Simulating Speciation and Extinction from East And

Home , Anancus, Chasmaporthetes, Deinotheriidae, Deinotherium, Elephantidae, Elephas

CONTINUOUS OR PULSE? SIMULATING SPECIATION AND EXTINCTION

FROM EAST AND SOUTH AFRICAN FAUNA AT PLIO-PLEISTOCENE

FOSSIL SITES

Thesis

Presented in partial fulfillment of the requirements for the degree Master of Arts in

the Graduate School of The Ohio State University

Daniel Chad Peart, B.A.

Graduate Program in Anthropology

The Ohio State University

2015

Thesis Committee:

Jeffrey K McKee, PhD, Advisor

Mark Hubbe, PhD

Debbie Guatelli-Steinberg, PhD

Daniel Chad Peart

2015

Abstract

Fossil fauna at paleoanthropological sites provides evidence for speciation and extinction events throughout the Plio-Pleistocene. Regarding fauna, first and last appearance dates are temporally clustered around time periods that correlate with climatic shift. The Turnover-Pulse

Hypothesis asserts climate change as the cause of punctuated speciation and extinction events.

Contending that climate is the cause of first and last appearance of species may be spurious due to inherent sampling biases in the fossil record. Species divergence and extinction may be influenced by climate, but the ultimate cause of species turnover is unclear. This research project required compilation of datasets of first and last appearance dates from South Africa and east Africa. These datasets were used to derive rates of species turnover in order to program a model to simulate the fossil record. Development of a species turnover simulation was undertaken utilizing mathematical modeling software (Matlab). Continuous speciation and extinction was simulated over 3.2 million years for South African fauna and 4.4 million years for east African fauna. Simulated continuous speciation and extinction produces peaks of turnover similar to turnover-pulses. Therefore, the fossil record is unable to support the Turnover-Pulse Hypothesis’s reliance on climate change as a causal mechanism for speciation and extinction. Rather, patterns of first and last appearance dates that indicate peaks of species turnover are a product of biased sampling.

Dedication

Dedicated to my grandmother, Nancy Lou Peart who always believed in me, no matter how grand my ambitions. Your support and love helped shape me into the person I am

today. You are, and will always be, remembered.

iii

Acknowledgements

I extend my heartfelt gratitude to Dr. Jeffrey McKee, who allowed my continuation of his work on computer simulation of evolutionary processes, as well as, use of his datasets. Thank you for your guidance and continued commitment to my success.

I am also thankful for the guidance and support of all faculty members in the

Department of Anthropology at The Ohio State University. Your shared knowledge and continuing advice have made me a better scholar. A special thank you to Mark Hubbe for guidance on quantitative methods, logic, and evolutionary principles.

Most importantly, thank you to my family. Mom, you have always pushed me to be the best I can be and achieve my dreams. Dad, you are the rock that supports our family and your continuing dedication to the dreams of your children is something I can only hope to one day live up to. Wes, you have always been a free-thinker, and I cannot thank you enough for helping to open my mind to possibilities I had never before considered. My family will always been an important part of my life. I love you all.

Vita

May 2002……………………………………………….…………….Brewer High School

December 2006…………………………B.S. Radio-TV-Film, Texas Christian University

May 2013…………………………….B.A. Anthropology, University of Texas, Arlington

2013 to present…………………………..Graduate Student, Department of Anthropology,

The Ohio State University

Fields of Study

Major Field: Anthropology

Specialization: Paleoanthropology

Table of Contents

Abstract…………………………………………………………………………………....ii

Dedication………………………………………………………………….…………..…iii

Acknowledgements……………………………………………………………………….iv

Vita…………………………………………………………………………...……………v

List of Tables…………………………………………………………………...... ……..viii

List of Figures…………………………...... ………………………………………...…....ix

Chapter 1. Introduction…………...... ……………………………………………………..1

Climate and Environment…………………………………………………………3

Fossil Record……………………………………………………………………...7

Evolution and Speciation………………………………………………………….9

Phyletic Gradualism…………………………………………………………….....9

Punctuated Equilibrium………………………………………………………...... 10

Autocatalysis, Eurytopy, and Stenotopy………………………………………….11

Extinction…………………………………………………………………...... 13

Migration…………………………………………………………………...... ….14

Modeling……………………………………………………………………...….15

Chapter 2. Materials…………………………………………………….………………..17

Chapter 3. Methods………………………………………………………………………20

Statistical Analyses……………………………………………………………....25

Chapter 4: Results……………………………………………………………………..…26

Tests between Observed and Simulated Data………………………………...... 27

Chapter 5: Discussion………………………………………………………………….…30

Limitations of Simulated Models………………………………………………...31

Chapter 6: Conclusion……………………………………………………………………33

References………………………………………………………………………………..34

Appendix A: Additional Tables………………………………………………………….42

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List of Tables

Table 1. Species Frequencies from Previous Publications……………...... ………………19

Table 2. P-Values for Kruskal-Wallis tests performed between observed and simulated data at 100,000 year intervals………………………………………………………………….28

Table 3. East African Fauna – First and Last Appearance Dates…………………………45

Table 4. South African Fauna – First and Last Appearance Dates………………………49

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List of Figures

Figure 1. Benthic d18O Enrichment through the Pliocene and Pleistocene………...... ……6

Figure 2. Fossil Record First and Last Appearance Dates – East Africa………………….21

Figure 3. Fossil Record First and Last Appearance Dates – South Africa………………...22

Chapter 1: Introduction

Climate is constantly changing. Throughout the Pliocene and Pleistocene, global climate cycled through a spectrum from warm to cold and wet to dry (deMenocal, 1995).

Cyclical climatic change impacts evolution and divergence of species since changes in long-term weather patterns are a major component of physical environment. Changes in a lineage’s physical surroundings may influence speciation and extinction. Species turnover has been interpreted from the fossil record as some fauna go extinct and other species appear. Through turnover of species, by means of speciation, extinction, and migration, different species will appear in the fossil record through time (Vrba, 1985). Change in physical environment is frequently cited as the ultimate cause of species turnover (Vrba,

1974, 1985, 2005). First appearances and last appearances of species in the fossil record are commonly interpreted as evidence in support of this turnover based on climatic shift; however, fossils are differentially preserved, and provide only a snapshot of current species temporally and spatially. Conclusions about species turnover based on the fossil record reflect differential preservation that may be accounted for by chance rather than by climatically driven punctuated events.

The fossil record should be intensely scrutinized when attempting to answer research questions, especially those about evolutionary processes. Although inferences can be made from the fossil record, causes for past events are difficult to decipher. Vrba (1985)

proposed the Turnover-Pulse Hypothesis to explain speciation, extinction, and migration events as an outcome of climate change. While species turnover occurs continuously at a low rate, Vrba noted punctuated periods of increased turnover. These periods correlate with short spans of intense global climate shift. Through the Turnover-Pulse Hypothesis, Vrba contends that periods of increased turnover are responsible for a majority of speciation and extinction while species remain stable without climatic forcing.

This thesis addresses causal mechanisms applied to first and last appearance dates in the fossil record. There are multiple mechanisms that could be responsible for patterns of faunal turnover. The goal of this inquiry is to investigate the potential of the fossil record as an interpretive proxy for climate induced species turnover. Was variance between rates of turnover in the fossil record indicative of climate shift, or are changes in frequency of first and last appearance dates of species due to chance?

In order to test the viability of the fossil record as an indicator of species turnover, a model of speciation and extinction was developed. Models have been used to simulate complexity of species radiations, extinctions, and phylogenetic diversification (Raup et al.,

1973; Hey, 1992; Rabosky and Lovette, 2008). Using rates of turnover observed in the fossil record, models simulating continuous and punctuated turnover were developed and compared to fossil frequencies of first and last appearances. If the fossil record was not significantly different than simulated continuous turnover, then apparent pulses of speciation and extinction that correlate with climatic shifts may have been the product of random chance.

Besides the turnover-pulse hypothesis, other scholars have cited problems with fossil record biases in drawing conclusions about catastrophic extinction of species (Signor and Lipps, 1982), and biodiversity changes through time (Smith, 2001). Understanding causal connections between physical environment and species turnover will bolster understanding of how researchers can use the fossil record as a proxy for past environmental processes.

Climate and Environment Environment and climate may be conflated in evolutionary discourse. Environment encompasses all aspects of an organism’s physical surroundings, both biotic and abiotic.

Flora and fauna constitute direct interactions between a species and its environment. Plants and animals serve as subsistence resources for all animal species. Humans manipulate biological aspects of their environment further, utilizing some species of flora and fauna for tools, such as carving of wood for weapons (Waguespack et al., 2009), shelter, such as using animal hide or bones for housing construction (Binford, 1990), and sources of labor

(Starkey, 2006). Subsistence alone is enough for natural selection to drive significant morphological change, such as the differences between dental morphology in carnivores and herbivores. While biological environment also includes microbiota, such as bacteria and microorganisms, non-osteological morphology such as skin and organs, and invertebrates such as insects, these would have been lost to taphonomic processes and are unavailable for corroboration with fossil material.

