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The geobiological role of bioturbating ecosystem engineers during key evolutionary intervals in Earth history
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The geobiological role of bioturbating ecosystem engineers during key evolutionary intervals in Earth history
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Content
THE GEOBIOLOGICAL ROLE OF BIOTURBATING ECOSYSTEM ENGINEERS DURING
KEY EVOLUTIONARY INTERVALS IN EARTH HISTORY
by
Alison Taveau Cribb
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 2023
Copyright 2023 Alison Taveau Cribb
ii
Dedication
This dissertation is dedicated to Dr. Molly F. Miller,
whose life and work has been a constant source of inspiration for my own.
iii
Acknowledgements
I do believe it takes a village to raise a dissertation, and this work would not exist if not for the
endless support I have received from mentors, peers, and family over the last five (plus) years.
First, I am endlessly grateful for my advisors, Frank Corsetti and Dave Bottjer. I truly
could not have asked for better mentorship and guidance throughout my PhD. Frank and Dave
have always encouraged me to keep pushing this research to new boundaries and, without them,
I certainly would not have developed or pursued the research in this dissertation. I am endlessly
thankful for their trust in me, both in letting me explore questions that were sometimes a little out
of left field, and for trusting me to do so sometimes on the other side of the world.
I have also been lucky to have had excellent dissertation and qualifying exams committee
members. I could not have undertaken this research without the support and guidance from Will
Berelson, who has never failed to ask me a question so thought provoking it pushed my research
to new levels, and whose cheering on has helped me face some of the toughest parts of this PhD
journey. I am extremely thankful to Naomi Levine, whose Modeling and Numerical Techniques
for Marine Sciences course and guidance during my qualifying exams gave me the confidence
and skills to do the more analytical portions of this dissertation. I am also thankful to Josh West,
who helped sharpen my geochemistry skills, and whose lessons about the scope and limits of
modeling in the Earth sciences shaped a lot of my philosophies about research in general.
I am so grateful to have received support from my mentors outside of USC as well. I am
especially thankful for Simon Darroch, who not only got me interested in trace fossils in my
undergraduate course and supervised my thesis research at Vanderbilt, but also has remained an
invaluable mentor. I would not be who I am as a scientist today if not for his support over the last
eight years. I am also grateful for the mentorship and guidance from Sebastiaan van de Velde,
iv
who I cannot thank enough for the endless emails helping me troubleshoot reactive-transport
models, for the inter-continental Zoom calls helping me with never ending reviewer comments,
for believing me when I said that carrying rocks up and down Andrews Mountain was a
worthwhile effort, and for trusting my questionable off-road driving.
I have had the immense pleasure of having the smartest, coolest, most supportive friends
at USC that I could possibly imagine. A special shoutout goes to the brilliant Kiersten Formoso,
who started this PhD journey with me in 2018, and has become one of my greatest friends in the
world. Kiersten has been a cheerleader for me at every step of the way throughout our PhDs, and
I feel so privileged to have partnered with her on the project that eventually became the fourth
chapter in this dissertation. Huge thanks also to Tori Cassady, who was the best roommate I
could have asked for during the pandemic, and whose unwavering support really helped me push
through many rough spots. Many thanks also to Hank Woolley, Amanda Godbold, and Katya
Larina, especially for their support early in my PhD that gave me the motivation to tackle big
questions. And thank you to James Beech, Shannon Brophy, Paul Byrne, Peter Maxeiner, Reena
Joubert, and Becky Wu, who are all brilliant, inspiring lab mates.
My PhD experience is, like so many others, split clearly into before-and-after March
2020. One of the silver linings amidst the horror that was the Covid-19 pandemic was the
incredible group of people I lived and pandemic-podded with in Palo Alto in 2020 and 2021. It
turns out things aren’t too terrible during the end of the world when you spend a pandemic with a
group of brilliant, supportive, inspiring, hilarious friends and scientists. Aaron Steelquist,
Mitchell Watt, Marisa Mayer, Matt Malkowski, Tom Boag, Elisa Hofmeister, and Tamara
Kahale are friends who I will always be indescribably grateful for. And, of course, much love
and thanks go to Frida (aka, the Palo Alto Princess).
v
I have also had the immense privilege to have spent the last year of my PhD as a visiting
academic at the University of Southampton’s School of Ocean and Earth Sciences. Many thanks
go to Tom Ezard and Paul Wilson, who have provided the mentorship and support that kept me
pushing this research and developing ideas for future projects over the last year. I am also so
thankful for the Fossil Hunting Friends in Southampton – Kerri, Lauren, Stephen, Ryan, Carin,
Chris, Phil, and Jeff – who all made the inter-continental move to England much more fun and
far less stressful.
I am, of course, incredibly thankful for my family’s support. I would not have pursued a
PhD without the support and guidance of my dad, who has always trusted me and cheered me on.
I have benefited immensely from the privilege of being his daughter, not only for the literal 27
years of geology lessons (so far), but also for the 27 years of life lessons that have taught me that
there is no mountain too high to tackle. My endless thanks also go to Caitlin for the kind of
unwavering support only sisters can really give, and to my niece, Annabelle, for being a constant
source of joy. I am truly eternally grateful for Morgan Bowling, who has been my best friend and
chosen family for, I often think, as long as our past lives must go back together, and whose
unwavering support has often been the only thing holding my sanity together (so much support
that one time a tweet went viral specifically about her hyping me up). Finally, endless thanks go
to Caroline, John, and Chris Stockey and Chuckie Rockett for welcoming me with open arms
into their family and always supporting me, particularly over the last several months.
There are many other people and groups I would be remiss to not acknowledge and
thank. To Emily Sharp and Bryce Koester for the support (emotional and in-the-field) over the
last eight years. To Kat Turk, who shares my love for weird worms and has been an incredible
friend and collaborator over the last five years. To Rachel Racicot, Charlotte Kenchington, Marc
vi
Laflamme, Katie Maloney, and Brandt Gibson for the support that has gotten me through long
drives and days in Namibia (and beyond). To Aaron Celestian for access to the µXRF at the
NHMLA and for help with analyzing the trace fossil samples. To Darlene Garza, Karen Young,
Alex Aloia, Vardui Ter-Simonian, John Yu, and Steve Lin for providing all of the logistical
support without which I suspect my entire PhD experience would have fallen apart. To the
members of the E6 NSF RCN, who have inspired a lot of the ideas I have about ecosystem
engineering. And finally to the Geological Society of America, the Society for Sedimentary
Geologists (SEPM), the USC Women in Science and Engineering (WiSE) chapter, and the USC
Graduate School for funding throughout my PhD.
And last, but definitely not least – all of my gratitude and love go to my husband and my
partner in everything, Richard Stockey. He was the first to hear any and every idea I have had
about this dissertation, the first person I talked with to go through the weeds of every problem I
ran into, the first person to cheer me on no matter what I was facing. Rich has been an
unwavering source of support and collaboration throughout my PhD, helping me face every
bump along the road no matter how big or small, always advocating for me, and always
reminding me that I can tackle any question I put my mind to. This dissertation absolutely would
not be what it is without him.
vii
Table of Contents
Dedication ....................................................................................................................................... ii
Acknowledgements ........................................................................................................................ iii
List of Tables .................................................................................................................................. x
List of Figures ............................................................................................................................... xii
Abstract ......................................................................................................................................... xv
I. Introduction .............................................................................................................................. 1
Ecosystem engineering: A history of concepts and definitions .................................... 2
Bioturbation: From Darwin’s vegetable garden to the Ediacaran seafloor .................. 8
Dissertation purpose and significance ........................................................................ 14
Figures ........................................................................................................................ 17
References ................................................................................................................... 19
Chapter 1: Ediacaran-Cambrian bioturbation did not extensively oxygenate sediments
in shallow marine ecosystems .................................................................................................... 25
Abstract ..................................................................................................................................... 25
1.1 Introduction .................................................................................................................. 25
1.2 Methods ........................................................................................................................ 29
Trace fossil dataset and bioturbation parameterization ....................................... 30
Reaction-transport model formulation .................................................................. 33
Modeling experiments and analyses ..................................................................... 36
1.3 Results .......................................................................................................................... 39
The Ediacaran-Cambrian trace fossil record of biomixing and bioirrigation
intensity ........................................................................................................................ 39
Sensitivity analyses of chosen biodiffusion and bioirrigation coefficients ........... 40
Sensitivity analyses of the OPD to bioturbation intensities .................................. 43
Impact of organic matter flux on the effect of bioturbation .................................. 44
Bioturbation intensities and oxygen consumption rates ....................................... 46
1.4 Discussion .................................................................................................................... 47
Geobiological implications for Ediacaran-Cambrian transition trace fossils ..... 47
Impact of Ediacaran-Cambrian bioturbation on sedimentary oxygen dynamics . 51
Potential environmental controls on benthic oxygen dynamics ............................ 56
1.5 Conclusions .................................................................................................................. 58
1.6 Tables and figures ........................................................................................................ 60
1.7 References .................................................................................................................... 68
Supplementary figures .............................................................................................................. 75
Supplementary tables ................................................................................................................ 83
Supplementary references ......................................................................................................... 93
viii
Chapter 2: Characterization of early Cambrian bioturbation ecosystem engineering
behaviors in the Deep Spring Formation, California, USA ................................................ 94
Abstract ..................................................................................................................................... 94
2.1 Introduction .................................................................................................................. 95
2.2 Geologic setting ........................................................................................................... 99
Previous ichnological studies ............................................................................. 100
2.3 Methods ...................................................................................................................... 101
2.4 Results ........................................................................................................................ 103
Ichnology of the Deep Spring Formation ........................................................... 103
Descriptive geochemistry of biomixers ............................................................... 106
Descriptive geochemistry of bioirrigators .......................................................... 106
Descriptive geochemistry of unbioturbated sediment laminae ........................... 111
Identification of distinct geochemical characteristics ........................................ 112
2.5 Discussion .................................................................................................................. 113
Ecosystem engineering impact of Deep Spring Formation bioturbators............ 113
Identifying biomixing versus bioirrigation trace fossils ..................................... 116
2.6 Conclusions ................................................................................................................ 118
2.7 Figures ........................................................................................................................ 120
2.8 References .................................................................................................................. 129
Supplementary tables .............................................................................................................. 135
Chapter 3: Complex marine bioturbation ecosystem engineering behaviors persisted in
the wake of the end-Permian mass extinction ........................................................................ 136
Abstract ................................................................................................................................... 136
3.1 Introduction ................................................................................................................ 137
3.2 Methods ...................................................................................................................... 139
Ecosystem engineering behaviors and trace fossil data collection .................... 139
Ecosystem engineering occupation cubes ........................................................... 140
Ecosystem engineering impact values ................................................................. 141
3.3 Results ........................................................................................................................ 142
3.4 Discussion .................................................................................................................. 144
Persistence of high-impact ecosystem engineering behaviors ............................ 144
Collapse of bioturbation depth in the Early Triassic .......................................... 145
Implications for ecosystem recovery ................................................................... 147
3.5 Conclusions ................................................................................................................ 149
3.6 Figures ........................................................................................................................ 150
3.7 References .................................................................................................................. 153
Supplementary figures ............................................................................................................ 158
Supplementary tables .............................................................................................................. 159
Supplementary references ....................................................................................................... 168
Chapter 4: Contrasting terrestrial and marine ecospace dynamics after the
end-Triassic mass extinction event .......................................................................................... 171
Abstract ................................................................................................................................... 171
4.1 Introduction ................................................................................................................ 171
4.2 Marine and terrestrial ecospace framework ............................................................... 174
4.3 Dataset assembly and analyses................................................................................... 176
ix
4.4 Results ........................................................................................................................ 178
Occupation of marine and terrestrial ecospace .................................................. 178
Taxonomic and functional richness .................................................................... 179
Within-functional group dynamics ...................................................................... 179
Within-functional group extinction severity ........................................................ 182
Ecological dissimilarity through time ........................................................................ 183
4.5 Discussion .................................................................................................................. 184
4.6 Conclusions ................................................................................................................ 192
4.7 Figures ........................................................................................................................ 193
4.8 References .................................................................................................................. 199
Supplementary figures ............................................................................................................ 203
Supplementary tables .............................................................................................................. 207
Chapter 5: Conclusions ............................................................................................................ 209
5.1 Dissertation overview ................................................................................................ 209
5.2 Local versus global trends in bioturbation ecosystem engineering ........................... 210
5.3 Ecosystem engineering in deep time: Bioturbation and beyond ................................ 212
5.4 References .................................................................................................................. 215
Bibliography ............................................................................................................................... 217
x
List of Tables
Table 1.1 Ediacaran and Terreneuvian ichnogenera. .................................................................... 60
Supplementary tables
Table S1.1 Biogeochemical reaction set used in model. .............................................................. 83
Table S1.2 Kinetic rate expressions for the reactions used in the model. .................................... 84
Table S1.3 Kinetic constants for reactions used in the model. ..................................................... 85
Table S1.4 Reaction rates for consumption and production of chemical species. ........................ 86
Table S1.5 Model parameters used in all simulations and experiments. ...................................... 87
Table S1.6 Boundary conditions for Figure 1.2 and S1.1 simulations. ........................................ 88
Table S1.7 Boundary conditions for Figure 1.3 and 1.4 simulations. .......................................... 89
Table S1.8 Boundary conditions for Figure 1.5 simulations. ....................................................... 90
Table S1.9 Boundary conditions for oxygen consumption simulations. ...................................... 91
Table S1.10 Parameterization of bioturbation coefficients........................................................... 92
Table S2.1 Elemental spectra for each sample and area. ............................................................ 135
Table S3.1 Tiering analysis......................................................................................................... 159
xi
Table S3.2 Ecosystem engineering group occupation analysis. ................................................. 160
Table S3.3 Dataset of trace fossil occurrences, EEI scores, and functional group
assignments used in analyses. ..................................................................................................... 161
Table S4.1 Marine ecospace tiering, motility, and feeding categories. ...................................... 207
Table S4.2 Terrestrial ecospace tiering, motility, and feeding categories. ................................. 208
xii
List of Figures
Figure I.1 Space-time diagram for modern ecosystem engineers. ................................................ 17
Figure I.2 Process- and outcome-based feedbacks for bioturbation ecosystem engineering. ...... 18
Figure 1.1 Relative abundance of biomixing and bioirrigation .................................................... 62
Figure 1.2 Sensitivity analyses of biodiffusion coefficients. ........................................................ 63
Figure 1.3 Simulated OPDs at increasing bioturbation intensities. .............................................. 64
Figure 1.4 Differences in OPD simulations between biomixing and bioirrigation. ..................... 65
Figure 1.5 Effect of organic carbon flux on the OPD. .................................................................. 66
Figure 1.6 Impact of bioturbation intensities on oxygen consumption rates. ............................... 77
Figure 2.1 Geologic setting of the Deep Spring Formation, White-Inyo Region,
California, USA. ......................................................................................................................... 120
Figure 2.2 Trace fossils and typical bedding-plane ichnofabrics from the Deep Spring
Formation at Hines Ridge and Andrews Mountain. ................................................................... 121
Figure 2.3 Morphological diversity of Treptichnus pedum from the Deep Spring Formation ... 122
Figure 2.4 Elemental maps of biomixing, Sample 19-HR-DS-3 ................................................ 123
Figure 2.5 Elemental maps of biomixing, Sample 19-HR-DS-R1. ............................................ 124
Figure 2.6 Elemental maps of T. pedum bioirrigation, Sample 19-HR-DS-1............................. 125
Figure 2.7 Elemental maps of T. pedum bioirrigation, Sample 19-AM-DS-9 ............................ 126
xiii
Figure 2.8 Elemental maps of unmixed sedimentary laminae, Sample 19-AM-DS-19 ............. 127
Figure 2.9 NMDS analyses for elemental composition of trace fossil samples ......................... 128
Figure 3.1 Relative abundance of burrow tiering across the Permian-Triassic .......................... 150
Figure 3.2 Ecosystem engineering occupation cubes ................................................................. 151
Figure 3.3 Ecosystem engineering impact (EEI) values and ranges ........................................... 152
Figure 4.1 Ecospace cubes and taxonomic occupation ............................................................... 193
Figure 4.2 Generic and functional group richness. ..................................................................... 194
Figure 4.3 Relative abundance of marine functional groups. ..................................................... 195
Figure 4.4 Relative abundance of terrestrial functional groups .................................................. 196
Figure 4.5 Within-functional group extinction severity ............................................................. 197
Figure 4.6 Bray-Curtis dissimilarity indices through the ETE ................................................... 198
Supplementary figures
Figure S1.1 Biodiffusion and bioirrigation depth profiles ............................................................ 75
Figure S1.2 Sensitivity analyses for bioirrigation coefficients ..................................................... 76
Figure S1.3 Oxygen consumption rates for different bioturbation parameters ............................ 77
Figure S1.4 Analysis of oxygen flux to the sediment ................................................................... 78
xiv
Figure S1.5 Organic matter burial simulations. ............................................................................ 79
Figure S1.6 Sediment profile simulations ..................................................................................... 80
Figure S1.7 Low oxygen end-member comparisons with SedChem ............................................ 81
Figure S1.8 High oxygen end-member comparisons with SedChem ........................................... 82
Figure S3.1 Ecosystem engineering occupation cubes for the entire dataset ............................. 158
Figure S4.1 Extinction severity versus functional group occupancy and generic richness
for the marine .............................................................................................................................. 203
Figure S4.2 Terrestrial paleoenvironments in each stage. .......................................................... 204
Figure S4.3 Marine rarefaction curves........................................................................................ 205
Figure S4.4 Terrestrial rarefaction curves .................................................................................. 206
xv
Abstract
The evolution of marine bioturbation is considered one of the most important events in
Earth history. This dissertation explores the evolutionary history and geobiological role of
bioturbators as ecosystem engineers that impacted the habitability of their environments. First, I
tested longstanding hypotheses that early bioturbators during the Ediacaran-Cambrian transition
significantly increased oxygen concentrations in shallow marine sediments. By using
biogeochemical models that incorporate ecologically-informed bioturbation parameters, I found
that early bioturbators were unlikely to have been capable of oxygenating their sedimentary
environments. Second, I investigated whether bioturbation behaviors with variable impacts on
sediment biogeochemistry could be geochemically identified. Although statistically unique
geochemical signatures did not emerge, differences in concentrations of certain elements in and
out of burrow structures indicate that the bioturbators were strong enough to have impacted early
diagenetic processes. Third, I investigated the impact of the end-Permian mass extinction on
bioturbation ecosystem engineering behaviors. I found that the ecosystem engineering strategies
which would have been most effective at maintaining resource availability persisted in the wake
of the extinction event. Finally, I investigated how functional ecology in terrestrial and marine
ecosystems changed across the end-Triassic mass extinction event, using a novel terrestrial
functional ecology framework that allows for direct comparison with the traditional frameworks
used in marine paleoecology. These results indicate that terrestrial ecosystems were much slower
to return to ecological stability than marine ecosystems. Overall, this dissertation provides new
insights into the geobiological role of bioturbators as ecosystem engineers during major
evolutionary intervals in Earth's history.
1
I. Introduction
The nature of life on Earth today is a result of the continuous co-evolution of life and
Earth systems processes that has occurred over billions of years. The Earth’s physical systems
(e.g., the hydrosphere, lithosphere, atmosphere) and its biosphere interact as synergistic
components of a larger, ever-evolving complex system – a concept most famously invoked by
the Gaia Hypothesis (Lovelock, 1972; Lovelock and Margulis, 1974). In this framework,
Lovelock (1995) defined the co-evolution of life and the Earth: “Biota influence their abiotic
environment, and that environment in turn influences the biota by Darwinian process.”
The latter half of Lovelock’s definition has been thoroughly investigated by
paleobiologists and geochemists interested in reconstructing the history of life on Earth. This is
most obvious in the abundance of literature linking together the fossil, sedimentological, and
geochemical records in order to understand how ancient environmental change (e.g., global
warming, ocean acidification, sea level rise) has impacted macroevolutionary processes. That the
environment influences biota and drives macroevolutionary processes is glaringly evident in the
face of modern and ancient climate change. Today, a rapidly warming planet is causing higher
extinction rates and permanently altering the state of Earth’s biosphere as it responds to
environmental stress from climate change, triggering Earth’s sixth mass extinction event
(Barnosky et al., 2011, 2012). Throughout Earth history, as well, biodiversity has at multiple
times dramatically plummeted in response to global climate change, and the structure of Earth’s
ecosystems at times has overturned so dramatically that new evolutionary faunas emerged (Raup
and Sepkoski, 1982; Sepkoski and Miller, 1986).
2
The former clause of Lovelock’s definition – that biota, in turn, influence their abiotic
environments – has often been overlooked in paleobiology as an important process in the drivers
of evolutionary dynamics. This is not to say that biotic interactions are under appreciated in
terms of their influence on macroevolutionary processes. For example, the co-evolution of
predators and their prey has long been considered an evolutionary “arms race” of sorts, resulting
in escalation that led to the occupation of new ecological niches, increased trophic complexity,
and new evolutionary innovations (e.g., Dawkins, 1986; Vermeij, 1987, 1994). However, direct
interactions of organisms with their environments are also important drivers of
macroevolutionary processes. The evolution of oxygenic photosynthesis clearly fundamentally
and permanently altered the Earth systems processes, as an oxygenated atmosphere and ocean
paved the way for a planet ripe to support complex ecosystems comprised of diverse, aerobic
animals with mobile, active ecological lifestyles. The evolution of calcifying taxa, such as
calcareous algae or carbonate-skeletal marine animals, impacted the physical Earth system so
profoundly that entirely new sedimentary rock types evolved – of which many deposits have
persisted for millions and millions of years to now serve as habitats for animals currently alive
on Earth (Erwin, 2008). Clearly, life is a powerful geological force, and the evolutionary history
of organism-environment interactions and feedbacks are a key part of understanding the history
of life on Earth.
Ecosystem engineering: A history of concepts and definitions
That animals interact with and influence their environments on a variety of scales, even
modifying their habitats for their own benefit, has been recognized for decades in evolutionary
biology and evolutionary ecology. In his now seminal paper on niche construction, Richard
3
Lewontin (1978) stated that “organisms do not experience environments passively; they create
and define the environment in which they live,” and further argued that there is “a constant
interplay of the organism and the environment, so that although natural selection may be
adapting the organism to a particular set of environmental circumstances, the evolution of the
organism itself changes those circumstances.” Lewontin (1978) therefore emphasized the critical
role of interplaying organism-environment interactions in key evolutionary ecological processes,
including niche construction. This idea also relates directly to Richard Dawkins’s concept of the
extended phenotype, as the environmental change caused by animals that modify their habitats is
an extension of the animals’ phenotypes (Dawkins, 1982). A beaver dam is perhaps the most
famous example of Dawkins’s extended phenotype. The dam and lake that beavers instinctually
construct is a clear organism-environment interaction and influences the fitness of the beavers
and other organisms in their environments (Dawkins, 1982). Through these key concepts in
evolutionary ecology – including niche construction and extended phenotypes – it has become
well-established that many animals play a major role in the creation and modification of
habitable space in their ecosystems.
The concept that animals interact with their environments to modify, create, and destroy
habitable space was formally defined as ecosystem engineering by Jones et al. (1994). Here,
Jones et al. (1994) defined ecosystem engineering as the behaviors or activities or organisms that
“directly or indirectly modulate the availability of resources (other than themselves) to other
species),” thereby resulting in the modification, maintenance, and/or creation of new habitats
within an ecosystem. Just as beaver dams are a classic example of the extended phenotype,
beavers are also quintessential examples of ecosystem engineers. Beavers construct their own
habitable environments that, in turn, impact the available habitable space for a wide variety of
4
plants and animals, as well as influence key Earth systems processes including carbon
sequestration and nutrient cycling (Jones et al., 1994). Jones et al. (1994) also defined two
categories of ecosystem engineers: autogenic and allogenic. Autogenic ecosystem engineers are
those that modify their environments via their own physical structures (excluding cases in which
the organism’s body itself is the physical structure, such as the trunks of trees serving as habitats
for cavity nesters). Coral reef-builders are arguably the ocean’s most important autogenic
ecosystem engineers. Their reef structures protect the environment from wave and current stress,
modulate sedimentation rates, and influence local marine biogeochemistry, thereby creating
unique habitable reef ecosystems that are hotspots for biodiversity today (Jones et al., 1994; Wild
et al., 2011). Allogenic ecosystem engineers, in contrast, are animals that transform materials in
their environments from one state to another. This can be as straightforward as the classic
example of beavers transforming dead trees into dams, or as biogeochemically complex as plants
increasing soil and rock weathering rates, thereby converting stable substrates into smaller
weathered geochemical materials (Jones et al., 1994).
These ecosystem engineering definitions from Jones et al. (1994) provided an important
and fundamental classification system for the early study of ecosystem engineering in ecology.
However, the clear boundaries of these definitions tend to break down in reality – which Jones et
al. (1994) did themselves highlight – and thus the precise definition and scope of ecosystem
engineering has been debated over the last three decades (e.g., Reichman and Seabloom, 2002b,
2002a; Wright and Jones., 2006; Berke, 2010). For example, in the attempts to move ecosystem
engineering beyond “just-so” stories (Berkenbusch and Rowden, 2003), debates ensued
regarding whether ecosystem engineering should be treated as process- or outcome-based
(Berke, 2010). A process-based framework of ecosystem engineering focuses on the direct
5
interactions between organisms and their environments and classifies any animal that directly
influences its physical environment as an ecosystem engineer (Jones et al., 2010). On the other
hand, an outcome-based framework of ecosystem engineering focuses on ecosystem-level effects
and changes to community ecology, and thus limits ecosystem engineers to animals whose direct
interactions with the environment also result in some observable ecological change (Reichman
and Seabloom, 2002b, 2002a).
Clearly, there are pros and cons to both the process- and outcome-based frameworks. A
process-based framework of ecosystem engineering may be overly broad, as nearly all organisms
impact their environments in some manner and scale, and therefor may render the concept of
ecosystem engineering essentially useless (Reichman and Seabloom, 2002a). An outcome-based
framework is more specific, in that it limits ecosystem engineers to only those animals that cause
environmental change that results in ecological effects for the organisms around them.
Identifying ecosystem-level changes can be difficult given the temporal scale of ecosystem
engineering impacts and difficulties in disentangling cause-and-effect relationships, so a process-
based definition may be preferable because it removes the burden of identifying statistically
significant ecological outcomes (Berke, 2010). A pure outcome-based framework also ignores
the environmental feedbacks that are so critical for the ecosystem engineering concept, and thus
outcome-based ecosystem engineering frameworks may become indistinguishable from keystone
species (Wright and Jones., 2006; Berke, 2010). Specific field-based studies of modern
ecosystem engineers most often fall into the process-based definition. Ecologists have often
sought to identify the environmental modifications that occur when ecosystem engineers are
introduced to the environment, such as the environmental modifications that directly occur from
ecosystem engineers that ameliorate environmental stressors (Byers et al., 2006). Larger studies
6
identifying the extent to which ecosystem engineers impact biodiversity – which is a critical
component of understanding their importance in evolutionary ecology – inherently rely on the
outcome-based definition (Romero et al., 2015; Guy‐Haim et al., 2018). The debate between
process- and outcome-based definitions of ecosystem engineering remains unresolved. In many
ways, they represent end-members and, in an ideal world, some combination of the two would
allow researchers to best identify cause-and-effect relationships at the ecosystem level.
Several other criticisms of ecosystem engineering have arisen over the years. For
example, assimilation (the conversion of vitamins, nutrients, and other bioavailable chemicals
into food) and dissimilation (the catabolic reactions that break down food to release energy) were
initially excluded from Jones et al.’s (1994) original definition of ecosystem engineering. Jones
et al. (1994) and others who exclude assimilation and dissimilation argued that this falls under
the umbrella of direct trophic interactions (which do not count as ecosystem engineering).
However, one can also argue that assimilation and dissimilation fall under the umbrella of
allogenic ecosystem engineering, as materials are transformed from one state to another.
Excluding assimilation and dissimilation from ecosystem engineering neglects the importance of
many so-called chemical engineers, which play important roles in creating biogeochemical
gradients (Berke, 2010). Researchers have also argued whether or not ecosystem engineering
should be scale-independent (Hastings et al., 2007; Berke, 2010). Modern ecosystem engineers
operate on a variety of temporal and spatial scales, from the small, ephemeral sedimentary
depressions excavated by stingrays in tidal environments to reef structures that persist on
geologic timescales (Figure I.1). Some have argued for a scale-dependent ecosystem engineering
definition, constraining the definition to only those animals that have very large effects relative
to their biomass, or much longer legacy effects than their lifespans. However, a more holistic,
7
scale-independent view of ecosystem engineering can reveal the importance of fine-scale effects
from small ecosystem engineers such as ants and termites, and expands ecosystem engineering
beyond the biggest, most charismatic animals (e.g., beavers, coral reef-builders, whales)
(Hastings et al., 2007).
Although this dissertation does not seek to redefine ecosystem engineering for the fossil
record, it is useful to briefly discuss the aspects of the history of ecosystem engineering
definitions and concepts that are most useful in the context of the fossil record. So much
ecological and environmental information is lost in the fossil record to taphonomic processes.
Direct biotic interactions are rarely preserved, biotic-abiotic interactions are generally limited to
animal-substrate interactions (such as those discussed here), and it is immensely difficult to
identify cause-and-effect relationships between ecosystem engineers, their environments, and
any ecological change. The ecosystem engineering concepts discussed here are largely process-
based, such that I have sought to constrain the sedimentological and geochemical impacts of
ecosystem engineers rather than directly investigating their impacts on paleoecology. However, a
unique power of the fossil record in comparison to modern ecological studies is the ability to
investigate long-term and large-scale ecological change. In other words, the fossil record can
allow us to investigate a combination of process- and outcome-based ecosystem engineering,
which I have attempted to address in discussions in the following chapters. Additionally, the
ecosystem engineering processes observed in the fossil record are inherently large in terms of
spatial and temporal scale. This is not necessarily because the effects have lasted hundreds of
millions of years in the rock record – although such is the case for the organisms that have
created modern shell beds (Erwin, 2008). Rather, outside of any ecosystem engineering observed
in lagerstätten, the processes must have occurred on a large enough temporal and/or spatial scale
8
to overcome the taphonomic filters that erase so many small-scale, ephemeral organism-
environment interactions from the rock record. In many ways, this is a simple question of
statistical likelihood – a single worm burrow is unlikely to survive taphonomy, but out of a
thousand worm burrows, perhaps one of them can be observed in the rock record today. There is,
therefore, likely a spatial, temporal, and density limit that defines ecosystem engineering that is
and is not preserved in the rock record, and this limit sets fossil record ecosystem engineering
apart from modern ecosystem engineering.
Bioturbation: From Darwin’s vegetable garden to the Ediacaran seafloor
Bioturbation refers to the behaviors and activities of animals that rework marine and
terrestrial sediments. Bioturbation has long been recognized as a major ecosystem engineering
behavior, well before ecosystem engineering was formally recognized and defined. Prior to the
publication and success of On the Origin of Species, Charles Darwin published three papers on
the role of earthworms in creating and changing the structure of soils: “On the formation of
mould” (Darwin, 1838), “On the formation of mould” (Darwin, 1840), and “On the origin of
mould” (Darwin, 1844). Darwin became particularly interested in the ways in which earthworms
contributed to soil displacement and impacted the formation of soil profiles, noting particularly
the ways in which earthworms were able to rapidly bury dead plant material and even stones. His
interest in earthworms ultimately culminated into the topic of Darwin’s last published book in
1881: The Formation of Vegetable Mould Through the Action of Worms with Observation of
Their Habits . Unfortunately, Darwin’s studies of earthworms and soil formations did not excite
his contemporaries (Darwin, 1887). Prior to publication of the book, he wrote to a colleague, “As
far as I can judge, it will be a curious little book. The subject has been to me a hobby-horse, and I
9
have perhaps treated it in foolish detail.” (Darwin, 1887). To his surprise, however, the book was
a great commercial and scientific success, with over 8500 copies sold in the first three years
(surpassing early sales of Origin of Species) (Darwin, 1887). The book and his initial
publications remain heavily cited across a variety of fields, including soil scientists, ecologists,
geomorphologists, paleontologists, hydrologists, and archeologists (Meysman et al., 2006). Since
Darwin’s first publication on bioturbation in the 1830s, it has become abundantly clear that
bioturbating organisms have impacted nearly all of Earth’s substrates, from forest soils to marine
sediments. Bioturbators are, and have long been, one of the most powerful ecosystem engineers
on Earth.
Marine bioturbation, in particular, has been recognized as one of the most important
ecosystem engineering behaviors in the ocean (Jones et al., 1994). Marine bioturbators act as
ecosystem engineers to modify resource availability in their ecosystems in two ways: by directly
impacting nutrient flows and by changing the physical properties of the substrate and sediment-
water interface (Figure I.2). For example, bioturbators can change the physical nature of their
environments simply in the creation of their burrows, which may be lined with organic matter
and influence the mechanics of the surrounding sediment grains, or by changing the porosity and
water content of the sediment to impact its rheological properties (Meysman et al., 2006; Tarhan,
2018). The physical structure of the burrow itself is also important, as large burrows increase the
surface area for important biogeochemical reactions (Aller, 1983; Lohrer et al., 2004; Huettel et
al., 2014); this is a particularly important interaction between bioturbation’s impact on the
physical properties of the sediment and its impact on nutrient flows (Figure I.2). Bioturbation can
also directly impact resource flows by influencing the structure of the sedimentary redox
gradient, thereby impacting benthic biogeochemical cycling of major nutrients such as nitrogen,
10
phosphorus, and organic matter (Aller, 1982; McIlroy and Logan, 1999). Bioturbation can
further stimulate or stymy microbial activity, either by changing the supply and bioavailability of
organic matter (Aller, 1994) or by creating biogeochemical micro-niches within the sediment
around the burrow structure (Bertics and Ziebis, 2010). Changes to these resource flows can also
directly impact the bioturbator itself, particularly if the bioturbation helps to ameliorate
inhospitable environmental conditions (e.g., burrow ventilators flushing toxic reduced
compounds out of the sediment). Finally, these effects on nutrient flows and the physical
characteristics of the environment ultimately translate to changes in resource availability, which
in turn can significantly affect the community structure in the ecosystem, as the ecological
structure is a function of resource availability for animals at various life stages (Figure I.2).
Then, because the individual or population of bioturbators is sensitive to ecological change as a
member of the ecosystem, the bioturbating ecosystem engineer may be a part of that biotic
response to its own activities (Figure I.2). These interacting feedback loops between the
bioturbating ecosystem engineer(s), the abiotic resource availability, and the ecological structure
of the ecosystem constitute a combined process- and outcome-based ecosystem engineering
framework (Darroch, Cribb et al., 2020) (Figure I.2). The best documented modern example of
this framework in action is, perhaps, observations of trophic group amensalism between
suspension feeders and deposit feeders, in which the bioturbation activities of deposit feeders can
profoundly shift the ecology of the benthic ecosystem by excluding suspension feeders from the
seafloor (Rhoads and Young, 1970).
