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The widespread dorsivittata reveals complex patterns of genetic diversity in

Danielle Rivera CUNY City College

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The widespread skink Mabuya dorsivittata reveals complex patterns of genetic

diversity in South America

Danielle Rivera

Department of Biology, City College of New York, City University of New York, New

York, NY 10031, USA

Masters Thesis Committee:

Ana Carnaval, Ph.D. (advisor)

Amy Berkov, Ph.D.

Elizabeth Alter, Ph.D.

10 August 2015 2

Contents

Abstract ...... 3

Introduction ...... 4

Materials & Methods ...... 8

Sampling and Molecular Data ...... 8 Phylogenetic and STRUCTURE Analyses ...... 8 Environmental Analyses...... 10 Multiple Matrix Regression with Randomization (MMRR) ...... 10 Results ...... 12

Phylogenetic and STRUCTURE Analyses ...... 12 Environmental Analyses and MMRR ...... 14 Discussion ...... 16

Acknowledgements ...... 18

References ...... 19

Figures ...... 35

Tables ...... 41

Supplementary Material ...... 42

Methods ...... 42 Figures ...... 43 Tables ...... 47

3

The widespread skink Mabuya dorsivittata reveals complex patterns of genetic diversity in South America

Abstract

It has become increasingly important to document, describe, and understand the mechanisms that shape biodiversity, especially in the species-rich, highly-threatened, and understudied tropics. In this study, I describe the patterns and levels of genetic diversity within a widespread South American skink, Mabuya dorsivittata, and examine whether and how it tracks regional changes in geography and the environment along a complex landscape. First, I assess whether this species is panmictic or if it is comprised of multiple, spatially structured lineages. To that end, I measure levels of genetic diversity, and ask if ecosystem boundaries have acted as barriers to gene flow across its range. I then employ multiple matrix regression with randomization (MMRR) to test the degree to which geographic and environmental differences have shaped spatial genetic structure in

M. dorsivittata. Bayesian inference trees recovered four well supported lineages, many of which overlap in geographic space and are not bound by specific geographic or environmental barriers. A hierarchical analysis of molecular variance (AMOVA) revealed that most of the genetic diversity occurs within ecosystem boundaries.

Although M. dorsivittata is widely-distributed and occurs across a range of environments,

MMRR results indicate that neither environmental nor geographic distances are highly correlated with intraspecific divergence. To shed more light on the drivers of genetic diversity within this species, future studies may benefit from the inclusion of data on physiology, behavior, and historical demography of M. dorsivittata. 4

Introduction

The heterogeneous landscape of South America provides an ideal system for studying how geography and the environment affect biodiversity: the highly contrasting habitat configurations of this continent, spanning from the wet forests of Amazonia and

Atlantic Forests, to the savannahs of the Cerrado and the xeric Caatinga, are known to harbor an expanse of biodiversity and endemic taxa (Carnaval et al., 2014; Carnaval, et al., 2009; Fouquet et al., 2015; Recoder et al., 2014; Smith et al., 2014; Valdujo, et al.,

2013; Werneck, et al., 2015). Studies of narrowly distributed species, especially those restricted to local forests, have been crucial to the documentation, understanding, and conservation of South America’s outstanding biodiversity (Amaro, et al., 2012; Böhm et al., 2013; Carnaval et al., 2009; Erwin, 2009; Fouquet et al., 2012, 2015; Galetti, et al.,

1997; Pelegrin, et al., 2013; Rodrigues et al., 2014; Turchetto-Zolet, et al., 2013). In contrast, much less emphasis has been given to the study of widespread South American taxa which occur over broad geographic areas and diverse environmental gradients – particularly those living in open areas (Recoder et al., 2014; Werneck, 2011; Wynn &

Heyer, 2001). This bias is unfortunate: open-area species that range across diverse eco- regions present a unique opportunity to study patterns and mechanisms of diversification and adaption across complex environmental boundaries (Gehara et al., 2014; Werneck, et al., 2015). This is especially so given the propensity of wide-ranging species to exhibit marked spatial genetic structure and cryptic diversity, even in species with little phenotypic variation (Gamble, et al., 2012; Gehara et al., 2014; Omland, et al., 2000;

Werneck et al., 2015).

Multiple processes are known to influence genetic differentiation across landscapes, including those promoted by geographic, environmental, and behavioral 5

differences (McRae, 2006; Nosil, et al., 2005; Slatkin, 1993; Wang & Bradburd, 2014;

Wang & Summers, 2010; Wright, 1943). In this study, I investigate the role of geography

and the environment as drivers of spatial genetic diversity in a widespread South

American skink, Mabuya dorsivittata. Specifically, I evaluate the potential impacts of

Isolation by Distance, Isolation by Resistance, and Isolation by Environment on the

genetic patterns of this broadly ranged species. The concept of Isolation by Distance

(IBD) has been widely tested and effectively applied in studies of spatial genetic differentiation for decades (McRae, 2006; Wright, 1943). IBD is a widespread phenomenon wherein populations separated by large geographic distances exhibit decreased levels of gene flow, thus diverging genetically over time (Slatkin, 1993;

Wright, 1943). Similarly, Isolation by Resistance (IBR) incorporates resistance to dispersal into the calculation of distance between each point across a landscape (McRae,

2006). Landscape resistance can be imposed by geographic barriers, unsuitable habitat, or

any biologically relevant feature. Resistance-based methods are thought to be better

predictors of actual dispersal distance in landscape genetics analyses than the strict

Euclidean distances represented in IBD (McRae, 2006); however, given the propensity

for widespread species to have more suitable habitat across a large range, it is expected

that Euclidean, least-cost path, and circuit distances will be very similar. The role of

Isolation by Environment (IBE), on the other hand, has reemerged in recent genetic

studies on natural populations, supported by the development of landscape genetics

techniques. IBE focuses on the influence that differing environments have on genetic

variation (McRae & Beier, 2007; Wang & Bradburd, 2014). Under IBE, ecologically

associated mechanisms such as adaptation to local environments, assortative mating, and

migration-balancing selection, reduce gene flow and promote genetic divergence between 6 populations (Edelaar & Bolnick, 2012; Nosil, et al., 2008; Nosil et al., 2005; Rundle &

Nosil, 2005; Wang & Bradburd, 2014; Wang & Summers, 2010). Recent studies have shown that these environmentally driven forces significantly affect population genetic structure, especially in widespread species (Manthey & Moyle, 2015; Wang, et al., 2013).

