The Pennsylvania State University

The Graduate School

HUMAN BEHAVIOR AS A DRIVER OF NON-HUMAN

MORPHOLOGICAL AND GENOMIC EVOLUTION

A Dissertation in

Biology

By

Alexis P. Sullivan

© 2020 Alexis P. Sullivan

Submitted in Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

December 2020

ii The dissertation of Alexis P. Sullivan was reviewed and approved by the following:

George H. Perry Chair of the Bioinformatics and Genomics Interdisciplinary Graduate Program Associate Professor of the Departments of Anthropology and Biology Huck Institutes of the Life Sciences and Center for Infectious Disease Dynamics Dissertation Advisor

Tracy Langkilde Professor of the Department of Biology Dean of the Eberly College of Science Chair of Committee

Jesse R. Lasky Professor of the Department of Biology

Timothy M. Ryan Professor and Head of the Department of Anthropology

Matthew Reimherr Professor of the Department of Statistics

Stephen W. Schaeffer Professor and Head of the Department of Biology

iii ABSTRACT The earliest fossil evidence of Homo sapiens in Africa dates to ~300,000 years before present, with signs of behavioral modernity appearing ~200,000 years later. If we condense the Earth’s entire timeline to just 24 hours, modern human behaviors only exist in the final 2 seconds. Yet in this very short time, humans have become highly adept at changing the world and the non-human lives around them. Plant and animal domestication are the obvious examples of the effects that human behavior can have on non-human morphological evolution, but we are only just realizing how many other non-human organisms have evolved due to indirect human influences. Modern studies have recorded evidence of rapid morphological trait change in a variety of non-human, non-domesticated species due to anthropogenic influences, including harvesting and predation pressures, landscape modification activities, and genetic modification. There is also long-term evidence of such processes from the archaeological record, extending as far back as ~50,000 years before present. We review these human behaviors in Chapter 1, and document that the most well-studied of these behaviors is size- or trait-selective hunting and harvesting. Consequently, most of the examples we include in the introductory chapter and two of the data chapters fall into this category. Another human behavioral model that we’ll be discussing in the third chapter is translocation, specifically how the introduction of a novel species can affect non-human morphological evolution in native species. We used an integrative suite of tools to study the ways that human harvesting and translocation behaviors can directly or indirectly effect morphological adaptation in non- model species. Our approach begins by quantifying a morphological change that has likely been precipitated by some anthropogenic influence. Once a morphological change (phenotype) is characterized in an organism, we sequence whole genomes from many individuals to identify genomic regions in the nuclear DNA associated with that phenotype with a genome-wide association study (GWAS). We also use population genetics techniques to detect any signatures of recent positive natural selection on alleles associated with that phenotype. These techniques can also be applied to examine allele frequency changes over time in ancient DNA recovered from well-preserved organisms, though extraction protocols may need to be modified for recovery from non-standard materials. Chapters 2, 3 and 4 detail the results of applying this approach to three different non-model study systems, one mammal, one reptile, and one invertebrate. In Chapter 2 we present the first formal comparative morphological analysis of non- human primate subfossil and modern skeletal remains to evaluate the hypothesis of recent dwarfism among the still-living lemurs of Madagascar. We investigated a purported morphological change due to size-selective human hunting pressures in a population of Propithecus verreauxi (Verreaux’s sifakas). We collected both traditional caliper measurements and high-resolution 3D surface scan data from 106 P. verreauxi sifaka long bones to test the hypothesis that sifaka body size decreased following the earliest appearance of cut-marked bones in the area. After comparing the body size-associated measurements of subfossil bones from an archaeological site at Taolambiby to those from modern osteological collection at the nearby Beza Mahafaly Special Reserve, we found that the subfossil sifaka bones were indeed larger than those of the same species currently occupying the area. Although we cannot determine the ultimate cause of this size difference on the basis of our limited data, we believe that the findings from our analysis and our publicly available 3D data repository on MorphoSource make an important contribution to several fields of study, from the potential evolutionary consequences of human-environment interactions to zooarchaeology and primatology. Expanded archaeological sampling could corroborate our findings, and evolutionary genomic analyses may support the adaptive hypothesis versus an assemblage or taphonomic scenario where only the largest of the subfossil bones were represented. Chapter 3 details our preliminary efforts in correlating Sceloporus undulatus (eastern fence lizard) phenotypic adaptations to predation by invasive Solenopsis invicta (red iv imported fire ants) in the southeast United States. Humans accidentally introduced the fire ants through the port in Mobile, Alabama in the 1930s, and the venomous ants have been known to swarm, attack, and prey upon fence lizards. Some fence lizard populations have been exposed to fire ant predation for more than 70 years. Adult fence lizards at sites with the longest times since ant invasion have 3.4% longer relative hind limb lengths and increased twitching and fleeing behavioral responses compared to lizards at not-yet invaded sites. These traits are heritable from mother to offspring and the relative hind limb length differences between sites are inconsistent with expected patterns of ecogeographic variation based on museum specimens collected prior to fire ant invasion. To identify the genetic basis of S. unudulatus morphological traits hypothesized to represent adaptations to predation by red imported fire ants, we extracted and sequenced DNA from 381 individual fence lizards and conducted an evolutionary analysis of the identified genetic regions using methods from genome-wide association studies. Heritable components that are associated with phenotypes such as limb length are often polygenic, an expectation borne out by our finding of several thousand genetic variants/regions that appear to underlie fence lizard hind limb length variation. Our preliminary genome-wide association study (GWAS) results indicate that the top 17,172 (0.1%) of all single-nucleotide polymorphisms are near genes significantly enriched for biological processes that could be related to both the increased hind limb length and behavioral twitching phenotypes, such as nervous system and cellular development and neural activity. We also generated deeper-coverage sequence data for 20 of the Alabama individuals, as well as 20 individuals each from a site with no fire ant exposure in Arkansas and Tennessee. These data were used to test both within and between these three populations whether the patterns of limb trait variation have been driven by positive natural selection. Initial population differentiation (Fst) results indicate that the Alabama and Arkansas populations differ in genetic regions that are associated with synaptic signaling and transmission. Our nucleotide diversity (π) and LASSI (T) scans for selective sweeps indicated a strong signal in chromosome 10 that overlaps with our GWAS results and suggests that the MYO9B gene could be of functional importance for muscular motor activity in the Alabama lizards. We are currently verifying the quality of the genome-wide variants and their corresponding signals in each of our association and selection tests. In Chapter 4 we describe our endeavors in protocol development for harnessing both modern and ancient DNA from challenging sources. Based on comparisons from a mid- Holocene fringing reef, late Holocene excavations, and live-caught specimens, Strombus pugilis (West Indian fighting conchs) overall body size has decreased by up to 40% due to thousands of years of low-level human harvesting. We collected both living conch and recently eaten and discarded shells, as well as archaeological and paleontological materials, from several sites in the Bocas del Toro archipelago. We assembled both a nuclear and mitochondrial reference genome for S. pugilis from a freshly preserved tissue sample, and evaluated several DNA extraction techniques that were created for mineralized substrates like ancient bone and bivalve shell materials. We developed a modified method to extract both modern and ancient DNA from the robust, crystalline calcium structures of the discarded shells and report our assessment of the mapping rates and DNA damage from modern live-caught, recently discarded, late Holocene, and mid-Holocene shell materials. We were able to successfully sequence nuclear DNA and proportionate amounts of mitochondrial DNA from all of the shell materials. We also confirmed the authenticity of ancient DNA recovered from the archaeological and paleontological shells based on the presence of typical molecular signatures of post-mortem DNA damage and fragment lengths. Our study represents the first conch shell DNA analysis and paleogenomic perspective on tropical marine shells. The resources we present here can be applied to middens worldwide to facilitate reconstructions of long-term records of the impacts of human behavior on mollusc and metagenomic evolutionary biology. We hope to sequence DNA from the remaining shell samples in our collection and incorporate more in-depth genomic analyses so that we might v see direct evidence of how the frequency of body size-associated alleles has changed over the past several thousand years. Though these morphological adaptations have arisen over remarkably short timescales, they are predicted by evolutionary theory, and detectable using the integrated approach and methods we describe herein. From invertebrates and reptiles in the new world to primates in the old, these studies illustrate how the unintended effects of both intentional (hunting) and accidental (release of invasive species) human activities have had detectable, functional consequences on a wide swathe of the natural world.

vi TABLE OF CONTENTS LIST OF FIGURES ...... viii LIST OF TABLES ...... ix ACKNOWLEDGEMENTS ...... x CHAPTER 1: Human behaviour as a long-term ecological driver of non-human evolution ..... 1 Abstract ...... 1 Introduction ...... 2 The antiquity of humans as keystone species and trophic regulators ...... 2 Human behavior as a driver of non-human evolution ...... 4 Response to human size- or trait-selective harvesting pressures ...... 5 Morphological evolution in response to human landscape modification and urbanization ...... 10 Morphological evolution in response to human-mediated ecosystem taxonomic turnover 11 Translocation ...... 11 Extinction...... 12 Connecting past, present, and future patterns of morphological evolution in response to human behavior ...... 13 Conclusion ...... 14 CHAPTER 2. Potential evolutionary body size reduction in a Malagasy primate (Propithecus verreauxi) in response to human size-selective hunting pressure ...... 15 Abstract ...... 15 Introduction ...... 16 Materials and Methods ...... 16 Osteological Collections ...... 16 Data Collection ...... 17 3D Surface Scan Post-Processing ...... 18 AMS Radiocarbon Dating ...... 20 Correlations Between 3D Scan and Caliper Measurements...... 22 3D Surface Scan Measurement Analyses...... 23 Results...... 24 Discussion ...... 27 Data, Code, and Materials ...... 28 CHAPTER 3. Genomic analysis of eastern fence lizard (Sceloporus undulatus) morphological adaptations to human-mediated fire ant invasion ...... 29 Abstract ...... 29 Introduction ...... 30 Materials and Methods ...... 32 Study Sites and Sample Selection ...... 32 DNA Extraction ...... 32 Library Preparation and Sequencing ...... 32 Read Mapping and Quality Filtering ...... 33 SNP Identification ...... 33 SNP Filtering and Quality Control ...... 34 Analysis of Population Structure and History ...... 34 vii Evolutionary Analyses ...... 35 Genome-wide Association Study (GWAS)...... 35 Results...... 35 Population Structure and History ...... 36 Evolutionary Analyses ...... 37 Genome-wide Association Study (GWAS)...... 38 Evolutionary Analyses and GWAS Overlaps ...... 40 Discussion ...... 40 Data, Code, and Materials ...... 42 CHAPTER 4. Modern, archaeological, and paleontological DNA analysis of a human- harvested marine gastropod (Strombus pugilis) from Caribbean Panama...... 43 Abstract ...... 43 Introduction ...... 44 Materials and Methods ...... 45 Sample Collection Sites ...... 45 Specimen Subsampling ...... 46 Extraction Protocols ...... 47 Library Preparation and Sequencing ...... 48 Reference Genome Assemblies ...... 48 Read Mapping ...... 48 Damage Characterization ...... 49 Results...... 49 Reference Genome Assemblies ...... 49 Developing a DNA Extraction Protocol for Modern and Ancient Conch Shells ...... 50 Comparison Among Modern, Archaeological, and Paleontological Specimens ...... 50 Discussion ...... 52 Data, Code, and Materials ...... 53 APPENDICES ...... 54 Appendix A: Supplementary Materials for Chapter 2 ...... 54 Appendix B: Supplementary Materials for Chapter 3 ...... 76 Appendix C: Supplementary Materials for Chapter 4 ...... 111 REFERENCES ...... 121

viii LIST OF FIGURES Figure 1-1. Model of how human behavior may effect non-human morphological evolution. 5 Figure 1-2. Selected modern examples of morphological change in response to human behavior...... 6 Figure 1-3. Evolutionary changes in bighorn sheep (Ovis canadensis) horn size in response to variable human trophy hunting pressures...... 7 Figure 1-4. Selected archaeological examples of morphological change in response to human behavior...... 8 Figure 1-5. Middle Stone Age (MSA) to Later Stone Age (LSA) size reductions of Cape turban shell (Turbo sarmaticus) opercula recovered from South African archaeological refuse dumps (shell middens)...... 9 Figure 2-1: Propithecus verreauxi osteological collection sites...... 17 Figure 2-2: Examples of osteological elements of P. verreauxi and the measurements collected for this study...... 19 Figure 2-3: Our comparative morphological results for the maximum number of subfossil individuals (MAX)...... 25 Figure 2-4: Comparative morphological results for the minimum number of subfossil individuals (MNI)...... 26 Figure 3-1: Dispersal of invasive red imported fire ants (S. invicta) since introduction in Mobile, Alabama ...... 31 Figure 3-2: Population structure and history of the S. undulatus in our study ...... 36 Figure 3-3: Results for natural selection scans in chromosomes 2, 3, 5, 6, and 10 for S. undulatus from Alabama, Arkansas, and Tennessee...... 38 Figure 3-4: Genome-wide association results for S. undulatus from Alabama ...... 39 Figure 4-1: Strombus pugilis study populations...... 45 Figure 4-2: Protocol summary for S. pugilis DNA extraction, library preparation, sequencing, and data analysis...... 47 Figure 4-3: Recovery of Strombus pugilis nuclear and mitochondrial DNA from shells of varying ages. A – Reads mapped to our S. pugilis nuclear assembly...... 52

ix LIST OF TABLES Table 2-1: Morphometric caliper and 3D surface scan measurement descriptions...... 20 Table 2-2: Subfossil P. verreauxi elements that were included in this study and their corresponding radiocarbon dates...... 22 Table 2-3: Measurement analysis calculations and examples...... 23

Table 3-1: Average Fst differentiation values between S. undulatus populations ...... 37

x ACKNOWLEDGEMENTS First and foremost, I have to thank my dissertation advisor, George (PJ) Perry, for all of the opportunities he gave me over the last 6 years. I have grown so much as a scientist and as a person because of you PJ. Without nearly constant encouragement and advice from Christina Bergey, Kathleen Grogan, and Stephanie Marciniak, graduate school would have been a rather different experience. Those three are power houses of awesome, and I will look up to them forever. Matthew Miller, you helped me cross the finish line and for that I will always be grateful. I also have to thank the former and present members of my lab for always being available to lend me an ear, show me how to run a lab protocol, make me smile, go have an adventure, try to teach me two new languages, learn how to write code with me, or eat whatever I felt like baking lately, even if they (intentionally) had crickets in them: Kathryn Turner, Raining Wang, Richard Bankoff, Richard George, Stephen Johnson, Emily Gagne, Diego Hernandez, Maggie Hernandez, Rindra Rakotoarivony, Ebony Creswell, Natalia Grube, David Villalta, Audrey Arner, Jacon Cohen, and Adrijana Vukelic.

I thank my dissertation committee members for their availability, support, and enthusiasm. I also thank Jen Knecht, Audrey Chambers, Betty Blair, and Robin Kephart, and Kathryn McClintock for helping life make sense and stay on track for the graduate students in the Biology and Anthropology departments. And Stephen Schaeffer: thank you for doing your best to take care of the Biology grad students and set examples for other departments for how things should be done.

I was so privileged to have so many amazing co-authors and collaborators from all over the world during my dissertation work. Thank you all for your patience and contributions: Gabriel Pigeon, Richard Klein, and Teresa Steele for providing the data from their studies used in Figures 1-3 and 1-5, Gabriel Pigeon and Emma Loftus for providing images, and Megan Aylward, Rebecca Bliege Bird, Brian Codding, Emily Davenport, and Richard Klein for helpful comments on earlier drafts of the Chapter 1 manuscript. I thank the Madagascar National Parks organization and colleagues at the Beza Mahafaly Special Reserve, including Joel Ratsirarson, Jeannin Ranaivonasy, Sibien Mahereza, Elaine Guevara, Brenda Bradley, Roshna Wunderlich, and Alison Richard. I also thank the University of Massachusetts Amherst Natural History Collections for allowing access to the Taolambiby collection, and Lily Doershuk for her advice on working with 3D data.

The fence lizard collections for Chapter 3 were approved by the Pennsylvania State University Animal Care and Use Committee, and the respective States permitted animal collection. Thank you Tonia Schwartz and the Sceloporus Genome Collaboration team, Tom Adams for amassing the Langkilde Lab specimens, and my wonderful undergraduate assistants Catherine Roberts and Katie Hickmann for their aid with the DNA extractions and shearing. Thank you Team Bocas 2017 for an amazing field experience in Panama and for your assistance in live conch collections: Ashley Sharpe, Nicole Smith-Guzman, Suzette Flantua, Jarrod Scott, Matthieu Leray, and Wilmer Elvir. Thank you Brigida de Gracia for assisting with the planning and coordination at the STRI Naos Marine and Molecular Laboratories. I also thank the Bocas del Toro Research Station team, especially Plinio Góndola and Urania González. Marco and Fausto Alvarez, Annick Belanger, and staff at Sweet Bocas kindly gave logistical support to collecting fossil conch. Permits to collect were provided by three Panamanian ministries; Ministerio de Comercio e Industria (MICI), MiAmbiente, and El Ministerio de Cultura de Panamá (formerly Instituto Nacional de Cultura, INAC). I thank the Dirección Nacional del Patrimonio Histórico, Instituto Nacional de Cultura, Republica de Panamá for issuing Resolución 153-14, permitting the excavation of units 60 and 61 at Sitio Drago and the Serracín family for access to the site – especially Juany and Willy Serracín and Ana Serracín de Shaffer. Last but certainly not least, thank you to the NYU Langone Genome Technology Center for sequencing my libraries.

xi There have been countless people in my life that have given me opportunities and support to become the person I am today. Though I don’t know your names, I want to thank the strangers whose smiles and acts of kindness have sustained me and reminded me that there are good people everywhere. Thank you to everyone who has been my friend, ally, fellow RA/chem prep assistant/coworker/intern, or all of the above. I also want to thank my mentors and favorite teachers from life before grad school: Guy Barbato, Tara Luke, Jim Brownhill from Stockton; Matthew Poach from NOAA; and literally every instructor from MAST, especially Liza Baskin.

I want to thank my momma Rani, dad Shannon, and sister Hayley for embracing and encouraging my passion for science from such a young age. You three never questioned my aspirations and only ever asked how you could help me get to where I wanted to go. I love you three and the rest of my family so much (especially Lorraine and Dennis, Marylyn, Stan and Alex, Kevin and Kyle, and Ira, Carol, Sarah, and Peter). Can’t wait to celebrate with you all once this damn pandemic gets under control. I wish you were here Grandpa Michael, you would have thought all of this was so amazing.

Last but certainly not least, I must thank my beloved little family. Margy and Moo, thank you for your feline shenanigans and snuggles, you both were a constant source of joy, comfort, and allergies during graduate school. And my partner, Daniel Schussheim. You read every single paper, presentation, job application, and even some emails. You talked me through my frustrations, and even machined the shell smasher that was essential to Chapter 4. You built a life with me in State College and now we get to embark on another journey together. I love and kin ye, always.

Components of this work were supported by the National Science Foundation Graduate Research Fellowship Program (DGE-1255832, to A.P.S.), the Smithsonian Tropical Research Institute Short-Term Fellowship Program (to A.P.S.), grants from the National Science Foundation (BCS-1554834 to G.H.P.; BCS-1459880, to D.W.B.; BCS-1750598 to L.R.G.), and the SNI program from the Secretaría Nacional de Ciencia y Tecnología e Innovación, Panamá (to A.O.). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. I would like to acknowledge support from the Peter A. and Marion W. Schwartz Family Foundation for providing funds (to L.R.G.) to help build the Beza Mahafaly Osteological Collection.

CHAPTER 1: Human behaviour as a long-term ecological driver of non-human evolution

Alexis P. Sullivan1*, Douglas W. Bird2, George H. Perry1,2,3*

Departments of 1Biology and 2Anthropology, Pennsylvania State University, University Park, PA, 16802, USA 3Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA

*Address for correspondence: [email protected]

Published in Nature Ecology and Evolution, doi: 10.1038/s41559-016-0065

Abstract Due to our intensive subsistence and habitat-modification strategies—including broad- spectrum harvesting and predation, widespread landscape burning, settlement construction, and translocation of other species—humans have major roles as ecological actors who influence fundamental trophic interactions. Here we review how the long-term history of human–environment interaction has shaped the evolutionary biology of diverse non-human, non-domesticated species. Clear examples of anthropogenic effects on non-human morphological evolution have been documented in modern studies of substantial changes to body size or other major traits in terrestrial and aquatic vertebrates, invertebrates, and plants in response to selective human harvesting, urbanized habitats, and human-mediated translocation. Meanwhile, archaeological records of harvested marine invertebrates and terrestrial vertebrates suggest that similar processes extend considerably into prehistory, perhaps to 50,000 yr bp or earlier. These results are consistent with palaeoenvironmental and other records that demonstrate long-term human habitat modification and intensive harvesting practices. Thus, while considerable attention has been focused on recent human impacts on ‘natural’ habitats, integrated evidence from modern biology and archaeology suggests a deep history of human entanglement with our ecosystems including substantial effects on the evolutionary biology of non-human taxa. The number and magnitude of such effects will probably increase given the continued intensification of anthropogenic activities and ecosystem impacts, including climate change and direct genetic modification.

2 Introduction The subsistence and habitat-modifying behaviors that typify how humans interact with their surrounding environments have broad and temporally profound effects on natural ecosystems (Boivin et al. 2016). At local levels, co-evolutionary relationships between small-scale human societies and non-human taxa in the same community can be so longstanding and immersive that the removal of humans results in simplified food webs or ecosystem collapse (Bliege Bird 2015; Castilla 1999), while in the context of global population growth the intensity of human- environment interactions has led to numerous documented cases of wildlife population decline and extinction (Jackson 2001; Ceballos et al. 2015). Given the extent and rate that human-mediated climate change and other byproducts of increasing human population density are predicted to impact future ecosystems (Western 2001; Corlett 2015), these issues are of major concern to biologists, ecological anthropologists, and policy makers. In addition to ecosystem health and wildlife extinction risk, human behavior also influences the evolutionary biology of non-human species. Extensive morphological evolution mediated by human behavior has been thoroughly documented for domesticated plants and animals (Diamond 2002; Larson and Fuller 2014), but these processes are in fact considerably more widespread, extending to non-domesticated species. That is, wild populations of non- human taxa can adapt via natural selection on functional genetic variation in response to human-induced ecological changes (Hutchings and Fraser 2008). In some cases, these morphological and behavioral adaptations may help certain species avoid extinction. These processes could have cascading effects on ecosystem function (Darimont et al. 2009; Western 2001), so they are an important factor to consider in the broader context of human- environment interactions. Clear examples of anthropogenic effects on the evolutionary biology of non-human, non-domesticated species have been documented in modern studies of body size or other morphological trait change in response to selective human harvesting of natural populations of vertebrates, invertebrates, and plants (Ratner and Lande 2001; Allendorf and Hard 2009; Darimont et al. 2009). Analyses of the archaeological record suggest that these processes likely extend considerably into prehistory (Erlandson and Rick 2010), perhaps even to earlier than 50,000 BP (years before present), and mediated by Neandertals (Stiner et al. 1999) in addition to anatomically modern humans. Non-human evolutionary adaptations to human- mediated ecological changes are also not limited to direct harvesting pressure effects. For example, human landscape modification practices, which predate the onset of agriculture ~12,000 BP and have since then intensified (Boivin et al. 2016), may too be catalysts for natural selection in affected non-human taxa (McDonnell and Hahs 2015; Western 2001). In this review we describe potential mechanisms for non-human adaptive evolution in response to human behavior, with human behavior defined broadly to encompass landscape modification (including niche construction), the harvesting, artificial selection, or translocation of non-human species, and even current and potential future directed practices involving genetic modification and de-extinction. These processes are illustrated by examples from modern evolutionary biology and enhanced with ethnographic and prehistoric perspectives that include discussion of evidence for the long-term history of anthropogenic effects on the environment. We focus primarily on morphological evolution, which does not encompass all adaptations (e.g., genetically-mediated behavioral or metabolic adaptations) but provides opportunities for insights from modern biology to be translated to the archaeological and paleontological records. The consideration of these data in a long-term, integrated framework will ultimately help us identify and model these evolutionary processes as they may occur in the future.

The antiquity of humans as keystone species and trophic regulators Disturbance ecology quantifies the effects of natural ecosystem disruptions that may result from abiotic forces (e.g. fire, flood, drought) or biotic variation (e.g. intensive predation, disease outbreak, depletion of keystone species) (Sousa 1984). Humans have major roles as biotic influencers of ecological disturbance due to our intensive subsistence strategies that 3 include broad-spectrum (of a high diversity of resources) harvesting and predation (Darimont et al. 2015; Rick and Erlandson 2009; Dunne et al. 2016), widespread landscape burning practices (Parker et al. 2016; Caldararo 2002; Scherjon et al. 2015; Bliege Bird et al. 2008), and habitat modification for agriculture, aquaculture, and shelter (Foster et al. 2003). In fact, the term ‘hyperkeystone species’ has recently been coined to describe humans, as our subsistence behaviors in a given habitat impact multiple other keystone species, with extensive resultant ecosystem effects (Worm and Paine 2016). Although many studies of the relationships between human activity and the population health and evolution of non-human species have focused on recent human impacts in ‘natural’ ecosystems, archaeological and paleoenvironmental records demonstrate a long- term history of major anthropogenic effects that began well prior to the origins of agriculture (Boivin et al. 2016). With respect to intensive harvesting, while there is fossil record evidence for the consumption of meat by African hominins by at least 2.5 million years BP (McPherron et al. 2010; Heinzelin 1999), it is unclear whether this early record reflects hunting or scavenging and whether this behavior was intense or sporadic (Lupo and O’Connell 2002; Ferraro et al. 2013). However, good evidence for repeated, systematic butchery of terrestrial vertebrate species by hominins consistent with targeted hunting and processing is observed by ~780,000 BP (Rabinovich, Gaudzinski-Windheuser, and Goren-Inbar 2008), and hafted spear technology appears in the archaeological record by at least ~500,000 BP (Wilkins et al. 2012). Archaeological data from South Africa (Marean et al. 2007) and Spain (Cortés-Sánchez et al. 2011) also show that broad use of coastal resources by hominin foragers began by at least 150,000 BP (Klein and Bird 2016). Analyses of pollen and charcoal records suggest the initiation of widespread anthropogenic landscape burning between ~50,000 and 40,000 BP in Borneo and New Guinea, coincident with the respective appearances of modern humans in each region (Hunt, Gilbertson, and Rushworth 2012; Hope 2009). Some scholars even infer that the purposeful use of fire for habitat modification – a practice common to both agriculturalists and hunter- gatherers worldwide today (Caldararo 2002; Scherjon et al. 2015) – may have roots in the evolution of the genus Homo close to two million years ago (Parker et al. 2016). Human-environment interactions later intensified further with the origins and spread of agriculture and animal husbandry beginning ~12,000 BP (Boivin et al. 2016). Human- mediated ecological impacts during this period have included extensive land clearing, terracing, waterway diversion, the construction of permanent settlements and ultimately urban centers, translocation of non-endemic species to new habitats, the potential for magnified selective hunting and fishing pressures on non-domesticated species associated with increasing human population sizes and densities, climate change, and now even direct genetic modification of non-human taxa. Through these various processes humans became deeply entangled in their ecosystems, as illustrated by the cascading effects that have been observed following the removal of human activity from some habitats (Castilla 1999). For example, in desert Australia, Aboriginal predation of monitor lizards involves disturbing climax vegetation via burning tracts of old-growth xeric grassland, creating a tight mosaic of vegetative succession that supports higher densities of many traditionally hunted species (Bliege Bird et al. 2013; Codding et al. 2014). A mid 20th-century hiatus in traditional burning led to trophic collapse and coincided with the extinction of at least 21 endemic marsupials (Bliege Bird 2015). Thus, while human predation and habitat modification can certainly affect non-human population distributions negatively, leading to extinction and food web simplification (Young et al. 2016; Ceballos et al. 2015; Bommarco, Kleijn, and Potts 2013), there is also strong evidence for significant ecological adaptation to the long-term keystone presence of intensive human disturbance, which could include adaptive morphological evolution in non-human taxa.

4 Human behavior as a driver of non-human evolution One of our goals in this Review is to demonstrate the antiquity of widespread phenotypic adaptation to human behavior by connecting observations and insights from modern evolutionary biology to archaeological and paleontological observations. To do so, we focus on the evolution of major morphological phenotypes – that is, traits that may be preserved in prehistoric records (unlike, for example, behavioral and metabolic changes) – as one subset of a diverse spectrum of genetic-based traits in non-human species that have evolved in response to human behavior (Palumbi 2001; Hendry, Gotanda, and Svensson 2016; Sarrazin and Lecomte 2016; Hendry, Farrugia, and Kinnison 2008). For the same reason we also do not discuss bacteria, parasites, and other microorganisms whose evolutionary biology is also affected by humans (Bull and Maron 2016; Perry 2014b; Rogalski et al. 2016; B. A. Jones et al. 2013). However, to the extent that human behavior is a long-term driver of macro-scale morphological change in non-human plants and animals, we would expect these processes to have likely affected the evolutionary histories of other phenotypes and organisms as well. Artificial selection associated with the domestication of non-human animals and plants is the best-known example of morphological evolution in response to human behavior. While agriculture and pastoralism originated by at least ~12,000 BP (Boivin et al. 2016), the roots of these processes likely extend considerably further into prehistory, for example to ~23,000 BP (Snir et al. 2015) or earlier (Allaby et al. 2015) for plants and >30,000 BP for dogs (Druzhkova et al. 2013). Here, because both the scale and archaeological prehistory of morphological evolution in domesticated taxa have been reviewed extensively (Larson and Fuller 2014; Diamond 2002; Meyer, DuVal, and Jensen 2012; Clutton-Brock 2012; Zeder 2006), we will instead focus our discussion on five other mechanisms by which human behaviors could be ecological drivers of morphological evolution: i) size- or trait-selective harvesting; ii) landscape modification and urbanization; iii) human-mediated ecosystem taxonomic turnover; and iv) looking towards the future, climate change and direct genetic modification (Figure 1-1).

5

Figure 1-1. Model of how human behavior may effect non-human morphological evolution.

In addition to marine and freshwater vertebrates, organism size changes in response to human harvesting pressures have also been reported in natural populations of terrestrial vertebrates and even plants (Figure 1-2). An excellent example, documented in a carefully controlled study, is that of the wild snow lotus Saussurea laniceps. This plant, from the eastern Himalayas, is used in traditional Tibetan and Chinese medicine for headache and high blood pressure treatments. Larger plants are considered more potent and thus collected preferentially. Based on herbarium specimens (of flowering plants; when maximum plant height is achieved), Law and Salick (2005) reported a ~45% decrease in S. laniceps heights over the past century. In contrast, flowering heights of the sympatric and related – but substantially less desired medicinally and little harvested – snow lotus Saussurea medusa were unchanged over the same period. Moreover, modern S. laniceps individuals subject to harvesting in non-protected areas were ~70% shorter than those in protected areas (Law and Salick 2005).

Response to human size- or trait-selective harvesting pressures Human patterns of prey selection can vary substantially from those of other predators. While likely not applicable to all societies, at least some human populations may harvest particular species with unparalleled intensity, and may disproportionately target large adults of a given prey species or individuals with a specific trophy feature, such as antlers or horns (Darimont et al. 2015). Rapid phenotypic evolution may result (Darimont et al. 2009).

6

Figure 1-2. Selected modern examples of morphological change in response to human behavior. The presented magnitudes of phenotypic change represent approximate percentages of difference from earliest measured value. The reported evolutionary rate (darwins; Haldane 1949) reflects the magnitude of morphological change (absolute value of the difference between the natural log of the starting trait value and the natural log of the ending trait value) per million years, for cases with available morphological trait measurements (which excludes the elephant presence/absence example) and information on the number of years over which the change occurred. Only one of multiple modern examples of these effects in different species of salmon (Quinn, McGinnity, and Cross 2006; Kendall et al. 2014; Bielak and Power 1986) and shellfish (Roy et al. 2003) were included in the figure. Bighorn sheep photo: Gabriel Pigeon. Other photos: Wikimedia Commons (credits to Joachim Huber, Alexandre Buisse, High Plains Grifter, Bering Land Bridge National Preserve, Peter Southwood, Peter van der Sluijs, Jerry Kirkhart, Scott Loarie, Don DeBold, Ron Knight, Geoff Gallice, CSIRO). 7 Morphological evolution has been especially well documented in fisheries, in which larger prey are typically of higher value and netting technology may greatly enrich the catch for larger individuals based on the sizes of the net openings (Kuparinen and Merilä 2007; Allendorf et al. 2008; Allendorf and Hard 2009; Law 2007). Body length and mass reductions of 25% or greater have been documented over time periods of only one or several decades in multiple independent taxa (Ricker 1981; Quinn, McGinnity, and Cross 2006; Hamilton et al. 2007; Kendall et al. 2014). Trophy hunters exert directional pressure on a particular phenotype, potentially leading to the evolution of smaller features across the population or higher rates of feature absence. For example, illegal ivory hunting in Zambia resulted in an increase of >300% (from 10.5% to 38.2%) of tuskless female African elephants (Loxodonta africana) from 1969 to 1989 (Jachmann, Berry, and Imae 1995). Trophy hunting may also have pleiotropic effects on other phenotypes that are not the direct targets of selection, as observed for bighorn sheep (Ovis canadensis) on Ram Mountain in Alberta, Canada. In addition to a ~30% reduction in male horn length over 23 years of horn size-focused hunting pressure (Figure 1-3), body mass also decreased ~23.5% over the same period (Coltman et al. 2003; Pigeon et al. 2016).

Figure 1-3. Evolutionary changes in bighorn sheep (Ovis canadensis) horn size in response to variable human trophy hunting pressures. A) Box and kernel density plots of the horn lengths of male 4-year-old sheep from 1975-2012 at Ram Mountain, Alberta, Canada, where 2.26 male rams were sport-harvested per year from 1973-1995 followed by a rate of only 0.27 rams per year from 1996-2011. Median values are represented by white circles; box bottoms and tops indicate the lower and upper quartiles, respectively. Data are reproduced from Pigeon et al. (2016) (with permission); see the original article for more comprehensive analyses, including with data from sheep of different age classes and estimates of changes in horn length genetic value. B) Ram Mountain bighorns. Photo: Gabriel Pigeon.

Is trophy hunting limited or widespread among human societies, and what is the antiquity of this behavior? In many societies, prestige goods do serve as costly signals of status and commitment and likely plan an important role in the development of inequality (Hayden and Villeneuve 2011; Bliege Bird and Smith 2005), and in foraging groups hunting is commonly as much a political activity as it is a provisioning practice (Bliege Bird and Power 2015; Hawkes et al. 2001). Discerning archaeological signatures of prestige hunting is difficult but has been proposed in several cases (McGuire and Hildebrandt 2005). Well-documented archaeological evidence for prey size changes in response to human harvesting pressure does exist in the record of intertidal mollusc exploitation (Erlandson and Rick 2010) (Figure 1-4). While many predators (marine, terrestrial, and avian) exploit shellfish, only humans transport large loads to central locales for repeated processing and deposition, creating substantial heaps or trash ‘middens’ of the inedible, durable mollusc 8 shells and the remains of other taxa that come to represent temporal records of both direct human predation on these animals and their sizes (Marean 2014; Simenstad, Estes, and Kenyon 1978). Such harvesting can have substantial effects, with selective pressures resulting in either morphological change in targeted molluscs or predation resulting in shellfish populations with persistently skewed age-size profiles.

Figure 1-4. Selected archaeological examples of morphological change in response to human behavior. The presented magnitudes of phenotypic change represent approximate percentages of difference from earliest measured value. The reported evolutionary rate (darwins; Haldane 1949) reflects the magnitude of morphological change (absolute value of the difference between the natural log of the starting trait value and the natural log of the ending trait value) per million years. Photos: Wikimedia Commons (credits to Justin Johnson, Alex Popovkin, Mayer Richard, Esculapio, H. Zell, Jerry Kirkhart, Jan Delsing).

Modern human intertidal foragers worldwide are selective and highly sensitive to changes in the trade-offs associated with searching for and handling different types and sizes of shellfish (Bird and Bliege Bird 1997; De Vynck et al. 2016; Meehan 1982). Because shellfish are sessile or slow on-encounter, shell size predicts the macro-nutritional return rate of molluscs, and correlates well with ethnographic measurements of the probability that foragers will select an individual encountered while foraging (Codding, O’Connell, and Bird 2014). Thus, observed foraging behavior suggests selective preferences for larger molluscs, generating conditions that could lead to evolutionary size decreases over time for species harvested intensely by humans. These expectations are consistently met by analyses of the archaeological record. Early evidence for routine, large-scale intertidal mollusc exploitation has been recorded beginning in the Middle Stone Age (~120,000 to 60,0000 BP) at coastal cave sites in South Africa (Jerardino 2016; Marean 2014). Avery et al. (2008) and Klein & Steele (2013) have compared these South African Middle Stone Age shellfish assemblages to those that accumulated in midden layers dated to the Later Stone Age (~12,000 to <1,000 BP), observing significant 9 reductions over time in the sizes of all represented species, including for the Cape turban shell (Turbo sarmaticus) as depicted in Figure 1-5.

Figure 1-5. Middle Stone Age (MSA) to Later Stone Age (LSA) size reductions of Cape turban shell (Turbo sarmaticus) opercula recovered from South African archaeological refuse dumps (shell middens). A) Box and kernel density plots of opercula maximum lengths from midden layers dating to the MSA (120-60 ky BP) and LSA (11-0.5 ky BP). Median values are represented by white circles; box bottoms and tops indicate the lower and upper quartiles, respectively. Dotted lines identify the dated layer boundaries for each sample. Data are reproduced from Klein and Steele (with permission). B) Modern Turbo sarmaticus, with operculum external surface indicated. Photo: MerlinCharon. C) Archaeological Turbo sarmaticus opercula that have been micromill sampled for seasonal palaeoclimate reconstruction, from a Nelson Bay Cave (South Africa) midden layer dated to 9–7 kyr bp. Photo: Emma Loftus.

Similar evolutionary trends are evident in many coastal environments elsewhere. For example, molluscs recovered from midden layers dated to ~19,000 to 9,000 BP at Riparo Mochi, Italy were ~30% smaller than those in earlier layers dated to ~36,000 to 24,000 BP (Stiner et al. 1999). Likewise, in the California Channel Islands, owl limpets (Lottia gigantea) have experienced ~40% size reductions since the onset of human foraging ~10,000 BP (Erlandson et al. 2011). In each of the above cases, the observed patterns of morphological change are inconsistent with known patterns of paleoenvironmental variation, suggesting that the morphological change likely reflects responses to anthropogenic activity. However, because growth is continuous throughout life for many of these taxa, confidently distinguishing adaptive morphological evolution from byproducts of changes in harvest age profiles based on archaeological midden data is also a challenge (Klein and Steele 2013). Still, such analyses are possible for species with clear markers of growth cessation (e.g., for skeletal long bones with their epiphyseal fusion) or at least of maturation. For example, sizes of both the largest 10 juveniles and the smallest adult conch (Strombus pugilis) shells became dramatically smaller over the past 7,000 years of human harvesting pressure in Caribbean Panama – conch harvested for food today have ~40% less meat than the conch that existed when human predation began (O’Dea et al. 2014) – demonstrating a morphological evolutionary effect rather than only a mortality profile change. Without the analytical benefits afforded by extensive shell midden deposits, archaeologists are more challenged to identify evidence of morphological evolution in response to human harvesting pressures on terrestrial vertebrates. Still, Stiner et al. (1999) reported a 19% size decrease (based on humeral shaft diameter as a proxy for body size) for spur-thighed tortoises (Testudo graeca) across archaeological layers dated to 200,000- 150,000 BP, 100,000-70,000 BP, and 28,000-11,000 BP that cannot be explained by patterns of climatic variation at Nahal Meged, a Neandertal and modern human site in Israel.

Morphological evolution in response to human landscape modification and urbanization Anthropogenic burning and clearing, irrigation infrastructure, horticultural and pastoral disturbance, and structural investment have widespread ecological consequences for other organisms sharing the affected habitats. Direct and indirect effects or responses to human landscape modification – both positive and negative – have been well documented for many non-human species, including changes to behavior, distribution, and abundance (Bliege Bird 2015; Bliege Bird et al. 2013; Codding et al. 2014; Smith 2005; Herzog et al. 2014; Hulme- Beaman et al. 2016; Trant et al. 2016). Anthropogenic landscape modifications also create contexts of novel selective pressure, providing a substrate for non-human morphological evolution (McDonnell and Hahs 2015; Turcotte et al. 2016). An exceptional example comes from a long-term study of cliff swallows (Petrochelidon pyrrhonota) in Nebraska, where roadside nesting behavior increased in frequency starting in the 1980s with expanding bridge and culvert construction (Brown and Bomberger Brown 2013). Overall population wing length decreased ~2% across a 30-year period, likely because shorter wings aided flight agility and reduced vehicle collision risk. Birds killed by cars were consistently longer-winged than the overall population, and the frequency of road-killed birds steadily declined despite the increasing proportion of sport- utility vehicles over the study period (Brown and Bomberger Brown 2013). Human-mediated habitat fragmentation may also exert evolutionary pressures on morphological traits – perhaps especially those related to dispersal (Cheptou et al. 2016). For example, increased wing pointedness in birds facilitates longer-distance mobility in the face of increased habitat isolation (Bowlin and Wikelski 2008). By studying museum specimens, Desrochers (2010) found that songbird relative wing pointedness increased by a magnitude of 7-28% over the last century in North American forests heavily affected by clear-cutting. The same pattern was not observed for birds in early-successional and intact forests (Desrochers 2010). Urban environments in particular are hotbeds for behavioral specializations and population density increases among non-human taxa, even while other species have been extirpated (McKinney 2008). These environments are also home to some of the strongest current examples of non-human adaptation in response to human landscape modification (Alberti, Marzluff, and Hunt 2016). In a recent study, Winchell et al. (2016) examined 319 adult male Puerto Rican crested anole lizards (Anolis cristaltellus) from three independent pairs of urban and nearby forested habitat populations. Average limb lengths for urban lizards were consistently and significantly longer (~2% longer), and urban individuals also had significantly greater numbers of subdigital scales (~3% greater), with both traits potentially facilitating more efficient locomotion on artificial surfaces. Results from a subsequent common garden experiment suggest a genetic, and thus potentially evolutionary, basis for the phenotypic differences (Winchell et al. 2016). House finches in urban Arizona have ~2% larger beaks relative to populations in nearby desert habitat, a putative genetic adaptation to facilitate greater bite forces for consumption of seeds in backyard feeders that are relatively 11 larger and harder-shelled than those typically processed by the non-urban finches (Badyaev et al. 2008). Adaptive beak shape changes associated with feeder-based diets are also hypothesized for Central European blackcaps (Sylvia atricapilla) (Rolshausen et al. 2009). However, because bird song acoustics may adapt to urban noise environments (Slabbekoorn 2013) and beak morphology also modulates song frequency (Giraudeau et al. 2014), the ultimate evolutionary pressure(s) affecting urban bird beak shape need to be confirmed.

Morphological evolution in response to human-mediated ecosystem taxonomic turnover Human population movement, landscape modification, and harvesting practices are (and have been) associated with widespread translocation and extinction of thousands of non- human species. For example, an incredible number of plant species – more than 13,000, or ~4% of the known extant flora – have been translocated by humans (van Kleunen et al. 2015). While expanding global travel and trade have led to an increased rate of human-mediated translocation in the past 150 years (Simberloff et al. 2013), the purposeful or accidental introduction of non-domesticated plants and animals to naïve ecosystems has a long prehistory, likely back to the earliest human colonizations of at least some global landmasses (Wilmshurst et al. 2008; Jones et al. 2013; Austin 1999). Invasive species affect endemic taxa by competitive exclusion, predation, and other processes, compounding the effects of human landscape modification and harvesting pressure to contribute to the ongoing anthropogenic extinction crisis (Young et al. 2016; Ceballos et al. 2015; Mooney and Cleland 2001; Clavero and García-Berthou 2005; Simberloff et al. 2013). In this section we consider the potential effects of human-mediated turnover in ecosystem species composition on morphological evolution in non-human plants and animals: either directly for translocated taxa themselves in response to their new ecosystems, or indirectly for endemic species in ecosystems affected by species turnover.

Translocation. Studies of potential morphological adaptation in translocated species are challenged by the high levels of genetic drift potentially associated with founder events that may also result in (non-adaptive) phenotypic change over time (Colautti and Lau 2015; Le Gros et al. 2016), but evolutionary genomic or fitness analyses may be used to evaluate hypotheses of neutral vs. adaptive evolution. For example, purple loosestrife (Lythrum salicaria), a European wetland plant invasive to North America, flowers ~20 days earlier and is ~50% smaller near the northern vs. southern extent of its East coast invasive range (Colautti and Barrett 2013). Based on common garden experiments, these traits are largely genetically controlled, and strong fitness advantages for earlier reproduction in the north and larger plant size in the south were identified in transplantation experiments (Colautti and Barrett 2013). Introduced species can also affect the behavioral ecology and evolutionary biology of endemic taxa. There is mounting evidence that prehistoric translocation events induced trophic cascades with continental-scale impacts on fauna and flora (Fillios, Crowther, and Letnic 2012). Contemporary examples of the role of invasives in shaping the morphology of endemic taxa include South American venomous fire ants (Solenopsis invicta), introduced by the 1940s into the southern United States, where they now prey on endemic fence lizards (Sceloporus undulatus). Adult fence lizards at sites with the longest times since ant invasion have 3.4% longer hindlimbs and have higher rates of body twitching and fleeing responses (which are more effective in longer-limbed individuals) to fire ant attack compared to lizards at not-yet invaded sites (Langkilde 2009). This pattern is inconsistent with expected ecogeographic variation based on morphology of museum specimens collected prior to fire ant invasion, altogether suggesting a recent history of phenotypic adaptation (Langkilde 2009). Meanwhile, the cane toad (Bufo marinus) was introduced into Australia in 1935. This invasive species is highly toxic upon consumption to some but not all endemic snakes, although smaller toads are less likely to deliver sufficient volumes of toxin to be fatal. Based on morphological analyses of museum specimens, two snake species (Pseudechis 12 porphyriacus and Dendrelaphis punctulatus) have evolved smaller heads over the past 70 years that putatively preclude the consumption of larger toads (Phillips and Shine 2004).

