOT SSW 0 27.4
SPOT 885m 885 27.2
MC-5B-1 (Core inc.) 885 26.4
5B-Final (Core inc.) 885 26.5
MC-2C OLW 885 29.6
MC-2D OLW 885 27.2
MC-5C OLW 885 27.8
MC-1D OLW 885 26.5
MC-2A OLW 885 26.1
Core
Depth,
cm
SO
4
,
mmol/L
MC-1D 1 26.5
MC-1D 3 24.4
MC-1D 5 26.1
MC-1D 9 26.3
MC-1D 16 26.0
MC-1D 24 24.5
MC-1D 29 24.9
MC-1D 33 23.5
MC-2A 0 26.1
MC-2A 4 25.6
MC-2A 12 25.4
MC-2A 20 24.7
MC-2A 29 25.4
267
Table B.4: Ge and Si concentrations during San Pedro Basin core incubations.
Sample Time, h
Corr. time,
d/m *
Ge,
pmol/L
Si,
µmol/L
Ge/Si,
µmol/mol
MC-3A
1 0 0 73 103 0.71
2 8.0 2.8 83 112 0.74
3 17.5 6.2 86 125 0.69
4 40.3 14.5 104 130 0.80
5 64.3 23.5 116 145 0.81
Final 65.8 24.1 118 142 0.83
MC-5D
1 0 0 83 105 0.79
2 6.0 1.8 86 108 0.79
3 23.4 7.2 88 115 0.76
4 45.4 14.2 87 120 0.73
5 68.4 21.7 94 127 0.74
Final 68.4 21.7 98 129 0.76
MC-4C
1 0 0 83 106 0.78
2 3.3 1.0 83 110 0.75
3 3.5 1.1 85 109 0.77
4 23.0 7.3 88 120 0.74
5 46.0 14.9 87 122 0.71
6 69.8 22.9 92 130 0.71
7 92.8 30.8 101 136 0.74
8 118.5 39.8 102 143 0.72
9 140.5 47.6 109 147 0.74
MC-5A
1 0 0 81 105 0.77
2 5.5 1.9 82 112 0.73
3 23.0 8.1 84 120 0.70
4 45.0 16.1 84 125 0.67
5 70.0 25.3 87 134 0.65
6 93.0 34.0 83 140 0.60
7 117.5 43.4 86 147 0.58
8 139.5 52.1 85 155 0.55
MC-5B
1 0 0 74 104 0.71
2 5.5 1.5 74 108 0.69
3 23.0 6.4 69 114 0.61
4 45.0 12.6 78 120 0.66
5 70.0 19.9 78 126 0.62
6 93.0 26.8 86 131 0.65
7 117.5 34.2 84 139 0.61
8 139.0 40.8 87 142 0.61
* Calculated as sum of incubation time in days divided by height of the
overlying incubated water column in m at a given time. The height of
the water column decreases throughout the incubation due to water
removal by sampling. This calculation corrects for the effect of
decreasing water volume, and is used to calculate the Ge and Si fluxes
reported in Table 3 of main text.
268
Table B.5: Ge and Si concentrations during Santa Monica Basin core incubations.
No measurements were made where data are not available.
Sample Time, h
Corr. time,
d/m *
Ge,
pmol/L
Si,
µmol/L
Ge/Si,
µmol/mol
D3-S2
1 11.5 4.9 96 126 0.76
2 24.6 10.6 104 131 0.79
3 31.2 13.6 107 133 0.80
4 49.7 22.2 114 140 0.81
5 119.2 56.0 145 154 0.94
D4-S1
1 9.8 3.5 95 129 0.74
2 22.7 8.3 -- 134 --
3 29.6 10.9 105 137 0.76
4 48.5 18.3 -- 143 --
5 118.4 46.4 147 165 0.89
D4-S4
1 9.8 4.4 99 129 0.76
2 22.8 10.5 -- 135 --
3 29.6 13.9 102 138 0.74
4 48.5 23.4 -- 146 --
5 118.2 59.7 147 170 0.86
D5-S1
1 8.5 2.9 91 131 0.70
2 21.6 7.4 -- 136 --
3 28.3 9.8 96 138 0.69
4 46.8 16.6 -- 139 --
5 116.9 42.9 151 189 0.80
D5-S4
1 8.6 2.4 88 131 0.67
2 23.0 6.4 -- 134 --
3 28.3 8.0 90 136 0.66
4 46.9 13.5 -- 145 --
5 116.8 34.6 116 163 0.71
* Calculated as sum of incubation time in days divided by height of the
overlying incubated water column in m at a given time. The height of
the water column decreases throughout the incubation due to water
removal by sampling. This calculation corrects for the effect of
decreasing water volume, and is used to calculate the Ge and Si fluxes
reported in Table 3 of main text.
269
Appendix C
La Selva interbasin groundwater
reactive transport model
C.1 Model description
The concentration and isotopic composition of solutes in flowing groundwater
is affected by 1) advection; 2) dissolution of primary minerals; and 3) precipitation
of secondary minerals, resulting in either co-precipitation or net adsorption of an
element of interest. Diffusion effects are assumed to be negligible relative to the
rate of fluid advection. Assuming that the shape of the fluid composition profile
along the flowpath does not change with time, the change in dissolved groundwater
Si concentration along the flowpath can be determined as:
d[
28
Si]
fluid
dx
=