The focal species in this study, Mabuya dorsivittata, is a widely distributed skink with a range originally thought to include portions of the northern and eastern

Argentinian Chaco, Paraguay, Bolivia, Uruguay, and southeastern Brazil; in addition, a number of populations have recently been identified across the southern Brazilian

Cerrado, as well as additional areas of Uruguay (Carranza & Arnold, 2003; Costa, et al.,

2009; Hedges & Conn, 2012; Recoder & Nogueira, 2007; Valdujo et al., 2009; Vaz-Silva et al., 2007; Williams & Kacoliris, 2011). Furthermore, recent fieldwork revealed previously unknown populations in open areas within the Atlantic forest throughout coastal Brazil, from the southernmost Brazilian states of Rio Grande do Sul and Santa

Catarina, to the montane regions of Bahia (Fig. 1). Yet, little is known about the amount of gene flow across this species’ range. Although Mabuya species are abundant in the

Neotropics, few phylogeographic studies have focused on intra-specific patterns of genetic structure and historical demography across the broad environmental gradients they occupy. Fewer still have addressed these issues through the use of multi-locus nuclear datasets.

The range of M. dorsivittata encompasses multiple contrasting ecosystems and climatic regimes. Individuals in the western range of the species occupy broadly connected, open areas of the Cerrado, the Chaco, the lowland Pampas, and the southern

Atlantic Forest (Fig. 1; Álvarez et al., 2009; Iganci, et al., 2011; Pelegrin & Bucher,

2012). In contrast, in the eastern portion of the range, the species occurs in the naturally 7

fragmented mountain ranges within the Atlantic forest ecosystem, either occupying forest

clearings, highly disturbed areas, or the montane campos rupestres. The latter are open

regions and often rocky grasslands, isolated by a matrix of dense rainforests (Araujo et

al., 2014; Costa et al., 2009; Gainsbury & Colli, 2014; Giulietti & Pirani, 1988; Recoder

& Nogueira, 2007; Townsend, et al., 2004; Valdujo et al., 2009; Vaz-Silva et al., 2007).

In this study, I investigate two major questions about the levels and patterns of

genetic diversity within M. dorsivittata. First, I ask whether M. dorsivittata is a wide-

ranging, panmictic species, or if it is comprised of multiple, spatially structured lineages.

Given the naturally fragmented nature of suitable habitat within the species range in the

Atlantic forest ecosystem relative to the more continuous habitats within the dry-diagonal

and the Pampas of Southern Brazil, I expect to find much higher levels of genetic

structure and differentiation across M. dorsivittata in the former relative to the latter.

Secondly, I ask if (and to which extent) local environmental conditions better

explain genetic structure compared to geography. In other words, is genetic diversity

within M. dorsivittata largely structured by differences in environmental conditions, as

recently documented for other widespread taxa, or by geographic distance? If local

adaptation has played a major role in shaping genetic structure within M. dorsivittata, I

expect IBE to be a much stronger predictor relative to distance. By identifying potential

key drivers of genetic diversity in M. dorsivittata, I hope to shed light on processes that may be likewise impacting other similarly distributed species across the heterogeneous landscapes of South America. 8

Materials & Methods

Sampling and Molecular Data

Dramatically increasing the sampled range of M. dorsivittata, genetic data from

82 specimens were generated from 35 different localities across the Brazilian Cerrado, the Pampas, the Argentinian Chaco, open areas of the Atlantic forest south of Rio de

Janeiro, and campos rupestres within the inland Espinhaço, Diamantina, and Mantiqueira mountain ranges (Table S1). DNA extraction was performed with a high salt protocol

(Carnaval & Bates, 2007). Three mitochondrial and seven nuclear genes were amplified; they include the mitochondrial 12S, 16S, and Cytochrome b (cytb) genes, and the nuclear markers BTB And CNC Homology 1 (BACH1), Oocyte Stimulating Factor Oocyte

Maturation Factor (CMOS), Dynein Axonemal Heavy Chain 3 (DNAH3),

Recombination Activating Gene 1 (RAG1), Ribosomal Protein L35 (RPL35), Synuclein

Alpha Interacting Protein (SINCAIP), and Myosin Heavy Chain (MYH). One sequence available in Genbank (DQ238876) was used to complement the sequenced panel. Primers and PCR profiles were adapted from the literature (Miralles et al., 2009; Pellegrino, 2001;

Townsend, et al., 2008; Whiting et al., 2006; Table S2).

Phylogenetic and STRUCTURE Analyses

DNA sequences were edited and aligned in Geneious vR6

(http://www.geneious.com, Kearse et al., 2012). Alignments were partitioned by gene;

best-fitting models of evolution were inferred per gene based on AIC scores, using

PartitionFinder v1.1.1 (Lanfear et al., 2012). Nuclear haplotypes of heterozygotes were

estimated with PHASE 2.1.1 (Flot, 2010; Stephens & Donnelly, 2003). Using M.

macrorhyncha as an outgroup, concatenated (mtDNA, nuDNA, mtDNA+nuDNA) 9

Bayesian inference trees were generated in MrBayes v3.2.1 (individual gene and

concatenated nuDNA trees presented in Fig. S1;Heulsenbeck et al., 2001) and run four

times independently, each for 20 million generations, discarding the first 25% of trees as

burn-in. Effective sample size (ESS) scores were assessed in Tracer v1.6 to ensure

stationarity (i.e. ESS>200; Rambaut, et al., 2014). All trees were run using the CUNY

HPC Center cluster.

To test for signals of genetic structure in the nuclear data, the Bayesian Markov

Chain Monte Carlo (MCMC) clustering program STRUCTURE V2.3.4 (Pritchard, et al.,

2000) was implemented in a single analysis that combined sequences of all seven nuclear

loci. STRUCTURE was run with the admixture model of ancestry and correlated allele

frequencies for 500,000 generations; 200,000 generations were discarded as burn-in.