Extinction. Human-mediated extinction may have cascading effects on the evolutionary biology of surviving non-human species in the same ecosystem, especially when the extinctions involve keystone species. We are currently experiencing a mass extinction crisis, with (for example) 8-100 times higher extinction rates over the past century for vertebrates than the long-term background rate (Ceballos et al. 2015). However, this crisis may be connected to longer-term extinction processes related to longstanding human landscape modification practices and harvesting pressures. Like humans, megafauna are generally keystone trophic facilitators for other species in the same habitats (Malhi et al. 2016). Of ~150 terrestrial mammalian genera >44 kg living on Earth’s continents 50,000 BP, approximately 65% were extinct by 10,000 BP (Barnosky 2004). Recent analyses of new, high-resolution paleoenvironmental and chronological datasets have given some (but not all) scholars increasing confidence that colonizing humans played at least indirect roles (e.g., via landscape burning) in prehistoric megafaunal extinctions in multiple large island habitats (Crowley et al. 2016; Boivin et al. 2016; Allentoft et al. 2014). At continental scales the role of humans in this process remains less clear (Stuart 2015; Wroe et al. 2013), with natural climate change likely a major factor in at least certain cases (Cooper et al. 2015). Our purpose is not to draw conclusions from these ongoing investigations or to review the associated debate. Rather, we suggest that if our ancestors did play roles in the extinctions of megafauna, then those events would represent another mechanism through which human behavior may have indirectly affected the evolutionary biology of other (non-megafaunal) non-human taxa. Regardless, by considering the outcomes of these past events we can raise our awareness of ongoing and future evolutionary responses to modern extinction processes. Ecosystem effects from the removal of keystone megafauna likely included niche space changes and openings for surviving taxa, resulting in opportunities for morphological evolution that might be detected in the paleontological record. For example, in the ~1,000 years following megafaunal extinctions in North America, coyote (Canis latrans) femur circumferences – a proxy for body size – became ~14% smaller relative to those of pre- megafaunal extinction coyotes (Meachen and Samuels 2012). The observed direction of phenotypic change is counter to expectations based on contemporary climate variation, suggesting to the authors that the coyote body size change may reflect adaptation to a smaller prey base, smaller competitors, or both (Meachen and Samuels 2012). Furthermore, compared to earlier coyotes, the mandibles of coyotes in post-megafaunal extinction North America were relatively less robust, and their dental morphology tended more toward tooth cusp morphology commonly associated with grinding and omnivory rather than shearing and carnivory (Meachen et al. 2014). These changes are consistent with morphological evolution in response to megafaunal extinction-associated dietary shifts. The vegetation consumption and trampling activities of herbivorous megafauna helped to maintain rich, mosaic-like landscapes; following megafaunal extinction these structured habitats were then replaced by denser and less diverse landscapes (Malhi et al. 2016; Johnson 2009; Bakker et al. 2016). Similar to the above-discussed evolutionary ecology implications of direct human landscape modification, such changes likely affected the abundance, behavior, and perhaps evolutionary biology of surviving species. Megafauna also had co-evolutionary relationships with many large-seeded plants, acting as primary dispersers (Janzen and Martin 1982). Larger seeds have higher seedling survival rates (Moles et al. 2005), which likely helps to explain the co-evolutionary optimization of seed size with dispersers. Following extinction of their primary dispersers, some megafauna-adapted plants still benefitted from secondary dispersers (Guimarães, Galetti, and Jordano 2008; Pires et al. 2014; Jansen et al. 2012), while others without extant dispersers (Federman et al. 2016) have experienced significant range contractions and even extinction in the wild (Kistler, Newsom, et al. 2015). We predict greater magnitude evolution 13 in fruit and seed phenotypes facilitating secondary dispersal for the megafaunal-adapted plants that have best survived this transition. Once in place, such morphological changes could have aided fruit accessibility for additional dispersers, likewise potentially affecting their evolutionary ecologies. In fact, a major component of this process in action has been documented in Brazil, where larger, seed-dispersing birds (e.g., toucans, Ramphastos dicolorus) have been locally extirpated over the past two centuries from many but not all forest fragments. Defaunation status accounted for ~34% of variance in seed size; the proportion of Euterpe edulis palm seeds with diameter >12 mm (the size limit for smaller birds) was ~32% in forests with larger birds but near 0% in defaunated sites (Galetti et al. 2013), providing a mechanism for directional selection on seed size.

Connecting past, present, and future patterns of morphological evolution in response to human behavior To explicitly compare the modern biology and archaeological observations of non-human morphological evolution in response to human behavior we computed the ‘darwin’ statistic as an estimate of the rate of evolutionary change (Haldane 1949) for each suitable example depicted in Figures 1-2 and 1-4. darwins reflect the magnitude of morphological change per million years. Overall, the observed rates of morphological evolution in natural (non-domestic taxa) modern systems are substantially higher than those from the archaeological record. Strikingly, present-day human behavior can apparently effect morphological evolution in non- human, non-domesticated species at rates similar to or greater than those associated with longer-term domestication processes. The substantially higher evolutionary rates observed in the modern vs. archaeological (non-domestication) records may reflect recently increased intensities of the human behaviors that are driving morphological evolution in non-human taxa, perhaps as byproducts of rapid human global population growth and commensurate industrialization. We do not believe that smaller-magnitude morphological changes are not also occurring in the present day – on the contrary, there are likely many, many ongoing processes that are more subtle and simply below the detection resolution of modern evolutionary biology studies. In contrast, from the archaeological record we would expect considerable power to detect non-human evolutionary responses to human behavior for slower but longer-lasting phenotypic responses to relatively less intensive human activities compared to the examples from modern biology (O’Dea et al. 2014). While it is possible that faster rates of evolutionary change may be less visible in lower resolution (incomplete) archaeological records, a temporal increase in the intensity of relevant human impacts on the environment is the most likely explanation for the observed difference between the two datasets. Looking forward, the intensity of anthropogenic impacts on the evolutionary biology of non-human species will likely continue to increase, including via additional types of human behaviors and impact. For example, we have entered a controversial era of human-directed genetic modification of non-human taxa that includes realized opportunities to effect morphological changes in existing species (Smith et al. 2010; Prado et al. 2014) and the potential to resurrect versions of extinct taxa (Richmond, Sinding, and Gilbert 2016). In particular, rapid climate change is an ongoing consequence of human behavior that can affect non-human evolution (Jump and Peñuelas 2005; Hoffmann and Sgrò 2011). To date, modern biology studies have been challenged to distinguish genetic-based morphological adaptations in response to human-induced climate change from environmental-based (plastic) responses (Merilä and Hendry 2014). Yet based on the global scale of climate change, we predict that an unprecedented number of non-human species (that are not otherwise driven to extinction) could eventually exhibit signs of morphological adaptation to this particular form of human- mediated ecological disturbance.

14 Conclusion Humans are keystone species; our pervasive habitat-modifying and subsistence behaviors have widespread ecosystem effects, including on the evolutionary biology of non-human species. Not restricted to the present, the keystone status of humans and the substantial ecological impacts of human behavior extends at least 50,000 years into prehistory, with associated long-term implications for the evolutionary biology of many non-human taxa. Archaeological and paleontological studies that identify non-human morphological evolutionary responses to past human behavior – the interpretation of which can be informed by studies of these evolutionary processes in modern systems and vice versa – are thus helping us to more comprehensively reconstruct the history and significance of anthropogenic impacts on worldwide ecosystems.

15 CHAPTER 2. Potential evolutionary body size reduction in a Malagasy primate (Propithecus verreauxi) in response to human size-selective hunting pressure

Alexis P. Sullivan1*, Laurie R. Godfrey3, Richard R. Lawler4, Heritiana Randrianatoandro5, Laurie Eccles2, Brendan Culleton6, Timothy M. Ryan2, George H. Perry1,2,7*

Departments of 1Biology and 2Anthropology, Pennsylvania State University, University Park, PA, 16802, USA 3Department of Anthropology, University of Massachusetts, Amherst, MA, 01003, USA 4Department of Sociology and Anthropology, James Madison University, Harrisonburg, VA, 22807, USA 5Department of Paleontology and Biological Anthropology, University of Antananarivo, 101, Madagascar 6Institutes of Energy and the Environment, Pennsylvania State University, University Park, PA, 16802, USA 7Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, 16802, USA

*Addresses for correspondence: [email protected]; [email protected]

Published in bioRxiv, doi: 10.1101/2020.03.23.004234

Abstract The Holocene arrival of humans on Madagascar precipitated major changes to the island’s biodiversity. The now-extinct, endemic “subfossil” megafauna of Madagascar were likely hunted by the island’s early human inhabitants. Perhaps in part due to preferential hunting of larger prey, no surviving species on Madagascar is larger than 10 kg. Moreover, some subfossil bones of extant lemurs are considerably larger than those of the modern members of their species, but due to locale differences it is uncertain whether these size differences reflect in situ change or pre-existing ecogeographic variation. Here, we used high-resolution 3D scan data to conduct comparative morphological analyses of subfossil and modern skeletal remains of one of the larger extant lemurs, Verreaux’s sifakas (Propithecus verreauxi) from subfossil and modern sites only ~10 km apart: Taolambiby (bones dated to 725-560 – 1075- 955 cal. years before present) and Beza Mahafaly Special Reserve, respectively. We found that the average subfossil sifaka bone (n=12) is 9% larger than that of modern sifakas (n=31 individuals; permutation test p=0.037). While we cannot yet conclude whether this size difference reflects evolutionary change or an archaeological aggregation/taphonomic process, if this is a case of phyletic dwarfism in response to human size-selective harvesting pressures then the estimated rate of change is slightly higher than those previously calculated for other archaeological cases of this phenomenon.

16 Introduction Intensive human behaviors including harvesting and predation, landscape modification, and translocation have effected non-human morphological evolution for at least tens of thousands of years (Sullivan, Bird, and Perry 2017). For example, size-selective hunting pressure by humans has resulted in documented body size or feature reduction for many different non- human taxa (Fenberg and Roy 2008; Darimont et al. 2009), from aquatic invertebrates and vertebrates such as snails (O’Dea et al. 2014; Roy et al. 2003) and salmon (Allendorf and Hard 2009; Ricker 1981) to terrestrial mammals like bighorn sheep (Pelletier, Festa-Bianchet, and Jorgenson 2012; Coltman et al. 2003). For terrestrial taxa, assessments of body size diminution due to human hunting pressures have been largely restricted to ungulates (Fenberg and Roy 2008; Darimont et al. 2009); no such process has yet been recorded for a non-human primate species. The Malagasy megafauna were comprised of at least 28 large-bodied species from ~11 kg to ~650 kg in size (Hansford and Turvey 2018), including at least 17 lemurs (primates), the largest of which had an estimated body mass of ~160 kg (Godfrey and Jungers 2003; Jungers, Demes, and Godfrey 2008). The timing of human arrival and permanent residence on Madagascar is uncertain (Dewar et al. 2013; Anderson et al. 2018) but may extend to ~10,500 years BP or earlier (Douglass et al. 2019; Hansford et al. 2018; Godfrey et al. 2019). There are reports of intentional human processing marks on lemur bones in southwest Madagascar dating to ~2,300 years BP (Burney et al. 2004; Perez et al. 2005; Godfrey and Jungers 2003). Human harvesting pressures, perhaps including preferential hunting of larger animals, have often been discussed as potential contributing factors in population declines and eventual extinctions of the island’s megafauna (Burney et al. 2004; Dewar 1984; Hixon et al. 2018; Kistler, Ratan, et al. 2015; Godfrey and Irwin 2007). Now, no surviving endemic terrestrial vertebrate species on Madagascar has a body mass larger than 10 kg (Crowley 2010). On Madagascar, subfossil bones of still-living lemur species recovered from archaeological and paleontological contexts are often considerably larger than those of their modern counterparts (Perez et al. 2005; Muldoon and Simons 2007; Godfrey et al. 1999; Albrecht, Jenkins, and Godfrey 1990). Godfrey et al. (1999) noted that there are many subfossil sites in the southwest with Propithecus verreauxi bones that are both more robust and longer than modern Propithecus in the same region. Indeed Lamberton (1939) had assigned a new species nomen, Propithecus verreauxoides, to subfossil specimens from Tsirave (south central Madagascar) because of its larger sizes for major skull measurements and long bone lengths relative to those of modern Propithecus verreauxi. Muldoon and Simons (2007) reported a similar size disparity between Lepilemur subfossils from Ankilitelo and extant Lepilemur from the same general region, but they also noted that prehistoric- extant size differences could simply reflect ecogeographic variation rather than recent body size evolution. For our present study, we identified an opportunity to investigate a potential case of non- human primate phyletic dwarfism due to human hunting pressures without the complicating factor of large geographic distance between where the compared subfossil and modern individuals lived. Specifically, we report the results from a morphological analysis of Verreaux’s sifaka (Propithecus verreauxi) postcranial skeletal remains from the subfossil Taolambiby site and the Beza Mahafaly Special Reserve, located ~10 km apart (Figure 2-1), to test the hypothesis that P. verreauxi body size decreased following the earliest appearance of cut-marked bones.

Materials and Methods Osteological Collections. We surveyed the osteological collections from two sites in southwestern Madagascar in an attempt to characterize a body size diminution in local Propithecus verreauxi over time. P. verreauxi has an adult body mass ranging from ~2.5 – 3.5 kg (Richard et al. 2000, 2002), and has historically been hunted by humans across multiple parts of its range (Razafimanahaka et al. 2012; Randrianandrianina, Racey, and Jenkins 2010; Gardner and Davies 2014). Specifically, more than 250 P. verreauxi skeletal 17 and craniodental elements were surface-collected from along the Taolambiby village river wash by Alan Walker in 1966 (Godfrey et al. 2019; Perez et al. 2005; Figure 2-1). This Taolambiby collection was donated to the Anthropological Primate Collection at the University of Massachusetts, Amherst (UM-TAO). Cut marks, chop marks, and/or spiral fractures indicative of human processing were identified on 62% of the P. verreauxi elements (Perez et al. 2005). The Taolambiby subfossil material is fragmentary; from the larger collection available to us there were a total of 15 elements that could be identified confidently as adult P. verreauxi femora and humeri that we included in our study. Immediately adjacent to this subfossil site/collection is the Beza Mahafaly Special Reserve (BMSR; Figure 2-1), which is home to an extant sifaka population that has been monitored and studied since 1984 (Sussman et al. 2012). In the first 25 years of BMSR collection and management of sifaka long-term data, 718 individuals have been captured, measured, and marked; now there are >900 individuals (Sussman et al. 2012). Since 1985, researchers have also been collecting and labeling faunal skeletal remains discovered in the course of observation or survey (Brockman et al. 2008). The resulting Beza Mahafaly Osteological Collection (BMOC) is comprised of skeletal elements from all four extent lemur species living in the area, along with invasive wildcats. P. verreauxi was represented in this collection by the remains of 31 adult individuals at the time of our data collection (see Supplementary Table 2-1 for full list of individuals).

Figure 2-1: Propithecus verreauxi osteological collection sites. Verreaux’s sifaka range data in green from IUCN Red List database [species 18354], last updated 2014. Subfossil osteological materials were collected from Taolambiby (T, tan dot), and modern remains from the Beza Mahafaly Special Reserve (B, brown dot).

Data Collection. We focused on femora and humeri because there is a strong positive correlation between measurements from these long bones and overall body size in Propithecus lemurs (Godfrey et al. 1995). Juvenile specimens, identified by the presence of an epiphyseal line or an unfused epiphysis (Egi 2001), were excluded from our analysis. Of the modern BMOC adults (n=31 total), sex was known for eight individuals (26%; n=4 males, n=4 females) based on the association of their remains with collars and/or other identifying features (see Supplementary Table 2-1). Since sifakas are not sexually dimorphic (Jenkins and Albrecht 1991; Kappeler 1991; Richard et al. 2000; Lawler 2009), male and female adults are expected to have approximately similar body sizes and sex was not included as a variable in our analyses. We collected 3D surface data from every adult P. verreauxi long bone that was available in each collection, fragmented or whole, right or left, with a portable Artec Space Spider (Artec 3D, Luxembourg; Figure 2-2; Table 2-1). All data collection at the Beza Mahafaly Special Reserve was approved by the Madagascar National Parks organization. The Spider scanner records high-resolution geometry and texture data at up to 0.1 mm resolution and 3D point accuracy up to 0.5 mm. Each of the 106 adult skeletal elements from these 18 collections (n=94 modern, 15 subfossil) was affixed to a turntable, and the Artec Space Spider was used to collect between 4-8 scans from multiple angles to capture the entirety of the bone. Linear caliper measurements (n=5 for humeri, 6 for femora; Table 2-1) were also collected from every bone (see Supplementary Table 2-2).

3D Surface Scan Post-Processing. The surface scan data were post-processed with Artec Studio 11 software to form 3D models for each element. Specifically, scans were first individually cleaned with the “Eraser” function to remove the turntable and other background noise. The multiple scans of the same element were then aligned to one another with at least three points of common geometry. The aligned scan data were then registered (“Global Registration” with settings for 50 mm minimal distance and 5000 iterations) and fused (“Fusion”: default “Outlier Removal” and “Sharp Fusion” settings with “Watertight” 0.3 mm resolution). 3D meshes of each element were then exported and individually measured as indicated in Table 1 and Figure 2 with Avizo 9.4 software (see Supplementary Table 2-3). We measured maximum length, midshaft diameters and circumferences for all whole femora, as well as femoral head heights, widths, and surface areas, bi-epicondylar breadth, and condylar widths and surface areas when available (Egi 2001; White, Black, and Folkens 2012; Godfrey et al. 1995). We measured maximum length and midshaft diameters for all whole humeri, as well as humeral head diameters, widths, and surface areas, and bi-epicondylar breadth when available (Egi 2001; White, Black, and Folkens 2012; Godfrey et al. 1995). Surface areas were determined by isolating the osteological region of interest from the rest of the bone, then using the “Materials Surface Area Statistics” tool available in Avizo. All 3D models are available on MorphoSource (“Sullivan/Perry Lab Propithecus verreauxi Surface Scans” Project ID 698). 19

Figure 2-2: Examples of osteological elements of P. verreauxi and the measurements collected for this study. Descriptions of each measurement are available in Table 2-1. (A) Two subfossil elements from Taolambiby. The humeral head (UM-TAO-66-2) was radiocarbon dated to ~1000 BP and the distal femur (UM-TAO-66-32) was dated to ~740 BP. Diagram of measurements collected from (B) humeri and (C) femora (distal condyle widths not pictured; example humerus and femur from BMOC-020).

20 Table 2-1: Morphometric caliper and 3D surface scan measurement descriptions. Number of Elements Measured 3D Scanner Radial Caliper Modern Subfossil Modern Subfossil Measurement Measurement Right Left R L R L R L Description Maximum length measured between top of humeral head MHL 18 22 0 0 16 19 0 0 and most distant point on distal humerus Maximum midshaft humeral MHD1 diameter, measured just 23 23 0 0 23 20 0 0 below deltoid Minimum midshaft humeral MHD2

diameter, measured just 23 23 0 0 NA NA NA NA [3D only] below deltoid Vertical head diameter, VHD 21 23 1 0 20 20 1 0 superoinferior diameter Humeral head width, external Humerus HHW transverse mediolateral 21 23 1 0 20 20 1 0 diameter Biepicondylar breadth, greatest distance between BBH medial and lateral 22 23 0 0 21 20 0 0 epicondyles, parallel to humeral shaft HHSA [3D Avizo-calculated humeral 21 23 1 0 NA NA NA NA only] head surface area Maximum length that can be measured between the top of MFL the greater trochanter and 20 19 0 0 21 18 0 0 bottom of the most distal condyle Anteroposterior (sagittal) MFD 23 24 0 0 24 24 0 0 midshaft diameter FMD Mediolateral (transverse) 23 24 0 0 NA NA NA NA [3D only] midshaft diameter MFC Midshaft circumference NA NA NA NA 23 24 0 0 [caliper only]

Femoral head height, FHH 24 20 2 2 24 20 2 2 superoinferior diameter Femoral head width, FHW 24 20 2 2 24 20 2 2 anteroposterior diameter Femur Femoral biepicondylar breadth, distance between FBE 23 21 2 1 23 21 1 2 medial-most and lateral-most points on epicondyles DML1 Width of medial distal 23 21 0 2 NA NA NA NA [3D only] condyle DML2 Width of lateral distal condyle 23 21 4 3 NA NA NA NA [3D only] FHSA Avizo-calculated femoral head 24 20 2 2 NA NA NA NA [3D only] surface area DSA Avizo-calculated condylar 23 21 0 1 NA NA NA NA [3D only] surface area Note: Unless specified, each measurement was collected with both linear calipers and 3D scan data.

AMS Radiocarbon Dating. It was important to establish the antiquity of each subfossil element included in our analysis with radiocarbon 14C dating methods. Of 18 Taolambiby P. verreauxi skeletal elements dated in a prior study, one (5.6%) was modern and the remaining 17 (94.4%) ranged from 605 to 1185 cal BP (Crowley and Godfrey 2013; Supplementary Table 2-3). Of those 17 previously dated, non-modern specimens, one was a proximal humerus and seven were femora. The humeral fragment and five of the femoral fragments were sufficiently intact for us to obtain at least one size measurement per bone (Table 2-2). 21 Nine additional (not previously radiocarbon dated) adult P. verreauxi femora from Taolambiby were also available for possible inclusion in our analysis. For each of these specimens, we first collected all measurements and 3D surface scan data before sampling 200- 500 mg from the subfossil skeletal remains for AMS radiocarbon 14C dating and stable isotope analyses at the Penn State University Human Paleoecology and Isotope Geochemistry Laboratory. The bone samples were scraped with blades to remove adhering material and clipped into small pieces. As a precaution, we removed possible conservants and adhesives by sonicating the scraped bone samples in washes of ACS-grade methanol, acetone, and dichloromethane for 20 minutes each at room temperature. Bone collagen was extracted and purified after sonication. Samples were demineralized for 1-3 days in 0.5N hydrochloric acid at 5°C. The demineralized pseudomorph was rinsed twice in 18.2Ω/cm Nanopure water for 20 minutes. The pseudomorph was gelatinized for 10 hours at 60°C in 0.01 Normal hydrochloric acid. The resulting gel was then lyophilized and weighed to determine percent yield as a first evaluation of the degree of bone collagen preservation. In this case the collagen samples were relatively poorly preserved and so they were pretreated using a modified XAD process (Stafford, Brendel, and Duhamel 1988; Stafford et al. 1991) after demineralization and gelatinization. The gelatin was hydrolyzed in 1.5 mL of 6 Normal hydrochloric acid for 24 hours at 110°C. Supelco ENVI-Chrome P SPE (Solid Phase Extraction) columns were fitted with a Millex HV PVDF 0.45 µm filter unit, and both were equilibrated with 50 mL of 6 Normal hydrochloric acid. The 1.5 mL sample hydrolyzate was pipetted into the SPE column and driven through with a syringe and an additional 10 mL of 6 N hydrochloric acid dropwise into a prepared 20 mm culture tube. The hydrolyzate (now bone collagen amino acids) was dried into a viscous syrup by passing UHP nitrogen gas over the heated (50°C) sample for about 8 hours. The XAD amino acids were analyzed for carbon and nitrogen concentrations and stable isotope ratios at the Yale Analytical and Stable Isotope Center with a Costech elemental analyzer (ECS 4010) and Thermo DeltaPlus isotope ratio mass spectrometer. Sample quality was evaluated by %C, %N, and the C:N ratio before AMS 14C dating. Good quality amino acid samples were then weighed (3.5-4.5 mg) into 8” quartz tubes, with 60 mg CuO and a ~2mm snip of 1mm diameter 99.9% silver wire, then sealed under vacuum and combusted at 800°C for 3 hours. The resulting CO2 was reduced to graphite at 550°C using UHP hydrogen gas and an iron catalyst, with the reaction water removed by magnesium perchlorate (Mg(ClO4)2). Graphite samples were pressed into targets and loaded onto a target wheel with oxalic acid (OXII) primary standards, known age bone secondaries and 14C free Pleistocene whale blank, and measured on a modified NEC 1.5SDH-1 500kV compact accelerator mass spectrometer housed in the Penn State Earth and Environmental Sustainability Laboratories. Three subfossil elements, UM-TAO-66-22, UM-TAO-66-28, and an unaccessioned femoral head, had insufficient remaining collagen for either isotopic or radiocarbon analysis, and thus were not included in our subsequent morphological analyses. Two samples were run on the AMS but not assigned PSUAMS lab codes, and should be considered provisional. C and N abundance and isotope ratios were unavailable for UM-TAO-66-31, and C:N ratio for UM- TAO-66-32 was 3.64, at the limits of the acceptable range. AMS radiocarbon results from this study and previous dates by Crowley and Godrey (Crowley and Godfrey 2013) were calibrated with OxCal v. 4.3.2 (Bronk Ramsey 2009) using the SHCal13 southern hemisphere curve (Hogg et al. 2013) and are presented in Table 2-2.

22 Table 2-2: Subfossil P. verreauxi elements that were included in this study and their corresponding radiocarbon dates. Element Human Calibrated Conventional Sample ID (Side, Processing 14C Lab ID 95.4% CI Reference 14C Age (BP) End) Mark(s) (cal. BP) UM-TAO- Femur (R, PSUAMS- Chop 1010 ± 15 925 - 800 This study 66-26 D) 2480 UM-TAO- Femur (R, PSUAMS- Chop 865 ± 20 770 - 680 This study 66-29 D) 2479 UM-TAO- Femur (R, Chop N/A* 740 ± 40 725 - 560 This study 66-32 D) UM-TAO- Femur (R, PSUAMS- Chop 975 ± 20 920 - 790 This study 66-33 D) 2477 UM-TAO- Femur (L, Chop N/A* 850 ± 35 775 - 670 This study 66-31 D) Chop, slice UM-TAO- Femur (L, PSUAMS- through 840 ± 20 740 - 675 This study 66-34 D) 2476 condyles (Crowley UM-TAO- Humerus Chop, cut CAMS- 1035 ± 25 960 - 800 and Godfrey 66-2 (R, P) marks 147111 2013) (Crowley UM-TAO- Femur (R, CAMS- Chop 1130 ± 30 1060 - 930 and Godfrey 66-21 P) 147036 2013) (Crowley UM-TAO- Femur (L, CAMS- Chop 1055 ± 30 965 - 805 and Godfrey 66-23 P) 147628 2013) (Crowley UM-TAO- Femur (L, CAMS- Chop 940 ± 40 915 - 730 and Godfrey 66-24 P) 147114 2013) (Crowley UM-TAO- Femur (R, CAMS- Chop 915 ± 35 905 - 685 and Godfrey 66-25 P) 147113 2013) (Crowley UM-TAO- Femur (L, CAMS- Chop 1165 ± 30 1075 - 955 and Godfrey 66-30 D) 147332 2013) *UM-TAO-66-31 and -32 were not assigned PSUAMS-# because there is no EA-IRMS data (-31) or poor C:N ratio (-32).

Correlations Between 3D Scan and Caliper Measurements. We directly compared the equivalent raw caliper and 3D scan measurements to each other as an accuracy check for each measurement technique. For every available measurement category (MFL, MFD, FHH, FHW, FBE; MHL, MHD1, VHD, HHW, BBH) we calculated the percent difference between the corresponding caliper measurement and 3D scan measurement taken for each individual skeletal element:

������� − 3� % ���������� = ∗ 100 (������� + 3�)/2

We determined that, on average, the caliper measurements were 0.68% smaller than the equivalent 3D measurements for the modern skeletal remains and 1.20% larger for the subfossil elements (Supplementary Table 2-4; see Supplementary Table 2-5 for all caliper versus 3D measurement comparisons). This difference is likely due to difficulties in identifying measurement landmarks with the calipers on the more broken subfossil materials. With the advantages of increased maximum/minimum measurement accuracy and surface area calculation tools, more 3D model measurements were able to be collected from the fragmented modern and subfossil materials relative to those with the caliper: 10 femoral 3D measurements versus 6 with caliper, and 7 humeral 3D versus 5 with caliper. For some bones, only 3D digital measurement was possible. Therefore, all results presented here are based on the 3D model measurements to increase the number of specimens included in the analyses. 23

3D Surface Scan Measurement Analyses. The limited number of 3D surface scan measurements that could be taken on each subfossil bone precluded our ability to directly estimate individual body sizes. Therefore, we compared the individual bone measurements to each other. To do so, for each measurement we first calculated the geometric mean (geoMean, GM; (Gordon, Green, and Richmond 2008)) from all available modern individuals for that measurement (Table 2-3, Step A). These geoMeans were then used to calculate a relative fold change (FC) value for each individual modern and subfossil skeletal element (Table 2-3, Step B). As an example, the geoMean of the modern right femoral head height (FHH) from the 3D surface scan data was 12.37 mm. The FHH measurements for modern BMOC-001 was 12.21 mm and for subfossil UM-TAO-66-25 was 12.90 mm. The FC for each of these individuals was calculated from the 12.37 mm GM: -0.01 and 0.04, respectively. Then, all of the FC values available for each separate sided bone (z) were arithmetically averaged (A; Table 2-3, Step C). Finally, for each modern individual an aggregate score (AS) was calculated as the mean of the averages from each element available for that individual (up to four; i.e. right and left, humeri and femora; Table 2-3, Step D). For the subfossil remains, the per-bone average FC value is the same as the individual AS, because potential individual- associations among any of the different subfossil elements are unknown.

Table 2-3: Measurement analysis calculations and examples. Step Variable Description(s) Formula A. geoMean of each measurement, �� where x = measurement �� calculated from the modern type samples (ex: FHH) = � ∙ � ∙ … ∙ � B. For each individual bone and �� where y = individual � element, fold change from �� = − 1 �� geoMean (ex: BMOC001) C. Average fold change for each � where z = sided element �� + ⋯ + �� � = skeletal element (ex: right femur) � D. Aggregate per-individual fold-

change scores from all available � + � + ⋯ + � �� �� = skeletal elements for that � individual

As an example, modern individual BMOC-001 had two complete femora and humeri, and all measurements were collected. The fold change difference was calculated for each of BMOC-001’s right femoral measurements, and the average of those fold changes was calculated to be -0.005. A negative average fold change (A) indicates that the right femoral bones of BMOC-001 were slightly smaller than the right femoral geoMean of the modern population of Beza Mahafaly lemurs. Each of the A values for BMOC-001’s two femora and humeri were averaged for a final aggregate score (AS). A positive AS of 0.003 indicates that the bones of BMOC-001 were slightly larger than the average AS of the modern population of Beza Mahafaly lemurs. Subfossil element UM-TAO-66-21 had only three measurements taken, and the AS of the fold changes for those three measurements was 0.142, indicating an element larger than BMOC-001 as well as the average AS of the modern population. We conducted two randomized subsampling permutation analyses to determine where the subfossil dataset’s average aggregate score would fall against distributions generated from the same amount of modern measurement data. One permutation analysis was conducted with the full n=12 subfossil elements treated as separate individuals and the other by analyzing only the skeletal element and side with the best representation in the dataset, or the minimum number of individuals (right distal femur; MNI n=4). A random subset of the modern data was partitioned to mimic those available for the subfossil individuals. In the case of the MNI group, this meant two right femoral FBE measurements and four DML2 measurements. The GM, FC, and AS were calculated from this subset of modern measurements, as described above, and then the entire subset procedure was repeated 10,000 times for comparison with 24 the average aggregate scores of the respective subfossil groupings. All code developed and used for this project is available in the GitHub repository https://github.com/AlexisPSullivan/Sifaka.

Results We compared long bone measurements as a proxy for body size between Propithecus verreauxi skeletal remains from the Taolambiby subfossil site (725-560 – 1075-955 calibrated 95.4% CI years before present) to those collected from modern individuals of the same species at the nearby Beza Mahafaly Special Reserve (<10km from Taolambiby) to evaluate whether this population experienced body size diminution since the first evidence of human hunting in the area. If so, then this result would be consistent with the hypothesis that human size- selective hunting pressures may have driven phenotypic evolutionary change in Madagascar’s surviving fauna. When comparing subfossil and modern specimens, we treated the entire scanned collection (n=12) as our maximum number of individuals (MAX, Figure 2-3A), and the four subfossil right distal femoral fragments (UM-TAO-66-26, -29, -32, -33) as our minimum number of individuals (MNI, Figure 2-4A). We calculated the fold-change difference between the measurements of each bone (subfossil) or individual (modern) and the geoMean of the modern population (Figures 2-3B; Figure 2-4B).

25

Figure 2-3: Our comparative morphological results for the maximum number of subfossil individuals (MAX). A: Each subfossil sample is separately colored to demonstrate progression through our pipeline. B: Fold-changes for both modern and subfossil 3D surface scan measurements for which there any subfossil data points are available. See Table 2-1 for the description of individual measurements. C: Aggregate scores for each subfossil skeletal element and modern individual. D: Permutation analysis depicting the distribution of average aggregate scores calculated from 10,000 subsets of modern measurements randomly selected to match the sample sizes of the MAX subfossil dataset. The actual average aggregate value (0.089) for the MAX subfossil sample is shown with a red circle. The indicated empirical p-value (p=0.037) represents the proportion of permuted modern values equal or greater to the actual subfossil value. 26 Figure 2-4: Comparative morphological results for the minimum number of subfossil individuals (MNI). A: Each subfossil sample is separately colored to demonstrate progression through our pipeline. B: Fold-changes for those modern and subfossil 3D surface scan measurements for which there are any subfossil data points. See Table 2-1 for the description of individual measurements. C: Aggregate scores for each subfossil and modern individual. D: Permutation analysis depicting the distribution of average aggregate scores calculated from 10,000 subsets of modern measurements randomly selected to match the sample sizes of the MNI subfossil dataset. The actual average aggregate value (0.103) for the MNI subfossil sample is shown with a red circle. The indicated empirical p-value (p=0.046) represents the proportion of permuted modern values equal or greater to the actual subfossil value. 27 As described in the Materials and Methods, each of the fold-change averages was arithmetically averaged for each individual across every element available for that individual to create an aggregate score (AS) that we used to directly compare modern and subfossil individuals to each other (Figure 2-3C; Figure 2-4C; Supplementary Table 2-6). The mean aggregate score for all subfossil elements (MAX; 0.089±0.117) is significantly greater than that for the modern individuals (0.009±0.045; Welch two-sample t-test; p=0.039; Figure 2-3C). With only four right distal subfossil femora, the mean aggregate score for subfossil MNI (0.103±0.107) was not significantly different than that for the modern individuals with a t-test (0.002±0.051; p=0.153; Figure 2-4C). We further used a permutation scheme to test the null hypothesis of no size difference between the modern and subfossil populations. Specifically, for each of the MAX and MNI comparisons we selected a random subset of the modern data to match the number of specimens and measurement types of the subfossil dataset, computed the aggregate score for that permuted modern dataset, and repeated that process 10,000 times. To compute empirical p-values, the observed subfossil aggregate scores were compared to the distributions of permuted results, with significant differences for both MAX (Figure 2-3D; p=0.037) and MNI (Figure 2-4D; p=0.046).

Discussion This work represents the first systematic assessment of the potential evolutionary effects of human size-selective hunting pressures on body size in a non-human primate. Using skeletal remains of both modern and subfossil P. verreauxi individuals from the same region of Southwest Madagascar and a high-resolution 3D surface scanning-based approach, we found that archaeological (725-560 – 1075-955 cal. years before present) body size-associated skeletal measurements were significantly larger than those of the modern sample. While this result is consistent with the hypothesis of recent phyletic dwarfism in response to size-selective hunting pressures by humans, our finding alone does not necessarily demonstrate a history of adaptive evolution for smaller body sizes in this population. As an alternative explanation, the archaeological sample could be biased by assemblage and/or taphonomic processes (Miller et al. 2014). For example, if past people were preferentially hunting larger sifaka with projectiles such as slingshots or blowguns (Dunham et al. 2008; S. H. Lehman and Ratsimbazafy 2001), then individuals who ended up in the archaeological sample may have been larger than the average for the overall population at the time. Additionally, larger bones may have been more likely to be preserved in the Taolambiby wash. The combination of expanded archaeological sampling and evolutionary genomic analyses with knowledge of P. verreauxi body size-associated alleles may ultimately be necessary to distinguish between the evolutionary vs. assemblage/taphonomic scenarios. Shifts in climate have been implicated in causing phyletic size increases/decreases in vertebrates, particularly on islands. We feel that climate change is a less parsimonious explanation for our results for the following two reasons. First, in an extensive examination of body size evolution in lemurs, Kamilar et al. (2012) note that, "diet and climate variables were weak predictors of lemur body mass," which suggests that the influence of climate on lemur body sizes are minimal. That said, Lehman, Mayor, and Wright (2005) suggested that body size evolution is negatively related to increasing resource seasonality, with smallest body sizes associated with increasing rainfall seasonality. Propithecus verreauxi inhabits a seasonal and unpredictable rainfall environment (Lawler et al. 2009). In fact, Madagascar's rainfall climate has been described as "hypervariable" and it selects for extreme "fastness" or "slowness" of life history schedules (Dewar and Richard 2007). A strong case for extreme "slowness" in sifaka is made by Richard et al. (2002) and Lawler et al. (2009), who show that P. verreauxi are characterized by long generation times, long lifespans, delayed maturation, and long reproductive careers; the evolution of this suite of life history traits is typically associated with increases in body size, not decreases (e.g., Promislow and Harvey 1990). Second, regarding phyletic size changes in body mass on islands, the general pattern is that small bodied animals get larger (island gigantism) and large-bodied animals get smaller (island dwarfism; Lomolino 28 et al. 2012). Sifakas are small-bodied folivores, and they do not show any patterns of dwarfism that characterize a history of body size reduction, such as increased litter size, molar simplification, molar reduction, and negative allometric trends of the skull and teeth (Marshall and Corruccini 1978; Ford 1980). If the inferred body size difference between the archaeological and modern P. verreauxi samples does reflect an evolutionary process in response to human size-selective hunting behavior, then it is of interest to compare the estimated rate of that evolutionary change to previous observations for other archaeological cases of this phenomenon. We estimated an evolutionary rate of 156 darwins, or the magnitude of morphological change (absolute value of the difference between the natural log of the starting trait value and the natural log of the ending trait value) per million years (Haldane 1949) using our data from the right femur width of lateral distal condyle (n=4 subfossil individuals, mean width=7.76 mm, average 771 cal. years BP, calculated with midpoints of 95.4% CI cal. years BP; n=23 modern individuals, mean width=6.88 mm). Using these values along with the P. verreauxi cohort generation time (average between the birth of a female and the birth of her daughters) of 18.5 years (Morris et al. 2011; Lawler et al. 2009), we also estimated a Haldane (H) evolutionary rate of -0.039 H, which represents the rate of change in standard deviations per generation (Gingerich 1993). While H is likely a more meaningful statistic, only darwin estimates are available for direct comparison with other taxa. Compared to seven previously-analyzed archaeological examples of purported morphological change in response to human behavior (darwin mean=21.4; range = 1-72; (Sullivan, Bird, and Perry 2017) the rate of 156 darwins for P. verreauxi is at least ~2x and an average of 7.3x greater. Our MNI and MAX analyses indicated that the larger average size of the subfossil P. verreauxi bones is not likely due to chance, and the average subfossil bone is about 9% larger than the modern sifaka bones. The Verreaux’s sifakas of 1,000 years ago might have been up to a pound larger than those living at Beza Mahafaly today. Several hundreds of years of human size-selective hunting pressures might have contributed to a smaller body size in this population of lemurs, but we cannot exclude the possibility of taphonomic bias in our archaeological sample.

Data, Code, and Materials All Artec Space Spider 3D surface scan data (.ply models) for the modern and subfossil Propithecus verreauxi individuals are available on MorphoSource (“Sullivan/Perry Lab Propithecus verreauxi Surface Scans” Project ID 698). All code developed and used for this research work are stored in GitHub (https://github.com/AlexisPSullivan/Sifaka) and have been archived within the Zenodo repository (http://doi.org/10.5281/zenodo.3765932).

29 CHAPTER 3. Genomic analysis of eastern fence lizard (Sceloporus undulatus) morphological adaptations to human-mediated fire ant invasion

Alexis P. Sullivan1*, Christina Bergey1,2,3, Matthew J. Miller1, Tracy Langkilde1, George H. Perry1,2,4*

Departments of Biology1 and Anthropology2, Pennsylvania State University, University Park, PA, USA 3Department of Genetics, Rutgers University 4Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA

* Addresses for correspondence: [email protected]; [email protected]

Abstract Humans accidentally introduced red imported fire ants (Solenopsis invicta) into the southeastern United States in the 1930s through the shipping port at Mobile, Alabama. As the fire ant range expanded, fence lizard (Sceloporus undulatus) populations were exposed to fire ant swarming and predation, some for more than 70 years (~42 generations). Adult fence lizards at sites with the longest times since fire ant invasion have 3.4% longer hind limbs and higher rates of body twitching and fleeing behavioral responses to fire ant attack compared to lizards at not-yet invaded sites. These traits are significantly heritable and hind limb differences are inconsistent with expected patterns of ecogeographic variation based on museum specimens collected prior to fire ant invasion, which suggests a recent history of genetic adaptation. Here, we investigate the genetic basis of fence lizard morphological traits hypothesized to represent adaptations to predation by invasive fire ants. We present a genome-wide association study conducted in 377 individuals from Solon Dixon Forestry Education Center, Alabama. Our preliminary results indicate that the top 0.01% of variable sites are located near genes related to cell development and morphogenesis and neurogenesis (FDR p = 1.06E-8, 4.46E-6, and 1.45E-8 respectively), though further testing is needed to rule out confounding familial relationships and chromosomal edge effects. We also tested whether the patterns of limb length trait variation were driven by positive natural selection using several population-level evolutionary analyses. Initial Fst results indicate enrichment in genes related to synaptic signaling and transmission in lizards from the Alabama population compared to a population in Arkansas (FDR p = 0.038), and no functionally enriched gene differences between the Alabama and Tennessee populations. Though our Fst analysis suggests that our two outgroup populations are more genetically differentiated from each other than to the Alabama population, our windowed-scans across the genome indicated increased differentiation signals in the Alabama-outgroup comparisons. Both our LASSI T and nucleotide diversity π detected a potential sweep for selection in Chromosome 10 that could be linked to the MYO9B gene and muscular motor activity. The MYO9B gene was also one of the most significant SNP associations in our limb length GWAS. Further investigations will include testing for mapping and SNP-calling errors to ensure that these signatures are not due to false positives.

30 Introduction Human behaviors such as global travel and trade have been associated with the widespread translocation of thousands of non-human species. These translocated taxa evolve in response to their new ecosystems, and the behavioral ecology and physical attributes of endemic taxa may evolve as well due to the presence of the introduced species (Sullivan, Bird, and Perry 2017; Mooney and Cleland 2001; Colautti and Lau 2015). One such translocation event in the United States occurred via the port city of Mobile, Alabama: humans accidentally introduced two invasive species of fire ants (black: Solenopsis richteri, red: S. invicta) in the 1920s and 1930s respectively (Callcott and Collins 1996). The toxic red imported fire ant is particularly well-studied because of the dramatic effects its invasion has had on human health and local biodiversity (Wojcik et al. 2001). These ants are highly aggressive, and will swarm their prey in coordinated biting attacks when disturbed (Kemp et al. 2000). The aftermath of such an encounter can result in symptoms that last anywhere from a few hours to several months in humans, and can even be fatal for certain animals (Vinson 1997). Even if the ants do not directly attack an individual, their swarms can be so large that they severely limit the resources available to entire populations in a particular ecosystem, leading to dehydration, starvation, and usurpation of valuable nesting grounds (Vinson 1997). The expanding range of the invasive red imported fire ants in the southeastern United States overlaps with the species range of the native eastern fence lizard (Sceloporus undulatus; Figure 3-1). Some lizard populations in Alabama have been exposed to red imported fire ant predation for more than 80 years (~42 generations; Parker, 1994), while others in more northern and western states, such as Tennessee and Arkansas, have not yet been exposed. Where they co-occur, the venomous red imported fire ants attack and prey on the fence lizards and vice versa (Venable, Adams, and Langkilde 2019). Interestingly, adults in the Alabama populations of fence lizards have 3.4% longer relative hind limb lengths and have higher rates of body twitching and fleeing behavioral to red imported fire ant attack compared to lizards at not-yet invaded sites (Langkilde 2009, 2010). The twitching behavior is more effective in longer-limbed individuals, and increased relative hind limb length is correlated with sprint performance in fence lizards (Langkilde 2009). Morphological data from museum specimens collected prior to red imported fire ant invasion suggest a significant positive correlation between latitudes and relative hind limb length, and the increase in hind limb length of lizards in more southern states directly contrasts the museum data (Langkilde 2009). There is also indirect evidence that this limb- lengthening trait is heritable: there is a positive correlation between the hind limb lengths of juveniles and those of their mothers (Langkilde 2009). The fence lizard populations in areas of long-term exposure to red imported fire ants might have experienced recent natural selection pressure for longer hind limbs and twitching escape behaviors. To test this hypothesis, we generated deeper-coverage population-level genome sequencing data from a random selection of lizards from the Alabama site, as well as two sites that have yet to be invaded by red imported fire ants, to test whether the patterns of limb trait variation have been driven by positive natural selection (Ellegren 2014; Field et al. 2016). We utilized several tests that were developed for detecting natural selection in genomic data both within and between species (Vitti, Grossman, and Sabeti 2013; Hohenlohe, Phillips, and Cresko 2010). Wright’s fixation index (Fst, represented by values ranging from 0 to 1) is one of the most commonly used metrics for evaluating population differentiation (Vitti, Grossman, and Sabeti 2013; Holsinger and Weir 2009; Weir and Cockerham 1984). An average Fst of 1 indicates complete differentiation, i.e. differing alleles at a particular locus, between two populations at a particular locus, and an average Fst of 0 implies no population differentiation (Vitti, Grossman, and Sabeti 2013; Hohenlohe, Phillips, and Cresko 2010). The Fst statistic is also valuable at the level of the allele: increased Fst values at particular loci could indicate multiple small effect loci throughout the genome (Bhatia et al. 2013). A large average genome-wide Fst value for two populations indicates that there is an increased amount of variation between them, and could indicate non-specific directional 31 selection, though it is unclear from just this statistic which population is currently undergoing the selective pressure (Vitti, Grossman, and Sabeti 2013; Holsinger and Weir 2009). Thus we also incorporated tests for measuring selective sweeps in our analyses: nucleotide diversity (π) and a haplotype-based likelihood ratio test statistic (T) (Nei and Li 1979; Harris and DeGiorgio 2020). Sweeps tend to signify large effect loci: as these beneficial mutations undergo strong selective pressure, the alleles around them will be “swept along”, even in cases of polygenic adaptation (Höllinger, Pennings, and Hermisson 2019).