Abiotic aspects of environment are primarily climatic. Climate refers to long term weather patterns, accounting for global, regional, and local variation in temperature,

humidity, precipitation, pressure, wind, and others (Metz, 2001). Climate also includes forcing mechanisms such as orbital cycles, volcanic ash, and anthropogenic greenhouse gas (Thornthwaite, 1948: 55). The relationship between climate and environment in evolutionary theory must be considered in research on species turnover since physical environment can vary drastically due to climate change. Climate may influence speciation and extinction through effects on physical environment, but correlation of global paleoclimatic fluctuation with species turnover in the fossil record can lead to spurious conclusions about causal mechanisms of speciation and extinction.

Universal forcing factors such as cosmic radiation, solar and orbital cycles, and volcanism are all mechanisms that influence climate on earth. Cosmic rays interact with nitrogen in the atmosphere to create carbon, which subsequently increases global temperature (Suess, 1968). As the Earth orbits the sun it receives varying amounts of solar radiation based on the eccentricity of the orbit and sunspot activity. Solar radiation can insulate the planet from cosmic radiation (Svensmark, 1998). Change in eccentricity of the

Earth’s orbit from circular to ovoid is cyclical and influences climate approximately every

100,000 years (Hays et al., 1976; Milankovitch, 1998). Precession and obliquity, two attributes of the earth’s axial tilt, are also cyclical. Precession refers to the Earths wobble on its axis that cycles every 22,000 years and this changes how solar radiation influences low-latitudes. Obliquity refers to the change in Earth’s tilt, which affects solar influence on the poles. These orbital drivers can influence the isotopic composition of the atmosphere which causes temperature, humidity, and precipitation to vary.

Despite all of these climatic driving forces, Earth’s climate has been stable enough to maintain life for more than 3.8 billion years (S J Mojzsis, 1996). Throughout the past five million years, climate has varied from cold and dry to warm and wet. As climate changes, the biosphere’s movement toward or away from the poles lags up to 1,500 years behind. Therefore, records of climate change will be out of phase with pollen records

(Webb III, 1986; Prentice et al., 1991; Menéndez et al., 2006). Vegetation and animals must either adapt to changing conditions or migrate to different geographic regions.

Using marine sediment cores, paleoclimate of the Pliocene and Pleistocene have been reconstructed (deMenocal, 1995; Haywood and Valdes, 2004; Lisiecki and Raymo,

2005; Raymo et al., 2006). During the Pliocene, global conditions were warmer than modern times with higher temperatures, more humidity, and increased precipitation. As the

Pliocene progressed, average temperatures slowly fell. By the beginning of the Pleistocene, temperatures cooled and climate became increasingly variable, cycling through glacial and interglacial phases (Fig. 1). Cool temperatures allowed formation of ice sheets that covered a significant portion of the northern hemisphere. As temperatures cooled, the variable climate fluctuated from glacial phases to interglacial phases marked by extent and retreat of ice sheets (Raymo et al., 2006). Beyond cold temperatures, formation of ice sheets caused global sea level to drop exposing continental shelves along many coastlines. The addition of large areas of land near coastal areas influenced faunal and floral habitats and migration (Shackleton, 1987).

Global climate and local environment are often incongruous. Local climate can be influenced by global climatic anomalies, but these microenvironments may not follow the

global pattern (Woodward and Williams, 1987). Global cooling would move temperate climatic zones toward lower latitudes, but changes in thermohaline circulation, or the movement of warm and cold water throughout the Earth’s oceans, and Hadley cells, which are movements of weather patterns away from the equator, may differentially influence local climate. These could cause warm episodes locally when global conditions are cooler, or local periods of cold when global climate remains warm. As these atmospheric processes fluctuate, warm and cold air, as well as warm and cold ocean currents can change local and regional climates without reflecting overall global processes. Mountain ranges, ocean currents, and volcanic activity can manipulate forcing events to generate a warmer and wetter local climate while the global climate is still cold and dry. Fauna and flora may be differentially affected by climate change due to these mechanisms which may insulate or exacerbate greater climatic phenomena.

Figure 1: Benthic delta Oxygen-18 record constructed from various marine cores. As the marine core becomes more enriched in 18O, more 16O is frozen in polar ice meaning that overall global temperatures are lower (Lisiecki and Raymo, 2005).

Correlating fast changing climatic events with fossil records of first and last species appearances may seem logical as peaks and troughs of fluctuation tend to line up. Although the variable climate of the Pleistocene fluctuates quickly on a geological timescale, these changes may take several thousand years. Difference between global climatic change and local environmental variability confounds correlation of climate and species turnover.

Although species have appeared and disappeared from the fossil record, attribution of their turnover to climatic processes is difficult due to the variable nature of climate over the past five million years. Climate is too complex to claim a global trend of climate change as the cause of species turnover; therefore further research into local and regional climate will help scaffold theories of speciation and extinction in the fossil record.

Fossil Record

The fossil record differentially preserves faunal remains. There are two main concerns when using the fossil record to support species turnover by climatic drivers. First, representation of faunal species in the fossil record are biased by differential preservation

(Dornburg et al., 2011). Fossil assemblages do not represent a random distribution of extant faunal material. Instead, prey species will be overrepresented due to the propensity of predator construction of faunal assemblages (Williams, 2003: 341-342). Faunal fossil assemblages are commonly associated with hominin ancestors. Hunting and scavenging strategies could lead to construction of prey species assemblages. Carnivores construct assemblages of prey species (Marean et al., 1992). In most faunal fossil assemblages, bovid taxa are more prevalent than other herbivore taxa. Carnivore taxa are less represented.

Fossil faunal assemblages are constructed by a combination of stochastic, taphonomic and intentional processes, and deciphering which process is the causal mechanism for first and last appearance dates is confounded by sampling biases. Random chance could be responsible for frequencies of first and last appearance dates that appear to be turnover pulses.

Second, fossils are differentially preserved after deposition. Fossilization is composed of a complex set of processes whereby the organic components of bone are slowly replaced with minerals. This effectively preserves the bone and creates fossils.

However, this replacement only happens under specific conditions. The ideal environment for fossilization is anaerobic, basic, dry, and undisturbed (Ascenzi, 1969: 526; Rolfe and

Brett, 1969: 215-216, 232-234). Ideal fossilization conditions are rare and thus the majority of osteological remains are lost to taphonomic processes. Bones that transition to fossils are preserved with variable levels of fidelity. Taphonomy, both natural and cultural, can warp and degrade bones and fossils, making it more difficult to identify specimens (Lyman,

1994: 1-4).

Identification of remains becomes increasingly difficult as taphonomic effects become more pronounced. Combined with inconsistent species conceptualization, poor fossil preservation makes differentiation between species more suspect. In many cases, taphonomy restricts identification of fossil elements to class level or above. Inferring relationships about morphology or lineage from a differentially preserved fossil record provides another bias with which researchers must contend. Climatic variation correlated with fossil faunal assemblages may provide evidence of taphonomic factors, as well as a

partial composition of past extant fauna, but it cannot functionally support climate as a causal mechanism of speciation and extinction.

Evolution and Speciation

Species definitions are contentious and do not allow definitive separations between populations. Separation of species based on one definition may not be true using other definitions. Intraspecies variation is fraught with significant morphological differences that result in contentious separations between groups of organisms (Holliday, 2003). Taxonomy of unique species was traditionally based on typological characterization of morphology

(Linnaeus, 1758), but in the last several decades the way species are identified has been considered from several perspectives (Simpson, 1961; Mayr, 1963, 2000; Cracraft, 1989;

Templeton, 1992; Holliday, 2003: 654-656). Delineation of modern species differs from what speciation separation using fossils. Speciation mechanisms and evolutionary theory provide further evidence for the inherent biases in the fossil record. The viability of the fossil record to answer questions about causes of speciation and extinction require understanding of how speciation can occur through various evolutionary processes.

Phyletic Gradualism

Phyletic gradualism is the divergence of species through long term accumulation of heritable variation. Gradualism requires extensive lengths of time for species to become morphologically and behaviorally novel (Darwin, 1859). Though evolutionary change occurs at a slow pace, changes are continuous as populations adapt to survive in dynamic

environments. Populations are adapting to their environments over multiple generations which essentially removes poorly adapted individuals from the population. Through phyletic gradualism, the appearance of species would be slow and appearances in the fossil record as punctuated events is misleading. Through phyletic gradualism, new species appear as a result of this constant change.

Punctuated Equilibrium

Through punctuated equilibrium, populations will remain relatively unchanged until influenced by external stimuli (Eldridge and Gould, 1972). This assumption builds on the niche conservatism model, whereby species maintain an ancestral niche as long as there is no pressure to evolve (Wiens, 2004). Punctuated equilibrium shifts focus from constant evolutionary change to long-term stability with short bursts of change. In a punctuated equilibrium model, individuals on the ecological fringes of a population may adapt to novel ways of surviving in their environment. Thereby, all new speciation events are outgrowths of larger populations rather than a split in a lineage.

Though novel species can arise logically due to evolution by punctuated equilibrium and phyletic gradualism, it is now commonly accepted that a combination of both apply (Bush, 1975; Stebbins and Ayala, 1981). Punctuated periods of rapid change based on environmental pressures are separated by long periods of relative stability where novel morphology slowly evolves as species become better adapted to their environments

(Johnson, 1982). During stable periods with gradual evolution, species may emerge slowly due to allopatric or sympatric mechanisms. During periods of intense environmental

pressure, species may arise quickly; however, punctuated events in geological time may last several thousand years (Gould, 1983; McKee, 1995).

