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From tree tops to river runoff: tracing plant wax biomarkers across the Peruvian Andes and Amazon

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From tree tops to river runoff: tracing plant wax biomarkers across the Peruvian Andes and Amazon

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FROM TREE TOPS TO RIVER RUNOFF –
TRACING PLANT WAX BIOMARKERS
ACROSS THE PERUVIAN ANDES AND AMAZON

by

Mong Sin (Christine) Wu

_________________________________________________

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
(GEOLOGICAL SCIENCES)

August 2019

Copyright 2019 Mong Sin Wu

i

Abstract
Plant wax biomarkers are popular proxies for reconstructing past climate and environment in a
variety of geologic archives including paleosol, lacustrine and marine sediments. Numerous
plant-based modern calibration studies have formed the basis of the application of plant wax
biomarkers as recorders of environmental conditions. However, knowledge about the processes
in between the source (plant production) and sink (sedimentary deposition) of plant wax
biomarkers, which contribute to their preservation, loss, and mobilization across the landscape,
remains limited. The molecular and isotopic signatures may undergo changes during these
processes, affecting our ability to directly translate plant-based calibrations into interpretations of
sedimentary records.
To address this gap in knowledge, this thesis studies plant wax biomarkers (long-chain n-alkanes
and n-alkanoic acids) along a 4 km elevation transect across the highly-biodiverse tropical
forests along the eastern flank of the Peruvian Andes to Amazon. My coauthors and I trace them
from tree leaves, through soils, and into river sediments to evaluate how they mobilize across the
landscape, and how their molecular and isotopic signatures may be preserved or altered in transit.
We surveyed the carbon isotopic compositions (δ
13
C) of long-chain n-alkanes in 405 canopy
leaves sampled across 129 species from nine forest plots, and n-alkanoic acids from a subset of
samples, along the Andes-Amazon transect. The study reveals an increase in δ
13
C with elevation
(+1.45 ± 0.33 ‰ km
-1
for C
29
n-alkane), highlighting the potential for this metric as tracer of
sourcing-elevation of biomarkers within catchment, and as proxy for paleoaltimetry.
We further study n-alkanes and n-alkanoic acids in soils across the Andes-Amazon transect,
which yield gradients in δ
13
C (c. 1.5 ‰ km
-1
) and δD (c. 10 ‰ km
-1
) that are similar to that in
ii

canopy leaves, suggesting the elevation signals are transferred from plant to soil (but with an
offset in δ
13
C of n-alkanes which will be addressed later). Combining dual isotopes and dual
compound classes, we evaluate the sourcing of plant wax biomarkers in rivers within the
catchment, and find that trends in river sediments generally follow isotopic gradients defined by
their mean catchment elevations, suggesting a relatively spatially-uniform integration. The last
part of the thesis investigates how plant wax signatures transfer from plants to soils. With
sampling of leaf litters and soils depth-profiles, we piece together a more detailed picture of how
plant wax signatures are altered from plants to soils during early diagenesis. Overall, this thesis
contributes to the understanding of how plant waxes are mobilized across the landscape, and the
processes that lead to the alteration of their molecular and isotopic signatures in transit.

iii

Acknowledgements

I would like to thank my advisor, Sarah Feakins, for your training and guidance, and for many
things that I have learned from you throughout all these years. Your support, encouragement, and
dedication are fundamental in making this research possible. Thanks to Josh West for the helpful
comments and discussions, and for years of support both scientifically and personally. Thanks to
Camilo Ponton for the guidance and scientific discussions, and for being a supportive friend.
Thanks to Valier Galy for my wonderful time at WHOI. Thanks to Will Berelson, Frank Corsetti,
and Doug Hammond for your encouragements during times of difficulties. Thanks to Miguel
Rincon for the lab training and instrument trouble-shooting. Thanks to all the co-authors of the
publications and submitted manuscripts arise from this piece of work, without whom these
studies would not have taken shape.
I want to thank my lab mates Hannah Liddy, Hyejung Lee, Mark Peaple, Emily Tibbett, Patrick
Cho, and Efrain Vidal for all the great friendship and wonderful times together, and the fun
discussions both scientifically and random. I also want to thank my dear friends for all the fun
food and game gatherings that keep me happy. Special thanks to Guang-Sin Lu and Xin Song for
the great companionship since my first year in the department.
I am very grateful to my parents for raising me up with love and care, and for taking me to
fantastic travels that exposed me to the wonderful nature that inspired me to pursue study in the
field of earth sciences.
Finally, I want to thank my dear husband and best friend, Gen Li, for all the support,
encouragement, devotion, and care throughout these years. I am lucky to have met you from
early on, and walk this journey of pursuing a PhD degree together with you.

iv

Table of Contents

ABSTRACT…………………………………………………………………………………. i
ACKNOWLEDGEMENTS…………………………………………………………………….. iii
LIST OF FIGURES……………………………………………………………………………. viii
LIST OF TABLES……………………………………………………………………………. xi

CHAPTER 1: INTRODUCTIONS………………………………………………………….. 1

1.1 Introduction to plant wax biomarkers……………………………………………… 1
1.2 Tracing plant wax biomarkers from source to sink……………………………...… 2
1.3 The Andes-Amazon transect as a case study……………………………………… 3
1.4 Dissertation chapters and research objectives………………………………………5
References………………………………………………………………………………….. 7

CHAPTER 2: ALTITUDE EFFECT ON LEAF WAX CARBON ISOTOPIC COMPOSITION IN
HUMID TROPICAL FORESTS …………........................................................................... 9

Abstract………………………………………………………………………………..….. 9
2.1 Introduction……………………………………………………………………..… 10
2.1.1
13
C fractionation during carbon fixation and growth of plant leaves…..…. 11
2.1.2
13
C fractionation during plant wax biosynthesis in C
3
plants …………...... 11
2.1.3 Altitude effect on δ
13
C in humid ecosystems……………………………... 12
2.2 Materials and Methods……………………………………………………………. 13
2.2.1 Study sites…………………………………………………………………. 13
2.2.2 Climate…………………………………………………………………….. 15
2.2.3 Collection of canopy leaf samples………………………………………… 16
2.2.4 Lipid extraction and compound identification and quantification………… 17
2.2.5 Compound-specific carbon isotopic analysis……………………………… 18
2.2.6 Bulk leaf carbon isotope analysis…………………………………………. 19
2.2.7 Carbon isotopic fractionation…………………………………………….. 19
2.2.8 Community average………………………………………………………. 20
2.3 Results…………………………………………………………………………….. 20
2.3.1 Leaf wax δ
13
C results…………………………………………………….. 20
2.3.2 Leaf wax δ
13
C values across the elevation profiles………………………. 22
2.3.2.1 Taxon-specific leaf wax δ
13
C gradients………………………….. 25
2.3.3 Comparison of leaf wax and bulk leaf δ
13
C………………………………. 26
2.3.4 Canopy effects……………………………………………………………. 28
2.3.4.1 Sunlit versus shaded canopy leaves………………………………. 28
2.3.4.2 Canopy versus understory………………………………………… 29
v

2.4 Discussions……………………………………………………………………….. 30
2.4.1 Comparison of leaf wax and bulk leaf properties……………………...…. 30
2.4.2 Environmental variables affecting carbon isotope fractionation………….. 31
2.4.2.1 Irradiance and canopy closure…………………………………….. 31
2.4.2.2 Dual C and H isotopic analyses: insights into leaf-atmosphere
exchange………………………………………………………….. 33
2.4.2.3 Adiabatic controls on carbon isotope fractionation………………. 34
2.4.3 Altitude effect on plant wax δ
13
C…………………………………………. 37
2.4.3.1 Evaluating robustness of the altitude effect…………………….…. 37
2.4.3.2 Global synthesis…………………………………………………… 38
2.4.4 General significance………………………………………………………. 40
2.4.4.1 Paleoaltimetry……………………………………………………… 41
2.4.4.2 Paleoecology………………………………………………………. 42
2.5 Conclusions……………………………………………………………………….. 43
Acknowledgements……………………………………………………………………….. 44
References………………………………………………………………………………… 46

CHAPTER 3: DUAL ISOTOPE EVIDENCE FOR SEDIMENTARY INTEGRATION OF PLANT
WAX BIOMARKERS ACROSS AN ANDES-AMAZON ELEVATION TRANSECT …….. 52

Abstract……………………………………………………………………………………. 52
3.1 Introduction……………………………………………………………………….. 53
3.2 Materials and Methods……………………………………………………………. 58
3.2.1 Study area…………………………………………………………………. 58
3.2.2 Field methods……………………………………………………………… 59
3.2.2.1 Soil samples……………………………………………………….. 59
3.2.2.2 River suspended sediment samples……………………………….. 60
3.2.3 Laboratory methods………………………………………………………. 63
3.2.3.1 Lipid extraction and compound identification……………………. 63
3.2.3.2 Compound-specific isotopic analysis……………………………... 64
3.2.3.3 Catchment hypsometry……………………………………………. 65
3.3 Results…………………………………………………………………………….. 65
3.3.1 Concentrations of organic carbon and plant wax biomarkers in soils…….. 65
3.3.2 Altitude effect in plant wax δ
13
C and δD in soils…………………………. 67
3.3.3 Altitude effect in plant wax δ
13
C and δD in surface river suspended
sediments………………………………………………………………….. 69
3.3.3.1 River depth profiles………………………………………………... 70
3.4 Discussions………………………………………………………………………… 73
3.4.1 Altitude effect in soils……………………………………………………… 73
3.4.1.1 Offset in n-alkane δ
13
C values between canopy and soils ………… 74
vi

3.4.2 The river-transported signal of plant wax δ
13
C and δD……………………. 76
3.4.3 Dual isotope analysis………………………………………………………. 79
3.4.4 Implications for geological and geochemical applications………………… 81
3.4.4.1 Understanding plant wax sedimentary integration………………… 81
3.4.4.2 Potential for dual isotope plant wax paleoaltimetry……………….. 86
3.5 Conclusions………………………………………………………………………… 89
Acknowledgements………………………………………………………………………… 91
References…………………………………………………………………………………. 91

CHAPTER 4: TROPICAL SOIL PROFILES REVEAL THE FATE OF PLANT WAX
BIOMARKERS DURING SOIL STORAGE ……………………...……………………….. 99

Abstract……………………………………………………………………………………. 99
4.1 Introduction……………………………………………………………………….. 100
4.1.1 Diagenesis of plant wax biomarkers………………………………………. 102
4.1.2 Tropical soils in an Andes-Amazon transect……………………………… 103
4.2 Materials and Methods……………………………………………………………. 105
4.2.1 Field sampling……………………………………………………………… 105
4.2.2 Bulk organic carbon analysis……………………………………………… 108
4.3.3 Lipid extractions…………………………………………………………… 109
4.2.4 Compound identification and quantification………………………………. 109
4.2.5 Compound-specific isotopic analysis……………………………………… 110
4.3 Results…………………………………………………………………………….. 111
4.3.1 TOC and plant wax abundance…………………………………………… 111
4.3.2 Chain length distributions…………………………………………………. 113
4.3.3 Hydrogen and carbon isotopic compositions……………………………… 116
4.3.3.1 Slope base-ridgetop comparisons…………………………………. 119
4.4 Discussions………………………………………………………………………… 119
4.4.1 Alteration of plant wax signatures across the litter-soil profile…………… 119
4.4.1.1 Plant wax transformation within leaf litter………………………… 119
4.4.1.2 Exponential decline of plant wax concentrations with depth in
soils……………………………………………………...…………. 121
4.4.1.3 The role of microbial activities in plant wax degradation in soils…. 125
4.4.1.4 Are root fungal contributions of plant waxes significant?………… 127
4.4.1.5 No systematic change in plant wax δD between canopy, litter
and soil………………………………………………….…………. 129
4.4.1.6 A systematic shift in plant wax δ
13
C between canopy, litter
and soil…………………………………………………………….. 131
4.4.2 Implications for plant wax calibration studies for paleoclimate
applications………………………………………………………………… 132
vii

4.4.2.1 Soil-based calibrations as integrator of plant signals………………. 132
4.4.2.2 Soils, not plants, are the major stock of plant wax………………… 135
4.4.2.3 Soil stocks are sources for fluvial erosion………………………… 138
4.5 Conclusions………………………………………………………………………… 139
Acknowledgements………………………………………………………………………… 141
References…………………………………………………………………………………. 142

CHAPTER 5: DISSERTATION CONCLUSIONS…………………………………………. 149

APPENDIX A: SUPPLEMENTARY INFORMATION FOR CHAPTER 2………………… 154
A.1 δ
13
C values of n-alkanoic acid homologues and comparison with n-alkanes……… 154
A.2 n-Alkanoic acid vs. bulk leaf δ
13
C………………………………………………… 156
A.3 n-Alkanoic acid δ
13
C across the elevation profile………………………………… 156
References…………………………………………………………………………………. 159

viii

List of Figures
Figure 1.1 The study area of this dissertation, along an Andes-Amazon transect in
Peru in the Madre de Dios River catchment………………………………. 4

Figure 2.1 Location of nine forest plots on an elevation transect along the eastern
flank of Andes in southern Peru…………………………………………… 15

Figure 2.2 Crossplots of δ
13
C
wax
values between odd-chain n-alkane homologues
measured from individual sunlit canopy leaf samples…………………..… 21

Figure 2.3 δ
13
C values of C
29
n-alkane from sunlit canopy leaf samples versus
elevation…………………………………………………………………… 23

Figure 2.4 Elevation trends in δ
13
C
29alk
from the same genus (Ocotea) and families
that span a wide range of elevations………………………………………. 26

Figure 2.5 Comparison of δ
13
C values between n-alkane homologues and bulk leaf… 27

Figure 2.6 Elevation trends in bulk leaf, δ
13
C of C
29
n-alkane, and ε
29alk/leaf
………… 28

Figure 2.7 Carbon isotopic offset between paired sunlit and shaded leaf samples,
and comparison of δ
13
C
29alk
values of sunlit canopy leaves and
understory leaves…………………………………………………………... 29

Figure 2.8 Comparison between C
29
n-alkane δ
13
C and δD…………………………… 33

Figure 2.9 Comparing the relationship between pCO
2
and carbon isotopic
fractionation from the Peru transect to growth chamber experiments……... 35

Figure 2.10 Assessment of sensitivity of regression to sample size using a Monte
Carlo subsampling approach………………………………………………. 38

Figure 2.11 Global elevation gradients in the carbon isotopic fractionations for bulk
leaf and leaf wax C
29
n-alkane…………………………………………….. 40

Figure 3.1 Shaded relief map showing soil and river sampling locations……………. 62

Figure 3.2 Soil isotopic gradients (δ
13
C and δD) of C
29
n-alkane and
C
30
n-alkanoic acid……………………………………………………….. 68

ix

Figure 3.3 River data showing δ
13
C and δD of C
29
n-alkane and C
30
n-alkanoic
acid from river suspended sediments from main stem and tributary………. 70

Figure 3.4 Properties of bulk sediment and plant wax biomarkers in the depth
profiles of the Madre de Dios River……………………………………….. 72

Figure 3.5 Comparison of δD and δ
13
C values of C
29
n-alkane and C
30
n-alkanoic
acid for river suspended sediments in wet and dry seasons, as well as the
depth profile samples………………………………………………………. 80

Figure 3.6 Synthesis of plant wax sedimentary integration studies…………………… 84

Figure 3.7 Comparison of δD and δ
13
C values of C
29
n-alkane and C
30
n-alkanoic
acid in both the soil O and M horizon samples……………………………. 87

Figure 4.1 Sampling locations across a 2740m Andes-Amazon transect in the Cusco
and Madre de Dios region of Peru………………………………………… 107

Figure 4.2 Vertical profiles of plant wax and bulk OC abundance at the four
study sites………………………………………………………………….. 112

Figure 4.3 Chain length distributions of n-alkanes and n-alkanoic acids in litter,
soil, and root at the four study sites……………………………………….. 114

Figure 4.4 Vertical profiles of carbon preference index (CPI) and average chain
length (ACL) of C
23-33
n-alkanes and C
22-32
n-alkanoic acids at the four
study sites………………………………………………………………….. 115

Figure 4.5 Vertical profiles of δD and δ
13
C of C
29
n-alkane and C
30
n-alkanoic acid
at the four study sites……………………………………………………… 118

Figure 4.6 Exponential decrease in the total abundance of bulk OC, n-alkanes (C
23-33
)
and n-alknaoic acids (C
22-32
) within soil profiles at the four sites……..….. 122

Figure 4.7 The rate of OC and plant wax loss with soil depth and litter
decomposition rate along the Andes-Amazon elevation transect……….… 124

Figure 4.8 Increase in mid-chain (C
23-25
) n-alkane fractional abundance down the
litter-soil profiles…………………………………………………………... 126

x

Figure 4.9 Estimates of the stock of plant waxes for C
23-33
n-alkanes and C
22-32

n-alkanoic acids in leaves and soil top 30cm across the Peruvian
Andes-Amazon elevation transect………………………………………… 137

Figure A.1 Comparison of δ
13
C values between consecutive n-alkanoic acid
homologues………………………………………………………..………. 154

Figure A.2 Comparison between δ
13
C
29alk
and δ
13
C
30acid
……………………………… 155

Figure A.3 Comparison between n-alkanoic acid δ
13
C values with δ
13
C
leaf
….……….. 156

Figure A.4 δ
13
C values of C
30
n-alkanoic acids from sunlit canopy leaf samples
versus elevation……………………………………………………………. 157
xi

List of Tables
Table 2.1 Environmental and ecological characteristics of 1-ha study plots along
a tropical montane elevation gradient, together with sample size and
representation………………………………………………………………. 14

Table 2.2 Unweighted site-mean carbon isotopic compositions and fractionations
in the Andes-Amazon transect…………………………………………….. 24

Table 2.3 Altitude effect demonstrated in dual isotopes for two classes of leaf wax
biomarkers…………………………………………………………………. 42

Table 3.1 Predicting paleoaltimetry using a dual isotope plant wax approach in
paleosols…………………………………………………………………… 59

Table 4.1 Locations and information of sampling sites along the Andes-Amazon
Transect………………………………………………………………….… 106

1

Chapter 1
Introductions
1.1 Introduction to plant wax biomarkers
Plant wax biomarkers are a group of lipids that coat the leaf surfaces of terrestrial plants,
typically including long-chain (C
24
– C
36
) n-alkanes, n-alkanoic acids, and n-alkanols, among
other compound classes that are less studied. Since the discovery of their persistence in the
environment and ubiquity in sedimentary archives (Eglinton and Hamilton, 1963), and advances
in analytical techniques namely the development of gas chromatography/mass spectrometry
(GC/MS) which allows compound identification, and gas chromatography/isotope ratio mass
spectrometry (GC/IRMS) which enables compound-specific isotopic analysis (e.g. Sessions,
2006), studies of these molecules in the nature have thrived in the recent decades. Plant wax
biomarkers exist in homologous series of compounds of different carbon numbers, with
characteristic odd (for n-alkanes) or even (for n-alkanoic acids and n-alkanols) carbon number
preference resulted from their biosynthetic pathways. They can be characterized in terms of their
molecular distributions (e.g. carbon preference index, average chain length, dominant chain
length), and carbon and hydrogen isotopic compositions (δ
13
C and δD), and many of these
metrics reflect on the environmental conditions at time of biosynthesis.
Due to the ability for these organic molecules to encode environmental and climate conditions,
they have found applications in paleoenvironmental reconstructions. Most common use of these
molecules include reconstructing past hydroclimate using plant wax δD as a proxy of
precipitation δD (Sachse et al., 2012), and investigating past vegetation changes (C
3
vs C
4
) using
plant wax δ
13
C, based on vastly different δ
13
C values between these plant types (Collister et al.,
2

1994). More recently, plant wax δD has been recognized as a paleoaltitude proxy (e.g. Polissar et
al., 2009; Kar et al., 2016) based on the effect of altitude on precipitation δD.
While applying plant waxes for past reconstructions represent one end of the plant wax research
spectrum, the other end is the modern calibration studies that seek to establish relationships
between plant wax signatures and various controlling factors. Many modern calibration studies
focus on characterizing plant wax biomarkers in living plants with respect to vegetation types or
environmental variables, while others approach the problem by studying plant waxes in soils and
sediments across environmental gradients to establish relationships. Researchers have
investigated the molecular and isotopic signatures of plant wax biomarkers in the context of
environmental factors such as hydrology (e.g. Sachse et al., 2006), elevation (e.g. Jia et al., 2008),
light (e.g. Yang et al., 2009), and temperature (e.g. Bush and McInerney, 2015), as well as
control from vegetation types including C
3
v C
4
(e.g. Collister et al., 1994), different types of C
3

plants (e.g. Diefendorf et al., 2015), and aquatic inputs (e.g. Aichner et al., 2010). Together these
studies form the foundation for the application of these molecules as paleoenvironmental proxies.
1.2 Tracing plant wax biomarkers from source to sink
We like to think that we understand plant wax biomarkers well enough to adequately interpret
them in geologic archives, but the fact that researchers are still conducting modern calibration
studies demonstrates that gaps exist in our current knowledge which may inhibit how reliably we
can infer past environmental and climate information. The majority of modern calibration studies
are geared towards understanding the factors controlling plant wax signatures during synthesis at
modern times. However, plant waxes that we find in sedimentary archives are probably not at the
same status as when they were biosynthesized in ancient times, considering the journeys these
3

molecules have been through from source (biosynthesis) to sink (sediments) and the processes
that may have altered their signatures when these molecules move from plants to soils, and from
soils to sediments as they are mobilized across the landscapes.
It has been recognized by the plant wax community that our level of understanding concerning
their source-to-sink processes is poor, both for δD (Sachse et al., 2012) and for δ
13
C (Diefendorf
and Freimuth, 2017). In recent years, more studies have been conducted to address this gap in
our knowledge, including tracing provenance of plant waxes within a catchment to contrast
seasonal changes in runoff (Hemingway et al., 2016), export from C
3
upland vs C
4
lowland
vegetation (Galy et al., 2011), between C3 forest and petrogenic sources (Häggi et al., 2016),
between river and estuary (Madeiros et al., 2012), and along elevation transects (Ponton et al.,
2014; Hoffman et al., 2016). Apart from providing implications to applications for past
reconstructions, these studies also advance our knowledge in the transport of biospheric carbon
that has implications in the global carbon cycle. However, despite the increase in the number
relevant studies in recent years, it is fair to say that we are just beginning to understand more
about plant wax source-to-sink processes, and gaps in our current knowledge are still large.
1.3 The Andes-Amazon transect as a case study
This dissertation aims to contribute to the on-going research in plant wax source-to-sink
processes and address some gaps in our understanding. It takes an elevation transect along the
eastern flank of the Peruvian Andes down to Amazon floodplains (Fig. 1.1) as a case study, and
seek to understand how plant waxes move across the landscape by characterizing their molecular
and isotopic signatures within different components along the journey. Few studies have
investigated plant waxes in transit along elevation gradients, including Galy et al. (2011) and
4

Hoffman et al. (2016) along the Himalayan front, and Ponton et al. (2014) along the Peruvian
Andes-Amazon, the same study area as in this dissertation. Studies of plant waxes in living
plants have also been conducted in the Andes-Amazon transect, including characterizing plant
wax δD (Feakins et al., 2016a) and n-alkane production (Feakins et al., 2016b) along the
elevation gradient. This dissertation builds upon these previous studies and addresses the
research gaps, by tracing plant waxes from living plants, to soils, to river sediments towards a
fuller understanding of the system. In particular, my major research questions include:
- Can plant wax δ
13
C reflect sourcing elevation similar to δD?
- How is plant wax biomarkers integrated in fluvial sediments across the catchment?
- Are plant waxes altered in their molecular and isotopic signatures throughout the journey?

Fig. 1.1. The study area of this dissertation, along an Andes-Amazon transect in Peru in the Madre de Dios River
catchment.
5

1.4 Dissertation chapters and research objectives
This dissertation summarizes my research on plant wax biomarkers across the Peruvian Andes-
Amazon transect at the University of Southern California from 2012 to 2018. The theme of this
dissertation is characterizing molecular and isotopic signatures of plant wax biomarkers (n-
alkanes and n-alkanoic acids) from tree tops, to soils, to river sediments to understand how these
molecules move across the landscape from source to sink, and how the environmental
information encoded in their signatures may be preserved or altered during the journey. This
dissertation is comprised of separate chapters focusing on different components under this
general theme, and each chapter is written as a unit submitted for publication. The research
objectives and papers published or submitted from each chapter are listed as below.
Chapter 2
This chapter focuses on the plant wax δ
13
C of tree canopy leaves across the Andes-Amazon
transect. This variable is generally considered a proxy for C
3
vs C
4
vegetation in paleoclimate
applications, however studies have revealed various environmental controls on the δ
13
C of bulk
leaf, as well as plant waxes, within the C
3
category. While water availability (mean annual
precipitation) is the dominant control of plant δ
13
C globally, elevation emerges as a driver of
δ
13
C of C3 plants in global wet regions (Diefendorf et al. 2010). This phenomena has been long
known for bulk δ
13
C (Korner et al. 1988), but had not been studied in plant wax biomarkers. This
study seeks to establish the elevation gradient in plant wax δ
13
C across the wet Andes-Amazon
transect, with an unprecedented sample size of >400 leaves to adequately capture the
environmental signal amid high species diversity. Findings from this chapter provide the basis of
using plant wax δ
13
C as an elevation proxy in the following chapters.
6

Chapter 3
This chapter seeks to understand how plant wax biomarkers transported by rivers are integrated
throughout the catchment, using plant wax δ
13
C and δD as tracers of sourcing elevation. By
analyzing these variables in soils across the Andes-Amazon transect, and comparing them to data
from river suspended sediments, inferences are made about the pattern of plant wax integration
in the catchment, and how that changes seasonally. This study extends the previous study by
Ponton et al. (2014) which focuses on δD of n-alkanoic acids; here we expand the analysis to
adopt a dual isotope (δ
13
C and δD) dual compound (n-alkanes and n-alkanoic acids) approach to
provide better constrains.
Chapter 4
This chapter takes a closer look at the soils of this Andes-Amazon transect, and studies how the
molecular and isotopic signatures of plant wax biomarkers change across the litter-soil profiles.
This study seeks to understand the surprising finding of δ
13
C offsets between plants and soils
from Chapter 3, and also explores whether plant wax δD and molecular signatures are also
altered during soil storage, as well as if there are difference in the degradation processes between
n-alkanes and n-alkanoic acids. This chapter is expected to provide implications for modern
calibration studies using soils as integrator of plant signals.

7

References
Aichner, B., Herzschuh, U., Wilkes, H., Vieth, A., Böhner, J. 2010. δD values of n-alkanes in
Tibetan lake sediments and aquatic macrophytes – A surface sediment study and
application to a 16 ka record from Lake Koucha. Organic Geochemistry 41, 779 – 790.
Bush, R.T., McInerney, F.A. 2015. Influence of temperature and C
4
abundance on n-alkane chain
length distributions across the central USA. Organic Geochemistry 79, 65 – 73.
Collister, J.W., Rieley, G., Stern, B., Eglinton, G. and Fry, B. 1994. Compound-specific δ
13
C
analyses of leaf lipids from plants with differing carbon dioxide metabolisms. Organic
Geochemistry 21, 619 – 627.
Diefendorf, A.F., Freimuth, E.J. 2017. Extracting the most from terrestrial plant-derived n-alkyl
lipids and their carbon isotopes from the sedimentary record: A review. Organic
Geochemistry 103, 1 – 21.
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Oecologia 160, 461 – 470.
9

Chapter 2
Altitude Effect on Leaf Wax Carbon Isotopic Composition in
Humid Tropical Forests
This chapter was published in 2017 as:
Wu, M.S., Feakins, S.J., Martin, R.E., Shenkin, A., Bentley, L.P., Blonder, B., Salinas, N., Asner,
G.P., Malhi, Y. 2017. Altitude effect on leaf wax carbon isotopic composition in humid tropical
forests. Geochimica et Cosmochimica Acta 206, 1 – 17.