Given the power of bioturbation as an ecosystem engineering force in the oceans today, it
is reasonable to assume that the evolutionary history of bioturbation is important when
considering the trajectory of the evolution of marine ecosystems. Bioturbation has long been
11
implicated in the Ediacaran-Cambrian transition (~571-509 Mya), which spans some of the most
important evolutionary events in Earth history, including the fall of the Ediacara Biota and the
subsequent rise of complex animal life in the Cambrian explosion (Darroch, Cribb et al., 2020).
Bioturbation was most famously implicated in driving some aspects of the Ediacaran-Cambrian
transition in the Agronomic Revolution (Seilacher and Pflüger, 1994) and Cambrian Substrate
Revolution (Bottjer et al., 2000). The Agronomic Revolution and Cambrian Substrate Revolution
both hypothesized that early bioturbation in the late Ediacaran and early Cambrian created
seafloor substrates that were well-suited for the ecological structure of complex benthic
ecosystems later in the Cambrian (Seilacher and Pflüger, 1994; Seilacher, 1999; Bottjer et al.,
2000). The primary mechanism invoked for driving the changes observed across the Ediacaran-
Cambrian boundary has long been the turnover from microbial matground seafloors in the
Ediacaran to mixground seafloors in the Cambrian (Seilacher and Pflüger, 1994; Seilacher, 1999;
Bottjer et al., 2000). During the Ediacaran, shallow marine seafloors are thought to have been
predominantly covered in microbial mats, evident in the sedimentological record of MISS fabrics
and wrinkle structures during the Ediacaran Period. The presence of matgrounds in Ediacaran
shallow marine environments stands in contrast to Cambrian shallow marine paleoenvironments,
which typically lack the same microbial mat-related sedimentological features, indicating the
decline of microbial mats on shallow marine seafloors through the Ediacaran-Cambrian
transition (Hagadorn and Bottjer, 1997; Seilacher, 1999; Bottjer et al., 2000; c.f. Davies et al.,
2016). Bioturbation has been suggested as the driver of this shift. Bioturbation may have
destabilized substrates and mixed away the microbial mats, ultimately producing a
biogeochemically active sedimentary mixed layer (Bottjer et al., 2000; Mángano and Buatois,
2020).
12
The shift from Ediacaran matgrounds to Cambrian mixgrounds was a major ecosystem
engineering event in terms of both environmental processes and ecological outcomes. At no
other point in Earth history has a group of ecosystem engineers so profoundly impacted the
nature of the seafloor (Herringshaw et al., 2017). Moreover, this matground to mixground shift
seems to have resulted in profound turnovers in benthic ecology. Some evidence suggests that
bioturbation driving the disappearance of microbial mats may have been the final nail in the
coffin for the Ediacara Biota, which were specially adapted for microbial matground seafloors
(Laflamme et al., 2013). The removal of microbial mats likely exacerbated stress on a
depauperate global population that was already threatened by unstable ocean redox conditions
(Darroch et al., 2018; Zhang et al., 2019; Cribb et al., 2019). Previous researchers have also
argued that the evolution of more complex vertical bioturbation and the removal of microbial
mats expanded the physical habitable zone in benthic ecosystems, which allowed animals to live
increasingly further below the sediment-water interface (McIlroy and Logan, 1999; Mángano
and Buatois, 2017) and opened up new ecological niche space (Erwin, 2008; Erwin and Tweedt,
2011). The evolution of strong, complex bioturbation and the formation of a sedimentary mixed
layer has also been suggested as a driver for stimulated biogeochemical cycles, notably the
global sulfur (Canfield and Farquhar, 2009; Tarhan et al., 2015) and phosphorus cycles (Tarhan
et al., 2021).
Bioturbation has also been studied in the context of benthic recovery after mass
extinction events throughout the Phanerozoic. In reconstructing how the sizes, depths, and
diversity of trace fossils changed during mass extinction intervals, previous researchers have
identified a number of clear patterns. Trace fossils tend to consistently decrease in size, depth,
mixing intensity (ichnofabrics), and the number of types of trace fossils (ichnodiversity) in the
13
wake of mass extinctions, and gradually return to their pre-extinction characteristics through the
biotic recovery interval (Twitchett and Barras, 2004). The most dramatic case of this occurred in
the wake of the end-Permian mass extinction (~252 Mya). The end-Permian mass extinction was
the most devastating mass extinction of marine life in the Phanerozoic (Raup and Sepkoski,
1982; Alroy et al., 2008; Alroy, 2008), driven by ocean warming, anoxia, and acidification
resulting from greenhouse gas emissions from the Siberian Large Igneous Province (Payne et al.,
2004; Clarkson et al., 2015; Penn et al., 2018). Trace fossils, which record the activity and
behaviors of typically soft-bodied benthic animals that are not well preserved as body fossils,
indicate severe extinctions on the Early Triassic seafloor. Bioturbation size, depth, and intensity
dramatically decreased, and the sedimentary mixed layer seems to have almost entirely collapsed
in the Early Triassic (Hofmann et al., 2011, 2015; Chen and Benton, 2012). However,
bioturbation has rarely been studied in the context of ecosystem engineering during mass
extinction intervals. This is perhaps a surprising research gap, given that ecosystem engineers
can increase the resilience of their ecosystems to environmental stress (Crain and Bertness,
2006). Recently, regional-scale patterns of end-Permian through Early Triassic trace fossils in
South China indicate that the number of ecological roles that bioturbation ecosystem engineers
fulfilled began to increase relatively early after the mass extinction interval (Feng et al., 2022).
This may suggest that early recovering bioturbating ecosystem engineers facilitated biotic
recovery by increasing the resilience of certain paleoenvironments during the protracted
inhospitable conditions and carbon cycle instability of the Early Triassic (Feng et al., 2022).
However, global patterns of how bioturbation and ecosystem engineering responded to the end-
Permian mass extinction event have remained largely unstudied.
14
Dissertation purpose and significance
The first three chapters of this dissertation represent three different approaches to the
same broad question: What was the geobiological role of bioturbation ecosystem engineers
during major evolutionary intervals in Earth history? Specifically, I have employed a
combination of biogeochemical, sedimentological, and geochemical approaches to address
knowledge gaps that remain for understanding both the role of bioturbation in driving ecological
change during the Ediacaran-Cambrian transition and in biotic recovery in the wake of the end-
Permian mass extinction. Chapters 1 and 2 focus on new methodologies for constraining the
effects of bioturbating ecosystem engineers during the Ediacaran-Cambrian transition, while
Chapter 3 focuses on ecosystem engineering during the Late Permian and Early Triassic.
Chapter 1, “Ediacaran-Cambrian bioturbation did not extensively oxygenate sediments
in shallow marine ecosystems”, addresses long-standing assumptions about the role of
bioturbation in sediment oxygenation during the Ediacaran-Cambrian transition. Previous
researchers have often claimed that as bioturbation increased in complexity, depth, and intensity
through the Ediacaran-Cambrian transition, more oxygen was transported downwards into the
sediment. This assumption has been key to the claim that bioturbators, as ecosystem engineers,
created more habitable environments for other aerobic infauna in the early Cambrian. However,
this assumption had never been quantitatively tested in terms of the trace fossil record of
different bioturbation behaviors that can have very different impacts on sediment redox
chemistry. Specifically, more recent work has demonstrated that biomixing (the mixing of solid-
phase sediment particles) and bioirrigation (the mixing of solutes in the sediment porewater) can
have opposite impacts on the fate of oxygen in the sediments (van de Velde and Meysman,
2016). In Chapter 1, I have reconstructed the record of biomixing and bioirrigation across the
15
Ediacaran-Cambrian boundary and used biogeochemical modeling to predict how those early
bioturbators impacted the oxic zone in the sediment.
Chapter 2, “Characterization of bioturbating ecosystem engineering behaviors in the
Deep Spring Formation, California, USA”, builds from Chapter 1 in terms of focusing on the
trace fossil record of biomixing and bioirrigation. Identification of bioturbation behaviors –
including biomixing and bioirrigation, as well as other functional groups involved in assessing
the ecosystem engineering impact of various trace fossils (Herringshaw et al., 2017; Minter et al.,
2017; Cribb et al., 2019) – largely relies on qualitative or semi-quantitative interpretations of
trace fossils’ ecology, architecture, and burrow construction. In Chapter 2, I have presented a
new geochemical technique to identify different bioturbation behaviors based on how they
influence early diagenesis.
Chapter 3, “Complex bioturbation ecosystem engineering behaviors persisted in the wake
of the end-Permian mass extinction”, focuses on how bioturbating ecosystem engineers respond
to catastrophic climate change events. Although global trends in the size, depth, and intensity of
bioturbators have been previously reconstructed for the end-Permian mass extinction, less
attention had been given to how the ecological strategies of bioturbators were impacted during
the mass extinction event. Moreover, no study had been conducted reconstructing how
bioturbation ecosystem engineering behaviors changed in response to any mass extinction event.
In Chapter 3, I have compiled data from the literature on trace fossil depth and ecosystem
engineering characteristics (e.g., ecological functional group, burrow construction mode,
bioirrigation potential) in order to reconstruct global trends that help demonstrate how
bioturbating ecosystem engineers were impacted by the end-Permian mass extinction event and
its associated environmental drivers.
16
Chapter 4, “Contrasting terrestrial and marine ecospace dynamics after the end-Triassic
mass extinction event”, does not strictly deal with bioturbation or ecosystem engineering. This
chapter presents the development of a novel paleoecological method – a terrestrial ecospace
framework – which was developed in order to directly compare contemporaneous changes in
marine and terrestrial functional ecology during key evolutionary intervals and climate change
events throughout Earth history. This new method is a major advancement in paleoecology, as it
is the first method that can directly compare changes in terrestrial and marine ecology based on
global, taxonomically diverse fossil datasets by comparing functional groups comprised of the
ecologically equivalent marine and terrestrial traits. Although this chapter does not test questions
about ecosystem engineers or bioturbators, it does deal with trends in benthic ecology through a
mass extinction interval, and I do discuss how the results presented in this chapter may be
impacted by the persistence or extinction of marine and terrestrial ecosystem engineers.
Chapter 1 was published in Geobiology in March 2023, and Chapter 3 was published in
Scientific Reports in January 2020. Chapter 1 remains unchanged from its published form. The
discussion section of Chapter 3 has been updated to incorporate new research that has been
published since the manuscript was initially accepted.
17
Figures
Figure I.1 Space-time diagram for modern ecosystem engineers.
Spatial scale refers to the size of the environmental modification or structure created by the
ecosystem engineer, and temporal scale refers to how long that modification lasts before it is
destroyed. Data from Hastings et al. (2007) and Albertson et al. (2022).
18
Figure I.2 Process- and outcome-based feedbacks for bioturbation ecosystem engineering.
Flowchart demonstrating a combined process- and outcome-based framework for bioturbation
ecosystem engineering. Ecosystem engineering processes include bioturbation’s impact to the
physical structure and nutrient flows (blue arrows) and the feedbacks between processes (orange
arrow to green box). Outcomes include the impacts directly to the bioturbator (green and orange
arrows to blue box), and the ultimate biotic response (green and orange arrows to yellow box),
which has an ecological impact on the bioturbator (yellow arrow to blue box). Figure from
Darroch, Cribb, et al. (2020).
19
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25
Chapter 1. Ediacaran-Cambrian bioturbation did not extensively oxygenate sediments
in shallow marine ecosystems
Abstract
The radiation of bioturbation during the Ediacaran– Cambrian transition has long been
hypothesized to have oxygenated sediments, triggering an expansion of the habitable benthic
zone and promoting increased infaunal tiering in early Paleozoic benthic com-munities.
However, the effects of bioturbation on sediment oxygen are underexplored with respect to the
importance of biomixing and bioirrigation, two bioturbation processes which can have opposite
effects on sediment redox chemistry. We categorized trace fossils from the Ediacaran and
Terreneuvian as biomixing or bioirrigation fossils and integrated sedimentological proxies for
bioturbation intensity with biogeochemical modeling to simulate oxygen penetration depths
through the Ediacaran– Cambrian transition. Ultimately, we find that despite dramatic increases
in ichnodiversity in the Terreneuvian, biomixing remains the dominant bioturbation behavior,
and in contrast to traditional assumptions, Ediacaran– Cambrian bioturbation was unlikely to
have resulted in extensive oxygenation of shallow marine sediments globally.
1.1 Introduction
The evolution of bioturbation during the Ediacaran Period and subsequent radiation of
complex burrowing behaviors during the Paleozoic Era is one of the most important
geobiological transitions in Earth history, as it fundamentally changed the nature of the
sediment-water interface and structure of benthic ecosystems. During the Ediacaran, shallow
marine (broadly defined as marine ecosystems above the ocean shelf break) sediments are
26
thought to have been predominantly anoxic with a very shallow oxic-anoxic interface due to
being unbioturbated, as well as commonly covered in microbial mats that are thought to have
sealed the sediment from oxygen exchange between porewaters and the overlying water column
(Seilacher and Pflüger, 1994). Then, in the early Cambrian, as the diversity of bioturbation
behaviors radiated in shallow marine environments (Buatois and Mángano, 2018), the sediment
tiers where animals predominantly lived are thought to have become increasingly oxygenated
due to burrowing macrofauna disrupting the microbial mats, stimulating oxygen diffusion into
the sediment, and, critically, by actively mixing oxygen into the sediments themselves via
bioturbation (Savrda and Bottjer, 1991; Seilacher, 1999). It has been proposed that the
bioturbation-driven oxygenation of the sediment further promoted more infaunal activity,
creating a positive bioturbation-oxygen feedback loop until the shallow seafloor was colonized
by infauna and fully bioturbated in the Early Paleozoic (McIlroy and Logan, 1999). Well-
bioturbated sediments, represented by ichnofabrics with fully disrupted sediment laminae, have
been observed in the lower Cambrian (Gougeon et al., 2018) and are traditionally assumed to
imply seafloors into which oxygen vertically penetrated (Savrda and Bottjer, 1991), although it
remains unclear whether or not deeper sediment tiers would have been extensively oxygenated.
Nevertheless, it has been suggested that the radiation of bioturbators during the Ediacaran-
Cambrian transition drove an expansion in habitability of the shallow to deep sediment tiers
(Mángano and Buatois, 2020), largely due to increased sediment oxygenation resulting from the
disappearance of microbial mats and the early evolution of oxygenating bioturbation behaviors
(McIlroy and Logan, 1999; Tarhan, 2018b). However, the extent to which the evolution of new
and more intense bioturbation behaviors during the Ediacaran-Cambrian transition might have
influenced oxygen penetration into the sediment – which broadly describes the thickness of the
27
sedimentary oxic zone before oxygen concentrations fall to zero (Cai and Sayles, 1996) – and the
habitability of the sediments for aerobic infaunal animals has been underexplored using
quantitative methods.
While bioturbation broadly refers to any biogenic sediment mixing, bioturbation behaviors
can be more precisely divided into which phase of the sediment is being mixed (Kristensen et al.,
2012). Previous work has demonstrated that biomixing (bioturbation behaviors which mix the
sediment solid-phase particles) and bioirrigation (those which enhance the exchange of solutes
between the porewater and the overlying water) can have opposite effects on the fate of oxygen
in the sediment (Aller, 1982; Kristensen et al., 2012; van de Velde and Meysman, 2016).
Biomixing ultimately restricts the depth of the oxic zone in the sediment primarily by
transporting more organic matter below the oxic-anoxic interface to stimulate anaerobic
respiration, producing reduced compounds that are eventually oxidized, thus contributing to the
consumption of oxygen deeper in the sediment. In contrast, bioirrigation increases oxygen
concentrations in deeper sediment tiers by supplying more oxygen to the sediment depth via
burrow flushing with oxygenated waters from the overlying water column (van de Velde and
Meysman, 2016). Previously proposed biogeochemical and paleoecological effects of the
evolution of bioturbation are underexplored with respect to the contrasting effects of biomixing
and bioirrigation on sediment redox chemistry (Canfield and Farquhar, 2009; Boyle et al., 2014;
Tarhan et al., 2015).
Here, we investigate whether early bioturbators in the Ediacaran and Terreneuvian could
have feasibly oxygenated sediments in shallow marine environments to increase the habitability
of deeper sediment tiers for aerobic macroinfauna, considering the record of biomixing and
bioirrigation in the trace fossil record (Buatois et al., 2020) and by integrating sedimentological
28
bioturbation intensity proxies with a sedimentary biogeochemical reaction-transport model.
Specifically, we simulate the oxygen penetration depth (OPD, or the minimum one-dimensional
sediment depth at which [O2,BW] < 1 µmol) by using data from the trace fossil record to
parameterize biomixing and bioirrigation intensities in the model. We also investigate the impact
of different bioturbation intensities on oxygen consumption rates and reactions to
mechanistically understand why different bioturbation parameters lead to different effects on the
OPD. Henceforth, we refer to bioturbation as meaning biomixing and bioirrigation operating
together, and to biomixing and bioirrigation as each behavior operating independently. We note
that here we explore the sole impact of bioturbation on the OPD, rather than the combined role of
bioturbators and the break-up of microbial mats. In addition to the differential impact of
biomixing vs. bioirrigation (van de Velde and Meysman, 2016), extrinsic environmental factors
also likely had a significant, and perhaps competing, impact on oxygen penetration into the
sediment during the Ediacaran and well into the Paleozoic. Specifically, oxygen concentrations
in the ocean and organic matter dynamics changed around the time of increases in bioturbation
intensity and bioturbator size (Lyons et al., 2014; Lu et al., 2018; van de Velde et al., 2018; Dahl
et al., 2019; Fakhraee et al., 2020), which would have also impacted the extent of oxygen
penetration into the sediment, particularly as organic matter dynamics are typically the driving
force of sediment oxygen dynamics in today’s ocean (Cai and Sayles, 1996; Kristensen, 2000).
Organic matter delivery to the seafloor may also impact oxygen penetration into the sediment
indirectly by influencing the physiology, activity, and abundances of bioturbators (Smith et al.,
1997; Sperling and Stockey, 2018). Thus, we also investigate the importance of organic matter
flux and its potentially competing or synergistic relationship with bioturbation on oxygen
penetration into the sediment.
29
1.2 Methods
We investigated two components of the Ediacaran-Cambrian trace fossil record to test the
hypothesis that the evolution of new bioturbation behaviors through the Ediacaran-Cambrian
transition resulted in macrofauna-driven oxygenation of shallow marine sediments. First, we
quantified the proportion of biomixers to bioirrigators and any changes in the relative abundance
of the two behaviors across the Ediacaran-Cambrian boundary. To achieve this, we use a
previously compiled global trace fossil dataset (Buatois et al., 2020), and classified each
ichnogenus as a biomixing or bioirrigation behavior. This record of biomixing and bioirrigation
serves as important context from the fossil record in which our model can be grounded and
addresses previous hypotheses that bioirrigators were the dominant bioturbation ecosystem
engineering behavior in early Cambrian ecosystems (McIlroy and Logan, 1999; Tarhan, 2018b).
Second, we compiled sedimentological proxies of bioturbation intensity to parameterize
biodiffusion and bioirrigation coefficients. Specifically, we compiled global average mixed layer
depths (Tarhan et al., 2015) and Bioturbation Index (BI) values (Taylor and Goldring, 1993) and
converted them to biodiffusion and bioirrigation coefficients, which were then integrated with a
biogeochemical reaction-transport model. We developed a one-dimensional reaction-transport
model simulating carbon, oxygen, nitrogen, and sulfur cycling. We have omitted iron and
manganese reactions from our model for sake of computational simplicity and efficiency in our
modeling experiments and sensitivity analyses, although we have run a subset of our analyses in
a more geochemically comprehensive and complex reaction-transport model (Zhao et al., 2020)
to compare results.
30
Using this reaction-transport model, we conducted two types of analyses. First, in three
different types of experiments, we simulated the OPD at various bottom water oxygen
concentrations and organic matter fluxes at different bioturbation intensities to investigate 1) the
broad impacts of early bioturbation on the OPD, and 2) the sensitivity of those impacts to bottom
water oxygen and organic matter dynamics. Second, we simulated the pathways of total oxygen
consumption in the sediment in order to investigate why different levels of bioturbation intensity
and types of bioturbation have potentially opposite impacts on the fate of oxygen in the
sediment. For each analysis, we simulated results with no bioturbation, Ediacaran bioturbation
parameters, Terreneuvian bioturbation parameters, and modern bioturbation parameters.
Trace fossil dataset and bioturbation parameterization
The trace fossil dataset used here is primarily from Buatois et al. (2020). Ediacaran
(Vendian and Nama) and Terreneuvian (Fortunian and Cambrian Stage 2) trace fossils were
selected from the larger dataset. Some Ediacaran ichnogenera which were not originally included
– such as Planolites (Corsetti and Hagadorn, 2000) and Lamonte (Meyer et al., 2014; O’Neil et
al., 2020) – were added to the dataset along with their stratigraphic occurrences. Trace fossils
were then classified as either biomixers or bioirrigators at the ichnogenus level based on feeding
behavior and burrow architecture, both of which are given for each ichnogenus in Buatois et al.
(2020). We follow previous interpretations (e.g., Herringshaw et al., 2017) that primarily
vertically oriented suspension feeding and predator ichnogenera represent bioirrigation, while
primarily horizontally-oriented deposit and predatory ichnogenera are biomixers. As a caveat, we
note that semi-permanent or permanent burrows constructed by suspension feeders necessarily
result in some redistribution of sediment particles, and thus bioirrigation is unlikely to occur
31
without some biomixing. However, the effects of bioirrigation and biomixing will primarily be
considered together in the following results. Ichnogenera that represent surficial scratches,
trackways, and imprints were removed, as they represent surficial modification that results in
negligible sediment mixing (Herringshaw et al., 2017). From the final trace fossil dataset, we
calculated two relative abundance metrics: the relative abundance of biomixing and bioirrigation
behaviors first from total unique ichnogenera, and second from unique stratigraphic occurrences
(as they were given in the original dataset (Buatois et al., 2020)). The resulting trace fossil data
of biomixers and bioirrigators is not directly incorporated into the models presented here, as the
biodiffusion and bioirrigation coefficients are derived from sedimentological proxies of sediment
mixing. However, the trace fossil record of biomixing and bioirrigation can serve as an important
conceptual comparison between the modeled results and the preserved evolutionary history of
bioturbation. For example, if the dominance of biomixers or bioirrigators changed across the
Ediacaran-Cambrian boundary, one might imagine that the resulting biogeochemical impacts
unique to biomixing or bioirrigation would have also changed across the Ediacaran-Cambrian
boundary.
Average mixed layer depths (xL) from Tarhan et al. (2015) and Bioturbation Index (BI)
values from Mángano and Buatois (2014) were used to calculate biodiffusion coefficients as they
relate to the mixed layer depth. For the Bioturbation Index, which measures the vertical
disruption of ichnofabrics (Taylor and Goldring, 1993), BI values were normalized to xL by
assuming the average mixed layer depth, xL = 10 cm (Boudreau, 1998), correlates to BI=6, the
maximum Bioturbation Index (BI) value (see Tarhan et al. (2015) for similar normalization of
modern ichnofabric index (ii) (Droser and Bottjer, 1986) values to mixed layer depths). Mixed
layer depths were then converted to biodiffusion coefficients using two different relationships.
32
First, we use the biomixing-mixed layer depth relationship described in van de Velde and
Meysman (2016),
𝑥 𝐿 =𝑥 𝐿 ,0
+𝑥 𝐿 ,𝑚𝑎𝑥 (1−exp(−𝐷 𝐵 ,0
/ 𝐷 𝐵 ,𝑟 𝑒𝑓
)),
where xL,0 is the minimum depth of bioturbation (0.1 cm), xL,max is the maximum depth of
bioturbation (xL,0 + xL,max = 10 cm; (Boudreau, 1998; van de Velde and Meysman, 2016)), and
DB, ref is a reference biomixing intensity for coastal sediments (DB,ref = 3 cm
2
yr
-1
; (Boudreau,
1998; van de Velde & Meysman, 2016). We also use the biomixing-mixed layer depth
relationship described by (Boudreau, 1998) to calculate a second biodiffusion coefficient,
𝑥 𝐿 =4√9∗
𝐷 𝐵 ,0
8𝑘 ,
where k is the organic matter reactivity. In the following models, we use a refractory organic
matter refractory of k = 0.1 yr
-1
and a labile organic matter refractory of k = 10 yr
-1
. The flux of
both fractions of organic matter is equal, so we calculate an average k-value of 5 yr
-1
for bulk
organic matter at the sediment-water interface. We use the k = 5 yr
-1
to calculate a biodiffusion
coefficient at the sediment-water interface (DB,0). The biodiffusion coefficients ultimately used
are the average of the biodiffusion coefficients calculated via these two different methods.
Finally, we note that, although other BI and ichnofabric index (ii) values are reported as local
maxima for Ediacaran and Terreneuvian localities, we emphasize that we do not use local
maxima mixed layer depths, BI values, or ii values, as very localized intense bioturbation can
occur which does not necessarily represent the entire marine environment’s nor the global
average sediment mixing (Tarhan, 2018b). Finally, we use modern biodiffusion coefficients to
serve as end-member comparison results. We use a modern global averaged biodiffusion
coefficient, which describes biomixing intensity, of 10 cm
2
yr
-1
(based on modern data quantified
using the
210
Pb method) (Lecroart et al., 2010; Solan et al., 2019). Bioirrigation rates are more
33
difficult to quantify, but diagenetic models tend to use a modern bioirrigation coefficient around
365 yr
-1
(van de Velde and Meysman, 2016). We note, however, these modern biodiffusion and
bioirrigation coefficients that have been selected only represent a subset of modern bioturbation
intensities, which vary quite extensively in the modern ocean, particularly in response to water
depth, net primary productivity, season, and climate (Solan et al., 2019).
Reaction-transport model formulation
We developed a one-dimension reaction-transport model to simulate carbon, oxygen,
nitrogen, and sulfur cycling in a sediment column. The reaction-transport model was developed
using the R package CRAN:ReacTran (Soetaert and Meysman, 2012), which is formulated from
two coupled mass balance equations for solids and solutes in the model sediment column
(Boudreau, 1997; Meysman et al., 2005):
{
𝜑 𝜕 𝐶 𝑖 𝜕𝑡
=
𝜕 𝜕 𝑧 (𝜑 𝐷 𝑖 𝜕 𝐶 𝑖 𝜕𝑧
−𝜑𝑣 𝐶 𝑖 )+ 𝜑𝛼 (𝑧 )(𝐶 𝑖 ,𝑠𝑤
−𝐶 𝑖 (𝑧 ))+ ∑𝑣 𝑖 ,𝑛 𝑅 𝑛 𝑛 (1−𝜑 )
𝜕 𝑆 𝑖 𝜕𝑡
=
𝜕 𝜕𝑧
((1−𝜑 )𝐷 𝐵 (𝑧 )
𝜕 𝑆 𝑖 𝜕𝑧
−(1−𝜑 )𝑤 𝑆 𝑖 ) + ∑𝑣 𝑖 ,𝑛 𝑅 𝑛 𝑛
In these equations, Ci represents the concentration of solute species, Si represents the
concentration of solid species, z is depth in the sediment column, and represents porosity. The
equation for the change in concentration of solutes includes two transport processes: first,
molecular advection and diffusion, following Fick’s first law (𝐽 𝐷 = - 𝜑 𝐷 𝑖 𝜕 𝐶 𝑖 𝜕𝑧
+𝜑𝑣 𝐶 𝑖 ) (Fick,
1855), and second, bioirrigation transport ((𝐼 𝑖𝑟𝑟 (𝑧 )= 𝛼 (𝑧 )(𝐶 𝑖 ,𝑠𝑤
−𝐶 𝑖 (𝑧 ))) (Boudreau, 1984;
Emerson et al., 1984). For molecular advection and diffusion, Di represents the molecular
diffusion coefficients for each solute species as a function of temperature and salinity, which we
34
calculate using the R package CRAN:marelac (Soetaert et al., 2010) and correct for tortuosity
with the correction factor
2
=1−2ln( ) (Boudreau, 1996), and v represents pore-water
sedimentation velocity in the compacted, impermeable sediment deposit. For bioirrigation
transport, 𝛼 (𝑧 ) represents the bioirrigation coefficient at each sediment depth, Ci,SW is the solute
concentration at the sediment-water interface, and Ci(z) is the solute concentration at each
sediment depth. The equation for the change in solids also includes two transport processes:
first, biodiffusion transport (𝐷 𝐵 (𝑧 )
𝜕 𝑆 𝑖 𝜕𝑧
), and second, advection due to sedimentation
((1−𝜑 )𝑤 𝑆 𝑖 ). For biodiffusion transport, DB(z) is solid-phase sediment volume-based and
represents the biodiffusion coefficients at each sediment depth. For advection due to
sedimentation, w represents the solid-phase sedimentation velocity.
The concentrations of both solids and solutes are changed by production and
consumption by biogeochemical reactions, expressed in each equation as ∑𝑣 𝑖 ,𝑛 𝑅 𝑛 𝑛 . Here, Rn
represents the reaction rate for the n-th reaction, and vi,n represents the stoichiometric coefficient
for the i-th species in the n-th reaction. Seven reactions describe carbon, oxygen, nitrogen, and
sulfur cycling (Table S1.1). The primary reactions are aerobic respiration, nitrate reduction, and
sulfate reduction. The secondary oxidation reactions are canonical sulfide oxidation, ammonium
oxidation, iron sulfide oxidation, and sulfide oxidation with nitrate. To account for a lack of full
iron cycle to interact with hydrogen sulfide, a portion of hydrogen sulfide that is produced by
sulfate reduction is precipitated as FeS. A full description of the biogeochemical reaction set,
kinetic rate expressions, reaction rate expressions, and associated reaction constants are in Table
S1.1-S1.4. The model domain is a 15-centimeter sediment column parametrized to reflect fine-
grained coastal sediments. For simplicity, we ignore compaction, so sediment porosity is
constant with depth at = 0.8. Sedimentation velocity for solutes and solids is fixed at v = w =
35
0.2 cm/yr, and the solid-phase sediment density ( ) is 2.6 g/cm
3
. Salinity (S) is 30, temperature
(T) is 25 C, and pH is 7.5. All parameters are held constant through the model experiments.
Full model parameters are described in Table S1.5. Finally, we again note that we omit Mn and
Fe reactions from our model for the sake of computational simplicity, as adding additional
complexity to this model would result in extended model solution times and lower oxygen
profile resolution, although a subset of simulations was conducted using a more geochemically
comprehensive reaction-transport model (Zhao et al., 2020).
Bioturbation is separated into and parameterized as biomixing and bioirrigation.
Following convention, biomixing is described as a diffusive transport process, or biodiffusion
(Boudreau, 1997; Meysman et al., 2010). The biodiffusion coefficients DB(z) for each depth in
the modeled sediment column follow a sigmoidal depth profile (𝐷 𝐵 (𝑧 )=
𝐷 𝐵 ,0
exp (−
(𝑧 −𝑋 𝐿 0.25𝑥 𝑏𝑚
) (1+exp(−
(𝑧 −𝑋 𝐿 0.25𝑥 𝑏𝑚
) ⁄ ), where z is the sediment depth and xbm is the depth
attenuation coefficient for biomixing (Figure S1.1) (Boudreau, 1998). Biodiffusion coefficients
are given a sigmoidal profile because bioturbating benthic macrofauna are physiologically
dependent on food and oxygen, which we assume are most available near the sediment-water
interface. Thus, biomixing is most intense in the shallow sediment tier and decreases with depth
(Emerson et al., 1984; Boudreau, 1998). For simplicity, we ignore reactivity selection, particle
size selection, and potential for differential transport in our biodiffusion coefficient profile.
Bioirrigation is described as a non-local exchange process in which sediment porewater is
exchanged with water at the sediment-water interface (Boudreau, 1984). Bioirrigation follows
an exponential depth profile (𝛼 (𝑧 )= 𝛼 0
exp (−
𝑧 𝑥 𝑖𝑟𝑟 )), where xirr is the depth attenuation
coefficient for bioirrigation (Figure S1.1) (Martin and Banta, 1992; Kristensen et al., 2018). As
with the depth-dependency of biomixing, this equation also assumes that bioirrigation activity is
36
most intense near the sediment-water interface, although instead follows an exponential profile
(van de Velde and Meysman, 2016).
Finally, we note that here we use a non-local model for bioirrigation, which is a
simplification of the complex, species-specific process of bioirrigation (Meile et al., 2005). We
choose the one-dimensional non-local exchange model because more complex two- or three-
dimensional models require assumptions about burrow structures, many of which cannot be
validated based on the limited data available from the trace fossil record. Additionally, we apply
one alpha for all solutes, meaning we ignore reactions at burrow walls (such as the oxidation of
hydrogen sulphide at the edge of bioirrigated burrows). This ultimately means the OPD
simulations in bioirrigated cases should be viewed as conservative maximum results, as
reoxidation reactions at the burrow wall would limit the removal of hydrogen sulphide, leading
to a higher oxygen consumption linked to sulphide oxidation. Thus, in reality, the OPDs for the
bioirrigated experiments were likely shallower than the results presented here. We keep the
alpha coefficient constant due to the lack of solute-specific alpha values for global application,
such as the results presented here.
