Phylogeny and Extinction Risk in Mammals Luis Darcy Verde Arregoitia

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Phylogeny and extinction risk in mammals Luis Darcy Verde Arregoitia

A thesis submitted for the degree of Doctor of Philosophy at

The University of Queensland in 2014

School of Biological Sciences

Abstract

Detailed knowledge of the evolutionary relationships between species can inform studies in ecology and conservation. This phylogenetic information is becoming a recognized basis for evaluating conservation priorities. However, associations between species risk of extinction and the properties of a phylogeny such as diversification rates and phylogenetic lineage ages remain unclear. Limited taxon-specific analyses suggest that species in older lineages are at greater risk. Extinction due to chance events is more likely over time, or groups may become more specialized over time, reducing their potential to adapt to new conditions.

This thesis has four main parts. The first aims to identify evolutionary characteristics that predict the fates of species, by testing for associations between lineage age, clade size, diversification rates, evolutionary distinctiveness and extinction risk for terrestrial mammals. There were no significant global or regional associations, and three significant results within two taxonomic groups. Extinction risk increases for evolutionarily distinct primates and decreases with lineage age when lemurs are excluded. Lagomorph species (rabbits, hares and pikas) that have more close relatives are less threatened.

The second part of this thesis investigates why primate species in both younger and evolutionarily distinct lineages are more likely to be threatened. This section aims to identify the immediate causes of extinction risk (e.g. factors such as hunting and habitat loss and their variation in space) and how they relate to evolutionary history and species biology. The relationships between phylogeny and extinction risk are mediated by the evolutionary trend of increasing body size through time. Evolutionarily distinct primate species are smaller- bodied and thus more susceptible to the effects of habitat fragmentation. Species in younger lineages generally have larger body sizes. Larger species are preferentially hunted and reproductively less resilient.

The third section of the thesis examines why lagomorph species with fewer close relatives are more likely to be threatened. The non-random extinction of small clades disproportionately threatens genetic diversity and phylogenetic history. This section explores the lagomorph phylogeny to test if differences in lagomorph evolution, biogeography and ecology explain current patterns of diversity and threat. Diversification was unrelated to climate, topography, geographic range size or geographic isolation. For lagomorphs in general, extinction risk was

ii positively correlated with increasing human population density and negatively correlated with occurrence in anthropogenically modified habitat. Habitat generalists were less likely to be threatened, and paleoendemic species with few close relatives tend to be habitat specialists.

The fourth and final section of the thesis expands on the use of phylogenies in ecology and conservation. This chapter aims to quantify ecological similarity between rodent species in order to compare it with phylogenetic relatedness. Results are related to ecological theory on ecological character displacement, and to conservation in the context of competition between introduced and native species. Morphological data can identify species with high probabilities of competing with invasive rats and house mice.

The findings of this thesis contribute to our understanding of the patterns of threat to evolutionary history. The results in each section incorporate spatially explicit data on threats to mammals, and a process-based approach to interpreting the patterns of diversity. The inclusion of paleobiogeographic data provides more information on the origins of diversity and how to best preserve it. These research outputs can assist in planning and implementing actions to mitigate threats and conserve multiple measures of diversity.

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Declaration by author

This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.

I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.

I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the General Award Rules of The University of Queensland, immediately made available for research and study in accordance with the Copyright Act 1968.

I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.

Publications during candidature

Peer-reviewed papers

Verde Arregoitia, L.D., Blomberg, S.P. & Fisher, D.O. (2013) Phylogenetic correlates of extinction risk in mammals: species in older lineages are not at greater risk. Proceedings of the Royal Society B: Biological Sciences, 280, 20131092.

Murray, K.A., Verde Arregoitia, L.D., Davidson, A., Di Marco, M. & Di Fonzo, M.M.I. (2014) Threat to the point: improving the value of comparative extinction risk analysis for conservation action. Global Change Biology, 20, 483-494.

Publications included in this thesis

Chapters 2-5 are included as papers in the format in which they were submitted for publication as encouraged by the University. Their supplementary materials appear at the end of each chapter. The text in these chapters is consistent with its published or submitted format. I use the plural first-person pronoun “we” because each chapter is a collaborative multi-authored paper. While efforts have been made to minimise the level of information overlap between papers, repetition of some material can be expected. Pages are numbered consecutively throughout the thesis for ease of use and a single bibliography is provided for brevity. All co-authors consented to the manuscripts being included in the thesis.

In Chapters 1 and 6, where the body of the thesis is introduced, interpreted and summarised, the singular first-person pronoun “I” is used and chapters are referred to by chapter number. This format is consistent with the fact that Chapters 1 and 6 are the Candidate’s work while Chapters 2-5 are collaborative papers.

CHAPTER 2: Verde Arregoitia, L.D., Blomberg, S.P. & Fisher, D.O. (2013) Phylogenetic correlates of extinction risk in mammals: species in older lineages are not at greater risk. Proceedings of the Royal Society B: Biological Sciences, 280, 20131092.

Contributor Statement of contribution

Luis D. Verde Arregoitia (Candidate) Designed study (70%)

Analysed data (80%)

Wrote and edited paper (90%)

Diana O. Fisher Designed study (30%)

Wrote and edited paper (10%)

Simon P. Blomberg Analysed data (20%)

CHAPTER 3: Verde Arregoitia, L.D., Blomberg, S.P. & Fisher, D.O. Body size and threatened evolutionary history in primates (in preparation for submission)

Contributor Statement of contribution

Luis D. Verde Arregoitia (Candidate) Designed study (80%)

Analysed data (70%)

Wrote and edited paper (80%)

Diana O. Fisher Designed study (20%)

Wrote and edited paper (10%)

Simon P. Blomberg Analysed data (30%)

Wrote and edited paper (10%)

CHAPTER 4: Verde Arregoitia, L. D., Leach, K., Reid, N., & Fisher, D.O. Diversity, extinction, and threat status in Lagomorphs (accepted pending major revisions, Ecography)

Contributor Statement of contribution

Luis D. Verde Arregoitia (Candidate) Designed study (85%)

Analysed data (60%)

Collected data (75%)

Wrote and edited paper (70%)

Katie Leach Analysed data (40%)

Collected data (25%)

Wrote and edited paper (10%)

Neil Reid Designed study (15%)

Wrote and edited paper (10%)

Diana O. Fisher Wrote and edited paper (10%)

CHAPTER 5: Verde Arregoitia, L. D. & Fisher, D.O. Likelihood of competition between invasive and native rodents revealed by global comparisons of morphological, ecological and phylogenetic similarity (in review, Journal of Mammalian Evolution)

Contributor Statement of contribution

Luis D. Verde Arregoitia (Candidate) Designed study (100%)

Collected and analysed data (100%)

Wrote and edited paper (60%)

Diana Fisher Wrote and edited paper (40%)

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Contributions by others to the thesis

Chapters 1 and 6 were written by the Candidate, with editorial advice from Diana Fisher. Simon Blomberg provided statistical advice for Chapters 4 and 5.

Statement of parts of the thesis submitted to qualify for the award of another degree

None

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Acknowledgements

I am grateful for the excellent supervision from Diana Fisher, Simon Blomberg and Kerrie Wilson. Throughout the whole process they gave me guidance when I needed it, but they also trusted me to learn from my mistakes. Diana: I know very few people as knowledgeable as you in mammal ecology and evolution, and I thank you for the constant feedback that always enriched the quality of my work. To Simon, your door was always open when I had trouble with my analyses, and you had the patience to help me while allowing me to be independent. To Kerrie, thank you for encouraging me to get involved with the EDG and the Wilson Lab, where I learned a great deal about the real-world aspects of conservation biology. Anne Goldizen and Rich Fuller went beyond their duties as readers for my thesis milestones. Their advice and constructive comments helped me stay on track and motivated.

Several funding sources contributed to supporting my research. Scholarships from Consejo Nacional de Ciencia y Tecnología (CONACYT, ref. 308685) and top-up funds from the University of Queensland made this candidature possible. Travel grants from the UQ Graduate School, the UQ School of Biological Sciences, and the British Ecological Society allowed me to present my research and network both internationally and within Australia.

A number of academic and professional staff at UQ made my candidature a smooth, thorough and multidisciplinary process. Meron, Gimme, Selena, Anthony, Peter and many others allowed me to participate in classroom and field courses where I gained valuable experience. Gail helped me navigate administrative hardships and complicated paperwork.

The support of many friends made my PhD an enriching and enjoyable process. Thank you to all members of the Behavioural Ecology Research Group and the Environmental Decisions Group for creating such a stimulating place to work. My office mates, everyone at the UQ Judo club, Jaime, Tyrone, Amanda, John, Renee, Nick, Andrew, Rachel, and many others kept me company and helped out in stressful times. Finally, Sandra, I am indebted for your patience and affection. I’m lucky to have someone so supportive and understanding.

My undergraduate advisors Cesar and Livia encouraged me to seek a PhD overseas, and obviously I wouldn’t be here without the support from my family. I’ve always enjoyed the interest that my brothers Alex and Bruno showed for natural history. Finally, Mom and Dad, thank you for your unconditional support.

Keywords diversification, ecomorphology, extinction risk, lagomorphs, lineages, mammals, phylogeny, primates, rodents

Australian and New Zealand Standard Research Classifications (ANZSRC)

ANZSRC code: 060309 Phylogeny and Comparative Analysis 60%

ANZSRC code: 050202 Conservation and Biodiversity 20%

ANZSRC code: 060311 Speciation and Extinction 20%

Fields of Research (FoR) Classification

FoR code: 0603 Evolutionary Biology 80%

FoR code: 0602 Ecology 20%

Table of Contents

Abstract ...... ii Publications during candidature ...... v Publications included in this thesis...... v Contributions by others to the thesis...... viii Statement of parts of the thesis submitted to qualify for the award of another degree ...... viii Acknowledgements ...... ix Australian and New Zealand Standard Research Classifications (ANZSRC)...... x Fields of Research (FoR) Classification ...... x Table of Contents ...... xi List of Figures and Tables ...... xiv List of Abbreviations used in the thesis ...... xvii Chapter 1. General introduction ...... 1 Phylogeny and extinction ...... 1 Evolutionary history as a predictor of extinction risk ...... 3 Themes in this thesis ...... 4 Thesis overview...... 5 Chapter 2. Phylogenetic correlates of extinction risk in mammals: species in older lineages are not at greater risk ...... 8 Summary ...... 8 Introduction ...... 8 Methods ...... 11 Data sources ...... 11 Statistical Analysis ...... 13 Genus-level analysis ...... 13 Species-level analysis ...... 15 Results ...... 16 Genus-level patterns ...... 16 Species-level patterns ...... 16 Discussion ...... 18 Acknowledgements ...... 20 Electronic supplementary material ...... 22 Chapter 3. Body size and threatened evolutionary history in primates ...... 25

Summary ...... 25 Introduction ...... 25 Methods ...... 28 Species Data...... 28 Spatial data ...... 29 Data Analysis ...... 30 Results ...... 32 Discussion ...... 37 Acknowledgements ...... 40 Chapter 4. Diversity, extinction, and threat status in Lagomorphs ...... 43 Summary ...... 43 Introduction ...... 43 Methods ...... 47 Phylogenetic data ...... 47 Species occurrence data ...... 48 Spatially explicit data...... 49 Phylogeny ...... 50 Principal Components Analysis...... 51 Correlates of extinction risk ...... 52 Correlates of clade richness...... 52 Results ...... 53 Principal Components Analysis...... 55 Correlates of extinction risk ...... 55 Discussion ...... 58 Acknowledgements ...... 63 Appendix: Paleobiogeographic summaries for all lagomorph genera ...... 64 Chapter 5. Likelihood of competition between invasive and native rodents revealed by global comparisons of morphological, ecological and phylogenetic similarity...... 70 Summary ...... 70 Introduction ...... 70 Methods ...... 72 Study taxon...... 72 Ecological similarity ...... 73 Phylogenetic and morphological data ...... 73 Data processing ...... 75

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Ecology and morphology ...... 76 Evolutionary trends ...... 77 Ecomorphological and phylogenetic similarity ...... 78 Ecological similarity and competition ...... 78 Results ...... 79 Ecology and morphology ...... 79 Similarity ...... 82 Evolutionary trends ...... 82 Similarity and competition ...... 83 Competition with introduced rodent species ...... 83 Discussion ...... 85 Acknowledgements ...... 87 Chapter 6. Discussion ...... 90 Global correlates of extinction risk ...... 90 Phylogeny, extinction risk and conservation...... 91 Evolutionary processes ...... 91 Phylogeny and ecology ...... 92 Comparative extinction risk modelling ...... 93 Conclusion ...... 95 Reference list ...... 96

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List of Figures and Tables

FIGURES

CHAPTER 2

Figure 1. Histogram for values of three phylogenetic variables 13

Figure 2. Significant species-level PGLMM results 17

CHAPTER 3

Figure 1. Directed acyclic graphs for six pre-specified cause-effect models linking 34 phylogenetic lineage age and extinction risk

Figure 2. Variable importance measures for the final conditional random forest 35 model

Figure 3. Conditional inference tree of species partitioning by predictors 36

CHAPTER 4

Figure 1. Distribution of lagomorph genus sizes and threat status 45

Figure 2. a) Relationship between the number of genera in each of 26 mammal 54 orders and the number of monotypic species for each order. b) Density distribution of clade sizes for all nodes in the lagomorph phylogeny compared with clade sizes for 1000 simulated trees

Figure 3. Phylogenetic inferences of diversity. Diversity trajectories were inferred 55 for Sylvilagus separately to all other extant lagomorphs, and both curves summed to obtain a total diversity curve.

Figure 4. BPMM results; predicted probabilities of falling into each threat category 58 when the effects of all other variables are kept constant

CHAPTER 5

Figure 1. Morphological characters measured 74

Figure 2. Residual sum of squares between phylogenetically-corrected matrices of 79 species measurements and ecological categories for different lambda values

Figure 3. Bivariate morphospace plots of DF1 and DF2 for discriminant analyses of 80 diet and locomotion type

Figure 4. Probability density functions of distances between species pairs that are 83 known to compete

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Figure 5. Bivariate plot of external and craniodental dissimilarity values showing 84 species predicted to compete with introduced rodents

TABLES

CHAPTER 2

Table S1. Summary of species-level models for mammalian orders 22

Table S2. Summary of species-level models for alternate taxonomic subsets 23

Table S3. Summary of species-level models for biogeographic subsets 23

Table S4. Summary of models for diversification rates and number of threatened 24 species per genus

CHAPTER 3

Table 1. Simplified correlation matrix of pairwise variable correlations on 32 phylogenetically independent contrasts

Table 2. Phylogenetic Path Analysis coefficients for the six pre-specified cause- 34 effect models for the directional relationships between phylogenetic age, body size, range size and extinction risk

Table 3. Simplified correlation matrix of pairwise variable correlations on 36 phylogenetically independent contrasts, including extrinsic factors

Table S1. Variable definitions and data sources 41

Table S2. Misclassified species in final conditional random forest model 42

CHAPTER 4

Table 1. Principal components analysis of environmental variables 56

Table 2. Bayesian Phylogenetic Mixed Model results 57

Table A1. Lagomorph species endemic to islands 65

Table A2. Examination of the properties of the lagomorph phylogeny. Results 67 obtained using alternate phylogeny (mitochondrial gene tree)

Table A3. Bayesian Phylogenetic Mixed Model results obtained using alternate 68 phylogeny (mitochondrial gene tree)

CHAPTER 5

Table 1. Description of morphological characters measured 74

Table 2. Descriptive discriminant analysis results 81

Table 3. Mantel correlation table from CADM analyses between matrices 82 representing phylogenetic and morphological distances

Table S1. * Large csv file plus ancillary data sources, available at web http://dx.doi.org/10.6084/m9.figshare.1054406

Table S2. Species likely to compete with introduced rodents (kde value next to each 88 scientific name, known competitors in bold)

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List of Abbreviations used in the thesis

BPMM Bayesian phylogenetic mixed models

CIC C statistic information criterion cRF Conditional Random Forest cTREE Conditional inference tree

DF Discriminant function d-sep Directional separation

ED Evolutionarily distinct

EDGE Evolutionarily distinct and globally endangered

EOO Extent of occurrence

GE Globally endangered

HII Human Influence Index

IUCN International Union for the Conservation of Nature

NDVI Normalized Difference Vegetation Index

NT Near Threatened

PCA Principal Components Analysis

PGLMM Phylogenetic generalised linear mixed models

PGLS Phylogenetic generalised least squares

PLS Partial least squares

PPA Phylogenetic confirmatory path analysis

YBP Years before present

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Chapter 1. General introduction

At least 77 mammal species worldwide have gone extinct in the past 500 years and recent estimates place between 21 and 25 percent of extant mammal species as threatened (International Union for Conservation of Nature, 2012). The risk of extinction for living species is defined by interactions between a species' intrinsic biological and ecological traits, geography, and a variety of environmental and anthropogenic factors (Fisher et al., 2003). Extinction risk is generally based on quantitative criteria in a temporal context, to reflect the likelihood of a species disappearing. For example: rapidly declining species with small populations occurring in reduced ranges are at higher risk than species that are widely distributed, abundant and have stable or increasing populations (Mace et al., 2008).

Because closely related species tend to share many biological traits as a result of their common ancestry, groups of related species with traits that increase their extinction proneness are at higher risk (Gaston & Blackburn, 1997a). Finding the biological traits that separate species at high risk from related but non-threatened species is a key issue in conservation biology (Safi & Kerth, 2004). Although extinction risk is a non-phenotypic, non-heritable, species attribute that does not evolve (Grandcolas et al., 2010), it correlates with heritable traits, and often shows a phylogenetic signal (Harcourt, 2005). It is therefore necessary to study patterns of extinction within a phylogenetic framework (Fisher & Owens, 2004). Understanding extinction vulnerability can inform proactive conservation efforts and avoid reactive approaches (McKinney, 1997).

Phylogeny and extinction

Conservation science now acknowledges the need to understand and preserve not just individual species, but also the evolutionary processes by which biodiversity arises and persists (Mace et al., 2003). This requires an understanding of the relationships between phylogeny and extinction, and the mechanisms that lead to these relationships. Studies of extinction that consider the evolutionary relationships among species usually incorporate phylogenetic information in three ways:

Using phylogenetic regression methods to test hypotheses that relate extinction risk to species traits

Evolutionarily related organisms resemble each other in their tendency to high extinction risk (Purvis et al., 2000a; Purvis, 2008). In mammals and other vertebrates, species traits such as larger body size, lower fecundity, narrow ecological niches and small geographic ranges are the most important predictors of vulnerability (González-Suárez & Revilla, 2013), and they are known to show a strong phylogenetic signal. Phylogenetically-informed statistical methods were first developed to avoid pseudoreplication when testing if species traits are correlated (Felsenstein, 1985). Phylogenetic comparative methods are widespread in extinction risk studies; their use helps to avoid Type I errors which would result from pseudoreplication of raw species values, and corrects for phylogenetic signal in the response variables. These correlational analyses can identify how changes in life history attributes during evolutionary history place species at relatively greater risk.

Testing for phylogenetically nonrandom patterns of extinction

If extinction is nonrandomly distributed amongst species, species that go extinct might not have surviving close relatives (Nee & May, 1997; Vamosi & Wilson, 2008). Observed patterns of extinction and threat show significant clustering when compared with randomized distributions across phylogenies. In birds, mammals, reptiles, and amphibians, the taxonomic distribution of extinction is almost always nonrandom, clustering in certain genera and families, and frequently in species-poor taxa (Bennett & Owens, 1997; Russell et al., 1998; Purvis et al., 2000a; Bielby et al., 2006; Fritz & Purvis, 2010a).

Determining if phylogenetically nonrandom extinction leads to loss of evolutionary history

Global species extinctions are often used to assess the extent of large-scale biodiversity loss (Collen et al., 2010). However, the evolutionary depth of different patterns of extinction is not apparent without phylogenetic information. Similar numbers of species losses can entail different losses of evolutionary history (Erwin, 2008). A number of metrics have been developed to measure the differences between species in terms of their shared evolutionary history (Faith, 1992; Faith & Baker, 2006; Redding & Mooers, 2006). Such metrics can be used to guide conservation priorities, to target species with longer periods of independent

evolution and few extant close relatives (Heard & Mooers, 2000; Isaac et al., 2007; Collen et al., 2011a; Safi et al., 2013; Jetz et al., 2014). Species in these lineages represent a greater amount of unique evolutionary history than recently evolved and species-rich taxa with faster diversification rates. They are thought to possess genotypic and phenotypic characteristics not found elsewhere in the tree of life (Redding et al., 2008).

More evolutionary history is lost when extinction is concentrated in groups with unusual evolutionary pathways and greater contribution to diversity. This is suggested to occur in primates (Purvis et al., 2000a), birds (von Euler, 2001), and marsupials (Johnson et al., 2002). For example, several marsupial families with only one living species such as the thylacine and numbat have become extinct or severely declined in the last 150 years (Johnson et al., 2002).

Evolutionary history as a predictor of extinction risk

Phylogenies can reveal more than just evolutionary relationships: they also contain information on ecological and evolutionary processes through time. Assuming a proportional relationship between divergence and time, genetic or morphological differences between species can be converted to approximate times since divergence, and then constrained by data from the fossil record for accuracy. The temporal spacing of nodes in a phylogenetic tree can reveal changes in diversification rates through time, and identify differences in the underlying probabilities of a lineage diversifying (Purvis et al., 2011).

Studies of several vertebrate and plant groups (Bennett & Owens, 1997; Gaston & Blackburn, 1997a; Russell et al., 1998; Jennings et al., 1999; Johnson et al., 2002; Meijaard et al., 2008; Vamosi & Wilson, 2008) have found that taxa from older and species-poor (more distinct) phylogenetic lineages are at greater risk of extinction. The opposite result, that faster- evolving and speciose lineages are more prone to extinction, was found in South African plants (Davies et al., 2011). These studies proposed a variety of mechanisms to explain the generally positive association between lineage age and risk of extinction. First, extinction due to chance events is more likely over time (Raup, 1981). Second, specialization is known to correlate with extinction risk (Colles et al., 2009) and older lineages may contain more specialized species that occupied marginal ecological niches early on, or that had more time to evolve a specialized ecology or morphology (Nosil & Mooers, 2005). Alternatively, older

lineages might be more robust if greater taxon age reflects better survival (Gaston & Blackburn, 1997a).

Themes in this thesis

My research in this thesis is directed towards improving our understanding of how evolutionary history relates to present day threat status in mammals. All the thesis sections include a number of recurring themes, relating concepts from ecology, biogeography, and conservation biology to evolutionary history. By considering ecological specialization, body size, geographic range and exposure to threatening processes in the analyses, phylogenetic correlates of extinction risk can be interpreted robustly and applied to other fields such as conservation planning and macroecology. These species attributes have a strong phylogenetic component, and are all known to play multiple roles in shaping species’ fates.

Ecology and life history

Body size has a well-known positive association with extinction risk for several mammal groups, but its value as a predictor comes from correlations with other life history traits more directly tied to persistence. Body size correlates with traits that determine how a species can recover from environmental changes, direct mortality or habitat degradation, but it may also correlate with phylogenetic lineage age. One direct effect that body size has on threat status relates to the preferential hunting or larger animals, perhaps because they are more conspicuous and provide a higher return for the effort associated with hunting them.