Autocatalysis, Stenotopy, and Eurytopy

Evolutionary mechanisms can also influence species divergence. Previous adaptations can lead to further evolutionary change. Autocatalytic evolution refers to previously adaptive traits that can act as constraints or, conversely, lift constraints for natural selection. Alternatively, autocatalysis can promote changes if previous adaptations necessitate or simplify further evolution (McKee, 2000). An example of autocatalysis is the evolution of the human brain. Debates persist regarding the origin of the large human brain, but one theory suggests that once encephalization reached a threshold, novel behaviors based on large brain morphology allowed for further evolution (Godfrey and

Jacobs, 1981; Sterelny, 2007). Allopatric speciation based on autocatalytic factors may be responsible for various species of early human ancestors. The human brain may have developed as a series of feedback loops whereby adaptations for culture allowed for further encephalization. These factors, which may have caused species divergence in the past may appear to due to climatic shift (Godfrey and Jacobs, 1981).

Two opposing strategies are employed when species interact with their physical environment. Stenotopic species have evolved to be optimally suited to a narrow environmental niche. While stenotopic species may be able to outcompete generalists in stable environments, they are incapable of contending with environmental change (Grant and Grant, 2011). Eurytopic species employ a generalist strategy of survival. Generalists

are better able to cope with climate change because they can rely on a wider range of environmental interactions. If one food source becomes unavailable due to climatic fluctuation, a generalist can make use of another (Futuyma and Moreno, 1988). All species fall within a spectrum of stenotopic to eurytopic survival strategies. Level of specialization is difficult to discern from morphological variation in the fossil record (Wood and Strait,

2004). To discern how species interacted with their environments, many assumptions must be made about their connections with extant species. Fossil morphology may be superficial and due to variation alone. In this case, separation based on morphology would be fallacious. For example, modern faunal species Ovis aries (sheep), and Capra hircis (goat) are mammals and ungulates. Sheep and goats can be differentiated based on bone structure but their morphology is very similar. Despite these morphological and taxonomic similarities, sheep and goats have very different ecological interactions. Sheep are ruminants that feed primarily on grass in fields. Goats can survive on grass, but they are much more eurytopic, and able to survive on a variety of foods such as grass, grains, and leaves. Sheep are flat-land dwellers but goats are able to climb rocky cliff faces and even trees (Schwartz and Ellis, 1981; Silanikove, 2000). Though sheep and goats are domesticated, owing much of their modern morphology to artificial selection and pristinely preserved specimens can be separated, this example accentuates the point that the way in which a species interact with their environments may be obscured in differentially preserved fossils. Therefore, separating species based on morphology may be flawed.

The fossil record may be biased in two ways due to the disparity between eurytopic and stenotopic species. First, eurytopic species may exist in larger geographical and

temporal distributions than stenotopic species. Therefore, the abundance of species at fossil sites would logically skew toward eurytopic species. Second, since stenotopic species are more influenced by climate change, interpretations of faunal turnover could be an artifact of small sample size of stenotopic species throughout the fossil record. Finding evidence of stenotopic species turnover due to climate is logical, but it means that the robusticity of the turnover-pulse hypothesis, which includes eurytopic and stenotopic species, is questionable.

Phyletic gradualism and punctuated equilibrium assume adaptation by means of natural selection; however, neutral evolution can also lead to speciation over a long time span. Neutral changes in morphology may have no influence on fitness but can eventually cause significant changes between populations causing them to become biologically distinct, or reproductively isolated (Hubbell, 2001: 231-232). In the fossil record, the appearance of novel forms does not necessarily indicate an adaptive shift.

Extinction

Evidence of extinctions are commonly observed in the fossil record. The cretaceous

– tertiary transition was marked in the fossil record by the extinction of an unprecedented number of genera (Alvarez et al., 1980). Approximately half of the genera extant 65 million years ago became extinct. The destruction of these species may have occurred from catastrophe or by subsequent environmental changes. An unknown forcing mechanism such as meteor impact or volcano would have stressed populations, causing species which could not adapt to rapidly changing conditions to be eradicated. Catastrophic and climatic

events have happened many times throughout the Earth’s history. Each period through geological time shows a peak of species extinctions. Poor resolution in paleoclimate records inhibits separation of climate change from forcing mechanisms; therefore, the causes of these mass extinctions may have been meteor impacts, but they may have been climatic changes reacting to the catastrophic forcing that actually led to extinction of species if they cannot survive rapidly changing conditions (Raup and Sepkoski, 1984: 806).

Causal mechanisms of extinction may be climatic, but they can also be due to other aspects of physical environmental. Adaptation to climatic conditions requires a degree of plasticity in behavior or phenotype. Punctuated catastrophic weather events can also cause the destruction of a population or a species. Hurricanes, monsoons, and landslides are all potentially detrimental to species. This type of punctuated event has little to do with greater aspects of climate change but can still cause species extinction. This type of catastrophic event was likely the cause of mass extinctions at the end of the cretaceous period (Alvarez et al., 1980). The sudden climatic response to a punctuated event, such as volcanos or meteors, must be separated from longer-term cyclical processes of climate change.

Migration

When habitats are destroyed, species must adapt, perish, or migrate. Migration of organisms is constantly occurring, whether it is birds flocking based on annual weather patterns or species moving over several hundred years based on shifting climatic conditions. Flora are generally conceptualized as sedentary but due to pollen and seed scatter, flora can also move with changing conditions (Pitelka, 1997). Therefore, habitat

zones may shift when warmer and wetter conditions move. If the extant fauna are able to maintain their niches by migration, they avoid extinction. Climate change may also release constraints on niche exploitation. As climate has changed in recent years, habitats that were previously unsuitable near the poles are becoming ideal for exploitation. Birds and squirrels have been slowly expanding their range northward as the northern hemisphere warms and ice retreats toward the north pole (Bradshaw and Holzapfel, 2006). The formation of new habitats and subsequent migration of faunal species is part of species turnover; however, movement to different habitats is masked by biased sampling in the fossil record which can only record a portion of extant species.

As species move due to climate change, they may seem to disappear from a particular environment. If a species migrates during a glacial period without record from other geographical areas, it may seem to disappear earlier than its true extinction.

Conversely, these species can appear suddenly in the fossil record as though a lineage diverged for the first time (Gingerich, 1977). The fossil record cannot record migration, and therefore it is one additional confounding factor for basing species turnover on climate change (Relethford, 1999). Migration is also problematic because the ideal circumstances for fossilization vary geographically, further biasing the fossil record.

Modeling

Climate change may have had a profound influence on speciation and extinction in the past four million years; however, the fossil record serves as a poor proxy for evidence of pulses of species turnover. Differential preservation, biases due to species

conceptualization, and inability of the fossil record to record climate change and migration requires multiple lines of evidence. Currently, the fossil record is the sole proxy for considering speciation in the past but modeling speciation and extinction may help researchers conceptualize how fossilization processes occur.

To help bolster the data from the fossil record, models of speciation, extinction, and random fossilization can provide a comparison for testing the validity of the fossil record to correlate with climate change. Previous modeling of turnover-pulses found that fossil site sampling was responsible for patterns of speciation and extinction rather than climatic causation (McKee, 1995; McKee, 1996; McKee, 2001). Over the last twenty years, more faunal assemblages have been found and their data added to the fossil record. Updating faunal datasets with new allowed for reassessment of turnover rates throughout the

Pliocene and Pleistocene.

In addition to updating observed datasets, development of a new model with updated software allowed for more robust simulations with addition of different variables.

A robust model of speciation and extinction allows for simulation of sampling found in the fossil record. Through comparison of gradual and punctuated models of species turnover, the fossil record may correlate with either continuous simulations or turnover-pulses. If not, the fossil record is more indicative of random processes and preservation biases than climate-driven speciation and extinction.

Chapter 2: Materials

Faunal fossil assemblages are prevalent at paleoanthropological sites. To consider first and last appearance dates, correlated with potential causal mechanisms such as climate change, a database was developed of both South African and eastern African species present in the fossil record of Plio-Pleistocene Africa. An ideal dataset would include all possible faunal assemblages throughout the entirety of Africa and would encompass contiguous geographic areas. Since a dataset of this kind is impossible due to the sparse nature of paleontological evidence, South African and east African fauna were considered independently. Separation between these two datasets was maintained due to the vast geographic expanse between regions. Though they are connected by similar fauna throughout the observed fossil record, their evolutionary relatedness is difficult to interpret spatially and temporally due to vast geographic expanses separating them. Therefore, eastern African nations of Ethiopia, Kenya, and Tanzania are assumed to be a continuous geographic expanse with ubiquitous faunal inhabitation for the past 4.4 million years.

Similarly, the nation of South Africa is assumed to be a continuous range for faunal species throughout the past 3.2 million years.