Modeling experiments and analyses
To investigate the sensitivity of the OPD to an increase in biomixing and bioirrigation
intensity across the Ediacaran-Cambrian boundary, we simulated the OPD over a range of
biodiffusion coefficients at four different oxygen levels. Biodiffusion coefficients were increased
from DB,0 = 0-3.0 cm
2
yr
-1
and bioirrigation coefficients were increased from 0
= 0-300 yr
-1
at
different bottom water oxygen concentrations and organic matter fluxes. Bottom water oxygen
concentrations tested are [O2,BW] = 0.014 mM (5% PAL), 0.028 mM (10% PAL), 0.07 mM (25%
37
PAL), 0.14 mM (50% PAL), and 0.196 mM (70% PAL) and 0.28 mM (100% PAL), and organic
matter fluxes tested are CH2Otot,F =150, 300, and 700 µmol cm
-2
yr
-1
. Bottom water oxygen
concentrations have a fixed concentration, and organic matter fluxes have a constant steady
delivery for this experiment and all further described model experiments. Sensitivity of the OPD
to bioirrigation coefficients was tested with the chosen Ediacaran and Terreneuvian biodiffusion
coefficients. Boundary conditions for these sensitivity analyses are given in Table S1.6. Second,
to investigate the impact of different bioturbation intensities (both separately as biomixing or
bioirrigation and as the two behaviors combine) over a large range of bottom water oxygen
concentrations and organic matter flux conditions, we conducted ten modeling experiments that
output simulated OPDs from [O2,BW] = 0.014-0.28 mM and the flux of total organic carbon from
CH2Otot,F = 100 – 450 µmol cm
2
yr
-1
. For oxygen, we focus specifically on [O2,BW] = 0.07-0.14
mM throughout the text, as a possible representation of 25-50% PAL oxygen concentrations of
the Ediacaran-Cambrian transition (Krause et al., 2018). For organic matter, we focus on fluxes
in the range of 150-450 µmol cm
-2
yr
-1
, which is within the low range of modern nearshore
organic carbon flux estimates (Dunne et al., 2007) and encompasses previously used boundary
conditions used to simulate early Paleozoic nearshore environments (Tarhan et al., 2021). Full
boundary conditions for these analyses are given in Table S1.7. Where Ediacaran-Cambrian
boundary conditions cannot be constrained based on available proxy information (e.g., for
temperature, salinity), we assume boundary conditions from modern nearshore environments
(Dunne et al., 2007; Mouret et al., 2009; Dale et al., 2015; van de Velde and Meysman, 2016) are
appropriate. One experiment was run with no bioturbation as a control to compare the other nine
experiments. The other nine experiments use bioturbation parameters for the Ediacaran,
Terreneuvian, and modern. For each time interval, three experiments were run: one with only
38
biomixing, one with only bioirrigation, and one with biomixing and bioirrigation together. For all
experiments, 10,000 steady state solutions were solved, representing the OPD simulated over
100 bottom water oxygen concentrations and 100 organic matter fluxes. In order to more clearly
visualize the role of organic matter flux and oxygen on the OPD as bioturbation evolved, from
these larger analyses, the OPD was also plotted for organic matter fluxes at CH2Otot,F = 100, 300,
and 450 µmol cm
2
yr
-1
at two different end-member bottom water oxygen concentrations for the
Ediacaran-Cambrian transition, [O2,BW] = 0.07 mM and 0.14 mM. All boundary conditions are
compiled for this experiment in Table S1.8. Finally, using our model outputs, we investigated the
effect of bioturbation on oxygen consumption profile, specifically focusing on oxygen
consumption via aerobic respiration and reoxidation pathways. We simulated reaction-rate
profiles for oxygen consumption. Boundary conditions for this experiment are given in Table
S1.9. We conducted ten simulations for the same ten different bioturbation intensity conditions
consisting of no bioturbation, Ediacaran bioturbation, Terreneuvian bioturbation, and modern
bioturbation parameters as the modeling experiments. We simulated total oxygen consumption,
oxygen consumption via aerobic respiration, and oxygen consumption via reoxidation pathways.
We also calculated burial of organic matter and iron sulphide. All models were constructed using
the R package CRAN:ReacTran (Soetaert and Meysman, 2012), and all equations were solved to
steady-state, which best represent geologic time scales, using the steady.1D function in the R
package CRAN:rootSolve (Soetaert, 2009; Soetaert and Herman, 2009).
39
1.3 Results
The Ediacaran-Cambrian trace fossil record of biomixing and bioirrigation intensity
There are a variety of both biomixing and bioirrigation ichnogenera in the Ediacaran and
Terreneuvian trace fossil records. Common biomixing trace fossils in both the Ediacaran and
Terreneuvian include small (sub-centimeter in diameter) horizontal Helminthopsis,
Helminthoidichnites, and Gordia and typically larger (centimeter-scale or larger in diameter)
horizontal Archaeonassa, Psammichnites, Palaeophycus, and Torrowangea (Table 1.1).
Predominantly vertical trace fossils that represent suspension- and predatory feeding behaviors
were classified as bioirrigators (Kristensen et al., 2012; Herringshaw et al., 2017). Small plug-
shaped burrows such as Conichnus and Bergaueria are common representatives of bioirrigators
in the Ediacaran and Terreneuvian, as well as larger, typically deeper U- and J-shaped trace
fossils such as Arenicolites and Diplocraterion which are more common in the Terreneuvian
(Table 1.1). Trace fossils such as Treptichnus and Streptichnus, which are vertically oriented and
created in a conveying-type behavior and would have likely connected the sediment-water
interface with the porewaters multiple times throughout the burrow length, were also classified
as bioirrigation trace fossils (Herringshaw et al., 2017) (Table 1.1).
Ichnodiversity increases significantly in the Terreneuvian (Table 1.1). In the Ediacaran,
17 ichnogenera are present, versus 53 ichnogenera in the Terreneuvian (Table 1.1) (Buatois et
al., 2020). However, there is only a small change in the ratio of biomixing to bioirrigation
ichnogenera. 70.5% (n=12) of ichnogenera in the Ediacaran and 67.9% (n=36) of ichnogenera in
the Terreneuvian are biomixers, and 29.5% (n=5) of ichnogenera in the Ediacaran and 32.1%
(n=17) of ichnogenera in the Terreneuvian are bioirrigators (Figure 1.1). In contrast, from the
unique stratigraphic occurrences (where an ichnogenus occurrence is equal to each unique
40
stratigraphic unit in which it occurs), there are 94 ichnogenera occurrences in the Ediacaran and
426 ichnogenera occurrences in the Terreneuvian (Buatois et al., 2020). When measured this
way, the proportion of biomixer to bioirrigator occurrences does observably change across the
Ediacaran-Cambrian boundary. In the Ediacaran, 87.2% (n=82) of ichnogenera occurrences
represent biomixing and only 12.8% (n=12) represent bioirrigation, in contrast to the
Terreneuvian, where only 63.4% (n=270) of ichnogenera occurrences are biomixers and 36.6%
(n=156) of ichnogenera occurrences represent bioirrigators.
Finally, we calculated a global average Ediacaran biodiffusion coefficient of DB,0 = 0.1
cm
2
yr
-1
and a Terreneuvian biodiffusion coefficient of DB,0 = 0.98 cm
2
yr
-1
. We note that the BI
values taken from the literature (Mángano and Buatois, 2014) may be sensitive to sedimentation
rates, but the BI values were compiled from similar shallow marine paleoenvironments and thus
we would not expect sedimentation rates to be a major caveat of our biodiffusion coefficient
parameterization. Assuming bioirrigation scales proportionately to modern values (DB,0 = 10
cm
2
yr
-1
, 0=365 yr
-1
) (van de Velde and Meysman, 2016; Solan et al., 2019) as biodiffusion
coefficients do, we estimate a bioirrigation coefficient of 0 = 3.65 yr
-1
for the Ediacaran (1% of
modern intensity) and 0 = 35.8 yr
-1
(9.8% of modern intensity) for the Terreneuvian.
Sensitivity analyses of chosen biodiffusion and bioirrigation coefficients
No matter the level of organic matter flux or bottom water oxygen concentrations, the
OPD shallows as the biodiffusion coefficient increases. Within the range around the Ediacaran
and Terreneuvian biodiffusion coefficients (0-1 cm
2
yr
-1
), the sensitivity of the OPD is dependent
on bottom water oxygen concentrations and the organic matter flux (Figure 1.2). For example, at
the lowest organic matter flux of CH2Otot,F = 150 µmol cm
-2
yr
-1
, increasing the biodiffusion from
41
0-1 cm
2
yr
-1
shoals the OPD only by greater than 0.5 cm for [O2,BW] = 0.14 mM and, most
significantly, only greater than 1 cm for [O2,BW] = 0.28 mM. At a higher organic matter flux of
CH2Otot,F = 300 µmol cm
-2
yr
-1
, the OPD is only shallowed by greater than 0.5 cm at [O2,BW] =
0.28 mM. Finally, at the highest, more modern-like organic matter flux of CH2Otot,F = 700 µmol
cm
-2
yr
-1
, increasing the biodiffusion coefficient in the same range does not shallow the OPD
significantly, even at the highest bottom water oxygen concentrations. The OPD, therefore, is
only significantly sensitive (shallowing > 1 cm) to small increases in low biodiffusion
coefficients at very low organic matter fluxes and near modern bottom water oxygen
concentrations ([O2,BW] = 0.28 mM), which are unlikely boundary conditions for the late
Ediacaran or Terreneuvian (Krause et al., 2018). In the bottom water oxygen concentration range
of [O2,BW] = 0.07 - 0.14 mM, however, the OPD is not particularly sensitive to small changes in
biomixing intensity, especially in the range between and around Ediacaran and Terreneuvian
biodiffusion coefficients (Figure 1.2).
Results are similar for the sensitivity of the OPD to increasing bioirrigation coefficients.
Most broadly, increasing bioirrigation intensity deepens the OPD. However, the sensitivity of the
OPD to increases in bioirrigation intensity decreases due to increasing organic matter flux,
decreasing bottom water oxygen concentrations, and increased biomixing intensity (Figure S1.2).
For most combinations of organic matter flux, bottom water oxygen concentration, and
biodiffusion coefficient, there is an inflection point in which a small change in the bioirrigation
coefficient shifts the OPD from a shallow and partially to a fully oxygenated model sediment
column (Figure S1.2). The bioirrigation coefficient at which this inflection point occurs,
however, becomes larger with increased organic matter input and lower bottom water oxygen
concentrations (Figure S1.2). For example, at an organic matter flux of CH2Otot,F = 150 µmol cm
-
42
2
yr
-1
, the inflection point only occurs at bottom water oxygen concentrations above [O2,BW] =
0.07 mM with Ediacaran biodiffusion and only above [O2,BW] = 0.014 mM with a Terreneuvian
biodiffusion coefficient (Figure S1.2). Furthermore, the inflection point occurs at higher
bioirrigation coefficients as bottom water oxygen concentrations are decreased (Figure S1.2). As
organic matter fluxes increase, higher bottom water oxygen concentrations and bioirrigation
coefficients are required to cause the inflection point (Figure S1.2). Thus, increasing the
bioirrigation coefficient only results in significant (>1cm) changes in the OPD at very high, near-
modern bioirrigation intensities, very low organic matter fluxes, and/or very high, near-modern
bottom water oxygen concentrations.
In summary, small changes to the already low estimated biodiffusion and bioirrigation
coefficients should not significantly change the OPD, given the most likely Ediacaran and
Terreneuvian bottom water oxygen concentrations and organic matter flux boundary conditions.
Furthermore, the estimated Ediacaran and Terreneuvian bioirrigation coefficients consistently
fall behind the inflection point for all boundary conditions (Figure S1.2), indicating that the OPD
is relatively insensitive to changes in bioirrigation intensity within the range of our bioturbation
parameters. Additionally, it is important to note that, although the estimated Ediacaran and
Terreneuvian biodiffusion and bioirrigation coefficients may not precisely represent the reality of
Ediacaran and Terreneuvian bioturbators, the insensitivity of the OPD at lower oxygen
concentrations and around an organic matter flux of CH2Otot,F = 300 µmol cm
-2
yr
-1
(Tarhan et al.,
2021) suggests that both time periods’ estimated biodiffusion and bioirrigation coefficients are
broadly accurate in predicting relative changes in the Ediacaran-Cambrian transition OPD.
43
Sensitivity analyses of the OPD to bioturbation intensities
The results of our sensitivity analyses underscore the similarity in magnitude of impact of
Ediacaran and Terreneuvian bioturbation on the OPD and its sensitivity to environmental
change, particularly in comparison to results for no bioturbation and modern bioturbation (Figure
1.3). At all organic matter fluxes, Ediacaran and Terreneuvian bioturbation have an insignificant
impact on the depth of oxygen penetration into the sediment. At the lowest organic matter flux in
our Ediacaran-Cambrian boundary conditions (150 µmol cm
-2
yr
-2
), the OPD without bioturbation
deepens by 0.68 cm when bottom water oxygen concentrations are increased from the low to
high range of Ediacaran-Cambrian boundary conditions (0.07-0.14 mM). In contrast, Ediacaran
and Terreneuvian bioturbation both shallow the OPD, but neither impact the OPD significantly
and not at magnitudes greater than 0.5 cm. As the organic matter flux increases, the impact of
Ediacaran and Terreneuvian bioturbation is even less significant. In contrast, with modern
bioturbation, the OPD is significantly deepened on the order of several centimeters when bottom
water oxygen concentrations are increased from 0.07-0.14 mM until the organic matter flux
reaches around 400 µmol cm
-2
yr
-1
. At very high organic matter concentrations, increasing bottom
water oxygen concentrations within the low Ediacaran-Cambrian boundary conditions does not
result in any major (>0.5 cm) change between no bioturbation, Ediacaran bioturbation,
Terreneuvian bioturbation, or modern bioturbation.
Our results also underscore the differences in effects on the OPD between biomixing and
bioirrigation (Figure 1.4). At all bottom water oxygen concentrations and organic matter fluxes,
biomixing shallows the OPD, and bioirrigation deepens it. Moreover, increased biomixing
decreases the sensitivity of the OPD to changes in bottom water oxygen concentrations, while
the opposite is true for bioirrigation. As biomixing intensity increases from Ediacaran to modern,
44
the OPD becomes increasingly insensitive to changes in organic matter or bottom water oxygen
concentrations (Figure 1.4). Interestingly, the difference in the magnitude of the OPD deepening
in the range of Ediacaran-Cambrian boundary condition bottom water oxygen concentrations
([O2,BW = 0.07-0.14 mM) between biomixing and bioirrigation in the Ediacaran and Terreneuvian
is not significant, particularly at high organic matter fluxes (Figure 1.4). However, modern
bioirrigation causes the OPD to be very significantly deepened, as the entire model sediment
column in oxygenated (Figure 1.4). These trends are reflected by the increasing size of shallow
OPD contour spaces as biomixing intensity increases versus the increasing size of the deep and
>10 cm OPD contour spaces as bioirrigation intensity increases. However, it is important to note
that these very deep simulated OPDs would not be found in modern environments, largely due to
the anactualistic boundary conditions of very high bottom water oxygen concentrations and very
low organic matter fluxes in shallow marine environments. In general, oxygen penetration depths
measured from the inner to outer shelf (0-200 m water depth) do not reach 2.5 cm (Jørgensen et
al., 2022). Modern environments with OPDs greater than 10 cm are limited to water depths
greater than 2000 m, where bottom water oxygen concentrations are high and net primary
productivity is low (Jørgensen et al., 2022).
Impact of organic matter flux on the effect of bioturbation
Our results also demonstrate that organic matter flux exerts a major control on the
magnitude of impact that bioturbation can have on the OPD (Figure 1.5). At bottom water
oxygen concentrations of [O2,BW]=0.07 mM, increasing bioturbation intensity has an
insignificant, sub-centimeter scale impact on the OPD, and increasing organic matter further
mutes the impact of bioturbation. For example, at an organic matter flux of CH2Otot,F = 150 µmol
45
cm
-2
yr
-1
, the OPD is 0.66 cm with no bioturbation, shallows by less than centimeter with
Ediacaran and Terreneuvian bioturbation and deepens by less than a centimeter with modern
bioturbation (Figure 1.5). As the organic matter flux increase to 300 and 450 µmol cm
-2
yr
-1
, the
OPD still shallows with Ediacaran and Terreneuvian bioturbation and deepens with modern
bioturbation, but the magnitudes of change become increasingly insignificant as organic matter
flux increases (Figure 1.5). In sum, Ediacaran and Terreneuvian bioturbation always shallow the
OPD while modern bioturbation always deepens the OPD, but the significance of those changes
decrease as the organic matter flux increases.
This pattern holds true and is even more pronounced when oxygen is increased to [O2,BW]
= 0.14 mM (Figure 1.5), such that more significant changes in the OPD can occur at higher
bottom water oxygen concentrations. At the lowest organic matter flux of CH2Otot,F = 150 µmol
cm
-2
yr
-1
, the OPD is 1.3 cm with no bioturbation, does not change with Ediacaran bioturbation,
shallows by less than 1 cm with Terreneuvian bioturbation, and significantly deepens to 6.2 cm
with modern bioturbation (Figure 1.4). When the organic matter flux is increased to CH2Otot,F =
300 µmol cm
-2
yr
-1
, the OPD shoals to 0.66 cm with no bioturbation, also shallows insignificantly
with both Ediacaran and Terreneuvian bioturbation, and still deepens, although with a much
smaller magnitude, to 1.2 cm with modern bioturbation (Figure 1.5). Finally, at the highest
organic matter flux of CH2Otot,F = 450 µmol cm
-2
yr
-1
, the OPD is further shallowed to 0.46 cm
with no bioturbation, shallows negligibly with both Ediacaran and Terreneuvian bioturbation,
and only slightly deepens with modern bioturbation. Thus, only at very low organic matter fluxes
and higher bottom water oxygen concentrations within the range of Ediacaran-Cambrian
boundary conditions is even modern bioturbation capable of significantly impacting the OPD on
a magnitude of greater than 1 cm. These results are consistent when the same OPD simulations
46
are conducted using a more geochemically complex model (Zhao et al., 2020), which results in
sediment oxygen profiles that are consistently not significantly impacted by Ediacaran or
Terreneuvian bioturbation (Figure S1.7; Figure S1.8).
Bioturbation intensities and oxygen consumption rates
Finally, we focus on the effect of biomixing versus bioirrigation and different intensities
of bioturbation on oxygen consumption rates to investigate a mechanism for why different
modes of bioturbation have such different effects on the OPD. Different intensities of
bioturbation alter the nature of oxygen consumption in the sediment. Without bioturbation, the
rate of maximum oxygen consumption occurs at the sediment-water interface primarily via
aerobic respiration. Oxygen consumption via reoxidation reactions consist of only a minor
component of total aerobic respiration but does become the dominant pathway below around
0.11 cm when aerobic respiration decreases significantly (Figure 1.6). With Ediacaran
bioturbation, the maximum rate of oxygen consumption decreases but still occurs at the
sediment-water interface. Oxygen is still primarily consumed via aerobic respiration until around
0.15 cm, where reoxidation reactions become slightly dominant (Figure 1.6). With Terreneuvian
bioturbation, the maximum oxygen consumption rate is further decreased and shifts away from
the sediment-water interface to a sediment depth of 0.11 cm and, for the first, primarily via
reoxidation instead of aerobic respiration (Figure 1.6). Finally, with modern bioturbation, oxygen
consumption rates are significantly decreased overall. The maximum oxygen consumption rate
occurs at a sediment depth of 0.30 cm and is consumed primarily via aerobic respiration until
around 0.5 cm, where oxygen consumption via reoxidation pathways becomes dominant (Figure
1.6).
47
Whether or not biomixing or bioirrigation is the dominant bioturbation behavior can also
significantly change the fate of oxygen in the sediment. Increasing biomixing both shifts the
maximum rate of oxygen consumption further below the sediment-water interface and increases
the rate of oxygen consumption via reoxidation reactions (Figure S1.3). In contrast, increasing
the intensity of bioirrigation does not change the depth nor dominant pathway of maximum
oxygen consumption. For Ediacaran, Terreneuvian, and modern bioirrigation intensities, all three
depths of maximum oxygen consumption are at the sediment-water interface and dominantly
driven by aerobic respiration. Increasing bioirrigation does, however, most significantly decrease
the total rate of oxygen consumption via reoxidation reactions, thereby also decreasing the total
oxygen consumption occurring below the zone in which aerobic respiration dominates (Figure
S1.3).
1.4 Discussion
Geobiological implications for Ediacaran-Cambrian transition trace fossils
The trace fossil record of the Ediacaran and Terreneuvian suggests that, globally,
biomixers were more common than bioirrigators during the Ediacaran-Cambrian transition.
Ichnodiversity clearly increases (around three-fold) from the Ediacaran to the Terreneuvian
(Table 1.1; Figure 1.1) (Buatois and Mángano, 2018; Buatois et al., 2020; Mángano and Buatois,
2020). However, there is no change in the proportion of biomixers to bioirrigators in newly
evolved behaviors (Figure 1.1). Whether the dominance of biomixing is measured in terms of
unique ichnogenera or unique stratigraphic occurrences – which are akin to ‘ichnorichness’ and
‘ichnoabundance’, respectively – biomixing is the dominant bioturbation behavior in the
Ediacaran and remains so in the Terreneuvian. This recorded dominance of biomixing is perhaps
48
surprising, given previous interpretations that the lack of a sedimentary mixed layer in the early
Cambrian is representative of bioirrigation being the dominant bioturbation behavior (Tarhan,
2018b), although ultimately reflects the overall weak intensity of Ediacaran-Cambrian
bioturbators.
Bioirrigation activity does appear to increase from the Ediacaran to the Terreneuvian
based on stratigraphic occurrences (Figure 1.1). This could reflect a variety of factors, ranging
from facies bias to an environmentally driven increase in bioirrigators’ activity or population
densities. First, this could reflect a paleoenvironmental bias, as early bioirrigators may be
restricted to certain nearshore paleoenvironments (Seilacher, 1967) that might be more
represented in the Terreneuvian trace fossil record. Alternatively, bioirrigators may become more
frequently preserved in the trace fossil record if biomixing intensity decreases, as strong
sediment disruption can erase sessile animals’ trace fossils from the mixed layer (Tarhan, 2018a)
and strong deposit feeder biomixing activity can exclude bioirrigating suspension feeders from
ecosystems (Rhoads and Young, 1970). However, the lack of a globally widespread, well-
developed mixed layer in shallow marine environments (Tarhan et al., 2015; Tarhan, 2018b) and
the dominance of biomixers over bioirrigators in the Ediacaran (Figure 1.1) suggests the increase
in bioirrigation trace fossil occurrences is not a taphonomic artifact. The most feasible
explanation of these patterns is that bioirrigators became more active in the Terreneuvian, likely
due to biotic innovations and changing global environmental conditions. In particular,
suspension feeders – which are generally effective bioirrigators – tend to be excluded from low
oxygen environments compared to deposit feeding biomixers (Rhoads, 1975), so an increase in
bottom water oxygen concentrations in global shallow marine environments would likely allow
for the preservation of an increase in bioirrigation trace fossil occurrences. Suspension feeders
49
also thrive in environments with high amounts of suspended detritus (Rhoads, 1975), so an
increase in their trace fossil abundances through the Ediacaran-Cambrian transition may also
reflect changes in the organic matter cycle which increased their food availability (Sperling and
Stockey, 2018).
Not all biomixing and bioirrigating fauna would be expected to have the same
environmental impact as each other. This is especially important in considering any local effects
that some rare, particularly large and architecturally complex ichnogenera in the Ediacaran and
Terreneuvian may have. The ecosystem engineering impacts of specific ichnogenera have been
previously explored in the Fortunian, and it was concluded that trace fossils such as
Thalassinoides, Treptichnus, Teichichnus, and Gyrolithes represent relatively high-impact
bioturbation behaviors with a more significant capability to alter the benthic environment for
other animals than other contemporary ichnogenera (Herringshaw et al., 2017). As these four
ichnogenera are bioirrigation trace fossils, our results support the notion that the animals that
made those trace fossils would have been well-suited for oxygenating the sediments around
them. In the Ediacaran, Parapsammichnites and Arenicolites likely represent the major
bioturbators most significant for impacting oxygen in the sediment (Table 1.1).
Parapsammichnites, which provides the earliest evidence of sediment bulldozing (relatively
large deposit feeding behaviors that displace significant amounts of sediment) (Buatois et al.,
2018; Darroch et al., 2020), represents the biomixing behavior most likely to have the strongest
impact. Parapsammichnites is the largest biomixing trace fossil in the Ediacaran, as it is the first
centimeter-scale coelomic-grade trace fossil that appears in the rock record (Buatois et al., 2018),
and thus likely resulted in the largest amount of organic matter mixing throughout the sediment
to stimulate oxygen consumption and ultimately shallow the OPD. Densely bioturbated bedding
50
planes covered in small trace fossils such as Helminthoidichnites and Helminthopsis (Darroch et
al., 2020) may also represent collectively strong biomixing by many bioturbators. However,
modern research suggests that biomixing rates are more impacted by animal size than density
(Sandnes et al., 2000). Thus, Parapsammichnites remains the best representation of early intense
biomixing. However, Parapsammichnites is rare in the Ediacaran and currently only identified in
one stratigraphic unit in the Nama Group, Namibia (Buatois et al., 2018; Darroch et al., 2020),
and thus it is unlikely to represent a global forcing for changes in benthic oxygen cycling.
Among Ediacaran bioirrigators, Arenicolites is the best evidence for the presence of macrofauna
that could have significantly oxygenated the sediment, as the trace-maker would have likely
ventilated its U- and J-shaped burrows down to a few centimeters (Oji et al., 2018). However,
like Parapsammichnites, Arenicolites is exceptionally rare in the Ediacaran, and in isolation may
have not had a large enough effect to significantly increase sediment oxygen concentrations, as
oxygen diffusion away from the burrow in the sediment would be quickly consumed by
biogeochemical reactions (Aller, 1980). More common terminal Ediacaran bioirrigation trace
fossils such as Treptichnus and Streptichnus, which have been previously interpreted as high-
impact Ediacaran ecosystem engineering trace fossils (Cribb et al., 2019), likely also represent
relatively intense Ediacaran bioirrigation, although their small size prior to the Ediacaran-
Cambrian boundary (Darroch et al., 2020) suggests their effects may be limited.
Finally, not every benthic ecosystem in the Ediacaran and Terreneuvian would be
expected to have the same composition of biomixers and bioirrigators, nor the same biological
activity. For example, from an ichnofacies perspective, bioirrigators would be expected to be
more common in nearshore environments with abundant wave-suspended detritus, whereas
biomixers would likely be more common in deeper offshore areas where organic matter is more
51
significantly deposited (Seilacher, 1967; Rhoads, 1975). However, we note that for the Ediacaran
in particular, biomixers may have been more dominant in nearshore environments where
microbial mats were abundant and could serve as a food source for deposit feeders. Similarly,
regional-scale palaeoceanographic and environmental differences would have certainly
influenced the distribution of bioturbation intensity at any given time. In the modern ocean,
bioturbation intensity and benthic community structure is often controlled by ecophysiological
factors such as food (e.g., organic matter flux), bottom water oxygen concentrations, and
temperature at the sea floor (Levin et al., 2001; Smith and Rabouille, 2002; Woolley et al.,
2016). These ecophysiological factors are certainly not homogenous throughout the ocean today,
and thus are not expected to have been equal in every paleoenvironment throughout Earth
history.
Impact of Ediacaran-Cambrian bioturbation on sedimentary oxygen dynamics
Our modeling results ultimately offer little support to the hypothesis that early
bioturbators could have significantly oxygenated the sediment column. When biomixing and
bioirrigation are considered separately, our results show that as biomixing intensity increases
from Ediacaran to modern, the OPD shoals significantly, and in contrast, increasing bioirrigation
intensity from Ediacaran to modern intensities deepens the OPD (Figure 1.4). This is a key
result, considering the dominance of biomixing in the Ediacaran and Terreneuvian fossil records
(Figure 1.1). Furthermore, and most critically, our modeling results ultimately indicate that
Ediacaran and Terreneuvian bioturbation had an insignificant impact on the OPD, even slightly
shoaling the OPD. Although bioirrigation trace fossil occurrences do increase in the
Terreneuvian (Figure 1.1), it is important to note that for bioirrigation alone there is a negligible
52
OPD-deepening effect within the plausible lower range of Ediacaran and Terreneuvian bottom
water oxygen concentrations ([O2,BW] = 0.07 – 0.14 mM) and organic matter fluxes (CH2Otot,F =
150 – 450 µmol cm
-2
yr
-1
) (Figure 1.4; Figure 1.5). Overall, given the context from the trace fossil
record that biomixers were more dominant that bioirrigators (Table 1.1; Figure 1.1) and
geochemical evidence that bottom water oxygen concentrations and organic matter fluxes were
lower than in modern environments (Krause et al., 2018; Lu et al., 2018; Sperling and Stockey,
2018; Fakhraee et al., 2020; Sperling et al., 2021), we take these modeling results as evidence
that neither Ediacaran nor Terreneuvian bioturbators were capable of significantly oxygenating
the sediment column. Thus, Ediacaran-Cambrian transition bioturbators were unlikely to have
been capable of promoting habitability of deeper sediment layers for other aerobic infauna (c.f.
Mángano and Buatois, 2017), nor were they likely capable of causing such significant changes to
the redox structure of the seafloor that it resulted in altered biogeochemical cycles that
influenced atmospheric oxygen concentrations (c.f. van de Velde et al., 2018; Dahl et al., 2019).
Previous work has argued that local well-developed mixed layer depths in the
Terreneuvian would increase oxygen concentrations by removing microbial mats to promote
oxygen diffusion into the sediment (Gougeon et al., 2018). However, sediment mixing is not
equivalent to sediment oxygenation. A well-developed mixed layer would result from strong
biomixing, not bioirrigation (Boudreau, 1998; Tarhan, 2018b), and our results suggest that such
an increase in biomixing intensity would result in a shallowing of the OPD (Figure 1.4).
Globally, the mixed layer depth does not appear to have been well developed until later in the
Paleozoic (Tarhan et al., 2015). We do find that the mixed layer depth deepens – and thereby
biomixing intensity increases – slightly in the Terreneuvian (Table S1.10), but our results
ultimately suggest that such a level of increase in bioturbation intensity from the Ediacaran to the
53
Terreneuvian had a negligible impact on the OPD (Figure 1.3). Previous work has also argued
that the evolution of early complex bioturbation ecosystem engineering behaviors may have
driven significant biogeochemical change in the Ediacaran (Cribb et al., 2019). Although
complex trace fossils that represent relatively impactful ecosystem engineering behaviors
increase in intensity in the terminal Ediacaran (Cribb et al., 2019) and into the Fortunian
(Herringshaw et al., 2017), not all high-impact benthic ecosystem engineering causes sediment
oxygenation, and it was unlikely that such change resulted in any large-scale sediment
oxygenation event.
These results all suggest a more protracted evolution and ecological impact of
bioturbation as significant global ecosystem engineers until later in the Paleozoic (Tarhan et al.,
2015). However, early bioturbators may have had impacts on other critical biogeochemical
cycles during this interval, including phosphorus, organic carbon, and sulfur (Canfield and
Farquhar, 2009; Boyle et al., 2014; van de Velde et al., 2018; Tarhan et al., 2021), which are
critical for the evolution of marine ecosystems but not extensively here. For example, we do find
that increasing bioturbation intensity changes the burial of organic matter and organic matter and
FeS, although the magnitude of that change is a factor of organic matter delivery to the seafloor
and bottom water oxygen concentrations (Figure S1.5). Additionally, the subset of simulations
we conducted using the more geochemically complex model from Zhao et al. (2020) does not
consistently result in the same OPD shoaling (Figure S1.7; Figure S1.8), underscoring the
potential importance of the complex, non-linear impacts that bioturbation has on biogeochemical
cycles like iron and phosphorus (van de Velde et al., 2020). Future work investigating the role
that early bioturbators play in other biogeochemical cycles that exert major controls on benthic
54
ecology will be critical for mechanistically linking the radiations observed in both the trace fossil
and body fossil records in the Cambrian and early Paleozoic.
Our modeled oxygen consumption profiles help mechanistically explain why biomixing
and bioirrigation, as well as the different bioturbation intensities, have such different impacts on
the OPD. The primary effect of increasing biomixing intensity on oxygen consumption is
shifting the maximum zone of oxygen consumption downwards and increasing oxygen
consumption via reoxidation (Figure S1.3). This occurs because biomixing transports more
organic matter downwards below the oxic-anoxic interface to stimulate anaerobic
remineralization pathways, which then results in the increased production of reduced compounds
that consume oxygen via reoxidation reactions near the oxic-anoxic interface (Figure S1.3).
Furthermore, as long as sediments are supplied enough organic matter for oxygen consumption
to occur, biomixing will shift the zone of maximum oxygen consumption away from the
sediment-water interface, thus making it more difficult for oxygen to diffuse into the sediment to
resupply what is lost to consumption (van de Velde and Meysman, 2016). In contrast, the
primary effect of increasing bioirrigation intensity is the injection of oxygen into the deeper
sediment column coupled with decreased oxygen consumption via reoxidation, which is due to
reduced compounds produced by anaerobic respiration being flushed out of the sediment column
by bioirrigation (Aller and Aller, 1998; van de Velde and Meysman, 2016). Additionally,
bioirrigation does not transport organic matter in the sediment, so it does not stimulate anaerobic
respiration the same way biomixing does (van de Velde and Meysman, 2016). This maintains an
oxygen consumption profile where oxygen is primarily consumed by aerobic respiration near the
sediment-water interface (Berner and Westrich, 1985) (Figure S1.3), in which oxygen can
resupply itself via diffusion or, more significantly, via bioirrigation to greater sediment depths.
55
When biomixing and bioirrigation are modeled together at different intensities, results show that
Ediacaran and Terreneuvian bioturbation more closely resemble the effects of biomixing, where
oxygen consumption via reoxidation reactions is stimulated, and for Terreneuvian bioturbation,
the zone of maximum oxygen consumption is shifted downwards (Figure 1.6). This suggests
bioturbation-driven sediment oxygenation during the early Paleozoic was delayed until the
effects of bioirrigation were, at some threshold, stronger than those of biomixing, resulting in a
stronger supply of oxygen downwards to resupply consumed oxygen.
Strong, modern-like bioturbation can deepen the OPD, even at relatively low-oxygen and
higher organic matter fluxes (Figure 1.3; Figure 1.5), demonstrating that bioturbators did, at
some point in time, evolve the capability of extensively oxygenating shallow marine sediments.
Further work is needed to constrain when modern-like bioturbation evolved, but it was likely not
until later in the Paleozoic or even Mesozoic (Tarhan et al., 2015). Rare occurrences of large,
deep trace fossils that were likely more strongly bioirrigated do occur in the early Paleozoic
(Zhang et al., 2017), but evidence for intense sediment churning and large three-dimension
network burrows is still uncommon globally even in late Ordovician trace fossils (Tarhan et al.,
2015; Tarhan, 2018b). The increase in trace fossil size and density in late Silurian trace fossil
assemblages, particularly among sediment bulldozers, may be the earliest evidence for strong
biomixing (Tarhan et al., 2015). However, strong biomixing and bioirrigation behaviors are not
commonly represented in the trace fossil record until the Mesozoic Marine Revolution, which
promoted the rise of the Modern evolutionary fauna (Vermeij, 1977; Sepkoski, 1981), including
more modern-like bioturbating infaunal communities with stronger intensities (Tackett and
Bottjer, 2012; Buatois and Mángano, 2018). For example, trace fossils made by irregular
echinoids first appear in the Mesozoic, and trace fossils created by crustaceans, bivalves, and
56
worms become increasingly abundant after the Paleozoic (Buatois and Mángano, 2018). These
deep network burrows that became more common in the Mesozoic are likely representative of
the first major radiation of strong bioirrigators and burrow ventilators that were capable of
significantly deepening the OPD and extensively oxygenating the sediments in their ecosystems.