Figure 3-1: Dispersal of invasive red imported fire ants (S. invicta) since introduction in Mobile, Alabama. Red imported fire ant county habitation data are from Callcott and Collins (1996) and the United States Department of Agriculture Animal and Plant Health Inspection Services records of Imported Fire Ant Federal Quarantines (USDA APHIS eCFR 7 301.81-3). Overlay of current S. undulatus range in blue (extends into Mexico, country borders not shown; from IUCN database, last updated 2007). Fence lizard data presented in this study were generated from individuals at three sites: Solon Dixon Forestry Education Center, Alabama (AL, n=381), Edgar Evins State Park, Tennessee (TN, n=20), and St. Francis National Forest, Arkansas (AR, n=20).

Nucleotide diversity (π) is a measure of genetic variation within a population, and a decrease in nucleotide diversity at a locus could indicate a hard selective sweep due to increased frequency of a beneficial mutation in that region of the genome (Hohenlohe, Phillips, and Cresko 2010). The T statistic is capable of detecting both hard and soft sweeps, or selective signatures resulting from previously neutral standing genetic variation becoming beneficial due to an environmental change, such as new predatory pressures (Harris and DeGiorgio 2020). The T statistic also focuses on phased genomic data, where haplotypes are statistically-estimated from polymorphic genotype data, thus this test could indicate larger linked regions of the genome inherited from parent to offspring that are under natural selection (Harris and DeGiorgio 2020). If a signature of adaptation is observed in the original population in Alabama and the evolutionary mechanism is indeed adaptation to invasive red imported fire ant predation, then we would expect to not observe the same signature in the populations without fire ants. We also implemented a genome-wide association study (GWAS; Bush and Moore, 2012). GWAS are powerful tools used to scan markers across the complete sets of DNA from 32 many individuals to find genetic variants/regions associated with a particular phenotype (McCarthy et al. 2008). In humans, GWAS have identified certain loci correlated with polygenic traits like height (Yang et al. 2010; Weedon et al. 2008) and body mass index (Akiyama et al. 2017). GWAS have also been successfully applied to complex trait variation in non-model organisms, especially those of commercial importance like salmon and cattle (Johnston et al. 2014; Chen et al. 2020). With these models in mind, we sequenced the whole genomes of a large group of lizards from a site in Alabama to identify the single nucleotide polymorphisms (SNPs) that underlie the variations in relative hind limb length.

Materials and Methods Study Sites and Sample Selection. We tested the hypothesis that S. undulatus (eastern fence lizards) populations in areas of long-term exposure to S. invicta (red imported fire ants) might have experienced recent natural selection pressure for longer hind limbs and increased twitching escape behaviors by extracting and sequencing DNA from lizards that live in both fire ant-invaded and fire ant-free sites. Over the past 13 years, the Langkilde Lab at Pennsylvania State University has collected fence lizard toe and/or tail clips, as well as biometric and behavioral data, from >2,000 individuals across multiple sites in the southeastern United States (Langkilde 2009; Thawley and Langkilde 2016; Thawley et al. 2019). For our genome-wide association study, we subsampled all of the available adult fence lizard samples in the collection (n=381) from Solon Dixon Forestry Education Center, Escambia County, Alabama (AL; 31°09’49” N, 86°42’10” W; 79 years of fire ant inhabitation; Figure 3-1; Supplemental Table 3-1). We also randomly selected 20 samples collected from adult fence lizard individuals at two sites that have yet to be invaded by fire ants for population-level genomic analyses: Edgar Evins State Park, Tennessee (TN; 36°19’10” N, 85°28’32” W) and St. Francis National Forest, Lee County, Arkansas (AR; 34°43’50” N, 90°42’18” W; Figure 3-1; Supplemental Table 3-1).

DNA Extraction. All the fence lizard tissue toe and/or tail samples in the Langkilde Lab’s collection are stored in 70% ethanol at 4°C. We used up to 30 mg of the 421 preserved fence lizard tissue samples for each E.Z.N.A.® tissue kit extraction (Omega Bio-Tek, Inc., Norcross, GA, USA). DNA extractions were performed following the manufacturer’s instructions with the following exceptions: each tissue sample was ground with a polypropylene pestle in a 1.5- mL microcentrifuge tube, total digestion time was increased to 14-15 hours in a 600 rpm shaking thermomixer, 1 µL of Pellet Paint® NF Co-Precipitant was added to each sample to increase DNA adherence in the HiBind® DNA Mini Column, and the total elution volume was halved (two 50-µL portions). Each sample’s DNA extraction concentration was obtained with a Qubit® 3.0 Fluorometer dsDNA High Sensitivity Assay Kit, and then stored at -20°C until library preparation.

Library Preparation and Sequencing. Portions of each DNA extract were sheared to a target length of 500 bp with a Covaris M220 Focused-ultra sonicator (Peak Incident Power: 50, Duty Factor: 20%, Cycles per Burst: 200). Libraries for each sample were prepared from 200 ng of sheared DNA with TruSeq® Nano DNA High Throughput Library Prep Kit (Illumina Inc., San Diego, CA, USA) and IDT for Illumina – TruSeq® DNA UD Indexes (Illumina Inc., San Diego, CA, USA). The libraries were pooled and sequenced with a paired-end 150 bp strategy on two Illumina NovaSeq 6000 S4 flowcells for 1.3 T of paired-end raw read data each. One pool (HC- 60) had 20 randomly selected AL individuals as well as the 20 lizards from the uninvaded sites TN and AR. An average of 165.24 million reads were generated for each sample in pool HC- 60, indicating an average maximum whole genome coverage of 13.01X for each individual ((sequenced read count * 150 bp read length) / 1.9 gigabase reference genome length). The other pool (LC-381) contained the full set of 381 AL individuals, with an average 27.88 million reads sequenced and 4.39 X average maximum whole genome coverage per individual. The raw sequence data have been deposited in NCBI SRA BioProject: PRJNA656311.

33 Read Mapping and Quality Filtering. We utilized a chromosome-level reference genome PBJelly assembly that was recently developed from two male S. undulatus individuals collected at Solon Dixon Forestry Education Center, AL (Westfall et al. 2020; English et al. 2012). The annotated reference assembly was indexed with bwa v0.7.16 index and SAMtools v1.5 faidx (Li et al. 2009; Li and Durbin 2009), and a sequence dictionary was created with picard CreateSequenceDictionary (Picard Toolkit 2019) for use in read mapping, SNP identification, and downstream analyses. The LC-381 group reads were sequenced without lanes in their NovaSeq S4 flowcell, but the HC-60 group reads were sequenced across four lanes and needed to be combined into one forward and reverse read prior to trimming and mapping to the reference genome. The raw reads were trimmed with Trimmomatic v0.36 to remove the Illumina TruSeq3-PE-2 adapters and other reads <36 bases long, as well as leading and trailing low quality or N bases (Bolger, Lohse, and Usadel 2014; Morin et al. 2018). The trimmed reads were aligned to the reference genome with bwa v0.7.16 mem (default settings), an alignment tool specialized for large genome sizes that seeds alignments with maximal exact matches and extends seeds with Smith-Waterman’s affine-gap penalty for insertions or deletions (Li 2013). SAMtools v1.5 flagstat was used to calculate estimated genome-wide coverage for the mapped reads, and view to convert the mapped .sam files to .bam files (Li et al. 2009). BAMtools v2.4.1 was used to filter out unmapped reads and mapped reads with mapQuality less than 50 (Barnett et al. 2011). Read groups were added to the mapped read files with Picard AddOrReplaceReadGroups (Picard Toolkit 2019), then the reads were sorted and indexed with SAMtools v1.5 (Li et al. 2009). For the remaining analyses, we found that several of the more computationally intensive programs required working at chromosome-level to finish processing within the limits postulated by our computational cluster system; we indicate such cases accordingly.

SNP Identification. We followed the Genome Analysis Toolkit (GATK, v4.1.3.0) “Best Practices” pipeline for germline short variant discovery in each of the sequencing pools (Poplin et al. 2017; McKenna et al. 2010; Van der Auwera et al. 2013; Depristo et al. 2011). Even though GATK’s pipeline was designed and optimized for analyzing human genetic data, it has been successfully utilized in several non-model systems with available high-quality reference genomes for evolutionary genomic inferences (Wang et al. 2020; Chen et al. 2020; Wright et al. 2019; Kryvokhyzha et al. 2019; Bernhardsson 2019) and outperforms other variant callers in capability and accuracy (Pirooznia et al. 2014; Liu et al. 2013). GATK’s pipeline began with HaplotypeCaller calling germline SNPs and indels for each individual via local de-novo assembly. In short, HaplotypeCaller defined active regions based on the presence of evidence for allele variation in each individual’s mapped reads, then built a De Bruijn-like graph to detect overlaps between sequences and reassemble the active region (Poplin et al. 2017). The possible active regions were realigned against the reference haplotype with the Smith-Waterman algorithm to identify potential variant sites, i.e. single nucleotide polymorphisms (SNPs) (Poplin et al. 2017). Likelihoods of alleles were determined using GATK’s PairHMM algorithm, and the most likely genotype per Bayes’ rule was assigned to each potentially variant site. HaplotypeCaller generated an intermediate GVCF file that contained likelihood data for every position in each of the top 24 largest chromosomes in every individual’s mapped read data. The per-chromosome GVCFs were indexed then merged GVCF files with GATK’s IndexFeatureFile program. Following the “Best Practices” pipeline, GenomicsDBImport was used to import the single-sample GVCFs into a per-chromosome database (GenomicsDB) before joint genotyping with GenotypeGVCFs. The chromosome VCFs were combined with VCFtools v0.1.15 vcf-concat (Danecek et al. 2011) into one file for each sequencing pool. There were 72,797,694 possible sites identified in the HC-60 group and 38,481,673 sites in the LC- 381 group.

34 SNP Filtering and Quality Control. The raw SNPs were filtered with a series of thresholds recommended by GATK (Poplin et al. 2017). SelectVariants kept only variants that were classified as SNPs, then VariantFiltration removed SNPs with hard-filters based on the INFO and FORMAT fields of the VCF files: quality score by depth (QD) <2.0, Phred-scaled p-value using Fisher’s exact test (FS) >60.0, and mapping quality score (MQ) < 40.0. SelectVariants was applied again to only keep SNPs that were not filtered out by VariantFiltration. After GATK filtering there remained 57,343,764 SNPs in the HC-60 group and 33,419,722 SNPs in the LC-381 group. The SNPs that remained in each pool after GATK’s suggested parameters were additionally filtered with VCFtools v0.1.15 for analysis (Danecek et al. 2011). Both pools were filtered to keep only biallelic sites (min-alleles 2, max-alleles 2) and remove sites with insertions and deletions (Danecek et al. 2011). The HC-60 pool was also filtered for Hardy- Weinberg Equilibrium with a low enough setting to remove sites that were likely to be erroneous variant calls (hwe 0.000001), leaving 50,373,197 SNPs for analysis (Danecek et al. 2011). The LC-381 group was also filtered for Hardy-Weinberg Equilibrium (hwe 0.001) and to remove sites with a minor allele frequency (maf, number of times an allele appears over all individuals at that site divided by the total number of non-missing alleles at that site) less than 0.05 to prevent inflation in downstream statistical estimates and during imputation with the remaining 350,281 SNPs (Danecek et al. 2011).

Analysis of Population Structure and History. We determined the genetic relationship between the Alabama, Arkansas, and Tennessee populations of lizards we sequenced in the HC-60 pool. We implemented additional filters with VCFtools v0.1.15 (Danecek et al. 2011) for our structure analyses. These included restricting the variant sites to only those with between 15X-30X coverage (min-meanDP 15, max-meanDP 30), and filtering out SNPs that are missing in more than 25% of the samples (max-missing 0.75) as well as those with a minimum allele frequency less than 10% (maf 0.10) to reduce structure errors due to mapping errors and gene duplications (Slifer 2018). We also reduced our number of SNPs to one random variant every 80,000 base pairs to reduce the density of the dataset (Mattingsdal et al. 2020). For our structure analysis, we converted the HC-60 VCF file into PLINK binary format (.bed, .fam, .bim) with PLINK v1.90b4.6 (Chang et al. 2015). Two of the individuals in this pool, one each from AR and AL, were removed due to missing call rates (mind 0.1), then we transcribed a minor allele frequency report (freq) and ran a principal components analysis (pca) to calculate the relationship matrix for 20 principal components of the variance- standardized relationship matrix (Chang et al. 2015; Purcell et al. 2007). We used ADMIXTURE v1.3.0 to infer the percent of admixture in each individual for K 2-6, where K is the number of populations (Alexander, Novembre, and Lange 2009; Alexander and Lange 2011; Shringarpure et al. 2016). All PCA and ADMIXTURE results were plotted in R v3.6.1. One random individual from each of the three populations was selected for Pairwise Sequentially Markovian Coalescent (PSMC) model development to infer respective population size history (Li and Durbin 2011). A PSMC model estimates the possible coalescence times, or events when two lineages combine to become one ancestral lineage, by the local genealogies at each locus across the genome (Mather, Traves, and Ho 2020). As coalescence events are more likely to occur when the population is small, we can use the rate of coalescence to estimate past population sizes, as well as expansions and bottlenecks (Mather, Traves, and Ho 2020). We used SAMtools v1.5 mpileup (read quality Q 30) and BCFtools v1.5 call in conjunction with vcfutils.pl vcf2fq (minimum read depth d 3, maximum read depth D 100, Q 30) to convert each individual’s mapped fasta file into a whole-genome diploid consensus sequence (Li and Durbin 2011; Li et al. 2009). PSMC fq2psmcfa converted the consensus sequence into PSMC-readable format, then PSMC was calculated in blocks of adjacent atomic intervals in a pattern suggested for human-sized genomes (-p “4+25*2+4+6”) (Li and Durbin 2011). A mutation rate of 2.1x10-10 per site per generation and generation time of 1 year were 35 assumed based on Anolis lizard data to translate coalescence times into years (Tollis and Boissinot 2014; Bourgeois et al. 2019). Even though all of the individuals in the LC-381 group were collected from the same area, closely related individuals and family lines could interfere with the inferred associations determined by our genome-wide association study (Bush and Moore 2012). We used KING v2.2.4 to test for kinship among the LC-381 pool of individuals and removed two individuals from second degree relationships (25% shared DNA) (Manichaikul et al. 2010). A preliminary PLINK v1.90b4.6 principal components and cluster neighbor analyses revealed another two likely second degree relationships, so another two individuals were removed prior to imputation for the GWAS (Slifer 2018; Chang et al. 2015; Purcell et al. 2007).

Evolutionary Analyses. Genomic scans for selection in the HC-60 group of S. undulatus were performed with three perspectives: differentiation between populations (Fst), nucleotide diversity within populations (π), and sweeps in haplotype data within population (LASSI T). We estimated both nucleotide diversity and Fst in 100,000 bp windows and 50,000 bp window steps with VCFtools v0.1.15 (Danecek et al. 2011; Holsinger and Weir 2009; Weir and Cockerham 1984; Chen et al. 2020). The LASSI software package required window sizes based on SNP distance instead of basepair distance along the genome, so we estimated levels of linkage disequilibrium (LD) over 50,000 bp windows within the phased HC-60 data with VCFtools v0.1.15 (Danecek et al. 2011; Harris and DeGiorgio 2020). Haplotype levels of LD (r2) were used to measure the LD decay by averaging the r2 values in each bin across each chromosome. We then followed the recommended pipeline for calculating the T statistic per chromosome in each population (Harris and DeGiorgio 2020). All evolutionary analyses results were plotted and analyzed for functional genetic correlations in R v3.6.1 and g:Profiler (Raudvere et al. 2019).

Genome-wide Association Study (GWAS). The VCFtools-filtered HC-60 group was split by chromosome with Tabix v1.5 (Li 2011; Danecek et al. 2011), then by population so that the high-coverage individuals from Alabama could be phased and utilized for imputation with the low-coverage data from the same population. Imputation is a method of using statistical models to replace missing genotype information in low-coverage data based on a phased reference panel, and is often utilized to increase power for GWAS (Pasaniuc et al. 2012; Browning, Zhou, and Browning 2018). We filtered the high-coverage Alabama chromosome VCFs for missingness per marker with PLINK v1.90b4.6 (geno 0.05), and then missingness per individual (mind 0.05) to prepare for statistical haplotype estimation, or phasing (Chang et al. 2015). The VCFs with the remaining 19 individuals were phased with Shape-IT v2.r837 (Delaneau, Coulonges, and Zagury 2008), and the resulting haplotype files were converted back into VCFs for Beagle v5.1 imputation with the low-coverage Alabama group (Browning, Zhou, and Browning 2018). Imputation increased variant count in the LC-377 group from 350,281 to 17,173,581 SNPs. The imputed LC-377 chromosome files were reassembled into a single VCF before filtering for maf 0.05 and max-maf 0.95 with VCFtools v0.1.15 (Danecek et al. 2011). We converted this filtered, imputed VCF into PLINK binary format with with PLINK v1.90b4.6, manually added the sex of each lizard into the .fam file, and ran a second principal components analysis to include the population structure in the GWAS (Bush and Moore 2012; Slifer 2018; Chang et al. 2015). PCs 1 and 2 and sex information were included as covariates with a PLINK v1.90b4.6 linear association test (Price et al. 2006; Chang et al. 2015). The additive effect values of both covariates were compiled for generating association (Manhattan) and quantile-quantile (QQ) plots with the qqman package, as well as functional and permutation analyses in R v3.6.1 and g:Profiler (Turner 2014; Raudvere et al. 2019).

Results In this study we identified variable sites within the nuclear genomes of over 400 S. undulatus lizards from the southeastern United States that are potentially correlated with morphological 36 adaptation to red imported fire ant (S. invicta) predation pressures. We generated whole genome data obtained from sequencing preserved lizard libraries in two pools: HC-60 contained high-coverage sequence data from 20 randomly-selected individuals collected from sites in Alabama, Arkansas, and Tennessee; and LC-381 was comprised of low-coverage data from 381 individuals from the Alabama site. We assessed each of these pools of individuals for genetic population structure, performed a genome-wide association study (GWAS) on the large Alabama group, and scanned for selection both within and between the high-coverage populations.

Population Structure and History. Both our PLINK principal components analysis (PCA) (Chang et al. 2015) and ADMIXTURE (Alexander, Novembre, and Lange 2009; Alexander and Lange 2011; Shringarpure et al. 2016) results also indicate that the Alabama, Arkansas, and Tennessee lizard populations are genetically distinct from each other (see Figure 3-2). There is no overlap between individuals in the first or second principal components. We inferred admixture within 58 individuals in the high-coverage data group, and a cluster size of K=3 had the lowest cross-validation error. Our coalescent demographic analysis (PSMC) suggests that the three populations of lizards in our study shared a population history up until the Alabama population split from the Arkansas and Tennessee populations around 500,000 years before present (see Figure 3-2) (Li and Durbin 2011; Mather, Traves, and Ho 2020). The Arkansas and Tennessee populations likely diverged about 200,000 years before present, and have maintained much lower effective population sizes than the Alabama population (80x104 AR, 20x104 TN, and 1480x104 AL; see Figure 3-2) (Li and Durbin 2011; Mather, Traves, and Ho 2020).

Figure 3-2: Population structure and history of the S. undulatus in our study. Fence lizards from Solon Dixon Forestry Education Center, Alabama are indicated in pink, from Edgar Evins, Tennessee in blue, and St. Francis, Arkansas in yellow (n=1 each for PSMC; n=19, 20, and 19, respectively).

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Evolutionary Analyses. Fst was calculated for the high-coverage data group in a sliding window of length 100,000 bp with a 50,000 bp steps size for three comparisons: Alabama (fire ant exposed) versus Tennessee, Alabama (fire ant exposed) versus Arkansas, and Tennessee versus Arkansas (see Table 3-1). The per-window Fst genetic differentiation values for each of three comparisons was standardized to Z-transformed Fst as follows: Z(Fst) = (per- window Fst – mean Fst of all genome-wide) / standard deviation of all genome-wide windows Fst (Wang et al. 2020). The average genome-wide Fst values for each comparison indicate that the differentiation between allele frequencies is greatest between the Tennessee and Arkansas populations that have not yet been exposed to fire ant predation pressures (see Table 3-1). There is more differentiation between the Alabama and Arkansas groups than between Alabama and Tennessee, which could be attributed to more recent shared population history, with the Mississippi River likely acting as a barrier to spread in the past (see Table 3-1). However, there are more significant (Z(Fst)>5) regions in the two Alabama comparisons (AL- AR 49 regions, AL-TN 41 regions) than in the outgroup comparison (11 regions; see Figure 3-3, Supplementary Figure 3-1 for whole-genome results, Supplementary Table 3-2 for Z(Fst)>5 results).

Table 3-1: Average Fst differentiation values between S. undulatus populations. AL vs. TN AL vs. AR TN vs. AR Mean non-windowed Fst estimate 0.1242 0.1479 0.2015 Mean windowed Fst 0.1274 ± 0.0264 0.1518 ± 0.0324 0.2027 ± 0.0452

There are possible signals across the genome for increased differentiation, i.e. signs of non-specific directional selection, between the fence lizard populations and selective sweeps within the populations (see Figure 3-3). There were 10,951 unique genes that overlap with the 50 kbp windows used for Fst and nucleotide diversity calculations. With a significance cutoff of Z(Fst) = 5, there were 41 windows containing a total of 47,462 SNPs near 27 genes in the Alabama-Tennessee comparison, and none of these genes were significantly enriched for functions in the GO database (see Supplementary Table 3-3). In the Alabama-Arkansas comparison, there were 49 windows with 55,273 SNPs near 33 genes with a Z(Fst)>5. These 33 AL-AR genes were enriched against the background Fst genes for GO BP “regulation of trans-synaptic signaling” and “modulation of chemical synaptic transmission” functions (FDR-adjusted p = 0.039), along with various CORUM categories (see Supplementary Table 3-3). All three between-population comparisons indicate increased allele frequency differentiation in chromosome 6. The Calpain 7 (CAPN7) and SH3 Domain Binding Protein 5 (SH3BP5) genes were represented by significant Z(Fst) values in both of the AL-versus- outgroup comparisons. The SH3BP5 gene starts at 53,560,205 bp along chromosome 6, with CAPN7 following close behind at 53,618,27 bp. There is a more pronounced decrease in nucleotide diversity in this region of the genome in both the Tennessee (π = 0.00075) and Arkansas (π = 0.00057) populations when compared to the Alabama population (π = 0.00148; see Supplementary Figure 3-2 for genome-wide π results). Likewise, both our nucleotide diversity and LASSI scans for selection in each of the three populations produced a similar peak (increased diversity and high T) at the end of chromosome 10 (see Figure 3-3; see Supplementary Figure 3-3 for LASSI T selection results in the first 12 chromosomes, and Supplementary Table 3-4 for Alabama LASSI T functional gene annotation overlaps). There are 100 windows with LASSI T > 25 across the Alabama genomes located near 24 genes significantly enriched against the background LASSI genes for GO CC “cell periphery” (FDR p = 0.021), “plasma membrane” and “cell-cell contact zone” (FDR p = 0.034; see Supplementary Table 3-5).

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Figure 3-3: Results for natural selection scans in chromosomes 2, 3, 5, 6, and 10 for S. undulatus from Alabama, Arkansas, and Tennessee. Each point in the population differentiation Z(Fst) plots represents the start of a 50,000 bp window, and its position on the y-axis indicates the amount of allele frequency variation between two populations. Nucleotide diversity, or π, is likewise depicted in 50,000 bp windows. Heterozygosity in variable sites is expected, so a decrease in π could indicate selection for a beneficial mutation in that region of the chromosome. LASSI T- statistics were calculated in SNP windows instead of bp windows, and an increase in T suggests that there are high-frequency haplotypes, and therefore selection, in that genomic region. See Supplementary Figures 3-1 – 3-3 for genome-wide Z(Fst), π, and LASSI T selection results.

Genome-wide Association Study (GWAS). Fence lizards from Solon Dixon Forestry Education Center, Alabama have significantly larger relative hind limb lengths [right hind limb length (cm)/snout-vent length (cm)] compared to sites with no fire ant exposure, and the effectiveness of body-twitch and flee behavior at removing fire ants is positively correlated with the relative hind limb length of the lizard performing those behaviors (Langkilde 2009). The relative hind limb length of the individuals in our Alabama population ranged from 0.29 to 0.53 (average 0.42±0.04, n=375; see Figure 3-4). We incorporated principal component analysis metrics for the imputed low-coverage data (PC1 and PC2) as well as sex for 377 individuals as covariates in our linear PLINK GWAS. The diagnostic quantile-quantile (QQ) plot generated from our association study shows a relatively early separation of the observed p value from the expected null distribution (McCarthy et al. 2008). This separation could be caused by the complexity of the quantitative trait in question (McCarthy et al. 2008; Stranger, Stahl, and Raj 2011), or it might indicate that we need to incorporate more principal component data from the imputed LC-377 PCA or switch to a mixed model program like GEMMA to better account for cryptic relatedness within the Alabama fence lizard group (Eu-ahsunthornwattana et al. 2014; Zhou and Stephens 2014).

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Figure 3-4: Genome-wide association results for S. undulatus from Alabama. (A) Associations between 17,173,581 genetic markers and relative hind limb length using data from 377 fence lizard individuals from Solon Dixon Forestry Education Center. (B) QQ-plot of the data shown in the Manhattan plot graphically represents the deviation of the observed p values from the null hypothesis. Observed p values that are more significant than expected under the null hypothesis depart from the diagonal line. (C) Each point in the Manhattan plot represents a single-nucleotide polymorphism (SNP), and position on the y-axis indicates the statistical significance of the association (p value) between that SNP and relative hind limb length. Dashed blue and black lines indicate - log10(p)=1E-8 and 1E-6, respectively.

SNPs in several regions across the genome are significantly associated with the relative hind limb length phenotype, particularly in chromosome 10 (see Manhattan plot in Figure 3-4). The S. undulatus reference genome generated with PBJelly contains 54,149 annotated genes and orthology information from human, chicken, mouse, and anole lizard protein databases (Westfall et al. 2020). However, 38,677 are listed without gene names and likely produce “hypothetical proteins” (Westfall et al. 2020). SNPs often activate or suppress gene expression by causing substitutions in regulatory sequences, so genes both upstream and downstream of SNPs should be considered in functional analyses. Several of the most- significantly associated SNPs, such as those in chromosomes 2 and 7 with p<1E-6, are within 2,500 basepairs (bp) of genes that code for “hypothetical proteins” or no genes whatsoever, and are thus not considered in our functional analyses. There are 10,962 unique gene names in the remaining 15,472 annotated genes for this species. There are 6 SNPs with p<1E-8, all in chromosome 10, that are located within 2,500 bp of known genes: CREB Regulated Transcription Coactivator 1 (CRTC1), Myosin IXB (MYO9B), and AT-rich Interaction Doman 3A (ARID3A; see Figure 3-4, dashed blue line). Along with those three, another 21 genes fall within 2,5000 bp of the 166 SNPs with p<1E-6 (see Figure 3-4, dashed black line; Supplementary Table 3-6). Five of the 166 SNPs were located in chromosome 1, one each in chromosomes 3 and 4, two in 5, and the remaining 157 in chromosome 10. Against the background of all 12,524 genes within 2,500 bp of a GWAS SNP, these 24 genes are enriched for functional groups catalogued the Comprehensive Resource of Mammalian protein complexes (CORUM) (Giurgiu et al. 2019) though not in any Gene Ontology (GO) groups (see Supplementary Table 3-7). 40 Since the increased relative hind limb length trait is likely associated with several regions across the genome, we expanded our functional enrichment tests to the 725 genes near the top 1,719 (0.01%) of all GWAS SNPs. These 725 genes were not significantly enriched for any functional categories against the GWAS background genes. We expanded again to consider the top 17,172 (0.1%) of all GWAS SNPs, and the 1,605 genes within 2,500 bp were significantly enriched for several GO categories in both the Molecular Function (MF) and Cellular Component (CC) umbrella groups (see Supplementary Table 3-8). The MF terms included iterations of “nucleotide binding” and “glutamate receptor activity”, and CC terms included “cell periphery”, “cell projection”, and various plasma membrane terms. As annotations with confirmed gene names only made up a small fraction of both the background GWAS genes and the S. undulatus reference genome, we adjusted the statistical scope to include all annotated Homo sapiens genes available in the g:Profiler database for analysis of the 1,605 genes. The most significantly enriched Biological Process (BP) gene categories included “nervous system development”, “cell development/morphogenesis”, as well as several terms related to neuron development and activity (see Supplementary Table 3-9).

Evolutionary Analyses and GWAS Overlaps. Recent studies of genetic adaptation have combined complementary approaches, including scans for signatures of selection, association tests, and gene expression, to detect small-effect SNPs near candidate genes for traits that are under natural selection (Igoshin et al. 2019; Yasumizu et al. 2020; You et al. 2018). We likewise surveyed our data for potential overlaps between our evolutionary analyses and Alabama GWAS. The CAPN7 and SH3BP5 genes were represented by significant Z(Fst) values in both of the AL-versus-outgroup comparisons, neither gene was significantly associated with SNPs in the Alabama GWAS. However, there is a weak signal in the GWAS just past the window of selection indicated by both the Z(Fst) and nucleotide diversity tests in chromosome 6 (see Figure 3-3 and Figure 3-4). Several GWAS SNPs around this signal are near the NIMA Related Kinase 11 (NEK11) gene, which has been associated with increased body weight in a human GWAS (Tachmazidou et al. 2017) and could be related to the increased relative hind limb length in our Alabama population. The peaks of increased nucleotide diversity and high LASSI T selection at the end of chromosome 10 are similar to the signature we observed in our GWAS (see Figure 3-3 and Figure 3-4). Three of the top 100 LASSI windows are located near the MYO9B gene, one of the top 3 genes from our GWAS. MYO9B gene expression has been linked to microfilament motor activity, i.e. the contraction in muscle fibers in animals (Ma et al. 2017).

Discussion We are studying the evolutionary process of eastern fence lizard (S. undulatus) morphological adaptation in response to the human introduction of an invasive species, red imported fire ants (S. invicta). Our population structure results are concordant with previous field observations that fence lizards from this species (S. undulatus) and western North America (S. occidentalis) move about 50 meters on average from their hatching locations, with a maximum dispersal distance of 400 meters in S. occidentalis (Massot 2003; Warner and Andrews 2002). Our tests for population stratification of S. undulatus support the hypothesis that there is no gene flow between the three populations in Alabama, Arkansas, and Tennessee that will negatively impact the imputation with the low-coverage data group or our evolutionary analyses. The preliminary association and selection test results we discuss here indicate that there are variants spanning the entire fence lizard genome that might play a small role in the evolution of increased relative hind limb length and increased twitching behaviors in the Alabama population. As an example, the decrease in nucleotide diversity paired with the lack of a strong signal in Z(Fst) suggests that the Arkansas and Tennessee populations may have had selective sweeps in this region of chromosome 6. The AL-AR and AL-TN differentiation at this spot is higher than other parts of the genome, which could suggest selection in the 41 Alabama population or the same shared sweep or two independent ones in the outgroups. We will continue deciphering our nucleotide diversity results in comparisons with other tests as decreases in diversity can indicate selection as well as centromeres and other highly- conserved regions of the genome, as is likely the case in chromosome 2 and 3. Based on these preliminary results and investigations of the genetic architecture of the phenotype in other organisms (Guo et al. 2015; Leamy et al. 2002; Škrabar et al. 2018; K. Cooper et al. 2020), relative hind limb length in lizards is likely a polygenic trait. However, it is unknown to what degree all genes underlying this phenotype would be impacted by selection for longer limbs. We may predict that selection would target fewer genes of large effect to rapidly change the trait, particularly in the case of rapid selection in response to an anthropogenic perturbation. In contrast, the same adaptation could arise via changes in the frequency of many small effect loci with minor directional shifts in allele frequencies. Either of these avenues could bring about the increased relative hind limb length phenotype in response to red imported fire ant predation pressures. To investigate this question concerning the targets and intensity of selection, we next intersected our results from the investigation of the genetic architecture of hind limb length to those of our identification of genomic regions under selection. Finding evidence of strong selection on genes with large effect on relative hind limb length would support a scenario of rapid change via classical sweeps on fewer large effect genes. Polygenic adaptation signatures will be more difficult to detect than hard sweeps, as a classic sweep is predicted to rapidly increase the frequency of a novel advantageous mutation and its surrounding alleles (Pritchard, Pickrell, and Coop 2010; Stephan 2016). Despite this difficulty in detecting polygenic selection, by analyzing our selection metrics across functional categories, it may be possible to detect polygenic adaptation, including that targeting standing variation in multiple areas of the genome. In this, we have followed the example of prior studies in which adaptive genetic architecture is revealed via intersection of signatures of positive selection and GWAS associations for complex traits (Igoshin et al. 2019; Yasumizu et al. 2020; You et al. 2018). Our GWAS in the Alabama fence lizards allows us to estimate the effect size of the SNPs associated with increased relative hind limb length, and our initial search for overlaps with the evolutionary analyses serves as a first step in understanding the genetic architecture of adaptation in this population (Barghi, Hermisson, and Schlötterer 2020). We are currently refining and expanding our methods to account for potential inaccuracies in our data. As an example, our test populations were mapped to a genome that was not masked for repetitive genetic regions, which may have resulted in increased SNP- calling errors (Treangen and Salzberg 2012; Jurka et al. 2007) though the case can be made to keep these repetitive regions in our analyses (Slotkin 2018). We will also be testing different SNP-calling and -filtering regimes that research groups have implemented for non-model organisms (Kryvokhyzha et al. 2019) as well as programs that might better account for existing familial relationships in our three populations (Eu-ahsunthornwattana et al. 2014; Zhou and Stephens 2014). Explicit comparison of population history models that include and exclude admixture would allow us to more conclusively rule out this possibility (Gutenkunst et al. 2010). The signals related to the MYO9B could indicate very exciting correlation with both the morphological and behavioral phenotypes in question, so we are amassing several quality control checks to ensure that the signal in chromosome 10 is not caused by false positives due to chromosomal edge effects. Evolutionary processes such as adaptations to human-mediated ecosystem changes are a major future concern with continued increases in the intensity of anthropogenic effects on worldwide ecosystems. The toolkit we utilized here – genome sequencing, GWAS, and population genetics tests for signatures of positive natural selection – can be applied to various non-human, non-model, non-domesticated organisms throughout the world, including other vertebrates, invertebrates, and plants. There is potential to expand this preliminary study to include genomic data from up to ~2,100 adult fence lizards, and produce one of the largest and most powerful phenotype-genotype association analyses in a non- human, non-model, non-domesticated species. Since the increased relative hind limb length 42 and twitching behaviors are traits that are influenced by both genetic and environmental factors, the incorporation of RNA-Seq data could allow us to directly analyze whether the genes we detect with these enrichment methods are expressed in wild populations (Yan et al. 2020). We could also utilize preserved DNA from formalin-fixed and ethanol-preserved museum specimens in a later study to directly track the change in trait-associated allele frequency over time with exposure to the invasive red imported fire ant. The Langkilde Lab at Pennsylvania State University has recorded phenotypic data of fence lizards stored in several museum collections, including specimens that were collected before red imported fire ant introduction in Alabama and spread through surrounding states. Preliminary extractions have produced authentic lizard DNA sequence data from formalin-fixed tissues, but further testing is needed to rule out preservation issues for ethanol-preserved specimens and/or certain collections.

Data, Code, and Materials Raw sequence reads and reference genomes are available at NCBI SRA BioProject: PRJNA656311. All code developed and used for this research work are stored in GitHub (https://github.com/AlexisPSullivan/Fence_Lizard).

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CHAPTER 4. Modern, archaeological, and paleontological DNA analysis of a human-harvested marine gastropod (Strombus pugilis) from Caribbean Panama

Alexis P. Sullivan1*, Stephanie Marciniak2, Aaron O’Dea3, Thomas Wake3,4, George H. Perry1,2,5*

Departments of Biology1 and Anthropology2, Pennsylvania State University, University Park, PA, USA 3Smithsonian Tropical Research Institute, Panama City, Panama 4Department of Anthropology and Costen Institute of Archaeology (CIOA), University of California, Los Angeles, CA, USA 5Huck Institutes of the Life Sciences, Pennsylvania State University, University Park, PA, USA

* Addresses for correspondence: [email protected]; [email protected]

Published in bioRxiv, doi: 10.1101/2020.08.26.269308

Abstract Although protocols exist for the recovery of ancient DNA from land snail and marine bivalve shells, marine conch shells have yet to be studied from a paleogenomic perspective. We first present reference assemblies for both a 623.7 Mbp nuclear genome and a 15.4 kbp mitochondrial genome for Strombus pugilis, the West Indian fighting conch. We next detail a method to extract and sequence DNA from conch shells and apply it to conch from Bocas del Toro, Panama across three time periods: recently-eaten and discarded (n=3), Late Holocene (984-1258 BP) archaeological midden (n=5), and a mid-Holocene (5711-7187 BP) paleontological fossil coral reef (n=5). These results are compared to control DNA extracted from live-caught tissue and fresh shells (n=5). Using high-throughput sequencing, we were able to obtain S. pugilis nuclear sequence reads from shells across all age periods: up to 92.5 thousand filtered reads per sample in live-caught shell material, 4.57 thousand for modern discarded shells, 12.1 thousand reads for archaeological shells, and 114 reads in paleontological shells. We confirmed authenticity of the ancient DNA recovered from the archaeological and paleontological shells based on 5.7x higher average frequency of deamination-driven misincorporations and 15% shorter average read lengths compared to the modern shells. Reads also mapped to the S. pugilis mitochondrial genome for all but the paleontological shells, with consistent ratios of mitochondrial to nuclear mapped reads across sample types. Our methods can be applied to diverse archaeological sites to facilitate reconstructions of the long-term impacts of human behavior on mollusc evolutionary biology.

44 Introduction Human behavior has directly or indirectly affected non-human morphological evolution in many ways, well beyond plant and animal domestication (Sullivan, Bird, and Perry 2017). One of the most well-studied of these mechanisms is that of non-human body size evolution due to size-selective human hunting and harvesting (Darimont et al. 2015, 2009). For example, intertidal mollusc exploitation by humans is well-documented throughout the archaeological record due to the trash heaps, or ‘middens’, of shells and other inedible materials deposited after processing (Erlandson and Rick 2010). Routine, large-scale shellfish acquisition for sustenance has been recorded from ~120,000 years before present (BP) in South Africa (Jerardino 2016), and the exploitation of this valuable resource continues to generate middens in certain areas of the world today (Bird and Bliege Bird 1997; O’Dea et al. 2014). While providing a record of human harvesting, these middens are also invaluable resources that can provide evidence of prey phenotypic change in response to these harvesting pressures through the ability to identify and quantify phyletic dwarfism over time (Avery et al. 2008; Erlandson et al. 2011; Klein and Steele 2013; Stiner et al. 1999). Interest in obtaining DNA from molluscan shells has increased over the past few years due to their abundance in museum collections and recovery rate in the wild (Geist, Wunderlich, and Kuehn 2008). By utilizing ancient DNA extraction and processing techniques, it is now possible to recover mitochondrial (Villanea, Parent, and Kemp 2016) and even nuclear (Der Sarkissian et al. 2017, 2020) DNA from shell materials, even from those that are thousands of years old. This DNA could then be utilized for analyzing the genomes of these species to infer population history, biogeography, and more, even in non-human, non- model organisms (Coutellec 2017; Perry 2014a). Once recovered from the shells, these temporal sequences of genetic records could potentially be used to confirm a genetic basis for the molluscan prey phenotypic change in response to human harvesting pressures and powerfully test adaptive hypotheses. In 2014, O’Dea et al. (2014) published a study in which they analyzed West Indian fighting conch (Strombus pugilis) body sizes from modern, prehistoric midden (540-1260 BP; Wake et al., 2013), and paleontological reef (not human harvested; 7187-5711 BP; Fredston- Hermann, O’Dea, Rodriguez, Thompson, & Todd, 2013) sites in the Bocas del Toro archipelago in Caribbean Panama. Even though S. pugilis is smaller than its sympatric relative Lobatus gigas (queen conch), S. pugilis greatly outnumbers L. gigas and has remained an important component of the subsistence diet of the local people for millennia, as evidenced by its overwhelming presence in Bocas del Toro (Fredston-Hermann et al. 2013; Wake et al. 2013) and year-round harvesting and consumption today. Presently shells are either discarded in mounds near the harvesters’ houses or sold as souvenirs to tourists in gift shops (O’Dea et al. 2014). By measuring the height, width, and lip thickness of S. pugilis shells from archaeological Sitio Drago (dated to AD 690-1410; Wake 2006; Wake et al. 2012, 2013), paleontological shells from a mid-Holocene fringing coral reef at Lennond (Fredston- Hermann et al. 2013; O’Dea et al. 2020; Lin et al. 2019), and modern sites spaced throughout the Bocas del Toro archipelago, O’Dea et al (2014) found that size at sexual maturity has decreased consecutively from the paleontological ‘pre-human’ period to the archaeological deposits to the present day. This size decrease is associated with a decline in edible meat weight by ~40% over the past 7,000 years (O’Dea et al. 2014). For our present study, we revisited the sites and collections studied by O’Dea et al (2014) (see Figure 4-1 A) with the initial goal of assembling both a reference nuclear and mitochondrial genome for S. pugilis from freshly-preserved tissue. We then evaluated several DNA extraction techniques that were developed for mineralized substrates like ancient bone and bivalve shell materials (Der Sarkissian et al. 2017; Gamba et al. 2016; Kemp et al. 2007; Villanea, Parent, and Kemp 2016; Yang et al. 1998; Gamba et al. 2014), and developed a modified method to extract modern and ancient DNA from the more robust recently-deceased and Holocene conch shells. We report our assessment of the mapping rates and DNA damage 45 from modern live-caught, recently discarded, Late Holocene archaeological, and mid- Holocene paleontological shell materials.

Figure 4-1: Strombus pugilis study populations. A – S. pugilis specimen collection sites. Live fighting conch individuals were collected from Boca del Drago and Cayo Agua 1 (green triangles), and shells from recently-eaten fighting conch were collected at Cayo Agua 2 (green circle). Late Holocene, archaeological, human-processed shells were collected from excavations at Sitio Drago (orange circle), and Mid-Holocene, pre-human shells from the exposed mid-Holocene fringing reef at the old town of Lennond, now called Sweet Bocas (blue circle). B – Estimated meat weight distributions for adult S. pugilis specimens. Height, width, and lip thickness measurements were collected for each shell, and calculations for estimated edible meat weight were calculated as described by O’Dea et al (2014). Filled violin plots represent distributions of specimens collected for this study, and unfilled plots depict adult shell distributions of shells presented in by O’Dea et al (2014; 144 from CA and 126 from Boca del Drago). The mean and median of each distribution are represented by the black and gray lines, respectively. Those individuals with larger points and colored interiors were sequenced for this study.

Materials and Methods Sample Collection Sites. We collected live Strombus pugilis individuals (n=10 each) for paired fresh tissue and shell subsamples from two of the contemporary sites sampled by O’Dea et al (2014): Cayo Agua (CA1, 9°08'46.6" N 82°03'8.9" W) and Boca del Drago (BD, 9°24'13.4" N 82°19'24.4" W; see Figure 4-1 A). Individual conchs were sited while snorkeling at the surface, then gathered from depths of approximately 4-6 meters. Sexually mature conchs were identified by an outer shell lip thickness of >1.8 mm (O’Dea et al. 2014); those with lip thicknesses less than or equal to 1.8 mm were released where they were caught. The live- caught mature individuals were transported in buckets to the Smithsonian Tropical Research Institute (STRI) Bocas del Toro Research Station (BDT) for subsampling and storage. Recently discarded shells were collected from underneath a dock at CA1 (n=5) as well as from refuse piles at a second Cayo Agua site (CA2, 9°09'31.5" N 82°03'23.9" W; n=15), with the permission of the people living in each location. According to conversations with the harvesters, these conchs had been boiled inside their shells in water, the flesh removed for consumption, and the shells and any remaining attached tissue discarded into the domestic 46 waste piles from which they were collected. The shells of these discarded individuals were rinsed with water and frozen whole in plastic zip bags at -20°C. The Late Holocene archaeological and mid-Holocene paleontological shells were sourced from collections housed at STRI’s BDT and the Earl S. Tupper Research Center, respectively. The archaeological site “Sitio Drago” (see Figure 4-1 A) has been excavated in a series of 1x1 m units, and materials collected from them were sorted into two ceramic phases based on their depth in 10-cm levels: the Bisquit Ware phase (AD 1100–AD 1400, 0-40 cm) and the pre-Bisquit Ware phase (690–AD 1200, 40-150+ cm) (Wake 2006; Wake et al. 2013). We amassed all of the whole adult S. pugilis shells that were collected from units 60 and 61 (9°24'58.7" N 82°18'59.9" W; n=23 total, 9 from U60 and 14 from U61). The paleontological S. pugilis remains (n=11 adult shells) were collected from a fossilized fringing reef excavated for construction purposes near the old town of Lennond (9°21'37.0" N 82°16'09.9" W; see Figure 4-1 A). Twenty four Uranium-Thorium and eight radiocarbon dates from 32 coral pieces date the reef at Lennond to the mid-Holocene (Mean = 6507, SD = 407, Min = 5711, Max = 7187 years BP; Fredston-Hermann et al., 2013; Lin et al., 2019; O’Dea et al., 2020; Dillon et al., in submission). This predates the earliest evidence of human interactions with marine species in the region which starts around around 4000 BP (Baldi 2011), and confirms previous conclusions that the Caribbean slope of Panama was prehistorically not extensively populated until the Late Holocene (Piperno, Bush, and Colinvaux 1990; Ranere and Cooke 1991; Olga F. Linares 1977; Wake, Doughty, and Kay 2013; Griggs 2005; Linares and White 1980).

