ORIGINAL ARTICLE

doi:10.1111/j.1558-5646.2012.01682.x

DEEP DIVERSIFICATION AND LONG-TERM PERSISTENCE IN THE SOUTH AMERICAN ‘DRY DIAGONAL’: INTEGRATING CONTINENT-WIDE PHYLOGEOGRAPHY AND DISTRIBUTION MODELING OF GECKOS

Fernanda P. Werneck,1,2 Tony Gamble,3,4 Guarino R. Colli,5 Miguel T. Rodrigues,6 and Jack W. Sites, Jr1,7 1Department of Biology, Brigham Young University, Provo, Utah 84602 2E-mail: [email protected] 3Department of Genetics, Cell Biology and Development, University of Minnesota, Minneapolis, Minnesota 55445 4Bell Museum of Natural History, University of Minnesota, St. Paul, Minnesota 55108 5Departamento de Zoologia, Universidade de Brasılia,´ 70910–900 Brasılia,´ DF, Brazil 6Departamento de Zoologia, Instituto de Biociencias,ˆ Universidade de Sao˜ Paulo, 05508–090 Sao˜ Paulo, SP, Brazil 7Bean Life Science Museum, Brigham Young University, Provo, Utah 84602

Received January 7, 2012 Accepted April 19, 2012

The relative influence of Neogene geomorphological events and Quaternary climatic changes as causal mechanisms on Neotropical diversification remains largely speculative, as most divergence timing inferences are based on a single locus and have limited taxonomic or geographic sampling. To investigate these influences, we use a multilocus (two mitochondrial and 11 nuclear genes) range-wide sampling of Phyllopezus pollicaris, a gecko complex widely distributed across the poorly studied South American ‘dry diagonal’ biomes. Our approach couples traditional and model-based phylogeography with geospatial methods, and demonstrates Miocene diversification and limited influence of Pleistocene climatic fluctuations on P. pollicaris. Phylogeographic structure and distribution models highlight that persistence across multiple isolated regions shaped the diversification of this species complex. Approximate Bayesian computation supports hypotheses of allopatric and ecological/sympatric speciation between lineages that largely coincide with genetic clusters associated with Chaco, Cerrado, and Caatinga, standing for complex diversification between the ‘dry diagonal’ biomes. We recover extremely high genetic diversity and suggest that eight well-supported clades may be valid species, with direct implications for taxonomy and conservation assessments. These patterns exemplify how low-vagility species complexes, characterized by strong genetic structure and pre-Pleistocene divergence histories, represent ideal radiations to investigate broad biogeographic histories of associated biomes.

KEY WORDS: ABC, Miocene, open biomes, Phyllopezus, Pleistocene, population structure, speciation.

Identification of underlying mechanisms shaping the diversifica- tial methods represent exciting advances in testing for alternative tion of intraspecific lineages and species complexes is essential models of population divergence at the species level, and histori- to understand speciation processes and ultimately biogeographic cal biogeography at the landscape level (Knowles and Alvarado- patterns across multiple spatial and temporal scales. Emergent Serrano 2010; Chan et al. 2011). The use of model-based inference approaches based on the coalescent theory coupled with geospa- in phylogeography (i.e., any method in which a fully specified

C 2012 The Author(s). 1 Evolution F. P. WERNECK ET AL.

probabilistic model describes how observed data are generated) is of these open biome biotas. For example, Miocene (23–5.3 Mya) not free of controversy (Templeton 2010). However, as more simu- geological events were interpreted as primary determinants of lation studies and empirical applications are completed, the debate diversification among lizard genera (Giugliano et al. 2007) and declines (Bloomquist et al. 2010), and their utility as a reason- species (Werneck et al. 2009), and within a frog species complex able statistical approach has now become consolidated (Beaumont (Maciel et al. 2010), whereas recent adaptive shifts driven by fire et al. 2010; Bertorelle et al. 2010; Garrick et al. 2010). dynamics (Simon et al. 2009) and Quaternary climatic changes Approximate Bayesian computation (ABC) is one of the most were identified as foremost processes of some plant (Ramos emblematic ‘likelihood-free’ model-based methods currently in et al. 2007; Caetano et al. 2008) and fruitfly (Moraes et al. 2009) use (Beaumont 2010). Briefly, ABC simulates millions of ge- groups. nealogies assuming different parameter and prior values under The role of relatively recent Quaternary changes versus Neo- competing models, and those producing patterns of genetic vari- gene geomorphological events in the origin and diversification of ation most similar to the observed data (determined by summary high Neotropical biodiversity has been a recurrent debate (Moritz statistics, SuSt) are then retained and analyzed in detail (Bertorelle et al. 2000; Rull 2008, 2011a; Hoorn et al. 2010). Each of these hy- et al. 2010; Csillery´ et al. 2010). Recent implementations of potheses predicts very different causal mechanisms (climatic vs. ABC to test complex evolutionary scenarios have brought sta- geomorphological) and patterns of genetic diversification. A po- tistical rigor and power to phylogeography, enhancing the po- tential role was attributed to Quaternary glacial cycles and habitat tential interfaces and outcomes of the field (Hickerson et al. stability (refugia), especially in the Northern Hemisphere (Hewitt 2011). 2004), but now also in the Southern Hemisphere where glaciers In a recent global survey, Beheregaray (2008) identified did not cover large areas but climate may have had consider- large phylogeographic knowledge gaps in most South American able impacts (Carnaval et al. 2009; Pepper et al. 2011; Sersic´ biomes. In the last decade or so, however, phylogeographic re- et al. 2011). Overall, it is expected that Quaternary stability search has accelerated throughout much of the continent, includ- will be reflected in higher genetic diversity and strong phylo- ing Amazonia (Geurgas and Rodrigues 2010; Ribas et al. 2012), geographic structure between refugia in opposition to unstable Atlantic Forest (Carnaval et al. 2009; Thome´ et al. 2010; Cabanne regions, where populations have genetic signatures of repeated et al. 2011), Andean regions (Cadena et al. 2011; Chaves and distributional shifts (Hewitt 2004). Smith 2011), and Patagonia (Lessa et al. 2010; Breitman et al. The alternative hypothesis predicts that species have been 2011; Nunez˜ et al. 2011), to cite some examples. Conspicuous little affected by Pleistocene fluctuations when compared to pre- gaps in this emerging coverage are the highly threatened open vious geomorphological events that remained largely unchanged. vegetation biomes of central-eastern South America, which ex- These older events are then predicted to leave stronger genetic tend diagonally across a large latitudinal range and include the signatures, characterized by deep phylogeographic structure with Seasonally Dry Tropical Forests (SDTFs, with the largest area, multiple well-supported haploclades, pre-Pleistocene divergence Caatinga, in northeastern Brazil), the Cerrado savanna (central times, and the presence of cryptic species and multiple evolu- Brazil), and the Chaco (southwestern South America, Fig. 1). tionary significant units (Moritz et al. 2000; Rull 2008; Hoorn Early studies interpreted these regions as a single complex of et al. 2010). A third hypothesis, although, remains largely unex- low-diversity open formations, the ‘diagonal of dry formations’ plored: that of Tertiary and Quaternary causal mechanisms leaving (Vanzolini 1963, 1976), without regionally distinct biotas, de- mutually detectable genetic signatures across different levels of spite their strong character (reviewed by Werneck 2011). How- sampled coalescent trees (Fujita et al. 2010; Rull 2011a). In this ever, species diversity and endemism is high for some taxa within case, most crown clades ages would date back to the Paleogene– these biomes, including fish (Leal et al. 2003), squamate reptiles Neogene, whereas most extant terminals would have diverged (Rodrigues 2003; Nogueira et al. 2011), Lepidoptera (Amorim during the Quaternary (Rull 2011a). Distinguishing among these et al. 2009), and plants (Oliveira and Marquis 2002; Simon et al. alternative hypotheses requires the use of multiple markers span- 2009). ning a wide range of coalescent times and mutation rates, coupled The combined influences of Paleogene–Neogene geologi- with dense population sampling, to estimate species trees and cal processes and Quaternary climatic and vegetational fluctu- divergence times. ations are hypothesized to have generated and maintained the Species or species complexes broadly distributed across high diversity levels, and to have driven the differentiation of the ‘dry diagonal’ biomes represent ideal models to investigate the open biome communities from adjacent rainforests and from subcontinental diversification patterns that span the Tertiary– each other (Werneck 2011). Recent studies have yielded mixed Quaternary boundary. The nocturnal gecko Phyllopezus polli- results concerning the primary diversification processes in some caris (Phyllodactylidae; Gekkota) fits this role (Gamble et al.

2 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

Figure 1. Distribution of sampled localities for Phyllopezus pollicaris relative to the refugial areas estimated through palaeomodeling for Seasonally Dry Tropical Forests (SDTFs; light gray refugia: Caatinga [Ca], Chiquitano [Chi], Missiones [Mi], and Mato Grosso do Sul [Ms] [from Werneck et al. 2011]), Cerrado [Ce] (Serra Geral de Goias´ refugium in medium gray [Werneck et al. in press]), and Chaco [Ch] refugium in dark gray (F. P. Werneck and G. C. Costa, unpubl. data). The insert map shows the approximate current distributions of the central-eastern South America ‘dry diagonal’ biomes. Numbers correspond to localities names in Table S1, and underlined numbers identify areas under legal preservation. Straight-line distance from locality 1 to 68 is approximately 3440 km. AR, Argentina; BO, Bolivia; BR, Brazil; PA, Paraguay. See text for details.

2011, 2012). Phyllopezus pollicaris distribution spans three South Methods American open biomes, with a slight break in the natural distri- SAMPLE COLLECTION bution near central Brazil. Current taxonomy recognizes two sub- Samples were obtained through fieldwork led by FPW, GRC, species: P. p. pollicaris and P. p. przewalskii (Fig. 1), but a recent or MTR, and through tissue loans obtained from colleagues study indicates that P. pollicaris is composed of multiple cryptic (Table S1). Vouchers were catalogued in the Colec¸ao˜ Her- lineages (Gamble et al. 2012). petologica´ da Universidade de Bras´ılia (CHUNB) or Museu de Here, we use a range-wide dataset sampled from multiple Zoologia da Universidade de Sao˜ Paulo (MZUSP). Phyllopezus loci to investigate the geographical and ecological factors pro- pollicaris is a habitat specialist on rock outcrops, but at regions moting the diversification of a ‘dry diagonal’ endemic. We in- where these are not abundant, individuals are sometimes associ- tegrate geospatial methods with coalescent phylogeographic and ated with human buildings (F. P. Werneck and M. T. Rodrigues, population genetic analyses to test the relative contributions of pers. obs.). Tertiary geomorphological versus Quaternary climatic events on The complete dataset for molecular analyses includes 393 P. pollicaris diversification. We subsequently use ABC analysis tissue samples from 68 localities, spanning the complete geo- to test divergence models under alternative biogeographic scenar- graphic, taxonomic, and morphological extent of variation cur- ios relevant at the subcontinental scale. Our integrative approach rently known in P. pollicaris. Sampling localities are distributed represents one of the best documented examples of the com- within and outside modeled historical refugia for P. pollicaris plex diversification histories of South American open biome taxa, and for the open biomes (Fig. 1). Phyllopezus periosus and P. highlighting possible species limits within the group and testing maranjonensis (Gamble et al. 2012) were used as outgroups in all biogeographic processes at the biome level. phylogenetic analyses.

EVOLUTION 2012 3 F. P. WERNECK ET AL.

SEQUENCE DATA COLLECTION (Macey et al. 1998), widely employed in dating squamate phy- We sequenced partial mtDNA sequences from cytochrome b logenies. For the nuclear loci, we used the default gamma prior (cytb) and NADH dehydrogenase subunit 2 (ND2) genes from for ucld.mean and exponential prior for ucld.stdev, with a mean most individuals (some samples failed to amplify for ND2). We of 0.5. then obtained a maximum likelihood (ML) gene tree to direct As with most species tree estimation methods, ∗BEAST re- subsampling efforts representing haplotype and geographical di- quires a priori assignment of individual alleles to a species (‘traits’ versity (one or two individuals representing all haplotypes at file) before estimating the relationships. There is no routine pro- each locality) for the nuclear loci. We screened approximately cedure on how to assign individuals to species in a poorly known 40 rapidly evolving nuclear markers from a variety of sources, complex, but any approach elected should avoid underestima- and amplified 11 variable loci within P. pollicaris (including tion of intraspecific diversity. We provisionally made assignments protein coding regions and introns). Supporting Information Ap- based on well-supported and geographically structured mtDNA pendix S2 and Table S2 summarize relevant details for the 13 haploclades. We selected at random one of the phased nuclear gene regions, primers, laboratory protocols, sequence assembling, gene copies to represent each individual on this and subsequent alignment, recombination tests, and treatment of heterozygous in- analyses to avoid extremely time-consuming computations. We dividuals. We deposited all sequences in GenBank (accession nos. implemented five independent runs of 100 million generations JQ825288-JQ828670) and trees in TreeBase (no. 12502). each (total 5 × 108 generations), and assessed convergence of MCMC runs (effective sample sizes, ESS values > 200) using GENE TREE ESTIMATION AND GENETIC DISTANCES Tracer version 1.4.1 (Rambaut and Drummond 2007). Stationar- To describe the overall phylogeographic structure of P. pollicaris, ity was reached before the 10% of the posterior samples, which we performed ML analyses on single-gene and partitioned con- was used as a conservative burn-in when pooling files from the catenated datasets (mtDNA and nuDNA), including both gene independent runs with LogCombiner (Drummond and Rambaut copies in the case of phased nuclear loci, using RAxML version 2007). We used BEAGLE version 1.0 (Ayres et al. 2012), a pro- 7.0.0 (Stamatakis 2006). Analyses were implemented with GTR + graming interface to accelerate analyses. Gamma model, 200 independent ML searches, and 1000 nonpara- metric bootstrap replicates to assess nodal support (Felsenstein POPULATION STRUCTURE AND ASSIGNMENTS 1985). We used Geophylobuilder version 1.0 (Kidd and Liu 2008) We investigated population structure with a Bayesian probabilis- to construct a tridimensional representation (geophylogeny) from tic genetic clustering implemented by STRUCTURE version 2.3 the mtDNA gene tree and associated geographical data, and visu- (Pritchard et al. 2000) using a genotype matrix of two mtDNA alized it with ArcGIS and ArcScene version 9.1 (ESRI). We cal- and four nuDNA. This subset of nuclear loci spanned a range culated net among-group distances between mtDNA haploclades of nucleotide diversities and coalescent times (KIF24, MYH2, with MEGA version 5.05 (Tamura et al. 2011), using uncorrected PRLR, and rpl35) in the complex, but allowed us to avoid the and Tamura–Nei (Tamura and Nei 1993) corrected distances, and exceptionally time-consuming computations typical of such large 500 bootstrap replicates to estimate standard errors. datasets. We are aware that in some instances (e.g. long history of isolation), STRUCTURE can create clusters that are inconsistent SPECIES TREE ESTIMATION AND DIVERGENCE with the main evolutionary divisions (Kalinowski 2011). This re- DATING sult seems to be forced by evaluation of an inappropriately small We estimated the P. pollicaris species tree from the multilocus number of discrete genetic clusters, K (Kalinowski 2011), so we trees under a coalescent model and simultaneously estimated di- explored a large range of values by running 20 replicated analyses vergence times using ∗BEAST version 1.6.2 (Drummond and over a range of K from 2 to 30. Each of these 580 independent Rambaut 2007; Heled and Drummond 2010). The lack of Phyl- runs implemented one million generations following a burn-in lopezus fossils prevented rigorous divergence dating, but we used of one million generations, and incorporated the possibility of a less-ideal indirect calibration based on substitution rates to pro- mixed ancestry. We visualized the optimal K structure based on vide a first estimate of divergence dates for the major nodes rel- the rate of change in the log probability of data between succes- evant to testing biogeographic scenarios. We used uncorrelated sive K values, K (Evanno et al. 2005), as calculated by Structure relaxed clocks to allow for rate heterogeneity among lineages, a Harvester. Yule prior for the species tree, and a normal prior on the global We combined the replicate analyses under the optimal K substitution rate to calibrate the estimation (mean = 0.0065 sub- identified with CLUMPP (Jakobsson and Rosenberg 2007), and stitutions/my; SD = 0.0025 for the ucld.mean parameter) based plotted results with individuals in the order of their appearance on on the mtDNA substitution rate of 0.65% changes/million years the mtDNA gene tree, to facilitate cross-visualization of results.

4 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

Because K may favor smaller values of K representing basal TESTS OF POPULATION DIVERGENCE levels of hierarchical structure (Evanno et al. 2005; Weisrock AND BIOGEOGRAPHIC SCENARIOS et al. 2010), we reran the STRUCTURE analyses (same set of We used an ABC approach to test three alternative scenarios for parameters) within each of the major basal clusters identified to the diversification of P.pollicaris relevant to hypothesized biogeo- investigate finer level structure. graphic scenarios (models and parameters described in Results). Analyses consisted of three steps: (1) draw parameter values from QUATERNARY PALAEOMODELING AND ANCESTRAL the prior distributions and simulate data under each model; (2) DISTRIBUTIONS compute SuSt and the distance between simulated and observed To estimate P. pollicaris species distribution models (SDMs) datasets; and (3) use a rejection algorithm to approximate the across Quaternary climatic fluctuations, we implemented posterior distribution of parameters by retaining the simulations the maximum entropy machine-learning algorithm MAXENT closest to the observed data, based on a specified threshold, that is (Phillips et al. 2006). The occurrence dataset included our field tolerance (Lopes and Beaumont 2010). Model choice can affect GPS records, georeferenced museum or published records, and estimates and conclusions, so alternative models should represent records from trusted collectors or on-line gazetteers (e.g., ACME biologically relevant hypotheses in the simplest way possible, Mapper version 2.0, and Global Gazetteer version 2.2), which and model fit should be explored (Carstens and Knowles 2010; were verified with Google Earth to ensure points were not lo- Csillery´ et al. 2010). We simulated 100,000 coalescent genealo- cated in heavily urbanized areas. Current climatic variables were gies under each model with msABC (Pavlidis et al. 2010), and downloaded from WorldClim (Hijmans et al. 2005), while Last simulated a total of six loci (the same used for Structure analyses) Glacial Maximum [21 ky BP, LGM] and Holocene [6 ky BP] (Pavlidis et al. 2010). Number of gene copies, number and length were based on ECHAM3 (Deutsches 1992), and Last Interglacial of loci, and prior distributions were chosen to reproduce observed [130 ky BP, LIG] data were obtained from Otto-Bliesner et al. values from the empirical dataset. (2006). We then obtained a historical stability map by overlap- We estimated empirical relative mutation rates of loci and av- ping the presence/absence projections under the four scenarios. erage theta across lineages specified in the models using Migrate- Methodology is described in detail in Werneck et al. (2011; in n version 3.2.15 (Beerli and Palczewski 2010) implementing the press), as applied to the open biomes. To estimate the ancestral same search strategy as above, and used those parameters to con- distribution of the basal node of P. pollicaris, we used PhyloMap- vert absolute times into coalescent times (units of 4No genera- per version 1b1 (Lemmon and Lemmon 2008) with nonparametric tions), assuming one generation per year. We selected all seven rate-smoothing to obtain an ultrametric tree from the mtDNA gene global SuSt to be computed with mean and variance calculated tree based on unique haplotypes, and corresponding geographical across the loci, but excluded population-specific SuSt that cannot coordinates. be compared between models with different numbers of popula- tions. We transformed observed sequence data into ms-like files GENETIC DIVERSITY AND TEST OF QUATERNARY and then calculated the same global SuSt across all loci. PHYLOGEOGRAPHIC PREDICTIONS We estimated posterior probabilities and models sup- We calculated population genetic metrics and tested general ge- port using ‘postpr’ and parameter posterior distributions us- netic predictions derived from the habitat stability (refugia) hy- ing ‘abc’ functions of the R package ‘abc’, move the comma pothesis (Table 1) based on mtDNA (Carnaval et al. 2009). We to out of the single quote implemented in R version 2.11.1 grouped samples based on the results of Structure, and into sta- (R Development Core Team 2010). We set tolerance to ble or unstable regions, and calculated coalescent estimators 0.001 and implemented a nonlinear neural network regression, of genetic diversity with Migrate-n version 3.2.15 (Beerli and which has been shown to outperform other ABC algorithms Palczewski 2010). We used two independent ML runs with 10 (Camargo et al. 2012). We summarized simulated model fit to short chains and five long chains, for a total of 2000 and 20,000 the observed data using a Principal Components Analysis (PCA): generations, a burn-in of 20,000 steps, and starting parameters predictive plots in which the observed SuSt occurred within the for the calculations derived from an Fst-like estimator (Beerli and cloud of simulated SuSt were interpreted as good fit. All inten- Palczewski 2010). We calculated the other genetic parameters in sive computational analyses were submitted to the BYU Fulton Table 1 and respective significance levels with 10,000 coalescent Supercomputing Cluster. simulations using DnaSP version 5 (Rozas et al. 2003). We also implemented Mantel tests with Zt (Bonnett and de Peer 2002) Results to investigate isolation-by-distance (IBD) patterns as character- MARKER AND DNA POLYMORPHISM ized by correlations between geographical and genetic distances We sequenced a total of 1828 and 7477 bp of mtDNA and nuclear matrixes. markers, respectively. Nucleotide diversity ranged from 0.59%

EVOLUTION 2012 5 F. P. WERNECK ET AL.

Table 1. Population genetic summary metrics used in model validation, and respective expectations for the estimated Quaternary historical stability surface.