Potential environmental controls on benthic oxygen dynamics
Organic matter has a competing role in the potential for bioturbation-driven sediment
oxygenation, as the magnitude of each intensity of bioturbation’s effect is increasingly muted
with increasing organic matter flux (Figure 1.5). Even at relatively high bottom water oxygen
concentrations, increasing the organic matter flux to the seafloor mutes the oxygenating impact
even of modern bioturbation intensities (Figure 1.5). Thus, one might expect that relatively
strong bioirrigators in well oxygenated benthic environments may have been able to deepen the
OPD, at least slightly, during the Ediacaran-Cambrian transition. However, long-term changes in
the organic carbon cycle – such as the efficiency of organic carbon export and rapid delivery to
the sediment surface – may have significantly increased carbon export efficiency at low
atmospheric oxygen concentrations due to faster sinking fecal pellets or changes in ocean
temperature (Lu et al., 2018; Fakhraee et al., 2020; Boscolo-Galazzo et al., 2021). Such an
increase in organic matter flux to the seafloor would likely have muted any oxygenating effect of
bioirrigators in the earliest Cambrian. Additionally, because more organic matter availability
likely physiologically fueled biomixing deposit feeders (Sperling and Stockey, 2018), an
increase in organic matter may have caused a shallowing of the OPD by both stimulating aerobic
respiration and by increasing biomixing intensity that promotes oxygen consumption via
reoxidation.
57
Finally, bioturbation can impact sediment oxygen dynamics in ways that have not been
explicitly tested here. First, we note that bioturbation may impact the flux of oxygen into the
sediment by influencing the benthic boundary layer, either by creating burrow structures that
change sediment topography near the sediment-water interface, or by changing the roughness of
the sediment-water interface due to changing the size or distribution of microbial mats on the
seafloor. This impact of bioturbation is likely only important on a very local scale, but
nonetheless it is an important avenue for future research on the role of bioturbation in local-scale
benthic ecology. Additionally, further work is needed to constrain how early bioturbators’
disruption of microbial mats alone impacted sediment oxygenation. The hypothesis that the
disappearance of microbial mats in the early Cambrian allowed for the diffusion of oxygen into
sediment porewaters has not been explicitly biogeochemically tested in terms of the impact of
the ecological structure and metabolic diversity of microbial mat communities. Nor has it been
tested in terms of the fate of the organic matter in the microbial mat as it is bioturbated. In terms
of the former, microbial mats dominated by photosynthetic cyanobacteria, which would be
feasible for the very shallow marine settings that early bioturbators occupied, can actually
increase oxygen concentrations in the porewaters relative to the water column (Gingras et al.,
2011). Thus, depending on the physiology of the microbial communities, bioturbation and
mining of microbial mats may have temporarily decreased sediment porewater oxygen
concentrations. In terms of the latter, any downwards mixing of organic matter derived from
microbial mats may have amplified the shallowing effect on the OPD, although the magnitude
and duration of such an effect would depend on the reactivity of the organic matter being
supplied to the sediment and within the microbial mat. We note that there may also be some
specific macrofauna-microbial mat interactions that uniquely alter sediment oxygen cycling due
58
to the nature of the microbial mat, but such interactions are difficult to constrain from the trace
fossil record alone. Additionally, the importance of the role of microbial mats as a supply of
organic matter depends on the prevalence of the mats in Ediacaran and Terreneuvian bioturbated
stratigraphic units, which may be influenced by both real changes in abundance of microbial
mats across the Ediacaran-Cambrian transition and the number of stratigraphic units which were
deposited in the photic zone. However, our results suggest that, in general, increased biomixing
of organic matter from the microbial mat downwards into the sediment would most likely
promote a shallowing of the OPD regardless of any increased diffusive oxygen exchange
between the sediment porewater and the overlying water column.
1.5 Conclusions
In conclusion, the results of this study highlight three main findings. First, the trace fossil
record of the Ediacaran and Terreneuvian suggests that bioturbators during the Ediacaran-
Cambrian transition were predominantly biomixers, not bioirrigators. Second, bioturbators
during the Ediacaran-Cambrian transition were most likely not capable of significantly
increasing oxygen concentrations in the shallow to deep sediment tiers, particularly given the
dominance of biomixers in the trace fossil record. Third, the magnitude of bioturbation’s
oxygenating effect is regulated by organic matter dynamics, which may be particularly important
as increased organic matter fluxes to the seafloor later in the Paleozoic (Fakhraee et al., 2020)
may have fueled biomixers (Sperling and Stockey, 2018) while muting the oxygenating effect of
later-evolved more intense bioirrigators. Further work is needed to constrain the role and nature
of the disappearance of microbial mats in the early Cambrian, as well as Ediacaran and
Terreneuvian biomixers’ and bioirrigators’ impacts on other key biogeochemical cycles, in order
59
to better understand bioturbation’s ecosystem engineering role in shaping benthic ecology in the
early Paleozoic. However, our results ultimately suggest that the effects on the fate of oxygen in
the sediment proposed by the Agronomic Revolution were delayed until after the Ediacaran and
Terreneuvian when a unique, optimal combination of organic matter dynamics, bottom water
oxygen concentrations, and biotic innovations allowed bioturbators to extensively oxygenate
their sedimentary environments.
60
1.6 Tables and figures
Table 1.1 Ediacaran and Terreneuvian ichnogenera.
Each ichnogenera is assigned bioturbation behavior (biomixing or bioirrigation) with data from
Buatois et al. (2020).
Ediacaran Terreneuvian
Ichnogenera Behavior Ichnogenera Behavior
Archaeonassa Biomixing Alcyonidiopsis Biomixing
Arenicolites Bioirrigation Allocotichnus Biomixing
Bergaueria Bioirrigation Altichnus Bioirrigation
Circulichnus Biomixing Archaeonassa Biomixing
Conichnus Bioirrigation Arenicolites Bioirrigation
Gordia Biomixing Asaphoidichnus Biomixing
Helminthoidichnites Biomixing Astropolichnus Bioirrigation
Helminthopsis Biomixing Bergaueria Bioirrigation
Lamonte Biomixing Ceiichnus Biomixing
Multina Biomixing Circulichnus Biomixing
Nenoxites Biomixing Cochlichnus Biomixing
Palaeophycus Biomixing Conichnus Bioirrigation
Parapsammichnites Biomixing Cruziana Biomixing
Planolites Biomixing Curvolithos Biomixing
Streptichnus Bioirrigation Cylindrichnus Biomixing
Torrowangea Biomixing Dactyloidites Biomixing
Treptichnus Bioirrigation Dendrorhaphe Biomixing
Diplocraterion Bioirrigation
Diplopodichnus Biomixing
Gordia Biomixing
Guanshanichnus Biomixing
Gyrolithes Bioirrigation
Heliochone Biomixing
Helminthoidichnites Biomixing
Helminthopsis Biomixing
Laevicyclus Bioirrigation
Lingulichnus Bioirrigation
Multina Biomixing
Oichnus Bioirrigation
Oldhamia Biomixing
Palaeophycus Biomixing
Paleodictyon Biomixing
Phycodes Bioirrigation
Pilichnus Biomixing
Planolites Biomixing
Protopaleodictyon Biomixing
Protovirgularia Biomixing
61
Psammichnites Biomixing
Rhizocorallium Biomixing
Rosselia Bioirrigation
Saerichnites Biomixing
Skolithos Bioirrigation
Syringomorpha Bioirrigation
Taenidium Biomixing
Tasmanadia Biomixing
Teichichnus Biomixing
Thalassinoides Bioirrigation
Torrowangea Biomixing
Treptichnus Bioirrigation
Trichophcyus Biomixing
Trypanites Bioirrigatoin
Volkichnium Biomixing
Zoophycos Biomixing
62
Figure 1.1 Relative abundance of biomixing and bioirrigation
Relative abundance of bioturbation behaviors based on ichnodiversity (number of unique
ichnogenera in Table 1.1) and ichnoabundance (number of unique stratigraphic occurrences of
ichnogenera in Table 1.1) for each time bin. Dark blue area of bar graphs indicates bioirrigation,
and dark orange area indicates biomixing. Data is based on Buatois et al. (2020).
63
Figure 1.2 Sensitivity analyses of biodiffusion coefficients.
Sensitivity analyses for chosen Ediacaran and Terreneuvian biodiffusion coefficients at
increasing bottom water oxygen concentrations and three different organic matter fluxes. Four
dotted lines are the simulated OPD as the biodiffusion coefficient increases at four different
bottom water oxygen levels. Oxygen concentrations increase as colors scale from beige to dark
red. The Ediacaran biodiffusion coefficient is 0.1 cm
2
yr
-1
and the Terreneuvian biodiffusion
coefficient is 0.98 cm
2
yr
-1
, which are plotted as the brown and purple dashed lines, respectively.
Bioirrigation coefficient here is 0 yr
-1
. Full boundary conditions are given in Table S1.6.
64
Figure 1.3 Simulated OPDs at increasing bioturbation intensities.
Contour plots of modeling results with no bioturbation and Ediacaran, Terreneuvian, and Modern
bioturbation parameters. Ediacaran bioturbation parameters are DB,0 = 0.1 cm
2
yr
-1
and 0 = 3.65
yr
-1
. Terreneuvian bioturbation parameters are DB,0 = 0.98 cm
2
yr
-1
and 0 = 35.8 yr
-1
. Modern
bioturbation parameters are DB,0 = 10 cm
2
yr
-1
and 0 = 365 yr
-1
. Each colored contour indicates
resulting simulated OPD depths at the same x – x+0.5 range. Colors are consistent across each
plot and correspond to the legend on the right, where dark purple colors are shallowest OPDs,
and light green colors are the deepest OPDs. Gray bars on axes are estimated ranges of organic
matter flux and bottom-water oxygen concentrations, which are discussed in the text. Full
boundary conditions are given in Table S1.7.
65
Figure 1.4 Differences in OPD simulations between biomixing and bioirrigation.
Contour plots of modeling results comparing biomixing versus bioirrigation at Ediacaran,
Terreneuvian, and modern intensities. Biodiffusion coefficients are DB,0 = 0.1 cm
2
yr
-1
for
Ediacaran biomixing, DB,0 = 0.98 cm
2
yr
-1
for Terreneuvian biomixing, and DB,0 = 10 cm
2
yr
-1
for
modern biomixing. Bioirrigation coefficients are 0 = 3.65 yr
-1
for Ediacaran bioirrigation, 0 =
35.8 yr
-1
for Terreneuvian bioirrigation, and 0 = 365 yr
-1
for modern bioirrigation. For
biomixing results, 0 = 0 yr
-1
for all three intensities. For bioirrigation results, DB,0 = 0 cm
2
yr
-1
for all three intensities. Color scale is consistent with Figure 1.3. Gray bars on axes are estimated
ranges of organic matter flux and bottom-water oxygen concentrations, which are discussed in
the text. Full boundary conditions are given in Table S1.7.
66
Figure 1.5 Effect of organic carbon flux on the OPD.
Effect of organic matter on OPD at different oxygen levels and bioturbation intensities.
Simulation of the OPD at low (left, [O2,BW]=0.07 mM, 25% PAL) and high (right, [O2,BW]=0.14
mM, 50% PAL) end members for Ediacaran-Cambrian transition bottom water oxygen
concentrations at three different organic matter fluxes (500, 300, and 450 µmol cm
2
yr
-1
) and four
intensities of bioturbation. See main text or Figure 1.3 caption for Ediacaran, Terreneuvian, and
modern bioturbation parameters. Specific OPDs for each bioturbation intensity in all six
scenarios given in the main text. Solid lines at 0 cm indicate the sediment-water interface. Full
boundary conditions are given in Table S1.8.
67
Figure 1.6 Impact of bioturbation intensities on oxygen consumption rates.
Oxygen consumption rate profiles in the upper 1 cm of the sediment column for no bioturbation
and Ediacaran, Terreneuvian, and modern bioturbation parameters. Total oxygen consumption
(dotted line) is broken up into aerobic respiration (red area) and reoxidation reactions (blue area).
Ediacaran, Terreneuvian, and modern bioturbation parameters can be found in the main text or
Figure 1.3 caption. Boundary conditions are listed in Table S1.9.
68
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75
S1. Supplementary appendix
Supplementary figures
Figure S1.1 Biodiffusion and bioirrigation depth profiles.
Biodiffusion profiles follow a sigmoidal curve, representing the relationship between
bioturbators and organic matter, in which biomixing is assumed to be strongest at the sediment-
water interface where organic matter is abundant. Bioirrigation follows an exponential profile,
representing a similar depth-dependent relationship between bioirrigation intensity and sediment
depth.
0
5
10
15
Depth (cm)
0
5
10
15
Depth (cm)
0
5
10
15
Depth (cm)
0
5
10
15
Depth (cm)
0
5
10
15
Depth (cm)
0
5
10
15
Depth (cm)
0.0 2.5 5.0 7.5 10.0
Ediacaran Biomixing (Db.0=0.1)
0 100 200 300
Ediacaran Bioirrigation (a.0=3.65)
0.0 2.5 5.0 7.5 10.0
Terreneuvian Biomixing (Db.0=0.98)
0 100 200 300
Terreneuvian Bioirrigation (a.0=35.77)
0.0 2.5 5.0 7.5 10.0
Modern Biomixing (Db.0=10)
0 100 200 300
Modern Bioirrigation (a.0=365)
76
Figure S1.2 Sensitivity analyses for bioirrigation coefficients
Plots in the same row have the same biodiffusion coefficients, and plots in the same column have
the same organic matter fluxes. Chosen Ediacaran and Terreneuvian bioirrigation coefficients are
given as tan and purple vertical dashed lines, respectively. OPD is plotted as bioirrigation
intensity increases for different bottom water oxygen concentrations, which increase from 0.014
– 0.28 mM. Increasing oxygen concentrations are represented by the color scale from tan to dark
red.
77
Figure S1.3 Oxygen consumption rates for different bioturbation parameters.
Oxygen consumption rates for the upper 1 cm of the sediment column for different intensities of
bioturbation (none, Ediacaran, Terreneuvian, and modern) and biomixing and bioirrigation
modelled separately. Total oxygen consumption (dotted line) is broken up into aerobic
respiration (red area) and reoxidation reactions (blue area). Consumption rates are relative to
sediment pore water volumes. Boundary conditions for this model are listed in Table S1.9.
78
Figure S1.4 Analysis of oxygen flux to the sediment.
Relative contributions of oxygen flux to the sediment via diffusive flux (dark blue) vs
bioirrigation (light blue) at different bioturbation parameterizations, increasing with intensity
from no bioturbation to modern. Organic matter flux increases downward, from 100 to 450 µmol
cm
-2
yr
-1
. Oxygen concentrations are 0.07 mM for the left column and 0.14 mM for the right
column.
79
Figure S1.5 Organic matter burial simulations.
Burial of organic matter (left) and FeS (right) for no bioturbation, Ediacaran bioturbation, and
Terreneuvian bioturbation. Organic matter flux is consistent across rows, increasing downwards
towards 700 µmol cm
-2
yr
-1
. Bottom water oxygen concentrations increase consistently across
each plate. Darker lines correspond to more intense bioturbation.
80
Figure S1.6 Sediment profile simulations.
Sediment profiles (0-10cm) of each chemical species in the model. Boundary conditions are the
high end-member values from Table S1.8. Gray lines are no bioturbation, green lines are
Ediacaran bioturbation, pink lines are Terreneuvian bioturbation, and purple lines are modern
bioturbation. Solid lines are bioturbation as biomixing and bioirrigation together, dashed lines
are biomixing alone, and dotted lines are bioirrigation alone.
81
Figure S1.7 Low oxygen end-member comparisons with SedChem.
Oxygen profiles over a 10 cm sediment column constructed from the SedChem reactive-transport
model (Zhao et al. 2020). Low end-member bottom water oxygen concentrations and same
organic matter flux conditions from Figure 1.4. Here, we note that despite SedChem’s more
comprehensive geochemical reaction network (including full sedimentary iron and phosphorus
cycles), neither Ediacaran nor Terreneuvian bioturbation significantly nor observably deepen the
OPD. Specifically, comparison to the results in presented Figure 1.4, the simulated OPD for each
bioturbation case is consistently deeper than those simulated by the model presented here, but
neither Ediacaran nor Terreneuvian bioturbation result in significant changes to the oxygen
profile and the depth at which oxygen reaches zero.
82
Figure S1.8 High oxygen end-member comparisons with SedChem.
Oxygen profiles over a 10 cm sediment column constructed from the SedChem reactive-transport
model (Zhao et al. 2020). High end-member bottom water oxygen concentrations and same
organic matter flux conditions from Figure 1.4. Similar to Figure S1.8, we note that despite
SedChem’s more comprehensive geochemical reaction network (including full sedimentary iron
and phosphorus cycles), as well as higher bottom water oxygen concentrations in this case,
neither Ediacaran nor Terreneuvian bioturbation significantly nor observably deepen the OPD.
Again, specifically comparing the results in Figure 1.4, the OPDs simulated by SedChem are
consistently deeper, but the broad results remain consistent between the two models that neither
Ediacaran nor Terreneuvian bioturbation significantly alters the sediment oxygen profile such
that the depth at which oxygen concentrations reach zero is deeper.
83
Supplementary tables
Table S1.1 Biogeochemical reaction set used in model.
Organic matter remineralization
R1: Aerobic respiration {CH 2O} f,s + O 2 → HCO 3
-
+ H
+
R2: Nitrate reduction
{CH 2O} f,s +
4
5
NO 3
-
→ HCO 3
-
+
2
5
N 2
+
2
5
H 2O +
1
5
H
+
R3: Sulfate reduction
{CH 2O} f,s +
1
2
SO 4
→ HCO 3
-
+
1−𝑓 .𝐹𝑒𝑆 2
HS
-
𝑓 .𝐹𝑒𝑆 2
FeS
1
2
H
+
Secondary redox reactions
R4: Canonical sulfur oxidation HS
-
+ 2O
2
→ SO
4
2-
+ H
+
R5: Ammonium oxidation NH 4
+
+ 2O 2 → NO 3
-
+ 2H
+
+ H 2O
R6: Iron sulfide oxidation
FeS +
9
4
O 2 +
3
2
H 2O → FeOOH + SO 4
2-
+ 2H
+
R7: Sulfide oxidation with nitrate
HS
-
+
8
5
NO 3
-
+
3
5
H
+
→
4
5
N 2 + SO 4
2-
+
4
5
H 2O
f.FeS = fraction of HS
-
precipitating as FeS
84
Table S1.2 Kinetic rate expressions for the reactions used in the model.
Reaction Kinetic rate expressions
M1 Fast organic matter
remineralization
(1- )kf[CH2Of]
M2 Slow organic matter
remineralization
(1- )ks[CH2Os]
R1 Aerobic respiration
[O
2
]
[O
2
]+ 𝐾 O
2
R
f
min
[O
2
]
[O
2
]+ 𝐾 O
2
R
s
min
R2 Nitrogen reduction
[NO
3
]
[NO
3
]+ 𝐾 NO
3
𝐾 𝑂 2
[O
2
]+ 𝐾 O
2
R
f
min
[NO
3
]
[NO
3
]+ 𝐾 NO
3
𝐾 𝑂 2
[O
2
]+ 𝐾 O
2
R
s
min
R3 Sulfate reduction
[SO4]
[SO4]+ 𝐾 SO4
𝐾 NO3
[NO3]+ 𝐾 NO3
𝐾 𝑂 2
[O2]+ 𝐾 O2
R
f
min
[SO
4
]
[SO
4
]+ K
SO
4
K
NO
3
[NO
3
]+ K
NO
3
K
O
2
[O
2
]+ K
O
2
R
s
min
R4 Canonical sulfur
oxidation
kCSO[O2][HS
-
]
R5 Ammonium oxidation kAMO[O2][NH4]
R6 Iron sulfide oxidation (1- )kISO[O2][FeS]
R7 Sulfide oxidation with
nitrate
kSNI[NO3][HS
-
]
85
Table S1.3 Kinetic constants for reactions used in the model.
Constant Symbol Unit Value Reference
Organic matter reduction
Decay constant for organic
matter – fast
kf year
-1
10.0 Katsev et
al. (2006)
Decay constant for organic
matter – slow
ks year
-1
0.1 Fossing et
al. (2004)
Monod constant for oxygen
consumption
KO2 µmol cm
-3
0.008 Meysman
et al.
(2015)
Monod constant for nitrate
reduction
KNO3 µmol cm
-3
0.008 Wang and
Van
Cappellen
(1996)
Monod constant for sulfate
reduction
KSO4 µmol cm
-3
0.9 Meysman
et al.
(2015)
Secondary redox reactions
Canonical sulfur oxidation kCSO µmol cm
-3
year
-1
10
+7
Meysman
et al.
(2015)
Nitrogen oxidation kAMO µmol cm
-3
year
-1
10
+7
Wang and
Van
Cappellen
(1996)
Iron sulfide oxidation kISO µmol cm
-3
year
-1
10
+7
Meysman
et al.
(2015)
Sulfide oxidation with nitrate kSNI µmol cm
-3
year
-1
10
+4
Wang and
Van
Cappellen
(1996)
86
Table S1.4 Reaction rates for consumption and production of chemical species.
Species Reaction rate expression
CH2Ofast
-k f* CH 2O fast
CH2Oslow
-k s* CH 2O slow
O2
-1*R1 – 2*R4 – 2*R5 – (9/4)*R6
SO4
2-
(-1/2)*R3 – R6
FeS
(1/2)*f.FeS*R3 – R6
HCO3
-
R1 + R2 + R3
NH4
+
(1/CNrat)*(R1 + R2 + R3)
NO3
-
(-4/5)*R2 + R5 – (8/5)*R7
HS
-
(1/2)*(1-f.FeS)*R3 – R4 – R7
87
Table S1.5 Model parameters used in all simulations and experiments.
Adapted from van de Velde and Meysman (2016) to reflect shallow marine conditions.
Parameter Depth Profile Symbol Unit Value
Sediment depth - L cm 15
Salinity Constant S - 30
Temperature Constant T °C 25
Pressure Constant P bar 1.013
pH Constant pH - 7.5
Porosity Constant - 0.8
Solid phase
density
Constant sed g cm
-3
2.6
Sedimentation
velocity
Constant Porewater v cm year
-1
0.2
Solid phase w cm year
-1
0.2
Biomixing Sigmoidal Biodiffusion coefficient
at the SWI
D B,0 cm
2
year
-1
No biomixing 0
Ediacaran biomixing 0.1
Terreneuvian biomixing 0.98
Moddern biomixing 10
Biodiffusion coefficient
at infinity
D B, cm
2
year
-1
0
Mixed layer Mixed layer depth x L cm
No bioturbation 0
Ediacaran 0.165
Terreneuvian 1.61
Modern 10
Attenuation coefficient x bm cm 2.0
Bioirrigation Exponential
decrease
Bioirrigation coefficient
at the SWI
0 year
-1
No bioirrigation 0
Ediacaran bioirrigation 3.65
Terreneuvian
bioirrigation
35.8
Modern bioirrigation 365
Bioirrigation coefficient
at infinity
0, year
-1
0.0
Bioirrigation depth x L,irr cm
Attenuation coefficient x irr cm 3.0
Fraction of
produced HS
-
precipitated as
FeS
- f.FeS - 0.1
C:N molar ratio - CNrat - 106.0
88
Table S1.6 Boundary conditions for Figure 1.2 and S1.1 simulations.
Species
Upper boundary
condition
Lower
boundary
condition
Value range Unit
CH2Ofast Flux No gradient 75, 150, and 350 µmol cm
-2
year
-
1
CH2Oslow Flux No gradient 75, 150, and 350 µmol cm
-2
year
-
1
FeS Flux No gradient 0 - 0 µmol cm
-2
year
-
1
O2 Fixed
concentration
No gradient 0.014 – 0.28 µmol cm
-3
HCO3 Fixed
concentration
No gradient 2.2 – 2.2 µmol cm
-3
NO3 Fixed
concentration
No gradient 0.005 – 0.1 µmol cm
-3
SO4 Fixed
concentration
No gradient 1.44 – 28.8 µmol cm
-3
NH4 Fixed
concentration
No gradient 0.0 – 0.0 µmol cm
-3
HS Fixed
concentration
No gradient 0.0 – 0.0 µmol cm
-3
89
Table S1.7 Boundary conditions for Figure 1.3 and 1.4 simulations.
Species
Upper boundary
condition
Lower
boundary
condition
Value range
(minimum to
maximum)
Unit
CH2Ofast Flux No gradient 50.0 – 225 µmol cm
-2
year
-
1
CH2Oslow Flux No gradient 50.0 – 225 µmol cm
-2
year
-
1
FeS Flux No gradient 0.0 – 0.0 µmol cm
-2
year
-
1
O2 Fixed
concentration
No gradient 0.014 – 0.28 µmol cm
-3
HCO3 Fixed
concentration
No gradient 2.2 - 2.2
NO3 Fixed
concentration
No gradient 0.005 – 0.1 µmol cm
-3
SO4 Fixed
concentration
No gradient 1.44 – 28.8 µmol cm
-3
NH4 Fixed
concentration
No gradient 0.0 – 0.0 µmol cm
-3
HS Fixed
concentration
No gradient 0.0 – 0.0 µmol cm
-3
90
Table S1.8 Boundary conditions for Figure 1.5 simulations.
High end-member oxygen values from Zhao et al. (2020).
Species
Upper boundary
condition
Lower
boundary
condition
Low end
member
values
High end
member
values
Unit
CH2Ofast Flux No gradient 75
150
225
75
150
225
µmol cm
-2
year
-1
CH2Oslow Flux No gradient 75
150
225
75
150
225
µmol cm
-2
year
-1
FeS Flux No gradient 0.0 0.0 µmol cm
-2
year
-1
O2 Fixed concentration No gradient 0.07 0.14 µmol cm
-3
HCO3 Fixed concentration No gradient 2.2 2.2 µmol cm
-3
NO3 Fixed concentration No gradient 0.003 0.012 µmol cm
-3
SO4 Fixed concentration No gradient 5.5 22 µmol cm
-3
NH4 Fixed concentration No gradient 0.0 0.0 µmol cm
-3
HS Fixed concentration No gradient 0.0 0.0 µmol cm
-3
91
Table S1.9 Boundary conditions for oxygen consumption simulations.
Species
Upper boundary
condition
Lower
boundary
condition
Values Unit
CH2Ofast Flux No gradient 225 µmol cm
-2
year
-
1
CH2Oslow Flux No gradient 225 µmol cm
-2
year
-
1
FeS Flux No gradient 0.0 µmol cm
-2
year
-
1
O2 Fixed
concentration
No gradient 0.14 µmol cm
-3
HCO3 Fixed
concentration
No gradient 2.0 µmol cm
-3
NO3 Fixed
concentration
No gradient 0.012 µmol cm
-3
SO4 Fixed
concentration
No gradient 22 µmol cm
-3
NH4 Fixed
concentration
No gradient 0.0 µmol cm
-3
HS Fixed
concentration
No gradient 0.0 µmol cm
-3
92
Table S1.10 Parameterization of bioturbation coefficients.
See materials and methods for details. Where NA is given for ii or BI is where mixed layer
depths are given for that reference.
Age Source ii or
BI
MLD
(cm)
Biodiffusion
coefficient - van de
Velde and Meysman
(2016)
Biodiffusion
coefficient -
(Boudreau,
1998)
k in
Boudreau
equation
Final
biodiffusion
coefficient
Nama Mángano
and Buatois
(2014)
0.1 0.17 0.02 0.01 5 0.01
Fortunian Mángano
and Buatois
(2014)
0.5 0.83 0.23 0.19 5 0.21
Cambrian
Stage 2
Mángano
and Buatois
(2014)
2.3 3.8 1.4 4.0 5 2.7
Early-
middle
Cambrian
Tarhan et al.
(2015)
NA 0.20
0.03 0.01 5 0.02
Cambro-
Ordovicia
n
Tarhan et al.
(2015)
NA 1.0 0.29 0.28 5 0.28
Ordovicia
n-Silurian
Tarhan et al.
(2015)
NA 1.5 0.46 0.63 5 0.54
Age MLD (cm) Final biodiffusion coefficient
Ediacaran 0.165 0.01
Terreneuvian 1.61 0.98
93
Supplementary references
Boudreau, B.P., 1998, Mean mixed depth of sediments: The wherefore and the why: Limnology
and Oceanography, v. 43, p. 542–526.
Fossing, H., Berg, P., Thamdrump, B., Rysgaard, S., Sørensen, H.M., and Nielsen, K., 2004, A
model set-up for an oxygen and nutrient flux model for Aarhus Bay (Denmark).: National
Environmental Research Institute.
Katsev, S., Sunby, B., and Mucci, A., 2006, Modeling vertical excursions of the redox boundary
in sediments: Application to deep basins of the Arctic Ocean: Limnology and
Oceanography, v. 54, p. 1581–1593.
Mángano, M.G., and Buatois, L.A., 2014, Decoupling of body-plan diversification and
ecological structuring during the Ediacaran-Cambrian transition: Evolutionary and
geobiological feedbacks: Proceedings of the Royal Society B: Biological Sciences, v.
281, p. 2040038.
Meysman, F.J.R., Risgaard-Petersen, N., Malkin, S.Y., and Nielsen, L.P., 2015, The geochemical
fingerprint of microbial long-distance electron transport in the seafloor: Geochimica et
Cosmochimica Acta, v. 152, p. 122–142, doi:10.1016/j.gca.2014.12.014.
Tarhan, L.G., Droser, M.L., Planavsky, N.J., and Johnston, D.T., 2015, Protracted development
of bioturbation through the early Palaeozoic Era: Nature Geoscience, v. 8, p. 865–869,
doi:10.1038/ngeo2537.
Wang, Y., and Van Cappellen, P., 1996, A multicomponent reactive transport model of early
diagenesis: Application to redox cycling in coastal marine sediments: Geochimica et
Cosmochimica Acta, v. 60, p. 2993–3014, doi:10.1016/0016-7037(96)00140-8.
Zhao, M., Zhang, S., Tarhan, L.G., Reinhard, C.T., and Planavsky, N., 2020, The role of calcium
in regulating marine phosphorus burial and atmospheric oxygenation: Nature
Communications, v. 11, p. 2232, doi:10.1038/s41467-020-15673-3.
94
Chapter 2. Characterization of early Cambrian bioturbation ecosystem engineering
behaviors in the Deep Spring Formation, California, USA
Abstract
The evolution of bioturbation during the Ediacaran-Cambrian transition was one of the
most important biotic innovations in Earth history. Early bioturbators acted as ecosystem
engineers – animals that influenced the habitability of their benthic environments by modifying
resource availability. As ecosystem engineers, bioturbators have long been hypothesized to have
driven profound changes in seafloor ecology through the Ediacaran-Cambrian transition. Here,
we characterize bioturbation ecosystem engineering behaviors from the lower Cambrian Deep
Spring Formation in the White-Inyo Succession of California. In addition to traditional
ecological approaches to characterizing ecosystem engineering strategies and impacts from trace
fossils, we also use micro X-ray fluorescence to characterize biogeochemical impacts and
identify any potential unique geochemical signatures of different ecosystem engineering
behaviors. We ultimately find that early Cambrian bioturbators from the Deep Spring Formation
were predominantly horizontal biodiffusors that intensely reworked sediments and created, at
least locally, a sedimentary mixed layer. Although rarer, there is also a morphological diversity
of complex, large Treptichnus pedum burrows, which would have been at least passively
bioirrigated. Although we did not find unique geochemical signatures for different bioturbation
behaviors, burrow structures and ichnofabrics are clearly identifiable based on qualitative
elemental maps, indicating that these bioturbators likely did drive biogeochemical changes that
impacted early diagenetic processes. These results are evidence for a Deep Spring Formation
trace fossil assemblage of early, moderately impactful bioturbation ecosystem engineers which
95
would have been capable of significantly changing sediment rheology and local biogeochemical
cycles.
2.1 Introduction
Bioturbation refers to the process by which animals living on and within the seafloor mix
and rework sediments. In work as early as Charles Darwin’s studies on earthworms (Darwin,
1892), the potential for small-scale bioturbators to cause large changes to their environments has
been well-recognized in marine and terrestrial research (Meysman et al., 2006). For this potential
to cause significant change, bioturbation has been recognized by modern ecologists and
paleoecologists alike as a major ecosystem engineering behavior (Jones et al., 1994; Erwin,
2008). Ecosystem engineers are organisms whose activities and behaviors change the habitability
of their environment by modifying its physical characteristics and/or resource flows (Jones et al.,
1994). Marine bioturbators can significantly impact both of their environment’s physical
characteristics and resource flows in their ecosystems. For example, marine bioturbators can
impact the physical nature of their environments by changing the rheology of seafloor sediments
by influencing porosity, water content, and microbial composition (Bottjer et al., 2000; Tarhan,
2018a). Bioturbation is also a well-established driver of changes to ancient and modern nutrients
by impacting biogeochemical cycles such as oxygen (Cribb et al., 2023), organic matter (Aller,
1994; van de Velde et al., 2018), phosphorus (Tarhan et al., 2021), nitrogen (Laverock et al.,
2011), and sulfur (Canfield and Farquhar, 2009; Tarhan et al., 2015; van de Velde and Meysman,
2016; Riemer et al., 2023). These changes ultimately impact resource availability and thus
control community ecology, as community composition is in part a function of the availability of
96
resources and habitable space for animals at various life stages. Therefore, the ecosystem
engineering activities of bioturbators can lead to profound ecological changes.
Because of the impacts of bioturbating ecosystem engineers (although not always
explicitly noted as ecosystem engineering), bioturbation has long been implicated in the
ecological turnover associated with the Ediacaran-Cambrian transition (Seilacher and Pflüger,
1994; Bottjer et al., 2000; Darroch et al., 2020). Shallow marine sediments during the Ediacaran
Period are thought to have been relatively ubiquitously covered in microbial mats due to the lack
of bioturbation (Seilacher and Pflüger, 1994; Bottjer et al., 2000). From a physical or rheological
perspective, this resulted in well-packed sediments with low water content and, due to the lack of
sediment reworking into the water column, a distinct sediment-water interface (Bottjer et al.,
2000). From a biogeochemical perspective, the microbial mats are thought to have acted as a cap
on the sediment preventing oxygen from being transported downwards into the porewaters,
causing anoxic shallow marine sediments (McIlroy and Logan, 1999). These microbial mat-
covered shallow marine sediments in the Ediacaran would have been inhospitable for aerobic
macroinfauna. Bioturbation increased in both complexity and intensity through the Ediacaran-
Cambrian transition and into the early Cambrian (Darroch et al., 2018, 2020; Cribb et al., 2019;
Mángano and Buatois, 2020). Microbial mat coverage seems to have declined in Early Cambrian
shallow marine sediments (Hagadorn and Bottjer, 1997; c.f. Davies et al., 2016), which is
attributed to the rise of benthic microbial mat-grazers and bioturbators mixing away the
microbial mats (Bottjer et al., 2000). Increased bioturbation activity destabilized the seafloor,
increased the sediment water content, contributed to a less distinct sediment-water interface, and
ultimately led to the development of a homogenized, ‘soupy’ sedimentary mixed layer at the top
of the sediment column (Bottjer et al., 2000). Additionally, the development of a mixed layer
97
would have stimulated benthic recycling in a number of key biogeochemical cycles (Tarhan,
2018b). These impacts ultimately led to profound changes in benthic ecology, including the
turnover of major groups across the Ediacaran-Cambrian boundary and the rise of new
ecological strategies (Seilacher, 1999; Bottjer et al., 2000; Dornbos and Bottjer, 2000; Laflamme
et al., 2013). These bioturbation-driven changes to the nature of the seafloor and their associated
changes in benthic ecology are encompassed by the Agronomic Revolution (Seilacher and
Pflüger, 1994) and Cambrian Substrate Revolution models (Bottjer et al., 2000).
