phylogeography of a widespread spider species

Phylogeography of a widespread spider species (Gasteracantha cancriformis): Gene 1

flow through geographical barriers shapes its diversification 2

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Fabian Camilo Salgado Roa 6

Student 7

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Eloisa Lasso de Paulis 10

Advisor 11

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Camilo Salazar Clavijo 14

Co-Advisor 15

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Universidad de los Andes 20

Departamento de Ciencias Biológicas 21

29 de Mayo 22

2019 23

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25 26 27 28 29 30 31 32 33 34 35 I am a firm believer, that without speculation there is no good & original observation 36 37 Charles Dawin to A. R. Wallace. 22 December 1857 38 39 40 41 42 43 44 45 Heureux ceux qui se divertissent en s'instruisant, et qui se plaisent a cultiver leur sprit par 46 les sciences 47 48 Francisco José de Caldas to Santiago Pérez de Arroyo y Valencia. 5 January 1799. 49 Cartas de Francisco José de Caldas. P. 46 50 51 52

Title: Phylogeography of a widespread spider species (Gasteracantha cancriformis): 53

Gene flow through geographical barriers shapes its diversification 54

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Fabian C. Salgado-Roa1,2, Carolina Pardo-Diaz2, Melissa Sanchez Herrera2, Diego F. 56

Cisneros-Herdia3,4, Camilo Salazar2, Eloisa Lasso1 57

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1Departamento de Ciencias Biológicas, Universidad de los Andes, Bogotá, Colombia 59

2Programa de Biología, Facultad de Ciencias Naturales y Matemáticas, Universidad del 60

Rosario, Bogotá, Colombia 61

3Department of Entomology, California Academy of Sciences, San Francisco, CA, USA 62

4 Universidad San Francisco de Quito USFQ, Colegio de Ciencias Biológicas y Ambientales, 63

Laboratorio de Zoología Terrestre & Museo de Zoología, Quito 170901, Ecuador 64

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Abstract 66

Species with a wide distribution range provide a great opportunity to test the role of 67

geographic barriers in biotic diversification. Gasteracantha cancriformis is a color 68

polymorphic orbweb spider widely distributed in the Americas, and due to the discrete 69

distribution of color morphs across its distribution, this species may represent a complex of 70

species or subspecies. In order to understand the spatial organization of the genetic diversity 71

of G. cancriformis throughout its distribution we used the mitochondrial COI locus in ~250 72

individuals from 42 localities from southern USA to southern Brazil, all the South American 73

samples obtained by us and the rest where downloaded from databases, covering almost 74

the entire range of the species. We also used NextRAD sequencing in a subset of South 75

American populations to explore the role of gene flow in the diversification of the species. By 76

using phylogenetic methods, along with other population genetics summary statistics, we 77

found four phylogenetic clades that are associated with geography rather than coloration. 78

Furthermore, we detected shared ancestry between geographical clades presumably due to 79

gene flow facilitated by geographic discontinuities such as altitudinal depressions of the 80

Andes. Our work is one of the few approximations to understand the evolutionary history of 81

an arachnid lineage with continental distribution. 82

83

INTRODUCTION 84 85

The tropical American biodiversity has been linked to a series of geological and climatic 86

changes over time (Hoorn et al., 2010; Rull, 2011) that hypothetically promoted divergence 87

and latter allopatric speciation by fragmenting the distribution of species that were formerly 88

continuous (i.e. Vicariance). A Vicariance model would generate a similar pattern of 89

diversification in different species independent of their dispersal capacity or ecological 90

characteristics where the divergence time between isolated lineages would match the origin 91

of the landscape reconfiguration. Dispersion is another model that is gaining attention to 92

explain the species richness in the Americas, where the landscape composition and the 93

species ability to disperse are the main factors influencing allopatric speciation (Sanmartín, 94

Van Der Mark, & Ronquist, 2008; Smith et al., 2014). Under this model, species with higher 95

capacity of space occupancy (i.e. long dispersal abilities) would have recent divergence time 96

and signature of gene flow between divergent populations (Claramunt, Derryberry, Remsen, 97

& Brumfield, 2012). In contrast species with poor dispersal capacity would have higher 98

diversification rates and subsequent greater accumulation of lineages (Salisbury, Seddon, 99

Cooney, & Tobias, 2012). 100

101

The Andean uplift caused a large-scale landscape transformation separating the continuous 102

distribution of the lowland rainforest, creating the Amazon river system, along with the 103

aridification of the Northeastern Colombia and Pacific coast below Ecuador (Hoorn et al., 104

2010). The link between Andean origin and neotropical diversification has been reported in 105

lots of lineages, specially associated with vicariance speciation models (Hoorn et al., 2010; 106

Turchetto-Zolet, Pinheiro, Salgueiro, & Palma-Silva, 2013). Nevertheless, recent evidence of 107

a comparative phylogeography study with birds (Smith et al., 2014) found a despair 108

divergence time between lineages that are distributed at both sides of the northern Andes, a 109

result that indicates that rather than a single vicariant event the dispersal ability of the 110

species is more important in shaping genetic differentiation. Also, The Andes is not a 111

mountain range with constant elevation throughout its range, instead it has at least five 112

altitudinal depressions (Cadena, Pedraza, & Brumfield, 2016) which probably facilitated 113

dispersion (Chapman, 1917, 1926) impacting the divergence times and promoting gene flow 114

between divergent lineages. 115

116

The dispersion model also explains the diversity found in islands. Most of the Pacific (e.g. 117

Galapagos) and Caribbean Islands have a volcanic origin (Hickman & Lipps, 1985; Pindell & 118

Barrett, 1991) meaning that since their uplift they have never been in contact with the 119

continent, suggesting that species colonization help to explain their biotic composition 120

(Grehan, 2001). The Caribbean islands biotic origin has been hypothesized in two ways, the 121

first one states that ca. 30-33 Ma low sea levels connected the Aves ridge with the Greater 122

Antilles forming a continuous land bridge between South America and the Caribbean Islands 123

(GAARlandia hypothesis; Iturralde-Vinent, 2006). The other hypothesis expressed that 124

lineages arrived at the Caribbean islands via overwater dispersal by airborne or vegetation 125

draft (Hedges, 1996). The latter hypothesis has also been formulated to explain the origin of 126

the Galapagos fauna, where overwater dispersion happened from the South America 127

mainland. However, some Galapagos lineages appear to have close phylogenetic 128

relationships with North American and Caribbean Islands species, which may be explained 129

by the connection throughout the circumtropical current (Grehan, 2001). 130

131

All the hypothesis of lineage diversification in the neotropics have been formulated specially 132

based on vertebrates’ evidence, leaving out animal groups with high levels of biodiversity in 133

the American tropics such as arthropods (Turchetto-Zolet et al., 2013). The few studies that 134

exist identified the diversification role of the Andes (Arias et al., 2014; Bartoleti, Peres, 135

Fontes, da Silva, & Solferini, 2018; Chazot et al., 2017; De-silva et al., 2017) and the ocean 136

as diversification drivers and proposed that gene flow could happen across these 137

geographical barriers (Čandek, Binford, Agnarsson, & Kuntner, 2018; Dick, Bermingham, 138

Lemes, & Gribel, 2007; Salgado-Roa et al., 2018). However, the individuals and molecular 139

sampling of these works are limited which restrain the construction of a holistic model that 140

explains the origin of high biodiversity in this region. 141

142

Species with a wide distribution range provide a great opportunity to test the role of 143

geographic barriers in biotic diversification (Lo et al., 2014). Here we use as a study model 144

Gasteracantha cancriformis (Linnaeus, 1758), a color polymorphic orbweb spider widely 145

distributed in the Americas, found from the south of USA to northern Argentina. This species 146

has a huge abdomen and color variation across its distribution (Levi, 1978), which led to 147

numerous descriptions of synonyms (Levi, 1996). In the last revision of the genus for 148

America, Levi (1996) claimed that because there are clines for the abdomens characters in 149

different directions and little variation in genitalia, the discrimination of morphological 150

subspecies is not possible. Given the complex panorama, molecular data could favor the 151

description of divergent lineages and possible cryptic species. Furthermore, molecular data 152

could disentangle the origin of this color pattern radiation and reveal the importance of 153

geography and demography in shaping its genetic variation. 154

155

Here, we used mtDNA to explore the spatial arrangements of G. cancriformis genetic 156

diversity across its entire distribution along with a genome-scale sequencing method to test if 157

the Andean altitudinal depressions promote cross-Andean connectivity. Based in the long 158

dispersal capacity of its sister taxa (Bell, Bohan, Shaw, & Weyman, 2005) and in its 159

widespread distribution, we predict that distinctive lineages will only be found in populations 160

separated by extreme geographical barriers like high and wide mountain formations or wide 161

ocean masses. We also expect that populations far from the Andean passes would have 162

less shared ancestry than those that are close. This work constitutes one of the few 163

approximations to elucidate the phylogeography of a widespread arachnid lineage in the 164

Americas. 165

166

METHODS 167

168

Sample collection 169

We collected 215 individuals of G. cancriformis from 34 locations distributed in Colombia, 170

Ecuador, Peru and Brazil (Figure 1, Supporting information Table S1). Specimens were color 171

coded following Gawryszewski (2007), preserved in a 20% dimethylsulfoxide (DMSO) 172

solution saturated with NaCl, and stored at -80°C. Samples were deposited in “Colección de 173