Results were analyzed with the Cluster Markov Packager Across K program

(CLUMPAK; Kopelman, Mayzel, Jakobsson, Rosenberg, & Mayrose, 2015), which

identified an optimal K value using the ΔK method (Evanno, et al., 2005). The software

CLUMPP (Jakobsson & Rosenberg, 2007) was then used to summarize and visualize the

outputs of multiple runs for the optimal K (Earl & vonHoldt, 2011; Pritchard et al.,

2000).

To ask whether discrete ecosystems explain observed genetic structure within this

species, I used pairwise genetic distances to estimate FSTs and implemented an analysis of

molecular variance (AMOVA) with three hierarchal levels in Arlequin v3.5.2.1

(Excoffier & Lischer, 2010). The AMOVA was designed to assess the percentage of

genetic variation across and within three ecosystems: Atlantic forest (including campos rupestres), Cerrado, and Pampas; the Chaco was excluded due to poor sampling. I also 10

built median joining (MJ) networks to portray haplotype relationships within and across

ecosystems in Network v4.6.1.3 (Tamura & Nei, 1993; Tamura, et al., 2013).

Environmental Analyses

To assess whether M. dorsivittata occupies a broad range of climatic regimes, or

if it instead occupies areas of similar climatic conditions across a range of vegetation

types (ecosystems), I ran a Principle Component Analysis (PCA) on species locality data using 19 freely available WorldClim bioclimatic layers (Hijmans, et al., 2005) in SPSS

(IBM Corp., 2012). I used known M. dorsivittata collection sites based on verified georeferenced records from tissue-sampled individuals used in this study, and vetted occurrence records available on VertNet (http://www.vertnet.org), speciesLink

(http://splink.cria.org.br/), GBIF (gbif.org) and literature records (database citations in

Supplements; Álvarez et al., 2009; Araujo et al., 2014; Costa et al., 2009; Filho &

Verrastro, 2012; Gainsbury & Colli, 2014; Iganci et al., 2011; Recoder & Nogueira,

2007; Valdujo et al., 2009; Vaz-Silva et al., 2007; Williams & Kacoliris, 2011). To describe climatic differences across the range of M. dorsivittata relative to the major ecosystems where it is found, I used ArcGIS v.10.2 (ESRI, 2013) to generate random points within the Atlantic forest, Cerrado, Chaco, and Pampas, and incorporated those points into my PCA for visualization purposes.

Multiple Matrix Regression with Randomization (MMRR)

To assess the contribution of environmental vs. geographical conditions on the

spatial genetic structure within M. dorsivittata, I used the multiple matrix regression with

randomization (MMRR) function in R (Wang, 2013), which allows for a number of

independent matrices to be compared to a dependent variable. Specifically, I tested the 11 relative contributions that Isolation by Distance (IBD), Isolation by Resistance (IBR), and

Isolation by Environment (IBE) have in explaining overall genetic diversity (Wang &

Bradburd, 2014; Wang et al., 2013; Wang, 2013). IBD was measured through a matrix of pairwise Euclidean distance between each locality. To evaluate the potential impact of

IBR, I built independent matrices of pairwise distances from the resistance-based methods of least-cost pathways (LCP) and circuit distances generated with ArcGIS and

Circuitscape (McRae & Beier, 2007). These methods have been shown to be more accurate measurements than strict Euclidean distances (McRae & Beier, 2007; McRae,

2006; Spear, et al., 2010; Wang, 2013), as well as to explore both single and multiple likely pathways between points (Howey, 2011; Wang et al., 2013).

To estimate landscape resistance and least-cost pathways, I developed an ecological niche model (ENM) for the species using the spatial jackknifing method in

SDMToolbox (Brown, 2014), which employs the software Maxent (Phillips, et al., 2006).

The model was built using 19 bioclimatic layers (Worldclim; Hijmans et al., 2005), as well as percent vegetation (MODIS) and global land cover (ESA) layers (Arino et al.,

2007; DiMiceli et al., 2011). Occurrence records were spatially rarefied (10 km distance) to reduce the effects of spatial auto-correlation (Boria, et al., 2014; Hijmans, 2012; Veloz,

2009); background selection was limited to a buffered minimum convex polygon. A minimum training presence threshold was applied, and neither clamping nor extrapolation was utilized in the model. The ENM was used to generate a friction layer, wherein grid cells with higher probabilities were assigned lower friction values (1- suitability score). Based on these values, pairwise LCP and circuit distances were calculated between all pairs of localities for which I had collected genetic data. To test the potential role of IBE, I generated pairwise distance matrices using the four principle 12

components extracted from the environmental PCA, as described above, on the same

pairs of M. dorsivittata localities. My response (dependent) variable matrix was comprised of pairwise p-distances for the concatenated mtDNA and nuDNA datasets

estimated in MEGA v6 (http://www.fluxus-engineering.com).

Results

Phylogenetic and STRUCTURE Analyses

Bayesian inference trees based solely on the mitochondrial genes and on

concatenated mitochondrial and nuclear gene sequences recovered four well-supported lineages (Fig. 2). The spatial distribution of the lineages does not clearly follow ecosystem boundaries, and the lineages also fail to clearly segregate in geographical space. Lineage A (blue squares, Fig. 2) is composed of individuals in mid- to high- elevation areas from the mid-Atlantic Forest along southeastern Brazil northward, including the montane campos rupestres (~1500 km), as well as in the Cerrado.

Individuals in lineage B (green circles, Fig. 2) are found in similar elevations in campos rupestres as well as open areas in the coastal, southern Atlantic forest. Lineage C (pink

triangles, Fig. 2) includes samples from a wide range of elevations and occupies sites in

the Cerrado, Chaco, open areas of the Atlantic forest, and the Pampas south and west of

the mid-Atlantic Forest. Lineage D (red diamonds, Fig. 2) is found in an open Atlantic

forest area and in the Pampas in southern Brazil, but also in a much more northern

locality within the Cerrado.

Despite the poor resolution of the individual and concatenated nuclear trees (Fig.