An important evolutionary generalization extracted from the mammalian fossil record is the tendency for some groups to evolve toward larger body size over time (Cope, 1887; Stanley, 1973; Monroe & Bokma, 2010). Body size is used as a covariate throughout the analyses in this thesis because even if size increase over evolutionary time scales is debated, it may cause spurious correlations between phylogeny and extinction risk.

Ecological specialization is a known predictor of both extinction risk and clade richness. Narrow habitat or diet breadths make ecological specialists susceptible to extrinsic threatening factors (Fisher & Owens, 2004; Williams et al., 2009; Fisher & Blomberg, 2011), and specialization to narrow niches leads to lower long term extinction and origination rates (Purvis et al., 2011). In cases for which specialization leads to increased extinction but

reduced speciation, specialization should be phylogenetically recent, because specialists would disappear too fast to leave a phylogenetic trace (Colles et al., 2009).

Geography

Post-speciation changes in geographic range size are not entirely random (Pigot et al., 2012); some older lineages of birds and mammals appear to have smaller range sizes than younger ones (Gaston & Blackburn, 1997b; Webb & Gaston, 2000; Jones et al., 2005a). Geographic range size can be simultaneously considered as a response to extrinsic factors or as a species attribute because: a) current range estimates may represent significantly smaller areas than those originally occupied before substantial human-mediated declines (Hanna & Cardillo, 2013) or b) species are habitat specialists, have naturally reduced populations, or occur in isolated places as a result of their evolutionary history.

A narrow distribution generally means higher extinction risk and this needs to be addressed in statistical models. In addition to having biological traits associated with greater extinction risk, older or younger lineages may be also exposed to more numerous or more severe extrinsic threats due to their spatial distribution. Closely related taxa usually occur close together, and exhibit varying degrees of niche conservatism (i.e. the tendency for species to retain their ancestral traits, resulting in closely related species being more ecologically similar than would be expected based on their phylogenetic relations). Different clades have different spatial centres of diversity; and older clades could be concentrated in locations where threats occur simply by chance (Purvis, 2008). If lineage ages follow the same latitudinal gradient of mammals as a whole and older lineages occur more frequently in the tropics, they could occur in regions that face profound socioeconomic difficulties that cause widespread habitat loss and degradation. The effects of extrinsic factors are known to vary according to each species' inherent biology, or they may be catastrophic enough to cause indiscriminate extinction regardless of any variation in the intrinsic biology or evolutionary history of the affected taxa (Russell et al., 1998).

Thesis overview

The first analysis section (Chapter 2) summarizes the existing literature on the study of phylogeny and extinction risk, and includes a rigorous statistical test for relationships between phylogenetic lineage age, evolutionary distinctiveness, clade size, diversification rates and extinction risk for terrestrial mammals. The key finding in this section is a clear

lack of significant global or regional associations, defined by mammalian biogeographic regions. Clade-specific processes may drive divergent patterns of extinction risk, and analysing taxonomic subsets of the global data revealed three significant relationships within taxonomic groups. Extinction risk increases for evolutionarily distinctive primates and decreases with lineage age when lemurs are excluded. Lagomorph species that have more close relatives are less threatened. Species in older, slower evolving and distinct lineages are less numerous than their rapidly diversifying or speciose counterparts, but they are not more threatened or extinction-prone.

Chapter 3 aims to explain the result from Chapter 2, that primate species in both younger and evolutionarily distinct lineages are more likely to be threatened. This section identifies the drivers of extinction risk for primates, and investigates how they relate to evolutionary history. Here, I explore the mechanisms that define species’ extinction risk. Human presence, land use, and the condition of species’ habitats are the main variables that predict primate threat status, and body size mediates species’ responses to the effects of these processes. The relationship between phylogeny and extinction risk in primates is mediated by the trend of increasing body size through time, which may result from a passive trend, or from possible selective advantages of larger size. Evolutionarily distinct species tend to be smaller-bodied and threatened mostly by habitat loss and fragmentation. Species in younger lineages tend to be larger and have smaller range sizes. Lemurs primarily drive the result of higher risk for distinct species, given the high proportion of species threatened in this ancient and endemic clade.

In Chapter 4, I investigate and explain why living lagomorphs belonging to species-poor genera are at particularly high risk of extinction - an unusual pattern among mammalian orders identified in Chapter 2. I build on the hypothesis that species-poor clades are geographically or ecologically marginal - and that marginal species are more likely to be at risk. This section incorporates other properties of the mammal phylogeny such as the shape and the frequency distribution of clade sizes to provide more clues on the taxonomic imbalance within rabbit and hare genera. I evaluate climatic variables, human population density, anthropogenic habitat conversion, sympatric species richness, and geographic range size as potential predictors of threat status, evolutionary distinctiveness, and genus size. Threatened species tend to occur in areas where human population densities are high or habitat-conversion levels are low, but none of the other variables predicted threat status,

evolutionary distinctiveness, or genus size. An additional review of the paleobiography of all living lagomorph genera is included, since data on living species may not reflect the effects of past environmental changes and high extinction fractions within certain lineages.

Chapter 5 introduces a new line of research: expanding the use of phylogenies in ecology and conservation. Closely related species are hypothesized to have similar morphologies and ecologies. To investigate this principle, I quantify the ecological similarity between rodent species, using morphological characters measured from museum specimens in order to compare ecological similarity with phylogenetic relatedness in rodents globally. The set of cranial, skeletal and external characters measured predict ecological attributes such as diet and locomotion type, so I conclude that morphological similarity relates to ecological similarity. Close relatives have similar ecologies, but biotic interactions recorded in the literature are predicted better by morphological traits. These results can inform attempts to relate phylogenetic diversity to functional diversity, and are relevant to conservation because we can predict competition between introduced and native rodent species that may not necessarily be closely related.

Chapter 2. Phylogenetic correlates of extinction risk in mammals: species in older lineages are not at greater risk

This chapter is published as a research article in Proceedings of the Royal Society B: Biological Sciences (2013), and is reprinted with permission. It is cited in other chapters of this thesis as Verde Arregoitia et al. (2013).

Summary

Phylogenetic information is becoming a recognized basis for evaluating conservation priorities, but associations between extinction risk and properties of a phylogeny such as diversification rates and phylogenetic lineage ages remain unclear. Limited taxon-specific analyses suggest that species in older lineages are at greater risk. We calculate quantitative properties of the mammalian phylogeny and model extinction risk as an ordinal index based on IUCN Red List categories. We test for associations between lineage age, clade size, evolutionary distinctiveness and extinction risk for 3308 species of terrestrial mammals. We show no significant global or regional associations, and three significant relationships within taxonomic groups. Extinction risk increases for evolutionarily distinctive primates and decreases with lineage age when lemurs are excluded. Lagomorph species (rabbits, hares and pikas) that have more close relatives are less threatened. We examine the relationship between net diversification rates and extinction risk for 173 genera and find no pattern. We conclude that despite being under-represented in the frequency distribution of lineage ages; species in older, slower-evolving and distinct lineages are not more threatened or extinction- prone. Their extinction however would represent a disproportionate loss of unique evolutionary history.

Introduction

Past extinctions and current extinction risk are not distributed randomly among taxa, and analyses of extant species have identified various factors that explain the selectivity of extinction risk (Purvis et al., 2000a; Purvis et al., 2000b). However, these types of studies often overlook the role of evolutionary history as an explicit predictor. Studies of several vertebrate groups (Bennett & Owens, 1997; Gaston & Blackburn, 1997a; Russell et al., 1998; Jennings et al., 1999; Purvis et al., 2000a; Johnson et al., 2002; Meijaard et al., 2008;

Redding et al., 2010) have found that taxa from older and species-poor lineages are most likely to face extinction. Davies et al. (Davies et al., 2011) found the opposite pattern in South African plants: fast-evolving and speciose lineages are more prone to extinction. Selectivity in extinction risk could be explained by evolutionary history, represented by a clade’s size and age. Older species usually occur in depauperate clades, which result from reduced geographical space, elevated extinction or low speciation (Purvis et al., 2011).

In the early 20th century, evolutionary biologists proposed that species in long-lived lineages were rare and ultimately fated with extinction (see Fiedler, 1986). Early analyses of survivorship curves from the fossil record found that the extinction probability of a taxon is independent of its age (Van Valen, 1973; Boyajian, 1991). Reinterpretation of paleontological studies by evolutionary geneticists briefly suggested that gene pools may introduce inferior morphotypes as they age (Ward, 1992).

Several mechanisms to explain the purported positive association between lineage age and risk of extinction have been proposed. First, extinction probability might stochastically increase through time (Gaston & Blackburn, 1997a; Pearson, 1998). Second, specialization is known to correlate with extinction risk (Arita et al., 1990; Williams et al., 2009). Older taxa might be more specialized through early occupation of fringe niches, phylogenetic constraint, or by having more time to evolve a specialized ecology, behaviour, or morphology (Nosil, 2002; Nosil & Mooers, 2005). Specialization to narrow adaptive zones reduces the likelihood of radiation, and of per-species background extinction in stable niches. This seems to be the case for relict mammal clades (e.g. monotremes (platypus and echidnas) and xenarthrans (anteaters, armadillos, and sloths)) (Purvis et al., 2011). Third, slow reproductive rates that evolve early in a lineage’s history and show a strong phylogenetic signal might limit the capacity for recovery under increased mortality from anthropogenic pressures (Bennett & Owens, 1997; McKinney, 1997).

Conversely, older lineages might be more robust if greater taxon age reflects better survival ability and resilience (Gaston & Blackburn, 1997a). An analysis of multiple animal datasets in the fossil record found that older lineages are closer to an average morphology, ecologically more generalist and able to survive a greater range of environmental changes (Liow, 2007). A third distinct possibility is the lack of any relationship. Perhaps those taxa susceptible to extrinsic stresses are already extinct and no pattern is evident. Turvey and Fritz (Turvey & Fritz, 2011) found evidence of an extinction filter operating in the Holocene, and

consequently some regional mammal faunas seem less threatened because the intrinsically susceptible species are extinct. If no relationship between lineage age and extinction risk exists, the taxonomic clustering of extinction biasing traits must not relate to a species’ lineage age. Robust or susceptible species would not be overrepresented in any part of the age distribution of extant species.

Mammals have high ecological, economic and social value (Fritz & Purvis, 2010c; González- Suárez et al., 2012). We focus on mammals because correlates of extinction risk have been investigated extensively using phylogenetic comparative studies. Life history datasets and phylogenies are available (González-Suárez et al., 2012), and studies incorporating paleontological data (Turvey & Fritz, 2011) (Mooers et al., 2009) enable interpretation of contemporary patterns on an evolutionary time scale.

If older lineages are intrinsically more extinction-prone, we expect lineage age to correlate positively with extinction risk in extant mammals, given the prevalence of external stresses and high proportion of threatened species. Patterns in this relationship might vary with geographical differences in mammalian diversity and threats. Dubey and Shine (2010) found spatial disparity in the mean species ages of reptiles and amphibians. At similar latitudes, species from the Southern Hemisphere are older than species from the Northern Hemisphere. Geographical disparity in mammalian lineage ages also exists in modern mammal assemblages because of historical changes in climate and topography. The prevalence of modern threats varies significantly among mammalian orders (Mace & Balmford, 2000), as well as the taxonomic clustering of different threat types. Fritz and Purvis (Fritz & Purvis, 2010b) found that the phylogenetic pattern of risk caused by harvesting is more strongly clumped than for species threatened by habitat loss or invasive species.

Phylogenies can be analysed to inform macroevolution (Purvis et al., 2011). The temporal spacing of nodes reveals changes in diversification rates over time (Bininda-Emonds et al., 2008), and asymmetries in a phylogeny illustrate how clades vary in their underlying probabilities of diversifying (Purvis et al., 2011). Genetic or morphological differences between species can be used to estimate approximate branching times, constrained by dates from the fossil record. Evolutionary age and clade size can be combined to calculate evolutionary distinctiveness. Evolutionarily distinct species have few living close relatives, slower diversification rates, greater lineage ages, and are known to have experienced greater levels of extinction, leading to imbalance in the phylogeny (Collen et al., 2011a).

Phylogenetic age, clade size, diversification and distinctiveness reflect key aspects of a species’ evolutionary history and allow us to analyse modern extinction risk at the species level.

Similar numbers of species extinctions can cause disparate losses of evolutionary history and potentially, uncommon phenotypic and functional diversity (Cadotte & Davies, 2010; Redding et al., 2010). Measures of taxonomic distinctiveness have implications for modern conservation practice. For example, the Zoological Society of London’s EDGE (Evolutionarily Distinct and Globally Endangered) (Redding & Mooers, 2006; Isaac et al., 2007; Collen et al., 2011a) initiative highlights and protects threatened species that represent unique evolutionary history. This system ranks species in terms of evolutionary distinctiveness (ED) and global endangerment (GE). So far, no association has been found between ED and GE in analyses of birds and primates (Redding & Mooers, 2006; Redding et al., 2010).

This paper investigates the relationship between phylogeny and the selectivity of extinction risk for extant and 14 recently extinct terrestrial non-volant mammal species. We build models at a global scale and for different taxonomic groups and geographic regions. We test for associations between phylogenetic age, genus size and evolutionary distinctiveness, net diversification rate, and extinction risk.

Methods

Data sources

We focused only on terrestrial, non-volant mammals. We followed the International Union for Conservation of Nature (IUCN) Red List (International Union for Conservation of Nature, 2012) nomenclature, and excluded data deficient, domestic and taxonomically uncertain species. The final dataset of species traits included 3294 extant species and 14 species that became extinct after 1800, appear in the chosen phylogeny and have information on geographic distribution (Supplementary dataset 1). We collected data on body size as adult mass in grams, following the methods in the PanTHERIA database (Jones et al., 2009) for calculating measures of central tendency.

Body size is frequently associated with vulnerability to extinction (Collen et al., 2011b), mainly due to its correlation with several other traits that are more directly tied to persistence

(e.g., speed of life history, home range size, conflict with humans) (Burton et al., 2011). To avoid spurious results we incorporated body size as a covariate in all of our analyses. We focused on obtaining body size information for all the species, supplementing information in the PanTHERIA (Jones et al., 2009), MOM 4.1 (Smith et al., 2003) and Morgan (Ernest, 2003) datasets with recently published, unpublished, museum, and grey literature data (Supplementary dataset 2). Body size data for 202 species could not be located (Supplementary dataset 3), so we imputed the missing values to avoid biases originating from casewise removal of species with missing data (Fisher et al., 2003). We applied Nonparametric Missing Value Imputation using Random Forests implemented in the R package missForest (Stekhoven & Bühlmann, 2011), using a random forest trained on the observed values to predict the missing values. We completed datasets of 17 mammal families that included species with no body size information, and evaluated the statistical deviations of the imputed datasets relative to the complete datasets and to randomly guessed values following Pantanowitz and Marwala (2009). We kept all imputed values, since the missing data imputation did not have a significant negative impact on the statistical properties of the data (Mean, 1st Quartile, Median, 3rd Quartile, Standard Deviation, Variance, Combined MSE, Mean Mahalanobis Distance, Linear Correlation with Target Set, and Maximum Percentage Deviation).

We obtained phylogenetic age estimates defined as branching times from sister taxa, from an update (Fritz et al., 2009) to a dated and calibrated species-level composite supertree of mammals (Bininda-Emonds et al., 2008). We defined genus size as the number of congeners, and used the evolutionary distinctiveness (ED) metric from the 2011 EDGE list (Collen et al., 2011a). We collected net diversification rates from Soria-Carrasco and Castresana (Soria- Carrasco & Castresana, 2012) for mammalian genera identified as monotypic in the same supertree (Fritz et al., 2009), calculated assuming no extinction, or a high extinction fraction.

We used the IUCN Red List status as our response variable of extinction risk. For species- level models, we converted the threat categories to an ordinal index from Least Concern (zero) to Extinct (five). For genus-level analyses, we used the Red List categories to define species as threatened or non-threatened to count the number of threatened species or calculate the proportion threatened per genus (number of threatened species divided by genus size). We counted those species considered Vulnerable, Endangered, Critically Endangered, or Extinct as “threatened”, and species classified as Least Concern or Near Threatened as “non- threatened”.

Statistical Analysis

All analyses were carried out in R v. 2.15.2 (R Development Core Team, 2012). An exploratory analysis of the frequency distribution of mammalian species’ lineage ages, evolutionary distinctiveness scores, and genus sizes revealed a right skew for all three variables (Figure 1). We initially tested for associations at the genus level and then focused on relationships at the species level.

600 not threatened threatened

600 400 400

400

number of species 200 200

200

0 0 0

0 20 40 60 0 20 40 60 0 20 40 60 phylogenetic age evolutionary distinctiveness genus size (branching times [mya]) score (number of congeners)

Figure 1. Frequency distributions of the quantitative variables derived from the phylogeny, showing threatened species per bin.

Genus-level analysis

Past extinctions are less likely to cause misleading branch length values at supraspecific taxonomic levels and at a global scale than at the species level and local scales. Missing taxa lead to overestimated branching times (Gittleman et al., 2004) and extinctions of entire genera are less common than species extinctions (McKinney, 1997). However, more higher- order taxon losses than expected by chance are expected under the current extinction regime (McKinney, 1998; Purvis et al., 2000a). Initial tests for global patterns followed Johnson et al. (2002), including the transformation of lineage ages and genus sizes to base two

logarithms. We calculated mean body size, genus age and size, and evolutionary distinctiveness for a global set of 896 genera and quantified extinction risk as the proportion of species threatened. We examined the relationships between extinction risk and the phylogenetic variables using Generalized Additive Models (logit link function, binomial distribution of variance, and an extra penalty added to each smooth term so that it can be penalized to zero) using the R package mgvc (Wood, 2011). This approach treats genera as independent, yet several groups of two or more genera arise from the same nodes in the phylogeny. We repeated the analyses using phylogenetic generalized linear mixed models (PGLMM) to incorporate phylogenetic information as a covariance matrix representing the amount of shared evolutionary history between taxa.We fit the models with the R package MCMCglmm (Hadfield, 2010).

For all PGLMM analyses, we used an uninformative prior for the random effect (see Rutkowska et al., 2012) and ran each chain for 555000 iterations with a thinning value of 500 after a burn-in of 50000, resulting in 1000 samples. All diagnostics of convergence of PGLMM parameters followed Rutkowska et al. (2012) using the Gelman-Rubin statistic. Potential scale reduction values were all < 1.1 among three parallel MCMC chains for models with different starting values (Gelman & Rubin, 1992) and the autocorrelations of posterior samples were all < 0.1. Effective sample sizes for all fixed effects were all >800. We report the posterior distributions from the three pooled chains as our parameter estimates, and considered fixed effects statistically significant when the 99% Credible Intervals (Highest Posterior Density) did not include zero.

Of the 896 genera with data, only 440 could be clearly identified as nodes in the phylogeny. We modeled the number of species threatened in a genus as a binomial response, treating species classified in the IUCN Red List as Extinct, Critically Endangered, Endangered, and Vulnerable as “threatened”, and species listed as Near Threatened and Least Concern as “not threatened”.

We analyzed the association between diversification rates and threatened species per genus separately, assuming no extinction or high extinction, and including body size as a covariate. We used the values from Soria-Carrasco and Castresana (Soria-Carrasco & Castresana, 2012) for 173 genera and modeled extinction risk as a binomial response. In reality no clear line separates threatened and non-threatened species (Robbirt et al., 2006). Species listed as Near

Threatened (NT) have been considered as both “threatened” and “not threatened” in previous studies (see (Agnarsson et al., 2010) and (Mooers et al., 2008)). To test whether changing the threat threshold influences our results, we repeated all genus-level analyses, considering NT species as not threatened, threatened, and excluding NT species altogether.

Species-level analysis

Threatened species within a genus may differ widely in their risk level and body sizes, so we also performed analyses for species. The amount of difference in a species’ actual extinction risk probably varies between threat categories, which are separated by unequal distances along the underlying continuous variable that they measure (Purvis et al., 2005; Matthews et al., 2011). We used phylogenetic generalized linear mixed models (PGLMM) to model extinction risk as an ordered response. Residual variance cannot be identified in ordinal probit models, so it was set at a fixed arbitrary value of one in the prior specification for the variance components of the fixed effects.

We tested for associations at the global scale using the full dataset of 3308 species. To address our biogeographic, taxonomic and ecological hypotheses, we then built models with species subsets defined by global biogeographic regions, orders, and for monophyletic groups of two or more orders with similar ecologies (Van Valen, 1971). We trimmed the complete phylogenetic tree to match the species subsets used for each model when creating the covariance matrices. We used species distributions from the IUCN spatial dataset (International Union for Conservation of Nature, 2012) and a digitalized map of mammalian zoogeographic regions (Cox, 2001) to divide the global species list into spatial subsets that only included those species that occur exclusively within the region boundary. We divided all mammals into eleven orders (Afrosoricida, Carnivora, Cetartiodactyla, Dasyuiromorphia, Didelphimorphia, Diprotodontia, Eulipotyphla, Lagomorpha, Perissodactyla, Primates, Rodentia) and six monophyletic groups: Afrotheria, Euarchonta, Glires, marsupials, ungulates and xenarthrans. We repeated the analysis of our primate dataset excluding lemurs (Lemuroidea, families Daubentoniidae, Indriidae, Lemuridae, and Lepilemuridae) to ensure that any result would not be entirely driven by this ancient, distinct and endemic clade with one of the highest levels of threat recorded for any vertebrate group (Spathelf & Waite, 2007; 2010).

Results

Genus-level patterns

Extinction risk increased with body size in the initial genus-level analysis for global data (Χ 2 = 173.63, estimated df = 8, n= 896, p < 0.0001) and in the PGLMM analysis that accounted for phylogenetically structured data (Table S3, electronic supplementary material). We found no relationships between genus age or distinctiveness and extinction risk.

We found no significant associations between net diversification rates and the number of threatened species in a genus (Table S4, electronic supplementary material) for both estimates (with or without extinction). The parameter estimates from both of our models are almost identical. Soria-Carrasco and Castresana (Soria-Carrasco & Castresana, 2012) found that both rate estimates are highly correlated and suggest that assuming extinction has little impact on comparative analyses.

Different “threatened” vs. “non threatened” thresholds for binomial models did not alter the parameter estimates or nature of the relationships, and resulted in reduced sample sizes when excluding Near Threatened species. Using the proportion or number of threatened species per genus has the disadvantage of losing the detail that a species-specific assessment provides, especially for smaller genera. This measure is sensitive to varying definitions of genus that may ultimately affect genus sizes in a quantitative analysis. The issue is confounded by the use of arbitrary taxonomic units that are not based on biological principles and might not be monophyletic.