r
rock
(1–f)
r
fluid
f
!
[
28
Si]
rock
28
k
diss
q
–[
28
Si]
fluid
28
k
rem
q
(C.1)
where [
28
Si] is the concentration of
28
Si isotope in [mmol L
–1
], x is distance in
[m], r is density in [g cm
–3
], q is fluid flow rate in [m y
–1
] and k
diss
and k
rem
are
the first-order rate constants of dissolution and precipitation, respectively, in [s
–1
].
The first term therefore represents the release of Si from the dissolution of primary
minerals and the second term represents removal (precipitation or adsorption) of
Si with secondary phases. The dissolution rate is defined as:
270
28
k
diss
= R
min
diss
A
rock
M
min
f
min
X
Si
min
(C.2)
where R
min
diss
is the primary mineral dissolution rate in [mol m
–2
y
–1
], A
rock
is
the specific rock surface area in [m
2
g
–1
], M
m
in is the molar mass of the dissolving
mineral in [mol g
–1
], f
min
is the weight fraction of the dissolving mineral in the
bedrock, and X
Si
min
is the stoichiometry of Si in the mineral (i.e. mol Si / mol
mineral).
To account for the kinetic rate dependence on the distance to equilibrium,
we use a simple expression of the transition state theory (TST) (Lasaga, 1984;
Brantley, 2008):
R
min
diss
= k
min
diss
(1–W) (C.3)
where k
min
diss
is the intrinsic mineral dissolution rate in [mol m
–2
s
–1
] and W is
the saturation state of the dissolving mineral.
Integrating Eq. C.1 for boundary conditions [
28
Si]
fluid
= [
28
Si]
0
fluid
when x =
0 and [
28
Si]
fluid
= [
28
Si]
x
fluid
when x = x yields:
[
28
Si]
x
fluid
=

r
rock
(1–f)
r
fluid
f
!
[
28
Si]
rock
28
k
diss
28
k
rem

1–e

–
28
k
rem
x
q

+ [
28
Si]
0
fluid
e

–
28
k
rem
x
q

(C.4)
Equation C.4 describes the concentration of an isotope at a given point along
the fluid flowpath, as a function of the rates of primary mineral dissolution and
secondary phase precipitation. At a certain point along the flowpath, as x
–
28
k
rem
q
, the age of fluid t
fluid
=
x
q
exceeds the residence time of Si in solution
271
t
res
=
1
28
k
rem
, the exponential terms in Eq. C.4 approach zero, and [
28
Si]
x
fluid
approaches steady state:
[
28
Si]
ss
fluid
=

r
rock
(1–f)
r
fluid
f
!
[
28
Si]
rock
28
k
diss
28
k
rem
(C.5)
Using the values for the various parameters given in Table C.1, Eq. C.2 and
C.3 can be combined to calculate
28
k
diss
, and Eq. C.5 can then be used to calcu-
late
28
k
rem
. It must be noted here that these are first-order rate constants that
are intrinsic to the dissolving and precipitating minerals and are assumed to be
constant along the flowpath, implying that the assemblage of dissolving and pre-
cipitating minerals remains constant.
An equivalent set of equations can be written for [
30
Si]. The isotopic frac-
tionation factors associated with dissolution and precipitation are defined as
30/28
a
diss
=
30
k
diss
/
28
k
diss
and
30/28
a
rem
=
30
k
rem
/
28
k
rem
. Dividing Eq. C.4 for
30
Si by that for
28
Si, plugging in the a expressions given above and performing
some algebraic manipulation yields an expression for the isotopic ratio of dissolved
Si:

30
Si
28
Si
!
x
fluid
=
[
28
Si]
ss
fluid
[
28
Si]
x
fluid

30
Si
28
Si
!
rock
30/28
a
diss
30/28
a
rem

1–e

–
28
k
rem
30/28
a
rem
x
q

+
[
28
Si]
0
fluid
[
28
Si]
x
fluid

30
Si
28
Si
!
0
x
e

–
28
k
rem
x
q
30/28
a
rem

(C.6)
From Eq. C.6, as x
–
28
k
rem
q
, the dissolved Si isotope ratio approaches steady
state:

30
Si
28
Si
!
ss
fluid
=
30/28
a
diss
30/28
a
rem

30
Si
28
Si
!
rock
(C.7)
272
The isotope ratio in the dissolving primary mineral is assumed to be equal to
the bulk bedrock value and it is further assumed that the IBGW fluid discharging
at La Selva has achieved a steady state (see main text Section 4.4.1), allowing us
to calculate the observed the net fractionation factor:
30/28
a
net
=
30/28
a
diss
30/28
a
rem
(C.8)
Assuming congruent mineral dissolution
30
k
diss
=
28
k
diss
and therefore
30/28
a
diss
= 1. Most field and experimental data suggest little to no fractiona-
tion during silicate mineral dissolution (Frings et al., 2016). Where fractionation
is observed, in all cases the dissolution of light isotopes was slightly faster. If this
is the case here, it could result in an underestimation of the D
30
Si fractionation
factor determined here, and the value in Table C.1 should therefore be seen as a
minimum estimate.
Similar equations can be written for germanium isotopes
74
Ge and
70
Ge to
calculate the [Ge] and d
74
Ge evolution of the IBGW fluid, which also yields an
expression of the fluid Ge/Si ratio along the pathway. Since the total dissolution
and the net release (i.e., addition to solution) rates are known for each element,
they can be combined to calculate the d
30
Si, d
74
Ge, and Ge/Si composition of the
precipitating secondary phases at any given point.
For simplicity’s sake, the primary dissolving mineral is taken to be albite, there-
fore a typical plagioclase Ge/Si ratio of 3 mmol/mol is used instead of the 2.1-2.3
mmol/mol measured in the bulk rock (Table 4.1). The bulk rock has a lower
ratio due to the presence of quartz, which typically has Ge/Si of 0.5 mmol/mol
(Murnane & Stallard, 1990; Kurtz et al., 2002) and is assumed to not dissolve
to any appreciable extent during groundwater-rock interaction. For d
74
Ge, we
assume that plagioclase is identical to the bulk rock, which is reasonable given the
273
extremely small variations in d
74
Ge signatures of various silicate rocks (Escoube
et al., 2012). Finally, we assume no d
74
Ge fractionation during plagioclase disso-
lution, i.e.
74/70
a
diss
= 1.
C.2 Model application
The thermodynamic dependence of the albite dissolution rate law (Eq. C.3) is
treated as follows. The albite IAP and W values for the initial fluid (represented by
rainwater sample LS16) and the final steady state fluid (represented by the IBGW
sample CR05) are calculated using data in Table 4.3 and Geochemist’s Workbench
Edition 11.0. As expected, rainwater is highly undersaturated (SI = log(W) =
–5.9), whereas the final IBGW fluid is supersaturated (SI = 0.6) with respect
to albite. Since Al, Na, and other solutes are not modeled along the flowpath,
albite saturation is assumed to be a linear function of the evolving dissolved Si
concentration. Then, according to Eq. C.3, the albite dissolution rate approaches
zeroasWapproaches1. Wealsoassumethatalbiteprecipitationratesarenegligible
under these conditions even when W is slightly above 1.
Thesolutionvolume-normalizedSidissolutionratein[molL
–1
s
–1
]iscalculated
as:
0
R
Si
diss
= R
Si
diss
A
min
r
rock
f
(C.9)
where R
Si
diss
is the mineral-normalized dissolution rate in [mol m
–2
s
–1
] (Table
C.1; Eq. C.3). The absolute Si removal (precipitation or adsorption) rate of Si
can then be calculated as the difference between
0
R
Si
diss
and the net Si addition to
solution over a given section of the fluid flowpath. The evolution of the Si dissolu-
tion and precipitation rates as a function of distance and [Si]
fluid
is shown in Fig.
274
C.1. The dissolution and precipitation rates for Ge can be obtained in the same
way. The absolute values depend primarily on the intrinsic mineral dissolution rate
supplied to the model (Table C.1). This value was chosen somewhat arbitrarily,
as it primarily only determines the distance over which the fluid achieves steady
state. We note that lab-measured albite dissolution rates are typically 100-1000
faster (e.g, Hellmann & Tisserand, 2006; Brantley, 2008; Gruber et al., 2014) and
yet another 2 orders of magnitude faster for Icelandic basalt dissolution (Gislason
& Oelkers, 2003). Selecting a 1000 times faster dissolution rate simply makes the
dissolved Si concentration and isotopic composition reach a steady state within
tens of meters (consistent with the modeling work of Maher (2010)) instead of
hundreds (Fig. 4.6), but does not change the final fluid composition. The other
parameters supplied to the model, such as rock surface area, fraction of reactive
mineral, and porosity (Table C.1) have a minor effect on the model output relative
to the dissolution rate.
It must be noted that the dissolved Si concentration in the IBGW fluid is∼3
times higher than the highest values observed in low temperature weathering fluid
(∼400 mmol/mol) that are assumed to represent the thermodynamic limit for sys-