S1), the STRUCTURE analysis of the pooled nuclear data identified four genetic clusters. 13

Similar to the mtDNA lineage arrangement, the spatial distribution of these nuclear gene

clusters does not clearly follow ecosystem boundaries, and also fail to clearly segregate in

geographical space (ΔK method; Fig. 3). Those individuals belonging to STRUCTURE clusters 1 and 2 (blue and orange; Fig. 3) are found solely in the Atlantic forest (open areas and campos rupestres). Conversely, those in STRUCTURE clusters 3 and 4 (purple and grey; Fig. 3) are broadly distributed; both contain individuals spanning the entirety of the species range in all ecosystems sampled (Fig. 3).

A comparison between the mtDNA genealogy and the STRUCTURE analyses demonstrates that individuals carrying mitochondrial lineages A and B, which are mostly distributed in the northern Atlantic forest and campos rupestres, usually belong to nuclear clusters 1 and 2 (blue and orange; Fig. 3). The only exceptions seem to happen in interior sites, where individuals with mitochondrial lineages A and B are inferred to have higher levels of admixture and assigned to nuclear clusters 3 and 4 (grey and purple; Fig 3). The latter, however, are pervasive in the Cerrado, Chaco, Pampas, and the southern Atlantic

Forest.

Between-site mitochondrial FSTs indicate that the levels of genetic structure across

the fragmented northern Atlantic forest are analogous to those across the open-area

localities of the Cerrado and Pampas, though many FST values were not significant (Table

S3). Overall high FSTs across sites in the Northern Atlantic forest (campos rupestres and

open forest; mean 0.81) reflect the observed phylogenetic structure in this region (Fig. 1).

Average FSTs within the Cerrado (0.82) and Pampas (0.71) were similarly high, as

individuals within these ecosystems shared membership with more than one

mitochondrial lineage (Fig. 2; Table S3). Comparatively, FSTs within the southern

Atlantic forest were much lower (open and cleared forest; 0.54; Table S3). 14

Mitochondrial FSTs across ecosystems demonstrate significant genetic

differentiation between individuals from the Atlantic forest, Pampas, and Cerrado (Table

1). Yet, an AMOVA shows that most of the genetic differentiation within this species

(61.43 %) is observed among sites within ecosystems; genetic differentiation between ecosystems and within sites explains only 24.60 % and 13.97 %, respectively, of the total variation (Table 1). While haplotype networks show that private alleles are found within

ecosystems, many of the most abundant nuclear haplotypes are shared across ecosystem boundaries (Fig. 4).

Environmental Analyses and MMRR

An ecological niche model (EMN) of the species predicts high climatic suitability

in the highlands of the Atlantic forest, including the narrowly ranged campos rupestres

and the Southern Highland Grasslands of Brazil, northern Pampas, and eastern Chaco

(Fig. 5). This model also predicts small patches of high suitability in portions of the

southeastern Cerrado and northern Chaco. Variable contributions (lambda values) and

variable jackknife responses indicate that land cover was not only an important feature,

but it also greatly influenced the resulting suitability across the landscape; the

incorporation of land cover data visibly fragmented suitable areas compared to models

generated without this information, especially outside of the Atlantic forest (Fig. 5).

The ecosystems occupied by M. dorsivittata span a wide range of climatic

conditions, not all of them exclusive to a particular ecosystem. The Cerrado, Chaco, and

Pampas regions, for instance, can be visually distinguished from each other through an

environmental PCA (Fig. 6A). However, the Atlantic forest climate, as described at the

spatial scale of the bioclimatic layers utilized here, partially overlaps with those of the 15

three ecosystems, particularly in its southern range (Fig. 6A); M. dorsivittata largely

occurs across this overlap, but is also found in climatic regimes specific to single

ecosystems (Fig. 6A).

M. dorsivittata occurs in a wide range of available temperatures and precipitation

regimes (annual mean temperature, 11 °C – 26 °C; annual precipitation, 679 mm – 2412

mm). Approximately 70% of the variation in the environmental space occupied by the

species is explained by PC1 (characterized mostly by temperature metrics; 42.7%) and

PC2 (characterized by precipitation metrics; 27.5%; Table S4).

Neither the mitochondrial lineages nor the nuclear STRUCTURE clusters within

M. dorivittata clearly segregate in environmental space: the environmental PCA shows overlap across multiple mtDNA lineages (Fig. 6B) and nuDNA STRUCTURE clusters

(Fig. 6C). The MMRR analysis broadly agrees with these findings, revealing that while

both environmental distance (IBE) and geographic distance (measured by either IBD or

IBR) are both significantly correlated to genetic structure patterns, neither strongly

explains genetic diversity within the species (Table 2). Environmental distance alone, characterized by pairwise differences of PC scores derived from the environmental PCA, explained about 21% of both mitochondrial and nuclear genetic differences (Table 2).

Resistance distances alone, which included least-cost pathway and circuit distances,

explained about 28% of mitochondrial and about 11% of nuclear genetic differences,

(Table 2). Similarly to resistance distances, Euclidean distance explained about 21% or

mitochondrial and 14% of nuclear distances (Table 2). 16

Discussion

M. dorsivittata shows considerable spatial structure across its broad range. We detect deep mitochondrial lineage divergence between lineages A and C in the mid-

Atlantic Forest, a region known to harbor phylogeographic breaks within other amphibian, , avian, and invertebrate species (Amaro et al., 2012; Batalha-Filho, et al., 2012; Batalha-Filho, et al., 2010; Cabanne, et al., 2008; Grazziotin, et al., 2006).

Genetic discontinuities around this region are found not only in the mtDNA data, but are also largely supported by the nuclear STRUCTURE analysis. Though the mechanism maintaining concurrent phylogenetic breaks in this region has not been clarified, it has been hypothesized that the reactivation of tectonic plates during the late promoted isolation and lineage divergence (Riccomini, et al., 2010).

Minor spatial structuring of the nuDNA STRUCTURE clusters correspond to mtDNA lineages; this pattern could be further supported through improved sampling both within and between individual localities. High levels of genetic structure were also detected in the northern Atlantic Forest. The naturally fragmented and open high elevation areas within the Atlantic forest (campos rupestres) seem to serve as sky islands, isolating populations on mountaintops and deterring gene flow despite the high inferred climatic suitability between these peaks, as observed both in the correlative distribution model and the mitochondrial data. This could indicate, as known localities corroborate, a preference for little-to-no forest cover, which would isolate and promote genetic divergence between individuals on open mountain peaks isolated by dense forest cover.