Species-level patterns

Body size was the only significant predictor of extinction risk in the global model (Figure 2a) and in most models for spatial and taxonomic subsets (Tables S1-S3, electronic supplementary material). We found significant effects for phylogenetic variables in three taxonomic subsets (Table S1, electronic supplementary material): extinction risk increases with evolutionary distinctiveness for primate species (Figure 2b), and lagomorph species (rabbits, hares, and pikas) in less speciose genera have higher probabilities of being classified as threatened (Figure 2c). Evolutionarily distinct primate species are more likely to be Critically Endangered than Least Concern. Lagomorph species in more speciose genera have the highest probability of being listed as Least Concern, while the probability of being

classified into the higher risk categories decreases with increasing number of species in the genus. We found no significant association between Evolutionary Distinctiveness and extinction risk once we excluded lemurs from the primate data. However, we found a significant negative association between phylogenetic age and extinction risk (Table S1, electronic supplementary material) in non-lemur primates. The probability of being classified as Least Concern category is greater with increasing phylogenetic age, and older species have lower probabilities of being classified as Endangered or Critically Endangered (Figure 2d).

(a) (b)

1 1 EX CR 0 1 0 1 CR 0 EN 1 0 EN 1 0 1 VU

0 VU 1 0 1 NT NT extinction risk category extinction risk category extinction 0 0 1 1 LC LC 0 0 0 5 10 15 0 1 0 20 3 0 4 0 50 log(body mass) Evolutionary Distinctiveness Score (c) (d)

1 1 CR CR

0 0 1 1 EN EN

0 0 1 1 VU VU

0 0 1 1 NT NT

extinction risk category extinction 0 risk category extinction 0 1 1 LC LC

0 0 01234 5 0 1 2 3 4 5 6 log2(genus size) log2(phylogenetic lineage age)

Figure 2. Effects of body size, evolutionary distinctiveness and genus size on the probabilities of falling into different extinction risk categories when the effects of other variables are held constant for (a) mammal species globally (b) lagomorphs (c) primates and (d) lagomorphs excluding lemurs. Confidence intervals (95%) are shaded in grey.

Discussion

The phylogenetic traits we chose to reflect a species’ evolutionary history do not generally predict extinction risk in mammals. We suggest that in mammals, there is an overall lack of biologically meaningful associations between evolutionary age, distinctiveness, clade size, diversification and any known extinction-biasing traits, such as body size or ecological versatility, even when these traits have a strong phylogenetic signal (Fritz et al., 2009).

Net diversification rates for genera had no association with extinction risk (Table S4, electronic supplementary material). Certain clades are characterized by either high diversity and rapid diversification (e.g. carnivores) or low diversity and systemic diversification rate slowing (e.g. Afrotheria, Perissodactyla) (Purvis et al., 2011). We did not find any general patterns in the species-level analyses of major lineages to suggest that historical differences in diversification influence current extinction risk.

Evolutionary Distinctiveness (ED) predicted extinction risk in primates (Figure 2b), a well- studied group with several known predictors of threat (Cowlishaw et al., 2009; Matthews et al., 2011). Redding et al. (2010) found that evolutionarily distinct primate species are ecomorphologically odd and geographically peripheral. The mechanisms that make trait oddness and distance from continental centroid significant drivers of extinction risk for evolutionarily distinct species are unknown, but our results point to lemurs driving the relationship between ED and threat status. We found no significant relationship once we excluded lemurs from the primate analysis (Table S1, electronic supplementary material). The pattern of higher risk for younger species of primates (excluding lemurs) is novel for vertebrates (Figure 2d).

If younger taxa occupy a smaller geographic range or adaptive space, they may be more sensitive to small-scale environmental perturbations, and more susceptible to extinction (Boyajian, 1991). Models in Purvis et al. (Purvis et al., 2000b) underestimated threat status for primates in tropical countries with especially high levels of deforestation, and concluded that species with lower risk than predicted by intrinsic traits occur in large areas of conserved habitat. Spatially and taxonomically nonrandom patterns of threat may explain our result of decreasing extinction risk with increasing lineage age for primates (excluding lemurs). For example: bushbabies (family Galagidae) are in an older lineage with no threatened species

out of 29. Threat level in the recent and diverse radiation of colobine monkeys (Subfamily Colobinae) exceeds 70% (40/53), including several Critically Endangered island endemics.

Unlike most mammalian lineages, lagomorphs exhibit higher species diversity in the fossil record than in the present, suggesting an ongoing decline in diversity (Lopez-Martinez, 2008). Eight out of 13 (61%) extant genera of lagomorphs are monotypic and six of these (75%) are threatened. Mooers et al. (Mooers et al., 2009) found that species in depauperate clades experienced disproportionately high extinction during the Holocene, and attribute the disparate loss to the island effect. Except for the extinct Sardinian pika Prolagus sardus (International Union for Conservation of Nature, 2012), no paleontological data indicates elevated levels of extinction for this group (Lopez-Martinez, 2008). Lagomorph extinctions were similarly minimal in the Late Pleistocene, when mammal extinctions preferentially affected large-bodied mammals (Cardillo et al., 2005). Our analysis of current extinction risk may have identified the ongoing decline in lagomorph diversity (Figure 2c). The decline in perissodactyl diversity evident in the fossil record, the clade’s downshifted diversification rate that is also predicted from branching models (McKinney, 1998; Bininda-Emonds et al., 2008; Lopez-Martinez, 2008; Purvis et al., 2011), was not evident in our results. A negative association between genus size and extinction risk supports the notion that taxa with more species may have phenotypes or ecologies that cause higher diversification rates (McPeek & Brown, 2007). Taxon size could relate to robustness towards external threats, efficient niche partitioning, strong dispersal abilities or wider geographic distributions and niche breadths, while specialization to narrow adaptive zones is perhaps the best predictor of species poor clades (Purvis et al., 2011).

The frequency distribution of mammalian taxon ages and distinctiveness values is right- skewed (Figure 1). We propose that this has led to misleading interpretations of the effect of taxon antiquity on modern extinction risk. Few living species are ancient or extremely distinct, yet most of these are not threatened with extinction. Species at the tail end of the age distribution are not intrinsically more susceptible to extinction, or more threatened than younger species. The significant negative relationship between phylogenetic age and extinction risk supports our view. We suspect that this right skew in lineage ages reflects the biased ratio between extinction and speciation in deep time, but extinction rates should not be estimated from molecular phylogenies (Purvis et al., 2011). Branching times from a molecular phylogeny do not reflect species lifespans or stratigraphic durations, and might not represent true node ages times when the phylogeny is built from relationships between extant

taxa (Johnson et al., 2002). We chose the phylogeny with the highest taxonomic coverage and a consistent dating process, based on multi-gene alignment and cladistically robust fossil calibration points (Bininda-Emonds et al., 2008). Our results from the genus-level analyses agree with the species-level models, supporting our conclusion that there is a general lack of significant associations between taxon age and extinction risk.

Treating the IUCN Red List categories as an ordered factor in analyses that correct for phylogenetic inertia provides a powerful method for understanding extinction risk. Ordered threat categories help to guide priorities for conservation investment among species, and produce a series of recommendations for conservation action for each category (Rodrigues et al., 2006). Our approach can identify trends within each threat category, and it avoids losing information by aggregating classifications into dichotomous variables. We avoided elevated type I error rates caused by not preserving the variance structure of the original ordinal ranks (Matthews et al., 2011) when assuming that categories are evenly spaced and continuously varying.

Large databases of life history traits (see González-Suárez et al., 2012), extinction risk assessments (International Union for Conservation of Nature, 2012), and species-level phylogenies (e.g. Fritz & Rahbek, 2012) for other vertebrate classes (birds, fish and amphibians) are increasingly available for comparative analyses. The methods in this study may be applied to investigate the role of phylogenies in the extinction risk patterns of taxa with different evolutionary dynamics.

Although we conclude that evolutionary history has no consistent association with extinction risk, prioritization methods that combine threat status and evolutionary history are critical because anthropogenic threats are increasingly pervasive regardless of species’ intrinsic traits (Fisher et al., 2003). Widespread threats like habitat loss, invasive species and overkill are sampling more of the taxon age distribution, including distinct and ancient species (Boyajian, 1991).

Acknowledgements

We thank the editor and two anonymous reviewers for comments that improved an earlier version of the manuscript. We are grateful to Nels Johnson for comments on ordinal regression, Jeffrey O. Hanson for assistance with R scripting, Kerrie A. Wilson for comments on manuscript structure, and N. Wood, J. Charters and K. Garland for help in error checking

the taxonomic and body size databases. LDVA was supported by funding from Consejo Nacional de Ciencia y Tecnología (CONACYT) scholarship 308685. DOF was supported by Australian Research Council fellowships DP0773920 and FT110100191.

Electronic supplementary material

Afrosoricida Carnivora Cetartiodactyla Dasyuromorphia (38) (226) (204) (63) intercept -1.59784 -2.43684 * -1.11322 -3.44535 * age -0.14709 -0.03907 -0.1492 0.15079 genus size -0.16218 -0.02352 ED 0.01769 -0.02588 0.04398 body size 0.34573 0.28296 * 0.17329 0.50216 * Didelphimorphia Diprotodontia Eulipotyphla Lagomorpha (68) (122) (330) (81) intercept 1.1708 -0.3931 -2.18149 * 1.38338 age -0.33217 -0.1682 0.3616 -0.18249 genus size 0.29621 -0.1955 0.03603 -0.44107 * ED 0.08379 body size -0.97152 * 0.123 -0.06075 -0.01799 Perissodactyla Primates [all] Primates [no lemurs] Rodentia (14) (312) (282) (1752) intercept 19.2763 -3.06633 * -2.99534 * -1.826312 * age 0.7787 -0.26501 -0.33242 * 0.056993 genus size -1.4653 -0.02014 -0.003506 ED -0.0652 0.06854 * 0.06094 body size -1.1825 0.45621 * 0.46516 * 0.138842 *

Table S1. Summary of species-level models for mammalian orders. Sample size in parenthesis. Non-significant terms were dropped from models when their exclusion led to better MCMC performance. * Significant; Highest Posterior Density (HPD) intervals did not include zero

Afrotheria Euarchonta Glires (56) (332) (1833) intercept -1.30489 -2.86456 * -1.76564 * age 0.42408 -0.14338 0.06201 genus size -0.36162 -0.01806 ED -0.08789 0.02234 body size 0.29117 * 0.43808 * 0.13089 * marsupials ungulates xenarthrans (275) (218) (26) intercept -0.8215 -2.023005 -4.5861 age -0.1119 -0.148791 0.5024 genus size -0.1581 0.008822 -0.234 ED body size 0.1476 * 0.23502 * 0.35

Table S2. Summary of species-level models for taxonomic subsets. Sample size in parenthesis. Non-significant terms were dropped from models when their exclusion led to better MCMC performance. * Significant; Highest Posterior Density (HPD) intervals did not include zero African Australian Oriental Eurasian

(730) (378) (330) (350) intercept -1.40914 * -1.20018 * -0.62674 -2.821061 * age -0.03433 -0.045185 -0.07364 0.195081 genus size 0.06319 -0.031675 -0.005551 ED 0.009704 -0.02899 -0.019296 body size 0.09683 * 0.165533 * 0.23372 * 0.276433 * North American South American Global Global1 (237) (896) (3308) (440 ) intercept -1.89156 * -2.406597 * -1.76756 * -3.7389 * age -0.16092 0.105819 0.01048 -0.117518 genus size 0.14845 0.060353 0.01712 0.081706 ED 0.002154 body size 0.08095 0.225759 * 0.17542 * 0.22088 *

Table S3. Summary of species-level models for biogeographic subsets. Sample size in parenthesis. Non-significant terms were dropped from models when their exclusion led to better MCMC performance. * Significant; Highest Posterior Density (HPD) intervals did not include zero 1 Analysis performed for genera using number of threatened species as a binomial response

assuming no extinction (µ=0) assuming extinction (µ =0.9) (173) (173)

-3.5592 * -3.5460 * intercept

crown group -0.1508 -0.1921 diversification rate 0.2130 * 0.2102 * body size

Table S4. Summary of models for diversification rates and number of threatened species per genus. Sample sizes in parenthesis. * Significant; Highest Posterior Density (HPD) intervals did not include zero

Chapter 3. Body size and threatened evolutionary history in primates

Summary

Evolutionary distinctiveness reflects the level of imperilment in primates but not in other mammals. Species in both younger and distinct lineages are more likely to be threatened. We identify drivers of extinction risk and investigate their relationship with evolutionary history. We collected data on life history, ecology, and geographic distribution for 312 species. To reflect extrinsic pressures, we quantified human presence, land use, and hunting. Using phylogenetic confirmatory path analysis and recursive partitioning, we investigate the interactions between predictor variables, and their relationship with extinction risk. We relate relevant predictors to quantitative properties of the phylogeny such as branching times and evolutionary distinctiveness. Evolutionarily distinct species tend to be smaller-bodied. Phylogenetically younger species tend to be larger and have smaller geographic ranges. Four extrinsic factors are associated with extinction risk: the magnitude and heterogeneity of human presence, the extent of croplands with livestock, and the occurrence of protected forest within species’ ranges. Body size evolution determines the relationships between phylogeny and extinction risk in primates. Body size mediates species’ responses to the effects of threats such as hunting and habitat fragmentation. Lemurs drive the result of higher risk for evolutionarily distinct species, given the high proportion of threatened species in this endemic clade. Understanding the patterns of threat to evolutionary history can assist in planning and implementing actions to mitigate threats and conserve multiple measures of diversity.

Introduction

Nearly half of all primate species are threatened with extinction by pressures such as hunting and habitat destruction through urbanization and agriculture (IUCN, 2013; Oates, 2013). Extinction risk in primates is clustered in certain families and genera, so if all threatened primates go extinct more diversity above the species level would be lost than by what is predicted by random extinction scenarios (Purvis et al., 2000a; Jono & Pavoine, 2012). In the study of phylogenetic hypotheses, species with long periods of independent evolution and few living relatives are considered to be evolutionarily distinct, and in the case of primates:

evolutionarily distinct and globally endangered species capture more phylogenetic and phenotypic diversity than expected by chance (Fritz & Purvis, 2010c; Redding et al., 2010). Extinction risk increases with evolutionary distinctiveness for primates, and decreases with phylogenetic lineage age - the time since a species’ divergence from its sister taxa (Verde Arregoitia et al., 2013). To assess the value of phylogeny as a predictor of extinction risk, we need to understand how it relates to species’ traits that increase extinction proneness. Intrinsic traits mediate each species’ response to external factors that vary in location and intensity.

Several intrinsic and extrinsic factors have been identified as correlates of extinction risk in primates. High resource requirements, slow reproductive rates and ecological specialization are biological traits that elevate species’ vulnerabilities (Purvis et al., 2000b; Harcourt et al., 2002). Species with low ecological versatility are more vulnerable to forestry, and larger, slow breeders are more prone to severe declines when they are hunted (Isaac & Cowlishaw, 2004). Threatened primates also experience higher human population densities across their geographic ranges than non-threatened species (Harcourt & Parks, 2003). Some factors that determine extinction risk are particularly relevant in the context of evolutionary history because they reflect deep time processes that are likely to be shared by related species: landmass type, harvesting pressure, geographic range, diet, and body size.

Landmass type

Landmass type is as a significant predictor of extinction risk for mammals and birds (Meijaard et al., 2008; Lee & Jetz, 2010). Island endemics usually have restricted geographical ranges and small populations. They are more susceptible to demographic stochasticity and overexploitation (Purvis et al., 2000b). Alcover et al. (1998) report selectivity and higher extinction proneness on islands for mammals with unusual or unique feeding and locomotor adaptations. Phylogenetic lineage ages and evolutionary distinctiveness scores may relate to phenotypic distinctiveness, and evolutionary isolation may reflect geographic isolation. Younger species may also be unusually common on islands. In southeast Asia, multiple mammal radiations relate to changing sea levels during glacial/interglacial cycles during the Late Pliocene (Meijaard et al., 2008).

Harvesting

Unsustainable exploitation can lead to local and species extinctions. Primates are among the most persecuted of tropical species. They are hunted for meat, fur and medicine, kept as pets,

and culled because of conflicts with agriculture (Oates, 2013). Significantly younger or older primate assemblages may occur in areas where current hunting pressure is disproportionately high because of local conditions (e.g. unrest and reduced enforcement of conservation measures).

Geographic range size

Geographic range size is widely associated with extinction risk (Purvis et al., 2000b), speciation (Gómez & Verdú, 2012) and taxon longevity (Foote et al., 2008). Although species sometimes undergo predictable phases of geographic expansion and contraction through time (Pigot et al., 2012), species’ range dynamics remain poorly understood (Jones et al., 2005a). Range dynamics may explain the relationship between phylogeny and extinction risk in primates.

Diet

Diet type can mediate species’ vulnerability to natural and anthropogenic pressures, and indirectly affect ‘availability’ for human exploitation. Specialized dietary requirements make species susceptible to seasonal fluctuations in resource supplies (Thoisy et al., 2009). Certain diet types often make individuals spend more time at forest edges, on the ground, or near water (Godfrey & Irwin, 2007). Diet determines a species’ ecological relationships with plants. Gómez and Verdú (2012) found that mutualism with plants is phylogenetically conserved. Interaction with plants is an important driver of diversification in primates. Mutualistic species have larger geographic ranges and higher diversification rates (Gómez & Verdú, 2012).

Body Size

All the traits mentioned above are related to or mediated by body size. Mammals generally follow Van Valen’s (1973) ‘island rule’ of body size variation. Small species evolve larger size on islands while large insular species dwarf. Bromham and Cardillo (2007) demonstrated this pattern for primates. Larger-bodied taxa are preferentially hunted (Thoisy et al., 2009) and have a lower reproductive resilience because fecundity, home range, group size and population density all show strong allometric scaling (Pearce et al., 2013). Geographic range size has a complex relationship with body size. Small species can have a variety of range sizes. Large-bodied species generally only have large ranges. For species smaller than the mode, range size decreases with body size (Agosta & Bernardo, 2013). Body size correlates

with the proportion of leaves in the diet. Folivorous primates are significantly larger than species with other diets (Gómez & Verdú, 2012), and species under 500 grams are usually strict faunivores (Matthews et al., 2011). Primate diversity has increased to a maximum in the Holocene, and diversification has been associated with increasing body size for most mammalian orders (Smith et al., 2010).

Extrinsic factors could explain the associations found by Verde Arregoitia et al. (2013). Distinct or younger primate species may occur close together in areas that experience high cumulative effects of threats. For example, a recent glacial-related radiation of rainforest species could occur on tropical island groups that face widespread habitat loss and degradation because of socioeconomic factors. Lemurs (superfamily Lemuroidea) represent a clear example of ancient species clustered in an area where threat intensity is high. Verde Arregoitia et al. (2013) and Redding et al. (2010) performed separate analyses that excluded lemurs to ensure that any result would not be driven entirely by this atypical lineage. The ancient single origin for Malagasy primates via dispersal across a formidable oceanic barrier makes them an extremely distinct, endemic clade (Yoder, 2013). Extant lemurs have one of the highest levels of threat recorded for any vertebrate group. Approximately 90% of species and subspecies are threatened, all confined to an island where threat intensity has increased since political changes in 2009 (Conservation International, 2012).

In this study, we examine the relationships between evolutionary distinctiveness, phylogenetic age and extinction risk found in primates. First, we identify which extrinsic pressures (e.g. habitat loss, hunting) and which intrinsic traits (e.g. body size, diet) make species more susceptible to extinction. We investigate how these traits relate to the phylogeny, and how the factors interact; resulting in an increased threat status.

Methods

Species Data

We collected data for 312 primate species with a known EDGE (Evolutionarily Distinct and Globally Endangered) score (http://www.edgeofexistence.org). We considered all species included in the phylogeny provided in Fritz et al. (2009) and not Data Deficient in the IUCN Red List (IUCN, 2013). We obtained data on body size and diet from the PanTHERIA (Jones et al., 2009) and MOM 4.1 (Smith et al., 2003) databases, and supplementary data from

Redding et al. (2010) and Gómez & Verdú (2012). Primates are one of the mammalian orders with the most detailed data about harvesting pressure (Thoisy et al., 2009). We assigned dichotomous values to the species listed as ‘hunted’ or ‘not hunted’ in Redmond et al. (2006). This list of hunted primates considers species as hunted when they are known to be hunted for bush meat sale and consumption. The data come from a variety of sources and does not have an explicit threshold for inclusion, it also does not consider species that are captured for the pet trade. Despite concealing variation in hunting intensity, this hunting variable can provide useful general information to identify common features of hunted species.

Spatial data

We used the IUCN spatial dataset’s Extent of Occurrence (EOO) polygons to represent species’ ranges, and collected spatially explicit data on extrinsic factors to assign species- specific values. We gathered data on Human Influence, Land Use, Zoogeography and Landmass type (see data definition, Table S1).

Multiple measures of extrinsic factors may be useful to represent the extent and magnitude of human activities across a species’ range without assuming that a unique central value (i.e. mean or median) is distributed uniformly throughout the area that a species inhabits (see Di Marco et al., 2013). For the spatial datasets describing a continuous gridded variable (the Human Influence Index) , we derived multiple species-specific values across each species’ EOO polygon. We used the Unites States Geological Survey’s NACT GIS toolbox (Price et al., 2010) and the native resolution of each spatial layer. We extracted the values for all cells within a species’ range, which could then be averaged, summed, or filtered for minimum and maximum values. This tool also provides a value of how many different values are present within the species’ range polygon.

The Human Influence Index (HII) (Sanderson et al., 2002) is a spatial measure of anthropogenic influence on ecosystems. It summarizes eight measures of human presence (e.g. human population density, urban areas, land cover and transport infrastructure), and has been associated with increased threat status in birds and mammals (Lee & Jetz, 2010; Yackulic et al., 2011). Different land use types imply varying levels of perturbation to natural habitats. We used the Land Use Systems of the World map (LADA, 2008) to quantify land use within each species’ range. This land use map contains data that describes different types of habitats in combination with human activities (production, habitation, agriculture, forestry,

etc), creating mutually exclusive categories. We quantified land use for each species by measuring what percentage of a range polygon’s area includes each land use category. Other spatially-derived species traits include landmass type and zoogeographic affinity, based on an island type dataset (Cardillo et al., 2008a) and a map of phylogenetically updated mammalian biogeographic regions (Holt et al., 2013).

Data Analysis

We implemented all statistical tests in R 3.0.2 (R Development Core Team, 2012), and all spatial analyses in ArcGIS 10.1. R code for all statistical analysis is provided as supplementary material. Given the major role of body size and geographic range as predictors of extinction risk in mammals (Cardillo et al., 2005), we first tested for pairwise correlations between evolutionary age, distinctiveness, body size and geographic range. We calculated noncentral correlations between phylogenetically independent contrasts because species do not represent independent data points. We also tested for partial correlations to identify spurious or hidden correlations after removing the effects of other variables. We repeated this correlation test on a dataset that excludes lemurs.

We used phylogenetic analysis of variance to test straight-forward a priori explanations for increased risk. We tested for differences in the evolutionary distinctiveness scores and lineage ages of species occurring in different landmass types and regions and for hunted versus non-hunted species.

