1 Pleistocene Climate Change and the Formation of Regional Species Pools

2 Joaquín Calatayud1,2,3*, Miguel Ángel Rodríguez4, Rafael Molina-Venegas5, María

3 Leo6, Jose Luis Horreo7 and Joaquín Hortal2

4

5 1. Departamento de Ciencias de la Vida, Universidad de Alcalá, Edificio de Ciencias,

6 Ctra. Madrid-Barcelona km. 33,6, 28871 Alcalá de Henares, Madrid, Spain.

7 2. Department of Biogeography and Global Change, Museo Nacional de Ciencias

8 Naturales (MNCN-CSIC), C/José Gutiérrez Abascal 2, 28006 Madrid, Spain.

9 3. Integrated Science Lab, Department of Physics, Umeå University, Naturvetarhuset,

10 byggnad G, NA plan 3, IceLab Umeå universitet, 901 87 Umeå, Sweden.

11 4. GLOCEE - Global Change Ecology and Evolution Group, Department of Life

12 Sciences, Universidad de Alcalá, 28805, Spain

13 5. Institute of Plant Sciences, University of Bern, Altenbergrain 21, Bern 3013,

14 Switzerland

15 6. Departamento de Biodiversidad y Conservación. Real Jardín Botánico de Madrid

16 (CSIC). 28014 Madrid, España

17 7. Departamento de Biodiversidad y Biología Evolutiva, Museo Nacional de Ciencias

18 Naturales (MNCN-CSIC), C/José Gutiérrez Abascal 2, 28006 Madrid, Spain.

19 * Corresponding author: [email protected]

1

20 Abstract

21 Despite the description of bioregions dates back to the origin of biogeography, the

22 processes originating their associated species pools have been seldom studied. Ancient

23 historical events are thought to play a fundamental role in configuring bioregions, but

24 the effects of more recent events on these regional biotas are largely unknown. We use a

25 network approach to identify regional and sub-regional faunas of European Carabus

26 beetles, and developed a method to explore the relative contribution of dispersal

27 barriers, niche similarities and phylogenetic history on their configuration. We identify

28 a transition zone matching the limit of the ice sheets at Last Glacial Maximum. While

29 southern species pools are mostly separated by dispersal barriers, in the north species

30 are mainly sorted by their environmental niches. Strikingly, most phylogenetic

31 structuration of Carabus faunas occurred during the Pleistocene. Our results show how

32 extreme recent historical events –such as Pleistocene climate cooling, rather than just

33 deep-time evolutionary processes, can profoundly modify the composition and structure

34 of geographic species pools.

35

36 Keywords: Pleistocene glaciations, historical biogeography, bioregions, dispersal,

37 niche tracking, Carabus ground beetles.

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38 Introduction

39 Naturalists have long been captivated by the geographic distribution of world biotas.

40 Rooted in the seminal ideas of Alexander von Humboldt, this fascination has promoted

41 a long-term research agenda aiming to delineate biogeographic regions according to

42 their faunas and floras (e.g. [1-3]). Besides this, the large-scale eco-evolutionary

43 processes that shape regional biotas are known to influence ecological and evolutionary

44 dynamics at finer scales [4]. For instance, regional species pools can modulate local

45 diversity patterns [5,6], the structure and functioning of ecosystems [7], or co-

46 evolutionary processes [8]. However, the processes that have configured regional biotas

47 have been seldom studied despite their fundamental importance, and most explanations

48 of their origin and dynamics remain largely narrative [9].

49 Perhaps the earliest speculations about the formation of regional species pools

50 date back to the 19th century (reviewed in [10]). At that time, some authors already

51 started to emphasize historical influences as key elements determining the configuration

52 of plant and animal regions. For instance, when Wallace [1] proposed his ground-

53 breaking zoogeographic regions, he argued that while the distribution of ancient linages

54 such as genera and families would likely reflect major geological and climatic changes

55 spanning the early and mid-Cenozoic, species distributions would be more influenced

56 by recent events such as Pleistocene glaciations (see [3]). These recent events could

57 have promoted many additions and subtractions of species to regional faunas through

58 dispersal and diversification processes. Indeed, increasing evidence suggests that

59 Pleistocene glacial-interglacial dynamics may have driven population extinctions (e.g.

60 [11]), allopatric speciation in glacial refugia (e.g. [12]) and post-glacial recolonization

61 events (e.g. [13-14]). Besides shaping phylogeographic patterns (e.g. [15-16]), all these

62 processes are likely underpinning diversity patterns for many taxa, particularly in the

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63 Holarctic (e.g. [17-19]). However, whether the signature of Pleistocene glaciations

64 scales up to the configuration of regional biotas remains largely unknown.

65 Historical contingencies should act over the intricate interplay between

66 ecological (i.e. environmental tolerances and dispersal) and evolutionary (i.e.

67 diversification and adaptation to new habitats) processes underpinning the composition

68 of regional species pools. On the one hand, niche-based processes may determine the

69 composition of regional species pools [20], mainly throughout their effects on species

70 distribution ranges [21]. These processes integrate responses to abiotic conditions and to

71 local and regional biotic environments [22], which may ultimately lead to the

72 appearance of distinct regional communities in areas of contrasted environmental

73 conditions. Although species with similar environmental tolerances can coexist in

74 regions of similar climate, their dispersal may be constrained by geographical barriers,

75 which may lead to divergent species pools under similar environmental conditions.

76 Finally, evolutionary processes also constrain all these mechanisms. For instance,

77 environmentally-driven regions may be expected if occupancy of new areas is

78 constrained by niche conservatism [18], which should also lead to pools of

79 evolutionarily related species (i.e. niche conservatism generating phylogenetically-

80 related species pools, Fig.1a). This pattern, however, can be also the output of

81 biogeographical processes. Indeed, diversification of lineages within regions separated

82 by strong dispersal barriers may also lead to phylogenetically related pools (i.e.

83 geographically-driven niche conservatism; Fig.1a; [8,23]). Historical contingencies may

84 contribute to the configuration of regional pools by modifying the balance between

85 these processes. For example, the accumulation of species due to diversification may be

86 the predominant driver of regional species pools during climatically stable periods [24].

87 Yet, regions with a greater influence of climatic fluctuations such as Pleistocene

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88 glaciations may harbour pools of species mostly shaped by the joint effects of current

89 climate and post-glacial colonization dynamics [25], as well as by species’ competition

90 during these colonization processes [26], thus eroding the signature of geographically-

91 structured diversification processes.

92 In this study we aim to disentangle the relative importance of the processes

93 that may contribute to the formation of regional species pools, using European Carabus

94 (Coleoptera: Carabidae) as a model lineage. Carabus is a species-rich ground beetle

95 genus of great popularity due to the beautiful jewel-like appearance of some species

96 [27]. In general, Carabus species are flightless nocturnal predators of snails,

97 earthworms and caterpillars. They hold hydrophilic adaptations and are typically

98 associated to deciduous forests [28]. Previous evidence suggests that the richness of

99 species from this genus in Europe is determined to a large extent by both current

100 climatic and habitat conditions and glacial-interglacial dynamics [19]. This makes

101 European Carabus an ideal case study to evaluate the joint effects of evolutionary,

102 ecological and historical contingency processes as drivers of regional species pools.

103 Specifically, we use data on the distribution and evolutionary relationships of

104 Carabus species, along with network and phylogenetic analyses, to evaluate six

105 hypotheses: First, given the presumed low dispersal capacity of the species from this

106 genus [27], we hypothesize that (H1) European Carabus species pools are mainly

107 shaped by the main orographic barriers of the continent, but also, that (H2) glacial-

108 interglacial dynamics have led to strong differentiation between northern and southern

109 regional species pools. If this differentiation exits, northern European Carabus faunas

110 will be comprised of species that colonized newly vacant habitats after the retreat of the

111 ice sheet, and hence (H3) their regional distribution will be mostly determined by

112 current climate. In contrast, (H4) southern faunas will be mainly shaped by the joint

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113 influence of diversification events and dispersal limitations, due to the combined effect

114 of higher climatic stability (e.g. climatic refugia) and a more complex orography (Alps,

115 Pyrenees, Carpathians). Therefore, (H5) species forming northern regional pools will

116 exhibit comparatively lower levels of regional endemicity, whereas those forming

117 southern regional pools will show comparatively higher levels of regional specificity.

118 Finally, according to Wallace [1], the advance and retreat of the ice sheets during the

119 Pleistocene should have determined the spatial distribution of lineages [e.g. 18,19],

120 eroding the effects of the former distribution of the main Carabus lineages. Therefore,

121 (H6) we expect a temporal signal coincident with the Pleistocene in the phylogenetic

122 structure of Carabus faunas, and no effect of deep-time events on the current

123 geographical distribution of these lineages.

124

125 Material and Methods

126 Rationale and Structure of the Analyses

127 Exploring the determinants of regional faunas requires jointly analysing ecological,

128 evolutionary and historical factors. We did so through three consecutive steps (Fig.1b).

129 First, we identified distinct regional species pools within Europe by using a network

130 community detection algorithm. From this analysis we derived a species pairwise

131 similarity matrix of occurrence into different modules, each one representing different

132 regions. Second, we assessed the relative importance of environmental, spatial and

133 evolutionary determinants of such similarity. To do so, we constructed four pairwise

134 matrices to describe ecological, topographical and evolutionary relationships among

135 species; namely, a matrix of climatic niche similarity, a matrix of habitat similarity, a

136 matrix of spatial connectivity among distributional ranges, and a phylogenetic distance

137 matrix. Then, we used generalized partial matrix regressions to model the similarity in

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138 species occurrences as a function of these four matrices (Fig.1b). We used this

139 workflow to explore the factors involved in the configuration of Carabus faunas at both

140 regional and sub-regional scales (i.e. through analysing species co-occurrence paterns

141 across regions and within sub-regions, respectively). Beyond scaling effects, the use of

142 these two scales allowed us to explore for differences among regions, and thus to delve

143 into our hypotheses 3 and 4. Finally, we also applied ancestral range estimation analysis

144 in order to identify the time period from which ancestral areas are estimated with less

145 uncertainty. By doing so, we aimed to detect important historical periods contributing to

146 the regional organization of Carabus lineages.

147 The interpretation of the joint and independent effects of explanatory matrices

148 can shed light on the different processes configuring regional faunas (see Fig.1a). Thus,

149 if niche similarities (i.e. represented by the climatic and habitat similarity matrices) and

150 phylogenetic distances altogether explained the regional co-occurrence of species, then

151 this could be interpreted as indicative of constrained niche evolution or a tendency to

152 resemble ancestral niches in shaping regional faunas (see Niche conservatism in Fig.1a).

153 However, if spatial connectivity also accounted for part of this co-occurrence, this

154 would indicate that this niche conservatism pattern can be caused by geographical

155 constrains (Spatially-driven niche conservatism in Fig.1a). Further, the effects of niche

156 similarities and spatial connectivity alone (i.e. without phylogenetic signal) can be most

157 likely the consequence of a convergence of climatic niches due to geographic isolation,

158 whereas the effects of connectivity and phylogeny would be indicative of a primacy of

159 intra-regional speciation driven by geographical barriers. Niche similarities alone would

160 point to unconstrained niche evolution shaping regional faunas (Niche convergence in

161 Fig.1a), while phylogeny alone would indicate a primacy of geographically

162 unconstrained intra-regional speciation events. Finally, the accumulation of species in

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163 past climatic refugia (the so-called cul-de-sac effect) or a primacy of vicariant

164 speciation events could lead to the existence of independent effects of connectivity on

165 regional co-occurrence (Vicariance or cul-de-sac in Fig.1a).

166

167 Identification of Regional Species Pools

168 We used network community detection analysis to identify Carabus regional species

169 pools in Europe. We first generated a bipartite network where species and grid cells

170 constitute two disjoint sets of nodes that are connected according to the presence of

171 species in grid cells (e.g. [8]). Species presence data comes from expert-based range

172 maps of all Carabus species inhabiting Europe (n = 131; [27]) overlaid into a 100x100

173 km equal-area grid based on the LAEA pan-European grid system (currently available

174 at https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2; see [19] for

175 details). Then, we conducted a modularity analysis using the index proposed by Barber

176 [29] and the Louvain algorithm [30] as implemented in the Matlab function “Gen

177 Louvain”, (available at http://netwiki.amath.unc.edu/; [31]). This analysis identified

178 groups of grid cells, each group sharing Carabus species mainly distributed within its

179 cells (i.e. regions and their associated faunas). The Louvain algorithm was run 500

180 times, and the network partition showing highest modularity value was retained. This

181 optimal solution was used to conduct all subsequent analyses, although all the solutions

182 were quantitatively and qualitatively similar (Appendix S1). We evaluated the statistical

183 significance of the modules by comparing their associated modularity to a null

184 distribution of values (n = 100) where the original presence-absence matrix was

185 randomized using the independent swap algorithm, a fixed-fixed null model

186 implemented in the R package “picante” [32]. Finally, to detect potential sub-modules

187 (i.e. sub-regions) nested within modules, we derived a new bipartite network from each

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188 of the previously identified modules, and applied the procedure described above in each

189 case.

190 It is important to note that despite species and grid cells were assigned to just

191 one module, they could also occur in other modules with different degrees of

192 specificity. Thus, we calculated the degree of module specificity for each node (i.e.

193 species and grid cells) as its number of links with nodes of its module divided by its

194 total number links. Higher module specificity would correspond to highly-endemic

195 species mainly distributed within its module, as well as to cells pertaining to well-

196 defined regions; whereas lower module specificity would indicate widespread species

197 and cells located in transition zones.