tems where primary minerals are dissolving and secondary phases are precipitating
(Maher & Chamberlain, 2014). This could be the result of elevated temperatures
within the slopes of Vulcan Barva, or more likely, enhanced solubility of primary
silicates and secondary minerals due to high acidity resulting from the degassing
and dissolution of volcanic CO
2
into the groundwater. Although both temperature
and pH measured in IBGW (Table 4.3) were no different from other local streams,
the high alkalinity, as well as previous studies of d
13
C and other tracers in this
fluid (Genereux et al., 2009) suggest a magmatic CO
2
source.
275
0
1×10
-13
0.01 0.1 1 10 100 1000 10000
Rate, mol L
-1
s
-1
Distance, m
Dissolution (Si)
Precipitation (Si)
0 200 400 600 800 1000 1200 1400
[Si]
fluid
, µmol L
-1
2×10
-13
3×10
-13
4×10
-13
0
1×10
-13
Rate, mol L
-1
s
-1
2×10
-13
3×10
-13
4×10
-13
a b
Figure C.1: The modeled evolution of dissolution and precipitation rates of Si
along the interbasin groundwater flowpath.
276
Table C.1: Input parameters and results of the model simulating the evolution of
interbasin groundwater (IBGW) composition.
Parameter Description Value Units Source
Inputs
φ Porosity 0.1 - Genereux et al. (2009)
ρ
rock
Rock density 2.6 g cm
-3
ρ
water
Water density 1.0 g cm
-3
q Water velocity 3.0 ± 0.7
m y
-1
Genereux et al. (2009)
A
rock
Rock specific surf area 0.1 m
2
g
-1
adjustable, see text
M
min
Mineral molar mass 263 g mol
-1
Albite
f
min
Fraction of dissolving mineral in rock 0.5 - adjustable, see text
k
min
diss
Mineral dissolution rate constant 10
-16
mol m
-2
s
-1
adjustable, see text
X
Si
min
Stoichiometric Si / mineral ratio 3 mol mol
-1
Albite
[
28
Si]
rock
Initial rock Si concentration (≈[Si]
rock
) 0.0114 mol g
-1
Measured (Table 1)
[
28
Si]
0
fluid
Initial fluid Si concentration (≈[Si]
rain
) 3.8 µmol L
-1
Sample LS16 (Table 3)
[
28
Si]
ss
fluid
IBGW fluid Si concentration (≈[Si]
GW
) 1293 µmol L
-1
Sample CR05 (Table 3)
30/28
α
diss
30
Si/
28
Si dissolution fractionation 1 - see text
(Ge/Si)
albite
Ge/Si ratio in dissolving mineral 3 µmol mol
-1
Froelich et al. (1992),
Kurtz et al. (2002)
X
Ge
min
Stoichiometric Ge / mineral ratio (=
X
Si
min
* (Ge/Si)
albite
)
9 µmol mol
-1
[Ge]
rock
(= [Si]
rock
* (Ge/Si)
albite
) 0.0342 µmol g
-1
[Ge]
0
fluid
Initial fluid Ge concentration (=[Ge]
rain
) 8.8 pmol L
-1
Sample LS16 (Table 3)
[Ge]
ss
fluid
IBGW fluid Ge concentration (=[Ge]
GW
) 405 pmol L
-1
Sample CR05 (Table 3)
74/70
α
diss
74
Ge/
70
Ge dissolution fractionation 1 - see text
Results
k
Si
rem
Si removal rate constant 2.7×10
-10
s
-1
Calc. using Eq. 3.6
30/28
α
rem
30
Si/
28
Si removal fractionation 0.9989 - Calc. using Eq. 3.8
k
Ge
rem
Ge removal rate constant 2.6×10
-9
s
-1
Calc. using Eq.
equivalent to Eq. 3.6
74/70
α
rem
74
Ge/
70
Ge removal fractionation 0.9966 -
Calc. using Eq.
equivalent to Eq. 3.8
277
Appendix D
La Selva lowlands isotopic mass
balance model
D.1 Model description
A simple non-dimensional mass balance steady-state model is used to to deter-
mine the Ge and Si isotope fractionation factors, while utilizing the observed Ge/Si
fractionation as an additional constraint. The lack of systematic pore water trends
with depth is taken as evidence that the percolating fluids are approximately at
equilibrium with the surrounding solids (see main text). As Ge or Si is released
from dissolution of soil material is either exported via advection of pore water
fluids or removed via adsorption or co-precipitation (Fig. ). At steady state the
supply by dissolution equals the advection and removal:
k
Ge
diss
= k
Ge
rem
+k
Ge
adv
(D.1a)
k
Si
diss
= k
Si
rem
+k
Si
adv
(D.1b)
where k is a relative flux of the element, diss = dissolution, adv = advection,
and rem = adsorption/co-precipitation. The fraction removed via adsorption/co-
precipitation can then be defined as:
278
f
Ge
rem
=
k
Ge
rem
k
Ge
rem
+k
Ge
adv
(D.2a)
f
Si
rem
=
k
Si
rem
k
Si
rem
+k
Si
adv
(D.2b)
The isotopic mass balance is then as follows:
k
Ge
diss
R
74/70
soil
= k
Ge
rem
R
74/70
clay
+k
Ge
adv
R
74/70
fluid
(D.3a)
k
Si
diss
R
30/28
soil
= k
Si
rem
R
30/28
clay
+k
Si
adv
R
30/28
fluid
(D.3b)
where R refers to the isotope ratio of either
74
Ge/
70
Ge or
30
Si/
28
Si. Under
steady state equilibrium, the fractionation factor is defined as the isotopic differ-
ence between the fluid and the precipitating clay (i.e., a
clay–fluid
= R
74/70
clay
/R
74/70
fluid
).
The measured isotopic difference between the fluid and the dissolving material
(assumed to be represented by bulk soil) will then depend on the fraction of the
element that is removed (adsorbed or co-precipitated)(Bouchez et al., 2013):
f
Ge
rem
=
R
74/70
soil
–R
74/70
fluid
R
74/70
clay
–R
74/70
fluid
(D.4a)
f
Si
rem
=
R
30/28
soil
–R
30/28
fluid
R
30/28
clay
–R
30/28
fluid
(D.4b)