Abstract
The carbon isotopic composition of plant leaf wax biomarkers is commonly used to reconstruct
paleoenvironmental conditions. Adding to the limited calibration information available for
modern tropical forests, we analyzed plant leaf and leaf wax carbon isotopic compositions in
forest canopy trees across a highly biodiverse, 3.3 km elevation gradient on the eastern flank of
the Andes Mountains. We sampled the dominant tree species and assessed their relative
abundance in each tree community. In total, 405 sunlit canopy leaves were sampled across 129
species and nine forest plots along the elevation profile for bulk leaf and leaf wax n-alkane (C
27
–
C
33
) concentration and carbon isotopic analyses (δ
13
C); a subset (76 individuals, 29 species, five
forest plots) were additionally analyzed for n-alkanoic acid (C
22
– C
32
) concentrations and δ
13
C.
δ
13
C values display trends of +0.87‰ km
−1
(r
2
= 0.96, p < 0.01) for bulk leaves and +1.45‰
km
−1
(r
2
= 0.94, p < 0.01) for C
29
n-alkane, the dominant chain length. These carbon isotopic
gradients are defined in multi-species sample sets and corroborated in a widespread genus and
several families, suggesting the biochemical response to environment is robust to taxonomic
10

turnover. We calculate fractionations and compare to adiabatic gradients, environmental
variables, leaf wax n-alkane concentrations, and sun/shade position to assess factors influencing
foliar chemical response. For the 4 km average height of the Andes, 4–6‰ higher δ
13
C values
are expected for upland versus lowland C
3
plant bulk leaves and their n-alkyl lipids, and we
expect this pattern to be a systematic feature of very wet tropical montane environments. This
elevation dependency of δ
13
C values should inform interpretations of sedimentary archives, as
13
C-enriched values may derive from C
4
grasses, petrogenic inputs or upland C
3
plants. Finally,
we outline the potential for leaf wax carbon isotopes to trace biomarker sourcing within
catchments and for paleoaltimetry.
2.1 Introduction
Plant leaf wax biomarkers and their carbon isotopic composition (δ
13
C
wax
) have found
application for sedimentary sourcing studies (Galy et al., 2011; Tao et al., 2015; Häggi et al.,
2016; Hemingway et al., 2016) and paleoenvironmental reconstructions (Freeman and Colarusso,
2001; Huang et al., 2001; Schefuß et al., 2003; Feakins et al., 2005; Whiteside et al., 2010;
Tipple et al., 2011). Modern plant sampling have built a foundation for biomarker-based
reconstructions (Collister et al., 1994; Chikaraishi and Naraoka, 2003; Bi et al., 2005; Garcin et
al., 2014). With relevance to tropical C
3
forests the data are limited, however a survey in west-
central Africa has found a trend towards higher δ
13
C
wax
in C
3
plants with increasing aridity
(Vogts et al., 2009; Garcin et al., 2014; Badewien et al., 2015). Within canopies there are also
vertical gradients (up tree) in δ
13
C
wax
(Graham et al., 2014). Although our theoretical
understanding of plant carbon fixation and fractionation is high, we have gaps in knowledge of
plant wax traits at the ecosystem-scale (Diefendorf and Freimuth, 2017). In particular, we need
broad sampling of productive, canopy foliage in highly biodiverse, tropical forests (Freeman and
11

Pancost, 2014), toward source-to-sink carbon cycle studies in tropical montane catchments and
paleoenvironmental reconstructions.
2.1.1
13
C fractionation during carbon fixation and growth of plant leaves
The carbon isotopic composition of plant organic matter is a product of atmospheric conditions
and plant biogeochemistry associated with the fixation of atmospheric CO
2
(Farquhar et al.,
1989). Extensive research has catalogued the environmental sensitivity of carbon isotopic
fractionations in C
3
plants in natural ecosystems and experimental settings (see Cernusak et al.,
2013 for a recent review). Most theory developed from measurements on bulk leaf (δ
13
C
leaf
).
δ
13
C
leaf
represents the average of all biochemicals within the leaf at time of measurement, each
the product of many biosynthesis steps. Variable proportions of non-structural carbohydrates
such as starches and simple sugars are exported from the leaf (Hoch and Körner, 2012) and
proportions of the foliar constituents (lignin, cellulose etc.) vary between species and across
environmental gradients (Asner et al., 2014b, 2016). Nevertheless, δ
13
C
leaf
represents a
convenient metric for the assessment of the relative fractionation of specific compounds.
2.1.2
13
C fractionation during plant wax biosynthesis in C
3
plants
Compound-specific approaches isolate a class of biomarkers for carbon isotopic analysis, for
example the n-alkanes, n-alkanoic acids or n-alcohols, commonly present in the cuticular and
epicuticular waxes on a plant leaf (Eglinton and Hamilton, 1967), and of these the n-alkanes are
the most commonly applied to paleoenvironmental reconstructions. Although compound-specific
approaches routinely collect data on multiple homologues within a series (Garcin et al., 2014),
often one chain length will be selected for sedimentary reconstructions, perhaps most often the
C
29
n-alkane (δ
13
C
29alk
). Compound-specific isotopic compositions reflect the net fractionation
12

from a sequence of biosynthesis steps; the pathway for production of n-alkyl lipids are known
(Zhou et al., 2010) and the isotopic composition of different chain lengths and compound classes
have been reported (Chikaraishi and Naraoka, 2007). However theory and applications would
benefit from a more comprehensive assessment of biomarker isotopic compositions in natural
ecosystems and experimental settings (Freeman and Pancost, 2014). Paired measurements of
compound-specific and bulk carbon isotopic compositions would help to capitalize on decades of
δ
13
C
leaf
research and established theory for C-fixation to build understanding of post-
photosynthetic fractionation processes (Brüggemann et al., 2011).
2.1.3 Altitude effect on δ
13
C in humid ecosystems
Altitudinal transects have revealed that δ
13
C
leaf
values increase with elevation by c. 1‰ km
−1
in
non-water-stressed regions (Körner et al., 1988, 1991), but the relationship may break down in
arid regions (Friend et al., 1989). Globally, δ
13
C
leaf
values in C
3
plants show mean annual
precipitation (MAP) as the primary variable, with elevation the secondary variable (Diefendorf et
al., 2010). But, the precipitation relationship is logarithmic, with greater sensitivity at lower
MAP (when plants restrict stomatal exchange of water and indirectly CO
2
), and very little
sensitivity in very wet climates (when stomata are open). Between 250 mm (semi-arid) and 1.5
m MAP δ
13
C values drop by c. 6‰, but <1‰ further drop occurs between 1.5 and 3.75 m MAP
(Diefendorf et al., 2010). Elevation is therefore expected to dominate in wet climates.
Humid tropical forests drape the eastern flank of the Andes, and yield a δ
13
C
leaf
increase with
elevation (Asner et al., 2014b). In this ecological survey and elevation-δ
13
C biomarker
calibration effort, we resample that profile and extend analyses to individual leaf wax
compounds, to survey post-photosynthetic carbon isotope biogeochemistry. We greatly expand
13

the available data on leaf wax carbon isotopic compositions and fractionations, and provide
landscape-level information with which to characterize tropical forests from lowland and
montane regions. This follows prior reports of elevation responses in the leaf wax δD values
(Feakins et al., 2016a) and in the concentration and productivity of n-alkanes with elevation
(Feakins et al., 2016b) in the same forest plots; as well as δD gradients in river-exported leaf wax
in the same watershed (Ponton et al., 2014). Establishing the gradient of plant biomarker δ
13
C
with elevation in this transect contributes to the study of source-to-sink carbon cycling and
paleoenvironmental reconstructions. The degree to which the findings can be generalized to
other elevation profiles (and to paleoaltimetry) will depend on geographic (and temporal)
differences in climate and ecosystem type (Körner, 2007).
2.2 Materials and Methods
2.2.1 Study sites
This study includes nine plots (Fig. 2.1; Table 2.1) that belong to a group of permanent 1-ha
plots operated by the Andes Biodiversity Ecosystems Research Group (ABERG,
http://www.andesconservation.org) and that are part of the ForestPlots
(https://www.forestplots.net/) and Global Ecosystems Monitoring Network (GEM;
http://gem.tropicalforests.ox.ac.uk/projects/aberg). Five montane plots (ACJ-01, ESP-01, TRU-
04, SPD-01, SPD-02) are located in the Kosñipata Valley in the province of Paucartambo, in the
department of Cusco, Perú; two submontane plots (PAN-03, PAN-02) are located in the
Pantiacolla front range of the Andes, and two lowland plots (TAM-05, TAM-06) are located in
Tambopata, in the department of Madre de Dios, Perú (Fig. 2.1, Table 2.1). All plots have
relatively homogeneous soil substrates and stand structure, and minimal evidence of human
14

disturbance (Girardin et al., 2014). The lowland plots were established in the early 1980s, and
the montane ones between 2003 and 2013, with all stems ≥10 cm diameter at breast height
tagged and identified to species-level, and plots have been annually measured for carbon
allocation and cycling following the standard GEM Network protocol (Marthews et al., 2014).
The productivity and carbon dynamics of these plots have been reported in Malhi et al. (2016).

Table 2.1. Environmental and ecological characteristics of 1-ha study plots along a tropical
montane elevation gradient, together with sample size and representation.
CHAMBASA
TAM-06 TAM-05 PAN-02 PAN-03 SPD-02 SPD-01 TRU-04 ESP-01 ACJ-01
site
Latitude -12.839 -12.831 -12.650 -12.638 -13.049 -13.048 -13.106 -13.175 -13.147
Longitude -69.296 -69.271 -71.263 -71.274 -71.537 -71.542 -71.589 -71.595 -71.632
Elevation* (m) 215 223 595 859 1494 1713 2719 2868 3537
Slope* (deg) 2.2 4.5 11.5 13.7 27.1 30.5 21.2 27.3 36.3
Aspect* (deg) 169 186 138 160.5 125 117 118 302 104
Mean annual air temp.** (°C) 24.4 24.4 23.5** 21.9** 18.8 17.4 13.5 13.1 9
Precipitation (mm yr
-1
) 1900 1900 2366** 2835** 5302 5302 2318 1560 1980
Relative humidity (%)
a
84.5 84.5 75.2 75.2 93.7 93.7 86.2 89.1 93.3
Vegetation height* 28.2 27.5 24.4 18.7 22.8 14 15.7 16.9 12.5
pCO 2 equivalency (ppm)
b
389 389 372 361 335 327 291 286 265
Sampled plant families 11 15 9 10 17 20 10 10 8
Sampled plant genera 14 21 10 14 21 22 11 10 8
Sampled plant species 15 24 11 15 22 26 14 13 11
Sampled plant individuals 38 58 25 36 57 58 49 44 40
Basal area represented (%)
c
18.3 45.9 29.0 33.2 53.4 36.5 43.6 63.4 72.9
*Derived from high-resolution airborne Light Detection and Ranging (LiDAR) data (see Asner et al., 2014a for methodology).

**Derived from observations between 6 Feb 2013 and 7 Jan 2014.

a
(Goldsmith et al., 2016).
b
Estimated based on elevation. See details in
Section 2.2 for methodology.
c
For n-alkanes and bulk samples.
c
Basal area represented by sampled species as a proportion of all trees for the n-
alkane survey. Data for n-alkanoic acids are taken from a subset, with lower basal area representation.

15

Fig. 2.1. Location of nine forest plots on an elevation transect along the eastern flank of Andes in southern Perú. Site
locations (open circles, site name annotated, vegetation zones, cloud base, and river proximity are indicated.
Elevation profile: grey line (elevation acquired from the Shuttle Radar Topographic Mission (SRTM) 90m Digital
Elevation Database 4.1 (Reuter et al., 2007), black line (smoothed elevation), grey envelope (± 2σ elevation from
1km-wide swath perpendicular to transect, re-centered to smoothed elevation).

2.2.2 Climate
Climate is humid throughout the elevation gradient, with mean annual precipitation (MAP)
ranging from 1.5–5.3 m yr
−1
, with peak precipitation at c. 1.5 km elevation in the lower montane
cloud forests (Table 2.1). Mean annual temperature decreases with increasing elevation from
24.4°C to 9°C. Relative humidity is consistently high, typically > 90%, but ranges from 75.2–
93.7%, and estimated Vapor Pressure Deficit (VPD) for plants ranges from 0.05 to 0.75 kPa
(Goldsmith et al., 2016). Along the transect from 0.2–3.5 km elevation, the atmospheric pressure
and hence the partial pressure of CO
2
(pCO
2
) decreases adiabatically following the equation:
16

Equivalent pCO
2
= P
0
× 𝑒𝑒 − ℎ/ ℎ
0
(2.1)
where P
0
is the pCO
2
at sea level, h is the elevation, and h
0
(approximately 8.5 km at 290 K) is
the scale height of Earth’s atmosphere. We have taken a sea level pCO
2
at 394 ppmv as of 1-yr
average prior to sampling in measured at Mauna Loa (Scripps CO
2
Program; Keeling et al.,
2001). We add 5 ppmv to reflect the higher CO
2
concentrations in the Amazon canopy compared
to the free atmosphere (Olsen and Randerson, 2004), although diurnal cycles are about 100 ppmv,
these are assumed constant across the profile. Thus, sampling across the 0.2 to 3.5 km elevation
transect is equivalent to sampling from 389–265 ppmv pCO
2
. For the same year the mean
atmospheric δ
13
C
CO2
reported from Mauna Loa is −8.4‰ (Keeling et al., 2001).
2.2.3 Collection of canopy leaf samples
Canopy leaf samples were collected as part of the CHAMBASA (CHallenging Attempt to
Measure Biotic Attributes along the Slopes of the Andes) project from April – November 2013.
Trees were chosen based on the most recently available census of tree diameter data. A sampling
protocol was adopted wherein species were sampled that maximally contributed to plot basal
area (a proxy for plot biomass or crown area). Within each species, three to five largest
individual trees were chosen for sampling (five trees in upland sites and three trees in lowland
sites depending on species diversity and practical time constraints). When a certain individual
was not available (due to death, tree fall, or inaccessibility), we sampled the next largest
individual tree from that species. If three trees were not available in the chosen plot, additional
individuals of the same species nearby were sampled. Leaf samples from one fully sunlit canopy
branch (of at least 1 cm diameter) were taken from selected trees. In addition, fully shaded
canopy leaves were collected from a fraction of the same trees. From each branch, five leaves
17

from simple-leaved species, or five individual leaflets from compound-leaved species (both
referred to as ‘leaf’ below) were sampled for trait measurements. In the case of compound leaves,
the entire compound leaf was also collected for whole-leaf area calculations. Leaves were chosen
with minimal damage (i.e. herbivory). Leaves were placed in coolers from the field plot to the
field lab for drying at c. 50 °C, and thereafter stored in paper envelopes prior to analysis. The
sample set analyzed for bulk leaf and n-alkanes includes 405 individual samples distributed
across nine forest plots, including 129 species from 47 families, representing 18–73% of the
forest population (Table 2.1), with n-alkane concentrations reported in Feakins et al. (2016b). A
smaller subset of leaf samples was analyzed for n-alkanoic acid δ
13
C (76 individuals, 29 species,
22 families) for a sample set where plant water and leaf wax concentrations and hydrogen
isotopic compositions of n-alkanes and n-alkanoic acids have been reported (Feakins et al.,
2016a).
2.2.4 Lipid extraction and compound identification and quantification
The dried leaves were cut using solvent-cleaned scissors, and leaf waxes were subsequently
extracted by immersing the leaf three times with dichloromethane (DCM):methanol (MeOH) 9:1
v/v using a Pasteur pipette. The lipid extract was separated into neutral and acid fractions by
column chromatography through LC-NH
2
gel, using 2:1 DCM:isopropanol (5 mL) and 4%
formic acid in diethyl ether (5 mL) respectively. The neutral fraction was separated by column
chromatography through 5% water-deactivated silica gel by hexane, DCM and MeOH (5 mL
each). The n-alkanes were eluted with hexane. The n-alkanoic acids, which are contained in the
acid fraction, were methylated into fatty acid methyl esters (FAMEs) using MeOH of known
isotopic composition with 5% hydrochloric acid at 70°C for 12 h. MilliQ water was then added
to the product, which was then partitioned into hexane. The hexane extract was further separated
18

through silica gel column chromatography by eluting hexane and DCM (5 mL each), the latter
fraction containing the FAMEs. Purified n-alkane and FAME samples were then dissolved in
hexane for compound identification, quantification, and isotopic measurements.
2.2.5 Compound-specific carbon isotopic analysis
Carbon isotopic composition (δ
13
C) of individual odd chain n-alkane and even chain n-alkanoic
acid compounds (C
27
to C
33
and C
22
to C
32
respectively) were measured using gas
chromatography isotopic ratio mass spectrometry (GC-IRMS; Thermo Scientific Trace gas
chromatograph connected to a Delta V Plus mass spectrometer via an Isolink combustion furnace
at 1000°C) at the University of Southern California. We checked the linearity in δ
13
C
determination across a range of peak amplitude (1–10 V) daily, with standard deviations
averaging 0.06‰. Only the compounds with peak amplitudes within this dynamic range were
accepted. δ
13
C values of target peaks were normalized to the Vienna Pee Dee Belemnite (VPDB)
standard by comparing with an external standard containing 15 n-alkane compounds (C
16
to C
30
),
with δ
13
C values ranging from −33.3 to −28.6‰, obtained from A. Schimmelmann, Indiana
University, Bloomington. The external standard was analyzed at least twice a day through the
course of the analysis, and the root-mean-square error (RMSE) was on average 0.2‰. An
internal standard (C
34
n-alkane) was co-injected with some of the samples to check for stability
through the course of the sequence. The δ
13
C values of the n-alkanoic acids were then calculated
from the δ
13
C values of the corresponding FAMEs using δ
13
C of the added methyl group by mass
balance (δ
13
C of methanol = −25.45 ± 0.37‰).

19

2.2.6 Bulk leaf carbon isotope analysis
Dried leaves were ground in a Wiley mill to a fine powder (20 mesh), packed in Sn capsules and
combusted via an Elemental Analyzer (Costech Analytical Technologies Inc. Valencia, CA,
USA) connected to a Picarro G2131-i cavity ring-down spectroscopy (CRDS) for determination
of δ
13
C
leaf
values. Samples were normalized to the VPDB scale by comparison to a Peach NIST
SRM 1547 standard (mean δ
13
C = −25.96 ± 0.12‰). An internal reference standard (Lemon Tree
Standard: mean δ
13
C = −28.12 ± 0.11‰) was interspersed every 20 samples to monitor stability.
2.2.7 Carbon isotopic fractionation
We report isotopic fractionations between the substrate, e.g., atmospheric carbon dioxide, a, of
carbon isotopic composition δ
13
C
a,
and product, e.g., plant tissue, p, with δ
13
C
p
, as enrichment
factors (ε
p/a
), calculated with the following equation following Hayes (1993):
ε
p/a
= α
p/a
− 1 = [(δ
13
C
p
+ 1) / (δ
13
C
a
+ 1)] − 1 (2.2)
where δ
13
C is in fractional notation, and ε
p/a
yields a negative number associated with the
discrimination against
13
CO
2
in plants. The capital delta symbol, Δ has been the convention in the
plant carbon isotope biochemistry community following (Farquhar et al., 1989):
Δ
a/p
= α
a/p
− 1 = [(δ
13
C
a
+ 1)/(δ
13
C
p
+ 1)] − 1 (2.3)
where Δ is positive when plants discriminate against
13
CO
2
whereas ε is negative. δ
13
C,

ε and Δ
are reported in per mil units (‰) which implies a factor of 1000 (Cohen et al., 2007).
Although not a product/substrate relationship, we also calculate the isotopic fractionation
(ε
29alk/leaf
) between δ
13
C
leaf
and δ
13
C
29alk
, where:
20

ε
29alk/leaf
= [(δ
13
C
29alk
+ 1)/(δ
13
C
leaf
+ 1)] – 1 (2.4)
after (Chikaraishi et al., 2004). δ
13
C
29alk
values are almost always lower than δ
13
C
leaf
resulting in
ε
29alk/leaf
values that range from –15 to +1‰ (n < 250 plants; compiled by Diefendorf & Freimuth,
2017). There may be systematic differences based on plant biology (Diefendorf and Freimuth,
2017) or climate (Freeman and Pancost, 2014), however inferences remain tentative given
limited sample sizes for biomarkers.
2.2.8 Community average
Average values for each 1-ha forest plot were estimated using both the unweighted mean of all
sampled individuals and community-weighted means. The community-weighted means (cwm)
and uncertainties (σ
w
) were calculated as follows:
𝑐𝑐𝑐𝑐 𝑐𝑐 =
∑ ( 𝑥𝑥 𝑖𝑖 × 𝑤𝑤 𝑖𝑖 )
𝑛𝑛 𝑖𝑖 = 1
∑ 𝑤𝑤 𝑖𝑖 𝑛𝑛 𝑖𝑖 = 1
(2.5)
𝜎𝜎 𝑤𝑤 = �
∑ 𝑤𝑤 𝑖𝑖 ×( 𝑥𝑥 𝑖𝑖 − 𝑐𝑐 𝑤𝑤 𝑐𝑐 )
2 𝑛𝑛 𝑖𝑖 = 1
∑ 𝑤𝑤 𝑖𝑖 𝑛𝑛 𝑖𝑖 = 1
(2.6)
where n is the number of species weighted, w
i
is the weight (concentration of the individual leaf
wax compound, if appropriate, and species basal area), and x
i
is trait value for the ith species. Eq.
6 accounts for interspecific variations, but does not propagate intraspecific variability.
2.3 Results
2.3.1 Leaf wax δ
13
C results
We report δ
13
C values for n-alkanes from 405 sunlit canopy leaf samples covering 129 species
from all nine sites, as well as n-alkanoic acids from 76 samples covering 29 species from five of
21

the nine sites (see Supplementary Information for results and discussions of n-alkanoic acids).
We additionally sampled 65 shaded canopy leaf samples, and 11 understory leaves (from ESP-
01). Production of different compounds and homologues varies between species. Thus, we report
δ
13
C measurements from C
27
, C
29
, C
31
and C
33
n-alkanes where those individual compounds are
present in sufficient concentrations for isotopic determination. We observe strong orthogonal
distance regressions (r > 0.9) among δ
13
C values of C
29
, C
31
and C
33
n-alkanes, close to the 1:1
line (Fig. 2.2a–c). The C
27
n-alkane yields weaker orthogonal distance regressions with other
homologues (r = 0.69–0.84; Fig. 2.2d–f), that are significantly offset from the 1:1 line by
Student’s t-test, on average by +0.7 ± 1.5‰ (relative to C
29
) and +1.2 ± 1.8‰ (relative to C
31
; 1σ;
p < 0.01). This may be analytical artefact, as C
27
is generally of lower concentration.

Fig. 2.2. Crossplots of δ
13
C
wax
values between odd-chain n-alkane homologues measured from individual sunlit
canopy leaf samples, showing (a-c) comparison among C
29
, C
31
and C
33
; (d-f) C
27
compared with other chain lengths.
Black lines indicate ordinary least square regressions (all with p<0.0001). Dashes show the 1:1 lines. n
diff
indicates
the difference in chain lengths between the compounds displayed.
22

2.3.2 Leaf wax δ
13
C values across the elevation profile
To investigate the altitudinal trend, we survey δ
13
C
wax
values in sunlit, upper canopy leaves. δ
13
C
values of C
29
n-alkane (the dominant chain length; Feakins et al., 2016b) from individual leaf
samples range from −44.1 to −29.2‰ across all sites. Despite ~10‰ variability between
individuals within each site, we observe a trend towards higher δ
13
C
29alk
as elevation increases
(Fig. 2.3). The elevation trends are similar among homologues (not illustrated).
Our sampling for n-alkanes represents 18–73% of the total basal area for each of the nine 1 ha
forest plots (Table 2.1). We calculated site mean traits based on the unweighted means of the
individuals (Table 2.2). We also calculated the community-weighted means, where species mean
values are weighted by wax abundance and species basal area (by Eq. 5). For δ
13
C
29alk
,
unweighted and community-weighted site mean values differ by ≤1‰ (Fig. 2.3) and they
produce a strong δ
13
C
29alk
-elevation correlation with similar gradients. Species-mean alkane
concentrations are not well determined from sample sizes of three to five leaves given the large
intra-species variability (Feakins et al., 2016b). The community weighting introduces large
uncertainty with poorly-constrained species-mean alkane concentrations. Therefore, we continue
only with discussion of the unweighted mean values as the best central estimates, similar to
Feakins et al. (2016b) and Asner et al. (2016).
We find an increasing trend in δ
13
C
29alk
with elevation from unweighted site mean values of
−39.1‰ at 215 m to −34.2‰ at 3.5 km, an increase of 5‰. Linear regression of δ
13
C
29alk
with
elevation yields a slope of +1.45 ± 0.33‰ km
−1
(95% CI; r
2
= 0.94, p < 0.01; Fig. 2.3) and
projected δ
13
C
29alk
at sea level (intercept) of −39.4 ± 0.6‰ (95% CI). To our knowledge this is
the first time that such an elevation response has been demonstrated in the carbon isotopic
23

composition of leaf wax biomarkers in living plants, let alone for a large number of samples in
humid tropical forests.

Fig. 2.3. δ
13
C values of C
29
n-alkane from sunlit canopy leaf samples versus elevation. Grey open circles indicate
individual data with sizes scaled to leaf wax abundance. Also showing unweighted mean (pink circles), and
community-weighted mean (blue circles) for each site, and ordinary least squares linear regression of the
unweighted site means (pink line). Hevea guianensis (rubber tree) has anomalously high δ
13
C values (still included
in calculation of site-means).

24

Table 2.2. Unweighted site-mean carbon isotopic compositions and fractionations in the Andes-Amazon transect.
Site summary data

Leaf wax biomarker (‰)

Bulk leaf (‰)
Site Elev.
(km)
pCO
2

(ppmv)
MAP
(mm)
n
spl.
δ
13
C
29alk
se ε
29alk/CO2
se ε
29alk/leaf
se δ
13
C
leaf
se ε
leaf/CO2
se Δ
leaf/CO2
se
TAM-06 0.215 389 1900 38 -39.1 0.3 -31.0 0.3 -8.2 0.3 -31.1 0.3 -22.9 0.3 23.5 0.3
TAM-05 0.223 389 1900 58 -39.3 0.3 -31.2 0.3 -8.2 0.3 -31.4 0.2 -23.2 0.2 23.7 0.2
PAN-02 0.595 372 2366 25 -38.8 0.5 -30.7 0.6 -8.1 0.5 -30.9 0.2 -22.7 0.2 23.3 0.2
PAN-03 0.859 361 2835 36 -37.5 0.5 -29.3 0.5 -7.5 0.4 -30.2 0.3 -21.9 0.3 22.4 0.3
SPD-02 1.494 335 5302 57 -37.3 0.3 -29.1 0.3 -7.6 0.3 -29.8 0.2 -21.6 0.2 22.1 0.2
SPD-01 1.713 327 5302 58 -36.7 0.3 -28.6 0.3 -7.3 0.2 -29.7 0.2 -21.4 0.2 21.9 0.2
TRU-04 2.719 291 2318 49 -34.8 0.3 -26.6 0.3 -6.1 0.3 -28.8 0.3 -20.6 0.3 21.0 0.3
ESP-01 2.868 286 1560 44 -36.0 0.4 -27.8 0.4 -7.5 0.3 -28.7 0.3 -20.4 0.3 20.9 0.3
ACJ-01 3.537 265 1980 40 -34.2 0.3 -26.0 0.3 -5.9 0.2 -28.5 0.1 -20.3 0.2 20.7 0.2
pCO
2
(ppmv) is an equivalent value determined based on elevation, with sea level pCO
2
taken at 394 ppm (1-year average prior to August 2013 sampling;
Keeling et al. (2001), with 5 ppmv added to reflect the higher CO
2
concentrations in the Amazon canopy compared to the free atmosphere (Olsen and
Randerson, 2004). Showing unweighted site mean and standard errors. n spl. = number of samples. All other abbreviations as in the text.
25

2.3.2.1 Taxon-specific leaf wax δ
13
C gradients
The elevation trends reported above represent the average of all sampled canopy tree species
(angiosperms). Due to the high turnover in the tree species community, we were unable to follow
a single species across the environmental gradient. One genus, Ocotea, has a broad geographic
range (0.215–2.719 km), with 19 sampled individuals (Fig. 2.4a), and four families (Clusiaceae,
Euphorbiaceae, Lauraceae, Rubiaceae) span a wide range of elevation (Fig. 2.4b). Within these
wide-spread taxa, some species display offsets from the site mean values. For example, Hevea
guianensis (rubber tree), present at PAN-02 and PAN-03 and belonging to the Euphorbiaceae
family, was found to be significantly enriched from other Euphorbiaceae by Student’s t test.
Being c. 5‰ heavier than site mean values, H. guianensis shows some of the most enriched
values of all sampled species at those sites (Fig. 2.3), and this biochemical offset may be related
to its notable sap production (latex), although latex is also produced by other species within
Clusiaceae. This results in the lack of elevation trend in Euphorbiaceae, although the elevation
trend is still not significant (p = 0.18) after excluding H. guianensis from the regression. Each of
these taxonomic groups, except Euphorbiaceae, show increasing δ
13
C
29alk
with elevation (Fig.
2.4), but small sample sizes impact uncertainties (see Section 4.3). This suggests that taxonomic
turnover does not determine the observed altitude effect (similar patterns are found for δ
13
C
leaf
,
not illustrated); but as cosmopolitan individuals are a small fraction of the overall sample set, it
remains untestable whether elevation response is driven by species turnover.
26

Fig. 2.4. Elevation trends in δ
13
C
29alk
from the same a) genus (Ocotea) and b) families that span a wide range of
elevations (grey: individual samples, red: unweighted site means). All elevation trends are significant (p < 0.01)
based on the individual samples, except for Euphorbiaceae (p = 0.18 excluding H. guianensis).

2.3.3 Comparison of leaf wax and bulk leaf δ
13
C
Comparing δ
13
C values of leaf wax and bulk leaf for the same tree (different branches), we find a
linear correlation between δ
13
C
leaf
and δ
13
C
29alk
(y = 1.95x – 21.3, r = 0.68, n = 399, p < 0.01), as
well as with other chain lengths (Fig. 2.5a). The comparison between ε
wax/leaf
values for different
chain lengths of n-alkanes are reported in Fig. 2.5b and Appendix A. We focus on the C
29
n-
alkane (the dominant homologue; Feakins et al., 2016b) vs. the bulk leaf (comprised of many
biochemicals) in order to evaluate the isotope systematics in individual leaf wax compounds. The
mean carbon isotopic fractionation between leaf wax C
29
n-alkane and bulk leaf (ε
29alk/leaf
) is −7.4
± 2.2‰ (1σ, n = 399) across all samples (Fig. 2.5b). We however find a reduction in ε
29alk/leaf

with increasing elevation (slope = 0.60 ± 0.30‰ km
−1
, 95% CI, r
2
= 0.76, p < 0.01; Fig. 2.6b), as
the elevational gradient in δ
13
C
29alk
is significantly steeper than that in δ
13
C
leaf
(+1.45 ± 0.33 and
27

+0.87 ± 0.16‰ km
−1
respectively; 95% CI; Fig. 2.6a). ε
29alk/leaf
ranges from −5.9 ± 1.5‰ (1σ, n =
40) at the highest site (ACJ-01) to −8.2 ± 1.6‰ (1σ, n = 38) at the lowest site (TAM-06). Using
the linear regression calculated here, the projected ε
29alk/leaf
at sea level is −8.3 ± 0.6‰ (95% CI;
Fig. 2.6b).

Fig. 2.5. Comparison of δ
13
C values between n-alkane homologues and bulk leaf, showing (a) scatter plot of
individual samples and ordinary least squares linear regressions (all with p < 0.001); (b) notched box and whisker
plots representing distributions of ε
wax/leaf
values of different n-alkane homologues (horizontal black lines: median;
boxes: upper and lower quartile; whiskers: 5
th
and 95
th
percentile; dots: data beyond 5
th
and 95
th
percentile). Data are
from individual sunlit leaf samples from all sites.
28

Fig. 2.6. Elevation trends in (a) bulk leaf (black) and δ
13
C of C
29
n-alkane (red), and (b) ε
29alk/leaf
(red), showing
unweighted site-means (circles), 1σ (error bars), and linear regressions (lines) and 95% confidence intervals
(shading).