Expectation (stable Expectation (unstable Metric Description areas) areas)

θ Theta diversity Higher genetic diversity Lower genetic diversity parameter Mean Da Average net nucleotide Higher differences Fewer differences across differences across across localities localities (lower localities reflecting higher geographic structure) geographic structure within refugia Fay and Wu’s Hs Test to detect population Lack of signature of Signatures of population (P-value) expansion signal population expansion expansion Mantel’s correlation Isolation-by-distance Presence of IBD patterns No IBD patterns coefficient (P-value) (IBD) test

Table 2. Phyllopezus pollicaris molecular markers ranked by hap- All but three nuclear loci (MC1R, MAP1B, DMXL1) had lotype and nucleotide diversity (%). H = number of haplotypes; at least one fixed indel among lineages, and MYH2 and rpl35 = = Hd haplotype diversity; Pi Nucleotide diversity (per site); regions had the largest number of indels. Barra do Garc¸as (no. 57) k = average number of nucleotide differences between sequences; and Sao˜ Geraldo do Araguaia (no. 23) had the highest fre- S = number of polymorphic (segregating) sites. quency of fixed indels (across five loci), followed by Babac¸ulandiaˆ Length (no. 24), Grao˜ Mogol (no. 51), and Mucugeˆ (no. 36), with indels Gene (bp) N/localities H/Hd Pi(%)/kS across three loci.

Cytb 942 393/68 164/0.99 15.65/98.4 344 ND2 886 312/62 130/0.98 16.85/97.72 340 GENE TREE ESTIMATION KIF24 565 241/67 142/0.97 3.65/20.37 263 The mtDNA tree identified multiple deep, moderately to strongly ACA 900 225/61 48/0.84 2.83/15.39 201 supported haploclades, with several localities exhibiting mono- MYH2 436 227/63 128/0.97 2.66/8.84 98 phyly. We identified 44 mtDNA haploclades (A–AR, Fig. 2, PRLR 563 265/68 98/0.96 2.65/13.70 174 rpl35 617 206/61 76/0.96 2.15/11.91 151 Table S1), which were used as assignments for species tree esti- MC1R 524 238/61 72/0.92 1.20/6.30 82 mation. We conservatively recognized some haploclades with low MAP1Bex5 888 226/64 55/0.89 1.15/8.81 119 support, if concordant with geography, to avoid underestimating MXRA5 919 258/68 96/0.94 1.09/9.75 166 diversity in downstream analysis. Corrected mtDNA distances SINCAIP 482 263/68 61/0.93 1.09/5.14 80 among haploclades ranged from 0.2% between geographically DMXL1 943 276/68 69/0.85 0.91/8.63 179 close sister-clades (Table S3) to values as high as 35% (uncor- RBMX 640 262/68 41/0.87 0.59/3.18 65 rected distances 0.2–25%, Table S4), with mean distance across the tree of 23.4% (SD = 0.9%). Some individual nuclear gene trees lacked resolution at the (RBMX) to 16.85% (ND2); and overall haplotype diversity and most basal levels (e.g., ACA), but most had moderate resolution average number of nucleotide differences were surprisingly high and support (Fig. S1). In contrast, the concatenated nuclear ML (Table 2). We found a high number of polymorphic sites for the tree recovered 12 strongly supported clades (Fig. 3). The overall mtDNA and most nuclear loci, including some protein coding genealogy of mtDNA and nuclear trees was congruent, with some genes (Table 2). The two mtDNA fragments were concatenated relationships consistently recovered and well supported across all and treated as a single locus in subsequent analyses; 269 of the trees, as P. p. przewalskii (from southwestern Brazil and through- 388 samples analyzed represented unique haplotypes restricted out Chaco) and the Caatinga group (Figs. 2, 3, and S1). Cerrado to single localities. Most well-sampled localities (n ≥ 4) were populations occupy three positions on the tree, two small basal characterized by different haplotypes, but haplotypes were shared lineages, including mtDNA haploclades A, B, and C, and nuclear once between two nearby localities (Xingo´ [no. 12] and Poc¸o clades (Mucugeˆ no. 36) and (Sao˜ Geraldo do Araguaia no. 23 + Redondo [no. 16]), and once between two disjunct populations Barra do Garc¸as no. 57), sister to all other lineages. A second (Tiangua´ [no. 20] and Peixe [no. 39]). clade, mtDNA haploclade E and nuclear clade (Serra da Mesa

6 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

Figure 2. Maximum likelihood mtDNA gene tree and correspondence between terminals and STRUCTURE clusters at the two levels investigated. Black squares represent bootstrap nodal support >0.70 (terminal names are omitted for clarity, but are available in Table S1). Each population cluster is represented by a different color, with horizontal bars representing individuals and the posterior probability that a given individual is assigned to a particular cluster. Clades A–AR correspond to the 44 mtDNA haploclades identified. Vertical bars and roman numerals represent the correspondence with clades identified from the Species Tree that are interpreted as ‘candidate species’ (Fig. 4 and text for details). Photo of Phyllopezus pollicaris by MTR. no. 44, Lajeado no. 37, and Palmas no. 38), is sister to the confidence intervals (95% highest posterior density, HPD) and, in strongly supported clade comprising all P. p. przewalskii pop- many instances, were not calculated at nodes of ambiguous rela- ulations (Fig. 3). The third group (mtDNA haploclades Q–U and tionships, that is, posterior probabilities lower than 0.5 (Fig. S2); V–W) is closely related to a clade including most Caatinga lo- thus, these are provisional conservative estimates. The species tree calities (haploclades X–AR). With a few exceptions, populations based on all 13 loci (Fig. 4) is mostly concordant in topology with from the Caatinga formed a well-supported clade deeply nested both mtDNA and nuclear concantenated ML trees. As in previous within the tree, but with internal relationships poorly resolved and analyses, Sao˜ Geraldo do Araguaia no. 23, Barra do Garc¸as no. short branches. 57, and Mucugeˆ no. 36 (clades I and II) are sister to all other P. pollicaris lineages, and diverged in the mid-Miocene (∼11.5 Ma). SPECIES TREE ESTIMATION AND DIVERGENCE The large clade of populations from Caatinga is deeply nested in DATING the species tree and diverged in the Mid–Late Miocene, but in- We observed large ESS (> 200) and convergence for most pa- ternal support is low (Fig. 4, green). The strongly supported P. p. rameters across independent and combined runs for the species przewalskii clade was isolated approximately 8.5 Ma, and shows tree analysis. Divergence time estimates were bound by broad moderate to strong support for internal relationships (Fig. 4, blue).

EVOLUTION 2012 7 F. P. WERNECK ET AL.

Figure 3. Phyllopezus pollicaris concatenated nuclear maximum likelihood tree; relationships within terminal clades are collapsed for ease of presentation and clades are labeled according to current subspecies designations or major localities. Parentheses include locality numbers (Fig. 1 and Table S1)/letters corresponding to the mtDNA haploclades (Fig. 2 and Table S1)/roman numerals corresponding to the clades recovered in the species tree (Fig. 4 and Table S1).

Divergence times estimated the origin and major crown gruent with geographic clusters in the Southwest (SW), Central clades splits of P. pollicaris to have occurred during the Miocene (CE), and Northeast (NE) of the P. pollicaris distribution, which (between 11 and 5 Ma), followed by divergence within these ma- are largely coincident with the distributions of the Chaco, Cerrado, jor groups during the Pliocene–Pleistocene transition (5–1.5 Ma). and Caatinga, respectively (Figs. 5, 6). One exception are samples These estimates are concordant with previous estimates for the ori- from a few close localities in Para´ıba state (Itaporanga [no. 7], gin of P. pollicaris (Gamble et al. 2011), and indicate that limited JuncodoSerido´ [no. 4], Patos [no. 5]), assigned to the Central diversification occurred during Late Pleistocene, mostly among cluster, while surrounded by populations assigned to the Northeast geographically close populations in the Chaco and Caatinga cluster (Fig. 5). There is also overall concordance between iden- (Fig. 4, blue and green). Most Cerrado populations initiated and tified multilocus clusters and mtDNA haploclades and geophy- ended their diversification during the Miocene, and are charac- logeny (Fig. 5). Some individuals located at the contact regions terized by long branches and older diversification dates (Fig. 4, between clusters revealed admixed assignments (Fig. 6). red). When we reran analyses within each of the major clusters, K detected K = 3, K = 9, and K = 3 (total K = 15) for the SW, POPULATION STRUCTURE AND ASSIGNMENTS CE, and NE clusters, respectively, demonstrating further popu- Calculations of K detected a peak at K = 3, representing the lation structure, more pronounced in the CE populations. How- most basal hierarchical structure in the data (Evanno et al. 2005; ever, although most individual membership posterior probabilities Weisrock et al. 2010). Assignments detected at K = 3 are con- were greater than 0.9 in the first round of analyses, coefficients

8 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

Figure 4. Phyllopezus pollicaris species tree (maximum clade credibility tree based on all 13 loci) and divergence time estimates, derived under a coalescent model with ∗BEAST. Clades colors correspond to population clusters defined by STRUCTURE analyses, and the squares correspond to nodes with posterior probability >0.75. Terminals A–AR identify the 44 mtDNA haploclades, and clades highlighted by roman numerals are interpreted as ‘candidate species’, pending confirmation from ecological and morphological studies. Credibility intervals are omitted for clarity, but see Figure S2. are overall lower at the finer level structure, and further divide extensive overlap of highly suitable areas across the four time both admixed and nonadmixed sets of individuals (Fig. 2). There- periods (i.e., large areas of stability; Fig. 6, yellow areas). Despite fore, as we considered using population assignments to develop minor oscillations in the species range, the P. pollicaris stabil- divergence models, and alternative models should preferentially ity map provides a close match for the ‘dry diagonal’ biomes represent biologically relevant questions in the simplest way pos- stability models (Werneck 2011; Werneck et al. in press). Thus, sible, we chose to focus subsequent analyses at the basal level of we are confident that P. pollicaris tracked changes of the open clustering (K = 3). biomes and that this complex represents a suitable proxy of their biogeographic patterns at both shallow and deep temporal scales. PALAEOMODELING AND ANCESTRAL The ancestral distribution was estimated by PhyloMapper to be DISTRIBUTIONS located in central Brazil, nearby CE and NE current distributions, Estimated palaeomodels depict a potential distribution of P. pol- where ancient Phyllopezus lineages became extinct (Fig. 6, black licaris contracted to the southwest and northeast of the ‘dry di- circle). agonal’ during the LGM, with a break in central Brazil. These two SDMs blocks closely track the distribution of population GENETIC POLYMORPHISM AND TEST clusters, SW to one extreme and [CE + NE] to the other OF PHYLOGEOGRAPHIC PREDICTIONS (Fig. 6). However, when considering all SDMs and the stabil- None of the groupings (or the total sample) revealed detectable ity surface, we observe that P. pollicaris potential distribution did signatures of population expansion or reduction (Table 3). We not change drastically across Quaternary climatic fluctuations, did detect significant IBD across all groupings, except for the especially from the Holocene to the present (Fig. 6). Note the SW cluster, which had overall lower diversity when compared to

EVOLUTION 2012 9 F. P. WERNECK ET AL.

Figure 5. Phyllopezus pollicaris and outgroups mtDNA geophylogeny as estimated by GeoPhyloBuilder and visualized by ArcScene. Colors shapes correspond to population clusters identified with STRUCTURE and the black arrow shows the tree root. the other clusters (Table 3). We did not find major differences and NE, as recovered by Structure), and represents a geologically between samples from stable and unstable areas with respect to older speciation scenario. Model C represents an early diversifi- most population genetic metrics, contradicting predictions from cation of the southwest (SW) group from the other groups (CE + the hypothesis of strong influence of Quaternary refugia on clade’s NE), followed by a more recent divergence between these last two. genetic structure (Hewitt 2004; Carnaval et al. 2009). In fact, This hypothesis would correspond to two speciation events within unstable areas presented higher genetic diversity than stable areas P. pollicaris, and it predicts a (SW [CE + NE]) topology (Fig. 7). with respect to several metrics (Table 3). We attribute the early divergence time (T1) to the final up- lift of the Central Brazilian Plateau that took place 7–5 Ma, an TESTS OF POPULATION DIVERGENCE important event hypothesized to have driven divergence across AND BIOGEOGRAPHIC SCENARIOS: ABC the open biomes (Werneck 2011). We hypothesize the more re- We developed three models representing alternative biogeo- cent divergence time (T2) to represent the final phase of the eco- graphic scenarios for the diversification of P. pollicaris across logical expansion of grass-dominated vegetation and increased the ‘dry diagonal’ based on (1) speciation expectations from esti- fire frequency (5–3 Mya), which may have promoted additional mated genealogies, population structure, and palaeomodels; and ecological diversification of the Cerrado from the adjacent open (2) available geological information with relevant times that fall biomes (Beerling and Osborne 2006; Simon et al. 2009; Edwards within the dated chronogram from ∗BEAST. The use of exter- et al. 2010). Both divergence times fall within estimated diver- nal information (e.g., dates of geological events) is crucial to gence times for P. pollicaris lineages (Fig. 4). Although the use narrow the universe of possible histories (Garrick et al. 2010). of three subgroups estimated by Structure does not fully corrob- The first model (A) represents a no-speciation null model and re- orate the phylogenetic results (which recover a paraphyletic CE flects views from early studies that the open biomes had a shared group), the models were intended to capture the broad substruc- history (Vanzolini 1976); it was modeled as a large population, ture and biogeographic patterns supported by available geological with no substructure and free migration. Model B hypothesizes an data for the ‘dry diagonal’. Because divergence with some degree approximately simultaneous early divergence from the ancestral of gene flow may be common (Nosil 2008), we also considered area (as estimated by PhyloMapper) into three clades (SW, CE, the possibility of migration in the diversification models B and C.

10 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

Figure 6. Modeled ranges of Phyllopezus pollicaris across Quaternary climatic fluctuations, and current climatic scenarios (left panels). Warmer colors of the logistic output format correspond to regions with higher probability of occurrence. Black dots in the logistic output for the Current climatic scenario (top left) represent the occurrence dataset implemented in the distribution modeling analyses, and depict the natural break in the P. pollicaris species complex distribution. On the right: Historical stability surface (gray tones and stable areas in yellow) obtained by overlapping predicted logistic outputs under the four climatic scenarios (current, 6, 21, and 120 kyr BP), and the geographical distribution of the Bayesian clustering results and ancestral distribution of P. pollicaris. All stability surfaces are plotted against a digital elevation model for South America, depicting major rivers and geomorphological features discussed in the text (brown represents higher altitudes, e.g., Central Brazilian Shield [CBS] and green represents lower altitudes, e.g., Chaco-Parana´ Basin [CPB]; a, Sao˜ Francisco river; b, Tocantins River; c, Espinhac¸o mountain range; d, Parana´ River). Each genetic cluster is designated by a different color, with pie charts representing individuals and the posterior probability that a given individual is assigned to a particular cluster.

Model A included the per locus mutation parameter θ (= 4Noμ) cally, diversification of P. pollicaris into two large groups, SW for a single lineage across multiple loci, whereas models B and and CE + NE (model C), was selected in every comparison C included additional parameters: the divergence times in coales- that included this model (Table 4). Posterior probabilities were cent units (τ, in units of 4No generations) and the migration rate overall lower for comparisons that included migration, relative to between divergent lineages (m, prior varied from 0, no migration, equivalent comparisons without migration, reflecting increased to 1, high migration). We assumed stable population sizes in all difficulty in distinguishing between speciation and no-speciation models. models when migration is present (Nielsen and Wakeley 2001). When compared simultaneously, ABC could not attain res- Bayes factors of pairwise comparisons to the preferred models at- olution between the three models, unless the tolerance rate was tained moderate values (up to ∼12). The observed SuSt occurred increased (Table 4). ABC favored the speciation scenarios over the within the bounds of the prior sample of simulated SuSt at PCA no-speciation scenario in all pairwise comparisons. More specifi- predictive plots, confirming good model fitting (Fig. S3).

EVOLUTION 2012 11 F. P. WERNECK ET AL.

Table 3. Population genetic summary metrics estimated from mtDNA (cytb + ND2, total 1828 bp) across stable and unstable areas, population clusters, and the total sample. N = sample size; θ = diversity parameter (per sequence); θsites = diversity parameter (per site);

Pi = nucleotide diversity (per site); k = average number of nucleotide differences; Mean Da = average net nucleotide differences across localities with more than two individuals sampled; Hs = Fay and Wu’s Hs test of population expansion. P-values in bold correspond to significant values at the 0.05 probability level.

Mantel’s θsites (95% correlation confidence Mean Da Hs coefficient Region N θ interval) Pi k (min.; max.) (P-value) (P-value)

Population cluster Central (CE) 131 185.23 0.120 (0.046–0.165) 0.15680 200.6 0.174 (0.010; 0.226) 0.029(0.330) 0.410 (0.002) Northeast (NE) 151 96.29 0.194 (0.083–0.364) 0.03736 64.478 0.037 (0.00006; 0.06132) −0.065(0.333) 0.303 (0.003) Southwest (SW) 105 91.38 0.058 (0.025–0.078) 0.07096 121.9 0.076 (0.001; 0.118) −0.082(0.332) 0.065 (0.439) Stability surface Stable 217 151.54 0.136 (0.114–0.165) 0.1405 202.3 0.118 (0.005; 0.216) 0.229(0.329) 0.507 (<0.0001) Unstable 170 169.89 0.084 (0.067–0.104) 0.1565 233.8 0.165 (0.001; 0.225) 1.520(0.323) 0.616 (<0.0001) Total 387 194.08 0.123 (0.108–0.142) 0.155 219.1 0.147 (0.001; 0.224) 0.048(0.331) 0.575 (<0.0001)

Table 4. Posterior probabilities of comparisons between no-speciation and speciation models, as analyzed with approximate Bayesian computation. Comparisons were done among all models simultaneously, between nonspeciation versus speciation models (two at a time), and between the speciation models only. Values represent comparisons including isolation models/migration models (for models B and C only). Preferred model at each comparison is marked in bold for emphasis, and i/m = isolation/migration.

Models comparisons

No-speciation versus speciation models, pairwise All models Speciation models only Model and parameters A versus B versus C i/m A versus B i/m A versus C i/m B versus C i/m

Model A; no-speciation 0.089/0.169∗ 0.158/0.159 0.08/0.169 NA Model B; early speciation 0.098/0.168∗ 0.842/0.841 NA 0.007/0.286 Model C; speciation in two 0.813/0.663∗ NA 0.84/0.831 0.993/0.714 clades (SW [NE+CE])

∗No resolution at tolerance rate = 0.001; results shown at an increased tolerance rate (tol = 0.1).

Discussion In this article, we present a densely sampled case study for The degree to which phylogeographic approaches can resolve a broadly distributed Neotropical habitat-specialist species com- broader biogeographic processes is directly dependent on the cov- plex. Our findings revealed a very deep mtDNA structure typical erage of geographical sampling, the biology of the study taxa, and of low-vagility species, unprecedented levels of cryptic genetic the nature of markers used. Single-locus (mtDNA) phylogeog- diversity, and diversification dating back to at least the Neogene raphy has been profusely criticized (Brito and Edwards 2009), with persistence across Quaternary fluctuations. Ultimately, we because it ignores coalescent stochasticity and tends to provide tested alternative divergence scenarios, which proved useful to biased and overly simplistic inferences (Knowles and Maddison infer major biogeographic patterns across the ‘dry diagonal’. 2002). Regarding sampling design, robust inferences require a trade-off between number of loci and individuals, but generally DNA POLYMORPHISM, GENETIC DISTANCES, an increased number of individuals within species/populations AND POPULATION STRUCTURE improve species tree accuracy (Maddison and Knowles 2006). Patterns of DNA polymorphism, gene and species trees are clearly However, most phylogeographic studies for Neotropical taxa are consistent with the occurrence of a complex of cryptic species still based mainly on mtDNA and limited population and indi- within P. pollicaris, as hypothesized by Gamble et al. (2011, vidual sampling, so their broader biogeographic implications are 2012). The large mtDNA genetic distances among clades found limited. here exceed typical values ranging from 5% to 11% reported

12 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

Figure 7. Alternative models for the phylogeographic history of P. pollicaris tested with multilocus ABC. Each model consisted of branching order and divergence times, which were converted from absolute times into coalescent times (units of 4No generations). Models B and C were also tested including migration rates as an additional parameter. Although the early divergence event in both speciation models (B and C) is referred to as T1 (because they are attributed to the same event) and the more recent divergence time at model C as T2, the coalescent simulations are preceded backwards on time (i.e., for model C, T2 is simulated before T1). See text for details. among gecko sister species (Rocha et al. 2009; Fujita et al. 2010), (Werneck 2011), if these changes result in reduced gene flow and and for gecko complexes with likewise high hidden genetic diver- isolation between lineages (Rundell and Price 2009). Geomor- sity and low haplotype sharing between localities (Geurgas and phological complexity and heterogeneity are then likely to have Rodrigues 2010). shaped the genetic structure among CE populations. The P. pollicaris species complex has a strong population structure in which the three major clusters at the deepest levels are concordant with the geographic limits of the Chaco, Cerrado, MAJOR RELATIONSHIPS AND DIVERGENCE TIMES and Caatinga biomes; and also approximately congruent in the Analyses of mtDNA and nuclear data showed overall congruence gene and species trees and geophylogeny, implying that these in the major relationships, differing mostly in their branch support ‘dry diagonal’ biomes do not share a single diversification his- at deeper levels (higher for most nuclear loci). We found higher tory. Although the SW cluster is more isolated from the others, support for the nuclear concatenated analysis relative to species the CE and NE are intermingled over several regions of overlap, trees, which have been suggested to be an artifact of uninformative as revealed by admixture assignments and lower diversity in the loci for short branches, and still comprise a challenge for species- SW Chaco cluster. The CE cluster is characterized by higher pop- tree methods for higher level phylogenies (Townsend et al. 2011). ulation subdivision, genetic diversity, and nucleotide differences As uninformative nuclear loci are more frequent at the lower across localities than the other two clusters. This cluster is mostly levels, we expected such an artifact to have stronger influences on confined to the Cerrado, which has a landscape compartmental- species-trees estimated from phylogeographic datasets. Indeed, ized between ancient plateaus and young valleys (Cole 1986). This McCormack et al. (2011) also reported slightly lower topological heterogeneity at local and regional scales potentially enhances en- support for the species tree in Aphelocoma jays, although their vironmental opportunity for ecological divergence and speciation study included only three nuclear markers.