Specimen Subsampling. The live S. pugilis individuals collected from each of the modern sites were kept in outdoor aquaria at the BDT Research Station prior to processing in the Station’s wet laboratory. The conchs were transferred to buckets, covered with seawater, and relaxed with 10 mL of a 2:1 solution of menthol oil and 95% ethanol (Sturm, Pearce, and Valdés 2006). A set of forceps, a scalpel, and the dissection surface were sanitized with bleach (3-4% sodium hypochlorite and <1% sodium hydroxide), then washed with freshly prepared 70% ethanol to remove the bleach residue between samples. Once the conchs were relaxed (~3 hours), forceps were used to grasp the operculum and remove the conch from its shell. The operculum was removed from the main body of the conch prior to subsampling a 1-cm disk of foot muscle tissue. The tissue disks were stored individually submerged in RNAlater in 1.5-mL microcentrifuge tubes at room temperature. All of the modern S. pugilis shells, from both the live-caught and recently discarded individuals, were rinsed with water, measured for height, width, and lip thickness (O’Dea et al., 2014; see Figure 4-1 B, Supplementary Table 4-1), then stored individually in plastic zip bags and frozen at -20°C. The archaeological and paleontological whole shells were also measured and stored in individual bags at room temperature. The shells were transported to the Pennsylvania State University for subsampling in an open-air facility. The subsampling surface and hood, forceps, and respective subsampling tool were cleaned with a 10% bleach solution followed by freshly prepared 70% ethanol between samples. The modern conch shells were rinsed with water to remove any residual frozen tissue or sand, and then the outer lip of each shell was sliced off in its entirety and stored in individual 50-mL falcon tubes at room temperature (see Figure 4-2). The Late Holocene archaeological conch shells were also brushed and rinsed with water to remove any adhering sand and soil. All of the archaeological and paleontological shells were individually measured (see Figure 4-1 B, Supplementary Table 4-1) before a new sterilized ½-inch diamond-coated drill bit was used to remove a portion of the outer whorl of each conch shell (see Figure 4-2). These portions were stored in individual 15-mL falcon tubes at room temperature.

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Figure 4-2: Protocol summary for S. pugilis DNA extraction, library preparation, sequencing, and data analysis.

Extraction Protocols. The subsampled preserved soft tissues and outer lip shell segments of the modern S. pugilis individuals were transferred to the Anthropological Genomics Laboratory for DNA extraction. The Holocene archaeological and paleontological subsamples were processed in a designated clean ancient DNA laboratory. Neither Penn State laboratory was exposed to molluscan material prior to this project, and extraction blank controls were incorporated in every step to monitor contamination. We minced preserved S. pugilis foot muscle tissue (≤30 mg) for standard E.Z.N.A.Tissue Kit (OMEGA bio-tek) extractions (see Supplementary Protocol 1 for full procedure). Trials were performed with the modern live-caught shells to test effectiveness of surface decontamination, extractions from several regions of the shell, amounts of starting material, extraction kits, and digestion time (Villanea, Parent, and Kemp 2016; Kemp et al. 2007; Der Sarkissian et al. 2017; Yang et al. 1998; Gamba et al. 2014, 2016). Based on positive preliminary results, the following protocol was utilized for all of the subsampled shell materials in the respective laboratory spaces. We prepared the S. pugilis shell fragments for extraction by removing the exterior surface of each shell with 220 grit sandpaper, rinsing throughout with deionized (DI) water, for decontamination. A customized stainless steel “shell smasher” was cleaned with detergent and DI water, disinfected with 10% bleach, rinsed with freshly prepared 70% ethanol, and dried before each new sample. The shell fragments were placed into the central chamber of the smasher and reduced to a powder, then ~1 g was transferred to a 50-mL falcon tube. Extraction buffer (0.5 M pH 8 EDTA, 0.5% sodium dodecyl sulfate, 0.25 mg/mL proteinase K) was prepared and warmed at 37°C until all precipitate had dissolved, then 4 mL was added to each sample. The samples were vortexed thoroughly then incubated at 55°C in a shaking heat block (≥ 750 rpm) for at least 24 hours. After this first digestion, the samples were centrifuged at 1500 x g for 2 minutes, and the supernatant transferred to a 15-mL falcon tube without disturbing or transferring any of the insoluble pellet or remaining shell. The first digestion aliquot was stored at -20°C, and a second digestion was performed on the remaining shell material at 37°C for at least 24 hours. The samples were centrifuged at 2000 x g for 5 minutes, then the second digestion aliquot was combined with the first, without transferring the remaining shell pellet. The combined aliquots were centrifuged at maximum speed (2520 x g) for 5 minutes, and the supernatant transferred into a 50-mL falcon tube. Five volumes of QIAquick PB Buffer were added to each sample, followed by 700 µL of sodium acetate (3 M, pH 5.5) and 15 µL of 48 Pellet Paint. This extraction solution was filtered through QIAquick spin columns fitted with Zymo-Spin V extenders secured in a vacuum manifold. Once all of the extraction solution for each sample had passed through the column, the QIAquick spin columns were transferred to the reserved 2-mL Collection Tubes. The DNA was washed on the column with 750 µL of QIAquick PE buffer, then centrifuged at 12,800 x g for 1 minute. The filtrate was discarded, then the empty spin columns were centrifuged at 12,800 x g for 1 minute to dry the columns. We aliquoted the DNA with two 25-µL portions of 55°C-heated nuclease-free water. DNA concentrations for all samples were obtained with a Qubit 3.0 dsDNA High Sensitivity Kit, and the extractions stored at -20°C (see Supplementary Protocol 4-1 for full procedure).

Library Preparation and Sequencing. As with the DNA extractions, the modern samples were processed in the Anthropological Genomics Laboratory and the ancient samples in the designated clean ancient DNA laboratory. Libraries were prepared for each DNA extraction following a slightly modified version of the Meyer and Kircher 2010 protocol. 2000 ng of each tissue DNA sample was sheared for 48 seconds in a 115- µL suspension with a Covaris M220 ultrasonicator (peak incident power 50, duty factor 20%, 200 cycles per burst, temperature 20°C). The shell DNA, even for the modern samples, was not long enough to merit shearing since the initial step of library preparation involved a size-selection bead cleanup step. 50 µL of each sheared tissue DNA sample and 25 µL of each shell DNA sample, modern or ancient, was brought into library preparation. The volumes for the blunt-end repair, single-adapter ligation, adapter fill-in, and indexing PCR master mixes were kept the same for both the tissue and shell library preparations (see Supplementary Protocol 4-2). The S. pugilis DNA libraries were shotgun sequenced on a NextSeq500 High Output 300 cycles in two batches: one for the five randomly-selected tissue samples, and one for the 18 randomly-selected shell samples. One random tissue sample, Cayo Agua 2.3, was selected and pooled accordingly for high-coverage sequencing to generate the reference nuclear and mitochondrial assemblies.

Reference Genome Assemblies. The Cayo Agua 2.3 reads for de novo reference assembly were trimmed with Trimmomatic (TruSeq2-PE adapters; Bolger, Lohse, & Usadel, 2014), and PCR duplicates were removed with filterPCRdupl.pl by Linnéa Smeds (https://github.com/linneas/condetri/blob/master/filterPCRdupl.pl). Kraken2 was used to taxonomically classify the trimmed reads, and, to avoid exogenous contamination, only those reads that were designated “unclassified”, “other sequences”, and “cellular organisms” were brought into assembly with soapdenovo v2.04 (kmer length 63; Wood, Lu, & Langmead, 2019). We removed all contigs smaller than 500 bp from the nuclear assembly prior to read mapping. We evaluated the quality of this nuclear assembly with QUAST, which determines assembly length, number of contigs, GC content, and contig N50, i.e. the minimum contig length that makes up half of the genome sequence (Gurevich et al. 2013). The same Kraken2- classified Cayo Agua 2.3 reads were also used for norgal mitochondrial genome assembly, with the nuclear genome assembly provided to skip the initial de novo assembly generated by norgal (Al-Nakeeb, Petersen, and Sicheritz-Pontén 2017). Both the nuclear and mitochondrial assemblies are available in SRA BioProject: PRJNA655996.

Read Mapping. All raw sequence reads are available in SRA BioProject: PRJNA655996. The raw read sequence data for both our S. pugilis shell and tissue samples were assessed with FastQC (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) then trimmed with leehom, with the –ancientdna flag for all of the Late Holocene archaeological and mid-Holocene paleontological shells (Renaud, Stenzel, and Kelso 2014). The tissue sequences were mapped to our nuclear and mitochondrial assemblies with BWA v0.7.16 mem (Li 2013), while the modern and ancient shells were mapped to each assembly with BWA v0.7.16 aln since aln has a higher mapping rate for shorter reads (Li and Durbin 2009). Once the reads for each sample were mapped, SAMtools v1.5 was used to convert the mapped SAM files to BAMs, sort the BAM files, remove duplicates, and filter the reads (minimum mapping quality of 30 and 30 49 base pair minimum length; Li et al., 2009). SAMtools v1.5 flagstat was used to count the number of alignments for every sample mapped to each assembly (Li et al. 2009).

Damage Characterization. mapDamage v2.0.8-1 was used to characterize ancient shell DNA in terms of fragmentation patterns, nucleotide mis-incorporation, and fragment size distributions (see Figure 4-3 B; Jónsson, Ginolhac, Schubert, Johnson, & Orlando, 2013). Specifically, we quantified patterns of the rate of nucleotide (base) misincorporation, fragment length distributions, and strand fragmentation (due to depurination before sequence read starts). Deamination of cytosine to uracil due to hydrolytic damage preferentially occurs at read ends and causes miscoding lesions, specifically C>T (cytosine to thymine) misincorporations on the 5` end and G>A (guanine to adenine) on the 3` strand (Briggs et al. 2007; Dabney, Meyer, and Paabo 2013). The elevated rates of nucleotide misincorporation in degraded/ancient material are a proxy of sequence validity, as modern DNA does not have this damage pattern (Krause et al. 2010; Hofreiter 2001). Post-mortem, DNA is enzymatically degraded into smaller molecules leading to an excess of short DNA fragments relative to longer DNA fragments, which contributes to a characteristic distribution of reads for DNA sequences from ancient material (Paabo 1989; Molak and Ho 2011). Strand fragmentation in DNA reads manifests as an increase of purines (due to post-mortem depurination or the loss of adenine and guanine bases) before read starts (Briggs et al. 2007; Ginolhac et al. 2011). The unique quality-filtered mapped read data for each shell category was combined with SAMtools v1.5 merge and filtered for duplicates for the total analysis (Li et al. 2009).

Results In this study we adapted existing DNA extraction protocols for use with the robust crystalline calcium matrix of conch shells. One of our primary motivations for doing so was to assess the amount and quality of DNA preserved in shell samples recovered from Late Holocene archaeological and mid-Holocene paleontological contexts. However, to be able to assess the effectiveness of our methods, we first needed to sequence and assemble both nuclear and mitochondrial reference genomes from a freshly preserved (modern) muscle tissue sample. Secondly, to assess the recovery of high-quality DNA from the mineralized shell matrix using our extraction protocol, we also generated sequencing data from DNA extracted from both modern conch muscle tissue and shell as baseline comparisons for the ancient DNA samples.

Reference Genome Assemblies. Genomic data for Strombus species are limited, with no complete genome for S. pugilis available prior to our study. We used soapdenovo v2.04 to assemble a de novo reference nuclear genome sequence from the shotgun-sequenced reads of a randomly-selected S. pugilis individual (Cayo Agua 2.3). A total of 125,083,906 raw sequence reads were used to generate a nuclear assembly with a total length of 623,741,713 base pairs (bp), which is within the range of genome lengths expected of gastropods, 432.3 – 1,865.4 Mbp (Sun et al. 2019). Our S. pugilis nuclear assembly is composed of 697,168 contigs, with GC content at 44%. The contig N50 value for this assembly, or the minimum length of contigs that cover 50% of the genome sequence, is 908 bp (see Supplementary Table 4-2 for full QUAST output). In the future, adding long-read sequencing data could help to increase the N50 value. We also assembled a 15,409 bp mitochondrial genome for S. pugilis from the same Cayo Agua 2.3 high-coverage reads. The length of our reference-guided mitochondrial genome assembly is also within the range of those of other gastropods, 13,856 – 15,461 bp (Grande, Templado, and Zardoya 2008; Márquez, Castro, and Alzate 2016). The BLAST “best hit” for this assembly to a database of complete mtDNA and plastid genomes (Altschul et al. 1990; Camacho et al. 2009) was a 7,864 bp alignment to a published Strombus (now Lobatus) gigas mitochondrion (e-value 0, bit-score 6999; see Supplementary Table 4-3 for full BLAST output; Márquez, Castro, and Alzate 2016).

50 Developing a DNA Extraction Protocol for Modern and Ancient Conch Shells. Several DNA extraction techniques have been used for mineralized substrates like ancient bone, land snail, and marine bivalve shells (Der Sarkissian et al. 2017; Gamba et al. 2014, 2016; Kemp et al. 2007; Villanea, Parent, and Kemp 2016; Yang et al. 1998). The protocol we describe here combines elements from each of these methods to address the difficulties in digesting robust conch shells. In brief, we initially digested 200 mg of freshly sanded and pulverized live- caught shell powder taken from the outer lip of the aperture (n=4) in 2 mL of SDS-based extraction buffer for 18 hours at 37°C and shaking at 750 rpm. SDS detergent is more effective than N-laurylsarcosyl in breaking down the mineralized matrix at room temperatures or higher. We added a second overnight shaking/heating step to digest more of the shell material: the samples first digested at 55°C, centrifuged to separate the extraction suspension and residual shell precipitate, their supernatant stored frozen at -20°C, and then a second overnight digest with fresh extraction buffer at 37°C. The initial fraction of supernatant was thawed at room temperature before being added back into the extraction solution. This addition of a two-day extraction period, plus Pellet Paint just before purification to help the DNA adhere to the spin column, yielded DNA for all samples. Due to the potential for intra-site variability and choice of extraction parameters in impacting DNA yield, we used samples of shell from one live-caught S. pugilis individual to measure DNA yields from (i) multiple regions of the shell and (ii) QIAquick versus MinElute DNA purification from the digestion supernatant. To experimentally test multiple regions of the shell, along with the body whorl/aperture regions previously sampled, we ground shell from the protoconch (top-most whorl of the shell) and the siphonal canal since they are also areas where shell material is added during maturation. After following the digestion protocol previously mentioned with the addition of the third overnight digestion, half of the DNA extracts were purified with QIAquick spin columns and the other half with MinElute columns. The MinElute spin-columns became visibly clogged and, likely as a consequence, purified less DNA from all of the shell regions than the QIAquick spin columns: average Qubit concentrations of 0.319±0.165 ng/µL and 0.757±0.204 ng/µL, respectively. The siphonal canal region of the shell yielded the least amount of DNA at 0.730 ng/µL, for QIAquick and 0.156 ng/µL for MinElute. The protoconch yielded concentrations of 0.974 ng/µL for QIAquick and 0.314 ng/µL for MinElute, though the protoconch proved more difficult to pulverize into fine-grained powder due to its rigid structure. Accordingly, using QIAquick silica spin columns and the easily accessible and most recently deposited outer lip shell material yielded the most DNA. The final adjustments included increasing the amount of starting material to 1.0 g of shell powder digested in 4-mL portions of the extraction buffer and using spin-column extenders during purification due to the buffer volume increase.

Comparison Among Modern, Archaeological, and Paleontological Specimens. We mapped whole-genome shotgun sequence data from samples of freshly-preserved (modern) S. pugilis tissue and shells of varying ages (age range 0-7187 BP) to our nuclear reference assembly (see Figure 4-3 A; see Supplementary Table 4-4 for filtered mapped read counts for all individuals). The muscle tissue sample group, including the one modern tissue sample we used to assemble our reference genome and four additional individuals, had an average of 15.9±20.1 million filtered reads map to the nuclear assembly out of 35.6±50.2 million total reads using the BWA long-read algorithm (v0.7.16, bwa-mem) (Li 2013). The sequencing libraries constructed from DNA extracted from modern and ancient conch shell samples (n=18) were pooled and sequenced with equimolar ratios, and then mapped to our nuclear reference genome using the BWA short-read algorithm bwa-aln v0.7.16 (Li and Durbin 2009). On average, the fresh (modern) shell samples taken from the same individuals as the tissue samples had the highest number of unique quality-filtered reads map to the S. pugilis nuclear reference assembly (33.4±36.3 thousand of 2.30±0.89 million total reads sequenced). The recently eaten and discarded modern shell samples had fewer unique quality-filtered mapped reads than the archaeological shell samples with an average of 2.05±2.20 thousand (of 3.80±0.57 million total reads sequenced) and 4.51±4.42 thousand 51 (of 3.56±1.06 million total reads sequenced), respectively. The proportions of reads that mapped to the nuclear reference assembly out of all sequenced reads from the discarded and archaeological shell samples are not significantly different (Welch two-sample t-test, p=0.2225). The shells from these groups were processed for consumption and then discarded either in soil or sea nearby, and their similar proportion of nuclear-mapped reads to total sequenced reads suggests long-term preservation of nuclear DNA in archaeological shells that is similar in quantity and quality to recently boiled shells. The paleontological shells had the fewest number of reads map to the nuclear assembly, with an average of just 44.0±44.2 (of 3.65±0.85 million total reads sequenced). The paleontological shells were likely exposed to much greater UV damage than any of the other shell groups, as evidenced by their bleached appearance. We characterized patterns of post-mortem DNA damage to assess the likelihood of endogenous nuclear DNA authenticity for each of our groups of S. pugilis shells (Figure 4-3 B; see Supplementary Figure 4-1 for individual shell results). As expected, the nuclear DNA reads for both of the modern groups of conch shells do not exhibit post-mortem DNA fragmentation patterns, though the fragment length distribution of trimmed single-end reads from the recently boiled and discarded shells are slightly shorter in length (average 96±27 bp) than those of the live-caught individuals (average 109±26 bp; Figure 4-3 B), which could be a potential impact from people boiling these shells. The authenticity of ancient DNA recovered from the archaeological and paleontological shells is confirmed based on the presence of typical molecular signatures of post-mortem DNA damage. Specifically, the sequence reads obtained from the ancient samples have shorter fragment lengths relative to those from the modern shells (average 97±28 bp for the archaeological shells, 76±28 bp for the paleontological; Figure 4-3 B). The ancient samples also exhibit higher rates of C>T nucleotide misincorporations at 5’ ends (G>A at the 3’ ends) due to post-mortem cytosine-driven deamination than the modern samples, resulting in the classic aDNA “smile” fragmentation pattern: average frequency of 0.005 for live shells, 0.010 for boiled, 0.041 for archaeological, and 0.42 for paleontological (Figure 4-3 B). Comparably, the rate of cytosine deamination in double- stranded regions (δd) is 0.0164±0.0013 for archaeological and 0.0174±0.0083 for paleontological, with lower rates in live and boiled shells (0.0003±0.0003 and 0.0034±0.0023, respectively). The more jagged “smile” fragmentation pattern and size distribution of the paleontological shells is attributed to the fewer high-quality mapped reads compared to the archaeological shells, potentially due to environmental/taphonomic conditions (e.g., bleaching by the sun in the exposed reef). We also mapped the tissue and shell sequence data to our mitochondrial reference assembly (see Figure 4-3 C; see Supplementary Table 4-4 for filtered mapped read counts for all individuals). The modern tissue samples mapped with BWA mem averaged 11.5±9.7 thousand unique quality-filtered mapped reads, and the shell samples from those same modern individuals mapped with BWA aln averaged 60±81 reads. When both the live- caught tissue and shell samples were mapped with the same program, BWA mem, the proportions of filtered mitochondrial reads to total reads were similar across both sample sources (0.071% mitochondrial reads out of total filtered mapped reads for the tissue samples, 0.074% for the shell samples; Fisher’s exact test, p=0.3691). The average proportions of filtered BWA aln-mapped mitochondrial reads relative to mitochondrial + nuclear mapped reads in the boiled and discarded modern shells (5.0±5.6) and archaeological shells (6.6±4.0) are similar to that observed for live-caught shell material (Fisher’s exact test; p=0.2247 and 0.3087, respectively). No filtered reads mapped to the mitochondrial genome for the paleontological shells, but this results still meets expectations given the ratio of nuclear:mtDNA mapped reads for modern shells (Fisher’s exact test; p=1), given the relatively small number of nuclear-mapped reads for the paleontological specimens.

52

Figure 4-3: Recovery of Strombus pugilis nuclear and mitochondrial DNA from shells of varying ages. A – Reads mapped to our S. pugilis nuclear assembly. The reference nuclear genome was assembled from the conch individual marked with a white asterisk, Cayo Agua 2.3. S. pugilis fresh tissue and fresh shell samples were collected from the same individuals. Tissue samples were mapped to the reference with BWA mem, all shells were mapped with BWA aln. B – Total nuclear DNA damage patterns and single-end read lengths for each group of S. pugilis shells. The mapped bam files were merged for each age category of shells for mapDamage nucleotide mis-incorporation analysis. Color codes for the misincorporation plots: C-to-T substitutions in red, G- to-A in blue, all other substitutions in gray, soft-clipped bases in orange, deletions or insertions relative to the reference in green or purple, respectively. Color codes for the length plots: positive DNA strands in red, negative strands in blue. C – Reads mapped to our S. pugilis mitochondrial assembly. The reference mitochondrial genome was assembled from the conch individual marked with a white asterisk, Cayo Agua 2.3. S. pugilis fresh tissue and fresh shell samples were collected from the same individuals. Tissue samples were mapped to the reference with BWA mem, all shells were mapped with BWA aln.

Discussion Genomics is an increasingly accessible tool for studying phylogenetics, trait evolution, adaptation, and population dynamics, and sequences for non-model organisms have been increasingly available over the past decade (Ellegren 2014). Molluscan DNA in particular is receiving attention given the fact that molluscs are an economically important marine resource, and are important for ecological, evolutionary, zooarchaeological, and even mechanical studies because of their shells (Gomes-dos-Santos et al. 2020; Ferreira et al. 2020). Molluscan shells, especially structurally robust conch shells, are a reservoir for both nuclear and mitochondrial DNA; their abundance in museum collections provides an opportunity to explore mollusc evolutionary change over time, including direct and indirect responses to human behavior. This study represents the first attempt to harness DNA from tropical marine shells. Our results demonstrate that authentic endogenous DNA can be extracted from Strombus pugilis shells, including specimens up to ~7,000 years old. DNA extraction was most successful (in terms of the number of unique mapped reads) from modern (fresh and boiled) and archaeological conchs, while paleontological conchs (5711-7187 BP) yielded the lowest amount of DNA, potentially due to post-mortem taphonomic conditions of UV exposure at the dry reef site. Macroscopically, the outer lip aperture of the mid-Holocene paleontological shells exhibited color bleaching and more brittle textures compared to the previously buried Late Holocene archaeological shells, as such we recommend researchers evaluate the integrity of this region when selecting samples for ancient DNA analysis. Our conch shell extraction protocol would be compatible with enrichment methods applications (e.g., hybridization-based DNA capture), which would further enhance the efficiency of 53 nuclear and mitochondrial DNA reconstruction from archaeological and paleontological conch shells. Our results confirm the long-term preservation of conch DNA in shell. This finding is consistent with those of other studies with molluscan shells dating to similar time periods (Der Sarkissian et al. 2017). With the ability to extract and sequence high-quality DNA from both modern and ancient conch shells, there is exciting potential in expanding the temporal range of evolutionary studies using these large marine snails across a range of contexts, including archaeological middens and pre-human sites. For our own research, we hope to identify genomic regions that correlate to the decreased body size phenotype via a genome- wide association study, use population genetics to test the hypothesis of natural selection on alleles associated with that phenotype, and incorporate ancient DNA for direct evidence of allele frequency change over time. There is further potential for future studies on S. pugilis utilizing the methods presented here. S. pugilis ranges from the Northern Caribbean to Brazil and has experienced vastly different degrees of intensity and duration of human harvesting, as documented in archaeological shell deposits and historical documents across this range. In some regions this conch species was commonly consumed in the past but is now rarely eaten. In other regions S. pugilis remains a popular and economically important delicacy or is frequently harvested for its shell, which is frequently sold as a souvenir for tourists. In both cases it is highly likely that harvesters focus on the largest individuals. Unpublished data show that size at maturity varies considerably across these gradients of human selection. Although further study is needed, evidence exists suggesting that when harvesting pressure is removed from a population, S. pugilis has larger body sizes compared to adjacent populations that continue to be harvested (O’Dea et al. 2014). Paleo- and modern genomics studies of widely harvested animals such as S. pugilis could therefore represent an important tool to better understand the impacts of human-selective harvesting, and could guide management approaches to slow, or even reverse, deleterious effects of human-induced evolution in wild populations. These general methods can also be applied to fossil and archaeological material more broadly to facilitate new explorations into ancient metagenomic evolutionary biology of carbonate- bound DNA.

Data, Code, and Materials Raw sequence reads and reference genomes are available at NCBI SRA BioProject: PRJNA655996. All code developed and used for this research work are stored in GitHub (https://github.com/AlexisPSullivan/Conch) and have been archived within the Zenodo repository (doi: 10.5281/zenodo.3993744).

54 APPENDICES Appendix A: Supplementary Materials for Chapter 2

Supplementary Table 2-1: List of modern Beza Mahafaly Propithecus verreauxi individuals and measured long bones (femur, humerus) DRAWER SAMPLE ID # SCANNED # SCANNED DATE #* # ELEMENTS FEMORA # SCANNED HUMERI RECOVERED 3 (right broken into 1 BMOC004 5 2 two pieces) 7/17/93 1 BMOC125 1 0 1 (right) 6/10/03 1 BMOC008 4 2 2 1/27/94 1 BMOC019 4 2 2 1 BMOC172 4 2 2 3/31/08 1 BMOC030 2 1 (right) 1 (right) 8/1/01 1 BMOC001 4 2 2 7/23/93 1 BMOC015 4 2 2 7/11/94 1 BMOC073 4 2 2 8/4/01 1 BMOC043 2 0 2 1 BMOC020 4 2 2 10/25/94 2 BMOC035 1 1 (right) 0 2 BMOC169 1 1 (right) 0 2 Fogel 2012 2 0 2 2/11/12 2 BMOC174 4 2 2 1/17/09 2 BMOC005 2 2 0 8/12/94 2 BMOC075 4 2 2 8/-/2001 2 BMOC021 2 2 0 1/11/95 2 BMOC156 1 1 (right) 0 6/30/06 2 BMOC197 4 2 2 6/28/06 2 BMOC157 4 2 2 7/3/06 2 BMOC014 2 2 0 6/21/94 2 BMOC142 4 2 2 8/1/05 2 BMOC180 3 1 (left) 2 11/27/07 2 BMOC028 4 2 2 2 BMOC191 1 1 (left) 0 After 9/2008 2 BMOC163 1 1 (left) 0 6/29/06 2 BMOC173 4 2 2 2 BMOC137 4 2 2 7/17/05 5 Indiv. #570 4 2 2 6/-/2012 5 Indiv. #550 4 2 2 5/-/2008 *Top- 49 (25 right, 24 >bottom 94 left) 45 (23 right, 21 left)

55 Supplementary Table 2-1 continued. SAMPLE ID COLLAR # FINDER LOCATION NUMBER/FN ID # SEX 20-30m West of Blue W/4m North BMOC004 Kashka Kubzdela of the trail South of Blue 1 Blue 1 between Orange W and Pink BMOC125 Krista Fish W but closer to Orange W 10m East of Blue W/3m North of BMOC008 Kashka Kubzdela Blue 2 3m West of Red W/45m North of BMOC019 Esoes Pink 3 40 BMOC172 Enafa 549 Male BMOC030 North of Pink 1/East of Orange W BMOC001 Kashka Kubzdela 20m East of Yellow W/Pink 2 242 2m East of Yellow W/30m North of BMOC015 Kashka Kubzdela Blue 3 204 Female Sifaka monitoring team BMOC073 (Diane Brockman) South of Blue 2/East of center 156 BMOC043 50m South of Pink 2/8m East of BMOC020 Esoahere and Esoes Yellow W 26 BMOC035 BMOC169 Orange E - river trail, Pink 2 - Pink3 Fogel 2012 Andy Fogel (23 39.041 S, 44 58.126 E) BMOC174 Enafa South Pink 2/West of Orange W 288 Male 50m West of Black/20m South of BMOC005 Enafa and Kashka Kubzdela Pink 1 74 Female BMOC075 Julie Parks Just West of center/North of Pink 2 224 30m North of Pink/25m West of BMOC021 Zavie Green W BMOC156 Within Parcel 1, near northern BMOC197 Anne Axel and Max boundary of res. BMOC157 25m West of Pink E/50m North of BMOC014 Anje Van Berckelaer Blue 2 ~10m South of Pink 2, 3m East of BMOC142 Diane Brockman Orange W 9093 Male BMOC180 Efitria Pink 4 5m from Red BMOC028 179 BMOC191 Efitria Parcel 1 764 Male BMOC163 Anne Axel BMOC173 Enafa North of Pink 1/East of Yellow E 367 Female Anne Axel, E. Between Pink 2 and 3, close to Green BMOC137 Rasoazanabary, L. Godfrey W and Blue W 282/8 100m West of Beza camp, across N/S Indiv. #570 Katie Grogan and Tahiri side road Nord vala nord (13m) SW la piste de Indiv. #550 Mahazarivo (red) Male

56 Supplementary Table 2-1 continued. SAMPLE ID # OTHER RECORDED INFORMATION BMOC004 Black collar BMOC125 BMOC008 BMOC019 Group Zavma1 BMOC172 Last seen 2/2008, group Saksud BMOC030 BMOC001 BMOC015 Found 5/12/1994 (dead ~16 days), group Lavaka, born 1981 BMOC073 BMOC043 BMOC020 Sifaka mate nimiro BMOC035 BMOC169 Fogel 2012 Indiv. # 288 (96), Borety; last seen 1/2007, discovered on a tree (7/2007), mort par un diarhee, BMOC174 buried by Bob Dewar (2007); broken left humerus BMOC005 Group Nify, born 1987, died 8/1993 BMOC075 BMOC021 BMOC156 Designated "Prop E"; WGS 84, UTM 38S, 460704, 7383447; rat damage Designated "Prop H 2006", grey collar, no tag; UTM 38S, WGS 84, 0462526, 7384463; older individual, skull (cranium and mandible), assorted postcrania including ilium, ischium, femora, BMOC197 ribs, tibia, 1 ulna, 1 clavicle, 1 scapula Designated "Prop F"; UTM 38S, WGS 84, 462571, 7384986; assorted post crania, *Kristi Lewton - BMOC157 3 tibia in bag BMOC014 Found with dry stomach contents BMOC142 Complete skeleton, 11 years old, group Borety BMOC180 Skull and mandible (complete), post crania bones BMOC028 BMOC191 Last seen 9/2008, NB: no data, skull, mandible, femur Designated "Prop L 2006"; UTM 38S, WGS 84, 0461187, 7383361; very worn teeth, scavenger damage, cranium and mandible, assorted postcranial material including 1 tibia, femora, left and BMOC163 right innominate, 2 ulna, 1 clavicle, sacrum, 1 partial fibula, misc. vertebrae and phallanges BMOC173 Born 1998, died 7/2008; group Vavigoa, Jonarisoa and Jadry BMOC137 Indiv. #570 Indiv. #550 Group NW1GPMAIO8, Nico group, Mont de la janvier 2009, Ralaivao

57 Supplementary Table 2-2: Modern and subfossil Propithecus verreauxi radial caliper measurements and fold-changes RAW MEASUREMENT (mm) SIDE ELEMENT AGE COLLECTION SAMPLE ID MFL MFD MFC FHH FHW FBE Right Femur Modern BMOC 550 169.34 8.59 28.14 11.71 12 18.96 Right Femur Modern BMOC 570 172.72 9.05 29.47 11.66 11.84 19.46 Right Femur Modern BMOC BMOC001 187.4 8.24 29.02 11.92 12.2 20.28 Right Femur Modern BMOC BMOC004 173.9 8.51 29.79 11.5 11.82 20.06 Right Femur Modern BMOC BMOC005 185.44 9.13 30.48 12.53 12.93 20.54 Right Femur Modern BMOC BMOC008 187.04 8.73 29.88 13.19 13.74 20.71 Right Femur Modern BMOC BMOC014 172.19 8.62 29.19 11.94 12.43 20.54 Right Femur Modern BMOC BMOC015 166.48 8.22 27.88 12.7 12.56 19.08 Right Femur Modern BMOC BMOC019 180.74 8.89 29.98 12.6 12.27 21.23 Right Femur Modern BMOC BMOC020 161.88 8.72 29.9 11.81 12.07 19.61 Right Femur Modern BMOC BMOC021 195.32 8.75 29.54 12.13 12.27 19.71 Right Femur Modern BMOC BMOC028 NA NA NA NA NA 20.06 Right Femur Modern BMOC BMOC030 NA 9.26 30.47 12.34 12.82 NA Right Femur Modern BMOC BMOC035 178.4 8.96 30.52 11.81 11.99 19.45 Right Femur Modern BMOC BMOC073 174.16 8.52 28.54 12.64 12.71 19.91 Right Femur Modern BMOC BMOC075 NA 8.33 27.58 11.95 12.45 20.03 Right Femur Modern BMOC BMOC137 186.57 9.04 31.26 13.21 13.65 21.93 Right Femur Modern BMOC BMOC142 176.94 9.05 29.71 13.19 13.3 20.06 Right Femur Modern BMOC BMOC156 180.63 9.9 30.49 12.12 11.73 20.33 Right Femur Modern BMOC BMOC157 188.36 8.89 29.46 12.39 12.91 19.84 Right Femur Modern BMOC BMOC169 179.27 8.89 28.42 8.05 8.26 21.5 Right Femur Modern BMOC BMOC172 168.99 8.32 25.36 11.88 12 19.26 Right Femur Modern BMOC BMOC173 181.36 8.34 27.59 12.03 12.28 19.84 Right Femur Modern BMOC BMOC174 182.7 8.86 28.05 12.12 12.26 20.66 Right Femur Modern BMOC BMOC197 NA 8.77 27.36 11.65 11.68 NA Right Femur Subfossil TAO TAO-66-21 NA NA NA 13.86 13.62 NA Right Femur Subfossil TAO TAO-66-25 NA NA NA 13.21 13.18 NA Right Femur Subfossil TAO TAO-66-32 NA NA NA NA NA 21.48

58 Supplementary Table 2-2 continued. FOLD-CHANGE (from modern geoMean (mm)) SAMPLE MFL MFD MFC FHH FHW FBE FOLD-CHANGE SIDE ELEMENT ID (178.38) (8.77) (29.06) (12.00) (12.21) (20.12) (SIDED) AVE. Right Femur 550 -0.05 -0.02 -0.03 -0.02 -0.02 -0.06 -0.03 Right Femur 570 -0.03 0.03 0.01 -0.03 -0.03 -0.03 -0.01 Right Femur BMOC001 0.05 -0.06 0 -0.01 0 0.01 0 Right Femur BMOC004 -0.03 -0.03 0.03 -0.04 -0.03 0 -0.02 Right Femur BMOC005 0.04 0.04 0.05 0.04 0.06 0.02 0.04 Right Femur BMOC008 0.05 0 0.03 0.1 0.13 0.03 0.05 Right Femur BMOC014 -0.03 -0.02 0 0 0.02 0.02 0 Right Femur BMOC015 -0.07 -0.06 -0.04 0.06 0.03 -0.05 -0.02 Right Femur BMOC019 0.01 0.01 0.03 0.05 0 0.06 0.03 Right Femur BMOC020 -0.09 -0.01 0.03 -0.02 -0.01 -0.03 -0.02 Right Femur BMOC021 0.09 0 0.02 0.01 0 -0.02 0.02 Right Femur BMOC028 NA NA NA NA NA 0 0 Right Femur BMOC030 NA 0.06 0.05 0.03 0.05 NA 0.05 Right Femur BMOC035 0 0.02 0.05 -0.02 -0.02 -0.03 0 Right Femur BMOC073 -0.02 -0.03 -0.02 0.05 0.04 -0.01 0 Right Femur BMOC075 NA -0.05 -0.05 0 0.02 0 -0.02 Right Femur BMOC137 0.05 0.03 0.08 0.1 0.12 0.09 0.08 Right Femur BMOC142 -0.01 0.03 0.02 0.1 0.09 0 0.04 Right Femur BMOC156 0.01 0.13 0.05 0.01 -0.04 0.01 0.03 Right Femur BMOC157 0.06 0.01 0.01 0.03 0.06 -0.01 0.03 Right Femur BMOC169 0.01 0.01 -0.02 -0.33 -0.32 0.07 -0.1 Right Femur BMOC172 -0.05 -0.05 -0.13 -0.01 -0.02 -0.04 -0.05 Right Femur BMOC173 0.02 -0.05 -0.05 0 0.01 -0.01 -0.01 Right Femur BMOC174 0.02 0.01 -0.03 0.01 0 0.03 0.01 Right Femur BMOC197 NA 0 -0.06 -0.03 -0.04 NA -0.03 Right Femur TAO-66-21 NA NA NA 0.16 0.12 NA 0.14 Right Femur TAO-66-25 NA NA NA 0.1 0.08 NA 0.09 Right Femur TAO-66-32 NA NA NA NA NA 0.07 0.07

59 Supplementary Table 2-2 continued. RAW MEASUREMENT (mm) SIDE ELEMENT AGE COLLECTION SAMPLE ID MFL MFD MFC FHH FHW FBE Left Femur Modern BMOC 550 167.34 8.24 28.31 11.83 12.04 19.41 Left Femur Modern BMOC 570 174.18 9.04 29.94 11.74 11.94 19.47 Left Femur Modern BMOC BMOC001 187.64 8.05 27.99 12.14 12.28 20.02 Left Femur Modern BMOC BMOC004 174.3 8.9 29.2 11.51 11.83 20.12 Left Femur Modern BMOC BMOC005 NA 9.1 30.09 NA NA 20.42 Left Femur Modern BMOC BMOC008 187.63 8.5 29.83 13.39 13.57 20.64 Left Femur Modern BMOC BMOC014 NA 8.22 23.8 NA NA 20.72 Left Femur Modern BMOC BMOC015 165.93 8.4 27.49 12.8 12.72 18.98 Left Femur Modern BMOC BMOC019 NA 8.85 31.55 NA NA NA Left Femur Modern BMOC BMOC020 167.05 8.7 29.62 11.97 12.22 19.84 Left Femur Modern BMOC BMOC021 174.93 8.62 30.02 12.23 12.37 19.56 Left Femur Modern BMOC BMOC028 185.8 8.54 30.5 12.52 12.7 20.36 Left Femur Modern BMOC BMOC073 174.65 8.62 28.45 12.2 12.55 19.76 Left Femur Modern BMOC BMOC075 171.54 8.29 27.88 12.09 12.47 20.12 Left Femur Modern BMOC BMOC137 187.06 9.28 31.66 13.41 13.56 21.76 Left Femur Modern BMOC BMOC142 177.34 8.78 29.02 13.1 13.21 20.25 Left Femur Modern BMOC BMOC157 189.02 8.63 29.9 12.4 12.84 20.73 Left Femur Modern BMOC BMOC163 NA 9.42 32.87 13.37 13.66 NA Left Femur Modern BMOC BMOC172 168.54 8.55 26.75 11.95 11.88 18.88 Left Femur Modern BMOC BMOC173 180.75 8.31 27.23 12.18 12.34 20.21 Left Femur Modern BMOC BMOC174 183.16 8.9 28.72 12.35 12.39 20.61 Left Femur Modern BMOC BMOC180 NA 9.04 29.95 12.02 12.82 NA Left Femur Modern BMOC BMOC191 NA 8.55 28.21 NA NA 20.56 Left Femur Modern BMOC BMOC197 172.43 8.51 28.48 11.7 11.69 19.73 Left Femur Subfossil TAO TAO-66-23 NA NA NA 13.06 13.52 NA Left Femur Subfossil TAO TAO-66-24 NA NA NA 13.24 13.1 NA Left Femur Subfossil TAO TAO-66-30 NA NA NA NA NA 21.23 Left Femur Subfossil TAO TAO-66-34 NA NA NA NA NA 20.99

60 Supplementary Table 2-2 continued. FOLD-CHANGE (from modern geoMean (mm)) FOLD- CHANGE SAMPLE MFL MFD MFC FHH FHW FBE (SIDED) SIDE ELEMENT COLLECTION ID (177.18) (8.66) (29.00) (12.33) (12.54) (20.09) AVE. Left Femur BMOC 550 -0.05 -0.05 -0.02 -0.04 -0.04 -0.03 -0.04 Left Femur BMOC 570 -0.02 0.04 0.03 -0.05 -0.05 -0.03 -0.01 Left Femur BMOC BMOC001 0.06 -0.07 -0.03 -0.02 -0.02 0 -0.01 Left Femur BMOC BMOC004 -0.02 0.03 0.01 -0.07 -0.06 0 -0.02 Left Femur BMOC BMOC005 NA 0.05 0.04 NA NA 0.02 0.03 Left Femur BMOC BMOC008 0.06 -0.02 0.03 0.09 0.08 0.03 0.04 Left Femur BMOC BMOC014 NA -0.05 -0.18 NA NA 0.03 -0.07 Left Femur BMOC BMOC015 -0.06 -0.03 -0.05 0.04 0.01 -0.06 -0.02 Left Femur BMOC BMOC019 NA 0.02 0.09 NA NA NA 0.05 Left Femur BMOC BMOC020 -0.06 0 0.02 -0.03 -0.03 -0.01 -0.02 Left Femur BMOC BMOC021 -0.01 0 0.04 -0.01 -0.01 -0.03 0 Left Femur BMOC BMOC028 0.05 -0.01 0.05 0.02 0.01 0.01 0.02 Left Femur BMOC BMOC073 -0.01 0 -0.02 -0.01 0 -0.02 -0.01 Left Femur BMOC BMOC075 -0.03 -0.04 -0.04 -0.02 -0.01 0 -0.02 Left Femur BMOC BMOC137 0.06 0.07 0.09 0.09 0.08 0.08 0.08 Left Femur BMOC BMOC142 0 0.01 0 0.06 0.05 0.01 0.02 Left Femur BMOC BMOC157 0.07 0 0.03 0.01 0.02 0.03 0.03 Left Femur BMOC BMOC163 NA 0.09 0.13 0.08 0.09 NA 0.1 Left Femur BMOC BMOC172 -0.05 -0.01 -0.08 -0.03 -0.05 -0.06 -0.05 Left Femur BMOC BMOC173 0.02 -0.04 -0.06 -0.01 -0.02 0.01 -0.02 Left Femur BMOC BMOC174 0.03 0.03 -0.01 0 -0.01 0.03 0.01 Left Femur BMOC BMOC180 NA 0.04 0.03 -0.03 0.02 NA 0.02 Left Femur BMOC BMOC191 NA -0.01 -0.03 NA NA 0.02 -0.01 Left Femur BMOC BMOC197 -0.03 -0.02 -0.02 -0.05 -0.07 -0.02 -0.03 TAO-66- Left Femur TAO 23 NA NA NA 0.06 0.08 NA 0.07 TAO-66- Left Femur TAO 24 NA NA NA 0.07 0.04 NA 0.06 TAO-66- Left Femur TAO 30 NA NA NA NA NA 0.06 0.06 TAO-66- Left Femur TAO 34 NA NA NA NA NA 0.04 0.04