Artrópodos de la Universidad del Rosario” (CAUR#229). 174

175

DNA extraction, amplification and sequencing 176

Genomic DNA was extracted from legs using Qiagen DNeasy blood and tissue kit (Qiagen, 177

Valencia, CA, USA), following the manufacture’s protocol. We amplified a fragment of the 178

mitochondrial gene Cytochrome Oxidase I (COI;500 pb; Folmer, Black, Hoeh, Lutz, & 179

Vrijenhoek, 1994) by polymerase chain reaction (PCR) using the conditions used in previous 180

studies. Fragments were cleaned by ExoSAP-IT (USA Corp., Cleveland, OH) and 181

sequenced at MACROGEN Inc. laboratories (Seoul, Korea). Finally, we downloaded all the 182

COI sequences of G. cancriformis available in GenBank and Bold system that correspond to 183

a different geographical region out of our sampling (Fig. 1, supplementary table 1). 184

Gene sequences were read, aligned and assembled in CLC MAIN WORKBENCH to obtain 185

a consensus sequence per individual. We used the MUSCLE algorithm in MEGA X (Kumar 186

et al., 2018) to create an alignment that was visually inspected and corrected. This alignment 187

was translated to protein to check for stop codons in Mesquite v.3.04 (Maddison & 188

Maddison, 2015). 189

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Molecular phylogenetics and divergence times 192

Phylogenetic analysis was performed with maximum likelihood (ML) in IQ-TREE (Nguyen, 193

Schmidt, von Haeseler, & Minh, 2015) letting the software to select the best substitution 194

model for our dataset and was K3Pu+F+I+G4. Node support was assessed with 10000 195

UltraFast bootstraps. Micrathena vigorsi, Cyclosa conica, Cyclosa turbinata, and 196

Gasteracantha Kuhlii were used as outgroup (Supplementary table 1). 197

We also estimated the coalescence best topology and divergence times by Bayesian 198

inference (BI) in BEAST v1.8 (Drummond, Suchard, Xie, & Rambaut, 2012) using the same 199

dataset and substitution model as in ML analysis. We applied a lognormal relaxed clock to 200

estimate divergence times using a substitution rate of 0.0112 (SD=0.001) 201

substitutions/site/million years previously used for node dating and calibration in spiders 202

(Bidegaray-Batista & Arnedo, 2011). We ran 100,000,000 generations sampling every 1,000 203

generations. We used TRACER v1.7 (Andrew Rambaut, Drummond, Xie, Baele, & Suchard, 204

2018) to confirm that the effective sample sizes (ESS) of the parameters were > 200 and to 205

confirm the convergence of the chains to a stationary distribution. The 10% of the trees were 206

discarded as burn-in in TreeAnnotator (Drummond et al., 2012) to selected the maximum 207

credibility tree that best represented the posterior distribution and was visualized and edited 208

in FigTree (A Rambaut, 2018). 209

210

We produced a lineage through time plot (LTT) to understand the accumulation of lineages 211

over time (Pybus, Rambaut, & Harvey, 2000). We used 100 trees form the posterior 212

probability of trees generated by BEAST and trimmed out the outgroups to calculate a 95% 213

confidence interval of lineage accumulation. We also compared our sample linages 214

accumulation to a set of simulated trees under a pure birth model (Yule speciation model). 215

This was all performed with functions from R packages phytools (Revell, 2012), ape 216

(Paradis, Claude, & Strimmer, 2004) and paleotree (Bapst, 2012). 217

218

Characterization of genetic variation 219

We calculated segregating sites (SS), nuclear diversity (pi), haplotype diversity (Hd) and 220

Tajima’s D for each geographical group depicted with dot colors in figure 1 using DNAsp 221

v5.0 (Librado & Rozas, 2009). Population structure (Fst) was estimated between the same 222

geographical groups its statistical significance was evaluated with the Hudson permutation 223

test (Hudson, Boos, & Kaplan, 1992). We used an Analysis of molecular variation (AMOVA) 224

with 10000 permutations in ARLEQUIN v3.5 (Excoffier & Lischer, 2010) to determine the 225

hierarchy of genetic variation using geographical regions as the higher-level grouping. 226

All the patterns observed can be obscured by isolation by distance (IBD), so we tested if this 227

pattern is present in our dataset using a Mantel test (Mantel, 1967); to do this, pairwise 228

geographic distance between localities were calculated with function distm from the package 229

geosphere (Hijmans, 2016), while genetics distances were estimated by linearizing Fst 230

values between localities. Considering the limitations of the Mantel test (Borcard, 2015; 231

Legendre & Fortin, 2010), we also calculated the linear correlations between the logarithm of 232

the geographical distances and genetic distances (Legendre & Fortin, 2010). We also 233

constructed a haplotype TCS network in POPART (Leigh, Bryant, & Nakagawa, 2015) 234

235

Species delimitation 236

Given the possibility that our phylogenetic grouping corresponds to different species, we 237

applied three species delimitation methods with different assumptions in order to select just 238

the groups congruent across methods such as recommended by Carstens, Pelletier, Reid, & 239

Satler (2013): (1) multi-rate Poisson tree processes (mPTP ; Kapli et al., 2017), (2) 240

Automatic Barcode Gap Discovery ABGD (Puillandre, Lambert, Brouillet, & Achaz, 2012) 241

and (3) Bayesian implementation of the general mixed Yule-coalescent model bGMYC (Reid 242

& Carstens, 2012). For mPTP, we first calculated the minimum branch length and used this 243

value as input together with the ML tree to ran 10 replicate Markov Chain Monte Carlo 244

MCMC) chains of 100,000,000 generations, sampling every 1000 with the burning of 10% of 245

the total chain’s length. We ran ABGD in a web interface. 246

247

(http://wwwabi.snv.jussieu.fr/public/abgd/abgdweb.html) with the JC69, K2P and simple 248

distance metrics using default parameters and a gap width parameter (X) of 4.0 in order to 249

guarantee divergence linages. bGMYC was performed sampling 100, 1000 and 10,000 trees 250

from our 100,000 trees estimated in BEAST, removing the 10% as burn-in to account for 251

error in gene tree estimation. Then we ran an MCMC chain of 50,000 steps with 40, 000 252

steps as burn-in and thinning intercept of 100 steps. We used a threshold of 0.9 above to 253

consider lineages as conspecific. Additionally, we generated morphological qualitative 254

descriptions for 10 females and 10 males genitalia from Quito, San Andrés and Lima 255

following Levi (1965). 256

257

Phenotype and genotype association 258

To test the association between the genetic variation of individuals and their coloration, we 259

performed a G-test using the function GTest from the R package DescTools, under the null 260

hypothesis of independence between coloration and genetic haplotypes. This analysis was 261

run following the color categories of Gawryszewski (2007) joint with our qualitative 262

description of new color morphs. We also ran this analysis using geographical regions 263

instead of coloration, to test the association between genetic haplotypes and geography. 264

265

NextRAD library preparation and sequencing 266

To better understand the patterns found by mtDNA we sampled thousands of single 267

nucleotide polymorphism (SNPs) through G. cancriformis genome. We selected a subset of 268

185 individuals from 32 populations across south America and used a derivative method of 269

RADseq called nextRAD (NextEra-tagmeted reductively-amplified DNA; Russello, 270

Waterhouse, Etter, & Johnson, 2015). This technique differs from the classical RADseq in 271

using a Nextera library preparation; that is based on engineered transposomes to fragment 272

and ligate PCR primers to sample thousands of loci across the genome, overcoming the 273

restriction fragment length bias of original RADseq (Davey et al., 2013). 274

275

Genomic DNA was converted into nextRAD genotyping-by-sequencing libraries (SNPsaurus, 276

LLC) as in Russello et al. (2015). The DNA was first fragmented with Nextera reagent 277

(Illumina, Inc), which also ligates short adapter sequences to the ends of the fragments. The 278

Nextera reaction was scaled for fragmenting 50 ng of genomic DNA in a 20 ul volume. 279

Fragmented DNA was then amplified for 27 cycles at 74 degrees, with one of the primers 280

matching the adapter and extending 10 nucleotides into the genomic DNA with the selective 281

sequence GTGTAGAGCC. Thus, only fragments starting with a sequence that can be 282

hybridized by the selective sequence of the primer will be efficiently amplified. The nextRAD 283

libraries were sequenced on a HiSeq 4000 with two lanes of 150 bp reads (University of 284

Oregon). 285

286

NextRAD SNPs filtering 287

The genotyping analysis used custom scripts (SNPsaurus, LLC) that trimmed the reads 288

using bbduk (BBMap tools; http://sourceforge.net/projects/bbmap/): 289

bbmap/bbduk.sh in=reads/run_2630/2630_GAACACTG-290

TAAGCCT_S567_L003_R1_001_subset.fastq.gz out=reads/run_2630/2630_GAACACTG-291

CTAAGCCT_S567_L003_R1_001_t.fastq.gz ktrim=r k=17 hdist=1 mink=8 292

ref=bbmap/resources/nextera.fa.gz minlen=100 ow=t qtrim=r trimq=10). 293

Next, a de novo reference was created by collecting 10 million reads in total, evenly from the 294

samples, and excluding reads that had counts fewer than 7 or more than 700. The remaining 295

loci were then aligned to each other to identify allelic loci and collapse allelic haplotypes to a 296

single representative. All reads were mapped to the reference with an alignment identity 297

threshold of 0.95 using bbmap (BBMap tools). Genotype calling was done using callvariants 298