EVOLUTION 2012 13 F. P. WERNECK ET AL.

Instances of topological discordance are evident at internal covered as highly distinct and early differentiated populations in nodes of more recent lineages, and at the admixture zones be- all analyses. Mucugeˆ together with other populations (Diamantina tween CE and NE phylogeographic groups. For example, rela- [no. 55/D] and Serro [no. 56/D]) are located in the high altitude tionships within the NE group are poorly resolved. This pat- “campos rupestres” in the Serra do Espinhac¸o mountain range tern can be attributed to the reduced topographic complexity (Fig. 6, letter c), a region well known for its high levels of en- of the Caatinga that is disrupted by few prominent landforms, demism, including squamates (Cassimiro and Rodrigues 2009; such as elevated plateaus (e.g., Chapadas do Araripe, Apodi, and Nogueira et al. 2011). Establishment of the Espinhac¸o range was Borborema, Serra do Espinhac¸o), and relictual rainforest enclaves a regional event that potentially influenced P. pollicaris diver- (Sampaio 1995). Fewer geological barriers in the Caatinga likely gence (see below). enhanced opportunity for gene flow, yielding less-resolved diver- Divergence within the P. pollicaris complex proceeded in a sification histories. nearly continuous fashion since about 11.5 Ma, but with crown A few samples are characterized by discordant placement clades originating in the Mid–Late Miocene. Phyllopezus pol- among datasets or methods. For example, the clade D (Diamantina licaris przewalskii from Chaco is strongly supported by gene no. 55 + Serro no. 56) occupying a basal position in the mtDNA and species tree, and diverged approximately 8.5 Ma. Pleistocene (Fig. 2) and the species trees (Fig. 4), but a more nested position in diversification is recovered among some geographically close the nuclear concatenated tree (Fig. 3). Similarly, Matias Cardoso Caatinga populations; a time correlating with geological evidence no. 50 + Augusto de Lima no. 54 and Grao˜ Mogol no. 51 (clades for the exposition of formerly submerged granite surfaces (laje- V and W) are basal in the species tree but deeply nested in both dos) where the species occur. These times suggest a major in- concatenated trees, and these samples show admixed structures fluence of divergence/geographical barriers during the Miocene, (Fig. 6). These cases show the confounding effect of contact zones followed by a nearly continuous diversification, with no clear between adjacent groups on species tree inference, and the impor- bursts of Quaternary diversifications (Rull 2011b). Long-term tance of multilocus coalescence methods for estimating species diversification with combined Neogene and Pleistocene causal trees in instances of discordance (Leache´ 2009). In these cases, mechanisms is expected to be apparent at higher taxonomic lev- we favor the species tree hypothesis that places some of the north- els (Rull 2011a), whereas the prolonged complex history of ‘dry ern Minas Gerais populations closer to the base of the tree (V, W, diagonal’ biomes is here evident within a single complex. and D), and remaining samples (Januaria´ [Q, no. 49] and Manga Likewise, ancient divergence dates have been reported in [R, no. 48]) as sister to the large clade of Caatinga populations other gecko complexes (Pellegrino et al. 2005; Geurgas and (Fig. 4). Rodrigues 2010; Oliver et al. 2010), and these are commonly The most interesting outcome of this hypothesis comes from older than codistributed taxonomic groups. Patterns of New World the disjunct placement of Manga (clade R, no. 48) and Matias gecko diversification exemplify most of the scenarios described Cardoso (clade V, no. 50) localities that are less than 15 km apart, for terrestrial vertebrates, including ancient vicariance, trans- but are separated by the Sao˜ Francisco River (Fig. 6, letter a, SFR). Atlantic rafting and temporary land bridge dispersal, and hu- The SFR is recognized as a barrier that has driven speciation at man introductions (Gamble et al. 2011), and reflect their ancient high taxonomic levels, with several endemic genera and species Gondwanan origin (Kluge 1987). Positive correlations between pairs isolated on opposite sides (Rodrigues 1996). More recently, clade age and species diversity described for geckos (Gamble et the SFR was suggested as an important phylogeographic barrier al. 2011) can account for some of the patterns we report for P. to Atlantic Forest taxa distributed closer to the delta (Pellegrino pollicaris complex (ancient divergence times and high diversity). et al. 2005; Carnaval and Moritz 2008). Recent divergence esti- mates in the lizard genera Eurolophosaurus (Passoni et al. 2008) MODEL-BASED POPULATION DIVERGENCE: and Calyptommatus (Siedchlag et al. 2010) suggest that the SFR BIOGEOGRAPHIC IMPLICATIONS probably formed a vicariant barrier during the Late Miocene, con- We are aware that our models represent simplifications (e.g., cordant with P. pollicaris divergence times. The SFR also likely no substructure within major clusters, use of combined splitting prevented secondary contact between previously diverged P. pol- times, and use of a nearly monophyletic group [CE]), but they do licaris lineages, reinforcing its importance as a singular center of reflect phylogeographic patterns within P.pollicaris that correlate diversification. Our results also suggest a role for the Tocantins with major biogeographic events across the ‘dry diagonal’. ABC River as barrier between CE and NE clusters (Fig. 6, letter b). favored the speciation scenarios over the no-speciation model, Denser sampling and investigation of other taxa distributed in the and we found stronger support for model C, representing an early region are, however, needed to further explore this hypothesis. diversification (∼7–5 Ma) of the southwest (SW) from the (CE + Sao˜ Geraldo do Araguaia (no. 23/A), Barra do Garc¸as NE) group, followed by a more recent divergence between these (no. 57/B), and Mucugeˆ (no. 36/C) on the other hand were re- last two (∼5–3 Ma). Comparisons of model C under isolation

14 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

and migration suggest that divergence may have occurred with cal barriers to prevent gene flow (Nosil 2008). The alternative of some gene flow. Due to the high correspondence between groups sympatric speciation is supported by an increasing body of evi- tested and the ‘dry diagonal’ biomes, we can infer geomorpholog- dence in marine vertebrates (Foote et al. 2009; Crow et al. 2010), ical events potentially responsible for an early diversification of where obvious barriers are less evident, but is also documented Chaco lineages followed by divergence of Cerrado and Caatinga for terrestrial vertebrates (Niemiller et al. 2008; VanderWerf et al. lineages, and their biogeographic implications. 2010), and must also be considered as a possibility here. Key Neogene events often emphasized as causal mechanisms Although the exact mechanisms are not clear, events at for Neotropical diversification (e.g., closure of the Panama Isth- the Tertiary–Quaternary boundary that significantly remodeled mus, Andean uplift, change of drainage patterns with the cessa- eastern Brazil’s landscapes may have confounded the genetic tion of marine incursions into the Amazon basin; Hoorn et al. signatures of the earlier isolating events, including vegetation 2010; Rull 2011a) almost certainly impacted indirectly the ‘dry shifts (Werneck et al. 2011, in press), and neotectonic activity diagonal’ biota diversification, which is largely confined to the (Riccomini and Assumpc¸ao˜ 1999). We suggest that the less Brazilian Shield (Werneck 2011). However, other correlated, but dramatic differentiation between Cerrado and Caatinga lineages less-understood, events were more relevant for this region and for could be due to a combination of more recent divergence and gene the diversification of P. pollicaris complex. From the hypothe- flow, and represent a possible example of ecological or sympatric sized ancestral area in central Brazil, some ancestral population speciation (Vanzolini 1976). dispersed across the ‘dry diagonal’ before being subject of the fol- lowing suggested biogeographic events that generated the three PAST DISTRIBUTIONS AND TESTS OF QUATERNARY major genetic lineages. PHYLOGEOGRAPHIC PREDICTIONS Marine transgressions during the Mid–Late Miocene (∼10– Our results suggest a limited influence for Quaternary climatic 5 Ma), supported by sedimentology and marine fossil records, fluctuations on the phylogeographic history and SDMs of P.polli- covered large portions of the Chaco-Parana´ Basin (CPB), and caris. Patterns of genetic variation and structure reflect the impact formed the second flooding pulse of the Paranaense Sea, iso- of earlier influences and a Miocene origin of the crown clades lating large areas of the Chaco depression and associated biota followed by persistence through the Pleistocene without major (Hernandez´ et al. 2005; Ruskin et al. 2011). Isolation of southwest demographic changes. Our modeling suggests a slight range re- populations of P. pollicaris initiated by these marine transgres- duction of P. pollicaris during the LGM with a break in central sions was most likely enhanced by the Chaco subsidence due to Brazil. This “two LGM refugia” scenario reflects very distinct an intense uplift phase of the Andes and the final uplift of the SDMs between the SW and the [CE + NE] clusters, and these Central Brazilian Shield during the Late Miocene–Early Pliocene ecological niche differences may have promoted additional diver- transition, 7–5 Mya (Gubbles et al. 1993; Uba et al. 2006; Mulch sification between these two groups. et al. 2010). We hypothesize that these events, in concert, drove Although Pleistocene distributional shifts shaped strong ge- allopatric speciation of the P. p. przewalskii clade (SW). The netic signatures in many groups (Carnaval et al. 2009; Knowles resulting altitudinal break acted as a geographical barrier and and Alvarado-Serrano 2010; Lessa et al. 2010), there are counter- prevented secondary contact, with the lowland populations iso- examples in which phylogeographic signatures reflect events lated on the SW side (CPB and Paraguay River basin) and the that have not been overwritten by Pleistocene climate dynamics higher elevation Brazilian Plateaus populations to the CE–NE side (Leavitt et al. 2007; Bell et al. 2010; Thome´ et al. 2010; Hoskin et (Fig. 6). Strong isolation coupled with low densities of P. polli- al. 2011). For P. pollicaris specifically, the influence of climatic caris preferred habitat (rock outcrops) in the Chaco, may have fluctuations seems minor when compared to geographical and limited population sizes, and thus explain the reduced molecular topographical influences that remained largely unchanged since variation in the SW clade. the Miocene, however, they can not be neglected, and likely played We also hypothesize that subsequent regional tectonic events a relevant role to promote further differentiation between and responsible for the final establishment of the Brazilian Plateau and within previously diverged lineages. enclosed regional mountain ranges (e.g., Serras do Espinhac¸o, do Mar, da Mantiqueira, 2–4 Mya) coupled with ecological events TAXONOMIC IMPLICATIONS AND PROVISIONAL such as expansion of grasslands and increased fire frequency SPECIES LIMITS (Beerling and Osborne 2006; Simon et al. 2009) would promote Phyllopezus pollicaris should clearly be treated as polytypic, and differentiation between Cerrado and Caatinga clades. However, because the consistently recovered clades are deeply divergent because CE and NE lineages are not reciprocally monophyletic and strongly supported in all analyses, they are almost certainly and show some admixture, they may have diverged through eco- valid species. Here, we comment briefly on species limits and logical speciation along environmental gradients, without physi- taxonomic implications within P. pollicaris complex.

EVOLUTION 2012 15 F. P. WERNECK ET AL.

The P. p. przewalskii clade is strongly supported across all serves have also been established in the transition zones between analyses, and it has a distinct structure and a singular SDM. CE and NE clusters (no. 23 and 26), but additional reserves in this Although morphological uniformity generally characterizes the region (e.g., no. 36, 37, 38, 51, 54, 55, 56,) and between the SW P. pollicaris complex, P. p. przewalskii is also morphologically populations and the other lineages (e.g. no. 57, 58, 61) should be diagnosable by the lack of postcloacal tubercles and slightly considered in regional conservation planning. lower ventral scales count (Vanzolini 1953), and in its karyotype Persistence through Quaternary fluctuations and high diver- (Pellegrino et al. 1997). Under a general lineage species concept sity within the P.pollicaris complex suggests that it has the poten- (de Queiroz 2007) and our empirical evidence for both ecolog- tial to tolerate adverse scenarios and to adapt to new conditions ical and genetic nonexchangeability (Crandall et al. 2000), P. (Davis et al. 2005), as long as rock outcrops persist. These patterns p. przewalskii should be elevated to species status. The nested provide important insights about responses to future environmen- placement of P. p. przewalskii within P. pollicaris renders the tal changes and long-term population viability, a critical vari- other subspecies, P. p. pollicaris, paraphyletic. We hypothesize able for establishing efficient conservation strategies (Diniz-Filho that the following clades (Roman numerals in Fig. 4 and Table et al. 2008). However, other taxa associated with ‘dry diagonal’ S1) should be treated as ‘candidate species’: Clades I (no. 38) features more susceptible to oscillations may be more susceptible and II (no. 7 and no. 54), which are the sister groups to all other to extinction due to climate change. Allocation of conservation P. pollicaris, should be treated as two candidate species, along resources will then be more effective if comparative studies can with these six remaining clades: Clade III (6 localities); Clade IV provide evolutionary histories of a diverse array of codistributed (3 localities); Clade V (P. przewaslkii, 11 localities); Clade VI ‘dry diagonal’ endemics. (6 localities); Clade VII (7 localities), and the Clade VIII (the 32 remaining Caatinga localities). See Figure S4 for the geo- graphic distribution of the eight ‘candidate species’. Aside from P. przewaslkii, we do not suggest formal names for any of these lin- Conclusions eages, but as ‘candidate species’ (Morando et al. 2003) they can This study highlights how phylogeographic patterns can be stud- guide biogeography-based conservation planning because they ied in a spatio-temporal framework to investigate broader his- register broad evolutionary patterns without the necessity of for- torical biogeographic processes through the coupling of SDMs, mal names (Bickford et al. 2007; Riddle et al. 2011). multilocus genetic data, and model-based approaches (Knowles 2009; Knowles and Alvarado-Serrano 2010). For species com- IMPLICATIONS FOR ‘DRY DIAGONAL’ plexes such as P. pollicaris, whose current and historical distribu- CONSERVATION tions overlap closely those of the relevant biomes, major phylo- Recent studies show that current knowledge of amphibian and geographic breaks may reflect influential historical biogeographic reptile species diversity in the Neotropics has been underestimated processes. These characteristics make such complexes important (Fouquet et al. 2007; Geurgas and Rodrigues 2010; Gamble et al. evolutionary radiations to study the deep history of associated 2011; Funk et al. 2012). The cryptic genetic diversity disclosed biomes. This approach is particularly relevant for poorly studied here reveals that the South American ‘dry diagonal’ biomes are regions, such as the South American ‘dry diagonal’ biomes where no exception. With the management task of preserving adaptive threats are escalating. diversity, evolutionary processes, and natural genetic connections In this perspective, the deeply divergent lineages of P. polli- across the range of species complexes (Crandall et al. 2000; Davis caris, which are geographically structured along the southwest– et al. 2008), we can propose some conservation implications of northeast ‘dry diagonal’, reflect complex diversification scenar- our results. ios and the primary influence of geologically old processes Our assessment of the P. pollicaris complex shows that pro- on the dry biomes diversification. Long-term isolation is not tection of the eight candidate species is necessary to preserve the essential to generate significant genetic differentiation (Knowles evolutionary processes that generated them, especially if these ge- and Alvarado-Serrano 2010), but this is the case for the P. polli- ographic regions have fostered codivergence in other taxa (Davis caris complex, whose distributions are characterized by substan- et al. 2008). Protection of long-isolated populations should be tial stability and genetic signatures of deep historical events that augmented by protection of admixture zones to maximize adap- were not erased by Pleistocene climate shifts. Recent environmen- tive diversity and evolutionary potential (Crandall et al. 2000; tal instability thus does not appear to be the primary parameter Smith et al. 2001; Moritz et al. 2009). This can be accomplished influencing the genetic outcomes of the diversification process in by placing reserves at the core of the three major clusters, and this taxon. several are established and were included in our sampling (e.g., Each biome has unique population clusters and lineages; no. 6, 10, 16, 19, 40, 42, 48, 49, 50, 64; Fig. 1). Fortunately, re- and our model-based approach yielded stronger support for the

16 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

diversification models, contradicting early views that the ‘dry LITERATURE CITED diagonal’ biomes would have a shared diversification history. Amorim, F. W., R. S. Avila,´ Jr., A. J. A. de Camargo, A. L. Vieira, and P. E. More precisely, the central portion of South America appears to Oliveira. 2009. A hawkmoth crossroads? Species richness, seasonality and biogeographical affinities of Sphingidae in a Brazilian Cerrado. J. have served as important diversification center over the last several Biogeogr. 36:662–674. million years, as a response to geomorphological rearrangements Ayres, D. L., A. Darling, D. J. Zwickl, P. Beerli, M. T. Holder, P. O. Lewis, J. P. of the landscape. These results have important implications Huelsenbeck, F. Ronquist, D. L. Swofford, M. P. Cummings, et al. 2012. for identification of areas to maximize the protection of rich BEAGLE: an application programming interface and high-performance computing library for statistical phylogenetics. Syst. Biol. 61:170–173. lineages unique to the region, which evolved in concert with the Beaumont, M. A. 2010. Approximate Bayesian computation in evolution and diversification of central-eastern South American ‘dry diagonal’ ecology. Annu. Rev. Ecol. Evol. Syst. 41:379–405. biomes. Beaumont, M. A., R. Nielsen, C. Robert, J. Hey, O. Gaggiotti, L. L. Knowles, A. Estoup, M. Panchal, J. Corander, M. J. Hickerson, et al. 2010. In defense of model-based inference in phylogeography. Mol. Ecol. 19: ACKNOWLEDGMENTS 436–446. FPW was supported by fellowships from CAPES/Fulbright (no. Beerli, P., and M. Palczewski. 2010. Unified framework to evaluate panmixia 15073722–2697/06–8) and by a graduate research assistantship from the and migration direction among multiple sampling locations. Genetics BYU Department of Biology. This work was funded by the National Geo- 185:313–326. graphic Society (NGS, no. 8642–09), National Science Foundation DDIG Beerling, D. J., and C. P. Osborne. 2006. The origin of the savanna biome. award (DEB-1210346), Society of Systematic Biologists (SSB), Neotrop- Glob. Change Biol. 12:2023–2031. ical Grassland Conservancy (NGC), Society for the Study of Amphibians Beheregaray, L. B. 2008. Twenty years of phylogeography: the state of the field and Reptiles (SSAR), Idea Wild, Conselho Nacional de Desenvolvimento and the challenges for the Southern Hemisphere. Mol. Ecol. 17:3754– Cient´ıfico e Tecnologico´ (CNPq), Fundac¸ao˜ de Amparo a` Pesquisa do 3774. Estado de Sao˜ Paulo (FAPESP), and Fundac¸ao˜ de Apoio a` Pesquisa do Bell, R. C., J. L. Parra, M. Tonione, C. J. Hoskin, J. B. Mackenzie, S. E. Distrito Federal (FAPDF). The work in the Sites lab was also supported Williams, and C. Moritz. 2010. Patterns of persistence and isolation by a BYU Mentoring Environment Grant (2011), and National Science indicate resilience to climate change in montane rainforest lizards. Mol. Foundation (NSF) awards EF 0334966 (“Deep Scaly” project) and OISE Ecol. 19:2531–2544. 0530267 (“PIRE – Patagonia” project); this last project to the follow- Bertorelle, G., A. Benazzo, and S. Mona. 2010. ABC as a flexible framework ing institutions (listed alphabetically): Brigham Young University, Cen- to estimate demography over space and time: some cons, many pros. tro Nacional Patagonico´ (AR), Dalhousie University, Instituto Botanico´ Mol. Ecol. 19:2609–2625. Darwinion (AR), Universidad Austral de Chile, Universidad de Con- Bickford, D., D. J. Lohman, N. S. Sodhi, P. K. L. Ng, R. Meier, K. Winker, K. cepcion,´ Universidad Nacional del Comahue, Universidad Nacional de K. Ingram, and I. Das. 2007. Cryptic species as a window on diversity Cordoba,´ and University of Nebraska. FPW thanks the Instituto Chico and conservation. Tree 22:148–155. Mendes de Conservac¸ao˜ da Biodiversidade (ICMBio; no. 16381–2) and Bloomquist, E. W., P. Lemey, and M. A. Suchard. 2010. Three roads diverged? Instituto Estadual de Florestas (IEF-MG; no. 066–070/09) for issuing Routes to phylogeographic inference. Tree 25:626–632. collecting permits. We thank the following researchers: D. Loebman, I. J. Bonnett, E., and V. de Peer. 2002. Zt: a software tool for simple and partial Roberto, F. S. Rodrigues, M. Persio,´ V. H. G. L. Cavalcante, P. Peloso, A. Mantel tests. J. Stat. Softw. 7:1–12. Prudente, D. M. Borges-Nojosa, J. Cassimiro, M. Teixeira, Jr., R. Recoder, Breitman, M. F., L. J. Avila, J. W. Sites, Jr., and M. Morando. 2011. Lizards F.F.Curcio,D.Pavan,J.Roscito,R.M.L.Santos,R.Villela,P.H.Valdujo, from the end of the world: Phylogenetic relationships of the Liolaemus F. L. Souza, M. Uetanabaro, C. Candido,ˆ D. O. Mesquita, F. R. Delfim, R. lineomaculatus section (Squamata: Iguania: Liolaemini). Mol. Phylo- G. Faria, C. B. Carvalho, R. A. Santos, H. Zaher, F. Graziotin, C. C. Mello genet. Evol. 59:364–376. D’Horta, V. L. Ferreira, C. Strussmann,¨ and curators of the following in- Brito, P. H., and S. V. Edwards. 2009. Multilocus phylogeography and phylo- stitutions for providing tissue samples for this study: CHUNB, MZUSP, genetics using sequence-based markers. Genetica 135:439–455. Colec¸ao˜ Herpetologica´ da Universidade Federal do Ceara´ (CHUFC), Cabanne, G. S., F. M. D’Horta, D. Meyer, J. M. C. Silva, and C. Y. Miyaki. Laboratorio´ de Zoologia da Universidade Federal do Piau´ı (LZUFPI), 2011. Evolution of Dendrocolaptes platyrostris (Aves: Furnariidae) be- Museu Paraense Em´ılio Goeldi (MPEG), Departamento de Zoologia da tween the South American open vegetation corridor and the Atlantic Universidade Federal do Mato Grosso (UFMT), Colec¸ao˜ Herpetologica´ forest. Bio. J. Linn. Soc. 103:801–820. da Universidade Federal do Mato Grosso do Sul (UFMS/CH), Museo Cadena, C. D., N. Gutierrez-Pinto,´ N. Davila,´ and R. T. Chesser. 2011. No Nacional de Historia Natural del Paraguay (MNHNP), Zoologisches population genetic structure in a widespread aquatic songbird from the Forschungsinstitut und Museum Alexander Koenig (ZFMK), Museum Neotropics. Mol. Phylogenet. Evol. 58:540–545. fur¨ Tierkunde Dresden (MTD), Museo Nacional de Ciencias Naturales Caetano, S., D. Prado, R. T. Pennington, S. Beck, A. T. Oliveira-Filho, R. (MNCN), Herpetological collection of L. J. Avila and M. Morando Spichiger, and Y. Naciri. 2008. The history of Seasonally Dry Tropical (LJAMM) of the Centro Nacional Patagonico´ (CENPAT–CONICET). Forests in eastern South America: inferences from the genetic structure FPWthanksA.H.S.B.Soares,R.Leite,D.C.dePaula,M.M.Esp´ırito- of the tree Astronium urundeuva (Anacardiaceae). Mol. Ecol. 17:3147– Santo, M. M. Caixeta, R. Recoder, S. F. Balbino, A. F. Beda,´ V. L. 3159. Ferreira, N. Rufino, F. C. Villela, L. J. Avila, N. Feltrin, N. Frutos, Camargo, A., M. Morando, L.J. Avila, and J.W. Sites, Jr. 2012. Species de- M. Motte, N. J. Scott, P. Cacciali, G. H. C. Vieira, D. O. Mesquita, limitation with ABC and other coalescent-based methods in lizards of S. Vieira, S. C. Ribeiro for their invaluable assistance in the field ex- the Liolaemus darwinii complex (Squamata: Liolaemidae). Evolution. peditions. This manuscript benefitted from discussions of the analyses doi:10.111/j.1558-5646.2012.01640.x. with A. Camargo, R. N. Leite, P. Pavlidis, J. E. McCormack, J. Brein- Carnaval, A. C., and C. Moritz. 2008. Historical climate modelling pre- holt, and G. C. Costa; and from helpful comments from two anonymous dicts patterns of current biodiversity in the Brazilian Atlantic forest. J. reviewers. Biogeogr. 35:1187–1201.