61 Supplementary Table 2-3: Modern and subfossil Propithecus verreauxi 3D scan Avizo measurements and fold-changes. Mod = modern samples; Sub = subfossil samples. SIDE, SAMPLE RAW MEASUREMENT (mm) ELEMENT AGE ID MHL MHD1 MHD2 VHD HHW BBH HHSA L, Hum Mod 550 88.15 6.69 5.54 10.71 8.28 20.22 153.45 L, Hum Mod 570 90.23 7.65 5.99 10.5 8.55 19.74 157.34 L, Hum Mod BMOC001 94.98 7.06 5.92 11.78 9.51 21.28 190.85 L, Hum Mod BMOC004 90.7 6.87 5.91 10.84 8.68 21.53 157.92 L, Hum Mod BMOC008 96.63 7.7 6.57 12.23 9.89 21.4 203.5 L, Hum Mod BMOC015 86.62 8.8 6.32 11.82 9.89 21.46 198.75 L, Hum Mod BMOC019 92.74 7.53 6.35 12.25 10 21.36 193.16 L, Hum Mod BMOC020 88.67 7.16 5.92 11.54 9.68 21.4 176.89 L, Hum Mod BMOC028 91.96 6.77 5.22 11.72 9.22 20.91 175.46 L, Hum Mod BMOC043 NA NA NA NA NA 20.72 NA L, Hum Mod BMOC073 89.01 7.95 6.15 11.72 9.17 21.19 181.07 L, Hum Mod BMOC075 87.49 7.41 6 11.06 8.83 19.92 173.39 L, Hum Mod BMOC137 96.75 7.69 6.58 12.56 10.4 23.62 205.02 L, Hum Mod BMOC142 92.11 7.96 5.98 11.93 9.33 21.33 190.92 L, Hum Mod BMOC157 NA 7.45 6.31 11.12 8.51 NA 172.16 L, Hum Mod BMOC172 85.93 7.49 5.52 11.38 9.26 19.56 185.17 L, Hum Mod BMOC173 90.96 7.48 6.09 11.29 8.97 20.66 165.49 L, Hum Mod BMOC174 89.67 7.84 6.47 11.54 8.77 20.99 173.95 L, Hum Mod BMOC180 92.92 6.75 7.37 11.21 9.85 20.96 196.03 L, Hum Mod BMOC197 90.74 7.52 6.33 10.89 8.72 20.34 150.57 L, Hum Mod Fogel 2012 89.63 7.37 6.02 10.44 8.71 19.7 167.27 R, Hum Mod 550 88.45 6.67 6.05 10.69 8.93 20.47 154.76 R, Hum Mod 570 89.62 7.7 5.98 10.69 8.53 20.01 144.88 R, Hum Mod BMOC001 95.06 6.87 5.95 11.12 9.34 21.24 168.95 R, Hum Mod BMOC004 NA 7.06 6.61 9.03 8.81 21.38 116.03 R, Hum Mod BMOC008 94.86 7.9 6.56 12.63 10.29 21.02 225.5 R, Hum Mod BMOC015 86.69 8.31 6.5 11.92 9.6 21.12 192.26 R, Hum Mod BMOC019 92.8 7.87 6.22 11.75 9.36 21.78 188.04 R, Hum Mod BMOC020 88.96 7.03 5.81 11.52 9.55 20.99 176.23 R, Hum Mod BMOC028 92.47 6.68 5.32 12.01 9.11 20.64 178.42 R, Hum Mod BMOC030 NA 7.53 6.66 11.05 8.85 NA 183.25 R, Hum Mod BMOC043 89.43 6.26 5.63 11.25 8.89 20.75 163.59 R, Hum Mod BMOC073 89.18 8.39 6.06 12.05 9.2 21.27 192.9 R, Hum Mod BMOC075 NA 7.56 6.07 NA NA 19.93 NA R, Hum Mod BMOC125 98.47 7.39 5.87 11.76 10.15 22.29 200.736 R, Hum Mod BMOC137 96.96 7.8 6.55 12.55 10.37 23.85 206.07 R, Hum Mod BMOC142 92.06 7.8 5.86 11.91 9.37 21.52 181.74 R, Hum Mod BMOC157 NA 7.4 6.46 NA NA 20.36 NA R, Hum Mod BMOC172 85.54 7.55 5.76 10.85 9.3 19.77 174.64 R, Hum Mod BMOC173 91.08 7.31 6.08 10.91 8.58 20.67 152.42 R, Hum Mod BMOC174 92.6 7.01 6.08 11.31 8.93 20.8 176.8 R, Hum Mod BMOC180 NA 7.7 6.21 NA NA 21.09 NA R, Hum Mod BMOC197 89.99 7.37 6.22 10.47 8.44 20.19 143.86 R, Hum Mod Fogel 2012 88.98 7.92 6.37 10.97 8.85 19.56 176.91 R, Hum Sub TAO-66-2 NA NA NA 14.21 10.61 NA 248.06 Supplementary Table 2-3 continued. 62 FOLD-CHANGE (from modern geoMean (mm)) FOLD- CHANGE SIDE, MHL MHD1 MHD2 VHD HHW BBH HHSA (SIDED) ELEMENT AGE SAMPLE ID (90.79) (7.44) (6.11) (11.41) (9.19) (20.90) (177.67) AVERAGE L, Hum Mod 550 -0.03 -0.1 -0.09 -0.06 -0.1 -0.03 -0.14 -0.08 L, Hum Mod 570 -0.01 0.03 -0.02 -0.08 -0.07 -0.06 -0.11 -0.05 L, Hum Mod BMOC001 0.05 -0.05 -0.03 0.03 0.03 0.02 0.07 0.02 L, Hum Mod BMOC004 0 -0.08 -0.03 -0.05 -0.06 0.03 -0.11 -0.04 L, Hum Mod BMOC008 0.06 0.03 0.07 0.07 0.08 0.02 0.15 0.07 L, Hum Mod BMOC015 -0.05 0.18 0.03 0.04 0.08 0.03 0.12 0.06 L, Hum Mod BMOC019 0.02 0.01 0.04 0.07 0.09 0.02 0.09 0.05 L, Hum Mod BMOC020 -0.02 -0.04 -0.03 0.01 0.05 0.02 0 0 L, Hum Mod BMOC028 0.01 -0.09 -0.15 0.03 0 0 -0.01 -0.03 L, Hum Mod BMOC043 NA NA NA NA NA -0.01 NA -0.01 L, Hum Mod BMOC073 -0.02 0.07 0.01 0.03 0 0.01 0.02 0.02 L, Hum Mod BMOC075 -0.04 0 -0.02 -0.03 -0.04 -0.05 -0.02 -0.03 L, Hum Mod BMOC137 0.07 0.03 0.08 0.1 0.13 0.13 0.15 0.1 L, Hum Mod BMOC142 0.01 0.07 -0.02 0.05 0.01 0.02 0.07 0.03 L, Hum Mod BMOC157 NA 0 0.03 -0.03 -0.07 NA -0.03 -0.02 L, Hum Mod BMOC172 -0.05 0.01 -0.1 0 0.01 -0.06 0.04 -0.02 L, Hum Mod BMOC173 0 0.01 0 -0.01 -0.02 -0.01 -0.07 -0.02 L, Hum Mod BMOC174 -0.01 0.05 0.06 0.01 -0.05 0 -0.02 0.01 L, Hum Mod BMOC180 0.02 -0.09 0.21 -0.02 0.07 0 0.1 0.04 L, Hum Mod BMOC197 0 0.01 0.04 -0.05 -0.05 -0.03 -0.15 -0.03 L, Hum Mod Fogel 2012 -0.01 -0.01 -0.02 -0.09 -0.05 -0.06 -0.06 -0.04 R, Hum Mod 550 -0.03 -0.1 -0.01 -0.05 -0.03 -0.02 -0.11 -0.05 R, Hum Mod 570 -0.02 0.04 -0.02 -0.05 -0.07 -0.04 -0.16 -0.05 R, Hum Mod BMOC001 0.04 -0.07 -0.03 -0.02 0.01 0.02 -0.02 -0.01 R, Hum Mod BMOC004 NA -0.05 0.08 -0.2 -0.04 0.02 -0.33 -0.09 R, Hum Mod BMOC008 0.04 0.06 0.07 0.12 0.12 0 0.3 0.1 R, Hum Mod BMOC015 -0.05 0.12 0.06 0.06 0.04 0.01 0.11 0.05 R, Hum Mod BMOC019 0.02 0.06 0.02 0.04 0.02 0.04 0.09 0.04 R, Hum Mod BMOC020 -0.02 -0.05 -0.05 0.02 0.04 0 0.02 -0.01 R, Hum Mod BMOC028 0.01 -0.1 -0.13 0.06 -0.01 -0.01 0.03 -0.02 R, Hum Mod BMOC030 NA 0.01 0.09 -0.02 -0.04 NA 0.06 0.02 R, Hum Mod BMOC043 -0.02 -0.16 -0.08 0 -0.03 -0.01 -0.06 -0.05 R, Hum Mod BMOC073 -0.02 0.13 -0.01 0.07 0 0.02 0.11 0.04 R, Hum Mod BMOC075 NA 0.02 -0.01 NA NA -0.05 NA -0.01 R, Hum Mod BMOC125 0.08 0 -0.04 0.04 0.1 0.07 0.16 0.06 R, Hum Mod BMOC137 0.06 0.05 0.07 0.11 0.13 0.14 0.19 0.11 R, Hum Mod BMOC142 0.01 0.05 -0.04 0.05 0.02 0.03 0.05 0.02 R, Hum Mod BMOC157 NA 0 0.06 NA NA -0.03 NA 0.01 R, Hum Mod BMOC172 -0.06 0.02 -0.06 -0.04 0.01 -0.06 0.01 -0.03 R, Hum Mod BMOC173 0 -0.01 -0.01 -0.03 -0.07 -0.01 -0.12 -0.04 R, Hum Mod BMOC174 0.02 -0.06 -0.01 0 -0.03 -0.01 0.02 -0.01 R, Hum Mod BMOC180 NA 0.04 0.02 NA NA 0.01 NA 0.02 R, Hum Mod BMOC197 -0.01 -0.01 0.02 -0.07 -0.08 -0.03 -0.17 -0.05 R, Hum Mod Fogel 2012 -0.02 0.07 0.04 -0.03 -0.04 -0.07 0.02 0 R, Hum Sub TAO-66-2 NA NA NA 0.26 0.15 NA 0.43 0.28 Supplementary Table 2-3 continued. 63 RAW MEASUREMENT (mm) SIDE, ELEMENT AGE SAMPLE ID MFL MFD FMD DSA FHH FHW FHSA FBE DML1 DML2 R, Fem Mod 550 169.34 8.45 8.57 630.44 11.85 11.74 323.02 18.85 4.9 6.45 R, Fem Mod 570 172.58 8.69 9.3 622.63 11.65 11.73 300.15 18.92 4.12 5.83 R, Fem Mod BMOC001 187.31 8.22 9.31 633.68 12.21 11.86 328.32 20.16 5.09 6.77 R, Fem Mod BMOC004 174.8 8.76 9.25 532.47 11.68 11.45 323.67 19.71 4.73 6.06 R, Fem Mod BMOC005 186.45 9.14 9.81 593.86 12.88 12.47 374.67 20.32 4.87 7.2 R, Fem Mod BMOC008 NA 8.55 9.49 646.02 13.64 13.4 425.78 20.53 5.4 6.43 R, Fem Mod BMOC014 171.89 8.67 9.22 650.34 12.07 12.26 323.44 20.53 5.4 6.97 R, Fem Mod BMOC015 165.44 8.28 8.83 751.98 12.8 12.19 375.79 18.71 4.97 6.87 R, Fem Mod BMOC019 180.28 9.07 9.43 744.49 12.57 12.33 360.5 20.91 5.38 7.35 R, Fem Mod BMOC020 167.2 8.72 9.35 638.83 11.93 11.69 318.53 19.65 5.17 7.09 R, Fem Mod BMOC021 175.3 8.89 9.02 532.14 12.26 12.01 338.87 19.5 4.75 7.1 R, Fem Mod BMOC028 NA NA NA 665.23 NA NA NA 19.52 6.05 8.02 R, Fem Mod BMOC030 NA 9.52 9.46 NA 12.72 12.43 363.45 NA NA NA R, Fem Mod BMOC035 177.86 9.37 10.1 596.6 11.8 11.9 331.5 19.6 5.41 7.2 R, Fem Mod BMOC073 174.02 8.37 9.4 584.62 12.72 12.67 359.59 19.54 5.17 6.96 R, Fem Mod BMOC075 NA 8.1 8.28 643.69 11.9 12.1 354.95 19.13 4.48 6.5 R, Fem Mod BMOC137 186.91 8.93 9.99 641.76 13.53 13.4 408.93 21.86 5.29 8.03 R, Fem Mod BMOC142 177.54 8.83 9.18 673.35 13.26 12.66 390.47 20.13 5.03 7.12 R, Fem Mod BMOC156 180.38 10.02 9.14 564.04 11.97 11.86 314.01 19.85 4.55 6.76 R, Fem Mod BMOC157 187.93 8.56 9.46 614.2 12.69 12.61 388.85 19.65 4.35 6.48 R, Fem Mod BMOC169 179.64 8.76 9.22 639.66 12.87 13.15 405.4 21.07 5.16 6.95 R, Fem Mod BMOC172 168.54 8.25 8.52 612.65 12.25 11.92 339.71 18.74 4.81 6.55 R, Fem Mod BMOC173 181.07 8.12 8.8 577.39 12.05 12.23 310.19 19.87 4.93 6.91 R, Fem Mod BMOC174 183.22 8.68 8.97 603.95 12.17 12.04 327.55 19.84 5.07 6.68 R, Fem Mod BMOC197 NA NA NA NA 11.6 11.37 296.7 NA NA NA R, Fem Sub TAO-66-21 NA NA NA NA 13.62 13.31 428.74 NA NA NA R, Fem Sub TAO-66-25 NA NA NA NA 12.9 13.01 352.67 NA NA NA R, Fem Sub TAO-66-26 NA NA NA NA NA NA NA NA NA 7 R, Fem Sub TAO-66-29 NA NA NA NA NA NA NA 19.16 NA 7.38 R, Fem Sub TAO-66-32 NA NA NA NA NA NA NA 21.24 NA 8.12 R, Fem Sub TAO-66-33 NA NA NA NA NA NA NA NA NA 8.54

64 Supplementary Table 2-3 continued. FOLD-CHANGE (from modern geoMean (mm)) SIDE, SAMPLE MFL MFD FMD DSA FHH FHW FHSA FBE DML1 DML2 ELEMENT ID (177.26) (8.73) (9.21) (623.65) (12.37) (12.22) (347.59) (19.84) (4.99) (6.86) R, Fem 550 -0.04 -0.03 -0.07 0.01 -0.04 -0.04 -0.07 -0.05 -0.02 -0.06 R, Fem 570 -0.03 0 0.01 0 -0.06 -0.04 -0.14 -0.05 -0.17 -0.15 R, Fem BMOC001 0.06 -0.06 0.01 0.02 -0.01 -0.03 -0.06 0.02 0.02 -0.01 R, Fem BMOC004 -0.01 0 0 -0.15 -0.06 -0.06 -0.07 -0.01 -0.05 -0.12 R, Fem BMOC005 0.05 0.05 0.06 -0.05 0.04 0.02 0.08 0.02 -0.02 0.05 R, Fem BMOC008 NA -0.02 0.03 0.04 0.1 0.1 0.22 0.03 0.08 -0.06 R, Fem BMOC014 -0.03 -0.01 0 0.04 -0.02 0 -0.07 0.03 0.08 0.02 R, Fem BMOC015 -0.07 -0.05 -0.04 0.21 0.04 0 0.08 -0.06 0 0 R, Fem BMOC019 0.02 0.04 0.02 0.19 0.02 0.01 0.04 0.05 0.08 0.07 R, Fem BMOC020 -0.06 0 0.02 0.02 -0.04 -0.04 -0.08 -0.01 0.04 0.03 R, Fem BMOC021 -0.01 0.02 -0.02 -0.15 -0.01 -0.02 -0.03 -0.02 -0.05 0.03 R, Fem BMOC028 NA NA NA 0.07 NA NA NA -0.02 0.21 0.17 R, Fem BMOC030 NA 0.09 0.03 NA 0.03 0.02 0.05 NA NA NA R, Fem BMOC035 0 0.07 0.1 -0.04 -0.05 -0.03 -0.05 -0.01 0.08 0.05 R, Fem BMOC073 -0.02 -0.04 0.02 -0.06 0.03 0.04 0.03 -0.01 0.04 0.01 R, Fem BMOC075 NA -0.07 -0.1 0.03 -0.04 -0.01 0.02 -0.04 -0.1 -0.05 R, Fem BMOC137 0.05 0.02 0.08 0.03 0.09 0.1 0.18 0.1 0.06 0.17 R, Fem BMOC142 0 0.01 0 0.08 0.07 0.04 0.12 0.01 0.01 0.04 R, Fem BMOC156 0.02 0.15 -0.01 -0.1 -0.03 -0.03 -0.1 0 -0.09 -0.02 R, Fem BMOC157 0.06 -0.02 0.03 -0.02 0.03 0.03 0.12 -0.01 -0.13 -0.06 R, Fem BMOC169 0.01 0 0 0.03 0.04 0.08 0.17 0.06 0.03 0.01 R, Fem BMOC172 -0.05 -0.05 -0.08 -0.02 -0.01 -0.02 -0.02 -0.06 -0.04 -0.05 R, Fem BMOC173 0.02 -0.07 -0.04 -0.07 -0.03 0 -0.11 0 -0.01 0.01 R, Fem BMOC174 0.03 -0.01 -0.03 -0.03 -0.02 -0.01 -0.06 0 0.02 -0.03 R, Fem BMOC197 NA NA NA NA -0.06 -0.07 -0.15 NA NA NA TAO-66- R, Fem 21 NA NA NA NA 0.1 0.09 0.23 NA NA NA TAO-66- R, Fem 25 NA NA NA NA 0.04 0.06 0.01 NA NA NA TAO-66- R, Fem 26 NA NA NA NA NA NA NA NA NA 0.02 TAO-66- R, Fem 29 NA NA NA NA NA NA NA -0.03 NA 0.08 TAO-66- R, Fem 32 NA NA NA NA NA NA NA 0.07 NA 0.18 TAO-66- R, Fem 33 NA NA NA NA NA NA NA NA NA 0.24

65 Supplementary Table 2-3 continued.

SIDE, ELEMENT AGE SAMPLE ID FOLD-CHANGE (SIDED) AVERAGE R, Fem Mod 550 -0.04 R, Fem Mod 570 -0.06 R, Fem Mod BMOC001 0 R, Fem Mod BMOC004 -0.05 R, Fem Mod BMOC005 0.03 R, Fem Mod BMOC008 0.06 R, Fem Mod BMOC014 0.01 R, Fem Mod BMOC015 0.01 R, Fem Mod BMOC019 0.05 R, Fem Mod BMOC020 -0.01 R, Fem Mod BMOC021 -0.02 R, Fem Mod BMOC028 0.11 R, Fem Mod BMOC030 0.04 R, Fem Mod BMOC035 0.01 R, Fem Mod BMOC073 0 R, Fem Mod BMOC075 -0.04 R, Fem Mod BMOC137 0.09 R, Fem Mod BMOC142 0.04 R, Fem Mod BMOC156 -0.02 R, Fem Mod BMOC157 0 R, Fem Mod BMOC169 0.04 R, Fem Mod BMOC172 -0.04 R, Fem Mod BMOC173 -0.03 R, Fem Mod BMOC174 -0.01 R, Fem Mod BMOC197 -0.09 R, Fem Sub TAO-66-21 0.14 R, Fem Sub TAO-66-25 0.04 R, Fem Sub TAO-66-26 0.02 R, Fem Sub TAO-66-29 0.02 R, Fem Sub TAO-66-32 0.13 R, Fem Sub TAO-66-33 0.24

66 Supplementary Table 2-3 continued. SIDE, RAW MEASUREMENT (mm) ELEMENT AGE SAMPLE ID MFL MFD FMD DSA FHH FHW FHSA FBE DML1 DML2 L, Fem Mod 550 167.06 8.36 9.01 498.82 11.64 11.74 296.91 18.18 4.16 6.34 L, Fem Mod 570 174.19 8.98 9.24 496.7 11.88 11.62 292.88 18.95 4.06 5.85 L, Fem Mod BMOC001 187.48 8.12 9.41 588.34 12.2 12.22 336.07 19.62 5.24 6.25 L, Fem Mod BMOC004 174.07 9.3 8.24 586.61 11.58 11.69 304.75 19.6 4.14 6.22 L, Fem Mod BMOC005 NA 9.18 9.76 561.73 NA NA NA 20.31 4.05 7.36 L, Fem Mod BMOC008 187.32 7.81 9.32 625.63 13.27 13.43 414.4 20.67 4.85 7.01 L, Fem Mod BMOC014 172.19 8.1 9.6 547.06 NA NA NA 20.34 4.69 6.6 L, Fem Mod BMOC015 165.79 8.23 9.04 514.23 12.74 12.48 368.81 18.93 4.68 6.7 L, Fem Mod BMOC019 NA 8.68 9.24 NA NA NA NA NA NA NA L, Fem Mod BMOC020 167.11 8.81 9.25 594.97 11.93 11.87 327.92 19.97 4.89 7.49 L, Fem Mod BMOC021 175.14 8.58 9.2 540.57 12.14 12.23 351.3 19.64 4.31 6.99 L, Fem Mod BMOC028 185.84 8.27 10.58 507.32 12.63 12.4 350.76 19.85 5.18 7.44 L, Fem Mod BMOC073 174.51 8.57 9.19 654.95 12.3 12.44 344.67 19.31 4.65 6.62 L, Fem Mod BMOC075 171.84 8.25 8.34 572.46 11.88 12.26 350.31 19.64 4.36 6.27 L, Fem Mod BMOC137 188.25 9.58 10.09 688.31 13.67 13.57 419.48 21.66 4.84 7.61 L, Fem Mod BMOC142 177.38 8.33 9.27 519.6 13.22 12.92 380.2 20.14 4.35 7.62 L, Fem Mod BMOC157 189.02 8.51 9.83 623.03 12.69 12.67 363.94 20.1 4.3 6.43 L, Fem Mod BMOC163 NA 9.32 11.34 NA 13.45 13.48 409.19 NA NA NA L, Fem Mod BMOC172 168.57 8.35 8.26 524 12.01 11.76 312.55 18.9 4.26 6.64 L, Fem Mod BMOC173 181.08 8.14 8.67 523.1 12.26 12.2 327.34 19.9 4.62 7.03 L, Fem Mod BMOC174 183.46 8.72 9.25 521.47 12.32 12.24 346.58 19.72 4.61 6.93 L, Fem Mod BMOC180 NA 8.52 9.71 NA 12.88 12.81 382.92 NA NA NA L, Fem Mod BMOC191 NA 8.48 8.9 642.44 NA NA NA 20.53 4.71 6.67 L, Fem Mod BMOC197 172.51 8.25 8.88 589.82 11.76 11.41 303.97 19.52 4.26 6.2 L, Fem Sub TAO-66-23 NA NA NA NA 13.04 13.67 389.45 NA NA NA L, Fem Sub TAO-66-24 NA NA NA NA 12.99 12.93 348.53 NA NA NA L, Fem Sub TAO-66-30 NA NA NA 658.3 NA NA NA 20.5 5.31 7.04 L, Fem Sub TAO-66-31 NA NA NA NA NA NA NA NA NA 5.59 L, Fem Sub TAO-66-34 NA NA NA NA NA NA NA NA 5.23 7.57

67 Supplementary Table 2-3 continued. FOLD-CHANGE (from modern geoMean (mm)) SIDE, SAMPLE MFL MFD FMD DSA FHH FHW FHSA FBE DML1 DML2 ELEMENT ID (176.83) (8.55) (9.29) (565.14) (12.41) (12.36) (347.28) (19.77) (4.52) (6.76) - L, Fem 550 -0.055 0.022 -0.03 -0.117 -0.062 -0.05 -0.145 -0.081 -0.08 -0.062 - L, Fem 570 -0.015 0.05 0.006 -0.121 -0.043 -0.06 -0.157 -0.042 -0.102 -0.134 L, Fem BMOC001 0.06 -0.05 0.013 0.041 -0.017 -0.011 -0.032 -0.008 0.159 -0.075 - L, Fem BMOC004 -0.016 0.088 -0.113 0.038 -0.067 -0.054 -0.122 -0.009 0.084 -0.079 L, Fem BMOC005 NA 0.074 0.05 -0.006 NA NA NA 0.027 -0.104 0.089 - L, Fem BMOC008 0.059 0.086 0.003 0.107 0.069 0.087 0.193 0.045 0.073 0.038 L, Fem BMOC014 -0.026 -0.053 0.033 -0.032 NA NA NA 0.029 0.037 -0.023 L, Fem BMOC015 -0.062 -0.037 -0.027 -0.09 0.027 0.01 0.062 -0.043 0.035 -0.008 - L, Fem BMOC019 NA 0.015 0.006 NA NA NA NA NA NA NA - L, Fem BMOC020 -0.055 0.03 0.005 0.053 -0.039 -0.039 -0.056 0.01 0.082 0.109 L, Fem BMOC021 -0.01 0.004 -0.01 -0.043 -0.022 -0.01 0.012 -0.007 -0.047 0.035 - L, Fem BMOC028 0.051 0.033 0.138 -0.102 0.018 0.003 0.01 0.004 0.146 0.101 L, Fem BMOC073 -0.013 0.002 -0.011 0.159 -0.009 0.007 -0.008 -0.023 0.028 -0.02 L, Fem BMOC075 -0.028 -0.035 -0.103 0.013 -0.043 -0.008 0.009 -0.007 -0.036 -0.072 L, Fem BMOC137 0.065 0.121 0.086 0.218 0.102 0.098 0.208 0.096 0.07 0.126 - - - L, Fem BMOC142 0.003 0.026 0.003 -0.081 0.065 0.046 0.095 0.019 0.038 0.128 - L, Fem BMOC157 0.069 0.005 0.058 0.102 0.023 0.025 0.048 0.017 -0.049 -0.048 L, Fem BMOC163 NA 0.09 0.22 NA 0.084 0.091 0.178 NA NA NA - L, Fem BMOC172 -0.047 0.023 -0.111 -0.073 -0.032 -0.048 -0.1 -0.044 -0.058 -0.017 - L, Fem BMOC173 0.024 0.048 -0.067 -0.074 -0.012 -0.013 -0.057 0.006 0.022 0.041 - L, Fem BMOC174 0.038 0.02 0.005 -0.077 -0.007 -0.009 -0.002 -0.003 0.02 0.026 - L, Fem BMOC180 NA 0.003 0.045 NA 0.038 0.037 0.103 NA NA NA - - L, Fem BMOC191 NA 0.008 0.042 0.137 NA NA NA 0.038 0.042 -0.013 - L, Fem BMOC197 -0.024 -0.035 0.044 0.044 -0.052 -0.077 -0.125 -0.013 -0.058 -0.082 TAO-66- L, Fem 23 NA NA NA NA 0.051 0.106 0.121 NA NA NA TAO-66- L, Fem 24 NA NA NA NA 0.047 0.046 0.004 NA NA NA TAO-66- L, Fem 30 NA NA NA 0.165 NA NA NA 0.037 0.174 0.042 TAO-66- L, Fem 31 NA NA NA NA NA NA NA NA NA -0.173 TAO-66- L, Fem 34 NA NA NA NA NA NA NA NA 0.157 0.12

68 Supplementary Table 2-3 continued. FOLD- CHANGE SIDE, SAMPLE (SIDED) ELEMENT AGE ID AVERAGE L, Fem Mod 550 -0.07

L, Fem Mod 570 -0.06

L, Fem Mod BMOC001 0.01

L, Fem Mod BMOC004 -0.04

L, Fem Mod BMOC005 0.02

L, Fem Mod BMOC008 0.06

L, Fem Mod BMOC014 0

L, Fem Mod BMOC015 -0.01

L, Fem Mod BMOC019 0

L, Fem Mod BMOC020 0.01

L, Fem Mod BMOC021 -0.01

L, Fem Mod BMOC028 0.03

L, Fem Mod BMOC073 0.01

L, Fem Mod BMOC075 -0.03

L, Fem Mod BMOC137 0.12

L, Fem Mod BMOC142 0.02

L, Fem Mod BMOC157 0.02

L, Fem Mod BMOC163 0.13

L, Fem Mod BMOC172 -0.06

L, Fem Mod BMOC173 -0.02

L, Fem Mod BMOC174 0

L, Fem Mod BMOC180 0.04

L, Fem Mod BMOC191 0.03

L, Fem Mod BMOC197 -0.05 TAO-66- L, Fem Sub 23 0.09 TAO-66- L, Fem Sub 24 0.03 TAO-66- L, Fem Sub 30 0.1 TAO-66- L, Fem Sub 31 -0.17 TAO-66- L, Fem Sub 34 0.14

69 Supplementary Table 2-4: Average percent differences between radial caliper and 3D model measurements MODERN SUBFOSSIL % DIFFERENCE STANDARD STANDARD % DIFFERENCE % DIFFERENCE T-TEST P DEVIATION DEVIATION Right Femur -0.03 3.02 1.66 0.58 0.0465 Left Femur 0.8 1.74 1.54 0.66 0.5851 Right -2.02 7.04 0.41 1.67 N/A Humerus Left -1.48 6.47 N/A N/A N/A Humerus

70 Supplementary Table 2-5: Radial caliper versus 3D scan Avizo measurements RAW MEASUREMENT % DIFFERENCE SAMPLE (mm) AVE SIDE ELEMENT AGE COLL. ID MFL MFD FHH FHW FBE % STDEV Right Femur Modern BMOC 550 0 1.64 -1.19 2.19 0.58 0.65 1.34 Right Femur Modern BMOC 570 0.08 4.06 0.09 0.93 2.81 1.59 1.77 Right Femur Modern BMOC BMOC001 0.05 0.24 -2.4 2.83 0.59 0.26 1.86 Right Femur Modern BMOC BMOC004 -0.52 -2.9 -1.55 3.18 1.76 0 2.46 Right Femur Modern BMOC BMOC005 -0.54 -0.11 -2.75 3.62 1.08 0.26 2.34 Right Femur Modern BMOC BMOC008 2.08 -3.35 2.51 0.87 0.53 2.68 Right Femur Modern BMOC BMOC014 0.17 -0.58 -1.08 1.38 0.05 -0.01 0.93 Right Femur Modern BMOC BMOC015 0.63 -0.73 -0.78 2.99 1.96 0.81 1.66 Right Femur Modern BMOC BMOC019 0.25 -2 0.24 -0.49 1.52 -0.1 1.29 Right Femur Modern BMOC BMOC020 -3.23 0 -1.01 3.2 -0.2 -0.25 2.31 Right Femur Modern BMOC BMOC021 10.8 -1.59 -1.07 2.14 1.07 2.27 5.01 Right Femur Modern BMOC BMOC028 2.73 2.73 NA Right Femur Modern BMOC BMOC030 -2.77 -3.03 3.09 -0.9 3.46 Right Femur Modern BMOC BMOC035 0.3 -4.47 0.08 0.75 -0.77 -0.82 2.12 Right Femur Modern BMOC BMOC073 0.08 1.78 -0.63 0.32 1.88 0.68 1.1 Right Femur Modern BMOC BMOC075 2.8 0.42 2.85 4.6 2.67 1.72 Right Femur Modern BMOC BMOC137 -0.18 1.22 -2.39 1.85 0.32 0.16 1.63 Right Femur Modern BMOC BMOC142 -0.34 2.46 -0.53 4.93 -0.35 1.24 2.41 Right Femur Modern BMOC BMOC156 0.14 -1.2 1.25 -1.1 2.39 0.29 1.54 Right Femur Modern BMOC BMOC157 0.23 3.78 -2.39 2.35 0.96 0.99 2.33 Right Femur Modern BMOC BMOC169 -0.21 1.47 -46.08 -45.68 2.02 -17.69 25.74 Right Femur Modern BMOC BMOC172 0.27 0.84 -3.07 0.67 2.74 0.29 2.1 Right Femur Modern BMOC BMOC173 0.16 2.67 -0.17 0.41 -0.15 0.58 1.19 Right Femur Modern BMOC BMOC174 -0.28 2.05 -0.41 1.81 4.05 1.44 1.85 Right Femur Modern BMOC BMOC197 0.43 2.69 1.56 1.6 Right Femur Subfossil TAO TAO-66-21 1.75 2.3 2.02 0.39 Right Femur Subfossil TAO TAO-66-25 2.37 1.3 1.84 0.76 Right Femur Subfossil TAO TAO-66-32 1.12 1.12 NA

71 Supplementary Table 2-5 continued. RAW MEASUREMENT % DIFFERENCE SAMPLE (mm) AVE SIDE ELEMENT AGE COLL. ID MFL MFD FHH FHW FBE % STDEV Left Femur Modern BMOC 550 0.17 -1.45 1.62 2.52 6.54 1.88 3.01 Left Femur Modern BMOC 570 -0.01 0.67 -1.19 2.72 2.71 0.98 1.71 Left Femur Modern BMOC BMOC001 0.09 -0.87 -0.49 0.49 2.02 0.25 1.12 Left Femur Modern BMOC BMOC004 0.13 -4.4 -0.61 1.19 2.62 -0.21 2.63 Left Femur Modern BMOC BMOC005 -0.88 0.54 -0.17 1 Left Femur Modern BMOC BMOC008 0.17 8.46 0.9 1.04 -0.15 2.08 3.6 Left Femur Modern BMOC BMOC014 1.47 1.85 1.66 0.27 Left Femur Modern BMOC BMOC015 0.08 2.04 0.47 1.9 0.26 0.95 0.94 Left Femur Modern BMOC BMOC019 1.94 1.94 NA Left Femur Modern BMOC BMOC020 -0.04 -1.26 0.33 2.91 -0.65 0.26 1.6 Left Femur Modern BMOC BMOC021 -0.12 0.47 0.74 1.14 -0.41 0.36 0.63 Left Femur Modern BMOC BMOC028 -0.02 3.21 -0.87 2.39 2.54 1.45 NA Left Femur Modern BMOC BMOC073 0.08 0.58 -0.82 0.88 2.3 0.61 1.15 Left Femur Modern BMOC BMOC075 -0.17 0.48 1.75 1.7 2.41 1.23 1.05 Left Femur Modern BMOC BMOC137 -0.63 -3.18 -1.92 -0.07 0.46 -1.07 1.48 Left Femur Modern BMOC BMOC142 -0.02 5.26 -0.91 2.22 0.54 1.42 2.43 Left Femur Modern BMOC BMOC157 0 1.4 -2.31 1.33 3.09 0.7 2.01 Left Femur Modern BMOC BMOC163 1.07 -0.6 1.33 0.6 1.04 Left Femur Modern BMOC BMOC172 -0.02 2.37 -0.5 1.02 -0.11 0.55 1.16 Left Femur Modern BMOC BMOC173 -0.18 2.07 -0.65 1.14 1.55 0.78 1.16 Left Femur Modern BMOC BMOC174 -0.16 2.04 0.24 1.22 4.41 1.55 1.82 Left Femur Modern BMOC BMOC180 5.92 -6.91 0.08 -0.3 6.42 Left Femur Modern BMOC BMOC191 0.82 0.15 0.48 0.48 Left Femur Modern BMOC BMOC197 -0.05 3.1 -0.51 2.42 1.07 1.21 1.55 Left Femur Subfossil TAO TAO-66-23 0.15 -1.1 -0.48 0.89 Left Femur Subfossil TAO TAO-66-24 1.91 1.31 1.61 0.42 Left Femur Subfossil TAO TAO-66-30 3.5 3.5 NA Left Femur Subfossil TAO TAO-66-34 NA NA

72 Supplementary Table 2-5 continued. RAW MEASUREMENT % DIFFERENCE SAMPLE (mm) AVE SIDE ELEMENT AGE COLL. ID MHL MHD VHD HHW BBH % STDEV Right Humerus Modern BMOC 550 0.11 -3.82 1.85 3.63 -0.29 0.3 2.78 Right Humerus Modern BMOC 570 0.04 -21.9 1.12 5.36 -1.26 -3.33 10.68 Right Humerus Modern BMOC BMOC001 -0.56 -8.34 -1.54 3.06 0.98 -1.28 4.31 Right Humerus Modern BMOC BMOC004 -3.31 11.58 2.36 -1.94 2.17 6.72 Right Humerus Modern BMOC BMOC008 -0.24 -10.11 -3.46 1.45 -3.09 5.1 Right Humerus Modern BMOC BMOC015 0.09 -14.59 -3.67 5.37 0.9 -2.38 7.54 Right Humerus Modern BMOC BMOC019 -0.13 -9.45 -3.02 8.98 -1.34 -0.99 6.63 Right Humerus Modern BMOC BMOC020 -0.11 0.57 -7.66 2.48 -0.38 -1.02 3.88 Right Humerus Modern BMOC BMOC028 0.02 -18.84 0.42 3.03 1.01 -2.87 9 Right Humerus Modern BMOC BMOC030 0.93 -0.91 8.44 2.82 4.95 Right Humerus Modern BMOC BMOC043 0.12 12.99 -5.29 5.47 1.1 2.88 6.83 Right Humerus Modern BMOC BMOC073 0.15 -24.03 -7.76 4.57 -0.38 -5.49 11.27 Right Humerus Modern BMOC BMOC075 -3.23 1.05 -1.09 3.02 Right Humerus Modern BMOC BMOC125 -0.64 -14.36 -2.15 7.22 1.43 -1.7 7.92 Right Humerus Modern BMOC BMOC137 0.02 -11.81 -0.8 3.04 -4.5 -2.81 5.7 Right Humerus Modern BMOC BMOC142 -0.17 -18.33 1.67 5.1 0 -2.35 9.18 Right Humerus Modern BMOC BMOC157 -14.34 0.64 -6.85 10.59 Right Humerus Modern BMOC BMOC172 -0.25 -19.32 -12.33 4.11 -0.41 -5.64 9.79 Right Humerus Modern BMOC BMOC173 0.21 -14.06 3.16 4.89 -0.05 -1.17 7.49 Right Humerus Modern BMOC BMOC174 -0.08 -8.79 2.1 1.22 1.48 -0.81 4.53 Right Humerus Modern BMOC BMOC180 -13.44 -1.19 -7.32 8.66 Right Humerus Modern BMOC BMOC197 0.07 -7.9 3.84 4.29 0.2 0.1 4.89 Right Humerus Modern BMOC Fogel 2012 -0.38 -22.94 0.27 1.68 -1.08 -4.49 10.36 Right Humerus Subfossil TAO TAO-66-2 -0.78 1.59 0.41 1.67

73 Supplementary Table 2-5 continued. RAW MEASUREMENT % DIFFERENCE SAMPLE (mm) AVE SIDE ELEMENT AGE COLL. ID MHL MHD VHD HHW BBH % STDEV Left Humerus Modern BMOC 550 0.08 -3.03 4.2 7.67 1.86 2.16 4.06 Left Humerus Modern BMOC 570 -0.14 -14.29 1.89 2.2 2.06 -1.66 7.12 Left Humerus Modern BMOC BMOC001 -0.65 -13.62 -6.4 3.41 1.68 -3.12 6.94 Left Humerus Modern BMOC BMOC004 0.1 -0.15 -2.14 4.5 -0.09 0.44 2.45 Left Humerus Modern BMOC BMOC008 -0.65 -14.19 1.3 5.51 -1.89 -1.98 7.38 Left Humerus Modern BMOC BMOC015 -0.07 -14.5 -2.14 0.81 -1.64 -3.51 6.26 Left Humerus Modern BMOC BMOC019 -0.42 -2.15 -7.01 2.47 -0.61 -1.54 3.48 Left Humerus Modern BMOC BMOC020 0.05 -4.43 -5.98 2.15 -4 -2.44 3.4 Left Humerus Modern BMOC BMOC028 0.02 -22.33 3.03 3.73 -0.77 -3.27 10.83 Left Humerus Modern BMOC BMOC043 0.77 0.77 NA Left Humerus Modern BMOC BMOC073 0.24 -7.57 0.6 3.85 0.05 -0.57 4.21 Left Humerus Modern BMOC BMOC075 -0.07 -15.88 4.77 6.36 0.2 -0.92 8.82 Left Humerus Modern BMOC BMOC137 0.13 -11.55 -1.77 1.05 -3.88 -3.2 5.04 Left Humerus Modern BMOC BMOC142 0.03 -25.02 -0.76 3.79 2.27 -3.94 11.92 Left Humerus Modern BMOC BMOC157 -14.85 2.84 8.33 -1.23 12.12 Left Humerus Modern BMOC BMOC172 -0.08 1.98 -5.23 5.26 -2.59 -0.13 4.05 Left Humerus Modern BMOC BMOC173 -0.09 -18.71 -1.7 0.67 -1.46 -4.26 8.14 Left Humerus Modern BMOC BMOC174 0.01 -11.46 -0.96 3.36 -0.81 -1.97 5.59 Left Humerus Modern BMOC BMOC180 0.04 9.19 3.33 4.47 -1.93 3.02 4.29 Left Humerus Modern BMOC BMOC197 0.13 -11.24 2.18 1.82 -0.64 -1.55 5.54 Left Humerus Modern BMOC Fogel 2012 -0.21 -15.65 2.46 3.83 -0.92 -2.1 7.82

74 Supplementary Table 2-6: Aggregate scores for all modern and subfossil Propithecus verreauxi individuals AVERAGE FOLD-CHANGE RIGHT LEFT RIGHT LEFT AGGREGATE MEAS. AGE COLLECTION SAMPLE ID FEMUR FEMUR HUMERUS HUMERUS SCORE 3D Modern BMOC 550 -0.041 -0.07 -0.051 -0.079 -0.06 3D Modern BMOC 570 -0.063 -0.063 -0.048 -0.045 -0.055 3D Modern BMOC BMOC001 -0.005 0.008 -0.01 0.018 0.003 3D Modern BMOC BMOC004 -0.051 -0.042 -0.087 -0.043 -0.056 3D Modern BMOC BMOC005 0.031 0.022 NA NA 0.026 3D Modern BMOC BMOC008 0.058 0.059 0.103 0.07 0.073 3D Modern BMOC BMOC014 0.005 -0.005 NA NA 0 3D Modern BMOC BMOC015 0.01 -0.013 0.05 0.061 0.027 3D Modern BMOC BMOC019 0.054 0.005 0.04 0.049 0.037 3D Modern BMOC BMOC020 -0.012 0.009 -0.007 -0.001 -0.003 3D Modern BMOC BMOC021 -0.024 -0.01 NA NA -0.017 3D Modern BMOC BMOC028 0.108 0.034 -0.021 -0.029 0.023 3D Modern BMOC BMOC030 0.042 NA 0.02 NA 0.031 3D Modern BMOC BMOC035 0.013 NA NA NA 0.013 3D Modern BMOC BMOC043 NA NA -0.051 -0.008 -0.03 3D Modern BMOC BMOC073 0.004 0.011 0.042 0.016 0.018 3D Modern BMOC BMOC075 -0.04 -0.031 -0.012 -0.029 -0.028 3D Modern BMOC BMOC125 NA NA 0.058 NA 0.058 3D Modern BMOC BMOC137 0.089 0.119 0.108 0.099 0.104 3D Modern BMOC BMOC142 0.038 0.021 0.024 0.031 0.029 3D Modern BMOC BMOC156 -0.02 NA NA NA -0.02 3D Modern BMOC BMOC157 0.004 0.024 0.009 -0.019 0.004 3D Modern BMOC BMOC163 NA 0.133 NA NA 0.133 3D Modern BMOC BMOC169 0.044 NA NA NA 0.044 3D Modern BMOC BMOC172 -0.039 -0.055 -0.026 -0.023 -0.036 3D Modern BMOC BMOC173 -0.03 -0.018 -0.037 -0.016 -0.025 3D Modern BMOC BMOC174 -0.013 0 -0.008 0.007 -0.004 3D Modern BMOC BMOC180 NA 0.044 0.02 0.042 0.035 3D Modern BMOC BMOC191 NA 0.026 NA NA 0.026 3D Modern BMOC BMOC197 -0.093 -0.047 -0.052 -0.033 -0.056 3D Modern BMOC Fogel 2012 NA NA -0.004 -0.042 -0.023 3D Subfossil TAO TAO-66-2 NA NA 0.281 NA 0.281 3D Subfossil TAO TAO-66-21 0.141 NA NA NA 0.141 3D Subfossil TAO TAO-66-23 NA 0.093 NA NA 0.093 3D Subfossil TAO TAO-66-24 NA 0.032 NA NA 0.032 3D Subfossil TAO TAO-66-25 0.041 NA NA NA 0.041 3D Subfossil TAO TAO-66-26 0.02 NA NA NA 0.02 3D Subfossil TAO TAO-66-29 0.021 NA NA NA 0.021 3D Subfossil TAO TAO-66-30 NA 0.105 NA NA 0.105 3D Subfossil TAO TAO-66-31 NA -0.173 NA NA -0.173 3D Subfossil TAO TAO-66-32 0.127 NA NA NA 0.127 3D Subfossil TAO TAO-66-33 0.244 NA NA NA 0.244 3D Subfossil TAO TAO-66-34 NA 0.139 NA NA 0.139

75 Supplementary Table 2-6 continued. AVERAGE FOLD-CHANGE RIGHT LEFT RIGHT LEFT AGGREGATE MEAS. AGE COLLECTION SAMPLE ID FEMUR FEMUR HUMERUS HUMERUS SCORE Caliper Modern BMOC 550 -0.034 -0.04 -0.03 -0.041 -0.036 Caliper Modern BMOC 570 -0.013 -0.011 -0.047 -0.05 -0.03 Caliper Modern BMOC BMOC001 -0.002 -0.014 -0.001 -0.01 -0.007 Caliper Modern BMOC BMOC004 -0.018 -0.017 -0.031 -0.023 -0.022 Caliper Modern BMOC BMOC005 0.042 0.035 NA NA 0.039 Caliper Modern BMOC BMOC008 0.054 0.044 0.072 0.038 0.052 Caliper Modern BMOC BMOC014 -0.002 -0.066 NA NA -0.034 Caliper Modern BMOC BMOC015 -0.022 -0.025 0.027 0.02 0 Caliper Modern BMOC BMOC019 0.028 0.055 0.042 0.031 0.039 Caliper Modern BMOC BMOC020 -0.02 -0.016 0.003 -0.016 -0.012 Caliper Modern BMOC BMOC021 0.018 -0.005 NA NA 0.006 Caliper Modern BMOC BMOC028 -0.003 0.021 -0.02 -0.033 -0.008 Caliper Modern BMOC BMOC030 0.046 NA 0.041 NA 0.044 Caliper Modern BMOC BMOC035 0.001 NA NA NA 0.001 Caliper Modern BMOC BMOC043 NA NA -0.002 0.005 0.002 Caliper Modern BMOC BMOC073 0.002 -0.011 -0.003 0.015 0.001 Caliper Modern BMOC BMOC075 -0.018 -0.023 0.032 0.394 0.096 Caliper Modern BMOC BMOC125 NA NA 0.057 NA 0.057 Caliper Modern BMOC BMOC137 0.077 0.079 0.085 0.062 0.076 Caliper Modern BMOC BMOC142 0.039 0.023 0.025 -0.002 0.021 Caliper Modern BMOC BMOC156 0.029 NA NA NA 0.029 Caliper Modern BMOC BMOC157 0.027 0.026 -0.028 -0.041 -0.004 Caliper Modern BMOC BMOC163 NA 0.099 NA NA 0.099 Caliper Modern BMOC BMOC169 -0.098 NA NA NA -0.098 Caliper Modern BMOC BMOC172 -0.05 -0.047 -0.064 -0.018 -0.045 Caliper Modern BMOC BMOC173 -0.015 -0.017 -0.022 -0.045 -0.025 Caliper Modern BMOC BMOC174 0.007 0.011 -0.007 -0.014 -0.001 Caliper Modern BMOC BMOC180 NA 0.018 0.004 0.03 0.018 Caliper Modern BMOC BMOC191 NA -0.006 NA NA -0.006 Caliper Modern BMOC BMOC197 -0.033 -0.033 -0.026 -0.035 -0.032 Caliper Modern BMOC Fogel 2012 NA NA -0.047 -0.06 -0.054 Caliper Subfossil TAO TAO-66-2 NA NA 0.192 NA 0.192 Caliper Subfossil TAO TAO-66-21 0.135 NA NA NA 0.135 Caliper Subfossil TAO TAO-66-23 NA 0.069 NA NA 0.069 Caliper Subfossil TAO TAO-66-24 NA 0.059 NA NA 0.059 Caliper Subfossil TAO TAO-66-25 0.09 NA NA NA 0.09 Caliper Subfossil TAO TAO-66-26 NA NA NA NA NA Caliper Subfossil TAO TAO-66-29 NA NA NA NA NA Caliper Subfossil TAO TAO-66-30 NA 0.057 NA NA 0.057 Caliper Subfossil TAO TAO-66-31 NA NA NA NA NA Caliper Subfossil TAO TAO-66-32 0.068 NA NA NA 0.068 Caliper Subfossil TAO TAO-66-33 NA NA NA NA NA Caliper Subfossil TAO TAO-66-34 NA 0.045 NA NA 0.045