2.3.4 Canopy effects
2.3.4.1 Sunlit versus shaded canopy leaves
To test whether light intensity affects δ
13
C
wax
values, we calculated the δ
13
C offsets between
paired sunlit and shaded leaves (ε
sun/shade
) for the bulk leaf, C
29
n-alkane, and ε
29alk/leaf
(Fig. 2.7a).
Sunlit leaves are on average
13
C-enriched in C
29
n-alkane relative to shaded leaves, but the
average difference is small (0.8 ± 2.3‰, 1σ; n = 62) relative to the very large range of
observations for sunlit (1σ = 2.9‰, n = 405) and shaded leaves (1σ = 2.9‰, n = 65). The
sun/shade enrichment is more evident in the bulk leaf, which shows a larger mean ε
sun/shade
of 1.3
± 1.3‰ (1σ, n = 57), with a narrower distribution for sunlit (1σ = 1.8‰, n = 405) and for shaded
(1σ = 1.6‰, n = 65) leaves. ε
sun/shade
for both measures of δ
13
C values are significantly below
29

zero by Student’s t-test (p < 0.01). In contrast, ε
29alk/leaf
shows no differences between sunlit and
shaded leaves (Fig. 2.7a).
2.3.4.2 Canopy versus understory
Although this study focuses on sampling the dominant production of leaves in the canopy, we
compare a small number of understory plants (10 samples, five species) collected at ESP-01 with
canopy leaves (Fig. 2.7b). δ
13
C
29alk
values of leaves on understory shrubs range from −44.4 to
−35.3‰, averaging −38.4 ± 2.5‰ (1σ, n = 10). In comparison, δ
13
C
29alk
of the sunlit canopy
leaves from trees span −42.2 to −30.5‰ and averaging −36.0 ± 2.8‰ (1σ, n = 43), and hence
significantly enriched (p = 0.013) relative to understory leaves.

Fig. 2.7. (a) Carbon isotopic offset between paired sunlit and shaded leaf samples (ε
sun/shade
) from the same tree, for
δ
13
C
leaf
(n = 57), δ
13
C
29alk
(n = 62), and ε
29alk/leaf
(n = 54). The first two distributions are significantly (p < 0.01) above
zero (dashed line) indicating sunlit leaves are enriched relative to shaded leaves, whereas the third distribution is not
significantly offset from zero at 95% confidence level. (b) Comparison of δ
13
C
29alk
values of sunlit canopy leaves (n
= 43) and understory leaves (n = 10) at ESP-01. The two distributions are different at 95% level (p = 0.013).
Notched box and whisker plots show median (horizontal lines), low and upper quartiles (boxes), 5
th
and 95
th

percentiles (whiskers), and data beyond 5
th
and 95
th
percentiles (dots).

30

2.4 Discussions
2.4.1 Comparison of leaf wax and bulk leaf properties
We measured bulk and compound specific carbon isotopic compositions on leaves on the same
trees. The average ε
29alk/leaf
(−7.4 ± 2.2‰, 1σ, n = 399; Fig. 2.5b) across all samples is similar to
values previously reported from eastern Africa (−7.4 ± 2.1‰, 1σ , n = 48; Magill et al., 2013)
and southern Alberta (−7.2 ± 0.9‰, 1σ , n = 15; Conte et al., 2003), and larger than that
compiled elsewhere (−5.2 ± 2.4‰, n = 109 for all environments; −5.5 ± 2.5‰, n = 74 for
tropical and subtropical climates; Diefendorf and Freimuth, 2017). Differences between study
averages may be due to plant life form (Diefendorf and Freimuth, 2017) and/or climate (Freeman
and Pancost, 2014). Here we add considerably to the data on tropical C
3
trees. Within our dataset,
we find larger fractionations in the lowland rainforest, and smaller fractionations in montane
forests. The systematic trend in ε
29alk/leaf
with elevation (Fig. 2.6b), means that the extrapolated
sea-level fractionation (−8.3 ± 0.6‰; 95% CI) may be more appropriate than the overall mean
for application in future tropical lowland studies. However, we caution that only 46% of
variability of δ
13
C
29alk
is explained by the observed variability in δ
13
C
leaf
(Fig. 2.5a) such that we
do not recommend relying heavily on of bulk leaf observations in the interpretation of leaf wax
biomarker records.
We emphasize that ε
29alk/leaf
represents the offset of a specific compound relative to the entire leaf
and is not a direct biosynthetic precursor-to-product step. The offset may reflect multiple isotope
effects, including changes in the magnitude of the fractionation as well as the proportions of
biochemicals in the leaves, each of which may vary with species and climate. Several aspects of
foliar physiology and biochemistry change upslope, including decreases in cellulose and lignin,
31

increases in LMA (leaf mass per area), soluble C (sugars), phosphorus (Asner et al., 2014b, 2016)
and n-alkane concentrations (Feakins et al., 2016b). We tested whether thicker leaves (higher
LMA) would reduce diffusion through the mesophyll cells and cause greater
13
C-enrichment, as
previously reported in a single species (Vitousek et al., 1990). We found a weak positive
correlation between LMA and δ
13
C
leaf
(r = 0.45, p < 0.01) and δ
13
C
29alk
(r = 0.31, p < 0.01),
suggesting
13
C-enrichment in thicker leaves corroborating with the previous study (Vitousek et
al., 1990). We observe no relationship between ε
29alk/leaf
and LMA, as well as phosphorous and
lignin concentrations, suggesting these leaf trait changes are not responsible for the gradient in
ε
29alk/leaf
. We find weak correlations between ε
29alk/leaf
and concentrations of cellulose (r = −0.12, p
= 0.02), soluble C (r = 0.10, p = 0.04), and n-alkanes (r = 0.22, p < 0.01). The latter suggests that
the pool of precursor may have been used more completely when making waxier leaves.
Similarly a decrease in the δ
13
C offset between bulk leaf and leaf wax was noted elsewhere as
total wax concentration increased (Zhou et al., 2015). Here, these relationships only account for a
small portion of the variance, with unexplained variance likely due to multiple variables in leaf
physiology and biochemistry that are not readily quantified at the leaf level.
2.4.2 Environmental variables affecting carbon isotope fractionation
2.4.2.1 Irradiance and canopy closure
Sunlit leaves are
13
C-enriched relative to shaded leaves in both bulk leaf and leaf wax (Fig. 2.7a),
suggesting influences from light intensity. Our results agree with previous theoretical and
experimental studies on bulk leaf (Farquhar et al., 1989) and leaf waxes (Graham et al., 2014).
13
C-enrichment in sunlit leaves reflects a decrease in c
i
/c
a
likely brought about by a higher
photosynthetic rate (Farquhar et al., 1989); an alternate possibility of lower stomatal conductance
32

is unlikely in this wet climate (Table 2.1) and is not supported by leaf water isotopic evidence
that indicates open exchange with the atmosphere (Feakins et al., 2016a). Leaf physiology may
contribute as sunlit leaves tend to be thicker (mean 20 ± 18.5% higher LMA) than shaded leaves,
and this may restrict diffusion of CO
2
into mesophyll cells. We find the sun/shade difference
(ε
sun/shade
) for δ
13
C
leaf
is positively correlated with the sun/shade LMA ratio (r
2
= 0.33, p < 0.01)
such that leaf thickness may explain 33% of the
13
C-enrichment in the bulk leaf; but no
significant correlation is observed for leaf wax. In addition, we find no significant sun/shade
difference in ε
29alk/leaf
, and thus infer light intensity exerts no effect on
13
C fractionation during
wax biosynthesis, but leaf waxes preserve the sun/shade signature of the original photosynthate.
We sampled the understory at a single site ESP-01 and observed
13
C-depletion relative to the
canopy (Fig. 2.7b). This is expected due to lower light intensities, and accumulation of
13
C-
depleted respired CO
2
under the dense closed canopy (Cerling et al., 2011). Similar canopy
effects have been observed in vertical canopy profiles in Panama, and that study concluded that
upper canopy leaves are the most relevant for geological archives given the greater productivity
(Graham et al., 2014). In contrast, individual tree height effects have been noted in other studies,
typically showing an increase in δ
13
C with increasing tree height within single forest plots for a
variety of biochemical and physiological reasons (McDowell et al., 2011; Kenzo et al., 2015).
Here, we do find a weak but significant positive correlation (r
2
< 0.3; p < 0.05) between tree
height and δ
13
C at some individual sites (lowland site TAM-06 for the C
29
n-alkanes only;
lowland to mid-elevation sites TAM-06 to SPD-02 for bulk leaf). At the landscape scale, tree
height increases toward lower elevations (Table 2.1), but canopy δ
13
C decreases (Fig. 2.6a),
which means that plot-altitude effects (not tree height) dominate here.

33

2.4.2.2 Dual C and H isotopic analyses: insights into leaf-atmosphere exchange
In dry environments, where stomata regulate water loss, they can also be a driver of carbon
dioxide limitation and thus
13
C-enrichment in both bulk leaf and leaf waxes. Water is not thought
to be limiting in these humid tropical forests with high relative humidities (> 90%) and
precipitation totals (> 1.5 m yr
−1
), and it has already been shown that leaf water content is
minimally D- and
18
O-enriched in these trees (Feakins et al., 2016a). Thus, we do not expect to
see signs of restricted stomatal conductance, but we test this by comparing the C
29
n-alkane
hydrogen isotopic compositions (δD
29alk
) measured from five study sites (Feakins et al., 2016a)
to δ
13
C
29alk
reported for the same samples here – the dual isotope measurements were made on
the same aliquots from the same leaves. To parse similar climatic and elevation zones, we divide
the sites into three groups: the wettest, mid-elevation montane sites SPD-01 and SPD-02, the
lowland sites TAM-05 and TAM-06, and the upper site ESP-01. We see no relationship between
δ
13
C
29alk
and δD
29alk
at all sites (Fig. 2.8), suggesting water limitation is not an effect on carbon
isotope fractionations in these tropical forests.

Fig. 2.8. Comparison between C
29
n-alkane δ
13
C (from this study) and δD (Feakins et al., 2016b). There is no
significant correlation.
34

2.4.2.3 Adiabatic controls on carbon isotope fractionation
Adiabatic changes with elevation imply that the pressure of the atmosphere, as well as all
component gases, decreases with elevation in a very predictable manner. Changes that may be
relevant to plant growth include adiabatic declines in pCO
2
, pO
2
, total pressure, temperature and
humidity (in addition to local climatic or ecological variables). Plants respond to these multiple
changes physiologically and biochemically, and the adiabatic processes may be encoded in their
leaf δ
13
C, but the driving mechanism are confounded (Körner, 2007). Experimental studies can
help to isolate a single process. We compare the elevation-based evidence, converted to pCO
2

equivalency (by Eq. 2) from this study for trees in a very wet, tropical climate with evidence
from Schubert and Jahren (2012) for forbs grown under controlled moisture and pCO
2

‘fertilization’ experiments in the laboratory reporting δ
13
C for both bulk and C
31
n-alkanes (Fig.
2.9). After converting the decline in partial pressure to ppmv equivalent units and fitting with a
linear regression, we find the Peruvian transect to be consistent but offset from the predicted
slope of the hyperbolic curve at lower pCO
2
, beyond the range of the earlier empirical work.
While experimentation allows the implications of variations in pCO
2
to be isolated from other
variables (CO
2
explains about 30% of the measured variance), experimental findings have been
dismissed as short-term responses that may be masked by plant adaptation in natural ecosystems
(Kohn, 2016). Our transect follows experimental predictions with an apparent response of plant
δ
13
C to low pCO
2
(200–400 ppmv) in a tropical elevation transect with fully-grown C
3
trees (Fig.
2.9). But, growth experiments should test the mechanism at low pCO
2
.
35

Fig. 2.9. Comparing the relationship between pCO
2
and carbon isotopic fractionation from the Perú transect (green;
this study) to growth chamber experiments (black; recalculated from Schubert and Jahren, 2012) with 1σ (error bars).
Equivalent pCO
2
for the Perú transect is determined based on elevation (see Eq. 2), with sea level pCO
2
taken at 394
ppmv, the 1-year average prior to August 2013 sampling (Keeling et al., 2001).

Competing hypotheses include the role of the pO
2
decline with elevation, which would act to
suppress photorespiration and increase photosynthesis efficiency (Berner et al., 2000). Few
growth experiments have studied the effect of pO
2
on plant
13
C-discrimination relevant to high
altitude. A growth experiment with Phaseolus vulgaris at 15% O
2
found δ
13
C
leaf
values were
+2.1‰ higher than plants grown at ambient conditions, or 21% O
2
(Beerling et al., 2002). While
the negative correlation between pO
2
and δ
13
C means this is a competing mechanism, the isotope
effect would have to be stronger than determined by experiments to explain the magnitude of the
observed shift across the elevation profile. Furthermore, at altitude the O
2
/CO
2
ratio does not
36

change and it is this ratio that is predicted to control photorespiration from first principles
(Beerling et al., 2002).
Each of the experimental studies selectively enrich the atmosphere in either pO
2
or pCO
2
. As
CO
2
and O
2
are mutually competitive inhibitors at their binding sites on RuBisCO, plants may be
responding to the changing partial pressure of either gas or to the changing O
2
/CO
2
mixing ratio
in each of the laboratory experiments (Beerling et al., 2002). But, either the O
2
or CO
2

mechanism must dominate, as the pO
2
/pCO
2
is unchanged adiabatically. Körner (2007) suggests
that plants make biochemical adaptations to use CO
2
more efficiently at high altitude, just as
animals increase ventilation to adapt to lower O
2
.
Adiabatic effects also determine the monotonic decline in temperature with altitude. However
elsewhere the effects of low temperature have been suggested to be minimal based on cold
tolerance, Arctic species that showed a response of δ
13
C to elevation in the Alps, but minimal
response to low temperatures (Zhu et al., 2010). Theoretical predictions that the decline in
absolute air pressure and humidity would aid diffusivity of CO
2
into the leaf (Terashima et al.,
1995) should increase selection against
13
C and thus cannot explain
13
C-enrichment at altitude.
Instead, various leaf physiological changes that inhibit diffusion between stomata and
carboxylation sites (Vitousek et al., 1990) are more likely to work in the direction of the
observed effect (discussed in Section 2.4.1). The mechanism must remain unresolved as we
cannot separate multiple variables (adiabatic changes and taxonomic turnover), but the net effect
observed in our finding of
13
C-enrichment with elevation is consistent with pCO
2
theory.

37

2.4.3 Altitude effect on plant wax δ
13
C
2.4.3.1 Evaluating robustness of the altitude effect
The large inter- and intra-species variability of plant δ
13
C values has previously been raised as a
concern for resolving environmental responses in the past as well as presenting challenges for
adequate representation of population mean values in modern calibration studies. A previous
study in a tropical closed-canopy forest used Monte Carlo resampling to find the number of
leaves (50) from leaf litter required to robustly capture canopy closure given the low proportion
of understory leaves (Graham et al., 2014). We used a similar Monte Carlo approach to evaluate
the sampling required for a robust elevation regression. We randomly subsampled (n = 1–50) our
δ
13
C
29alk
dataset from each of the nine sites (with replacement when n > number of measured
δ
13
C
29alk
data), and calculated the site means and linear regressions with elevation for 2000
iterations. Uncertainties increased as the number of samples per site decreases (Fig. 2.10a).
These tests reveal that the sample size of our δ
13
C
29alk
dataset (n per site = 25–58) is sufficient to
capture a robust regression; beyond n = 20 there is limited improvement and false negatives
increase sharply for n < 10.
In order to generalize our findings, we generated a synthetic δ
13
C
29alk
dataset for the elevation
profile and subsampled different numbers of evenly-spaced sites (n sites = 3–50) along the 3.3
km elevation transect with 2000 iterations. The synthetic dataset was defined to match the
properties of the measured δ
13
C
29alk
(site mean values determined from the δ
13
C
29alk
regression
with elevation; 10 000 individuals per site with a Gaussian distribution, 1σ = 2.4‰, the transect
average), and we repeated the analysis on 10 synthetic datasets. We found the number of sites are
more important than the number of samples per site when seeking the minimum total sample size
38

to obtain a robust regression of the population (Fig. 2.10b). For example, for a total sample size
of 50, the chance of a statistically-significant elevation gradient (at 95% confidence level) is
highest when collecting one sample each from 50 sites (Fraction
p>0.05
= 0.021), compared to 5
samples each from 10 sites (Fraction
p>0.05
= 0.037) and 10 samples each from 5 sites
(Fraction
p>0.05
= 0.16). Our elevation transects for taxon-specific δ
13
C
29alk
(Fig. 2.4) and δ
13
C
30acid
subsets (Fig. A4) were based on 3–5 unevenly spaced sites, which have a high chance of a false
negative (Fig. 2.10b).

Fig. 2.10. Assessment of sensitivity of regression to sample size using a Monte Carlo subsampling approach (2000
iterations) a) for different numbers of individuals per site from the measured δ
13
C
29alk
values at all nine study sites;
and b) for a synthetic δ
13
C dataset matching the characteristics of the δ
13
C
29alk
dataset (mean 1σ distribution per site
= 2.4‰, slope = 1.45‰ km
-1
, RMSE of linear regression = 0.45‰) with different numbers of individuals per site (x
axis) for different numbers of equally-spaced sites along a 3.3 km elevation transect (lines).

2.4.3.2 Global synthesis
Although our study constitutes an unprecedented sample-size for the study of plant wax carbon
isotope systematics in tropical forest ecosystems, it only considers data from one geographic
region. Additional transect-based calibration efforts in other tropical forests, e.g. central Africa
39

and southeast Asia, could be well-justified, given taxonomic differences may incur carbon
isotope effects (e.g. H. guianensis, rubber tree), and given the high species diversity in the
tropics (Kreft and Jetz, 2007; Ter Steege et al., 2010). Phylogenetic sampling schemes have
indicated variations in carbon isotope fractionations between plant groups (Diefendorf et al.,
2010; Diefendorf et al., 2015). However, variations in precipitation amount are globally the
dominant control on carbon isotope fractionations (Diefendorf et al., 2010), with the effect most
apparent below ~1.5 m MAP. At higher MAP (above ~1.5 m), the effect becomes smaller
because of the logarithmic relationship (Diefendorf et al., 2010). If carbon isotope fractionations
are insensitive to precipitation amount in very wet climate regimes >1.5 m MAP, this allows
other variables to be discerned. The western Amazon and Andean cloud forest regions included
in this elevation transect have very high MAP (1.5–5.3 m) and relative humidity (75.2–93.7%;
Table 2.1), and as expected, we find no correlation between δ
13
C and MAP in our transect. We
compile previously published data from locations with >1.5 m MAP in order to globally assess
the altitude effects in very wet climates (Fig. 2.11). We find that the altitude effect identified in
Perú is repeated in the global data compilation in both bulk leaf (Fig. 2.11a) and leaf wax n-
alkanes (Fig. 2.11b). Globally there is insufficient data to test this in the n-alkanoic acids. One
limitation is the difference in data density across elevations (Fig. 2.11 insets), and our evidence
for an altitude effect may perhaps encourage more data collection and reporting of data from
altitudinal transects.
40

Fig. 2.11. Global elevation gradients in the carbon isotopic fractionations for (a) bulk leaf, ε
leaf/CO2
and (b) leaf wax
C
29
n-alkane, ε
29alk/CO2
. Data are from this study (triangles); Diefendorf et al. (2010) compilation (circles); Körner et
al. (1988) compilation (diamonds); and other sources (crosses; Chikaraishi and Naraoka, 2003; Diefendorf et al.,
2011; Garcin et al., 2014; Vogts et al., 2009). We only include data from angiosperm trees from locations with
MAP > 1.5 m, to eliminate the aridity effect on δ
13
C (see inset). Each individual data point (grey) represents a
species which may come from a single measurement or an average from multiple samples. The number of samples,
species at each site varies between studies and the coverage across elevations is also uneven (see inset). To account
for the unevenness in data distribution, we plot elevation average data (red squares) and 1σ distribution (error bars),
at increments of 50 m and 100 m for ε
leaf/CO2
and ε
29alk/CO2
respectively below 0.5 km, and 250 m increments above
0.5 km, with linear regression (red line) and 95% confidence interval (shading). Both elevation regressions are
statistically significant (p < 0.001).

2.4.4 General significance
This large-scale study of carbon isotope systematics in tropical lowland and montane rainforests
in Perú provides modern observational data to support tropical paleoenvironmental
reconstructions. We characterize the δ
13
C composition of lowland tropical rainforest and
demonstrate an altitude effect of c. +1‰ km
−1
in this wet, forested gradient (Fig. 2.6) and find
the pattern to be generalizable to other moist forests, based on a collation from the literature (Fig.
2.11). The sensitivity of site mean δ
13
C values to elevation gain in the Andes Mountains (+4 – 6‰
41

gain from sea level to the tree line c. 4 km) is an order of magnitude greater than measurement
uncertainties (c. 0.2‰), and although dwarfed by plant-to-plant variability (Fig. 2.3), with
sufficient sample sizes (Fig. 2.10) the signals of elevation in site mean values clearly emerge in
these humid forests.
2.4.4.1 Paleoaltimetry
Biomarker traits that are sensitive to elevation offer possibilities for reconstructing changes in
elevation if preserved in suitable sedimentary archives, as well as differentiation of the source
elevation of organic matter transported in rivers. Hydrogen isotopes in plant waxes (Polissar et
al., 2009; Hren et al., 2010; Ernst et al., 2013; Ponton et al., 2014; Kar et al., 2016) and oxygen
isotopes in carbonates (Poage and Chamberlain, 2001; Quade et al., 2011) each perform this
function by recording the altitudinal gradient in isotopes in precipitation. In addition, the
‘clumped’
13
C-
18
O isotopic bond ordering in carbonates provides evidence for temperature
(Ghosh and Brand, 2003; Huntington et al., 2010; Quade et al., 2013).
Paleoaltimetry with plant wax biomarkers has the potential to be based upon both the altitude
response of δD
wax
to precipitation δD (previously reported in this transect; Feakins et al., 2016a)
and the adiabatic response of δ
13
C
wax
(demonstrated here) in humid tropical forests (Table 2.3).
The main complications for altitude proxies arise when shifting into dry climates. Aridity leads
to closure of stomata to limit transpiration, with attendant
13
C-enrichment (Cernusak et al., 2013).
Thus δ
13
C is confounded in profiles where aridity is a variable (Friend et al., 1989, Wei and Jia,
2009) but not in wet catchments, such as this one and that reported by Körner et al. (1991). The
addition of another elevation-sensitive proxy, and especially the ability to measure two isotope
systems (δ
13
C and δD) in the same molecules, offers the possibility to cross-check aridity effects
42

(positive correlation) versus altitude effects (negative correlation). Uniquely the use of dual C
and H isotopes should provide a convenient tracer of aridity effects as explained in Section 4.2.2.
Here, both respond as altimeters.

Table 2.3. Altitude effect demonstrated in dual isotopes for two classes of leaf wax biomarkers.

δ
13
C (‰)

δD (‰)
Site
Elev.
(km)
pCO
2

(ppmv)
C29alk se C28acid se C29alk se C28acid se
TAM-06 0.215 389

-39.1 0.7 -35.7 0.6

-168 3 -159 7
TAM-05 0.223 389

-39.3 0.6 -35.5 0.5

-166 3 -153 3
SPD-02 1.494 335

-37.3 0.6 -33.6 0.7

-177 3 -180 5
SPD-01 1.713 327

-36.7 0.7 -34.5 0.9

-176 4 -168 6
ESP-01 2.868 286 -36.0 0.6 -34.0 0.6 -216 3 -212 5
Gradient (‰ km
−1
) 1.28

0.67

-16.5

-19.4

RMSE 0.36

0.62 10.1 9.9
Gradient (‰ 100 ppmv
-1
)
a,b
-3.27 -1.74
RMSE 0.29 0.58

We report a summary of data for the homologues of each compound class used in river applications.
a
Assumes
that the dominant control on δ
13
C
wax
is pCO
2
.
b
The δD value of precipitation is identified as the dominant control
of δD
wax
(Feakins et al., 2016a).

2.4.4.2 Paleoecology
One common application of plant wax δ
13
C values to lake or ocean sediment core
reconstructions is to discern proportions of plants using the C
3
versus C
4
pathway (Schefuß et al.,
2003; Castaneda et al., 2009; Feakins et al., 2013). Such studies may assume C
3
forests have a
δ
13
C
29alk
of c. −38‰ as in the lowland sites here. Since contributions from high elevation sites
may bias the sedimentary δ
13
C record toward more enriched values, it would be important to rule
43

out substantial high elevation contributions when attempting to reconstruct C
4
plant contributions
to an otherwise C
3
ecosystem.
In fluvial studies, more
13
C-enriched values of plant waxes in transit have previously been
interpreted as indicating C
4
plant inputs from the lowlands (Galy et al., 2011), or petrogenic
inputs (Häggi et al., 2016), and each of these explanations is plausible. However, our data raise a
third possibility, that contributions from upland ecosystems may also contribute a
13
C-enriched
signal. This may be particularly relevant for interpretation of n-alkanes, which were identified as
a more persistent and upstream component, versus n-alkanoic acids and n-alcohols which
displayed a ‘flashier’ response with local inputs in the Congo River (Hemingway et al., 2016).
In most cases C
4
interpretation are likely secure, but in tropical montane catchments, particularly
in foreland basins, the contribution of upland,
13
C-enriched, plant-derived contributions should
be carefully considered. Findings of an altitude effect in plant wax δ
13
C are expected to be most
pronounced in sedimentary records draining high elevations, when these are a significant
proportion of the catchment (Ponton et al., 2014), and when forest productivity, precipitation and
erosion rates conspire to make these montane regions a significant source of plant wax to the
downstream archive (Hoffmann et al., 2016).
2.5 Conclusions
We have conducted a multi-species survey of plant leaf wax carbon isotope biogeochemistry in
tropical forests spanning an elevation gradient extending from tropical lowland rainforest to
montane cloud forest ecosystems in Perú. Our elevation transect supports the use of leaf wax
biomarkers for a range of applications including provenance studies in fluvial transport and
paleoaltimetry studies. We find strong evidence for an altitude effect on plant wax δ
13
C across
44

the profile, mirrored in both biomarker constituents as well as in bulk tissue with offsets well-
characterized from large tropical forest sample sets. Notably this change occurs in a context of
high species diversity and community turnover in the Andes-Amazon region. While single
species transects are not possible, genus and family-specific transects suggest this is a robust
response to environment and not solely a function of taxonomic turnover. Our plot-based
elevation transect provides new understanding of plant carbon isotopic compositions across
tropical lowland rainforest and montane cloud forests. These data can inform future sedimentary
applications, including tracking sourcing within the Madre de Dios River network and lowland
Amazon basin. More generally, our finding of an altitude effect is consistent with globally
compiled data from wet (>1.5 m MAP) climatic regions. The mechanism is likely adiabatic, and
our results follow experimental predictions for humid environments that low pCO
2
will lead to
13
C enrichment of c. +1‰ km
−1
. Although the altitudinal effect is clear in this humid tropical
forest transect, aridity may confound these presumed-adiabatic signals elsewhere. Dual isotope
analysis of C and H in plant waxes offers a practical means to monitor for secondary climatic
controls.
Acknowledgements
Contributing authors are part of the Andes Biodiversity and Ecosystems Research Group
ABERG (andesresearch.org), the Global Ecosystems Monitoring (GEM) network
(gem.tropicalforests.ox.ac.uk) and the Amazon Forest Inventory Network RAINFOR
(www.rainfor.org) research consortia. Field sampling: The field campaign was funded by grants
to Y.M. from the UK Natural Environment Research Council (Grants NE/D01025X/1,
NE/D014174/1). The research leading to these results has received funding from the European
Research Council under the European Union's Seventh Framework Programme (FP/2007-2013) /
45

ERC Grant Agreement n. 321131 and 291585 (GEM-TRAITS and T-FORCES) as well as the
Jackson Foundation to Y.M. and a John D. and Catherine T. MacArthur Foundation (US) grant
to GA. GA and the spectranomics team were supported by the endowment of the Carnegie
Institution for Science, and by the National Science Foundation (DEB-1146206), supporting the
taxonomic contributions to the project. Carnegie Airborne Observatory data collection,
processing and analyses were funded solely by the John D. and Catherine T. MacArthur
Foundation. The Carnegie Airborne Observatory is supported by the Avatar Alliance Foundation,
John D. and Catherine T. MacArthur Foundation, Andrew Mellon Foundation, David and Lucile
Packard Foundation, Mary Anne Nyburg Baker and G. Leonard Baker Jr., and William R. Hearst
III (all US). BB acknowledges a NSF doctoral dissertation improvement grant (EF-1209287) and
a NERC independent research fellowship (NE/M019160/1). Laboratory work at USC: This
material is based upon work supported by the US National Science Foundation under Grant No.
EAR-1227192 to S.F. Acknowledgment is made to the donors of the American Chemical Society
Petroleum Research Fund for partial support of this research (53747-ND2) to S.F. In Perú, we
thank the Servicio Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) and
personnel of Manu and Tambopata National Parks for logistical assistance and permission to
work in the protected areas. We also thank the Explorers’ Inn and the Pontifical Catholic
University of Perú (PUCP), as well as Amazon Conservation Association for use of the
Tambopata and Wayqecha Research Stations, respectively. Many researchers were involved in
the field, in particular we would like to thank E. Cosio, W. Huaraca-Huasca and J. Huaman for
advising on field logistics; tree climbers: C. Costas, D. Chacón, H. Ninatay; field project
supervision: T. Boza, M. Raurau; species identification and basal area: W. Farfan, F. Sinca; leaf
areas R.M. Castro, G. Rayme, A. Robles, Y. Choque and Y. Valdez. We thank USC lab
46

assistants: C. Hua, K. McPherson, E. Rosca, A. Figueroa, T. Peters and J. Sunwoo. We thank
Kate Freeman, Aaron Diefendorf and Josh West for helpful discussions.
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Oecologia 162, 853-863.

52

Chapter 3
Dual Isotope Evidence for Sedimentary Integration of Plant Wax
Biomarkers across an Andes-Amazon Elevation Transect
This manuscript was published in 2018 as:
Feakins, S.J., Wu, M.S., Ponton, C., Galy, V., West, A.J. Dual isotope evidence for sedimentary
integration of plant wax biomarkers across an Andes-Amazon elevation transect. Geochimica et
Cosmochimica Acta 242, 64-81.