EVOLUTION 2012 17 F. P. WERNECK ET AL.

Carnaval, A. C., M. J. Hickerson, C. F. B. Haddad, M. T. Rodrigues, and C. Funk, W. C., M. Caminer, and S. R. Ron. 2012. High levels of cryptic species Moritz. 2009. Stability predicts genetic diversity in the Brazilian Atlantic diversity uncovered in Amazonian frogs. Proc. R. Soc. Lond. B Biol. Forest hotspot. Science 323:785–789. 279:1806–1814. Carstens, B. C., and L. L. Knowles. 2010. Navigating the unknown: model Gamble, T., A. M. Bauer, G. R. Colli, E. Greenbaum, T. R. Jackman, L. J. selection in phylogeography. Mol. Ecol. 19:4581–4582. Vitt, and A. M. Simons. 2011. Coming to America: multiple origins of Cassimiro, J., and M. T. Rodrigues. 2009. A new species of lizard genus New World geckos. J. Evol. Biol. 24:231–244. Gymnodactylus Spix, 1825 (Squamata: Gekkota: Phyllodactylidae) from Gamble, T., G. R. Colli, M. T. Rodrigues, F. P. Werneck, and A. M. Serra do Sincora,´ northeastern Brazil, and the status of G. carvalhoi Simons. 2012. Phylogeny and cryptic diversity in geckos (Phyllopezus; Vanzolini, 2005. Zootaxa 2008:38–52. Phyllodactylidae; Gekkota) from South America’s open biomes. Mol. Chan, L. M., J. L. Brown, and A. D. Yoder. 2011. Integrating statistical Phylogenet. Evol. 62:943–953. genetic and geospatial methods brings new power to phylogeography. Garrick, R. C., A. Caccone, and P. Sunnucks. 2010. Inference of popula- Mol. Phylogenet. Evol. 59:523–537. tion history by coupling exploratory and model-driven phylogeographic Chaves, J. A., and T. B. Smith. 2011. Evolutionary patterns of diversification analyses. Int. J. Mol. Sci. 11:1190–1227. in the Andean hummingbird genus Adelomyia. Mol. Phylogenet. Evol. Geurgas, S. R., and M. T. Rodrigues. 2010. The hidden diversity of Coleo- 60:207–218. dactylus amazonicus (Sphaerodactylinae, Gekkota) revealed by molec- Cole, M. 1986. The Savannas: biogeography and geobotany. Academic Press, ular data. Mol. Phylogenet. Evol. 54:583–593. London. Giugliano, L. G., R. G. Collevatti, and G. R. Colli. 2007. Molecular dating Crandall, K. A., O. R. P. Bininda-Emonds, G. M. Mace, and R. K. Wayne. and phylogenetic relationships among Teiidae (Squamata) inferred by 2000. Considering evolutionary processes in conservation biology. Tree molecular and morphological data. Mol. Phylogenet. Evol. 45:168–179. 15:290–295. Gubbles, T. L., B. L. Isacks, and E. Farrar. 1993. High-level surfaces, plateau Crow, K. D., H. Munehara, and G. Bernardi. 2010. Sympatric speciation in a uplift, and foreland development, Bolivian central Andes. Geology genus of marine reef fishes. Mol. Ecol. 19:2089–2105. 21:695–698. Csillery,´ K., M. G. B. Blum, O. E. Gaggiotti, and O. Franc¸ois. 2010. Approx- Heled, J., and A. J. Drummond. 2010. Bayesian inference of species trees imate Bayesian computation (ABC) in practice. Tree 25:410–418. from multilocus data. Mol. Biol. Evol. 27:570–580. Davis, E. B., M. S. Koo, C. Conroy, J. L. Patton, and C. Moritz. 2008. The Hernandez,´ R. M., T. E. Jordan, A. D. Farjat, L. Echavarr´ıa, B. D. Idleman, and California Hotspots Project: identifying regions of rapid diversification J. H. Reynolds. 2005. Age, distribution, tectonics, and eustatic controls of of mammals. Mol. Ecol. 17:120–138. the Paranense and Caribbean marine transgressions in southern Bolivia Davis, M. B., R. G. Shaw, and J. R. Etterson. 2005. Evolutionary responses and Argentina. J. South Am. Earth Sci. 19:495–512. to changing climate. Ecology 86:1704–1714. Hewitt, G. M. 2004. Genetic consequences of climatic oscillations in the de Queiroz, K. 2007. Species concepts and species delimitation. Syst. Biol. Quaternary. Philos. Trans. R. Soc. Lond. B 359:183–195. 56:879–886. Hickerson, M. J., B. C. Carstens, J. Cavender-Bares, K. A. Crandall, C. H. Deutsches, K. M. 1992. The ECHAM3 atmospheric general circulation model. Graham, J. B. Johnson, L. Rissler, P. F. Victoriano, and A. D. Yoder. Pp. 1–184. DKRZ Techinical Report No. 6. Deutsches Klimarechenzen- 2011. Phylogeography’s past, present, and future: 10 years after Avise, trum Modellbetreuungsgruppe Hamburg, Germany. 2000. Mol. Phylogenet. Evol. 54:291–301. Diniz-Filho, J. A. F., M. P. C. Telles, S. L. Bonatto, E. Eizirik, T. R. O. Hijmans, R. J., S. E. Cameron, J. L. Parra, P. G. Jones, and A. Jarvis. 2005. Freitas, P. Marco, Jr., F. R. Santos, A. Sole-Cava, and T. N. Soares. 2008. Very high resolution interpolated climate surfaces for global land areas. Mapping the evolutionary twilight zone: molecular markers, populations Int. J. Climatol. 25:1965–1978. and geography. J. Biogeogr. 35:753–763. Hoorn, C., F. P. Wesselingh, H. ter Steege, M. A. Bermudez, A. Mora, Drummond, A. J., and A. Rambaut. 2007. BEAST: Bayesian evolutionary J. Sevink, I. Sanmart´ın, A. Sanchez-Meseguer, C. L. Anderson, J. P. analysis by sampling trees. BMC Evol. Biol. It is a on-line access jour- Figueiredo, et al. 2010. Amazonia through time: Andean uplift, climate nal, and this is the way they cite it (including other Evolution papers). change, landscape evolution, and biodiversity. Science 330:927–931. Alternatively, could be 7: 1–8. Hoskin, C. J., M. Tonione, M. Higgie, J. B. Mackenzie, S. E. Williams, Edwards, E. J., C. P. Osborne, C. A. E. Stromberg, S. A. Smith, W. J. Bond, J. VanderWal, and C. Moritz. 2011. Persistence in peripheral refugia P. A. Christin, A. B. Cousins, M. R. Duvall, D. L. Fox, R. P. Freckleton, promotes phenotypic divergence and speciation in a rainforest frog. et al. 2010. The origins of C-4 grasslands: integrating evolutionary and Am. Nat. 178:561–578. ecosystem science. Science 328:587–591. Jakobsson, M., and N. A. Rosenberg. 2007. CLUMPP: a cluster matching and Evanno, G., S. Regnaut, and J. Goudet. 2005. Detecting the number of clusters permutation program for dealing with label switching and multimodality of individuals using the software STRUCTURE: a simulation study. Mol. in analysis of population structure. Bioinformatics 23:1801–1806. Ecol. 14:2611–2620. Kalinowski, S. T. 2011. The computer program STRUCTURE does not reli- Felsenstein, J. 1985. Confidence-limits on phylogenies—an approach using ably identify the main genetic clusters within species: simulations and the bootstrap. Evolution 39:783–791. implications for human population structure. Heredity 106:625–632. Foote, A. D., J. Newton, S. B. Piertney, E. Willerslev, and M. T. Gilbert. 2009. Kidd, D. M., and X. Liu. 2008. GEOPHYLOBUILDER 1.0: an ARCGIS Ecological, morphological and genetic divergence of sympatric North extension for creating ‘geophylogenies’. Mol. Ecol. Res. 8:88–91. Atlantic killer whale populations. Mol. Ecol. 18:5207–5217. Kluge, A. G. 1987. Cladistic relationships in the Gekkonoidea (Squamata, Fouquet, A., A. Gilles, M. Vences, C. Marty, M. Blanc, and N. J. Gemmell. Sauria). Miscellaneous Publications, Museum of Zoology, University of 2007. Underestimation of species richness in Neotropical frogs revealed Michigan 173:1–54. by mtDNA analyses. PLoS One 10:1–10. Knowles, L. L. 2009. Statistical phylogeography. Annu. Rev. Ecol. Evol. Syst. Fujita, M. K., J. A. McGuire, S. C. Donnellan, and C. Moritz. 2010. Diversi- 40:593–612. fication and persistence at the arid-monsoonal interface: Australia-wide Knowles, L. L., and D. F. Alvarado-Serrano. 2010. Exploring the population biogeography of the Bynoe’s gecko (Heteronotia bionoei: Gekkonidae). genetic consequences of the colonization process with spatio-temporally Evolution 64:2293–2314. explicit models: insights from coupled ecological, demographic and

18 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

genetic models in montane grasshoppers. Mol. Ecol. 19:3727– Nogueira, C., S. Ribeiro, G. C. Costa, and G. R. Colli. 2011. Vicariance 3745. and endemism in a Neotropical savanna hotspot: distribution patterns of Knowles, L. L., and W. P. Maddison. 2002. Statistical phylogeography. Mol. Cerrado squamate reptiles. J. Biogeogr. 38:1907–1922. Ecol. 11:2623–2635. Nosil, P. 2008. Speciation with gene flow could be common. Mol. Ecol. Leache,´ A. D. 2009. Species tree discordance traces to phylogeographic clade 17:2103–2106. boundaries in North American fence lizards (Sceloporus) Syst. Biol. Nunez,˜ J. J., N. K. Wood, F. E. Rabanal, F. M. Fontanella, and J. W. Sites Jr. 58:547–559. 2011. Amphibian phylogeography in the Antipodes: refugia and post- Leal, I. R., M. Tabarelli, and J. M. C. Silva. 2003. Ecologia e Conservac¸ao˜ da glacial colonization explain mitochondrial haplotype distribution in the Caatinga. Universidade Federal de Pernambuco, Recife. Patagonian frog Eupsophus calcaratus (Cycloramphidae). Mol. Phylo- Leavitt, D. H., R. L. Bezy, K. A. Crandall, and J. W. Sites, Jr. 2007. Multi- genet. Evol. 58:343–352. locus DNA sequence data reveal a history of deep cryptic vicariance Oliveira, P. S., and R. J. Marquis. 2002. The Cerrados of Brazil: ecology and and habitat-driven convergence in the desert night lizard Xantusia vig- natural history of a Neotropical Savanna. Columbia Univ. Press, New ilis species complex (Squamata: Xantusiidae). Mol. Ecol. 16:4455– York. 4481. Oliver, P. M., M. Adams, and P. Doughty. 2010. Molecular evidence for Lemmon, A. R., and E. M. Lemmon. 2008. A Likelihood framework for ten species and Oligo-Miocene vicariance within a nominal Australian estimating phylogeographic history on a continuous landscape. Syst. gecko species (Crenadactylus ocellatus, Diplodactylidae). BMC Evol. Biol. 57:544–561. Biol. 10:1–11. Lessa, E. P., G. D’El´ıa, and U. F. J. Pardinas.˜ 2010. Genetic footprints of Otto-Bliesner, B. L., S. J. Marshall, J. T. Overpeck, G. H. Miller, A. Hu, and late Quaternary climate change in the diversity of Patagonian-Fueguian L. I. P. m. CAPE. 2006. Simulating Arctic climate warmth and icefield rodents. Mol. Ecol. 19:3031–3037. retreat in the Last Interglaciation. Science 311:1751–1753. Lopes, J. A., and M. A. Beaumont. 2010. ABC: a useful Bayesian tool for the Passoni, J. C., M. L. Benozzati, and M. T. Rodrigues. 2008. Phylogeny, analysis of population data. Infect. Genet. Evol. 10:826–833. species limits, and biogeography of the Brazilian lizards of the genus Eu- Macey, J. R., J. A. Schulte II, N. B. Ananjeva, A. Larson, N. Rastegar-Pouyani, rolophosaurus (Squamata: Tropiduridae) as inferred from mitochondrial S. M. Shammakov, and T. J. Papenfuss. 1998. Phylogenetic relationships DNA sequences. Mol. Phylogenet. Evol. 46:403–414. among agamid lizards of the Laudakia caucasia species group: testing Pavlidis, P., S. Laurent, and W. Stephan. 2010. msABC: a modification of hypotheses of biogeographic fragmentation and an area cladogram for Hudson’s ms to facilitate multi-locus ABC analysis. Mol. Ecol. Res. the Iranian Plateau. Mol. Phylogenet. Evol. 10:118–131. 10:723–727. Maciel, N. M., R. G. Collevatti, G. R. Colli, and E. F. Schwartz. 2010. Pellegrino, K. C., S. Kasahara, M. T. Rodrigues, and Y. Yonenaga- Late Miocene diversification and phylogenetic relationships of the huge Yassuda. 1997. Pericentric inversion events in karyotypic distinction of toads in the Rhinella marina (Linnaeus, 1758) species group (Anura: Brazilian lizards of genus Phyllopezus (Squamata, Gekkonidae) detected Bufonidae). Mol. Phylogenet. Evol. 57:787–797. by chromosomal banding patterns. Hereditas 127:255–262. Maddison, W. P., and L. L. Knowles. 2006. Inferring phylogeny despite in- Pellegrino, K. C. M., M. T. Rodrigues, A. N. Waite, M. Morando, Y. Y. complete lineage sorting. Syst. Biol. 55:21–30. Yassuda, and J. W. Sites, Jr. 2005. Phylogeography and species limits in McCormack, J. E., J. Heled, K. S. Delaney, A. T. Peterson, and L. L. the Gymnodactylus darwinii complex (Gekkonidae, Squamata): genetic Knowles. 2011. Calibrating divergence times on species trees versus structure coincides with river systems in the Brazilian Atlantic Forest. gene trees: implications for speciation histoty of Aphelocoma jays. Evo- Biol. J. Linn. Soc. 84:13–26. lution 65:184–202. Pepper, M., M. K. Fujita, C. Moritz, and S. Keogh. 2011. Palaeoclimate change Moraes, E. M., K. S. C. Yotoko, M. H. Manfrin, V.N. Solferini, and F. M. Sene. drove diversification among isolated mountain refugia in the Australian 2009. Phylogeography of the cactophilic species Drosophila gouveai: arid zone. Mol. Ecol. 20:1529–1545. demographic events and divergence timing in dry vegetation enclaves in Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy eastern Brazil. J. Biogeogr. 36:2136–2147. modeling of species geographic distributions. Ecol. Model 190:231–259. Morando, M., L. J. Avila, and J. W. S. Sites, Jr. 2003. Sampling strategies Pritchard, J. K., M. Stephens, and P. Donnelly. 2000. Inference of population for delimiting species: genes, individuals, and populations in the Lio- structure using multilocus genotype data. Genetics 155:945–959. laemus elongatus-kriegi complex (Squamata: Liolaemidae) in Andean- R Development Core Team. 2010. R: a language and environment for statistical Patagonian South America. Syst. Biol. 52:159–185. computing. R Foundation for Statistical Computing, Vienna, Austria. Moritz, C., J. L. Patton, C. J. Schneider, and T. B. Smith. 2000. Diversification Available at http://www.R-project.org. of rainforest faunas: an integrated molecular approach. Annu. Rev. Ecol. Rambaut, A., and A. J. Drummond. 2007. Tracer Analysis Tool Syst. 31:533–563. v1.4. Tracer University of Oxford, Oxford, U.K. Available at Moritz, C., C. J. Hoskin, J. B. Mackenzie, B. L. Phillips, M. Tonione, N. Silva, http://beast.bio.ed.ac.uk/. J. VanderWal, S. E. Williams, and C. H. Graham. 2009. Identification Ramos, A. C. S., J. P. Lemos-Filho, R. A. Ribeiro, F. R. Santos, and M. and dynamics of a cryptic suture zone in tropical rainforest. Proc. R. B. Lovato. 2007. Phylogeography of the tree Hymenaea stigonocarpa Soc. Lond. B Biol. 276:1235–1244. (Fabaceae: Caesalpinioideae) and the influence of Quaternary climate Mulch, A., C. E. Uba, M. R. Strecker, R. Schoenberg, and Chamberlain. 2010. changes in the Brazilian Cerrado. Ann. Bot. 100:1219–1228. Late Miocene climate variability and surface elevation in the central Ribas, C. C., A. Aleixo, A. C. R. Nogueira, C. Y. Miyaki, and J. Cracraft. Andes. Earth Planet. Sci. Lett. 290:173–182. 2012. A palaeobiogeographic model for biotic diversification within Nielsen, R., and J. Wakeley. 2001. Distinguishing migration from isolation: a Amazonia over the past three million years. Proc. R. Soc. Lond. B Biol. Markov Chain Monte Carlo approach. Genetics 158:885–896. 279:681–689. Niemiller, M. L., B. M. Fitzpatrick, and B. T. Miller. 2008. Recent diver- Riccomini, C., and M. Assumpc¸ao.˜ 1999. Quaternary tectonics in Brazil. gence with gene flow in Tennessee cave salamanders (Plethodontidae: Episodes 22:221–225. Gyrinophilus) inferred from gene genealogies. Mol. Ecol. 17:2258– Riddle, B. R., R. J. Ladle, S. A. Lourie, and R. J. Whittaker. 2011. Basic 2275. biogeography: estimating biodiversity and mapping nature. Pp. 47–92

EVOLUTION 2012 19 F. P. WERNECK ET AL.