76 Appendix B: Supplementary Materials for Chapter 3

Supplementary Table 3-1: List of Sceloporus undulatus individuals and associated biometric and behavioral data (RHL = right hind limb length, RRHL = relative hind limb length) SITE MASS TWITCHES INDIVIDUAL ID (STATE) TUBE NUMBER YEAR SEX (g) RHL SVL RRHL (No.) EE-2011-0010 EE(TN) 10 2011 M 5.25 0.43 5.4 2.3 EE-2011-0053 EE(TN) 53 2011 M 7.26 0.42 5.9 2.5 EE-2011-0062 EE(TN) 62 2011 M 4.49 0.43 5.1 2.2 EE-2011-0115 EE(TN) 115 2011 F 6.07 0.42 5.7 2.4 EE-2011-0147 EE(TN) 147 2011 F 4.99 0.43 5.4 2.3 EE-2013-0019 EE(TN) 19 2013 F 10.77 0.38 7.4 2.8 2 EE-2013-0034 EE(TN) 34 2013 F 11.49 0.41 6.9 2.8 1 EE-2013-0047 EE(TN) 47 2013 M 5.13 0.42 5.4 2.25 1 EE-2013-0071 EE(TN) 71 2013 F 7.45 0.41 6.4 2.65 0 EE-2013-0081 EE(TN) 81 2013 M 11.02 0.39 6.9 2.7 0 EE-2013-0113 EE(TN) 113 2013 M 6.76 0.41 6 2.45 0 EE-2013-0117 EE(TN) 117 2013 F 6.19 0.40 5.9 2.35 0 EE-2013-0120 EE(TN) 120 2013 F 12.26 0.41 6.9 2.8 0 EE-2013-0151 EE(TN) 151 2013 F 5.3 0.43 5.5 2.35 0 EE-2013-0164 EE(TN) 164 2013 M 4.57 0.42 5.3 2.2 6 EE-2013-0213 EE(TN) 213 2013 F 3.91 0.41 5.1 2.1 0 EE-2013-0248 EE(TN) 248 2013 F 4.97 0.39 5.6 2.2 0 EE-2013-0274 EE(TN) 274 2013 M 6.13 0.32 5.9 1.9 2 EE-2013-0292 EE(TN) 292 2013 M 6.19 0.40 6.1 2.45 0 EE-2013-0346 EE(TN) 346 2013 M 9.18 0.39 6.5 2.55 2 SF-2006-0062 SF(AR) 62 2006 M 5 0.41 5.4 2.2 0 SF-2006-0083 SF(AR) 83 2006 F 20 0.37 8.1 3 0 SF-2006-0087 SF(AR) 87 2006 F 14.5 0.40 7.8 3.1 0 SF-2006-0091 SF(AR) 91 2006 F 14 0.37 7.1 2.6 0 SF-2006-0096 SF(AR) 96 2006 F 8 0.41 6.1 2.5 0 SF-2006-0101 SF(AR) 101 2006 M 12.5 0.39 7.2 2.8 0 SF-2006-0112 SF(AR) 112 2006 F 9 0.38 6.3 2.4 0 SF-2006-0118 SF(AR) 118 2006 F 17.25 0.38 7.9 3 0 SF-2006-0125 SF(AR) 125 2006 M 6.1 0.41 5.6 2.3 0 SF-2006-0130 SF(AR) 130 2006 M 7.7 0.41 6.3 2.6 0 SF-2006-0132 SF(AR) 132 2006 M 7.3 0.42 6 2.5 0 SF-2006-0144 SF(AR) 144 2006 F 7.2 0.42 5.7 2.4 0 SF-2006-0157 SF(AR) 157 2006 M 14 0.39 7.6 3 1 SF-2006-0167 SF(AR) 167 2006 F 9.9 0.39 6.7 2.6 0 SF-2006-0170 SF(AR) 170 2006 M 6.8 0.39 5.9 2.3 0 SF-2006-0171 SF(AR) 171 2006 M 10.25 0.43 6.8 2.9 0 SF-2006-0172 SF(AR) 172 2006 M 12.5 0.41 7.3 3 0 SF-2006-0214 SF(AR) 214 2006 F 17.75 0.38 7.7 2.9 0 SF-2006-0226 SF(AR) 226 2006 F 17.75 0.38 7.8 3 0 SF-2006-0229 SF(AR) 229 2006 F 20.5 0.36 8.3 3 3 SD-06-0001-S1_ SD(AL) TL 0001 2006 M 10.3 0.40 6.8 2.7 7 SD-06-0002-S69 SD(AL) TL 0002 2006 F 7.9 0.40 6.3 2.5 9 SD-06-0007-S17 SD(AL) TL 0007 2006 M 6.85 0.47 5.1 2.4 4 SD-06-0009-S70 SD(AL) TL 0009 2006 F 8 0.38 7.6 2.9 10 77 SD-06-0012-S73 SD(AL) TL 0012 2006 F 7.2 0.42 6 2.5 5 SD-06-0013-S15 SD(AL) TL 0013 2006 M 10.25 0.42 6.5 2.7 4 SD-06-0014-S13 SD(AL) TL 0014 2006 M 7 0.46 5.9 2.7 8 SD-06-0018-S68 SD(AL) TL 0018 2006 F 7.6 0.39 6.4 2.5 4 SD-06-0021-S214 SD(AL) TL 0021 2006 M 9.2 0.38 6.5 2.5 6 SD-06-0023-S64 SD(AL) TL 0023 2006 F 7.3 0.40 6.2 2.5 6 SD-06-0027-S77 SD(AL) TL 0027 2006 F 6.5 0.40 6.3 2.5 8 SD-06-0029-S10 SD(AL) TL 0029 2006 M 7 0.44 5.7 2.5 5 SD-06-0031-S14 SD(AL) TL 0031 2006 M 7.2 0.43 6.1 2.6 12 SD-06-0032-S136 SD(AL) TL 0032 2006 M 7 0.40 5.7 2.3 2 SD-06-0034-S12 SD(AL) TL 0034 2006 M 10.25 0.40 6.8 2.7 5 SD-06-0035-S16 SD(AL) TL 0035 2006 M 8.1 0.41 6.1 2.5 5 SD-06-0036-S62 SD(AL) TL 0036 2006 F 18.75 0.39 7.7 3 6 SD-06-0037-S9 SD(AL) TL 0037 2006 M 6 0.44 5.5 2.4 8 SD-06-0039-S66 SD(AL) TL 0039 2006 F 15.25 0.39 7.2 2.8 4 SD-06-0040-S74 SD(AL) TL 0040 2006 F 6.3 0.41 5.8 2.4 4 SD-06-0041-S61 SD(AL) TL 0041 2006 F 9.75 0.40 6.3 2.5 3 SD-06-0042-S80 SD(AL) TL 0042 2006 F 13 0.41 7 2.9 6 SD-06-0043-S75 SD(AL) TL 0043 2006 F 11.5 0.45 5.6 2.5 5 SD-06-0044-S3 SD(AL) TL 0044 2006 M 6.7 0.44 5.7 2.5 10 SD-06-0046-S65 SD(AL) TL 0046 2006 F 13.75 0.39 7.4 2.9 5 SD-06-0047-S76 SD(AL) TL 0047 2006 F 8.8 0.39 6.2 2.4 7 SD-06-0048-S78 SD(AL) TL 0048 2006 F 17.4 0.37 7.3 2.7 3 SD-06-0050-S137 SD(AL) TL 0050 2006 M 12.75 0.41 7.5 3.1 11 SD-06-0051-S67 SD(AL) TL 0051 2006 F 12.75 0.39 7 2.7 8 SD-06-0052-S79 SD(AL) TL 0052 2006 F 15.75 0.41 7.1 2.9 8 SD-06-0053-S11 SD(AL) TL 0053 2006 M 5 0.45 5.1 2.3 87 SD-06-0054-S141 SD(AL) TL 0054 2006 M 6 0.46 5.7 2.6 7 SD-06-0055-S140 SD(AL) TL 0055 2006 M 7.5 0.44 6.1 2.7 2 SD-06-0056-S6 SD(AL) TL 0056 2006 M 10.5 0.38 6.8 2.6 9 SD-06-0057-S63 SD(AL) TL 0057 2006 F 9.25 0.42 6.5 2.7 5 SD-06-0058-S7 SD(AL) TL 0058 2006 M 12.75 0.43 7 3 10 SD-06-0059-S71 SD(AL) TL 0059 2006 F 12 0.41 7.3 3 3 SD-06-0127-S18 SD(AL) TL 0127 / R 16380 2006 M 9.4 0.39 6.6 2.6 16 SD-07-0004-S84 SD(AL) TL 0004 2007 F 7.2 0.42 5.7 2.4 2 SD-07-0301-S91 SD(AL) TL 0301 2007 F 12.5 0.41 6.8 2.8 5 SD-07-0302-S83 SD(AL) TL 0302 2007 F 6.2 0.44 5.5 2.4 8 SD-07-0304-S98 SD(AL) TL 0304 2007 F 9.2 0.41 6.1 2.5 SD-07-0305-S2 SD(AL) TL 0305 2007 M 5.5 0.43 5.6 2.4 7 SD-07-0306-S181 SD(AL) TL 0306 2007 F 8.1 0.43 6.5 2.8 3 SD-07-0308-S93 SD(AL) TL 0308 2007 F 7.3 0.41 5.6 2.3 5 SD-07-0309-S88 SD(AL) TL 0309 2007 F 11 0.40 6.5 2.6 SD-07-0311-S5 SD(AL) TL 0311 2007 M 12.7 0.43 7.4 3.2 5 SD-07-0312-S36 SD(AL) TL 0312 2007 M 7.6 0.45 5.6 2.5 3 SD-07-0313-S142 SD(AL) TL 0313 2007 M 5.9 0.44 5.2 2.3 SD-07-0314-S100 SD(AL) TL 0314 2007 F 9.8 0.40 6.5 2.6 SD-07-0315-S87 SD(AL) TL 0315 2007 F 18.5 0.40 7.3 2.9 SD-07-0316-S139 SD(AL) TL 0316 2007 M 4.8 0.43 5.3 2.3 SD-07-0317-S37 SD(AL) TL 0317 2007 M 5.4 0.44 5.4 2.4 3 78 SD-07-0319-S95 SD(AL) TL 0319 2007 F 9.4 0.43 6.3 2.7 1 SD-07-0320-S103 SD(AL) TL 0320 2007 F 9.1 0.39 6.2 2.4 6 SD-07-0321-S25 SD(AL) TL 0321 2007 M 6.4 0.43 5.6 2.4 4 SD-07-0323-S22 SD(AL) TL 0323 2007 M 5.9 0.46 5.4 2.5 4 SD-07-0324-S92 SD(AL) TL 0324 2007 F 14.5 0.41 7.4 3 5 SD-07-0325-S104 SD(AL) TL 0325 2007 F 9.4 0.44 6.4 2.8 5 SD-07-0327-S33 SD(AL) TL 0327 2007 M 11.5 0.44 6.8 3 SD-07-0328-S35 SD(AL) TL 0328 2007 M 4.4 0.45 5.3 2.4 2 SD-07-0329-S23 SD(AL) TL 0329 2007 M 6 0.45 5.5 2.5 5 SD-07-0330-S21 SD(AL) TL 0330 2007 M 9.7 0.45 6.2 2.8 4 SD-07-0331-S85 SD(AL) TL 0331 2007 F 8.1 0.42 5.7 2.4 SD-07-0333-S96 SD(AL) TL 0333 2007 F 15.3 0.42 6.9 2.9 SD-07-0334-S213 SD(AL) TL 0334 2007 M 6.7 0.43 5.8 2.5 6 SD-07-0336-S105 SD(AL) TL 0336 2007 F 12.7 0.42 6.7 2.8 5 SD-07-0337-S82 SD(AL) TL 0337 2007 F 6.4 0.40 5.5 2.2 3 SD-07-0339-S106 SD(AL) TL 0339 2007 F 11 0.41 6.6 2.7 SD-07-0340-S97 SD(AL) TL 0340 2007 F 9.9 0.43 6.3 2.7 SD-07-0341-S86 SD(AL) TL 0341 2007 F 6.8 0.44 5.5 2.4 3 SD-07-0342-S89 SD(AL) TL 0342 2007 F 8.4 0.42 6 2.5 SD-07-0343-S215 SD(AL) TL 0343 2007 M 9.3 0.42 6.5 2.7 SD-07-0344-S30 SD(AL) TL 0344 2007 M 5.1 0.47 5.1 2.4 SD-07-0345-S138 SD(AL) TL 0345 2007 M 5.7 0.45 5.3 2.4 SD-07-0346-S81 SD(AL) TL 0346 2007 F 13.7 0.41 7.1 2.9 3 SD-07-0348-S99 SD(AL) TL 0348 2007 F 12.2 0.42 6.7 2.8 3 SD-07-0350-S180 SD(AL) TL 0350 2007 F 7.4 0.43 5.6 2.4 SD-07-0351-S31 SD(AL) TL 0351 2007 M 7.6 0.45 5.8 2.6 4 SD-07-0352-S94 SD(AL) TL 0352 2007 F 9 0.41 5.9 2.4 SD-07-0354-S101 SD(AL) TL 0354 2007 F 6.7 0.43 6.1 2.6 4 SD-07-0356-S4 SD(AL) TL 0356 2007 M 6.5 0.42 5.7 2.4 7 SD-07-0357-S90 SD(AL) TL 0357 2007 F 10.7 0.45 6.2 2.8 3 SD-07-0358-S102 SD(AL) TL 0358 2007 F 7.7 0.42 6 2.5 7 SD-07-0360-S145 SD(AL) TL 0360 2007 M 7.4 0.42 5.9 2.5 5 SD-07-0527-S107 SD(AL) TL 0527 2007 F 8.1 0.41 5.8 2.4 2 SD-08-0025-S149 SD(AL) 2008_0025 2008 M 9.42 0.44 6.3 2.8 SD-08-0055-S266 SD(AL) 2008_0055 2008 F 18.45 0.41 7.4 3 SD-08-0063-S39 SD(AL) 2008_0063 2008 M 8.01 0.46 5.9 2.7 SD-08-0065-S267 SD(AL) 2008_0065 2008 F 8.43 0.43 6 2.6 SD-08-0068-S24 SD(AL) 2008_0068 2008 M 5.65 0.45 5.5 2.5 SD-08-0071-S121 SD(AL) 2008_0071 2008 F 7.1 0.42 5.9 2.5 SD-08-0077-S112 SD(AL) 2008_0077 2008 F 9.1 0.43 6.3 2.7 SD-08-0078-S8 SD(AL) 2008_0078 2008 M 12.71 0.46 6.9 3.2 SD-08-0080-S118 SD(AL) 2008_0080 2008 F 12.67 0.44 6.4 2.8 SD-08-0081-S41 SD(AL) 2008_0081 2008 M 10.39 0.45 6.5 2.9 SD-08-0085-S116 SD(AL) 2008_0085 2008 F 9.25 0.44 6.2 2.7 SD-08-0086-S111 SD(AL) 2008_0086 2008 F 15.33 0.41 7 2.9 SD-08-0088-S114 SD(AL) 2008_0088 2008 F 8.73 0.42 6.2 2.6 SD-08-0089-S49 SD(AL) 2008_0089 2008 M 12 0.46 6.8 3.1 SD-08-0091-S183 SD(AL) 2008_0091 2008 F 9.06 0.44 6.2 2.7 SD-08-0092-S264 SD(AL) 2008_0092 2008 F 14.74 0.41 7.4 3 79 SD-08-0093-S150 SD(AL) 2008_0093 2008 M 8.12 0.47 5.8 2.7 SD-08-0095-S27 SD(AL) 2008_0095 2008 M 10.2 0.47 6.6 3.1 SD-08-0097-S45 SD(AL) 2008_0097 2008 M 6.5 0.47 5.8 2.7 SD-08-0098-S29 SD(AL) 2008_0098 2008 M 7.77 0.43 6.1 2.6 SD-08-0100-S38 SD(AL) 2008_0100 2008 M 11.32 0.44 6.8 3 SD-08-0101-S346 SD(AL) 2008_0101 2008 F 7.6 0.40 5.8 2.3 SD-08-0102-S182 SD(AL) 2008_0102 2008 F 13.78 0.40 7 2.8 SD-08-0103-S120 SD(AL) 2008_0103 2008 F 12.97 0.40 7 2.8 SD-08-0104-S108 SD(AL) 2008_0104 2008 F 11.65 0.44 6.6 2.9 SD-08-0105-S28 SD(AL) 2008_0105 2008 M 5.7 0.53 5.5 2.9 SD-08-0106-S115 SD(AL) 2008_0106 2008 F 16.2 0.42 7.1 3 SD-08-0108-S146 SD(AL) 2008_0108 2008 M 12.3 0.46 6.9 3.2 SD-08-0109-S147 SD(AL) 2008_0109 2008 M 5.03 0.45 5.3 2.4 SD-08-0110-S143 SD(AL) 2008_0110 2008 M 5.46 0.44 5.2 2.3 SD-08-0111-S117 SD(AL) 2008_0111 2008 F 7.29 0.41 5.9 2.4 SD-08-0112-S113 SD(AL) 2008_0112 2008 F 16.5 0.41 7 2.9 SD-08-0113-S119 SD(AL) 2008_0113 2008 F 7 0.43 6 2.6 SD-08-0114-S144 SD(AL) 2008_0114 2008 M 7.42 0.43 5.8 2.5 SD-08-0116-S26 SD(AL) 2008_0116 2008 M 5.92 0.43 5.6 2.4 SD-08-0117-S54 SD(AL) 2008_0117 2008 M 10.6 0.46 6.8 3.1 SD-08-0118-S20 SD(AL) 2008_0118 2008 M 5.92 0.44 5.5 2.4 SD-08-0119-S52 SD(AL) 2008_0119 2008 M 6.89 0.43 5.8 2.5 SD-08-0120-S110 SD(AL) 2008_0120 2008 F 11.43 0.40 7 2.8 SD-08-0121-S265 SD(AL) 2008_0121 2008 F 12.4 0.43 6.8 2.9 SD-08-0122-S43 SD(AL) 2008_0122 2008 M 4.8 0.43 5.3 2.3 SD-08-0123-S34 SD(AL) 2008_0123 2008 M 13.69 0.44 7.1 3.1 SD-08-0124-S46 SD(AL) 2008_0124 2008 M 4.96 0.46 5.2 2.4 SD-08-0125-S122 SD(AL) 2008_0125 2008 F 5.99 0.42 5.5 2.3 SD-08-0126-S109 SD(AL) 2008_0126 2008 F 7.74 0.42 6.4 2.7 SD-08-0127-S184 SD(AL) 2008_0127 2008 F 7.1 0.41 5.8 2.4 SD-08-0128-S32 SD(AL) 2008_0128 2008 M 6.57 0.43 5.6 2.4 SD-08-0130-S40 SD(AL) 2008_0130 2008 M 6.95 0.45 5.8 2.6 SD-08-0131-S337 SD(AL) 2008_0131 2008 M 5.1 0.44 5.4 2.4 SD-08-0151-S19 SD(AL) 2008_0151 2008 M 7.22 0.43 6 2.6 SD-09-0001-S269 SD(AL) 2009_0001 2009 F 6.84 0.31 5.8 1.8 SD-09-0004-S48 SD(AL) 2009_0004 2009 M 4.58 0.44 5.2 2.3 SD-09-0006-S57 SD(AL) 2009_0006 2009 M 9.64 0.32 6.6 2.1 SD-09-0008-S123 SD(AL) 2009_0008 2009 F 12.18 0.43 6.5 2.8 SD-09-0009-S53 SD(AL) 2009_0009 2009 M 7.74 0.47 6 2.8 SD-09-0013-S148 SD(AL) 2009_0013 2009 M 6.66 0.45 5.8 2.6 SD-09-0014-S126 SD(AL) 2009_0014 2009 M 8.47 0.44 6.3 2.8 SD-09-0016-S50 SD(AL) 2009_0016 2009 M 6.39 0.45 5.6 2.5 SD-09-0019-S152 SD(AL) 2009_0019 2009 M 7 0.44 5.9 2.6 SD-09-0020-S338 SD(AL) 2009_0020 2009 M 12.35 0.42 6.6 2.8 SD-09-0025-S134 SD(AL) 2009_0025 2009 F 12.65 0.31 7.1 2.2 SD-09-0026-S44 SD(AL) 2009_0026 2009 M 6.53 0.43 6 2.6 SD-09-0027-S216 SD(AL) 2009_0027 2009 M 6.18 0.45 5.6 2.5 SD-09-0028-S124 SD(AL) 2009_0028 2009 F 8.42 0.43 6 2.6 SD-09-0031-S153 SD(AL) 2009_0031 2009 M 6.34 0.45 5.6 2.5 80 SD-09-0032-S151 SD(AL) 2009_0032 2009 M 4.42 0.43 5.1 2.2 SD-09-0042-S132 SD(AL) 2009_0042 2009 F 7.32 0.31 5.9 1.8 SD-09-0045-S127 SD(AL) 2009_0045 2009 F 8.66 0.31 6.1 1.9 SD-09-0047-S187 SD(AL) 2009_0047 2009 F 10.58 0.30 6.3 1.9 SD-09-0051-S55 SD(AL) 2009_0051 2009 M 5.72 0.45 5.3 2.4 SD-09-0054-S42 SD(AL) 2009_0054 2009 M 4.73 0.42 5.2 2.2 SD-09-0058-S129 SD(AL) 2009_0058 2009 F 4.01 0.44 5 2.2 SD-09-0059-S130 SD(AL) 2009_0059 2009 F 9.35 0.44 6.2 2.7 SD-09-0061-S135 SD(AL) 2009_0061 2009 F 6.26 0.31 5.4 1.7 SD-09-0064-S128 SD(AL) 2009_0064 2009 F 8.46 0.31 5.9 1.8 SD-09-0065-S56 SD(AL) 2009_0065 2009 M 5.57 0.46 5.4 2.5 SD-09-0068-S125 SD(AL) 2009_0068 2009 M 9.95 0.42 6.6 2.8 SD-09-0072-S133 SD(AL) 2009_0072 2009 F 7.38 0.30 5.7 1.7 SD-09-0077-S131 SD(AL) 2009_0077 2009 F 11.13 0.42 6.9 2.9 SD-09-0081-S268 SD(AL) 2009_0081 2009 M 7.33 0.44 6.2 2.7 SD-09-0083-S188 SD(AL) 2009_0083 2009 F 9.09 0.40 6.2 2.5 SD-09-0091-S185 SD(AL) 2009_0091 2009 F 8.56 0.32 6.5 2.1 SD-09-0095-S186 SD(AL) 2009_0095 2009 F 8.08 0.46 5.9 2.7 SD-09-0097-S47 SD(AL) 2009_0097 2009 M 5.98 0.45 5.5 2.5 SD-09-0098-S51 SD(AL) 2009_0098 2009 M 5.07 0.46 5.4 2.5 SD-10-0003-S248 SD(AL) 2010_0003 2010 M 6.5 0.43 5.8 2.5 1 SD-10-0004-S341 SD(AL) 2010_0004 2010 M 8.54 0.43 6.3 2.7 SD-10-0006-S252 SD(AL) 2010_0006 2010 M 10.41 0.43 6.5 2.8 SD-10-0008-S225 SD(AL) 2010_0008 2010 M 6.46 0.43 5.8 2.5 SD-10-0009-S228 SD(AL) 2010_0009 2010 M 10.52 0.43 6.9 3 SD-10-0010-S255 SD(AL) 2010_0010 2010 M 2.45 0.48 4 1.9 SD-10-0011-S349 SD(AL) 2010_0011 2010 F 1.31 0.43 3.5 1.5 SD-10-0012-S220 SD(AL) 2010_0012 2010 M 4.69 0.43 5.3 2.3 SD-10-0013-S350 SD(AL) 2010_0013 2010 F 1.27 0.46 3.5 1.6 SD-10-0014-S271 SD(AL) 2010_0014 2010 F 8.02 0.42 6.2 2.6 SD-10-0019-S226 SD(AL) 2010_0019 2010 M 4.38 0.43 5.1 2.2 SD-10-0020-S155 SD(AL) 2010_0020 2010 M 4.73 0.44 5.2 2.3 6 SD-10-0021-S272 SD(AL) 2010_0021 2010 F 2.27 0.46 3.9 1.8 SD-10-0022-S227 SD(AL) 2010_0022 2010 M 5.15 0.42 5.7 2.4 SD-10-0023-S270 SD(AL) 2010_0023 2010 F 2.61 0.46 4.1 1.9 SD-10-0025-S250 SD(AL) 2010_0025 2010 M 1.37 0.42 3.3 1.4 SD-10-0030-S351 SD(AL) 2010_0030 2010 F 1.43 0.46 3.5 1.6 SD-10-0037-S257 SD(AL) 2010_0037 2010 M 2.51 0.37 4.1 1.5 SD-10-0041-S342 SD(AL) 2010_0041 2010 M 12.32 0.29 6.9 2 0 SD-10-0042-S251 SD(AL) 2010_0042 2010 M 9.12 0.46 6.3 2.9 3 SD-10-0044-S352 SD(AL) 2010_0044 2010 F 2.19 0.44 4.1 1.8 SD-10-0045-S249 SD(AL) 2010_0045 2010 M 5.46 0.31 5.5 1.7 SD-10-0046-S218 SD(AL) 2010_0046 2010 M 9.67 0.45 6.6 3 SD-10-0048-S224 SD(AL) 2010_0048 2010 M 4.89 0.41 5.4 2.2 4 SD-10-0055-S343 SD(AL) 2010_0055 2010 M 3.87 0.46 4.6 2.1 SD-10-0059-S154 SD(AL) 2010_0059 2010 M 11.03 0.44 6.8 3 3 SD-10-0064-S353 SD(AL) 2010_0064 2010 F 8.1 0.40 6.2 2.5 SD-10-0069-S354 SD(AL) 2010_0069 2010 F 11.63 0.42 7.1 3 SD-10-0073-S223 SD(AL) 2010_0073 2010 M 2.55 0.46 3.9 1.8 81 SD-10-0081-S378 SD(AL) 2010_0081 2010 F 2.1 0.50 3.6 1.8 SD-10-0098-S156 SD(AL) 2010_0098 2010 M 9.96 0.31 6.7 2.1 36.4 SD-10-0104-S273 SD(AL) 2010_0104 2010 F 2.9 0.44 4.3 1.9 SD-10-0110-S340 SD(AL) 2010_0110 2010 M 2.07 0.46 3.9 1.8 SD-10-0111-S355 SD(AL) 2010_0111 2010 F 1.61 0.33 3.6 1.2 SD-10-0113-S356 SD(AL) 2010_0113 2010 F 2.76 0.44 4.3 1.9 SD-10-0127-S274 SD(AL) 2010_0127 2010 F 3.29 0.48 4.4 2.1 SD-10-0128-S221 SD(AL) 2010_0128 2010 M 3.15 0.43 4.4 1.9 SD-10-0130-S357 SD(AL) 2010_0130 2010 F 2.19 0.48 4 1.9 SD-10-0131-S217 SD(AL) 2010_0131 2010 M 2.61 0.45 4.2 1.9 SD-10-0135-S222 SD(AL) 2010_0135 2010 M 3.48 0.47 4.5 2.1 SD-10-0139-S233 SD(AL) 2010_0139 2010 M 2.97 0.47 4.3 2 SD-10-0141-S347 SD(AL) 2010_0141 2010 F 2.73 0.46 4.1 1.9 SD-10-0163-S256 SD(AL) 2010_0163 2010 M 2.11 0.46 3.9 1.8 SD-10-0202-S253 SD(AL) 2010_0202 2010 M 1.23 0.44 3.4 1.5 SD-10-0205-S254 SD(AL) 2010_0205 2010 M 1.4 0.44 3.6 1.6 SD-10-0213-S358 SD(AL) 2010_0213 2010 F 5.06 0.33 5.2 1.7 SD-10-0218-S359 SD(AL) 2010_0218 2010 F 3.96 0.33 4.8 1.6 SD-10-0223-S360 SD(AL) 2010_0223 2010 F 2.14 0.34 3.8 1.3 SD-10-0225-S275 SD(AL) 2010_0225 2010 F 2.76 0.48 4.4 2.1 SD-10-0226-S276 SD(AL) 2010_0226 2010 F 4.36 0.35 4.9 1.7 SD-10-0230-S277 SD(AL) 2010_0230 2010 F 2.01 0.46 3.9 1.8 SD-10-0235-S348 SD(AL) 2010_0235 2010 F 3.53 0.47 4.5 2.1 SD-10-0336-S344 SD(AL) 2010_0336 2010 M 2.35 0.46 3.9 1.8 SD-10-0344-S339 SD(AL) 2010_0344 2010 M 0.86 0.47 3 1.4 SD-10-0348-S219 SD(AL) 2010_0348 2010 M 3.15 0.45 4.4 2 SD-10-0355-S361 SD(AL) 2010_0355 2010 F 1.69 0.43 3.7 1.6 SD-10-0359-S345 SD(AL) 2010_0359 2010 M 1.24 0.45 3.3 1.5 SD-10-0361-S362 SD(AL) 2010_0361 2010 F 1.3 0.45 3.3 1.5 SD-11-0027-S230 SD(AL) 2011_0027 2011 M 7.51 0.42 6.2 2.6 SD-11-0054-S234 SD(AL) 2011_0054 2011 M 10.7 0.46 6.5 3 SD-11-0055-S299 SD(AL) 2011_0055 2011 F 7.62 0.46 5.9 2.7 SD-11-0175-S302 SD(AL) 2011_0175 2011 F 10.24 0.41 6.4 2.6 SD-11-0177-S301 SD(AL) 2011_0177 2011 F 11.96 0.43 6.8 2.9 SD-11-0180-S297 SD(AL) 2011_0180 2011 F 13.25 0.42 6.6 2.8 SD-11-0186-S235 SD(AL) 2011_0186 2011 M 9.85 0.45 6.4 2.9 SD-11-0189-S295 SD(AL) 2011_0189 2011 F 13.25 0.45 6.5 2.9 SD-11-0192-S59 SD(AL) 2011_0192 2011 M 10.45 0.49 6.1 3 SD-11-0201-S197 SD(AL) 2011_0201 2011 F 9.5 0.44 6.1 2.7 SD-11-0206-S165 SD(AL) 2011_0206 2011 M 11.52 0.45 6.5 2.9 SD-11-0209B-S382 SD(AL) 2011_0209 2011 M 8.33 0.44 6.3 2.8 SD-11-0213-S195 SD(AL) 2011_0213 2011 F 6.37 0.47 5.5 2.6 SD-11-0215-S196 SD(AL) 2011_0215 2011 F 10.46 0.44 6.6 2.9 SD-11-0220-S194 SD(AL) 2011_0220 2011 F 7.35 0.43 5.6 2.4 SD-11-0235-S204 SD(AL) 2011_0235 2011 F 1.68 0.44 3.6 1.6 SD-11-0238-S370 SD(AL) 2011_0238 2011 F 0.47 0.46 2.4 1.1 SD-11-0239-S371 SD(AL) 2011_0239 2011 F 0.47 0.46 2.4 1.1 SD-11-0245-S304 SD(AL) 2011_0245 2011 F 0.77 0.43 2.8 1.2 SD-11-0251-S367 SD(AL) 2011_0251 2011 F 0.41 0.42 2.6 1.1 82 SD-11-0258-S305 SD(AL) 2011_0258 2011 F 0.89 0.43 3 1.3 SD-11-0272-S294 SD(AL) 2011_0272 2011 F 1.68 0.43 3.7 1.6 SD-11-0274-S369 SD(AL) 2011_0274 2011 F? 0.42 0.42 2.4 1 SD-11-0278-S368 SD(AL) 2011_0278 2011 F 0.65 0.46 2.6 1.2 SD-11-0279-S300 SD(AL) 2011_0279 2011 F 0.47 0.43 2.3 1 SD-11-0286-S296 SD(AL) 2011_0286 2011 F? 0.63 0.46 2.6 1.2 SD-11-0294-S198 SD(AL) 2011_0294 2011 F 14.4 0.42 7.1 3 SD-11-0313-S193 SD(AL) 2011_0313 2011 F 1.25 0.47 3.2 1.5 SD-11-0317-S199 SD(AL) 2011_0317 2011 F 1.4 0.42 3.3 1.4 SD-11-0322-S298 SD(AL) 2011_0322 2011 F 13.55 0.43 6.8 2.9 SD-11-0323-S245 SD(AL) 2011_0323 2011 M 10.82 0.43 6.3 2.7 SD-11-0324-S303 SD(AL) 2011_0324 2011 F 15.38 0.40 7.8 3.1 SD-11-0325-S242 SD(AL) 2011_0325 2011 M 7.81 0.46 5.9 2.7 SD-11-0326-S205 SD(AL) 2011_0326 2017 f 13.01 0.44 6.8 3 SD-11-0327-S314 SD(AL) 2011_0327 2017 f 6.21 0.33 6.3 2.1 SD-11-0335-S313 SD(AL) 2011_0335 2011 F 11.7 0.44 7.1 3.1 SD-11-0336-S316 SD(AL) 2011_0336 2011 F 6.01 0.44 5.7 2.5 SD-11-0338-S239 SD(AL) 2011_0338 2011 M 9.99 0.44 6.4 2.8 SD-11-0341-S329 SD(AL) 2011_0341 2011 F 10.01 0.44 6.2 2.7 SD-11-0343-S376 SD(AL) 2011_0343 2011 F 13.58 0.43 7.2 3.1 SD-11-0344-S58 SD(AL) 2011_0344 2011 M 8.16 0.43 6 2.6 SD-11-0347-S315 SD(AL) 2011_0347 2011 F 11.29 0.40 7 2.8 SD-11-0349-S229 SD(AL) 2011_0349 2011 M 12.31 0.43 6.8 2.9 SD-11-0351-S241 SD(AL) 2011_0351 2011 M 8.24 0.47 5.7 2.7 SD-11-0360-S168 SD(AL) 2011_0360 2017 m 4.76 0.45 4.9 2.2 SD-11-0442-S157 SD(AL) 2011_0442 2017 m 7.99 0.39 6.4 2.5 SD-11-0467-S162 SD(AL) 2011_0467 2017 m 11.88 0.40 6.8 2.7 SD-12-0007-S332 SD(AL) 2012_0007 2012 F 9.08 0.44 6.1 2.7 SD-12-0025-S160 SD(AL) 2012_0025 2012 M 6.66 N/A 5.5 SD-12-0038-S247 SD(AL) 2012_0038 2012 M 11.34 N/A 6.4 SD-12-0041-S321 SD(AL) 2012_0041 2012 F 8.22 0.41 5.9 2.4 SD-12-0042-S200 SD(AL) 2012_0042 2012 F 6.62 0.43 5.6 2.4 SD-12-0044-S333 SD(AL) 2012_0044 2012 F 7.02 0.40 6.2 2.5 SD-12-0045-S212 SD(AL) 2012_0045 2012 F 6.37 0.41 5.6 2.3 SD-12-0050-S320 SD(AL) 2012_0050 2012 F 9.72 0.40 6.2 2.5 SD-12-0056-S377 SD(AL) 2012_0056 2012 F 6.66 0.41 5.8 2.4 SD-12-0083-S202 SD(AL) 2012_0083 2012 F 14.67 0.40 7.2 2.9 SD-12-0092-S334 SD(AL) 2012_0092 2012 F 8.91 0.40 6.2 2.5 SD-12-0093-S211 SD(AL) 2012_0093 2012 F 11.1 0.42 6.7 2.8 SD-12-0113-S324 SD(AL) 2012_0113 2012 F 8.78 0.40 6.2 2.5 SD-12-0121-S238 SD(AL) 2012_0121 2012 M 9.96 0.42 6.7 2.8 SD-12-0123-S322 SD(AL) 2012_0123 2012 F 10 0.41 6.1 2.5 SD-12-0130-S201 SD(AL) 2012_0130 2012 F 15.3 0.45 6.7 3 SD-12-0152-S206 SD(AL) 2012_0152 2012 F 5.83 0.41 5.9 2.4 SD-12-0155-S203 SD(AL) 2012_0155 2012 F 11.72 0.41 6.6 2.7 SD-12-0164-S243 SD(AL) 2012_0164 2012 M 4.75 0.46 5.4 2.5 SD-12-0174-S380 SD(AL) 2012_0174 2012 F 7.44 0.40 5.8 2.3 SD-12-0183-S174 SD(AL) 2012_0183 2012 M 6.17 0.45 5.8 2.6 SD-12-0186-S246 SD(AL) 2012_0186 2012 M 6.77 0.43 5.8 2.5 83 SD-12-0187-S319 SD(AL) 2012_0187 2012 F 9.43 0.44 6.6 2.9 SD-12-0203-S309 SD(AL) 2012_0203 2012 F 7.48 0.40 6 2.4 SD-12-0206-S207 SD(AL) 2012_0206 2012 F 9.24 0.43 6 2.6 SD-12-0207-S244 SD(AL) 2012_0207 2012 M 5.34 0.47 5.3 2.5 SD-12-0214-S237 SD(AL) 2012_0214 2012 M 9.07 0.43 6.3 2.7 SD-12-0222-S166 SD(AL) 2012_0222 2012 M 8.53 0.44 6.2 2.7 SD-12-0225-S163 SD(AL) 2012_0225 2012 M 5.98 0.44 5.5 2.4 SD-12-0227-S60 SD(AL) 2012_0227 2012 M 8.72 0.44 6.2 2.7 SD-12-0230-S375 SD(AL) 2012_0230 2012 F 13.01 0.41 6.9 2.8 SD-12-0245-S161 SD(AL) 2012_0245 2012 M 7.45 0.44 6.2 2.7 SD-12-0250-S167 SD(AL) 2012_0250 2012 M 5.32 0.44 5.4 2.4 SD-12-0268-S158 SD(AL) 2012_0268 2012 M 6.65 0.46 5.7 2.6 SD-12-0271-S231 SD(AL) 2012_0271 2012 M 7.59 0.46 6.1 2.8 SD-12-0272-S232 SD(AL) 2012_0272 2012 M 6.92 0.44 5.9 2.6 SD-12-0280-S379 SD(AL) 2012_0280 2012 F 8.95 0.45 6.2 2.8 SD-12-0285-S336 SD(AL) 2012_0285 2012 M 11 0.45 6.6 3 SD-12-0290-S381 SD(AL) 2012_0290 2012 F 14.27 0.42 7.2 3 SD-12-0294-S374 SD(AL) 2012_0294 2012 F 9.94 0.41 6.3 2.6 SD-12-0304-S164 SD(AL) 2012_0304 2012 M 9.21 0.42 6.6 2.8 SD-12-0325-S159 SD(AL) 2012_0325 2012 M 5.8 0.45 5.6 2.5 SD-12-0329P-S335 SD(AL) 2012_0329 2012 M 5.84 0.47 5.5 2.6 SD-12-0332-S331 SD(AL) 2012_0332 2012 F 12.04 0.37 7 2.6 SD-12-0341-S240 SD(AL) 2012_0341 2012 M 5.67 0.48 5.4 2.6 SD-12-0352-S323 SD(AL) 2012_0352 2012 F 6.07 0.42 5.7 2.4 SD-13-0053-S286 SD(AL) 2013_0053 2013 F 14.28 0.30 7.3 2.2 SD-13-0057-S285 SD(AL) 2013_0057 2013 F 4.72 0.36 5.3 1.9 SD-13-0070-S171 SD(AL) 2013_0070 2013 M 9.04 0.33 6.6 2.2 SD-13-0077-S278 SD(AL) 2013_0077 2013 F 8.22 0.34 6.1 2.1 SD-13-0098-S281 SD(AL) 2013_0098 2013 F 7.69 0.33 6.2 2.05 SD-13-0122-S283 SD(AL) 2013_0122 2013 F 7.06 0.34 5.8 1.95 SD-13-0124-S173 SD(AL) 2013_0124 2013 M 6.46 0.35 5.7 2 SD-13-0178-S282 SD(AL) 2013_0178 2013 F 4.8 0.35 5.4 1.9 SD-13-0186-S169 SD(AL) 2013_0186 2013 M 4.72 0.35 5.3 1.85 SD-13-0188-S363 SD(AL) 2013_0188 2013 F 6.72 0.36 6 2.15 SD-13-0209-S288 SD(AL) 2013_0209 2013 F 8.2 0.35 6.2 2.2 SD-13-0218-S279 SD(AL) 2013_0218 2013 F 9.63 0.33 6.6 2.15 SD-13-0227-S170 SD(AL) 2013_0227 2013 M 5.71 0.36 5.5 2 SD-13-0230-S172 SD(AL) 2013_0230 2013 M 9.33 0.36 6.3 2.25 SD-13-0236-S260 SD(AL) 2013_0236 2013 M 4.33 0.34 5.3 1.8 SD-13-0267-S284 SD(AL) 2013_0267 2013 F 8.89 0.34 6.15 2.1 SD-13-0283-S259 SD(AL) 2013_0283 2013 M 8.62 0.33 6.3 2.1 SD-13-0305-S258 SD(AL) 2013_0305 2013 M 5.8 0.33 5.75 1.9 SD-14-0011-S287 SD(AL) 2014_0011 2017 f 10.18 0.40 6.2 2.5 SD-14-0095-S280 SD(AL) 2014_0095 2017 f 12.48 0.39 6.5 2.55 0214 M CNF 5/12/15 SD-15-0214-S262 SD(AL) Adult 2015 M 10.08 0.42 6.6 2.8 1135 CNF F 5/12/15 SD-15-1135-S325 SD(AL) Adult 2015 F 12.04 0.39 6.6 2.6 SD-15-123/4/50- 123/4/50 CNF F S292 SD(AL) 5/12/15 Adult 2015 F 13.69 0.39 7.2 2.8 84 1352 M SD 5/19/15 SD-15-1352-S72 SD(AL) Adult 2015 M 3.79 0.47 4.7 2.2 2205 F 5/14/15 CNF SD-15-2205-S189 SD(AL) Adult 2015 F 14.37 0.43 6.9 3 2552 M CNF 5/12/15 SD-15-2552-S263 SD(AL) Adult 2015 M 10.09 0.42 6.6 2.8 3203 F 5/12/15 CNF SD-15-3203-S366 SD(AL) Adult 2015 F 10.53 0.41 6.3 2.6 SD-15-3205-S293 SD(AL) 3205 SD 5/18/15 Adult 2015 F 7.79 0.42 5.7 2.4 3301 F 5/12/15 CNF SD-15-3301-S365 SD(AL) Adult 2015 F 13.59 0.40 6.8 2.7 3312 F 5/12/15 CNF SD-15-3312-S190 SD(AL) Adult 2015 F 7.59 0.45 5.6 2.5 3333 F SD 5/19/15 SD-15-3333-S191 SD(AL) Adult 2015 F 17.29 0.46 7.2 3.3 SD-15-3415-S209 SD(AL) 3415 F SD 5/18/15 Adult 2015 F 10.76 0.40 6.3 2.5 3420 F 5/12/15 CNF SD-15-3420-S208 SD(AL) Adult 2015 F 6.88 0.40 6 2.4 3432 F SD 5/20/15 SD-15-3432-S317 SD(AL) Adult 2015 F 8.79 0.35 6 2.1 3452 F SD 5/19/15 SD-15-3452-S192 SD(AL) Adult 2015 F 8.75 0.42 5.9 2.5 4350 M CNF 5/14/15 SD-15-4350-S179 SD(AL) Adult 2015 M 11.42 0.42 6.6 2.8 SD-15-5031-S318 SD(AL) 5031 F SD 5/21/15 Adult 2015 F 13.33 0.42 6.7 2.8 5100 M CNF 5/12/15 SD-15-5100-S261 SD(AL) Adult 2015 M 4.53 0.43 5.1 2.2 5501 F CNF 5/15/15 SD-15-5501-S291 SD(AL) Adult 2015 F 6.54 0.44 5.5 2.4 SD-16-0309-S364 SD(AL) 2016_0309 2017 f 13.34 0.48 7.1 3.4 SD-16-0310-S289 SD(AL) 2016_0310 2017 f 7.46 0.41 6.8 2.8 SD-16-0319-S290 SD(AL) 2016_0319 2017 f 6.49 0.46 5.9 2.7 SD-17-0013-S178 SD(AL) 2017_0013 2017 f 11.85 0.45 6.7 3 SD-17-0013-S308 SD(AL) 2014_0013 2017 m 4.77 0.45 4.9 2.2 SD-17-0020-S210 SD(AL) 2017_0020 2017 f 9.18 0.47 6.4 3 SD-17-0027-S307 SD(AL) 2017_0027 2017 f 8.79 0.43 6 2.6 SD-17-0050-S328 SD(AL) 2017_0050 2017 f 7.87 0.45 6 2.7 SD-17-0056-S176 SD(AL) 2014_0056 2017 m 11.57 0.44 6.8 3 SD-17-0062-S177 SD(AL) 2014_0062 2017 m 7.37 0.41 5.8 2.4 SD-17-0088-S175 SD(AL) 2014_0088 2017 m 5.84 0.42 5.5 2.3 SD-17-0097-S326 SD(AL) 2017_0097 2017 f 5.48 0.39 5.9 2.3 SD-17-0139-S372 SD(AL) 2017_0139 2017 f 14.43 0.43 6.7 2.9 SD-17-0144-S327 SD(AL) 2017_0144 2017 f 13.67 0.45 6.9 3.1 SD-17-0166-S311 SD(AL) 2017_0166 2017 f 6.54 0.46 5.6 2.6 SD-17-0183-S310 SD(AL) 2017_0183 2017 f 7.15 0.43 5.4 2.3 SD-17-0210-S373 SD(AL) 2017_0210 2017 f 11.8 0.44 6.6 2.9 SD-17-0237-S312 SD(AL) 2017_0237 2017 f 11.77 0.46 6.7 3.1 SD-17-0278-S330 SD(AL) 2017_0278 2017 f 9.39 0.45 6.4 2.9 SD-17-0310-S306 SD(AL) 2017_0310 2017 f 10.43 0.45 6.5 2.9 SD-AL-058-S236 SD(AL) AL_058 N/A