198

199 Assessing the Determinants of Regional Species Pools

200 To disentangle the determinants of the current configuration of Carabus faunas in

201 Europe, we first measured the regional co-ocurrence similarity of species pairs based on

202 the proportion of their ranges present in each module. For this, we used Schoener’s

203 index [33], which measures the proportion of overlap between pairs of species. The

204 resultant occurrence pairwise similarity matrix was used as dependent variable. Then,

205 we generated four pairwise dis/similarity matrices used as explanatory variables. Two of

206 them were used to account for environmental factors: a climatic-niche similarity matrix

207 and a habitat similarity matrix. The remaining two considered geographical and

208 evolutionary factors: a spatial-connectivity matrix and a phylogenetic distance matrix.

209

210 Climatic-niche similarity matrix.— We characterized the climatic niche of each

211 Carabus species in the dataset following a similar approach as proposed by

212 Broennimann et al. [34]. We selected six bioclimatic variables to account for the main

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213 water and energy aspects of climate –namely mean annual temperature, temperature of

214 the warmest quarter, temperature of the driest quarter, total annual precipitation, total

215 precipitation of the warmest quarter and total precipitation of the driest quarter, as well

216 as altitudinal range to account for the effects of mesoclimatic gradients within each grid

217 cell. These variables may be among the main determinants of the distribution of

218 Carabus species diversity within Europe (see [19]). Bioclimatic variables were

219 extracted from Worldclim (v1.4; available at http://www.worldclim.org/; [35]), whereas

220 altitudinal data were derived from the 30-arcsecond digital elevation model GTOPO30

221 (available at https://lta.cr.usgs.gov/GTOPO30/). We conducted a principal component

222 analysis on these variables to obtain a bidimensional climatic space defined by the two

223 main axes that explained 81.4% of the variance (Fig. S1). We divided this climatic

224 space into a 100x100 grid system and computed the frecuency of occurrence of each

225 species in each grid cell. Finally, we measured the species overlap in the gridded space

226 using Schoener’s index (see above).

227

228 Habitat similarity matrix. — The distribution of Carabus species may also be shaped by

229 forest preferences [27]. Accordingly, we used ten vegetation categories derived from

230 MODIS Land Cover at 5-minute resolution (Evergreen broadleaf forest, deciduous

231 needle-leaf forest, deciduous broadleaf forest, mixed forest, closed shrub lands, open

232 shrub lands, woody savannas, savannas and grasslands; available at

233 http://glcf.umd.edu/data/lc/; [36]). For each species we computed the proportion of each

234 category overlaying its range. With this, we computed pairwise similarities in the

235 preference for different vegetation types using Schoener’s index (see above).

236

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237 Spatial connectivity matrix. — To evaluate the potential influence of geophysical

238 barriers to dispersal on the current distribution of Carabus species, the study area was

239 divided in 1 km2 grid cells. For each cell, we obtained both mean elevation –from

240 GTOPO30 digital elevation model, and the presence of waterbodies – from Natural

241 Earth database (available at http://www.naturalearthdata.com/). Then, we derived a

242 spatial conductance network, where grid cells were nodes linked to the 8 neighbouring

243 cells. Link weights represent the probability of transit from cell i to cell j . This

244 probability was inversely proportional to the difference in elevation between cells,

245 weighted by the presence of water bodies:

246 𝑃𝑖↔𝑗 1 𝑃 ,

247 where 𝑅𝑖𝑠𝑒 is the difference in altitude, 𝑅𝑢𝑛 the great-circle distance between

248 the centroids of the cells, and 𝑃 the probability to transit across water bodies. We

249 used a 𝑃 of 0.5 to build up the conductance network, altough different values

250 provided similar results (see Table S1). Then, the matrix of connectivity between the

251 centroids of all pairs of cells at 100x100 km was calculated as the accumulated cost for

252 a random walker to commute from cell i to cell j and back to cell i across the

253 conductance network [37]. Finally, the spatial connectivity between each pair of

254 species’ distributional ranges in the dataset was estimated as the average distance

255 among all grid cells at 100x100km within the range of each species. All analyses were

256 conducted using the “gdistance” R package [37].

257

258 Phylogenetic distance matrix. — To unravel the evolutionary history of Carabus

259 lineages and assess the potential importance of evolutionary processes in determining

260 the formation of species pools, we reconstructed a time-calibrated phylogeny including

261 the 89 species for which we found available DNA information on ten markers

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262 (Appendix S2 and Tables S2 and S3). The final dataset was concatenated in 5,603

263 basepairs following a total-evidence approach [38], and used to conduct Bayesian

264 phylogenetic inference with BEAST v.2.4.6 software ([39]; Appendix S2). Molecular

265 dating was done with two different scenarios. In the first, the crown age of Carabus was

266 set at 17.3 Mya, according to Deuve et al.’s [28] molecular dating. In the second, the

267 origin of the group was set at 25.16 Mya, according to Andujar et al. [40] (Appendix

268 S2). To account for topological and time-calibration uncertainties, we used 100

269 posterior phylogenies for each molecular dating scenario. In addition, we used

270 taxonomic information and phylogenetic uncertainty methods [41] to place species

271 lacking molecular information into the phylogeny (Appendix S2). Thus, we derived 100

272 different phylogenetic hypotheses from each Bayesian posterior phylogeny by randomly

273 inserting missing species within their most derived consensus clade based on taxonomic

274 knowledge. In total, we generated 10,000 phylogenetic hypotheses for each dating

275 scenario, and randomly selected 1,000 for subsequent analyses. Finally, we computed

276 multiple patristic distances between species pairs using raw branch lengths and several

277 branch-length transformations to accommodate different evolutionary models

278 (Appendix S3). Patristic distance calculations and branch-length transformations were

279 conducted using the R packages “ape” and “geiger” respectively.

280 We used generalized multiple regression on distance matrices and deviance

281 partitioning to disentangle the relative importance of climatic niche, habitat preferences,

282 dispersal barriers and evolutionary history in determining Carabus species pools in

283 Europe. First, we conducted single regressions between the occurrence pairwise

284 similarity matrix and each of the four explanatory matrices described above to seek for

285 significant associations. Since the dependent variable is basically a proportion (i.e. the

286 proportion of overlap), we set a binomial family for error distribution and a logit link

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287 function (see [8] for a similar approach). To assess for significance, we randomized the

288 observed species per module matrix using the independent swap algorithm (see above)

289 to derive 999 null occurrence similarity matrices. Then, we used simple regressions to

290 relate each null similarity matrix with each one of the explanatory matrices. The

291 relationship between an explanatory matrix and the observed species per module matrix

292 was considered to be significant when explaining a higher proportion of the deviance

293 than 99% of the regressions performed on the null matrices. In the case of phylogenetic

294 distances we repeated this procedure for each phylogenetic hypothesis, and considered a

295 relationship to be significant when 99% of phylogenetic hypotheses explained higher

296 deviance than 99% of corresponding null matrices. Finally, we retained those variables

297 that showed significant relationships, and conducted deviance partitioning to explore for

298 patterns of covariation among niche similarities (i.e. climatic and habitat similarity

299 matrices), dispersal barriers and phylogenetic history. The explained deviance was

300 computed as McFadden's pseudo-R2, and we followed the partitioning approach

301 presented in Legendre [42] (see Appendix S4). We first conducted the analyses for the

302 co-occurrence into modules (i.e. regions) and sub-modules (i.e. sub-regions) at a

303 European scale. Then, we conducted independent analyses for the co-occurrence into

304 submodules of the species grouped in each module. Regresion analyses were conducted

305 in R [43].

306

307 Ancestral Range Estimation

308 To assess whether deep historical signals were eroded by Pleistocene glaciations we

309 used probabilistic models of geographic range evolution. We used the Dispersal–

310 Extinction–Cladogenesis model of range evolution (DEC; [44]) implemented in the R

311 package BioGeoBears [45]. Species ranges were coded as present/absent in each

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312 module. If the Pleistocene glacial periods had important effects on the lineage

313 distributions it could be expected that ancestral range estimations will increase in

314 accuracy around the Pleistocene. To explore this, we evaluated the existence of changes

315 in the relationship between node age and the marginal probability of the single most-

316 probable ancestral state at each internal node by fitting general additive mixed models

317 (GAMMs), including the phylogenetic hypothesis as a random factor. We also used

318 generalized linear mixed models (GLMMs) combined with piecewise regression to

319 detect potential major breakpoints (i.e. temporal shifts) in the relationship between

320 marginal probability and node age (Appendix S5). We used a binomial family and a

321 loglink function to fit all models. General assumptions of probabilistic ancestral range

322 estimation models may compromise subsequent interpretations [46]. Hence, we

323 conducted additional analyses to provide further evidence on the temporal signal of

324 Pleistocene glaciations in the phylogenetic structuration of Carabus faunas (Apendix

325 S5). Finally, to explore for different Pleistocene effects across regions, we also

326 calculated the probability that a phylogenetic node has all its descendant species within

327 a given region, independently for nodes ocurring either before and after the beginning of

328 the Pleistocene (2.59 Mya; herein pre-Pleistocene and post-Pleistocene nodes).

329 All analyses were carried out in R [43], using the function bam of the package

330 mcvg for GAMM [47] and the package Lme4 for GLMM analyses [48].

331

332 Results

333 Identification of Regional Faunas

334 The Carabus occurrence network was significantly modular (M=0.385, P=0.01),

335 dividing Europe in seven modules that group zoogeographically distinct regions with

336 their associated faunas (i.e., different regional species pools; Figs. 2a and S2).

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337 Furthermore, all modules but the southernmost one (SM) showed significant sub-

338 modular structure, presenting a decrease in modularity with latitude (mean M=0.316,

339 ranging from 0.154 to 0.468; all P < 0.05, see Table S4 and Appendix S6). The

340 transition zones between regions were clearly associated with geographical barriers such

341 as the Pyrenees, the Alps, the Carpathian and the Ural Mountains, as well as the Turkish

342 Straits System (Fig. 2b), in agreement with our first hypothesis (H1). Interestingly, we

343 also identified a west-to-east transitional belt between southern and northern regions

344 that closely followed the southern limits of the ice sheet at the Last Glacial Maximum

345 (LGM). This transitional zone further suggested a link between the configuration of

346 Carabus regional faunas and Pleistocene glacial conditions, supporting our hypothesis

347 H2.

348

349 Correlates of Regional Co-occurrence

350 Matrix regressions showed that deviance of species co-occurrences across regions,

351 across sub-regions and within each region was significantly explained (P<0.01),

352 primarily by spatial connectivity, and secondarily by environmental niche similarity,

353 except for northern regions (i.e. northweastern and northeastern modules; NW and NE

354 respectively; Fig. 3 and Tables S1 and S5). In contrast, relationships with evolutionary

355 relatedness were non significant in most instances (Table S6). Moreover, in the cases

356 where we found a significant effect (i.e. when analysing co-ocurrences in modules,

357 submodules and central-western module, CW), the deviance explained by phylogenetic

358 distances was rather low and mostly overlapped with that explained by spatial

359 connectivity (Fig.3). Comparing both scales, environmental niche similarity explained

360 more deviance across sub-regions than across regions, whereas spatial connectivity did

361 the reverse (see Fig. 3). Comparing explained deviances between regions, the primacy

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362 of environmental niche similarity (mostly climate, see Table S5) in the northern ones

363 (NW and NE) is consistent with the notion that northern regional pools are

364 geographically sorted by current climate (our hypothesis H3), whereas the importance

365 shown by spatial connectivity in the remaining regions is consistent with the more

366 complex orography of central and southern Europe (consistent with hypothesis H4).

367

368 Ancestral Range Estimation

369 Both phylogenetic datasets (i.e. alternative calibration scenarios) yielded similar

370 qualitative and quantitative results (Appendix S5). Thus, we only present here ancestral

371 range estimations based on Deuve’s et al. [28] calibration. GAMM results showed that

372 node marginal probability of the most probable state increased towards younger nodes

373 (P<0.01, explained deviance =7.49%, Fig. 4a). However, this increase showed a steep

374 increment coinciding with the Pleistocene. Indeed, piecewise regression revealed that

375 the relationship between marginal state probability and node age changed at 1.51 Mya

376 (median value; with 45th and 55th percentiles at 1.24 and 1.89 Mya., respectively; P <

377 0.01; Figs. 4a and S3), suggesting that most of the phylogenetic structuration of

378 Carabus faunas began around the Pleistocene, supporting hypothesis H6.

379 Complementary analyses yield similar results (see Appendix S5).

380 In agreement with these results, we found that the probability of finding

381 phylogenetic nodes having all descendants belonging to the same region was higher for

382 post-Pleistocene nodes (median at 0.21; 25th and 75th percentiles at 0.20 and 0.23,

383 respectively; Figs. 4b and S4) than for pre-Pleistocene ones (median at 0.10; 25th and

384 75th percentiles at 0.08 and 0.11). These low probabilities are congruent with the lack of

385 phylogenetic signal in module co-occurrence previously found. Interestingly, the

386 probabilities found for both types of nodes (i.e. pre- and post-Pleistocene) were higher

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387 in southern and central regions than in northern ones (Fig. 4b). This suggests that

388 regions not covered by ice during the LGM can still reflect some old historical legacies

389 (as shown by the higher pre-Pleistocene node probability) while accumulating some

390 related lineages that diversify during and after the Pleistocene (as indicated by the

391 higher post-Pleistocene node probability).

392

393 Discussion

394 More than 140 years ago, Wallace [1] foresaw that the influence of Pleistocene

395 glaciations on the distribution of diversity had been strong enough to erode the imprint

396 of previous events. Our results support Wallace's thoughts, showing a remarkable

397 coincidence between the distribution of the ice sheets at the Last Glacial Maximum and

398 the current configuration and evolutionary structure of European Carabus Faunas.