in R. J. Ladle and R. J. Whittaker, eds. Conservation biogeography. Tamura, K., D. Peterson, G. Stecher, M. Nei, and S. Kumar. 2011. MEGA5: Wiley-Blackwell, Chichester, UK. molecular evolutionary genetics analysis using maximum likelihood, Rocha, S., M. Vences, F. Glaw, D. Posada, and D. J. Harris. 2009. Multi- evolutionary distance, and maximum parsimony methods. Mol. Biol. gene phylogeny of Malagasy day geckos of the genus Phelsuma.Mol. Evol. 28:2731–2739. Phylogenet. Evol. 52:530–537. Templeton, A. R. 2010. Coherent and incoherent inference in phylogeography Rodrigues, M. T. 1996. Lizards, snakes, and amphisbaenians from the Qua- and human evolution. Proc. Natl. Acad. Sci. USA 107:6376–6381. ternary sand dunes of the middle Rio Sao˜ Francisco, Bahia, Brazil. J. Thome,´ M. T. C., K. R. Zamudio, J. G. R. Giovanelli, C. F. B. Haddad, F. A. Herpetol. 30:513–523. Baldissera, Jr., and J. Alexandrino. 2010. Phylogeography of endemic ———. 2003. Herpetofauna da Caatinga. Pp. 181–236 in I. R. Leal, M. toads and post-Pliocene persistence of the Brazilian Atlantic Forest. Tabarelli, and J. M. C. Silva, eds. Ecologia e Conservac¸ao˜ da Caatinga. Mol. Phylogenet. Evol. 55:1018–1031. Editora Universitaria´ da UFPE, Recife. Townsend, T. M., D. G. Mulcahy, B. P. Noonan, J. W. Sites Jr., C. A. Kuczyn- Rozas, J., J. C. Sanchez-DelBarrio,´ X. Messeguer, and R. Rozas. 2003. DnaSP, ski, J. J. Wiens, and T. W. Reeder. 2011. Phylogeny of iguanian lizards DNA polymorphism analyses by the coalescent and other methods. inferred from 29 nuclear loci, and a comparison of concatenated and Bioinformatics 19:2496–2497. species-tree approaches for an ancient, rapid radiation. Mol. Phylogenet. Rull, V. 2008. Speciation timing and neotropical biodiversity: the Tertiary– Evol. 61:363–380. Quaternary debate in the light of molecular phylogenetic evidence. Mol. Uba, C. E., C. Heubeck, and C. Hulka. 2006. Evolution of the Late Cenozoic Ecol. 17:2722–2729. Chaco foreland basin, Southern Bol´ıvia. Basin Res. 18:145–170. ———. 2011a. Neotropical biodiversity: timing and potential drivers. Tree VanderWerf, E. A., L. C. Young, N. W. Yeung, and D. B. Carlon. 2010. Step- 26:508–513. ping stone speciation in Hawaii’s flycatchers: molecular divergence sup- ———. 2011b. Origins of biodiversity. Science 331:398–399. ports new island endemics within the elepaio. Conserv. Genet. 11:1283– Rundell, R. J., and T. D. Price. 2009. Adaptive radiation, nonadaptive radiation, 1298. ecological speciation and nonecological speciation. Tree 24:394–398. Vanzolini, P. E. 1953. Sobre o generoˆ Phyllopezus Peters (Sauria, Ruskin, B. G., F. M. Davila,´ G. D. Hoke, T. E. Jordan, R. A. Astini, and R. Gekkonidae). Papeis´ Avulsos de Zoologia, Sao˜ Paulo 11:353–369. Alonso. 2011. Stable isotope composition of middle Miocene carbonates ———. 1963. Problemas faun´ısticos do Cerrado. Pp. 305–321 in M. Ferri, of the Frontal Cordillera and Sierras Pampeanas: did the Paranaense sea- ed. Simposio´ sobre o Cerrado. Editora da Universidade de Sao˜ Paulo., way flood western and central Argentina? Palaeogeogr. Palaeoclimatol. Sao˜ Paulo. Palaeocol. 308:293–303. ———. 1976. On the lizards of a Cerrado-Caatinga contact, evolutionary and Sampaio, E. V. S. B. 1995. Overview of the Brazilian Caatinga. Pp. 35–63 zoogeographical implications (Sauria). Papeis´ Avulsos de Zoologia, Sao˜ in S. H. Bullock, H. A. Mooney, and E. Medina, eds. Seasonally Dry Paulo 29:111–119. Tropical Forests. Cambridge Univ. Press, Cambridge, UK. Weisrock, D. W., R. M. Rasoloarison, I. Fiorentino, J. M. Ralison, S. M. Sersic,´ A. N., A. Cosacov, A. A. Cocucci, L. A. Johnson, R. Pozner, L. J. Avila, Goodman, P. M. Kappeler, and A. D. Yoder. 2010. Delimiting species J. W. Sites, Jr., and M. Morando. 2011. Emerging phylogeographical without nuclear monophyly in Madagascar’s mouse lemurs. PLoS Biol. patterns of plants and terrestrial vertebrates from Patagonia. Biol. J. 5:e9883. Linn. Soc. 103:475–494. Werneck, F. P. 2011. The diversification of eastern South American open Siedchlag, A. C., M. L. Benozzati, J. C. Passoni, and M. T. Rodrigues. 2010. vegetation biomes: historical biogeography and perspectives. Quat. Sci. Genetic structure, phylogeny, and biogeography of Brazilian eyelid- Rev. 30:1630–1648. less lizards of genera Calyptommatus and Nothobachia (Squamata, Werneck, F. P., L. G. Giugliano, R. G. Collevatti, and G. R. Colli. 2009. Gymnophthalmidae) as inferred from mitochondrial DNA sequences. Phylogeny, biogeography and evolution of clutch size in South American Mol Phylogenet Evol 56:622–630. lizards of the genus Kentropyx (Squamata: Teiidae). Mol. Ecol. 18:262– Simon, M. F., R. Grether, L. P.Queiroz, C. Skema, and R. T. Pennington. 2009. 278. Recent assembly of the Cerrado, a neotropical plant diversity hotspot, Werneck, F. P., G. C. Costa, G. R. Colli, D. Prado, and J. W. Sites, Jr. 2011. by in situ evolution of adaptations to fire. Proc. Natl. Acad. Sci. USA Revisiting the historical distribution of Seasonally Dry Tropical Forests: 106:20359–20364. new insights based on palaeodistribution modelling and palynological Smith, T. B., S. Kark, C. J. Schneider, R. K. Wayne, and C. Moritz. 2001. evidence. Glob. Ecol. Biogeogr. 20:272–288. Biodiversity hotspots and beyond: the need for preserving environmental Werneck, F. P., C. Nogueira, G. R. Colli, J. W. Sites, Jr., and G. C. Costa. transitions. Tree 16:431. Climatic stability in the Brazilian Cerrado: implications for biogeo- Stamatakis, A. 2006. RAxML-VI-HPC: maximum likelihood-based phyloge- graphical connection of South American savannas, species richness, and netic analyses with thousands of taxa and mixed models. Bioinformatics conservation in a biodiversity hotspot. J. Biogeogr. doi:10.1111/j.1365- 22:2688–2690. 2699.2012.02715.x. Tamura, K., and M. Nei. 1993. Estimation of the number of nucleotide sub- stitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol. Biol. Evol. 10:512–526. Associate Editor: B. Fitzpatrick

20 EVOLUTION 2012 DEEP DIVERGENCE OF SOUTH AMERICAN ‘DRY DIAGONAL’ BIOMES

Supporting Information The following supporting information is available for this article:

Appendix S1: Sample collection

Table S1. Details of material examined, with locality data for all species examined, mtDNA haploclades assignments, species tree clades correspondence, and major population clusters assignments.

Appendix S2: Genetic markers, lab protocols, and sequence editing/alignment

Table S2. Mitochondrial and nuclear genes and primers used, with details of PCR conditions.

Table S3. Net among-group distances between sampled clades for mtDNA data.

Table S4. Net among-group uncorrected distances between sampled clades for mtDNA data.

Figure S1. Individual nuclear gene maximum likelihood trees.

Figure S2. Species tree for Phyllopezus pollicaris with estimated divergence times and credibility intervals.

Figure S3. Principal Components Analysis vectors predictive plots (PC1 × PC2 and PC1 × PC3) for the prior predictive distributions of Summary Statistics (SuSt) for all models compared using the approximate Bayesian computation approach. Figure S4. Geographic distribution of the eight ‘candidate species’ clades within the Phyllopezus pollicaris complex.

Supporting Information may be found in the online version of this article.

Please note: Wiley-Blackwell is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing material) should be directed to the corresponding author for the article.

EVOLUTION 2012 21 SUPPORTING INFORMATION

S1: Sample collection

Lizards were collected by hand either when active at night, or by lifting stones or other cover, and also using gluetraps.

Specimens were euthanized with Tiopental; tissue samples (liver or tail tips) were collected and stored in 95-100% ethanol or liquid nitrogen, and subsequently in −80◦C freezers.

Table S1: Details of material examined, with locality data for all species examined, mtDNA haploclades assignments, Species tree clades correspondence, and population clusters assignments (multilocus Structure analyses; CE = Central, NE = Northeast, and SW =

Southwest). Brazilian states: AL = Alagoas, BA = Bahia, CE = Ceará, GO = Goiás, MA = Maranhão, MG = Minas Gerais, MT =

Mato Grosso, MS = Mato Grosso do Sul, TO = Tocantins, PA = Pará, PE = Pernambuco, PB = Paraíba, PI = Piauí, SE = Sergipe.

UHE = Usina Hidrelétrica (Hydroelectric power project). Specimens ID acronyms: MTR = Miguel Trefault Rodrigues Field series deposited at the Coleção de Tecidos de Vertebrados do Departamento de Zoologia, Instituto de Biociências, Universidade de São

Paulo; FRD = Fagner Ribeiro Delfim field series; CHUNB = Coleção Herpetológica da Universidade de Brasília; CHUFCL = Coleção

Herpetológica da Universidade Federal do Ceará Lagartos; RAS = Rafael Alves dos Santos field series; MPEG = Museu Paraense

Emílio Goeldi; MZUSP = Museu de Zoologia da Universidade de São Paulo; CASTH = Castelo - Herpetologia; PHV = Paula Hanna

Valdujo field series; RRT = Resgate de Rondonópolis field series; CENPAT = Centro Nacional Patagónico; MNHNP = Museo Nacional de Historia Natural del Paraguay; CH = Coleção Herpetológica da Universidade Federal do Mato Grosso do Sul; TR =

Transnordestina field series; ZFMK = Zoologisches Forschungsinstitut und Museum Alexander Koeni. Asterisc * marks areas under legal protection.

Candidate species mtDNA clades from Species Major pop. Locality Species Specimen ID haplo-clade tree Cluster Sampling location no. São Geraldo do Araguaia/ Serra dos Martírios State Park, PA, P. p. pollicaris MTR(PV1053) A II Central Brazil 23* São Geraldo do Araguaia/ Serra dos Martírios State Park, PA, P. p. pollicaris MTR(PV1059) A II Central Brazil 23* Uruçuí-Una Ecological Station, P. p. pollicaris MTR2810 AA VIII Northeast PI, Brazil 26* Uruçuí-Una Ecological Station, P. p. pollicaris MTR5744 AA VIII Northeast PI, Brazil 26* Uruçuí-Una Ecological Station, P. p. pollicaris MTR2346 AA VIII Northeast PI, Brazil 26* Uruçuí-Una Ecological Station, P. p. pollicaris MTR2807 AA VIII Northeast PI, Brazil 26* Uruçuí-Una Ecological Station, P. p. pollicaris MTR2958 AA VIII Northeast PI, Brazil 26* Uruçuí-Una Ecological Station, P. p. pollicaris MTR2959 AA VIII Northeast PI, Brazil 26* P. p. pollicaris MTR(LG1342) AB VIII Northeast Campo Formoso, BA, Brazil 35 P. p. pollicaris MTR(LG1343) AB VIII Northeast Campo Formoso, BA, Brazil 35 Serra das Confusões National P. p. pollicaris MTR4563 AB VIII Northeast Park, PI, Brazil 30* P. p. pollicaris MTR4648 AB VIII Northeast Serra das Confusões National 30* Park, PI, Brazil Serra das Confusões National P. p. pollicaris MTR4850 AB VIII Northeast Park, PI, Brazil 30* Serra das Confusões National P. p. pollicaris MTR4851 AB VIII Northeast Park, PI, Brazil 30* P. p. pollicaris MTR11757 AB VIII Northeast Alagoado, BA, Brazil 32 P. p. pollicaris MTR3681 AB VIII Northeast Ilha do Gado Bravo, BA, Brazil 31 Serra das Confusões National P. p. pollicaris MTR4960 AB VIII Northeast Park, PI, Brazil 30* Buíque/ Catimbau National Park, P. p. pollicaris MTR15361 AC VIII Northeast PE, Brazil 6* P. p. pollicaris MTR887015 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris MTR887020 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris FRD847 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris FRD848 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris FRD849 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris FRD850 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris FRD865 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris FRD866 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris FRD867 AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris S/N AD VIII Northeast Cabaceiras, PB, Brazil 3 P. p. pollicaris CHUNB61897 AE VIII Northeast Aiuaba State Park, CE, Brazil 10* P. p. pollicaris CHUNB61898 AE VIII Northeast Aiuaba State Park, CE, Brazil 10* P. p. pollicaris CHUNB61899 AE VIII Northeast Aiuaba State Park, CE, Brazil 10* P. p. pollicaris CHUNB61900 AE VIII Northeast Aiuaba State Park, CE, Brazil 10* P. p. pollicaris CHUNB61901 AE VIII Northeast Aiuaba State Park, CE, Brazil 10* P. p. pollicaris CHUNB61907 AE VIII Northeast Exu, PE, Brazil 11 P. p. pollicaris CHUFC L2852 AF VIII Northeast Betânia, PE, Brazil 8 P. p. pollicaris CHUFC L2854 AF VIII Northeast Betânia, PE, Brazil 8 P. p. pollicaris CHUNB56579 AF VIII Northeast Milagres, CE, Brazil 9 P. p. pollicaris CHUNB61906 AF VIII Northeast Exu, PE, Brazil 11 P. p. pollicaris CHUNB61908 AF VIII Northeast Exu, PE, Brazil 11 P. p. pollicaris CHUNB61909 AF VIII Northeast Exu, PE, Brazil 11 P. p. pollicaris CHUNB61910 AF VIII Northeast Exu, PE, Brazil 11 P. p. pollicaris CHUNB61911 AF VIII Northeast Exu, PE, Brazil 11 P. p. pollicaris CHUNB61912 AF VIII Northeast Exu, PE, Brazil 11 Canindé de São Francisco, SE, P. p. pollicaris A143 AG VIII Northeast Brazil 14 P. p. pollicaris A98 AG VIII Northeast Paulo Afonso, BA, Brazil 13 P. p. pollicaris MTR(LG1011) AG VIII Northeast Porto Seguro, BA, Brazil 52 P. p. pollicaris MTR(LG1106) AG VIII Northeast Xingó, AL, Brazil 12 P. p. pollicaris MTR(LG1107) AG VIII Northeast Xingó, AL, Brazil 12 P. p. pollicaris MTR(LG1110) AG VIII Northeast Xingó, AL, Brazil 12 P. p. pollicaris MTR(LG807) AG VIII Northeast Xingó, AL, Brazil 12 P. p. pollicaris MTR(LG808) AG VIII Northeast Xingó, AL, Brazil 12 P. p. pollicaris MTR(LG922) AG VIII Northeast Xingó, AL, Brazil 12 Poço Redondo/ Grota do Angico P. p. pollicaris RAS000 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS009 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS010 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS027 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS028 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS029 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS030 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS031 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS038 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS040 AG VIII Northeast National Monument, SE, Brazil 16* Poço Redondo/ Grota do Angico P. p. pollicaris RAS041 AG VIII Northeast National Monument, SE, Brazil 16* P. p. pollicaris MTR3743 AH VIII Northeast Alagoado, BA, Brazil 32 P. p. pollicaris MTR3745 AH VIII Northeast Alagoado, BA, Brazil 32 P. p. pollicaris MTR3748 AH VIII Northeast Alagoado, BA, Brazil 32 P. p. pollicaris CHUNB51124 AI VIII Northeast São Desidério, BA, Brazil 43 P. p. pollicaris CHUNB51125 AI VIII Northeast São Desidério, BA, Brazil 43 P. p. pollicaris CHUNB51126 AI VIII Northeast São Desidério, BA, Brazil 43 P. p. pollicaris CHUNB51127 AI VIII Northeast São Desidério, BA, Brazil 43 P. p. pollicaris CHUNB51128 AI VIII Northeast São Desidério, BA, Brazil 43 Formosa do Rio Preto/ Serra Geral P. p. pollicaris MTR14878 AJ VIII Northeast Ecological Station, BA, Brazil 42* Formosa do Rio Preto/ Serra Geral P. p. pollicaris MTR14879 AJ VIII Northeast Ecological Station, BA, Brazil 42* Formosa do Rio Preto/ Serra Geral P. p. pollicaris MTR14888 AJ VIII Northeast Ecological Station, BA, Brazil 42* Formosa do Rio Preto/ Serra Geral P. p. pollicaris MTR14889 AJ VIII Northeast Ecological Station, BA, Brazil 42* Formosa do Rio Preto/ Serra Geral P. p. pollicaris MTR14890 AJ VIII Northeast Ecological Station, BA, Brazil 42* Formosa do Rio Preto/ Serra Geral P. p. pollicaris MTR14891 AJ VIII Northeast Ecological Station, BA, Brazil 42* Formosa do Rio Preto/ Serra Geral P. p. pollicaris MTR14894 AJ VIII Northeast Ecological Station, BA, Brazil 42* P. p. pollicaris MTR3746 AK VIII Northeast Alagoado, BA, Brazil 32 P. p. pollicaris MPEG26878 AL VIII Northeast Floriano, PI, Brazil 22 P. p. pollicaris MTR(ESTR 1124) AM VIII Northeast Carolina, MA, Brazil 25 P. p. pollicaris MTR7788 AM VIII Northeast Babaçulândia, TO, Brazil 24 Capitão Gervásio de Oliveira, PI, P. p. pollicaris MTR(CGERV083) AN VIII Northeast Brazil 28 Capitão Gervásio de Oliveira, PI, P. p. pollicaris MTR(CGERV084) AN VIII Northeast Brazil 28 P. p. pollicaris FRD444 AN VIII Northeast São Francisco de Assis, SE, Brazil 15 P. p. pollicaris CHUNB56064 AO VIII Northeast São João do Piauí, PI, Brazil 27 P. p. pollicaris CHUNB56065 AO VIII Northeast São João do Piauí, PI, Brazil 27 P. p. pollicaris CHUNB56066 AO VIII Northeast São João do Piauí, PI, Brazil 27 P. p. pollicaris MPEG26880 AO VIII Northeast Floriano, PI, Brazil 22 São Raimundo Nonato/ Serra da P. p. pollicaris MTR15002 AO VIII Northeast Capivara National Park, PI, Brazil 29* São Raimundo Nonato/ Serra da P. p. pollicaris MTR15004 AO VIII Northeast Capivara National Park, PI, Brazil 29* São Raimundo Nonato/ Serra da P. p. pollicaris MTR15005 AO VIII Northeast Capivara National Park, PI, Brazil 29* P. p. pollicaris AO VIII Buíque, Catimbau National Park, MTR15387 Northeast PE, Brazil 6* Buíque, Catimbau National Park, P. p. pollicaris MTR(VC36) AO VIII Northeast PE, Brazil 6* Piracuruca/ Sete Cidades National P. p. pollicaris CHUNB61031 AP VIII Northeast Park, PI, Brazil 21* Piracuruca/ Sete Cidades National P. p. pollicaris CHUNB61032 AP VIII Northeast Park, PI, Brazil 21* Piracuruca/ Sete Cidades National P. p. pollicaris CHUNB61033 AP VIII Northeast Park, PI, Brazil 21* P. p. pollicaris CHUNB57391 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57392 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57395 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57397 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57400 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57402 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57406 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57409 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57412 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57413 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57416 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57418 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57419 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57422 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57423 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57425 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57428 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57429 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57430 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57431 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57432 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57433 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57434 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57435 AQ VIII Northeast Ubajara National Park, CE, Brazil 19* P. p. pollicaris CHUNB57388 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57389 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57390 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57393 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57394 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57396 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57398 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57399 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57403 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57404 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57405 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57407 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57408 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57410 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57411 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57414 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57415 AR VIII Northeast Ibiapina, CE, Brazil 18 P. p. pollicaris CHUNB57417 AR VIII Northeast Ibiapina, CE, Brazil 18 P. p. pollicaris CHUNB57420 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57421 AR VIII Northeast Ibiapina, CE, Brazil 18 P. p. pollicaris CHUNB57424 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57426 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57427 AR VIII Northeast Tianguá, CE, Brazil 20 P. p. pollicaris CHUNB57436 AR VIII Northeast Ibiapina, CE, Brazil 18 P. p. pollicaris MZUSP95898 AR VIII Northeast Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris CHUNB55904 B II Central Barra do Garças, MT, Brazil 57 P. p. pollicaris CHUNB50457 C I Central Mucugê, BA, Brazil 36 P. p. pollicaris CHUNB50458 C I Central Mucugê, BA, Brazil 36 P. p. pollicaris MTR(JC1153) C I Central Mucugê, BA, Brazil 36 P. p. pollicaris MTR(JC1182) C I Central Mucugê, BA, Brazil 36 P. p. pollicaris MTR(JC1183) C I Central Mucugê, BA, Brazil 36 P. p. pollicaris MTR(JC1185) C I Central Mucugê, BA, Brazil 36 P. p. pollicaris MTR(JC1234) C I Central Mucugê, BA, Brazil 36 P. p. pollicaris MTR887703 D III Central Diamantina, MG, Brazil 55 P. p. pollicaris MTR(JC1503) D III Central Serro, MG, Brazil 56 P. p. pollicaris MTR66316 E IV Central Serra da Mesa, GO, Brazil 44 P. p. pollicaris MTR(LG1309) E IV Central Serra da Mesa, GO, Brazil 44 P. p. pollicaris MTR(LG1310b) E IV Central Serra da Mesa, GO, Brazil 44 P. p. pollicaris MTR(LG1792) E IV Central Palmas, TO, Brazil 38 P. p. pollicaris MTR6686 E IV Central UHE Lajeado, TO, Brazil 37 P. p. pollicaris MTR6728 E IV Central UHE Lajeado, TO, Brazil 37 P. p. pollicaris MTR6733 E IV Central UHE Lajeado, TO, Brazil 37 P. p. pollicaris MTR6822 E IV Central UHE Lajeado, TO, Brazil 37 P. p. pollicaris MTR6839 E IV Central UHE Lajeado, TO, Brazil 37 P. p. pollicaris MTR6841 E IV Central UHE Lajeado, TO, Brazil 37 P. p. pollicaris TG00105 I V Southwest Paraguay (no specific locality) 65 P. p. pollicaris MTR(LG1845) M IV Central UHE Lajeado, TO, Brazil 37 P. p. pollicaris MTR13452 N III Central Trancoso, BA, Brazil 53 P. p. pollicaris MTR13497 N III Central Trancoso, BA, Brazil 53 P. p. pollicaris MTR906406 O VI Central Santo Inácio, BA, Brazil 33 P. p. pollicaris MTR2981 O VI Central Santo Inácio, BA, Brazil 33 P. p. pollicaris MTR11129 O VI Central Santo Inácio, BA, Brazil 33 P. p. pollicaris MTR11202 O VI Central Santo Inácio, BA, Brazil 33 P. p. pollicaris MTR3074 O VI Central Santo Inácio, BA, Brazil 33 P. p. pollicaris MTR3263 O VI Central Gentio do Ouro, BA, Brazil 34 P. p. pollicaris MTR3287 O VI Central Santo Inácio, BA, Brazil 33 P. p. pollicaris MTR887011 P VI Central Cabaceiras, PB, Brazil 3 P. p. pollicaris MTR887019 P VI Central Cabaceiras, PB, Brazil 3 P. p. pollicaris CHUNB61919 P VI Central Itaporanga, PB, Brazil 7 P. p. pollicaris CHUNB61928 P VI Central Patos, PB, Brazil 5 P. p. pollicaris CHUNB61929 P VI Central Patos, PB, Brazil 5 P. p. pollicaris CHUNB61930 P VI Central Patos, PB, Brazil 5 P. p. pollicaris CHUNB61931 P VI Central Patos, PB, Brazil 5 P. p. pollicaris CHUNB61932 P VI Central Patos, PB, Brazil 5 P. p. pollicaris CHUNB61933 P VI Central Patos, PB, Brazil 5 P. p. pollicaris CHUNB61934 P VI Central Patos, PB, Brazil 5 P. p. pollicaris CHUNB61940 P VI Central Junco do Seridó, PB, Brazil 4 P. p. pollicaris CHUNB61941 P VI Central Junco do Seridó, PB, Brazil 4 P. p. pollicaris CHUNB61942 P VI Central Junco do Seridó, PB, Brazil 4 P. p. pollicaris CHUNB61943 P VI Central Junco do Seridó, PB, Brazil 4 P. p. pollicaris CHUNB61944 P VI Central Junco do Seridó, PB, Brazil 4 P. p. pollicaris CHUNB61945 P VI Central Junco do Seridó, PB, Brazil 4 Januária/ Cavernas do Peruaçu P. p. pollicaris MTR(MTJ0126) Q VII Central National Park, MG, Brazil 49* Januária/ Cavernas do Peruaçu P. p. pollicaris MTR(MTJ0221) Q VII Central National Park, MG, Brazil 49* Januária/ Cavernas do Peruaçu P. p. pollicaris MTR(MTJ0224) Q VII Central National Park, MG, Brazil 49* Januária/ Cavernas do Peruaçu P. p. pollicaris MTR(MTJ0225) Q VII Central National Park, MG, Brazil 49* Januária/ Cavernas do Peruaçu P. p. pollicaris MTR(MTJ0294) Q VII Central National Park, MG, Brazil 49* Januária/ Cavernas do Peruaçu P. p. pollicaris MTR(MTJ0381) Q VII Central National Park, MG, Brazil 49* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58309 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58310 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58311 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58312 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58313 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58314 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58315 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58316 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58317 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58318 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58319 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58320 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58321 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58325 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58326 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58344 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58345 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58352 R VII Central MG, Brazil 48* Manga/ Mata Seca State Park, P. p. pollicaris CHUNB58353 R VII Central MG, Brazil 48* P. p. pollicaris CHUNB33583 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB33584 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB33585 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB35234 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB35235 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB35240 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB35241 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB35243 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB35244 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB35246 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB43850 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB43852 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB43854 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB43855 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB43856 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB43857 S VII Central São Domingos, GO, Brazil 47 P. p. pollicaris CHUNB59067 T VII Central Alto Paraíso de Goiás, GO, Brazil 46 P. p. pollicaris CHUNB59068 T VII Central Alto Paraíso de Goiás, GO, Brazil 46 P. p. pollicaris MTR(LG1310) T VII Central Niquelândia, GO, Brazil 45 P. p. pollicaris CHUNB36987 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB36991 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB36992 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB36998 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB37001 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB37002 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB37003 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB37004 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB37005 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris CHUNB52602 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MTR4248 U VII Central Paranã, TO, Brazil 41 P. p. pollicaris MZUSP95893 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95895 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95896 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95897 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95899 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95900 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95901 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95902 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95903 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95904 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95905 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP95906 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP96212 U VII Central Peixe Angical, UHE, TO, Brazil 39 P. p. pollicaris MZUSP9894 U VII Central Peixe Angical, UHE, TO, Brazil 39 Matias Cardoso/ Cajueiro State P. p. pollicaris CHUNB58343 V III Central Park, MG, Brazil 50* Matias Cardoso/ Cajueiro State P. p. pollicaris CHUNB58347 V III Central Park, MG, Brazil 50* Matias Cardoso/ Cajueiro State P. p. pollicaris CHUNB58349 V III Central Park, MG, Brazil 50* Matias Cardoso/ Cajueiro State P. p. pollicaris CHUNB58351 V III Central Park, MG, Brazil 50* P. p. pollicaris MTR(JC1283) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1325) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1326) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1493) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1494) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1496) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1497) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1498) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1499) W III Central Augusto de Lima, MG, Brazil 54 P. p. pollicaris MTR(JC1501) W III Central Grão Mogol, MG, Brazil 51 P. p. pollicaris MTR(JC1502) W III Central Grão Mogol, MG, Brazil 51 P. p. pollicaris CASTH0661 X VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CASTH0280 Y VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CASTH0288 Y VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CASTH0389 Y VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CASTH0423 Y VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CASTH0494 Y VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CASTH0495 Y VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CASTH0783 Y VIII Northeast Castelo do Piauí, PI, Brazil 17 P. p. pollicaris CHUNB56580 Y VIII Northeast Milagres, CE, Brazil 9 Mateiros/ Serra Geral Ecological P. p. pollicaris MTR(PHV2198) Z VIII Northeast Station, TO, Brazil 40* Mateiros/ Serra Geral Ecological P. p. pollicaris MTR(PHV2199) Z VIII Northeast Station, TO, Brazil 40* P. p. przewalskii MTR(RRT29) F V Southwest Rondonópolis, MT, Brazil 58 P. p. przewalskii CHUNB58656 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58657 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58658 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58659 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58660 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58661 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58662 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58663 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58664 G V Southwest Bonito, MS, Brazil 62 P. p. przewalskii CHUNB58665 G V Southwest Bonito, MS, Brazil 62 P. p. CHUNB58666 G V Southwest Bonito, MS, Brazil 62 przewalskii P. p. przewalskii CHUNB58667 G V Southwest Bonito, MS, Brazil 62 P. p. Serra da Bodoquena National przewalskii CHUNB58668 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58669 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58670 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58671 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58672 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58673 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58674 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58675 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58676 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58677 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58678 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58679 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58680 H V Southwest Park, MS, Brazil 60* P. p. Serra da Bodoquena National przewalskii CHUNB58681 H V Southwest Park, MS, Brazil 60* P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12071 I V Southwest Provincia del Formosa, Argentina 66 P. p. CENPAT12072 I V Southwest Ingeniero Guillermo N. Juarez, 66 przewalskii Provincia del Formosa, Argentina P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12073 I V Southwest Provincia del Formosa, Argentina 66 P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12074 I V Southwest Provincia del Formosa, Argentina 66 P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12080 I V Southwest Provincia del Formosa, Argentina 66 P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12081 I V Southwest Provincia del Formosa, Argentina 66 P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12082 I V Southwest Provincia del Formosa, Argentina 66 P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12083 I V Southwest Provincia del Formosa, Argentina 66 P. p. Ingeniero Guillermo N. Juarez, przewalskii CENPAT12084 I V Southwest Provincia del Formosa, Argentina 66 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12088 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12089 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12090 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12091 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12092 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12093 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12094 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii CENPAT12095 I V Southwest Chaco, Argentina 67 P. p. Fuerte Esperanza, Provincia del przewalskii MTR(LG1093) I V Southwest Chaco, Argentina 67 P. p. MNCN5903 I V Southwest Serranía Aguarague, Tarija, 68 przewalskii Bolivia Depto. Boquerrón, Tenente P. p. Agripino Enciso National Park, przewalskii MNHNP11412 I V Southwest Paraguay 64* Depto. Boquerrón, Tenente P. p. Agripino Enciso National Park, przewalskii MNHNP11413 I V Southwest Paraguay 64* Depto. Boquerrón, Tenente P. p. Agripino Enciso National Park, przewalskii MNHNP11415 I V Southwest Paraguay 64* Depto. Boquerrón, Tenente P. p. Agripino Enciso National Park, przewalskii MNHNP11417 I V Southwest Paraguay 64* Depto. Boquerrón, Tenente P. p. Agripino Enciso National Park, przewalskii MNHNP11426 I V Southwest Paraguay 64* P. p. przewalskii CHUNB58682 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58683 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58684 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58685 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58686 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58687 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58688 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58689 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58690 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58691 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58692 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58693 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58694 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58695 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58696 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58697 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58698 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58699 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58700 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58701 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58702 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58703 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58704 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58705 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CHUNB58706 J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii MTR (FSFL1016) J V Southwest Corumbá, MS, Brazil 59 P. p. przewalskii CH272 K V Southwest Porto Murtinho, MS, Brazil 63 P. p. przewalskii CH560 K V Southwest Porto Murtinho, MS, Brazil 63 P. p. przewalskii CH561 K V Southwest Porto Murtinho, MS, Brazil 63 P. p. przewalskii CH562 K V Southwest Porto Murtinho, MS, Brazil 63 P. p. przewalskii CHUNB58631 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58632 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58633 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58634 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58635 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58636 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58637 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58638 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58639 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58640 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58641 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58642 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58643 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58644 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58645 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58646 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58647 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58649 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58650 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58651 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58652 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58653 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58654 L V Southwest Aquidauana, MS, Brazil 61 P. p. przewalskii CHUNB58655 L V Southwest Aquidauana, MS, Brazil 61 P. periosus MTR887014 Cabaceiras, PB, Brazil 3 P. periosus MTR887022 Cabaceiras, PB, Brazil 3 P. periosus CHUNB49501 Campina Grande, PB, Brazil 2 P. periosus CHUNB56581 Milagres, CE, Brazil 9 P. periosus CHUNB61920 Itaporanga, PB, Brazil 7 P. periosus CHUNB61921 Itaporanga, PB, Brazil 7 P. periosus CHUNB61922 Itaporanga, PB, Brazil 7 P. periosus CHUNB61923 Itaporanga, PB, Brazil 7 P. periosus CHUNB61924 Itaporanga, PB, Brazil 7 P. periosus FRD754 Areia, PB, Brazil 1 P. periosus FRD851 Cabaceiras, PB, Brazil 3 P. periosus FRD852 Cabaceiras, PB, Brazil 3 P. periosus TR297 Milagres, CE, Brazil 9 P. maranjonensis ZFMK84995 Balsas, Peru S2: Genetic markers, lab protocols, and sequence editing/alignment