Path Analysis

We investigated the causal effects linking variables known to predict with extinction risk. We used phylogenetic confirmatory path analysis (PPA) following Hardenberg and Gonzalez- Voyer (2013) to test the likelihood of predefined causal hypotheses about the role of phylogenetic lineage age, body size and range size in determining threat status. We did not test Evolutionary Distinctiveness scores in this analysis because it correlates tightly with lineage age, is a compound metric, and is not easily interpretable in a cause-effect context.

PPA combines d-sep tests (Shipley, 2000) and phylogenetic generalized least squares (PGLS). The d-sep test evaluates the conditional probabilistic independences implied in a causal model. It identifies the minimal set of conditional independences that must all be true if the model is correct. Conditional independence is tested by fitting linear models and

calculating the probability that the partial regression coefficients between variables hypothesized to be independent is zero (Shipley, 2000). When data has an underlying phylogenetic structure, models can be fit using PGLS. The correlation structure of the data is determined by the expected covariance of species traits, given the phylogenetic tree and evolutionary model.

We performed PPA following Hardenberg and Gonzalez-Voyer (2013). We built PGLS models using the phylogeny in Fritz et al. (2009). We used the IUCN Red List categories as our measure of extinction risk, converted into a coarse continuous index in the same manner as Cardillo et al. (2005). For primates, Matthews et al. (2011) tested whether the ordinal nature of Red List categories affects the statistical performance of comparative analyses that use it as a dependent variable. The ordinal coding did not produce elevated Type I errors in their primate dataset. To avoid potential circularity in all analyses that included geographic range as a predictor of extinction risk, we excluded threatened species listed under criterion B of the IUCN Red List, which focuses on range restriction (IUCN, 2013), so that threat status is independent of geographic range.

Predictors of extinction risk

Almost half (46%) of the species in our dataset have an average body mass under three kilograms. If biologically intrinsic life-history traits are less likely to influence extinction risk for this high proportion of species (Cardillo et al., 2005), extrinsic factors and the size and location of their geographic ranges should have a major role in placing species at risk. Variation in the spatial distribution and intensity of extrinsic pressures can result in complex, context-dependent and indirect pathways to decline. The inclusion of extrinsic factors and threat variables improves model accuracy and interpretation (Murray et al., 2013).

We used conditional random forests (cRFs) to identify relevant extrinsic drivers of extinction risk so we could then examine their relationships with evolutionary history. Although these models do not take into account phylogenetic covariance, random forest (RF) models can provide accurate predictions and useful information about the underlying data (Murray et al., 2011). RFs recursively partition data into groups of increasingly similar observations based on the predictors, and average the results over a set of trees built from bootstraping observations (Strobl et al., 2009). Conditional random forests are based on conditional inference trees (cTREEs), built by performing a signiﬁcance test on independence between

predictors and response. Branches are split when the p-value is smaller than a prespeciﬁed nominal level (Everitt & Hothorn, 2010).

We considered species listed in the IUCN Red List as Vulnerable, Endangered, or Critically Endangered as ‘threatened’, and species listed as Least Concern or Near Threatened as ‘non- threatened’. We used a dichotomous measure to minimize the effect of the skewed distribution of categorical threat values on model accuracy (Price & Gittleman, 2007). A coarse continuous index is more informative for the correlative tests used in PPA, but a binary threat designation for classification analysis yields more robust results. The initial cRF model included all our variables (Table S1). We used the unbiased variable importance measures to discard non-significant predictors for easier interpretation, and to build a reduced model for single-tree methods. It is difficult to trace an individual species’ path through a random forest and arrive at the cause of its classification (Murray et al., 2011). We used a single-tree method for visualization. Single-tree methods are generally less accurate and more sensitive to small changes in the data than ensemble methods. However, they can display the partitioning of species by predictors visually. We created 10,000 cTREEs, allowing for random sampling of input variables at each node and Bonferroni correction of the nominal p- values for multiple comparisons. We kept the models with the highest accuracy in relation to the more complicated cRF model.

Results

We found three significant pairwise correlations in the full dataset and three in the dataset that excluded lemurs (Table 1). Phylogenetic age is tightly associated with evolutionary distinctiveness, which is calculated using branch lengths and only varies with changes in diversification rates. Evolutionarily distinct species are consistently smaller-bodied. When excluding lemurs, geographic range size increases with evolutionary age. Tests for partial correlations have similar results, and establish collinearity between phylogenetic age and evolutionary distinctiveness.

noncentral correlations partial noncentral correlations all species (n=311, df=310) all species (n=311, df=303) BM GR AGE BM GR AGE GR 0.142 GR 0.126 (0.071) (0.165) AGE -0.261 0.042 AGE 0.052 0.093

(0) (1) (1) (0.617) ED -0.481 -0.055 0.633 ED -0.414 -0.044 0.601 (0) (1) (0) (0) (1) (0) no lemurs (n=270, df=269) no lemurs (n=270, df=262) BM GR AGE BM GR AGE GR 0.055 GR 0.107 (1) (0.082) AGE -0.133 0.192 AGE 0.069 0.122 (0.169) (0.008) (1) (0.281) ED -0.349 0.15 0.565 ED -0.342 0.085 0.542 (0) (0.076) (0) (0) (0.989) (0)

Table 1. Simplified correlation matrix of pairwise variable correlations on phylogenetically independent contrasts. Non-central Pearson’s Correlation Coefficients and Bonferroni- adjusted probabilities in brackets (values greater than 1 expressed as “1” and values smaller than 0.001 expressed as “0”). Significant correlations in bold. BM = body size, GR = geographic range size, AGE = phylogenetic lineage age, ER = extinction risk.

We found no significant differences between the lineage ages or Evolutionary Distinctiveness scores of species occurring in different landmass types (F2,309 = 0.018, p=0.98;

F2,309=0.45312, p=0.631) or reported as hunted vs. not hunted (F1,310 =0.0, p=1). We found no differences in the ED scores or lineage ages of primate species occurring in different biogeographic regions (F12,299= 0.36, p=0.97; F12,299 = 0.44, p=0.94). Overall, Madagascan species have more distinct representatives. Species in north-eastern mainland Asia have the lowest values for both variables.

PPA Model Selection

When a model is correctly parameterized and all assumptions are met in a Phylogenetic Path Analysis, the C statistic follows a X2 distribution. A path model is considered to fit the data when the p-value for the C statistic is not significant (Shipley, 2009). Models A and B are not rejected by the data, and represent possible explanations of how the association between evolutionary age and extinction risk is mediated by a relationships between age, body size and geographic range size (Figs 1(a), 1(b) and Table 2). The assumed independence between evolutionary age and extinction risk is supported in both models. Models C to F represent all other combinations of causal relationships, which also have plausible biological and

evolutionary interpretations. Model selection based on the C statistic information criterion (CICc) identifies model A as the best fitting of the supported models when Δ CICc < 2 (Hardenberg & Gonzalez-Voyer, 2013), supporting the stated independence between body size and geographic range size.

a) b) c) AGE AGE AGE

BM GR BM GR BM GR

ER ER ER

d) e) f) AGE AGE AGE

BM GR BM GR BM GR

ER ER ER

Figure 1. Directed acyclic graphs for pre-specified cause-effect models for the relationships between body size (BM), geographic range size (GR), phylogenetic lineage age (AGE) and extinction risk (ER).

Model C k q P-value CICc Δ CICc A 2.765 2 8 0.598 19.292 0 B 2.765 1 9 0.251 21.472 2.135 C 12.137 3 7 0.059 26.546 7.254 D 13.343 3 7 0.038 27.752 8.46 E 20.372 4 6 0.009 32.678 13.386 F 37.424 4 6 0 49.73 30.438

Table 2. Phylogenetic Path Analysis coefficients for the six pre-specified cause-effect models for the directional relationships between phylogenetic age, body size, range size and extinction risk. C=Fisher’s C statistic; k=number of independence statements; q= number of parameters; CICc = C statistic information criterion; Δ CICc = difference in CICc from the best fitting model (lowest CICc score)

Conditional Random Forest models

The final model, built with only nine variables, correctly classified 91.13 per cent (257 of 282 species) as threatened or non-threatened. Model sensitivity (threatened species correctly classified) was 93.6 per cent. Specificity (non-threatened species correctly classified) was 88.6. Cohen’s kappa index was 0.82, indicating better than random discrimination.

After eliminating nonsignificant variables, the extent of croplands with moderate livestock density and protected forest and were the most important extrinsic predictors of extinction risk after geographic range and body size. The variety of values and the minimum values of the Human Influence Index were also significant extrinsic variables (Fig. 2).

Figure 2. Variable importance geographic range measures for the final body size crops + moderate livestock dens. conditional random forest model protected forest region predicting extinction risk. HII variety landmass type Importance values for each diet HII minimum predictor variable are calculated hunting as the decrease in classification 0.00 0.02 0.04 0.06 0.08 0.10 accuracy after predictor removal variable importance in a forest of 1000 trees.

For the single tree method we kept the tree structure with the highest accuracy measures (sensitivity = 0.808, specificity= 0.836, percentage of cases correctly classified =0.822). The most important primary split in the conditional inference tree was geographic range size, followed by splits based on land use across species ranges (Figure 3). In this tree, species with geographic ranges smaller than the threshold (86371 km2) and with almost no (< 1%) protected forest in their range have the highest probabilities of being threatened out of all terminal node groupings. Large-bodied species (over four kilograms) with larger ranges have a high probability of being threatened when more than 5 % of their range includes croplands with moderate livestock density. Large body size also predicts threat in some species with a small extent of croplands in their range, even when over 20% of their range includes protected forest. Species node memberships are provided in supplementary material.

geographic range size (km2 ) crops & moderate < 86371.1 > 86371.1 livestock density protected forest (% of range) (% of range)

body size < 5.2 > 5.2 body size (grams) (grams)

< 0.9 > 0.9 < 4345 > 4345 protected forest (% of range) < 4155 > 4155

< 23.3 > 23.3

Node 3 Node 4 Node 7 Node 9 Node 10 Node 12 Node 13 (n = 77) (n = 14) (n = 61) (n = 29) (n = 12) (n = 41) (n = 48) 0.88 0.75 0.77 0.43 0.17 0.37 0.02

Figure 3. Conditional inference tree of species partitioning by predictors. Terminal node barplots represent the average proportion of species classified as threatened.

Lastly, we investigated the relationships between the most important extrinsic factors identified in the reduced conditional Random Forest model with evolutionary distinctiveness and lineage age for all species. We used the same approach as in the correlation test with intrinsic traits (above) and found no significant associations (Table 3).

a) noncentral correlation (all species, n=311, df=310) HIIVAR PCPrFr Crops HIIMIN AGE ED BM PCPrFr -0.097 (1) Crops 0.354 -0.101 (0) (1) HIIMIN -0.607 0.095 -0.136 (0) (1) (0.463) AGE -0.088 0.095 -0.015 0.111 (1) (1) (1) (1) ED -0.088 -0.041 -0.041 0.165 0.633 (1) (1) (1) (0.096) (0) BM 0.061 0.085 0.047 -0.122 -0.261 -0.481 (1) (1) (1) (0.869) (0) (0) GR 0.102 0.259 -0.007 -0.113 0.043 -0.056 0.142 (1) (0) (1) (1) (1) (1) (0.332)

b) partial noncentral correlation (all species, n=311,df=299) HIIVAR PCPrFr Crops HIIMIN AGE ED BM PCPrFr -0.026 (1) Crops 0.346 -0.077 (0) (1) HIIMIN -0.591 (0.085) 0.114 (0) (1) (1) AGE -0.055 0.133 0.046 -0.028 (1) (0.588) (1) (1) ED 0.04 -0.097 -0.034 0.101 0.602 (1) (1) (1) (1) 0 BM -0.023 0.043 0.038 -0.044 0.044 -0.4 (1) (1) (1) (1) (1) 0 GR 0.067 0.253 -0.028 -0.066 0.061 -0.012 0.109 (1) (0) (1) (1) (1) (1) (1)

Table 3. Simplified correlation matrix of pairwise variable correlations on phylogenetically independent contrasts. Non-central Pearson’s Correlation Coefficients and Bonferroni- adjusted probabilities in brackets (values greater than 1 expressed as “1” and values smaller than 0.001 expressed as “0”). Significant correlations in bold. BM = body size, GR = geographic range size, AGE = phylogenetic lineage age, ER = extinction risk.

Discussion

The evolution of mammalian body size may explain the higher extinction risk for species in both evolutionarily distinct and younger lineages (Verde Arregoitia et al., 2013). Mammalian body mass evolution in turn is influenced by a combination of geography, climate, and history (Cooper & Purvis, 2010). A trend of increasing body size is more parsimonious than the unclear dynamics of changing geographic range sizes over time and in relation to body size. We did not find significant relationships between extrinsic factors like land use or human influence and phylogenetic lineage age or evolutionary distinctiveness (Table 3). With the exception of Madagacascar, extrinsic factors are not disproportionately affecting younger, older, or distinct lineages. These lineages are not overrepresented on islands, or under preferential persecution.

Evolutionary distinctiveness is high in lineages with early divergence and low net diversification resulting from low speciation or high extinction. Lemurs represent less than a

tenth of all primate diversity, yet their current threat pattern and unique evolutionary history drives the association between distinctiveness and extinction risk. The primate fauna in Madagascar has experienced several extinctions in recent history and in the last 2000 years. At least one-third of Madagascar’s known primate species became extinct by the late Holocene, including all species larger than nine kilograms (Jungers et al., 2002). Globally, selective extinctions of larger-bodied taxa across all primate families and within genera confer a higher distinctiveness score to any remaining smaller-bodied species, especially in species-poor clades.

Our results suggest that extinction risk decreases with phylogenetic lineage age because younger lineages have smaller geographic ranges and larger body sizes. Conversely, older and more distinct lineages have larger ranges and smaller body sizes. These patterns have all been proposed separately in the macroecological literature and in cross-species studies of extinction risk (Bennett & Owens, 1997; Jones et al., 2005a; Cardillo et al., 2008b). Supported models in the path analysis include a directional relationship between taxonomic age and body size. Younger species are generally larger than older ones and larger species are more threatened, especially in the tropics (Fritz et al., 2009). Traits linked to large body size might have opposing effects on primate extinction risk. Large-bodied species are preferentially hunted and more susceptible to trait-based decline. They are slower breeders, and need larger home ranges and habitat areas to maintain viable populations and meet their metabolic requirements (Fritz & Purvis, 2010c). However, they are also more tolerant of habitat disturbance because of their higher vagility and leaf intake.

Cope’s (1887) rule states that lineages tend to evolve towards a larger body size because of selective advantages conferred by larger sizes. Data from extant primates provide no support for trait evolution models that assume Cope’s rule (Monroe & Bokma, 2010). The primate fossil record shows an increase in mammalian body size over time, and body size correlates positively with taxon longevity (Gómez & Verdú, 2012). If the trend is not driven by selection, there may still be a passive tendency for descendants to be larger than their predecessors during diversification. Novel environments can promote body size diversification in lineages relative to their ancestral stock (Thomas et al., 2009). Directional changes in body size related to ecological release may be greater in tropical forests. These environments can support more medium and large species because of their high productivity and three-dimensional structure.

Phylogenetic path analysis supported a directional relationship between taxon age and geographic range size (Figs 1(a), 1(b) and Table 2). The correlation between age and range size (Table 1) also contributes to higher extinction risk (Fig. 2). Geographic range size may correlate with taxon age because species with small or declining ranges are less likely to survive to the present (Pigot et al., 2012), and we focused on extant taxa. Despite their global distribution, most primate species are concentrated within tropical forests, which are being lost at an alarming rate. Many species that experienced substantial declines now have small geographic ranges. Past range declines complicate any attempts to separate the role of range size as a predictor of extinction risk from its role as a response to external stresses (Hanna & Cardillo, 2013).

Although evolutionarily distinct and threatened primates possess characteristics that make them seem odd (Redding et al., 2010), oddity does not seem to be associated with increased persecution. ‘Hunting status’ had the lowest importance measure in the recursive partitioning model. A dichotomous measure obscures variation in intensity and location. It does not differentiate intense exploitation of rare species in disturbed and declining habitats like Hose’s leaf monkey in Borneo (Thoisy et al., 2009), from localized subsistence hunting of abundant and widespread species such as ‘vermin’ Cercopithecids in Africa (Johnson et al., 2010). Hunting of larger-bodied species could explain the high proportion of species predicted as threatened, even when human activities are low in species with large areas of protected forest within their distributions (Fig. 3, Node 10).

Our analyses identified anthropogenic threats with the potential to threaten evolutionarily distinct primates. The extent of protected forest and agricultural areas with livestock within species’ may be a proxy for habitat status, quality and degradation. The variety of different unique human influence index (HII) and the minimum HII values across species’ ranges capture the heterogeneity and intensity of anthropogenic pressure. Three of these variables are correlated (Table 3b). Minimum HII values and extents of croplands with moderate livestock density are greater for species that have a wider variety of HII values. Habitat loss, fragmentation and degradation represent a major threat for older and evolutionarily distinct species. These species tend to be smaller and less folivorous, and thus more susceptible to fragmentation or disturbance from extractive activities (Isaac & Cowlishaw, 2004). Smaller arboreal mammals are less able to disperse between forest fragments. Smaller species also need larger fragments because of the sparse or patchy distribution of their non-foliage food resources (Godfrey & Irwin, 2007).

A recursive partitioning approach helped us reduce the number of predictor variables and draw conclusions without much loss of information. We were able to trace the effects of influential predictors on the classification of individual species. Misclassified cases from our final model were mostly Near Threatened species according to their IUCN Red List entry. Other misclassified species have unclear taxonomies or distributions that make assessment unclear (see Table S2 for details).

After excluding lemurs, our models support the notion of no general associations between Evolutionary Distinctiveness and extinction risk for non-marine, non-volant mammals (Jono & Pavoine, 2012; Verde Arregoitia et al., 2013). Because evolutionarily distinct species are not more threatened, branch lengths can be used in compound metrics of evolutionary distinctiveness and threat status for conservation applications (Mooers et al., 2008). The importance and evolutionary trends of primate body size need to be considered whenever unique evolutionary history is being targeted for conservation.

Acknowledgements

We thank Jeffrey O. Hanson for R scripting assistance and Amélie Lootvoet for comments that helped improve manuscript clarity. LDVA was supported by funding from Consejo Nacional de Ciencia y Tecnología (CONACYT) scholarship 308685.

Supporting Information

Data supplement. Includes Supplementary Tables S1 and S2

Data accesibility

All data tables, R scripts and descriptions are available on Figshare at: http://dx.doi.org/10.6084/m9.figshare.851302

Supplementary tables

Variable Units or categories Derived Source

Gómez and Verdú Diet type Faunivore neontological literature data (2012) (Categorical) Folivore Frugivore Gumnivore

Landmass type continental Intersection of species’ EOO centroid Cardillo et al. (2008) (Categorical) landbridge island with landmass polygons oceanic island

Geographic range measured from EOO polygons (equal area IUCN (2013) spatial square kilometers (Continous) projection) data

Human influence Overlay of species’ EOO polygons with HII index minimum value raster data Sanderson et al. (2002)

(Continuous) maximum value using the NACT ArcMap Toolbox

range of values

mean value

variety

majority value

minority value

median value Body Size grams Mean values multiple sources (Continuous)

32 categories Overlay of species’ EOO polygons with Land Land use Use Systems raster data using the NACT FAO-LADA (2008) ArcMap Toolbox to measure % of species’ (Continuous) range occupied by each land use type

Zoogeographic Intersection of species’ EOO polygons affinity 12 categories Holt et al. (2013) (Categorical) with zoogeographic region polygons

Threat status 0 (not threatened) LC and NT speces considered not threatened IUCN Red List (Categorical) 1 (threatened) VU, EN, CR species considered threatened

Lineage age Verde Arregoitia et al. time since divergence (mya) from phylogeny in Fritz et al. (2013) (Continuous) (2013)

Evolutionary compound value, fair Verde Arregoitia et al. distinctiveness from EDGE of Existence project proportions (2013) (Continuous)

Hunting status (Categorical) 0 (not hunted), 1 (hunted) from Ape Alliance Bushmeat report website Redmond et al. (2006)

IUCN Red List threat status (Continuous) Ordinal index 0 to 4 from IUCN Red List website IUCN Red List

Table S1. Variable definitions

Species Red Notes Major threats List Ateles belzebuth EN Unclear distribution Heavily hunted, occurs in several protected forests Ateles chamek EN May be conspecific with other spider monkeys Heavily hunted

In urgent need of a taxonomic review and Forest loss and hunting. Distribution along rivers may Cacajao calvus VU reappraisal of subspecies status make this taxon more vulnerable to human impacts.

Callimico VU May co-occur with an undescribed congener Projected habitat loss from logging goeldii

Chiropotes Unusual geographic distribution at its southern EN Projected rapid agricultural expansion, hunting albinasus and western range edges

Exceptionally high levels of hunting and poaching and the Ebola epizootic even in remote or Gorilla gorilla CR disease-induced mortality protected areas

Possibly a synonym for Lagothrix Heavy deforestation and hunting, infants popular in pet Lagothrix cana EN lagotricha subspecies cana trade, targeted at very low human population densities

Pan troglodytes EN Most widespread hominid ape High levels of exploitation, disease, and loss of habitat Pithecia VU Taxonomy currently being revised. Hunting albicans Alouatta LC Widespread distribution Present in numerous protected areas palliata Localized habitat destruction, less susceptible to Avahi laniger LC Distribution may need revision hunting because of behavioural traits

Unclear taxonomy, Almost qualifies as Callithrix kuhlii NT Forest loss and fragmentation, captured as pets threatened under criterion A2c

Cebus Widespread in Central America and Northern LC Kept as pets and hunted capucinus Colombia

Disjunct populations. Almost qualifies as Forest destruction, due primarily to slash-and-burn Eulemur fulvus NT threatened under criterion A2cd practices, charcoal production and illegal logging

Galagoides Unclear taxonomy, considered within Galago LC Widespread in Tanzania, occurs in secondary forests zanzibaricus by some authorities Macaca LC Downlisted from VU in previous Red List Lowland populations threatened by agriculture cyclopis Microcebus LC Thought to be common Forest loss from charcoal and maize production griseorufus Poorly known, appears to be relatively Habitat loss from slash-and-burn agriculture and illegal Phaner furcifer LC widespread in the eastern forests logging

Presbytis Disputed taxonomy. Almost qualifies as NT Habitat loss, oil palm expansion femoralis threatened under criterion A2c

Least threatened colobine on Borneo, Presbytis LC adaptable, wide distribution and occurrence at Hunting, oil palm expansion rubicunda high altitudes Presbytis In need of reassessment once taxonomy Habitat loss in lowland areas, expansion of oil palm NT siamensis becomes clearer plantations Almost qualifies as threatened (A2cd). Cryptic, Procolobus NT able to survive in small forest fragments Forest loss and hunting verus disturbed habitats Polytypic; if each form would be recognized as Semnopithecus NT distinct species they would be listed as Hunting and habitat loss, occurs in few protected areas priam threatened Trachypithecus NT Almost qualifies as threatened; A2cd Hunting, expansion of oil palm, kept as pets, wildfires cristatus Trachypithecus Uncertain taxonomy, Almost qualifies as Hunting, expansion of oil palm plantations, frequent NT obscurus threatened under criterion A2cd. road mortality Table S2. Misclassified species in final conditional random forest model. All data sourced from each species' IUCN Red List entry (accessed October 2013)

Chapter 4. Diversity, extinction, and threat status in Lagomorphs

Summary

A quarter of all lagomorphs (pikas, rabbits, hares and jackrabbits) are threatened with extinction, including several genera that contain only one species. The number of species in a genus correlates with extinction risk in lagomorphs, but not in other mammal groups, and this is concerning because the non-random extinction of small clades disproportionately threatens genetic diversity and phylogenetic history. Here, we use phylogenetic analyses to explore the properties of the lagomorph phylogeny and test if variation in evolution, biogeography and ecology between taxa explains current patterns of diversity and extinction risk. Threat status was not related body size (and, by inference, its biological correlates), and there was no phylogenetic signal in extinction risk. We show that the lagomorph phylogeny has a similar clade-size distribution to other mammals, and found that genus size was unrelated to present climate, topography, or geographic range size. Extinction risk was greater in areas of higher human population density and negatively correlated with anthropogenically modified habitat. Consistent with this, habitat generalists were less likely to be threatened. Our models did not predict threat status accurately for taxa that experience region-specific threats. We suggest that pressure from human populations is so severe and widespread that it overrides ecological, biological and geographic variation in extant lagomorphs.