399 The first line of evidence supporting this idea comes from the close spatial

400 relationship between the southern limits of the ice sheet at LGM and the transition zone

401 separating the southern and northern regions. This border also coincides with the line

402 identified by Calatayud et al. [19] where the relationship between Carabus species

403 richness and current climate changes (Fig. 2). Thus, it seems that Pleistocene climate

404 changes not only shaped phylogeographic [11-16,49] and species richness patterns [17-

405 19], but that Ice ages also left a strong imprint on the geographical structure of species

406 composition at a regional scale. Accordingly, the species from the northwestern region

407 (NW) show the lowest level of endemism (Fig. S5), as expected for regional faunas

408 composed of species that have recently colonized the north of Europe from southern

409 glacial refugia [19], our hypothesis H5. In fact, although these species show large

410 distribution ranges in different parts of southern Europe, their ranges only overlap near

411 the northern Carpathian Mountains (Fig. S6). This area was a glacial refugia for a large

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412 and taxonomically diverse array of northern European species (e.g. [16] and references

413 therein), including Carabus [50]. Additionally, the decrease in modularity values with

414 latitude also points to a lesser geographical structure of northern assemblages, which

415 can be interpreted as the result of a post-glacial colonization, together with less

416 geographic complexity in some areas.

417 Besides the Pleistocene effects in the definition and geographical structure of

418 regional species pools, we also found evidence of the imprint of this geological period

419 on the processes configuring the distribution of Carabus faunas. The general strong

420 relationship between regional patterns of co-occurrence and both niche similarities and

421 spatial connectivity shows that co-occurring species tend to have similar realized

422 environmental niches and that also tend to be geographically constrained by the same

423 dispersal barriers. This latter result was expected given the –presumed– low dispersal

424 capacity of Carabus species [27], which is likely to be behind the spatial coincidence of

425 module transition zones and geographical barriers. Perhaps more unexpected is the

426 weak effect of phylogenetic distances despite the strong relationship between regional

427 co-occurrence and niche similarities. This implies that geographical barriers rather than

428 climatic-niche conservatism have restricted species distributions even within regions of

429 similar climate. These results also point to Carabus niche evolution being, to some

430 extent, evolutionarily unconstrained, which is congruent with the generally high

431 adaptive capacity of insects (e.g. [51]).

432 Whatever the origin of the relationship between species occurrence and

433 environmental conditions, what is certainly true is that its strength changes between

434 regions. These changes follow a latitudinal gradient in the importance of environmental

435 niche similarities (Fig. 3). The occurrence into sub-regions is more strongly related to

436 the similarity in the realized niche in the north than in the south. This might be a direct

18

437 consequence of the effects of post-glacial colonization, where formerly glaciated areas

438 show a clear sorting of species due to its environmental preferences. On the contrary, in

439 southern regions species are expected to have had more time to diversify and sort

440 geographically by other factors besides climate [18]. Our findings corroborated this idea

441 since we found strong effects of dispersal barriers in these areas. Moreover, although we

442 did not find a significant phylogenetic signal in the subregional co-ocurrence over these

443 regions (except for the central-western module), our analyses revealed that they hold a

444 small but still larger number of related species compared to northern ones, supporting

445 that more stable regions are more prone to accumulate related species.

446 Despite of these related species of southern regions, we found a generalized

447 lack of phylogenetic structuration of Carabus faunas. This can be the outcome of

448 relatively recent speciation events due to vicariance and/or a cul-de-sac effect [52]. The

449 former would imply the formation of dispersal barriers promoting the geographical split

450 of many lineages and subsequent allopatric speciation [53]. Yet, the geophysical

451 accidents that can be associated with the limits of the Carabus regions largely predate

452 the origin of the genus [28,54]. On the other hand, a generalized dispersion into climatic

453 refugia, together with a subsequent stagnancy within them (i.e. a cul-de-sac effect) may

454 also produce the observed mixing of unrelated linages into regions. Although it is

455 difficult to distinguish between both processes, the latter seems more plausible, with

456 southern regions accumulating unrelated species while acting as glacial refugia, and

457 northern ones being recolonized by unrelated species with similar environmental niches

458 and/or simply higher dispersal capacity [17].

459 Supporting the Pleistocene signature, our results showed a temporal coincidence

460 between this geological period and the phylogenetic structuration of Carabus faunas.

461 This result was consistent regardless of the different approaches used and across the

19

462 different time calibration scenarios. This robust temporal coincidence supports that the

463 current regional organization of Carabus lineages is rooted at the Pleistocene, which

464 also explains the general lack of phylogenetic structure. Our results partially contrast

465 with ancestral range estimations for clades inhabiting areas that were never glaciated,

466 where more ancient signals were found in the spatial sorting of lineages [55-58]. These

467 previous findings are, nonetheless, congruent with the higher probability of holding

468 related Carabus species of southern and more stable European regions. In sum, these

469 results suggest that the repeated advances and retreats of ice sheets and glacial

470 conditions that characterize the European Pleistocene produced repeated cycles of

471 retreat to southern regions and advance towards the north of Carabus species, a hustle-

472 and-bustle process that ultimately led to the observed mixing of unrelated lineages, with

473 few related species inhabiting in less affected regions.

474 To summarize, our results provide solid arguments in favour of the importance

475 of Pleistocene glaciations along with geographical barriers and niche-based processes in

476 structuring the regional faunas of European Carabus. On the one hand, this group’s

477 faunas are primarily delimited by the location of the southern limit of the ice sheet at

478 LGM, which separates two large regions that differ not only in species composition, but

479 also in the processes underlying the spatial organization of these species. On the other

480 hand, the phylogenetic structure of these faunas coincides with the beginning of the

481 Pleistocene. This implies that the geographical distribution of species and lineages is

482 profoundly shaped by past climates. Moreover, our results also suggest that ecological

483 [7,59] and evolutionary mechanisms [8,60] that rely on procceses operating at regional

484 scales can be profoundly affected by the history of Earth’s climates. Hence, the study of

485 these historical events may be essential to unravel both large and local scale diversity

486 patterns.

20

487

488 Acknowledgements

489 We are very grateful to Achille Casale for providing data on Carabus habitat

490 preferences and comments on an early version of the phylogeny, and the Scientific

491 Computation Centre of Andalusia (CICA) for the computing services they provided. We

492 acknowledge insightful discussions with Hortal lab members. This work was supported

493 by the Spanish projects SCARPO (CGL2011-29317) , UNITED (CGL2016-78070-P)

494 and BIOREGIONS 2.0 (CGL2017-86926-P) , funded respectively by the Spanish

495 Ministry of Economy, Industry and Competitiveness (MINECO), the Spanish Ministry

496 for Science, Innovation and Universities (MICINN) and AEI/FEDER, UE. JC was

497 supported by a FPU-fellowship of the Spanish Ministry of Education (FPU12/00575),

498 and ML and JLH by MINECO FPI and Juan de la Cierva-Incorporación (ref. IJCI-2015-

499 23618) grants, respectively.

500

501 Author contributions

502 JC and JH conceived the ideas. JC designed the study with contributions of all authors.

503 JC, RMV, ML and JLH analysed the data. All authors discussed results. JC wrote the

504 paper with contributions of all authors.

505

506 References

507 1. Wallace AR. 1876 The geographical distribution of animals: with a study of the

508 relations of living and extinct faunas as elucidating the past changes of the earth’s

509 surface. Cambridge, UK: Cambridge University Press.

510 2. Holt BG et al. 2013 An update of Wallace’s zoogeographic regions of the world.

511 Science 339, 74–78. (doi:10.1126/science.1228282)

21

512 3. Rueda M, Rodríguez MÁ, Hawkins BA. 2013 Identifying global zoogeographical

513 regions: lessons from Wallace. J. Biogeogr. 40, 2215–2225. (doi:10.1111/jbi.12214)

514 4. Ricklefs RE. 2008 Disintegration of the ecological community. Am. Nat. 172, 741–

515 750. (doi:10.1086/593002)

516 5. Medina NG, Albertos B, Lara F, Mazimpaka V, Garilleti R, Draper D, Hortal

517 J. 2014 Species richness of epiphytic bryophytes: drivers across scales on the edge

518 of the Mediterranean. Ecography 37, 80–93. (doi:10.1111/j.1600-

519 0587.2013.00095.x)

520 6. Ricklefs RE, He F. 2016 Region effects influence local tree species diversity. Proc.

521 Natl. Acad. Sci. USA 113, 674–679. (doi:10.1073/pnas.1523683113)

522 7. Naeslund B, Norberg J. 2006 Ecosystem consequences of the regional species

523 pool. Oikos 115, 504-512. (doi:10.1111/j.2006.0030-1299.14782.x)

524 8. Calatayud J, Hórreo JL, Madrigal-González J, Migeon A, Rodríguez

525 MÁ, Magalhães S, Hortal J. 2016 Geography and major host evolutionary

526 transitions shape the resource use of plant parasites. Proc. Natl. Acad. Sci. USA 113,

527 9840–9845. (doi:10.1073/pnas.1608381113)

528 9. Crisp MD, Trewick SA, Cook LG. 2011. Hypothesis testing in biogeography.

529 Trends Ecol. Evol. 26, 66–72. (doi:10.1016/j.tree.2010.11.005)

530 10. Ebach MC. 2015. Origins of Biogeography. New York, USA: Springer.

531 11. Barnes I, Matheus P, Shapiro B, Jensen D, Cooper A. 2002 Dynamics of Pleistocene

532 population extinctions in Beringian brown bears. Science 295, 2267–2270.

533 (doi:10.1126/science.1067814)

534 12. Johnson NK, Cicero C, Shaw K. 2004 New mitochondrial DNA data affirm the

535 importance of Pleistocene speciation in North American birds. Evolution 58, 122–

536 1130. (doi:10.1111/j.0014-3820.2004.tb00445.x)

22

537 13. Hewitt GM. 1999 Post-glacial re-colonization of European biota. Biol. J. Linn. Soc.

538 68, 87–112. (doi:10.1111/j.1095-8312.1999.tb01160.x)

539 14. Theissinger K, Bálint M, Feldheim KA, Haase P, Johannesen J, Laube I, Pauls SU.

540 2013 Glacial survival and post-glacial recolonization of an arctic–alpine freshwater

541 insect (Arcynopteryx dichroa, Plecoptera, Perlodidae) in Europe. J. Biogeogr. 40,

542 236–248. (doi:10.1111/j.1365-2699.2012.02793.x)

543 15. Avise JC, Walker D, Johns GC. 1998 Speciation durations and Pleistocene effects

544 on vertebrate phylogeography. Proc. R. Soc. Lond. B Biol. Sci. 265, 1707-1712

545 16. Provan J, Bennett KD. 2008 Phylogeographic insights into cryptic glacial

546 refugia. Trends Ecol. Evol. 23, 564-571. (doi:10.1098/rspb.1998.0492)

547 17. Svenning JC, Skov F. 2007 Could the tree diversity pattern in Europe be generated

548 by postglacial dispersal limitation? Ecol. Lett. 10, 453–460. (doi:10.1111/j.1461-

549 0148.2007.01038)

550 18. Hortal J, Diniz-Filho JAF, Bini LM, Rodríguez MÁ, Baselga A, Nogués-Bravo D,

551 Rangel TF, Hawkins BA, Lobo JM. 2011. Ice age climate, evolutionary constraints

552 and diversity patterns of European dung beetles. Ecol. Lett. 14, 741–748.

553 (doi:10.1111/j.1461-0248.2011.01634.x)

554 19. Calatayud J, Hortal J, Medina NG, Turin H, Bernard R, Casale A, Ortuño

555 VM, Penev L, Rodríguez MÁ. 2016 Glaciations, deciduous forests, water

556 availability and current geographical patterns in the diversity of European Carabus

557 species. J Biogeogr. 43, 2343–2353. (doi:10.1111/jbi.12811)

558 20. Mittelbach GG, Schemske DW. 2015 Ecological and evolutionary perspectives on

559 community assembly. Trends Ecol. Evol. 30, 241–247.

560 (doi:10.1016/j.tree.2015.02.008)

23

561 21. Hortal J, Roura-Pascual N, Sanders N, Rahbek C. 2010 Understanding (insect)

562 species distributions across spatial scales. Ecography 33, 51. (doi:10.1111/j.1600-

563 0587.2009.06428.x)

564 22. Colwell RK, Rangel TF. 2009 Hutchinson’s duality: the once and future niche.

565 Proc. Natl. Acad. Sci. USA 106, 19651–19658. (doi:10.1073/pnas.0901650106)

566 23. Warren DL, Cardillo M, Rosauer DF, Bolnick DI. 2014 Mistaking geography for

567 biology: inferring processes from species distributions. Trends Ecol. Evol. 29, 572–

568 580. (doi:10.1016/j.tree.2014.08.003)

569 24. Cardillo M. 2011 Phylogenetic structure of mammal assemblages at large

570 geographical scales: linking phylogenetic community ecology with macroecology.

571 Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 2545–2553.

572 (doi:10.1098/rstb.2011.0021)

573 25. Svenning JC, Eiserhardt WL, Normand S, Ordonez A, Sandel B. 2015 The influence

574 of paleoclimate on present-day patterns in biodiversity and ecosystems. Annu. Rev.

575 Ecol. Evol. Syst. 46, 551–572. (doi:10.1146/annurev-ecolsys-112414-054314)

576 26. Horreo JL, Peláez ML, Suárez T, Breedveld MC, Heulin B, Surget-Groba Y,

577 Oksanen TA, Fitze PS. 2018. Phylogeography, evolutionary history, and effects of

578 glaciations in a species (Zootoca vivipara) inhabiting multiple biogeographic

579 regions. J. Biogeogr. In press. (doi:10.1111/jbi.13349)

580 27. Turin H, Penev L, Casale A, Arndt E, Assmann T, Makarov K, Mossakowski

581 D, Szél G, Weber F. 2003 Species accounts. In The Genus Carabus in Europe: A

582 Synthesis (eds Turin H., Penev L., Casale A., editors), pp. 151–284. Sofia, Bulgaria:

583 Pensoft Publishers.

24

584 28. Deuve T, Cruaud A, Genson G, Rasplus J-Y. 2012 Molecular systematics and

585 evolutionary history of the genus Carabus (Col. Carabidae). Mol. Phylogenet. Evol.

586 65, 259–275. (doi:10.1016/j.ympev.2012.06.015)

587 29. Barber MJ. 2007 Modularity and community detection in bipartite networks. Phys.