Mitochondrial data also show disjunct phylogeographic patterns. Lineage D, for instance, is found to occur in the Cerrado, but also in the southern Atlantic Forest and

Pampas, with no intervening populations found. Similar patterns have been observed in 17

Tropidurus catalenensis lizards (Sena, 2015), yet the taxonomic instability of that group, tied to poor sampling, renders any meaningful comparison difficult at best. It is plausible that improved sampling in intermediate areas (Mato Grosso do Sul, Paraná and Santa

Catarina in Brazil, as well as Argentina, and Paraguay) could reveal this lineage to be more widespread than presently known.

Levels of gene flow (average FSTs) measured within the fragmented northern

Atlantic forest were not markedly different from that of areas outside of the Atlantic forest. However, mitochondrial divergence was greater across sites in the northern

Atlantic forest than in the cooler southern Atlantic forest. It is possible that genetic

structure in these areas was influenced by paleoclimatic events and effects of historical

climatic stability. In the Atlantic forest, late Quaternary climatic fluctuations have been

associated with geographic shifts in the distribution of several species (Carnaval et al.,

2014, 2009). Given its association with cooler environmental conditions (e.g. mountain

tops), I expect that correlative paleodistribution models for M. dorsivittata might suggest range expansions in the Atlantic forest during the Last Glacial Maximum, and range contraction during the comparatively warmer Interglacial periods. These montane lineages are likely experiencing bottlenecks in their current climatically and geographically restricted ranges. However, if vegetation (or landscape) configuration is essential for species occurrence, as suggested by the species distribution models and the genetic data, then paleoclimatic distribution models of M. dorsivittata must include information about past vegetation configuration, and not only past climates. To appropriately model the former range of this species, and likely other open-area taxa, it will be necessary to incorporate inferences about changes in vegetation structure over time. 18

The contributions from IBD and IBR to both mitochondrial and nuclear genetic

divergence were very similar, supporting the expectation that these two metrics would

yield nearly identical results in a widespread species. Environmental differences were

previously found to have an underestimated amount of influence on the genetic diversity

of widespread taxa (Wang, 2013). However, most of the genetic variation within M.

dorsivittata occurs within, instead of across, ecosystems. This indicates that ecosystem

boundaries have not been acting as strong barriers to dispersal and gene flow. Still, the

MMRR analysis detects significant, although low, correlation between environmental and

genetic distance, suggesting that environmental conditions do play some role in shaping

genetic diversity in M. dorsivittata, or are correlated with other important processes not

yet described. Process-based studies that explore plausible mechanisms underscoring

patterns of genetic diversity will likely shed more light on the drivers of genetic structure

in this and similarly distributed species.

Acknowledgements

I thank the members of the Carnaval Lab, both past and present, for years of research

guidance and support, as well as those from the other Ecology, Evolution, and Behavior

Labs at The City College of New York. I also thank Miguel T. Rodrigues and his lab at the

Universidade de São Paulo for collecting and providing all tissue samples used in this study, assistance in the field, and vital information regarding ideas presented in this paper. I thank

Ivan Prates and Diego Alvarado-Serrano for unyielding support, valued feedback, and

company during late lab sessions. Jason L. Brown and Maria Strangas provided

invaluable feedback and assistance with analyses presented here. Gustavo Monteiro,

Pedro Peloso, Silvia Pavan, Robert Boria, Peter Galante, and Beth Gerstner provided 19 much-needed temporary respite from lab work. Finally, I’d like to thank my mentor, Dr. Ana

Carnaval, for her unrelenting positivity, support, and significance in guiding my present and future endeavors.

Supported to D. Rivera was provided by the Louis Stokes Alliance for Minority

Participation and The City College Academy for Professional Preparation. Research was funded by FAPESP (BIOTA, 2013/50297-0), NSF (DEB 1343578) and NASA, through the Dimensions of Biodiversity Program. This research benefited from the City

University of New York High Performance Computing Center at the College of Staten

Island (NSF CNS-0958379, CNS-0855217, ACI-1126113).

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35

Figures

Figure 1. Map of major ecosystems, classified by combined eco-regions defined by

World Wildlife Fund (Olson et al., 2001).

36

Figure 2: Left. Map of M. dorsivittata sampling localities. Right. Bayesian inference tree of M. dorsivittata based on the mtDNA dataset. Loci are listed in Table S2. Asterisks indicate posterior probability >0.99.

37

Figure 3. Left. Map of M. dorsivittata localities. Colors correspond to STRUCTURE clusters. Right. STRUCUTRE plots. Levels of admixture are indicated at each locality.

38

Figure 4. Haplotype networks for each gene used in this study, colored by ecoregion.

39

Figure 5. Ecological niche model (ENM) developed for Mabuya dorsivittata. Warmer

colors indicate higher suitability.

40

Figure 6. Environmental PCAs. Symbols and colors represent (A) all M. dorsivittata known locality points with background ecosystem points identified, (B) a subset of M. dorsivittata sampled localities identified by mtDNA lineages, and (C) the same sampled subset identified by nuDNA Structure clusters. Variation: PC1=42.7%; PC2=27.5; total=70.2%.

41

Tables

Table 1. (A) Pairwise mtDNA FSTs calculated between ecosystems using pairwise differences, and (B) AMOVA results based on mtDNA distances. Populations are grouped by major ecosystem. Significant values are bolded.

(A) (B) Atlantic Cerrado Forest Source of variation % of variation Atlantic 0.16 --- among ecosystems 24.6 Forest among sites within Pampas 0.21 0.29 61.43 ecosystems within sites 13.97

Table 2. Multiple Matrix Regression with Randomization (MMRR) results for Mabuya dorsivittata. Included are the fit of the model (R2) and regression coefficients (β) for each matrix used to explain Isolation by Environment (IBE), Isolation by Resistance (IBR), and Isolation by Distance (IBD). Significant values are bolded.