More recent work has demonstrated not all bioturbation ecosystem engineering behaviors
have the same impact on their environments (Herringshaw et al., 2017). Additionally, different
modes of bioturbation have been demonstrated to have opposite impacts on sediment redox
chemistry, and thus their impacts on any nutrient cycles with redox-sensitive benthic processes
will vary (van de Velde and Meysman, 2016; Tarhan et al., 2021; Cribb et al., 2023).
Specifically, bioturbation can be broken down into two end-member mixing processes:
biomixing and bioirrigation (Kristensen et al., 2012). Biomixing refers to the (bio)diffusive
mixing of the solid-phase sediments, including organic matter and mineral grains, whereas
bioirrigation refers to the mixing of solutes in sediment porewaters with the overlying water
column via burrow ventilation (Kristensen et al., 2012). Although most bioturbation, in reality,
consists of a combination of these two end-member behaviors, modeling and experimental
studies have shown that biomixing and bioirrigation have opposite impacts on sediment redox
geochemistry (van de Velde and Meysman, 2016; van de Velde et al., 2020; Cribb et al., 2023).
Biomixing tends to shoal the oxic zone of the sediment by mixing organic matter downwards to
the suboxic and anoxic sediment layers, stimulating oxygen consumption at the oxic-anoxic
interface via reoxidation, where oxygen cannot be resupplied by oxygen diffusion from the water
98
column alone (van de Velde and Meysman, 2016; Cribb et al., 2023). In contrast, bioirrigation
expands the oxic zone by supplying more oxygen to deeper sediment layers, notably faster than
would be supplied by the downward diffusion of oxygen into the sediment alone (van de Velde
and Meysman, 2016; Cribb et al., 2023). Because these two behaviors have such opposite
impacts on sediment redox chemistry, they can also have variable impacts on the biogeochemical
cycling of major nutrients (van de Velde et al., 2018; Tarhan et al., 2021).
Neither the Agronomic Revolution (Seilacher and Pflüger, 1994) nor the Cambrian
Substrate Revolution (Bottjer et al., 2000) models originally accounted for these differences in
impacts from different bioturbation modes and behaviors. Without a precise understanding of the
evolutionary history of biomixing and bioirrigation, it is difficult to predict bioturbation’s
ecosystem engineering impact during the Ediacaran-Cambrian transition to understand the extent
to which bioturbators drove benthic ecological turnover observed during this time interval.
However, it is difficult to robustly identify biomixing and bioirrigation in the trace fossil record –
particularly for early trace fossils in the Ediacaran and early Cambrian. Furthermore, it is
virtually impossible to precisely and reliably quantify rates of biomixing and bioirrigation from
the trace fossil record. In modern studies, biomixing and bioirrigation can be recognized from
direct observations of the bioturbating fauna. Rates of biomixing are often quantified using a
combination particle tracers including radionuclides (e.g.,
210
Pb,
234
Th,
14
C,
7
Be), chlorophyll a,
and luminophores (Maire et al., 2008), while bioirrigation rates are estimated using a
combination of inert tracers and mechanistic models (Berelson et al., 1998; Borger et al., 2020).
However, the information that these methods use is typically lost to long-term diagenetic
processes and is thus not available in the trace fossil record. A first step to constraining the
evolutionary history of the ecological strategies of bioturbation as ecosystem engineers during
99
the Ediacaran-Cambrian transition is to develop methods that allow for the robust identification
of biomixing and bioirrigation behaviors. In modern, unlithified sediments, bioirrigation and
biomixing can often be visually recognized without biogeochemical methods. Well-ventilated
burrows, for example, may exhibit a color zonation due to oxidation of the sediment in and
around the burrow (Kristensen et al., 2012). For biomixing, the mixed layer can be observed and
measured using in situ sediment profile imaging due to clear sedimentological characteristics
(Rhoads and Cande, 1971). These sedimentological and redox-driven early diagenetic
characteristics are more easily preserved in the rock record than what is recorded by traditional
biomixing and bioirrigation measurements, and thus may serve as useful proxies for identifying
the two end-member bioturbation processes. By identifying sedimentological and geochemical
fingerprints left by biomixers and bioirrigators, each behavior can be better identified and
constrained in the trace fossil record, further untangling the evolutionary history of bioturbating
ecosystem engineers that may have very different impacts on benthic ecology.
2.2 Geologic setting
The White-Inyo Succession of eastern California hosts well-preserved, diverse trace fossil
assemblages from the early Cambrian (Figure 2.1). This is one of the four distinct interfingering
successions (the White-Inyo, Death Valley, Craton, and Craton Margin Successions) in the
Neoproterozoic-Cambrian strata in the southwestern United States (Figure 2.1A) (Fedo and
Cooper, 2001). The White-Inyo Succession consists of more than 6000 m of lightly
metamorphosed mixed siliciclastic and carbonate units representing proximal- to mid-shelf
deposits (Nelson, 1962, 1978; Stewart, 1970; Corsetti and Hagadorn, 2000). The entire
succession consists of, in ascending order, the Wyman, Reed, Deep Spring, Campito, Poleta,
100
Harkless, Saline Valley, and Mule Spring Formations (Nelson, 1962) (Figure 2.1B). The
Ediacaran-Cambrian Boundary (ECB) (538.8 ± 3 Ma) (Bowyer et al., 2022) is recorded in the
Deep Spring Formation (Corsetti and Hagadorn, 2003). The Deep Spring Formation is a 200-400
m thick unit of shoreface to outer shelf deposits (Stewart, 1970; Nelson, 1978) (Figure 2.1). The
Deep Spring Formation is divided into three members: the Lower Member, the Middle Member,
and the Upper Member (Nelson, 1978) – also sometimes referred to as the Dunfee, Esmerelda,
and Gold Point Members, respectively (Ahn et al., 2012) (Figure 2.1). The ECB has been
identified in a siliciclastic unit in the Middle Member based on the first occurrence of
Treptichnus pedum, the GSSP-recognized index fossil for the ECB, and a negative carbon
isotope excursion potentially representing the Basal Cambrian Carbon Isotope Excursion
(BACE) (Corsetti and Kaufman, 1994; Corsetti and Hagadorn, 2000, 2003; Smith et al., 2016).
Trace fossils for this study were collected from siliciclastic units in two Deep Spring Formation
localities: Hines Ridge (37.102° N, 118.095° W, WGS84) and Andrews Mountain (37.081° N,
118.076° W, WGS84) (Figure 2.1A). At both Hines Ridge and Andrews Mountain, the
uppermost Wyman, Reed, Deep Spring, and Campito Formations are exposed.
Previous ichnological studies
Previous work on the ichnology of Early Cambrian units in the White-Inyo Succession has
been conducted by numerous groups over the last five decades (e.g., Alpert, 1976; Droser and
Bottjer, 1988; Hagadorn and Bottjer, 1997; Miller and Smail, 1997; Corsetti and Hagadorn,
2003; O’Neil et al., 2022). Like other Ediacaran-Cambrian transition shallow marine
siliciclastics, these units typically contain limited vertical disruption of sediments and often
preserve microbially-mediated sedimentary structures (MISS), particularly below the first
101
occurrence of T. pedum (Hagadorn and Bottjer, 1997). Bedding-plane parallel trace fossils are
common at Hines Ridge and Andrews Mountain, including Helminthoidichnites, Planolites,
Plagiogmus, and Torrowangea (Corsetti and Hagadorn, 2003). Other Deep Spring Formation
localities in the White-Inyo Region host similar trace fossils, such as Helminthopsis, Lamonte,
and Gordia (Marenco and Bottjer, 2008; O’Neil et al., 2020, 2022; Tarhan et al., 2020).
Treptichnus isp. has also been reported below T. pedum in the Lower and Middle Members
(Tarhan et al., 2020; O’Neil et al., 2022). T. pedum appears in the Middle Member, representing
the onset of vertical-penetrating bioturbation behaviors and the ECB (Corsetti and Hagadorn,
2003; Smith et al., 2016). Above T. pedum in the Upper Member, a suite of trilobite trace fossils
appear, including common Rusophycus, Cruziana, and Diplichnites, and vertically-penetrating
trace fossils such as Skolithos (Alpert, 1975, 1976; Nelson, 1978).
2.3 Methods
Trace fossils were collected from the Deep Spring Formation during two field excursions
to the Hines Ridge (May 2019) and Andrews Mountain (October 2019) localities. At both
localities, sedimentary units tend to be fissile and exposure of bedding planes is limited, so
finding trace fossils that could be extracted in situ was rare. The trace fossils reported here were
collected in float and thus are not constrained to a particular stratigraphic horizon. However, the
assemblage of ichnogenera, lack of trilobite burrows, and topography indicate the trace fossil
slabs were not extensively transported far from their original position at either locality and thus
can be unambiguously placed in stratigraphic context. Slabs containing trace fossils were
transported back to the University of Southern California to be photographed in order to identify
any trace fossils and sedimentological features that were not visible in natural light. Ichnogenera,
102
their ecological strategies, and their ecosystem engineering impact were then identified and
characterized based on previously reported descriptions of trace fossils from shallow marine
settings of Ediacaran and Early Cambrian age (e.g., Marenco and Bottjer, 2008; Carbone and
Narbonne, 2014; Herringshaw et al., 2017; Cribb et al., 2019; Darroch et al., 2020; O’Neil et al.,
2022). Bedding-plane bioturbation indices (BPBI) (Miller and Smail, 1997) were estimated for
each trace fossil slab. Burrow size and ichnofabric thickness were measured in ImageJ. The least
fissile slabs with the best-preserved trace fossils and sedimentological features were selected to
be cut, polished, and imaged in cross section.
A further subset of trace fossil slabs was then selected for geochemical analysis. Two
trace fossil slaps which record bioirrigated trace fossils, three trace fossils slabs which record
strong biomixing, and one trace fossil slab with undisturbed sediment laminae were analyzed.
Geochemical analyses were conducted using X-ray microfluorescence (µXRF). These analyses
were conducted using a Horiba XGT-7200 Analytical Microscope. For each sample, qualitative
elemental area maps were created for five elements (silicon, calcium, potassium, iron, and
titanium). These elements were consistently the most abundant across samples. Particular
attention was given to iron, which has been previously shown to preserve the structure of much
younger trace fossils that represent bioturbation behaviors that influence sediment redox
geochemistry (Harazim et al., 2015; Reolid and Reolid, 2020). Each sample was also analyzed
for sulfur and magnesium, but concentrations of neither element were high enough to create
qualitative maps (Table S1). Multiple areas were analyzed for each trace fossil slab to compare
the unmixed sedimentary fabric and either the discrete burrow structure (for bioirrigators) or the
ichnofabrics (for biomixers). Areas were scanned at a power voltage tube setting of 50 kV and a
spot size diameter of 50 µm, which is well below the size of small features of the trace fossil
103
burrow structures. Spectra were generated for each area scanned to produce a relative abundance
of elemental compositions to compare between areas and between samples. Eight elements were
selected for this spectral analysis: silicon, calcium, potassium, iron, titanium, magnesium, and
aluminum. Using the relative abundance of elemental compositions from the spectra that were
generated (Table S1), a non-metric multidimensional scaling (NMDS) analysis was conducted to
identify unique geochemical signatures that may exist between either burrowing strategies or
other abiotic influences.
2.4 Results
Ichnology of the Deep Spring Formation
A diverse assemblage of trace fossils and a variety of ichnofabrics were recovered from
the Deep Spring Formation (Figure 2.2, 2.3). The majority of trace fossil slabs recovered are
moderately to extensively reworked bedding planes (BPBI = 3-4) that preserve dense bedding-
plane oriented trace fossils (Figure 2.2C-G). Due to the intense reworking, discrete ichnogenera
are often difficult to identify. However, common ichnogenera occurrences include Planolites,
Helminthoidichnites, and Torrowangea (Figure 2.2). Burrow widths are never larger than 1 cm
and are most often around 0.5 cm. Trace fossil slabs seem to most often be monotypic
(comprised of the same ichnogenus) most often by dense, short Planolites (Figure 2.2C, G). The
largest trace fossil that occurs is consistently Torrowangea, which is longer and has a slightly
larger diameter than other ichnogenera and tends to cross-cut other trace fossils across the
bedding planes (Figure 2.2E).
Two slabs were collected that deviate from this trend of densely bioturbated bedding
planes. The first slab preserves a horizontal, curving trail in negative epirelief which cross-cuts a
104
MISS wrinkle fabric (Figure 2.2A). This slab is the only direct bioturbator-microbial mat
interaction recovered during the two field excursions from Hines Ridge or Andrews Mountain.
The second slab preserves a sole putative biogenic sedimentological feature preserved in positive
epirelief (Figure 2.2B). This feature is likely biogenic in origin given its consistent width along
the entire feature. Annulated segments of the feature are also preserved, albeit poorly. The
feature takes a sharp, angular turn, which is more characteristic of a body fossil than a trace
fossil. This may be the fossil of a tube-like organism, resembling the cm-scale Saarina and
Costatubus tubiculous taxa recently described from the Deep Spring and Wood Canyon
Formations that outcrop in Nevada (Selly et al., 2020). Alternatively, this feature may be the
trace fossil Plagiogmus, which is a common early Cambrian trace fossil and has been previously
reported in the Deep Spring Formation at these localities (Corsetti and Hagadorn, 2003).
Plagiogmus is characterized by bedding-plane parallel burrows with “ladder trails” that may be
of variable width. The annulated segments of the feature resemble these “ladder trails”
Plagiogmus often consists of a medium furrow, which is not observed here, but the median
furrow is only diagnostic of certain ichnospecies (McIlroy and Heys, 1997). Although angular
turns are not typical of trace fossils, relatively sharply-turning specimens of Plagiogmus have
been reported (McIlroy and Heys, 1997). This feature remains problematic due to the detail lost
from poor preservation, but it is almost certainly biogenic in origin.
Finally, there is a diversity of Treptichnus morphologies collected from the two localities
(Figure 2.3). Each Treptichnus specimen is preserved in positive epirelief and consists of small
burrow segments joined in a rope-like pattern to form one larger burrow trail. Treptichnus has
been interpreted as the three-dimensional burrow system of an infaunal scavenging or predatory
priapulid worms, constructed by upward conveying-type burrowing mechanisms (Vannier et al.,
105
2010; Herringshaw et al., 2017; Kesidis et al., 2019). Because these priapulids lived within the
seafloor sediments and formed repeated branches that connected with an oxygenated sediment-
water interface, Treptichnus has been interpreted as a likely bioirrigated burrow system
(Herringshaw et al., 2017). The various morphologies observed among Treptichnus specimens in
the Deep Spring Formation all share the common characteristics (probing, connected, short
burrow segments) that lend to these ecological and ecosystem engineering characterizations, but
the overall geometry of the trace fossils can vary (Figure 2.3). There are a number of highly
curving Treptichnus specimen (Figure 2.3C, E). These consist of C-shaped or nearly ring-shaped
traces, with burrow segments probing off to the outside of the curve. More common are
straighter, but still sinuous, Treptichnus (Figure 2.3B, D). These tend to reach several
centimeters in length and may cross-cut other Treptichnus burrows (Figure 2.3B). A possible
third morphology of Treptichnus may exist as a blend of these two end-members, with gently
curving Treptichnus consisting of shorter probes (Figure 2.3F). Priapulid treptichnid burrowers
can change their precise burrowing behaviors depending on the type of sediment they encounter
(Vannier et al., 2010; K. Turk, pers. comm.), but because different morphologies can occur on
the same bedding plane (Figure 2.3A), we cannot entirely attribute the various Treptichnus
morphologies to facies or lithology. Instead, this morphological diversity suggests that priapulid
treptichnid-burrowers used different burrowing strategies to exploit food at the sediment-water
interface (Vannier et al., 2010). Despite the various morphologies, we interpret all Treptichnus
found in the Deep Spring Formation as T. pedum.
106
Descriptive geochemistry of biomixers
Geochemical analyses were conducted for two biomixing samples (Figure 2.4, Figure
2.5). The first sample is a cut section from an intensely reworked bedding plan of dense
Planolites (Figure 2.2F, Sample 19-HR-DS-3). In cross-section, there are two distinct
sedimentary fabrics which nearly divide the sample in half. On the half of the section away from
the bioturbated bedding plane (bottom half in Figure 2.4), there are fairly discrete, undisrupted
sedimentary laminae alternating between brown and dark gray colors every few millimeters. In
contrast, in the top ~0.75 cm of section closest to the (top) bioturbated bedding plane, there is a
clear ichnofabric, where the discrete sedimentary laminae disappear and are replaced by a
mottled texture (Figure 2.4). We interpret this as evidence for a sedimentary mixed layer. There
are no discrete burrow structures in this sample. As with many slabs cut and polished from the
Deep Spring Formation, there are relatively large orange to red grains distributed throughout the
sample.
For this specimen, there are some geochemical differences between the ichnofabric
(mixed layer) and the rest of the undisturbed sedimentary fabric (Figure 2.4). Silicon does not
exhibit any structure or differences between the two and is evenly distributed throughout the
sample. Calcium does exhibit structure based on the unmixed sedimentary laminae and
ichnofabric, where it is more concentrated throughout the mixed layer ichnofabric and in the
brown layers of the undisturbed sedimentary laminae. Potassium also exhibits an observable
structure and has an opposite pattern to calcium, where potassium is also elevated in the mixed
layer fabric and in the gray layers of the undisturbed sedimentary laminae. Iron exhibits little to
no structure other than where it is intensely concentrated in the larger orange-to-red grains,
indicating the presence of iron-rich mineral gains throughout the sample. Titanium also exhibits
107
little to no structure, with no differences between the mixed layer fabric and the underlying
sediment laminae.
The second biomixing sample (Figure 2.5) is a section cut from an even more intensely
reworked bedding plane, which also contains one large, long Torrowangea which cuts through
the bedding plane ichnofabric (Figure 2.2E; Sample 19-HR-DS-R-1). This slab was cut through
the Torrowangea burrow in order to expose its internal burrow structure. In cross-section, this
sample exhibits significant reworking, with no discrete sedimentary laminae. Fine-grained layers
of clay of inconsistent width which do not reach across the entire specimen are interfingered with
the coarser grain matrix. Some of the lighter coarser-grained sediments which form ovular
shapes throughout the matrix resemble discrete, horizontal trace fossils, potentially representing
Planolites (which is exposed at the bedding plane). However, the contacts between these features
and the surrounding sediment are not sharp enough to definitively identify them as trace fossils.
One clear burrow structure is present where the Torrowangea was transected. Connecting with
the Torrowangea on the bedding plane, there is a clear broad D-shaped feature (Figure 2.4, Area
A). This burrow structure is approximately 2 cm at its widest at the bedding plane, and vertically
penetrates 0.5 cm into the sedimentary matrix.
Within the Torrowangea burrow (Figure 2.5, Area A), there are no clear patterns for
calcium or iron. The burrow structure is subtly observable in silicon, where concentrations are
lower around one side of the burrow edge. The burrow structure is most clear in potassium,
where the inside of the burrow is observably depleted in potassium concentrations. Small, linear
layers of titanium are present throughout the sample, and may at some points follow the contour
of the Torrowangea burrow. In general, calcium concentrations are low, especially compared to
the other biomixing sample (Figure 2.4). In the rest of the burrow matrix, there is similarly little
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structure between the elemental concentrations. A subtle structure exists in silicon, potassium,
and titanium concentrations following the sediment grains within the sample. Silicon
concentrations seem to be elevated in the finer-grained sediments, whereas potassium and
titanium concentrations are elevated in the coarser grained sediments. This is perhaps surprising,
given the expectation for potassium and titanium to be more concentrated in clay layers. Iron is
relatively homogenously distributed, with none of the iron-rich mineral grains that are present in
other samples. There is very little calcium in this sample in general, and thus it exhibits no
structure.
Descriptive geochemistry of bioirrigators
Geochemical analyses were also conducted for two T. pedum samples representing
bioirrigators (Figure 2.6, 2.7). The first sample is a section cut from one of the more gently
curving T. pedum morphologies, and cuts through two relatively parallel segments of T. pedum
trails (Figure 2.3D, Sample 19-HR-DS-1). The majority of the sedimentary matrix is
undisturbed, with a gray fine-grained sediment layer on the bottom (away from the T. pedum
bedding plane) portion of the sample and light brown, coarser sediments on the top (towards the
T. pedum bedding plane) 0.3 – 0.5 cm of the sample. The T. pedum penetrates vertically through
the entire section of the rock, which is around 1.2 cm thick. This is indicated by the cloud-like
sedimentary fabric (Figure 2.6, Area A), which is likely formed by stacking and cross-cutting T.
pedum burrow knobs. This fabric was most likely created by vertical movements of the trace-
maker, with the vertical stacking of the Treptichnus knobs either due to successive vertical
probing through the sediment or because of upwards-movement in response to rapid
sedimentation rates. A clear darker, fine-grain boundary exists between each of the discrete
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burrow knobs. Area A of this sample is the only section where the T. pedum penetrates vertically
through the entire specimen to the other bedding plane, but the same sedimentological texture is
seen in the area between the two parallel T. pedum trails, in the upper ~0.3 cm between Area A
and the topographical knob on the bedding plane to the right of Area B. Orange and black
sedimentary grains are found throughout the sample, particularly concentrated between the two
parallel T. pedum trails.
Within the T. pedum burrows (Figure 2.6, Areas A and B) there is little structure
exhibited in silicon, iron, and titanium. Silicon is homogenously distributed throughout each
area, other than in the orange- and dark gray colored large sediment grains. In these grains, iron
concentrations are significantly elevated, but otherwise iron is also homogenously distributed
throughout the areas. The most structure in elemental composition and concentrations occurs for
calcium and potassium. Calcium concentrations are elevated within the coarse-grain sediments
that are in the T. pedum burrow knobs and concentrations are very low in the dark gray contacts
between them. Potassium concentrations exhibit the opposite pattern, concentrated in the lining
between each burrow knob and relatively depleted within the burrows themselves. This may
indicate a potassium-rich burrow lining, possibly due to reactions between potassium-rich clays
and a burrow mucous lining, which would contribute to the preservation of the burrow as a trace
fossil (Vannier et al., 2010). In the fine grain sedimentary matrix which has not been impacted
by the T. pedum burrow fabric (Figure 2.6, Area C), each of the five elements are evenly
distributed, except for the iron-rich orange- and dark-gray colored mineral grains.
The second bioirrigated T. pedum sample analyzed is a section cut through a C-shaped
trace fossil (Figure 2.3C, Sample 19-AM-DS-9). Similar to the other T. pedum analyzed, there is
a clear burrow knob shape left by the treptichnid-maker. This burrow knob protrudes into the
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bedding plane where the T. pedum is exposed at the surface (Figure 2.7, Area A). This structure
is filled with light coarse grain sediments and connects with a laterally continuous 0.3 cm thick
layer of the same sediments, which appears to pinch out beyond the burrow knob. There is a
subtle boundary of finer, darker sediment grains between the burrow knob (Area A) and the
coarse-grain layer. This may be an artifact of the same successive vertical probing that resulted
in the cloud-like sedimentological fabric described in the other T. pedum specimen. Along the
top (towards the T. pedum bedding plane) of the coarse grain layer, there are small bumps along
an otherwise smooth area, perhaps indicative of relic, unpreserved burrow knobs from the trace
maker probing upwards from the sediment to the surface to feed. There is a subtle, coarser grain
area that connects the coarse grain layer to the bedding-plane, for example, at the far end of this
sample which does not make direct contact with a positive epirelief T. pedum burrow. Beyond
these features and the coarse-grain layer, there are two areas of finer grain sediment. Underlying
the coarse-grain layer are dark, medium-to-fine grain sediment grains with very fine continuous
sediment laminae and small orange-colored mineral grains. Overlying the coarse-grain layer and
making direct contact with the T. pedum bedding plane is an extremely fine grain dark layer,
which hosts the burrow knob (Area A) and but otherwise has no defining sedimentological
features.
Geochemical mapping of the burrow knob (Figure 2.7, Area A) yields the most structure
related to a trace fossil burrow of any sample or area previously described. Calcium
concentrations are negligible and thus do not exhibit any structure. For the other elements,
however, distinct regions emerge. Silicon concentrations are elevated within the burrow,
indicating a coarse-grain quartz-rich sandstone layer. Concentrations are lower outside of the
burrow structure, and significantly so in the overlying very fine-grain layer. Potassium
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concentrations exhibit the opposite pattern, where its concentrations are depleted within the
burrow knob itself and particularly elevated in the very fine-grain sediment overlying the coarse
sandstone layer. Iron concentrations also exhibit a clear structure relative to the burrow knob.
Within the burrow itself and in the coarse grain sediment layer, iron concentrations are lowest in
the sample. Iron concentrations are higher outside of the burrow in the finer-grain sediments,
particularly in the underlying medium-to-find grain sediment layer. Finally, titanium
concentrations seem to follow the contours of the burrow structure. Titanium concentrations are
elevated in the contact between the burrow knob and underlying the continuous sandstone. In the
overlying very fine-grain layer, titanium is evenly distributed throughout with little structure,
whereas in the underlying more medium-grain layer, elevated concentrations of titanium are
arranged in thin layers. The same patterns are found away from the T. pedum burrow knob
(Figure 2.7, Area B), where the coarse-layer is elevated in silicon and depleted in iron, the
overlying very fine-grain layer is depleted in silicon and elevated in potassium, iron, and
titanium compared to the burrow, and the underlying more coarse-layer is more depleted in
silicon and elevated in potassium and iron compared to the burrow, with clear thin layers of
titanium. In Area B, however, the contact between the coarser sandstone layer and the underlying
medium-grain layer is less clear based on the distribution of elemental concentrations alone.
Descriptive geochemistry of unbioturbated sediment laminae
Finally, one geochemical analysis was conducted for a sample with undisrupted sediment
laminae, representing no infaunal bioturbation (Figure 2.8). This sample is a section cut across
the horizontal trace that cross-cuts MISS fabric (Figure 2.2A). Despite the trace fossil observed
on the bedding plane, there is no evidence for vertical sediment mixing. This is clearly evident
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due to the distinct, laterally continuous sediment laminae (Figure 2.8). One vertical feature cuts
through the sediment laminae, but it is more than likely an abiogenic sedimentary feature. This
feature causes some offset between the otherwise continuous sediment laminae and thus is
probably an artifact arising from a combination of weathering and jointing in the rock.
Geochemical analysis of areas related to this feature were avoided. Otherwise, there are distinct
alternating layers of dark- and light gray sediment laminae of various widths. The top (towards
the MISS and trace fossil bedding plane) of the sample has a particularly thick dark, fine-grain
layer of sediment. Orange-colored mineral grains of various size and shape are distributed
throughout the sample.
No obvious structure is clear from any of the five elements scanned in either areas of this
sample (Figure 2.8). As with other samples, the orange-colored sediment grains are iron-rich.
Some subtle layers can be seen where potassium and titanium concentrations are slightly
elevated in some sediment laminae. However, there does not seem to be any geochemical
difference between the different sediment laminae throughout the sample, and with no
bioturbation influence, no biogenic structures are evident.
Identification of distinct geochemical characteristics
The relative abundance of each element (as displayed in the bar graphs in Figures 2.4-2.8;
Table S2.1) were statistically compared to identify common geochemical characteristics between
samples. Condensing the variance in elemental composition between all scanned areas into
NMDS space (k=2) reveals how the geochemically analyzed samples do or do not cluster based
on shared geochemical characteristics (Figure 2.9). There are no distinct geochemical signatures
when comparing biomixing to bioirrigation (ANOSIM [Analysis Of Similarities], R = -0.21, P >
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0.5). Visually, this is evident by the overlapping biomixing and bioirrigation groups (Figure
2.9A). Moreover, environmental vectors of each element indicate that Fe (the only redox-
sensitive major element analyzed) is not associated with the differences between biomixing and
bioirrigation (Figure 2.9A). When grouping samples by locality between Hines Ridge and
Andrews Mountain, the groups of samples do visually plot separately in NMDS space (Figure
2.9B), but geochemical differences between the two groups is still statistically insignificant
(ANOSIM, R = 0.16, P > 0.5).
2.5 Discussion
Ecosystem engineering impact of Deep Spring Formation bioturbators
Trace fossils in the Deep Spring Formation represent a variety of bioturbation behaviors
with varying ecosystem engineering consequences. Intense sediment reworking is clearly evident
from the number of trace fossil slabs that preserve highly reworked bedding planes (Figure 2.3)
and the sedimentological evidence of a mixed layer in these samples (Figure 2.4). This intense
sediment mixing was largely driven by horizontal deposit feeders which produced trace fossil
such as Planolites, Torrowangea, and Helminthoidichnites (Figure 2.3). The larger Planolites
and Torrowangea trace-makers were semi-infaunal biodiffusive bioturbators, which moved
sediment and organic matter throughout the shallow sedimentary mixed layer (Herringshaw et
al., 2017). The Helminthoidichnites-making bioturbators were clearly smaller (Figure 2.3I), but
the extremely dense bedding plane coverage of these trace fossils suggest they still reworked
significant amounts of sediment and organic matter, albeit perhaps over smaller depths in the
sedimentary mixed layer. These trace fossils contrast with more surficial trace fossils that only
mix sediment and organic matter across very short distances near the sediment-water interface
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and likely do not contribute to the development of a sedimentary mixed layer. This is best
represented by the surficial horizontal trace fossil and MISS fabric which still preserves
undisturbed sedimentary laminae (Figure 2.2A; Figure 2.8).
T. pedum represents the highest-impact ecosystem engineering behaviors in the Deep
Spring Formation. Treptichnus most likely represents a combination of biodiffusion and
conveying behaviors as the trace-maker constructed its burrow. Modern observations of
priapulids that construct Treptichnus-like burrows have noted that the trace-makers rework
sediments both horizontally and vertically (Vannier et al., 2010; Kesidis et al., 2019). As the
priapulids construct the horizontal components of their burrows, they likely acted as biodiffusors
similar to Planolites and Torrowangea, transporting sediment over short distances throughout the
shallowest sediment tier. As the priapulids constructed the vertical components of the burrows,
which are the probes that connect the burrow network back up to the sediment surface, they
likely acted as sediment conveyors and either actively or passively transported sediment towards
and away from the sediment-water interface (Herringshaw et al., 2017). The vertically
penetrating burrow knob and cloud-like structure exhibited combined with the ichnofabric
preserved between the two parallel T. pedum burrows is good evidence for this combined
biodiffusion and bioirrigation behavior. The T. pedum which connects with the coarse sandy
layer in Figure 2.5 is curious, but is most likely evidence that the trace maker exploited a sand-
mud boundary that hosted a higher concentration of organic matter (or perhaps some unique
redox boundary). This behavior has been observed in modern priapulids that burrow into a
combination of sandy and muddy sediment layers (Kesidis et al., 2019). This may not be a
significant ecosystem engineering effect, but does suggest a behavioral advancement in
bioturbators being able to detect and exploit resources at specific depths within the sediment,
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similar to those described through the Ediacaran-Cambrian transition for other deposit feeders
(Carbone and Narbonne, 2014).
These bioturbating ecosystem engineers would have driven both biogeochemical and
physical changes to their benthic environments. In an organic-rich benthic environment, the
intense sediment reworking would have resulted in changes in sediment biogeochemistry due to
the vertical transport of organic matter. In particular, the downward mixing of organic matter
throughout the sediment would have likely stimulated the consumption of oxygen due to
reoxidation reactions at the oxic-anoxic interface, potentially shrinking the oxic layer of the
sediments (van de Velde and Meysman, 2016; Cribb et al., 2023). However, the precise effects
of this process are extremely dependent on extrinsic factors including bottom water oxygen
concentrations and the quality and quantity of organic matter available at the sediment-water
interface (Cribb et al., 2023), meaning these impacts are difficult to predict without robust
geochemical data constraining the local environmental conditions. Bioirrigation by Treptichnus
would have had the opposite impact, as the bioturbator would have mediated the transport of
oxygen into the sediment (van de Velde and Meysman, 2016; Cribb et al., 2023). Sedimentary
mixed layers are zones of stimulated biogeochemical activity within the seafloor, and their
formation by early Cambrian bioturbators may have resulted in stimulated biogeochemical
cycles, including sulfur, organic matter, and phosphorus (Canfield and Farquhar, 2009; Tarhan et
al., 2015, 2021). However, a sedimentary mixed layer is not a common, uniting feature of all
bioturbated sedimentary fabrics in the Deep Spring Formation, suggesting that the development
of a sedimentary mixed layer was very localized and ephemeral with a limited biogeochemical
effect. Intense horizontal reworking likely did, however, contribute to changes in sediment
rheology, which could have had downstream effects on benthic ecology depending on the
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ecological strategies and life habits which were previously adapted to microbial mat-stabilized
sediments (Seilacher, 1999; Bottjer et al., 2000). Changes in sediment rheology are difficult to
assess in the rock record, but the lack of fine-scale sedimentological features suggests that the
sediments were not entirely stabilized by microbial mats (Tarhan, 2018a).
Identifying biomixing versus bioirrigation trace fossils
Unique geochemical signatures for biomixing or bioirrigation did not emerge from the
geochemical analyses. A number of factors may explain this null result. First, early Cambrian
bioturbators in the Deep Spring Formation may have been too weak to impact the early
diagenetic processes that would create unique geochemical signatures for each bioturbation
behavior. This is likely, given previous work demonstrating that bioturbation intensity at this
time period was relatively weak in terms of its impacts on biogeochemical processes (Tarhan et
al., 2015; Cribb et al., 2023). Second, T. pedum is not an end-member bioirrigation trace fossil. It
is constructed by a combination of biodiffusion and conveying behaviors, which would have
transported sediment and organic matter around in a similar manner to the other deposit feeders
in this environment, and it was probably not actively bioirrigated via burrow ventilation during
respiration. Therefore, T. pedum likely maintains signatures of biomixing, especially if the
passive bioirrigation was not particularly impactful due to low bottom water oxygen
concentrations relative to organic matter availability (Cribb et al., 2023). Finally, the Deep
Spring Formation has been subject to low-grade metamorphism (Corsetti and Kaufman, 1994),
which may have altered the preservation of any geochemical fingerprints of different
bioturbation behaviors.