Introduction

Identifying the processes that generate striking differences in the number of species among lineages is fundamental to evolutionary biology, and understanding the origins of biodiversity can inform efforts to preserve it (Jones et al., 2005b; Soria-Carrasco & Castresana, 2011; Pyron & Burbrink, 2012). Phylogenetic analyses can reveal the effects of speciation and extinction on biodiversity (Rabosky, 2009; Morlon et al., 2011), and the ecological mechanisms that influence diversification (Wiens, 2011). Species-poor clades are common in vertebrate groups; they exist at higher proportions than predicted by a random processes with constant evolutionary rates (Ricklefs et al., 2007). Some small clades are remnants of formerly diverse lineages. Others survive as relict clades characterised by low net diversification, persisting in geographically isolated or ecologically marginal environments

(Ricklefs, 2003; Ricklefs, 2005). Relict species often have small populations, making them more likely to be threatened with extinction. The antiquity of relict taxa makes them important for the conservation of genetic diversity, phylogenetic history and evolutionary potential (Hampe & Petit, 2005).

Global changes (e.g. climate change and large-scale habitat modification) are causing declines in species with restricted climatic niches, habitats or diets (Clavel et al., 2010). This non-random pattern threatens species-poor lineages disproportionately, because they frequently occur at the edges of geographic or ecological space. In birds, small clades are generally isolated from continental landmasses, occur in habitats with low avian diversity or have unusual diets (Ricklefs, 2003). Marginal niches tend to be occupied by early-diverged lineages with few close relatives (Purvis et al., 2011).

Differentiated species-poor lineages persist within vertebrate groups at multiple taxonomic levels. In land mammals, the aardvark (Orycteropus) is the only representative in its order, and there are at least six monotypic families within the diversely radiated order Rodentia. Within the order Lagomorpha (pikas, rabbits, hares and jackrabbits), small genera outnumber diverse ones. Of twelve extant genera, three species-rich genera (Ochotona, Sylvilagus and Lepus) contain 74 (85%) of the 87 species. Seven (78%) of the remaining nine genera are monotypic. Humphreys (2014) found significant phylogenetic clustering and trait coherence above the species level for several mammal groups including lagomorphs. Thus, genera reflect underlying evolutionary processes, even when they are defined by taxonomists based primarily on patterns of morphological and genetic variation.

Almost a quarter of all 87 lagomorph species are threatened with extinction (Chapman & Flux, 2008; Figure 1). This proportion is similar to other mammalian orders (Hoffmann et al., 2011). However, over half of the species-poor genera in the Lagomorpha are threatened. Genus size correlates with extinction risk for lagomorphs, but not for any other order of terrestrial mammals (Verde Arregoitia et al., 2013). Such a pattern threatens more diversity above the species level than predicted by random extinction (see Purvis et al., 2000a). Extinctions of entire genera are less likely if extinctions are random, unless clades are species-poor. Species with fewer close relatives are evolutionarily distinct and may possess unique genetic information or ecological functions requiring conservation action (Redding & Mooers, 2006).

Red List Status Brachylagus Poelagus DD 1 Bunolagus Pentalagus Caprolagus Romerolagus Oryctolagus LC NT 2 Nesolagus VU EN CR 4 Pronolagus genus size genus 17 Sylvilagus

25 Ochotona

32 Lepus

10 20 30 number of species

Figure 1. Distribution of lagomorph genus sizes and threat status. Stacked bars display relative number of species in different IUCN threat categories for each genus size. Genera listed next to each bar. DD: Data Deficient; LC: Least Concern; NT: Near Threatened; VU: Vulnerable; EN: Endangered; CR: Critically Endangered.

Species-poor lagomorph clades require detailed study, particularly when small genera face extinction. Lagomorphs as a group show biological adaptations to a wide range of environments, from deserts to arctic regions (Chapman & Flux, 2008). Notably, faecal re- ingestion increases their digestive efficiency in habitats with poor nutrient availability. This specialized digestion is virtually non-existent in other mammals and may explain the success of lagomorphs in harsh environments where few other mammalian herbivores are present (Hirakawa, 2001; Hackländer et al., 2008). Occupation of isolated niches represents a plausible mechanism for the persistence of small, early-diverged clades at low diversity (Purvis et al., 2011)

Environmental changes and the composition of vegetation have played an important role in shaping current lagomorph diversity since the early Miocene 23-15 million years ago (Mya) (Ge et al., 2012; Flynn et al., 2013; Ge et al., 2013). Ecological opportunity and biogeographic factors could explain the uneven distribution of species among genera. Descriptors of the present-day environment and species’ distributions capture only snapshots of the environmental histories of lineages, yet both have been found to explain variation in

clade richness. Measures of clade richness at various taxonomic levels (genera, species and subspecies) represent useful indicators of phenotypic divergence and diversification. In agreement with allopatric and parapatric models of divergence, Phillimore et al. (2007) found a positive correlation between the range size of bird species and subspecies richness; bird families that diversified faster have smaller ranges. Botero et al. (2013) found that harsh climatic conditions are positively correlated with greater subspecies richness in birds and terrestrial mammals.

Higher extinction risk is predicted for species occurring on islands, mountain summits or habitats with extreme climates and low diversity (Ricketts et al., 2005). Monotypic species often occur in these marginal habitats, so environmental and biogeographic variables could explain both the prevalence of small lagomorph clades and their current patterns of endangerment. Associations between harsh climate or low sympatric mammal species richness (as a proxy for geographic and ecological niche isolation) and small genus size would support the notion that marginal species tend to be in species-poor clades. Alternatively, ecological pressure from human populations may be strong enough to cut across the considerable variation in lagomorph biology and the remarkable range of environments in which they are found.

The present distribution of lagomorph genus sizes is a result of multiple different processes that are difficult to identify using information for crown groups only. At present, no comparative analyses of diversification or extinction risk have focused on lagomorphs, an important mammalian group with roles in ecosystem function, invasive representatives, and commercial species (Chapman & Flux, 2008).

In this study, we test whether differences in lagomorph evolution, biogeography and ecology explain current patterns of diversity and extinction risk. First, we compare the shape of the extant lagomorph tree to those of other mammalian orders and to expectations from a null model of equal speciation and extinction. We then infer diversification from the lagomorph phylogeny to test speciation and extinction rates. Differences with the general patterns for other mammals could hint at differences in diversification over time. We test if extinction risk is phylogenetically clustered, because related species often share extinction biasing traits. Extinction risk is not always explained by species’ biology, so we include measures of human presence and activity into an examination of climatic variables, sympatric species richness, and geographic range size as potential predictors of both threat status and genus

size. Variables that predict clade size or extinction risk provide information about how evolutionary history is accumulated or lost. Additionally, we review the paleobiogeographic history of the species-poor clades, identifying geographically or ecologically marginal species and the threatening processes that act upon them.

Methods

Phylogenetic data Taxonomically problematic groups pose a challenge to comparative studies because taxonomic changes can influence our measures of both diversification and conservation status. We consider 87 extant lagomorph species. The species-level taxonomy of most of the lagomorphs in this study is well supported by molecular and morphological data (Matthee et al., 2004; Lanier & Olson, 2009), and we followed taxonomic expertise from Andrey Lissovsky (Zoological Museum of Moscow State University) to avoid taxonomic inflation in the pikas. We classed Ochotona nigritia and Ochotona gaoligongensis as morphs of Ochotona forresti, Ochotona muliensis as a morph of Ochotona gloveri, Ochotona himalayana as a morph of Ochotona roylei and Ochotona huangensis as a morph of Ochotona thibetana.

We extracted the lagomorph clade from the maximum clade credibility tree built by Rolland et al. (2014) from a distribution of 100 trees after redating previous mammalian supertrees (Bininda-Emonds et al., 2007; Fritz et al., 2009). This phylogeny incorporates the alternative dating of the mammal tree of life proposed by Meredith et al. (2011), because the branching time estimates from Bininda-Emonds et al. (2007) are debated (dos Reis et al., 2012). There are substantial differences in topology between this tree and recent work focused on lagomorphs (Lanier & Olson, 2009; Ge et al., 2012; Ge et al., 2013). The cladistic positions of threatened monospecific genera are noticeably different, and three clades of pikas that are well supported by mitochondrial and nuclear data (Matthee et al., 2004) were not recovered in the chosen supertree. To evaluate the robustness of our results to differences in topology and branch lengths, we repeated all analyses with the phylogeny from Ge et al. (2013), built using three mitochondrial genes.

Phylogenetically informed analyses perform best if all living species in a focal clade or region are accounted for (Thomas et al., 2013). The lagomorph clade in the redated supertree is well-resolved, but did not include five species from our current taxonomy. The phylogeny proposed by Ge et al. (2013) includes only 59 (68%) species out of 87. We determined the

likely clade membership for the missing species based on taxonomic data and used two different approaches to include any missing species.

We used an expanded tree approach to graft the missing species to the lagomorph clade from the redated supertree. For this method, missing tips were randomly inserted along the branches belonging to its likely clade (e.g. Day et al., 2008). We generated random phylogenies for each group of missing species to graft them onto the tree. Species were not grafted directly at the tip of the existing tree. Instead, a random fraction of the existing branch length was removed and the simulated tree scaled to have the same length as the fraction removed. To complete the Ge et al. (2013) phylogeny, we used phylogenetic assembly with soft taxonomic inferences (‘PASTIS’; Thomas et al., 2013). This method allows us to incorporate the species lacking genetic data at the tree inference stage. Using the topology, fossil calibration points, and underlying sequence data from Ge et al. (2013), we integrated additional species using taxonomic statements as prior information on the affinities of taxa with no sequence data. We then executed the PASTIS output in MrBayes 3.2 (Ronquist et al., 2012) to produce a posterior distribution of complete ultrametric trees, and performed post- hoc dating of the consensus tree using PATHd8 (Britton et al., 2007).

Species occurrence data

We obtained a total of 139,686 occurrence records for all 87 lagomorph species. We collated data from the Global Biodiversity Information Facility (GBIF) Data Portal (http://data.gbif.org), from species experts, members of the IUCN Species Survival Commission (SSC) Lagomorph Specialist Group (LSG), and/or from the literature. After resolving taxonomic and spatial accuracy issues, 41,874 records remained. We checked all records against the latest IUCN taxonomy; we rejected records if names did not match after considering taxonomic synonyms. We also rejected obviously erroneous records for the target species if they fell outside the extent of the IUCN geographic range polygon. Finally, we discarded duplicated records and point data with spatial error estimates >2km(see Leach et al., 2014)(see Leach et al. 2014). These occurrence points were used to extract spatial data by taking the mean values across all grid cells in which these points fell for each species.

We consider point data a suitable proxy of species’ distribution for our purpose of quantifying spatially explicit data describing the environment and human activity. We did not use Extent of Occurrence (EOO) maps because they generate commission errors by assuming

homogenous distributions, and they frequently overestimate species presence (Rondinini et al., 2006; Gaston & Fuller, 2009). Murray et al. (2011) determined that commission errors arising from the use of polygon range maps significantly and non-uniformly influenced the quantification of spatial data when compared to the influence of omission errors arising from the use of point occurrences. In addition, several species of lagomorph are poorly known and point data accounts for all their known populations. Species distribution models can overcome sampling bias but they risk overestimating the small-scale occurrence of poorly- known, ecologically specialized species. In measuring species’ environmental characteristics, roadside bias in species observation points did not deteriorate the accuracy of predictive bioclimatic models (Kadmon et al., 2004), implying that even biased point data captures species’ bioclimatic envelopes adequately. Point data also contains temporal information, which we used when associating species occurrence with climatic information.

Spatially explicit data

We collected and measured spatially explicit variables for each species (see Data Definition in Supplementary Material), including data on geographic range size (Rondinini et al., 2011), number of sympatric terrestrial mammals, habitat conversion, human population density, ecoregional climatic stability, climate and topography. We used the IUCN spatial dataset of species distribution maps to create a global grid of mammal species richness (except bats and marine species) within 100km2 grid cells using a Mollweide projection. We downloaded projected human population density estimates for 2015 from the NASA Socioeconomic Data and Applications Center (SEDAC) (http://sedac.ciesin.columbia.edu/data/set/gpw-v3- population-density-future-estimates). Rather than using recorded population densities, we chose this dataset because it is available for all continents with no access restrictions, and it represents a time period very close to present. These estimates approximate recent recorded population densities, calculated independently for Southeast Asia (Gaughan et al., 2013). Takuya Iwamura provided the ecoregional climate stability dataset (Iwamura et al., 2013). To represent a coarse measure of threat from human activities, we included the proportion of species occurrence points occurring in converted habitats (following Hoekstra et al. (2005)) and the minimum human population density value present across each set of species occurrence points. This definition of converted habitats includes land area classified as cultivated or managed, as well as artificial surfaces present in a global land cover dataset. These areas are only a subset of (and should not be equated with) all habitats affected by

human activities. We identified island endemics, and whether or not their island is a landbridge island following (Cardillo et al., 2008a), i.e. separated from continental land masses after the last glacial period (Table A1).

We downloaded empirical climate and altitude data from WorldClim at 30 arc-second resolution (≈1km grid cells at the equator). We associated occurrence records pre-1950 with mean climate data from 1900-1949 and records post-1950 with mean data from 1950-2000. We used eight climatic variables in their raw format and calculated three composite variables (see Data Definition in Supplementary Material). We obtained Mean annual Normalized Difference Vegetation Index (NDVI) data from the EDIT Geoplatform (http://edit.csic.es/Soil-Vegetation-LandCover.html). Finally, we derived mean, minimum and maximum elevation, as well as a surface roughness index, from the WorldClim digital elevation model.

Phylogeny

We initially investigated the quantitative properties of the lagomorph phylogeny. First, we examined the proportion of monotypic species in all mammal orders in comparison to the Lagomorpha. We counted the total number of genera and the number of monotypic genera for each mammalian order based on the current IUCN taxonomy. We then compared the distribution of clade sizes in the lagomorph tree with a random diversification process. We measured the clade size for each internal node in the lagomorph phylogeny, and for 100 random trees with the same number of tips. We built the random trees using an equal-rates Markov (ERM) model, in which each branch had an equal probability of splitting. The Markov model does not explicitly incorporate extinction, but the modelled rate of net diversification is considered to be the rate of speciation minus the rate of extinction. If extinction is random across lineages it does not affect tree balance, so a model of net diversification is adequate for testing tree shape hypotheses (Mooers & Heard, 1997). We compared the density distributions of real and simulated clade sizes using the R package 'sm’ (Bowman & Azzalini, 2012). Significance testing was based on permutations on the D statistic for which and are kernel-type density estimators for frequency distributions (Bowman & Azzalini, 1997). The number of extant species varies considerably between sister clades in the mammalian phylogeny. Phylogenetic tree imbalance suggests that contemporaneous lineages had unequal chances of diversifying (Purvis et al.,

2011). We used Colless’ Index (Colless, 1982) as a conservative statistical measure of topological tree imbalance using the functions in the R package ‘apTreeshape’ (Bortolussi et al., 2006). Colless’ Index is estimated as the sum of absolute differences in species richness between sister clades at each internal node.

We employed a likelihood-based method to analyse the temporal pattern of speciation and extinction. We compared ten scenarios of diversification following Morlon et al. (2011) that span from simple models with null extinction, through time-varying speciation and/or extinction rates, to diversity-saturated models (Rabosky, 2009).Jones et al. (2005b) found significant variation in among-clade diversification rates for the Lagomorpha, and Purvis et al. (2011) identified a significant shift in diversification rates for Sylvilagus within the entire order. Diverse and recently radiating clades can mask the signal of extinctions in other parts of a phylogeny, but extinctions can be detected from after accounting for rate heterogeneity among clades and through time (Morlon et al., 2011). We analysed Sylvilagus and all other lagomorphs separately using the R package ‘PANDA’ (beta version available at https://github.com/hmorlon/PANDA).

Phylogenetic signal is the tendency for closely related taxa to resemble each other more than distant ones (Blomberg et al., 2003). Phylogenetic signal is high when closely related species have similar trait values. A trait with a weak phylogenetic signal may vary randomly across a phylogeny, or distantly related species could converge on similar values. Phylogenetic signal can be measured for binary traits, including a dichotomous measure of threatened versus non- threatened species. We characterized the clustering of extinction risk using the D statistic (Fritz & Purvis, 2010a) implemented in the R package ‘caper’ (Orme, 2012). D represents the sum of sister-clade disparities. When D equals 0, trait values are phylogenetically clumped and when D approaches 1, trait values are random. Significance is tested against values of D for phylogenetically random patterns, generated by shuffling the tip values along the phylogeny.

Principal Components Analysis

We used principal component analysis (PCA) to reduce an initial set of 16 continuous measurements of climate and topography across species’ ranges to a smaller number of composite variables. We ran PCA on the set values for each environmental layer for each species, measured in its native resolution. When needed, we transformed variables in the

original set for normality prior to PCA, implemented using the R package ‘FactoMineR’ (Husson et al., 2012).

Correlates of extinction risk

We investigated the correlates of extinction risk for all lagomorphs using a Generalized Linear Mixed Modelling (GLMM) approach, hereafter referred to as Bayesian Phylogenetic Mixed Models (BPMM) following Botero et al. (2013). This method incorporates phylogenetic information as a covariance matrix representing the amount of shared evolutionary history between taxa, and can accommodate an ordinal response variable. We used the IUCN Red List Categories as the dependent variable (see Verde Arregoitia et al., 2013). We fitted a single model with environmental PCA variables, habitat conversion, human population density and body size as possible correlates of extinction risk. We implemented the model in the R package ‘MCMCglmm’ (Hadfield, 2010). We ran all mixed model analyses for 2.5M iterations with a burn-in of 50,000 samples and a thinning value of 2,000 samples.

Correlates of clade richness

Genus size may be modelled at the species level using a quasi-Poisson error distribution, but information is lost when aggregating species level data to genus level data, and congeners with identical values for the response variable (e.g. all 17 species of Sylvilgaus will necessarily have the same genus size) should not be treated as separate data points. Genus size is an emergent property of genera, so it should be analysed at the level of genus. However, a genus level analysis with only 12 genera would lead to low statistical power. Species-specific measures of how unique a species is such as evolutionary distinctiveness (Isaac et al., 2007; Collen et al., 2011a) can be calculated using branch lengths and node sizes from phylogenies. These metrics reflect evolutionary isolation at the species level and capture the evolutionary processes shared by congeners as well as within-genus variation in diversification. High values for evolutionary distinctiveness identify taxa with few close relatives and long periods of independent evolution. Low values occur in recent radiations of speciose lineages. Evolutionary Distinctiveness (ED) is higher for monotypic genera in the Leporidae (rabbits, hares and jackrabbits), and higher values identify early-diverged species even within diverse genera including Ochotona and Lepus. We used a fair proportions

measure of ED (Isaac et al., 2007) to reflect evolutionary isolation and diversification at the species level. We built separate species-level models with environmental PCA variables, sympatric terrestrial mammal richness, and geographic range size as predictors of genus diversity, represented indirectly by ED values for each species. All statistical tests were carried out in R version 3.0.2 (R Core Team, 2013). Data and scripts are provided as Supplementary Material.

Results

The results from all our analyses were consistent for both sources of phylogenetic data. We report the statistics for tests using the phylogenetic data from our modification to the lagomorph clade in the redated supertree from Rolland et al. (2014) because of the widespread use of this topology in comparative analyses. It is worth noting that the estimated age of crown genus Ochotona in the supertree adopted here is exceptionally old (~35 Mya). The fossil record of ochotonids is reasonably dense, and paleontologists consider the genus Ochotona to have originated in the late Miocene; their oldest known fossils are no older than 10 Mya (Lopez-Martinez, 2008; Erbajeva et al., 2011). Molecular-clock estimates are generally 10-12 Mya, with the oldest estimate being ca. 20 Mya (Lanier & Olson, 2009; Ge et al., 2012). Fortunately, this does not affect our interpretation because the same conclusions are drawn from analyses using the tree from Ge et al. (2013). However, the Evolutionary Distinctiveness values for the Ochotona obtained from the tree in Rolland et al.’s (2014) may be overestimated and should therefore be regarded with caution. We summarize the results from tests using the completed Ge et al. (2013) phylogeny in Tables A2 and A3.

The number of monotypic genera in the mammalian phylogeny increased with clade size, and lagomorphs did not differ from this general pattern (Figure 2a). The frequency distribution of clade sizes in the lagomorph phylogeny was not significantly different from a geometric distribution (Figure 2b), but the lagomorph tree was less balanced than predicted by an Equal-Rates Markov model (Colless Index = 3.04, p = 0.001). We found no phylogenetic signal associated with IUCN Red List threat status. The distribution of threatened species was not significantly different from random (D = 0.90; p(D > 0) = 0.001; p(D < 1) = n.s.). The phylogenetic signal of extinction risk changed with the inclusion of Data Deficient species (either assuming that they were threatened (D= 0.89; (D > 0) = 0.005; p (D<1) = n.s), or not

threatened (D=0.89; p(D>0) = 0.004; p(D<1) = n.s)), but we found no significant departure from a phylogenetically random distribution.

a) b)

80 simulated

0.08 tree 60 40 0.04 density 20 numbermonotypicof species 0 0.00

0 50 100 150 200 20 60 100

number of genera in order clade size

Figure 2. a) Relationship between the number of genera in each of 26 mammal orders and the number of monotypic species for each order. b) Density distribution of clade sizes for all nodes in the lagomorph phylogeny compared with clade sizes for 1000 simulated trees

When we analysed diversification using a rate-heterogeneous approach, the most likely model for Sylvilagus featured no extinction (birth-only model) and an exponentially variable speciation rate such that net diversification switched from negative to positive over time. The best fit model for the remaining 70 species of extant lagomorphs was a birth-death model with linear variation of extinction and speciation rates over time, such that net diversification changes from positive to negative over time. Summing these two diversity trajectories, we obtain an inferred diversity curve consistent with a peak in diversity during the middle Miocene and a subsequent decline over the past ca. 10 million years (Figure 3).