Equations D.4 are obtained from combining Eqs. D.2 and D.3. Using the
measurements of bulk soil, tertiary clays, and pore waters allows to determine
f
Ge
rem
and f
Si
rem
. In cases where f
rem
values can be determined independently, the
279
k
Si
diss
k
Ge
diss
k
Si
adv
k
Ge
adv
k
Si
diss
k
Ge
diss
k
Si
rem
k
Ge
rem
R
Ge/Si
soil
R
74/70
soil
R
30/28
soil
R
Ge/Si
uid
R
74/70
uid
R
30/28
uid
R
Ge/Si
clay
R
74/70
clay
R
30/28
clay
Figure D.1: The steady state Ge and Si isotope mass balance.
fractionation factor can be determined only from the isotopic difference between
the fluids and the dissolving material:
a
74/70
clay–fluid
= 1–
R
74/70
fluid
–R
74/70
soil
f
Ge
rem
(D.5a)
a
30/28
clay–fluid
= 1–
R
30/28
fluid
–R
30/28
soil
f
Si
rem
(D.5b)
280
Which is equivalent to the "batch" or steady state equilibrium equation
expressed in d notation and utilized in many previous studies of isotope fraction-
ation during weathering (e.g., Georg et al., 2007; Hughes et al., 2013; Dellinger et
al., 2015):
d
74
Ge
fluid
≈ d
74
Ge
soil
–D
74
Ge
clay–fluid
×f
Ge
rem
(D.6a)
d
30
Si
fluid
≈ d
30
Si
soil
–D
30
Si
clay–fluid
×f
Si
rem
(D.6b)
The advantage of simultaneous Ge and Si isotope measurements is that the
measured Ge/Si ratios can be used to overconstrain the system and check the
validity of the calculated f
rem
values. First, the Ge/Si ratio in the advected fluids
and the precipitated clays has to equal the relative fluxes of each element:
R
Ge/Si
fluid
=
k
Ge
adv
k
Si
adv
(D.7a)
R
Ge/Si
clay
=
k
Ge
rem
k
Si
rem
(D.7b)
Then, Eqs. D.7 can be combined with Eqs. D.2 to obtain:
R
Ge/Si
fluid
= R
Ge/Si
clay
×
f
Si
rem
(1–f
Ge
rem
)
f
Ge
rem
(1–f
Si
rem
)
(D.8)
Combining the f
rem
values calculated above from the respective isotope mass
balances of each element with the the measured R
Ge/Si
clay
allows to predict R
Ge/Si
fluid
.
Additionally, Ge/Si fractionation during tertiary clay formation depends on a par-
titioning coefficient K
d
(as defined by Murnane & Stallard (1990) and Froelich et
al. (1992)):
281
R
Ge/Si
clay
= K
d
×R
Ge/Si
soil
(D.9)
where K
d
=
f
Ge
rem
f
Si
rem
and again can be calculated using the f
rem
values determined
from the isotopic mass balance. The R
Ge/Si
fluid
and R
Ge/Si
clay
values calculated from Eqs.
D.8 and D.9 can be compared to measured values to optimize model results as
decribed below.
D.2 Model results
Thetwo-elementisotopicmassbalancewascalculatedasdescribedabove, using
the measured d
74
Ge and d
30
Si composition in the bulk soil (dissolving material),
separated fine clay fraction (representative of precipitating tertiary clays) and low-
land weathering fluids not impacted by the interbasin groundwater inputs. For the
soil value, all bulk soil measurements in the two soil pits were used (n = 6; Table
4.1. For the clay value, all separate clay measurements were used (n = 4). For
the fluid value, all available pore water, lowland groundwater, and Taconazo River
(unaffected by IBGW fluids) values were used (n = 3 for d
74
Ge n = 15 for d
30
Si;
Table 4.3).
Table D.1: Steady-state model input values.
d
74
Ge, % d
30
Si, % Ge/Si, mmol/mol
mean s mean s mean s
Soil -0.12 0.12 -2.07 0.25 6.32 0.22
Clay -0.16 0.09 -2.53 0.15 7.35 0.47
Fluid 2.41 0.17 -0.32 0.64 1.41 0.49
282
To assess the uncertainty of the calculated fractionation factors, a Monte Carlo
approach was used. The measurements were assumed to represent a normal dis-
tribution around the mean value for each of the three reservoirs (soils, clays, and
fluids). The used input values are given in Table D.1. From within each distribu-
tion, a random value was picked and Eqs. D.4 and D.5 were used to calculate f
rem
anda
clay–fluid
(orD
clay–fluid
when expressed in %) values for Ge and Si. Equations
D.8 and D.9 were then used to calculate the predicted fluid and clay Ge/Si ratios.
This procedure was repeated 100,000 times to generate a distribution of all the
calculated values (Fig. D.2a-c). Finally, the results were optimized by retaining
only those model runs were the calculated fluid and clay Ge/Si ratios fell within
the 3s bounds of the distributions given in Table D.1. The optimized distributions
are shown in Fig. D.2d-f. This optimization routine had a very small impact on
the calculated D
clay–fluid
values (Table D.2). The optimized values are used in the
discussion of the main text.
Table D.2: Steady-state model results.
Parameter Initial Optimized
D
30
Si
clay–fluid
-2.2± 0.6 -2.4± 0.6
D
74
Ge
clay–fluid
-2.6± 0.2 -2.6± 0.2
K
d
1.21± 0.08 1.21± 0.08
283
-4 -3 -2 -1 0
0
500
1000
1500
2000
a
30
Si
Δ
74
Ge
5 10 15 20
Ge/Si
clay
0
1000
2000
3000
4000
b
0 1 2 3 4
Ge/Si
fluid
0
500
1000
1500
2000
c
-4 -3 -2 -1 0
Δ = δ
clay
- δ
fluid
0
200
400
600
800
1000
d
4 6 8 10
Ge/Si
clay
0
200
400
600
800
e
0 1 2 3 4
Ge/Si
fluid
0
100
200
300
400
500
600
700
f
Δ = δ
clay
- δ
fluid
Δ
calc.
meas.
calc.
meas.
calc.
meas.
30
Si
Δ
74
Ge
Δ
calc.