(BBMap tools) (callvariants.sh list=ref_spider_rm.txt.align_samples out=spider_total.vcf 299

ref=ref_spider.fasta ploidy=2 multisample=t rarity=0.05 minallelefraction=0.05 usebias=f 300

ow=t nopassdot=f minedistmax=5 minedist=5 minavgmapq=15 minreadmapq=15 301

minstrandratio=0.0 strandedcov=t). 302

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We filtered the obtained VCF file with VCFtools (Danecek et al., 2011) to remove sites with a 304

frequency of less than 10%, indels and quality score lower than 30. We also trimmed all the 305

individuals with more than 60% of missing data. PLINK v1.90 (Purcell et al., 2007) was used 306

to prune linked sites and format the data for some of the subsequent analysis. 307

308

NextRAD Analysis 309

Population structure was explored using two approaches. The first one was implemented in 310

fastSTRUCTURE (Raj, Stephens, & Pritchard, 2014). We ran the analysis with cross 311

validation error (cv) with 20 test sets, looking for the K value with the lower CV. We also 312

drew barplots for several K (1 to 10) values using the function make.structure.plot from the R 313

package conStructv1.0.3. The second validation of the genetic clusters was done via 314

multivariate analysis. The VCF file was transformed into a genind object with the package 315

vcfR (Knaus & Grünwald, 2017) and loaded into adegenet R package (Jombart & Ahmed, 316

2011) where we retained the number of PCAs that better explained our variation (~60%). 317

Using those PCAs, we performed a discriminant analysis of principal components (DAPC). 318

A preliminary evaluation of gene flow under drifting populations was done with TREEMIX 319

(Pickrell & Pritchard, 2012). First, it constructs a Maximum Likelihood topology of the 320

relationships between populations and for those populations that are more closely related 321

than the model, the program attempts to infer admixture events between them. The groups 322

obtained in fastSTRCUTURE were used as apriori input for TREEMIX. We ran migration 323

edges from 0 to 4 and using the East cluster to root the tree since, it had lower number of 324

individuals with mixed ancestry. 325

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RESULTS 327

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Molecular phylogenetics and divergence times 329

BI and ML topologies were concordant in their phylogenetic pattern, differentiating two major 330

clades with high node support (Fig. 2). The first clade has two subclades that correspond to 331

populations from the eastern side of the Andes (EA) and dry Pacific region from Perú and 332

Ecuador (DP). The second major clade groups together two subclades consistent with 333

populations from the Western side of the Andes (WE), excluding the dry Pacific populations, 334

and the Caribbean and Galapagos Islands (CG). All these subclades have low support for 335

their internal nodes and have shared haplotypes (Fig. 3). This phylogenetic grouping 336

highlights the role of the Andes and the ocean as effective dispersal barriers, excluding other 337

geographical barriers across the distribution of G. cancriformis such as the western and 338

central cordilleras of the Colombian Andes, the Brazilian dry diagonal and the Central 339

America topographical barriers. 340

341

BEAST estimated a divergence time for the two major clades of ca. 3.88 Ma (95% HPD = 342

2.73-5.13 Ma; Fig. 2), this date is around the Miocene/Pliocene boundary which is 343

concordant with the uplift of the northern Andes. The divergence time between EA and DP is 344

ca. 2.57 Ma (95% HPD = 1.66-3.56 Ma; Fig. 2) and between WE and GP is ca. 2.21 Ma 345

(95% HPD = 1.46-3.01 Ma; Fig. 2). Both dates are close to the Pliocene/Pleistocene 346

boundary. LTT plots demonstrated that our data did not fit to a Yule speciation model, but it 347

is more likely to follow a coalescence model were the lineage diversification increased in 348

recent times (Fig. 4). 349

350

351

Characterization of genetic variation 352

TCS networks supported the groups and relationships found in the phylogenetic analysis 353

and revealed shared haplotypes between geographical regions. Clusters are separated by at 354

least 13 mutational steps, being the separation of EA and DP the highest with 24 mutations 355

(Fig. 3). We found significant genetic differentiation between clusters (p < 0.05 in the Hudson 356

permutation test), Fst values ranging from 0.16 to 0.68 (Table 1) where the lower genetic 357

differentiation occurs between WE and CG. This pattern was also observed in other 358

divergence statistics (Table 1). 359

360

Genetic diversity summary statistics were not different between geographical clusters (Table 361

2). However, the WE populations have the lowest genetic diversity, which could be 362

associated with the presence of a unique haplotype in high frequency (Figure 4). None of the 363

geographical groups presents a significative Tajimas’ D, suggesting neutral evolution in the 364

mitochondrial locus. Our AMOVA analysis reflected that the variation observed in this locus 365

was better explained by differences among regions than between or within populations 366

(Supplementary table 2). Nevertheless, we detected genetic structure between populations 367

and within groups (Supplementary table 1). Isolation by distance did not contribute to the 368

structure pattern described above, thus divergence is mainly caused by geographical 369

barriers (mantel-r=0.071, p-value=0.148; Figure S1). 370

371

Species delimitation 372

The bGMYC, ABGD and mPTP and analysis gave different results, identifying two, five and 373

seven species respectively. Qualitative examination of the genitalia morphology did not 374

reveal any apparent differences between females epigynum from distinct regions. In 375

contrast, male palpus shows slight differences in the palpus base, embolus (E), median (M) 376

and paramedian (PM) apophysis (Figure 5). Males from San Andrés/Quito have a thick M 377

with a marked and sharp distal upper and lower knob, they also have ellipsoidal PM 378

compared with Lima and Puerto Rican males. Lima males have thin M with a wide distal 379

knob, their PM has triangular shape compare with the other males. Puerto Rican male has a 380

slightly M knob, the shortest E and distinctively palpus base with lack of sharp left site. 381

These morphological variations are concordant with the ABGD and mPTP delimitated 382

lineages (Supplementary figure 2), which discriminates individuals from Lima, San 383

Andrés/Quito and Puerto Rico as genetically differentiated species. 384

385

Phenotype and genotype association 386

Even though we found that some coloration morphs (First described here), such as orange-387

black, white-with-red-spines, black-with-red-spines, and Galapagos’ morph are unique for 388

some locations, (Fig. 6, Supplementary figure 3) there is no phenotype by mtDNA haplotype 389

association (G =425.8, df= 400, p-values=0.18). The lack of this signal could be due to the 390

fact that the rest of the morphs are widely distributed and/or nuclear gene(s) are involved in 391

generating these color phenotypes. On the other hand, we did find relations between 392

geography and genetic variation as expected based on our previous analyses (G =374.1, 393

df= 150, p-values=2.2e-16). 394

395

NextRAD analysis 396

We obtained a total of 3262 SNPs after applying the described data quality filters. The 397

fastSTRUCTURE analysis revealed an optimal range of K values that goes from K=3 to K=5. 398

The K=3 categorization reconstructed the South American geographical grouping identical to 399

mitochondrial data (Fig. 6; EA, WE and DP clades), with shared ancestry between them. 400

K=4 recovered the EA and DP groups but splits the WE group in Ecuadorian Andes-Wet 401

pacific and northern Andes (Supplementary figure 4). K=5 reconstructed the same four 402

categories of the later K but showed the individuals from Baños-Ecuador as a new block 403

(Supplementary figure 4). In all K values several individuals had mixed ancestry between 404

well resolved groups (Fig 6, Supplementary figure 4). In agreement with fastSTRCTURE, the 405

DAPC analysis clearly separates the three South American clades (Fig. 7, supplementary 406

figure 5). Both clustering methods were consistent with the pattern observed in the 407

mitochondrial locus (Fig. 2 and 3). However, discriminant analysis of principal components 408

suggests that the WE and EA are more closely related (Fig. 7), which differs from the mtDNA 409

topology (Fig. 2). We got preliminary insights that admixture in both kinds of molecular data 410

(mtDNA and nextRAD) could be due to genetic interchange. The genetic drift model 411

(Treemix) showed non-zero migration weights (arrows) across Andean regions 412

(Supplementary figure 6). Only one of the possible migration events differs between both 413

kind of molecular data. Individuals from Villavicencio had shared ancestry with the WE clade 414

in mtDNA but, nuclear SNPs showed the opposite signal (i.e. WE populations shared 415

ancestry with EA). Anyway, both datasets showed genetic interchange between WE and EA. 416

417

DISCUSSION 418

Phylogeography and admixture patterns 419

Our mtDNA data showed four differentiated and supported groups that are concordant with 420

geographical patterns, where the Andes and the ocean are the main factors driving G. 421

cancriformis diversification. The Andes separates populations at the west (WE and DP) from 422

populations at the east (EA), supporting that this geological formation is a barrier to 423

dispersion and promotes allopatry as documented in several lineages (Hoorn et al., 2010; 424

Miller et al., 2008; Smith et al., 2014). However, both clades from the west of the Andes are 425

not closely related to each other, even though there is no topographical barrier separating 426

them. On the other hand, DP is a sister clade of EA, a pattern that has been poorly 427

documented in other studies (Musher & Cracraft, 2018; Oswald, Overcast, Mauck, 428

Andersen, & Smith, 2017), where cross Andean dispersion gave origin to this phylogenetic 429

relationship. Our result of the divergence between both clades (mean=2.57, 95% HPD = 430