Abstract
Tropical montane regions tend to have high rates of precipitation, biological production, erosion,
and sediment export, which together move material off the landscape and toward sedimentary
deposits downstream. Plant wax biomarkers can be used to investigate sourcing of organic matter
and are often used as proxies to reconstruct past climate and environment in sedimentary
deposits. To understand how plant waxes are sourced within a wet, tropical montane catchment,
we measure the stable C and H isotope composition (δ
13
C and δD) of n-alkanes and n-alkanoic
acids in soils along an elevation transect and from sediments within the Madre de Dios River
network along the eastern flank of the Peruvian Andes, draining an area of 75,400 km
2
and 6 km
of elevation. Soils yield systematic trends in plant wax δ
13
C (+1.75 and +1.31‰ km
–1
, for the C
29

n-alkanes and C
30
n-alkanoic acids respectively in the mineral horizon) and δD values (–10 and -
12‰ km
–1
, respectively) across a 3.5 km elevation transect, which approximates trends
previously reported from canopy leaves, though we find offsets between δ
13
C values in plants
and soils. River suspended sediments generally follow soil isotopic gradients defined by
53

catchment elevations (δ
13
C: +1.03 and +0.99‰ km
–1
and δD: –10 to –7‰ km
–1
, for the C
29

n-
alkanes and C
30
n-alkanoic acids respectively) in the wet season, with a lowering in the dry
season that is less well-constrained. In a few river suspended sediments, petrogenic contributions
and depth-sorting influence the n-alkane δ
13
C signal. Our dual isotope, dual compound class and
seasonal sampling approach reveals no Andean-dominance in plant wax export, and instead that
the sourcing of plant waxes in this very wet, forested catchment approximates that expected for
spatial integration of the upstream catchment, thus with a lowland dominance on areal basis,
guiding paleoenvironmental reconstructions in tropical montane regions. The dual isotope
approach provides cross-check on the altitudinal signals and can resolve ambiguity such as might
be associated with vegetation change or aridity in paleoclimate records. Further, the altitude
effect encoded within plant waxes presents a novel dual-isotope biomarker approach to
paleoaltimetry.
3.1 Introduction
The transport of organic carbon (OC) by large river systems is a crucial component of the Earth’s
carbon cycle. Rivers erode and transport plant, soil and rock-derived (petrogenic) OC across the
landscape, and that which survives degradation may be deposited in sedimentary basins. Among
the world’s river systems, the Amazon River is the largest in terms of drainage area (6.4 million
km
2
) and discharge (200,000 m
3
s
–1
) (Meybeck and Ragu, 2012), and its export of particulate
organic carbon (POC; c. 11.6 Tg yr
–1
to the Atlantic) represents c. 6% of the global riverine POC
input to the oceans (Beusen et al., 2005; Galy et al., 2015; Richey et al., 1990). Understanding
the sourcing, degradation, and transport of POC in the Amazon fluvial system is thus significant
in budgeting the global carbon cycle, and has been extensively studied in both the lowland
mainstreams and Andean head waters (e.g., Bouchez et al., 2014; Clark et al., 2013; Hedges et al.,
54

2000; Townsend-Small et al., 2005; Townsend-Small et al., 2008). The Andes Mountains
represent just 11% of the Amazon River catchment area, but may account for 90% of rock-debris
exported from the Amazon River to the Atlantic Ocean (Meade et al., 1985). While globally,
biospheric OC export generally scales with sediment load (Galy et al., 2015), POC carried by the
lowland Amazon River is thought to be dominantly sourced from lowland forests implying a
near-complete degradation of Andean-derived POC in transit or swamping by more extensive
lowland contributions (Mayorga et al., 2005). The biospheric OC represents carbon fixed from
the atmosphere, and if it escapes oxidation in transit (Cole et al., 2007; Hedges and Oades, 1997)
and is sequestered in ocean sediments for up to 10
8
yrs, it represents a long-term sink of
atmospheric CO
2
(France-Lanord and Derry, 1997; Galy et al., 2007). In contrast, if biospheric
carbon decomposes in soils (Koven et al., 2017) or in transit (Richey et al., 2002), CO
2
is
returned to the atmosphere relatively rapidly, on decade-century timescales, related to the age of
carbon in soils (Trumbore, 1993) and in rivers (Clark et al., 2013; Townsend-Small et al., 2007).
Sourcing and degradation processes within the catchment need to be understood to determine
which regions contribute to marine repositories both for carbon budget and paleoclimate
applications. Yet, many prior source-to-sink carbon cycle studies are based upon bulk POC,
which is a complex mixture of components with diverse age, residence time, degradation
potentials and geochemical signatures that can be difficult to tease apart (Mayorga et al., 2005).
Biological marker molecules, or biomarkers, not only derive from a specific class of organism
but also carry signatures of environment. These biomarkers can be variously used to investigate
sourcing and track molecules in transit, and they are often used as proxies to reconstruct past
climate and environment, which is predicated on understanding sourcing. Biomarkers provide
tracers for specific components of terrestrial OC cycling and thus provide a clearer view of
55

sourcing and fluvial integration processes than bulk OC. For example, lignin (Aufdenkampe et
al., 2007; Goñi et al., 2000), terpenoids (Giri et al., 2015; Medeiros et al., 2012) and plant wax
biomarkers (Freymond et al., 2018; Galy et al., 2011; Häggi et al., 2016; Hemingway et al., 2016;
Hoffmann et al., 2016; Tao et al., 2015) have been used in riverine systems to trace biogenic OC
derived from vascular plant biomass. In addition, biomarkers for microbial activity have been
used to trace bacterial and archaeal components of terrestrial biospheric OC production exported
by rivers (Hanna et al., 2016; Hemingway et al., 2017; Kim et al., 2012; Wagner et al., 2014).
Plant wax hydrogen (δD
wax
) and carbon isotope compositions (δ
13
C
wax
) reflect environmental and
ecological conditions and thus may be able to reveal sourcing within a catchment, provided it is
characterized by a gradient in environmental conditions. The carbon isotopic signatures of plant
waxes in river sediment has been used to trace the evolving character of suspended sediment OC
between mountain-front tributaries and the river mouth, based on the contrast between C
3
upland
vegetation and C
4
lowland vegetation (Galy et al., 2011), between river and estuary (Medeiros et
al., 2012) and between C
3
forest and petrogenic sources (Häggi et al., 2016). Sourcing has also
been differentiated based on hydrogen isotopes in precipitation that vary spatially within
catchments, including with elevation (Galy et al., 2011; Häggi et al., 2016; Hoffmann et al., 2016;
Ponton et al., 2014). A few studies are beginning to combine information from different
compound classes as erosion and preservation pathways may differ between compounds, for
example with diterpenoids (derived from conifers) being over contributed relative to
triterpenoids (derived from angiosperms; Giri et al., 2015) or with n-alkanoic acids and n-
alcohols having a more rapid response to changes in runoff and thus more local input relative to
n-alkanes (Hemingway et al., 2016). Studies of river suspended sediment using radiocarbon have
demonstrated the potential for pre-aged carbon to contribute to the n-alkane load (including rock-
56

derived sources, that are radiocarbon dead), whereas the radiocarbon ages for the n-alkanoic
acids suggest considerably less (but not negligible) influence from soil storage (Galy and
Eglinton, 2011; Kusch et al., 2010; Tao et al., 2015). However, despite the complementary
information provided by different isotope systems and biomarker compounds, few fluvial studies
have combined information from C and H isotopes (Galy et al., 2011; Häggi et al., 2016), and
fewer have compared n-alkane and n-alkanoic acid compound classes (Hemingway et al., 2016)
or C and H isotope systems in multiple compound classes (Chikaraishi et al., 2005).
In a series of biomarker studies along the eastern flank of the Andes in Perú, the authors of this
paper and other collaborators have shown that biomarkers record aspects of environment and that
these properties may tag biospheric carbon in fluvial transit. The altitude effect in the isotopic
composition of precipitation has been demonstrated locally in precipitation, plant waters, and
plant wax n-alkanes and n-alkanoic acids in the canopy leaves of the modern forest (Feakins et
al., 2016a). Similarly, the δD value of plant wax C
28
n-alkanoic acids transported by rivers in the
Madre de Dios River network records the altitude effect in soils in the same catchment (Ponton
et al., 2014). Lignin biomarkers trace soil degradation and erosion processes represented in river
sediments (Feng et al., 2016), and also signal that soil-river is the dominant pathway for most
organic carbon including plant waxes (rather than direct input of plant leaves or leaf wax
aerosols into the river). More recently it has been shown that plant leaf (Asner et al., 2014) and
plant leaf wax n-alkane and n-alkanoic acids (Wu et al., 2017) are
13
C-enriched with altitude.
This pattern in bulk leaves has also been seen in other wet tropical montane forests (Asner and
Martin, 2016; Körner et al., 1988), but can be complicated elsewhere by the stronger control
exerted by aridity as shown in bulk leaves and leaf wax n-alkanes (Diefendorf et al., 2010), and
in tropical lowlands, there can be a significant contribution from species using the C
4
pathway
57

(Galy et al., 2011; Häggi et al., 2016; Hemingway et al., 2016). The utility of δ
13
C
wax
as an
altitudinal sourcing tracer has not yet been demonstrated in upland soils and river exported
sediments, although plant-based evidence indicates potential (Wu et al., 2017).
Here, we present a dual isotope (C and H) and a dual compound class (n-alkanes and n-alkanoic
acid) study of soils and river sediments in the Madre de Dios River basin. We hypothesize that
the trends seen in plants for D-depletion (Feakins et al., 2016a) and
13
C-enrichment with altitude
both in bulk leaves (Asner et al., 2014) and leaf wax n-alkanes and n-alkanoic acids (Wu et al.,
2017) from canopy leaves, will be imprinted into the soils and allow us to track inputs to the
river. We seek to test whether both n-alkanes and n-alkanoic acids in soils equally record the
canopy vegetation trends reported for δD (Feakins et al., 2016a) and δ
13
C (Wu et al., 2017), or
whether the post-mortem biogeochemical processes differ between the two leaf wax compound
classes within soil storage. Further, we seek to describe how those signals are represented in
river suspended sediments. We take a detailed elevation sampling approach to the soils and river
network, with new analyses of the same sample set reported in prior studies of n-alkanoic acid
δD (Ponton et al., 2014) and lignin (Feng et al., 2016) from the region. Through these
observations we gather information about biogeochemical processes from plant-to-soil-to-river,
in a large tropical montane river system. This dual isotope approach complements prior work on
another large tropical river network, the Congo River basin (Hemingway et al., 2016), which has
a lowland catchment. We seek to contribute to understanding the processes of sedimentary
integration identified by the community as major gaps in knowledge in several recent review
papers for both plant wax hydrogen (Sachse et al., 2012) and carbon isotopes (Diefendorf and
Freimuth, 2017). The ultimate goals of this dual isotope, dual compound class and soil-to-river
approach are to 1) better understand processes along source-to-sink pathways in the plant wax
58

component of the carbon cycle (Clark et al., 2017; Galy et al., 2015), 2) aid interpretations of
sedimentary records from rivers draining large, tropical montane watersheds (Bendle et al., 2010;
Freeman and Colarusso, 2001; Hein et al., 2017); and 3) understand the signatures of elevation
encoded in plant waxes in soils towards paleoaltimetry applications including a familiar
hydrogen isotope approach (Kar et al., 2016; Polissar et al., 2009; Zhuang et al., 2015) here
combined with carbon isotopes showing potential for dual-isotope plant wax paleoaltimetry.
3.2 Materials and Methods
3.2.1 Study area
Our study area is in the Cusco and Madre de Dios regions of Perú, from the mountainous terrain
along the eastern flank of the Andes to the extensive Amazon lowland floodplain (Fig. 3.1a). The
South American Low Level Jet brings atmospheric moisture westward, and together with the
steep topography along the eastern flank of the Andes, drives high mean annual precipitation
(MAP; 1.5 – 5 m yr
-1
) in the study region (Killeen et al., 2007). High MAP supports lush tropical
forests, with highly-biodiverse tropical rain forests in the lowlands transitioning into tropical
montane cloud forests at c. 1.5 km above sea level (asl), where precipitation peaks (Halladay et
al., 2012). Above the tree line at c. 3.5 km asl, vegetation is dominated by puna grassland and
shrubland. The catchment is drained by the Madre de Dios River which feeds into the Madeira
River, a major Amazon tributary.

59

3.2.2 Field methods
3.2.2.1 Soil samples
Soil samples were previously collected from 14 well-studied forest plots along the elevation
transect spanning 194 – 3644 m asl (Fig. 3.1a, b). All sites are covered with tropical rain forests
or montane cloud forests, except the uppermost site (TC), just above the tree line and dominated
by puna grassland. Subsamples were taken from five soil pits (40 × 40 cm) distributed
systematically across each 1 ha plot and homogenized. Samples were taken from the soil organic
(O) and mineral (M) horizon, differentiated by color, where the top 10 cm from each horizon was
sampled. If the O horizon was less than 10 cm thick (O-horizon thickness ranges from 0.7 – 22.8
cm and generally increases with elevation; Table 3.1), then the entire O horizon was sampled.
These soil samples were previously analyzed for the δD value of the C
28
n-alkanoic acid from the
M horizon only (Ponton et al., 2014) and for bulk organic carbon content (OC%) and lignin
biomarker distribution, for both O and M horizon (Feng et al., 2016). Here we add δ
13
C for the n-
alkanes and n-alkanoic acids in both horizons, δD for the n-alkanes in both horizons, and δD
values for the n-alkanoic acids in the O horizon, complementing the M horizon data from Ponton
et al. (2014) that we reanalyze here for analytical consistency.
Table 3.1. Predicting paleoaltimetry using a dual isotope plant wax approach in paleosols.
Proxy
Elevation response
(‰ km
-1
)
Predicted change after
+2 km uplift (‰)
Uncertainty based on the
modern regression (km)*
δ
13
C
29alk (soil M)
+1.75 ± 0.19 +3.5 ± 0.4 0.2
δ
13
C
30acid (soil M)
+1.31 ± 0.21 +1.6 ± 0.4 0.3
δ D
29alk

(soil M)
–10 ± 2 –20 ± 4 0.3
δ D
30acid (soil M)
–12 ± 2 –24 ± 4 0.3
*Additional uncertainties from aridity and plant type change may be constrained by dual isotope analyses, pollen
information on plant communities, or other environmental evidence. These effects have the potential to confound
estimates of elevation to the order of 2 or 20‰, for δ
13
C or δ D respectively, equivalent to 2 km.

60

3.2.2.2 River suspended sediment samples
River water samples were previously collected from the Kosñipata and Madre de Dios River
main stem and major tributaries during March (wet season) and August (dry season) 2013 (Fig.
3.1). We followed the river downstream with samples collected at point locations across a range
of elevations (c. 180 – 2280 masl) but representing contributions from the catchment above the
sampling point. For example, at Wayqecha, our highest river sampling point (2271 masl), the
river drains a catchment size of c. 50 km
2
with elevation that extends up to 3933 masl (mean
catchment elevation = 3203 m asl). Here, the Kosñipata River was turbulent and well-mixed,
carrying a high sediment load in the wet season but very little in the dry season (63 and 6 g L
–1
at
WAY and SP in the wet season, vs. 0.03g L
–1
at SP in the dry season). In these mountain streams,
suspended sediment sampling was performed from the river bank using a 10 L bucket. At lower
elevations where the Madre de Dios River was navigable, suspended sediment samples were
collected from the middle of the river, where the velocities are the fastest, accessed by a small
boat, repeatedly returning to the same location in the river (tracked by Global Positioning System)
and sampling surface water samples using a 10 L bucket. We collected depth profiles in the
lowland river with the same river navigation methods and using specially-designed 10 L
horizontal isokinetic depth-sampler with a fin for orientation and a pneumatic trap door closure
mechanism. At our lowest river sampling point near Puerto Maldonado (CMD35; 180 m asl), the
mainstem drains a catchment size of c. 75,000 km
2
with elevation that extends up to 6062 m asl
(mean catchment elevation = 1178 m asl), after the confluence with the Inambari River (#44, in
Fig 1a) which drains the very highest regions. Within the river network, there is therefore
considerable heterogeneity in catchment hypsometry, only partially reflected in the catchment
61

mean elevation reported in addition to sampling elevation and distance along river channel
(calculated as km upstream from Puerto Maldonado).
At each river sampling location, large volumes of river water (60–180 L) were collected in 10 L
increments; 100% of the sample was transferred into wine bags with a liner of ethylene vinyl
alcohol (EVOH). Bags were transported and stored in the dark prior to filtering. Water samples
were filtered within 12 hrs at 0.2 µm on polyethersulfone (PES) filters (90 and 147 mm diameter)
using pressurized filtration units, similar to Galy et al. (2011). Filters containing the particulate
organic matter (POM) were stored in Whirlpak bags under cool conditions during the 2-week
fieldwork in remote areas until transport back to the laboratory for refrigeration. In the laboratory,
the POM was rinsed off the filter with milliQ water and subsequently freeze-dried using a Virtis
2k unit. Dry samples were disaggregated and material coarser than 1 mm was removed using a
sieve. Coarse material was infrequently recovered and comprised occasional leaf and wood
debris. The organic matter content of the sieved suspended sediments (0.2−1000 µm) constitutes
the POM. These are the same river suspended sediment samples collected, extracted, and
purified as reported in Ponton et al. (2014), where δD values of C
28
n-alkanoic acids only were
presented. Those samples are reanalyzed here for dual compound class (n-alkane and n-alkanoic
acid) quantification and for dual C and H isotopic composition analysis on both compound
classes. We also add results from depth profile samples, representing some of the only
compound-specific stable isotope data reported to date from river depth profiles.

62

Fig. 3.1. Shaded relief map showing soil and river sampling locations in (a) the Madre de Dios River network and (b)
the Kosñipata River which drains into the Madre de Dios River. River sample code prefix (wet season: CMD; dry
season: MMD) is omitted on map. River sample codes are differentiated into wet (blue) or dry (red) season samples,
mainstem (Kosñipata River and Madre de Dios river; no underline) or tributary samples (underlined), or stormflow
samples (parentheses). Soil sample codes are in brown. Digital elevation model is derived from the 3 arc-second
(approx. 90 m) Shuttle Radar Topography Mission (SRTM) data (Jarvis et al., 2008).

63

3.2.3 Laboratory methods
3.2.3.1 Lipid extraction and compound identification
Total lipid extracts (TLE) were extracted from freeze-dried and homogenized samples with 9:1
v/v dichloromethane (DCM) to methanol (MeOH) using an Accelerated Solvent Extraction
system (ASE 350, Dionex), at 100°C and 1500 psi for 2 cycles of 15 mins. TLE were then
separated by column chromatography through LC-NH
2
gel into a neutral (containing n-alkanes)
and acid (containing n-alkanoic acids) fraction, eluted by 2:1 v/v DCM to isopropanol and 4%
formic acid in diethyl ether respectively. The acid fraction was methylated using MeOH of
known C and H stable isotopic compositions in HCl (19:1 v/v) at 70°C for 12 h. The product was
then diluted with milliQ water and partitioned in hexane, which was then further separated by
column chromatography through 5% water-deactivated silica gel, using hexane and DCM
respectively. DCM elutes the fatty acid methyl esters (FAMEs). The fractions that contained the
n-alkanes and FAMEs were blown dry with N
2
gas and dissolved in hexane ready for compound
identification and quantification using gas chromatography (Agilent 6890) coupled with mass-
selective detector (Agilent 5973) and flame ionization detection (GC-MS/FID). The instrument
was equipped with a Rxi-5 ms column (30 m x 0.25 mm, film thickness 0.25 µm) with column
flow split between MS and FID. Absolute abundance was calculated using a calibration curve of
an in-house standard mixture of n-alkanes and n-alkanoic acids and their peak area response on
the FID, with the same analytical conditions between standard and sample. For those samples
where an unresolved complex mixture was found during GC-MS analysis of the n-alkane
fraction, the n-alkanes were isolated by urea-adduction prior to isotopic analyses.

64

We report the concentration of individual homologues and the sum of the C
23
-
33
n-alkanes (∑alk)
and the C
22-32
n-alkanoic acids (∑acid) on a µg g
–1
sediment and µg g
–1
OC basis (Λalk and
Λacid), summing compounds likely to be mostly plant wax derived based on both reported plant
wax distributions (Feakins et al., 2016b) as well as the consistency of the isotopic composition of
these homologues in the sedimentary samples studied here. We also calculate the average chain
length (ACL) and carbon preference index (CPI) of n-alkanes and n-alkanoic acids using the
following equations:
ACL = Σ (n x [C
n
]) / Σ [C
n
]

(3.1)
and
CPI = 2 [C
n
]/ ([C
n-1
]+ [C
n+1
]) (3.2)
where n indicates the chain length, n = 23 – 33 for n-alkanes and n = 22 – 32 for n-alkanoic acids.
We also report the modal chain length (C
max
). These data are compared to sediment load, OC
concentrations, lignin biomarkers and specific surface area (SSA) previously reported in Feng et
al. (2016).
3.2.3.2 Compound-specific isotopic analysis
Carbon and hydrogen isotopic compositions of individual compounds were measured using gas
chromatography – isotopic ratio mass spectrometry (GC-IRMS; Thermo Scientific Trace gas
chromatograph connected to a Delta V Plus mass spectrometer, via an Isolink combustion
furnace at 1000°C for δ
13
C measurement, and an Isolink pyrolysis furnace at 1400°C for δD
measurement). We checked the linearity in isotopic determination across a range of peak
amplitude (1 – 10 V) daily, and only the measurements from compounds with peak amplitudes
65

within the range of linearity were accepted. δ
13
C values were normalized to the Vienna Pee Dee
Belemnite (VPDB) standard, and δD values were normalized to the Vienna Standard Mean
Ocean Water/Standard Light Antarctic Precipitation (VSMOW/SLAP) standard, by comparing
with an external standard, the A3-mix (supplied by A. Schimmelmann, Indiana University),
containing 15 n-alkane compounds (C
16
– C
30
) with δ
13
C and δD values spanning −33.3 to
−28.6‰, and −9 to −254‰ respectively, with RMS uncertainties better than 0.2 and 5‰,
representing the dominant component of analytical uncertainty. We correct measured n-alkanoic
acid methyl esters by mass balance for the added methyl group (δ
13
C −25.45 ± 0.37‰,
determined offline by combustion of methanol, and δD −198.3 ± 3.9‰, determined by
methylation of a phthalic acid standard supplied by A. Schimmelmann) (Lee et al., 2017) to
report the δ
13
C and δD values of n-alkanoic acids.
3.2.3.3 Catchment hypsometry
We delineated the Madre de Dios River network and catchments of river sediment samples (Fig.
3.1a) in ArcGIS using 3 arc-second (c. 90 m) resolution Shuttle Radar Topography Mission
(SRTM) digital elevation model (DEM; Fig. 3.1b) (Jarvis et al., 2008). River profile and
elevation distributions were then determined from the delineated catchments. These data were
used to identify the catchment mean elevation of individual river suspended sediment samples.
3.3 Results
3.3.1 Concentrations of organic carbon and plant wax biomarkers in soils
Plant wax biomarker concentrations vary by two orders of magnitude in sampled soils. We find
the expected distributions of n-alkane and n-alkanoic acid homologues indicative of plant wax
sources. We sum the homologues to report total compound class concentrations. In the O horizon,
66

∑alk range from 2 – 144 (median = 22.5) μg g
–1
and ∑acid range from 14 – 290 (median = 109)
μg g
–1
. In the M horizon, ∑alk values range from 3 – 24 (median = 11) μg g
–1
and ∑acid from 12
– 422 (median = 71) μg g
–1
.
The ∑alk is less abundant than ∑acid and both decrease in concentration between the O and the
M horizon at each site. We find no systematic change in the ∑acid /∑alk between the O and M
horizons on a site-by-site basis or along the elevation profile, suggesting no uniform difference
in net changes in inputs and preservation.
Considering plant wax as a proportion of soil organic matter (Λalk = ∑alk/OC) accounts for part
of the variability in plant wax concentrations. OC varies by an order of magnitude from 4.3% to
38.8% in the O horizon (OC < 10% at TP3, TP4, SP1) and 1.9% to 13.0% in the M horizon (OC >
10% at WAY) as reported in Feng et al. (2016). We find Λalk ranges from 56 – 889 (median =
122) μg g
–1
OC, and Λacid ranges from 154 – 1330 (median = 723) μg g
–1
OC in the O-horizon.
Λalk ranges from 116 – 320 (median = 181) μg g
–1
OC, and Λacid ranges from 384 – 5485
(median = 1140) μg g
–1
OC in the M-horizon. Overall plant wax concentrations vary by an order
of magnitude more than bulk soil organic matter concentrations. Some higher elevation sites
have higher concentrations of plant waxes (one site TU4 has exceptionally high concentrations).
However, the altitudinal trend is small compared to the scatter between sites, soils being
notoriously heterogeneous. Vertically within soils we find a decrease in n-alkane and n-alkanoic
acid concentration between O and M horizons, but n-alkanes and n-alkanoic acids represent an
increasing (but still trace) component of organic matter in deeper layers of soil.

67

3.3.2 Altitude effect in plant wax δ
13
C and δD in soils
We measured δ
13
C and δD values of odd chain length n-alkanes (C
27
– C
31
) and even chain
length n-alkanoic acids (C
22
– C
32
) in soils across the elevation transect (as homologue
abundances allow). All plant wax homologues in both O and M horizons show the same trends:
an increase in δ
13
C and a decrease in δD values with increasing elevation (except the C
27
n-
alkanes which are present in low concentrations). We focus on C
29
n-alkanes (δ
13
C
29alk
) and C
30

n-alkanoic acids (δ
13
C
30acid
) as they are the most abundant long-chain compounds in each class,
both in soils (this study) and canopy leaves (Feakins et al., 2016a; Feakins et al., 2016b).
δ
13
C
29alk
increases with elevation in soil O and M horizons (+1.37 and +1.75‰ km
–1
respectively)
with gradients similar to canopy leaves within uncertainties (Wu et al., 2017), but with a c. +2‰
increase from leaves to soil (Fig. 3.2a). δ
13
C
30acid
also increases with elevation (+1.06 and +1.31‰
km
–1
in O and M horizons respectively; Fig. 3.2b). In canopy leaves (Fig 2b), the elevation
relation was not statistically significant given the small number of forest plots sampled for
δ
13
C
30acid
analyses (Wu et al., 2017), but site mean plant values are similar to soils (Fig 3b).
Comparing the two soil horizons, we find a c. +1‰ increase on average from O to M horizon in
both C
29
n-alkane and C
30
n-alkanoic acid (Fig. 3.2a, b). This O to M offset is not apparent in the
lowest elevation sites (TP3 and TP4), but the O horizon at these two sites is very thin (0.7 – 2.5
cm; Table 3.1), and the samples collected show relatively low OC% (4.3 – 9.3%), thus not
meeting the typical >10% criterion for an O horizon (Feng et al., 2016). δD
29alk
decreases with
elevation in soil O and M horizons (–13.2 and –9.7‰ km
–1
respectively) with shallower but
similar gradients (within the large uncertainties) to canopy leaves (–16.4‰ km
–1
; Fig. 3.2c) and
with similar absolute values. δD
30acid
in both horizons show the same trend with similar slopes (–
10.1 and –11.8‰ km
–1
for O and M horizon respectively; Fig. 3.2d). In contrast to δ
13
C, we do
68

not observe systematic isotopic offsets in δD values between plant and soil, or between O and M
horizons in either compound classes (Fig. 3.2c, d) although for the highest altitude plant site, the
site mean plant δD
29alk
value is more negative than soils, which has the effect of steepening the
apparent plant slope, although this is not well defined by 5 sites resulting in a large uncertainty
(Fig. 3.2c). Overall, the trend with altitude is the major finding for both isotopes and compound
classes for the catchment scale interpretations (Fig. 3.2).

Fig. 3.2. Soil isotopic gradients showing (a) δ
13
C C
29
n-alkane, (b) δ
13
C C
30
n-alkanoic acid, (c) δD C
29
n-alkane and
(d) δD C
30
n-alkanoic acid in soil O (violet) and M (orange) horizons, as well as canopy leaf site-means (green; δ
13
C
Wu et al., 2017, δD Feakins et al., 2016), showing 1σ uncertainties (error bars), ordinary least squares (OLS) linear
regressions (lines; all with p<0.0001) with regression uncertainties (1σ: shading). No regression plotted for canopy
leaves in b or d because the relationship is not significant, but this is thought to be a type II error, due to the
insufficient number of sites along the transect (n = 5). Insets show the relationship between soil O and M horizons.
Note that soil O horizon samples at sites TP3 and TP4 contain <10% OC (open squares, included in the regression).
69

3.3.3 Altitude effect in plant wax δ
13
C and δD in surface river suspended sediments
We measured δ
13
C and δD values of odd chain length n-alkanes (C
27
– C
31
) and even chain
length n-alkanoic acids (C
24
– C
30
; as homologue concentrations allow) in river suspended
sediment collected across the elevation transect. We note that molecular abundance distributions
of both compound classes, and the isotopic similarities across dominant homologues, are
consistent with those found in soils. All plant wax homologues show the same trends: an increase
in δ
13
C and a decrease in δD values with increasing elevation (except the C
27
n-alkane, which is
more
13
C-enriched). Here we focus on C
29
n-alkanes (δ
13
C
29alk
) and C
30
n-alkanoic acids
(δ
13
C
30acid
) as they are the most abundant long-chain compounds in each class in rivers and soils,
as well as in canopy leaves as noted above (Feakins et al., 2016a; Feakins et al., 2016b). In the
wet season δ
13
C values increase with elevation in rivers for both δ
13
C
29alk
(+1.01‰ km
–1
; Fig.
3.3a) and δ
13
C
30acid
(+0.96 ‰ km
–1
; Fig. 3.3b). We find very little Andean export in the dry
season, i.e. sediment loads of OC and ∑
alk
and ∑
acid
are lower in the Andean rivers during the dry
season relative to the wet season and relative to the downstream reaches. Following this, δ
13
C
values increase less with sampling elevation in the dry season, but the trend is not sufficiently
described given the small sample size (n < 7) available. Availability of dry-season suspended
sediments was limited in the upland, because even very large samples (>300 L of river water at
the uppermost sampling point) did not yield sufficient plant wax for isotopic analyses.
70

Fig. 3.3. River data (wet season: blue circle; dry season: red diamond) showing δ
13
C (top) and δD (bottom) of C
29
n-
alkane (left) and C
30
n-alkanoic acid (right) from river suspended sediments (error bars: 1σ uncertainties) from main
stem (solid symbol) and tributary (open symbol). OLS linear regressions (blue lines) are shown for wet season
samples with regression uncertainties (1σ: shading). CMD 16 and 31 are outliers, with low CPI evidence for
petrogenic contamination (crosses, not included in regression). Regressions for dry season samples are not
significant (dashed line; p > 0.05). Also showing data from depth profile samples (grey, not included in the
regression).