85 Supplementary Table 3-2: List of S. undulatus Z(Fst)>5 basepair windows and overlapping annotated genes Compariso CHR_STAR BIN_START. BIN_END. N_VARIANTS. n T Name CHR_BP x x y Window_Z scaffold_13 - 13 - AL-TN 4937051 CD47_2 4900001 4900001 5000000 95 5.92918331 scaffold_13 - 13 - AL-TN 4993296 CD47_4 4900001 4900001 5000000 95 5.92918331 scaffold_14 - 14 - AL-TN 2083991 SMCHD1_2 2100001 2100001 2200000 28 10.7449999 scaffold_14 - 14 - AL-TN 3075220 AKAIN1_2 3000001 3000001 3100000 173 7.13700335 scaffold_14 - 14 - AL-TN 3095749 ZBTB14_2 3000001 3000001 3100000 173 7.13700335 scaffold_15 - 15 - AL-TN 1107511 AKAP7_1 1050001 1050001 1150000 60 9.57918476 scaffold_15 - 15 - AL-TN 1855648 DLGAP1_1 1850001 1850001 1950000 106 5.51654486 scaffold_15 - 15 - AL-TN 1911624 DLGAP1_2 1850001 1850001 1950000 106 5.51654486 scaffold_15 - 15 - AL-TN 3601992 ROCK1_2 3600001 3600001 3700000 98 5.74312927 scaffold_15 - 15 - AL-TN 3601992 ROCK1_2 3650001 3650001 3750000 145 5.19109207 scaffold_16 - 16 - AL-TN 4339565 ELOVL4_2 4350001 4350001 4450000 71 7.53969825 scaffold_2 - 2 - AL-TN 13579086 TRIM7_2 13500001 13500001 13600000 1828 9.62924279 scaffold_2 - 2 - AL-TN 13579086 TRIM7_2 13550001 13550001 13650000 1393 7.74284158 scaffold_2 - 2 - AL-TN 141132447 ACSF2_2 141100001 141100001 141200000 2505 6.00574487 scaffold_2 - 2 - AL-TN 141132447 ACSF2_2 141150001 141150001 141250000 1997 5.63431733 scaffold_2 - 2 - AL-TN 141227374 RSAD1 141150001 141150001 141250000 1997 5.63431733 scaffold_23 - 23 - AL-TN 313828 GLTSCR1_1 300001 300001 400000 44 8.84396695 scaffold_23 - 23 - AL-TN 342157 CAPN12_1 300001 300001 400000 44 8.84396695 scaffold_23 - 23 - AL-TN 342157 CAPN12_1 350001 350001 450000 53 8.48153776 scaffold_23 - 23 - AL-TN 364369 CAPN12_3 300001 300001 400000 44 8.84396695 scaffold_23 - 23 - AL-TN 364369 CAPN12_3 350001 350001 450000 53 8.48153776 scaffold_3 - 3 - AL-TN 214393557 ENTPD7_1 214400001 214400001 214500000 2075 5.16228224 scaffold_3 - 3 - AL-TN 214408135 ENTPD7_2 214400001 214400001 214500000 2075 5.16228224 scaffold_3 - 3 - AL-TN 214420413 COX15 214400001 214400001 214500000 2075 5.16228224 scaffold_3 - 3 - AL-TN 214472559 ABCC2 214450001 214450001 214550000 1882 8.38902115 scaffold_3 - 3 - AL-TN 214472559 ABCC2 214400001 214400001 214500000 2075 5.16228224 86 scaffold_3 - IL1RAPL1_ 3 - AL-TN 81115253 2 81100001 81100001 81200000 2169 5.15585485 scaffold_5 - 5 - AL-TN 45091413 TMCC3 45050001 45050001 45150000 2625 5.35930065 scaffold_6 - 6 - AL-TN 52794169 ABHD5 52700001 52700001 52800000 1544 5.71065204 scaffold_6 - 6 - AL-TN 53560205 SH3BP5 53600001 53600001 53700000 865 7.45750327 scaffold_6 - 6 - AL-TN 53560205 SH3BP5 53550001 53550001 53650000 894 5.5625196 scaffold_6 - 6 - AL-TN 53618427 CAPN7 53600001 53600001 53700000 865 7.45750327 scaffold_6 - 6 - AL-TN 53618427 CAPN7 53550001 53550001 53650000 894 5.5625196 scaffold_7 - 7 - AL-TN 11807202 CLIC3 11800001 11800001 11900000 2351 5.54599743 scaffold_7 - 7 - AL-TN 11816110 ABCA2 11800001 11800001 11900000 2351 5.54599743 scaffold_7 - 7 - AL-TN 11863534 FUT7 11800001 11800001 11900000 2351 5.54599743 scaffold_7 - 7 - AL-TN 11892723 NPDC1_1 11800001 11800001 11900000 2351 5.54599743 scaffold_7 - 7 - AL-TN 3215824 SLC2A5_1 3200001 3200001 3300000 2304 5.22855997 scaffold_7 - 7 - AL-TN 3271749 SLC2A5_2 3200001 3200001 3300000 2304 5.22855997 scaffold_7 - 7 - AL-TN 3288271 CA6 3200001 3200001 3300000 2304 5.22855997 1 - scaffold_1 - 23695000 AL-AR 236949684 DTX3L 1 236950001 237050000 2039 5.30730662 1 - scaffold_1 - 23695000 AL-AR 236973244 PARP14_1 1 236950001 237050000 2039 5.30730662 1 - scaffold_1 - 23695000 AL-AR 237015229 PARP14_2 1 236950001 237050000 2039 5.30730662 scaffold_1 - 1 - 5.8283204 AL-AR 295652315 SHANK2_2 295650001 295650001 295750000 2305 4 scaffold_14 - 14 - AL-AR 2083991 SMCHD1_2 2100001 2100001 2200000 21 7.99777161 scaffold_14 - 14 - AL-AR 3075220 AKAIN1_2 3000001 3000001 3100000 212 5.44990053 scaffold_14 - 14 - AL-AR 3095749 ZBTB14_2 3000001 3000001 3100000 212 5.44990053 scaffold_15 - 15 - 6.0392804 AL-AR 1107511 AKAP7_1 1050001 1050001 1150000 61 4 scaffold_16 - 16 - AL-AR 4339565 ELOVL4_2 4350001 4350001 4450000 81 7.84487343 scaffold_17 - 17 - AL-AR 206840 CDH2_1 200001 200001 300000 113 5.46356757 scaffold_17 - 17 - AL-AR 206840 CDH2_1 250001 250001 350000 79 7.68469439 scaffold_2 - 2 - AL-AR 110250490 ERGIC1 110250001 110250001 110350000 1836 5.31125557 87 2 - scaffold_2 - 33430000 AL-AR 334294195 TESK1 1 334300001 334400000 656 5.41121321 scaffold_21 - 21 - AL-AR 854192 DHX35_2 850001 850001 950000 138 5.55497946 scaffold_21 - 21 - AL-AR 876270 DHX35_3 850001 850001 950000 138 5.55497946 scaffold_21 - 21 - AL-AR 923108 FAM83D_2 850001 850001 950000 138 5.55497946 scaffold_24 - 24 - AL-AR 381281 EHD2 400001 400001 500000 9 5.30968216 scaffold_24 - 24 - AL-AR 475934 NKX2-1_1 400001 400001 500000 9 5.30968216 scaffold_24 - 24 - AL-AR 497214 ASPSCR1_1 400001 400001 500000 9 5.30968216 scaffold_3 - 3 - AL-AR 227705855 CLSTN2 227700001 227700001 227800000 1737 5.56093373 4 - scaffold_4 - 22825000 AL-AR 228294793 CSMD3_1 1 228250001 228350000 1525 5.33346838 4 - scaffold_4 - 22825000 AL-AR 228334991 CSMD3_2 1 228250001 228350000 1525 5.33346838 scaffold_4 - 4 - 6.0706560 AL-AR 33213190 PLCG1_1 33200001 33200001 33300000 1667 3 scaffold_4 - 4 - AL-AR 45869708 ELF3 45850001 45850001 45950000 1347 5.01872519 scaffold_4 - 4 - AL-AR 59148123 RBBP5 59100001 59100001 59200000 1421 5.23727457 scaffold_4 - 4 - AL-AR 59181327 TMEM81 59100001 59100001 59200000 1421 5.23727457 scaffold_4 - 4 - AL-AR 59148123 RBBP5 59150001 59150001 59250000 1531 6.19412224 scaffold_4 - 4 - AL-AR 59181327 TMEM81 59150001 59150001 59250000 1531 6.19412224 scaffold_4 - 4 - AL-AR 59222083 CNTN2 59150001 59150001 59250000 1531 6.19412224 scaffold_4 - 4 - AL-AR 87048396 HOOK1_2 87050001 87050001 87150000 1062 6.2835905 scaffold_4 - 4 - AL-AR 87075969 CYP2J2_1 87050001 87050001 87150000 1062 6.2835905 scaffold_4 - 4 - AL-AR 87110905 CYP2J2_2 87050001 87050001 87150000 1062 6.2835905 scaffold_4 - 4 - AL-AR 87075969 CYP2J2_1 87100001 87100001 87200000 856 5.90165356 scaffold_4 - 4 - AL-AR 87110905 CYP2J2_2 87100001 87100001 87200000 856 5.90165356 scaffold_4 - 4 - AL-AR 87155063 CYP2J2_3 87100001 87100001 87200000 856 5.90165356 5 - scaffold_5 - 13880000 AL-AR 138854324 KLHL2 1 138800001 138900000 1365 6.52845589 5 - scaffold_5 - 13880000 AL-AR 138869507 MSMO1 1 138800001 138900000 1365 6.52845589 scaffold_5 - 5 - AL-AR 138854324 KLHL2 138850001 138850001 138950000 1722 6.93717161 88 scaffold_5 - 5 - AL-AR 138869507 MSMO1 138850001 138850001 138950000 1722 6.93717161 scaffold_6 - 6 - AL-AR 135545768 GRIK4 135450001 135450001 135550000 1857 7.06273569 scaffold_6 - 6 - AL-AR 135545768 GRIK4 135500001 135500001 135600000 1804 8.63342821 scaffold_6 - 6 - AL-AR 53560205 SH3BP5 53600001 53600001 53700000 800 5.03510714 scaffold_6 - 6 - AL-AR 53618427 CAPN7 53600001 53600001 53700000 800 5.03510714 scaffold_6 - 6 - AL-AR 54018554 ATP2C1 54050001 54050001 54150000 2212 6.94957376 scaffold_6 - 6 - AL-AR 54074068 ASTE1 54050001 54050001 54150000 2212 6.94957376 scaffold_6 - 6 - AL-AR 54090562 NEK11 54050001 54050001 54150000 2212 6.94957376 scaffold_6 - 6 - AL-AR 54090562 NEK11 54100001 54100001 54200000 2490 9.19223466 scaffold_6 - 6 - AL-AR 54090562 NEK11 54150001 54150001 54250000 1588 7.35594479 scaffold_15 - 15 - TN-AR 3979878 ABHD3_2 4000001 4000001 4100000 43 5.21586086 scaffold_15 - 15 - TN-AR 4010307 MIB1_2 4000001 4000001 4100000 43 5.21586086 scaffold_5 - 5 - TN-AR 39248475 CYP2D6_2 39300001 39300001 39400000 645 5.29549798 scaffold_5 - 5 - TN-AR 39319087 CYP2D6_3 39300001 39300001 39400000 645 5.29549798 scaffold_5 - 5 - TN-AR 39339454 CYP2D6_4 39350001 39350001 39450000 633 5.67406217 scaffold_5 - 5 - TN-AR 39339454 CYP2D6_4 39300001 39300001 39400000 645 5.29549798 scaffold_5 - 5 - TN-AR 39374043 TCF20 39350001 39350001 39450000 633 5.67406217 scaffold_5 - 5 - TN-AR 39374043 TCF20 39300001 39300001 39400000 645 5.29549798 scaffold_6 - 6 - TN-AR 54090562 NEK11 54100001 54100001 54200000 1886 5.96198267 scaffold_6 - 6 - TN-AR 54090562 NEK11 54150001 54150001 54250000 1189 5.52634817

89 Supplementary Table 3-3: List of functionally enriched genes overlapping S. undulatus Z(Fst)>5 basepair windows Comparison source term_name term_id adjusted_p_value REAC:R-HSA- AL-TN REAC Metabolism of porphyrins 189445 0.02789741 Factor: CSX; motif: NNCACTTGNRN; match AL-TN TF class: 1 TF:M02108_1 0.03385114 AL-TN CORUM ITGAV-ITGB3-CD47-FCER2 complex CORUM:2355 0.00653594 AL-TN CORUM ITGB3-ITGAV-CD47 complex CORUM:2356 0.00653594 AL-TN CORUM ITGA2b-ITGB3-CD47-SRC complex CORUM:2377 0.00653594 AL-TN CORUM ITGA4-ITGB1-CD47 complex CORUM:2423 0.00653594 AL-TN CORUM ITGA2-ITGB1-CD47 complex CORUM:2429 0.00653594 AL-TN CORUM DLG4-DLGAP1-SHANK3 complex CORUM:6321 0.00653594 AL-TN CORUM DLC1-DLGAP1-MYO5A complex CORUM:7233 0.00653594 AL-TN CORUM DLGAP1-DLG4-DYNLL2 complex CORUM:7234 0.00653594 AL-TN CORUM ISLR2-ROCK1 complex CORUM:7424 0.00653594 AL-TN CORUM ITGA2b-ITGB3-CD47-FAK complex CORUM:2896 0.00783868 AL-TN CORUM ITGA2b-ITGB3-CD9-GP1b-CD47 complex CORUM:2872 0.00890255 AL-TN CORUM Cytochrome c oxidase, mitochondrial CORUM:6442 0.01627514 AL-AR GO:BP regulation of trans-synaptic signaling GO:0099177 0.0390125 AL-AR GO:BP modulation of chemical synaptic transmission GO:0050804 0.0390125 AL-AR CORUM Hook1-Vps18 complex CORUM:6117 0.01770237 AL-AR CORUM DTX3L-PARP9 complex CORUM:7384 0.01770237 AL-AR CORUM FYN-KHDRBS1-PLCG1 complex CORUM:7379 0.01770237 AL-AR CORUM AIP4-DTX3L complex CORUM:7286 0.01770237 AL-AR CORUM WRA complex (WDR5, RBBP5, ASH2L) CORUM:6850 0.01770237 AL-AR CORUM NIF1-ASH2L-RBBP5-WDR5 complex CORUM:6845 0.01770237 AL-AR CORUM CUL4A-DDB1-RBBP5 complex CORUM:6491 0.01770237 AL-AR CORUM DLL1-CTNNB1-CDH2 complex CORUM:6269 0.01770237 AL-AR CORUM Hook1-Vps41 complex CORUM:6119 0.01770237 AL-AR CORUM Hook1-Vps39 complex CORUM:6118 0.01770237 AL-AR CORUM DTX3L-PARP9-STAT1 complex CORUM:7385 0.01770237 AL-AR CORUM Hook1-Vps16 complex CORUM:6116 0.01770237 AL-AR CORUM FHF complex CORUM:6109 0.01770237 SLP-76-PLC-gamma-1-VAV complex, alpha-TCR AL-AR CORUM stimulated CORUM:2961 0.01770237 SLP-76-PLC-gamma-1-ITK complex, alpha-TCR AL-AR CORUM stimulated CORUM:2960 0.01770237 AL-AR CORUM HOOK1-SHP2 complex CORUM:7441 0.01770237 LCK-SLP76-PLC-gamma-1-LAT complex, AL-AR CORUM pervanadate-activated CORUM:2955 0.01770237 PLC-gamma-1-LAT-c-CBL complex, OKT3 AL-AR CORUM stimulated CORUM:2956 0.01770237 PDGFRA-PLC-gamma-1-PI3K-SHP-2 complex, AL-AR CORUM PDGF stimulated CORUM:2551 0.01770237 AL-AR CORUM PLC-gamma-1-SLP-76-SOS1-LAT complex CORUM:2547 0.01770237 AL-AR CORUM NEK2-NEK11 complex CORUM:1932 0.01770237 LAT-PLC-gamma-1-p85-GRB2-SOS signaling AL-AR CORUM complex, C305 activated CORUM:2922 0.0190425 AL-AR CORUM WDR5-ASH2L-RBBP5-MLL2 complex CORUM:1399 0.0190425 AL-AR CORUM ASH2L-KDM6B-KDM6B-WDR5 complex CORUM:7367 0.0190425 AL-AR CORUM WRAD complex (WDR5, RBBP5, ASH2L, DPY30) CORUM:6849 0.0190425 90 AL-AR CORUM MLL1 core complex CORUM:6457 0.0190425 MWRAD complex (MLL1, WDR5, RBBP5, AL-AR CORUM ASH2L, DPY30) CORUM:6851 0.02131656 LAT-PLC-gamma-1-p85-GRB2-CBL-VAV-SLP-76 AL-AR CORUM signaling complex, C305 activated CORUM:2529 0.02131656 AL-AR CORUM PITX2-MLL4-ASH2L-RBBP5-PTIP complex CORUM:6340 0.02131656 AL-AR CORUM MLL-HCF complex CORUM:1256 0.02179342 AL-AR CORUM Set1B complex CORUM:2730 0.02179342 AL-AR CORUM Set1A complex CORUM:2731 0.02179342 Menin-associated histone methyltransferase AL-AR CORUM complex CORUM:1254 0.02179342 AL-AR CORUM ASCOM complex CORUM:1400 0.02179342 AL-AR CORUM MLL1 complex CORUM:6462 0.02467124 AL-AR CORUM MLL4 complex CORUM:6467 0.02594033 AL-AR CORUM MLL3 complex CORUM:6461 0.02594033 AL-AR CORUM PTIP-HMT complex CORUM:5195 0.02594033 AL-AR CORUM Set1A complex CORUM:6469 0.02769235 AL-AR CORUM MLL2 complex CORUM:6460 0.02769235 AL-AR CORUM MOF complex CORUM:1401 0.02998483 AL-AR CORUM Set1B complex CORUM:6470 0.03141362 AL-AR CORUM UTX-MLL2/3 complex CORUM:3075 0.03141362

91 Supplementary Table 3-4: List of S. undulatus LASSI T>25 SNP windows and overlapping annotated genes T m E pK Coordinates Chr Start Stop Name 55.8594368 3 0.00066667 0.03522132 16586132 scaffold_10 16573236 16613420 MYO9B_1 41.3140564 3 0.00066667 0.03522132 16606059 scaffold_10 16573236 16613420 MYO9B_1 31.2962412 5 0.00066667 0.03522132 16605724 scaffold_10 16573236 16613420 MYO9B_1 68.4901876 1 0.00066667 0.04679325 12754026 scaffold_11 12745113 12774651 RAP1GAP2 44.5126643 2 0.00066667 0.04679325 12753908 scaffold_11 12745113 12774651 RAP1GAP2 37.4280369 1 0.01866667 0.04679325 12753332 scaffold_11 12745113 12774651 RAP1GAP2 37.4280369 1 0.01866667 0.04679325 12755197 scaffold_11 12745113 12774651 RAP1GAP2 34.7787446 2 0.00066667 0.04679325 12754287 scaffold_11 12745113 12774651 RAP1GAP2 32.7454956 2 0.00333333 0.04679325 12754439 scaffold_11 12745113 12774651 RAP1GAP2 32.7454956 2 0.00333333 0.04679325 12754667 scaffold_11 12745113 12774651 RAP1GAP2 32.6816594 1 0.014 0.04679325 12753784 scaffold_11 12745113 12774651 RAP1GAP2 32.2880821 1 0.01866667 0.04679325 12753223 scaffold_11 12745113 12774651 RAP1GAP2 31.3587556 1 0.02133333 0.04679325 12753488 scaffold_11 12745113 12774651 RAP1GAP2 31.0953401 1 0.02066667 0.04679325 12755485 scaffold_11 12745113 12774651 RAP1GAP2 28.0181713 1 0.02 0.04679325 12753679 scaffold_11 12745113 12774651 RAP1GAP2 27.5315524 1 0.024 0.04679325 12753545 scaffold_11 12745113 12774651 RAP1GAP2 26.1679626 1 0.02133333 0.04679325 12753598 scaffold_11 12745113 12774651 RAP1GAP2 26.0462882 2 0.01133333 0.04679325 12753015 scaffold_11 12745113 12774651 RAP1GAP2 43.0980429 1 0.00066667 0.037517 19002758 scaffold_2 19001827 19008456 Y-LEC1 38.706011 3 0.00066667 0.037517 19004415 scaffold_2 19001827 19008456 Y-LEC1 38.706011 3 0.00066667 0.037517 19005349 scaffold_2 19001827 19008456 Y-LEC1 35.5731672 3 0.00066667 0.037517 19005465 scaffold_2 19001827 19008456 Y-LEC1 35.5731672 3 0.00066667 0.037517 19005526 scaffold_2 19001827 19008456 Y-LEC1 33.7408153 4 0.00066667 0.037517 19005660 scaffold_2 19001827 19008456 Y-LEC1 33.7408153 4 0.00066667 0.037517 19005750 scaffold_2 19001827 19008456 Y-LEC1 33.7408153 4 0.00066667 0.037517 19005876 scaffold_2 19001827 19008456 Y-LEC1 33.7408153 4 0.00066667 0.037517 19006644 scaffold_2 19001827 19008456 Y-LEC1 32.2845422 2 0.00066667 0.037517 18952848 scaffold_2 18944967 18959850 OLR1 27.730447 4 0.00066667 0.037517 19002841 scaffold_2 19001827 19008456 Y-LEC1 27.730447 4 0.00066667 0.037517 19003101 scaffold_2 19001827 19008456 Y-LEC1 27.7059647 2 0.00066667 0.037517 17922599 scaffold_2 17910941 17938464 FLOT1 25.0090513 4 0.00066667 0.037517 17937377 scaffold_2 17910941 17938464 FLOT1 30.665681 1 0.02333333 0.03811274 18826996 scaffold_5 18828285 18845943 CACNA1C_3 26.2658637 1 0.02466667 0.03811274 6614940 scaffold_5 6570495 6669151 PLXNA4_3 34.4347247 3 0.00066667 0.03935839 26825253 scaffold_6 26821481 26877068 MRC1_3 34.4347247 3 0.00066667 0.03935839 26825724 scaffold_6 26821481 26877068 MRC1_3 31.2051356 2 0.00066667 0.03935839 26826035 scaffold_6 26821481 26877068 MRC1_3 27.3759265 1 0.01333333 0.03935839 26838943 scaffold_6 26821481 26877068 MRC1_3 27.3759265 1 0.01333333 0.03935839 26839197 scaffold_6 26821481 26877068 MRC1_3 43.8791291 1 0.00266667 0.04141927 17159841 scaffold_7 17108447 17157820 MVB12B 41.2830849 3 0.00066667 0.04141927 11828438 scaffold_7 11816110 11858343 ABCA2 40.6287878 2 0.00066667 0.04141927 11828815 scaffold_7 11816110 11858343 ABCA2 35.2366977 1 0.00066667 0.04141927 3219189 scaffold_7 3215824 3219672 SLC2A5_1 35.0056017 3 0.00066667 0.04141927 11828073 scaffold_7 11816110 11858343 ABCA2 32.9626943 2 0.00066667 0.04141927 11829308 scaffold_7 11816110 11858343 ABCA2 32.9626943 2 0.00066667 0.04141927 11829682 scaffold_7 11816110 11858343 ABCA2 92 29.6944201 1 0.022 0.04141927 17160079 scaffold_7 17108447 17157820 MVB12B 25.7948678 3 0.00066667 0.04141927 11830167 scaffold_7 11816110 11858343 ABCA2 25.372514 1 0.01466667 0.04141927 3218936 scaffold_7 3215824 3219672 SLC2A5_1 26.7664927 3 0.00066667 0.04467766 1663017 scaffold_8 1656945 1661815 FHOD1_1 26.7664927 3 0.00066667 0.04467766 1663017 scaffold_8 1664245 1677265 FHOD1_2 26.2823225 1 0.02333333 0.04467766 14442827 scaffold_8 14423602 14467142 ADCY7 68.0888327 1 0.00066667 0.04345308 18390056 scaffold_9 18387964 18391251 FBXO17 63.5278482 1 0.00066667 0.04345308 18389813 scaffold_9 18387964 18391251 FBXO17 63.3439176 1 0.00066667 0.04345308 18390311 scaffold_9 18387964 18391251 FBXO17 53.8987545 1 0.00066667 0.04345308 18383261 scaffold_9 18374168 18383090 ACP7 53.8987545 1 0.00066667 0.04345308 18383423 scaffold_9 18374168 18383090 ACP7 52.5033771 1 0.00066667 0.04345308 18387422 scaffold_9 18387964 18391251 FBXO17 47.9423926 1 0.00066667 0.04345308 18389088 scaffold_9 18387964 18391251 FBXO17 47.9423926 1 0.00066667 0.04345308 18389712 scaffold_9 18387964 18391251 FBXO17 47.6888302 1 0.00066667 0.04345308 18387644 scaffold_9 18387964 18391251 FBXO17 46.5903966 1 0.006 0.04345308 18386925 scaffold_9 18387964 18391251 FBXO17 43.1278457 1 0.00066667 0.04345308 18271645 scaffold_9 18260422 18274277 NKG7_1 43.1278457 1 0.00066667 0.04345308 18383010 scaffold_9 18374168 18383090 ACP7 43.1278457 1 0.00066667 0.04345308 18383663 scaffold_9 18374168 18383090 ACP7 43.1278457 1 0.00066667 0.04345308 18383974 scaffold_9 18374168 18383090 ACP7 41.3589276 1 0.01266667 0.04345308 18386180 scaffold_9 18387964 18391251 FBXO17 37.9586745 1 0.008 0.04345308 18388394 scaffold_9 18387964 18391251 FBXO17 37.5074809 1 0.008 0.04345308 18385052 scaffold_9 18374168 18383090 ACP7 37.5074809 1 0.008 0.04345308 18385567 scaffold_9 18374168 18383090 ACP7 37.5074809 1 0.008 0.04345308 18385567 scaffold_9 18387964 18391251 FBXO17 37.1117283 1 0.01333333 0.04345308 18384416 scaffold_9 18374168 18383090 ACP7 36.7475861 2 0.00066667 0.04345308 18390446 scaffold_9 18387964 18391251 FBXO17 34.4562034 1 0.00333333 0.04345308 18269317 scaffold_9 18260422 18274277 NKG7_1 33.9428534 1 0.00266667 0.04345308 18388679 scaffold_9 18387964 18391251 FBXO17 33.9322755 3 0.00066667 0.04345308 20289090 scaffold_9 20276722 20290613 DHX34 32.587879 1 0.01466667 0.04345308 18271905 scaffold_9 18260422 18274277 NKG7_1 32.2813938 1 0.01466667 0.04345308 18385886 scaffold_9 18387964 18391251 FBXO17 32.1509959 2 0.00466667 0.04345308 18390922 scaffold_9 18387964 18391251 FBXO17 30.7166034 2 0.00466667 0.04345308 16444330 scaffold_9 16397658 16453965 KIRREL2 29.9198301 2 0.00066667 0.04345308 20486478 scaffold_9 20458173 20499415 LTBP4 29.306921 1 0.01066667 0.04345308 18269918 scaffold_9 18260422 18274277 NKG7_1 28.8324239 4 0.00066667 0.04345308 21105190 scaffold_9 21095989 21112288 CAPN12_5 28.2992902 1 0.02066667 0.04345308 20516771 scaffold_9 20514218 20519244 SHKBP1_1 28.2992902 1 0.02066667 0.04345308 20516771 scaffold_9 20519251 20522883 SHKBP1_2 28.0093137 3 0.00066667 0.04345308 20289307 scaffold_9 20276722 20290613 DHX34 27.188271 1 0.022 0.04345308 18379553 scaffold_9 18374168 18383090 ACP7 26.612171 1 0.02133333 0.04345308 18272205 scaffold_9 18260422 18274277 NKG7_1 26.612171 1 0.02133333 0.04345308 20516104 scaffold_9 20514218 20519244 SHKBP1_1 26.2681816 1 0.02866667 0.04345308 20524899 scaffold_9 20519251 20522883 SHKBP1_2 26.2681816 1 0.02866667 0.04345308 20527201 scaffold_9 20528689 20582439 SPTBN4 26.0085484 3 0.00066667 0.04345308 19757948 scaffold_9 19750461 19762670 LYPD3 26.0085484 3 0.00066667 0.04345308 19758108 scaffold_9 19750461 19762670 LYPD3 25.8665375 4 0.00066667 0.04345308 19683788 scaffold_9 19680276 19686741 HSPA8_1 25.7062261 1 0.01866667 0.04345308 18379692 scaffold_9 18374168 18383090 ACP7 93 25.489179 5 0.00066667 0.04345308 21105344 scaffold_9 21095989 21112288 CAPN12_5 25.4666261 1 0.02333333 0.04345308 18375397 scaffold_9 18370326 18373184 NKG7_3 25.4666261 1 0.02333333 0.04345308 18375397 scaffold_9 18374168 18383090 ACP7 25.2265821 3 0.00066667 0.04345308 19758393 scaffold_9 19750461 19762670 LYPD3

94 Supplementary Table 3-5: List of functionally enriched genes overlapping S. undulatus LASSI T>25 SNP windows source term_name term_id adjusted_p_value GO:CC cell periphery GO:0071944 0.02050979 GO:CC plasma membrane GO:0005886 0.03445887 GO:CC cell-cell contact zone GO:0044291 0.03445887 CORUM DNJC3-DNAJB1-HSPA8 complex CORUM:6396 0.01327643 CORUM CAP-cbl-flotilin complex CORUM:6587 0.01327643 CORUM PLXNA4-RANBPM complex CORUM:5760 0.01327643 CORUM Profilin 1 complex CORUM:2837 0.01327643 CORUM DNAJC7-HSPA8-HSP90AA1 complex CORUM:6399 0.01327643 CORUM BAG1-HSC70 complex CORUM:6114 0.01327643 CORUM CHIP-HSC70-TAU-UBCH5B complex CORUM:6037 0.01327643 CORUM PABPC1-HSPA8-HNRPD-EIF4G1 complex CORUM:1308 0.01327643 CORUM DNAJB2-HSPA8-PSMA3 complex CORUM:2129 0.01327643 CORUM FARP2-NRP1-PlexinA4 complex CORUM:5649 0.01703323 CORUM Ubiquitin E3 ligase (CUL1, FBXO17, RBX1, SKP1) CORUM:6902 0.01703323 CORUM Profilin 2 complex CORUM:2300 0.01703323 CORUM HMGB1-HMGB2-HSC70-ERP60-GAPDH complex CORUM:280 0.01703323 CORUM BAG3-HSC70-HSPB8-CHIP complex CORUM:6052 0.01703323 CORUM BCOR complex CORUM:1178 0.02230833 CORUM CDC5L core complex CORUM:1182 0.02230833 CORUM P2X7 receptor signalling complex CORUM:725 0.02793492 CORUM HCF-1 complex CORUM:2721 0.04587461 CORUM CDC5L complex CORUM:1183 0.04708435 CORUM CEN complex CORUM:929 0.04708435 HP Patent foramen ovale HP:0001655 0.04207418

95 Supplementary Table 3-6: List of Alabama S. undulatus p<1E-6 GWAS SNPs and annotated genes within 2,500 bp CHR BP A1 NMISS BETA STAT P Feature Start Stop Name scaffold_10 16266096 A 374 0.01483 6.374 5.49E-10 CDS 16267450 16290227 CRTC1_1 scaffold_10 16266100 G 374 0.01485 6.357 6.08E-10 CDS 16267450 16290227 CRTC1_1 scaffold_10 16595356 C 374 0.0249 6.266 1.03E-09 CDS 16573236 16613420 MYO9B_1 scaffold_10 15551591 T 374 0.01356 6.111 2.51E-09 CDS 15548154 15552180 ARID3A_2 scaffold_10 15519511 G 374 0.02027 5.997 4.77E-09 CDS 15513052 15544028 ARID3A_1 scaffold_10 15551605 A 374 0.01309 5.942 6.50E-09 CDS 15548154 15552180 ARID3A_2 scaffold_10 15880073 A 374 0.02028 5.852 1.07E-08 CDS 15871803 15881098 ARMC6_2 scaffold_10 15551366 C 374 0.01284 5.818 1.29E-08 CDS 15548154 15552180 ARID3A_2 scaffold_10 15551689 A 374 0.01322 5.795 1.47E-08 CDS 15548154 15552180 ARID3A_2 scaffold_10 16615828 T 374 0.01393 5.769 1.68E-08 CDS 16573236 16613420 MYO9B_1 scaffold_1 169890752 T 374 0.02468 5.716 2.24E-08 CDS 169887491 169919808 RB1_1 scaffold_10 16030216 G 374 0.01926 5.694 2.53E-08 CDS 16030473 16034870 DDX49_2 scaffold_10 16030201 T 374 0.01885 5.678 2.77E-08 CDS 16030473 16034870 DDX49_2 scaffold_10 15878266 T 374 0.01441 5.651 3.18E-08 CDS 15871803 15881098 ARMC6_2 scaffold_10 15519522 C 374 0.0191 5.643 3.32E-08 CDS 15513052 15544028 ARID3A_1 scaffold_10 16615818 C 374 0.01353 5.61 3.96E-08 CDS 16573236 16613420 MYO9B_1 scaffold_10 16030190 T 374 0.01883 5.61 3.97E-08 CDS 16030473 16034870 DDX49_2 scaffold_10 15519536 A 374 0.01812 5.593 4.35E-08 CDS 15513052 15544028 ARID3A_1 4.94E- scaffold_10 15922908 T 374 0.01464 5.569 08 CDS 15909164 15931201 SUGP2_2 scaffold_10 16444126 G 374 0.01432 5.559 5.21E-08 CDS 16420428 16450701 TMEM59L scaffold_10 16441910 G 374 0.01421 5.546 5.58E-08 CDS 16420428 16450701 TMEM59L scaffold_10 16607908 C 374 0.01781 5.544 5.64E-08 CDS 16573236 16613420 MYO9B_1 scaffold_10 16607910 G 374 0.01781 5.544 5.64E-08 CDS 16573236 16613420 MYO9B_1 6.02E- scaffold_10 15909003 T 374 0.0145 5.531 08 CDS 15906948 15909027 SUGP2_1 6.02E- scaffold_10 15909003 T 374 0.0145 5.531 08 CDS 15909164 15931201 SUGP2_2 6.46E- scaffold_10 16441879 A 374 0.01396 5.518 08 CDS 16420428 16450701 TMEM59L scaffold_10 15879821 T 374 0.0188 5.506 6.87E-08 CDS 15871803 15881098 ARMC6_2 scaffold_10 15364300 T 374 0.01586 5.505 6.93E-08 CDS 15363968 15399135 PTBP1 scaffold_10 15924831 G 374 0.01658 5.477 8.01E-08 CDS 15909164 15931201 SUGP2_2 scaffold_10 15878161 A 374 0.01366 5.462 8.65E-08 CDS 15871803 15881098 ARMC6_2 scaffold_10 15878163 T 374 0.01366 5.462 8.65E-08 CDS 15871803 15881098 ARMC6_2 8.87E- scaffold_10 15880110 T 374 0.01846 5.457 08 CDS 15871803 15881098 ARMC6_2 scaffold_10 15914011 T 374 0.0144 5.456 8.91E-08 CDS 15909164 15931201 SUGP2_2 scaffold_10 15551409 A 374 0.01194 5.428 1.03E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15924866 C 374 0.01726 5.427 1.04E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 16585217 C 374 0.01808 5.425 1.05E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16615880 T 374 0.01333 5.425 1.05E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15551465 A 374 0.01208 5.406 1.16E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 16030169 C 374 0.0182 5.397 1.21E-07 CDS 16030473 16034870 DDX49_2 scaffold_10 15880174 A 374 0.01767 5.388 1.27E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 16585212 T 374 0.01873 5.382 1.31E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16139777 T 374 0.01508 5.378 1.34E-07 CDS 16130320 16161116 UPF1 scaffold_10 15551337 C 374 0.01245 5.373 1.37E-07 CDS 15548154 15552180 ARID3A_2 96 scaffold_10 16266330 T 374 0.0156 5.373 1.37E-07 CDS 16267450 16290227 CRTC1_1 scaffold_10 15364259 T 374 0.01532 5.358 1.49E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15933130 G 374 0.01671 5.348 1.56E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 16140028 A 374 0.01356 5.334 1.68E-07 CDS 16130320 16161116 UPF1 scaffold_10 15924850 T 374 0.01679 5.332 1.70E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15880161 C 374 0.01778 5.316 1.84E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 16589857 G 374 0.01136 5.316 1.84E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15551332 T 374 0.01235 5.307 1.92E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15526889 A 374 0.01724 5.305 1.94E-07 CDS 15513052 15544028 ARID3A_1 scaffold_10 15521616 C 374 0.01242 5.289 2.10E-07 CDS 15513052 15544028 ARID3A_1 scaffold_1 169890731 A 374 0.02302 5.285 2.15E-07 CDS 169887491 169919808 RB1_1 scaffold_10 15364006 G 374 0.01515 5.283 2.18E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15924891 C 374 0.01642 5.278 2.23E-07 CDS 15909164 15931201 SUGP2_2 - scaffold_1 171973051 T 374 0.02838 -5.275 2.26E-07 CDS 171972426 171972930 MEP1A scaffold_10 15933094 A 374 0.01692 5.266 2.38E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 16594265 A 374 0.01752 5.259 2.46E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16589849 A 374 0.0113 5.254 2.52E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16615787 C 374 0.01317 5.253 2.53E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16265884 C 374 0.013 5.251 2.56E-07 CDS 16267450 16290227 CRTC1_1 scaffold_10 15551317 T 374 0.01243 5.246 2.62E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 16615892 T 374 0.01268 5.243 2.66E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15517701 G 374 0.01727 5.238 2.74E-07 CDS 15513052 15544028 ARID3A_1 scaffold_10 15426622 T 374 0.01697 5.232 2.81E-07 CDS 15424621 15431216 PLPPR3_2 scaffold_10 16615904 A 374 0.01279 5.231 2.83E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16266604 A 374 0.01322 5.23 2.84E-07 CDS 16267450 16290227 CRTC1_1 scaffold_10 15551315 A 374 0.01244 5.23 2.85E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 16605747 C 374 0.01267 5.227 2.88E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15388178 T 374 0.01262 5.227 2.89E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15388155 C 374 0.01281 5.226 2.90E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15475569 C 374 0.01731 5.225 2.91E-07 CDS 15460838 15489324 KISS1R scaffold_10 15878467 A 374 0.01332 5.222 2.96E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 15878468 A 374 0.01332 5.222 2.96E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 15166367 T 374 0.01763 5.218 3.02E-07 CDS 15165003 15169235 ISYNA1A scaffold_10 15393633 A 374 0.01317 5.217 3.04E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15878272 T 374 0.01309 5.215 3.06E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 16594273 A 374 0.01754 5.206 3.21E-07 CDS 16573236 16613420 MYO9B_1 scaffold_5 73154582 G 374 -0.02017 -5.201 3.29E-07 CDS 73155319 73156135 HYAL4_1 scaffold_10 15393654 T 374 0.01325 5.2 3.30E-07 CDS 15363968 15399135 PTBP1 scaffold_10 16134026 C 374 0.01619 5.184 3.58E-07 CDS 16130320 16161116 UPF1 scaffold_10 15629459 G 374 0.0145 5.178 3.69E-07 CDS 15626802 15665715 ARHGAP45 scaffold_10 16607974 T 374 0.01698 5.178 3.69E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15642044 T 374 0.01557 5.174 3.77E-07 CDS 15626802 15665715 ARHGAP45 scaffold_10 15386514 T 374 0.01395 5.173 3.79E-07 CDS 15363968 15399135 PTBP1 scaffold_5 73158749 T 374 -0.02708 -5.171 3.83E-07 CDS 73159319 73173169 HYAL4_2 scaffold_10 16605892 A 374 0.01684 5.166 3.92E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15388145 G 374 0.01265 5.162 3.99E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15932072 G 374 0.01744 5.16 4.03E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15932074 G 374 0.01744 5.16 4.03E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15364232 A 374 0.01546 5.156 4.12E-07 CDS 15363968 15399135 PTBP1 97 scaffold_10 15517710 G 374 0.01712 5.146 4.34E-07 CDS 15513052 15544028 ARID3A_1 scaffold_10 15475584 A 374 0.01707 5.141 4.43E-07 CDS 15460838 15489324 KISS1R scaffold_10 16605734 T 374 0.01252 5.135 4.57E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15166137 G 374 0.01731 5.135 4.57E-07 CDS 15165003 15169235 ISYNA1A scaffold_10 16605732 T 374 0.0125 5.13 4.70E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15551415 T 374 0.01133 5.128 4.72E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15919654 T 374 0.01191 5.127 4.77E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15166691 T 374 0.01655 5.123 4.85E-07 CDS 15165003 15169235 ISYNA1A scaffold_10 15877463 A 374 0.01649 5.123 4.85E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 15551713 A 374 0.01171 5.123 4.86E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15551717 A 374 0.01171 5.123 4.86E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15639407 T 374 0.01286 5.12 4.92E-07 CDS 15626802 15665715 ARHGAP45 scaffold_10 15517696 T 374 0.01658 5.114 5.06E-07 CDS 15513052 15544028 ARID3A_1 scaffold_1 106248885 T 374 -0.02164 -5.11 5.18E-07 CDS 106226129 106261826 NT5DC1_1 scaffold_10 15549365 T 374 0.01278 5.098 5.48E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15878283 T 374 0.01304 5.097 5.51E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 16605870 C 374 0.01622 5.095 5.57E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15526603 C 374 0.0122 5.095 5.57E-07 CDS 15513052 15544028 ARID3A_1 scaffold_10 15166112 T 374 0.01714 5.094 5.60E-07 CDS 15165003 15169235 ISYNA1A scaffold_10 15387527 T 374 0.01353 5.094 5.61E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15519825 T 374 0.01549 5.087 5.78E-07 CDS 15513052 15544028 ARID3A_1 scaffold_10 16543558 G 374 0.01194 5.087 5.79E-07 CDS 16538988 16542166 RAB3A_1 scaffold_10 16594277 C 374 0.01719 5.086 5.84E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16594261 T 374 0.01654 5.086 5.84E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16142298 G 374 0.01227 5.085 5.85E-07 CDS 16130320 16161116 UPF1 scaffold_10 15908937 C 374 0.01344 5.081 5.97E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15908937 C 374 0.01344 5.081 5.97E-07 CDS 15906948 15909027 SUGP2_1 scaffold_10 15908938 A 374 0.01344 5.081 5.97E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15908938 A 374 0.01344 5.081 5.97E-07 CDS 15906948 15909027 SUGP2_1 scaffold_10 15919771 A 374 0.01159 5.08 6.01E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15364282 T 374 0.01483 5.079 6.04E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15364283 T 374 0.01483 5.079 6.04E-07 CDS 15363968 15399135 PTBP1 scaffold_10 16421936 A 374 0.01742 5.074 6.19E-07 CDS 16420428 16450701 TMEM59L scaffold_10 15909019 A 374 0.01317 5.073 6.21E-07 CDS 15906948 15909027 SUGP2_1 scaffold_10 15909019 A 374 0.01317 5.073 6.21E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15520825 G 374 0.01573 5.073 6.22E-07 CDS 15513052 15544028 ARID3A_1 scaffold_10 15629437 C 374 0.01394 5.072 6.23E-07 CDS 15626802 15665715 ARHGAP45 scaffold_10 16543616 T 374 0.01253 5.071 6.27E-07 CDS 16538988 16542166 RAB3A_1 scaffold_10 15364417 C 374 0.01518 5.07 6.31E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15364421 C 374 0.01518 5.07 6.31E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15364428 T 374 0.01518 5.07 6.31E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15364431 A 374 0.01518 5.07 6.31E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15364432 G 374 0.01518 5.07 6.31E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15877435 A 374 0.0166 5.069 6.34E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 15922951 C 374 0.01333 5.065 6.44E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15879079 C 374 0.01662 5.059 6.66E-07 CDS 15871803 15881098 ARMC6_2 scaffold_10 16273028 C 374 0.01329 5.057 6.73E-07 CDS 16267450 16290227 CRTC1_1 scaffold_10 15678643 T 374 0.01583 5.056 6.75E-07 CDS 15675167 15689232 CNN2 scaffold_10 15364079 T 374 0.01508 5.055 6.77E-07 CDS 15363968 15399135 PTBP1 98 scaffold_10 16265859 A 374 0.0122 5.051 6.92E-07 CDS 16267450 16290227 CRTC1_1 scaffold_10 15526606 A 374 0.01213 5.046 7.08E-07 CDS 15513052 15544028 ARID3A_1 scaffold_3 140369802 T 374 -0.02315 -5.038 7.38E-07 CDS 140371347 140396699 TNFSF11 scaffold_10 11680659 T 374 0.01809 5.035 7.50E-07 CDS 11680586 11694876 UBE2L3_1 scaffold_10 16142271 C 374 0.01217 5.026 7.82E-07 CDS 16130320 16161116 UPF1 scaffold_10 15388109 A 374 0.01204 5.024 7.87E-07 CDS 15363968 15399135 PTBP1 scaffold_10 15551528 A 374 0.01153 5.022 7.97E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15539934 T 374 0.01418 5.02 8.05E-07 CDS 15513052 15544028 ARID3A_1 scaffold_10 16615772 T 374 0.01279 5.018 8.12E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16615773 G 374 0.01279 5.018 8.12E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15521591 G 374 0.01182 5.018 8.14E-07 CDS 15513052 15544028 ARID3A_1 scaffold_1 169890233 G 374 0.02243 5.01 8.46E-07 CDS 169887491 169919808 RB1_1 - scaffold_4 137683714 A 374 -0.01844 5.003 8.75E-07 CDS 137641578 137704585 SORCS2_2 scaffold_10 11680656 A 374 0.01778 5.002 8.77E-07 CDS 11680586 11694876 UBE2L3_1 scaffold_10 15438794 G 374 0.0151 5.002 8.79E-07 CDS 15440586 15457993 MED16 scaffold_10 16019285 A 374 0.01264 4.999 8.90E-07 CDS 16020391 16025233 DDX49_1 scaffold_10 15919790 C 374 0.0113 4.996 9.03E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15491783 T 374 0.01676 4.996 9.05E-07 CDS 15460838 15489324 KISS1R scaffold_10 16615589 T 374 0.01074 4.995 9.07E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 16615590 G 374 0.01074 4.995 9.07E-07 CDS 16573236 16613420 MYO9B_1 scaffold_10 15247119 G 374 0.01137 4.986 9.48E-07 CDS 15242660 15254152 GTPBP3_2 scaffold_10 15552681 T 374 0.0125 4.986 9.50E-07 CDS 15548154 15552180 ARID3A_2 scaffold_10 15230087 C 374 0.01102 4.984 9.57E-07 CDS 15229351 15231795 GTPBP3_1 scaffold_10 15908942 A 374 0.01312 4.981 9.74E-07 CDS 15909164 15931201 SUGP2_2 scaffold_10 15908942 A 374 0.01312 4.981 9.74E-07 CDS 15906948 15909027 SUGP2_1