2 IBE R βPC1 βPC2 βPC3 βPC4 mtDNA 0.212 0.041 0.053 0.314 0.3 nuDNA 0.213 0.137 -0.069 0.446 0.007 2 2 IBR R βLCP βCIR IBD R βEuclid mtDNA 0.283 0.163 0.396 0.226 0.476 nuDNA 0.112 0.239 0.114 0.135 0.367

42

Supplementary Material

Methods

Locality sources:

HerpNet: California Academy of Sciences: CAS Herpetology (HERP), California

Academy of Sciences. Museum of Comparative Zoology, Harvard University (MCZ),

Harvard University. TCWC Herpetology (TCWC), Texas Cooperative Wildlife

Collection. The National Collection of Amphibians and Reptiles (USNM), National

Museum of Natural History. http://portal.vertnet.org/search?q=mabuya+dorsivittata

(accessed 2015-06-14), CM Herps Collection (CM), Carnegie Museums. The National

Collection of Amphibians and Reptiles (USNM), National Museum of Natural History

http://portal.vertnet.org/search?q=aspronema+dorsivittatum (accessed 2015-06-14)

speciesLink: Coleção de Répteis (MCP-Repteis), Sistema de Informação do Programa

Biota/Fapesp (SinBiota), Coleção de Répteis do Centro de Coleções Taxonômicas da

UFMG (UFMG-REP), Coleção Zoológica da UFMT- Setor Herpetologia-Répteis

(UFMT-R), Coleção de Répteis do Museu de Zoologia da UNICAMP (ZUEC-REP), available at speciesLink network (http://www.splink.org.br) in 2015, April 14.

GBIF: Museum of Vertebrate Zoology: MVZ Herp Collection (Arctos), 2015-03-20.

Accessed via http://www.gbif.org/occurrence/1050956142 on 2015-07-01

43

Figures

Figure S1. Bayesian inference nuclear trees of M. dorsivittata: (a) concatenated nuDNA

(all seven loci); (b) BACH1; (c) CMOS; (d) DNAH3; (e) MYH; (f) RAG1; (g)

RPL35; (h) SINCAIP. 44

45

46

47

Tables

Table S1: Sample field numbers and localities. Abbreviations: (countries) BR – Brazil;

ARG – Argentina; (states) BA – Bahia; CR – Corrientes; ES – Espirito Santo; MG –

Minas Gerais; MS – Matto Grosso do Sul; MT – Matto Grosso; RJ – Rio de Janeiro; RS

– Rio Grande do Sul; SC – Santa Catarina; SP - São Paulo.

Field Number Locality State 3410 Cunha SP, BR 3411 Santana do Parnaíba SP, BR 3529 Cunha SP, BR 3578 Cunha SP, BR 3579 Cunha SP, BR AAGARDA7024 Chapada Diamantina, Palmeiras BA, BR CTMZ03172 Par. Nacional da Serra da Canastra MG, BR CTMZ03197 Estação Ecológica de Bananal SP, BR CTMZ03203 Par. Nacional da Serra da Canastra MG, BR CTMZ03207 Estação Ecológica de Bananal SP, BR CTMZ03266 Estação Ecológica de Bananal SP, BR CTMZ03288 Área Alfa DF, BR CTMZ03300 Estação Ecológica do Bananal SP, BR CTMZ03341 Estação Ecológica do Bananal SP, BR CTMZ03351 Estação Ecológica de Bananal SP, BR CTMZ03361 Estação Ecológica de Bananal SP, BR CTMZ03579 P. E. Jacupiranga, Núcleo Caverna do Diabo SP, BR CTMZ03630 P. E. Carlos Botelho SP, BR GJC27 Botucatu, Fazenda Edgardia SP, BR H0368 Buri SP, BR LG0173 São Paulo SP, BR LG0941 São Paulo SP, BR LG1089 São Paulo, Cidade Universitária SP, BR LG1273 Ribeirão Grande SP, BR LG1274 Ribeirão Grande SP, BR LG1784 Cotia SP, BR LG2173 Par. Nacional do Caparaó MG, BR LGE01527 Estancia Santo Domingo, Ituzaingó CR, ARG MCP11037 Dom Feliciano RS, BR MCP14737 São Jerônimo RS, BR MTR10550 UHE Ponte de Pedra MS/MT, BR MTR10553 UHE Ponte de Pedra MS/MT, BR MTR10713 Par. Nacional do Caparaó MG, BR MTR10714 Par. Nacional do Caparaó, acima da Tronqueira MG, BR MTR10821 Par. Nacional do Caparaó, Linha 5 MG, BR MTR10906 Par. Nacional do Caparaó MG, BR MTR10910 Par. Nacional do Caparaó MG, BR MTR10920 Par. Nacional do Caparaó MG, BR MTR11605 Par. Nacional do Caparaó, Pico da Bandeira MG, BR MTR11606 Par. Nacional do Caparaó, Pico da Bandeira MG, BR MTR15551 Flor. Nacional de Ipanema SP, BR MTR15552 Iperó, Flor. Nacional de Ipanema SP, BR MTR17039 E.B. Boracéia SP, BR MTR19446 Serra do Cipó MG, BR MTR19617 Serra do Cipó MG, BR MTR20104 Pico do Barbado BA, BR MTR20363 Serra do Cipó MG, BR MTR20364 Serra do Cipó MG, BR MTR21342 E.B. Boracéia SP, BR MTR21942 Serra do Cipó MG, BR MTR26063 Par. Nacional de Itatiaia RJ, BR MTR26094 Par. Nacional de Itatiaia RJ, BR MTR26124 Par. Nacional do Caparaó, acima terreirão MG, BR 48