Genomic DNA was extracted with the DNAeasy Qiagen kit or with QIAamp DNA Micro Kit for samples with small amounts of starting material. PCR amplification conditions were different across loci, but basically included a initial denaturation at 94°C 5 min, followed by 37-40 cycles consisting of 94°C denaturation (1 min); 45-72 °C annealing (0:30-1 min), 72 °C extension (1 min), and a final extension at 72 °C (5-7 min). Details for each locus are given in Table S2. We vacuum purified PCR products using

MANU 30 PCR plates Millipore and subsequently resuspended the DNA with ultra-pure water. Sequencing reactions used the ABI

Big-Dye Terminator v3.1 Cycle Sequencing Kit in an ABI GeneAmp PCR 9700 thermal cycler. Sequencing products were purified with Sephadex G-50 Fine (GE Healthcare) and sequenced on an ABI 3730xl DNA Analyzer at the BYU DNA Sequencing Center.

We screened approximately 40 rapidly-evolving nuclear markers from a variety of sources, including protein coding

(Townsend et al. 2008; Portik et al. 2010; Wiens et al. 2010), intron, and exon regions (Waltari and Edwards 2002; Dolman and

Moritz 2006; Fujita et al. 2010; Gamble et al. 2011), and amplified 11 variable loci for P. pollicaris and outgroups. Chromatograms were assembled and edited with Sequencher v4.7 (Gene Codes Corporation), aligned with Muscle (Edgar 2004), and open reading frames checked using MacClade v4.06 (Maddison and Maddison 1999) to confirm alignment and gap placement. When present, poorly aligned regions were removed using GBlocks (Castresana 2000), and models of DNA evolution were selected based on AIC criterion with jModelTest v0.1.1 (Posada 2008). We performed recombination tests for the nuclear loci using the Difference of Sum of

Squares test with TOPALi v2.5 (Milne et al. 2009), and none revealed significant recombination regions. We resolved gametic phase of heterozygous individuals with PHASE v2.1.1 (Stephens et al. 2001), implementing 500 main iterations, 500 burn-in iterations, one thinning interval per iteration, and confidence probability thresholds of 0.90. The omission of unresolved genotypes can potentially introduce biases into downstream phylogeographic inferences (Garrick et al. 2010), but the accuracy of haplotypes reconstructed by

PHASE based on lower probability thresholds is acceptable compared to the time and cost of experimentally-phasing them (Harrigan et al. 2008). We therefore selected haplotype reconstructions with the highest posterior probability, when phase certainty was inferior to the probability threshold.

Table S2: Mitochondrial and nuclear genes and primers used in this study, with details of PCR conditions. NPCL = nuclear protein coding locus. Gene Kind of Length PCR Primers Author Model marker (bp) profile Cytb mtDNA 942 Cytb45 IguaCytob_F2: CCACCGTTGTTATTCAACTAC (Corl et al. TIM2+G (45) PhyCytob_R2: GCTGCGGTAKTTTGAGGTGT 2010) and this study ND2 mtDNA 886 1-touch51 L4437: AAGCTTTCGGGCCCATA (Macey and TIM3+I+G tRNA-Asn-2R: Verma TAGATTAAAGTCCGCTGGNTTGG 1997) KIF24 NPCL, 565 ANL63 F1: SAAACGTRTCTCCMAAACGCATCC (Portik et al. TVM+I+G kinesin R1: WGGCTGCTGRAAYTGCTGGTG 2010) family member 24 gene ACA alpha-cardiac 900 1-touch57 F: GAGCGTGGCTAYTCCTTTGT (Waltari and TPM2uf+G actin gene, R: GTGGCCATTTCATTCTCAAA Edwards intron 3 and 2002) exon 4 MYH2 myosin heavy 436 34cycle50 F: GAACACCAGCCTCATCAACC (Dolman TrN+G chain gene, R: TGGTGTCCTGCTCCTTCTTC and Moritz intron 2 and 2006) flanking exons PRLR NPCL, 563 1-touch51 F1: GACARYGARGACCAGCAACTRATGCC (Townsend TIM1+G prolactin R3: GACYTTGTGRACTTCYACRTAATCCAT et al. 2008) receptor gene rpl35 ribosomal 617 NuIntr72 exon 2: (Fujita et al. TPM1uf+G protein L35 CAGAGTGCTGACAGTCATTAACCAGAC 2010) gene, intron 2 exon 3: GTCTTCAGACCCTCTTCGTGCTTG and exon 3 MC1R NPCL, 524 ANL63 F1: TGGGGCTGGTGAGCYTGGTG (Rosenblum HKY+I melanocortin R1: CCCAGSAGGATGGTGAGGGTG et al. 2004) 1 receptor gene MAP1B microtubule- 888 1-touch57 F2: GCAGCCAAYKGTTACACAGAAAGA T. Gamble TIM2+G associated R2: CTCTGGTGAAGACATRAGGGATCT unpub. data protein 1B (this study) gene, exon 5 MXRA5 NPCL, 919 ANL63 F2: KGCTGAGCCTKCCTGGGTGA D. Portik, HKY+G encoding R2: YCTMCGGCCYTCTGCAACATTK unpub. data matrix- remodelling associated 5 gene SINCAIP NPCL, 482 ANL63 F10: CGCCAGYTGYTGGGRAARGAWAT (Townsend GTR+G synuclein R13: GGWGAYTTGAGDGCACTCTTRGGRCT et al. 2008) alpha interacting protein gene DMXL1 NPCL, dmx- 943 1-touch57 F2: GTCTAGGGAGGATGGTTCACATA T. Gamble TPM1uf+G like 1 gene R2: GAATGAAGCAAGTGACSAGAAAGA unpub. data (this study) RBMX X-linked 640 1-touch57 F1: TCCTCTTACAGTGAYCGTGATG (Gamble et TPM1uf+I RNA binding R1: TCCCGTAATCATCATAGCGACT al. 2011) motif protein gene, intron 8 and flanking exons Some regions were amplified with one of the following standard touchdown cycles: 94°C–2:45min, 40X [94°C–15s, 51°C or 57°C –20s (−0.1°C/cycle), 72°C–1min], 72°C–1min, final rest at 4°C (called 1-touch51 or 1-touch57); or 95°C–1:30min, 10X [95°C–35s, 63°C–35s (−0.5°C/cycle), 72°C–1min], 10X (95°C–35s, 58°C–35s, 72°C–1min), 15X (95°C– 35s, 52°C–35s, 72°C–1min; final rest at 10°C; called ANL63).

Supporting information references

Castresana, J. 2000. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol Biol Evol 17:540-552. Corl, A., A. R. Davis, S. R. Kuchta, T. Comendant, and B. Sinervo. 2010. Alternative mating strategies and the evolution of sexula size dimorphism in the side-blotched lizard, Uta stansburiana: a population-level comparative analysis. Evolution 64:79-96. Dolman, G., and C. Moritz. 2006. A multilocus perspective on refugial isolation and divergence in rainforest skinks (Carlia). Evolution 60:573-582. Edgar, R. C. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nuclei Acids Res 32:1792-1797. Fujita, M. K., J. A. McGuire, S. C. Donnellan, and C. Moritz. 2010. Diversification and persistence at the arid-monsoonal interface: Australia-wide biogeography of the Bynoe's gecko (Heteronotia bionoei: Gekkonidae). Evolution 64:2293-2314. Gamble, T., J. D. Daza, G. R. Colli, L. J. Vitt, and A. M. Bauer. 2011. A new genus of miniaturized and pug-nosed gecko from South America (Sphaerodactylidae: Gekkota). Zool J Linn Soc-Lond 163:1244-1266. Garrick, R. C., P. Sunnucks, and R. J. Dyer. 2010. Nuclear gene phylogeography using PHASE: dealing with unresolved genotypes, lost alleles, and systematic bias in parameter estimation. BMC Evolutionary Biology 10:1-17. Harrigan, R. J., M. E. Mazza, and M. D. Sorenson. 2008. Computation vs. cloning: evaluation of two methods for haplotype determination. Molecular Ecology Resources 8:1239-1248. Macey, J. R., and A. Verma. 1997. Homology in phylogenetic analysis: alignment of transfer RNA genes and the phylogenetic position of snakes. Mol Phylogenet Evol 7:272-279. Maddison, W. P., and D. R. Maddison. 1999. MacClade: analysis of phylogeny and character evolution. v 4.06. Sinauer Associates, Inc., Sunderland, Massachusetts. Milne, I., D. Lindner, M. Bayer, D. Husmeier, G. McGuirem, D. F. Marshall, and F. Wright. 2009. TOPALi v2: a rich graphical interface for evolutionary analyses of multiple alignments on HPC clusters and multi-core desktops. Bioinformatics 25:126- 127. Portik, D. M., A. M. Bauer, and T. R. Jackman. 2010. The phylogenetic Affinities of Trachylepis sulcata nigra and the intraspecific evolution of coastal melanism in the western rock skink. African Zoology 45:147-159. Posada, D. 2008. jModelTest: Phylogenetic Model Averaging Mol Biol Evol 25:1253-1256. Rosenblum, E. B., H. E. Hoekstra, and M. W. Nachman. 2004. Adaptive reptile color variation and the evolution of the MC1R gene. Evolution 58:1794-1808. Stephens, M., N. Smith, and P. Donnelly. 2001. A new statistical method for haplotype reconstruction from population data. American Journal of Human Genetics 68. Tamura, K., and M. Nei. 1993. Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol 10:512-526. Tamura, K., D. Peterson, G. Stecher, M. Nei, and S. Kumar. 2011. MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods. Mol Biol Evol. Townsend, T. M., R. E. Alegre, S. T. Kelley, J. J. Wiens, and T. W. Reeder. 2008. Rapid development of multiple nuclear loci for phylogenetic analysis using genomic resources: an example from squamate reptiles. Mol Phylogenet Evol 47:129-142. Waltari, E., and S. V. Edwards. 2002. Evolutionary dynamics of intron size, genome size, and physiological correlates in Archosaurs Am Nat 160:539-552. Wiens, J. J., C. A. Kuczynski, T. M. Townsend, T. W. Reeder, D. G. Mulcahy, and J. W. Sites Jr. 2010. Combining phylogenomics and fossils in higher-level squamate reptile phylogeny: molecular data change the placement of fossil taxa. Syst Biol 59:674- 688. Table S3. Net among group distances between sampled clades for mtDNA data. Values below the diagonal (black) are corrected p-distances and values above the diagonal (blue) respective standard errors, calculated using 500 bootstrap replicates. Analyses were conducted using the