1.66-3.56 Ma) occurs after the middle Andes uplift (Mora et al., 2009), supporting a 431

dispersion model instead of a vicariance origin that would be expected if DP and WE 432

diverged around the middle Miocene. 433

434

The individuals from the Caribbean and Galapagos islands are grouped within a single clade 435

closely related to continental populations from WE, except for the San Andrés island 436

population, that although it belongs to the Caribbean plate (Vargas-Cuervo, 2004), falls 437

within the WE clade. The absence of shared genetic composition of San Andrés’ population 438

with those from the Caribbean islands may indicate that this population was recently 439

colonized by a WE lineage. WE and CG phylogenetic closeness supports the hypothesis 440

that arachnids from the Caribbean and Galapagos originated from the America mainland 441

(Agnarsson et al., 2016a; Čandek et al., 2018; McHugh, Yablonsky, Binford, & Agnarsson, 442

2014), Our divergence time estimation between these clades is around the early Pleistocene 443

(mean=2.21 Ma, 95% HPD = 1.46-3.01 Ma; supplementary figure 2) meaning that Caribbean 444

G. cancriformis most likely originated by an overwater dispersal event rather than by the 445

GAArlandia hypothesis that would be supported by a divergence time close to 35-33 Ma 446

(Iturralde-Vinent, 2006). 447

448

The phylogenetic relationship between Galapagos and Caribbean islands seems 449

improbable, due to the huge geographical distance and the presence of landmasses 450

between them. Nonetheless, this pattern has been reported for birds (Funk & Burns, 2018), 451

snakes, iguanas, moths, isopods, sponges (Grehan, 2001) and plants (Andrus et al., 2009). 452

A biogeographical hypothesis to explain this pattern is that the circumtropical current 453

connected the Cocos-Carnegie Ridge with the Caribbean islands before the closure of the 454

Panamanian Isthmus, promoting dispersion (Grehan, 2001). However, our divergence time 455

(mean= 1.62 Ma, 95% HPD = 0.99-2.31 Ma), is posterior to the closure of the Panamanian 456

Isthmus (15 to 3 Ma; Montes et al., 2015; ODea et al., 2016), which agrees with a scenario 457

of recent dispersion, a case that has been also hypothesized for Darwin finches (Funk & 458

Burns, 2018). A bigger sample from Galapagos and Caribbean islands is needed to 459

construct a more accurate hypothesis. 460

461

The Andes and the ocean have been reported as barriers that limit dispersion, which 462

promotes diversification in several terrestrial lineages. However, dispersion and posterior 463

gene flow across these barriers is still enigmatic, especially for arthropods (Turchetto-Zolet 464

et al., 2013). Here, we found that despite the clear topological clustering by geography, 465

some mtDNA haplotypes are shared between groups what could be related to gene flow 466

between distant populations (Fig. 2, supplementary figure 7). North American populations 467

(i.e. Florida and Texas) share ancestry with the Caribbean islands, this pattern was already 468

reported for spiders with long dispersal abilities (Agnarsson et al., 2016b; Čandek et al., 469

2018), contrasting with the well resolved and not mixed pattern observed in poor dispersal 470

lineages with higher diversification rates (Čandek, Agnarsson, Binford, & Kuntner, 2019; 471

McHugh et al., 2014). The South American clades (EA, WE, and DP) also share mtDNA 472

haplotypes between them (supplementary figure 7). This could be promoted by the altitudinal 473

depressions of the middle and northern Andes, where lowland individuals could easily move 474

from one flank to another The EA clade includes individuals that geographically correspond 475

to DP, which suggest dispersion and gene flow from EA to DP through the north Peruvian 476

low (Porculla pass, Huancabamba depression) or the Loja valley (Chapman, 1926). This 477

corridor was hypothesized by Chapman (1926) and Haffer (1967) based on distribution 478

data, and recently supported by Cadena et al. (2016) and Oswald et al. (2017) with climatic 479

and molecular data. 480

481

We also found that some individuals from Villavicencio and Buenavista (EA clade) had 482

haplotypes that corresponded to WE group. A pattern that could be promoted by dispersion 483

across the northern Andes with subsequent gene flow. This hypothesis was formulated by 484

Chapman (1917), who said that altitudinal depressions such as the Andalucía pass could 485

promote the movement of individuals across this mountain range, latter Haffer (1967) 486

proposed a more plausible scenario of dispersion through the northern tip of the Colombian 487

Andes (Táchira depression), nevertheless our sampling could not discriminate between 488

Chapman and Haffer scenarios. 489

490

We reported that despite DP and WE are distantly related, pacific populations from Peru and 491

Ecuador (DP clade) had genetic interchange with WE populations (Fig. 2, supplementary 492

figure 7). The presence of northern and southern clades through the Pacific has been 493

previously documented (Haffer, 1967), even though there is no topographical barrier that 494

could promote the divergence between these two clades. A possible explanation is that a 495

climatic gradient from a humid north to a dry south had an effect on individual’s dispersion. 496

In the same way forest density (high in the north) would be also affecting the distribution of 497

the mtDNA haplotypes. Thus, an environmental cline could promote isolation by environment 498

(Wang & Bradburd, 2014) which leads to divergence and gene flow in the climatic transition 499

zone (Haffer, 1967). 500

501

Consistently with our mtDNA signal, the 3200 nuclear SNPs revealed that G. cancriformis 502

populations are clustered in the same three genetic groups (EA, WE and DP). However, 503

based in the multivariate analysis only, seems that EA and WE are more closely related to 504

each other than each of them with DP (Fig. 7, supplementary figure 5) contrasting with the 505

mtDNA topology. Further phylogenetic analyses with the SNPs dataset are required to 506

validate this apparent incongruence. Our assignment test also suggests that four and five 507

clusters were probable (Supplementary figure 4). The two extra groups correspond to 508

Ecuadorian Andes-Wet pacific populations and Baños. The common signature of both was 509

their complete mixed ancestry. This is not unexpected, since high hybridization between 510

subspecies has been documented in these two regions in butterflies (Jiggins & Davies, 511

2008). 512

513

We also observed admixture in the nuclear DNA between members of the three clusters 514

similar to the mtDNA pattern (Fig. 6 and 7). As expected, the populations closer to the 515

Andean altitudinal depressions presented higher mixed ancestry than those that are more 516

distant (Fig. 6). As a first aim to characterize if the shared ancestry in these depressions 517

was due to gene flow, we ran a drift model with different migration edges. We found at least 518

four migration events occurred between the recovered clusters. However, we rule out 519

incomplete lineage sorting as an explanation to this pattern, because individuals with 520

admixture were located near the mountain altitudinal depressions, and those that were away 521

from these Andean passes, did not share genetic variation with other geographical regions. 522

This result needs to be confirmed with other model-based (PHRAPL; Jackson, Morales, 523

Carstens, & O’Meara, 2017) and frequentist analysis (i.e D stadistics family and recent 524

derivations; Hahn & Hibbins, 2019). 525

526

Even though we observed discrete polymorphic color morphs in some geographical regions, 527

we did not find an association between color and mtDNA haplotypes. Although, here we did 528

not explore the SNPs dataset to look for phenotype/genotype statistical association, our 529

clustering analyses showed a main geographic signal rather than color clustering. However, 530

exploration of genetic and color segregation inside each cluster deserves future evaluation. 531

It’s possible that genetic connectivity among genetic clusters facilitates color morph 532

interchange (Fig. 6) but, this hypothesis remains to be tested. 533

534

One geographical structured species vs. multiple species 535

Despite the species delimitations methods used here showed the existence of multiple 536

differentiated lineages, there is no consistency between them (Supplementary figure 2). This 537

incongruence across analysis may be an artifact of the low sample size of some distinctive 538

lineages and the statistical power of each method, which could limit the performance of the 539

analysis that depends on where the lineages are in the speciation continuum (Carstens et 540

al., 2013). Along with this, some species delimitation methods are biased to delimit 541

population structure instead of species (Sukumaran & Knowles, 2017), which could be the 542

case in our analysis, because as shown by our LTT plots (Fig. 4), the G. cancriformis 543

diversification is recent and fits better with a unique species coalescent model instead of a 544

Yule-type speciation model. Regardless of the recent diversification of G. cancriformis, we 545

found some morphological differentiation in the male genitalia that could lead to reproductive 546

Isolation. 547

548

Although, speciation could be occurring in this arachnid lineage, we decided to let the 549

question open due to lack of enough evidence from different sources. We call to avoid the 550

recent praxis in arachnids of describing species with not enough evidence (Agnarsson et al., 551

2016b; Čandek et al., 2019, 2018), such as over splitting based only in “divergent” mtDNA 552

haplotypes. This praxis is dangerous for some practical issues such as the conservation of 553

biodiversity. 554

555

Conclusion 556

This work constitutes one of the few phylogeographical studies of a widespread arthropod, 557

demonstrating that the ocean, the Andes and climatic boundaries are permeable barriers. 558

However, further sampling of the Islands lineages is essential to clarify the evolutionary 559

history of G. cancriformis in this geographical area. Also, a more focal experimental design 560

must be made to answer the questions related to the color polymorphism of this species. 561

562

Acknowledgment 563

We want thank Diana Silva for all the help in obtaining the permits of specimen collection in 564

Peru. We are very grateful to Juan Pablo Jordan, Francisco Velázquez, Meiss Lozano and 565

other field work volunteers for their logistic support. We also thank Valentina Muñoz for 566

checking the Gasteracantha genitalia and illustrating the images. Thanks to Mateo Davila 567

and Daniela Garcia for correcting the grammar of this manuscript. Finally, we want to thank 568

all the friends that in some way made enjoyable these two years at Universidad de los 569