588 Rev. E 76, 066102. (doi:10.1103/PhysRevE.76.066102)

589 30. Blondel VD, Guillaume J-L, Lambiotte R, Lefebvre E. 2008 Fast unfolding of

590 communities in large networks. J. Stat. Mech. Theory. Exp. 2008, P10008.

591 (doi:10.1088/1742-5468/2008/10/P10008)

592 31. Mucha PJ, Richardson T, Macon K, Porter MA, Onnela J-P. 2010. Community

593 structure in time-dependent, multiscale, and multiplex networks. Science 328, 876–

594 878. (doi:10.1126/science.1184819)

595 32. Kembel SW, Cowan PD, Helmus MR, Cornwell WK, Morlon H, Ackerly DD,

596 Blomberg SP, Webb CO. 2010 Picante: R tools for integrating phylogenies and

597 ecology. Bioinformatics 26, 1463–1464. (doi:10.1093/bioinformatics/btq166)

598 33. Schoener TW. 1970 Nonsynchronous spatial overlap of lizards in patchy habitats.

599 Ecology 51, 408–418. (doi:10.2307/1935376)

600 34. Broennimann O, et al. 2012 Measuring ecological niche overlap from occurrence

601 and spatial environmental data. Glob. Ecol. Biogeogr. 21, 481–497.

602 (doi:10.1111/j.1466-8238.2011.00698.x)

603 35. Hijmans RJ, Cameron SE, Parra JL, Jones PG, Jarvis A. 2005 Very high resolution

604 interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978.

605 (doi:10.1002/joc.1276)

606 36. Channan S, Collins K, Emanuel W. 2014 Global mosaics of the standard MODIS

607 land cover type data. University of Maryland and the Pacific Northwest National

608 Laboratory, College Park, Maryland, USA.

25

609 37. van Etten J. 2015 gdistance: Distances and Routes on Geographical Grids.

610 http://CRAN.R-project.org/package=gdistance.

611 38. Kluge AG. 1998 Total evidence or taxonomic congruence: cladistics or consensu

612 classification. Cladistics 14, 151-158. (doi:10.1006/clad.1997.0056)

613 39. Bouckaert R, Heled J, Kühnert D, Vaughan T, Wu CH, Xie D., Suchard MA,

614 Rambaut A, Drummond AJ. 2014 BEAST 2: a software platform for Bayesian

615 evolutionary analysis. PLoS Comput. Boil. 10, e1003537.

616 (doi:10.1371/journal.pcbi.1003537)

617 40. Andújar C, Serrano J, Gómez-Zurita J. 2012 Winding up the molecular clock in the

618 genus Carabus (Coleoptera: Carabidae): assessment of methodological decisions on

619 rate and node age estimation. BMC Evol. Biol. 12, 40. (doi:/10.1186/1471-2148-12-

620 40)

621 41. Rangel TF, Colwell RK, Graves GR, Fučíková K, Rahbek C, Diniz-Filho JAF. 2015

622 Phylogenetic uncertainty revisited: Implications for ecological analyses. Evolution

623 69, 1301–1312. (doi:10.1111/evo.12644)

624 42. Legendre P. 1993 Spatial autocorrelation: trouble or new paradigm? Ecology 74,

625 1659–1673 (doi: 10.2307/1939924)

626 43. R Core Team 2015. R: A Language and Environment for Statistical Computing. R

627 Foundation for Statistical Computing, Vienna, Austria.

628 44. Ree RH, Smith SA. 2008 Maximum likelihood inference of geographic range

629 evolution by dispersal, local extinction, and cladogenesis. Syst. boil. 57, 4-14.

630 (doi:10.1080/10635150701883881)

631 45. Matzke N. J., 2013. BioGeoBEARS: BioGeography with Bayesian (and Likelihood)

632 Evolutionary Analysis in R Scripts. University of California, Berkeley, USA.

26

633 46. Ree RH, Sanmartin I. 2018 Conceptual and statistical problems with the DEC+J

634 model of founder-event speciation and its comparison with DEC via model

635 selection. J. Boigeogr. 45, 741-749. (doi:10.1111/jbi.13173)

636 47. Wood SN, Goude Y, Shaw S. 2015 Generalized additive models for large datasets. J.

637 R. Stat. Soc. Ser. C Appl. Stat. 64, 139–155. (doi:/10.1111/rssc.12068)

638 48. Bates D, Maechler M, Bolker B, Walker S. 2014 lme4: Linear mixed-effects models

639 using Eigen and S4. R package version 1.

640 49. Horreo JL, Jimenez-Valverde A, Fitze PS. 2016. Ecological change predicts

641 population dynamics and genetic diversity over 120 000 years. Glob. Change Biol.

642 22, 1737-1745. (doi:10.1111/gcb.13196)

643 50. Homburg K, Drees C, Gossner MM, Rakosy L, Vrezec A, Assmann T. 2013

644 Multiple glacial refugia of the low-dispersal ground beetle Carabus irregularis:

645 molecular data support predictions of species distribution models. PloS One 8,

646 e61185. (doi:10.1371/journal.pone.0061185)

647 51. Overgaard J, Sørensen JG. 2008. Rapid thermal adaptation during field temperature

648 variations in Drosophila melanogaster. Cryobiology 56, 159–162.

649 (doi:10.1016/j.cryobiol.2008.01.001)

650 52. O’Regan HJ. 2008 The Iberian Peninsula–corridor or cul-de-sac? Mammalian faunal

651 change and possible routes of dispersal in the last 2 million years. Quat. Sci. Rev.

652 27, 2136–2144. (doi:10.1016/j.quascirev.2008.08.007)

653 53. Weeks BC, Claramunt S, Cracraft J. 2016 Integrating systematics and biogeography

654 to disentangle the roles of history and ecology in biotic assembly. J. of Biogeogr.

655 43, 1546–1559. (doi:10.1111/jbi.12747)

656 54. Moores EM, Fairbridge RW. 1997 Encyclopedia of European and Asian Regional

657 Geology. Berlin/Heidelberg, Germany: Springer Science & Business Media.

27

658 55. Condamine FL, Toussaint EF, Clamens A-L, Genson G, Sperling FA, Kergoat GJ.

659 2015 Deciphering the evolution of birdwing butterflies 150 years after Alfred Russel

660 Wallace. Sci. Rep. 5, 11860. (doi:10.1038/srep11860)

661 56. Economo EP, et al. 2015 Breaking out of biogeographical modules: range expansion

662 and taxon cycles in the hyperdiverse ant genus Pheidole. J. Biogeogr. 42, 2289–

663 2301. (doi:10.1111/jbi.12592)

664 57. Tänzler R, Van Dam MH, Toussaint EF, Suhardjono YR, Balke M, Riedel A. 2016.

665 Macroevolution of hyperdiverse flightless beetles reflects the complex geological

666 history of the Sunda Arc. Sci. Rep. 6, 18793. (doi:0.1038/srep18793)

667 58. Toussaint EF, Balke M. 2016 Historical biogeography of Polyura butterflies in the

668 oriental Palaeotropics: trans-archipelagic routes and South Pacific island hopping. J.

669 Biogeogr. 43, 1560–1572. (doi:10.1111/jbi.12741)

670 59. Madrigal-González J, Ruiz-Benito P, Ratcliffe S, Calatayud J, Kändler G, Lehtonen

671 A, Dahlgren J, Wirth C, Zavala MA. 2016 Complementarity effects on tree growth

672 are contingent on tree size and climatic conditions across Europe. Sci. Rep. 6,

673 32233. (doi:10.1038/srep32233)

674 60. Wüest RO, Antonelli A, Zimmermann NE, Linder HP. 2015 Available climate

675 regimes drive niche diversification during range expansion. Am. Nat. 185, 640–652.

676 (doi:10.1086/680551)

677 61. Ehlers J, Gibbard PL. 2004 Quaternary glaciations-extent and chronology: part I:

678 Europe. London, UK: Elsevier.

679

28

680 Figure captions

681 Figure 1. Identifying the factors configuring regional faunas. a) Four out of seven

682 (see b) hypothetical processes that may configure Regional faunas. Dotted lines depict

683 different regions while colours correspond with different climates. The tips of the

684 phylogeny point to the distribution of the species. b) Workflow and potential results: 1)

685 Hypothetical results of modularity analysis over the occurrence network; 2) similarity

686 matrix of occurrence into modules; 3) pairwise matrix of environmental niche

687 similarities; phylogenetic distances and topographical connectivity; and 4) hypothetical

688 results and interpretations of a partial matrix regression on species occurrence

689 similarities as a function of niche similarities, phylogenetic distances and connectivity.

690

691 Figure 2. Transition zones between regions were associated to geophysical

692 accidents and the border of the ice sheet at LGM. European Carabus regions found

693 by the network community detection analysis. a) Geographical location of modules (i.e.

694 regions) and submodules (i.e. sub-regions). Module labels correspond with: SW:

695 southwestern module; SM: southernmost module; SE: southeastern module; CW:

696 central-western module; CE: central-eastern module; NW: northweastern module; and

697 NE: northeastern module. b) Values of module specificity per grid cell; green colours

698 (i.e. cells with low specificity) identify transition zones. The dotted black line

699 corresponds with the southern limit of the ice sheet at LGM (extracted from [61]). The

700 blue line depicts the breakpoint where the temperature-Carabus richness relationship

701 changes, as found in Calatayud et al. [19].

702

703 Figure 3. Regional co-occurrence was mostly explained by geographical

704 connectivity and environmental niche similarities. Results of the partial generalized

29

705 matrix regression of similarity in regional co-occurrence as a function of environmental

706 niche similarity (climate and habitat, E), topographical connectivity (C) and

707 phylogenetic distances (P). The first and second bars correspond with the models

708 including occurrence similarities among all modules and submodules, respectively. The

709 remaining bars correspond with the models where the similarities in submodule

710 occurrence were analysed independently for the species of each module. We used

711 average results derived from phylogenies time-calibrated following Deuve et al. [28].

712 Both calibration scenarios provided similar results (see Table S6). Modules are labeled

713 according to Fig. 2a.

714

715 Figure 4. Temporal coindidence between the Pleistocene and the phylogenetic

716 structuration of Carabus regions. a) GAMM predictions of the marginal probability

717 of the most probable state as a function of node age. The dashed red lines correspond

718 with the confidence interval at 95%. The dotted black line represents the median of the

719 breakpoint found by piecewise GLM regressions. The boxplot at the bottom represent

720 the 45th and 55th percentile breakpoint values, whereas the whiskers depict the 25th and

721 75th percentiles. b) Probability of finding a phylogenetic node having all descendant

722 species grouped in the same region for pre-Pleistocene (pink) and post- Pleistocene

723 nodes (blue). This probability was calculated for all modules both jointly (“All” in x

724 axis) and independently (labelled according to Fig. 2a in x axis).

725

30

726 Figure 1.

727

31

728 Figure 2.

729

32

730 Figure 3.

731

33

732 Figure 4.

733

34

Pleistocene climate change and the for- mation of regional species pools

Calatayud, J., Rodr´ıguez, M. A.,´ Molina-Vengas, R., Leo,M., H´orreo, J.L. and Hortal, J.

Supporting Information

Appendices

1 Modularity uncertainty3

2 Phylogenetic inference and uncertainties4

3 Phylogenetic distances7

4 Deviance partitioning8

5 Phylogenetic history of regional faunas9

6 Module description 11

7 Supplementary figures 12

8 Supplementary tables 20

9 References 26

List of Figures

S1 First and second axis of the Principal Components Analysis used in niche estimation analysis...... 12 S2 Map showing the regions and subregions found by modularity analyses 13 S3 Relationship between the node marginal probability and node age.. 13

1 S4 Probability of pre- and post-Pleistocene nodes of having all descen- dants in the same region...... 14 S5 Distribution of module affinity of Carabus species...... 15 S6 Richness distribution of the Carabus species grouped in each module 16 S7 Molecular phylogeny of European Carabus species. Calibration I... 17 S8 Molecular phylogeny of European Carabus species. Calibration II.. 18 S9 Relationship between the probability of having all descendants in the same region and node age...... 19 S10 Relationship between the probability based on Fisher descendants range similarity and node age...... 19

List of Tables

S1 Results of regional and subregional co-occurrence similarity as func- tion of topographical connectivity...... 20 S2 Genbank accession numbers for Carabus mitochondrial markers... 21 S3 Genbank accession numbers for Carabus nuclear markers...... 22 S4 Summary of modularity analyses at a regional and subregional levels 23 S5 Results of regional and subregional co-occurrence similarity as func- tion of environmental variables...... 24 S6 Results of regional and subregional co-occurrence similarity as func- tion of phylogenetic distance...... 24 S7 Species insertions in molecular phylogenies...... 25

2 1 Modularity uncertainty

The network community detection used relies on the optimisation a of modularity index by employing a heuristic search. Given the stochasticity inherent to this analysis we conducted 500 optimisations and selected the network partition showing the highest modularity. This should ensures the achievement of a quasi-optimal partition. However, modularity analysis may suffer from a degeneracy problem (i.e. the potential achievement of different partitions with similar modularity, Good et al. 2010) and this may affect subsequent inferences. We therefore explored whether our analyses were degenerated. To do so, we first measured the similarity between the best partition (i.e. the one showing the highest modularity) and the rest of partitions using the Normalize Mutual Information index (Danon et al. 2005) as implemented in the Igraph package (Csardi and Nepusz 2006). This index ranges between 1, when partitions are identical, to 0. Then, we calculated the mean similarity weighted by the modularity of the partitions. If our results are highly degenerated, we should find low a mean weighted similarity. We repeated this analysis for the Pan European dataset and for each of the regions. In all situations the mean weighted similarity was considerably high (0.83, 0.95, 0.91, 0.82, 0.94, 0.98 and 0.75; respectively for Europe and modules 1, 3, 4, 5, 6 and 7 according to Fig. S2), showing the consistency of the regions and subregions found and validating the used of the best partition.