100

all other lagomorphs 50 Sylvilagus

cumulative diversity number of species

50 40 30 20 10 0 time (Mya)

Figure 3. Phylogenetic inferences of diversity. Diversity trajectories were inferred for Sylvilagus separately to all other extant lagomorphs, and both curves summed to obtain a total diversity curve.

Principal Components Analysis

The first principal component axis (eigenvalue = 7.284) captured 45.5% of environmental variation, relating to productive environments with year-round higher temperature and precipitation (Table 1). The second axis (eigenvalue = 3.118) captured 19.5% of environmental variation, relating to elevation and topography (Table 1). Species with higher values on PC1 occur in more tropical conditions. Species with higher values on PC2 occur at higher elevations where precipitation is seasonal.

Correlates of extinction risk

Our model resulted in 1,000 samples from the posterior distribution for which autocorrelation between successive samples for all parameters was <0.05, and all effective sample sizes for fixed effects and ordinal cutpoint values were >900 (good MCMC convergence and mixing). We assessed significance from 95% Credible Intervals that included zero, and found two

significant associations. Extinction risk increased with a) increasing values of minimum human population density and b) decreasing percentage of converted habitat across species’ ranges. Species probabilities of falling into the highest threat categories were highest when minimum human population density was high, and probabilities of falling into the lowest threat category was highest at low minimum human population density values (Table 2, Figure 4).

Environmental variable PC1 PC2

Precipitation Seasonality -0.1623 0.6026 Minimum temperature of the coldest month 0.9261 0.1653 Mean Annual Temperature 0.9105 0.1167 Annual Evapotranspiration 0.7899 0.2712 NDVI 0.7991 0.1465 Climate Stability 0.432 0.6441 Sqrt (Precipitation of the wettest month) 0.7668 0.3094 log (Precipitation of the driest month) 0.4502 -0.3784 Sqrt (Mean Annual Precipitation) 0.8158 0.1273 Sqrt (Temperature Seasonality) -0.7936 -0.4147 Sqrt (Annual Water Balance) 0.798 0.1201 Sqrt (Surface Roughness Index) -0.3592 0.6558 Sqrt (Mean Elevation) -0.5587 0.7547 Sqrt (Minimum Elevation) -0.4499 0.6531 Sqrt (Maximum Elevation) -0.5143 0.548 (Maximum temperature of the warmest month)2 0.7009 -0.191

Table 1. Principal components analysis of environmental variables. Standardized loadings (correlations of X with PC) of variables with the greatest contributions are highlighted in bold. Component 1 explains 45.5 % of variance in the data; Component 2 explains 19.5%.

Parameter Posterior Lower CI Upper CI

mean (95%) (95%)

Extinction risk Intercept -0.262 -1.072 0.635

Principal Component 1 0.183 -0.046 0.404

Principal Component 2 -0.169 -0.374 0.042

Minimum human population density value 0.016 * 0.006 0.026

Proportion of occurrences in converted -0.023 * -0.046 -0.001 habitats Body size -0.001 -0.001 0.001 Evolutionary Distinctiveness Intercept 0.566 -1.846 2.872

Sympatric mammal richness 0.020 -0.059 0.099

Principal Component 1 -0.316 -1.453 0.614

Principal Component 2 0.008 -1.004 0.981

Geographic range size 0.072 -0.378 0.499

Table 2. Bayesian Phylogenetic Mixed Model results. Parameter estimates and confidence intervals calculated from the highest posterior density. * statistically significant.

a) b) 1.00

extinction risk 0.75 (Red List Status) LC NT 0.50 VU

probability EN CR 0.25

0.00 0 50 100 150 200 250 0 25 50 75 minimum human population density value percent converted habitat 2 (persons per km )

Figure 4. BPMM results; predicted probabilities of falling into each threat category when the effects of all other variables are kept constant.

Discussion

The Lagomorpha have a similar proportion of monotypic and threatened species to other mammalian orders. Despite a geometric clade-size distribution in the lagomorph phylogeny, the sizes of genera vary considerably. This disparity may arise because congeners share evolutionary processes such as geographical isolation and ecological divergence. An unbalanced phylogeny points to multiple diversification rates within the order. Purvis et al. (2011) detected a significantly higher diversification rate for Sylvilagus than for other genera within the Leporidae. Our diversification analysis supports this purported radiation, and the inferred diversification for the remaining taxa corresponds with fossil diversity curves and supports the notion of long term survival of some early-diverged species during a prolonged period of declining diversity. Rapid radiation in Sylvilagus and also Lepus, attributed by Ge et al. (2013) to expansion of grasslands, may account for the tree imbalance within the Leporidae. In novel environments, species with generalist feeding habits are more likely to become established, which provides the opportunity for speciation (Phillimore et al., 2006). This process may apply to Sylvilagus and Lepus, aided by relatively large body sizes, higher mobility and the ability to exploit abundant and poor-quality foods. The nutrient-poor grass families that became widespread at the end of the Miocene may have provided this opportunity (Ge et al., 2012; Ge et al., 2013).

We found that extinction risk was not phylogenetically clustered. At broad scales, phylogenetically random extinction risk can imply that species’ threat status was driven by traits that did not show a strong phylogenetic signal, such as the nature and intensity of the threats acting on species. At local scales, spatial clustering of related species and clades can make geographically restricted threats show a phylogenetic signal (Fritz & Purvis, 2010a). Davies et al. (2008) found that small mammals were more likely to be threatened if they had small geographic ranges, lived in temperate areas, and faced high human population densities. These characteristics do not usually show a phylogenetic signal. Intrinsic life- history traits like body size (and its allometric correlates) are phylogenetically conserved, but less likely to influence extinction risk for smaller species (Cardillo et al., 2005). Another explanation is that in a small group like lagomorphs, there is less variation in any given trait with which to explain variation in extinction risk.

All species-poor lagomorph genera are members of the family Leporidae. We identify two explanations for why these lineages are so species-poor, and summarize the paleobiogeography of all extant lagomorph genera in Appendix A. Bunolagus, Nesolagus, Pentalagus, Pronolagus and Romerolagus represent relict taxa characterized by survival in refugial habitats and close associations with specific vegetation or substrate types. In these cases, small genus size results from long-term lineage survival with low net diversification. These paleoendemic species face further loss of habitat from habitat conversion and global climate change. Although relict taxa are proven survivors from the evolutionary past (Hampe & Jump, 2011), their long-term refugia are being altered or destroyed by anthropogenic pressures. The distributions of Bunolagus, Caprolagus and Romerolagus represent a fraction of their historical range, largely because of recent changes in land use (Hughes et al., 2008; Anderson et al., 2009). Small-ranged species are at even higher risk because they have smaller populations, and localized threats may affect their entire distribution (Fisher & Blomberg, 2011).

Species of Caprolagus, Oryctolagus, Sylvilagus and Lepus are surviving members of more recent and diverse radiations that lost some, if not most, species to environmental changes and anthropogenic pressures (Lopez-Martinez, 2008). They are not usually associated with isolated refugial habitats, and in the current study, their present-day threat status varied with their tolerance for habitat modification. Severe ecosystem instability from environmental changes during the Pleistocene (2.6 million – 12,000 years ago), and human activity

throughout the Holocene (12,000 years ago to present) caused extinctions in these four genera, among other small-bodied mammals (see Appendix).

Variation in ecology and behavior might also contribute to differences in clade species richness. Some small mammals can shield themselves from environmental fluctuations through sleeping or hiding behaviors including: torpor, hibernation, burrow use, hollow use, etc. (Liow et al., 2009). Over geologic time-scales, these species are less extinction-prone, buffered from a deteriorating environment until conditions become suitable. They contribute to higher survivorship and lower origination probabilities (Liow et al., 2008). Detailed ecological field data are lacking for several species, but in general, burrowing and rock- dwelling habits are common for monotypic leporids. The more mobile, diverse and widespread species of Lepus rarely dig or occupy burrows or tunnels (Stoner et al., 2003). Qualitatively, the sleep/hide hypothesis is supported for the family Leporidae but not for the Ochotonidae. Pikas are diverse both at the species (e.g. within extant Ochotona) and genus level (in the fossil record). Extant pikas display burrowing and rock-dwelling behaviors but do not fit the picture of small ‘sleeper/hider’ mammals with lower diversification probabilities. Many other factors influence diversification, and ochotonid diversity may relate to the timing and location of the group’s radiation into cooler, heterogeneous landscapes (see Appendix).

We did not find any general correlates of evolutionary distinctiveness (as a measure of diversification). Environmental and biogeographic variables did not relate to species level values of Evolutionary Distinctiveness. Safi et al. (2013) found that environmental variables did not correlate with spatial patterns of Evolutionary Distinctiveness for amphibians and mammals, and diversification rates in carnivores are decoupled from geographic range overlap (within each clade and for all carnivores), rates of niche evolution, clade area, opportunity for geographic expansion, and ecological similarity of sympatric species (Machac et al., 2013). Fifteen lagomorph species are listed as threatened because of their restricted geographic ranges, yet Evolutionary Distinctiveness did not correlate with geographic range size. Unlike some very small passerine bird clades (Ricklefs, 2003) where ecological and spatial marginality are associated, species-poor lagomorph genera were not restricted to isolated, harsh environments with low mammalian diversity. The richness of sympatric mammals did not relate to evolutionary isolation and contrary to expectations, the lagomorphs that occupy environments with low mammalian diversity are species within the diverse genera Sylvilagus and Lepus that occur in high latitude Arctic environments, oceanic

islands or heavily modified habitat in Western Europe. Except for Pentalagus (isolated in a non-equilibrium archipelago where extinction > colonization), threatened and species-poor genera such as Bunolagus, Nesolagus, Caprolagus and Romerolagus were not marginal and instead occur in productive megadiverse areas (e.g. Southern Africa, Sumatra, India and Neotropical Mexico). These species are all relatively rare, possibly as a result of historically isolated habitats or strong biotic interactions with multiple competitors and predators (Mittelbach et al., 2007).

The proportion of species occurrences in converted landscapes had a significant relationship with extinction risk. However, the relationship we found does not follow other comparative studies in which habitat degradation predicts higher extinction risk (e.g. Murray & Hose, 2005). This opposing result most likely reflects the success of several species of Ochotona, Sylvilagus and Lepus in heavily modified agricultural areas. The four threatened species of Lepus (L. castroviejoi, L. corsicanus, L. flavigularis and L. hainanus) are found in areas with high population densities and widespread habitat degradation, perhaps reflecting the limits of tolerance to anthropogenic pressures. Alternatively, the problematic taxonomy of hares may influence this pattern. Except for the Tehuantepec hare (L. flavigularis), a restricted lowland species facing multiple interacting threats, all other threatened hares have been previously considered as subspecies or races of other widespread species.

Although our point data were represented in similar proportions across converted and non- converted habitat, spatial measures of habitat conversion and human population are sensitive to sampling bias. Sampling biases are often determined by accessibility; species are more likely sampled in easily reachable areas (Rondinini et al., 2006; Di Marco et al., 2013), which may not necessarily be converted habitats by our definition. Perhaps threatened and rare species with smaller ranges were sampled exclusively within non-converted habitat. Range declines may have already shifted sensitive species to remnant natural habitat, so this measure of threat from habitat degradation actually represents species’ specialization and tolerances to modification. Our results should be robust to the use of point occurrence data for quantifying human influence; values of human footprint measured using point occurrences for mammals in SE Asia approximated values measured using detailed habitat suitability maps, and these two proxies provided more consistent measurements than EOO maps (Di Marco et al., 2013). For species with few occurrence points the sampling variance for the external threat estimates tends to be larger than the variance for species with more point data. This was the case for some species in our data (figure A1), but rather than

implying biased threat estimates, this reflects the spatially discontinuous distribution of human activities across landscapes.

In agreement with previous studies on mammals (Cardillo et al., 2004; Cardillo et al., 2008b; Fisher, 2011), we found that extinction risk increased with exposure to higher human population density. This result is common for small mammals, although our model did not accurately predict threat status for several threatened species. For example, low risk was predicted for the critically endangered San José brush rabbit (Sylvilagus mansuetus), the riverine rabbit (Bunolagus monticularis) and the silver pika (Ochotona argentata). Each of these species faces idiosyncratic threats in addition to rapid habitat loss and fragmentation that were not quantified and incorporated as predictor variables. Our model overestimated the threat status of the Hainan hare (Lepus hainanus), perhaps reflecting its high tolerance to human activity and the species’ unexpected survival in artificially cleared deer ranches. Unaddressed processes in the extinction risk model include taxon and region-specific threats such as introduced carnivores on islands, and diseases such as Myxomatosis or Rabbit Haemorrhagic Disease in Western Europe. Some species are at risk from human activities even at low levels of human population density and habitat conversion. Poisoning of meadow-dwelling pikas and rabbits in uninhabited areas as a pastureland management strategy (e.g. in China) has caused significant declines in recent years (Davidson et al., 2012). Reviewing the lagomorph fossil record in its dynamic paleoenvironmental context helped us identify and separate the processes that explain the prevalence of small clades within the lineage. Recognizing declines over geologic time scales can enhance our interpretation of current patterns of species endangerment from anthropogenic threats. The limited heat tolerance of some lagomorphs makes them vulnerable to the effects of global climate change. Globally, climatic warming is predicted to reduce suitable habitat for high-altitude species and increase physiological stress for thermally specialized taxa (Beever et al., 2010; Kearney et al., 2010). Tropical, small-ranged mammals may be at risk because their phylogenetically conserved thermal niches restrict their upper thermal limits (Araújo et al., 2013). Overall, tropical mammals are disproportionately affected by overexploitation and habitat loss (Cooper et al., 2011). Consequently, extinction risk from climate change may be much higher than expected for lagomorphs, and this is yet to be modelled or considered in threat assessment. The lack of associations between threat status and climate (even when the first PCA axis described tropical climatic conditions) could be because lagomorphs do not

follow the typical mammalian latitudinal diversity gradient in which diversity peaks in the tropics but for lagomorphs peaks between 30-50o north of the equator (Rolland et al., 2014).

Conclusion

Genera within the Lagomorpha have differed with respect to their past speciation and extinction probabilities, leaving many species-poor and a few species-rich clades. The phylogenetic distribution of extinction risk in lagomorphs is not significantly clustered in particular lineages, but enough species in depauperate or monotypic genera are threatened by modern extrinsic factors to drive a relationship between genus size and extinction risk. The combined effects of human alterations to a multitude of environments create a pattern of biodiversity loss whereby extinction risk is unpredictable in relation to species’ biological attributes. Threatened lagomorphs representing unique branches of evolution are not necessarily poor competitors relegated to pioneer or marginal conditions, but human encroachment can substantially reduce their ecological distribution. For a large number of lagomorphs, what once was a stable niche for a specialized species is now a dead-end strategy, reminiscent of the taxon cycle hypothesis of habitat specialization.

Acknowledgements

Luis Verde Arregoitia was supported by funding from Consejo Nacional de Ciencia y Tecnología (CONACYT) scholarship 308685. Katie Leach was supported by Quercus, Queen’s University Belfast. Neil Reid was supported by the Natural Heritage Research Partnership (NHRP) between the Northern Ireland Environment Agency (NIEA) and Quercus, Queen’s University Belfast (QUB). Diana Fisher was supported by an ARC ARF fellowship and a Future Fellowship (DP0773920 and FT110100191). We thank Simon P. Blomberg for providing statistical advice and supervision. We are grateful to Andrey A. Lissovsky, Zoological Museum of Moscow State University for providing taxonomic expertise and additional species data. Alison Boyer and two anonymous referees contributed to an earlier version of the manuscript.

Appendix: Paleobiogeographic summaries for all lagomorph genera Paleobiogeographic summaries for all extant lagomorph genera.

Pikas – Family Ochotonidae The maximum diversity and geographic extent of pikas occurred during the global climate optimum from the late-Oligocene to middle-Miocene (Ge et al., 2012). When species evolve and diversify at higher temperatures, opportunities for speciation and evolution of thermal niches are likely through adaptive radiation in relatively colder and species poor areas (Araújo et al., 2013). Extant Ochotonids may be marginal (ecologically and geographically) but diverse because they occur in topographically complex areas where habitat diversity is greater and landscape units are smaller (Shvarts et al., 1995). Topographical complexity creates new habitat, enlarges environmental gradients, establishes barriers to dispersal, and isolates populations. All these conditions can contribute to adaptation to new environmental conditions and speciation in excess of extinction for terrestrial species (Badgley, 2010).

Hares and rabbits - Family Leporidae Pronolagus, Bunolagus, Romerolagus, Pentalagus and Nesolagus may belong to lineages that were abundant and widespread in the Oligocene and subsequently lost most (if not all) species. Lepus, Sylvilagus, Caprolagus and Oryctolagus represent more recent radiations which lost species unevenly during the late Pleistocene. Living species in these four genera display more generalist diet and habitat preferences, and are better represented in the fossil record. (Lopez-Martinez, 2008).

Island endemics Nesolagus netscheri and Pentalagus furnessi are both restricted to islands and threatened. Nesolagus shows a number of primitive morphological features, with no derived characters that link it clearly to any of the other leporids (Matthee et al., 2004). Both species of Nesolagus are amongst the least-known and rarest mammals in the world (McCarthy et al., 2012). Other lagomorph species confined to islands are three species of Lepus and two of Sylvilagus with relatively recent branching time estimates. They occur on islands that separated from continental landmasses at the last glaciation, or on oceanic islands for which dispersal from continents is common (Table A1). Ancestral leporids most likely originated in forested environments. Nesolagus and Pentalagus are the only genera that are both plesiomorphic and confined to dense vegetation (Matthee et al., 2004).

Table A1. Lagomorph species endemic to islands. Island type follows Cardillo et al. (2008a).

Species and common name Distribution Island Type

Lepus brachyurus Japan Oceanic

Japanese hare multiple islands (~150 km offshore)

Lepus hainanus Hainan Island, China Landbridge island

Hainan Hare

Lepus insularis Espiritu Santo Island, Mexico Landbridge island

Black jackrabbit

Nesolagus netscheri Sumatra, Indonesia Landbridge island

Sumatran Striped Rabbit

Pentalagus furnessi Amami-Oshima and Oceanic

Amami Rabbit Tokuno-Shima Islands, Japan (~650 km offshore)

Sylvilagus graysoni Tres Marias Islands, Mexico Oceanic

Tres Marias Cottontail (~100 km offshore)

Sylvilagus mansuetus San José Island, Mexico Landbridge island

San Jose Brush Rabbit

Phylogeographic analyses of south east Asian vertebrates, and the extinct form Nesolagus sinensis suggest that the Sumatran striped rabbit (N. netscheri) may have descended from a formerly widespread ancestral species on the Sunda Shelf . Rising sea levels and contracting forests in the Pliocene are likely causes for its geographic isolation (Sterling & Hurley, 2005). The Japanese Ryukyu archipelago was connected with the Eurasian continent during the middle to late Miocene, and became isolated in the Pliocene (Millien-Parra & Jaeger, 1999). The extinct form Pliopentalagus from the European and Asiatic Pliocene supports the

‘refugee’ status of the surviving Amami rabbit (Lopez-Martinez, 2008). Molecular data suggests that Pentalagus furnessi (and the ancient rodent Tokudaia osimensis) maintained relict populations on these islands after divergence and isolation, whilst relatives on the continental mainland went extinct (Yamada et al., 2002).

Europe Oryctolagus has the oldest fossil record among extant leporid genera, first appearing in Europe about 3.5 Ma. O. cuniculus is first recorded in southern Spain in the Middle Pleistocene (about 0.6 Ma), and associated with a relict, warm-adapted fauna usually restricted to the tropics. In Europe, fossil materials record the extinction of at least five Oryctolagus species during this period (Lopez-Martinez, 2008).

Indian subcontinent The fossil record of the hispid hare (Caprolagus hispidus) points to a Pliocene/early Pleistocene origin (Lopez-Martinez, 2008). Its distribution is currently restricted to a few remnant fragments of tall, alluvial grasslands in the north of the Indian subcontinent. Caprolagus experienced a similar decline in distribution to other grassland mammals in the region, especially the Indian Rhino (Rhinoceros unicornis) and pygmy hog Porcula salvania, which were associated with ongoing human-mediated changes in grassland land use during the Holocene (Peet et al., 1999).

Africa Ancestral stocks of Pronolagus and Poelagus differentiated from an Asian leporid entering Africa during the Miocene (Matthee et al., 2004). Red rock “hares” (Pronolagus) in southern Africa are the most speciose of the species-poor leporids, with four species. The fossil record for Pronolagus is confined to southern Africa and the oldest report is for the early Pliocene. The fossil history of Poelagus is uncertain (Winkler & Avery, 2010). Plio-Pleistocene climatic oscillations heavily influenced small mammal assemblages in Africa (Montgelard & Matthee, 2012), but the rock-dwelling nature of these species may aided their long-term survival in stable microhabitats. Multiple topographical features may have served as widespread refugia for rock dwellers during severe environmental changes. Diversification in Pronolagus may be similar to that of other rock-dwelling vertebrates in southern Africa, driven by changes in habitat availability from coastal regression and transgression during the Pliocene, or population isolation during peaks of aridity in later periods (Diedericks &

Daniels, 2014). Bunolagus is a later arrival to Africa, possibly in the middle Pleistocene. The fossil record is unclear, but it appears that the Riverine rabbit has had a consistently restricted distribution in central South Africa (Winkler & Avery, 2010).

North America Analysing disjunct distributions of Mexican mammals, Ceballos et al. (2010) suggest that some species in central Mexico became isolated long before the Pleistocene. The Volcano rabbit (Romerolagus diazi) and other endemics survived the complex environmental changes of the Plio-Pleistocene in the mountains of the Mexican Transvolcanic Belt. These volcanoes acted as a likely refuge after the species was isolated during late Tertiary aridity. Pygmy rabbits (Brachylagus) have a disjunct distribution in boreal sagebrush habitat throughout the American Great Basin. The isolation of Brachylagus may be more recent, and has been associated with the loss of sagebrush habitat during end-Pleistocene environmental changes and mid-Holocene expansion of pinyon-juniper woodland within the region (Grayson, 2006).

Table A2. Examination of the properties of the lagomorph phylogeny. Results obtained using alternate phylogeny (mitochondrial gene tree).

Frequency distribution of clade sizes Test of equal densities; p=0.31

Topological tree imbalance Colless index = 1.569564, p = 0.032

Phylogenetic signal of extinction risk D= 0.79; p(D > 0) = 0.001; p(D < 1) = n.s.

Table A3. Bayesian Phylogenetic Mixed Model results obtained using alternate phylogeny (mitochondrial gene tree). Parameter estimates and confidence intervals calculated from the highest posterior density.

* statistically significant.