3.3.3.1 River depth profiles
Sediment settling within river channels leads to higher sediment loads at depth, with significant
effects in the rivers studied here, which range up to 12 m deep (Fig. 3.4a). OC concentrations
tend to decline with depth, however there is a ‘woody undercurrent’ of visible coarse plant debris
just above the peak in sediment load within some deep channels (Fig. 3.4b, Feng et al., 2016).
71

OC variations largely control n-alkane concentrations, with ∑
alk
varying by less than a factor of
two in most depth profiles, apart from high ∑
alk
in the surface and deepest samples of one profile
(CMD 25, Fig. 3.4c). We do not have robust quantification data for the n-alkanoic acids;
however they are more abundant than the n-alkanes. The finer sediments in the upper water
column have higher specific surface area (SSA; Fig. 3.4e; Feng et al., 2016). CPI
alk
varies
between location and depth, but tends to decrease towards the surface, suggesting transport of
more degraded n-alkanes in association with fine grains with high SSA (Fig. 3.4f), such as those
from the weathered soils. This is consistent with evidence of in-river depth differentiation in
δ
13
C
alk
values (Fig. 3.4f): the upper samples are consistent with degraded M horizons, whereas
the deep samples are
13
C-depleted and this might reflect the ‘woody undercurrent’ with
overprinting of some larger, fresher material from plants and from the O horizon. We find
essentially no sorting of δ
13
C
30acid
(Fig. 3.4g), δD
acid
(Fig. 3.4h), or δD
alk
(Fig. 3.4i) consistent
with an absence of offset between soil O and M horizons. For δ
13
C
alk
, it appears that the
differential effects between O and M horizon also complicates assessment of sediment sourcing
in rivers, whereas δ
13
C values of the n-alkanoic acids and the δD values of both compound
classes provide elevation markers immune to these effects.
72

Fig. 3.4. Properties of bulk sediment and plant wax biomarkers in the depth profiles of the Madre de Dios River: (a)
sediment load, (b) organic carbon content (OC%), (c) OC-normalized total n-alkane abundance (Λ
alk
), (d) carbon
preference index of C
27-33
n-alkanes (CPI
alk
), (e) specific surface area (SSA), (f) δ
13
C
29alk
, (g) δ
13
C
30acid
, (h) δD
30acid
, (i)
δD
29alk
.
73

3.4 Discussions
3.4.1 Altitude effect in soils
The sensitivity of plant wax δ
13
C values in soils to elevation (Fig. 3.2a, b) provides new evidence
that carbon isotopes may be useful as an elevation proxy in paleosols and for sedimentary
sourcing studies. This study confirms that the slope of canopy plant wax δ
13
C values with
altitude (Wu et al., 2017) is robustly captured in soils here. The major caveat with this approach
is that aridity is a larger factor in carbon isotope fractionations relative to elevation effects
(Diefendorf et al., 2010) and this can confound an altitude effect; for example a prior application
of plant wax δ
13
C in soils on Mt. Gongga, China, found the patterns to be dominated by aridity
(Wei and Jia, 2009). However, in this very wet region of the Madre de Dios, the altitude
sensitivity encoded in soils provides a robust signal that can be used for our catchment sourcing
questions.
The altitude effect in hydrogen isotopes in precipitation has been established in plant wax (Bai et
al., 2011) and applied for paleoaltimetry (Polissar et al., 2009). Such an elevation effect has
already been reported for plant wax δD in the tree canopy (Feakins et al., 2016a) and soils
(Ponton et al., 2014) in the Madre de Dios. This study extends those findings, confirming the
presence of the altitude effect in dual plant wax compound classes in soils, i.e. that it is robust to
the early diagenesis changes between leaf and soil. Thus, we find both compound classes and
both isotope systems have potential for ‘tagging’ the elevation of origin.
Overall, the altitude trends seen in prior tree canopy surveys (Feakins et al., 2016a; Wu et al.,
2017) are found to be reproduced in tree canopy and in soil O and M horizons for δ
13
C of n-
alkanoic acids and δD values of both n-alkanoic acids and n-alkanes here (Fig. 3.2). But, while
74

the carbon isotopic trend in the canopy is indeed transferred to soils, there is a >2‰
13
C-
enrichment in n-alkanes in soils relative to leaves (considered separately below). Considering the
greater plant wax stock in soils relative to tree canopy, the multi-centennial age of most river-
exported plant wax (Galy and Eglinton, 2011; Kusch et al., 2010) and the coherence between
soils and river samples (considered later), the soils, especially the M horizon, represent the major
source of plant wax to the river. Thus, we find the soil-based calibrations most relevant for
interpretation of catchment sourcing.
3.4.1.1 Offset in n-alkane δ
13
C values between canopy and soils
What may explain the offset in δ
13
C values (Fig. 3.2a) between tree canopy n-alkanes and soils
and between O and M horizon in the Andes? First, the Suess effect (anthropogenic alteration of
the δ
13
C of CO
2
in the atmosphere due to the combustion of fossil fuels) has lowered the δ
13
C of
the atmosphere between c. –6.4‰ in 1750 AD (Friedli et al., 1986) to –8.3‰ in 2013, when
samples were collected – amounting to a lowering of –1.9‰. Today’s atmosphere is the substrate
for modern plant leaves, whereas the OC in soil was fixed from an atmosphere with higher δ
13
C.
Plant wax in the O horizon likely integrates several recent decades, and since 1960 there has
been a 1‰ lowering of the atmospheric δ
13
C (Friedli et al., 1986) and thus we might expect up to
a –1‰ offset of the O horizon from canopy leaves based on the Suess effect alone. Plant wax in
M horizons may be much older, perhaps integrating several centuries, including (but likely not
exclusively) preindustrial and thus we might expect up to a –2‰ offset between the canopy and
the M horizon. However additional factors still must be invoked to explain the offsets as not all
soil O and M horizon carbon dates from prior to 1960 and 1750 respectively.
75

Degradation processes may account for part of the offset between the canopy, O horizon and M
horizon. Indeed this magnitude of effect is possible with >2‰ increase in n-alkane δ
13
C values
reported elsewhere between fresh leaves and leaf litter (Nguyen Tu et al., 2004). Degradation
gradients exist along the elevation transect in the Madre de Dios; for example, the O horizon is
thinner and has a lower OC content at the lowest elevation site, in addition to local variations
associated with waterlogging. Fungal communities have been shown to add n-alkanes at depth
(Marseille et al., 1999) and could carry a distinct carbon isotopic signature, though fractionations
are unknown.
Other inputs to soils should be considered. Understory plant wax contributions to soils are
unlikely to drive the offset as productivity is lower than that of the canopy and
13
C-depleted
values due to respired carbon would be expected, counter to the observed offset. Root wax
contributions to soils are possible, as agricultural studies have reported that root derived lipids
may be better preserved than leaf lipids in soils and may be
13
C-enriched (Rasse et al., 2005;
Wiesenberg et al., 2004). Root wax inputs were not characterized as part of this study, and we
note that root species identification in a mixed rainforest is difficult, in contrast to crop
monoculture. However fine root productivity increases with altitude in this region (Girardin et al.,
2013), so root wax inputs may be greater at higher altitudes, and this could contribute to the
different slopes of δ
13
C for the O and M horizon (Fig. 3.2 a, b), however fresh root production
would be unlikely to explain the offset as the Suess effect would act counter to the direction of
offset observed.
Generally, plant wax concentrations reveal an increase in Λ
alk
and Λ
acid
by about a factor of two
between the O and the M horizon, which likely reflects the slower rates of loss relative to other
compounds in soils such as cellulose (Kögel-Knabner and Amelung, 2013; Schmidt et al., 2011)
76

and explains the importance of waxes as geological biomarkers (Eglinton and Eglinton, 2008). A
couple of studies have indicated that there can be deep soil additions of n-alkanes from fungi
(Marseille et al., 1999) and of n-alkanoic acids from roots (Wiesenberg et al., 2004), although it
is not clear how widespread such inputs are, and how much this may confound the “leaf” wax
environmental interpretation. How inputs and preservation vary between compound classes
merits further study.
3.4.2 The river-transported signal of plant wax δ
13
C and δD
During the wet season, plant wax δ
13
C and δD values in both compound classes in river
suspended sediments (surface water) correlate with mean catchment elevation (blue symbols and
regression, p < 0.001; Fig. 3.3), following similar patterns as found in soils (both soil O and M
regression envelopes shown for comparison; Fig. 3.3). A previous study of δD values of C
28
n-
alkanoic acids in the same samples used the median catchment elevation as the central estimate
of the skewed hypsometric distribution (Ponton et al., 2014). Our broader suite of measurements
(dual isotope, dual compound class) supports the earlier suggestion that plant waxes in
mountainous river systems record the isotopic gradients defined by their catchment elevations.
Here we analyzed the river data with both the median and the mean catchment elevation, and we
find catchment mean elevation best represents the catchment sourcing (higher R
2
by 0.11 - 0.25),
which implies averaging of all altitudes uniformly. This general interpretation applies to both n-
alkanes and n-alkanoic acids and both hydrogen and carbon isotopic composition in our wet
season dataset (Fig. 3.3). Altogether, our broader suite of measurements (dual isotope, dual
compound class) supports the earlier suggestion that plant waxes in the Madre de Dios system
record the isotopic gradients defined by their catchment elevations.
77

In order to explore differences from the soil-predicted regression based on catchment
hypsometry represented by the catchment mean elevation (i.e. uniform integration case), we
calculate the offset for individual river samples from the soil regression. In most cases the
discrepancies between measured values and those predicted for uniform integration are neither
large nor consistent between compound class or isotope system, for example at CICRA (821 m
catchment mean elevation), we find a high altitude bias in the δD
30acid
only. Overall, we do not
find a systematic bias from the uniform catchment integration case, except at the mountain front
location, where there are seasonal effects, considered next.
During the dry season, sediment loads were low, especially at the Andean sites, limiting potential
for plant wax analyses despite very large volume sampling, i.e. it was obvious in the field that
the signal exported during the dry season has more of a lowland bias than the wet season uniform
catchment integration case. Overall, the restricted elevation range and small number of the dry
season plant wax isotopic data indicate non-significant relationships with catchment elevation
(Fig. 3.3). Given the scatter in the dry season elevation trend, we compare the isotopic
composition at point locations across seasons. At the mountain front location (MLC, 450 m asl,
2006 m catchment mean elevation), we observe seasonal differences, with the dry season offset
from the wet by –1.5‰ (δ
13
C
30acid
) and +26‰ (δD
30acid
), –3‰ (δ
13
C
29alk
), with insufficient dry
season sample for δD
29alk
determination resulting in no comparison. The seasonal offsets include
some inconsistency between proxies, but each indicate lowering of the locus of erosion by c. 1–2
km relative to the uniform catchment integration found in the wet season at this site (MLC wet
season values were within uncertainty of soil-regression predicted values). At other locations
where we have data in both seasons, the differences are not larger than analytical uncertainties.
78

Our two-season sampling approach demonstrates that the wet season accounts for most upland
erosion within the catchment: the river is visibly turbid with sediments and plant wax ∑alk
concentrations are 49 and 10.2 µg L
–1
at WAY and SP. In the dry season upland transport is
reduced (∑alk was not measurable at WAY and were just 0.1µg L
–1
at SP) and thus the locus of
wax sourcing lowers. We find that when Andean erosion peaks in the wet season, catchment
integration approximates uniform integration at all elevations (Fig. 3.3). During the dry season,
Andean export diminishes, and this results in a pronounced seasonality in sourcing at the
mountain-front location, corroborating the finding of Ponton et al., (2014), now with dual
isotopes and compound classes. At the floodplain locations, catchment hypsometry is so skewed
to the lowlands on an areal basis, that the seasonal variation in Andean export is barely
perceptible. Thus, we establish that there is no Andean bias in plant wax sourcing, even in the
wet season, in contrast to the clastic flux carried by the river.
Analysis of biomarker distributions reveals differences in degradation: downstream we observe
lower CPI consistent with more weathering of n-alkanes during storage and remobilization in the
floodplain for the n-alkanes overall (p < 0.01) and especially in the wet season (p < 0.001). No
such degradation trend is observed in the n-alkanoic acids. Other plant biomarkers, lignins,
similarly show degradation and soil storage prior to fluvial export in this system (Feng et al.,
2016). Both n-alkanes and n-alkanoic acids show a slight tendency to increased chain length in
the lowlands, similar to patterns reported for plants in the catchment suggesting the river
sediment composition reflects incorporation of a lowland signal in the floodplain.

79

3.4.3 Dual isotope analysis
If we examine soil data in dual isotope space (Fig. 3.5), we observe the general inverse
relationship expected from the altitudinal relationships in each isotope system, and that river
samples mostly fall on or near the dual-isotope relationship described in the transect of soil plots
(Fig. 3.5). We note one outlier among the river samples that does not fit with the soil or river
samples in dual isotope space (CMD 31; Fig. 3.5). The n-alkanes derived from CMD 31, a
mainstem, lowland sample, deviate from other soil and river samples in dual isotope space, with
more
13
C-enrichment than expected based on the δD value. In addition, CMD 31 has a low CPI.
n-Alkanes in plants and most sediments have a high CPI, whereas low CPI is generally indicative
of thermal alteration and as such is indicative of petrogenic sources. Petrogenic inputs tend to
drive
13
C-enriched values which could account for the isotopic deviation here (CMD 31 is an
outlier on both Fig. 3.3 and 3.5). However, this sample was from the main stem of the Madre de
Dios and no influence was detected at the next station downstream station, thus this outlier was
not volumetrically relevant for sourcing in this catchment. Petrogenic inputs have been
documented elsewhere in sedimentary n-alkanes (Häggi et al., 2016; Pearson and Eglinton,
2000), and hence many studies have favored n-alkanoic acids for fluvial sourcing (Galy et al.,
2011; Kusch et al., 2010) and paleoenvironmental reconstruction (Feakins et al., 2013;
Niedermeyer et al., 2014; Tierney et al., 2008). Nevertheless, alkanes have value for fluvial
sourcing (Hemingway et al., 2016) and have been widely applied for paleoenvironmental
reconstructions (Freeman and Colarusso, 2001; Pagani et al., 2006; Schefuss et al., 2011;
Schefuss et al., 2003). Dual isotope analysis is demonstrated here as useful validation between
two ‘altimeters’, here confirming strong agreement overall, as well as isolating a sample with
petrogenic influence (confirming the primary evidence from that sample’s low CPI). Although
80

not applicable in this forested, very wet catchment, we suggest the dual isotope approach would
also be well-suited to identify and diagnose potential causes of divergence from the altitude
relation in other catchments, such as inputs from a lowland C
4
savanna. In other climates, aridity
could also cause the altitude effect to be disrupted, potentially leading to both a
13
C- and D-
enrichment (of different magnitudes); again the dual approach can provide more information
than a single-isotope based system.

Fig. 3.5. Comparison of δD and δ
13
C values of (a) C
29
n-alkane and (b) C
30
n-alkanoic acid for river suspended
sediments in wet (blue circles) and dry (red diamonds) seasons, as well as the depth profile samples (grey). OLS
linear regressions are shown for the river wet season and soil O (violet) and M (orange) horizons (individual data of
soil is shown in Fig. 3.6) with 1σ regression uncertainties (shading). CMD 31 is an outlier, with low CPI evidence
for petroleum contamination.

Other river n-alkane data display minor deviations from the soil relationship in dual isotope
space, with a tendency to
13
C- and/or D-depleted values for n-alkanes. These offsets are not
consistent with a bias in spatial integration as each isotope has opposing implications (
13
C-
depleted would imply lower elevation, while D-depleted would imply higher elevation).
81

Generally high CPI allows us to discount a petrogenic source in most samples, although the CPI
systematically declines (p < 0.01) toward the lowland and is more scattered in the lower
elevations and between tributaries; thus stored input from different environments or times may
explain some of the scatter. Depletion of both isotopes in the same molecules may be explained
if: a) the range of sources in the catchment have not been completely represented by the transect
of soil samples, e.g. if a lens of ancient substrate is exported from a river bank (Householder et
al., 2012; Rigsby et al., 2009), b) analytical uncertainties may contribute to ‘scatter’ in the river
samples around the regression envelope of uncertainties (with overall hydrogen isotope
analytical uncertainties on the order of 5‰, and carbon on the order of 0.2‰) or c) point
sampling of the river may not fully represent the full transport load of the river (despite
contrasting two seasons, we have not accomplished sampling of detailed seasonality
(Hemingway et al., 2016; Tao et al., 2015), extreme flood hydrographs (Freymond et al., 2018),
or interannual variability.
3.4.4 Implications for geological and geochemical applications
3.4.4.1 Understanding plant wax sedimentary integration
Where plant wax biomarker reconstructions are derived from large catchments with high
elevation, questions have been raised about the source area represented in the downstream
sedimentary deposits as well as the temporal components of erosion, storage and sedimentary
sourcing (e.g., Hein et al., 2017). These questions have been summarized as “the nature of
sedimentary integration” (Diefendorf and Freimuth, 2017; Sachse et al., 2012) with a reported
state of knowledge ranked as low (1 out of 5). Several studies have ventured to answer these
questions, tracking plant wax molecules through catchments, including in the Amazon River, the
82

largest river drainage network in the world. From an elevation transect in the wet, forested,
mountain sector of the Madre de Dios, Perú, an earlier study reported the δD values of plant wax
n-alkanoic acids showing spatial averaging (Ponton et al., 2014). That finding is corroborated
here, and further we find similar patterns of plant wax integration using both δ
13
C and δD,
analyzed in both the n-alkanoic acids and n-alkanes. In the lowland Amazon River network,
Häggi et al. (2016) identified signals from the western basin as well as each of the tributaries
contributing to the mainstem of the Amazon River, confirming the concept of spatial integration
much further downstream for the n-alkanes.
If uniform spatial integration is a reasonable approximation for plant wax sourcing, in many
catchments that means a lowland-dominated record based on areal extent, for example c. 90% of
the Amazon Basin is lowland. River floodplains further appear to reset the transported plant wax
signal to lowland values, not only because of additive inputs from greater lowland areal extent,
but also because of degradation of the upland material in transit across floodplains (Galy et al.,
2011; Häggi et al., 2016) or estuaries (Medeiros et al., 2012). Here, we find the concentrations of
plant wax n-alkanes in river suspended sediments ( Σ
alk
and Λ
alk
, Appendix A) remain relatively
constant downstream despite increased catchment area, indicating degradation and replacement,
rather than accumulation. Evidence for floodplain replacement also comes from the Ganges-
Brahmaputra River network, where Galy et al. (2011) analyzed the δ
13
C and δD values of plant
wax n-alkanoic acids in suspended sediment, finding differences between mountain-front
tributaries, but ultimately showing a lowland signal upon export to the ocean.
Although steep terrain enables sedimentary erosion, the aforementioned studies have shown that
such regions are unlikely to dominate plant wax sedimentary records, except when sampling in
proximal locations. At mountain-front locations, there may be time-variable inputs associated
83

with changing flow induced by storms and seasonal climate regimes (Clark et al., 2013), and by
low frequency, large magnitude landslide events (Clark et al., 2016). For example, in the
mountain-front river sampling of the Madre de Dios, seasonal Andean export and episodic storm
inputs perturb the river suspended plant wax signal (Ponton et al., 2014).
While the Madre de Dios catchment is fully forested, other catchments include regions with
sparse vegetation that yield little plant wax, e.g. the Tibetan portion of the Arun River catchment
in Nepal (Hoffmann et al., 2016). Such regions are underrepresented in the exported plant wax
signal relative to their spatial extent. However, various studies reveal that it is the soil stocks of
plant waxes and not modern plant wax productivity that is critical to determining uneven
contributions. From this Madre de Dios transect, n-alkane productivity in modern forests
(Feakins et al., 2016a), together with n-alkane concentrations and δ
13
C signals in soils and rivers
(this study) and analogous data for lignin (Feng et al., 2016), indicate that soil stocks are the
major source of plant biomarkers to rivers (i.e. plant-to-soil-to-river), rather than direct from
plant-to-river. Other studies find that this soil-to-river pathway is accentuated for those rivers
that travel through regions with abundant stored sedimentary material. The Yellow River drains
the loess plateau, and Tao et al. (2015) used compound-specific radiocarbon to establish that a
large fraction of the exported plant wax was eroded from storage with an age of ~1000 years for
the n-alkanoic acids and >26,000 years for the n-alkanes, in the extreme case of the loess storage.
Some lowland tropical basins (including the Congo and Amazon) contain vast swamp areas, and
these regions may at times be sinks or sources that contribute to the exported biomarker signal
(Hemingway et al., 2016; Wagner et al., 2014). As river meanders migrate, they erode
sedimentary deposits, likely remobilizing older plant wax (Torres et al., 2017), such as from
84

Holocene peats (Householder et al., 2012) and Pleistocene age river terraces (Rigsby et al., 2009)
in the Madre de Dios River basin.

Fig. 3.6. Synthesis of plant wax sedimentary integration studies (Galy et al., 2011; Häggi et al., 2016; Hemingway et
al., 2016; Hoffmann et al., 2016; Ponton et al., 2014; Tao et al., 2015).

We summarize these observations of plant waxes in transit in this and varied modern systems in
Fig. 3.6 with a view to interpreting plant wax studies of paleoclimate and paleoenvironment.
Broadly, uniform spatial averaging is a fair approximation for plant wax sourcing in many
environments. Mountain lakes will necessarily receive a local signal. Mountain-front
85

sedimentary deposits may record a variable signal, but lowlands will dominate the exported
signal in most catchments because of their areal basis and ability to degrade upland organic
matter and replace with lowland organic matter inputs, as well as their proximity to sedimentary
sinks. Uniform averaging can be disrupted if a river erodes thick sedimentary sources that
effectively ‘over contribute’. Conversely, when the river erodes land that may be barren (or have
low amounts) of vegetation and soil those areas may ‘under contribute’ based on their areal
extent.
A contribution from this study is that both carbon and hydrogen isotopes can reveal remarkably
consistent stories about catchment sourcing, in both the alkane and alkanoic acid compound
classes. One interpretation could then be that either isotope system or compound class can be
used to track sourcing. However, single isotope approaches in other settings may find that one or
other of the isotope systems may be biased. For example, lowland aridity may shift the carbon
isotopic composition to more positive values such that the altitude trend is diminished, or may
shift the values at high elevation, such that the trend is accentuated. Or, lowland vegetation may
include C
4
grasses, and this would counteract the altitude trend. Alternatively, hydroclimate
changes can alter the pattern of precipitation isotopes. In such cases the direction of divergence
between the two isotope systems may reveal the explanation. In submarine fans, containing
ancient river-transported sediments, several studies have shown that the dual isotope approach
can be insightful about climate and vegetation (Hein et al., 2017; Schefuss et al., 2011; Schefuss
et al., 2005). Although spatial patterns may remain obscure from single marine core
reconstructions, the purpose of catchment sourcing studies is to reveal the source region
integrated in river-transported sediments, even though sourcing may change over time. A
powerful solution can be the study of multiple sedimentary deposits across catchments, to reveal
86

the spatial distribution of changes on the landscape, e.g. the combination of terrestrial (Siwaliks)
and marine (Bengal Fan) records (Freeman and Colarusso, 2001), or the comparison of the
nested catchments of the Congo Fan (Schefuss et al., 2005) and Lake Tanganyika records
(Tierney et al., 2008), reported in Sachse et al. (2012).
3.4.4.2 Potential for dual isotope plant wax paleoaltimetry
Reconstructing the past elevation of landscapes is of interest for understanding tectonic processes
(Garzione, 2008), climate dynamics (Molnar et al., 2010) and biological evolution (Antonelli et
al., 2009; Hoorn et al., 2010). Many proxies for paleoelevation make use of the stable isotope
gradient in meteoric water with elevation, as recorded in the δ
18
O value of carbonates (Blisniuk
and Stern, 2005; Rowley, 2007). More recently, it has been noticed that plant wax organic matter
archives the altitudinal gradient in the δD value of meteoric waters in soils (Bai et al., 2011; Jia
et al., 2008; Luo et al., 2011; Ponton et al., 2014) and based on such observations, the δD
wax

value has been used as a paleoaltimetry proxy on the Tibetan Plateau, on the Andean Altiplano
and in the New Zealand Alps (Kar et al., 2016; Polissar et al., 2009; Zhuang et al., 2014; Zhuang
et al., 2015). Our results provide additional evidence to support the use of hydrogen isotopes in
plant wax as a paleoaltimetry proxy in ancient soils and fluvial deposits; further we introduce the
concept of dual C and H isotopes in the same plant wax molecules for paleoaltimetry.
Altitude effects empirically determined in both the hydrogen and carbon isotopic composition of
the forest canopy (Feakins et al., 2016a; Wu et al., 2017) are transferred to soils (Fig. 3.2) and to
river sediments (Fig. 3.3), in two compound classes of leaf wax biomarkers (n-alkanes and n-
alkanoic acids). In theory, river sediments carry altitudinal information, although these would
reconstruct the catchment integrated elevation rather than being an in situ estimate, making
87

paleosols the simpler case. Based on the relationship observed in soil M horizons (the major
stock) across the elevation transect in the Madre de Dios basin (Fig. 3.2), we predict the isotopic
signal that would be anticipated in paleosol samples with uplift from 2 to 4 km asl, together with
uncertainties solely from the M horizon soil regression with elevation (Table 3.1). We also
illustrate the elevation of all soil data (including O and M horizons) in dual isotope space,
illustrating the power to differentiate sites separated in elevation by 0.5-1 km but no finer (Fig.
3.7), which provides a guide to paleoaltimetry potential.

Fig. 3.7. Comparison of δD and δ
13
C values of (a) C
29
n-alkanes and (b) C
30
n-alkanoic acids in both the soil O and
M horizon samples (undifferentiated). Symbol colors correspond to sampling elevation (see legend). Orthogonal
distance regressions (black lines) are shown with 1σ regression uncertainties (grey shading).

Additional uncertainties exist, beyond that captured in the modern soil survey. For plant wax δD
the principal uncertainty is usually the choice of the appropriate net fractionation factor between
source water and plant wax needed to evaluate the isotopic composition of past precipitation. Net
fractionations vary between plant type and climatic regime, but the largest shift in the net
88

fractionation is typically seen when transitioning to grasslands which have larger D/H
fractionations than other lifeforms. A second concern surrounds aridity. Although plants can
regulate water loss by their stomata, net carbon and hydrogen fractionations are generally smaller
in drier environments than wet environments (Diefendorf et al., 2010; Feakins and Sessions,
2010; Sachse et al., 2012; Wei and Jia, 2009). Both plant type and climatic change likely
accompany uplift, and such changes would alter the response in the plant wax archive from that
seen in this forested, very wet altitude transect today.
Overall, the major concern with stable isotope approaches to paleoaltimetry is that uplift often
engenders drying, such as is the case in the Andean Altiplano or Tibetan Plateau, and this drying
affects stable isotope based altimetry as recorded in carbonates (Rowley and Garzione, 2007).
The plant wax proxy should be less sensitive to aridity as transpiration losses from plants are
restricted unlike evaporative losses affecting carbonates. Further the plant wax archive is
uniquely suited to monitor for the confounding effect of aridity, because of the ability to measure
both stable C and H isotopes in the same molecule. We find a negative correlation between δD
and δ
13
C values across the elevation transect in the ever-wet scenario studied here (Fig. 3.4),
whereas drying would be expected to lead to a positive correlation between δD and δ
13
C. The
combination of plant wax carbon and hydrogen isotopic analyses in the same molecules therefore
provides a check on hydrological changes that may compromise either altimeter. Thus, for
paleoaltimetry applications of the plant wax paleoprecipitation proxy, we recommend dual
analysis of C and H, spanning time in an ancient deposit, or ideally a series of deposits across a
topographic feature, to discriminate between a past scenario with a) high δ
13
C
wax
and low δD
wax

that would be interpreted as higher elevation than modern or b) high δ
13
C
wax
and high δD
wax
that
would be interpreted as drier than modern. Past vegetation change would ideally be also
89

monitored (e.g., using plant wax chain length distributions, pollen or macrofossil analyses), and
we suggest such concerns about plant type bias would be greater in soils or different facies than
in broadly integrated deposits that average plant inputs. In this way we hope that the dual-isotope
information from plant waxes can usefully add additional monitoring and diagnostic power to
prior efforts at paleoaltimetry with δD
wax
. This dual isotope plant wax proxy may be a welcome
addition to the available toolkit for paleoaltimetry including carbonate and other secondary
mineral archives of precipitation oxygen isotopes (Poage and Chamberlain, 2002), clumped
isotope estimates of temperature (Huntington et al., 2010), and microbial proxies for temperature
(Peterse et al., 2009), each may variously find application in different sedimentary archives and
contribute to multi-proxy efforts to robustly determine paleoelevation in the Andes and
elsewhere.
3.5 Conclusions
This study takes a dual compound class and dual isotope approach to survey plant waxes in soils
and river sediments in a transect across the eastern flank of the Andes in a forested, steep, very
wet, tropical mountain watershed. We observe elevation trends in both C and H isotopes and n-
alkane and n-alkanoic acid compound classes stored in soils and transported in fluvial suspended
sediments. The altitudinal trend in the C and H isotopic composition of plant wax hydrocarbons
represents a powerful diagnostic tool for tracking elevation in catchment sourcing studies, and
offers potential for paleoaltimetry.
In river transported sediments, we test whether sedimentary integration is uniform or whether
steep regions with high sedimentary erosion rates lead to bias in the plant wax sedimentary
record. In our field collection we find plant wax concentrations and suspended sediment
90

concentrations are very low in the dry season in the upper reaches, and that most of the erosion
occurs in the wet season. Comparing the empirical regression from the transect of soil plots to
the river exported signal based on the catchment mean elevation, the slope of the regression is
shallower in the rivers implying that the upland sources are under-represented, but this is not
robust to the uncertainty on the regression and the implications are in any case erased by the
expansive lowlands where uniform spatial integration is a good approximation. Uplands are
never over-represented, but their export occurs primarily during the more erosive wet season,
and in the dry season the locus of erosion reduces by at least 1 km at the mountain front station,
with less seasonality seen further downstream. Within deep rivers, we observe modest depth
differentiation for the carbon isotopic composition of the n-alkanes with more degraded
components derived from weathered soils in the upper water column, but no depth differentiation
for the other isotopes studied here.
We introduce the concept of a dual isotope approach for plant wax studies as a diagnostic tool to
discern source characteristics other than elevation signals in some river samples. The strong
elevation signal in this study is confirmed through this approach and indicates the power in
combining dual compound classes and dual isotopes to analyze source-to-sink processes for plant
wax biomarkers. Overall, the large lowland area of many river catchments means that upland
sources of plant wax will become negligible in many distal archives, and the lowland floodplains
are thus important for the replacement of the fluvially-exported plant wax component.