MTR26126 Par. Nacional do Caparaó MG, BR MTR26127 Par. Nacional do Caparaó MG, BR MTR26161 Par. Nacional do Caparaó MG/ES, BR MTR26218 Par. Nacional do Caparaó, Pedra Menina ES, BR MTR26219 P.N. Caparaó, Pedra Menina ES, BR MTR26252 Par. Nacional do Caparaó ES, BR MTR26254 Par. Nacional do Caparaó ES, BR MTR26261 Par. Nacional do Caparaó ES, BR MTR26262 Par. Nacional do Caparaó ES, BR MTR26874 ParNa São Joaquim SC, BR MTR26875 ParNa São Joaquim SC, BR MTR26890 ParNa São Joaquim SC, BR MTR6147 Presidente Epitácio/Rio do Peixe MS, BR MTR9824 UHE Ponte de Pedra MS/MT, BR MTR9827 UHE Ponte de Pedra MS/MT, BR MTR9830 UHE Ponte de Pedra MS/MT, BR RPD137 P.E. Sete Passagens, Miguel Calmon BA, BR RPD182 P.E. Sete Passagens, Miguel Calmon BA, BR RPD183 P.E. Sete Passagens, Miguel Calmon BA, BR RPD192 P.E. Sete Passagens, Miguel Calmon BA, BR RPD232 P.E. Sete Passagens, Miguel Calmon BA, BR RPD233 P.E. Sete Passagens, Miguel Calmon BA, BR UFRGST0122 Alegrete RS, BR UFRGST0344 Bom Jesus RS, BR UFRGST0823 Bom Jesus RS, BR UFRGST0824 Bom Jesus RS, BR UFRGST1292 São Francisco de Assis RS, BR UFRGST1293 São Francisco de Assis RS, BR UFRGST1849 Eldorado do Sul RS, BR UFRGST1946 Caibaté RS, BR UFRGST2324 Pedras Altas RS, BR UFRGST2766 São Jerônimo RS, BR UFRGST2790 São Jerônimo RS, BR UFRGST2957 São Jerônimo RS, BR UFRGST3177 Balneário Gaivota SC, BR UFUB497 Poços de Caldas MG, BR

Table S2: Primers, Sequences, and PCR Protocols

Gene/primers Sequence Profile Reference 12S (Kocher et al., 12Saa CTG GGA TTA GAT ACC CCA CTA [94°C (3:00); 94°C (0:45); 50°C (0:45); 1989) 12Sba TGA GGA GGG TGA CGG GCG GT 72°C (1:30) for 35 cycles; 72°C (7:00)] 16S (Whiting, Bauer, 16Sar CGCCTGTTTAYCAAAAACAT [94°C (3:00); 94°C (0:45); 55°C (0:45); & Sites Jr, 2003) 16Sbr CCGGTCTGAACTCAGATCACGT 72°C (1:30) for 35 cycles; 72°C (7:00)] BACH1 (Townsend et al., Bach1 f1 GATTTGAHCCYTTRCTTCAGTTTGC [94°C (3:00); 94°C (0:45); 54°C (0:45); 2008) Bach1 r2 ACCTCACATTCYTGTTCYCTRGC 72°C (1:30) for 35 cycles; 72°C (7:00)] CMOS (Whiting et al., Mos-F CTCTGGKGGCTTTGGKKCTGTSTACAAG [95°C (12:00); 94°C (1:00); 56°C (1:00); 2003) Mos-R GGTGATGGCAAANGAGTAGATGTCTGC 72°C (1:00) for 35 cycles; 72°C (5:00)] Cyt b (Irwin, et al., L15146 CATGAGGACAAATATCATTCTGAG [94°C (3:00); 94°C (0:45); 53°C (0:45); 1991) H15915sh TTCATCTCTCCGGTTTACAAGAC 72°C (1:30) for 35 cycles; 72°C (7:00)] DNAH3 (Townsend et al., Dnah3 f1 GGTAAAATGATAGAAGAYTACTG [94°C (3:00); 94°C (0:45); 54°C (0:45); 2008) Dnah3 r6 CTKGAGTTRGAHACAATKATGCCAT 72°C (1:30) for 35 cycles; 72°C (7:00)] RAG1 (Gartner, et al., RAG1-AnF1 GAAATTCAAGCTCTTCAAAGTGAGAT [94°C (3:00); 94°C (0:45); 58°C (0:45); 2013) RAG1-AnR1 TGTCAAKGAAAGTAAGTGTTGTCTTG 72°C (1:30) for 35 cycles; 72°C (7:00)] RPL35 (Fujita, et al., rpl35 ex2 CAGAGTGCTGACATCATTAACCAGAC [95°C (10:00); 94°C (0:30); 54°C (0:35); 2010) rlp35 ex3 GTCTTCAGACCCTCTTCGTGCTTG 72°C (1:00) for 35 cycles; 72°C (10:00)] MYH (Dolman et al., Myh2 F GAACACCAGCCTCATCAACC [94°C (3:00); 94°C (0:45); 62°C (0:45); 2004; Lyons et Myh2_R TGGTGTCCTGCTCCTTCTTC 72°C (1:30) for 35 cycles; 72°C (7:00)] al., 1997) SINCAIP (T. M. Townsend Sincaip-F10 CGCCAGYTGYTGGGRAARGAWAT [94°C (3:00); 94°C (0:45); 55°C (0:45); 49

Sincaip-R13 GGWGAYTTGAGDGCACTCTTRGGRCT 72°C (1:30) for 35 cycles; 72°C (7:00)] et al., 2008)

Table S3. Pairwise FSTs between sites within the (a) fragmented northern Atlantic Forest,

(b) Cerrado, and (c) Pampas. Significant values are bolded.

Sites: 1: Área Alfa, DF 17: Presidente Epitácio/Rio do Peixe, MS 2: ParNa São Joaquim, SC 18: Balneário Gaivota, SC 3: P.E. Sete Passagens, Miguel Calmon, BA 19: P. E. Carlos Botelho, SP 4: Par. Nacional da Serra da Canastra, MG 20: São Paulo, SP 5: Pico do Barbado, BA 21: Santana do Parnaíba, SP 6: Par. Nacional de Itatiaia, RJ 22: Cotia, SP 7: Par. Nacional do Caparaó, MG 23: Flor. Nacional de Ipanema, SP 8: Serra do Cipó, MG 24: P. E. Jacupiranga, Núcleo Caverna do Diabo, SP 9: Chapada Diamantina, Palmeiras, BA 25: UHE Ponte de Pedra, MS/MT 10: E.B. Boracéia, SP 26: Caibaté, RSc 11: Estação Ecológica de Bananal, SP 27: São Francisco de Assis, RS 12: Bom Jesus, BA 28: Alegrete, RS 13: Cunha, SP 29: Eldorado do Sul, RS 14: Botucatu, Fazenda Edgardia, SP 30: São Jerônimo, RS 15: Buri, SP 31: Dom Feliciano, RS 16: Ribeirão Grande, SP 32: Pedras Altas, RS