The South African and eastern African faunal datasets were compiled from published paleoanthropological research dating from 1995 through 2015. Mckee (1995) developed a faunal dataset for South and east Africa which includes first and last

appearance dates of fossil fauna. Since then, additional sites have been discovered with new faunal assemblages. In addition to McKee, 13 publications comprise the South African dataset (Plug, 1997; Keyser et al., 2000; Klein and Cruz-Uribe, 2000; Henshilwood et al., 2001;

Klein et al., 2007; Reynolds et al., 2007; de Ruiter et al., 2008, 2009; Rector and Reed, 2010; Clark,

2011; Faith, 2012a; b; Faith and Behrensmeyer, 2013) and 10 articles were referenced to construct the east African dataset (Potts and Deino, 1995; Kimbel et al., 1996; Heinzelin et al., 1999; Bobe and Eck, 2001; Asfaw et al., 2002; Suwa et al., 2003; Geraads et al.,

2004; Faith, 2012b, 2013). Using data from these publications, first appearance dates were obtained based on the most ancient date for the species. Alternatively, last appearance dates reflect the most recent inclusion of species in the fossil record. First appearance and last appearance dates for all species from new sites were included in the dataset used for this thesis (Table 1).

The South African faunal fossil datasets consists of 179 identified species while the eastern African dataset is composed of 189. If a fossil element was not identified to the species level in a publication, that individual was disregarded because it is likely already represented in the database. In some cases, subspecies names were included as distinct species. For example, if Equus burchelli was included as a distinct species instead of delineating Equus quagga burchelli, its subspecies category, then it was included in the database as a separate identified species.

To keep the two datasets consistent with observed evolutionary time, faunal dates at hominin fossil sites were recorded. Assemblages in South Africa span the past 3.2 million years from the late Pliocene to the Holocene. Material in east Africa begins 4.4

million years ago and ends in the Holocene. First appearance dates and last appearance dates rely on dated faunal material. Volcanic deposits are rare in South Africa and most of the faunal material must be correlated with species in eastern Africa where absolute radioisotopic dates can be obtained using potassium-argon, argon-argon, or carbon 14 dating. However, a few sites in South Africa used uranium-series dating to obtain absolute dates. Periods of less than 10,000 years would likely have been too short to succumb to environmental pressures; therefore, faunal FADs and LADs were truncated into 100,000 year periods. The fossil record is separated by more than 100,000 year intervals in many cases. Therefore, these 100,000 year intervals represent a range of first and last appearance dates that still constitute a punctuated event within fossil contexts.

Table 1: Frequency of species within each faunal family for east and South Africa. The first appearance and last appearance of species were used as proxy for rates of species turnover in the fossil record. Species were truncated into their family categories due to space constrains. A full list of species and their first and last appearance dates can be found in Appendix 1 (Table 3 and 4).

Family Number of Number of Family Number of Number of South East South East African African African African Species Species Species Species Bathyergidae 1 Hystricidae 1 1 Bovidae 52 73 Leporidae 4 1 Canidae 8 5 Manidae 1 Cercopithecidae 19 15 Muridae 2 Chalicotheriidae 1 1 Mustelidae 4 2 Deinotheriidae 1 Nesomyidae 1 Elephantidae 4 5 Orycteropodidae 1 1 Equidae 4 17 Otariidae 2 Erinaceidae 1 Pedetidae 1 Felidae 16 11 Pholidota 1 Giraffidae 2 8 Procaviidae 3 Gomphotheriidae 1 1 Rhinocerotidae 2 3 Herpestidae 7 Sciuridae 1 Hippopotamidae 2 8 Suidae 13 17 Hominidae 7 9 Thryonomyidae 1 Hyaenidae 8 4 Ursidae 1 Hyracoidea 1 Viverridae 8 1

Chapter 3: Methods

Modeling the fossil record requires simulation of evolutionary events as well as differential sampling based on biases in the fossil record. Observed fossil data were used to derive rates of speciation and extinction to develop realistic models of evolution that imitate continuous and punctuated species turnover. The goal of these models was to mimic the fossil record by simulating how fossils may have been distributed at sites by chance and determine whether they are similar to patterns predicted by the Turnover-Pulse

Hypothesis.

The eastern African and South African databases were used to derive these rates of first and last appearance of species throughout the timespan of current literature. Currently, the eastern African faunal database in the Plio-Pleistocene spans 4.4 mya to the onset of the Holocene at 11,700 years before present. Publications for South Africa encompass a timespan from 3.2 million years ago to the Holocene. The databases were compiled by extracting faunal lists from articles published within the last 20 years. These data were added to an original database from McKee (1995) that was already composed of first and last appearance dates for previous literature. The earliest dated appearance of each species at any site within the relevant region was added to the dataset as its first appearance date.

The last appearance of a species in any fossil assemblage within the region was added to the dataset as its last appearance date. Peaks of frequency of first appearance dates are at

some points correlated with high peaks of last appearance dates providing what has been interpreted as turnover-pulses (Fig. 2 and Fig. 3). These data comprise the observed fossil record which were then compared to simulated models in order to determine the likelihood that these observed frequencies, and by extension turnover-pulses, were a product of chance preservation rather than climatic forcing.

Figure 2: East African fossil record first and last appearance dates for the past 4.4 million years.

The observed fossil record was used to derive average rates of species turnover.

Since the fossil record is biased by differential preservation, these rates of first and last appearances do not represent true speciation and extinction rates. Instead, they represent an average rate of preservation of species that have appeared in the fossil record of the Plio-

Pleistocene. For the eastern African fossil record, an average rate of 5.95%, or 4-5 novel species appear in the fossil record every 100,000 years. The last appearance dates of species

occur at an average rate of 10.57% or between 9 and 10 species every 100,000 years.

Analysis of the South African fossil record provided rates of first appearance as 7.97% and

last appearance of 7.27%. These rates indicate turnover of between 7 and 8 species every

100,000 years. Since these rates are averaged over the temporal spans for each region, they

represent a range of turnover rates. The models account for this range by assuming that

potential speciation and extinction events will occur at a rate within the aforementioned

percentages for each time interval.

Figure 3: South African fossil record first and last appearance dates for the past 3.2 million years

The program designed to model the fossil record was developed using Matlab

computer software. Coding in Matlab consists of using commands to work with arrays of

numbers. The first operation of the program generates an array of numbers from 1 to 2000

that represent potential fauna that may appear on the landscape. Individual fauna in this

array are given two random numbers between 0 and 1. The first represents the potential of

the species to diverge, creating a new species and new first appearance. The second random number represents the potential of that species to go extinct. The initial array of 2000 simulated species is used to populate arrays in the program.

The program’s second operation generates a second array that represents all extant fauna that are on the landscape at the beginning of the simulation. The number of extant species is set to 84 for eastern African simulations and to 88 for South African simulations.

These values were derived from the observed fossil record and represent the maximum number of extant species in each region during the Plio-Pleistocene. This maximum value was chosen to provide full potential for peaks of speciation and extinction. The extant fauna array is populated with this maximum number of species. They are drawn randomly from the initial 2000 simulated species and inserted in the extant array. The extant array is then updated by speciation and extinction loops that simulate first and last appearance dates.

The speciation loop uses the extant species array to simulate the emergence of new species. A randomized comparison value is generated between 0 and the speciation rate for the region. The speciation loop then cycles through all species in the extant array comparing the speciation propensity for each species with the comparison value. If the propensity of the species is within this range, the species diverges and a new species is added to the extant array. This new species is drawn randomly from the simulated fauna and inserted as a new species in the extant array. After the speciation loop, the extant array is used to simulate extinction.

The extinction loop works much like the speciation loop. A comparison value is randomly generated between 0 and the extinction rate derived from the fossil record of

each region. If the extinction propensity of each species in the extant array is within the generated range, the species goes extinct and is deleted from the extant array. Extinct species are unique and if deleted, they will never appear in the extant array again.

The speciation and extinction loops are repeated every at intervals of 100,000 years for the length of each regions fossil record. For eastern Africa, the loops are repeated 44 times and for South Africa they are repeated 32 times to simulate first and last appearance date frequencies at everyone 100,000 year interval over the course of the region’s fossil record. Before each repetition, the values of speciation and extinction potentials for all species in the extant array are reset to new random values between 0 and 1. These changing values at every 100,000 year interval simulate minute changes to the physical environment of the individual simulated species.

The values for each speciation and extinction event are added to final arrays which keep track of the numbers of first appearances and last appearances in each simulated run.

The program runs 1000 times and the values for each simulated fossil record are entered into two final arrays. One saves the frequencies of first-appearance dates for each 100,000 year interval of each simulated fossil record. The other saves the last-appearance dates.

Simulation of turnover-pulses required adding a subroutine to the continuous species turnover program. This subroutine changes speciation and extinction rate at 1.0 million years and 2.8 million years. The continuous background turnover rate was halved to simulate a low level of environmental change while the intervals at 1.0 and 2.8 million years was doubled. These two intervals of increased turnover simulate two drastic climatic shifts that would be responsible for more turnover than the low background rate. The

turnover model also repeats 1000 times and records the first and last appearance dates. The final arrays for first and last appearance from each of program were compared for each region using SPSS. The final array for first appearances and last appearances were then compared to the observed fossil record to consider whether the sampling observed in the observed fossil record was significantly different from the two simulated models.