Parameter Posterior Lower CI Upper CI

mean (99%) (99%)

Extinction risk

Intercept -0.260 -1.201 0.532

Principal Component 1 0.180 -0.042 0.396

Principal Component 2 -0.183 -0.413 0.031

Minimum human population density value 0.016 * 0.006 0.026

Proportion of occurrences in converted -0.023 * -0.046 -0.0002 habitats Body size -0.0002 -0.0007 0.0002

Evolutionary Distinctiveness

Intercept 0.1779 * 0.0756 0.2847

Sympatric mammal richness 0.0001 -0.0002 0.0004

Principal Component 1 -0.0020 -0.0059 0.0014

Principal Component 2 0.0015 -0.0022 0.0059

Geographic range size -0.0002 -0.0020 0.0016

15 10 5 log. variance in HPD 0

2 4 6 8 10 log. number of occurrence points

Figure A1. Variance in human population density measured for species using occurrence points.

Chapter 5. Likelihood of competition between invasive and native rodents revealed by global comparisons of morphological, ecological and phylogenetic similarity

Summary

Recent studies have challenged the idea that closely related species have similar ecologies. Ecological and phylogenetic similarities often are not related, thus limiting the application of phylogenetic data in ecological research. Here, we quantify the ecomorphological similarity of 146 rodent species from four different major bioregions and investigate how morphological similarity and phylogenetic relatedness influence interspecific competition. Competition is a key biotic interaction with evolutionary and ecological consequences, and it plays an important role in current anthropogenically-facilitated biological invasions. To quantify ecological similarity, we used data on morphological characters hypothesized to be ecology-dependent. These cranial, dental, and external (e.g. tail and ear length) characters predicted diet and locomotion type in a sample of Australian rodents with detailed ecological data. Comparative analyses of evolutionary trends show that our chosen sets of characters involved in feeding and locomotion evolve at different rates, but under shared functional demands. We find that morphological similarity is a better predictor of competition than phylogenetic relatedness, and apply this finding to identify similar species - in morphology and hence ecology - that are likely to compete with problematic invasive rodents.

Introduction

Closely-related species often share many traits as a result of common ancestry. In some cases, phylogenetic relatedness reflects ecological similarity due to conserved phenotypes (Losos, 2008). This tendency for species to retain ancestral ecological characteristics is a key concept in the study of speciation, biogeography, and community structure (Wiens & Graham, 2005). As more comprehensive phylogenetic data become available, detailed knowledge of the evolutionary relationships between species can inform ecological studies (Webb et al., 2002). However, the relevance of phylogenies to ecological studies is limited by convergent evolution. Convergent evolution results when distant phylogenetic relatives solve ecological problems in a similar way (Schaad & Poe, 2010). Close relatives share more

phenotypic features than distant ones, but beyond a certain threshold; increasingly unrelated taxa are not more divergent (Kelly et al., 2014).

When phylogenetic information cannot inform ecology and data are scarce, another way of making plausible ecological inferences is to identify morphological features specialized for the occupation of particular niches. Form may not always correlate with function, but this approach has been an important tool for inferring ecological attributes in birds and mammals (e.g. Safi & Kerth, 2004; Habib & Ruff, 2008; Reese et al., 2013). Morphological traits are often accessible and functionally important, with the potential to be measured in significant numbers of ecologically undescribed and poorly-known species (Yates et al., 2014). In this paper, we focus on traits likely to be associated with feeding and locomotion in rodents.

To understand biotic interactions between coexisting organisms, we need to measure ecological similarity between species. Competition is one of the most important of these interactions, because it may determine range limits, community structure, and demography (Gause, 1934; MacArthur, 1972). Contemporary biological invasions exemplify how competition for food and space generally occurs between invaders and those species in the recipient community that are ecologically similar (Shea & Chesson, 2002). On an evolutionary timescale, competition may also drive evolutionary diversification and adaptive radiation. Brown and Wilson (1956) proposed that ecological character displacement, or the evolutionary divergence between competing species, drives speciation and dictates the structure of communities. The presence of ecologically similar species can also influence the diversification of lineages that have colonizing new areas (Schenk et al., 2013). Interspecific competition can either cause character displacement; or inhibit the rate of evolution by the increased packing of niche space (Cooper & Purvis, 2009).

Competition often takes place between closely related species, since their ecological requirements tend to be similar. However, ecological similarity may predict competition better than phylogenetic relatedness alone, because the intensity of interspecific competition depends on the similarity in ecological requirements between competing species (Valkenburgh, 1988; Dayan et al., 1992).

The concept of ecological similarity can be applied in a contemporary conservation context to predict the likelihood of adverse effects of introduced species on native species. Three rodents are now globally widespread because of their commensal relationship with humans: the black rat Rattus rattus, the brown rat Rattus norvegicus and the house mouse Mus

musculus. Rodent pests cause widespread and costly damage to agriculture, forestry and human health (Capizzi et al., 2014). These species pose a major global threat to biodiversity. They can cause extinctions and displace native rodents and other fauna through predation, herbivory, and competition (Stokes et al., 2009a). A third of the species accounts of extinct rodents in the IUCN red list note that introduced rats are a likely cause of extinction, due to competition, predation or disease spread by rats to native rodents (IUCN, 2013). Disruption of small mammal communities has broad ecosystem-level effects, given the position of these animals in food chains, their importance in soil tillage, seed predation and dispersal, and pollination (Shepherd & Ditgen, 2012).

In this study, we characterize the ecology of 146 rodent species using a morphological approach. First, we use twelve morphological traits to estimate ecological similarity, and validate the ecological relevance of our chosen traits for a sample of 28 Australian species. We compare ecological versus phylogenetic similarity by testing the congruence between distance matrices that describe phylogenetic and ecomorphological similarity. There should be significant correlation between phylogeny and ecology if closely related species share similar ecologies, and the strength of the correlations between matrices indicates how phylogeny predicts ecology. Because the characters we measured may be under selective pressure for feeding and locomotion, we calculated their rates of evolution. Different rates of evolution for craniodental vs. skeletal and external traits could have important effects on the coexistence of similar species, for example: in partitioning food resources in species with similar body constructions or microhabitat segregation in species with similar diets. We apply our measures of ecomorphological similarity to explore the potential for competition between species in our sample, and expand this approach to identify species likely to compete with invasive species. Finally, we discuss the application of our method in conservation science and ecology, and the relevance of characterizing poorly-known species using morphological data.

Methods

All statistical tests were carried out in R version 3.0.2 (R Core Team, 2013). All data and code are provided as Supplementary Material.

Study taxon

Rodents are the most diverse extant mammal group, with over 2100 species. The order spans a wide array of body sizes and shows great diversity in locomotor habits; having evolved aquatic, arboreal, fossorial, hopping, and gliding forms (Carleton & Musser, 2005). The average rodent can climb, dig, and swim without extensive morphological specializations (Samuels & Van Valkenburgh, 2008). Nonetheless, specialist forms have evolved (often independently) and can be found in nearly every terrestrial habitat.

Ecological similarity

We applied a morphological approach to estimate ecological similarity between species. We focused on ecology-dependent morphological characters, measured from preserved museum specimens. We chose measurements which would maximize variability between species, and minimize variability within species. Each measurement correlates with a known ecological strategy observed in the field or derived from data on well-studied species (Miljutin, 1997). We assume that variation in external characters reflects locomotor strategies, while craniodental characters reflect feeding strategies (see Table 1 for character descriptions).

Previous studies on rodents identified consistent differences in morphology that relate to functionally important traits. Climbing, digging, swimming and jumping Sigmodontine rats have statistical differences in postcranial skeletal characters (Carrizo et al., 2013). Samuels and Van Valkenburgh (2008) also inferred ecological strategies from postcranial appendicular measurements. Feeding strategies show strong correlations with morphology. Molar crown descriptors discriminated the diets of extant muroid rodents (Tiphaine et al., 2013) and incisor morphology reflected the diet of 11 caviomorph genera at a fine scale. For example, long incisors with a wider occlusal diameter predicted the fruit and leaf diet of the spotted Paca (Cuniculus paca) in discriminant analyses (Croft et al., 2011).

Phylogenetic and morphological data

We used published phylogenetic data to quantify evolutionary relatedness between species. We extracted the rodent clade from the maximum clade credibility (consensus) tree provided by Rolland et al. (2014), built from a distribution of 100 trees after redating and randomly resolving previous mammalian supertrees (Bininda-Emonds et al., 2007; Fritz et al., 2009). We collated measurements for 146 rodent species, using data from previous studies (Miljutin, 1997; Miljutin, 1998, 1999, 2006, 2008; Miljutin & Lehtonen, 2008; Miljutin, 2011) and

from 584 specimens measured at the mammal collections of the Queensland Museum, Australian Museum, and the ‘‘Alfonso L. Herrera’’ Zoology Museum (UNAM, Mexico). The choice of species was opportunistic, based on data availability and how much material was available in collections in good condition. We were unable to explicitly select a sample of species with wide ecological variation and varying levels of interaction with invasive rodents.

Figure 1. a) Craniodental and b) external characters measured. Modified with permission from Miljutin (1997)

Table 1. Description of morphological characters. All characters were measured in millimetres except ACP, measured in degrees.

External characters

HB head and body length: distance from the tip of the nose to the base of the tail

T length of tail: distance from the base of the tail to its tip without terminal hairs

E ear length: distance from the basal notch to the tip without terminal hairs

Vib length of vibrissae: length of the longest vibrissa from base to tip in its natural position

HF length of hind foot: distance from the heel to the tip of the longest digit without claw

FF length of forefoot: distance from the notch between the radius and carpus to the tip of the longest digit without claw, measured parallel to the manual axis

UM length of forefoot claw: distance from the base of the longest claw on its inferior surface to the tip

Craniodental characters

CBL condylobasal length: distance from the border between the anterior surface of the upper incisors and intermaxilla to the posterior surfaces of the occipital condyles measured parallel to the cranial axis

LR length of rostrum: distance from the tip of the nasal bones to the anterior edge of the zygomatic arch, measured level with the nasals and parallel to the cranial axis

ZB zygomatic breadth: greatest breadth across the zygomatic arches

BIT breadth across incisor tips: distance across the tips of the incisors

LMT alveolar length of maxillary tooth row: distance from the anterior edge of the alveolus of the maxillary tooth row’s first tooth to the posterior edge of the alveolus of the third molar

HMd height of mandibular corpus: distance from the anteriodorsal part of the first molar to the ventral surface of the mandibular corpus, measured perpendicular to the masticatory surface of the mandibular tooth row

ACP angle of condylar process: angle between the tangent to the ventral surface of the mandibular corpus parallel to the masticatory surface of the mandibular tooth row and the line connecting the tangent’s contact point with the axis of the mandibular condyle

Data processing

To compare species of different sizes, we converted raw values to ratios. We divided the absolute value of each external and character by the head and body length, and the absolute value of each craniodental character (except the angle of the condylar process) by the

condylobasal length. We multiplied each resulting proportion by 100 to express it as a percentage. The use of ratios can be problematic without acknowledging the difference between statistical control and proportionality, which focuses on the relationship between two traits. Misuse of ratio variables can lead to data with poor distributional properties, increased measurement error and the potential for spurious correlations (Smith, 2005).

We used ranging (Gower, 1971) to normalize the data in gross size and variability, as well as to remove heteroscedasticity. In ranging, the smallest value for the character is subtracted from each value and the result is divided by the range: Xr = [(X’–X’min) / (X’ max–X’min)] , where X’ is the ratio and Xr is its ranged value. The maximum and minimum values (X’max and X’min) are not from the sample but for the majority of rodents. Minimum values are for rodents in general, and maximum values reflect a derived condition. We assumed that a value of about 75% of the actual known maximum in rodents represents a derived condition of a character, representative of specialization to a particular ecology. This makes the results comparable with data on other rodents. We performed cluster analysis of the ranged character values using Manhattan (City-block) distances and Ward’s minimum variance method in order to study the morphological pair-wise similarities and the underlying hierarchical classification structure (Sneath & Sokal, 1973).

Ecology and morphology

Miljutin (1997) identified ecology-dependent morphological characters using data from Baltic rodents. Our sample has species from other regions, different taxonomic affiliations, and ecologies not represented in the Balkan dataset. We needed to confirm the ecological relevance of the measured characters for other rodents. Before using morphology to investigate ecology and evolution, we validate the ecological relevance of the chosen traits with data from 28 Australian rodents from our sample for which diet, habitat and locomotion are known in detail from a recent standardized database [unpublished dataset, D. Fisher and M. Lawes]. We used discriminant analyses to test whether these 28 species could be classified into their correct ecological category based on morphological measurements.

Losos (2008) reviewed multiple case studies of ecological niche conservatism in plants and animals and found that ecological and phylogenetic similarities are not often related. If phylogenetic signal is present in a dataset, it must be accounted for in statistical analyses. Previous studies on vertebrates have detected phylogenetic conservatism in dietary and

locomotor traits (Lindeman, 2000; Lovegrove, 2004; Vitt & Pianka, 2005). Morphological traits are less labile than climatic envelopes or behaviour (Blomberg et al., 2003) and more likely to reflect the amount of shared evolutionary history between species.

The species with reliable ecological data in our dataset represent a small, non-random sample of spatial and taxonomic levels. Because small and biased samples can lead to over or underestimation of phylogenetic signal, we did not test for it explicitly. Instead, we estimated a value of Pagel’s lambda following Motani and Schmitz (2011) that would minimize the Residual Sum of Squares (RSS) between the phylogenetically-corrected matrices containing the continuous measurements and the categorical ecological data for each species. A lambda value of zero indicates that phylogeny has no importance in the model, equivalent to a nonphylogenetic analysis. If lambda equals one, phylogeny is an important component of the model, with the residuals following a Brownian motion model of evolution (Revell, 2010; Hall et al., 2012). In this case, phylogenetic correction is necessary to avoid issues with non- independence (see Freckleton, 2009).

Evolutionary trends

Traits that share common developmental pathways, or those that experience similar functional demands may be expected to evolve jointly with one another (Olson & Miller, 1958). Measurements of craniodental and external characters don’t necessarily represent separate character systems with unique biological properties. Although the crania, postcrania and dentition of mammals are equally prone to evolutionary change (Sánchez-Villagra & Williams, 1998), they may still evolve at different rates because of differences in structural or developmental constraints, or selective pressures. These differences in rates of evolution could have important effects on the coexistence of similar species.

Phenotypic traits subject to strong selective pressures are of little value in cladistic analyses aiming to construct a phylogeny using morphological data. If a trait can be shown to have arisen on multiple occasions (e.g. when phylogenetically unrelated species radiate into similar niches), it may not be a reliable indicator of phylogenetic affinity (Williams, 2007). Because we use ecology-dependent traits in this study, we do not expect strong phylogenetic patterns. However, if the characters we measured are under intense selective pressures for feeding and locomotion, their rates of evolution may vary. Resource partitioning could drive

faster dietary divergence within similar habitats while external body constructions remain similar.

The degree to which sets of organismal traits covary with one another, or morphological integration, can be estimated for two sets of variables using partial least squares and assessed using phylogenetic permutation. We estimated the degree of phylogenetic morphological covariation between craniodental and external characters following Adams and Felice (2014). We then tested if the evolutionary rates between craniodental and external characters vary. 2 Adams (2013) introduced a matrix-based metric (σ mult) to quantify phylogenetic evolutionary rates for high-dimensional multivariate data. We used this method to calculate the evolutionary rates for craniodental and external measurements, and to test the null hypothesis that rates of evolution are equal for both types of measurements.

Ecomorphological and phylogenetic similarity

To test for congruence between phylogenetic and ecomorphological distances, we used Congruence Among Distance Matrices (CADM) tests (Campbell et al., 2011). CADM is a generalization of a Mantel test that allows for simultaneous comparison of multiple distance matrices. We used 9999 permutations to assess the significance of matrix congruence between phylogenetic distance and morphological distances calculated using: all characters, craniodental characters only, and external characters only.

Ecological similarity and competition

In a literature search, we found 61 instances of competitive biotic interactions between species pairs for which we had morphological data, twelve of which involved introduced species (Supplementary Table S1). We recorded competition following Grant (1972), when published studies identified clear microhabitat segregation between sympatric species, as well as direct behavioral interactions, observations from removal experiments, or changes in community structure that imply competitive interactions. We extracted the distances between species pairs for those that are known to compete, in order to obtain a preliminary estimate of the frequency of competition across a range of ecomorphological distances between species pairs. To identify similar species that may therefore have been more likely to compete with invasive rodents, we extracted the ecomorphological distances between all species and the three possible non-native competitors.

Results

Ecology and morphology

We used nonphylogenetic discriminant analyses because we found that a value of zero for Pagel’s lambda minimized the Residual Sum of Squares for diet type and and locomotor mode (Figure 2). The discriminant model with 28 Australian species showed significant separation of dietary (Wilk’s Lambda = 0.043, F(3,39) = 2.05, p= 0.014) and locomotor (Wilk’s Lambda = 0.001, F(4,48) =4.45, p<0.001) categories in morphological space.

Figure 2. Residual sum of squares between phylogenetically-corrected matrices of species measurements and ecological categories plotted for Pagel’s lambda values from cero to one.

Descriptive Discriminant analysis for dietary categories returned two significant discriminant functions that accounted for 99.7% of variance in the data. Herbivorous and carnivorous species are clearly separated, and nonphylogenetic morphological clustering is a characteristic of ecological similarity (Corbin et al., 2013). Omnivores that share diet items overlap in morphological space (Figure 3). Discriminant function 1 accounted for 86.5% of the total variance. Measurements of the masticatory apparatus (LMT, ACP and HMd) positively influenced scores on this axis (Table 2). This discriminant function separated herbivorous from carnivorous species, with herbivorous species having longer mandibular tooth rows, more pronounced condylar process angles and a higher mandibular corpus at the first molar; omnivorous species had intermediate values on this axis.

Discriminant function 2 separated herbivorous species from all other species, mostly on the basis of broader incisors (BIT). Croft et al. (2011) found similar results in caviomorph genera, and suggest that broad incisors are useful for cropping pliable but tough foods such as

grasses or leaves. Two external characters showed significant discriminant power in relation to diet type: ear length (E) and vibrissae length (Vib). These sensory traits may play a role in food location and prey-capture within different microhabitats (Sánchez-Villagra & Williams, 1998).

Figure 3. Bivariate morphospace plots of DF1 and DF2 for discriminant analyses of diet and locomotion type.

Locomotor strategies were associated with both external and craniodental morphology. The first two Discriminant Functions (DF) explain 91.2% of the variance in the data. DF1 shows strong positive correlation with ear length, vibrissae length, rostrum length, and the angle of the condylar process. Two craniodental characters such as the length of the rostrum and the angle of the condylar process had a strong influence on predicting locomotion type, perhaps because they are involved posture, digging, and because the skull carries important sensory organs (Dayan & Simberloff, 1994). Burrowing rodents use their snout for pushing and ramming excavated soil, while holding a specialized mandibular position when attacking soil and hard obstacles with their deeply inserted incisors (Verzi & Olivares, 2006). DF2 separated jumping species with elongated hind feet, the character with the strongest correlation with the discriminant function values.

Table 2. Discriminant analysis results character Correlation Ratio Wilk's lambda F p DF1 DF2 Dietary categories T 0.341 0.659 4.315 0.014 0.026 -0.68 E 0.304 0.696 3.636 0.026 0.435 -0.44 Vib 0.346 0.654 4.41 0.013 0.262 -0.648 UM 0.076 0.924 0.689 0.567 0.101 0.247 HF 0.06 0.94 0.53 0.666 0.049 -0.309 FF 0.044 0.956 0.381 0.768 0.159 -0.116 LR 0.285 0.715 3.316 0.036 0.517 -0.228 ZB 0.235 0.765 2.557 0.078 0.492 0.121 BIT 0.302 0.698 3.599 0.027 0.306 0.605 LMT 0.537 0.463 9.667 0 0.638 0.541 HMd 0.499 0.501 8.294 0.001 0.702 0.336 ACP 0.506 0.494 8.53 0 0.744 0.11 Locomotor strategies T 0.448 0.552 4.879 0.005 0.297 -0.494 E 0.669 0.331 12.135 0 0.603 -0.555 Vib 0.549 0.451 7.308 0.001 0.55 -0.437 UM 0.045 0.955 0.28 0.888 0.05 0.048 HF 0.671 0.329 12.227 0 0.229 -0.812 FF 0.133 0.867 0.921 0.468 0.158 0.33 LR 0.339 0.661 3.076 0.035 0.601 -0.02 ZB 0.399 0.601 3.991 0.013 0.518 -0.383 BIT 0.252 0.748 2.018 0.124 0.028 0.498 LMT 0.23 0.77 1.793 0.163 0.448 0.242 HMd 0.436 0.564 4.633 0.006 0.545 0.389 ACP 0.447 0.553 4.854 0.005 0.65 0.264

Similarity

The CADM test rejected the null models of incongruence among the path-length distance matrices representing phylogenetic and ecomorphological similarity. The association between each pair of distance matrices is significantly higher than would be expected at random, and we found partial congruence between all four matrices (W = 0.529, p = 0.010); whereby Kendall’s coefficient of concordance W ranges from 0 (no agreement) to 1 (complete agreement). Mantel correlations between matrices show that craniodental characters have a stronger phylogenetic pattern than external characters (Table 3). The connection between phylogenetic relatedness and morphological similarity decays rapidly, so the relationship between shared morphological features and tree distance is often not distinguishable from a random pattern across much of the tree (Kelly et al., 2014). This is evidenced by the low Mantel correlation (<0.5; Table 3).

Table 3. Mantel correlations between distance matrices from phylogeny and morphological character sets, all (Holm-corrected) probabilities < 0.01 based on 9999 permutations Phylogenetic All Craniodental Phylogenetic 1 All 0.415 1 Craniodental 0.395 0.584 1 External 0.164 0.445 0.233

Evolutionary trends

Multivariate phylogenetic evolutionary rates for craniodental and external characters differed 2 2 significantly (σ mult.A / σ mult.B =1.42; Psim <0.01). The relative rate of craniodental evolution is 2 slightly faster than the relative rate of evolution of external body configurations (σ craniodental = 2 0.003 and σ external = 0.002). These similar evolutionary rates indicate significant morphological integration between craniodental and external characters (rPLS =

0.581; Prandom = 0.048); integration is the cohesion among traits or sets of traits that results from interactions between the biological processes (development , structure, and function) that produce them (Klingenberg, 2008).

Similarity and competition

The highest frequency of competition, as estimated by probability density functions, does not occur for very closely related species with distance values close to zero. Bivariate comparisons indicate that morphological similarity may be a more consistent predictor of competition than phylogenetic relatedness, and that competition is more frequent at lower dissimilarity values for craniodental configurations (Fig 4).

morphological

0.15 craniodental 0.020 phylogenetic external 0.015 0.10 0.010 ProbabilityDensity 0.05 ProbabilityDensity 0.005 0.000 0.00 0 50 100 150 0 10 20 30 40 dissimilarity morphological dissimilarity

Figure 4. Probability density functions of distances between species pairs that are known to compete.

Competition with introduced rodent species

Given the small number of recorded biotic interactions between Rattus rattus, R. norvegicus and Mus musculus with the rest of the species in our dataset, we aggregated them in this analysis. To characterize the morphology of the taxa that are likely to interact with introduced species, we calculated a kernel density estimate of their craniodental and external distance values in two-dimensional space. We then retrieved the values of the estimated density at the coordinates for the remaining species (Fig 5). We consider these densities as a proxy to how similar each species is to those that are known to interact with introduced rats and house mice. We used the minimum z-axis score (density estimate) for the species with known biotic interactions with introduced rodents as a conservative estimate to split the dataset into species

likely to compete and species not likely to compete. Nearly sixty percent of native species in the data set (85/143) are likely to compete with introduced species (Supplementary table S2).