Tamura-Nei model (Tamura and Nei 1993) in MEGA5 (Tamura et al. 2011). mara = P. maranjonensis. peri = P. periosus. A AA AB AC AD AE AF AG AH AI AJ AK AL AM AN AO AP AQ AR B C D E F G H I J K L M N O P Q R S T U V W X Y Z mara peri A 0.020 0.020 0.026 0.021 0.021 0.021 0.021 0.020 0.020 0.020 0.020 0.026 0.021 0.021 0.019 0.020 0.021 0.020 0.020 0.027 0.022 0.023 0.023 0.022 0.021 0.021 0.021 0.020 0.021 0.034 0.018 0.026 0.019 0.020 0.021 0.021 0.020 0.021 0.027 0.019 0.023 0.020 0.022 0.034 0.036 AA 0.297 0.007 0.011 0.007 0.008 0.007 0.008 0.008 0.008 0.007 0.007 0.010 0.008 0.007 0.008 0.008 0.007 0.008 0.021 0.029 0.018 0.019 0.023 0.022 0.021 0.020 0.019 0.021 0.021 0.038 0.020 0.022 0.019 0.019 0.019 0.019 0.019 0.018 0.020 0.018 0.017 0.008 0.008 0.033 0.032 AB 0.313 0.055 0.006 0.004 0.004 0.004 0.004 0.005 0.006 0.006 0.004 0.006 0.005 0.004 0.005 0.005 0.005 0.005 0.021 0.027 0.018 0.021 0.023 0.021 0.020 0.020 0.019 0.022 0.021 0.034 0.019 0.021 0.020 0.016 0.015 0.016 0.015 0.015 0.019 0.018 0.016 0.006 0.006 0.034 0.028 AC 0.274 0.074 0.033 0.006 0.005 0.006 0.006 0.009 0.009 0.009 0.008 0.008 0.008 0.008 0.009 0.008 0.008 0.008 0.029 0.027 0.023 0.026 0.028 0.026 0.027 0.026 0.023 0.029 0.023 0.033 0.023 0.023 0.029 0.020 0.021 0.018 0.018 0.021 0.018 0.020 0.025 0.009 0.012 0.036 0.032 AD 0.327 0.063 0.029 0.028 0.004 0.003 0.004 0.006 0.005 0.006 0.005 0.007 0.005 0.005 0.006 0.006 0.005 0.006 0.021 0.027 0.018 0.024 0.022 0.020 0.019 0.019 0.018 0.022 0.021 0.030 0.020 0.021 0.019 0.017 0.015 0.015 0.014 0.015 0.018 0.016 0.011 0.006 0.007 0.037 0.031 AE 0.326 0.064 0.030 0.028 0.025 0.003 0.003 0.006 0.006 0.006 0.005 0.007 0.005 0.005 0.006 0.006 0.005 0.006 0.022 0.027 0.019 0.024 0.022 0.019 0.019 0.020 0.018 0.022 0.022 0.031 0.020 0.021 0.020 0.017 0.016 0.015 0.014 0.015 0.019 0.017 0.011 0.007 0.007 0.035 0.032 AF 0.324 0.060 0.026 0.028 0.021 0.018 0.002 0.005 0.006 0.006 0.005 0.007 0.005 0.004 0.006 0.005 0.005 0.005 0.021 0.028 0.018 0.023 0.022 0.019 0.019 0.019 0.018 0.023 0.021 0.031 0.020 0.021 0.019 0.016 0.015 0.014 0.014 0.015 0.019 0.017 0.011 0.006 0.006 0.037 0.032 AG 0.333 0.062 0.032 0.034 0.023 0.021 0.007 0.006 0.007 0.007 0.005 0.007 0.005 0.005 0.007 0.006 0.006 0.006 0.021 0.028 0.018 0.021 0.022 0.020 0.020 0.019 0.019 0.023 0.021 0.032 0.020 0.021 0.020 0.017 0.016 0.015 0.014 0.015 0.019 0.017 0.013 0.007 0.007 0.039 0.031 AH 0.315 0.056 0.031 0.047 0.041 0.038 0.036 0.039 0.006 0.006 0.005 0.008 0.005 0.005 0.006 0.006 0.005 0.006 0.022 0.027 0.019 0.020 0.022 0.020 0.020 0.020 0.019 0.022 0.020 0.033 0.019 0.024 0.021 0.017 0.017 0.018 0.016 0.016 0.019 0.019 0.016 0.007 0.008 0.036 0.031 AI 0.322 0.061 0.041 0.051 0.043 0.043 0.044 0.050 0.039 0.005 0.005 0.008 0.005 0.005 0.006 0.006 0.005 0.006 0.021 0.026 0.018 0.021 0.023 0.020 0.019 0.020 0.020 0.022 0.021 0.032 0.019 0.021 0.019 0.018 0.017 0.018 0.017 0.016 0.019 0.019 0.014 0.007 0.007 0.039 0.032 AJ 0.323 0.061 0.040 0.057 0.048 0.047 0.046 0.052 0.042 0.044 0.005 0.008 0.006 0.005 0.006 0.006 0.005 0.006 0.021 0.027 0.019 0.021 0.023 0.020 0.019 0.020 0.019 0.021 0.019 0.033 0.019 0.021 0.021 0.018 0.017 0.018 0.017 0.017 0.021 0.020 0.013 0.007 0.008 0.036 0.030 AK 0.319 0.053 0.028 0.046 0.039 0.037 0.034 0.037 0.032 0.037 0.036 0.007 0.004 0.004 0.005 0.005 0.004 0.005 0.021 0.027 0.018 0.023 0.023 0.020 0.019 0.020 0.019 0.022 0.020 0.033 0.020 0.022 0.019 0.017 0.016 0.016 0.016 0.015 0.018 0.018 0.010 0.007 0.007 0.037 0.030 AL 0.280 0.063 0.036 0.045 0.036 0.038 0.036 0.041 0.038 0.045 0.047 0.035 0.007 0.006 0.007 0.007 0.007 0.007 0.027 0.027 0.024 0.025 0.032 0.027 0.026 0.024 0.023 0.027 0.024 0.033 0.022 0.024 0.027 0.023 0.023 0.020 0.021 0.021 0.020 0.023 0.025 0.008 0.010 0.038 0.030 AM 0.327 0.064 0.037 0.048 0.043 0.044 0.037 0.041 0.037 0.042 0.045 0.028 0.041 0.003 0.005 0.004 0.004 0.004 0.022 0.026 0.019 0.024 0.024 0.020 0.019 0.021 0.019 0.022 0.020 0.032 0.020 0.022 0.019 0.016 0.016 0.016 0.017 0.015 0.019 0.018 0.011 0.007 0.007 0.037 0.031 AN 0.324 0.055 0.028 0.041 0.036 0.037 0.032 0.037 0.035 0.038 0.038 0.021 0.030 0.023 0.005 0.004 0.004 0.004 0.021 0.026 0.019 0.023 0.024 0.020 0.018 0.020 0.018 0.021 0.019 0.031 0.020 0.021 0.019 0.018 0.017 0.017 0.016 0.016 0.019 0.019 0.012 0.006 0.007 0.038 0.030 AO 0.298 0.057 0.034 0.048 0.044 0.041 0.041 0.045 0.036 0.042 0.046 0.033 0.038 0.037 0.032 0.005 0.005 0.005 0.020 0.027 0.018 0.019 0.023 0.021 0.020 0.019 0.020 0.021 0.020 0.029 0.018 0.020 0.018 0.017 0.016 0.017 0.016 0.017 0.018 0.018 0.015 0.006 0.008 0.035 0.030 AP 0.309 0.062 0.034 0.043 0.039 0.042 0.036 0.043 0.035 0.040 0.041 0.032 0.035 0.030 0.024 0.034 0.003 0.004 0.022 0.026 0.020 0.019 0.025 0.021 0.020 0.020 0.020 0.023 0.020 0.034 0.019 0.024 0.021 0.019 0.018 0.018 0.018 0.017 0.021 0.020 0.017 0.007 0.007 0.037 0.031 AQ 0.326 0.061 0.029 0.040 0.039 0.039 0.034 0.038 0.035 0.040 0.039 0.025 0.035 0.027 0.022 0.032 0.013 0.001 0.021 0.025 0.018 0.022 0.023 0.020 0.019 0.019 0.018 0.022 0.019 0.032 0.021 0.022 0.020 0.018 0.016 0.017 0.016 0.015 0.019 0.018 0.012 0.007 0.007 0.035 0.030 AR 0.320 0.065 0.031 0.043 0.040 0.041 0.036 0.041 0.037 0.042 0.043 0.028 0.039 0.029 0.024 0.034 0.015 0.002 0.021 0.025 0.019 0.020 0.023 0.020 0.019 0.019 0.019 0.022 0.019 0.032 0.020 0.022 0.020 0.018 0.017 0.017 0.017 0.016 0.019 0.019 0.014 0.007 0.007 0.035 0.030 B 0.320 0.309 0.311 0.293 0.333 0.340 0.340 0.341 0.333 0.330 0.338 0.331 0.286 0.345 0.329 0.300 0.326 0.339 0.337 0.027 0.020 0.026 0.020 0.022 0.021 0.021 0.019 0.020 0.019 0.035 0.020 0.024 0.023 0.020 0.019 0.021 0.019 0.021 0.028 0.020 0.021 0.021 0.023 0.033 0.032 C 0.279 0.320 0.315 0.316 0.319 0.322 0.328 0.337 0.324 0.318 0.330 0.311 0.312 0.307 0.302 0.306 0.313 0.309 0.311 0.288 0.027 0.026 0.027 0.030 0.027 0.025 0.025 0.029 0.025 0.030 0.023 0.030 0.030 0.032 0.034 0.028 0.028 0.029 0.031 0.025 0.029 0.026 0.030 0.033 0.031 D 0.307 0.246 0.247 0.221 0.255 0.262 0.257 0.256 0.255 0.253 0.255 0.255 0.225 0.273 0.258 0.236 0.251 0.261 0.261 0.313 0.281 0.020 0.021 0.022 0.022 0.020 0.019 0.020 0.020 0.030 0.019 0.022 0.023 0.018 0.017 0.020 0.019 0.020 0.030 0.016 0.020 0.018 0.019 0.036 0.029 E 0.317 0.299 0.309 0.247 0.323 0.328 0.322 0.318 0.304 0.320 0.311 0.316 0.252 0.332 0.307 0.294 0.296 0.311 0.306 0.355 0.270 0.256 0.022 0.019 0.019 0.019 0.018 0.019 0.020 0.024 0.020 0.020 0.024 0.023 0.022 0.020 0.021 0.022 0.024 0.020 0.021 0.019 0.026 0.031 0.033 F 0.355 0.318 0.329 0.304 0.337 0.335 0.337 0.342 0.322 0.327 0.339 0.337 0.322 0.349 0.342 0.317 0.342 0.346 0.349 0.330 0.309 0.296 0.287 0.014 0.015 0.015 0.014 0.015 0.014 0.035 0.020 0.027 0.023 0.021 0.021 0.022 0.022 0.021 0.030 0.020 0.021 0.023 0.024 0.036 0.036 G 0.344 0.294 0.305 0.282 0.309 0.303 0.307 0.310 0.298 0.311 0.310 0.309 0.280 0.318 0.310 0.293 0.307 0.314 0.318 0.351 0.318 0.302 0.264 0.168 0.007 0.010 0.011 0.011 0.010 0.036 0.018 0.027 0.023 0.022 0.020 0.018 0.019 0.019 0.029 0.021 0.020 0.022 0.020 0.033 0.033 H 0.336 0.286 0.294 0.280 0.304 0.303 0.300 0.303 0.282 0.304 0.307 0.299 0.271 0.303 0.298 0.286 0.296 0.304 0.303 0.347 0.303 0.289 0.263 0.180 0.062 0.011 0.011 0.012 0.011 0.033 0.018 0.025 0.021 0.021 0.019 0.020 0.021 0.019 0.033 0.022 0.018 0.022 0.020 0.035 0.032 I 0.330 0.290 0.284 0.270 0.298 0.303 0.295 0.299 0.287 0.304 0.293 0.298 0.250 0.308 0.293 0.283 0.288 0.300 0.299 0.347 0.292 0.266 0.250 0.183 0.119 0.127 0.007 0.008 0.007 0.035 0.018 0.026 0.021 0.022 0.021 0.021 0.020 0.021 0.029 0.020 0.018 0.020 0.021 0.035 0.032 J 0.339 0.284 0.287 0.265 0.290 0.303 0.292 0.298 0.284 0.298 0.294 0.301 0.264 0.305 0.295 0.288 0.293 0.301 0.303 0.320 0.303 0.263 0.253 0.191 0.133 0.130 0.064 0.008 0.007 0.031 0.018 0.027 0.021 0.021 0.019 0.019 0.020 0.022 0.029 0.020 0.017 0.020 0.020 0.036 0.029 K 0.321 0.304 0.320 0.294 0.332 0.327 0.334 0.341 0.319 0.329 0.316 0.336 0.295 0.340 0.322 0.317 0.322 0.332 0.332 0.336 0.319 0.282 0.258 0.192 0.146 0.153 0.078 0.073 0.008 0.034 0.020 0.023 0.024 0.023 0.021 0.021 0.022 0.023 0.032 0.022 0.020 0.021 0.022 0.039 0.033 L 0.337 0.301 0.301 0.246 0.316 0.325 0.322 0.324 0.302 0.315 0.302 0.314 0.275 0.324 0.309 0.293 0.300 0.319 0.313 0.335 0.293 0.283 0.261 0.186 0.131 0.141 0.079 0.075 0.085 0.033 0.018 0.023 0.021 0.023 0.022 0.020 0.021 0.022 0.032 0.020 0.020 0.020 0.022 0.034 0.030 M 0.310 0.281 0.269 0.263 0.244 0.251 0.254 0.257 0.275 0.274 0.269 0.269 0.262 0.279 0.258 0.240 0.271 0.260 0.263 0.302 0.284 0.232 0.204 0.305 0.315 0.282 0.289 0.253 0.291 0.280 0.027 0.027 0.032 0.029 0.027 0.027 0.024 0.028 0.027 0.030 0.026 0.032 0.037 0.039 0.040 N 0.290 0.261 0.266 0.226 0.273 0.282 0.273 0.271 0.262 0.267 0.277 0.276 0.213 0.289 0.278 0.252 0.254 0.283 0.273 0.304 0.260 0.242 0.262 0.294 0.282 0.280 0.286 0.273 0.302 0.284 0.215 0.023 0.019 0.016 0.016 0.016 0.017 0.016 0.022 0.016 0.019 0.018 0.020 0.031 0.031 O 0.298 0.240 0.226 0.225 0.232 0.216 0.226 0.224 0.237 0.229 0.228 0.229 0.233 0.236 0.230 0.214 0.239 0.232 0.229 0.312 0.333 0.263 0.274 0.312 0.310 0.288 0.294 0.313 0.293 0.284 0.271 0.251 0.018 0.024 0.022 0.020 0.021 0.020 0.024 0.020 0.024 0.022 0.022 0.032 0.034 P 0.307 0.243 0.251 0.247 0.258 0.265 0.254 0.261 0.254 0.258 0.258 0.265 0.239 0.262 0.262 0.238 0.252 0.261 0.258 0.364 0.315 0.316 0.327 0.325 0.332 0.319 0.321 0.330 0.352 0.338 0.273 0.283 0.185 0.019 0.016 0.019 0.018 0.018 0.022 0.017 0.019 0.020 0.019 0.031 0.033 Q 0.305 0.221 0.212 0.199 0.222 0.219 0.217 0.219 0.213 0.226 0.222 0.220 0.212 0.226 0.225 0.212 0.227 0.229 0.230 0.305 0.336 0.242 0.302 0.297 0.295 0.283 0.311 0.305 0.318 0.313 0.251 0.241 0.236 0.237 0.007 0.013 0.012 0.013 0.019 0.016 0.018 0.018 0.019 0.035 0.030 R 0.319 0.241 0.212 0.205 0.223 0.221 0.216 0.223 0.222 0.242 0.228 0.225 0.223 0.232 0.226 0.212 0.230 0.227 0.229 0.319 0.343 0.244 0.309 0.313 0.294 0.289 0.309 0.296 0.324 0.317 0.239 0.256 0.241 0.233 0.064 0.013 0.011 0.013 0.019 0.014 0.017 0.017 0.017 0.034 0.031 S 0.299 0.214 0.195 0.183 0.194 0.200 0.199 0.203 0.204 0.210 0.205 0.205 0.191 0.211 0.208 0.196 0.204 0.204 0.206 0.289 0.313 0.259 0.273 0.278 0.246 0.255 0.282 0.265 0.284 0.266 0.227 0.214 0.202 0.225 0.131 0.132 0.009 0.010 0.019 0.019 0.019 0.017 0.018 0.032 0.026 T 0.288 0.206 0.180 0.169 0.186 0.187 0.184 0.189 0.195 0.207 0.197 0.193 0.193 0.197 0.195 0.188 0.198 0.199 0.199 0.288 0.313 0.236 0.273 0.290 0.275 0.278 0.278 0.276 0.298 0.283 0.198 0.215 0.200 0.213 0.129 0.126 0.087 0.011 0.017 0.016 0.018 0.016 0.016 0.032 0.029 U 0.328 0.246 0.223 0.227 0.233 0.228 0.227 0.232 0.229 0.235 0.242 0.231 0.227 0.237 0.236 0.234 0.229 0.235 0.234 0.317 0.326 0.287 0.300 0.296 0.287 0.283 0.307 0.300 0.320 0.324 0.249 0.240 0.240 0.244 0.155 0.165 0.111 0.101 0.020 0.018 0.020 0.018 0.016 0.033 0.029 V 0.276 0.198 0.184 0.181 0.174 0.185 0.184 0.191 0.184 0.192 0.191 0.176 0.190 0.188 0.184 0.174 0.197 0.195 0.197 0.269 0.327 0.243 0.221 0.280 0.295 0.299 0.287 0.274 0.293 0.280 0.210 0.200 0.232 0.209 0.187 0.194 0.179 0.160 0.198 0.018 0.028 0.019 0.022 0.030 0.030 W 0.289 0.204 0.207 0.190 0.210 0.213 0.207 0.206 0.207 0.217 0.216 0.212 0.210 0.227 0.220 0.203 0.222 0.218 0.222 0.297 0.305 0.218 0.265 0.278 0.299 0.293 0.292 0.275 0.316 0.287 0.233 0.217 0.222 0.213 0.185 0.195 0.193 0.177 0.212 0.145 0.018 0.019 0.019 0.031 0.030 X 0.329 0.212 0.195 0.267 0.157 0.155 0.151 0.177 0.195 0.185 0.169 0.149 0.275 0.152 0.151 0.188 0.204 0.158 0.181 0.331 0.307 0.280 0.237 0.284 0.279 0.266 0.267 0.258 0.296 0.290 0.233 0.280 0.266 0.276 0.248 0.247 0.248 0.233 0.277 0.266 0.237 0.017 0.012 0.031 0.032 Y 0.286 0.054 0.039 0.058 0.051 0.050 0.047 0.054 0.050 0.055 0.056 0.049 0.050 0.055 0.050 0.046 0.050 0.049 0.050 0.293 0.309 0.230 0.298 0.309 0.300 0.293 0.278 0.283 0.304 0.298 0.253 0.246 0.224 0.237 0.217 0.223 0.202 0.181 0.231 0.183 0.212 0.197 0.007 0.032 0.030 Z 0.340 0.060 0.056 0.082 0.061 0.063 0.057 0.063 0.066 0.070 0.064 0.059 0.070 0.064 0.059 0.066 0.063 0.059 0.062 0.343 0.351 0.263 0.350 0.353 0.321 0.316 0.323 0.324 0.346 0.349 0.296 0.288 0.243 0.257 0.245 0.248 0.230 0.211 0.251 0.202 0.231 0.159 0.056 0.034 0.034 mara 0.359 0.363 0.356 0.369 0.368 0.367 0.382 0.399 0.378 0.386 0.364 0.378 0.393 0.379 0.379 0.359 0.371 0.361 0.361 0.354 0.363 0.334 0.317 0.390 0.356 0.362 0.370 0.372 0.395 0.352 0.377 0.327 0.334 0.332 0.356 0.350 0.320 0.319 0.341 0.317 0.318 0.333 0.356 0.375 0.034 peri 0.393 0.351 0.324 0.343 0.330 0.348 0.342 0.343 0.347 0.338 0.328 0.340 0.334 0.355 0.333 0.334 0.340 0.336 0.339 0.369 0.340 0.304 0.325 0.382 0.362 0.346 0.347 0.327 0.359 0.355 0.390 0.309 0.363 0.367 0.340 0.340 0.303 0.332 0.321 0.326 0.333 0.339 0.332 0.355 0.369 Table S4. Net among group uncorrected distances between sampled clades for mtDNA data.