Statistical Analyses

Kruskal-Wallis non-parametric tests for analysis of variance (ANOVA) were used to compare simulated data and observed data. These tests were used to determine variance between simulated continuous data, simulated pulse data, and the observed fossil record at each 100,000 year interval. The observed fossil record was compared through non- parametric ANOVA with both simulated continuous and simulated turnover-pulse models.

Subsequently, continuous samples were tested against pulse samples to ensure their statistical difference. Statistical similarities between continuous simulations and turnover- pulse simulations would nullify comparisons between each simulation and the observed fossil record. Finally, each set of samples was tested against observed data to determine if either continuous or pulse samples were statistically similar to observed fossil data.

Chapter 4: Results

Between zero and ten species appeared during each interval in eastern African simulations of continuous speciation. Though the distribution is fairly regular, outliers are present for a majority of intervals during the 4.4 million years lending further credence to the chance inclusion of fossils in assemblages. To visualize these distributions, ten simulates were chosen at random and plotted in Figure 4. The simulations of speciation pulses produced zero to seven new species at each

100,000 year interval. A random sampling of ten simulations allow a look at the distributions of the turnover pulse model (Fig. 5). The greatest amount of speciation distribution in the turnover simulations occurred at 1mya where a center of faunal turnover was programmed; however, the interval at 2.8 million years had no more species appearance than background turnover. The eastern

African last appearance dates for simulated continuous turnover produced frequencies of species last appearance between zero and ten. A random sample of ten simulations can be visualized in

Figure 6. The turnover-pulse model produced last appearance dates between zero and eight for background extinction with peaks of at 2.8 million years. For last appearance dates, 1 million years did not produce any significantly different distributions than background turnover. A random sample of ten simulations was plotted in Figure 7.

Prior to comparing observed fossil record to simulated datasets, it was necessary to control for possible similarities between the two simulated datasets. If they were statistically undifferentiated, then further tests between the fossil record and simulations would be valid for comparison with observed data. A Kruskal-Wallis test was performed between simulation of continuous turnover and punctuated turnover and resulted in statistically significant difference

between the two simulations at every 100,000 year interval with all p-values <.001. Constant turnover ranges were higher in species first and last appearances at a majority of intervals. Though the turnover-pulse model was set to a higher rate of turnover for 2.8mya and 1mya, these intervals were not significantly higher in overall speciation frequency than other 100,000 year intervals.

Statistical difference between samples of the two simulations provided justification for their meaningful difference in speciation and extinction processes; therefore, these models could be compared to the fossil record.

Tests between Observed and Simulated Data

East African simulations were compared with observed first and last appearance dates using Kruskal-Wallis non-parametric tests. East African continuous simulations show no statistical difference from observed first-appearance dates (FADS) (Table 2). Observed east African last- appearance dates are not significantly different than last appearance (LADS) dates of continuous simulations. East African turnover-pulse simulations were statistically different from the observed fossil record at only seven intervals: 2.4 million, 2.5 million, 3 million, 3.3 million, 3.4 million, 3.8 million, and 4.2 million years before present. To ensure these pairwise comparisons did not signify a meaningful difference, Mann-Whitney tests were performed between east African simulated data and observed data at every interval. This test showed no significant difference between distributions of the observed fossil record to the simulated pulse speciation model. The turnover-pulse last appearance dates in simulated data were not significantly different than the observed fossil record at any 100,000 year interval.

Comparisons were made between the observed fossil record of South Africa and simulated turnover data (Table 2). For South African data, there were no significant difference between continuous simulated distributions and observed fossil data for first and last appearance dates at

any 100,000 year interval. Similarly, the turnover-pulse data for South Africa was not significantly different than observed fossil data at any interval for first or last appearance dates.

Table 2: P-Values for Kruskal-Wallis Tests between the observed fossil record and simulations of faunal turnover (Significance P < .05)

Obs – Cont - FAD Obs – Pulse - FAD Obs – Cont - LAD Obs – Pulse - LAD EA SA EA SA EA SA EA SA 0 .113 .114 1.000 .895 .804 .123 .219 .851 100,000 .110 .623 1.000 .073 1.000 1.000 1.000 .311 200,000 .108 .673 1.000 .075 .834 .842 .238 .122 300,000 .115 .124 1.000 .815 .824 .125 .257 .834 400,000 1.000 .124 1.000 .810 .808 .635 .244 .073 500,000 .101 .110 1.000 .827 .331 .118 1.000 .885 600,000 1.000 .108 .618 .889 .096 1.000 .468 .785 700,000 .108 .380 1.000 1.000 .103 .115 .436 .862 800,000 .476 .109 1.000 .869 .103 .122 .443 .813 900,000 .289 .108 1.000 .882 .851 1.000 .248 .832 1,000,000 .396 .057 .161 .764 1.000 .800 .960 .064 1,100,000 .101 .111 1.000 .872 .969 .112 1.000 .912 1,200,000 1.000 1.000 1.000 .400 .895 1.000 .289 .340 1,300,000 .102 1.000 1.000 1.000 .152 .467 .560 1.000 1,400,000 1.000 1.000 .567 1.000 .102 .873 .428 .102 1,500,000 .105 .679 1.000 .068 .109 .116 .410 .883 1,600,000 1.000 .104 .644 .852 1.000 .655 1.000 .075 1,700,000 1.000 .649 .301 .070 .107 .652 .407 .067 1,800,000 1.000 .653 .141 .068 .892 .120 .316 .863 1,900,000 1.000 .099 .056 .873 .103 1.000 .426 1.000 2,000,000 1.000 .735 .058 .089 .147 1.000 .490 .956 2,100,000 .095 .101 1.000 .867 .910 .104 .323 .880 2,200,000 .093 1.000 1.000 .469 .880 .113 .335 .868 2,300,000 1.000 .102 .155 .871 .110 .104 .389 .889 2,400,000 .938 .096 .038 .918 .105 1.000 .408 .969 2,500,000 .970 .102 .036 .856 .105 .778 .402 .078 2,600,000 1.000 .064 .320 .686 .921 .361 .376 1.000 2,700,000 .090 .088 1.000 .923 .971 .101 .370 .883 2,800,000 .347 .897 .162 .078 .698 1.000 .075 .451 2,900,000 .094 .099 1.000 .849 .196 .114 .637 .883 3,000,000 1.000 .967 .034 1.000 1.000 .890 .401 .102 3,100,000 .089 .092 1.000 .904 .111 .095 .370 .924 3,200,000 1.000 .097 .704 .901 1.000 .098 1.000 .908 3,300,000 1.000 - .045 - 1.000 - 1.000 - 3,400,000 1.000 - .034 - 1.000 - .394 - 3,500,000 .500 - 1.000 - 1.000 - .418 - 3,600,000 .096 - 1.000 - .204 - .556 - 3,700,000 .096 - 1.000 - 1.000 - .419 - 3,800,000 1.000 - .031 - 1.000 - .450 - 3,900,000 .094 - 1.000 - 1.000 - 1.000 - 4,000,000 1.000 - 1.000 - 1.000 - .426 - 4,100,000 1.000 - .243 - 1.000 - 1.000 - 4,200,000 1.000 - .041 - 1.000 - .486 - 4,300,000 .097 - 1.000 - .183 - .507 - 4,400,000 .098 - 1.000 - 1.000 - .483 -

Observed first and last appearance dates for east African and South African fauna fit with distributions for both simulated continuous turnover and simulated pulses of turnover. Simulations of continuous turnover produced peaks of first and last appearance dates that are similar to the observed fossil record. Similarly, the turnover-pulse simulation produced peaks of species turnover at various intervals and the programmed pulses did not necessarily predict peaks. Therefore, the observed fossil record could represent either continuous turnover or pulses of turnover. Frequency of turnover was similarly produced in both models for eastern Africa and South Africa.

Chapter 5: Discussion

The datasets generated by the simulations, as well as the observed fossil record, were relatively simple; however, statistical comparisons were based on 1000 simulations, which increases the fidelity of the models and their usefulness for comparison. If these models of species turnover were more complex, it would have been more difficult to interpret meanings of significance. For this model of species turnover, the program has enough flexibility to manipulate variables to answer various research questions.

The first question answered by comparison of simulated and observed data is whether the fossil record is indicative of continuous turnover, pulses of turnover, or chance occurrences. There was no significant difference between simulated continuous speciation and the observed fossil record. Continuous speciation events could have produced the patterns of species turnover in fossil fauna. However, turnover-pulse simulations also showed no significant difference from fossil fauna. Continuous faunal turnover or pulses of faunal turnover could have been responsible for patterns observed in the fossil record. Since there was no evidence to support delineation of patterns of turnover in the fossil record as either continuous or pulse, it is likely that the fossil record represents a series of random chances mediated by preservation biases.

The second question to explore is whether forcing mechanisms such as climate change are visible in the fossil record. Evidence from this model, as well as from other studies in paleoanthropology and paleontology (Signor and Lipps, 1982; Behrensmeyer, 2006) provide evidence that differential preservation of fossils throughout the fossil record caused peaks of first and last appearance dates that are not necessarily due to causal forcing mechanisms. Biases to the

30 fossil record include pre-depositional (Lam et al., 1999), taphonomic (Lyman, 1994), and excavation biases.