Pairwise FST (sites) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

1 -

2 0.99 -

3 0.98 1.00 -

4 0.98 1.00 1.00 -

5 0.94 0.77 0 0 -

6 0.98 1.00 1.00 1.00 0 -

7 0.99 1.00 1.00 1.00 0.90 1.00 -

8 0.98 0.95 0.97 0.97 0.93 0.97 0.97 -

9 0.98 0.81 0.95 0.95 0.86 0.95 0.98 0.95 -

10 0.99 1.00 1.00 1.00 0.77 1.00 1.00 0.80 0.93 -

11 0.98 1.00 1.00 1.00 0.73 1.00 1.00 0.96 0.94 1.00 -

12 0.99 0.95 0.98 0.98 0.86 0.98 0.99 0.96 0.85 0.98 0.98 -

13 0.77 0.58 0.60 0.63 0.64 0.61 0.44 0.71 0.60 0.58 0.34 0.59 -

14 0.97 0.81 0.95 0.95 0.91 0.95 0.96 0.95 0.09 0.93 0.94 0.81 0.64 -

15 0.99 1.00 1.00 1.00 0.77 1.00 1.00 0.80 0.93 1.00 1.00 0.98 0.59 0.93 -

16 0.99 1.00 1.00 1.00 0.89 1.00 1.00 0.96 0.95 1.00 1.00 0.99 0.61 0.93 1.00 - 17 0.96 0.87 0.94 0.94 0.91 0.94 0.93 0.92 0.88 0.89 0.91 0.89 0.68 0.89 0.89 0.63 18 0.99 0.99 0.95 0.86 0.49 0.94 0.99 0.98 0.98 0.99 0.99 0.99 0.70 0.97 0.99 0.99 19 0.97 1.00 1.00 1.00 0.80 1.00 1.00 0.97 0.96 1.00 1.00 0.99 0.71 0.96 1.00 1.00 20 0.98 1.00 1.00 1.00 0 1.00 1.00 0.97 0.95 1.00 1.00 0.98 0.61 0.95 1.00 1.00 21 0.98 0.98 0 0.90 0.30 0.83 0.99 0.97 0.97 0.98 0.97 0.98 0.67 0.96 0.98 0.99 22 0.98 1.00 0 1.00 0 1.00 1.00 0.97 0.95 1.00 1.00 0.98 0.60 0.95 1.00 1.00 23 0.98 1.00 0 1.00 0 1.00 1.00 0.97 0.95 1.00 1.00 0.98 0.60 0.95 1.00 1.00 24 0.98 0.98 0 0.93 0.12 0.87 0.99 0.97 0.97 0.98 0.98 0.98 0.65 0.96 0.98 0.99 25 0.95 0.80 0.88 0.89 0.86 0.88 0.91 0.90 0.84 0.82 0.85 0.85 0.64 0.87 0.82 0.07 26 0.98 1.00 1.00 1.00 0 1.00 1.00 0.97 0.95 1.00 1.00 0.98 0.64 0.95 1.00 1.00 27 0.99 1.00 1.00 1.00 0.30 1.00 1.00 0.97 0.98 1.00 1.00 0.99 0.68 0.96 1.00 1.00 28 0.98 1.00 1.00 1.00 0 1.00 1.00 0.97 0.95 1.00 1.00 0.98 0.64 0.95 1.00 1.00 50

29 0.99 1.00 1.00 1.00 0.07 1.00 1.00 0.97 0.95 1.00 1.00 0.98 0.66 0.95 1.00 1.00 30 0.99 1.00 1.00 1.00 0.60 1.00 1.00 0.98 0.99 1.00 1.00 1.00 0.71 0.97 1.00 1.00 31 0.98 1.00 1.00 1.00 0 1.00 1.00 0.97 0.95 1.00 1.00 0.98 0.64 0.95 1.00 1.00 32 0.97 1.00 1.00 1.00 0.80 1.00 1.00 0.97 0.96 1.00 1.00 0.99 0.71 0.96 1.00 1.00 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 17 - 18 0.95 - 19 0.95 0.99 - 20 0.94 0.96 1.00 - 21 0.95 0.93 0.98 0.50 - 22 0.94 0.95 1.00 1.00 0 - 23 0.94 0.95 1.00 1.00 0 0.00 - 24 0.94 0.95 0.99 0.60 0 0 0 - 25 0.41 0.93 0.90 0.88 0.92 0.88 0.88 0.91 - 26 0.94 0.60 1.00 1.00 0.91 1.00 1.00 0.93 0.89 - 27 0.95 0.37 1.00 1.00 0.94 1.00 1.00 0.96 0.92 1.00 - 28 0.94 0.00 1.00 1.00 0.89 1.00 1.00 0.92 0.89 1.00 0 - 29 0.94 0.88 1.00 1.00 0.93 1.00 1.00 0.95 0.90 1.00 1.00 1.00 - 30 0.96 0.58 1.00 1.00 0.96 1.00 1.00 0.98 0.95 1.00 0 0 1.00 - 31 0.94 0.00 1.00 1.00 0.90 1.00 1.00 0.93 0.89 1.00 0 0 1.00 0 - 32 0.95 0.99 1.00 1.00 0.98 1.00 1.00 0.99 0.90 1.00 1.00 1.00 1.00 1.00 1.00 -

Average FST: N-AF = 0.814 S-AF = 0.54 Cerrado = 0.821 Pampas = 0.714

Table S4. Principal component loadings, based on environmental PCA (Fig. 6).

Component Loadings PC1 PC2 PC3 PC4 Precipitation of Driest Mean Temperature of Precipitation of Temperature seasonality Quarter Warmest Quarter Warmest Quarter Temperature annual Precipitation of Driest Max Temperature of Vegetation Continuous range Month Warmest Month Fields Precipitation of Coldest Mean Temperature of Isothermality Quarter Wettest Quarter Minimum Temperature Annual Mean Precipitation Seasonality of the Coldest Month Temperature Precipitation of Wettest Mean Temperature of Mean Diurnal Range Quarter Coldest Quarter Mean Temperature of

Coldest Quarter Precipitation of Wettest

Month Mean Temperature of

Driest Quarter Annual Mean

Temperature