Values below the diagonal (black) are uncorrected p-distances and values above the diagonal

(blue) respective standard errors, calculated using 500 bootstrap replicates. Analyses were conducted in MEGA5 (Tamura et al. 2011). mara = P. maranjonensis. peri = P. periosus. A AA AB AC AD AE AF AG AH AI AJ AK AL AM AN AO AP AQ AR B C D E F G H I J K L M N O P Q R S T U V W X Y Z mara peri A 0.009 0.009 0.012 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.012 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.013 0.009 0.009 0.010 0.009 0.009 0.009 0.009 0.009 0.010 0.014 0.009 0.012 0.009 0.009 0.009 0.009 0.009 0.009 0.013 0.008 0.010 0.010 0.009 0.014 0.014 AA 0.202 0.005 0.008 0.006 0.007 0.006 0.006 0.006 0.006 0.006 0.006 0.008 0.006 0.005 0.006 0.006 0.006 0.006 0.010 0.012 0.009 0.008 0.010 0.009 0.010 0.010 0.009 0.010 0.009 0.015 0.009 0.011 0.009 0.009 0.009 0.010 0.008 0.008 0.010 0.009 0.009 0.006 0.006 0.013 0.013 AB 0.205 0.047 0.005 0.004 0.003 0.003 0.004 0.004 0.004 0.005 0.004 0.005 0.004 0.003 0.004 0.004 0.004 0.004 0.009 0.012 0.009 0.008 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.014 0.008 0.011 0.009 0.008 0.007 0.008 0.008 0.007 0.010 0.008 0.008 0.005 0.005 0.014 0.012 AC 0.192 0.064 0.030 0.005 0.005 0.005 0.005 0.007 0.007 0.008 0.006 0.006 0.007 0.006 0.007 0.007 0.006 0.006 0.013 0.012 0.012 0.012 0.012 0.012 0.012 0.013 0.011 0.013 0.011 0.015 0.011 0.011 0.013 0.011 0.010 0.010 0.010 0.010 0.010 0.010 0.013 0.007 0.008 0.014 0.013 AD 0.217 0.055 0.027 0.026 0.003 0.003 0.003 0.005 0.004 0.005 0.004 0.006 0.005 0.004 0.005 0.005 0.004 0.005 0.009 0.012 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.014 0.009 0.010 0.009 0.008 0.007 0.008 0.007 0.007 0.010 0.008 0.007 0.005 0.005 0.014 0.013 AE 0.215 0.055 0.027 0.026 0.024 0.003 0.003 0.005 0.005 0.005 0.004 0.006 0.004 0.004 0.005 0.005 0.004 0.005 0.009 0.012 0.009 0.009 0.009 0.009 0.009 0.009 0.008 0.009 0.009 0.014 0.009 0.010 0.008 0.008 0.008 0.008 0.007 0.007 0.010 0.008 0.007 0.005 0.005 0.014 0.013 AF 0.215 0.053 0.024 0.026 0.020 0.017 0.002 0.004 0.005 0.005 0.004 0.006 0.004 0.004 0.005 0.004 0.004 0.004 0.009 0.012 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.010 0.009 0.014 0.009 0.010 0.009 0.008 0.007 0.008 0.007 0.007 0.010 0.008 0.007 0.005 0.005 0.014 0.013 AG 0.219 0.055 0.029 0.031 0.022 0.020 0.007 0.005 0.005 0.005 0.004 0.006 0.004 0.004 0.005 0.005 0.004 0.005 0.009 0.012 0.009 0.008 0.009 0.009 0.009 0.009 0.009 0.010 0.009 0.014 0.008 0.011 0.009 0.008 0.008 0.008 0.007 0.008 0.010 0.008 0.008 0.006 0.005 0.014 0.013 AH 0.211 0.049 0.028 0.043 0.037 0.035 0.033 0.036 0.005 0.005 0.004 0.006 0.005 0.004 0.005 0.005 0.005 0.005 0.010 0.011 0.009 0.008 0.009 0.009 0.010 0.010 0.009 0.010 0.009 0.014 0.009 0.012 0.009 0.009 0.008 0.009 0.008 0.008 0.011 0.009 0.009 0.006 0.006 0.014 0.013 AI 0.214 0.054 0.036 0.046 0.039 0.039 0.040 0.045 0.036 0.005 0.004 0.007 0.004 0.004 0.005 0.005 0.005 0.005 0.009 0.011 0.009 0.008 0.009 0.009 0.009 0.010 0.009 0.010 0.009 0.014 0.009 0.011 0.009 0.009 0.008 0.009 0.008 0.008 0.010 0.009 0.008 0.006 0.006 0.014 0.013 AJ 0.215 0.054 0.036 0.051 0.044 0.042 0.042 0.047 0.039 0.040 0.005 0.007 0.005 0.004 0.005 0.005 0.005 0.005 0.009 0.011 0.009 0.008 0.009 0.009 0.009 0.010 0.009 0.010 0.009 0.015 0.008 0.011 0.009 0.009 0.008 0.010 0.008 0.008 0.011 0.009 0.007 0.006 0.006 0.014 0.013 AK 0.214 0.047 0.026 0.042 0.036 0.034 0.031 0.035 0.030 0.034 0.034 0.006 0.004 0.003 0.004 0.004 0.004 0.004 0.009 0.012 0.008 0.009 0.009 0.009 0.009 0.009 0.009 0.010 0.009 0.015 0.009 0.011 0.008 0.009 0.008 0.009 0.008 0.007 0.010 0.008 0.007 0.005 0.005 0.014 0.013 AL 0.196 0.056 0.033 0.041 0.034 0.035 0.033 0.039 0.036 0.042 0.044 0.033 0.006 0.005 0.006 0.006 0.006 0.006 0.013 0.012 0.012 0.012 0.012 0.012 0.013 0.013 0.011 0.013 0.011 0.014 0.011 0.012 0.013 0.012 0.011 0.011 0.010 0.011 0.011 0.011 0.012 0.007 0.008 0.014 0.012 AM 0.216 0.056 0.033 0.044 0.040 0.039 0.034 0.038 0.034 0.038 0.041 0.027 0.038 0.003 0.005 0.004 0.003 0.004 0.009 0.011 0.009 0.009 0.009 0.009 0.009 0.010 0.009 0.010 0.009 0.014 0.009 0.011 0.009 0.008 0.008 0.008 0.008 0.007 0.010 0.009 0.007 0.005 0.006 0.014 0.012 AN 0.212 0.048 0.025 0.037 0.033 0.033 0.029 0.033 0.032 0.035 0.035 0.020 0.028 0.022 0.004 0.004 0.003 0.003 0.009 0.012 0.009 0.009 0.009 0.009 0.009 0.010 0.009 0.009 0.009 0.014 0.009 0.011 0.008 0.009 0.008 0.009 0.008 0.007 0.010 0.009 0.007 0.005 0.005 0.014 0.012 AO 0.202 0.050 0.030 0.043 0.040 0.037 0.037 0.040 0.033 0.038 0.041 0.031 0.035 0.034 0.029 0.005 0.004 0.004 0.009 0.012 0.009 0.008 0.009 0.009 0.009 0.010 0.009 0.010 0.009 0.014 0.008 0.011 0.009 0.009 0.008 0.009 0.008 0.008 0.010 0.009 0.009 0.005 0.006 0.014 0.012 AP 0.209 0.054 0.031 0.039 0.036 0.038 0.033 0.039 0.033 0.037 0.037 0.030 0.033 0.029 0.023 0.031 0.003 0.003 0.009 0.012 0.010 0.008 0.010 0.009 0.009 0.010 0.010 0.010 0.009 0.015 0.009 0.012 0.010 0.010 0.009 0.010 0.009 0.008 0.011 0.010 0.009 0.005 0.006 0.014 0.013 AQ 0.217 0.054 0.027 0.037 0.035 0.035 0.031 0.035 0.032 0.037 0.036 0.024 0.033 0.025 0.020 0.029 0.012 0.001 0.009 0.011 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.014 0.009 0.011 0.009 0.009 0.008 0.009 0.008 0.007 0.010 0.008 0.007 0.005 0.005 0.013 0.013 AR 0.214 0.056 0.028 0.040 0.036 0.037 0.032 0.037 0.034 0.039 0.039 0.026 0.036 0.027 0.022 0.031 0.014 0.002 0.009 0.011 0.009 0.008 0.009 0.009 0.009 0.009 0.009 0.010 0.009 0.014 0.009 0.012 0.009 0.009 0.008 0.009 0.008 0.008 0.010 0.009 0.008 0.005 0.006 0.013 0.013 B 0.216 0.209 0.204 0.202 0.221 0.221 0.222 0.224 0.220 0.219 0.223 0.219 0.199 0.225 0.216 0.203 0.218 0.224 0.222 0.013 0.009 0.010 0.009 0.009 0.009 0.009 0.009 0.009 0.009 0.015 0.009 0.010 0.009 0.009 0.009 0.009 0.009 0.009 0.013 0.008 0.010 0.010 0.009 0.014 0.012 C 0.196 0.212 0.207 0.212 0.214 0.214 0.217 0.221 0.216 0.213 0.219 0.211 0.210 0.208 0.203 0.206 0.212 0.210 0.211 0.201 0.011 0.012 0.012 0.013 0.012 0.012 0.012 0.012 0.012 0.013 0.011 0.013 0.013 0.012 0.013 0.012 0.011 0.012 0.013 0.011 0.013 0.012 0.012 0.014 0.013 D 0.197 0.164 0.161 0.155 0.172 0.172 0.171 0.171 0.171 0.170 0.171 0.170 0.157 0.178 0.169 0.160 0.170 0.175 0.174 0.201 0.184 0.008 0.009 0.009 0.009 0.010 0.009 0.009 0.009 0.013 0.008 0.010 0.009 0.008 0.008 0.009 0.009 0.008 0.012 0.008 0.009 0.009 0.009 0.014 0.013 E 0.202 0.187 0.187 0.168 0.199 0.199 0.197 0.197 0.191 0.198 0.195 0.196 0.170 0.201 0.190 0.184 0.189 0.195 0.193 0.217 0.180 0.159 0.009 0.008 0.008 0.008 0.008 0.009 0.008 0.012 0.009 0.009 0.009 0.009 0.009 0.009 0.008 0.009 0.011 0.008 0.008 0.008 0.009 0.013 0.013 F 0.232 0.209 0.209 0.205 0.219 0.217 0.218 0.221 0.212 0.215 0.219 0.219 0.210 0.224 0.217 0.208 0.220 0.222 0.223 0.222 0.211 0.188 0.182 0.008 0.008 0.008 0.008 0.008 0.008 0.014 0.009 0.011 0.009 0.009 0.009 0.010 0.009 0.009 0.013 0.008 0.009 0.009 0.009 0.013 0.014 G 0.224 0.197 0.199 0.194 0.207 0.203 0.205 0.207 0.201 0.207 0.208 0.207 0.193 0.211 0.205 0.197 0.206 0.210 0.210 0.227 0.213 0.188 0.171 0.129 0.005 0.007 0.007 0.007 0.007 0.014 0.009 0.011 0.009 0.009 0.009 0.009 0.009 0.008 0.012 0.008 0.009 0.010 0.009 0.013 0.014 H 0.222 0.194 0.195 0.194 0.206 0.204 0.203 0.205 0.195 0.205 0.207 0.203 0.190 0.205 0.200 0.194 0.202 0.206 0.205 0.227 0.208 0.184 0.171 0.137 0.055 0.007 0.007 0.007 0.008 0.014 0.009 0.012 0.009 0.010 0.009 0.010 0.009 0.009 0.013 0.009 0.009 0.010 0.009 0.014 0.014 I 0.221 0.197 0.192 0.190 0.204 0.204 0.201 0.204 0.198 0.206 0.202 0.204 0.180 0.208 0.199 0.194 0.199 0.205 0.204 0.228 0.204 0.176 0.167 0.137 0.097 0.102 0.006 0.006 0.006 0.015 0.009 0.012 0.009 0.009 0.009 0.010 0.009 0.009 0.012 0.009 0.009 0.010 0.009 0.015 0.013 J 0.223 0.194 0.192 0.187 0.200 0.204 0.200 0.203 0.196 0.203 0.202 0.205 0.187 0.206 0.200 0.196 0.201 0.205 0.205 0.216 0.209 0.174 0.168 0.141 0.105 0.105 0.057 0.006 0.005 0.013 0.009 0.011 0.009 0.009 0.008 0.009 0.009 0.010 0.012 0.009 0.009 0.009 0.009 0.014 0.013 K 0.217 0.204 0.207 0.201 0.218 0.215 0.218 0.221 0.212 0.216 0.212 0.220 0.202 0.221 0.211 0.209 0.214 0.218 0.218 0.224 0.216 0.184 0.171 0.143 0.114 0.118 0.068 0.064 0.006 0.015 0.009 0.011 0.009 0.009 0.009 0.010 0.009 0.010 0.013 0.009 0.009 0.010 0.009 0.015 0.014 L 0.222 0.201 0.198 0.178 0.211 0.213 0.212 0.214 0.204 0.210 0.205 0.210 0.192 0.214 0.205 0.198 0.204 0.212 0.209 0.223 0.205 0.183 0.171 0.139 0.104 0.111 0.068 0.065 0.073 0.013 0.009 0.011 0.009 0.010 0.009 0.009 0.009 0.009 0.013 0.009 0.009 0.009 0.009 0.014 0.012 M 0.207 0.185 0.177 0.180 0.171 0.175 0.176 0.178 0.186 0.185 0.184 0.183 0.180 0.188 0.176 0.168 0.184 0.179 0.180 0.204 0.195 0.153 0.143 0.193 0.197 0.189 0.192 0.175 0.194 0.185 0.014 0.013 0.014 0.013 0.013 0.013 0.012 0.014 0.013 0.013 0.013 0.014 0.015 0.015 0.014 N 0.196 0.173 0.171 0.159 0.182 0.183 0.180 0.180 0.175 0.178 0.183 0.182 0.151 0.187 0.180 0.169 0.172 0.185 0.180 0.202 0.179 0.157 0.168 0.196 0.189 0.189 0.191 0.185 0.199 0.191 0.152 0.011 0.008 0.008 0.008 0.009 0.008 0.008 0.012 0.008 0.009 0.008 0.008 0.013 0.013 O 0.204 0.169 0.158 0.162 0.165 0.156 0.162 0.162 0.168 0.165 0.164 0.164 0.167 0.168 0.163 0.155 0.170 0.166 0.165 0.210 0.221 0.173 0.178 0.209 0.206 0.198 0.200 0.208 0.202 0.197 0.188 0.172 0.010 0.011 0.010 0.011 0.009 0.010 0.012 0.010 0.011 0.011 0.011 0.013 0.013 P 0.207 0.167 0.167 0.170 0.176 0.176 0.173 0.178 0.173 0.176 0.176 0.178 0.167 0.177 0.175 0.165 0.173 0.177 0.176 0.229 0.211 0.193 0.197 0.211 0.212 0.209 0.210 0.213 0.222 0.216 0.185 0.185 0.137 0.009 0.007 0.009 0.008 0.008 0.011 0.008 0.008 0.009 0.009 0.013 0.013 Q 0.206 0.156 0.148 0.146 0.159 0.155 0.156 0.158 0.154 0.160 0.159 0.157 0.153 0.160 0.157 0.152 0.161 0.163 0.163 0.206 0.216 0.163 0.187 0.202 0.198 0.194 0.206 0.204 0.209 0.206 0.171 0.168 0.165 0.165 0.005 0.008 0.007 0.008 0.010 0.008 0.009 0.009 0.009 0.013 0.013 R 0.210 0.162 0.147 0.148 0.158 0.156 0.154 0.158 0.156 0.166 0.160 0.158 0.157 0.162 0.156 0.149 0.161 0.160 0.160 0.211 0.218 0.162 0.188 0.206 0.196 0.194 0.203 0.197 0.209 0.205 0.165 0.173 0.168 0.163 0.056 0.008 0.007 0.007 0.010 0.007 0.008 0.008 0.008 0.013 0.013 S 0.201 0.151 0.138 0.136 0.143 0.145 0.145 0.148 0.148 0.151 0.149 0.149 0.141 0.152 0.148 0.142 0.148 0.148 0.149 0.195 0.206 0.168 0.175 0.190 0.173 0.179 0.190 0.183 0.192 0.182 0.158 0.152 0.149 0.158 0.103 0.102 0.006 0.007 0.011 0.009 0.010 0.009 0.009 0.013 0.012 T 0.186 0.139 0.123 0.122 0.131 0.130 0.129 0.134 0.135 0.140 0.136 0.134 0.134 0.136 0.133 0.130 0.137 0.137 0.137 0.188 0.197 0.148 0.165 0.185 0.178 0.181 0.180 0.180 0.189 0.181 0.139 0.144 0.138 0.144 0.096 0.094 0.069 0.007 0.009 0.007 0.009 0.008 0.008 0.013 0.011 U 0.214 0.168 0.153 0.161 0.164 0.159 0.160 0.163 0.161 0.164 0.167 0.162 0.162 0.165 0.162 0.162 0.162 0.164 0.164 0.210 0.214 0.180 0.187 0.200 0.193 0.192 0.203 0.199 0.207 0.208 0.172 0.165 0.168 0.168 0.118 0.123 0.089 0.078 0.010 0.008 0.009 0.008 0.008 0.013 0.013 V 0.191 0.145 0.134 0.137 0.133 0.138 0.138 0.144 0.139 0.143 0.142 0.134 0.142 0.141 0.136 0.131 0.146 0.145 0.146 0.186 0.215 0.158 0.151 0.188 0.193 0.195 0.189 0.185 0.192 0.186 0.150 0.145 0.165 0.152 0.140 0.142 0.132 0.116 0.144 0.010 0.012 0.010 0.011 0.013 0.013 W 0.191 0.141 0.138 0.135 0.145 0.144 0.143 0.144 0.144 0.148 0.148 0.145 0.145 0.153 0.146 0.139 0.151 0.149 0.150 0.194 0.199 0.143 0.165 0.182 0.190 0.189 0.188 0.181 0.198 0.186 0.156 0.147 0.154 0.148 0.132 0.136 0.135 0.118 0.143 0.109 0.009 0.009 0.009 0.012 0.012 X 0.217 0.155 0.139 0.189 0.124 0.122 0.120 0.135 0.146 0.141 0.132 0.120 0.192 0.121 0.119 0.141 0.151 0.125 0.137 0.221 0.209 0.180 0.149 0.194 0.192 0.187 0.188 0.183 0.201 0.197 0.166 0.186 0.185 0.186 0.175 0.172 0.175 0.157 0.186 0.182 0.162 0.009 0.007 0.014 0.014 Y 0.195 0.047 0.034 0.050 0.045 0.044 0.041 0.048 0.044 0.048 0.049 0.044 0.045 0.048 0.044 0.041 0.044 0.044 0.044 0.198 0.207 0.154 0.184 0.201 0.198 0.196 0.190 0.191 0.201 0.198 0.172 0.164 0.158 0.162 0.151 0.153 0.143 0.125 0.159 0.135 0.142 0.142 0.006 0.013 0.012 Z 0.222 0.052 0.048 0.070 0.054 0.055 0.050 0.056 0.058 0.061 0.056 0.053 0.061 0.056 0.051 0.057 0.056 0.052 0.055 0.225 0.226 0.174 0.208 0.224 0.212 0.210 0.213 0.214 0.223 0.223 0.194 0.187 0.172 0.176 0.170 0.170 0.162 0.143 0.172 0.149 0.155 0.126 0.049 0.013 0.013 mara 0.237 0.232 0.224 0.235 0.235 0.233 0.240 0.246 0.239 0.242 0.235 0.239 0.244 0.238 0.235 0.229 0.236 0.231 0.231 0.232 0.239 0.210 0.205 0.247 0.232 0.236 0.239 0.239 0.250 0.232 0.238 0.215 0.220 0.220 0.231 0.225 0.210 0.201 0.221 0.214 0.206 0.225 0.228 0.239 0.014 peri 0.248 0.229 0.214 0.228 0.222 0.228 0.226 0.228 0.228 0.225 0.222 0.226 0.224 0.232 0.220 0.222 0.226 0.225 0.226 0.240 0.226 0.201 0.209 0.246 0.236 0.231 0.231 0.222 0.236 0.234 0.246 0.207 0.236 0.236 0.227 0.222 0.207 0.208 0.216 0.222 0.215 0.227 0.218 0.233 0.241 Figure S1: Individual nuclear gene ML trees. A) ACA, DMXL1, KIF24, and MAP1B; B)

MC1R, MXRA5, MYH2, and PRLR; and C) RBMX, rpl35, and SINCAIP. Green boxes highlight the P. p. przewalskii clades on each gene tree. A

ACA DMXL1

K1F24 MAP1B B

MC1R MXRA5

PRLR MYH2 C

rpl35

RBMX

SINCAIP Figure S2: Species tree for Phyllopezus pollicaris with estimated divergence times. Nodes for which the credibility intervals (height 95% HDP) are not displayed correspond to nodes of ambiguous relationships (posterior clade probabilities lower than 0.5). 2.0

Miocene Pliocene Pleist. 12.5 10.0 7.5 5.0 2.5 0.0 Figure S3: PCA vectors predictive plots (PC1 X PC2 and PC1 X PC3) for the prior predictive distributions of Summary Statistics (SuSt) for all models compared using the ABC approach.

The observed SuSt are shown with red circles.

Figure S4: Geographic distribution of the eight ‘candidate species’ clades within the Phyllopezus pollicaris complex relative to the refugial areas estimated through palaeomodeling for the ‘dry diagonal’ biomes. Each color correspond to a different ‘candidate species’ numbered from I to

VIII. For a detailed description of palaeomodels see Figure 1. AR = Argentina, BO = Bolivia,

BR = Brazil, PA = Paraguay. 0o Amazon Forest Ca 20 19 Ce VIII 21 Ce 18

II 17 VI SDTF Ch 23 Ca 4 2 Cerrado Atlantic 22 10 9 7 Forest 24 25 1 Chaco 27 5 3 26 28 11 8 6 37 29 12 16 IV 30 13 31 14 38 42 33 15 BR 40 32 35 39 34 41 43 IV 36 44 47 VI 48 I Chi 57 45 46 II 49 50 52 58 51 53 VII 54 BO 55 V 59 56 III Ch 60 61 64 63 Ms 62 66 65 67 Sampling localities 68 PA P. p. pollicaris Mi P. p. przewalskii AR

750 Kilometers