Pre-depositional biases refer to all processes that happen to faunal remains prior to their deposition in a fossil assemblage. Predepositional biases are important for research into paleoanthropology because prey choices of predators are recorded in the fossil record. Carnivore gnaw marks, dry-stick and green-stick fractures, cut marks and more are indicative of processes which construct fossil assemblages. Taphonomic biases include all processes that influence preservation of bones after deposition has occurred. These processes include such processes as weathering, burning, root etchings, and diagenetic processes associated with burial. Through interpretation of taphonomy, the fossil record can be used to interpret how bones have been differentially preserved through the past. Excavation biases include choices researchers make in excavating faunal assemblages. Paleontologists and Paleoanthropologists researching evolution in the past choose particular sites based on geological and environmental factors that favor preservation. This bias is influenced by research projects and sociopolitical boundaries that maintain spatial and temporal gaps. Due to the variable nature of fossil preservation and excavation, the fossil record does not support differentiating between constant turnover of species and long periods of stability with punctuated bursts of turnover. Instead, it records biases and must be interpreted to answer questions about human interaction with physical environment instead of the environmental impact on humans.

Limitations of Simulated Models

Though this model of species turnover is robust, in that it allows for manipulation of variables without sacrificing the fidelity of the simulations, there are confounding factors. The rates of speciation and extinction were derived from the fossil record, which were used to simulate a fossil record. This reflexive structure is bolstered by the inclusion of random chance within each

31 interval. The rates of speciation and extinction were averages in the fossil record and they are assumed to reflect the paradigm, either constant or punctuated, that created it. Further, the simulations incorporated random chance into the model. Simulated species were individually stressed by natural selection factors and were able to speciate, persist, or go extinct. The fact that the first and last appearance dates are based on the fossil record allows for an assumption that each complete run represents a random sample of values which mimics differential preservation in the fossil record. This is of concern with all computer modeling because random numbers are selected from a distribution of random values instead of independently selected cases. Due to this computing phenomenon, the test is repeatable and every run will be exactly the same. It could not, however, represent the true stochastic nature of fossil record sampling. Matlab documentation assures randomly chosen integers within the program are approaching randomness with enough success that it would be indistinguishable from true random values (Hahn and Valentine, 2013).

The robusticity and simplicity of these species turnover programs opens up new possibilities for answering questions about fossil fauna, and the Plio-Pleistocene landscape.

Addition of data based on ecological, biological, and behavioral factors could add further constraints for fossil record deposition. The fossil record remains the only proxy for theoretical insights into organismal interaction in the past and through use of this model, interpretations may be correlated based on what can and will happen under certain conditions.

Chapter 6: Conclusion

The fossil record is a useful proxy for considering past processes, but it does not provide supporting evidence for climate as a causal mechanism of speciation and extinction. Climate shifted from warm and wet during the Pliocene to highly variable with periods of cold and dry glaciation interspersed with warmer and wetter interglacials. As climate changed, species would have adapted to changing conditions based on unique life-history traits of lineages. Adaptations may have led to species divergence and extinction but this is difficult to decipher from the fossil record. Instead, fossilization biases differentially preserve faunal evidence which may appear as pulses of species turnover. Correlating these peaks of species turnover with climatic shifts could lead to spurious conclusions about the causes of speciation and extinction throughout prehistory.

Reexamining environmental determinism of evolution requires eschewing previous assumptions about the fossil record’s viability to answer questions about prehistoric processes.

Modeling speciation and extinction provides evidence of how fossils may be deposited in assemblages. This thesis provided a set of models for simulating species turnover. They provide robust a robust means of simulating speciation and extinction that allows manipulation of variables without sacrificing its integrity. Through further refinement of the model and addition of new faunal fossil assemblages, future research on species biodiversity in fossil assemblages will benefit from a solid theoretical foundation regarding the fossil record.

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Appendix A: Tables

Table 3: East African Fauna – First and Last Appearance Dates

Family Genus/Species FAD LAD Bovidae Aepyceros melampus 4,100,000 0 Bovidae Aepyceros shungurae 4,100,000 1,100,000 Bovidae Alcelaphus buselaphus 600,000 0 Bovidae Antidorcas recki 3,300,000 1,000,000 Bovidae Awashia suwai 2,500,000 2,400,000 Bovidae Beatragus antiquus 2,300,000 1,500,000 Bovidae Beatragus hunteri 1,400,000 0 Bovidae Beatragus whitei 2,500,000 2,400,000 Bovidae Bos makapani 1,800,000 1,700,000 Bovidae Bouria anngettyae 1,042,000 800,000 Bovidae Brabovus nanincisus 3,800,000 3,600,000 Bovidae Cephalophus sylvicultor 392,000 330,000 Bovidae Connochaetes africanus 1,700,000 1,500,000 Bovidae Connochaetes gentryi 2,600,000 1,350,000 Bovidae Connochaetes taurinus 1,800,000 0 Bovidae Damaliscus ademassui 2,500,000 2,500,000 Bovidae Damaliscus aglaius 1,700,000 1,000,000 Bovidae Damaliscus dorcas 392,000 330,000 Bovidae Damaliscus hunteri 392,000 330,000 Bovidae Damaliscus hypsodon 390,000 10,000 Bovidae Damaliscus lunatus 392,000 330,000 Bovidae Damaliscus niro 1,700,000 600,000 Bovidae Damalops paleindicus 3,800,000 2,900,000 Bovidae Gazella granti 2,600,000 330,000 Bovidae Gazella janenschi 3,800,000 1,400,000 Bovidae Gazella praethomsoni 2,400,000 1,300,000 Bovidae Gazella thomsonii 600,000 330,000 Bovidae Hippotragus equinus 392,000 330,000 Bovidae Hippotragus gigas 2,800,000 1,000,000 Bovidae Kobus ancystrocera 3,400,000 1,700,000 Bovidae Kobus ellipsiprymnus 2,300,000 0 Bovidae Kobus kob 3,400,000 0 Bovidae Kobus leche 1,900,000 0 Bovidae Kobus oricornus 4,100,000 2,400,000 Bovidae Kobus sigmoidalis 3,300,000 800,000 Bovidae Litocranius walleri 392,000 330,000 Bovidae Megalotragus avifluminis 3,800,000 2,500,000 Bovidae Megalotragus isaaci 2,300,000 1,500,000 Continued 43

Table 3 Continued

Family Genus/Species FAD LAD Bovidae megalotragus kattwinkeli 3,000,000 700,000 Bovidae Megantereon cultridens 4,400,000 1,500,000 Bovidae Menelikia leakeyi 4,100,000 2,520,000 Bovidae Menelikia lyrocera 3,400,000 1,400,000 Bovidae Nitidarcus asfawi 1,042,000 800,000 Bovidae Numidocapra crassicornis 2,500,000 800,000 Bovidae Oryx beisa 392,000 330,000 Bovidae Parestigorgon gadgingeri 2,600,000 2,500,000 Bovidae Parmularius altidens 3,200,000 1,700,000 Bovidae Parmularius angusticornis 1,900,000 800,000 Bovidae Parmularius eppsi 2,800,000 1,500,000 Bovidae Parmularius pandatus 3,800,000 1,600,000 Bovidae Parmularius parvus 900,000 700,000 Bovidae Parmularius rugosus 2,500,000 700,000 Bovidae Pelorovis antiquus 1,042,000 600,000 Bovidae Pelorovis oldowayensis 2,800,000 1,000,000 Bovidae Pelorovis turkanensis 2,000,000 1,300,000 Bovidae praedamalis deturi 3,800,000 2,500,000 Bovidae Rabaticeras arambourgi 2,500,000 700,000 Bovidae Rabaticeras lemutai 1,450,000 1,400,000 Bovidae Redunca fulvorufula 2,800,000 0 Bovidae Rusingoryx atopocranion 50,000 40,000 Bovidae Simatherium kohlarseni 3,800,000 3,600,000 Bovidae Syncerus acoelotus 2,850,000 600,000 Bovidae Syncerus caffer 1,600,000 0 Bovidae Taurotragus arkelli 1,200,000 700,000 Bovidae Taurotragus oryx 900,000 0 Bovidae Tragelaphus buxtoni 2,600,000 0 Bovidae Tragelaphus gaudryi 2,400,000 1,700,000 Bovidae Tragelaphus kyaloae 4,400,000 3,200,000 Bovidae Tragelaphus nakuae 4,100,000 1,700,000 Bovidae Tragelaphus pricei 2,800,000 2,400,000 Bovidae Tragelaphus scriptus 3,300,000 0 Bovidae Tragelaphus spekei 1,450,000 1,400,000 Bovidae Tragelaphus strepsiceros 2,500,000 0 Canidae Canis adustus 392,000 330,000 Canidae Canis mesomelas 392,000 0 Canidae Lycaon pictus 2,400,000 0

