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Phylogeography of a widespread spider species (Gasteracantha cancriformis): Gene 1
flow through geographical barriers shapes its diversification 2
3
4
5
Fabian Camilo Salgado Roa 6
Student 7
8
9
Eloisa Lasso de Paulis 10
Advisor 11
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13
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
55
Fabian C. Salgado-Roa1,2, Carolina Pardo-Diaz2, Melissa Sanchez Herrera2, Diego F. 56
Cisneros-Herdia3,4, Camilo Salazar2, Eloisa Lasso1 57
58
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
65
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
190
191
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
303
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
326
RESULTS 327
328
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
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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 3 Acre Brazil -9.982 -67.811 Female X X
Gasteracantha cancriformis Manualy collected 4 Acre Brazil -9.982 -67.811 Female X X
Gasteracantha cancriformis Manualy collected 5 Acre Brazil -9.982 -67.811 Female X X
Gasteracantha cancriformis Manualy collected 6 Acre Brazil -9.982 -67.811 Female X X
Gasteracantha cancriformis Manualy collected 7 Acre Brazil -9.982 -67.811 Female X X
Gasteracantha cancriformis Manualy collected 8 Acre Brazil -9.982 -67.811 Female X X
Gasteracantha cancriformis Manualy collected 9 Praia do Forte Brazil -12.525 -38.015 Female X X
Gasteracantha cancriformis Manualy collected 10 Praia do Forte Brazil -12.525 -38.015 Female X X
Gasteracantha cancriformis Manualy collected 11 Praia do Forte Brazil -12.525 -38.015 Female X X
Gasteracantha cancriformis Manualy collected 12 Praia do Forte Brazil -12.525 -38.015 Female X X
Gasteracantha cancriformis Manualy collected 13 Praia do Forte Brazil -12.525 -38.015 Female X X
Gasteracantha cancriformis Manualy collected 14 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 17 Lencois Brazil -12.561 -41.386 Female X X
Gasteracantha cancriformis Manualy collected 18 Lencois Brazil -12.561 -41.386 Female X X
Gasteracantha cancriformis Manualy collected 19 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 21 Lencois Brazil -12.561 -41.386 Female X X
Gasteracantha cancriformis Manualy collected 22 Lencois Brazil -12.561 -41.386 Female X X
Gasteracantha cancriformis Manualy collected 23 Lencois Brazil -12.561 -41.386 Female X 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 26 Cali Colombia 3.568 -76.574 Female X X
Gasteracantha cancriformis Manualy collected 27 Cali Colombia 3.568 -76.574 Female X X
Gasteracantha cancriformis Manualy collected 28 Cali Colombia 3.568 -76.574 Female X X
Gasteracantha cancriformis Manualy collected 29 Cali Colombia 3.568 -76.574 Female X X
Gasteracantha cancriformis Manualy collected 30 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 34 Palomino Colombia 11.252 -73.558 Female X
Gasteracantha cancriformis Manualy collected 35 Palomino Colombia 11.252 -73.558 Female X
Gasteracantha cancriformis Manualy collected 36 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 38 Armero Colombia 5.002 -74.908 Female X
Gasteracantha cancriformis Manualy collected 39 Armero Colombia 5.002 -74.908 Female X
Gasteracantha cancriformis Manualy collected 40 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 42 Villavicencio Colombia 4.073 -73.587 Female X
Gasteracantha cancriformis Manualy collected 43 Villavicencio Colombia 4.073 -73.587 Female X
Gasteracantha cancriformis Manualy collected 44 Villavicencio Colombia 4.073 -73.587 Female X
Gasteracantha cancriformis Manualy collected 45 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 47 Villavicencio Colombia 4.073 -73.587 Female 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 50 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 51 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 52 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 53 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 54 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 55 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 56 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 57 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 58 Villavicencio Colombia 4.073 -73.587 Female X X
Gasteracantha cancriformis Manualy collected 59 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 63 Boquia Colombia 4.638 -75.587 Female X X
Gasteracantha cancriformis Manualy collected 64 Boquia Colombia 4.638 -75.587 Female X X
Gasteracantha cancriformis Manualy collected 65 Boquia Colombia 4.638 -75.587 Female X X
Gasteracantha cancriformis Manualy collected 66 Boquia Colombia 4.638 -75.587 Female X X
Gasteracantha cancriformis Manualy collected 67 Boquia Colombia 4.638 -75.587 Female X X
Gasteracantha cancriformis Manualy collected 68 Boquia Colombia 4.638 -75.587 Female X X
Gasteracantha cancriformis Manualy collected 69 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 71 Ibagué Colombia 4.428 -75.213 Female X
Gasteracantha cancriformis Manualy collected 72 Ibagué Colombia 4.428 -75.213 Female X
Gasteracantha cancriformis Manualy collected 73 Ibagué Colombia 4.428 -75.213 Female X
Gasteracantha cancriformis Manualy collected 74 Ibagué Colombia 4.428 -75.213 Female X
Gasteracantha cancriformis Manualy collected 75 Ibagué Colombia 4.428 -75.213 Female X
Gasteracantha cancriformis Manualy collected 76 Ibagué Colombia 4.428 -75.213 Female X
Gasteracantha cancriformis Manualy collected 77 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 79 Ibagué Colombia 4.428 -75.213 Female X X
Gasteracantha cancriformis Manualy collected 80 Ibagué Colombia 4.428 -75.213 Female X X
Gasteracantha cancriformis Manualy collected 81 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 84 Bahia Malaga Colombia 4.102 -77.491 Female X
Gasteracantha cancriformis Manualy collected 85 Bahia Malaga Colombia 4.102 -77.491 Female X
Gasteracantha cancriformis Manualy collected 86 Bahia Malaga Colombia 4.102 -77.491 Female X
Gasteracantha cancriformis Manualy collected 87 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 89 Bahia Malaga Colombia 4.102 -77.491 Female X X
Gasteracantha cancriformis Manualy collected 90 Bahia Malaga Colombia 4.102 -77.491 Female X X
Gasteracantha cancriformis Manualy collected 91 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 93 Bucaramanga Colombia 7.142 -73.119 Female X