Andes and Universidad del Rosario. 570

571

LITERATURE 572

Agnarsson, I., LeQuier, S. M., Kuntner, M., Cheng, R. C., Coddington, J. A., & Binford, G. 573

(2016a). Phylogeography of a good Caribbean disperser: Argiope argentata (Araneae, 574

Araneidae) and a new ‘cryptic’ species from Cuba. ZooKeys, 2016(625), 25–44. 575

https://doi.org/10.3897/zookeys.625.8729 576

Agnarsson, I., LeQuier, S. M., Kuntner, M., Cheng, R. C., Coddington, J. A., & Binford, G. 577

(2016b). Phylogeography of a good Caribbean disperser: Argiope argentata (Araneae, 578

Araneidae) and a new ‘cryptic’ species from Cuba. ZooKeys, 2016(625), 25–44. 579

https://doi.org/10.3897/zookeys.625.8729 580

Andrus, N., Tye, A., Nesom, G., Bogler, D., Lewis, C., Noyes, R., … Francisco-Ortega, J. 581

(2009). Phylogenetics of Darwiniothamnus (Asteraceae: Astereae) - Molecular evidence 582

for multiple origins in the endemic flora of the Galápagos Islands. Journal of 583

Biogeography, 36(6), 1055–1069. https://doi.org/10.1111/j.1365-2699.2008.02064.x 584

Arias, C. F., Salazar, C., Rosales, C., Kronforst, M. R., Linares, M., Bermingham, E., & 585

McMillan, W. O. (2014). Phylogeography of Heliconius cydno and its closest relatives: 586

Disentangling their origin and diversification. Molecular Ecology, 23(16), 4137–4152. 587

https://doi.org/10.1111/mec.12844 588

Bapst, D. W. (2012). paleotree : an R package for paleontological and phylogenetic analyses 589

of evolution. Methods in Ecology and Evolution, 3(5), 803–807. 590

https://doi.org/10.1111/j.2041-210X.2012.00223.x 591

Bartoleti, L. F. de M., Peres, E. A., Fontes, F. von H. M., da Silva, M. J., & Solferini, V. N. 592

(2018). Phylogeography of the widespread spider Nephila clavipes (Araneae: 593

Araneidae) in South America indicates geologically and climatically driven lineage 594

diversification. Journal of Biogeography, 45(6), 1246–1260. 595

https://doi.org/10.1111/jbi.13217 596

Bell, J. R., Bohan, D. A., Shaw, E. M., & Weyman, G. S. (2005). Ballooning dispersal using 597

silk: world fauna, phylogenies, genetics and models. Bulletin of Entomological 598

Research, 95(02), 69–114. https://doi.org/10.1079/BER2004350 599

Bidegaray-Batista, L., & Arnedo, M. A. (2011). Gone with the plate: the opening of the 600

Western Mediterranean basin drove the diversification of ground-dweller spiders. BMC 601

Evolutionary Biology, 11(1), 317. https://doi.org/10.1186/1471-2148-11-317 602

Borcard, D. (2015). Should the Mantel test be used in spatial analysis ?, 1239–1247. 603

https://doi.org/10.1111/2041-210X.12425 604

Cadena, C. D., Pedraza, C. A., & Brumfield, R. T. (2016). Climate, habitat associations and 605

the potential distributions of Neotropical birds: Implications for diversification across the 606

Andes. Revista de La Academia Colombiana de Ciencias Exactas, Físicas y Naturales, 607

40(155), 275–287. https://doi.org/10.18257/RACCEFYN.280 608

Čandek, K., Agnarsson, I., Binford, G. J., & Kuntner, M. (2019). Biogeography of the 609

Caribbean Cyrtognatha spiders. Scientific Reports, 9(1), 1–14. 610

https://doi.org/10.1038/s41598-018-36590-y 611

Čandek, K., Binford, G. J., Agnarsson, I., & Kuntner, M. (2018). Caribbean golden 612

orbweaving spiders maintain gene flow with North America. BioRxiv. 613

Carstens, B. C., Pelletier, T. A., Reid, N. M., & Satler, J. D. (2013). How to fail at species 614

delimitation. Molecular Ecology, 22(17), 4369–4383. https://doi.org/10.1111/mec.12413 615

Chapman, F. M. (1917). The Distribution of Bird-Life in Colombia: A Contribution to a 616

Biological Survey of South America. 617

Chapman, F. M. (1926). The Distribution of Bird-Life in Ecuador: A Contribution to a Study of 618

the Origin of Andean Bird-Life. Bulletin of the American Museum of Natural History, 55, 619

784. 620

Chazot, N., Willmott, K. R., Lamas, G., Freitas, A. V. L., Piron-prunier, F., Arias, C. F., … 621

Elias, M. (2017). Renewed diversification following Miocene landscape turnover in a 622

Neotropical butterfly radiation. BioRxiv. 623

Claramunt, S., Derryberry, E. P., Remsen, J. V., & Brumfield, R. T. (2012). High dispersal 624

ability inhibits speciation in a continental radiation of passerine birds. Proceedings of the 625

Royal Society B: Biological Sciences, 279(1733), 1567–1574. 626

https://doi.org/10.1098/rspb.2011.1922 627

Danecek, P., Auton, A., Abecasis, G., Albers, C. A., Banks, E., Depristo, M. A., … Vcf, T. 628

(2011). The variant call format and VCFtools, 27(15), 2156–2158. 629

https://doi.org/10.1093/bioinformatics/btr330 630

Davey, J. W., Cezard, T., Fuentes-Utrilla, P., Eland, C., Gharbi, K., & Blaxter, M. L. (2013). 631

Special features of RAD Sequencing data: implications for genotyping. Molecular 632

Ecology, 22(11), 3151–3164. https://doi.org/10.1111/mec.12084 633

De-silva, D. L., Mota, L. L., Chazot, N., Ricardo, M., Silva-, K. L., Miryam, L., … Elias, M. 634

(2017). North Andean origin and diversification of the largest ithomiine butterfly genus, 635

1–17. https://doi.org/10.1038/srep45966 636

Dick, C. W., Bermingham, E., Lemes, M. R., & Gribel, R. (2007). Extreme long-distance 637

dispersal of the lowland tropical rainforest tree Ceiba pentandra L. (Malvaceae) in Africa 638

and the Neotropics. Molecular Ecology, 16(14), 3039–3049. 639

https://doi.org/10.1111/j.1365-294X.2007.03341.x 640

Drummond, A. J., Suchard, M. A., Xie, D., & Rambaut, A. (2012). Bayesian Phylogenetics 641

with BEAUti and the BEAST 1.7. Molecular Biology and Evolution , 29(8), 1969–1973. 642

https://doi.org/10.1093/molbev/mss075 643

Excoffier, L., & Lischer, H. E. L. (2010). Arlequin suite ver 3.5: a new series of programs to 644

perform population genetics analyses under Linux and Windows. Molecular Ecology 645

Resources, 10(3), 564–567. https://doi.org/10.1111/j.1755-0998.2010.02847.x 646

Folmer, O., Black, M., Hoeh, W., Lutz, R., & Vrijenhoek, R. (1994). DNA primers for 647

amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan 648

invertebrates. Molecular Marine Biology and Biotechnology, 3(5), 294–299. 649

Funk, E. R., & Burns, K. J. (2018). Biogeographic origins of Darwin’s finches (Thraupidae: 650

Coerebinae). The Auk, 135(3), 561–571. https://doi.org/10.1642/AUK-17-215.1 651

Gawryszewski, F. M. (2007). Policromatismo e stabilimentum em Gasteracantha 652

cancriformis ( Araneae , Araneidae ): caracterização e as hipóteses da atração de 653

presas e da proteção da teia. 654

Grehan, J. (2001). Biogeography and evolution of the Galapagos: Integration of the 655

biological and geological evidence. Biological Journal of the Linnean Society, 74(3), 656

267–287. https://doi.org/10.1006/bijl.2001.0576 657

Haffer, J. (1967). Speciation in Colombian forest birds west of Andes. Novitates Zoologicae, 658

2294(2294), 1–57. 659

Hahn, M. W., & Hibbins, M. S. (2019). A three-sample test for introgression. BioRxiv, 660

594333. https://doi.org/10.1101/594333 661

Hickman, C. S., & Lipps, J. H. (1985). Geologic Youth of Galápagos Islands Confirmed by 662

Marine Stratigraphy and Paleontology. Science, 227(4694), 1578–1580. 663

https://doi.org/10.1126/science.227.4694.1578 664

Hijmans, R. J. (2016). geosphere: Spherical Trigonometry. 665

Hoorn, C., Wesselingh, F. P., ter Steege, H., Bermudez, M. a, Mora, a, Sevink, J., … 666

Antonelli, a. (2010). Amazonia through time: Andean uplift, climate change, landscape 667

evolution, and biodiversity. Science (New York, N.Y.), 330(6006), 927–931. 668

https://doi.org/10.1126/science.1194585 669

Hudson, R. R., Boos, D. D., & Kaplan, N. L. (1992). A statistical test for detecting geographic 670

subdivision. Molecular Biology and Evolution, 9(1), 138–151. 671

Iturralde-Vinent, M. A. (2006). Meso-Cenozoic Caribbean Paleogeography: Implications for 672

the Historical Biogeography of the Region. International Geology Review, 48(9), 791–673