99 Supplementary Table 3-7: List of functionally enriched genes with 2500 bp of Alabama S. undulatus p<1E-6 GWAS SNPs against GWAS background source term_name term_id adjusted_p_value intersections CORUM DCS complex CORUM:7381 0.02181757 PTBP1 CORUM DCS complex (PTBP1, PTBP2, HNRPH1, HNRPF) CORUM:1288 0.02181757 PTBP1 CORUM RB-E2F1 complex CORUM:1250 0.02181757 RB1 CORUM Rb-HDAC1 complex CORUM:3853 0.02181757 RB1 CORUM RB1(hypophosphorylated)-E2F4 complex CORUM:5099 0.02181757 RB1 CORUM RB-E2F1 complex CORUM:5143 0.02181757 RB1 CORUM Upf complex (UPF1, UPF2, UPF3b) CORUM:813 0.02181757 UPF1 CORUM BRCA1-BARD1-UbcH7c complex CORUM:2823 0.02181757 UBE2L3 CORUM Upf complex (UPF1, UPF2, UPF3a) CORUM:812 0.02181757 UPF1 CORUM MRG15-PAM14-RB complex CORUM:722 0.02181757 RB1 CORUM RB1-TFAP2A complex CORUM:5146 0.02181757 RB1 CORUM CEBPE-E2F1-RB1 complex CORUM:5656 0.02181757 RB1 CORUM TRIM27-RB1 complex CORUM:5663 0.02181757 RB1 CORUM Meprin A CORUM:426 0.02181757 MEP1A CORUM REP-RGGT-Rab complex CORUM:6956 0.02181757 RAB3A CORUM MRG15-PAM14-RB complex CORUM:723 0.02181757 RB1 CORUM RB1-HDAC1-BRG1 complex CORUM:3269 0.02181757 RB1 CORUM SMG-1-UPF-ERF1-ERF3 complex (SURF) CORUM:784 0.02351797 UPF1 CORUM DCP1A-NR3C1-PNRC2-UPF1-complex CORUM:7030 0.02351797 UPF1 CORUM Rb-tal-1-E2A-Lmo2-Ldb1 complex CORUM:1372 0.02351797 RB1 CORUM DNMT1-RB1-HDAC1-E2F1 complex CORUM:1488 0.02351797 RB1 CORUM Emerin complex 24 CORUM:5611 0.03586923 RB1 CORUM TRAP complex CORUM:66 0.03586923 MED16 CORUM Postsplicing complex CORUM:814 0.03586923 UPF1 mRNA decay complex (UPF1, UPF2, UPF3B, DCP2, CORUM XRN1, XRN2, EXOSC2, EXOSC4, EXOSC10, PARN) CORUM:822 0.04415593 UPF1 CORUM TFTC-type histone acetyl transferase complex CORUM:441 0.0453679 MED16 CORUM CRSP-Mediator 2 complex CORUM:910 0.0453679 MED16 CORUM DRIP complex CORUM:549 0.04640161 MED16 CORUM DRIP complex CORUM:548 0.04640161 MED16 CORUM PC2 complex CORUM:300 0.04729159 MED16 CORUM ARC92-Mediator complex CORUM:909 0.04729159 MED16 CORUM SMCC complex CORUM:547 0.049566 MED16

100 Supplementary Table 3-8: List of functionally enriched genes with 2500 bp of Alabama S. undulatus top 17,172 (0.1%) GWAS SNPs against GWAS background source term_name term_id adjusted_p_value GO:MF adenyl nucleotide binding GO:0030554 0.00998189 GO:MF adenyl ribonucleotide binding GO:0032559 0.00998189 GO:MF glutamate receptor activity GO:0008066 0.02560077 GO:MF ATP binding GO:0005524 0.04148991 GO:MF anion binding GO:0043168 0.04148991 GO:MF nucleotide binding GO:0000166 0.0469637 GO:MF small molecule binding GO:0036094 0.0469637 GO:MF purine ribonucleotide binding GO:0032555 0.0469637 GO:MF ribonucleotide binding GO:0032553 0.0469637 GO:MF purine nucleotide binding GO:0017076 0.0469637 GO:MF ATPase activity GO:0016887 0.0469637 GO:MF nucleoside phosphate binding GO:1901265 0.0469637 GO:MF 3',5'-cyclic-GMP phosphodiesterase activity GO:0047555 0.0469637 GO:CC cell periphery GO:0071944 0.00729465 GO:CC cell projection GO:0042995 0.00729465 GO:CC intrinsic component of plasma membrane GO:0031226 0.00729465 GO:CC plasma membrane GO:0005886 0.00729465 GO:CC integral component of plasma membrane GO:0005887 0.00729465 GO:CC neuron projection GO:0043005 0.02280714 GO:CC plasma membrane bounded cell projection GO:0120025 0.03288106 GO:CC plasma membrane region GO:0098590 0.03288106 TF Factor: ZNF460; motif: NNACNCCCCCCNN; match class: 1 TF:M12313_1 0.04137236 Factor: DB1; motif: GGRRRRGRRGGAGGGGGNGRRR; match class: TF 1 TF:M10107_1 0.04137236

101 Supplementary Table 3-9: List of GO:BP functionally enriched genes with 2500 bp of Alabama S. undulatus top 17,172 (0.1%) GWAS SNPs against g:Profiler all annotated genes term_name term_id adjusted_p_value term_name term_id adjusted_p_value nervous system actin filament development GO:0007399 1.06E-08 organization GO:0007015 0.01026094 cellular component organization or post-Golgi vesicle- biogenesis GO:0071840 1.06E-08 mediated transport GO:0006892 0.0103825 developmental cell cell development GO:0048468 1.06E-08 growth GO:0048588 0.01046518 dichotomous subdivision of terminal units cellular component involved in salivary organization GO:0016043 1.17E-08 gland branching GO:0060666 0.01046518 membrane depolarization during neuron SA node cell action differentiation GO:0030182 1.17E-08 potential GO:0086046 0.01046518 cGMP catabolic neurogenesis GO:0022008 1.45E-08 process GO:0046069 0.01046518 plasma membrane bounded cell positive regulation of projection transcription, DNA- organization GO:0120036 1.19E-07 templated GO:0045893 0.01048657 generation of cGMP metabolic neurons GO:0048699 1.58E-07 process GO:0046068 0.01050207 cell projection post-embryonic organization GO:0030030 7.15E-07 development GO:0009791 0.01056399 neuron projection development GO:0031175 8.37E-07 heart process GO:0003015 0.01056399 regulation of extent neuron development GO:0048666 1.57E-06 of cell growth GO:0061387 0.01118799 actin filament-based actin cytoskeleton process GO:0030029 2.35E-06 reorganization GO:0031532 0.01118799 cell morphogenesis involved in regulation of heart differentiation GO:0000904 2.41E-06 contraction GO:0008016 0.01243042 stem cell cell morphogenesis GO:0000902 3.73E-06 development GO:0048864 0.01310165 neuron projection morphogenesis GO:0048812 3.88E-06 cell-cell signaling GO:0007267 0.01358886 positive regulation of regulation of blood molecular function GO:0044093 4.13E-06 circulation GO:1903522 0.01358886 cell differentiation GO:0030154 4.46E-06 organelle localization GO:0051640 0.01358886 plasma membrane semaphorin-plexin bounded cell signaling pathway projection involved in neuron morphogenesis GO:0120039 4.60E-06 projection guidance GO:1902285 0.013726 regulation of regulation of cell developmental development GO:0060284 5.45E-06 growth GO:0048638 0.01396022 modulation of cell projection chemical synaptic morphogenesis GO:0048858 5.45E-06 transmission GO:0050804 0.01396022 regulation of cellular positive regulation of component phosphorus organization GO:0051128 8.25E-06 metabolic process GO:0010562 0.01424058 102 regulation of positive regulation of phosphorus cellular process GO:0048522 9.66E-06 metabolic process GO:0051174 0.01424058 cellular component microtubule-based morphogenesis GO:0032989 1.05E-05 movement GO:0007018 0.01424058 positive regulation of cell part phosphate metabolic morphogenesis GO:0032990 1.82E-05 process GO:0045937 0.01424058 developmental regulation of trans- process GO:0032502 2.09E-05 synaptic signaling GO:0099177 0.01424058 cellular organonitrogen developmental compound metabolic process GO:0048869 2.09E-05 process GO:1901564 0.01424058 cell morphogenesis regulation of involved in neuron glutamate receptor differentiation GO:0048667 2.15E-05 signaling pathway GO:1900449 0.01456694 regulation of neuron positive regulation of projection RIG-I signaling development GO:0010975 2.17E-05 pathway GO:1900246 0.01486503 positive regulation of cell morphogenesis regulation of cell involved in differentiation GO:0045595 2.37E-05 differentiation GO:0010770 0.01486503 establishment of axonogenesis GO:0007409 2.92E-05 vesicle localization GO:0051650 0.014905 regulation of protein system development GO:0048731 4.56E-05 modification process GO:0031399 0.014905 regulation of anterograde trans- neurogenesis GO:0050767 4.56E-05 synaptic signaling GO:0098916 0.01677648 regulation of anterograde axonal signaling GO:0023051 4.67E-05 transport GO:0008089 0.01677648 regulation of neuron establishment of differentiation GO:0045664 4.67E-05 localization GO:0051234 0.01677648 regulation of nervous system chemical synaptic development GO:0051960 5.68E-05 transmission GO:0007268 0.01677648 regulation of plasma membrane bounded cell projection regulation of NMDA organization GO:0120035 5.76E-05 receptor activity GO:2000310 0.01695844 regulation of positive regulation of phosphate metabolic biological process GO:0048518 5.76E-05 process GO:0019220 0.01695844 membrane regulation of cell depolarization during communication GO:0010646 6.77E-05 action potential GO:0086010 0.01695844 multicellular axon extension organism involved in axon development GO:0007275 8.43E-05 guidance GO:0048846 0.01695844 neuron projection extension involved in positive regulation of neuron projection catalytic activity GO:0043085 8.57E-05 guidance GO:1902284 0.01695844 axon development GO:0061564 8.57E-05 dephosphorylation GO:0016311 0.0172102 cytoskeleton organization GO:0007010 8.63E-05 mitotic cell cycle GO:0000278 0.01742591 circulatory system localization GO:0051179 8.63E-05 process GO:0003013 0.01763884 103 anatomical structure development GO:0048856 8.80E-05 cellular localization GO:0051641 0.01795953 regulation of cell projection organization GO:0031344 8.89E-05 behavior GO:0007610 0.01808752 regulation of cell morphogenesis involved in differentiation GO:0010769 8.89E-05 cellular process GO:0009987 0.01808752 positive regulation of organelle actin cytoskeleton organization GO:0006996 0.00010758 reorganization GO:2000251 0.01836329 cardiac right positive regulation of ventricle metabolic process GO:0009893 0.00016392 morphogenesis GO:0003215 0.01836329 phosphate- containing compound metabolic process GO:0006796 0.00016624 blood circulation GO:0008015 0.01836329 positive regulation of actin cytoskeleton macromolecule organization GO:0030036 0.00016911 biosynthetic process GO:0010557 0.01836329 phosphorus neural crest cell metabolic process GO:0006793 0.0001741 development GO:0014032 0.01836329 movement of cell or subcellular establishment of cell component GO:0006928 0.00025478 polarity GO:0030010 0.01836329 small GTPase mediated signal spontaneous synaptic transduction GO:0007264 0.00026745 transmission GO:0098814 0.01846401 positive regulation of cellular metabolic positive regulation of process GO:0031325 0.00030062 gene expression GO:0010628 0.02015539 regulation of multicellular organismal process GO:0051239 0.00030062 synaptic signaling GO:0099536 0.02040478 positive regulation of Golgi vesicle cell differentiation GO:0045597 0.00031397 transport GO:0048193 0.02049788 ionotropic glutamate organophosphate receptor signaling catabolic process GO:0046434 0.0003861 pathway GO:0035235 0.02085302 negative regulation of axon extension regulation of involved in axon molecular function GO:0065009 0.00039245 guidance GO:0048843 0.02085302 regulation of axon regulation of extension involved in membrane potential GO:0042391 0.00041338 axon guidance GO:0048841 0.02094209 forebrain ventricular ribonucleotide zone progenitor cell catabolic process GO:0009261 0.00044079 division GO:0021869 0.02111662 cell junction organization GO:0034330 0.00048586 cardiac conduction GO:0061337 0.02111662 branchiomotor regulation of neuron axon localization GO:0032879 0.00053068 guidance GO:0021785 0.02111662 semaphorin-plexin trans-synaptic signaling pathway GO:0071526 0.00054049 signaling GO:0099537 0.02111662 104 regulation of neuron signaling GO:0023052 0.00069779 migration GO:2001222 0.02111662 positive regulation of supramolecular fiber cellular biosynthetic organization GO:0097435 0.00072581 process GO:0031328 0.02111662 regulation of purine nucleotide axonogenesis GO:0050770 0.00072962 catabolic process GO:0006195 0.02111662 central nervous regulation of cellular system development GO:0007417 0.00072962 localization GO:0060341 0.02127725 regulation of small GTPase mediated regulation of signal transduction GO:0051056 0.00073828 biological process GO:0050789 0.02193994 cellular component positive regulation of assembly GO:0022607 0.00075847 cell growth GO:0030307 0.02221566 intracellular signal GDP metabolic transduction GO:0035556 0.00080443 process GO:0046710 0.02229041 regulation of axon guidance GO:0007411 0.00080443 hydrolase activity GO:0051336 0.02229041 synaptic transmission, cell growth GO:0016049 0.00082385 glutamatergic GO:0035249 0.02237404 developmental growth involved in establishment of morphogenesis GO:0060560 0.00086427 localization in cell GO:0051649 0.02237404 nucleobase- containing small neuron projection molecule extension GO:1990138 0.00086427 biosynthetic process GO:0034404 0.02237404 positive regulation of cellular component chondrocyte biogenesis GO:0044085 0.00086427 differentiation GO:0032332 0.02347453 regulation of actin cytoskeleton histone H4-K16 reorganization GO:2000249 0.00086427 acetylation GO:0043984 0.02347453 positive regulation of macromolecule microtubule-based metabolic process GO:0010604 0.00088044 process GO:0007017 0.0237828 neuron projection guidance GO:0097485 0.00088204 transport GO:0006810 0.02390434 negative regulation of cellular glutamate receptor component signaling pathway GO:0007215 0.00106097 organization GO:0051129 0.02390434 cell surface receptor signaling pathway positive regulation of involved in cell-cell cell development GO:0010720 0.0010759 signaling GO:1905114 0.02390434 anatomical structure peptidyl-amino acid morphogenesis GO:0009653 0.00125609 modification GO:0018193 0.0241677 regulation of cell morphogenesis GO:0022604 0.00140878 cell adhesion GO:0007155 0.02489579 positive regulation of nitrogen compound microtubule-based metabolic process GO:0051173 0.00140878 transport GO:0099111 0.02508935 positive regulation of neuron projection positive regulation of development GO:0010976 0.00140976 biosynthetic process GO:0009891 0.02532357 105 purine positive regulation of ribonucleotide protein modification catabolic process GO:0009154 0.0014919 process GO:0031401 0.0253915 regulation of cell regulation of growth GO:0001558 0.00152972 response to stimulus GO:0048583 0.02556248 positive regulation of cell projection positive regulation of organization GO:0031346 0.00177364 phosphorylation GO:0042327 0.02695015 regulation of regulation of anatomical structure biological quality GO:0065008 0.00177982 morphogenesis GO:0022603 0.02695015 positive regulation of neuron mesenchymal cell differentiation GO:0045666 0.00177982 development GO:0014031 0.02709286 protein phosphorylation GO:0006468 0.00241707 pigmentation GO:0043473 0.02709286 cytoskeleton- dependent positive regulation of intracellular cellular component transport GO:0030705 0.00254437 organization GO:0051130 0.02739674 actin filament-based protein metabolic movement GO:0030048 0.00273901 process GO:0019538 0.0289528 cellular protein modification process GO:0006464 0.00273901 biological adhesion GO:0022610 0.02910915 protein modification cGMP-mediated process GO:0036211 0.00273901 signaling GO:0019934 0.02960139 stem cell growth GO:0040007 0.00273913 differentiation GO:0048863 0.02984983 phospholipid actin filament-based dephosphorylation GO:0046839 0.00273913 transport GO:0099515 0.02984983 regulation of signal positive regulation of transduction GO:0009966 0.00273913 growth GO:0045927 0.02984983 nucleoside phosphate catabolic sympathetic nervous process GO:1901292 0.00273913 system development GO:0048485 0.02984983 plasma membrane regulation of bounded cell catalytic activity GO:0050790 0.00273913 projection assembly GO:0120031 0.03000919 myelination in multicellular peripheral nervous organismal signaling GO:0035637 0.00282233 system GO:0022011 0.03062837 peripheral nervous anatomical structure system axon arrangement GO:0048532 0.00282233 ensheathment GO:0032292 0.03062837 regulation of cellular component cell communication GO:0007154 0.00291267 biogenesis GO:0044087 0.03241131 regulation of GTPase dense core granule activity GO:0043087 0.00291267 cytoskeletal transport GO:0099519 0.03288545 nucleotide catabolic cell projection process GO:0009166 0.00300818 assembly GO:0030031 0.03288545 regulation of developmental dense core granule process GO:0050793 0.00316181 localization GO:0032253 0.03288545 multicellular dense core granule organismal process GO:0032501 0.00335937 transport GO:1901950 0.03288545 106 positive regulation of developmental organelle transport process GO:0051094 0.00359731 along microtubule GO:0072384 0.03307012 regulation of actin positive regulation of filament-based calcineurin-NFAT process GO:0032970 0.00359731 signaling cascade GO:0070886 0.03330957 positive regulation of calcineurin-mediated axon extension GO:0048675 0.00388977 signaling GO:0106058 0.03330957 positive regulation of macromolecule protein kinase modification GO:0043412 0.00388977 activity GO:0045860 0.03330957 positive regulation of multicellular regulation of protein organismal process GO:0051240 0.00413852 phosphorylation GO:0001932 0.03425909 neuronal action vesicle localization GO:0051648 0.00442576 potential GO:0019228 0.03483394 cellular response to cardiac muscle cell stimulus GO:0051716 0.00443583 proliferation GO:0060038 0.03538497 positive regulation of negative regulation GTPase activity GO:0043547 0.00531269 of axonogenesis GO:0050771 0.03538497 cyclic nucleotide cyclic nucleotide catabolic process GO:0009214 0.00531874 metabolic process GO:0009187 0.0367795 regulation of transport GO:0051049 0.00531874 neuron migration GO:0001764 0.03698507 adenylate cyclase- inhibiting G protein- coupled glutamate receptor signaling synapse organization GO:0050808 0.00538501 pathway GO:0007196 0.03714621 positive regulation of nucleobase- containing compound metabolic intracellular process GO:0045935 0.00538501 transport GO:0046907 0.03714621 membrane positive regulation of depolarization during nervous system cardiac muscle cell development GO:0051962 0.00538501 action potential GO:0086012 0.03728087 regulation of cytoskeleton RIG-I signaling organization GO:0051493 0.00538501 pathway GO:0039529 0.03728087 negative regulation regulation of axon of histone extension GO:0030516 0.00538501 methylation GO:0031061 0.03728087 regulation of ion regulation of growth GO:0040008 0.00546903 transport GO:0043269 0.03764809 establishment or maintenance of cell heart contraction GO:0060047 0.00620907 polarity GO:0007163 0.0380709 regulation of actin regulation of axon cytoskeleton guidance GO:1902667 0.0063629 organization GO:0032956 0.03839464 sensory system heart development GO:0007507 0.00680304 development GO:0048880 0.04037031 cell junction assembly GO:0034329 0.00682571 eye development GO:0001654 0.04037031 107 positive regulation of RNA metabolic process GO:0051254 0.00736075 protein acylation GO:0043543 0.04052061 regulation of positive regulation of postsynaptic developmental membrane potential GO:0060078 0.00764429 growth GO:0048639 0.04103264 regulation of organelle negative regulation organization GO:0033043 0.00764429 of axon extension GO:0030517 0.0425926 regulation of microtubule-based phosphorylation GO:0016310 0.00841491 process GO:0032886 0.04292114 regulation of multicellular organismal mesenchyme development GO:2000026 0.00853312 development GO:0060485 0.04328625 establishment of organelle neural crest cell localization GO:0051656 0.00906463 differentiation GO:0014033 0.04402676 positive regulation of nucleic acid- regulation of templated supramolecular fiber transcription GO:1903508 0.00907238 organization GO:1902903 0.04402676 spontaneous neurotransmitter secretion GO:0061669 0.0091893 locomotion GO:0040011 0.04491633 positive regulation of RNA biosynthetic cellular response to process GO:1902680 0.00921855 endogenous stimulus GO:0071495 0.04521687 positive regulation of neurogenesis GO:0050769 0.00924698 signal transduction GO:0007165 0.04551808 cranial nerve structural negative chemotaxis GO:0050919 0.00930312 organization GO:0021604 0.04691931 semaphorin-plexin signaling pathway branching involved involved in axon in salivary gland guidance GO:1902287 0.00941805 morphogenesis GO:0060445 0.0469532 negative regulation negative regulation of axon guidance GO:1902668 0.00953576 of cell development GO:0010721 0.04793102 visual system biological regulation GO:0065007 0.00953576 development GO:0150063 0.04793102 vesicle cytoskeletal trafficking GO:0099518 0.00978096 response to stimulus GO:0050896 0.04855342 developmental growth GO:0048589 0.00978096 DNA alkylation GO:0006305 0.04868471 positive regulation of transferase activity GO:0051347 0.00978096 DNA methylation GO:0006306 0.04868471 regulation of cellular regulation of process GO:0050794 0.01015902 transferase activity GO:0051338 0.0489609 positive regulation of actin-mediated cell hydrolase activity GO:0051345 0.01015902 contraction GO:0070252 0.0489609 positive regulation of kinase activity GO:0033674 0.01015902

108

Supplementary Figure 3-1: Fst results for genome-wide high-coverage data from Alabama, Arkansas, and Tennessee S. undulatus populations

109 Supplementary Figure 3-2: Nucleotide diversity results for genome-wide high-coverage data from Alabama, Arkansas, and Tennessee S. undulatus populations

110 Supplementary Figure 3-3: LASSI T-statistic results for first twelve chromosomes of high- coverage data from Alabama, Arkansas, and Tennessee S. undulatus populations

111 Appendix C: Supplementary Materials for Chapter 4 Supplementary Database: Poster (https://scholarsphere.psu.edu/concern/generic_works/41n79h518r)

Supplementary Protocol 4-1: Checklist-formatted protocol for DNA extraction from Strombus pugilis shells (https://scholarsphere.psu.edu/concern/generic_works/41n79h518r)

Supplementary Protocol 4-2: Checklist-formatted protocol for our modified library preparation protocol for single-indexed, whole-genome shotgun sequencing (https://scholarsphere.psu.edu/concern/generic_works/41n79h518r)

112 Supplementary Table 4-1: Morphometric data for all collected Strombus pugilis specimens (sequenced individuals highlighted in green) LIP LENGTH WIDTH THICKNESS INDIVIDUAL SITE AGE SITE NAME (MM) (MM) (MM) ModSite-1-1 Modern ModSite 77.4 52.05 4.4 ModSite-1-2 Modern ModSite 83.14 52.44 4.32 ModSite-1-3 Modern ModSite 80.49 56.58 4.94 ModSite-1-4 Modern ModSite 75.92 50.8 3.42 ModSite-1-5 Modern ModSite 69.62 42.64 3.11 ModSite-1-6 Modern ModSite 78.41 53.15 4.02 ModSite-1-7 Modern ModSite 78.74 53.37 5.23 ModSite-1-8 Modern ModSite 85.21 54.58 4.77 ModSite-1-9 Modern ModSite 73.71 47.04 4.43 ModSite-1-10 Modern ModSite 82.79 53.33 4.4 CayoAgua-2-1 Modern CayoAgua1 71.16 40.74 4.68 CayoAgua-2-2 Modern CayoAgua1 69.31 43.2 5.27 CayoAgua-2-3 Modern CayoAgua1 70.11 40.78 4.8 CayoAgua-2-4 Modern CayoAgua1 61.55 36.97 4.59 CayoAgua-2-5 Modern CayoAgua1 66.06 43.49 3.85 CayoAgua-2-6 Modern CayoAgua1 67.57 44.05 5.48 CayoAgua-2-7 Modern CayoAgua1 60.26 34.89 3.13 CayoAgua-2-8 Modern CayoAgua1 58.72 33.66 3.38 CayoAgua-2-9 Modern CayoAgua1 65.72 41.86 4.71 CayoAgua-2-10 Modern CayoAgua1 71.64 47.88 3.99 BocaDrago-3-1 Modern BocaDrago 70.28 44.02 4.13 BocaDrago-3-2 Modern BocaDrago 68.12 40.64 3.1 BocaDrago-3-3 Modern BocaDrago 71.62 47.72 4.07 BocaDrago-3-4 Modern BocaDrago 63.14 39.1 4.44 BocaDrago-3-5 Modern BocaDrago 66.31 42.71 3.55 BocaDrago-3-6 Modern BocaDrago 66.08 41.58 3.33 BocaDrago-3-7 Modern BocaDrago 71.37 43.46 4.72 BocaDrago-3-8 Modern BocaDrago 63.07 39.26 3.58 BocaDrago-3-9 Modern BocaDrago 65.3 43.29 5.17 BocaDrago-3-10 Modern BocaDrago 77.08 49.21 4.94 SitioDrago-U61-0_10-5223 Archaeological SitioDrago-U61 70.23 44.92 5.61 SitioDrago-U61-10_20-5305A Archaeological SitioDrago-U61 63.32 42.91 3.62 SitioDrago-U61-10_20-5305B Archaeological SitioDrago-U61 62.46 39.2 3.4 SitioDrago-U61-20_30 Archaeological SitioDrago-U61 67.41 43.9 4.21 SitioDrago-U61-50_60-5454 Archaeological SitioDrago-U61 73.93 47.2 3.98 SitioDrago-U61-70_80 Archaeological SitioDrago-U61 78.75 47.45 4.61 SitioDrago-U61-80_90-5596A Archaeological SitioDrago-U61 67.51 41.44 3.41 SitioDrago-U61-80_90-5596B Archaeological SitioDrago-U61 66.46 40.52 4.65 SitioDrago-U61-80_90-5596C Archaeological SitioDrago-U61 69.3 45.01 4.71 SitioDrago-U61-90_100A Archaeological SitioDrago-U61 67.38 46.09 4.98 SitioDrago-U61-90_100B Archaeological SitioDrago-U61 61.88 43.9 3.77 SitioDrago-U61-100_110 Archaeological SitioDrago-U61 69.76 45.64 4.66 SitioDrago-U61-110_120-5651A Archaeological SitioDrago-U61 71.28 43.14 4.01 SitioDrago-U61-110_120-5651B Archaeological SitioDrago-U61 68.48 48.21 4.38 SitioDrago-U60-10_20 Archaeological SitioDrago-U60 70.15 48.52 4.66 113 SitioDrago-U60-20_30-5310 Archaeological SitioDrago-U60 64.67 44.68 4.13 SitioDrago-U60-30_40A Archaeological SitioDrago-U60 74.06 49.14 4.82 SitioDrago-U60-30_40B Archaeological SitioDrago-U60 58.72 39.61 3.75 SitioDrago-U60-40_50 Archaeological SitioDrago-U60 86.09 55.76 3.51 SitioDrago-U60-70_80-5530A Archaeological SitioDrago-U60 67.89 44.07 2.84 SitioDrago-U60-70_80-5530B Archaeological SitioDrago-U60 67.72 43.08 4.1 SitioDrago-U60-90_100-5564 Archaeological SitioDrago-U60 61.96 43.03 4.51 SitioDrago-U60-110_120-5659 Archaeological SitioDrago-U60 70.44 46.6 4.72 Lennond-SweetBocas-MS-17-2-78 Paleontological Lennond-SweetBocas 70.21 43.01 4.98 Lennond-SweetBocas-MS-F-2-217 Paleontological Lennond-SweetBocas 72.14 44.76 3.59 Lennond-SweetBocas-MS-F-5-157 Paleontological Lennond-SweetBocas 74.3 46.59 4.22 Lennond-SweetBocas-MS-F-5-166 Paleontological Lennond-SweetBocas 65.67 41.78 4.54 Lennond-SweetBocas-MS-F-7-128 Paleontological Lennond-SweetBocas 76.03 47.55 4.36 Lennond-SweetBocas-MS-F-10- 112 Paleontological Lennond-SweetBocas 72.33 49.63 4.05 Lennond-SweetBocas-MS-F-10- 110 Paleontological Lennond-SweetBocas 69.84 41.65 4.11 Lennond-SweetBocas-MS-F-15-1 Paleontological Lennond-SweetBocas 69.66 45.53 4.1 Lennond-SweetBocas-MS-F-15-2 Paleontological Lennond-SweetBocas 66.62 45.31 3.56 Lennond-SweetBocas-MS-F-15-3 Paleontological Lennond-SweetBocas 69.04 43.27 3.16 Lennond-SweetBocas-MS-F-15-4 Paleontological Lennond-SweetBocas 67.82 44.94 3.55 CayoAgua-Boil1 Modern CayoAgua2 70.48 43.71 4.38 CayoAgua-Boil2 Modern CayoAgua2 77.26 46.96 4.21 CayoAgua-Boil3 Modern CayoAgua2 64.28 42.97 5.19 CayoAgua-Boil4 Modern CayoAgua2 77.44 46.63 2.31 CayoAgua-Boil5 Modern CayoAgua2 79.84 44.58 4.21 CayoAgua-Boil6 Modern CayoAgua2 71.4 41.43 4.78 CayoAgua-Boil7 Modern CayoAgua2 74.28 44.42 3.29 CayoAgua-Boil8 Modern CayoAgua2 62.65 39.82 4.02 CayoAgua-Boil9 Modern CayoAgua2 78.71 48.11 4.67 CayoAgua-Boil10 Modern CayoAgua2 78.64 49.61 4.33 CayoAgua-Boil11 Modern CayoAgua2 73.34 43.52 3.43 CayoAgua-Dock1 Modern CayoAgua1 56.94 35.46 4.86 CayoAgua-Dock2 Modern CayoAgua1 63.41 42.34 4.64 CayoAgua-Dock3 Modern CayoAgua1 60.07 39 4.11 CayoAgua-Dock4 Modern CayoAgua1 58.4 35.49 4.4 CayoAgua-Dock5 Modern CayoAgua1 62.67 39.4 4.63

114 Supplementary Table 4-1 continued. ESTIMATED MEAT INDIVIDUAL WEIGHT (G) SEX SHELL CONDITION ModSite-1-1 3.657219182 Female ModSite-1-2 4.693077546 Female ModSite-1-3 4.191952558 Male ModSite-1-4 3.419178006 Female ModSite-1-5 2.527963447 Female ModSite-1-6 3.826298858 Male ModSite-1-7 3.882730085 Male ModSite-1-8 5.113168569 Female ModSite-1-9 3.084587323 Male ModSite-1-10 4.624565114 Female CayoAgua-2-1 2.728314837 Female CayoAgua-2-2 2.488940436 Male CayoAgua-2-3 2.590532024 Male CayoAgua-2-4 1.645314813 Male CayoAgua-2-5 2.105260552 Female CayoAgua-2-6 2.277834473 Male CayoAgua-2-7 1.528204724 Male CayoAgua-2-8 1.396330291 Male CayoAgua-2-9 2.067729272 Male CayoAgua-2-10 2.793009044 Female BocaDrago-3-1 2.612495135 Female BocaDrago-3-2 2.343124648 Female BocaDrago-3-3 2.790291833 Male BocaDrago-3-4 1.798298398 Male BocaDrago-3-5 2.133165206 Female BocaDrago-3-6 2.107483289 Male BocaDrago-3-7 2.756485542 Female BocaDrago-3-8 1.791358005 Male BocaDrago-3-9 2.02202886 Male BocaDrago-3-10 3.604780184 Female SitioDrago-U61-0_10-5223 2.606021666 NA top dull SitioDrago-U61-10_20-5305A 1.816233153 NA SitioDrago-U61-10_20-5305B 1.731683432 NA bottom dull SitioDrago-U61-20_30 2.25908725 NA SitioDrago-U61-50_60-5454 3.116800334 NA SitioDrago-U61-70_80 3.88444933 NA SitioDrago-U61-80_90-5596A 2.270791315 NA SitioDrago-U61-80_90-5596B 2.150034034 NA SitioDrago-U61-80_90-5596C 2.487688828 NA SitioDrago-U61-90_100A 2.255584436 NA SitioDrago-U61-90_100B 1.676271508 NA SitioDrago-U61-100_110 2.545728949 NA SitioDrago-U61-110_120-5651A 2.744387111 NA SitioDrago-U61-110_120-5651B 2.386575832 NA SitioDrago-U60-10_20 2.595687922 NA SitioDrago-U60-20_30-5310 1.954835302 NA SitioDrago-U60-30_40A 3.135947657 NA 115

SitioDrago-U60-30_40B 1.396330291 NA SitioDrago-U60-40_50 5.299624992 NA SitioDrago-U60-70_80-5530A 2.315661361 NA SitioDrago-U60-70_80-5530B 2.295510511 NA SitioDrago-U60-90_100-5564 1.683838255 NA SitioDrago-U60-110_120-5659 2.6332873 NA Lennond-SweetBocas-MS-17-2-78 2.603435485 NA Lennond-SweetBocas-MS-F-2-217 2.861554469 NA Lennond-SweetBocas-MS-F-5-157 3.171516716 NA Lennond-SweetBocas-MS-F-5-166 2.062250506 NA Lennond-SweetBocas-MS-F-7-128 3.436478862 NA Lennond-SweetBocas-MS-F-10-112 2.887913423 NA Lennond-SweetBocas-MS-F-10-110 2.555920541 NA Lennond-SweetBocas-MS-F-15-1 2.533030253 NA Lennond-SweetBocas-MS-F-15-2 2.168132075 NA Lennond-SweetBocas-MS-F-15-3 2.455304323 NA Lennond-SweetBocas-MS-F-15-4 2.30734874 NA CayoAgua-Boil1 2.638503723 NA CayoAgua-Boil2 3.634210656 NA CayoAgua-Boil3 1.914046464 NA CayoAgua-Boil4 3.66381208 NA CayoAgua-Boil5 4.075123375 NA CayoAgua-Boil6 2.76052679 NA CayoAgua-Boil7 3.168541694 NA CayoAgua-Boil8 1.750116134 NA CayoAgua-Boil9 3.877575606 NA CayoAgua-Boil10 3.865567469 NA CayoAgua-Boil11 3.030947382 NA top/bottom dull CayoAgua-Dock1 1.254253366 NA bottom dull CayoAgua-Dock2 1.825248195 NA top/bottom dull top/bottom dull, lip CayoAgua-Dock3 1.511473388 NA broken CayoAgua-Dock4 1.369983022 NA top dull CayoAgua-Dock5 1.752064523 NA top dull

116 Supplementary Table 4-2: S. pugilis nuclear reference assembly QUAST metrics ASSEMBLY CAYO_AGUA_2-3_S1_KRAKEN4 CAYO_AGUA_2-3_S1_KRAKEN4_BROKEN # contigs (>= 0 bp) 697,168 - # contigs (>= 1000 bp) 180,494 80,727 # contigs (>= 5000 bp) 490 129 # contigs (>= 10000 bp) 7 2 # contigs (>= 25000 bp) 0 0 # contigs (>= 50000 bp) 0 0 Total length (>= 0 bp) 624,646,899 - Total length (>= 1000 bp) 268,783,557 115,504,023 Total length (>= 5000 bp) 2,981,588 782,733 Total length (>= 10000 bp) 75,057 20,895 Total length (>= 25000 bp) 0 0 Total length (>= 50000 bp) 0 0 # contigs 695,354 478,846 Largest contig 12,028 10,766 Total length 623,741,713 379,042,087 GC (%) 44 44 N50 908 771 N75 673 606 L50 225,771 165,931 L75 427,335 305,789 # N's per 100 kbp 14,883 0

117 Supplementary Table 4-3: S. pugilis mitochondrial reference assembly norgal BLAST metrics SCAFFOLD:SCAFFOLD- ALIGNMENT- REF. E- BIT- TYPE LENGTH IDENTITY LENGTH LENGTH VALUE SCORE m scaffold_0:15409 bp 82.93 7864 15461 0 6999 p scaffold_79678:1116 bp 87.05 139 156073 6.00E-35 152 m scaffold_27902:946 bp 88.50 113 17572 2.00E-29 134 p scaffold_20072:772 bp 94.52 73 153429 2.00E-23 113 m scaffold_51871:610 bp 95.08 61 16000 1.00E-18 97.1 m scaffold_27962:714 bp 97.14 35 43660 8.00E-07 58.4 m scaffold_60762:583 bp 100 31 42448 7.00E-07 58.4 p scaffold_5546:1007 bp 100 30 62891 4.00E-06 56.5 m scaffold_22387:753 bp 100 30 704100 3.00E-06 56.5 m scaffold_134222:448 bp 100 30 62978 2.00E-06 56.5 m scaffold_6410:981 bp 100 29 978846 1.00E-05 54.7 m scaffold_20195:771 bp 100 29 16975 1.00E-05 54.7 p scaffold_30473:699 bp 100 29 147896 1.00E-05 54.7 p scaffold_45799:630 bp 100 29 165372 9.00E-06 54.7 p scaffold_47242:625 bp 100 29 193197 9.00E-06 54.7 m scaffold_51060:612 bp 100 29 70578 9.00E-06 54.7 m scaffold_116963:472 bp 96.97 33 30782 7.00E-06 54.7 m scaffold_141505:439 bp 92.31 39 49539 6.00E-06 54.7 m scaffold_209314:365 bp 100 29 364070 5.00E-06 54.7 m scaffold_209583:365 bp 100 29 16120 5.00E-06 54.7 m scaffold_305995:250 bp 100 29 201763 3.00E-06 54.7 m scaffold_5538:1008 bp 100 28 53262 5.00E-05 52.8 p scaffold_6935:966 bp 100 28 102657 5.00E-05 52.8 m scaffold_17488:1269 bp 100 28 16767 7.00E-05 52.8 m scaffold_20021:772 bp 100 28 42781 4.00E-05 52.8 p scaffold_32121:690 bp 100 28 145303 4.00E-05 52.8 m scaffold_49416:618 bp 100 28 71124 3.00E-05 52.8 m scaffold_92258:513 bp 100 28 71335 3.00E-05 52.8 m scaffold_93459:511 bp 100 28 982833 3.00E-05 52.8 m scaffold_119425:469 bp 100 28 31825 2.00E-05 52.8 m scaffold_153656:424 bp 100 28 680603 2.00E-05 52.8 m scaffold_226398:350 bp 100 28 105383 2.00E-05 52.8 p scaffold_239857:338 bp 100 28 171634 2.00E-05 52.8 p scaffold_272582:311 bp 94.29 35 91517 2.00E-05 52.8 m scaffold_298848:268 bp 100 28 76453 1.00E-05 52.8 m scaffold_330255:204 bp 100 28 51679 1.00E-05 52.8

118 Supplementary Table 4-3 continued. SCAFFOLD:SCAFFOLD- TYPE LENGTH BEST-HIT REFERENCE m scaffold_0:15409 bp Strombus gigas isolate Sg300-UNAL-SAA mitochondrion, complete genome p scaffold_79678:1116 bp Nicotiana otophora chloroplast, complete genome Anomaloglossus baeobatrachus voucher AF2590 mitochondrion, complete m scaffold_27902:946 bp genome p scaffold_20072:772 bp Bryopsis hypnoides chloroplast, complete genome m scaffold_51871:610 bp Fergusonina taylori mitochondrion, complete genome m scaffold_27962:714 bp Paramecium caudatum mitochondrion, complete genome m scaffold_60762:583 bp Coprinopsis cinerea okayama7#130 cont3.68, whole genome shotgun sequence Phelipanche purpurea chloroplast complete genome, specimen voucher p scaffold_5546:1007 bp BONN:S. Wicke Op38/39 m scaffold_22387:753 bp Tripsacum dactyloides mitochondrion, complete genome m scaffold_134222:448 bp Fusarium solani mitochondrion, complete genome m scaffold_6410:981 bp Welwitschia mirabilis mitochondrion, complete genome m scaffold_20195:771 bp Jacana jacana voucher STRI:BC4055 mitochondrion, complete genome p scaffold_30473:699 bp Silene conoidea chloroplast, complete genome p scaffold_45799:630 bp Zygnema circumcarinatum chloroplast, complete genome p scaffold_47242:625 bp Chlorotetraedron incus strain SAG 43.81 chloroplast, complete genome Saccharomyces pastorianus Weihenstephan 34/70 mitochondrion, complete m scaffold_51060:612 bp genome m scaffold_116963:472 bp Saccharomyces servazzii mitochondrion, complete genome m scaffold_141505:439 bp Rhynchosporium orthosporum mitochondrion, complete genome m scaffold_209314:365 bp Psilotum nudum isolate v16 chromosome 1 mitochondrion, complete sequence m scaffold_209583:365 bp Pyrophorus divergens mitochondrion, complete genome m scaffold_305995:250 bp Chlorokybus atmophyticus mitochondrion, complete genome m scaffold_5538:1008 bp Ogataea thermophila strain NCAIM Y.01608 mitochondrion, complete genome p scaffold_6935:966 bp Cistanche deserticola chloroplast, complete genome m scaffold_17488:1269 bp Castor canadensis mitochondrion, complete genome m scaffold_20021:772 bp Tetradesmus obliquus strain KS3-2 mitochondrion, complete genome p scaffold_32121:690 bp Isoetes flaccida chloroplast, complete genome m scaffold_49416:618 bp Saccharomyces arboricola strain CBS 10644 mitochondrion, complete genome m scaffold_92258:513 bp Paracoccidioides brasiliensis mitochondrion, complete genome m scaffold_93459:511 bp Cucurbita pepo mitochondrion, complete genome m scaffold_119425:469 bp Phakopsora pachyrhizi mitochondrion, complete genome m scaffold_153656:424 bp Zea mays subsp. parviglumis mitochondrion, complete genome Gaeumannomyces graminis var. tritici R3-111a-1 mitochondrial scaffold m scaffold_226398:350 bp supercont1.514, whole genome shotgun sequence p scaffold_239857:338 bp Ceramium japonicum plastid, complete genome p scaffold_272582:311 bp Orobanche rapum-genistae chloroplast, complete genome m scaffold_298848:268 bp Dekkera bruxellensis mitochondrion, complete genome m scaffold_330255:204 bp Lachancea kluyveri mitochondrion, complete genome

119 Supplementary Table 4-4: Filtered mapped read count for all sequenced S. pugilis specimens NUMBER OF READS MAPPED SOURCE NUCLEAR READS MITOCHONDRIAL READS INDIVIDUAL SITE AGE MATERIAL BWA MEM BWA ALN BWA MEM BWA ALN BocaDrago3-10 Modern Tissue 10,596,655 1,152,019 10,751 683 BocaDrago3-3 Modern Tissue 6,608,838 632,340 4,555 2,151 CayoAgua2-3 Modern Tissue 51,560,307 9,218,590 28,023 10,970 CayoAgua2-5 Modern Tissue 3,499,281 459,756 4,329 1,315 CayoAgua2-6 Modern Tissue 7,153,558 762,193 9,389 1,826 BocaDrago3-10 Modern Shell 25,361 1,697 5 2 BocaDrago3-3 Modern Shell 147,458 8,172 10 5 CayoAgua2-3 Modern Shell 466,588 92,528 570 192 CayoAgua2-5 Modern Shell 281,446 40,506 208 83 CayoAgua2-6 Modern Shell 218,071 24,145 51 18 CayoAguaBoil1 Modern Shell 1,240 525 4 0 CayoAguaBoil2 Modern Shell 2,488 1,063 2 4 CayoAguaBoil3 Modern Shell 7,647 4,574 6 11 U60-10_20 Archaeological Shell 390 3,269 0 7 U60-110-120-5659 Archaeological Shell 1,138 12,060 1 10 U61-20-30 Archaeological Shell 349 1,392 0 3 U61-50_60-5454 Archaeological Shell 266 1,375 0 2 U61-80_90_5596C Archaeological Shell 476 4,459 0 11 MS-F-10-110 Paleontological Shell 236 4 0 0 MS-F-15-4 Paleontological Shell 195 41 0 0 MS-F-2-78 Paleontological Shell 285 9 0 0 MS-F-5-157 Paleontological Shell 282 114 0 0 MS-F-7-128 Paleontological Shell 293 52 0 0

120 Supplementary Figure 4-1: Nuclear DNA damage and read length results for individual shell samples

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VITA

Alexis Paige Sullivan

EDUCATION PhD Biology, Pennsylvania State University 2020 BS Biology, Chemistry Minor, Stockton University 2014

SELECTED PUBLICATIONS Sullivan AP, Marciniak S, O’Dea A, Wake TA, Perry GH. Modern, archaeological, and paleontological DNA analysis of a human-harvested marine gastropod (Strombus pugilis) from Caribbean Panama. bioRxiv. doi: 10.1101/2020.08.26.269308.

Sullivan AP, Godfrey LR, Lawler RR, Randrianatoandro H, Eccles L, Culleton B, Ryan TM, Perry GH. 2020. Potential evolutionary body size reduction in a Malagasy primate (Propithecus verreauxi) in response to human size-selective hunting pressure. bioRxiv. doi: 10.1101/2020.03.23.004234.

Sullivan AP, Bird DW, Perry GH. 2017. Human behavior as a long-term ecological driver of non-human evolution. Nature Ecology and Evolution 1: 0065. doi: 10.1038/s41559-016- 0065.

INVITED SEMINARS AND SELECTED PRESENTATIONS Human-induced evolution in lemurs, lizards, and l’escargot. Fall 2019 Seminar Series. University Park, Pennsylvania, USA. Pennsylvania State Biology Department.

Genomic analysis of eastern fence lizard (Sceloporus undulatus) morphological adaptation to human-mediated fire ant invasion. Education/outreach 2 Symposium. Providence, Rhode Island. 2019 Evolution Meeting. Poster Session.

Modern, archaeological, and paleontological DNA analysis of a marine gastropod from Caribbean Panama. The Evolution of Complex Traits and Polygenic Adaptation: Where Do We Stand? Symposium. Montpellier, France. 2018 Evolution Meeting. Poster Session.

Human behavior as a long-term ecological driver of non-human evolution. 2017 Tupper Seminar. Ancón, Panama. Smithsonian Tropical Research Institute.

Are jumping tree animals getting smaller over time because humans catch and eat the larger ones? Invited symposium: Up Goer Five PhysAnth Edition: Communicate Your Science Using English’s Ten Hundred Most Common Words. New Orleans, LA. 2017 Annual meeting of the American Association of Physical Anthropologists. Podium presentation.

FELLOWSHIPS, HONORS, AND AWARDS Jeanette Ritter Mohnkern Graduate Student Scholarship in Biology 2018 Smithsonian Tropical Research Institute Short Term Fellowship 2017 National Science Foundation Graduate Research Fellowship Program (NSF GRFP) 2016 Homer F. Braddock Award 2014 School of Natural Sciences and Mathematics Research Poster Symposium 2012, 2013 Stockton Scholarship 2010

TEACHING AND MENTORSHIP Teaching Assistant, BIOL 110, Pennsylvania State University 2015, 2016, 2020 Undergraduate Research Volunteers (wet lab safety and sterile techniques) Spring 2018