Gasteracantha cancriformis Manualy collected 94 Bucaramanga Colombia 7.142 -73.119 Female X
Gasteracantha cancriformis Manualy collected 95 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 99 Buenavista Colombia 4.175 -73.681 Female X X
Gasteracantha cancriformis Manualy collected 100 Buenavista Colombia 4.175 -73.681 Female X X
Gasteracantha cancriformis Manualy collected 101 Buenavista Colombia 4.175 -73.681 Female X X
Gasteracantha cancriformis Manualy collected 102 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 105 Tolú Colombia 9.593 -75.571 Female X X
Gasteracantha cancriformis Manualy collected 106 Tolú Colombia 9.593 -75.571 Female X X
Gasteracantha cancriformis Manualy collected 107 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 110 Guaviare Colombia 2.576 -72.714 Female X X
Gasteracantha cancriformis Manualy collected 111 Guaviare Colombia 2.576 -72.714 Female X X
Gasteracantha cancriformis Manualy collected 112 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 126 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 127 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 128 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 129 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 130 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 131 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 132 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 133 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 134 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 135 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 136 Lima Peru -12.209 -76.987 Female X X
Gasteracantha cancriformis Manualy collected 137 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 144 Moyobamba Peru -6.024 -76.965 Female X X
Gasteracantha cancriformis Manualy collected 145 Moyobamba Peru -6.024 -76.965 Female X X
Gasteracantha cancriformis Manualy collected 146 Moyobamba Peru -6.024 -76.965 Female X X
Gasteracantha cancriformis Manualy collected 147 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 154 Jaen Peru -5.635 -78.782 Female X X
Gasteracantha cancriformis Manualy collected 155 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 158 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 159 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 160 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 161 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 162 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 163 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 164 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 165 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 166 Piura Peru -5.508 -80.894 Female X X
Gasteracantha cancriformis Manualy collected 167 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 170 Alamor Ecuador -4.018 -80.02 Female X X
Gasteracantha cancriformis Manualy collected 171 Alamor Ecuador -4.018 -80.02 Female X X
Gasteracantha cancriformis Manualy collected 172 Alamor Ecuador -4.018 -80.02 Female X X
Gasteracantha cancriformis Manualy collected 173 Alamor Ecuador -4.018 -80.02 Female X X
Gasteracantha cancriformis Manualy collected 174 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 177 Vilcabamba Ecuador -4.26 -79.217 Female X X
Gasteracantha cancriformis Manualy collected 178 Vilcabamba Ecuador -4.26 -79.217 Female X X
Gasteracantha cancriformis Manualy collected 179 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 182 El Pangui Ecuador -3.618 -78.582 Female X X
Gasteracantha cancriformis Manualy collected 183 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 186 Sucúa Ecuador -2.402 -78.159 Female X X
Gasteracantha cancriformis Manualy collected 187 Sucúa Ecuador -2.402 -78.159 Female X X
Gasteracantha cancriformis Manualy collected 188 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 190 Baños Ecuador -1.401 -78.423 Female X X
Gasteracantha cancriformis Manualy collected 191 Baños Ecuador -1.401 -78.423 Female X X
Gasteracantha cancriformis Manualy collected 192 Baños Ecuador -1.401 -78.423 Female X X
Gasteracantha cancriformis Manualy collected 193 Baños Ecuador -1.401 -78.423 Female X X
Gasteracantha cancriformis Manualy collected 194 Baños Ecuador -1.401 -78.423 Female X X
Gasteracantha cancriformis Manualy collected 195 Baños Ecuador -1.401 -78.423 Female X X
Gasteracantha cancriformis Manualy collected 196 Baños Ecuador -1.401 -78.423 Female X X
Gasteracantha cancriformis Manualy collected 197 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 201 Misahualli Ecuador 1.038 -77.671 Female X X
Gasteracantha cancriformis Manualy collected 202 Misahualli Ecuador 1.038 -77.671 Female X X
Gasteracantha cancriformis Manualy collected 203 Misahualli Ecuador 1.038 -77.671 Female X X
Gasteracantha cancriformis Manualy collected 204 Misahualli Ecuador 1.038 -77.671 Female X X
Gasteracantha cancriformis Manualy collected 205 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 208 Santo_Domingo Ecuador -0.2504 -79.158 Female X X
Gasteracantha cancriformis Manualy collected 209 Santo_Domingo Ecuador -0.2504 -79.158 Female X X
Gasteracantha cancriformis Manualy collected 210 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 214 Quito Ecuador -0.191 -78.1435 Female X X
Gasteracantha cancriformis Manualy collected 215 Quito Ecuador -0.191 -78.1435 Female X X
Gasteracantha cancriformis Manualy collected 216 Quito Ecuador -0.191 -78.1435 Female X X
Gasteracantha cancriformis Manualy collected 217 Quito Ecuador -0.191 -78.1435 Female X X
Gasteracantha cancriformis Manualy collected 218 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 BBUSE1625-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE1918-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE1956-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE1959-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE1977-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE1978-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE1979-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE2114-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE2214-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE2256-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE3109-12 Texas USA 29.371 -95.631 Female X
Gasteracantha cancriformis BoldSystem BBUSE3200-12 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
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