827. https://doi.org/10.2747/0020-6814.48.9.791 674

Jackson, N. D., Morales, A. E., Carstens, B. C., & O’Meara, B. C. (2017). PHRAPL: 675

Phylogeographic Inference Using Approximate Likelihoods. Systematic Biology, 19, 676

431–435. https://doi.org/10.1093/sysbio/syx001 677

Jiggins, C. D., & Davies, N. (2008). Genetic evidence for a sibling species of Heliconius 678

charithonia (Lepidoptera; Nymphalidae). Biological Journal of the Linnean Society, 679

64(1), 57–67. https://doi.org/10.1111/j.1095-8312.1998.tb01533.x 680

Jombart, T., & Ahmed, I. (2011). adegenet 1 . 3-1 : new tools for the analysis of genome-681

wide SNP data, 27(21), 3070–3071. https://doi.org/10.1093/bioinformatics/btr521 682

Kapli, P., Lutteropp, S., Zhang, J., Kobert, K., Pavlidis, P., Stamatakis, A., & Flouri, T. 683

(2017). Multi-rate Poisson Tree Processes for single-locus species delimitation under 684

Maximum Likelihood and Markov Chain Monte Carlo. Bioinformatics, 33(11), btx025. 685

https://doi.org/10.1093/bioinformatics/btx025 686

Knaus, B. J., & Grünwald, N. J. (2017). vcfr : a package to manipulate and visualize variant 687

call format data in R. Molecular Ecology Resources, 17(1), 44–53. 688

https://doi.org/10.1111/1755-0998.12549 689

Kumar, S., Stecher, G., Li, M., Knyaz, C., Tamura, K., & Battistuzzi, F. U. (2018). MEGA X: 690

Molecular Evolutionary Genetics Analysis across Computing Platforms. Molecular 691

Biology and Evolution, 35(6), 1547–1549. https://doi.org/10.1093/molbev/msy096 692

Legendre, P., & Fortin, M.-J. (2010). Comparison of the Mantel test and alternative 693

approaches for detecting complex multivariate relationships in the spatial analysis of 694

genetic data. Molecular Ecology Resources, 10(5), 831–844. 695

https://doi.org/10.1111/j.1755-0998.2010.02866.x 696

Leigh, J. W., Bryant, D., & Nakagawa, S. (2015). popart: full-feature software for haplotype 697

network construction. Methods in Ecology and Evolution, 6(9), 1110–1116. 698

https://doi.org/10.1111/2041-210X.12410 699

Levi, H. W. (1965). Techniques for the Study of Spider Genitalia. Psyche (New York), 72(2), 700

152–158. https://doi.org/10.1155/1965/94978 701

Levi, H. W. (1978). The American Orb-weaver Genera Colphepeira, Micrathena and 702

Gasteracantha North of Mexico (Araneae, Araneidae). Bulletin of the Museum of 703

Comparative Zoology (Vol. 148). 704

Levi, H. W. (1996). The American Orb Weavers Hypognatha, Encyosaccus, Xylethrus, 705

Gasteracantha, and Enacrosoma (Araneae, Araneidae). Bulletin of the Museum of 706

Comparative Zoology, 155(November), 89–157. 707

Librado, P., & Rozas, J. (2009). DnaSP v5 : a software for comprehensive analysis of DNA 708

polymorphism data, 25(11), 1451–1452. https://doi.org/10.1093/bioinformatics/btp187 709

Lo, S., Gehara, M., Crawford, A. J., Orrico, V. G. D., Rodrı, A., Fouquet, A., … Vences, M. 710

(2014). High Levels of Diversity Uncovered in a Widespread Nominal Taxon : 711

Continental Phylogeography of the Neotropical Tree Frog Dendropsophus minutus, 712

9(9). https://doi.org/10.1371/journal.pone.0103958 713

Maddison, W., & Maddison, D. (2015). Mesquite: A modular system for evolutionary 714

analysis, version 3.04. 2015 Available: http://mesquiteproject. 715

org/mesquite/download/download. html. 716

Mantel, N. (1967). The Detection of Disease Clustering and a Generalized Regression 717

Approach. Cancer Research, 27(2 Part 1), 209 LP-220. 718

McHugh, A., Yablonsky, C., Binford, G. J., & Agnarsson, I. (2014). Molecular phylogenetics 719

of Caribbean Micrathena (Araneae: Araneidae) suggests multiple colonization events 720

and single island endemism. Invertebrate Systematics, Accepted F(2012), 337–349. 721

https://doi.org/10.1071/IS13051 722

Miller, M. J., Bermingham, E., Klicka, J., Escalante, P., do Amaral, F. S. R., Weir, J. T., & 723

Winker, K. (2008). Out of Amazonia again and again: episodic crossing of the Andes 724

promotes diversification in a lowland forest flycatcher. Proceedings. Biological Sciences, 725

275(1639), 1133–1142. https://doi.org/10.1098/rspb.2008.0015 726

Montes, C., Cardona, A., Jaramillo, C., Pardo, A., Silva, J. C., Valencia, V., … Niño, H. 727

(2015). Middle Miocene closure of the Central American Seaway. Science, 348(6231), 728

226–229. https://doi.org/10.1126/science.aaa2815 729

Mora, A., Baby, P., Roddaz, M., Parra, M., Brusset, S., Hermoza, W., & Espurt, N. (2009, 730

January 8). Tectonic History of the Andes and Sub-Andean Zones: Implications for the 731

Development of the Amazon Drainage Basin. Amazonia: Landscape and Species 732

Evolution. https://doi.org/doi:10.1002/9781444306408.ch4 733

Musher, L. J., & Cracraft, J. (2018). Phylogenomics and species delimitation of a complex 734

radiation of Neotropical suboscine birds (Pachyramphus). Molecular Phylogenetics and 735

Evolution, 118(September 2017), 204–221. https://doi.org/10.1016/j.ympev.2017.09.013 736

Nguyen, L.-T., Schmidt, H. A., von Haeseler, A., & Minh, B. Q. (2015). IQ-TREE: A Fast and 737

Effective Stochastic Algorithm for Estimating Maximum-Likelihood Phylogenies. 738

Molecular Biology and Evolution, 32(1), 268–274. Retrieved from 739

http://dx.doi.org/10.1093/molbev/msu300 740

ODea, A., Lessios, H. A., Coates, A. G., Eytan, R. I., Restrepo-Moreno, S. A., Cione, A. L., 741

… Jackson, J. B. C. (2016). Formation of the Isthmus of Panama. Science Advances, 742

2(8), e1600883–e1600883. https://doi.org/10.1126/sciadv.1600883 743

Oswald, J. A., Overcast, I., Mauck, W. M., Andersen, M. J., & Smith, B. T. (2017). Isolation 744

with asymmetric gene flow during the nonsynchronous divergence of dry forest birds. 745

Molecular Ecology, 26(5), 1386–1400. https://doi.org/10.1111/mec.14013 746

Paradis, E., Claude, J., & Strimmer, K. (2004). APE: Analyses of Phylogenetics and 747

Evolution in R language. Bioinformatics, 20(2), 289–290. 748

https://doi.org/10.1093/bioinformatics/btg412 749

Pickrell, J. K., & Pritchard, J. K. (2012). Inference of Population Splits and Mixtures from 750

Genome-Wide Allele Frequency Data. PLoS Genetics, 8(11), e1002967. 751

https://doi.org/10.1371/journal.pgen.1002967 752

Pindell, J. L., & Barrett, S. F. (1991, January 1). Geological evolution of the Caribbean 753

region; A plate-tectonic perspective. (G. Dengo & J. E. Case, Eds.), The Caribbean 754

Region. Geological Society of America. https://doi.org/10.1130/DNAG-GNA-H.405 755

Puillandre, N., Lambert, A., Brouillet, S., & Achaz, G. (2012). ABGD, Automatic Barcode Gap 756

Discovery for primary species delimitation. Molecular Ecology, 21(8), 1864–1877. 757

https://doi.org/10.1111/j.1365-294X.2011.05239.x 758

Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M. A. R., Bender, D., … Sham, 759

P. C. (2007). PLINK: A Tool Set for Whole-Genome Association and Population-Based 760

Linkage Analyses. The American Journal of Human Genetics, 81(3), 559–575. 761

https://doi.org/10.1086/519795 762

Pybus, O. G., Rambaut, A., & Harvey, P. H. (2000). An Integrated Framework for the 763

Inference of Viral Population History From Reconstructed Genealogies. Genetics, 764

155(3), 1429–1437. Retrieved from http://www.genetics.org/content/155/3/1429 765

Raj, A., Stephens, M., & Pritchard, J. K. (2014). fastSTRUCTURE: variational inference of 766

population structure in large SNP data sets. Genetics, 197(2), 573–589. 767

https://doi.org/10.1534/genetics.114.164350 768

Rambaut, A. (2018). FigTree. Retrieved from https://github.com/rambaut/figtree/releases 769

Rambaut, A., Drummond, A. J., Xie, D., Baele, G., & Suchard, M. A. (2018). Posterior 770

Summarization in Bayesian Phylogenetics Using Tracer 1.7. Systematic Biology, 67(5), 771

901–904. https://doi.org/10.1093/sysbio/syy032 772

Reid, N. M., & Carstens, B. C. (2012). Phylogenetic estimation error can decrease the 773

accuracy of species delimitation: a Bayesian implementation of the general mixed Yule-774

coalescent model. BMC Evolutionary Biology, 12(1), 196. https://doi.org/10.1186/1471-775