Continued 44

Table 3 Continued

Family Genus/Species FAD LAD Canidae Otocyon megalotis 392,000 330,000 Canidae Otocyon recki 1,800,000 1,700,000 Cercopithecidae Cercopithecus aethiops 1,900,000 0 Cercopithecidae Cercopithecus kimeui 1,700,000 700,000 Cercopithecidae Gorgopithecus major 2,800,000 1,700,000 Cercopithecidae Papio hamadryas 1,200,000 0 Cercopithecidae Papio quadratirostris 3,000,000 2,300,000 Cercopithecidae Paracolobus chemeroni 2,500,000 2,400,000 Cercopithecidae Paracolobus mutiwa 3,000,000 1,900,000 Cercopithecidae Parapapio ado 4,200,000 2,500,000 Cercopithecidae Parapapio jonesi 3,200,000 3,100,000 Cercopithecidae Parapapio whitei 3,300,000 2,500,000 Cercopithecidae Rhinocolobus turkanensis 3,400,000 1,100,000 Cercopithecidae Theropithecus brumpti 3,300,000 1,900,000 Cercopithecidae Theropithecus darti 3,400,000 3,100,000 Cercopithecidae Theropithecus oswaldi 2,500,000 50,000 Cercopithecidae Thryonomys swinderianus 2,400,000 2,000,000 Chalicotheriidae Ancylotherium hennigi 3,800,000 1,350,000 Deinotheriidae Deinotherium bozasi 4,200,000 1,400,000 Elephantidae Elaphas ekorensis 4,200,000 2,900,000 Elephantidae Elephas recki 3,500,000 500,000 Elephantidae Loxodonta adaurora 4,400,000 1,900,000 Elephantidae Loxodonta africana 400,000 0 Elephantidae Loxodonta atlantica 2,500,000 2,400,000 Elephantidae Loxodonta exoptata 3,800,000 2,500,000 Equidae Equus burchelli 1,800,000 0 Equidae Equus grevyi 1,600,000 0 Equidae Equus koobiforensis 2,000,000 1,700,000 Equidae Equus numidicus 2,400,000 2,300,000 Equidae Equus oldowayensis 2,400,000 50,000 Equidae Equus tabeti 1,900,000 1,500,000 Equidae Hipparion afarense 3,400,000 2,400,000 Equidae Hipparion albertenae 2,800,000 600,000 Equidae Hipparion baardi 4,000,000 2,300,000 Equidae Hipparion cornelianum 2,000,000 1,700,000 Equidae Hipparion ethiopicum 2,800,000 700,000 Equidae Hipparion hasumense 4,400,000 1,900,000 Equidae Hipparion libycum 2,400,000 700,000

Continued 45

Table 3 Continued

Family Genus/Species FAD LAD Equidae Hipparion namaquense 4,000,000 2,300,000 Equidae Hipparion primigenium 4,200,000 2,300,000 Equidae Hipparion sitifense 4,200,000 1,500,000 Equidae Hipparion turkanense 4,200,000 2,300,000 Felidae Acinonyx crassidens 3,200,000 3,100,000 Felidae Acinonyx jubatus 2,800,000 0 Felidae Dinofelis barlowi 3,000,000 1,900,000 Felidae Dinofelis piveteaui 3,000,000 1,400,000 Felidae Felis caracal 3,000,000 0 Felidae Felis libyca 900,000 0 Felidae Felis serval 3,400,000 0 Felidae Homotherium crenatidens 3,800,000 1,500,000 Felidae Homotherium problematicum 2,500,000 2,500,000 Felidae Panthera leo 3,800,000 0 Felidae Panthera pardus 3,800,000 0 Giraffidae Giraffa atilei 4,400,000 3,600,000 Giraffidae Giraffa camelopardalis 800,000 0 Giraffidae Giraffa gracilis 3,000,000 600,000 Giraffidae Giraffa jumae 4,400,000 700,000 Giraffidae Giraffa pygmaeus 4,400,000 1,500,000 Giraffidae Giraffa stillei 4,400,000 700,000 Giraffidae Sivatherium maurusium 3,800,000 1,400,000 Giraffidae Sivatherium olduvaiensis 1,800,000 600,000 Gomphotheriid Anancus kenyensis 4,200,000 3,900,000 Hippopotamidae Hexaprotodon coryndoni 3,400,000 3,100,000 Hippopotamidae Hexaprotodon harvardi 4,200,000 4,100,000 Hippopotamidae Hexaprotodon karumensis 2,000,000 1,400,000 Hippopotamidae Hexaprotodon protamphibius 4,400,000 4,300,000 Hippopotamidae Hippopotamus amphibius 1,900,000 0 Hippopotamidae Hippopotamus gorgops 2,000,000 500,000 Hippopotamidae Hippopotomus aethiopicus 2,800,000 1,400,000 Hippopotamidae Hippopotomus shungurensis 3,000,000 2,900,000 Hominidae Ardipithecus ramidus 4,400,000 4,300,000 Hominidae Australopithecus aethiopicus 2,800,000 1,900,000 Hominidae Australopithecus afarensis 4,200,000 3,100,000 Hominidae Australopithecus boisei 2,300,000 1,400,000 Hominidae Australopithecus garhi 2,500,000 2,400,000 Hominidae Homo erectus 2,000,000 800,000

Continued 46

Table 3 Continued

Family Genus/Species FAD LAD Hominidae Homo habilis 2,400,000 1,500,000 Hominidae Homo rudolfensis 2,000,000 1,900,000 Hominidae Homo sapiens 600,000 0 Hyaenidae Crocuta crocuta 3,800,000 0 Hyaenidae Hyaena brunnea 3,000,000 0 Hyaenidae Hyaena hyaena 3,000,000 0 Hyaenidae Pachycrocuta aff. brevirostris 1,042,000 800,000 Hystricidae Hystrix cristatus 2,400,000 2,000,000 Leporidae Lepus capensis 392,000 330,000 Muridae Golunda gurai 2,400,000 2,000,000 Muridae Millardia coppensi 2,400,000 2,000,000 Mustelidae Aonyx capensis 2,500,000 2,500,000 Mustelidae Mellivora capensis 392,000 330,000 Orycteropodidae Orycteropus afer 392,000 330,000 Rhinocerotidae Ceratotherium praecox 4,400,000 3,300,000 Rhinocerotidae Ceratotherium simum 3,000,000 0 Rhinocerotidae Diceros bicornis 3,800,000 0 Suidae Hylochoerus meinertzhageni 400,000 100,000 Suidae Kolpochoerus afarensis 3,400,000 2,400,000 Suidae Kolpochoerus limnetes 3,300,000 100,000 Suidae Kolpochoerus majus 1,900,000 500,000 Suidae Kolpochoerus olduvaiensis 1,600,000 800,000 Suidae Metridiochoerus andrewsi 3,300,000 600,000 Suidae Metridiochoerus compactus 1,900,000 700,000 Suidae Metridiochoerus hopwoodi 2,400,000 1,000,000 Suidae Metridiochoerus modestus 2,400,000 600,000 Suidae Notochoerus euilus 4,400,000 2,400,000 Suidae Notochoerus scotti 3,300,000 1,700,000 Suidae Nyanzachoerus jaegeri 4,400,000 2,400,000 Suidae Nyanzachoerus kanamensis 4,400,000 2,400,000 Suidae Phacochoerus aethiopicus 2,300,000 0 Suidae Phacochoerus antiquus 900,000 700,000 Suidae Potamochoerus porcus 4,400,000 0 Suidae Trilobophorus afarensis 3,400,000 3,100,000 Ursidae Helogale kitafe 4,400,000 4,300,000 Viverridae Ichneumia albicauda 392,000 330,000

Table 4: South African Fauna – First and Last Appearance Dates

Family Genus/Species FAD LAD Bathyergidae Bathyergus suillus 1,000,000 57,000 Bovidae Aepyceros melampus 65,000 0 Bovidae Alcelaphus buselaphus 174,000 0 Bovidae Antidorcas australis 1,000,000 400,000 Bovidae Antidorcas bondi 89,000 10,000 Bovidae Antidorcas marsupialis 1,500,000 0 Bovidae Antidorcas recki 2,000,000 40,000 Bovidae Bos taurus 128,000 0 Bovidae Capra hircus 3,000 0 Bovidae Cephalophus natalensis 65,000 0 Bovidae Cephalophus parvus 2,800,000 2,600,000 Bovidae Connochaetes gnou 1,000,000 0 Bovidae Connochaetes taurinus 1,200,000 0 Bovidae Damaliscus dorcas 1,200,000 0 Bovidae Damaliscus lunatus 1,000,000 0 Bovidae Damaliscus niro 1,000,000 10,000 Bovidae Damaliscus pygargus 151,000 10,000 Bovidae Gazella gracilior 3,200,000 3,000,000 Bovidae Gazella helmoedi 1,000,000 900,000 Bovidae Gazella vanhoepeni 3,200,000 2,000,000 Bovidae Hippotragus cookei 3,200,000 2,500,000 Bovidae Hippotragus equinus 2,600,000 0 Bovidae Hippotragus gigas 1,700,000 400,000 Bovidae Hippotragus leucophaeus 1,000,000 0 Bovidae Hippotragus niger 1,400,000 0 Bovidae Kobus ellipsiprymnus 1,200,000 0 Bovidae Kobus leche 1,700,000 0 Bovidae Makapania broomi 3,200,000 2,500,000 Bovidae Megalotragus priscus 1,200,000 0 Bovidae Oreotragus oreotragus 3,200,000 0 Bovidae Oryx gazella 1,000,000 600,000 Bovidae Ourebia ourebi 21,000 0 Bovidae Ovis aries 128,000 0 Bovidae Parmularius braini 3,200,000 3,000,000 Bovidae Pelea capreolus 1,800,000 0 Bovidae Pelorovis antiquus 1,000,000 57,000 Bovidae Philantomba monticola 64,700 0 Bovidae Rabaticeras arambourgi 1,000,000 400,000