3 2 Phylogenetic inference and uncertainties

Phylogenetic inference extended

To unravel the evolutionary history of the Carabus lineage and assess the poten- tial importance of evolutionary processes in determining the formation of Carabus species pool, we reconstructed a species-level time-calibrated molecular phylogeny including the 89 species for which we found available DNA information on ten mark- ers (eight mitochondrial regions: 12S rDNA,16S rDNA, 28S rDNA, ND4, ND5, COI, Cytb and PEPCK; plus two nuclear ones: anonymous locus and wingless; Tables S2 and S3). We aligned each marker independently using different algorithms: MAFFT (Katoh and Standley 2013), Clustal X (Thompson et al. 1994, Larkin et al. 2007), MUSCLE (Edgar 2004) and Kalign (Lassmann and Sonnhammer 2005), and selected the most reliable alignment for each marker using the multiple overlap score (MOS) provided by MUMSA (Lassmann and Sonnhammer 2006). We removed ambigu- ous or poorly aligned positions from the alignments with trimAl (Capella-Guti´errez et al. 2009). The final dataset was concatenated in 5603 basepairs following a total- evidence approach (Kluge 1998), and was used to conduct Bayesian phylogenetic inference with BEAST v.2.4.6 software (Bouckaert et al. 2014). We used a GTR model for sequence evolution, a Random Local Clock, a birth-death model prior, and 100 million of MCMC chain length searching for convergence. We used the soft- ware Tracer v 1.6 (available at http://tree.bio.ed.ac.uk/software/tracer/) to check for MCMC chains convergence, and FigTree v.1.4.2 (available at http://tree.bio.ed.ac.uk/software/figtree/) for viewing and editing the phylogenetic trees (see Figures S7 and S8).

Phylogenetic uncertainties

We considered different sources of uncertainties during the reconstruction of Carabus phylogenetic hypothesis. These uncertainties were related to 1) time estimations of the origin of the group; 2) molecular reconstruction and 3) species lacking molecular information. In this appendix we provide an explanation on how we deal with them.

4 Appendix 2 5

Time calibration uncertainties There exists controversies in the origin of Carabus genus. Indeed, the two most up-to-date studies on the relationships of the group provided contrasting time es- timations (Deuve et al. 2012, And´ujar et al. 2012). While And´ujar et al.(2012) estimated the origin of the group at 25.2 Mya (95% HDP: 18.4-33.0), Deuve et al. (2012) prodived a much younger origin at 17.3 Mya (95% HDP: 12.8-23.3). These differences might compromise subsequent results, specially those ones related to the effects of Pleistocene glaciations on the phylogenetic configuration of regional faunas. To account for this, we conducted two independent phylogenetic recon- structions, where both estimations of the crown age of Carabus were used as time calibration points.

Molecular reconstruction uncertainties During the molecular reconstruction some uncertainties may be also expected, re- garding both tree topology and nodes’ age estimations. The Bayesian approximation used here allowed to account for them, by sampling several trees from the posterior distribution. That is, instead of using a consensus phylogeny in subsequent analyses, we sampled 100 trees from the posterior distribution and repeated the analyses using each tree. By doing so, we were considering both uncertainties in tree topology and node dating.

Species lacking molecular information In order to incorporate species that were missing from the phylogenetic tree due to unavailability of molecular data, we followed the procedure described by Rangel et al.(2015). These authors defined a phylogenetically uncertain taxon (PUT) as a taxonomic unit that is recognized as valid by experts and is also accepted as belong- ing to a particular clade, but is missing from the available phylogenetic tree. Thus, in order to account for phylogenetic uncertainty of the PUTs of our species list (n = 42), we used taxonomical information to define the clade that unequivocally con- tains each PUT (the most derived consensus clade, MDCC). Then, several expanded trees can be generated by randomly inserting each PUT at a certain point below the corresponding MDCC. Notice that Rangel et al.’s(2015) approach is mostly design to work with just one molecular phylogeny, so that PUTs can be associated to a phy- logenetic node representing a MDCC. In our case, we used 100 phylogenies, which may differ in the MDCC where a species should be associated. For example, a given species could be associated to a given subgenera that may appear as monophyletic or phylophyletic depending on the tree considered. To account for this, we first defined a list of species that are expected to be close relative of a given PUT, based on both taxonomic information and previous and our phylogenies (see Table S7 for the species list of each PUT). Then, we set the MDCC as the phylogenetic node that include all of these species. By doing so, we were considering potential incon- gruences between taxonomic and molecular classifications across different molecular phylogenies, in a conservative way. Following the previous example, if a species is expected to belong a subgenera that is polyphyletic, the MDCC will be the phyloge- netic node including all species belonging to the subgenera, which will also include other species. Once MDCCs were identified we used the software SUNPLIN Martins Appendix 2 6 et al.(2013) to generated 100 expanded trees for each molecular phylogeny. In total we generated 20,000 phylogenetic hypotheses (two time calibration sce- narios per 100 sampled phylogenies per 100 taxonomically expanded trees) and we randomly selected 1,000 phylogenies per calibration scenario in subsequent analyses. 3 Phylogenetic distances

The correlation between phylogenetic distances and similarity in regional occur- rence can be indicative of different processes. On the one hand, a strong correlation between both variables may result from diversification events occurring within re- gions, so that we could expect close relative inhabiting same region(s). On the other hand, a significant correlation may emerge if environmental niches are evolutionar- ily conserved and regions hold different environments. In this case, we could expect close relative inhabiting regions of similar climates. In the former case, phylogenetic distances based on time since diversification (e.g. patristic distances in dated phy- logenies) should capture well the process. Yet, such distances may not accurately describe evolutionary processes leading to phylogenetically conserved niches as niche resemblance may follow different evolutionary models (such as the Brownian motion or Ornstein-Uhlenbeck models). To have this into account, we conducted different branch length transformations prior to compute patristic distances. We first calcu- lated patristic distances based on original branch lengths. Then, to accommodate a Brownian motion model of niche evolution we conducted a squared root transforma- tion of patristic distances (Letten and Cornwell 2015). We also transformed branch lengths to accommodate a Ornstein-Uhlenbeck model (OU) with different values for the attraction parameter (1, 2.5, 5 and 10) and computed the patristic distances using the transformed phylogenies. We used the six distance matrices (i.e. patristic distances based on original branch lengths, its squared root transformation and the OU transformations with 1, 2.5, 5, and 10 attraction parameters) as explanatory variables of the similarity in regional co-occurrence independently. Finally, we se- lected the distance matrices showing the highest correlations to asses significance and in subsequent deviance partitioning analyses (see Table S5).

7 4 Deviance partitioning

Variation partitioning allows to separate the independent and shared variation of a dependent variable explained by different groups of explanatory variables (Legendre 1993). Thus, this approach allows to quantify how much variation is explained by each group independently of co-linearities among them. In an analogous way, here we explored the patterns of covariation between environmental niche similarities (i.e. climatic and habitat similarity matrices; E), geophysical connectivity (C) and phylogenetic history (P) when explaining regional occurrence similarities by using deviance partitioning. We first modelled occurrence similarity considering all explanatory groups (DALL = E + C + P ). Then, we obtained the deviance explained by all possible pairs of ex- planatory groups (i.e. DE+C , DE+P , DC+P ). In that way, the deviance explained by niche similarities alone was calculated as De = DALL − DC+P . The deviance explained by connectivity alone (Dc) and phylogenetic distance (Dp) alone was cal- culated in a similar way. Then, the deviance explained exclusively by niche similarity and connectivity jointly was calculated as De+c = DALL − DP − De − Dc. We cal- culated the deviance explained exclusively by the remaining pairs of variables (De+c and Dc+p) in a similar way. Finally, the deviance explained by the three groups of variables jointly was computed as De+c+p = DALL−De−Dc−Dp−De+c−De+p−Dc+p. In several occasions we found that only niche similarities and connectivity were significantly related to occurrence similarities. In these cases, we first calculated the deviance explained by these two groups of variables (DE+C ). Then, the independent deviance explained by niche similarities was computed as De = DE+C − DC ; and in a similar way we calculated the deviance explained exclusively by connectivity (Dc). Finally, the shared explained deviance was computed as De+c = DE+C − De − Dc.

8 5 Phylogenetic history of regional faunas

Breakpoint estimation

We explored for a breakpoint in the relationship between node age and the prob- ability of the most likely node state. Given the large amount of observations (130 nodes x 1,000 phylogenies) we firstly estimated the breakpoint independently for each phylogenetic hypothesis. To do so, we included the breakpoint as a new pa- rameter in a generalized linear model of marginal probabilities as a function of node age, minimizing the deviance of the fitted model using the function ”optimize” in the R package lme4 (Bates et al. 2014). Finally, to assess significance we used the av- eraged breakpoint value in a GLMM with the same fixed formulation but including the phylogenetic hypothesis as a random factor.

Complementary analyses

Ancestral range estimation analysis has been proven of great utility to unravel historical processes configuring species distribution. Yet, general assumptions of probabilistic ancestral range estimation models may compromise subsequent inter- pretations (Ree and Sanmart´ın 2018). Moreover, our dataset does not fulfil the assumption of ancestral range estimation models that phylogenies should include all extant lineages, since some Carabus linages have representatives from outside Europe (mainly Asiatic species) that were not included in the analyses. Hence, we also employed two complementary approaches that do not violate any assumption to assess the role of history in shaping Carabus regional faunas. Here we explain both approaches and present the results of all analyses (i.e. ancestral estimation models plus complementary analyses) obtained when using phylogenies calibrated according to Deuve et al.(2012) and to And´ujar et al.(2012). We first explored for temporal signals in the regional organization of Carabus lineages by generating a binomial variable where a phylogenetic node received a 1 if all its descendant species were grouped in the same region, and 0 otherwise. We determined that a species belongs to a region based on results from modularity anal-

9 Appendix 5 10 ysis, which groups species to the single region where they show the highest specificity (i.e. the largest portion of their ranges occur within the region).This analysis would further elucidate if there is a breakpoint in the relationship between node age and the probability that all descendant species belong to the same region. Although this procedure should work fine for most Carabus species, in some cases it ignores the fact that a species can have similar specifity for more than one region. To account for this, we conducted a second complementary analysis. We also generated a binomial variable but in this case it was based on the statistical significance of the similitude in the regional distribution of phylogenetic nodes. That is, for the two descendants node of each phylogenetic node we first obtained the area of their range in each mod- ule (estimate as the number of grid cells). Then, we compared these distributions among modules using the Fisher’s exact test (Fisher 1922). Nodes whose descendant nodes do not have significantly different (at P > 0.05) distributions among regions received a value of 1. Whereas nodes having a significantly different distribution received a value of 0. We repeated this procedure for 1000 randomly chosen phylo- genies of each time calibration scheme. Finally, we model this binomial variable as a function of node age following the analytical procedure explained in the main text (i.e. GAMM, piecewise GLM and GLMM). Results from both approaches were congruent with ancestral range estimation results and across both phylogenetic calibration schemes. First, results from an- cestral estimation showed very similar patterns for the two phylogenetic datasets (Fig. S3). Indeed, the breakpoint where probabilities began a stepper increase was estimated at 1.51 Mya (median value, 45th and 55th percentiles at 1.22 and 1.89 Mya, respectively) using Deuve et al.(2012) time estimations; and at 2.14 Mya (median value, 45th and 55th percentiles at 1.92 and 2.29 Mya) following And´ujar et al.(2012) ones (Fig. S3). These findings were congruent with results based on the modularity based binomial variable, although in this case breakpoint estimations were older and there was more variability between phylogenetic datasets (Fig. S9). Specifically, the breakpoint for Deuve et al.(2012) based phylogenies was estimated at 3.70 Mya (45 th and 55th percentiles at 2.47 and 4.48 Mya); whereas for And´ujar et al.(2012) based ones was at 6.83 Mya (45th and 55th percentiles at 4.97 and 8.11 Mya). It could be argued that this last much older breakpoint restrained robustness to the tempo- ral signal of Pleistocene glaciations. However, it should be notice that in this case GAMM revealed a second breakpoint much closer to the Pleistocene (Fig. S9). Fi- nally, analyses of the Fisher based binomial variable also showed a marked temporal coincidence between the Pleistocene and the phylogenetic configuration of Carabus faunas (Fig. S10). Indeed, while the breakpoint for Deuve et al.(2012) based phylo- genies was estimated at 3.43 Mya (45th and 55th percentiles at 2.39 and 3.94 Mya), for And´ujar et al.(2012) based ones was at 1.07 Mya (45 th and 55th percentiles at 0.89 and 1.40 Mya). Notice also, that in this last case, GAMM revealed a second breakpoint associated with the Plio-Pleistocene transition (Fig. S10). In sum, the congruent results across methods and time calibrations provided confident support for the important role of Pleistocene glaciations in the geographical configuration of Carabus lineages in Europe. 6 Module description

Module 1 holds 21 species mainly living in South-western Palearctic (Iberian Penin- sula, North of Africa, Balearic Islands, Corsica, Sardinia and the western half of Sicilia). This module was subdivided into four submodules. Module 2 included only two species, both endemic of Crete. Module 3 identified an East Mediterranean region including the Italic Peninsula, part of Greece and Turkey. This module holds 18 species and was subdivided into five submodules. Module 4 depicted a Central European region embracing the Alps and the Carpathian Mountains, as well as Cen- tral European plains. This module showed the highest species richness, including 49 Carabus species, and was split into four submodules. Module 5 and module 6 comprised northern regions and showed the lowest species richness values, holding 10 species each. The former comprised Iceland and the British Isles and extended eastward up to the vicinity of the Ural Mountains. The latter included this mountain range and expanded to the easternmost zone of the study area. Both modules were divided into 3 submodules. Finally, module 7 included 21 species and embraced a south-eastern central European region expanding from the Carpathian Mountains to the south Ural Mountains. This module was split into three submodules.