meas.
Figure D.2: The distribution of calculated Ge and Si isotope fractionation factors
(a) and the measured and calculated Ge/Si ratios in the forming tertiary clays
(b) and the fluids (c). Panels d-f show the optimized distributions of the same
parameters after retaining only those model runs where the calculated Ge/Si ratios
fall within 3s bounds of the measured distributions.
284
Appendix E
Ge and Si isotope geochemistry in
global rivers: Supplementary data
Table E.1: Additional global river data.
Ge, Si, Ge/Si, Na, K, Ca, Mg,
pmol/L µmol/L µmol/mol µmol/L µmol/L µmol/L µmol/L
San Jacinto, California SJ14 2014-12-04 15:30 33.731 -116.809 18 352 0.05 626 89.6 166.7 51.8
Basse Terre, Guadeloupe AN-14-40 2014-06-14 -- 16.178 -61.693 70 477 0.15 352.4
a
18.1
a
139.5
a
89.6
a
Mackenzie basin
Salt CAN-09-27 2009-07-17 -- 60.021 -112.351 46 158 0.29 35217
b
61.4
b
1646
b
6608
b
-- CAN-10-18 2010 -- -- -- 27 51 0.53 -- -- -- --
Hay CAN-10-59 2010 -- -- -- 57 104 0.55 13.8
b
1.0
b
35.5
b
10.8
b
Amazon basin
Trombetas AM89_TR 1989-05 -- -1.748 -55.895 71 127 0.56 31.0
c
23.0
c
11.0
c
9.0
c
Tapajos AM14_TA 2014-07-27 -- -2.373 -54.808 64 157 0.41 34.0 21.4 25.1 20.5
Obidos AM14_00 2014-04-01 -- -1.943 -55.501 109 138 0.79 72.0 29.1 137.5 57.2
Obidos AM14_01 2014-07-28 -- -1.943 -55.501 75 130 0.58 66.2 21.5 97.8 35.9
a
Gaillardet & Bouchez, unpublished
b
Dellinger, PhD (2013)
c
Allègre et al. (1996); Gaillardet et al. (1997); Dosseto et al. (2006); Dellinger et al.(2015)
River Sample ID Date Hour Lat., ° Long., °
285
Table E.2: Major ion chemistry for river samples presented in Tables 6.1 and 6.5.
Na, K, Ca, Mg, Cl, SO
4
,
µmol/L µmol/L µmol/L µmol/L µmol/L µmol/L
North America
Mississippi* (Aug 2011) MI11 1369 137 1382 884 -- -- Baronas et al. (2017a)
Mississippi* (Jul 2015) GRO001622 835 101 988 434 705 394
B. Peucker-Ehrenbrink
et al., unpub.
Kaweah KW13 292 39 291 73 -- -- this study
Lone Pine Creek LPC13 120 26 188 31 -- -- this study
Kern (North Fork) KN09 384 38 343 79 -- -- Baronas et al. (2017a)
Kern* (Lower) KL09 596 44 430 105 -- -- Baronas et al. (2017a)
San Gabriel (North Fork) SGN13 475 112 1063 484 -- -- Baronas et al. (2017a)
Santa Clara SC13 6068 269 -- 734 -- -- this study
Hondo* RH08 693 75 1141 606 -- -- Baronas et al. (2017a)
Los Angeles River* LAR13 -- -- -- -- -- --
Mackenzie CAN10-(11+14) 309 22 994 454 184 437 M. Dellinger, PhD (2013)
Liard CAN10-(46+48) 2.7 0.7 37 10.5 22 471 M. Dellinger, PhD (2013)
Peel CAN10-(03+05) 183 13.8 1227 632 39 713 M. Dellinger, PhD (2013)
Fraser FR15 -- -- -- -- -- --
Greenland
Watson JO-14 51 33 49 17.2 -- -- this study
Volcanic islands
Waimano OA14-1 355 24 66 126 -- -- this study
Uhva OA14-2 576 28 215 333 -- -- this study
Kahaua OA14-3 420 16 149 210 -- -- this study
Molokai ML09 -- 184 377 610 -- -- Baronas et al. (2017a)
Iao Valley MA09 247 14.6 147 130 -- -- Baronas et al. (2017a)
Ytra Ranga YR15 -- -- -- -- -- --
Grande Riviere de Goyave AN-14-(42-44) 308 13.7 128 67 269 30
J. Gaillardet & J.
Bouchez, unpub.
Asia & Oceania
Changjiang* (Nov 2014) CJ14 529 84 770 356 -- -- this study
Changjiang* (Jan 2015) CJ15 678 89 818 386 -- -- this study
Mekong ME14 278 40 449 236 -- -- this study
Nam Xong NX14 174 11.1 729 345 -- -- this study
Otira OT14 56 4.8 107 13.0 -- -- this study
South America
Kosñipata (Aug 2013) MMD-02 127 10.2 202 131 4.0 292 Torres et al. (2016)
Kosñipata (Oct 2015) KOS15 -- -- -- -- -- --
Carbon MMD-05 76 22 82 29 0.7 2.7 Torres et al. (2016)
Madre de Dios MMD-28 92 26 344 86 5.0 54 Torres et al. (2016)
Madre de Dios MMD-32 112 26 270 81 9.7 69 Torres et al. (2016)
Inambari MMD-29 94 17 116 51 19.8 112 Torres et al. (2016)
Piedras MMD-34 318 59 427 182 5.2 12.6 Torres et al. (2016)
Solimões (Jun 2005) AM-05-(05-08) 126 23 49 212 87 29 J. Bouchez, PhD (2009)
Solimões (Dec 2014) AM14-36 139 27 185 58 -- -- this study
Negro AM14-40 20.0 11.3 12.5 7.6 -- -- this study
Madeira AM06-(34-43) 58 32 58 98 17 49 J. Bouchez, PhD (2009)
Amazon AM06-(23-30) 103 20 47 204 83 35 J. Bouchez, PhD (2009)
All samples were taken at the surface (0m), with the following exceptions: AM-05-(05-08) composite of 0-21m; AM06-(23-30) composite of 0-45m
(two depth profiles); AM06-(34-43) composite of 0-15m; AN-14-(42-44) composite of surf. samples taken at Duclas and at the mouth; CAN-10-
(03+05) composite of 2.5 and 8.5m; CAN-10-(11+14) composite of 5 and 19.4m; CAN-10-(46+48) composite of 1.5 and 4.8m.
*Affected by anthropogenic activity. The Mississippi, Hondo, and Los Angeles rivers are incorporate significant industrial and urban runoff.
Changjiang chemistry is affected by extensive irrigation and farming of rice paddies in the floodplain (Ding et al., 2004). Lower Kern river was
sampled downstream of a dam reservoir in which water chemistry is strongly modified by diatom growth (Baronas et al., 2017a).
Sample ID Reference River
286