Continued 48

Table 4 Continued

Family Genus/Species FAD LAD Bovidae Rabaticeras porrocornutu 1,700,000 1,600,000 Bovidae Raphicerus campestris 1,700,000 0 Bovidae Raphicerus melanotis 1,000,000 0 Bovidae Redunca arundinum 1,800,000 0 Bovidae Redunca darti 3,200,000 2,500,000 Bovidae Redunca fulvorufula 1,500,000 0 Bovidae Sylvicapra grimmia 252,600 0 Bovidae Syncerus acoelotus 2,600,000 1,900,000 Bovidae Syncerus caffer 151,000 0 Bovidae Taurotragus oryx 1,800,000 0 Bovidae Tragelaphus angasi 3,200,000 0 Bovidae Tragelaphus pricei 3,200,000 3,000,000 Bovidae Tragelaphus scriptus 1,800,000 0 Bovidae Tragelaphus strepsiceros 1,800,000 0 Bovidae Wellsiana torticornuta 3,200,000 3,000,000 Canidae Canis atrox 1,800,000 1,700,000 Canidae Canis familiaris 3,000 0 Canidae Canis mesomelas 3,200,000 0 Canidae Lycaon pictus 1,000,000 0 Canidae Nyctereutes terblanchei 1,800,000 1,700,000 Canidae Otocyon megalotis 253,000 0 Canidae Vulpes chama 3,200,000 0 Canidae Vulpes pulcher 1,800,000 1,600,000 Cercopithecidae Cercopithecus aethiops 9,000 0 Cercopithecidae Cercopithecus albogularis 65,000 62,000 Cercopithecidae Cercopithecus pygerythrus 65,000 58,000 Cercopithecidae Cercopithecus williamsi 3,200,000 1,600,000 Cercopithecidae Chlorocebus aethiops 63,000 0 Cercopithecidae Dinopithecus ingens 1,700,000 1,600,000 Cercopithecidae Gorgopithecus major 1,800,000 1,700,000 Cercopithecidae Papio angusticeps 2,000,000 1,700,000 Cercopithecidae Papio cynocephalus 253,000 0 Cercopithecidae Papio hamadryas 1,500,000 62,000 Cercopithecidae Papio izodi 2,600,000 2,400,000 Cercopithecidae Papio robinsoni 2,000,000 1,000,000 Cercopithecidae Papio ursinus 151,000 0 Cercopithecidae Parapapio antiquus 2,600,000 2,400,000 Cercopithecidae Parapapio broomi 3,200,000 2,500,000

Continued 49

Table 4 Continued

Family Genus/Species FAD LAD Cercopithecidae Parapapio jonesi 3,200,000 1,600,000 Cercopithecidae Parapapio whitei 3,200,000 2,500,000 Cercopithecidae Theropithecus darti 3,200,000 2,800,000 Cercopithecidae Theropithecus oswaldi 1,700,000 1,400,000 Chalicotheriidae Anclotherium hennigi 3,200,000 3,000,000 Elephantidae Elephas iolensis 1,300,000 1,200,000 Elephantidae Elephas recki 3,200,000 2,000,000 Elephantidae Loxodonta africana 128,000 57,000 Elephantidae Loxodonta atlantica 1,000,000 400,000 Equidae Equus burchelli 2,200,000 0 Equidae Equus capensis 2,200,000 10,000 Equidae Equus quagga 1,000,000 0 Equidae Hipparion libycum 3,200,000 900,000 Erinaceidae Erinaceus frontalis 128,000 57,000 Felidae Acinonyx jubatus 3,200,000 0 Felidae Dinofelis barlowi 3,200,000 115000 Felidae Dinofelis piveteaui 1,800,000 1,700,000 Felidae Felis caracal 2,000,000 0 Felidae Felis crassidens 1,800,000 1,700,000 Felidae Felis lybica 3,200,000 0 Felidae Felis nigripes 100,000 0 Felidae Felis serval 3,200,000 0 Felidae Homotherium crenatidens 2,600,000 1,700,000 Felidae Homotherium nestianus 3,200,000 3,000,000 Felidae Megantereon cultridens 2,600,000 1,200,000 Felidae Megantereon eurydon 1,800,000 1,700,000 Felidae Megantereon gracile 2,000,000 400,000 Felidae Megantereon whitei 1,500,000 1,400,000 Felidae Panthera leo 2,600,000 0 Felidae Panthera pardus 3,200,000 0 Giraffidae Giraffa camelopardalis 1,000,000 0 Giraffidae Sivatherium maurusium 3,200,000 400,000 Gomphotheriidae Anancus kenyensis 3,000,000 2,800,000 Herpestidae Atliax palundinosus 65,000 62,000 Herpestidae Crossarchus transvaalens 1,800,000 1,700,000 Herpestidae Galerella pulverulenta 100,000 0 Herpestidae Galerella sanguinea 65,000 62,000 Herpestidae Herpestes mesotes 1,800,000 1,700,000

Continued 50

Table 4 Continued

Family Genus/Species FAD LAD Herpestidae Herpestes pulverulentus 151,000 57,000 Herpestidae Herpestes sanguineus 200,000 100,000 Hippopotamidae Hippopotamus amphibius 3,200,000 0 Hippopotamidae Hippopotamus protamphibius 2,000,000 1,900,000 Hominidae Australopithecus africanus 3,200,000 2,500,000 Hominidae Australopithecus robustus 2,000,000 1,200,000 Hominidae Homo erectus 1,700,000 1,300,000 Hominidae Homo ergaster 253,000 115,000 Hominidae Homo habilis 2,200,000 2,000,000 Hominidae Homo heidelbergensis 1,000,000 600,000 Hominidae Homo sapiens 700,000 0 Hyaenidae Chasmaporthetes nitidula 2,600,000 1,200,000 Hyaenidae Chasmaporthetes silberbergi 2,600,000 1,600,000 Hyaenidae Crocuta crocuta 3,000,000 0 Hyaenidae Hyaena brunnea 2,600,000 0 Hyaenidae Hyaena hyaena 3,200,000 1,700,000 Hyaenidae Hyaenictis forfex 1,700,000 1,600,000 Hyaenidae Pachycrocuta bellax 3,200,000 1,700,000 Hyaenidae Proteles cristatus 1,500,000 1,400,000 Hyracoidea Procavia capensis 253,000 0 Hystricidae Hystrix africaeaustralis 1,500,000 0 Leporidae Lepus capensis 1,000,000 115,000 Leporidae Lepus saxatilis 128,000 0 Leporidae Pronolagus crassicaudatus 65,000 0 Leporidae Pronolagus rupestris 3,000 0 Manidae Manis temmincki 3,000 0 Mustelidae Aonyx capensis 1,700,000 0 Mustelidae Ictonyx striatus 1,000,000 0 Mustelidae Mellivora capensis 1,000,000 0 Mustelidae Poecilogale albinucha 1,500,000 1,400,000 Nesomyidae Cricetomys gambianus 65,000 58,300 Orycteropodidae Orcyteropus afer 3,200,000 0 Otariidae Arctocephalus pusillus 134,000 57,000 Otariidae Mirounga leonina 71,000 57,000 Pedetidae Pedetes capensis 253,000 0 Pholidota Phataginus pangolin 1,000,000 600,000 Procaviidae Gigantohyrax maguirei 3,200,000 2,800,000 Procaviidae Procavia antiqua 3,200,000 1,000,000

Continued 51

Table 4 Continued

Family Genus/Species FAD LAD Procaviidae Procavia transvaalensis 2,600,000 1,400,000 Rhinocerotidae Ceratotherium simum 3,200,000 0 Rhinocerotidae Diceros bicornis 3,200,000 57,000 Sciuridae Xerus inauris 9,000 0 Suidae Kolpochoerus paiceae 1,000,000 400,000 Suidae Metridiochoerus andrewsi 1,500,000 400,000 Suidae Metridiochoerus jacksoni 1,700,000 1,600,000 Suidae Metridiochoerus modestus 1,500,000 1,400,000 Suidae Notochoerus scotti 3,200,000 2,400,000 Suidae Phacochoerus aethiopicus 134,000 0 Suidae Phacochoerus africanus 252,600 58,000 Suidae Phacochoerus modestus 2,200,000 90,000 Suidae Potamochoeroides shawi 3,200,000 2,500,000 Suidae Potamochoerus larvatus 65,000 0 Suidae Potamochoerus porcus 71,000 0 Suidae Stylochoerus compactus 1,000,000 900,000 Suidae Sus scrofa 3,000 0 Thryonomyidae Thryonomys swinderianus 65,000 58,000 Viverridae Atilax paludinosus 1,000,000 0 Viverridae Cynictis penicillata 3,200,000 0 Viverridae Galarella sanguinea 62,000 0 Viverridae Genetta tigrina 1,300,000 0 Viverridae Herpestes ichneumon 1,700,000 0 Viverridae Mungos mungo 62,000 0 Viverridae Suricata suricatta 1,400,000 0 Viverridae Viverra civetta 1,000,000 0