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However, these geochemical analyses do underscore that the early Cambrian bioturbators
did cause changes to the seafloor sediments that impacted early diagenesis and allowed for the
burrow structures to be identifiable from geochemical mapping. The depleted iron concentrations
within bioirrigated burrow structures (Figure 2.8) and elevated calcium concentrations within the
mixed layer fabrics are the best evidence that bioturbators impacted biogeochemical processes in
their seafloor environments. Differences in iron concentrations between bioirrigated burrows and
the sedimentary matrix suggest that the trace-makers were involving some burrow ventilation
process, flushing water into their burrows from the sediment-water interface. This likely
removed reduced compounds and oxidized less stable iron sulfide mineral phases, whereas
dissolved iron outside of these burrows remained in the sediment porewaters to precipitate iron
minerals. Elevated calcium concentrations within ichnofabrics may arise one of two ways. First,
sediment mixing may increase the sediment porosity, allowing for more space for the
precipitation of calcium carbonate cements. Second, bioturbation-driven changes to
biogeochemical processes within the sediment may change the alkalinity of sediment porewaters.
Anaerobic sulfate reduction, for example, drives increased concentrations of inorganic carbon
and alkalinity, favoring the precipitation of calcium carbonate cements (Coleman, 1993; Present
et al., 2021). This process is particularly compelling to potentially explain the increased calcium
concentrations in ichnofabrics, as anaerobic sulfate reduction would have been fueled by
biomixers transporting organic matter below the oxic-anoxic interface (van de Velde and
Meysman, 2016).
A number of avenues of future work would be promising for improving the geochemical
characterization of ecosystem engineers. First, data collected here is limited, so further field
work and trace fossil collection to the Deep Spring Formation would result in more robust data to
118
determine whether or not unique geochemical signatures exist between biomixing and
bioirrigation. Geochemical analyses of more clear, end-member examples of bioirrigation – such
as the suspension feeding trace fossil Arenicolites – would be useful additions to this dataset.
Second, similar geochemical analyses conducted on younger rocks would serve as useful
comparisons. Elemental mapping has been conducted on much younger trace fossils (Harazim et
al., 2015; Reolid and Reolid, 2020), but constructing a time series of emerging (or lack thereof)
geochemical characteristics through the Paleozoic in particular would be useful for identifying
when the impact of bioturbators began to significantly impact early diagenetic processes. Finally,
applying this technique to searching for unique geochemical characteristics between bioturbation
functional groups which are hypothesized to have different degrees of ecosystem engineering
impact (Solan and Wigham, 2005; Herringshaw et al., 2017) would be useful for better
quantifying those ecosystem engineering impacts.
2.6 Conclusions
Bioturbating ecosystem engineers in the Deep Spring Formation were predominantly
shallow biomixers that occasionally formed sedimentary mixed layers, representing moderate
ecosystem engineering impacts (Herringshaw et al., 2017). T. pedum burrowers would have been
particularly impactful ecosystem engineers, especially if the burrows were efficiently
bioirrigated, but they are relatively rare compared to the biomixers. This pattern of trace fossil
assemblages comprised predominantly of simple, horizontal biomixers with rare, complex
bioirrigators is similar to the Ediacaran trace fossil record of ecosystem engineers, which consists
of shallow horizontal bioturbators with very small vertical bioirrigators (Cribb et al., 2019;
Darroch et al., 2020), albeit with more intense mixing due to larger trace fossils in general. The
119
dominance of biomixers over bioirrigators is also similar to global trends in biomixing versus
bioirrigation in the Ediacaran-Cambrian transition (Cribb et al., 2023). While these ecological
characterizations of bioturbation are useful for qualitatively assessing the ecosystem engineering
impact of Deep Spring Formation bioturbators, no unique geochemical signatures were found
between biomixers and bioirrigators to help characterize any varying levels of or even opposite
ecosystem engineering impacts. However, the elemental mapping clearly indicates that
bioturbators had some impact on sediment biogeochemistry and early diagenetic processes,
thereby creating clear structures in the concentrations of certain elements, suggesting that these
bioturbators were at least efficient ecosystem engineers on very small scales.
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2.7 Figures
Figure 2.1 Geologic setting of the Deep Spring Formation, White-Inyo Region, California,
USA.
A) Map of White-Inyo Region. Gray areas on map indicate outcrop of other Ediacaran-Cambrian
transition sections. Highlighted green area is the White-Inyo Region. Pink and yellow points
indicate locations of Hines Ridge and Andrews Mountain, respectively. Square point is the
location of Big Pine, California. B) Photograph of Hines Ridge locality from Andrews Mountain,
with idealized illustration showing the sequence of geological units. C) Composite stratigraphic
column of the Reed Formation and Deep Spring Formation. Figures adapted from Corsetti and
Hagadorn (2000).
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Figure 2.2 Trace fossils and typical bedding-plane ichnofabrics from the Deep Spring
Formation at Hines Ridge and Andrews Mountain.
A) Horizontal, C-shaped trace fossil cutting through MISS wrinkle fabric collected from
Andrews Mountain. Sample 19-AM-DS-19. B) ?Plagiogmus collected from Andrews Mountain.
Sample 19-AM-DS-3B. C) Dense Planolites bedding plane fabric collected from Andrews
Mountain. Sample 19-AM-DS-13. D) Dense Planolites bedding plane fabric collected from
Hines Ridge. Sample 19-HR-DS-6. E) Large Torrowangea cutting through dense Planolites
bedding plane fabric collected from Hines Ridge. Sample 19-HR-DS-R1. F-H) Dense Planolites
bedding plane fabrics collected from Hines Ridge. F is Sample 19-HR-DS-3. G is Sample 19-
HR-DS-5. H is Sample 19-HR-DS-9. I) Dense Helminthoidichnites bedding plane fabric
collected from Hines Ridge. Sample 19-HR-DS-R3. J) Dense Planolites bedding plane fabric
cross-cut by two larger Planolites (or ?Plagiogmus) collected from Hines Ridge. Sample 19-HR-
DS-10. Scale bars are 1 cm.
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Figure 2.3 Morphological diversity of Treptichnus pedum from the Deep Spring Formation.
A) Large slab with multiple T. pedum of different morphologies collected from Andrews
Mountain. Sample 19-AM-DS-9. B-C) Insets from panel A showing detailed views of the two T.
pedum morphologies. D) Two, broadly parallel T. pedum trails collected from Hines Ridge.
Sample 19-HR-DS-1. E) One small tightly curved, C- to O-shaped T. pedum collected from
Hines Ridge. Sample 19-HR-DS-11. F) Multiple, relatively densely packed shorter T. pedum
burrows from Hines Ridge. Sample 19-HR-DS-4. Scale bars are 1 cm.
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Figure 2.4 Elemental maps of biomixing, Sample 19-HR-DS-3
Areas A and B which were scanned for elemental maps and spectra are shown by the dashed
boxes on the sample in cross-section. Scale bar on sample figure is 1 cm. ‘ML’ = Mixed layer.
Bedding plane photograph in Figure 2.2F.
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Figure 2.5 Elemental maps of biomixing, Sample 19-HR-DS-R1.
Areas A and B which were scanned for elemental maps and spectra are shown by the dashed
boxes on the sample in cross-section. Scale bar on sample figure is 1 cm. Bedding plane
photograph in Figure 2.2E.
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Figure 2.6 Elemental maps of T. pedum bioirrigation, Sample 19-HR-DS-1.
Areas A, B, and C which were scanned for elemental maps and spectra are shown by the dashed
boxes on the sample in cross-section. Scale bar on sample figure is 1 cm. Bedding plane
photograph in Figure 2.3D.
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Figure 2.7 Elemental maps of T. pedum bioirrigation, Sample 19-AM-DS-9.
Areas A and B which were scanned for elemental maps and spectra are shown by the dashed
boxes on the sample in cross-section. Scale bar on sample figure is 1 cm. Bedding plane
photograph is in Figure 2.3C.
127
Figure 2.8 Elemental maps of unmixed sedimentary laminae, Sample 19-AM-DS-19.
Areas A and B which were scanned for elemental maps and spectra are shown by the dashed
boxes on the sample in cross-section. Scale bar on sample figure is 1 cm. Bedding plane
photograph in Figure 2.2A.
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Figure 2.9 NMDS analyses for elemental composition of trace fossil samples.
A) Samples grouped by bioturbation behavior into biomixing (pink), bioirrigation (blue), and
unmixed sedimentary laminae (gray). Inset gives environmental vectors for each element, where
longer arrows indicate stronger association moving in that direction through the ordination space.
B) Samples grouped by locality into Hines Ridge (purple) and Andrews Mountain (orange).
Environmental vectors in the insert in panel A also apply to panel B.
129
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S2. Supplementary appendix
Supplementary tables
Table S2.1 Elemental spectra for each sample and area.
Spectra of Mg, Al, Si, S, K, Ca, Ti, and Fe oxides collected during µXRF analyses demonstrating
relative abundance of elements in each sample. ‘HR’ = Hines Ridge. ‘AM’ = Andrews
Mountain.
Sample Area Behavior Locality MgO Al2O3 SiO2 SO3 K2O CaO TiO2 Fe2O3
19-HR-DS-1 C Bioirrigation HR 1.21 7.24 70.88 0.03 3.09 3.33 1.49 12.73
19-HR-DS-1 B Bioirrigation HR 1.95 6.28 70.92 0.06 2.97 4.15 2.06 11.62
19-HR-DS-1 A Bioirrigation HR 1.55 6.84 70.62 0.04 3.07 8.22 1.18 8.49
19-AM-DS-9 A Bioirrigation AM 0.76 9.60 75.54 0.02 5.07 0.62 1.20 7.21
19-AM-DS-9 B Bioirrigation AM 1.73 9.51 65.85 0.01 5.61 0.75 2.61 13.94
19-HR-DS-3 A Biomixing HR 2.07 7.29 65.41 0.06 3.09 9.57 1.44 11.07
19-HR-DS-3 B Biomixing AM 1.92 5.97 68.47 0.02 2.51 10.1 1.3 9.71
19-HR-DS-R1 A Biomixing HR 1.19 6.28 72.61 0.03 3.41 0.79 3.7 12.01
19-HR-DS-R1 B Biomixing HR 2.06 7.32 70.36 0.01 3.96 0.47 2.40 13.42
19-AM-DS-19 A Unmixed AM 1.04 6.81 68.24 0.02 2.64 0.52 1.41 19.33
19-AM-DS-19 B Unmixed AM 1.33 7.55 80.10 0.02 3.05 0.46 0.59 6.89
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Chapter 3. Complex marine bioturbation ecosystem engineering behaviors persisted in
the wake of the end-Permian mass extinction
Abstract
The end-Permian mass extinction was the most severe mass extinction event of the
Phanerozoic and was followed by a several million-year delay in benthic ecosystem recovery.
While much work has been done to understand biotic recovery in both the body and trace fossil
records of the Early Triassic, almost no focus has previously been given to analyzing patterns in
ecosystem engineering complexity as a result of the extinction drivers. Bioturbation is a key
ecosystem engineering behavior in marine environments, as it results in changes to resource
flows and the physical environment. Thus, the trace fossil record can be used to examine the
effect of the end-Permian mass extinction on bioturbating ecosystem engineers. We present a
dataset compiled from previously published literature to analyze burrowing ecosystem
engineering behaviors through the Permian-Triassic boundary. We report two key observations:
first, that there is no loss in bioturbation ecosystem engineering behaviors after the mass
extinction, and second, that these persisting behaviors include deep tier, high-impact, complex
ecosystem engineering. These findings suggest that while environmental conditions may have
limited deeper burrowing, complex ecosystem engineering behaviors were able to persist in the
Early Triassic. Furthermore, the persistence of deep tier bioirrigated three-dimensional network
burrows implies that benthic biogeochemical cycling could have been maintained at pre-
extinction states in some local environments, stimulating ecosystem productivity and promoting
biotic recovery in the Early Triassic.
137
3.1 Introduction
The end-Permian mass extinction is recognized as the most devastating mass extinction
event of the Phanerozoic, resulting in an estimated loss of 81% of all marine species
(Stanley,
2016) and a turnover of the Paleozoic evolutionary fauna to the Modern evolutionary fauna
(Sepkoski, 1981). The proposed trigger of this extinction event – the eruption of the Siberian
traps (Campbell et al., 1992; Reichow et al., 2009; Burgess et al., 2014) – resulted in global
warming, ocean anoxia, ocean acidification, and habitat loss (Erwin, 1994; Clarkson et al., 2015;
Song et al., 2015). The environmental conditions following the eruption of the Siberian traps
contributed to prolonged instability of biogeochemical cycles and inhabitable environments that
led to a delay in global ecosystem and biotic recovery of several million years (Payne et al.,
2004; Lehrmann et al., 2006; Bottjer et al., 2008). The nature of how ecosystems during the
Early Triassic returned to stability is not currently well understood. Here, we examine the trace
fossil record, an overlooked dataset to understanding biotic recovery. Compared to body fossils,
trace fossils not only record the response of soft bodied organisms not easily preserved as fossils
to mass extinction events (Twitchett and Barras, 2004), but, more importantly, record the
diversity and complexity of these organisms’ behaviors and, in particular, those which involve
ecosystem engineering.
Ecosystem engineering refers to the behaviors of organisms which modify, create, and
maintain habitable environments (Jones et al., 1994). Ecosystem engineering can be classified as
autogenic or allogenic. Autogenic ecosystem engineers modify environments by providing a
physical structure for other organisms (e.g. reef-building corals), while allogenic ecosystem
engineers modify environments by creating new habitats and altering resource flows by
redistributing materials within the environment (e.g. vegetation-clearing goats) (Jones et al.,
138
1994). In the marine environment, bioturbation is the major ecosystem engineering behavior
(Jones et al., 1994; Herringshaw et al., 2017; Minter et al., 2017), due to the resulting alteration
of substrate rheology, the mixing and redistribution of nutrients and sediments, the shift in
sediment redox gradients, the creation of new habitats, and the construction of new ecospace
(Aller, 1982; McIlroy and Logan, 1999; Erwin and Tweedt, 2011). Bioturbation in marine
environments typically creates a surficial mixed layer, transition layer, and historical layer
(Savrda and Bottjer, 1991). The mixed layer is commonly bioturbated by mobile as well as
sedentary organisms and has the highest water content. In the mixed layer, the record of
bioturbation is represented by sediment mixing and a lack of preservation of identifiable trace
fossils with sharp outlines. The underlying transition layer begins a number of centimeters
below the sediment-water interface. The transition layer has a lower water content, as it
represents the beginning of compaction, which allows for the preservation of identifiable trace
fossils. Bioturbation does not typically occur in the historical layer below the transition layer.
The historical layer therefore typically represents bioturbation as it ultimately appears in the
stratigraphic record. In marine environments, bioturbation can be divided into various functional
groups based on how bioturbators interact with the substrate within these layers
(Solan and
Wigham, 2004; Minter et al., 2017)
.
Each behavior has its own degree of impact on the sediment
chemistry, biogeochemical cycling, and benthic ecosystems (Herringshaw et al., 2017). Thus, the
persistence or disappearance of these high-impact ecosystem engineering burrowing behaviors
across the end-Permian mass extinction is key to understanding how marine ecosystems
recovered in the Early Triassic.
Previous research on the trace fossil record across the Permian-Triassic boundary has
primarily focused on tiering, ichnofabrics, and burrow size. Induan trace fossils tend to be
139
simple, shallow burrows
(Twitchett and Barras, 2004; Pruss and Bottjer, 2004). As early as the
early Olenekian (Smithian), trace fossil assemblages exhibit recovery to pre-extinction levels of
ichnodiversity, ichnofabric indices, and burrow size (Hofmann et al., 2011; Chen et al., 2012).
However, tiering did not return to pre-extinction depths until the beginning of the Middle
Triassic (Hofmann et al., 2015). Herein, we apply two ecosystem engineering framework
analyses to previously published Permian-Triassic trace fossil datasets to understand the patterns
of ecosystem engineering behaviors across the mass extinction boundary and how these
behaviors may have influenced ecosystem recovery in the Early Triassic.
3.2 Methods
Ecosystem engineering behaviors and trace fossil data collection
The objective of this research is to use the Permian-Triassic trace fossil record to compile
and analyze ecosystem engineering behaviors and their impacts on the benthic environment.
Trace fossil occurrences were compiled from previously published literature and included the
entire Permian through the first stage of the Middle Triassic (Asselian through the Anisian, 299 –
237 Ma
17
). Only trace fossils which could be confidently assigned to a tier – surficial, semi-
infaunal, shallow, intermediate, or deep (Ausich and Bottjer, 1982; Bottjer and Ausich, 1986;
Mángano and Buatois, 2014)
– were added to the dataset. Tiering is a fundamental part of the
ecosystem engineering analyses used in this research, and thus trace fossils without a precise
description of vertical penetration depth or tiering cannot be accurately analyzed in terms of
ecosystem engineering behavior and the effect it may have had on the benthic environment. Each
ichnogenus was counted once per tier because the presence or absence of each ecosystem
engineering behavior represents the necessary data to investigate the effect of the extinction on
140
each behavior and the roll that each behavior may have played in the recovery from the
extinction. Data were also limited to shallow marine trace fossil occurrences as they generally
provide the richest trace fossil record (Hofmann et al., 2015). Due to data filtering based on the
lack of precise tiering descriptions, Roadian and Capitanian data are absent. Ultimately, 164
unique ichnogenera were entered into the database given the parameters of the study (confident
tiering assignment, one occurrence per tier, one occurrence per time period, and shallow marine).
These data were analyzed using two frameworks: the ecosystem engineering occupation cube
method (Minter et al., 2017)and the ecosystem engineering impact (EEI) values method
(Herringshaw et al., 2017; Cribb et al., 2019). Trace fossils and their categorical assignments for
both ecosystem engineering analyses were grouped into stages based on stratigraphic
descriptions in the primary literature.
Ecosystem engineering occupation cubes
The ecosystem engineering occupation cube method assigns ‘cube spaces’ to ichnogenera
according to tiering, sediment interaction
32
, and sediment modification
(Solan and Wigham,
2004; Minter et al., 2017). For tiering, literature descriptions were used to classify each trace
fossil entry as surficial, semi-infaunal (0 – 0.5 cm), shallow (0.5 – 6 cm), intermediate (6 – 12
cm), or deep (>12 cm) (Ausich and Bottjer, 1982; Bottjer and Ausich, 1986; Mángano and
Buatois, 2014). Surficial, semi-infaunal, and shallow tier bioturbating organisms commonly
occupy the mixed layer, while bioturbators occupying intermediate or deep tiers occupy the
transition layer. Each ichnogenus was assigned one of four sediment interaction classifications:
intrusion, compression, backfilling, and excavation
(Buatois and Mangano, 2011). Intrusion
describes the displacement of the sediment as the animal burrows and sediment closes up behind
141
it; compression describes the movement and compaction around the burrowing animal;
backfilling describes the backward passage of sediment either around or through the burrower;
and excavation describes the active loosening and transportation of sediment from one point
along the burrow path to another
(Buatois and Mangano, 2011). Each ichnogenus was also
assigned one of four sediment modification classifications: biodiffusion, gallery biodiffusion,
conveyor, and regenerator
(Solan and Wigham, 2004). Biodiffusion involves the movement of
sediment particles over short distances; gallery biodiffusion involves the redistribution of
sediment particles from one part of the sediment profile to another; conveying involves
transporting sediment particles across and within tiers; and regenerating involves moving
sediment up to the surface from below the sediment-water interface (Solan and Wigham, 2004).
When possible, each ichnogenus was given sediment modification and sediment interaction
assignments based on previous descriptions (Minter et al., 2017). For ichnogenera which had not
been previously described in this framework, the original literature which described the
ichnogenera was consulted for the assignments. From these tiering, sediment modification, and
sediment interaction categories, each ichnogenus was given an occupied cube which represents a
certain ecosystem engineering behavior within a given tiering depth. For each of the time
intervals across the Permian-Triassic boundary, the number of occupied cubes is summed to
represent the total number of ecosystem engineering behaviors present.
Ecosystem engineering impact values
The ecosystem engineering impact (EEI) value method
(Herringshaw et al., 2017) rank-
order scores trace fossils on the basis of tiering, bioturbation behavior functional group
(Solan
and Wigham, 2004), and bioirrigation potential. We have modified this method to use the same
142
tiering categories used in the ecosystem engineering occupation cube scheme in order to make
the two frameworks more comparable. Potential functional group, which describes how the
burrowing organism modifies the substrate, scores trace fossils as 1 = epifaunal locomotion, 2 =
surficial modification, 3 = biodiffusion, 4 = regeneration, 5 = downward conveying, 6 = upward
conveying, or 7 = gallery biodiffusion. Biodiffusion, regeneration, and gallery biodiffusion are
the same descriptions as those in the ecosystem engineering occupation cube framework (Solan
and Wigham, 2004; Minter et al., 2017). Epifaunal locomotion describes a surficial animal’s
movement which does not penetrate the sediment-water interface. Surficial modification
describes burrowing which moves particles over short distances only within 2 cm of the
sediment-water interface. Conveying is divided into downward conveying, which describes
head-down orientation movement, and upward-conveying, which describes movement of an
organism with its head at or close to the surface (Solan and Wigham, 2004). Bioirrigation
potential, which describes the likelihood that the burrow was flushed with sediment, is scored as
1 = improbable, 2 = probable, and 3 = possible (Herringshaw et al., 2017). Many ichnogenera
can occupy a range in values for each category, so they are given a summated EEI range. The
final EEI value range for an ichnogenus is calculated by summing the minimum scores and the
maximum scores. Trace fossils which represent simple, low-impact ecosystem engineering have
scores lower in range and value, and trace fossils which represent complex, high-impact
ecosystem engineering have scores higher in range and value (Herringshaw et al., 2017).
3.3 Results
The Permian primarily consists of deep and intermediate tier burrows, with a smaller
component of shallow tier burrows and no reported semi-infaunal and surficial burrows (Figure
143
3.1). The relative abundance of deep tier burrows is stable until it decreases by about a third from
the Wordian to the Wuchiapingian and increases again in the Changhsingian. Intermediate tier
burrows follow a similar pattern but disappear in the Wuchiapingian and are only a tenth of the
trace fossils in the Changhsingian. Abundance of shallow tier burrows is stable from the Asselian
to the Kungurian, decreases in the Wordian, increases during the Wuchiapingian, and decreases
in the Changhsingian. Surficial and semi-infaunal tier burrows were not reported in the Permian.
The Triassic marks a shift to a majority of shallow and semi-infaunal burrows. Surficial
trace fossils are only present in the Induan and Olenekian. Semi-infaunal tier burrows increase in
abundance from the Induan to the Olenekian and decrease in the Anisian (Figure 3.1). Shallow
tier burrows represent nearly half of the trace fossils in the Induan and continue to make up at
least 40% of all trace fossils at each stage throughout the Triassic. Intermediate and deep
burrows combined represent less than a fifth of total trace fossils in the Induan and Olenekian
and increase to half of all trace fossils in the Anisian.
During the Permian, there are four occupied cubes in the Asselian, six in the Sakmarian,
six in the Artinskian, five in the Kungurian, six in the Wordian, four in the Wuchiapingian, and
six in the Changhsingian. During the Triassic, there are thirteen occupied cubes in the Induan
and in the Olenekian (the maximum at all time intervals), and seven in the Anisian (Figure 3.2;
Table S3.2). Five behaviors represented by interaction-modification combinations comprise all
cubes across the Permian-Triassic: regenerator-excavation, compression-biodiffusion, intrusion-
biodiffusion, backfill-conveyor, and gallery biodiffusion-compression. Gallery biodiffusion-
compression is the most common ecosystem engineering behavior (Figure 3.2; Table S3.2; Table
S3.3).
144
From the Asselian to the Kungurian, ecosystem engineering impact is high in both value
and range at EEI=7-14 (Figure 3.3). In the Wordian, EEI values increase in range to EEI=5-14.
EEI values remain high but decrease in range to EEI=7-14 for the Wuchiapingian and
Changhsingian. In the Induan, EEI values increase to EEI=3-14 and remain consistent into the
Olenekian. EEI values decrease in range to EEI=4-14 in the Anisian.
3.4 Discussion
Persistence of high-impact ecosystem engineering behaviors
The ecosystem engineering occupation cubes reveal that there are no ecosystem
engineering bioturbation behaviors (sediment interaction-sediment modification combination)
lost in the aftermath of the end-Permian mass extinction (Figure 3.2). We note that some
complex trace fossils, such as Zoophycos, do disappear during the Early Triassic, but other trace
fossils representing the same ecosystem engineering behaviors continue to persist. The three
ecosystem engineering behaviors present in the Permian – compression-gallery biodiffusion,
backfill conveyor, and compression-biodiffusion – are still present even in the Induan. These
results are in agreement with previous observations that, despite the high extinction rates and
selectivity associated with the end-Permian mass extinction, there was little change in functional
group diversity between pre- and post-extinction benthic ecosystems (Foster and Twitchett,
2014; Dineen et al., 2019).
Notably, gallery biodiffusion, the highest impact sediment modification functional group
(Herringshaw et al., 2017), is present even in the deepest tiers in the Induan and Olenekian
following the mass extinction event due to the persistence of trace fossils such as Skolithos,
Diplocraterion, and Thalassinoides
(Table S3.3). Moreover, the continuation of high EEI values
145
through the Early Triassic reveals that ecosystem engineering remains both complex and high-
impact across the Permian-Triassic boundary (Figure 3.3). Any effect of the mass extinction on
ecosystem engineering is evident only in a decrease in minimum EEI values from the
Changhsingian to the Induan (Figure 3.3), but this decrease in EEI values without a contraction
in range during the Triassic reflects presence of semi-infaunal and surficial trace fossils rather
than the loss of high-impact ecosystem engineering (Figure 3.1; Figure 3.2). The persistence of
all ecosystem engineering behaviors likely reflects either behavioral redundancy in bioturbating
ecosystem engineers or that new Early Triassic bioturbators rapidly refilled empty roles of the
extinct Permian ecosystem engineers.
Collapse of bioturbation depth in the Early Triassic
Although no loss in ecosystem engineering behaviors is evident, the data do reveal a
collapse in bioturbation depth associated with the extinction (Figure 3.1). It is also evident that
there are fewer ichnogenera that represent high-impact ecosystem engineering behaviors present
in the deep and intermdiate tiers during the Early Triassic than during the Permian (Figure 3.2;
Table S3.1, Table S3.2). For example, only three ichnogenera in the Induan occupy the deep
compression-gallery biodiffusion cube space, whereas five ichnogenera occupy the same cube
space during the Wordian. The lack of surficial and semi-infaunal trace fossils during the
Permian is best explained by the existence of a well-developed mixed layer (Buatois and
Gabriela Mángano, 2013), while the predominance of shallow tier burrows and loss of deep
tiering burrows in the Triassic implies the loss of deep sedimentary mixing in shallow marine
environments (Figure 3.1). This has been observed in previous research at a variety of temporal
146
and spatial scales
(Ausich and Bottjer, 2001; Twitchett and Barras, 2004; Twitchett, 2006;
Hofmann et al., 2015).
What caused the loss of deep tiered burrows remains unclear. On the one hand, the Early
Triassic has been widely associated with widespread marine anoxia (Wignall and Twitchett,
1996; Song et al., 2015). In modern environments and controlled experiments, some animal
burrows become shallower in hypoxic settings
(Savrda and Bottjer, 1986; Diaz and Rosenberg,
1995; Weissberger et al., 2009). Thus, the collapse of deep sedimentary bioturbation and lack of
recovery by the end of the Early Triassic could be interpreted as the persistence of low-oxygen
conditions in shallow marine environments. The persistence of some high-impact ecosystem
engineering behaviors (e.g. gallery biodiffusion) in the deep tiers in the Early Triassic, however,
most likely suggests that across all localities in the dataset, environmental stress limited
burrowing organisms to shallower depths, but some local conditions may have permitted deeper
burrowing. This explanation is in agreement with the differences in functional group extinction
across the Permian-Triassic boundary reported in different depositional environments and
paleolatitudes
(Foster and Twitchett, 2014). Furthermore, deep tier trace fossils tend to occur in
the shallowest marine environments, where coarse sediment allows for easier oxygen diffusion
deeper into the substrate from the sediment-water interface. These shallow facies are not present
in trace-fossil bearing Olenekian sections, which likely explains the lower proportion of deep tier
trace fossils after the Induan. Finally, we note that the proxies by which marine anoxia are
determined may be locally controlled by the intensity of bioturbation, as bioirrigation promotes
oxygen penetration into the sediment
1
(Aller, 1982; Hofmann et al., 2015). The geochemical
signal of anoxia may, therefore, be amplified by the lack of deeper bioturbation.
147
Implications for ecosystem recovery
The persistence of high-impact ecosystem engineering behaviors and loss of deep
bioturbation across the end-Permian mass extinction both likely affected benthic biogeochemical
cycling during the Early Triassic. Bioturbation exerts a control on the rate of nutrient cycling in
marine ecosystems (Biles et al., 2002; Laverock et al., 2011). Globally, a cessation of deep tiered
bioturbation in general may have increased nutrient burial, as nutrients that reach the seafloor
would not have been as likely to be reworked by conveyors and regenerators and resuspended
back into the water column. Additionally, a shallowing of the mixed and transition layers would
have caused the redox profile discontinuity (RPD) to migrate upwards towards the sediment-
water interface (Weissberger et al., 2009), which may have affected redox-sensitive nutrient flux
and benthic microbial processes (Bertics and Ziebis, 2009). However, in some local
environments, deep high-impact ecosystem engineering behaviors would have maintained
benthic nutrient flux and the RPD depth by transporting nutrients throughout the sediment and
promoting nutrient flow to microbial communities (Mermollid-Blondin and Rosenberg, 2006).
More specifically, gallery biodiffusion (the highest impact bioturbation functional group)
(Herringshaw et al., 2017), vastly increases the surface area for solute exchange and active
biogeochemical reaction sites (Aller, 1983; Lohrer et al., 2004; Huettel et al., 2014). This means
that these complex bioturbation behaviors are a critical aspect of benthic nitrogen cycling, for
example, as nitrification-denitrification processes require an oxic-anoxic interface (Jenkins and
Kemp, 1984). Increased bioirrigation through gallery network burrows thus can result in
increased areas of stimulated nitrification (Huettel et al., 1998)
and decrease the flux of inorganic
nitrogen to the water column (Nilsson and Rosenberg, 2000). Bioirrigation also strongly
influences benthic phosphorus cycling (Lohrer et al., 2004; Boyle et al., 2014; van de Velde et
148
al., 2018, 2020; Tarhan et al., 2021), thus exerting a major control on the limiting nutrient for
marine ecosystems. Increased bioturbation intensity by animals which create bioirrigated gallery
networks has also been found to cause increased abundance of microbial communities which are
unique to the burrow, either due to increased oxygen (Bertics and Ziebis, 2009) or organic matter
in the burrow wall (Papaspyrou et al., 2006). Thus, as these high-impact ecosystem engineering
behaviors and burrow size returned to pre-extinction levels within the intermediate and deep
tiers, progressively more complex bioturbation would have allowed benthic biogeochemical
cycling to return to its pre-extinction state.
Precisely how these bioturbation-induced changes in biogeochemical cycling may have
influenced biotic recovery of benthic macrofauna is not entirely resolved, and research
investigating the links between these benthic processes remains limited. However, a number of
studies of modern benthic ecology have linked habitat quality and ecosystem functioning to
oxygenated and bioturbated substrates (Nilsson and Rosenberg, 2000; Teal et al., 2010). An
increased supply of nutrients to microbes due to complex bioturbation behaviors would have
stimulated marine productivity, which may have maintained or increased the diversity of life
(Martin et al., 2008) in the wake of the end-Permian mass extinction. However, it is still
implausible that ecosystems would have fully recovered until larger, deep tier bioturbators
regained pre-extinction levels on a global scale (Foster and Twitchett, 2014). Although, there is
still potential for high-impact ecosystem engineering behaviors, most notably gallery
biodiffusion, to maintain habitable environments during biotic crises and mass extinctions,
particularly in environments where deep tier burrows persisted in the Early Triassic.
149
3.5 Conclusions
The data presented here show two key observations: 1) there is no loss in ecosystem
engineering bioturbation behaviors after the end-Permian mass extinction, and 2) the highest-
impact ecosystem engineering behaviors (deep tier gallery biodiffusion) persisted in the Early
Triassic. There is a pronounced shift from deep and intermediate tier burrows in the Permian to
shallow and semi-infaunal tier burrows in the Early Triassic, likely related to significant loss of
mixed layer development and shallowing of the transition layer. However, ecosystem
engineering complexity does not seem to be majorly affected, as highly effective sediment
mixing behaviors were still present throughout the entire Early Triassic. These two key
observations suggest that although shallow marine environments may have still been
inhospitable during the Early Triassic, generally causing bioturbating organisms to be limited to
the shallow tier, some local environmental conditions were favorable enough to bioturbators to
reach the deep sediment tier with rather complex bioturbation behaviors. The persistence of
complex engineering behaviors across the most devastating mass extinction in Earth history
suggests that there was still potential for these organisms to maintain habitable benthic
environments by increasing nutrient flow throughout the sediment profile and to the surface.
150
3.6 Figures
Figure 3.1 Relative abundance of burrow tiering across the Permian-Triassic.
Trace fossils for the Roadian and Capitanian are absent (see details in Methods), represented by
vertical gray dashed lines. Permian-Triassic mass extinction is represented by the vertical red
dashed line. Percentages for trace fossils representing each tier in a given time interval are in
Table S3.1.
151
Figure 3.2 Ecosystem engineering occupation cubes.
Teal shaded cubes indicate ecosystem engineering behavior which is represented by a present
ichnogenus in a given tiering depth. The Permian-Triassic mass extinction event is represented
by the vertical red dashed line. Number of ichnogenera which occupy each cube are given in
Table S3.2. Occupied cubes for the Asselian to the Anisian given in Figure S3.1.
152
Figure 3.3 Ecosystem engineering impact (EEI) values and ranges.
Teal bars represent the range between the minimum and maximum EEI value scores for all the
ichnogenera in a given time interval. Mass extinction event occurs between the Changhsingian
and the Induan.
153
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S3. Supplementary appendix
Supplementary figures
Figure S3.1 Ecosystem engineering occupation cubes for the entire dataset.
All data from the Asselian through the mass extinction interval to the Anisian. Full data given in
Table S3.3, and occupation data in Table S3.2.
159
Supplementary tables
Table S3.1 Tiering analysis.
Percentage of trace fossils belonging to each burrow tier in each stage included in the analyses,
along with sample size of each stage.
Stage/Substage %
surficial
% semi-
infaunal
%
shallow
%
intermediate
% deep Sample
size (n)
Asselian 0 0 33.3 44.4 22.2 9
Sakmarian 0 0 31.3 37.5 31.2 16
Artinskian 0 0 28.6 28.6 42.8 14
Kungurian 0 0 33.3 33.3 33.3 6
Wordian 0 0 11.8 41.2 47.1 17
Wuchiapingian 0 0 75.0 0 25.0 4
Changhsingian 0 0 40.0 10.0 50.0 9
Induan 21.6 18.9 43.2 2.7 13.5 37
Olenekian 7.0 30.1 41.9 14.0 7.0 43
Anisian 0 11.1 33.3 44.4 11.1 9
160
Table S3.2 Ecosystem engineering group occupation analysis.