11 7 Supplementary figures

Eigenvalues

Temp. warmest

Anual Temp. Temp. driest

Altitude

Prep. warmest

Prep. driest Anual prep.

Fig. S1: First and second axis of Principal Components Analysis showing the used variables (arrows). Temp. warmest: Temperature during the warmest quarter; An- nual Temp: Mean annual temperature; Temp. driest: Temperature during the driest quarter; Altitude: Altitudinal range; Annual prep.: Annual precipitation; Prep. dri- est: precipitation during the driest quarter; and Prep. warmest: precipitation during the warmest quarter. The eigenvalues of the two selected axes are shown in the top right corner.

12 Appendix 7 13

3.2 3.3 2 0 2.300 4.600 Km.

Fig. S2: Map showing the regions and subregions found by modularity analyses. Labels correspond with Fig.2 a and Table S4.

a) b)

Exp. dev. = 7.31% Exp. dev. = 7.49%

Fig. S3: GAMM predictions of the marginal probability of the most probable state as a function of node age for the phylogenies calibrated following a) And´ujar et al.(2012) and b) Deuve et al.(2012). The doted red lines corresponds with the confidence interval at 95%. The dashed black line represents the median of the breakpoint found by piecewise GLM regressions. The boxplot at the bottom represent the 45th and 55th percentile breakpoint values, whereas the whiskers depict the 25th and 75th percentiles. Appendix 7 14

a) b)

All All

Fig. S4: Boxplot showing the probability of finding a phylogenetic node hav- ing all descendant species grouped in the same region for pre-Pleistocene (pink) and post- Pleistocene nodes (blue). This probability was calculated for all re- gions jointly (”All” in x axis) and independently (labelled according to Fig. S2 in x axis)deuve2012molecular. For phylogenies calibrated using a) And´ujar et al.’s(2012) age estimations and b) using Deuve et al.’s(2012) ones. Appendix 7 15

Module 1 Module 3 Frequency Frequency 0 5 10 15 0 2 4 6

0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0

Module 4 Module 5 Frequency Frequency 0 10 20 30 0 2 4 6

0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0

Module 6 Module 7 Frequency Frequency 0 2 4 6 0 2 4 6 8

0.2 0.4 0.6 0.8 1.0 0.2 0.4 0.6 0.8 1.0

endemicity endemicity Fig. S5: Distribution of module affinity (i.e. endemicity) values for the Carabus species grouped in each module. Note that modele affinity range between zero and one, being one the value of maximum endemicity. Modules are labelled according to Fig. S2. Appendix 7 16

Module 1 Module 3

0 0

9 7

Module 4 Module 7

0 0

19 12

Module 5 Module 6

0 0

10 8

Fig. S6: Maps showing the richness distribution of the Carabus species grouped in each module. Modules are labelled according to Fig. S2. Note that module 2 only grouped 2 species which are endemic of Crete, so we did not map it Appendix 7 17

0,99 Orinocarabus_linnei 1 Diocarabus_loschnikovi 1 Tomocarabus_convexus 0,98 Tomocarabus_marginalis 1 Eurycarabus_genei Eurycarabus_famini 1 Oreocarabus_hortensis 1 1 Oreocarabus_glabratus 1 Pachystus_cavernosus 1 Aulonocarabus_canaliculatus Aulonocarabus_truncaticollis 1 Mesocarabus_lusitanicus 1 1 Mesocarabus_macrocephalus 1 Mesocarabus_problematicus Mesocarabus_dufouri 1 1 Oreocarabus_amplipennis 1 Oreocarabus_ghilianii Oreocarabus_guadarramus 1 0,95Orinocarabus_heteromorphus 1 Orinocarabus_concolor 1 Orinocarabus_fairmairei 1 Orinocarabus_adamellicola 1 Orinocarabus_sylvestris Orinocarabus_putzeysianus 1 Archicarabus_nemoralis 1 Archicarabus_pseudomonticola 0,52 Archicarabus_monticola 1 Archicarabus_rossii Archicarabus_steuarti 1 Carabus_granulatus 1 1 Eucarabus_deyrollei Eucarabus_arvensis 1 1 Eucarabus_catenulatus 1 1 Eucarabus_parreyssi 0,7 Eucarabus_ulrichii Eucarabus_italicus Macrothorax_morbillosus 1 0,6 Macrothorax_rugosus Macrothorax_planatus 0,66 1 Chrysocarabus_lineatus 1 1 Chrysocarabus_rutilans 1 Chrysocarabus_splendens 0,97 Chrysocarabus_hispanus 1 Chrysocarabus_auronitens 0 630,8 Iniopachys_pyrenaeus 1 Chrysocarabus_olympiae Chrysocarabus_solieri Sphodristocarabus_janthinus Megodontus_violaceus 0,98 Pachycranion_ermaki 1 1 1 Pachycranion_schoenherri 1 Megodontus_exaratus 1 Megodontus_septemcarinatus 1 Megodontus_germarii 1 1 1 0,99 Procerus_gigas Procerus_scabrosus 1 Megodontus_caelatus 1 Procrustes_coriaceus Lamprostus_torosus Morphocarabus_monilis 1Morphocarabus_comptus 1 1 Morphocarabus_hampei 0,62 Morphocarabus_kollari 0,57 1 Morphocarabus_scheidleri 1 Morphocarabus_henningi 0,99 Morphocarabus_regalis 0,49 Morphocarabus_hummeli 0,55 1 Morphocarabus_aeruginosus Morphocarabus_odoratus 1 1 Trachycarabus_estreicheri Trachycarabus_haeres 1 Morphocarabus_scabriusculus 1 Rhabdotocarabus_melancholicus 1 Ctenocarabus_galicianus 1 Autocarabus_cancellatus 0,95 Autocarabus_auratus Orinocarabus_latreilleanus 1 Hemicarabus_nitens Autocarabus_cristofori 1 Limnocarabus_clathratus Platycarabus_irregularis 1 1 Platycarabus_depressus 1 Platycarabus_fabricii Platycarabus_creutzeri 1 1 1 Chaetocarabus_lefebvrei 1 Chaetocarabus_intricarius 1 Chaetocarabus_merlini 1 Heterocarabus_marietii Hygrocarabus_nodulosus 1 Cychrus_attenuatus Cychrus_caraboides

-30 -25 -20 -15 -10 -5 0 Fig. S7: Molecular phylogeny of European Carabus species with molecular dating based on Deuve et al.’s(2012) crown age estimation. Node posterior probabilities are shown. Bars 95% HPD intervals for node ages in Mya. Appendix 7 18

Orinocarabus_linnei 0,97 Diocarabus_loschnikovi 1 Tomocarabus_convexus 1 1 Tomocarabus_marginalis Eurycarabus_genei 1 Eurycarabus_famini 1 Oreocarabus_hortensis 1 Oreocarabus_glabratus 1 1 Pachystus_cavernosus Aulonocarabus_canaliculatus 1

Aulonocarabus_trunca¡ collis Mesocarabus_lusitanicus 1 1 1 Mesocarabus_macrocephalus

1 Mesocarabus_problema¡ cus Mesocarabus_dufouri 1 Oreocarabus_amplipennis 1 1 Oreocarabus_ghilianii Oreocarabus_guadarramus Orinocarabus_heteromorphus 1 0,89 1 Orinocarabus_concolor Orinocarabus_fairmairei 1 Orinocarabus_adamellicola 1 1 1 Orinocarabus_sylvestris Orinocarabus_putzeysianus Hemicarabus_nitens 0,92 Rhabdotocarabus_melancholicus 1 0,74 Ctenocarabus_galicianus Autocarabus_cancellatus 1 1 Autocarabus_auratus Autocarabus_cristofori Orinocarabus_latreilleanus 1 Platycarabus_irregularis 1 Platycarabus_depressus 1 1Platycarabus_fabricii 1 Platycarabus_creutzeri 1 Chaetocarabus_lefebvrei 1 1 Chaetocarabus_intricarius 0,98 1 Chaetocarabus_merlini

Heterocarabus_marie ¡i 1 1 Hygrocarabus_nodulosus Limnocarabus_clathratus Archicarabus_nemoralis 1 1 Archicarabus_pseudomon¡ cola

0,59 Archicarabus_mon¡ cola 1 Archicarabus_rossii

Archicarabus_steuar¡ 1 Carabus_granulatus 1 Eucarabus_deyrollei 1 Eucarabus_arvensis 1 Eucarabus_catenulatus 1 Eucarabus_parreyssi 1 Eucarabus_ulrichii 0,7 Eucarabus_italicus Macrothorax_morbillosus 1 Macrothorax_rugosus 0,56 Macrothorax_planatus Chrysocarabus_lineatus 1 1 1 Chrysocarabus_ru ¡lans 0,83 1 Chrysocarabus_splendens 1 Chrysocarabus_hispanus 1 0,96 Chrysocarabus_auronitens 0 84 0,7 Iniopachys_pyrenaeus Chrysocarabus_olympiae 1 Chrysocarabus_solieri Sphodristocarabus_janthinus Megodontus_violaceus 0,98 Pachycranion_ermaki 1 1 1 Pachycranion_schoenherri Megodontus_exaratus 1 0,98 Megodontus_septemcarinatus 0,43 Megodontus_germarii 1 0,99 Procerus_gigas 0,98 Procerus_scabrosus 1 Megodontus_caelatus Procrustes_coriaceus 1 Lamprostus_torosus Morphocarabus_hummeli Morphocarabus_monilis 0,47 Morphocarabus_comptus 1 1 1 Morphocarabus_hampei 0,61 Morphocarabus_kollari 1 Morphocarabus_scheidleri 0,55 Morphocarabus_henningi 1 1 Morphocarabus_regalis Morphocarabus_scabriusculus Trachycarabus_estreicheri 1 1 Trachycarabus_haeres Morphocarabus_aeruginosus 0,99 Morphocarabus_odoratus

Cychrus_a¢enuatus 1 Cychrus_caraboides

-50 -40 -30 -20 -10 0 Fig. S8: Molecular phylogeny of European Carabus species with molecular dating based on And´ujar et al.’s(2012) crown age estimation. Node posterior probabilities are shown. Bars 95% HPD intervals for node ages in Mya. Appendix 7 19 a) b)

Exp. dev. = 17.3% Exp. dev. = 14.2%

Fig. S9: GAMM predictions of the the probability of having all descendants be- longing to the same region as a function of node age for the phylogenies calobrated following a) And´ujar et al.(2012) and b) Deuve et al.(2012). The doted red lines corresponds with the confidence interval at 95%. The dashed black line represents the median of the breakpoint found by piecewise GLM regressions. The boxplot at the bottom represent the 45th and 55th percentile breakpoint values, whereas the whiskers depict the 25th and 75th percentiles. Horizontal dotted black line represents a 0.5 probability.

a) b)

Exp. dev. = 14.5% Exp. dev. = 8.5%

Fig. S10: GAMM predictions of the probability of having daughter nodes having non-significant differences in their distribution across modules as a function of node age for the phylogenies calibrated following a) And´ujar et al.(2012) and b) Deuve et al.(2012). The doted red lines corresponds with the confidence interval at 95%. The dashed black line represents the median of the breakpoint found by piecewise GLM regressions. The boxplot at the bottom represent the 45th and 55th percentile breakpoint values, whereas the whiskers depict the 25th and 75th percentiles. 8 Supplementary tables

Table S1: Results of regional and subregional co-occurrence similarity as func- tion of topographical connectivity with different probabilities to transit across water bodies (Pwater). Explained deviance (Exp.Dev.) and regression coefficients (Coeff.) are shown for models including all species (Modules and Submodules) and models including only species of each module (Module 1 to Module 7). Models in red are those where statistical significance was assessed and used for deviance partitioning. * Indicates significant relationship at P≤0.01.

Level Pwater = 0.1 Pwater = 0.25 Pwater = 0.5 Pwater = 0.75 Pwater = 0.9 Modules Exp. Dev 0.477 0.525 0,509* 0.477 0.457 Coeff. -2.009 -2.200 -2.245 -2.188 -2.132 Submodules ALL Exp. Dev 0.463 0.533 0,534* 0.511 0.495 Coeff. -1.728 -1.945 -2.039 -2.033 -2.006 Submodules M1 Exp. Dev 0.419 0.544 0,609* 0.638 0.648 Coeff. -1.953 -2.294 -2.289 -2.246 -2.224 Submodules M3 Exp. Dev 0.584 0.762 0,801* 0.773 0.750 Coeff. -1.506 -1.852 -2.052 -2.109 -2.112 Submodules M4 Exp. Dev 0.430 0.493 0,516* 0.522 0.522 Coeff. -1.242 -1.393 -1.468 -1.498 -1.507 Submodules M5 Exp. Dev 0.344 0.345 0,287* 0.231 0.201 Coeff. -0.399 -0.400 -0.364 -0.327 -0.306 Submodules M6 Exp. Dev 0.143 0.249 0,324* 0.351 0.357 Coeff. -0.572 -0.833 -1.042 -1.127 -1.146 Submodules M7 Exp. Dev 0.152 0.413 0,476* 0.450 0.428 Coeff. -0.552 -0.969 -1.071 -1.036 -1.005

20 Appendix 8 21

Table S2: Genbank accession numbers for Carabus mitochondrial markers sps ND5 12rna 16rna ND4 coi cytb pepCK rna