91

Acknowledgements
This material is based upon work supported by the US National Science Foundation under Grant
No. EAR-1227192 to A.J.W and S.J.F for the river work. In Perú, we thank the Servicio
Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) and personnel of Manu and
Tambopata National Parks for logistical assistance and permission to work in the protected areas.
We thank the Explorers’ Inn and the Pontifical Catholic University of Perú (PUCP), as well as
Amazon Conservation Association for use of the Tambopata and Wayqecha Research Stations,
respectively. For river fieldwork assistance, we thank M. Torres, A. Robles and A. Ccahuana.
Soil samples were contributed by Andrew Nottingham and Patrick Meir. Logistical support was
provided by Y. Malhi, J. Huaman, W. Huaraca Huasco and other collaborators as part of the
Andes Biodiversity and Ecosystems Research Group ABERG (andesresearch.org). We thank
USC lab assistants: C. Hua, K. McPherson and E. Rosca. We thank B. Bookhagen, J. Polk, G. Li,
K. Clark, G. Asner, Y. Malhi and X. Feng for helpful discussions. This manuscript was revised
with comments from Associate Editor Tom Wagner, reviewer Clay Magill and two anonymous
reviewers.
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Chapter 4
Tropical Soil Profiles Reveal the Fate of Plant Wax Biomarkers
during Soil Storage
The manuscript was published in 2019 as:
Wu, M.S., West, A.J., Feakins, S.J. Tropical soil profiles reveal the fate of plant wax biomarkers
during soil storage. Organic Geochemistry 128, 1 – 15.

Abstract
The waxy coating that protects the leaves and other soft tissues of plants includes n-alkane and n-
alkanoic acid compounds that are commonly used as biomarkers to reconstruct past environment.
Plant waxes have geological relevance given their persistence in soils and paleosols, as well as in
lake and marine sediments, yet diagenesis may alter their molecular and isotopic signatures from
synthesis to deposition. This study seeks to understand the fate of plant wax biomarkers in soils
after leaf-fall as characterized by a series of tropical soil profiles. We investigate the changes in
abundance, molecular distributions, and hydrogen (δD) and carbon isotopic compositions (δ
13
C)
of plant waxes (n-alkanes and n-alkanoic acids) in six litter-to-soil profiles along a 2740 m
elevation transect from the eastern flank of the Andes mountains down to the lowland Amazon
floodplain in Peru. From litter to soil, we find acid/alkane ratios increase, while absolute
abundances decrease. In contrast, within each soil, acid/alkane ratios are roughly constant and we
find an equivalent exponential decline in concentration in both compound classes with depth;
with molecular distributions indicating some new production. We observe a 4 – 6‰
13
C-
enrichment from litter to deeper soils for both C
29
n-alkanes and C
30
n-alkanoic acids; of which
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the Suess effect accounts for ≤ 2‰. We infer that microbial degradation and production (or
‘turnover’) processes influence the δ
13
C of plant waxes that survive in soils; in contrast, no
systematic change in δD values is observed. The plant wax signal in soils includes averaging of
inputs and diagenetic effects, so this signature is particularly relevant for the interpretation of
plant waxes archives in paleosols and the plant waxes eroded from soils and exported to
downstream sedimentary archives. We show that soils represent the major stock of plant wax
under living ecosystems, suggesting that soils may be a quantitatively-important source of plant
waxes available for fluvial erosion, with implications for studies of carbon cycling and
paleoenvironmental reconstructions from downstream geological archives.
4.1 Introduction
Plant wax biomarkers are commonly used to reconstruct past environments based upon the
carbon and hydrogen isotopic compositions that reflect aspects of vegetation and climate
(Eglinton & Eglinton, 2008). Geological applications focus on sedimentary deposits that archive
the spatial and temporal record of these molecular fossils, and plant waxes have been found
preserved in paleosols (e.g., Magill et al., 2016), lake sediments (e.g., Fornace et al., 2014) and
marine sediments (e.g., Tipple and Pagani, 2010). In order to calibrate how the plant wax proxy
records aspects of vegetation and climate, many studies have sampled leaves from living
vegetation, including studies of temperate forests (Sachse et al., 2006), arid ecosystems (Feakins
& Sessions, 2010), tropical forests (Vogts et al., 2009) and high latitude ecosystems (Wilkie et
al., 2013). Modern lake sediments (Sachse et al., 2004) and marine core tops (Rommerskirchen
et al., 2003) have been used to study plant wax delivered by wind and water transport. Soils have
also been surveyed to characterize plant wax variations along altitudinal transects (Jia et al., 2008;
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Bai et al., 2011), latitudinal transects (Bush and McInerney, 2015; Bakkelund et al., 2018) and
aridity gradients (Schwab et al., 2015).
Given the ~2000 Pg of organic carbon stored in soils globally (Batjes, 1996; Jobbágy & Jackson,
2000), soils are a major source of the organic carbon (including plant waxes) eroded from the
continents to lake and ocean sediments (Blair et al., 2004). Soils are a particularly important
storage step (Blair et al., 2004) between new plant production and erosion by rivers given the age
of plant waxes transported by rivers revealed by compound specific radiocarbon (Kusch et al.,
2010; French et al., 2018), that suggests storage from decades to thousands of years.
Soils can be sampled as an archive of environmental information in situ integrating the time of
soil formation, and given requisite burial or protection from erosion soils may be preserved in the
form of paleosols, yielding information based on pedogenic structures and thicknesses (e.g.,
Retallack, 2013) as well as pedogenic carbonate nodules (e.g., Cerling and Quade, 1989, Quade
et al., 2013) and more recently plant waxes (e.g. Magill et al., 2016).
When interpreting plant waxes stored in paleosols or derived from soil erosion, we need to
understand how plant wax biomarkers are incorporated into soils and how diagenesis may alter
their molecular and isotopic signatures from synthesis to deposition. Once a leaf falls from the
canopy it forms the litter layer on top of the soil, with leaves comprising the majority,
often >60%, of litterfall (Kögel-Knabner & Amelung, 2014). Removal processes associated with
herbivory and microbial degradation (and/or runoff erosion on steep slopes) may be considerable,
but litter represents the input of organic matter at the top of the soil profile and can contribute to
the upward accumulation of soils. In lower layers of the soil, weathering of parent rock may
deepen the soil downwards (Amundson, 2014). Over time, soil erosion by water or wind may
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remove surficial layers of the soil, or landsliding may remove forest, soil and rock such as in the
steep-sided Andes (Clark et al., 2016) or the banks of meandering lowland rivers (Torres et al.,
2017). The residence time of soil is therefore controlled by the balance of additions from above
and below, and removal processes of degradation and erosion (Heimsath et al., 1997). The
persistence of soil organic matter, and its individual compounds, is decoupled from the intrinsic
thermodynamic stability expected based on molecular structure: many compounds persist
decades beyond their expected residence time, reflecting the importance of packaging within soil
aggregates and adsorption to minerals (Schmidt et al., 2011). If the persistence of soil organic
carbon is an “ecosystem property” (Schmidt et al., 2011), then more work needs to be done to
characterize the fate of individual compounds in a range of ecosystems and terrains for carbon
cycle applications, as well as for paleoclimate reconstructions using biomarkers.
4.1.1 Diagenesis of plant wax biomarkers
Previous research to understand the effect of early diagenesis on plant wax biomarkers has
included field studies in low-diversity temperate ecosystems, comparing fresh leaves with litter
and soil (Nguyen Tu et al., 2004; Chikaraishi & Naraoka, 2006; Zhang et al., 2017), and
monitoring changes with time in litterbag experiments (Huang et al., 1997; Nguyen Tu et al.,
2017, 2011; Zech et al., 2011; Wang et al., 2014; Li et al., 2017). Most have studied n-alkanes
only, with the exception of Chikaraishi & Naraoka (2006) who studied a suite of lipids including
n-alkanes and n-alkanoic acids.
Study of hydrogen isotope effects associated with diagenesis is limited. In a litter bag experiment
of three broadleaf tree species, Zech et al. (2011) found seasonal variations of 10 – 20 ‰ in n-
alkane δD values that were attributed to microbial n-alkane production, but they found no
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systematic overall trend across the 2 year study. In contrast, a study of a soil profile in a Japanese
maple forest found D-depletion (by ~50‰) in both n-alkanes and n-alkanoic acids from leaf to
soil, suggesting a significant hydrogen isotope effect during early diagenesis in soils (Chikaraishi
& Naraoka, 2006).
In contrast, the carbon isotopic effect associated with plant wax degradation is relatively well-
known. Prior studies have reported an increase (~1 – 2‰) in plant wax δ
13
C during early
diagenesis, as reflected in differences between fresh leaves and leaf litter, and also seen in
changes during 1-3 yrs of litter decomposition in experiments (Nguyen Tu et al., 2004;
Chikaraishi & Naraoka, 2006; Wang et al., 2014; Li et al., 2017; Zhang et al., 2017), although
two shrub species showed no temporal change in δ
13
C (Huang et al., 1997; Li et al., 2017).
Considering the diversity in species (including maple, ginkgo, bamboo, C3 and C4 grasses, moss)
and sites studied so far, a 1 – 2‰
13
C-enrichment appears to be a widespread signature
associated with degradation.
4.1.2 Tropical soils in an Andes-Amazon transect
A litter translocation experiment across the Andes-Amazon transition in Peru has found a strong
dependence of litter degradation on soil temperatures, with ~3-fold higher degradation rates at
lowland sites of 24°C compared to upland sites of 12°C mean annual soil temperature (Salinas et
al., 2010). The dependence of degradation rates on temperature also leads to thicker soils and
higher soil organic carbon (OC) contents in the colder montane cloud forests compared to
lowland tropical rainforests (Whitaker et al., 2014). Microbial community also changes in
response to elevation, with increased microbial biomass and fungi relative to bacteria with
increasing altitude, which affects soil respiration rates (Whitaker et al., 2014). These
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environmental controls and microbial processes not only determine the fate of bulk OC as a
whole, but also may have different influence on various types of organic compounds, including
plant wax n-alkanes and n-alkanoic acids.
Here, we study plant waxes in leaf litters and soils from a series of soil pits under tropical forests
at contrasting altitudes, spanning sites from the eastern flank of the Peruvian Andes to the
Amazon floodplain. We quantify how bulk organic carbon and plant wax molecular and isotopic
signatures vary during soil storage by sampling the progression from leaf litter down through the
soil profile, with detailed sampling of soil pits dug along the elevation transect across a wide
range of temperatures and soil organic layer thickness. We study molecular abundances and C
and H isotope compositions of both n-alkanes and n-alkanoic acids, aiming towards a more
comprehensive understanding of the preservation/alteration of plant wax biomarkers from plants
to soils.
This study adds to prior work on plant waxes in the Madre de Dios region of Peru including
canopy surveys of leaf wax n-alkane molecular abundance distributions and productivity
(Feakins et al., 2016a); canopy bulk leaf and leaf wax carbon isotopic composition (Wu et al.,
2017); and canopy leaf wax hydrogen isotopic composition together with plant ecohydrology
(Feakins et al., 2016b), as well as river export of plant waxes n-alkanoic acids related to soil
mineral horizon hydrogen isotopic composition (Ponton et al., 2014) and dual isotope
comparison of plant wax n-alkanes and n-alkanoic acids in soils and river (Feakins et al., 2018).
Those studies found a linear trend in both δ
13
C and δD values of both n-alkanes and n-alkanoic
acids in canopy leaves and soils with elevation, supporting the use of these metrics as proxies for
elevation for paleoaltimetry for ancient deposits and to indicate sourcing-elevation of plant
waxes exported by rivers within the catchment. The latter study also found an isotopic offset in
105

δ
13
C values of the C
29
n-alkane between canopy and soils (Feakins et al., 2018) that we
investigate further here.
4.2 Materials and Methods
4.2.1 Field sampling
We collected samples from four sites across our study area located in the Madre de Dios region
of Peru, spanning elevations from 286 m in the Amazon floodplain to 3025 m along the eastern
flank of the Andes (Fig. 4.1, Table 1). The region receives 1560 – 5300 mm mean annual
precipitation (MAP) and is fully forested (tropical montane cloud forest to lowland rainforest).
The sample sites span a temperature range of 11.1 – 24.4 °C. All sites are primary forests with
one secondary growth forest site in the foothills, at Villa Carmen (VC), previously logged and
now dominated by bamboos. The primary forest sites are highly-biodiverse. Tree species with
high abundance include Weinmannia crassifolia, Clusia alata cf., and Hesperomeles ferruginea
at Wayqecha (WAY), as well as Alchornea latifolia, Tachigali setifera, and Tapirira obtuse at
San Pedro (SP). The lowland tropical rainforest (TR) is characterized by even higher biodiversity,
but abundant Amazonian lineages include Inga, Swartzia, Protieae, and Guatteria including
presence of species of those genera at the Los Amigos (LA) site (Dexter et al., 2017). Soil types
include Umbrisol at WAY, Cambisol at SP (Whitaker et al., 2014), and Ultisol LA (Pittman et al.,
2001), but have not been previously classified at our VC site. Soil organic layer thickness varies
from 1 to 26 cm with a tendency towards increasing thickness at higher altitudes (Table 1).
Along this transect are a series of permanent forest plots that are part of the Global Ecosystems
Monitoring Network (GEM; http://gem.tropicalforests.ox.ac.uk/projects/aberg), where canopy
leaf wax has been studied before (Feakins et al., 2016a,b; Wu et al., 2017), and where aggregate
106

soil organic (O) and mineral (M) samples have been collected and studied by amalgamating soils
from five locations at each plot (Ponton et al., 2014; Nottingham et al., 2015; Feng et al., 2016;
Feakins et al., 2018). Here we study individual vertical soil profiles, sampling within a single pit
to investigate degradation processes and transformation of plant wax signatures during soil
storage at each of 4 sites along the elevation transect. Although this region experiences
landslides in areas of steep relief (Clark et al., 2016), we selected soil pits at locations where the
surface did not appear to be disturbed. Within the soil pits, examination of the color, texture, and
structure of the soil profiles suggested that the soils had formed from downward weathering and
upward accumulation of leaf litter, without sedimentary structures indicative of disturbance by
erosional reworking. At two sites (VC and LA), an additional site was sampled to contrast
hillslope setting, by digging one pit at the ridgetop and one at the slope base. In total we present
data for 6 pits (Table 1). This soil profile study overlaps with the prior plant wax study of soil O
and M layers (Feakins et al., 2018) at WAY and SP where we can make direct comparisons. In
addition, we can compare the litter-soil profile at SP with data from canopy leaves from previous
studies (Feakins et al., 2016a,b; Wu et al., 2017).
Table 4.1: Locations and information of sampling sites along the Andes-Amazon transect.
Site name
Site
code
Elev
(m)
Lat Long
Temp
(°C)
Forest
type*
Soil type Soil pit
Organic
layer
thickness
(cm)
Soil pit
depth
(cm)
Litter
collection
Wayqecha WAY 3025 -13.1926 -71.5880 11.1 TMCF Umbrisol single pit 26 90 Y
San Pedro SP 1500 -13.0490 -71.5370 18.8 TMCF Cambisol single pit 16 40 Y
Villa
Carmen
VC 614 -12.8961 -71.4183 22.9 TR n.a.
ridgetop 5 63 N
slope base 23 100 Y
Los
Amigos
LA 286 -12.5588 -70.0993 24.4 TR n.a.
ridgetop 1 90 Y
slope base 13 150 N
* TMCF: tropical montane cloud forest; TR: tropical rainforest. All are primary forests except VC, which is a bamboo-dominated secondary
growth forest.

107

Fig. 4.1. Sampling locations across a 2740 m elevation Andes-Amazon transect in the Cusco and Madre de Dios
region of Peru (square symbols, color indicates elevation). Andean sites: Wayqecha (WAY) and San Pedro (SP).
Foothills: Villa Carmen (VC) Lowland: Los Amigos (LA). Circles show major cities in the region.

The two soil profiles at the high elevation sites at Wayqecha (WAY) and San Pedro (SP) are
located under tropical montane cloud forest and correspond to the RAINFOR sites of the same
names, with the SP site equivalent to RAINFOR SP-1500 (www.rainfor.org). The Villa Carmen
(VC) plot is under a secondary forest in the foothills of Andes, and Los Amigos (LA) is located
in the tropical rain forest of the Amazon floodplain. Two soil pits were dug each at VC and LA,
with one located at a slope base (VC2 and LA5) and the other located on top of a nearby ridge
(VC3 and LA4).
108

We collected leaf litter at WAY, SP, VC2 and LA4, with the litter at WAY (the upper site)
divided into top, middle, and bottom litter because of its thickness (~12 cm). Soil pits were dug
to ~90 – 150 cm depth, and 3 – 4 samples (integrating 5 – 50 cm of soil vertically) were taken at
each pit based on the soil profile characterization (based on color and physical properties). We
also sampled roots at SP and LA4. Samples were stored under cool conditions in the field until
transport back to the laboratory where they were stored in a freezer at -20°C, before being
freeze-dried. As rock fragments were present in many soil samples, clasts >2 mm were removed
by sieving. The soil samples were then ground in a pestle and mortar to homogenize for
geochemical analyses.
4.2.2 Bulk organic carbon analysis
Aliquots of the soil samples were taken for total organic carbon (TOC) and bulk organic carbon
isotope (δ
13
C
OC
) analysis. The samples were heated in dilute (10%) HCl to 70°C in a water bath
for 1 h to remove carbonates. The decarbonated samples were then rinsed three times with
deionized water, and dried in an oven at 56°C. The dried samples were analyzed for TOC and
δ
13
C
OC
using a Costech Elemental Combustion System (EA 4010) connected via a Picarro
Liaison (A0301) to a Picarro cavity ring down spectrometer (G2131-i). A USGS-40 standard
(Glutamic Acid with δ
13
C
OC
= -26.6‰ in VPBD scale) was run with replicates at different
weights at the beginning and end of the sequence to provide a calibration curve for the measured
TOC, as well as an assessment of the precision in δ
13
C
OC
measurements (determined to be better
than 0.2‰).

109

4.2.3 Lipid extraction
Total lipid extracts (TLE) were extracted from freeze-dried samples with 9:1 dichloromethane
(DCM) to methane (MeOH) using an Accelerated Solvent Extraction system (ASE 350, Dionex)
at 100°C and 1500 psi for 2 cycles of 15 mins. The TLE was separated into neutral (FN;
containing n-alkanes) and acid (FA; containing n-alkanoic acids) fractions by eluting 2:1 DCM
to isopropanol and 4% formic acid in ethyl ether respectively through a column of LC-NH
2
gel.
The n-alkanes were then further separated from the FN fraction by eluting with hexane through a
silica gel column. The FA fraction was methylated in 5% HCl in MeOH of known isotopic
compositions at 70°C overnight, during which the n-alkanoic acids were reacted into fatty acid
methyl esters (FAMEs). The product was diluted with milliQ water and partitioned in hexane
using liquid-liquid extraction. The extract was further separated by eluting through a silica gel
column using hexane and DCM, with the DCM fraction carrying the FAMEs.
4.2.4 Compound identification and quantification
Samples were dissolved in hexane ready for compound identification and quantification using a
gas chromatograph (Agilent 6890) coupled with a mass spectrometer (Agilent 5973) and flame
ionization detector (GC-MS/FID). Compound identification was based on retention time and
mass spectra of target peaks. Absolute abundance was calculated from peak area response on the
FID, based on a calibration curve of an in-house standard mixture of n-alkanes and n-alkanoic
acids of known abundance. We recorded the abundance of n-alkanes (C
23
– C
33
) and n-alkanoic
acids (C
22
– C
32
), individual homologues conventionally considered terrestrial plant-derived (G.
Eglinton & Hamilton, 1967), and calculated their total abundance on a µg g
-1
dry weight basis
(∑alk and ∑acid) as well as normalized to TOC, i.e., µg g OC
-1
(Λalk and Λacid). To represent
110

the molecular distributions of the plant wax homologues, we also calculated the average chain
length (ACL) and carbon preference index (CPI) using the following equations:
ACL = ∑(n x [C
n
]) / ∑[C
n
] (4.1)
CPI = 2 [C
n
] / ([C
n-1
]+ [C
n+1
]) (4.2)
where n indicates the chain length (n = 23 – 33 for n-alkanes and n = 22 – 32 for n-alkanoic
acids), and [C
n
] indicates the abundance of that chain length.
4.2.5 Compound-specific isotopic analysis
The compound-specific carbon and hydrogen isotopic compositions (δD and δ
13
C) were
measured by gas chromatography – isotopic ratio mass spectrometry (GC-IRMS) using a
Thermo Scientific Trace gas chromatograph connected to a Delta V Plus mass spectrometer via
an Isolink pyrolysis furnace at 1400°C for δD, and a combustion furnace at 1000°C for δ
13
C. We
monitored the linearity of isotopic determinations across 1-7 V peak amplitude daily and only
accepted measurements from peaks with amplitude within the range of acceptable linearity. δD
and δ
13
C measurements were normalized to VSMOW/SLAP and VPDB standards respectively,
by calibrating against an external standard (A6-mix obtained from A. Schimmelmann, Indiana
University) containing a mixture of 15 n-alkane compounds (C
16
– C
30
) with δD and δ
13
C values
ranging from -9 to -263 and -26.2 to -33.8‰ respectively. The isotopic values of n-alkanoic
acids were then calculated from measured values of FAMEs and known values of the added
methyl group by mass balance.

111

4.3 Results
4.3.1 TOC and plant wax abundance
Here we report total abundance of organic carbon (TOC, mass C per gram sediment), and plant
wax mid- and long-chain (C
23-33
) n-alkanes and (C
22-32
) n-alkanoic acids normalized to per gram
dry sample (∑alk and ∑acid) and per gram OC (Λalk and Λacid) (Fig. 4.2; Appendix A). TOC
ranges from 35.6 – 46.7% in leaf litter, and 0.5 – 19.8% in top soil samples declining to 0.1 – 2%
in the deepest soil samples. ∑alk and ∑acid range from 104 – 2279 and 330 – 1200 µg g
-1

in leaf
litter, and from 0.05 – 115 and 0.2 – 603 µg g
-1

in soil respectively. TOC-normalized Λalk and
Λacid range from 248 – 4878 and 786 – 2674 µg g
-1

in leaf litter, and from 27 – 578 and 170 –
4297 µg g
-1

in soil respectively. TOC and plant wax abundance varies significantly between sites,
and with depth in soils. In general, TOC, n-alkanes, and n-alkanoic acids trend towards lower
abundance with decreasing site elevation, and with increasing depth in each soil profile. At
WAY and SP where multiple litter layers were collected, wax abundance shows lower values in
the more-degraded litters (lower litter / smaller debris; Fig. 4.2a,b). In roots, ∑alk and ∑acid
range from 0.6 – 5.1 and 42 – 55 µg g
-1
respectively, which represent <0.6% and <16% of the
abundance found in leaf litter. While n-alkane and n-alkanoic acid abundance both decrease from
top to bottom in each profile, the ratio of relative abundance between the two compounds classes
exhibits a vertical trend; the fraction of n-alkanoic acid, Facid = ∑acid/(∑alk+∑acid), shows
increasing values from litter (ranging 0.2 – 0.8) to soil (generally >0.8). Furthermore, Λalk
decreases from litter to top soil whereas Λacid generally increases for all four litter-soil profiles
in this study. These trends indicate an increase in abundance of n-alkanoic acids relative to n-
alkanes from litter to soil.
112

Fig. 4.2. Vertical profiles of plant wax and bulk OC abundance at the four study sites (pink: C
23-33
n-alkanes; blue:
C
22-32
n-alkanoic acids ), showing (a-d) total organic carbon concentrations, (e-h) abundance per gram dry weight
(∑alk and ∑acid), (i-l) abundance normalized to OC (Λalk and Λacid), (m-p) n-alkanoic acid fraction. Data shown
are from this study (litter: triangle; root: inverted triangle; soil: square), as well as from previous studies (canopy:
diamond; soil: circle) of overlapping sites at WAY and SP (Feakins et al. 2016a,b; Feakins et al., 2018). Open
symbols at LA and VC denote additional pits at these sites at the ridge top, with closed symbols representing slope
base. Vertical bars indicate the depth range from which the soil profile samples were taken. Horizontal bars of
canopy data at SP represent standard error of the site means (n = 39). Note the change in x-axes for soil data on the
left panels.
113

4.3.2 Chain length distributions
In litter and soil, n-alkanes show a strong odd-over-even preference from C
23
– C
33
, with C
29
and
C
31
being the dominant compounds, trending towards higher C
31
/C
29
ratio in the lower-elevation
sites (Fig. 4.3, left). From litter to soil, we observe a general increase in the mid-chains (C
23-27
)
leading to lower relative abundance of C
29
and C
31
. The two root samples have different chain
length distributions of n-alkanes, with the sample from SP showing similar distributions to that
of litter and soil (C
29
and C
31
dominating with low C
23-27
; Fig. 4.3c), while the sample from LA
shows exceptionally high C
23
and C
25
that is distinct from litter and soil distributions at this site
(Fig. 4.3g).
n-Alkanoic acids exhibit an even-over-odd preference from C
22
– C
32
, with C
30
or C
32
being the
dominant compound in litter, whereas soil shows a more ‘flat’ distribution across chain lengths
owing to an increase in the abundance of mid-chains C
22-26
(Fig. 4.3). Similar to n-alkanes, we
also observe a trend towards longer chain lengths (higher C
32
/C
30
ratio) towards lower-elevation
sites. Roots show distinctively different distributions with dominance by mid-chains in both sites
(Fig. 4.3d,h).
Carbon preference index (CPI) and average chain length (ACL) calculations provide more
quantitative comparisons of molecular distributions between samples (Fig. 4.4). We find that CPI
of n-alkanes exhibits a wide range from ~4 – 16, with decreasing values from litter to soil at all
sites. Lower n-alkane CPI values are found in roots (CPI = 4.1–7.1) compared to litter (CPI =
6.6–16.1). In contrast, CPI of n-alkanoic acids shows a relatively invariant vertical profile among
litter, roots, and soil, with only the top litter at WAY and the canopy leaves at SP being
exceptions with slightly elevated CPI.
114

ACL of both n-alkanes and n-alkanoic acids shows a decrease (by c.1) from litter to soil at WAY,
VC and LA, but a relatively straight profile at SP. Litter shows a trend towards higher ACL at
lower-elevation sites. Roots exhibit lower ACL compared to litter and soil, related to high
abundance of mid-chain length compounds (Fig. 4.3). Overall, the soil CPI and ACL results from
this study match well with data from previous soil studies (Fig. 4.4 circles; Feakins et al. 2016a,b;
Feakins et al., 2018) at WAY and SP.

Fig. 4.3. Chain length distributions of n-alkanes (left) and n-alkanoic acids (right) in litter (green), soil (yellow), and
roots (purple) at the four study sites. Error bars represent 1σ deviation from the mean values when multiple litter or
soil samples are present at a single site.
115

116

Fig. 4.4. Vertical profiles of (left) carbon preference index (CPI) and (right) average chain length (ACL) of C
23-33
n-
alkanes (pink) and C
22-32
n-alkanoic acids (blue) at the four study sites, showing data from this study (litter: triangle;
roots: inverted triangle; soil: square). Open symbols at LA and VC denote additional pits at these sites at the ridge
top, with closed symbols representing slope base. Also shown are canopy (diamond) and soil (circle) data from
previous studies of overlapping sites at WAY and SP (Feakins et al. 2016a,b; Feakins et al., 2018). Vertical bars
indicate the depth range from which the soil profile samples were taken. Horizontal error bars of canopy data at SP
represent standard error of the site means (n = 39).

4.3.3 Hydrogen and carbon isotopic compositions
We report δD and δ
13
C values of C
27-31
odd-chain n-alkanes and C
22-32
even-chain n-alkanoic
acids when reliable isotopic measurements could be made on these samples (Appendix A). We
focus our attention on the most dominant chain length of each compound class, C
29
n-alkane and
C
30
n-alkanoic acid. Although we do not show data from all the chain lengths on Fig. 4.5, the
general isotopic patterns described below are shared among the homologues of each compound
class, as reported in Appendix A. We also report bulk δ
13
C
OC
values to compare with the plant
wax data.
The hydrogen isotopic composition of C
29
n-alkane (δD
29alk
) and C
30
n-alkanoic acid (δD
30acid
)
ranges from -173 to -210‰ and -158 to -207‰ respectively across all sites, with a general trend
towards more enriched values at lower-elevation sites (Fig. 4.5). C
30
n-alkanoic acid is generally
about 5 – 20‰ enriched relative to C
29
n-alkane in the same samples. Both δD
29alk
and δD
30acid

show relatively small (<20‰) variations within the soil profiles. However we see no systematic
patterns in δD with depth across the four sites: a 5 – 20‰ decreasing trend at WAY and VC
slope base for C
29
n-alkane, and at WAY, SP, VC slope base and LA ridge top for C
30
n-alkanoic
acid; a ~5‰ increase for C
30
n-alkanoic acid at LA slope base; a ~10‰ increase towards ~40cm
depth followed by a 5‰ decrease below at VC ridgetop for both compounds; and no trend for
C
29
n-alkane at SP and LA (Fig. 4.5). We find a much depleted δD
30acid
value (-206‰) for the
117

root sample from LA compared to soil at this site (ranging ~160 – 170‰), though abundance of
C
29
n-alkane was insufficient for δD analysis. For the root sample from SP (10 – 20g mass),
neither compound was sufficiently abundant for δD analysis.
The carbon isotopic compositions of C
29
n-alkane (δ
13
C
29alk
), C
30
n-alkanoic acid (δ
13
C
30acid
), and
bulk OC (δ
13
C
bulk
) range from -32.4 to -42.9‰, -31.5 to -40.4‰, and -24.5 to –33‰ respectively
across all sites (Fig. 4.5). C
29
n-alkanes are depleted relative to C
30
n-alkanoic acids (by ~2‰)
which are in turn depleted by ~6‰ from bulk OC in the same samples. We find consistent
patterns of δ
13
C with depth for bulk OC and both plant wax compounds across all four sites: a
trend of c. 4 – 6‰ enrichment from litter to soil at depth, which is a combination of c. 2 – 4‰
enrichment between litter and top-layer soil, and c. 2‰ gradual enrichment down the soil profile
(Fig. 4.5). Roots yield δ
13
C values that are similar to litter or top-layer soil but more depleted
than soils at depth (Fig. 4.5f,h). Across the elevation transect, we find a general trend towards
more depleted δ
13
C values in lower-elevation sites.
When comparing soil data from this study (Fig. 4.5, squares) to that at WAY and SP from
previous studies (Fig. 4.5, circles; data from Feakins et al., 2018) in which isotopic data were
measured at two depth ranges (organic and mineral horizons), we find results are consistent, with
both sets of data showing no systematic patterns in δD with depth, and a
13
C-enrichment in the
deeper soil. We find the vertical profiles of δD (no consistent trend) and δ
13
C (deeper layers are
more enriched) are consistent among all chain lengths of n-alkanes and n-alkanoic acids
(Appendix A).