Number of ichnogenera in each time interval that are present for each ecosystem engineering
cube space, denoted by the ecosystem engineering behavior tier + interaction + modification
combinations. Highlighted cells indicate ecosystem engineering behaviors which are represented
in the trace fossil record, and bolded numbers correspond to the number of ichnogenera at each
stage that correspond to that particular ecosystem engineering behavior. Ichnogenera occurrences
are counted only once in each tier for each time interval.
Impacts upon sediment
Asselian
Sakmarian
Artinskian
Kungurian
Wordian
Wuchiapingian
Changhsingian
Induan
Olenekian
Anisian
Surficial + Excavation + Regenerator 0 0 0 0 0 0 0 2 0 0
Surficial + Compression + Biodiffusion 0 0 0 0 0 0 0 6 3 0
Semi-infaunal + Backfill + Conveyor 0 0 0 0 0 0 0 1 3 0
Semi-infaunal + Compression + Gallery Biodiffusion 0 0 0 0 0 0 0 1 3 0
Semi-infaunal + Intrusion + Biodiffusion 0 0 0 0 0 0 0 1 0 0
Semi-Infaunal + Excavation + Regenerator 0 0 0 0 0 0 0 0 1 0
Semi-infaunal + Compression + Biodiffusion 0 0 0 0 0 0 0 4 6 1
Shallow + Compression + Biodiffusion 0 0 0 0 0 0 0 1 1 0
Shallow + Compression + Gallery Biodiffusion 0 1 1 1 1 1 1 7 10 2
Shallow + Backfill + Conveyor 3 4 3 1 1 1 2 5 5 2
Shallow + Excavation + Regenerator 0 0 0 0 0 1 1 3 2 0
Intermediate + Compression + Gallery Biodiffusion 3 3 2 0 2 0 1 1 4 2
Intermediate + Backfill + Conveyor 0 3 2 2 5 0 0 0 2 1
Intermediate + Excavation + Regenerator 1 0 0 0 0 0 0 0 0 1
Deep + Compression + Gallery Biodiffusion 2 3 5 1 5 1 3 3 2 0
Deep + Backfill + Conveyor 0 2 1 1 3 0 1 2 0 0
Deep + Excavation + Regenerator 0 0 0 0 0 0 0 0 1 1
161
Table S3.3 Dataset of trace fossil occurrences, EEI scores, and functional group
assignments used in analyses.
Reference numbers: 1 – Bann and Fielding (2004); 2 – Bann et al. (2004); 3 – Chen et al. (2012);
4 – Chen et al. (2011); 5 – Ding et al. (2016); 6 – Feng et al. (2017); 7 – Feng et al. (2019); 8 –
Fielding et al. (2007); 9 – Fraser and Bottjer (2009); 10 – Hofmann et al. (2011); 11 – Hofmann
et al. (2013a); 12 – Hofmann et al. (2013b); 13 – Knaust (2010); 14 – Luo et al. (2016); 15 –
MacNaughton and Zonneveld (2010); 16 – Mason et al. (1983); 17 – McCarthy (1979); 18 –
Pruss and Bottjer (2004); 19 – Stanistreet et al. (1980); 20 – Tavener-Smith and Mason (1983);
21 – Twitchett and Wignall (1996); 23 – Twitchett (1999); 24 – Wignall et al. (1998); 25 –
Zhang et al. (2019); 26 – Zhao and Tong (2010); 27 – Zhao et al. (2015); 28 – Zonneveld et al.
(2007); 29 – Zonneveld et al. (2010)
Stage Ichnogenera EEI
max
EEI
min
Tier Modification Interaction Ref.
Artinskian Arenicolites 14 14 deep gallery
biodiffusion
compression 1
Wordian Arenicolites 14 14 deep gallery
biodiffusion
compression 1
Sakmarian Arenicolites 14 14 deep gallery
biodiffusion
compression 2
Olenekian Arenicolites 13 13 shallow gallery
biodiffusion
compression 3
Anisian Arenicolites 14 14 mid gallery
biodiffusion
compression 6
Induan Arenicolites 13 13 shallow gallery
biodiffusion
compression 7
Olenekian Arenicolites 13 13 shallow gallery
biodiffusion
compression 18
Induan Asteriacites 7 8 shallow biodiffusive compression 7
Induan Asteriacites 3 4 surficial biodiffusive compression 9
Olenekian Asteriacites 4 7 semi-
infaunal
biodiffusive compression 18
Wordian Asterosoma 12 13 mid conveyor backfill 1
Sakmarian Asterosoma 12 13 mid conveyor backfill 2
Induan Asterosoma 7 8 shallow conveyor backfill 29
Induan Catenichnus 9 10 shallow regenerator excavation 10
Olenekian Catenichnus 9 10 shallow regenerator excavation 21
Artinskian Chondrites 13 14 deep gallery
biodiffusion
compression 1
Changhsing
ian
Chondrites 13 14 deep gallery
biodiffusion
compression 5
Wuchiaping
ian
Chondrites 13 14 deep gallery
biodiffusion
compression 5
Olenekian Chondrites 13 14 deep gallery
biodiffusion
compression 28
162
Olenekian Circulichnis 6 7 semi-
infaunal
conveyor backfill 27
Induan Cochlichnus 4 7 semi-
infaunal
biodiffusive compression 21
Olenekian Cochlichnus 4 7 semi-
infaunal
biodiffusive compression 21
Wordian Conichnus 5 6 mid gallery
biodiffusion
compression 1
Olenekian Conichnus 5 6 mid gallery
biodiffusion
compression 28
Olenekian Cruziana 7 7 semi-
infaunal
regenerator excavation 12
Induan Cruziana 6 6 surficial regenerator excavation 15
Wordian Cylindrichnus 13 13 deep gallery
biodiffusion
compression 1
Sakmarian Cylindrichnus 12 12 mid gallery
biodiffusion
compression 2
Asselian Cylindrichnus 12 12 mid gallery
biodiffusion
compression 17
Olenekian Didymaulichnus 3 3 surficial biodiffusive compression 3
Induan Didymaulichnus 3 3 surficial biodiffusive compression 29
Induan Dimorphichnus 3 3 surficial biodiffusive compression 7
Olenekian Diplichnites 3 3 surficial biodiffusive compression 3
Induan Diplichnites 3 3 surficial biodiffusive compression 15
Wordian Diplocraterion 12 14 deep gallery
biodiffusion
compression 1
Sakmarian Diplocraterion 12 14 deep gallery
biodiffusion
compression 2
Olenekian Diplocraterion 11 13 shallow gallery
biodiffusion
compression 3
Induan Diplocraterion 13 13 shallow gallery
biodiffusion
compression 13
Asselian Diplocraterion 12 14 deep gallery
biodiffusion
compression 17
Artinskian Diplocraterion 12 14 deep gallery
biodiffusion
compression 20
Induan Diplocraterion 12 14 deep gallery
biodiffusion
compression 23
Changhsing
ian
Diplocraterion 12 14 deep gallery
biodiffusion
compression 24
Olenekian Diplocraterion 11 13 mid gallery
biodiffusion
compression 25
Olenekian Diplopodichnus 7 8 shallow biodiffusive compression 25
Olenekian Gnathichnus 3 7 semi-
infaunal
biodiffusive compression 3
Olenekian Gordia 4 7 semi-
infaunal
biodiffusive compression 3
163
Induan Gordia 4 7 semi-
infaunal
biodiffusive compression 11
Olenekian Gyrochorte 6 7 semi-
infaunal
conveyor backfill 4
Induan Gyrochorte 7 8 shallow conveyor backfill 11
Olenekian Gyrochorte 10 11 shallow conveyor backfill 18
Wordian Gyrolithes 11 12 mid conveyor backfill 8
Induan Halopoa 6 7 semi-
infaunal
conveyor backfill 29
Induan Helicodromites 9 10 semi-
infaunal
gallery
biodiffusion
compression 29
Artinskian Helminthopsis 7 7 shallow conveyor backfill 1
Sakmarian Helminthopsis 7 7 shallow conveyor backfill 2
Induan Helminthopsis 7 7 shallow conveyor backfill 10
Olenekian Helminthopsis 7 7 shallow conveyor backfill 25
Induan Kouphichnium 3 3 surficial biodiffusive compression 7
Olenekian Laevicyclus 12 13 shallow gallery
biodiffusion
compression 3
Induan Laevicyclus 11 13 shallow gallery
biodiffusion
compression 7
Sakmarian Lingulichnus 8 9 mid gallery
biodiffusion
compression 2
Olenekian Lockeia 4 7 semi-
infaunal
biodiffusive compression 3
Induan Lockeia 4 7 semi-
infaunal
biodiffusive compression 10
Anisian Lockeia 4 7 semi-
infaunal
biodiffusive compression 28
Kungurian Macaronichnus 13 14 deep conveyor backfill 1
Wordian Macaronichnus 13 14 deep conveyor backfill 1
Sakmarian Macaronichnus 13 14 deep conveyor backfill 2
Olenekian Monocraterion 8 12 shallow gallery
biodiffusion
compression 4
Induan Monomorphichn
us
3 3 surficial biodiffusive compression 15
Wordian Nereites 12 13 mid conveyor backfill 8
Olenekian Ophiomorpha 4 7 semi-
infaunal
biodiffusive compression 3
Artinskian Palaeophycus 7 8 shallow gallery
biodiffusion
compression 1
Kungurian Palaeophycus 7 8 shallow gallery
biodiffusion
compression 1
Wordian Palaeophycus 7 8 shallow gallery
biodiffusion
compression 1
Sakmarian Palaeophycus 7 8 shallow gallery
biodiffusion
compression 2
164
Olenekian Palaeophycus 7 8 shallow gallery
biodiffusion
compression 3
Changhsing
ian
Palaeophycus 7 8 shallow gallery
biodiffusion
compression 5
Wuchiaping
ian
Palaeophycus 7 8 shallow gallery
biodiffusion
compression 5
Induan Palaeophycus 7 8 shallow gallery
biodiffusion
compression 10
Anisian Palaeophycus 7 8 shallow gallery
biodiffusion
compression 28
Olenekian Paleodictyon 6 6 semi-
infaunal
conveyor backfill 27
Wordian Parahaentzschel
iana
11 14 deep conveyor backfill 1
Induan Phycodes 12 13 shallow gallery
biodiffusion
compression 7
Olenekian Phycodes 5 7 semi-
infaunal
gallery
biodiffusion
compression 21
Artinskian Phycosiphon 11 12 mid conveyor backfill 1
Kungurian Phycosiphon 11 12 mid conveyor backfill 1
Wordian Phycosiphon 11 12 mid conveyor backfill 1
Sakmarian Phycosiphon 11 12 mid conveyor backfill 2
Olenekian Phycosiphon 11 12 mid conveyor backfill 28
Artinskian Planolites 7 8 shallow conveyor backfill 1
Kungurian Planolites 7 8 shallow conveyor backfill 1
Wordian Planolites 7 8 shallow conveyor backfill 1
Sakmarian Planolites 7 8 shallow conveyor backfill 2
Olenekian Planolites 7 8 shallow conveyor backfill 3
Changhsing
ian
Planolites 7 8 shallow conveyor backfill 5
Wuchiaping
ian
Planolites 7 8 shallow conveyor backfill 5
Induan Planolites 7 8 shallow conveyor backfill 10
Anisian Planolites 7 8 shallow conveyor backfill 14
Asselian Planolites 7 8 shallow conveyor backfill 17
Induan Protovirgularia 7 7 semi-
infaunal
biodiffusive intrusion 7
Sakmarian Psammichnites 7 8 shallow conveyor backfill 2
Asselian Psammichnites 7 8 shallow conveyor backfill 17
Induan Ptychoplasma 7 7 semi-
infaunal
biodiffusive compression 7
Olenekian Radulichnus 3 3 surficial biodiffusive compression 3
Artinskian Rhizocorallium 13 13 mid gallery
biodiffusion
compression 1
165
Wordian Rhizocorallium 13 13 mid gallery
biodiffusion
compression 1
Sakmarian Rhizocorallium 13 13 mid gallery
biodiffusion
compression 2
Anisian Rhizocorallium 13 13 mid gallery
biodiffusion
compression 6
Olenekian Rhizocorallium 11 11 semi-
infaunal
gallery
biodiffusion
compression 9
Induan Rhizocorallium 12 12 shallow gallery
biodiffusion
compression 10
Asselian Rhizocorallium 13 13 mid gallery
biodiffusion
compression 17
Olenekian Rhizocorallium 12 12 shallow gallery
biodiffusion
compression 18
Artinskian Rhizocorallium 13 13 mid gallery
biodiffusion
compression 19
Induan Rhizocorallium 13 13 mid gallery
biodiffusion
compression 23
Olenekian Rhizocorallium 13 13 mid gallery
biodiffusion
compression 25
Changhsing
ian
Rhizocorallium 13 13 mid gallery
biodiffusion
compression 26
Artinskian Rosselia 13 13 deep gallery
biodiffusion
compression 1
Kungurian Rosselia 13 13 deep gallery
biodiffusion
compression 1
Sakmarian Rosselia 13 13 deep gallery
biodiffusion
compression 2
Wordian Rosselia 13 13 deep gallery
biodiffusion
compression 8
Asselian Rosselia 13 13 deep gallery
biodiffusion
compression 17
Induan Rusophycus 6 6 surficial regenerator excavation 15
Induan Siphonichnus 13 13 deep gallery
biodiffusion
compression 25
Olenekian Siphonichnus 12 13 shallow gallery
biodiffusion
compression 25
Wordian Skolithos 10 14 deep gallery
biodiffusion
compression 1
Artinskian Skolithos 10 14 deep gallery
biodiffusion
compression 16
Asselian Skolithos 10 14 mid gallery
biodiffusion
compression 17
Olenekian Skolithos 8 12 shallow gallery
biodiffusion
compression 21
Induan Skolithos 10 14 deep gallery
biodiffusion
compression 23
Changhsing
ian
Skolithos 10 14 deep gallery
biodiffusion
compression 26
166
Olenekian Skolithos 9 9 mid gallery
biodiffusion
compression 27
Olenekian Skolithos 10 14 deep gallery
biodiffusion
compression 28
Artinskian Spirodesmos 10 11 shallow conveyor backfill 16
Induan Spongeliomorph
a
9 9 shallow regenerator excavation 10
Sakmarian Taenidium 10 11 shallow conveyor backfill 2
Olenekian Taenidium 10 11 shallow conveyor backfill 3
Induan Taenidium 10 11 shallow conveyor backfill 10
Asselian Taenidium 10 11 shallow conveyor backfill 17
Olenekian Taphrehelmintho
psis
7 8 shallow conveyor backfill 4
Artinskian Teichichnus 11 12 mid conveyor backfill 1
Kungurian Teichichnus 11 12 mid conveyor backfill 1
Sakmarian Teichichnus 11 12 mid conveyor backfill 2
Wordian Teichichnus 11 12 mid conveyor backfill 8
Induan Teichichnus 11 12 deep conveyor backfill 25
Anisian Teichichnus 11 12 mid conveyor backfill 28
Olenekian Teichichnus 11 12 mid conveyor backfill 28
Olenekian Thalassinoides 10 12 shallow regenerator excavation 3
Changhsing
ian
Thalassinoides 10 12 shallow regenerator excavation 5
Wuchiaping
ian
Thalassinoides 10 12 shallow regenerator excavation 5
Anisian Thalassinoides 11 13 mid regenerator excavation 6
Induan Thalassinoides 10 12 shallow regenerator excavation 10
Asselian Thalassinoides 11 13 mid regenerator excavation 17
Anisian Thalassinoides 11 13 deep regenerator excavation 28
Olenekian Thalassinoides 11 13 deep regenerator excavation 28
Olenekian Treptichnus 12 13 shallow gallery
biodiffusion
compression 3
Olenekian Treptichnus 11 12 semi-
infaunal
gallery
biodiffusion
compression 4
Induan Treptichnus 12 13 shallow gallery
biodiffusion
compression 7
Anisian Zoophycos 10 11 shallow conveyor backfill 6
Wordian Zoophycos 11 12 deep conveyor backfill 8
Induan Zoophycos 11 12 deep conveyor backfill 23
Changhsing
ian
Zoophycos 11 12 deep conveyor backfill 24
Sakmarian Zoophycus 11 12 deep conveyor backfill 2
Artinskian Zoopychos 11 12 deep conveyor backfill 1
167
Changhsing
ian
Zoopychus 10 11 shallow conveyor backfill 5
168
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171
Chapter 4. Contrasting terrestrial and marine ecospace dynamics after the end-Triassic
mass extinction event
Abstract
Mass extinctions have fundamentally altered the structure of the biosphere throughout
Earth history. The ecological severity of mass extinctions has been well studied in marine
ecosystems by categorizing marine taxa into functional groups based on ‘ecospace’ approaches,
but the ecological response of terrestrial ecosystems to mass extinctions is less well understood
due to the lack of a comparable ecospace methodology. Here, we present a novel terrestrial
ecospace framework that categorizes fauna into functional groups as defined by tiering, motility,
and feeding traits. We applied the new terrestrial and traditional marine ecospace analyses to
occurrences data from the Paleobiology Database across the end-Triassic mass extinction, a time
of significant CO2 rise, to compare taxonomic and ecological change between the marine and
terrestrial biospheres. We found that terrestrial functional groups experienced higher extinction
severity than the marine, that taxonomic and functional richness are more tightly coupled in the
terrestrial than in the marine, and that the terrestrial realm continued to experience high
ecological dissimilarity in the wake of the extinction. These findings suggest greater ecological
pressure from the end-Triassic mass extinction on terrestrial ecosystems than marine
ecosystems.
4.1 Introduction
Mass extinction events caused by global warming and climate-related stressors
throughout Earth history have profoundly impacted the ecological structure of ancient
ecosystems (Erwin, 2001; Bush and Bambach, 2011; Cole and Hopkins, 2021). In particular,
172
previous studies focusing on marine ecospace (the combination of ecological traits that
categorizes an organism’s mode of life based on shared ecological roles (Bambach et al., 2007))
have noted that mass extinctions have altered the occupation of ecospace and composition of
functional groups in marine ecosystems (Bush and Bambach, 2011; Dineen et al., 2014; Foster
and Twitchett, 2014; Aberhan and Kiessling, 2015; Cole and Hopkins, 2021). These studies are
critical, as functional diversity is known to play a major role in modern ecosystem processes
(Tilman et al., 1997). Moreover, understanding how past environmental change has affected
functional diversity from the fossil record is essential for predicting the ecological impacts of
modern climate change. However, direct comparisons between contemporaneous changes in
marine and terrestrial functional ecology from the fossil record have not previously been
investigated, thus limiting our comprehensive understanding of global ecological change
throughout Earth history in response to ancient global warming events.
The end-Triassic mass extinction (ETE) (ca. 201.5 Ma) was the fourth of the ‘Big Five’
Phanerozoic mass extinction events (Raup and Sepkoski, 1982) and profoundly impacted both
marine and terrestrial ecosystems. The ETE was caused by a rapid rise in greenhouse gases due
to increased volcanism from the Central Atlantic Magmatic Province (CAMP), a large igneous
province associated with the rifting of the supercontinent Pangea (Schaller et al., 2011;
Blackburn et al., 2013). The increase in greenhouse gases due to CAMP volcanism resulted in
rapid global warming, causing catastrophic effects on marine and terrestrial environments and
biota (Schoepfer et al., 2022). In marine ecosystems, ocean anoxia (Jost et al., 2017), ocean
acidification (Greene et al., 2012; Landwehrs et al., 2020), and decreased primary productivity
(Van de Schootbrugge et al., 2013) resulted in the total extinction of conodonts, severe losses in
scleractinian corals, ammonoids, and reef-building sponges, and elevated extinction rates among
173
articulate brachiopods, bivalves, and marine vertebrates (Hautmann et al., 2008; Kiessling et al.,
2009; Thorne et al., 2011). Meanwhile, terrestrial ecosystems suffered from deforestation, soil
loss, wildfires, and major changes in the hydrological cycle (Schoepfer et al., 2022), resulting in
significant turnovers in floral assemblages (Lindström, 2016), increased provinciality among
tetrapods (Whiteside et al., 2011), and the disappearance of dominant tetrapod groups such as
temnospondyl amphibians, all but one lineage of Pseudosuchians (‘crocodile-like’ archosaurs)
(Stubbs et al., 2013), and non-mammalian synapsids (Tanner et al., 2004; Schoepfer et al., 2022).
After the ETE, Jurassic global ecosystems were further impacted by volcanism from the Karoo-
Ferrar Large Igneous Province (KFLIP) and the associated Toarcian Ocean Anoxia Event (T-
OAE) (Schoepfer et al., 2022). Marine macroevolutionary processes continued to be influenced
by the ongoing Mesozoic Marine Revolution (MMR) (Vermeij, 1977; Reeves et al., 2021), while
terrestrial macroevolutionary processes were characterized by the diversification of dinosaurs
(Olsen et al., 2002). Because marine and terrestrial ecosystems both experienced major
taxonomic and ecological changes during and after the ETE, this time interval offers unique,
untapped insights into comparative ecological changes in the marine and terrestrial fossil
records.
Marine functional ecology in animals has been previously studied across the ETE in
terms of occupation of ecospace (Dunhill et al., 2018). Similar to previous studies of marine
ecospace across other mass extinction boundaries (Foster and Twitchett, 2014), on a global scale,
no functional groups were lost across the ETE, and there was no significant loss in functional
diversity as a result of the mass extinction (Dunhill et al., 2018). However, extinction intensity
was not spread evenly across all functional groups. Sessile suspension feeders and functional
groups containing calcifying taxa experienced particularly high taxonomic extinction severity
174
during the ETE (Dunhill et al., 2018). With regards to terrestrial ecosystems, previous work
focusing on niche partitioning in tetrapods has identified trends in terrestrial feeding ecospace
through the Triassic and Early Jurassic (Singh et al., 2021). However, no development or
application of a terrestrial ecospace using the same marine ecospace ecological traits (Bambach
et al., 2007) has existed to compare terrestrial and marine functional ecology. Thus, our
understanding of how marine and terrestrial ecosystems comparatively change in response to the
ETE, as well as other major climate change events and mass extinctions through Earth history,
has been limited.
4.2 Marine and terrestrial ecospace framework
Marine ecospace has previously been defined as the combination of three ecological
categories: 1) location in physical space at, above, or below the sediment-water interface
(tiering), 2) potential for either motion or maintenance of a stationary position (motility), and 3)
method of acquiring energy (feeding) (Bambach et al., 2007; Bush et al., 2007) (Figure 4.1;
Table S4.1). This definition of ecospace has resulted in a three-dimensional marine ecospace,
where an animal’s functional group or mode of life is defined as their tiering-motility-feeding
classifications (Bambach et al., 2007; Bush et al., 2007). While previous terrestrial ecospace
frameworks have been proposed (Chen et al., 2019; Munstermann et al., 2021), these methods
sought to answer different ecological questions and lack the same marine ecospace tiering-
motility-feeding ecological categories, limiting their utility for comparing trends in ecospace and
functional ecology between the marine and terrestrial fossil records.
We present a novel terrestrial ecospace framework specifically designed to be as
analogous as possible to the tiering-motility-feeding ecospace framework used for marine
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ecosystems (Bambach et al., 2007). The terrestrial ecospace framework has tiering, motility, and
feeding ecological categories (Figure 4.1; Table S4.2). The tiering categories are: 1) aerial, 2)
arboreal, 3) ground-dwelling, 4) soil-dwelling, 5) troglobitic, and 6) aquatic. Tiering is
conceptualized as the primary environment an animal is adapted for and utilizes for life history.
The aerial tier applies to animals that spend significant time in the air, relying on the air for a
multitude of behaviors, and are flight adapted. Arboreal tiering applies to animals that primarily
spend their time living above the ground level in tall vegetation, like trees. Ground-dwelling
tiering applies to animals that spend most of their time living and feeding on the ground. Soil-
dwelling tiering applies to animals who primarily burrow into soils and live and feed
underground. Troglobitic tiering refers to animals living primarily in caves. We also include an
aquatic tier to include fresh-water animals, such as those living in lakes and rivers, as they are
often sensitive to the environmental dynamics of terrestrial ecosystems and not included in
marine ecospace analyses. The motility categories in the terrestrial ecospace are 1) migratory, 2)
freely, far moving, 3) freely, short moving, 4) tethered, and 5) non-motile. Migratory animals
seasonally migrate over long distances. Freely, far moving animals can move freely away from
an initial habitat and over long distances (e.g., bears with large territories), whereas freely, short
moving animals can also leave their initial habitats but only over relatively short distances (e.g.,
beavers that remain near their dams). Tethered animals are those that cannot survive if they leave
their small initial habitats (e.g., icebugs that are constrained to glaciers). Non-motile animals are
those which do not move at all. The feeding categories in the terrestrial ecospace are 1) predator
(big), 2) predator (small), 3) omnivore, 4) herbivore, 5) scavenger, and 6) other. Predators are
animals which feed on other animals as prey. Animals are classified as predator (big) or predator
(small) based on the maximum size of the prey that they were likely to eat. For example, an
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animal classified as predator (big) can prey on megafauna or macrofauna, such as a grizzly bear
preying on fish. In contrast, an animal classified as predator (big) can only prey on mesofauna,
such as an insectivorous predator. Omnivores are animals which consume both plants and
animals as prey. Herbivores are animals which consume plants via behaviors like browsing,
foraging, and grazing. Scavengers are animals that consume dead and decaying biomass. The
‘other’ feeding category encapsulates any feeding behavior which does not fit into the other five
categories. Within this ecospace, we define a terrestrial functional group as a unique tiering-
motility-feeding combination.
This terrestrial ecospace framework is a theoretical cubic ecospace designed with modern
terrestrial invertebrates and vertebrates in mind. The ecological traits within each category were
chosen to maximize the amount of occupiable ecospace for all terrestrial fauna. However, not
every theoretical functional group of the cube is realistically occupiable. For example, an aerial-
soil dwelling-non motile combination is an impossible mode of life, similar to how a pelagic-
mining-attached stationary category is impossible in the marine ecospace (Bambach et al., 2007).
We also note that we do not expect the cube to be entirely occupied, particularly around the
Triassic-Jurassic boundary, as animals have increasingly filled functional ecospace moving
through Earth history towards the modern (Bush et al., 2007).
4.3 Dataset assembly and analyses
Marine and terrestrial fossil occurrence data from the Carnian to the Aalenian (c. 237 – 174
Ma) were downloaded from the Paleobiology Database (PBDB) (accessed in June 2021). Data
were processed to exclude ichnotaxa, form taxa, and uncertain taxonomic assignments. Marine
and terrestrial data were initially extracted based on environment and lithology categories in the
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PBDB, and data were further cleaned to correct for marine and terrestrial taxa entered in
incorrect paleoenvironments (e.g., terrestrial vertebrates preserved in marine sediments due to
bloat-and-float). Ecospace assignments for tiering, motility, and feeding were made at the genus
level. Ecospace assignments were based on a combination of previous ecospace literature (Foster
and Twitchett, 2014; Dunhill et al., 2018), functional morphology, ecological information of
extant relatives, and according to the ecology categories information given in the PBDB. Genera
were removed from the dataset when a functional group assignment could not be made due to
insufficient information. After data cleaning and removal of taxa without functional group
assignments, the dataset ultimately comprises of 56,173 marine fossil occurrences with 2481
unique genera, and 2028 terrestrial fossil occurrences with 630 unique genera.
To assess the impact of the ETE on functional ecology, we conducted analyses on
functional and taxonomic diversity, extinction dynamics within functional groups, and ecological
dissimilarity during the ETE and the Early Jurassic. Our analyses largely follow previous marine
ecospace work (Foster and Twitchett, 2014; Dunhill et al., 2018) in order to make cross-study
comparisons using different datasets and subsampling methods. Functional and taxonomic
diversity were determined by calculating functional group and generic richness (the number of
unique functional groups or genera in each stage, respectively). For within-functional group
dynamics, we calculated the relative abundance of functional groups in each stage in terms of
occurrences and generic richness, as well as the extinction severity of each functional group.
Extinction severity was determined by calculating the percentage of pre-ETE (Rhaetian) genera
that do not reappear in the Early Jurassic. Finally, to assess ecological stability, we calculated
Bray-Curtis dissimilarity indices comparing the functional group composition between preceding
stages (e.g., Norian and Carnian, Pliensbachian and Sinemurian) and between each Jurassic stage
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and the Rhaetian (Dunhill et al., 2018). To account for sampling biases in the marine and
terrestrial fossil records, we applied a collection-level bootstrap subsampling protocol, with
subsampling quotas set at n=400 collections for marine analyses and n=35 collections for
terrestrial analyses. For the terrestrial analyses, the Carnian and Aalenian were excluded from
subsampling due to very low sample size (and thus they are omitted from terrestrial results). For
each analysis, the subsampling protocol was applied for 1000 iterations to obtain a distribution of
results with a final mean value and 95% confidence intervals.
4.4 Results
Occupation of marine and terrestrial ecospace
In the marine realm, 39 ecospace functional groups were present from the Carnian
through the Aalenian. The majority of functional groups present were in the surficial, semi-
infaunal, and shallow infaunal tiers, although the most abundantly occupied functional group was
the pelagic, freely fast-moving predators (FG 115) (Figure 4.1A). This group includes animals
like cartilaginous fishes, secondarily aquatic tetrapods, and ammonites. Epifaunal attached
stationary suspension feeders (FG 361) were the second most occupied functional group,
occupied by fauna such as demosponges and brachiopods, followed by surficial freely slow-
moving grazers (FG 324), which are largely represented by gastropods and ostracods. In the
terrestrial realm, 33 ecospace functional groups were present, with most occupied functional
groups in the ground-dwelling and aquatic tiers (Figure 4.1B). Ground-dwelling freely far-
moving herbivores (FG 324), represented by dicynodonts and early dinosaurs, and aquatic freely
short-moving large predators (FG 631), like non-marine hybodont sharks, were the most
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common terrestrial functional groups. We identified no taxa as cave-dwelling animals from the
Carnian to the Aalenian, so the troglobitic ecospace tier is unoccupied.
Taxonomic and functional richness
The marine and terrestrial records of generic and functional richness differ. Marine
generic richness was highest prior to the extinction interval in the Norian, followed by a
significant decline through the Rhaetian and into the Hettangian. Marine generic richness then
increased gradually in the Jurassic until a slight decline between the Pliensbachian and the
Toarcian (Figure 4.2A). In contrast, marine functional richness increased through the Late
Triassic, reaching its acme in the Rhaetian, followed by a marginal decline across the mass
extinction interval into the Hettangian. Marine functional richness was then relatively stable until
a slight peak in the Pliensbachian, followed by a gradual decline into the Toarcian and Aalenian
(Figure 4.2A). Terrestrial generic richness was relatively stable between the Norian and
Rhaetian, followed by a significant decline into the Hettangian. Terrestrial generic richness then
continued to increase through the Early Jurassic, until reaching its peak in the Toarcian (Figure
4.2B). Terrestrial functional richness followed a similar pattern, with unchanging functional
richness between the Norian and the Rhaetian followed by a decline in the Hettangian, and then
continuously increasing until the Pliensbachian (Figure 4.2B).
Within-functional group dynamics
The patterns in functional group occurrences and generic richness changed in both the
marine and the terrestrial records. In the marine, pelagic freely fast-moving predators (FG 115)
and epifaunal attached stationary suspension feeders (FG 361) were consistently the two
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functional groups occupied by the most genera. However, which of the two groups was the
dominant (i.e., most taxonomically occupied) functional group changed across the extinction
interval (Figure 4.3A). In the Norian and Rhaetian, epifaunal attached stationary suspension
feeders were the dominant functional group, comprising about 36% of all marine genera
occurrences. From the Hettangian to the Toarcian, the pelagic freely fast-moving predators were
the dominant functional groups, representing 36% to 47% of all Early Jurassic marine genera.
The third most dominant marine functional group was the erect attached stationary suspension
feeders (FG 261), which represented about 13% of all marine genera occurrences in the Norian
and Rhaetian and then declined to only around 3% of marine genera beginning in the Hettangian
and through to the Toarcian. In their place, epifaunal freely slow-moving grazers (FG 324)
became the third most occupied functional group beginning in the Hettangian. In terms of marine
generic richness, we found the same general patterns as with marine generic occurrences,
although the magnitude of the changes is lower (Figure 4.3B). Marine generic richness is more
evenly distributed among the functional groups, particularly after the extinction interval. For
example, pelagic freely fast-moving predators and epifaunal attached stationary suspension
feeders were still the functional groups with the most unique genera, but they only represented a
combined approximately 45% of the unique genera present beginning in the Hettangian and
continuing through the Early Jurassic. Less well occupied functional groups such as epifaunal
freely slow-moving grazers, shallow-infaunal attached stationary suspension feeders (FG 461),
and deep-infaunal unattached facultatively mobile suspension feeders (FG 631) were better
represented in terms of generic richness. Erect attached stationary suspension feeders (FG 261)
were also impacted in terms of generic richness, but less severely than in terms of occurrences
(Figure 4.3B).
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Patterns in terrestrial functional groups are even more dynamic for both occurrences and
generic richness (Figure 4.4). The most significant change in terrestrial functional group
occurrences is in the aquatic tier, in which aquatic freely short-moving large predators (FG 631)
comprised of approximately 25% of all terrestrial generic occurrences in the Norian and
Rhaetian but collapsed significantly to less than 5% of generic occurrences after the mass
extinction interval (Figure 4.4A). The same pattern and magnitude are true for the aquatic-tier
functional groups when considering generic richness within terrestrial functional groups (Figure
4.4B). Outside of the aquatic tier, terrestrial functional group dominance changed dynamically
between stages. For example, in the Norian, ground dwelling freely far-moving herbivores (FG
324) were the most dominant group, but it quickly collapsed, and ground-dwelling and arboreal
freely short-moving small predators (FG 232 and FG 332, respectively) became the dominant
functional groups in the Rhaetian (Figure 4.4A). Through the mass extinction interval and into
the Hettangian, the ground-dwelling freely far-moving herbivores regained dominance,
representing over a third of all terrestrial genera occurrences. Concurrently, there was a
significant expansion within the ground-dwelling tier. Over three-quarters of all terrestrial genera
occurrences in the Hettangian belonged to ground-dwelling functional groups (Figure 4.4A).
Occurrences of arboreal-tier functional groups increased through the Jurassic, particularly driven
by the rise of arboreal freely short-moving herbivores (FG 234) beginning in the Sinemurian,
which is the most occupied arboreal functional group by the Toarcian (Figure 4.4A). Aerial-tier
functional groups, which were barely represented in the Triassic, also represent an increasing
proportion of terrestrial genera occurrences in the Jurassic (Figure 4.4A). By the Toarcian, aerial
freely short-moving small predators (FG 132) (e.g., crown group dragonflies) and herbivores
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(FG 134) represented over 40% of all terrestrial genera occurrences. When accounting for the
relative abundance of terrestrial functional groups in terms of generic richness, we find the same
patterns occur at very similar magnitudes (Figure 4.4B). The only major difference between the
results based on occurrences versus functional richness is in the ground-dwelling freely short-
moving large predator functional group (FG 321), which increased in the total number of genera
occurring in the functional group from the Rhaetian to the Hettangian, but not in terms of the
generic richness of the functional group (Figure 4.4).