Archicarabus monticola AB047259 - - - - - AY183551 - Archicarabus nemoralis JQ689858 JQ647020 JQ647138 - JQ646569 JQ646676 AY183552 JQ647360 Archicarabus steuarti JX279696 - - - - JX279600 - JX279668 Aulonocarabus canaliculatus AB031472 - - - DQ645037 - AY183559 AB031415 Aulonocarabus truncaticollis AB031475 ------AB031417 Autocarabus auratus CCOMTNDS5B JQ647050 JQ647169 - JQ646600 JQ646704 JQ646874 JQ647390 Autocarabus cancellatus AB092702 JQ647049 JQ647168 - JQ646599 JQ646703 AY183574 JQ647404 Autocarabus cristoforii AB092705 JQ647047 JQ647166 JQ663419 JQ646597 JQ646701 - JQ647387 Carabus granulatus AB093174 JQ647017 JQ647135 JQ663403 - JQ646673 AF219518 JQ647357 Chaetocarabus intricarius AB047197 JQ647016 JQ647134 JQ663392 JQ646552 JQ646648 AY183503 JQ647356 Chrysocarabus auronitens AB101014 JQ647018 JQ647136 JQ663404 JQ646567 JQ689823 AY183519 JQ647358 Chrysocarabus hispanus AB101016 JQ647063 JQ647182 - JQ646611 JQ646716 AY183521 JQ647403 Chrysocarabus olympiae AB101020 - - JQ663373 - JQ646620 AY183520 - Chrysocarabus rutilans AB101017 JQ646962 JQ647080 JQ663377 JQ646528 JQ646624 AY183524 JQ647307 Chrysocarabus splendens AF190050 JQ646994 JQ647112 - KJ158785 JQ646652 AY183525 JQ647335 Ctenocarabus galicianus AB101008 JQ646977 JQ647095 - JQ646542 JQ646637 JQ646829 JQ647212 Diocarabus loschnikovi AB050767 ------Eucarabus arvensis AB041059 JQ647019 JQ647137 JQ663405 JQ646568 JQ646675 - JQ647359 Eucarabus catenulatus AB093179 JQ647044 JQ647163 JQ663417 JQ646594 JQ646698 AY183549 JQ647384 Eucarabus deyrollei JQ689848 JQ647038 JQ647157 JQ663414 JQ646588 JQ646694 - JQ647378 Eucarabus italicus AB093180 ------Eurycarabus famini AB092706 JQ646978 JQ647096 JQ663384 JQ646543 JQ646638 JQ646830 JQ647320 Eurycarabus genei AF231691 JQ646995 JQ647113 - JQ646619 JQ646653 JQ646843 JQ647336 Hemicarabus nitens AF231687 JQ647074 JQ647192 - JQ646618 JQ646726 - GU347502 Heterocarabus marietii AB047196 ------Hygrocarabus nodulosus AB047195 JQ647062 JQ647187 JQ663430 JQ646615 JQ646715 JQ646882 JQ647408 Lamprostus torosus AB101114 ------Limnocarabus clathratus JQ689869 JQ647052 JQ647171 - JQ646602 JQ646706 JQ646876 JQ647392 Macrothorax morbillosus AB101013 JQ647041 JQ647160 - JQ646591 JQ689820 AY183518 JQ647381 Macrothorax rugosus JQ689861 - - - - JQ689829 - - Megodontus exaratus AB101066 ------Megodontus germarii AB101085 - - - - - AY183514 - Megodontus septemcarinatus AB101068 ------Megodontus violaceus AB101083 JQ647026 JQ647144 - JQ646575 JQ646682 AY183513 JQ647366 Mesocarabus dufouri JX279461 - - - - JQ689841 AY183557 JQ689722 Mesocarabus lusitanicus JX279459 JQ646983 JQ647101 JQ663388 JQ646547 JQ646642 AY183556 JQ647325 Mesocarabus macrocephalus JX279510 - - - - JX277917 - JQ689700 Mesocarabus problematicus JX279582 JQ646991 JQ647132 JQ663393 JQ646553 JQ646649 AY183554 JQ647354 Morphocarabus aeruginosus AB053550 ------Morphocarabus comptus AB053532 ------Morphocarabus hampei AB053529 ------Morphocarabus henningi AB053542 ------Morphocarabus hummeli AB053567 - - - - - AY183540 - Morphocarabus kollari AB053530 ------Morphocarabus monilis JQ689859 JQ647023 JQ647140 - JQ646572 JQ646679 JQ646863 JQ647362 Morphocarabus odoratus AB053543 ------Morphocarabus regalis AB053540 ------Morphocarabus scabriusculus AB053524 ------Morphocarabus scheidleri AB053525 ------Oreocarabus amplipennis JX279700 JQ646981 JQ647099 - - JX279605 AY183558 JQ647323 Oreocarabus ghiliani JX279695 - - - - JX279599 - JX279667 Oreocarabus glabratus AB017469 JQ647015 JQ647133 JQ663401 JQ646565 - AY183571 JQ647355 Oreocarabus guadarramus JX279698 - - - - JX279604 - JX279674 Oreocarabus hortensis AB017464 JQ647033 JQ647152 JQ663412 JQ646583 JQ646690 AY183572 JQ647374 Orinocarabus adamellicola KJ546524 - - - KJ546470 - - - Orinocarabus concolor JX279705 JQ646963 JQ647081 - JQ646529 JQ646625 AY183577 JQ647308 Orinocarabus fairmairei JX279706 JQ647045 JQ647164 - JQ646595 JQ646699 - JQ647385 Orinocarabus heteromorphus AB017455 JQ646961 JQ647079 JQ663376 JQ646527 JQ646623 JQ646820 JQ647306 Orinocarabus latreilleanus AB017478 JQ646960 JQ647084 JQ663378 JQ646526 JQ646628 AY183576 JQ647305 Orinocarabus linnei AB017472 JQ647027 JQ647145 JQ663408 JQ646576 JQ646683 JQ646867 JQ647367 Orinocarabus putzeysianus AB017451 JQ646958 JQ647076 - - - - JQ647303 Orinocarabus sylvestris AB017459 - - - KJ546498 - - - Pachycranion ermaki AB101076 ------Pachycranion schoenherri AB101072 ------Pachystus cavernosus AB092719 - - - - - AY183573 - Platycarabus creutzeri KC686502 - - - KP067564 - AY183505 - Platycarabus depressus AB047199 JQ646965 JQ647083 - JQ646530 JQ646627 AY183504 - Platycarabus fabricii AF231699 ------Platycarabus irregularis JQ689856 JQ647043 JQ647162 JQ663416 JQ646593 JQ646697 JQ646872 JQ647383 Procerus gigas AB101086 ------Procerus scabrosus AB101088 ------Procrustes coriaceus AB101104 JQ647071 JQ647190 - JQ646617 JQ689839 AY183517 JQ647410 Rhabdotocarabus melancholicus AB101007 JQ647010 JQ647128 JQ663398 JQ646560 JQ646667 AY183575 JQ647244 Sphodristocarabus janthinus AB101029 ------Tomocarabus convexus AB092757 JQ647032 JQ647151 JQ663411 JQ646582 JQ646689 AY183539 JQ647373 Tomocarabus marginalis AB050756 ------Trachycarabus estreicheri AB053519 ------Trachycarabus haeres AB053520 ------Archicarabus pseudomonticola - JQ647012 JQ647130 - JQ646562 JQ646669 - JQ647352 Chaetocarabus lefebrvrei - JQ647040 JQ647159 JQ663415 JQ646590 JQ646695 - JQ647380 Chaetocarabus merlini - JQ646989 JQ647107 JQ663391 JQ646551 JQ646647 JQ646838 JQ647331 Chrysocarabus lineatus - JQ646979 JQ647097 JQ663385 JQ646544 JQ646639 AY183522 JQ647321 Macrothorax planatus - JQ647039 JQ647158 - JQ646589 - - JQ647379 Megodontus caelatus - JQ647042 JQ647161 - JQ646592 JQ646696 AY183516 JQ647382 Iniopachys pyrenaeus - - JQ647150 - JQ646581 JQ646688 JQ646870 JQ647372 Chrysocarabus solieri - - - - KJ158782 KJ158793 AY183526 KJ158801 Eucarabus parreyssi - - - - KP067568 - - - Eucarabus ulrichii - - - - KP067555 - - - Archicarabus rossii ------AY183553 - Appendix 8 22

Table S3: Genbank accession numbers for Carabus nuclear markers

sps anonimus wg

Archicarabus monticola - AY183630 Archicarabus nemoralis JQ646923 JQ646774 Archicarabus steuarti - - Aulonocarabus canaliculatus - AY183638 Aulonocarabus truncaticollis - - Autocarabus auratus JQ646944 JQ646800 Autocarabus cancellatus JQ646943 JQ646799 Autocarabus cristoforii JQ646924 JQ646797 Carabus granulatus JQ646920 JQ646771 Chaetocarabus intricarius JQ646904 AY183582 Chrysocarabus auronitens JQ646921 JQ646772 Chrysocarabus hispanus JQ646953 JQ646812 Chrysocarabus olympiae JQ646886 AY183599 Chrysocarabus rutilans JQ646888 JQ646731 Chrysocarabus splendens JQ646907 JQ646753 Ctenocarabus galicianus JQ646895 JQ646743 Diocarabus loschnikovi - - Eucarabus arvensis JQ646922 JQ646773 Eucarabus catenulatus JQ646939 JQ646794 Eucarabus deyrollei - - Eucarabus italicus - - Eurycarabus famini JQ646896 - Eurycarabus genei JQ646908 - Hemicarabus nitens - - Heterocarabus marietii - - Hygrocarabus nodulosus - JQ646811 Lamprostus torosus - - Limnocarabus clathratus JQ646945 JQ646802 Macrothorax morbillosus JQ646936 JQ646791 Macrothorax rugosus - - Megodontus exaratus - - Megodontus germarii - AY183593 Megodontus septemcarinatus - - Megodontus violaceus JQ646926 JQ646778 Mesocarabus dufouri - AY183636 Mesocarabus lusitanicus JQ646899 JQ646748 Mesocarabus macrocephalus - - Mesocarabus problematicus JQ646905 JQ646751 Morphocarabus aeruginosus - - Morphocarabus comptus - - Morphocarabus hampei - - Morphocarabus henningi - - Morphocarabus hummeli - AY183619 Morphocarabus kollari - - Morphocarabus monilis - JQ646776 Morphocarabus odoratus - - Morphocarabus regalis - - Morphocarabus scabriusculus - - Morphocarabus scheidleri - - Oreocarabus amplipennis JQ646897 JQ646746 Oreocarabus ghiliani - - Oreocarabus glabratus - JQ646770 Oreocarabus guadarramus - - Oreocarabus hortensis JQ646931 JQ646786 Orinocarabus adamellicola - - Orinocarabus concolor - JQ646732 Orinocarabus fairmairei JQ646940 JQ646795 Orinocarabus heteromorphus - JQ646730 Orinocarabus latreilleanus JQ646887 JQ646733 Orinocarabus linnei JQ646927 JQ646779 Orinocarabus putzeysianus - - Orinocarabus sylvestris - - Pachycranion ermaki - - Pachycranion schoenherri - - Pachystus cavernosus - AY183652 Platycarabus creutzeri - AY183584 Platycarabus depressus - AY183583 Platycarabus fabricii - - Platycarabus irregularis JQ646938 JQ646793 Procerus gigas - - Procerus scabrosus - - Procrustes coriaceus - JQ646817 Rhabdotocarabus melancholicus JQ646914 JQ646766 Sphodristocarabus janthinus - - Tomocarabus convexus - JQ646785 Tomocarabus marginalis - - Trachycarabus estreicheri - - Trachycarabus haeres - - Archicarabus pseudomonticola JQ646916 - Chaetocarabus lefebrvrei JQ646935 - Chaetocarabus merlini JQ646903 JQ646750 Chrysocarabus lineatus - JQ646744 Macrothorax planatus JQ646934 JQ646790 Megodontus caelatus JQ646937 JQ646792 Iniopachys pyrenaeus JQ646930 JQ646784 Chrysocarabus solieri - AY183605 Eucarabus parreyssi - - Eucarabus ulrichii - - Archicarabus rossii - AY183632 Appendix 8 23

Table S4: Summary of modularity analyses at a regional and subregional levels. No sps: number of species grouped in each module. No cells: number of grid cells grouped in each module. Modules are labelled according to Fig. S2

No sps No cells Modularity p-value No modules Europe 131 1487 0.385 0.01 7 Module 1 21 138 0.468 0.01 4 1.1 6 50 1.2 3 50 1.3 7 29 1.4 5 9 Module 2 2 4 NA NA Module 3 18 139 0.427 0.01 5 3.1 3 48 3.2 6 30 3.3 1 12 3.4 3 34 3.5 5 15 Module 4 49 222 0.344 0.01 4 4.1 7 84 4.2 10 49 4.3 21 26 4.4 11 63 Module 5 10 482 0.154 0.02 3 5.1 5 196 5.2 2 146 5.3 3 140 Module 6 10 236 0.189 0.01 3 6.1 4 68 6.2 5 129 6.3 1 39 Module 7 21 268 0.314 0.01 3 7.1 7 73 7.2 7 69 7.3 7 126 Appendix 8 24

Table S5: Results of regional and subregional co-occurrence similarity as function of environmental variables. Explained deviance (Exp.Dev.) and regression coefficients (Coeff.) are shown for models including all species (Modules and Submodules) and models including only species of each module (Module 1 to Module 7). * Indicates significant relationship at P≤0.01.

Level Climate Forest Modules Exp.Dev. 0.183* 0.108* Coeff 0.952 0.668 Submodules ALL Exp.Dev. 0.447* 0.250* Coeff 1.039 1.015 Submodules M1 Exp.Dev. 0.536* 0.337* Coeff 1.691 1.222 Submodules M3 Exp.Dev. 0.549* 0.171* Coeff 1.324 0.806 Submodules M4 Exp.Dev. 0.262* 0.066* Coeff 0.862 0.405 Submodules M5 Exp.Dev. 0.869* 0.520* Coeff 0.667 0.495 Submodules M6 Exp.Dev. 0.552* 0.109 Coeff 1.235 0.475 Submodules M7 Exp.Dev. 0.440* 0.241* Coeff 0.930 0.703

Table S6: Results of regional and subregional co-occurrence similarity as function of phylogenetic distances obtained after conducting different branch length trans- formations. Explained deviance (Exp.Dev.) and regression coefficients (Coeff.) are shown for models including all species (Modules and Submodules) and models in- cluding only species of each module (Module 1 to Module 7). Models in red are those where statistical significance was assessed and used for deviance partitioning. * Indicates significant relationship (i.e. when 99% of phylogenetic hypotheses show significance at P≤0.01). Results of phylogenies calibrated using both molecular dat- ing scenarios are provided (Deuve and And´ujar). Ori.: original branch length. Sqrt.: squared root transformation of branch length. OU 1: branch length transformation to accommodate a Ornstein-Uhlenbeck model with an attraction strength equal to 1. OU 2.5: Ornstein-Uhlenbeck model with an attraction strength equal to 2.5. OU 5: Ornstein-Uhlenbeck model with an attraction strength equal to 5. OU 10: Ornstein-Uhlenbeck model with an attraction strength equal to 10.