Abstract (if available)

Abstract The conditions on the surface of planet Earth, including climate and its ability to support life, are controlled by a complex interaction of physical and chemical processes, taking place over a range of spatial and temporal scales. Due to the ubiquity of silicon (Si) in the solid Earth, a number of these processes involve the transformation and translocation of silica-containing compounds (i.e., the global Si cycle). The rates of the various chemical reactions and mass fluxes within the Si cycle are often difficult to assess directly, especially in the geological past. Therefore, geochemical tracers can help evaluate their importance in controlling the evolution of Earth's climate, landscape, and life. ❧ In this PhD dissertation, I investigate the use of germanium (Ge) and Si isotope composition (δ⁷⁴Ge and δ³⁰Si, respectively) in natural fluids and solids to trace various Earth surface processes, including silicate rock weathering and marine sediment authigenesis. Ge, like Si, is primarily supplied by the weathering of silicate rocks on continents, and consumed during biogenic silica production in the oceans, making it a reliable tracer of the Si cycle. In particular, different chapters of this dissertation are focused on establishing Ge isotope systematics in various different Earth surface environments, as there is very little prior data available. A second objective is to demonstrate how the combined use of δ⁷⁴Ge, δ³⁰Si, and Ge/Si signatures can yield insights otherwise unobtainable if either proxy was applied in isolation. ❧ Ge and Si are primarily supplied to the ocean via rivers and hydrothermal fluids, and removed via burial of biogenic silica and authigenic phases that precipitate within marine sediments. In Chapter 2, I presented the first δ⁷⁴Ge data from a number of analytically challenging low temperature fluid samples, including seawater and river water. The dissolved δ⁷⁴Ge composition of rivers and seawater was significantly heavier (2.0-5.6 ‰ and 3.2±0.4 ‰, respectively) than the source silicate rocks (0.4-0.8 ‰), indicating significant Ge isotope fractionation during low temperature processes. High temperature ridge-axis hydrothermal fluids exhibited δ⁷⁴Ge of 0.7-1.6 ‰ and are likely controlled by isotopic equilibration with hydrothermal quartz. Low temperature ridge-flank hydrothermal fluids had δ⁷⁴Ge of 2.9-4.1 ‰, consistent with isotopic fractionation during Ge adsorption or co-precipitation with Fe oxyhydroxides. A steady state isotopic mass budget for the ocean was used to predict that Ge sequestration during sediment authigenesis (i.e. the non-opal Ge sink) must involve a Δ⁷⁴Ge_{authigenic-seawater} fractionation factor of -0.6±1.8 ‰. ❧ In Chapter 3 I sought to directly observe and quantify the δ⁷⁴Ge fractionation associated with marine sediment authigenesis, using Ge/Si and δ³⁰Si data to provide additional constraints. The same set of sedimentary processes appeared to control dissolved Ge dynamics in all three studied continental margin sites (San Pedro Basin, Santa Monica Basin, and Gulf of Mexico continental shelf). First, biogenic Si (bSi) dissolution supplies dissolved Ge to the pore waters with δ⁷⁴Ge = ~3 ‰, identical to seawater composition. Second, reductive dissolution of Fe oxides (FeOx) in the subsurface sediments results in pore water δ⁷⁴Ge as low as 1.3-1.9 ‰, coinciding with a Ge/Si maximum of up to 3 µmol/mol. It is unclear whether the Fe oxides are of lithogenic or authigenic origin (i.e., supplied as settling detrital particles or previously formed in-situ. However, variations in the benthic dissolved Ge flux and its isotopic composition suggest sensitivity to redox conditions, indicating −1.2 ‰ difference in δ⁷⁴Ge between FeOx and bSi. Third, δ³⁰Si fractionation indicates that authigenic aluminosilicates precipitate throughout the sediments. The latter process results in significant dissolved Ge draw down (Ge/Si ratio decreases to 0.3 µmol/mol) without any significant Ge isotope fractionation (pore water δ⁷⁴Ge ~2 ‰). Overall, authigenically buried Ge is ~1 ‰ lighter than seawater, close to the value calculated in Chapter 2. ❧ Chapters 4-6 focus on Ge and Si isotope systematics during continental weathering. In Chapter 4, a detailed study of soils, streams, and ground water in the tropical, supply-limited weathering environment of La Selva, Costa Rica was used to elucidate the competing controls of secondary mineral precipitation and vegetation uptake on δ⁷⁴Ge, δ³⁰Si, and Ge/Si signatures. The soils of La Selva lowlands are strongly weathered and composed almost exclusively of secondary minerals. The streams, however, reflect a mixture of lowland soil waters and interbasin groundwater, the latter representing volcanic rock weathering at higher elevations. Similar degrees of isotopic fractionation were observed during weathering of fresh volcanic rock by groundwater (Δ³⁰Si_{clay-fluid} = −1.2 ± 0.1 ‰ and Δ⁷⁴Ge_{clay-fluid} = −2.6 ± 0.2 ‰) and of chemically depleted lowland soils by rainwater (Δ³⁰Si_{clay-fluid} = −2.4 ± 0.6 ‰ and Δ⁷⁴Ge_{clay-fluid} = −3.0 ± 0.5 ‰). The observed Ge/Si and Ge and Si isotope signatures are best