118

Fig. 4.5. Vertical profiles of (left, a-d) δD and (right, e-h) δ
13
C of C
29
n-alkane (pink), C
30
n-alkanoic acid (blue),
and bulk OC (grey) at the four study sites, showing data from this study (litter: triangle; root: inverted triangle; soil:
square). Open symbols at LA and VC denote additional pits at these sites at the ridge top, with closed symbols
representing slope base. Also shown are canopy (diamond) and soil (circle) data from previous studies of
overlapping sites at WAY and SP (Feakins et al. 2016a; Wu et al. 2017; Feakins et al., 2018) for comparison.
Vertical bars indicate the depth range from which the soil profile samples were taken. Horizontal bars indicate 1σ
error from replicate measurements (for soil and litter data) or standard errors of site mean (canopy data at SP).
119

4.3.3.1 Slope base-ridgetop comparisons
We contrasted slope base and ridgetop settings by digging two soil pits each at VC and LA, to
reveal possible difference at locations that are less well-drained with thicker O-layer (slope base)
and more well-drained with thin O-layer (ridgetop). While n-alkane δD at LA has limited data to
allow comparison, we find that in general, the ridgetop shows enrichment in both H (by ~10–
20‰) and C (by ~1–3‰) isotopes relative to slope base. At VC, while the uppermost soil
samples show similar δD and δ
13
C values, the ridgetop location appears to be more enriched in
both isotopes at deeper depths. At LA, there is consistently isotopic enrichment from the top soil
to deeper soil.
4.4 Discussions
4.4.1 Alteration of plant wax signatures across the litter-soil profile
Numerous plant-based surveys have characterized how plant waxes record environmental
variables, providing a basis for interpreting the chemical fingerprints in these biomarkers as
climate proxies in sedimentary archives. But an unresolved question relates to possible changes
in plant wax signatures between plant and sediment which may compromise the environmental
information they carry. In this section, we evaluate the changes observed in abundance,
molecular distributions, and isotopic compositions of plant wax from litter to soil in the Peru
transect studied here, and we discuss the processes that may lead to these changes.
4.4.1.1 Plant wax transformation within leaf litter
Sampling of thick leaf litter accumulations at WAY and SP reveals substantial loss of plant
waxes within the leaf litter, in contrast to a limited OC loss (~2 – 5%). We find a decrease in
120

concentrations (in terms of both per gram dry weight and OC-normalized) by ~77 – 87% for n-
alkanes and ~10 – 45% for n-alkanoic acids between top litter (large litter) and bottom litter
(litter debris) at both sites. We note that there is an increase in ∑acid in the middle litter layer at
WAY, which may imply new additions (perhaps by microbial productions during litter
diagenesis) or simply heterogeneity within the coarse debris. The overall significant decrease in
n-alkane abundance within litter suggests rapid degradation of these molecules during early
diagenesis. Such rapid loss via degradation has also been observed from litterbag experiments
that show >80% loss of plant waxes within 1-3 years, as a result of microbial degradation and
perhaps also consumption by herbivores such as mesofauna (Zech et al., 2011; Li et al., 2017;
Nguyen Tu et al., 2011, 2017). Our field data corroborate these experimental observations.
Although both compound classes lose concentrations within litter, there appears a better
preservation for n-alkanoic acids, as shown by an increase of their abundance relative to n-
alkanes from top (large) to lower (small) litter at WAY and SP (Fig. 4.2i-l). The better
preservation for n-alkanoic acids is also evidenced by generally higher litter-to-soil Λacid in
contrast to the decrease in Λalk, indicating a greater portion of n-alkanoic acids survive litter
degradation and enter the soil (Fig. 4.2e-h). It is unclear why n-alkanes may be lost at a faster
rate than n-alkanoic acids during litter decomposition; this phenomenon has not been observed in
the previous leaf litterbag studies which lack the compound class comparison. We speculate that
a different rate of loss between the two compound classes is possible if they are not homogenous
on the leaf surface, such as if they exhibit different morphological structures, and if n-alkanes
dominate the more fragile surface wax crystals whereas n-alkanoic acids form the lower wax
layers. For example, under scanning electron microscopy of plant leaves of other species, C
24-30

n-alkanoic acids are observed to form smooth wax films, whereas C
29-31
n-alkanes form various
121

wax types including films, crust, and ridged rodlets (Koch et al., 2009), with crystal structures
that extrude from the surface and hence may be more easily lost. Though the exact mechanism
unclear, the observation that n-alkanes are preferentially lost within leaf litter bridges the
discrepancy in the relative concentrations of these two compounds between canopy leaves and
soils: canopy leaves commonly contain more n-alkanes than n-alkanoic acids (abundance data
from Feakins et al., 2016b, Wu et al., 2017) whereas n-alkanoic acids dominate in soils and river
suspended sediments (abundance data from Feakins et al., 2018).
4.4.1.2 Exponential decline of plant wax concentrations with depth in soils
Below the litter layer, we find further significant drop in plant wax concentrations in soils with
depth (by ~65 – 99% from top soil to 50 cm depth for both compounds, similar to the decline in
TOC by ~57 – 90% from top to 50 cm; Fig. 4.2). Since the absolute concentrations at different
sites significantly vary, in order to compare sites, we first calculated fractional concentrations
relative to the top sample (within O horizon) at each site, and then characterized the rate of loss
(k
z
; depth-dependent decay rate) by fitting an exponential decay function. We find exponential
loss in concentrations in soils with k
z
ranging from 2.1±0.9 to 6.2±1.4 m
-1
for OC, from 2.3±0.6
to 16.1±1.2 m
-1
for n-alkanes, and from 2.9±0.3 to 15.2±0.7 m
-1
for n-alkanoic acids (Fig. 4.6).
In contrast to the greater loss of n-alkanes relative to n-alkanoic acids observed in litter as
described in section 4.1.1, the two compound classes appear to drop in concentration with depth
at the same rate (no significant difference in k
z
values) except at WAY where the k
z
value of n-
alkanes is about double that of n-alkanoic acids. This distinction between plant wax loss in litter
and within soil profiles soil implies different mechanisms governing the resilience of plant waxes
in litter vs. soils. Within soils, physical protection in soil aggregates and absorption to soil
122

minerals is known to play an important role in the stability of soil organic matter (Schmidt et al.,
2011). Previous soil studies have found turnover times for both n-alkanes and n-alkanoic acids
also to be similar and on the order of several decades (Wiesenberg et al. 2004; Schmidt et al.,
2011).

Fig. 4.6. Exponential decrease in the total abundance of (a) bulk OC, (b) n-alkanes (C
23-33
), and (c) n-alkanoic acids
(C
22-32
) within soils profiles at the four sites. Data show fractional concentrations relative to the top soil samples at
each site. Soil data are plotted on the mean sample depth below the top sample. Depth-dependent rate constants (k
z
)
are estimated with 1σ uncertainties, which show the same values within uncertainties among compounds, except at
for n-alkanes WAY and bulk OC at LA. Soil profiles at ridgetop (open symbol) and slope base (solid symbol) at VC
are grouped for curve fitting. Note that the ridgetop soil pit at LA is excluded in this analysis due to the very thin
soil O layer (1 cm) at much finer resolution than the top sample (0-7 cm).

We cannot infer turnover times from the k
z
values calculated based on the decreases in
concentration within depth in our soil profiles, because we lack chronological information for the
soils in this study. Further, we note that the exponential decline in concentrations with depth
observed in this study may be affected by downward mobilization in addition to decomposition,
although downward transport would not be expected to produce a carbon isotope fractionation
with depth. Overall, the exponential decrease in plant wax concentrations is probably determined
by a combination of accumulation of plant wax inputs on the soil top, downward-transportation
123

by mesofauna such as earthworms (Oades et al., 1993) and decomposition of plant waxes within
the soil over time.
Across the four sites, we observe up to six-fold difference in the rate of loss with soil depth, with
increase in k
z
values from VC, to WAY, SP and LA (except for OC at LA). What determines the
difference among sites? The tendency is for an increase in k
z
as elevation decreases and
temperature increases, which is a known factor for determining rates of respiration. Across the
same Andes-Amazon transect, a litterbag translocation experiment which tracked litter
decomposition of 15 species over 1.2 yr (Salinas et al., 2010) found that while species type has a
large influence on the decomposition rate (k), soil temperature stands out as the main control
when averaging all species, such that a five-fold increase in the k value is observed from the high
Andes (WAY at 3025 m, 11.1°C) to the lowland Amazon (Tambopata at 210 m, 23.9°C; similar
elevation to LA site in this study). This temperature-sensitivity for litter decomposition rates (k;
Salinas et al., 2010) is reflected in the different k
z
values of plant waxes at WAY, SP and LA
(Fig. 4.7), which increase progressively from high to low elevation, implying that temperature
may also be the main control on the rate of plant wax loss. The k
z
values of OC at WAY and SP
also follow the same trend as plant waxes, but the apparent lower rate of OC loss at LA is
surprising (Fig. 4.6a; Fig. 4.7). It is possible that the OC left in these deeper soils at LA is
relatively recalcitrant, if the majority of labile OC have already been degraded near the top of the
soil due to the warm temperature at this site. This change in recalcitrance would not be expected
to be seen in the plant waxes, perhaps explaining with the trends in k
z
for waxes are more
systematically related to temperature than bulk OK. Overall, we infer that temperature is a
primary control on the rate of decline of plant wax concentration and that decomposition likely
dominates the depth-decay of plant wax concentration profiles.
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Fig. 4.7. The rate of OC and plant wax loss with soil depth (k
z
; this study) and litter decomposition rate (k; Salinas et
al., 2010) along the Andes-Amazon elevation transect. Note that the soil under secondary growth at VC is an outlier
that does not follow the temperature-dependency of k
z
values. Litter bag experiments were not conducted at the
secondary forest sites at VC. The lower than expected k
z
value for bulk OC at LA (the lowest elevation site) may be
in part explained by the recalcitrance of residual bulk organic material at depth in these soils.

An exception to the overall temperature trend for the k
z
of the plant waxes is found at VC, which
shows much slower loss (lower k
z
) than predicted for its elevation (Fig. 4.7). We note that while
the other three sites are in primary tropical forests, VC is in a secondary forest previously logged
for timber (Table 1), with secondary-growth bamboos dominating the canopy. Bamboos are
known as one of the fastest growing plants on earth, with relatively slow litter decomposition
rates (Liu et al., 2010) partly due to the abundance of phytoliths (Piperno and Pearsall, 1998),
hence having significant implications for carbon accumulation and storage in soils (Zhou et al.,
2005). However, little plant wax research has been done on bamboo leaves (Li et al., 2012, 2016),
and the preservation of plant waxes in soils of bamboo forests remains unknown. Here, we find a
125

lower than expected k
z
which may imply a greater accumulation of soil organic matter in this
presently bamboo-dominated forest, or some other aspect of the landscape disturbance, such as
any use of fire which may add to soil organic carbon and slow decomposition. This exception is
a useful reminder that while temperature may be a major control on the rate of organic carbon
and plant wax loss in well-drained soils, other factors like waterlogging, disturbance and species
succession may also modify soil organic properties.
4.4.1.3 The role of microbial activities in plant wax degradation in soils
Microbial activities are prevalent in the tropical soils along the Andes-Amazon transect, and
prior studies found them to be critical in soil carbon cycling processes (Whitaker et al., 2014).
The degradation and alteration of plant waxes in the soils studied here is likely also significantly
governed by soil microbial activities. Laboratory-based experiments have provided direct
evidence for microbial activity increasing alongside plant wax decay. During a 3-year room-
condition soil storage experiment, Brittingham et al. (2017) found increased genes linked to n-
alkane degrading enzymes, alongside a loss of long-chain (C
29-31
) and rise in shorter-chain (C
21-25
)
n-alkane relative abundance, indicating microbial reworking of long-chain into mid-chain
homologues. This change in the molecular distribution was also marked by decreased ACL and
CPI in the experiments. In line with those experimental results, an increase in mid-chain (C
23-25
)
n-alkanes relative to total (Fig. 4.8), together with a decrease in CPI (Fig. 4.4), is apparent down
profile in our study, consistent with the occurrence of microbial degradation of n-alkanes. The
soil profiles suggest that microbial degradation affects plant waxes from the very earliest stages
of diagenesis; for example the biggest drop in n-alkane CPI happens at the litter-soil interface
(Fig. 4.4).
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Fig. 4.8. Increase in mid-chain (C
23-25
) n-alkane fractional abundance down the litter-soil profiles, showing data
from litter (triangles) and soils (squares) from the four sites. Note that both ridgetop (open symbols) and slope base
(solid symbols) pits are shown for VC and LA. We find no such consistent increase in mid-chain proportions for n-
alkanoic acids across the sites.

Apart from degradation, microbial activities can also affect plant wax signatures by contributing
n-alkanes and n-alkanoic acids to the soil pool. Though commonly assumed to be dominated by
terrestrial vascular plant sources in paleo reconstructions, long-chain n-alkanes (>C
27
) and n-
alkanoic acids (>C
28
) could also be produced by microbes as previous studies have shown
(Nguyen Tu et al., 2011; Summons et al., 2013; Makou et al., 2018). It is possible that both
degradation and addition of these compound classes may occur in soils, and this can be detected
by examination of molecular abundance distributions. In a 1.5-year soil incubation experiment,
researchers detected microbial production of long-chain (C
27-31
) n-alkanes with an estimated
turnover rate of ~0.1% per year (for n-C
29
) under aerobic conditions, though no significant
production was detected under anaerobic conditions (Li et al., 2018). n-Alkane-degrading
127

microbes can convert these molecules into n-alkanoic acids following identified aerobic and
anaerobic degradation pathways involving alkane hydroxylases (Ji et al., 2013), leading to
accumulation of n-alkanoic acids relative to n-alkanes, which is favored in soils with low pH
~3.8 as observed in a soil experiment, whereas higher pH ~7.3 favors higher abundance of n-
alkanes (Bull et al., 2000). We note that soils throughout the Peruvian Andes-Amazon transect
have low pH values ~4 (Whitaker et al., 2014), which based on these experimental results may
enhance the accumulation of n-alkanoic acids relative to n-alkanes as we see in our soils (Fig.
4.2).
4.4.1.4 Are root and fungal contributions of plant waxes significant?
Root-derived organic carbon (OC) represents a significant source of soil organic matter, in part
due to enhanced protection mechanisms of root-derived versus shoot-derived OC in soils, e.g. as
root-hairs can burrow inside of soil aggregates providing physical protection of root-derived OC
(Rasse et al., 2005). In terms of plant waxes, grass roots have been found to produce long-chain
n-alkanes with high odd-over-even preference and C
31
dominance (Marseille et al., 1999), as
well as n-alkanoic acids with a distinct chain length distribution (C
22-24
dominance) compared to
leaves and stems (C
28
and C
30
dominance) (Wiesenberg et al., 2012). From a litter-soil profile in
a grass-dominated landscape, Naafs et al. (2004) deduced substantial root input of lipids,
including long-chain n-alkanes and n-alkanoic acids, into soils. It is unclear how much roots
contribute to plant waxes in soils in natural tropical forests such as in our study area, as such data
are lacking perhaps in part due to the difficulties in field collection and identification of
entangled roots from diverse tree species, especially in the context of very high biodiversity in
the western Amazon and Andes (Silman, 2014). However, given the anatomy of plants, root-
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derived OC is presumably less important in forests than in grasslands (Oades, 1993), given the
higher above-ground biomass of trees relative to grasses.
Although we only have studied two available root samples from SP and LA, the results provide
some clues to whether root inputs of n-alkanes and n-alkanoic acids are important in these soils.
These two roots show very low plant wax abundance, with <1% n-alkane and 6-16% n-alkanoic
acid concentrations compared to litter (Fig. 4.2b,d). Given the lower net primary productivity
allocated to roots than canopy across the same Andean transect (Malhi et al., 2016), a low plant
wax concentration in roots suggests a tiny root contribution to soil on a biomass basis, although
these may be overrepresented given the greater preservation potential previously noted. Another
line of evidence comes from the molecular distributions. If plant waxes in soils are mainly
derived from roots, we would expect to see molecular distributions in soils that are more similar
to roots compared to leaf litter. While the n-alkane molecular distributions are similar between
litter and roots at SP (Fig. 4.3c), confounding any separation on this basis, the molecular
distributions of n-alkanes at LA (Fig. 4.3g) and n-alkanoic acids at both SP and LA (Fig. 4.3d,h)
are distinct in roots versus litter. In general, we do not find evidence of significant root inputs of
n-alkanes and n-alkanoic acids, as soils show more similar molecular distributions to that of litter
(C
29
and C
31
n-alkane, and C
30
and C
32
n-alkanoic acid dominance), and lack the distinct
signatures shown in roots (C
23
and C
25
n-alkane dominance at LA, C
22-26
n-alkanoic acid
dominance with low even-over-odd preference in that range). Moreover, the one available δD
measurement of root n-alkanoic acid at LA is significantly D-depleted (by ~40‰) relative to the
adjacent soil profile (Fig. 4.5d), further supporting a minor influence of root inputs to soil plant
waxes.
129

Fungi have been reported to contain n-alkanes often with C
27
, C
29
and C
31
dominance similar to
that of vascular plants (Weete, 1972), but not long-chain (>C
28
) n-alkanoic acids (Weete, 1972;
Madan et al., 2002). Other studies have found increases in C
25
and C
27
n-alkanes in sub-surface
horizons and attributed these to fungal production (Huang et al., 1996; Marseille et al., 1999). It
may be difficult to use n-alkane molecular distributions to detect fungal inputs given the wide
diversity of fungi, poor characterization of n-alkane production (only few species characterized),
and possible confounded distributions with vascular plants (Weete, 1972). A sub-surface
increase in abundance of total n-alkanes or particular chain lengths such as C
27
is expected if
fungal input is substantial as in previous reports (Huang et al., 1996; Marseille et al., 1999), and
we do not observe any such feature in our profiles. However, we note that in those prior fungal
studies, the sub-surface increase in fungal n-alkanes occurs within a discrete, thin layer a few to
ten centimeters down from soil tops, perhaps guided by visual evidence during sampling. If any
fungal n-alkane production happens at such shallow depth within our soils, this production would
not be observed by our relatively coarse profile sampling, and no fungal evidence was observed
in the field.
4.4.1.5 No systematic change in plant wax δD between canopy, litter and soil
The hydrogen isotopic compositions of both C
29
n-alkane and C
30
n-alkanoic acid (the dominant
chain length of each compound class) show minor variations (<20‰) down profile with no
systematic pattern observed across sites (Fig. 4.5). The lack of systematic trend in plant wax δD
values within the litter layer and soil profiles in this study means we have no evidence for any
isotopic effect, whether via new inputs or below ground processes such as degradation and
remobilization, during soil formation. One possibility is that downward-transport of plant waxes
(such as by mesofauna), may have homogenized plant wax characteristics; however the different
130

patterns of δD and δ
13
C values with depth, measured on the same molecules, do not support
mixing as a major process for these soils. Concentration data have been interpreted as indicating
microbial decomposition within litter and soil during the timescales of soil formation. As we do
not find a systematic change in δD values, we infer no evidence for any consistent hydrogen
isotope fractionation effects associated with early diagenesis here.
This interpretation is consistent with a 27-month litterbag degradation study conducted on three
higher plant species in a German spruce forest (Zech et al., 2011). In that study, researchers
found no overall trend in C
27-31
n-alkane δD over the course of the study. They suggested minor
(~10 – 20‰) fluctuations were linked to seasonal variations of soil water δD on the microbial
community, but no systematic change in n-alkane δD was observed in their 2-year litterbag
experiment. Our study extends from leaf litter to consider the soil profile and finds that there is
no change in plant wax δD during the timescale of soil formation in this system.
In contrast, a leaf-litter-soil profile in a maple forest in Japan (Chikaraishi & Naraoka, 2006)
found D-depletion by 33 – 77‰ for both compound classes. Most of that D-depletion occurred
between canopy and leaves on the ground (litter), whereas the D-depletion within the litter-soil
profile was only ~5 – 20‰. We find no such systematic directional change, and at SP, we
observe that litter is 5 – 15‰ depleted for C
29
n-alkane, but 20 – 30‰ enriched for C
30
n-
alkanoic acid relative to average canopy (Fig. 4.5b). Although it is hard to reconcile the different
findings in a Japanese temperate maple forest dominated by just two species (Acer argutum and
Acer carpinifolium) and the Peruvian tropical high biodiversity forest sites spanning an altitude
range (this study), one possibility is that there has been a directional change in the hydroclimate
at the Japanese location during the time of soil formation. Another possibility is that high
131

biodiversity at our Peruvian sites masks any diagenetic changes, with variability down-profile
driven primarily by different species inputs over time. Future work might study the isotope
effects down-profile in a wider range of soil types and ecosystems in order to better constrain
plant wax δD values in soil archives.
4.4.1.6 A systematic shift in plant wax δ
13
C across between canopy, litter and soil
This study was motivated by the observation of plant wax δ
13
C offsets between canopy leaves
and soils (Feakins et al., 2018). In the current detailed study of leaf litter and soil profiles we
confirm that offset and study the progression via more detailed sampling within soil pits. We find
a 4 – 6‰ enrichment in both plant wax compounds from litter to deeper soils (Fig. 4.5). The
larger enrichment step happens between the litter and top soil (~2 – 4‰) followed by a smaller
change (~2‰) deeper in the soil, and the profiles in plant waxes mirror that of bulk OC. The
13
C-
depletion in litter relative to canopy leaves at SP (~1‰ for C
29
n-alkane and ~3‰ for C
30
n-
alkanoic acid, Fig. 4.5f) may indicate the addition of relatively
13
C-depleted understory leaves
(Wu et al., 2017). Up to 2‰ of the down-profile
13
C-enrichment may be explained by the more
enriched pre-industrial atmospheric CO
2
compared to today due to the Suess effect (Francey et
al., 2002; Scripps CO
2
program), if the plant waxes in the deeper soils were entirely pre-
industrial. Root inputs cannot explain the enrichment in soils, as the roots are 2 – 4‰ depleted
relative to soils (Fig. 4.5f,h). Plant wax δ
13
C entering the soils could have shifted through time if
there was a directional change in plant type/composition over the timescale of decades; however
this is very unlikely in these pristine highly biodiverse tropical forests (except VC) where no
single tree species dominate the landscape. Hence, after accounting for Suess effect, we infer at
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least ~2 – 4‰ of post-mortem
13
C-enrichment of plant waxes within soils which is likely a result
of diagenesis.
Our profiles corroborate previous studies of a leaf-litter-soil sequence in a Japanese maple forest
that found ~2.5 – 4‰
13
C-enrichment between leaf and surface soil (Chikaraishi and Naraoka,
2006), and depth profiles of three types of tundra-covered British acid upland soils that found ~2
– 4‰
13
C-enrichment of C
27-31
n-alkanes downwards (Huang et al., 1996). Considering very
different settings among the three studies (and the continued Suess effect over recent decades),
the
13
C-enrichment of plant waxes during soil storage appears to be a common feature across a
range of environmental and soil conditions.
13
C-enrichment of n-alkanes during early diagenesis
has also been observed in several litterbag experiments in mid-latitude temperate forests, mostly
showing 1 – 2‰ enrichment within 1 – 3 years, likely a result of microbial processes (Nguyen
Tu et al., 2004; Wang et al., 2014; Li et al., 2017; Zhang et al., 2017). Our study confirms that
direction and trend, and reveals additional change down profile, which could be due to the much
longer time scale of soil formation compared to the litterbag experiments. Not only do we find
this result for the n-alkanes, we also confirm this transition in litter and soil profiles for the n-
alkanoic acids, which have been less often reported in litterbag degradation experiments.
4.4.2 Implications for plant wax calibration studies for paleoclimate applications
4.4.2.1 Soil-based calibrations as integrators of plant signals
Much attention for modern plant wax studies has focused on leaves from living plants (as
summarized in review papers by Sachse et al., 2012; Diefendorf & Freimuth, 2017), but soil-
based studies (e.g. Jia et al., 2008) provide integrated records of multi-species plant inputs and
post-mortem soil processes that may affect plant wax signatures in the transition from leaf to soil
133

(Nguyen Tu et al., 2004; Chikaraishi & Naraoka, 2006). Several studies have surveyed soils
across environmental transects using soils to understand molecular abundance distribution (Bush
& McInerney, 2015), carbon isotopic composition (Wei & Jia, 2009; Schwab et al., 2015;
Feakins et al., 2018) and hydrogen isotopic composition (e.g. Jia et al., 2008; Bai et al., 2011;
Zhang & Liu, 2011; Ernst et al., 2013; Ponton et al., 2014; Zhuang et al., 2015; Nieto-Moreno et
al., 2016; Wang et al., 2017; Feakins et al., 2018), almost all of which studied n-alkanes, with
only few exceptions that have studied n-alkanoic acids (Ponton et al., 2014; Feakins et al., 2018;
Bakkelund et al., 2018). Although both plant and soil-based approaches have merits, plant-based
calibrations include significant scatter among individual plants associated with differences in
plant type, species, biosynthetic processes, seasonality and microclimate, whereas soils provide
an average of plant inputs and reveal how environmental controls are represented in the soil
archive. For example, along a slope of Mount Taibai in China, soil n-alkane δD values capture
the altitudinal gradient in source water composition which was not observed in plant
measurements due to significant scatter among individuals, especially between woody plants and
grasses (Zhang & Liu, 2011).
Regions of high biodiversity, such as tropical forests, pose even bigger challenges for plant-
based calibrations, as large-quantity sampling and knowledge of species dominance may be
required to adequately capture the ecosystem-scale average signatures. Recent surveys of plant
wax δD and δ
13
C in canopy leaves in the same region as this study, along a 3320 m elevation
transect in the highly-biodiverse tropical forests of the Peruvian Andes, sampled at an
unprecedented scale (>300 samples) and revealed significant scatter among individual tree leaves
and species. Despite the scatter these studies could identify a robust altitudinal trend (Feakins et
al., 2016a,b; Wu et al., 2017), but one that would have been difficult to reveal without substantial
134

sampling of leaves and sites as demonstrated by Monte Carlo simulations (Wu et al., 2017). In
contrast, a relatively small number of soil samples may be needed to calibrate the archived proxy
across an environmental transect (e.g., as shown for this region in Ponton et al., 2014 and
Feakins et al., 2018). In a series of studies in this region, we have both constrained the isotopic
signal fixed in the plant canopy (Feakins et al., 2016a,b; Wu et al., 2017), the processes of
alteration down profile (this study) and the archived proxy in soils (Ponton et al., 2014; Feakins
et al., 2018).
Another advantage of soil-based calibrations is that these capture the post-mortem alterations to
plant wax signatures during residence in soil, which may modify the environmental information
being recorded from time of synthesis. While we find that δD does not systematically vary, we
find a 4 – 6‰
13
C-enrichment in both plant wax compounds from litter to deeper soils (Fig. 4.5).
The larger enrichment step happens between the litter and top soil (~2 – 4‰) followed by a
smaller change (~2‰) deeper in the soil, and the profiles in plant waxes mirror that of bulk OC.
Knowing this, what are the implications for application of plant-based calibrations to the
sedimentary record? Corrections for the changing δ
13
C values of atmospheric CO
2
(Suess effect)
based on the year of plant collection can be readily applied, when using modern plant-based
calibrations for interpretations of the pre-industrial geologic record. Corrections associated with
diagenetic processes in soils will be harder to quantify and we anticipate that the diagenetic
effect will likely vary with climate, soil type and microbial community. Based on this study of
tropical forests, a 2 – 4‰ diagenetic correction may be relevant for vegetation reconstructions in
tropical settings based on soils, paleosols, and sedimentary archives where plant waxes are
mainly derived from pre-aged plant waxes that have been diagenetically altered in soils. In
135

contrast, archives that mainly integrate leaves that did not go through soil storage (e.g. swamps,
some lakes) may not experience such diagenetic effects.
Without a diagenetic correction for soil-stored plant waxes, the reconstruction of vegetation
composition using soil-derived plant wax δ
13
C may be subject to bias relative to calibrations
based on living plants. For example, a common tropical application of carbon isotopic analyses is
to estimate the proportion of plants using the C4 pathway (e.g., Schefuß et al., 2003, Castaneda
et al., 2009). Based on our C3 tropical forest soils, a 3‰ post-mortem diagenetic enrichment
would lead to a ~20% overestimation of C4 coverage (based on a 14‰ difference between C3
and C4 end-members in Cerling et al., 1997). Although our study shows a relatively consistent
magnitude of
13
C-enrichment across a range of elevation and forest types (tropical rain forest,
bamboo forest, montane cloud forest), it is likely that the diagenetic isotope effect may vary with
climate, soil and ecosystem (e.g. Arctic tundra vs. tropical savanna) as diagenetic changes are
likely controlled by various biologic and environmental factors (e.g., leaf structure, temperature,
soil porosity, soil wetness and microbial community). Further studies would ideally characterize
diagenetic alteration of plant wax in a broad range of in situ soil profiles (ideally with age
information) and controlled experimental studies of individual variables.
4.4.2.2 Soils, not plants, are the major stock of plant wax
We posit that soils are the dominant plant wax stock relative to plant biomass in these tropical
forest ecosystems. This inference comes from our estimates of the stock of plant waxes in plants
vs soils (Appendix A), explained as follows and calculated along a series of tropical rainforest
(TR) and tropical montane cloud forest (TMCF) sites across the same Andes-Amazon transect
136