2148-12-196 776

Revell, L. J. (2012). phytools: an R package for phylogenetic comparative biology (and other 777

things). Methods in Ecology and Evolution, 3(2), 217–223. 778

https://doi.org/10.1111/j.2041-210X.2011.00169.x 779

Rull, V. (2011). Neotropical biodiversity: Timing and potential drivers. Trends in Ecology and 780

Evolution, 26(10), 508–513. https://doi.org/10.1016/j.tree.2011.05.011 781

Russello, M. A., Waterhouse, M. D., Etter, P. D., & Johnson, E. A. (2015). From promise to 782

practice: pairing non-invasive sampling with genomics in conservation. PeerJ, 3, e1106. 783

https://doi.org/10.7717/peerj.1106 784

Salgado-Roa, F. C., Pardo-Diaz, C., Lasso, E., Arias, C. F., Solferini, V. N., & Salazar, C. 785

(2018). Gene flow and Andean uplift shape the diversification of Gasteracantha 786

cancriformis (Araneae: Araneidae) in Northern South America. Ecology and Evolution, 787

8(14), 7131–7142. https://doi.org/10.1002/ece3.4237 788

Salisbury, C. L., Seddon, N., Cooney, C. R., & Tobias, J. A. (2012). The latitudinal gradient 789

in dispersal constraints: ecological specialisation drives diversification in tropical birds. 790

Ecology Letters, 15(8), 847–855. https://doi.org/10.1111/j.1461-0248.2012.01806.x 791

Sanmartín, I., Van Der Mark, P., & Ronquist, F. (2008). Inferring dispersal: A Bayesian 792

approach to phylogeny-based island biogeography, with special reference to the Canary 793

Islands. Journal of Biogeography, 35(3), 428–449. https://doi.org/10.1111/j.1365-794

2699.2008.01885.x 795

Smith, B. T., McCormack, J. E., Cuervo, A. M., Hickerson, M. J., Aleixo, A., Cadena, C. D., 796

… Brumfield, R. T. (2014). The drivers of tropical speciation. Nature, 515(7527), 406–797

409. https://doi.org/10.1038/nature13687 798

Sukumaran, J., & Knowles, L. L. (2017). Multispecies coalescent delimits structure, not 799

species. Proceedings of the National Academy of Sciences, 114(7), 1607–1612. 800

https://doi.org/10.1073/pnas.1607921114 801

Turchetto-Zolet, A. C., Pinheiro, F., Salgueiro, F., & Palma-Silva, C. (2013). 802

Phylogeographical patterns shed light on evolutionary process in South America. 803

Molecular Ecology, 22(5), 1193–1213. https://doi.org/10.1111/mec.12164 804

Vargas-Cuervo, G. (2004). Geología y Aspectos Geográficos de la Isla de San Andrés, 805

Colombia. Geología Colombiana, 29(8), 71–87. Retrieved from 806

http://www.bdigital.unal.edu.co/32406/1/31915-116650-1-PB.pdf 807

Wang, I. J., & Bradburd, G. S. (2014). Isolation by environment. Molecular Ecology, 23(23), 808

5649–5662. https://doi.org/10.1111/mec.12938 809

810

811

Table 1. Population divergence metrics between clades. Eastern of the Andes clade (EA), 812 Western of the Andes clade, Dry pacific clade (DP) and Caribbean and Galapagos islands 813 (CG) 814 815

POP 1 POP 2 FST DXY DA EA WE 0.641 0.051 0.033 EA DP 0.506 0.048 0.024 EA CG 0.562 0.05 0.032 WE DP 0.686 0.064 0.044 WE CG 0.156 0.026 0.004 DP CG 0.598 0.067 0.04

816 817 Table 2. mtDNA Population genetic summary statistics for the Eastern of the Andes clade 818 (EA), Western of the Andes clade, Dry pacific clade (DP) and Caribbean and Galapagos 819 islands (CG) 820 821

POP N S ϴ Π HD D EA 87 62 0.025 0.021 0.889 -0.4426 WE 92 67 0.029 0.015 0.881 -1.489 DP 36 52 0.025 0.028 0.824 0.3456 CG 11 38 0.027 0.03 0.945 0.268

Notes. POP: population, N: number of individuals, S: segregating sites, ϴ: Watterson 822 estimator of genetic diversity, π: Nucleotide diversity, Hd: Haplotype diversity, D: Tajima’s D 823 824 825 826 827 828 829

830

831 Figure 1. Sampling locations. Color points represent the geographical regions as follows. 832 Green: East of the Andes (EA). Red: West of the Andes (WE). Blue: Dry pacific coast of 833 Perú and Ecuador (DP). Yellow: Volcanic Islands (CG). Populations B15, B16, B17, D3, D4, 834 D5 corresponds to sequences downloaded from databases (see methods section). 835 836 837

838

Figure 2. Mitochondrial phylogeny. Node supports are represented with squares, where the 839 upper part correspond to maximum-likelihood ultrafast bootstrap after 10,000 840 pseudoreplicates, and the lower part is the posterior probability obtained by Bayesian 841 inferences. The color squares over the tree represents the geographical region matching 842 each clade as follow. Green: East of the Andes (EA). Red: West of the Andes (WE). Blue: 843 Dry Pacific coast of Perú and Ecuador (DP). Yellow: Caribbean and Galapagos Volcanic 844 Islands (CG). Lines in front of the tips represent individuals or set of individuals that were 845 sampled from a geographical region at their phylogenetic relationships are closer to 846 individuals from another geography location. 847

848 Figure 3. Haplotype TCS network. Colors represents geographical regions as in Fig. 2. 849 Numbers next to lines indicates the number of mutational steps 850 851 852

853 854 855 Figure 4. Lineages through time (LTT plots). Left panel compares a set of simulated trees under Yule process (gray lines with 856 mean as red line) to the obtained mtDNA phylogeny (black line). Right panel represents the linage accumulation through time in 857 our dataset 858 859

860 Figure 5. Male palpus from structures for three geographical clades. Western of the Andes 861 corresponds to individuals from Quito and San Andrés. Dry pacific coast of Perú belongs to 862 to Lima locations. Caribbean Islands is an illustration obtained from Levi (1978). M: Median 863 apophysis, PM: Paramedian apophysis, E: Embolus 864 865

866

867 Figure 6. Bayesian population assignment test based on genome SNPs. Bar plots 868 represents Bayesian assignment probabilities for individuals where color bars represent the 869 most probable ancestry of each individual (green: EA, red: WE and blue: DP). Populations 870 are coded as Fig. 1 with their corresponding color phenotypes besides. 871 872

873 Figure 7. Discriminant Analysis of Principal Components (DAPC). Colors match 874 geographical locations as follow: Green: East of the Andes (EA). Red: West of the Andes 875 (WE). Blue: Dry Pacific coast of Perú and Ecuador (DP). Top left panel represents the 876 number of retained components for the analysis. Bottom left panel shows the Discriminant 877 Analysis Eigen values. 878 879 880

881

Supplementary figure 1. Isolation by distance Plot. Red line is the trending line for the 882 linear regression. At the top left are the values of the linear regression and correlation 883 analysis. 884 885

886

Supplementary figure 2. mtDNA species delimitation. Bars in front of the tips corresponds 887 to the species delimited by each method. Blue bar in the nodes are the divergence time 95% 888 confidence interval estimated in BEAST 889 890

891 892 Supplementary figure 3. South American geographical clades as in Fig. 2 with their 893 corresponding color morphs frequencies 894 895

896 Supplementary figure 4. Bayesian population assignment test based on genome SNPs. Bar plots represents Bayesian 897 assignment probabilities for individuals where color bars represent the most probable ancestry of each individual. green: EA, 898 red: WE, blue: DP., yellow: in Ecuadorian Andes-Wet pacific, Purple: Baños-Ecuador. A. genetic clustering for K=4. B Genetic 899 clustering for K=5. Populations are coded as Fig. 1 at the top of each panel 900 901 902

903 Supplementary figure 5. Discriminant Analysis of Pricipal Components density plot results. 904 Color correspond to geographical regions as in Fig. 2. 905 906

907 908 Supplementary Figure 6. Treemix result. Each yellow to red lines represents a migration 909 event 910 911 912

913 914

915 916 917 918

919 920

921 Supplementary figure 7. Shared mtDNA haplotypes between locations. Each panel represents the geographical origin of each 922 clade tip. A: East of the Andes (EA). B: West of the Andes (WE). C: Dry Pacific coast of Perú and Ecuador (DP). D: Caribbean 923 and Galapagos Volcanic Islands (CG) 924 925

Supplementary figure 1. Collecting data information 926 927

Species Source Code Location Country X Y Sex COI RADs

Gasteracantha cancriformis Manualy collected 1 Acre Brazil -9.982 -67.811 Female X X

Gasteracantha cancriformis Manualy collected 2 Acre Brazil -9.982 -67.811 Female X







Gasteracantha cancriformis Manualy collected 9 Praia do Forte Brazil -12.525 -38.015 Female X X






Gasteracantha cancriformis Manualy collected 15 Praia do Forte Brazil -12.525 -38.015 Female X

Gasteracantha cancriformis Manualy collected 16 Lencois Brazil -12.561 -41.386 Female X X




Gasteracantha cancriformis Manualy collected 20 Lencois Brazil -12.561 -41.386 Female X