Within-functional group extinction severity
There were 33 marine functional groups present in the Rhaetian, which all experienced
some level of extinction. The least impacted functional groups were the deep-infaunal unattached
facultatively mobile deposit feeders (FG 632) and the shallow-infaunal attached facultatively
mobile ‘other’ feeders (FG 546). For both groups, less than 1% of the Rhaetian genera
disappeared in the Early Jurassic. Within 22 marine functional groups, at least half of the
Rhaetian genera went extinct in the Hettangian and did not reappear by the Aalenian, and 8
functional groups experienced 100% extinction of all the Rhaetian genera (Figure 4.5A).
However, the majority of the functional groups that lost all of their Rhaetian genera had enough
taxonomic turnover that they did not disappear. For example, within the erect facultatively
attached suspension feeders group (FG 241), only two crinoid genera from the Paracomatuliae
and Tulipacrinidae families occupied the group in the Rhaetian, which are replaced by crinoids
in the Pentacrinitidae family in the Hettangian and Sinemurian, and then replaced by stalked
benthic crinoids from the Isocrinida family by the Toarcian. Only the semi-infaunal unattached
stationary ‘other’ feeders (FG 456) and the shallow-infaunal freely short-moving predators (FG
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521) disappeared in the Hettangian and did not reappear by the Aalenian, driven by the
extinction of various bivalve taxa.
Terrestrial functional groups experienced higher extinction severity compared to the
marine functional groups. There were 24 terrestrial functional groups in the Rhaetian. In all 24
functional groups, at least half of the pre-extinction genera disappeared between the Rhaetian
and Hettangian and did not reappear by the Aalenian. Seventeen functional groups lost 100% of
the genera that were present in the Rhaetian (Figure 4.5B). However, much like the marine
functional groups with very high extinction severity, not all of those 17 functional groups
disappeared, as there was sufficient turnover for the majority of the terrestrial function groups to
persist. For example, in the arboreal, freely short moving small predators group (FG 232), the
mammaliaform haramiyidans and lizard-like kuehneosaurids were replaced by other small
mammaliaform insectivores, including triconodontids and cotylosaurs, in the Hettangian and
Sinemurian, while morganucodonts persisted in the group across the extinction interval. Only
three terrestrial functional groups disappeared entirely between the Hettangian and Aalenian:
arboreal freely short-moving ‘other’ feeders (FG 236), ground-dwelling freely far-moving
omnivores (FG 323), and aquatic freely far-moving large predators (FG 621). Their
disappearances were driven by the extinction among Triassic reptiles, including drepanosaurs
and parasuchid phytosaurs.
Ecological dissimilarity through time
Bray-Curtis dissimilarity indices indicate differences in ecological similarity and stability
through time between the marine and terrestrial fossil records (Figure 4.6). When comparing the
ecological structures of consecutive marine stages, the highest dissimilarity is observed between
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the Rhaetian and the Hettangian, just after the mass extinction interval. Dissimilarity then
decreased through the Jurassic to its lowest point between the Sinemurian and the Pliensbachian,
and slightly increased again to the Toarcian and Aalenian (Figure 4.6A). In the terrestrial, the
greatest dissimilarity between preceding stages also occurred in between the Rhaetian and the
Hettangian, just after the mass extinction interval. Dissimilarity then decreased between the
Hettangian and Sinemurian but increased again through the Pliensbachian and Toarcian (Figure
4.6B). Overall, dissimilarity between consecutive terrestrial stages was significantly higher than
dissimilarity between consecutive marine stages (Figure 4.6).
The marine and terrestrial records also differ when comparing ecological composition of
the Jurassic stages to the Rhaetian. In the marine, all the Jurassic stages were more dissimilar to
the Rhaetian than they are to each other. Ecological dissimilarity to the Rhaetian decreased only
slightly through time in the Pliensbachian but then increased again in the Toarcian and Aalenian
(Figure 4.6A). In general, however, all the marine stages were relatively equally dissimilar to the
Rhaetian, as the Jurassic Bray-Curtis indices broadly average around 0.30 (Figure 4.6A). In
comparison, the Bray-Curtis indices comparing terrestrial Jurassic stages to the Rhaetian in the
terrestrial are, again, much higher than they are in the marine (Figure 4.6B). Additionally, the
ecological dissimilarity increased through the Early Jurassic (Figure 4.6B).
4.5 Discussion
When comparing marine and terrestrial ecospace dynamics across the end-Triassic mass
extinction event, three key differences emerge. First, taxonomic and functional richness were
more tightly coupled in the terrestrial fossil record than they were in the marine fossil record
across the ETE. Second, extinction severity within terrestrial functional groups was much higher
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than within marine functional groups. Third, terrestrial ecosystems sustained high ecological
dissimilarity through the wake of the ETE, suggesting protracted ecological flux in the terrestrial
realm through the Early Jurassic.
Terrestrial functional group richness decreased across the extinction interval and tracked
terrestrial generic richness (Figure 4.2). These results indicate coupling of terrestrial taxonomic
and ecological extinction severity through the mass extinction interval. This stands in contrast
both to the marine taxonomic and ecological decoupling observed here (Figure 4.2A) and to
other mass extinction intervals (McGhee et al., 2004; Foster and Twitchett, 2014; Dunhill et al.,
2018; Song et al., 2018). Coupling between taxonomic and functional group richness in the
terrestrial through the ETE most likely occurred due to low taxonomic redundancy in terrestrial
functional groups, where very few animal taxa occupied a single functional group. This lack of
taxonomic redundancy is particularly evident when comparing the relative abundance of
terrestrial functional groups in terms of generic richness and occurrences, which follow each
other closely (Figure 4.4). Terrestrial functional group richness was sensitive to taxonomic losses
because the functional groups were only occupied by small number of taxa and thus relatively
unbuffered to taxonomic extinction severity. In contrast, marine functional groups were
represented by many taxonomic occurrences often belonging to the same genera, and this
taxonomic redundancy within functional groups did buffer marine functional group richness
against high extinction severity during the ETE (Foster and Twitchett, 2014; Dunhill et al.,
2018). Taxonomic and ecological decoupling during mass extinction intervals is important for
the persistence of ecosystem stability, as persistent functional diversity may keep marine
ecosystems from ecological collapse, particularly if the surviving functional groups represent
ecologically important groups such as keystone species (McGhee et al., 2004). Thus, lack of
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taxonomic redundancy within terrestrial functional groups may have ultimately caused terrestrial
ecosystems that were particularly sensitive to ecological collapse. However, the continuous
changing of availability in ecological niche space due to constant taxonomic turnover within
functional groups in the wake of the ETE may have also enabled the major diversifications in
certain terrestrial clades observed in the Early Jurassic (Benton et al., 2014). Moreover, strong
extinction selectivity of certain functional groups in the terrestrial may have further driven the
coupling between ecological and taxonomic extinction severity (Dick et al., 2022).
Extinction severity within terrestrial functional groups was also significantly higher than
in marine functional groups (Figure 4.5). In all of the terrestrial functional groups which
occurred in the Rhaetian, at least half of the genera were lost and did not reappear by the
Aalenian (Figure 4.5B). Furthermore, a much higher proportion of terrestrial functional groups
experienced 100% generic extinction versus marine functional groups (Figure 4.5). In the
marine, functional groups with high extinction severity are generally associated with low
occupancy and low functional richness. For example, almost all marine functional groups that
lost at least 90% of their genera are in the bottom quartile for the taxonomic occupation and
generic richness in the Rhaetian (Figure S4.1). In contrast, the terrestrial functional groups are
not as strongly associated with functional group occupancy and generic richness. The terrestrial
functional groups with over 90% generic extinction include functional groups that are well
within the third quartiles for taxonomic occupancy and generic richness, significantly contrasting
the characteristics of marine functional groups with high extinction severity (Figure S4.1). For
example, the arboreal freely short moving small predator functional group (FG 232) experiences
100% generic extinction (Figure 4.5B) despite being the third most occupied and generically rich
terrestrial functional group. The lack of correlation between terrestrial functional group
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occupancy or generic richness and extinction severity is most likely due to the low number of
taxa present in terrestrial functional groups in general, as even the most occupied terrestrial
functional groups contain a relatively small number of taxa when compared to the marine (Figure
4.1; Figure S4.1). Thus, it is possible that there were not enough terrestrial taxa during the Late
Triassic to buffer functional groups from high extinction severity, so even the most well-
occupied and generically rich terrestrial functional groups are still sensitive to extinction.
Finally, the differences in marine and terrestrial ecological dissimilarity after the ETE
suggest that terrestrial ecosystems were in a more prolonged state of ecological flux. For both the
marine and terrestrial realms, the highest ecological dissimilarity occurred between the Rhaetian
and the Hettangian (Figure 4.6). This is unsurprising, given that ecosystems are expected to be in
a state of ecological transition or flux just after the mass extinction event, resulting in ecological
turnover and low functional diversity (Hull and Darroch, 2013; Hull, 2015). While the marine
realm gradually returned to low ecological dissimilarity in the wake of the ETE, terrestrial
ecological dissimilarity remained high (Figure 4.6). Marine ecosystems were likely returning to a
state of equilibrium by the Sinemurian and Pliensbachian, while terrestrial ecosystems continued
to experience ecological flux due to prolonged impacts of the ETE (Hull and Darroch, 2013;
Hull, 2015). Additionally, terrestrial ecological dissimilarity between stages was always higher
than marine dissimilarity (Figure 4.6) due to the more dynamic changes in functional group
dominance during and after the ETE (Figure 4.4). The low taxonomic occupancy within
terrestrial functional groups that led to high within-functional group extinction severity also
caused the dramatic changes in the functional group composition of each stage (Figure 4.4)
which ultimately led to high ecological dissimilarity between stages through time.
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When comparing the ecological dissimilarity of each Jurassic stage to the Rhaetian,
terrestrial ecosystems appear to have been evolving into a very different ecological realm, while
marine ecosystems returned to a pre-ETE global ecology. The dissimilarity of marine ecosystems
never fell to zero, largely due to the increase in abundance of the pelagic freely fast-moving
predators in the Jurassic (Figure 4.3A), but dissimilarity comparing each Jurassic stage to the
Rhaetian is relatively stable through time (Figure 4.6A). In contrast, dissimilarity between
Jurassic stages and the Rhaetian for terrestrial ecosystems was much higher than in the marine
and, most importantly, continued to increase through each Jurassic stage (Figure 4.6B). This
implies that terrestrial ecosystems were in such flux that they were evolving into a new, post-
extinction ecological state, apparently largely driven by expansions in ground dwelling- and
aerial-tier functional groups and the near total collapse of the aquatic tier in the Jurassic (Figure
4.4).
In general, our results for both marine and terrestrial ecospace changes are aligned with a
number of conclusions from previous research that utilize different methodologies. First,
unsurprisingly, the results presented here align well with previous marine ecospace research
across the ETE, even while using slightly different datasets and subsampling protocols (Dunhill
et al., 2018). Second, the survival of most functional groups despite high extinction severity in
both the marine and terrestrial realms aligns with previous research which demonstrated that
functional groups are difficult to lose as long as they are occupied by a few resilient taxa – or
manned by a ‘skeleton crew’ (Foster and Twitchett, 2014). Third, ecological flux being driven by
ground-dwelling and aerial-tier functional groups is supported by previous research which
indicates that broad-scale faunal turnover in Early Jurassic ecosystems was driven by the rise of
large herbivorous dinosaurs (Sookias et al., 2012) and pterosaurs (Butler et al., 2013). Finally,
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previous research has focused on turnover among insects through this time interval, and although
it is unclear from our results whether the ETE significantly affected insect diversification and
extinction rates (McGhee et al., 2004; Condamine et al., 2016), the changes exhibited in
ecospace occupation in major insect groups (e.g., cockroaches, omnivorous beetles) suggest that
major disruptions to ecosystem structure substantially affected insect ecological partitioning.
Thus, although our terrestrial ecospace dataset is smaller than the marine ecospace dataset, our
results largely align with previous observations and interpretations of broader terrestrial faunal
dynamics preceding and after the ETE. This also underscores the validity of the new terrestrial
ecospace framework as a wholistic and statistically robust framework which allows for more
direct comparisons between marine and terrestrial realms. Ultimately, these results strongly
indicate that the terrestrial ecological realm was severely impacted by the ETE, which aligns
with previous hypotheses that the effects of the ETE on land were more severe than those
experienced in oceans (McGhee et al., 2004).
There are a number of biases unique to the terrestrial fossil record during our sampled
time interval that are likely to influence the results presented here. First, the terrestrial fossil
record passes through more significant taphonomic filters than does the marine fossil record.
Sediments deposited in fluvial and lacustrine systems preserve the bulk of our sampled
vertebrate fossils (Figure S4.2). The constant cycling between erosion, transport, and deposition
in these systems significantly decreases the preservation potential of any organism, but
especially for animals that preserve disarticulated parts (e.g., vertebrates, arthropods). This
geologic and taphonomic bias almost certainly contributes to a lower number of terrestrial fossils
in general. There are also taphonomic biases within the terrestrial cube itself. For example, while
the aquatic tier is particularly sensitive to global warming in terms of physiological stress on
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animals, it is also sensitive to global warming with respect to the preservation of the tier. As
terrestrial ecosystems shift towards hotter, more arid climates, non-marine aquatic ecosystems
such as lakes dry up, thus decreasing the preservation potential of the animals living in those
ecosystems. This may drive some of the results in terms of the collapse of the aquatic tier,
although we note that such a taphonomic bias would not occur independently of taxonomic loss,
as it also results in massive habitat loss.
There are also unique taxonomic biases in the terrestrial fossil record. Specifically,
outdated taxonomic assessments in the terrestrial fossil record for this time interval did hamper
our ability to assess functional ecology of many taxa belonging to most groups within the
terrestrial fauna. Additionally, because the terrestrial fauna during this time period are often
represented by disarticulated fossils, the availability of limited anatomical information available
creates some degree of taxonomic and ecological uncertainty for these animals. For example,
insect ecospace assignments were often made based on taxonomic relationships and inferred
similarity to modern insects, although future work utilizing recent advances in dietary inferences
in small, extinct taxa (Stockey et al., 2022) would decrease this particular bias. We note that it is
important to consider these taphonomic and taxonomic biases when interpreting broad-scale
patterns in the record of terrestrial ecological change through time. However, given the context
of our results within previously described faunal restructuring in terrestrial ecosystems, we
conclude that our results are still illustrative of a genuine ecological signal across the ETE.
The results of the new terrestrial ecospace framework presented here are promising in
terms of avenues for future work. First, while it is evident that Early Jurassic terrestrial
ecosystems experienced considerably high ecological flux, it is not necessarily clear what the
abiotic and/or biotic drivers were. It is possible that some very ecologically important terrestrial
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taxa such as keystone species or ecosystem engineers were disproportionately impacted by the
ETE and ultimately resulted in disruptions to key terrestrial Earth systems processes that
contributed to persistent ecological disequilibrium (Hull, 2015). For example, the re-
establishment of marine reefs, which are both keystone taxa and marine ecosystem engineers
(Jones et al., 1994), likely contributed to marine ecosystem equilibrium by re-establishing the
ecological and Earth systems processes that reefs support. Identifying and studying terrestrial
taxa which were similarly important for macroecological stability and earth systems processes
may be important for understanding the nature of terrestrial ecosystem succession in the wake of
the ETE, particularly if the terrestrial taxonomic-ecological coupling was driven by the
selectivity of functional groups that were critical for these fundamental biotic interactions (Dick
et al., 2022). Additionally, there is some evidence that the terrestrial fossil record is extremely
spatially ecologically heterogenous. This is suggested by the very high uncertainty in the
terrestrial results, as the fossil records were subsampled by PBDB collection rather than by time
bin. This sampling protocol thus considers some spatial variance, as each collection represents
geographically co-located fossils. This interpretation would be unsurprising given previously
reported increased provinciality among some taxonomic groups in terrestrial ecosystems after the
ETE (Whiteside et al., 2011), but future work investigating the spatial structure of terrestrial
functional ecology using this ecospace framework will prove valuable for understanding the
nature of terrestrial biodiversity through space and time (Close et al., 2020; Benson et al., 2021).
Finally, the terrestrial fossil record should certainly be investigated at other time intervals using
the ecospace framework presented here. It is inconclusive whether the trends of ecological
instability in the terrestrial realm are unique to the ETE, a recurring pattern during other mass
extinction events and periods of major global environmental perturbations, or a general
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characteristic of the terrestrial fossil record. Therefore, applying this new terrestrial ecospace
framework to other time periods throughout Earth history will be especially important for
contextualizing the results presented here.
4.6 Conclusions
The development of a terrestrial ecospace framework to reconstruct trends in functional
ecology comparative to marine trends has illuminated new insights in how terrestrial ecosystems
responded to the ETE, a time of catastrophic rise in greenhouse gases. A lack of functional
redundancy in the terrestrial realm likely drove taxonomic-ecological coupling in terms of
extinction severity, which is not observed in the marine. Terrestrial functional groups also
experienced much higher extinction severity than marine functional groups did, which was most
likely driven by the small number of taxa that occupied terrestrial functional groups. Finally, our
results overall indicate continuous functional group turnover and ecological flux in terrestrial
ecosystems through the Jurassic, contrasting with the earliest establishment of ecological
stability in the marine. Important future work should constrain whether these differences
between the marine and terrestrial records are unique to the ETE, consistent across extinction
events, or a general characteristic of terrestrial ecosystems. However, these results demonstrate
the unique utility of the novel terrestrial ecospace framework presented here, underscore the
severity of the ETE for ecosystems beyond the marine biosphere, and suggest that the extinction
event was most ecologically severe for terrestrial ecosystems. These results could be critical to
understanding the future of the terrestrial biosphere in light of rapid anthropogenic climate
change.
193
4.7 Figures
Figure 4.1 Ecospace cubes and taxonomic occupation.
Marine (A) and terrestrial (B) ecospace cubes with functional groups color coded for occupation.
Filled in cubes represent functional groups that are present at least once in the Carnian to
Aalenian dataset. Functional groups represented by dark purple cubes are sparsely occupied,
whereas functional groups represented by light yellow groups are abundantly occupied. Color
scale corresponds to the log number of genera occupying each functional group.
194
Figure 4.2 Generic and functional group richness.
Taxonomic (generic) and functional richness for the marine (a) and terrestrial (b) fossil records.
Generic richness curves are plotted in purple and functional richness curves are plotted in
orange. Error bars are 95% confidence intervals calculated from bootstrap subsampling. Carnian
and Aalenian data is missing for the terrestrial due to insufficient raw Paleobiology Database
(PBDB) data size for subsampling.
195
Figure 4.3 Relative abundance of marine functional groups.
Relative abundance of marine functional groups calculated from total taxonomic occurrences (A)
and from unique genera (B). Colors are divided into the ecospace tier, and shades within each
color are different feeding-motility functional group combinations.
.
196
Figure 4.4 Relative abundance of terrestrial functional groups.
Relative abundance of terrestrial functional groups calculated from total taxonomic occurrences
(A) and from unique genera (B). Colors are divided into the ecospace tier, and shades within
each color are different feeding-motility functional group combinations. Data is not presented for
the Carnian or Aalenian due to their PBDB sample sizes being too small to subsample.
197
Figure 4.5 Within-functional group extinction severity.
Generic extinction severity of marine functional groups (A) and terrestrial functional groups (B).
Extinction severity is calculated as the percentage of genera in the Rhaetian that are not present
in the Jurassic from the Hettangian to the Aalenian. Error bars are 95% confidence intervals from
bootstrap subsampling.
198
Figure 4.6 Bray-Curtis dissimilarity indices through the ETE.
Ecological dissimilarity of functional group composition between stages for the marine (A) and
terrestrial (B) fossil records. Ecological dissimilarity calculated as Bray-Curtis dissimilarity
indices, where 1 is most dissimilar and 0 is the same functional group composition. Star-dash
lines are comparing each Jurassic stage to the Rhaetian, and circle-dash lines are comparing each
stage to its preceding stage. Error bars are 95% confidence intervals calculated from bootstrap
subsampling. Red dashed line represents the extinction event.
199
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203
S4. Supplementary appendix
Supplementary figures
Figure S4.1 Extinction severity versus functional group occupancy and generic richness for
the marine.
For the top two rows, marine data is the left column and terrestrial data is the right column, and
extinction vs. functional group taxonomic occupancy is the first row and extinction vs. functional
group generic richness is the second row. For the bottom row, marine and terrestrial data are
plotted on the same axes, where marine data is blue and terrestrial data is gray, and occupancy is
the left plate and richness is the right plate. Error bars are 95% confidence intervals from
bootstrap subsampling.
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Figure S4.2 Terrestrial paleoenvironments in each stage.
Raw total number of terrestrial paleoenvironments (left) and relative abundance of terrestrial
paleoenvironments (right) in the Aalenian to Toarcian terrestrial dataset. Paleoenvironments are
divided into seven categories – alluvial, channel, delta, floodplain, fluvial, lacustrine, and other –
and classified according to the ‘environment’ category in the PBDB. Data is not subsampled.
0
300
600
900
n
0.00
0.25
0.50
0.75
1.00
Relative Abundance
Number of Terrestrial Paleoenvironments Relative Abundance of Terrestrial Paleoenvironments
Aalenian Carnian Hettangian Norian PliensbachianRhaetian Sinemurian Toarcian
stage
Aalenian Carnian Hettangian Norian PliensbachianRhaetian Sinemurian Toarcian
stage
environment
alluvial
channel
delta
floodplain
fluvial
lacustrine
other
205
Figure S4.3 Marine rarefaction curves.
Rarefaction curves for each stage for taxonomic richness (left) and functional group richness
(right). Envelopes are 95% confidence intervals for each richness metric at each sampling
intensity (number of collections subsampled). Gray dashed line is the subsampling quota set for
all analyses (n=400 collections).
0
250
500
750
1000
Taxonomic (Generic) Richness
0
10
20
30
Functional Group Richness
0 1000 2000 3000 4000
Sampling Intensity (# Collections)
0 1000 2000 3000 4000
Sampling Intensity (# Collections)
stage
Aalenain
Carnian
Hettangian
Norian
Pliensbachian
Rhaetian
sinemurian
Toarcian
206
Figure S4.4 Terrestrial rarefaction curves
Rarefaction curves for each stage for taxonomic richness (left) and functional group richness
(right). Envelopes are 95% confidence intervals for each richness metric at each sampling
intensity (number of collections subsampled). Gray dashed line is the subsampling quota set for
all analyses (n=35 collections).
0
100
200
300
Taxonomic (Generic) Richness
0
10
20
30
Functional Group Richness
0 100 200 300 400
Sampling Intensity (# Collections)
0 100 200 300 400
Sampling Intensity (# Collections)
stage
Aalenain
Carnian
Hettangian
Norian
Pliensbachian
Rhaetian
sinemurian
Toarcian
207
Supplementary tables
Table S4.1 Marine ecospace tiering, motility, and feeding categories.
Tiering Motility Feeding
1 – Pelagic 1 – Fully, fast motile 1 – Suspension feeder
2 – Erect 2 – Fully, slow motile 2 – Deposit feeder
3 – Surficial 3 – Facultative, unattached 3 – Miner
4 – Semi-infaunal 4 – Facultative, attached 4 – Grazer
5 – Shallow (0.05 – 5 cm) 5 – Stationary, unattached 5 – Predator
6 – Deep (>5 cm) 6 – Stationary, attached 6 – Other
208
Table S4.2 Terrestrial ecospace tiering, motility, and feeding categories.
Tiering Motility Feeding
1 – Aerial 1 – Migratory 1 – Predator (big)
2 – Arboreal 2 – Fully motile, far 2 – Predator (small)
3 – Ground-dwelling 3 – Fully motile, short 3 – Omnivore
4 – Soil-dwelling 4 – Tethered 4 – Herbivore
5 – Troglobitic 5 – Non-motile 5 – Scavenger
6 – Aquatic 6 – Other
209
Chapter 5. Conclusions
5.1 Dissertation overview
The research presented in this dissertation broadly addresses the question: What is the
evolutionary history and geobiological role of bioturbating ecosystem engineers? In Chapter 1, I
have addressed long-standing assumptions that the evolution of bioturbation across the
Ediacaran-Cambrian boundary caused increased oxygen concentrations in deeper sediment tiers,
thus expanding the habitable zone in benthic ecosystems. In fact, the results presented in Chapter
1 overturn this assumption. First, it is clear that biomixing, rather than bioirrigation, was the
dominant behavior in both the late Ediacaran and earliest Cambrian. Second, integrating proxies
for bioturbation intensity with a sedimentary biogeochemical model gives results that firmly
indicate that bioturbation across the Ediacaran-Cambrian boundary did not result in the
significant oxygenation of shallow marine sediments. This was due to a combination of weak
bioturbation intensities and low bottom water oxygen concentrations relative to the organic
matter input to the seafloor. In Chapter 2, I have presented the first geochemical analysis of trace
fossils from the Ediacaran-Cambrian transition (Deep Spring Formation, California, USA), with
the aim of identifying unique geochemical signatures that could be used to help categorize trace
fossils as biomixing or bioirrigation behaviors. Although unique geochemical signatures for
biomixing and bioirrigation did not emerge, the geochemical evidence does demonstrate that
early bioturbators from the Deep Spring Formation were strong enough ecosystem engineers in
their sedimentary environments to impact early diagenetic processes. In Chapter 3, I have
investigated how the functional ecology of bioturbating ecosystem engineers changed in
response to the end-Permian mass extinction (EPME). These results demonstrate that high-
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impact ecosystem engineers survived the EPME, despite being more physiologically intensive
bioturbation strategies. This suggests that the surviving ecosystem engineers may have been able
to buffer their habitats against persisting environment stress related to the EPME. Finally, in
Chapter 4, I have investigated how functional ecology changed across the planet in response to
the end-Triassic mass extinction (ETE), utilizing a novel terrestrial ‘ecospace’ functional ecology
framework that allows for direct comparisons with marine functional ecology. These results
demonstrate earlier ecological recovery in marine ecosystems compared to terrestrial
ecosystems, which had sustained ecological instability through the Early Triassic.
5.2 Local versus global trends in bioturbation ecosystem engineering
This work largely deals with global trends that were reconstructed from the fossil record.
Chapters 1, 3, and 4 all use global data compilations, either from literature review (Chapters 1,3)
or the Paleobiology Database (pbdb.org) (Chapter 4). While reconstructing global trends is
valuable for understanding broad, macroevolutionary changes throughout Earth history, it may
not be entirely representative of reality. The Earth is, and always has been, heterogenous in terms
of environmental conditions and resource availability. Bioturbation intensity in the modern is
extremely spatially variable. Biomixing and bioirrigation intensities change rapidly across basin
transects and through seasons, driven by heterogeneity in environmental conditions such as
organic carbon delivery (Solan et al., 2019). Ecological and taxonomic diversity of the fossil
record is also spatially heterogenous, driven by similar factors (Close et al., 2020; Benson et al.,
2021). Chapter 2, in contrast, only reconstructs local trends by focusing solely on trace fossils
from the Deep Spring Formation. There is value in both contextualizing these local trends in the
global trace fossil record and in comparing the global trends to case studies from specific regions
211
and localities. The trace fossil record of the Deep Spring Formation resembles the global trends
reconstructed in Chapter 1, in that the majority of sediment mixing in the earliest Cambrian was
done by biomixing deposit feeders, with rarer occurrences of bioirrigators such as Treptichnus
pedum. One could assume, therefore, that Deep Spring bioturbators had a similar impact on
sediment oxygen and organic matter as was presented for Terreneuvian bioturbation in Chapter
1, with the stronger biomixers shoaling the oxic zone in the sediment. However, as the numerous
sensitivity analyses in Chapter 1 exhibit, the precise impact of the Deep Spring bioturbators
would have been dependent on local environmental conditions.
The ecosystem engineering impacts of bioturbation are unlikely to have operated on a
global scale. Rather, any seemingly global scale impacts would have more likely been the
aggregated results of many local and regional scale ecosystem engineering effects. Therefore,
future work comparing local and global trends in bioturbation ecosystem engineering will be
important. For the Ediacaran-Cambrian transition, this would involve applying these
biogeochemical and ecosystem engineering perspectives to trace fossil records from new regions,
particularly focusing on the most well-studied localities such as Newfoundland, Namibia, South
Australia, and Northwest Canada, which largely drive our understanding of global bioturbation
trends. For the Early Triassic, it will be important to identify environments where ecosystem
engineering behaviors persist and what those environmental conditions were like by utilizing
local geochemical proxies and Earth systems modeling. This may give indications about the
environmental conditions that are within the physiological limits of high-impact ecosystem
engineers, such as those that make deep, three-dimensional network Thalassinoides burrows
which persisted in the Early Triassic. Moreover, spatially constraining the survival of these
ecosystem engineers is the first step in associating their persistence with metrics of biotic
212
recovery, ecological stability, and lower extinction severity in the Early Triassic. Finally,
incorporating local bioturbation parameters and environmental conditions into biogeochemical
models such as those presented in Chapter 1 will be important for reconstructing how
bioturbation may drive biogeochemical changes that vary from region to region.
5.3 Ecosystem engineering in deep time: Bioturbation and beyond
Moving bioturbation beyond “just-so” stories (Berkenbusch and Rowden, 2003) has
perhaps been hampered by the limits of process- and outcome-based ecosystem engineering
frameworks. Process-based ecosystem engineering frameworks focus on the mechanisms and
environmental changes that arise from ecosystem engineering behaviors and are thus limiting in
that they do not address any resulting biotic change. Outcome-based ecosystem engineering
frameworks focus instead on associating the presence and activities of ecosystem engineers with
a biotic change in their ecosystems, and thus tend to lack a direct, mechanistic link that
underscores the importance of how the ecosystem engineer impacts its environment (Berke,
2010). This work has largely been focused on process-based ecosystem engineering: Chapter 1
deals with how bioturbation impacts oxygen and organic matter, Chapter 2 deals with the how
bioturbation impacts early diagenetic processes, and the functional groups used in Chapter 3 are
derived from process-based traits, such as sediment modification (Herringshaw et al., 2017;
Minter et al., 2017).
The rock and fossil record may hold unique opportunities for bridging the gap between
process- and outcome-based ecosystem engineering frameworks which cannot necessarily be
achieved by modern ecological studies. For one, the fossil record and, in particular, large open-
source databases such as the PBDB allow outcome-based ecosystem engineering hypotheses to
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be directly tested. Future studies along this route would be the first in addressing longstanding
questions about the relationship between the rise of ecosystem engineers and increased
biodiversity throughout history (Erwin, 2008). Advancements in biogeochemical and
paleoecological modeling with the fossil record to link ecosystem engineering processes and
ecosystem engineering outcomes would push the scope of the field even further. For example,
recent developments in quantitative paleoecology that incorporate agent-based modeling and
ecological niche modeling (see, for example, Saupe et al., 2012, 2018; Myers et al., 2015) offer
an exciting avenue of future work for ecosystem engineering. Specifically, combining these
paleoecological models with the appropriate biogeochemical models can allow us to test how
ecosystem engineers impact or maintain the properties that define an ecological niche, thereby
bridging the ecosystem engineering process with the ecological outcome.
There is also now clear scope to move ecosystem engineering in the fossil record beyond
bioturbation. Marine bioturbators are by no means the only ecosystem engineers with robust
fossil records. Reef-building organism – including various coralline taxa, sponges, and mollusks
– have even more robust fossil records than bioturbators. Terrestrial ecosystem engineers, as
well, have usable fossil records, including land plants and vegetation-trampling megafauna. The
role of both marine and terrestrial ecosystem engineers should certainly be investigated in future
work in order to understand the extent of their role in driving macroevolutionary trends observed
in the fossil record, particularly if terrestrial and marine ecosystem engineers impact their
ecosystems differently. In fact, the development of the terrestrial ecospace cube presented in
Chapter 4 will be useful in this avenue of future research, as it will allow for studies of how
terrestrial and marine ecosystem engineers impact various ecological measures and processes in
their ecosystems. Finally, moving deep time ecosystem engineering studies beyond bioturbation
214
will open the research topic to questions more related to conservation paleobiology. In the
modern, ecosystem engineers have previously been proposed as marine conservation targets, as
maintaining their populations is expected to bolster the buffer they provide to their ecosystems
against climate-related stressors (Crain and Bertness, 2006). As paleoecologists are increasingly
moving towards research that is related to the modern climate and extinction crisis (Kiessling et
al., 2023), there is clear scope to incorporate ecosystem engineering into research questions
related to the objectives of conservation paleobiology. In particular, outcome-based ecosystem
engineering questions focused on ecosystem engineers’ association with instability will be useful
for understanding when and under what conditions ecosystem engineers are associated with
increased ecological stability. In sum, the research presented in this dissertation should provide a
valuable springboard for advancing the study of ecosystem engineering in deep time, with future
studies focused on advancements in paleoecological modeling, incorporating a diverse suite of
ecosystem engineering taxa, and increasing the relevance of research questions for conservation
paleobiology.
215
5.4 References
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space and time in the fossil record: Current Biology, v. 31, p. R1225–R1236,
doi:10.1016/j.cub.2021.07.071.
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Abstract (if available)
Abstract
The evolution of marine bioturbation is considered one of the most important events in Earth history. This dissertation explores the evolutionary history and geobiological role of bioturbators as ecosystem engineers that impacted the habitability of their environments. First, I tested longstanding hypotheses that early bioturbators during the Ediacaran-Cambrian transition significantly increased oxygen concentrations in shallow marine sediments. By using biogeochemical models that incorporate ecologically-informed bioturbation parameters, I found that early bioturbators were unlikely to have been capable of oxygenating their sedimentary environments. Second, I investigated whether bioturbation behaviors with variable impacts on sediment biogeochemistry could be geochemically identified. Although statistically unique geochemical signatures did not emerge, differences in concentrations of certain elements in and out of burrow structures indicate that the bioturbators were strong enough to have impacted early diagenetic processes. Third, I investigated the impact of the end-Permian mass extinction on bioturbation ecosystem engineering behaviors. I found that the ecosystem engineering strategies which would have been most effective at maintaining resource availability persisted in the wake of the extinction event. Finally, I investigated how functional ecology in terrestrial and marine ecosystems changed across the end-Triassic mass extinction event, using a novel terrestrial functional ecology framework that allows for direct comparison with the traditional frameworks used in marine paleoecology. These results indicate that terrestrial ecosystems were much slower to return to ecological stability than marine ecosystems. Overall, this dissertation provides new insights into the geobiological role of bioturbators as ecosystem engineers during major evolutionary intervals in Earth's history.
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Cribb, Alison Taveau
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The geobiological role of bioturbating ecosystem engineers during key evolutionary intervals in Earth history
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Doctor of Philosophy
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Geological Sciences
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2023-08
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