that were previously studied for plant wax work (Ponton et al., 2014; Feakins et al., 2016a,b; Wu
et al., 2017) and are analogous to those studied in more detailed here.
To estimate plant wax stock in the leaves of living trees, we take the OC-normalized plant wax
concentration data from prior studies (Feakins et al., 2016a,b) multiplied by leaf net primary
productivity NPP
leaf
(Malhi et al. 2016) and average leaf lifespan (1 yr in tropical rainforest and 3
yr in tropical montane cloud forest sites; Girardin et al., 2014; Huaraca Huasco et al., 2014) to
estimate plant wax stock in the leaves. We estimate that plant wax stock ranges from 0.06 – 1
Mg km
-2
for C
23-33
n-alkanes, and from 0.03 – 0.52 Mg km
-2
for C
22-32
n-alkanoic acids, with a
tendency towards greater stocks of plant waxes on living plants in the TMCF sites.
We estimate plant wax stock in soils for the top 30 cm of soils only, using a two-layer (organic
and mineral layer) approach, based on OC-normalized plant wax concentration data (Feakins et
al., 2018) and soil OC stock estimates (Girardin et al., 2014). We find an estimated 0.6 – 3.9 Mg
km
-2
for n-alkanes and 3.2 – 21.5 Mg km
-2
for n-alkanoic acids for the top 30 cm of soils again
with a tendency towards bigger stocks in the TMCF. Together these results show a much bigger
plant wax stock in soils compared to canopy, which accounts for only 17% and 23% of total n-
alkane stock, and 1% and 5% of total n-alkanoic acid stock, on average for TR and TMCF sites
respectively (Fig. 4.9).
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Fig. 4.9. Estimates of the stock of plant waxes for (a) C
23-33
n-alkanes (b) C
22-32
n-alkanoic acids in leaves (green)
and soil top 30cm (brown) across the Peruvian Andes-Amazon elevation transect (sites are ordered in increasing
elevation). Plant wax stock in leaves is calculated based on OC-normalized plant wax concentration data (Feakins et
al., 2016a,b) multiplied by leaf net primary productivity NPP
leaf
(Malhi et al., 2016) and then leaf lifespan, using 1 yr
in tropical rainforest (TR) sites, and 3 yr in tropical montane cloud forest (TMCF) sites (Girardin et al., 2014;
Huaraca Huasco et al., 2014). Soil wax stock is estimated based on OC-normalized plant wax concentration data
from soil organic and mineral layers (Feakins et al., 2018) and OC stock estimates for the top 30cm of soils
(Girardin et al., 2014). The average of TR and TMCF sites is shown in the summary, with numbers in green
indicating the fraction of plant wax stock allocated to the leaves. Crosses denote where leaf or soil estimate is
unavailable for the site. Readers are referred to previous publications (Wu et al., 2017, Feakins et al., 2018) for site
information.
While these estimates are subject to caveats such as not accounting for non-leaf plant waxes in
living biomass, and simplification of the two-layer model for soil (top 30 cm) estimates, they
depict an overall picture that the vast majority of terrestrial plant waxes is stored in the soils
138

rather than in the living biomass. Moreover, the soil top 30 cm stock represents an
underestimation of the overall soil stock, especially in the higher-elevation sites where the
organic layers are deeper (Table 1) and plant wax loss with depth is gentler (Fig. 4.7). Although
it has long been known that soils are important archives of OC (Blair et al., 2004), the effort to
quantify plant wax production (Feakins et al., 2016a) and here to quantify and compare plant
waxes stocks above and below ground, is a new contribution. It would be interesting for carbon
cycle quantification and tracking to see this biomarker approach to stocks and fluxes expanded to
more climates and ecosystems.
4.4.2.3 Soil stocks are sources for fluvial erosion
Given the much greater stock of plant waxes in soils relative to living plants (Fig. 4.9), soils are
the likely source for the majority of riverine-erosion and export of plant waxes used in studies of
catchment sourcing (e.g. Ponton et al., 2014; Häggi et al., 2016; Hemingway et al., 2016), and
thus for the plant waxes deposited downstream in sedimentary repositories used for paleoclimate
reconstructions (e.g., Tipple and Pagani, 2010; Schefuß et al., 2011; Hein et al., 2017). We
acknowledge that the actual sourcing from plants vs soils depends not just on the stock, but also
on the erosional processes. For example, it has been suggested that landsliding plays an
important role in soil OC sourcing to rivers in the Peruvian Andes, stripping 80% of the OC from
soils and 20% from vegetation (Clark et al., 2016). Landslides would enhance supply of soil
plant waxes from deeper depths (beyond 30 cm), as well as the plant waxes directly from the
living biomass. While estimating the exact living vegetation vs soil plant wax sourcing is beyond
the scope of this study, we suggest that the stock estimates gives us a first-order view on the
relative importance of these two pools, such that soil-based calibrations carry merit of likely
being the pool from which most riverine plant waxes are sourced. In terms of parsing fluvial
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sourcing proportions between living plants and soil stocks of plant wax, compound specific
radiocarbon analysis is needed (French et al., 2018).
The idea that soil is the major source of sedimentary and riverine plant waxes has important
implications especially for elevation-sourcing studies. For example, Feakins et al. (2018)
evaluated fluvial sourcing of plant waxes within the Andes-Amazon Madre de Dios catchment
based on plant wax isotopic gradients in soils. If the δ
13
C gradient in canopy leaves (which is c. -
1 and -2‰ offset from soil organic and mineral layer respectively) were to be used instead, this
would result in an overestimate of the average sourcing elevation by more than 700 m (taking
c.1.5‰ km
-1
altitudinal gradient for C
29
n-alkane; Feakins et al., 2018). The degree of litter-to-
soil
13
C-enrichment, however, appears similar across the four sites that span a range in elevation
(286 – 3025 m), forest structure (from montane cloud forests to lowland tropical rain forests),
and soil organic content (soil organic layer 1 – 26 cm thick, thinner towards lower elevation),
such that the δ
13
C altitudinal gradients are kept nearly constant between plant and soil, though
their values may be offset (Feakins et al., 2018). This implies that when relative isotopic changes
are interpreted (e.g., as relative shifts in C3/C4 coverage, or when applying δ
13
C in plant waxes
for paleoaltimetry reconstruction) instead of the absolute values, the problem caused by
13
C-
enrichment within soils may be avoided.
4.5 Conclusions
Here we studied plant wax biomarkers (n-alkanes and n-alkanoic acids) from four locations (six
soil pits) along a 2740 m elevation transect in the Andes-Amazon. We measured plant wax
concentrations, molecular distributions, and hydrogen and carbon isotopic compositions, as well
as bulk organic carbon and carbon isotopic composition, in these litter-soil profiles. Based on the
140

observations within these profiles we draw inferences about inputs, degradation processes and
alteration of plant wax properties within these tropical soil profiles. Within leaf litter, although
both compound classes decline in absolute abundance, we find that n-alkanes are lost relative to
n-alkanoic acids, and it is this greater loss of n-alkanes (rather than in-soil inputs of n-alkanoic
acids) which leads to an increase in n-alkanoic acid relative abundance from litter to soil. Within
the soil profiles, concentrations of both compound classes decline with depth (k
z
) at similar rates
within a site. Between sites, k
z
decreases with elevation, such that k
z
is smaller at higher
elevation (colder) sites and larger at lowland (warmer) sites. The only exception to this trend is at
VC, the only soil sampled under a secondary growth forest, now dominated by bamboo, and
aspects of the disturbance history or bamboo regrowth may explain the lower than expected k
z
.
We find signs of microbial activities altering molecular distributions of plant waxes, but no
evidence of root and fungal contributions being quantitatively important. Across the litter-soil
profiles, we find no systematic change in δD values, but a consistent 4 – 6‰ increase in δ
13
C
down-profile, which is attributed to a combination of Suess effect (≤2‰) and diagenetic
processes (2 – 4‰), corroborating results from previous litter degradation experiments. With
these observations, we suggest that soil-based calibrations carry considerable merit as integrated
recorder of plant signals, and this approach is especially relevant in high biodiversity ecosystems
where it reduces the number of samples needed to adequately characterize the system. Further,
soil-based calibrations capture the post-mortem diagenetic processes that affect plant wax. It is
important to characterize plant waxes in soils for a range of applications. Most obviously,
surveying plant waxes within modern soil profiles is important for calibration of the recorded
signals that may inform applications to paleosol archives of the plant wax proxy. As we show the
below-ground stock of plant wax is much greater than that of the living forest here, further
141

quantification and characterization of the soil stock of plant waxes in a range of environments
and ecosystems would be informative for carbon cycle and sourcing studies. While not all soils
are connected to fluvial systems, some soils are episodically eroded in this system by landslides
in high relief areas and by migrating river meanders in lowland systems. Overall, river studies of
plant wax export have shown that soil storage contributes pre-aged plant waxes to the export flux,
although to variable extent in different climates and river systems (Kusch et al., 2010; Feng et al.,
2015; French et al., 2018). We therefore encourage further modern soil-surveys of plant waxes to
understand the exported signal of eroded soils and plant waxes for carbon cycle implications, and
when deposited in sedimentary archives, for paleoclimate reconstructions.
Acknowledgements
This material is based upon work supported by the US National Science Foundation under Grant
Nos. EAR-1455352 to A.J.W. and EAR-1703141 to S.J.F. In Perú, we thank the Servicio
Nacional de Áreas Naturales Protegidas por el Estado (SERNANP) for logistical assistance and
permission to work in the protected areas. We thank the Amazon Conservation Association for
use of the Wayqecha, Villa Carmen, and CICRA-Los Amigos Research Stations, and for help
with field logistics, as well as the Andes Biodiversity and Ecosystems Research Group ABERG
(andesresearch.org). Soil pits were dug, described and sampled by the USC GEOL 465 class of
2016, with thanks especially to A. Figueroa, K. Morales and K. O’Rourke. We thank Nick
Rollins for laboratory assistance. We thank the Associate Editor Philip Meyers and the reviewers
(Aaron Diefendorf, Rencheng Li, and an anonymous reviewer) for their thoughtful comments
that helped improve this manuscript.

142

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Chapter 5
Dissertation Conclusions
Plant wax biomarkers are popular proxies for reconstructing past terrestrial vegetation and
hydroclimate recorded in sedimentary archives. Their applications have been built upon
numerous modern calibration studies that draw relationships between environmental factors and
plant wax signatures (molecular distributions and isotopic compositions). Interpreting past
environmental information encoded in plant waxes in geological archives based on relationships
observed from modern calibration studies requires the general assumption that the plant wax
signatures are not significantly altered from source (modern study) to sink (geological archive).
However, how plant wax signatures are transformed or preserved between source and sink
remains not well understood. Various factors, such as integration from individual plants to
sediments, and alteration of plant wax signatures during diagenesis, may influence how the
environmental information encoded during plant wax synthesis is reflected in sedimentary
archives. By studying molecular and isotopic signatures of plant wax biomarkers (long-chain n-
alkanes and n-alkanoic acids) from tree top to soils to river sediments across an Andean-Amazon
transect in Peru, this thesis provides some new insights concerning source-to-sink processes of
plant wax biomarkers:
- Chapter 2: Plant wax carbon isotopic composition as elevation proxy
- Chapter 3: Fluvial integration of plant wax biomarkers across the catchment
- Chapter 4: The fate of plant wax biomarkers during soil storage
150

Chapter 2 presents plant wax δ
13
C data from canopy tree leaves across the 3.3 km Andes-
Amazon elevation transect. This study constitutes an unprecedented sampling of tropical
vegetation for plant wax proxy calibration (>400 samples, 129 species, nine forest plots). Not
only is the number of samples large for any plant wax calibration, but a large number of
parameters were measured including bulk leaf δ
13
C, leaf wax n-alkane concentration and δ
13
C
and leaf wax n-alkanoic acid concentration and δ
13
C, generating new knowledge in tropical plant
wax biochemistry. Our analysis reveals that despite large (~10‰) scatter in plant wax n-alkane
δ
13
C values within sites, clear positive elevation gradients in C
29
n-alkane (+1.45 ± 0.33‰ km
-1
)
and bulk leaves (+0.87 ± 0.16‰ km
-1
), as well as ε
29alk/bulk
emerge from site averages due to the
large sampling size (trend in C
30
n-alkanoic acid not visible due to smaller sample size and
number of sites). We find that the elevation trends are robust at family and genus level
suggesting that environment (likely adiabatic gradients in pCO
2
), not taxonomic turnover, is the
major control of plant wax δ
13
C (and also bulk leaf δ
13
C) in this humid tropical forest transect.
While plant wax δD values have been well known to correspond to elevation and recently
introduced as a paleoaltimetry proxy, our study provides basis for an additional elevation tracer,
plant wax δ
13
C, such that dual isotopic analysis (C and H) may offer better constraints for
paleoaltimetry and sourcing elevation of sediments exported in river catchments.
With plant wax C and H isotopes proven useful elevation tracers, Chapter 3 takes a dual isotope
approach to investigate how plant wax biomarkers are integrated in the river catchment. We
analyzed plant wax δ
13
C and δD in soil organic (O) and mineral (M) horizons and river
suspended sediments in the Madre de Dios River catchment which drains the eastern flank of the
Peruvian Andes down to the Amazon floodplain. We find that soils show elevation trends in both
C and H isotopes with gradients similar to previously observed in canopy leaves, though with
151

systematic offsets in δ
13
C between plants and soils. Using soil gradients as reference lines, we
find that wet season river suspended sediments generally follow isotopic gradients defined by
mean catchment elevations indistinguishable from soil gradients, suggesting sourcing of plant
waxes approximating that expected from an areal-based spatial integration, hence we find no
evidence of Andean-biased contribution despite greater erosion. The dry season samples, though
less well-constrained, reveal a lowering of sourcing elevation by ~1 km in average. Taken
together, the near areal-based integration suggests that upland plant wax contributions will
become swamped by additions from lowlands as the river progresses into the floodplain, such
that lowland contributions dominate especially in sediments from large river systems with
extensive lowland areas in their catchments. Our dual isotope approach provides means to cross-
check elevation signals and resolve ambiguity such as that arise from petrogenic contributions
and depth-sorting which influence δ
13
C in a few samples. Apart from tracing sourcing elevation
in modern rivers as demonstrated by this study, we further suggest this dual isotope approach is
useful to paleoaltitude reconstructions from geologic archives.
Chapter 4 investigates an offset discovered between the plant wax δ
13
C values in the canopy
(Chapter 2) and the soils (Chapter 3), and studies the transformation of plant waxes from plants
to soils. By analyzing the concentrations, molecular and isotopic signatures of n-alkanes and n-
alkanoic acids from six litter-soil profiles at four sites across the Andes-Amazon transect, we
investigate the fate of plant wax biomarkers during their residence in soils. We find that n-
alkanes appear to be preferentially degraded relative to n-alkanoic acids within litters resulting in
an increase in acid/alkane ratio from plants to soils, whereas both compound classes are lost at
similar rates with depth within soils, suggesting different mechanisms governing their resilience
once they move from litters into the soils. We determined the depth-dependent decay rate
152

constant (k
z
) of plant waxes within soils from different sites, and find elevation (temperature) to
be the dominant control, such that plant wax concentrations are lost at faster rates with depth at
lower-elevation (warmer) sites. The one exception to this trend from a bamboo-dominated
secondary forest suggests additional influences from ecosystem properties. From litter to soils,
we observe no systematic trends in plant wax δD, but a consistent increase in δ
13
C by 4 – 6‰
down-profile resulted from a combination of Suess effect and diagenetic processes. With these
observations from these litter-soil profiles, we propose that soil-based modern calibrations carry
considerable merits as soils not only integrate signals from individual leaves/plants and capture
post-mortem alterations due to diagenetic processes, but also represent a much bigger stock than
living biomass hence likely the dominant source of plant waxes in sedimentary archives.
By systematically investigating plant waxes in different components in this tropical forested
Andes-Amazon transect, this thesis presents a relatively well-characterized system which may
provide insights generalizable to other regions, at least those with similar settings. However,
some important questions concerning plant waxes’ journey from biosynthesis to deposition in
sedimentary archives remain to be answered by future studies. For example, the factors (climatic,
geomorphic, and environmental) behind the fluvial integration of plant waxes in different
catchments around the world, and in the same catchment over geological time with tectonic,
climate and ecological change, merit further investigations. The relative contributions of plant
waxes between plants and soils of different depths, which depend not only on stock but also on
erosional processes, remain poorly constrained. Despite the number of samples analyzed in this
thesis, it is an understatement to say that the diversity of the Amazon Basin remains
undersampled. Future studies could apply similar approaches to characterize other settings, for
example, seasonally dry tropical forests and savannas. Ultimately this thesis advances our
153

understanding of how plant wax signatures are preserved or altered during source-to-sink
processes, but also opens up questions that future studies could seek to investigate.

154

Appendix A
Supplementary Information for Chapter 2
A.1 δ
13
C values of n-alkanoic acid homologues and comparison with n-alkanes
We report δ
13
C values for n-alkanoic acids (C
22
, C
24
, C
26
, C
28
, C
30
, and C
32
) from 76 plant
samples covering 29 species from five of the nine sites (ESP-01, SPD-01, SPD-02, TAM-05,
TAM-06). We observe strong linear correlations (r > 0.85) between δ
13
C values of consecutive
chain-lengths (C
n
vs. C
n+2
), except for between C
22
and C
24
(r = 0.66; Fig. A.1).

Fig. A.1. Comparison of δ
13
C values between consecutive n-alkanoic acid homologues. Grey circles show all
individual samples, with orthogonal distance linear correlations (black) all significant (p<0.01). Also showing 1:1
lines (dashed line).

Comparison of δ
13
C values among different leaf wax homologues and between compound
classes within individual plants is of fundamental interest to evaluate isotopic fractionations
during biosynthesis (Zhou et al., 2010). During biosynthesis of leaf waxes, C
n
alkanoic acids,
and the corresponding C
n-1
alkanes, are derived from a common n-alkyl-ACP precursor (Zhou et
al., 2010), with a carboxyl group lost to produce the C
n-1
alkane. This decarboxylation step is
predicted to have a normal isotope effect, with preferential breaking of HOO
12
C-
12
CH
2
bonds
(Hayes, 2004). Our samples confirm that prediction of n-alkane being
13
C-depleted relative to the
155

n-alkanoic acid (ε
29alk/30acid
= −2.5 ± 2.4‰, 1σ, n = 74; Fig. A.2). In contrast, a prior study
reported an average ε
29alk/30acid
of +1.4 ± 1.1‰ (1σ, n = 25; Chikaraishi and Naraoka, 2007). The
δ
13
C values of the two molecules are linearly correlated (y = 1.05x – 0.5; r = 0.67; p < 0.001; Fig.
A.2).

Fig. A.2. Comparison between δ
13
C
29alk
and δ
13
C
30acid
. Figure showing individual samples (grey circles) and
orthogonal distance linear regression (black line).

Both n-alkanes and n-alkanoic acids are popular compounds in paleoenvironmental
reconstructions, yet few modern plant studies have measured δ
13
C from both. Thus, the
fractionation between the two compound classes is only known from a very small sample set
(Chikaraishi and Naraoka, 2007; Hou et al., 2007; Zhou et al., 2015). Calibration of both
compound classes is of interest to support sedimentary studies (Hemingway et al., 2016), and
paleoenvironmental interpretations that may use one or (as yet rarely) both compounds. Our
study adds δ
13
C data for many tropical trees for both n-alkanes and n-alkanoic acids, which allow
156

for the synthesis and inter-comparison of data from different leaf wax homologues from multiple
studies.
A.2 n-Alkanoic acid vs. bulk leaf δ
13
C
Comparing n-alkanoic acid δ
13
C values with δ
13
C
leaf
, we find orthogonal distance linear
correlations (correlation coefficient ranging 0.33 – 0.48; Fig. A.3a). Correlations are weaker than
between n-alkanes and bulk leaf (Fig. 2.6), possibly due to the smaller sample size. The ε
wax/leaf

values for different n-alkanoic acid chain lengths are reported in Fig. A.3b.

Fig. A.3. Comparison between n-alkanoic acid δ
13
C values with δ
13
C
leaf
. (a) δ
13
C
leaf
versus δ
13
C values of n-alkanoic
acid homologues for individual samples and orthogonal distance linear regressions (all with p < 0.05). (b) Notched
box and whisker plots showing distributions of ε
wax/leaf
for n-alkanoic acid homologues (circle: mean; horizontal
black line: median; boxes: upper and lower quartile; whiskers: 5
th
and 95
th
percentile; dots: outliers beyond the 5
th

and 95
th
percentiles).

A.3 n-Alkanoic acid δ
13
C across the elevation profile
To investigate the elevational trend in n-alkanoic acid δ
13
C, we focus on that of C
30
n-alkanoic
(δ
13
C
30acid
), the modal chain length. Given the strong linear correlations among C
24
– C
32
157

homologues (Fig. A.1), trends observed in δ
13
C
30acid
are generalizable for other chain lengths.
δ
13
C
30acid
ranges −39.3 to −27.4‰ across the five sites, and within-site variability spans the same
range at some sites (Fig. A.4).

Fig. A.4. δ
13
C values of C
30
n-alkanoic acids from sunlit canopy leaf samples versus elevation. Grey open circles
indicate individual data with sizes scaled to leaf wax abundance. Also showing unweighted mean (pink circles), and
community-weighted mean (blue circles) for each site, and ordinary least squares linear regression of the
unweighted site mean values (pink line).

Our sampling for n-alkanoic acids is for a subset of the sampled plants representing 4–53% of
the total basal area for each of five 1-ha forest plots. Unweighted and community-weighted mean
δ
13
C
30acid
values are similar (≤ 1.3‰), except at SPD-02 (Fig. A.4) where we find an offset of 3‰
driven by one species, Alchornea latifolia (mean δ
13
C
30acid
= −28.7 ± 1.5‰, n = 3), representing
41% of basal area of the six, sampled species. C
30
n-alkanoic acid concentrations vary by a factor
of three in Alchornea latifolia, and this is likely associated with sampling leaves of mixed ages.
Given the population species mean is not well represented by 3 samples and this species has a
158

strong effect on the community-weighted mean, we therefore suggest the unweighted mean is the
more appropriate central estimate.
Unweighted site mean δ
13
C
30acid
values tend to increase with elevation. Linear regression yields a
slope of +0.81 ± 1.5‰ km
−1
(95% CI; r
2
= 0.47; Fig. A.4) and a projected δ
13
C
30acid
at sea level
of −36.0 ± 2.5‰ (95% CI), however, the linear regression is not significant (p = 0.19). Based on
the biosynthetic relationship of the C
30
n-alkanoic acid and C
29
n-alkane, and the correlation
shown in Fig. A.2, we expect both homologues to be recording the same processes. We suggest
that the non-significance of the regression is likely due to the inability for the smaller number of
samples and sites to capture a robust elevational gradient, not inherent lack of elevational
gradient (see Section 2.4.3.1 in the main text). Given the δ
13
C trend with elevational shown in
the 9-site survey of plants for the C
29
n-alkane (Fig. 2.3), and that δ
13
C
30acid
and δ
13
C
29alk
are
linearly correlated where both are measured (Fig. A.2), we predicted that δ
13
C
30acid
values would
also increase with elevation (although the slopes may differ). The n-alkanoic acid plant-based
elevation calibration would ideally be enhanced by data from more sites along the profile.
Alternatively, calibration efforts toward paleoenvironmental reconstructions could include soil
sampling along the profile to offer an integrative perspective on the plant canopy, or a sequence
of river sediments that integrate across the landscape scale. Paleoenvironmental reconstructions
have reported similar signatures in both compound classes: for example, the C
4
expansion in the
Miocene has been revealed by δ
13
C values of n-alkanes (Freeman and Colarusso, 2001) and n-
alkanoic acids (Feakins et al., 2013) in distal geographic locations, as well as with both
compound-classes in the same sediment (Liddy et al., 2016).
In summary, this large survey of living trees across an Andes-Amazon transect demonstrates the
post-photosynthetic fractionations encoded in two compound classes of plant waxes. We report
159

the correlation and isotopic fractionation between the carbon isotopic composition of n-alkanoic
acids and n-alkanes in tropical trees. Sampling of the bulk leaves and n-alkanes reveal a
significant correlation with elevation, but the subset of sites is found insufficient to robustly
identify a trend in n-alkanoic acids.
References
Chikaraishi, Y. and Naraoka, H. (2007) δ
13
C and δD relationships among three n-alkyl
compound classes (n-alkanoic acid, n-alkane and n-alkanol) of terrestrial higher plants.
Org. Geochem. 38, 198–215.
Feakins, S.J., Levin, N.E., Liddy, H.M., Sieracki, A., Eglinton, T.E. and Bonnefille, R. (2013)
Northeast African vegetation change over 12 m.y. Geology 41, 295 – 298.
Freeman, K.H. and Colarusso, L.A. (2001) Molecular and isotopic records of C4 grassland
expansion in the late miocene. Geochim. Cosmochim. Acta 65, 1439 – 1454.
Hayes, J.M. (2004) Isotopic order, biogeochemical processes, and earth history: Goldschmidt
lecture, Davos, Switzerland, August 2002. Geochim. Cosmochim. Acta 68, 1691–1700.
Hemingway, J.D., Schefuß, E., Dinga, B.J., Pryer, H. and Galy, V.V. (2016) Multiple plant-wax
compounds record differential sources and ecosystem structure in large river catchments.
Geochim. Cosmochim. Acta 184, 20–40.
Hou, J., D'Andrea, W.J., MacDonald, D. and Huang, Y. (2007) Evidence for water use efficiency
as an important factor in determining the δD values of tree leaf waxes. Org. Geochem. 38,
1251–1255.
Liddy, H. M., Feakins, S. J., Clift, P.D., Tauxe, L., Kulhanek, D.K., Scardia, G., Warny,
S., Bendle, J., Galy, V., Zhou, P., and IODP Expedition 355 Science Party. Late
Miocene hydrological change in the Indus River catchment. Fall AGU (San Francisco).
Abstract. 2016.
Zhou, Y.P., Grice, K., Stuart-Williams, H., Farquhar, G.D., Hocart, C.H., Lu, H. and Liu, W.G.
(2010) Biosynthetic origin of the saw-toothed profile in δ
13
C and δ
2
H of n-alkanes and
systematic isotopic differences between n-, iso- and anteiso-alkanes in leaf waxes of land
plants. Phytochemistry 71, 388–403.
Zhou, Y., Stuart-Williams, H., Grice, K., Kayler, Z.E., Zavadlav, S., Vogts, A., Rommerskirchen,
F., Farquhar, G.D. and Gessler, A. (2015) Allocate carbon for a reason: Priorities are
reflected in the
13
C/
12
C ratios of plant lipids synthesized via three independent
biosynthetic pathways. Phytochemistry 111, 14–20.

Abstract (if available)

Abstract Plant wax biomarkers are popular proxies for reconstructing past climate and environment in a variety of geologic archives including paleosol, lacustrine and marine sediments. Numerous plant-based modern calibration studies have formed the basis of the application of plant wax biomarkers as recorders of environmental conditions. However, knowledge about the processes in between the source (plant production) and sink (sedimentary deposition) of plant wax biomarkers, which contribute to their preservation, loss, and mobilization across the landscape, remains limited. The molecular and isotopic signatures may undergo changes during these processes, affecting our ability to directly translate plant-based calibrations into interpretations of sedimentary records. ❧ To address this gap in knowledge, this thesis studies plant wax biomarkers (long-chain n-alkanes and n-alkanoic acids) along a 4 km elevation transect across the highly-biodiverse tropical forests along the eastern flank of the Peruvian Andes to Amazon. My coauthors and I trace them from tree leaves, through soils, and into river sediments to evaluate how they mobilize across the landscape, and how their molecular and isotopic signatures may be preserved or altered in transit. We surveyed the carbon isotopic compositions (δ¹³C) of long-chain n-alkanes in 405 canopy leaves sampled across 129 species from nine forest plots, and n-alkanoic acids from a subset of samples, along the Andes-Amazon transect. The study reveals an increase in δ¹³C with elevation (+1.45 ± 0.33 ‰ km⁻¹ for C₂₉ n-alkane), highlighting the potential for this metric as tracer of sourcing-elevation of biomarkers within catchment, and as proxy for paleoaltimetry. ❧ We further study n-alkanes and n-alkanoic acids in soils across the Andes-Amazon transect, which yield gradients in δ¹³C (c. 1.5 ‰ km⁻¹) and δD (c. 10 ‰ km⁻¹) that are similar to that in canopy leaves, suggesting the elevation signals are transferred from plant to soil (but with an offset in δ¹³C of n-alkanes which will be addressed later). Combining dual isotopes and dual compound classes, we evaluate the sourcing of plant wax biomarkers in rivers within the catchment, and find that trends in river sediments generally follow isotopic gradients defined by their mean catchment elevations, suggesting a relatively spatially-uniform integration. The last part of the thesis investigates how plant wax signatures transfer from plants to soils. With sampling of leaf litters and soils depth-profiles, we piece together a more detailed picture of how plant wax signatures are altered from plants to soils during early diagenesis. Overall, this thesis contributes to the understanding of how plant waxes are mobilized across the landscape, and the processes that lead to the alteration of their molecular and isotopic signatures in transit.

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Asset Metadata

Creator Wu, Mong Sin (Christine) (author)

Core Title From tree tops to river runoff: tracing plant wax biomarkers across the Peruvian Andes and Amazon

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Geological Sciences

Publication Date 06/23/2020

Defense Date 08/09/2019

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

Tag Amazon,Andes,OAI-PMH Harvest,plant wax biomarkers

Format application/pdf (imt)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Feakins, Sarah J. (committee chair), Brutchey, Richard (committee member), West, A. Joshua (committee member)

Creator Email mongsinw@usc.edu,wumongsin@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-c89-177972

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