explained by the precipitation of secondary and tertiary clays, with vegetation playing a negligible role. The magnitude of fractionation observed in the fluids and the solids was shown to depend on the chemical mobility of each element. Due to the high degree of Ge retention in secondary products, weathering fluid δ⁷⁴Ge signatures were more strongly fractionated than δ³⁰Si. The opposite was true for the δ⁷⁴Ge and δ³⁰Si composition of the residual soils. ❧ Chapter 5 focuses on the other climatic extreme, investigating Ge/Si and δ⁷⁴Ge behavior during glacial weathering processes in West Greenland. The field study was coupled with a long-term river water and sediment incubation experiment to provide a direct observation of Ge/Si and δ⁷⁴Ge evolution with continued weathering. The dissolved Ge/Si ratios in periglacial streams, the Watson River, and its tributaries ranged from 0.9 to 2.2 µmol/mol, higher than most non-glacial rivers around the world, and likely reflecting preferential dissolution of Ge-rich biotite during subglacial weathering. Dissolved δ⁷⁴Ge of the Watson River was 0.86±0.24 ‰, only slightly heavier than the river suspended load (0.48±0.23 ‰), indicating limited precipitation of secondary weathering products during the short rock-water contact times associated with glacial weathering. Incubating unfiltered river water with its suspended sediment in the laboratory for 1.5‐2 years has resulted in the reduction of dissolved Ge/Si to ~0.5 µmol/mol, indicating significant Ge removal from solution with increased rock-water contact time, most likely due to adsorption to Fe oxyhydroxides. At the same time, dissolved δ⁷⁴Ge increased to 1.9‐2.2 ‰. The Δ⁷⁴Ge_{sec-diss} fractionation factor was calculated as −2.1±1.4 ‰, in good agreement with previously determined values for Ge adsorption onto Fe oxide particles and similar to the values determined for tropical weathering in Chapter 4. ❧ Chapter 6 presents an overview of δ⁷⁴Ge, δ³⁰Si, and Ge/Si data in a number of world's rivers, spanning different climatic and geomorphic regimes. Co-variation of all three proxy signatures confirmed that fluid composition is primarily determined by the precipitation of secondary weathering products. Ge/Si and δ³⁰Si signatures deviated from this trend only where biological uptake of Si is thought to have a major effect on the river chemistry. In contrast, δ⁷⁴Ge appeared unaffected by the limited uptake of Ge by diatoms or vegetation. Despite the broad similarity between the riverine δ⁷⁴Ge, δ³⁰Si, and δ⁷Li behavior, each proxy displayed a unique global relationship with silicate chemical weathering intensity. These differences likely stem from variable affinity of each element to different secondary weathering phases (Fe and Al oxides, various aluminosilicates). ❧ Finally, Chapter 7 presents and updated global Ge isotope budget, taking into account the revised global riverine δ⁷⁴Ge composition from Chapter 6. The knowledge of δ⁷⁴Ge dynamics gained from the preceding chapters is used to investigate the glacial-interglacial changes in Ge and Si cycles, using the paleorecord previously presented by Mantoura (2006). Since diatom δ⁷⁴Ge composition appears insensitive to vital effects, sedimentary diatom δ⁷⁴Ge paleorecords can be used to track secular changes in the seawater signature that result from the shifting balance between the different Ge sources and sinks. However, similar δ⁷⁴Ge composition of various input fluxes and limited isotopic fractionation during authigenic Ge removal from pore water (~−1 ‰ relative to seawater), coupled with short Ge residence time in the ocean, results in only small δ⁷⁴Ge_{SW} variations over glacial-interglacial cycles. More work is needed to confirm and explain the small ~0.5 ‰ positive excursion during glacial termination T2 at 130 ka. In summary, δ⁷⁴Ge paleorecords should be insensitive to local biological overprinting and could help identify large magnitude perturbations in the global Ge and Si cycles.

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Asset Metadata

Creator Baronas, Jokubas Jotautas (author)

Core Title Germanium and silicon isotope geochemistry in terrestrial and marine low-temperature environments

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Geological Sciences

Publication Date 07/18/2017

Defense Date 05/11/2017

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag germanium,global budget,isotope geochemistry,marine sediment diagenesis,OAI-PMH Harvest,rivers,seawater,silicon,Soils,weathering

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Hammond, Douglas E. (committee chair), de Barros, Felipe (committee member), West, Abraham Joshua (committee member)

Creator Email baronas@usc.edu,jotautas.baronas@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c40-403765

Unique identifier UC11263211

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Legacy Identifier etd-BaronasJok-5538.pdf

Dmrecord 403765

Document Type Dissertation

Rights Baronas, Jokubas Jotautas

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Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the a...

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