Level Deuve And´ujar Ori. Sqrt. OU 1 OU 2.5 OU 5 OU 10 Ori. Sqrt. OU 1 OU 2.5 OU 5 OU 10 Modules Exp. Dev 0.000 0.009 0.004 0.012 0,016* 0.012 0.011 0.010 0.016 0.019 0.017 0.011 Coeff. -0.029 -0.207 -0.126 -0.248 -0.357 -0.417 -0.217 -0.215 -0.267 -0.344 -0.421 -0.457 Submodules ALL Exp. Dev 0.003 0.009 0.016 0.034 0,037* 0.027 0.027 0.011 0.039 0.045 0.037 0.025 Coeff. -0.115 -0.201 -0.247 -0.343 -0.394 -0.416 -0.325 -0.216 -0.366 -0.397 -0.427 -0.449 Submodules M1 Exp. Dev 0.006 0.005 0.005 0.003 0.001 0.001 0.003 0.003 0.003 0.002 0.001 0.001 Coeff. -0.182 -0.225 -0.194 -0.202 -0.143 0.204 -0.133 -0.175 -0.159 -0.185 -0.119 0.408 Submodules M3 Exp. Dev 0.004 0.005 0.006 0.008 0.008 0.008 0.006 0.009 0.009 0.013 0.016 0.014 Coeff. -0.121 -0.171 -0.174 -0.236 -0.269 -0.245 -0.173 -0.240 -0.218 -0.293 -0.395 -0.521 Submodules M4 Exp. Dev 0.015 0.034 0.045 0.079 0,085* 0.066 0.098 0.107 0.109 0.104 0.087 0.063 Coeff. -0.202 -0.330 -0.365 -0.544 -0.685 -0.783 -0.530 -0.622 -0.601 -0.678 -0.778 -0.915 Submodules M5 Exp. Dev 0.007 0.004 0.002 0.000 0.001 0.002 0.000 0.001 0.001 0.001 0.002 0.002 Coeff. 0.080 0.095 0.062 0.014 -0.108 -0.823 -0.023 -0.042 -0.046 -0.105 -0.397 -9.339 Submodules M6 Exp. Dev 0.081 0.082 0.081 0.076 0.060 0.046 0.003 0.007 0.016 0.045 0.055 0.042 Coeff. -0.593 -0.831 -0.698 -0.869 -1.103 -1.780 -0.051 -0.212 -0.321 -0.836 -1.817 -5.563 Submodules M7 Exp. Dev 0.002 0.002 0.002 0.001 0.002 0.002 0.005 0.004 0.003 0.002 0.002 0.002 Coeff. -0.065 -0.065 -0.059 -0.046 -0.024 -0.005 -0.103 -0.110 -0.095 -0.065 -0.032 -0.042 Appendix 8 25

Table S7: List of species used to determine de MDCC of each PUT

PUT MDCC Archicarabus alysidotus Archicarabus monticola, Archicarabus nemoralis, Archicarabus pseudomonticola, Archicarabus rossii, Archicarabus steuarti Archicarabus montivagus Archicarabus monticola, Archicarabus nemoralis, Archicarabus pseudomonticola, Archicarabus rossii, Archicarabus steuarti Archicarabus wiedemanni Archicarabus monticola, Archicarabus nemoralis, Archicarabus pseudomonticola, Archicarabus rossii, Archicarabus steuarti Autocarabus vagans Autocarabus auratus, Autocarabus cancellatus Carabus menetriesi Carabu granulatus, Eucarabus arvensis, Eucarabus deyrollei Chaetocarabus arcadicus Chaetocarabus intricarius, Chaetocarabus lefebrvrei, Chaetocarabus merlini Chaetocarabus krueperi Chaetocarabus intricarius, Chaetocarabus lefebrvrei, Chaetocarabus merlini Eucarabus obsoletus Eucarabus catenulatus, Eucarabus italicus, Eucarabus parreyssi, Eucarabus ulrichii Eucarabus stscheglowi Eucarabus catenulatus, Eucarabus italicus, Eucarabus parreyssi, Eucarabus ulrichii Hygrocarabus variolosus Hygrocarabus nodulosus Iniopachys auriculatus Iniopachys pyrenaeus Megodontus aurolimbatus Megodontus caelatus, Megodontus exaratus, Megodontus germarii, Megodontus septemcarinatus, Megodontus violaceus, Pachycranion ermaki, Pachycranion schoenherri Megodontus croaticus Megodontus caelatus, Megodontus exaratus, Megodontus germarii, Megodontus septemcarinatus, Megodontus violaceus, Pachycranion ermaki, Pachycranion schoenherri Megodontus gyllenhali Megodontus caelatus, Megodontus exaratus, Megodontus germarii, Megodontus septemcarinatus, Megodontus violaceus, Pachycranion ermaki, Pachycranion schoenherri Megodontus planicolis Megodontus caelatus, Megodontus exaratus, Megodontus germarii, Megodontus septemcarinatus, Megodontus violaceus, Pachycranion ermaki, Pachycranion schoenherri Morphocarabus excellens Morphocarabus aeruginosus, Morphocarabus comptus, Morphocarabus hampei, Morphocarabus henningi, Morphocarabus hummeli, Morphocarabus kollari, Morphocarabus monilis, Morpho- carabus odoratus, Morphocarabus regalis, Morphocarabus scabriusculus, Morphocarabus scheid- leri, Trachycarabus estreicheri, Trachycarabus haeres Morphocarabus karpinskii Morphocarabus aeruginosus, Morphocarabus comptus, Morphocarabus hampei, Morphocarabus henningi, Morphocarabus hummeli, Morphocarabus kollari, Morphocarabus monilis, Morpho- carabus odoratus, Morphocarabus regalis, Morphocarabus scabriusculus, Morphocarabus scheid- leri, Trachycarabus estreicheri, Trachycarabus haeres Morphocarabus perrini Morphocarabus aeruginosus, Morphocarabus comptus, Morphocarabus hampei, Morphocarabus henningi, Morphocarabus hummeli, Morphocarabus kollari, Morphocarabus monilis, Morpho- carabus odoratus, Morphocarabus regalis, Morphocarabus scabriusculus, Morphocarabus scheid- leri, Trachycarabus estreicheri, Trachycarabus haeres Morphocarabus rothi Morphocarabus comptus, Morphocarabus hampei Morphocarabus rybinskii Trachycarabus haeres Morphocarabus sibiricus Trachycarabus haeres Morphocarabus versicolor Morphocarabus aeruginosus, Morphocarabus comptus, Morphocarabus hampei, Morphocarabus henningi, Morphocarabus hummeli, Morphocarabus kollari, Morphocarabus monilis, Morpho- carabus odoratus, Morphocarabus regalis, Morphocarabus scabriusculus, Morphocarabus scheid- leri, Trachycarabus estreicheri, Trachycarabus haeres Morphocarabus zawadzki Morphocarabus aeruginosus, Morphocarabus comptus, Morphocarabus hampei, Morphocarabus henningi, Morphocarabus hummeli, Morphocarabus kollari, Morphocarabus monilis, Morpho- carabus odoratus, Morphocarabus regalis, Morphocarabus scabriusculus, Morphocarabus scheid- leri, Trachycarabus estreicheri, Trachycarabus haeres Oreocarabus carinthiacus Oreocarabus amplipennis, Oreocarabus ghiliani, Oreocarabus guadarramus Orinocarabus alpestris Orinocarabus adamellicola, Orinocarabus concolor, Orinocarabus fairmairei, Orinocarabus het- eromorphus, Orinocarabus putzeysianus, Orinocarabus sylvestris Orinocarabus bertolinii Orinocarabus adamellicola, Orinocarabus concolor, Orinocarabus fairmairei, Orinocarabus het- eromorphus, Orinocarabus putzeysianus, Orinocarabus sylvestris Orinocarabus castanopterus Orinocarabus adamellicola, Orinocarabus concolor, Orinocarabus fairmairei, Orinocarabus het- eromorphus, Orinocarabus putzeysianus, Orinocarabus sylvestris Orinocarabus cenisius Orinocarabus fairmairei Orinocarabus lepontinus Orinocarabus adamellicola, Orinocarabus concolor, Orinocarabus fairmairei, Orinocarabus het- eromorphus, Orinocarabus putzeysianus, Orinocarabus sylvestris Pachystus cribellatus Oreocarabus glabratus, Oreocarabus hortensis, Pachystus cavernosus Pachystus graecus Oreocarabus glabratus, Oreocarabus hortensis, Pachystus cavernosus Pachystus hungaricus Oreocarabus glabratus, Oreocarabus hortensis, Pachystus cavernosus Pachystus preslii Oreocarabus glabratus, Oreocarabus hortensis, Pachystus cavernosus Pachystus trojanus Oreocarabus glabratus, Oreocarabus hortensis, Pachystus cavernosus Platycarabus cychroides Platycarabus creutzeri, Platycarabus depressus, Platycarabus fabricii, Platycarabus irregularis Procerus duponcheli Procerus gigas, Procerus scabrosus Procerus sommeri Procerus gigas, Procerus scabrosus Procrustes banoni Procrustes coriaceus Tomocarabus bessarabicus Tomocarabus convexus, Tomocarabus marginalis Trachycarabus besseri Trachycarabus estreicheri, Trachycarabus haeres Trachycarabus bosphoranus Trachycarabus estreicheri, Trachycarabus haeres Trachycarabus errans Trachycarabus estreicheri, Trachycarabus haeres 9 References

And´ujar, C., J. Serrano, and J. G´omez-Zurita. 2012. Winding up the molecular clock in the genus Carabus (Coleoptera: Carabidae): assessment of methodological decisions on rate and node age estimation. BMC evolutionary biology 12:40.

Bates, D., M. Maechler, B. Bolker, S. Walker, et al. 2014. lme4: Linear mixed- effects models using Eigen and S4. R package version 1.

Bouckaert, R., J. Heled, D. Kuhnert,¨ T. Vaughan, C.-H. Wu, D. Xie, M. A. Suchard, A. Rambaut, and A. J. Drummond. 2014. BEAST 2: a software platform for Bayesian evolutionary analysis. PLoS computational biology 10:e1003537.

Capella-Guti´errez, S., J. M. Silla-Mart´ınez, and T. Gabald´on. 2009. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinfor- matics 25:1972–1973.

Csardi, G., and T. Nepusz. 2006. The igraph software package for complex network research. InterJournal, Complex Systems 1695:1–9.

Danon, L., A. Diaz-Guilera, J. Duch, and A. Arenas. 2005. Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment 2005:P09008.

Deuve, T., A. Cruaud, G. Genson, and J.-Y. Rasplus. 2012. Molecular system- atics and evolutionary history of the genus Carabus (Col. Carabidae). Molecular Phylogenetics and Evolution 65:259–275.

Edgar, R. C. 2004. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Research 32:1792–1797.

Fisher, R. A. 1922. On the interpretation of χ 2 from contingency tables, and the calculation of P. Journal of the Royal Statistical Society 85:87–94.

Good, B. H., Y.-A. de Montjoye, and A. Clauset. 2010. Performance of modularity maximization in practical contexts. Physical Review E 81:046106.

26 Appendix 9 27

Katoh, K., and D. M. Standley. 2013. MAFFT multiple sequence alignment soft- ware version 7: improvements in performance and usability. Molecular Biology and Evolution 30:772–780.

Larkin, M. A., G. Blackshields, N. Brown, R. Chenna, P. A. McGettigan, H. McWilliam, F. Valentin, I. M. Wallace, A. Wilm, R. Lopez, et al. 2007. Clustal W and Clustal X version 2.0. bioinformatics 23:2947–2948.

Lassmann, T., and E. L. Sonnhammer. 2005. Kalign–an accurate and fast multiple sequence alignment algorithm. BMC Bioinformatics 6:298.

Lassmann, T., and E. L. Sonnhammer. 2006. Kalign, Kalignvu and Mumsa: web servers for multiple sequence alignment. Nucleic Acids Research 34:W596–W599.

Legendre, P. 1993. Spatial autocorrelation: trouble or new paradigm? Ecology 74:1659–1673.

Letten, A. D., and W. K. Cornwell. 2015. Trees, branches and (square) roots: why evolutionary relatedness is not linearly related to functional distance. Methods in Ecology and Evolution 6:439–444.

Martins, W. S., W. C. Carmo, H. J. Longo, T. C. Rosa, and T. F. Rangel. 2013. SUNPLIN: simulation with uncertainty for phylogenetic investigations. BMC Bioin- formatics 14:324.

Rangel, T. F., R. K. Colwell, G. R. Graves, K. Fuˇc´ıkov´a, C. Rahbek, and J. A. F. Diniz-Filho. 2015. Phylogenetic uncertainty revisited: Implications for ecological analyses. Evolution 69:1301–1312.

Ree, R. H., and I. Sanmart´ın. 2018. Conceptual and statistical problems with the DEC+ J model of founder-event speciation and its comparison with DEC via model selection. Journal of Biogeography 45:741–749.

Thompson, J. D., D. G. Higgins, and T. J. Gibson. 1994. Clustal W: improving the sensitivity of progressive multiple sequence alignment through sequence weighting, position-specific gap penalties and weight matrix choice. Nucleic Acids Research 22:4673–80.