Figure 5. Bivariate plot of external and craniodental dissimilarity values for 143 species. Sizes of grey circles around each point scale with kernel density estimate built using values for species known to compete with introduce rodents (filled triangles). Solid points = species predicted to compete with introduced rodents, hollow points = species not predicted to compete with introduced rodents.

In this preliminary exercise, we find that introduced Rattus and Mus are likely to compete with a wide variety of species with generalized morphologies that occupy different habitats and climates. Introduced rodents do not generally occupy the same ecomorphological space as species with specialized lifestyles, or with those that show a marked disparity in their craniodental and external features. These species include those with relatively unspecialized cranial features and dentition but highly modified body constructions (e.g. dwarf hamsters, genus Cricetulus) and a small number of species with generalized body constructions but specialized feeding adaptations (e.g. moss mice, genus Pseudohydromys). We did not have sufficient data to draw out sympatric pairs of native species with different levels of impact from invasive species (i.e. a test of the hypothesis).

Discussion

The relationship between morphological, ecological, and phylogenetic similarity is not straightforward, but can be investigated using a combination of molecular, morphological and ecological field data. We find that species compete with morphologically similar species that are not necessarily their close relatives. Because of the weak correlation between phylogeny and ecomorphology, we cannot adequately predict the ecological similarity between species using only phylogenies (e.g. Davis, 2005).

However, there are other ecologically important traits (e.g. behaviour or physiology) with strong phylogenetic signal involved in species’ niches, to the point at which phylogenetic information was a suitable proxy of species niches when combined with data on environmental complexity to predict rodent community composition (Stevens et al., 2012). Stevens’ (2012) study on Mojave rodent communities found that as species diversity increases along a gradient of increasing environmental heterogeneity, communities are composed of increasingly related species, implicating niche packing of similar species into more diverse communities (Brown, 2012).

Our examination of Australian species supports the ecological relevance of the chosen characters, even when our sample was small and did not include representatives with extremely specialized lifestyles (e.g. gophers, which burrow). The tests for phylogenetic signal in diet and locomotion assume a Brownian motion model of evolution (kappa=1). Low lambda values can only be considered as evidence against a hypothesis of heritability with rapid evolution in which traits reflect the selective constraint of the environment rather than their history. We suggest that inferring ecology from morphology is an accessible alternative to currently available data, which may be lacking or too broad to be useful. For instance, even in well studied groups like primates we lack detailed data on food items consumed by many species (Kamilar & Cooper, 2013), and widely-used designations of substrate use that classify arboreal vs. terrestrial species conceal the variation in the nesting or foraging preferences of the species (e.g. Cardillo et al., 2008b; Johnson & Isaac, 2009).

Analysing craniodental and external characters separately provided useful evolutionary insights. We find significantly different evolutionary rates between these characters. This supports previous work that highlighted the importance of differences in the masticatory apparatus (dentition, mandible, cranium) in resource partitioning (Dayan & Simberloff,

1994). Species with the same locomotor strategy can occur in a wide array of habitats without stark differences in external morphology, as long as they are divergent in their feeding morphology. For example, Rattus genera in Australia and New Guinea (once connected during quaternary glaciations) colonized every available habitat while remaining morphologically conservative (Rowe et al., 2011). We found that craniodental characters evolve at a faster rate than external characters, and Wu et al. (2014) also found that locomotor and dental character evolution is decoupled for jerboas (family Dipodidae). Despite having different rates of evolution, the significant evolutionary covariation (integration) between craniodental and external characters with different developmental pathways supports the notion of common functional demands for our sets of ‘diet-related’ and ‘locomotion-related’ traits.

We focused on one aspect of phylogenetic information: the evolutionary relationships between species. However, Tobias et al. (2014) found that phenotypic divergence in ovenbirds was best predicted by how long ago species evolved. Trait differences among lineages could simply accumulate a result of genetic drift and ecological adaptation. Speciation is almost always vicariant; the geographical range of a taxon is split by the formation of a barrier to gene flow or dispersal, so related lineages may be ancient by the time that they interact in sympatry, especially if competitive exclusion limits how much they overlap (Davies et al., 2007). Rather than representing increased evolution away from the morphology and niche space of a competitor, phenotypic divergence among sympatric species may simply reflect trait differences acquired in allopatry (Tobias et al., 2014).

Competition data from the literature provided a helpful baseline to explore how phylogenetic relatedness and morphological similarity influence biotic interactions, and to make predictions about taxa that lack field data on resource and habitat use, and on their interactions with other species. We were able to predict which species in our data set might potentially compete with invasive rodents. A more definitive test would require the effects of invasive species on native ones to be quantified adequately for coexisting taxa. Variation in the effects of competition with invasive species could then be related to ecomorphology. More comprehensive data could also allow for estimating the predictive power of competition and similarity data by using degraded datasets to test for cross-prediction. Identifying which species are more likely to compete can direct study efforts or conservation actions. Native species that are ecologically similar to invasive rodents may be at risk of declining due to competition. Conversely, ecologically similar native species might limit introduced species

through competitive exclusion, reducing the need for management and its associated costs (Stokes et al., 2009b; Stokes et al., 2009a). Other applications of this approach include predicting competition between threatened species introduced into predator-free conservation enclosures or islands (Abbott, 2000) where competition is more likely because of limited space and resources. As well as competition, the other major effect of invasive rodents on native rodents is spreading new diseases, like the well documented pathogenic trypanosome that caused the extinction of two endemic Christmas Island rats, spread by Rattus rattus after a shipwreck (Wyatt et al., 2008). Competition is important, but our method can’t predict all biotic interactions that can drive native species extinct.

The environment can act as a filter that favours species with certain traits (Keddy, 1992). Morphological traits are associated with particular environments because they affect mobility, diet and microhabitat use (Hanspach et al., 2012). For sigmodontine rats, habitat type relates to external morphology better than locomotion type (Rivas-Rodríguez et al., 2010). Such association between habitat and morphology can inform targeted search efforts for data deficient or possibly extinct rodents, usually known from a limited number of museum specimens. For example, the possibly extinct Puebla deer mouse Peromyscus mekisturus is only known from two specimens, last recorded over 60 years ago (Fisher & Blomberg, 2011). The specimen information suggests that they were collected in arid shrubland (Castañeda-Rico et al., 2014), but morphological features (e.g. very long tufted tail and elongated fifth digit on the hindfoot) suggest that it may be an arboreal species (Carleton, 1989), so search effort should focus on wooded habitats in the region.

Ecological metrics derived from morphology can overcome the limitations of limited data and convergent evolution in comparative studies. This method can be used for other taxonomic groups, and the results can inform macroecological theory and conservation. Our approach underlines the important role that museum collections can play in not just taxonomic or phylogenetic studies, but also in advancing the study of ecology (Graham et al., 2004; Pyke & Ehrlich, 2010).

Acknowledgements

We are grateful to Andrei Miljutin for providing data and valuable comments on the manuscript. We thank Simon Blomberg for statistical advice and supervision. Sandy Ingleby, Heather Jantezki, Anja Divljan, Livia León Paniagua, and Zamira Ávila-Valle provided access to collections and assistance with specimens. L.V. was supported by funding from Consejo Nacional de Ciencia y Tecnología (CONACYT) scholarship 308685. D.O.F. was supported by Australian Research Council fellowship DP0773920.

Table S2. a) Species likely to compete with introduced rodents. KDE values next to each species name, known competitor highlighted in bold.

Rattus fuscipes 0.047 Chiruromys lamia 0.022 Notomys cervinus 0.047 Rattus tanezumi 0.021 Nesomys rufus 0.047 Pseudomys hermannsburgensis 0.021 Pogonomys macrourus 0.047 Chiruromys forbesi 0.02 Zyzomys argurus 0.047 Reithrodontomys mexicanus 0.02 Micromys minutus 0.046 Dendromus insignis 0.02 Pseudomys delicatulus 0.045 Zapus hudsonius 0.02 Abeomelomys sevia 0.045 Osgoodomys banderanus 0.019 Leptomys elegans 0.045 Pseudomys higginsi 0.019 Eliurus webbi 0.045 Leptomys ernstmayri 0.018 Rattus richardsoni 0.043 Peromyscus yucatanicus 0.018 Sicista pseudonapaea 0.043 Chiruromys vates 0.017 Melomys burtoni 0.043 Melomys leucogaster 0.017 Eliurus minor 0.042 Mallomys aroaensis 0.016 Rattus tunneyi 0.042 Mastomys natalensis 0.016 Apodemus uralensis 0.042 Rattus villosissimus 0.016 Sicista betulina 0.042 Sciurus vulgaris 0.016 Sicista tianshanica 0.041 Leggadina forresti 0.013 Sicista subtilis 0.041 Neotoma mexicana 0.011 Eliurus tanala 0.04 Mastacomys fuscus 0.011 Nesomys audeberti 0.04 Pteromys volans 0.011 Pseudomys novaehollandiae 0.039 Uromys caudimaculatus 0.011 Pseudomys gracilicaudatus 0.039 Rattus leucopus 0.01 Dryomys nitedula 0.038 Rattus exulans 0.008 Pseudomys bolami 0.038 Glaucomys volans 0.008 Melomys capensis 0.038 Xenuromys barbatus 0.008 Mesembriomys gouldii 0.037 Peromyscus furvus 0.007 Rattus tiomanicus 0.037 Habromys lophurus 0.007 Sicista napaea 0.037 Stylodipus andrewsi 0.006 Eliomys quercinus 0.037 Brachytarsomys albicauda 0.006 Liomys pictus 0.036 Sicista caudata 0.006 Eliurus myoxinus 0.036 Melomys cervinipes 0.006

Dendromus mesomelas 0.036 Megadontomys thomasi 0.006 Dendromus mystacalis 0.036 Notomys alexis 0.006 Hyomys goliath 0.035 Onychomys leucogaster 0.005 Leporillus conditor 0.034 Rattus sordidus 0.005 Hydromys chrysogaster 0.03 Rattus lutreolus 0.005 Apodemus flavicollis 0.027 Apodemus agrarius 0.005 Pseudomys australis 0.025 Macrotarsomys ingens 0.005 Dendromus melanotis 0.025 Sicista caucasica 0.005 Muscardinus avellanarius 0.024 Notomys mitchellii 0.004 Tylomys nudicaudus 0.024 Macrotarsomys bastardi 0.004 Table S2. b) Species not likely to compete with introduced rodents. KDE values next to each species name.

Gymnuromys roberti 0.023 Oligoryzomys fulvescens 0.003 Allactaga vinogradovi 0 Allactaga bullata 0.003 Phodopus sungorus 0 Chaetodipus baileyi 0.003 Jaculus blanfordi 0 Notomys fuscus 0.003 Allactaga balikunica 0 Cardiocranius paradoxus 0.003 Mesocricetus newtoni 0 Pseudohydromys ellermani 0.001 Mesocricetus auratus 0 Brachyuromys betsileoensis 0.001 Microtus arvalis 0 Tscherskia triton 0 Allactodipus bobrinskii 0 Cricetulus longicaudatus 0 Pygeretmus pumilio 0 Cricetulus kamensis 0 Allocricetulus eversmanni 0 Allactaga major 0 Microtus agrestis 0 Eremodipus lichtensteini 0 Phodopus roborovskii 0 Dipus sagitta 0 Ondatra zibethicus 0 Cricetulus sokolovi 0 Arvicola amphibius 0 Allactaga severtzovi 0 Mesocricetus raddei 0 Cricetulus barabensis 0 Cratogeomys fumosus 0 Cricetulus migratorius 0 Microtus oaxacensis 0 Oryzomys couesi 0 Pygeretmus platyurus 0 Allocricetulus curtatus 0 Mesocricetus brandti 0 Microtus subterraneus 0 Salpingotus crassicauda 0 Microtus oeconomus 0 Anisomys imitator 0 Dipodomys nelsoni 0 Microtus levis 0 Allactaga williamsi 0 Euchoreutes naso 0 Allactaga sibirica 0 Castor fiber 0 Cricetus cricetus 0 Allactaga elater 0 Paradipus ctenodactylus 0 Ototylomys phyllotis 0 Pygeretmus shitkovi 0

Chapter 6. Discussion

The objectives of this PhD project were achieved by 1) identifying, for the first time in mammals, the phylogenetic correlates of extinction risk and 2) testing the hypothesized relationships proposed in previous studies. In this final chapter I present a summary of what has been learned, reflect on possible limitations and discuss potentially rewarding areas of future research. Remarks on the conservation and macroecological implications of this research are given throughout.

Global correlates of extinction risk

In Chapter 2, I conducted the first dedicated study investigating the role of evolutionary history in extinction risk, using a global dataset, a comprehensive phylogeny, and innovative statistical methods. No previous global study has considered phylogeny as a predictor of extinction risk in mammals, and the overall lack of meaningful associations is novel. Evolutionary age is not expected to be independent of current extinction risk by authors of the limited literature on this topic, and has only been suggested from comparisons of taxonomic longevity and survivorship from the fossil record (Boyajian, 1991). Evolutionary distinctness is also not a surrogate for imperilment in mammals according to my investigation, and support for this result was also recently found globally in birds (Jetz et al., 2014). The results from this chapter allow the confident conclusion of no global or regional associations between phylogeny and extinction risk, but also highlight the need to interpret, report and publish negative results. The taxon-specific results from this chapter were investigated in follow-up studies (Chapters 3 and 4) in a way that incorporated the objectives and themes proposed for the thesis as a whole.

Previous studies that reported phylogenetic clustering of extinction risk in some older lineages are often interpreted in a way that suggests that such lineages may be maladapted to current conditions and intrinsically vulnerable to extinction (Bennett et al., 2014). This interpretation could potentially bias conservation efforts, and it might stem from the idea that older lineages are senescent, isolated, and geographically restricted, such that neutral or deleterious genes are more likely to spread and reduce competitive or adaptive abilities (Cain, 1940; Fiedler, 1986). No elements in this thesis support the notion of lineage senescence, and

the complex dynamics of changing geographical range through time (‘age and area’ debate) are still being investigated (Pigot et al., 2012). An interesting offshoot may be a comparative analysis of heritable genetic variation across lineages of different ages, with a subsequent investigation on how genetic heterogeneity relates with species-wide extinction risk.

Phylogeny, extinction risk and conservation

Information derived from detailed phylogenies is increasing its influence in the field of conservation. Although species diversity is a reliable measure for spatial conservation planning (Rodrigues et al., 2011), conservation programmes are shifting from maximizing the total number of species conserved, to maximizing conserved phylogenetic diversity (Bennett et al., 2014). Variables that explicitly quantify species’ evolutionary history are overlooked in comparative studies of extinction risk, so any possible relationships between a species phylogenetic age or distinctness and its ability to adapt to emerging conditions remain unclear. The overall results from the thesis should provide evidence to dispel deterministic views on lineage antiquity equating with a high probability of extinction.

Evolutionary processes

Throughout this work I place a large emphasis on phylogenetic lineage age and measures of evolutionary distinctness derived from it, but much more information can be measured from phylogenies and related to current extinction risk. Diverse lineages are assumed to have greater genetic redundancy (May, 1990; Vane-Wright et al., 1991; Crozier, 1997), so clade richness is sometimes equated with evolutionary potential. Rapid evolutionary potential in diverse, recently-radiated clades is already being targeted for conservation, but it may also influence present day-extinction proneness.

The biological properties or environmental context that shaped clade diversity within highly radiated lineages could influence their ability to adapt to changing conditions. In this case, these groups would have fewer species that are extinct or currently threatened. In Chapter 2, I find no support for this notion. Net diversification rates in mammalian genera do not predict the proportion of threatened species per genus.

The taxonomic distribution of species diversity among lagomorph genera suggests that clades have differed with respect to their past speciation and extinction probabilities, with many species-poor and a few species-rich clades. The phylogenetic distribution of extinction risk in

lagomorphs is not significantly clustered in particular lineages, but enough species in monotypic genera are threatened (by modern extrinsic factors) to drive a relationship between clade size and extinction risk, identified in Chapter 2. Chapter 4 examines this relationship, because a combination of taxonomic imbalance and threatened species in monotypic groups threatens more evolutionary history than a random pattern, even when the overall proportion of threatened lagomorph species is similar to those observed in other mammalian orders (~25%).

None of the variables that could define a species as marginal (e.g. living in fringe environments with harsh climates and few sympatric mammals) had associations with clade size or extinction risk in lagomorphs. Modern pressures such as habitat loss and conversion, introduced predators, and disease are strong enough to cause indiscriminate decline across the varied range of lagomorph biology and distribution. These results contribute to ecological and evolutionary theory, because threatened lagomorphs representing unique branches of evolution are not necessarily poor competitors relegated to pioneer or marginal conditions (Stebbins, 1942).

Although Chapter 4 was written in collaboration with lagomorph specialists, taxonomic difficulties prevented fossil lagomorphs from being fully integrated with the phylogeny of living lagomorphs for analysis. Reviewing the lagomorph fossil record in its dynamic paleoenvironmental context was crucial, in order to understand the present-day variations in evolutionary distinctiveness and genus size.

Phylogeny and ecology

Some species have biological traits that make them ecologically important to ecosystem processes. Because phylogenetic information is growing more rapidly than ecological information for poorly-studied taxa, there have been numerous efforts to capture functional diversity using phylogenies as an indirect measure, assuming that ecologically important traits evolve along the branches of the phylogeny. The objective addressed in Chapter 5 was to test if rodent species with similar morphologies have similar ecologies, and how these measures of similarity relate to evolutionary relatedness. I found that morphology is a better predictor of ecology than phylogeny, so evolutionary distinctness is not necessarily an appropriate reflection of the long-term ecological importance of species or assemblages.

More importantly, in this section I developed an accessible way to characterize species ecologically using informative traits and data from museum specimens. An accessible measure of ecology can be related to ecological function and help to inform conservation planning schemes aiming to measure, protect, and maintain ecosystem function in a rapidly changing world. Multivariate descriptions of a species’ ecology can be used to investigate a number of ecological hypotheses such as ecological character displacement and phylogenetic niche conservatism. The outputs from Chapter 5 contribute to ecological theory because I showed that competition is more likely for morphologically similar species than it is for closely related ones, and that this approach can be used to predict the likelihood of competition between problematic invasive species with native ones regardless of relatedness or ecological ‘guild’.

A comprehensive analysis following from this study could include morphological data for an entire mammal lineage (e.g. a family or order), to measure divergence and similarity and relate it to phylogenetic lineage age following Tobias et al. (2014), who found that increased trait differences among coexisting lineages of ovenbirds (Furnariidae) are explained by their greater evolutionary age in relation to non-interacting lineages.

Comparative extinction risk modelling

Comparative extinction risk analyses are the most common approaches to determine the likelihood of a species going extinct in the foreseeable future. The process involves searching for associations between a species’ level of endangerment and information on biology, geography, threats experienced, etc. Comparative studies need to address a number of methodological considerations such as missing data, spatial variation, and uncertainty in order to ensure the biological reality of any findings and avoid statistical artefacts.

Missing values in comparative data in both the predictor and response variables can lead to casewise removal of species, which biases any further analysis through non-random taxon selection (Ackerly, 2000; Nakagawa & Freckleton, 2008). In Chapter 2, I used nonparametric machine learning methods to estimate missing body mass values. This approach does not consider the statistical uncertainty in model parameters due to missing data, but Di Marco et al. (2012) found the variation in results across multiple imputed data sets to be equal or smaller than 1% in all orders when performing similar analyses. Chapter 4 employed two different techniques (expanded trees and phylogenetic assembly with soft taxonomic

inferences) to obtain a complete lagomorph phylogeny and avoid discarding species from the taxonomy that were not present in the available phylogenetic literature.

The main response variable throughout this thesis was current extinction risk, and the IUCN Red List of Threatened Species is the most important mechanism for classifying species based on their extinction risk (Rodrigues et al., 2006; Hoffmann et al., 2011). I discarded Data Deficient species with no extinction risk assessment from most analyses, but a recent analysis of amphibians found that over half of all Data Deficient species are likely to be threatened (Howard & Bickford, 2014). An in-depth examination of the phylogenetic distribution of Data Deficient species and an approach to assign preliminary extinction risk values could be informative (González-Suárez et al., 2013; Morais et al., 2013). This thesis includes the first published study that models extinction risk using the IUCN Red List categories as an ordinal, rather than a continuous index, using phylogenetic generalized linear mixed models with ordinal probit link function (Chapters 2 and 4).Ordered threat categories have specific recommendations for conservation action for each category (Rodrigues et al., 2006), and this approach can helps avoid information loss and elevated error rates associated with the assumption that categories are evenly spaced and continuously varying.

Taxon-specific analyses in Chapters 3 and 4 both consider spatial variation in exposure to threats and threat magnitude, using different proxies of species distribution, human presence, and the status of species’ habitats. Human presence and the condition of habitat across a species’ geographic range was associated with extinction risk, and the biological characteristics of primate and lagomorph species mediate their response to these extrinsic factors. Because primate body size evolution shows an increase with time and larger bodied species are more likely to be threatened (Cardillo et al., 2005), more species in younger lineages are threatened throughout the tropical areas where they live. In the case of lagomorphs, non-threatened species occur in converted habitats, perhaps reflecting the tolerance of some hares and rabbits that thrive in agricultural areas.

The phylogentically-informed analyses in this thesis relied on maximum clade credibility (consensus) trees. However, incorporating the inherent uncertainty in the topology and branch length of a phylogeny is important because it can affect the conclusions that are drawn from a comparative analysis (Matthews et al., 2011). Statistical modelling approaches that account for phylogenetic uncertainty are good ways ensure that any results are robust to legitimate uncertainty in the state of mammalian phylogenetics. The results in Chapter 4 were

consistent even when two phylogenetic trees with major differences in branching times and cladistic positions of threatened species were used. Chapter 3, which focuses on primates, followed the nomenclature stated in the IUCN Red List, but multiple primate taxonomies exist (Groves, 2001; Wilson & Reeder, 2005; Corbet & Hill, 1991) and possible measurement error in how species are delimited could obscure the results. An avenue for future research could include a global sensitivity analyses across multiple trees and taxonomies employing analytical tools recently made available and following models for birds (Jetz et al., 2012; Botero et al., 2013).

Conclusion

Phylogeny is often corrected for in comparative analyses, but overlooked as a source of information when predicting present-day extinction risk. Cross-species studies of extinction and extinction risk usually have a strong conservation viewpoint, so phylogeny has been put aside to identify instead predictors of extinction risk more relevant to applied conservation management interventions. This study combined correlative methods, machine learning, phylogenetic simulations, and ecomorphological and spatial analyses to maximize the information contained in the mammalian phylogeny and apply it to multiple questions in conservation, ecology and evolution. Although this research demonstrated that significant associations are lacking, the methods used can be replicated to answer the same questions for other vertebrate groups with different evolutionary trajectories, for which species data and comprehensive phylogenies are available.

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