Gasteracantha cancriformis Manualy collected 24 Campinas Brazil -22.819 -47.070 Female X X

Gasteracantha cancriformis Manualy collected 25 Cali Colombia 3.568 -76.574 Female X X






Gasteracantha cancriformis Manualy collected 31 Cali Colombia 3.568 -76.574 Female X

Gasteracantha cancriformis Manualy collected 32 Cali Colombia 3.568 -76.574 Female X

Gasteracantha cancriformis Manualy collected 33 Palomino Colombia 11.252 -73.558 Female X




Gasteracantha cancriformis Manualy collected 37 Armero Colombia 5.002 -74.908 Female X




Gasteracantha cancriformis Manualy collected 41 Villavicencio Colombia 4.073 -73.587 Female X





Gasteracantha cancriformis Manualy collected 46 Villavicencio Colombia 4.073 -73.587 Male X


Gasteracantha cancriformis Manualy collected 48 Villavicencio Colombia 4.073 -73.587 Male X X

Gasteracantha cancriformis Manualy collected 49 Villavicencio Colombia 4.073 -73.587 Female X X











Gasteracantha cancriformis Manualy collected 60 Boquia Colombia 4.638 -75.587 Female X

Gasteracantha cancriformis Manualy collected 61 Boquia Colombia 4.638 -75.587 Female X

Gasteracantha cancriformis Manualy collected 62 Boquia Colombia 4.638 -75.587 Female X X








Gasteracantha cancriformis Manualy collected 70 Ibagué Colombia 4.428 -75.213 Female X








Gasteracantha cancriformis Manualy collected 78 Ibagué Colombia 4.428 -75.213 Female X X




Gasteracantha cancriformis Manualy collected 82 Choco Colombia 6.385 -77.399 Female X

Gasteracantha cancriformis Manualy collected 83 Bahia Malaga Colombia 4.102 -77.491 Female X





Gasteracantha cancriformis Manualy collected 88 Bahia Malaga Colombia 4.102 -77.491 Female X X




Gasteracantha cancriformis Manualy collected 92 Bucaramanga Colombia 7.142 -73.119 Female X




Gasteracantha cancriformis Manualy collected 96 Palmira Colombia 4.178 -76.205 Female X X

Gasteracantha cancriformis Manualy collected 97 Palmira Colombia 4.178 -76.205 Female X X

Gasteracantha cancriformis Manualy collected 98 Buenavista Colombia 4.175 -73.681 Female X X





Gasteracantha cancriformis Manualy collected 103 Tolú Colombia 9.593 -75.571 Female X

Gasteracantha cancriformis Manualy collected 104 Tolú Colombia 9.593 -75.571 Female X X




Gasteracantha cancriformis Manualy collected 108 Cartagena Colombia 10.353 -75.427 Female X

Gasteracantha cancriformis Manualy collected 109 Guaviare Colombia 2.576 -72.714 Female X X




Gasteracantha cancriformis Manualy collected 113 Medellin Colombia 6.207 -75.569 Female X X

Gasteracantha cancriformis Manualy collected 114 Medellin Colombia 6.207 -75.569 Female X X

Gasteracantha cancriformis Manualy collected 115 Medellin Colombia 6.207 -75.569 Female X

Gasteracantha cancriformis Manualy collected 116 Cucuta Colombia 7.795 -72.523 Female X X

Gasteracantha cancriformis Manualy collected 117 San_Andres Colombia 12.542 -81.812 Male X

Gasteracantha cancriformis Manualy collected 118 San_Andres Colombia 12.542 -81.812 Male X X

Gasteracantha cancriformis Manualy collected 119 San_Andres Colombia 12.542 -81.812 Male X X

Gasteracantha cancriformis Manualy collected 120 San_Andres Colombia 12.542 -81.812 Female X X

Gasteracantha cancriformis Manualy collected 121 San_Andres Colombia 12.542 -81.812 Female X X

Gasteracantha cancriformis Manualy collected 122 Leticia Colombia -4.181 -69.951 Female X

Gasteracantha cancriformis Manualy collected 123 Leticia Colombia -4.181 -69.951 Female X X

Gasteracantha cancriformis Manualy collected 124 Lima Peru -12.209 -76.987 Female X

Gasteracantha cancriformis Manualy collected 125 Lima Peru -12.209 -76.987 Female X X













Gasteracantha cancriformis Manualy collected 138 Chiclayo Peru -6.641 -79.396 Female X

Gasteracantha cancriformis Manualy collected 139 Chiclayo Peru -6.641 -79.396 Female X X

Gasteracantha cancriformis Manualy collected 140 Chiclayo Peru -6.641 -79.396 Female X X

Gasteracantha cancriformis Manualy collected 141 Moyobamba Peru -6.024 -76.965 Female X

Gasteracantha cancriformis Manualy collected 142 Moyobamba Peru -6.024 -76.965 Female X

Gasteracantha cancriformis Manualy collected 143 Moyobamba Peru -6.024 -76.965 Female X X





Gasteracantha cancriformis Manualy collected 148 Tarapoto Peru -6.478 -76.353 Female X

Gasteracantha cancriformis Manualy collected 149 Tarapoto Peru -6.478 -76.353 Female X X

Gasteracantha cancriformis Manualy collected 150 Tarapoto Peru -6.478 -76.353 Female X X

Gasteracantha cancriformis Manualy collected 151 Jaen Peru -5.635 -78.782 Female X

Gasteracantha cancriformis Manualy collected 152 Jaen Peru -5.635 -78.782 Male X X

Gasteracantha cancriformis Manualy collected 153 Jaen Peru -5.635 -78.782 Female X X



Gasteracantha cancriformis Manualy collected 156 Piura Peru -5.508 -80.894 Female X

Gasteracantha cancriformis Manualy collected 157 Piura Peru -5.508 -80.894 Female X X











Gasteracantha cancriformis Manualy collected 168 Alamor Ecuador -4.018 -80.02 Female X

Gasteracantha cancriformis Manualy collected 169 Alamor Ecuador -4.018 -80.02 Female X X






Gasteracantha cancriformis Manualy collected 175 Vilcabamba Ecuador -4.26 -79.217 Female X

Gasteracantha cancriformis Manualy collected 176 Vilcabamba Ecuador -4.26 -79.217 Female X X




Gasteracantha cancriformis Manualy collected 180 El Pangui Ecuador -3.618 -78.582 Female X

Gasteracantha cancriformis Manualy collected 181 El Pangui Ecuador -3.618 -78.582 Female X X



Gasteracantha cancriformis Manualy collected 184 Sucúa Ecuador -2.402 -78.159 Female X

Gasteracantha cancriformis Manualy collected 185 Sucúa Ecuador -2.402 -78.159 Female X X




Gasteracantha cancriformis Manualy collected 189 Baños Ecuador -1.401 -78.423 Female X X









Gasteracantha cancriformis Manualy collected 198 Misahualli Ecuador 1.038 -77.671 Female X

Gasteracantha cancriformis Manualy collected 199 Misahualli Ecuador 1.038 -77.671 Male X X

Gasteracantha cancriformis Manualy collected 200 Misahualli Ecuador 1.038 -77.671 Female X X






Gasteracantha cancriformis Manualy collected 206 Santo_Domingo Ecuador -0.2504 -79.158 Female X

Gasteracantha cancriformis Manualy collected 207 Santo_Domingo Ecuador -0.2504 -79.158 Female X X




Gasteracantha cancriformis Manualy collected 211 Quito Ecuador -0.191 -78.1435 Female X

Gasteracantha cancriformis Manualy collected 212 Quito Ecuador -0.191 -78.1435 Female X

Gasteracantha cancriformis Manualy collected 213 Quito Ecuador -0.191 -78.1435 Female X X






Gasteracantha cancriformis Manualy collected 219 Pedrera Colombia -1.3176 -69.587 Female X

Gasteracantha cancriformis Manualy collected 220 Pedrera Colombia -1.3176 -69.587 Female X

Gasteracantha cancriformis Manualy collected 221 Galapagos Ecuador -

0.696171 -90.9787 Female X

Gasteracantha cancriformis BoldSystem BBUSU269-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU270-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU602-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU609-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU628-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU646-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU653-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU654-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU655-15 Florida USA 26.271 -80.821 Female X Gasteracantha cancriformis BoldSystem BBUSU660-15 Florida USA 26.271 -80.821 Female X

Gasteracantha cancriformis BoldSystem BBUSE1509-12 Texas USA 29.371 -95.631 Female X













Gasteracantha cancriformis BoldSystem CENAM020-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM075-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM076-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM077-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM082-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM101-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM102-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM103-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM110-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM111-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM112-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM113-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM126-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM127-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM137-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM188-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM195-12 CostaRica CostaRica 10.300 -85.837 Female X Gasteracantha cancriformis BoldSystem CENAM196-12 CostaRica CostaRica 10.300 -85.837 Female X

Gasteracantha cancriformis BoldSystem CARSP304-14 SintMarteen SintMarteen 18.070 -63.050 Female X Gasteracantha cancriformis GenBank KJ157214.1 PuertoRico PuertoRico 18.349 -66.077 Female X Gasteracantha cancriformis GenBank J157213.1 Hispaniola Hispaniola 18.984 -71.572 Female X Gasteracantha cancriformis GenBank KJ157212.1 Hispanola Hispanola 18.984 -71.572 Female X

928 929 930

phylogeography of a widespread spider species

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