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Journal of Ecology

Ant functional responses along environmental gradients

Journal: Journal of Animal Ecology

ManuscriptFor ID: JAE-2013-00735.R1 Review Only

Manuscript Type: Standard Paper

Date Submitted by the Author: 29-Jan-2014

Complete List of Authors: Arnan, Xavier; CREAF, Cerdá, Xim; CSIC, Estación Biológica de Doñana Retana, Javier; CREAF,

biomes, climate, Formicidae, foraging strategy, functional composition, Key-words: functional traits, productivity, worker size

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1 Running headline: functional responses to the environment

2

3 Ant functional responses along environmental gradients

4

5

6 Xavier Arnan 1,2* , Xim Cerdá 3, and Javier Retana 1,4 7 For Review Only 8 1 CREAF, Cerdanyola del Vallès, 08193 Catalunya, Spain. 9 2 Faculty of Biology, TU Darmstadt, Schnittspahnstrasse 3, D-64287 Darmstadt, Germany. 10 3 Estación Biológica de Doñana, CSIC, Avda Américo Vespucio, s/n, E-41092 Sevilla, Spain. 11 4 Univ Autònoma Barcelona, Cerdanyola del Vallès, 08193 Catalunya, Spain. 12 13 *Corresponding author 14 15 16

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17 ABSTRACT

18 • Understanding distributions and diversity gradients is a central challenge in

19 ecology and requires prior knowledge of the functional traits mediating species’ survival

20 under particular environmental conditions. While the functional ecology of plants has been

21 reasonably well explored, much less is known about that of . are among the

22 most diverse, abundant, and ecologically significant organisms on earth, and they perform 23 a great variety ofFor ecological Review functions. Only

24 • In this study, we analyze how the functional species traits present in ant communities vary

25 along broad gradients in climate, productivity, and vegetation type in the southwestern

26 Mediterranean.

27 • To this end, we complied one of the largest animal databases to date: it contains

28 information on 211 local ant communities (including eight climate variables, productivity,

29 and vegetation type) and 124 ant species, for which 10 functional traits are described. We

30 calculated two complementary functional trait community indices (‘trait average’ and ‘trait

31 dissimilarity’) for each trait, and we analyzed how they varied along the three different

32 gradients using generalized least squares (GLS) models that accounted for spatial

33 autocorrelation.

34 • Our results show that productivity, vegetation type, and, to a lesser extent, each climate

35 variable per se might play an important role in shaping the occurrence of functional species

36 traits in ant communities. Among the climate variables, temperature and precipitation

37 seasonality had a much higher influence on functional responses than their mean values,

38 whose effects were almost lacking.

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39 • Our results suggest that strong relationships might exist between the abiotic environment

40 and the distribution of functional traits among southwestern Mediterranean ant

41 communities. This finding indicates that functional traits may modulate the responses of

42 ant species to the environment. Since these traits act as the link between species

43 distributions and the environment, they could potentially be used to predict community

44 changes under future global change scenarios.

45 Keywords : biomes, climate,For diet, Formicidae,Review foraging strat Onlyegy, functional composition, functional

46 traits, productivity, worker size

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47 INTRODUCTION

48 Understanding the processes responsible for variation in species richness and community

49 composition along gradients is a central challenge in ecology. Many studies suggest that the

50 environment is a key factor driving such variation (e.g. Pausas & Austin 2001; Werner et al. 2007;

51 Dunn et al. 2009). Although the link between the environment and species distributions or

52 community composition has largely been addressed from a taxonomic or phylogenetic standpoint,

53 the importance of usingFor a functional Review approach has recently Only been underscored (McGill et al. 2006).

54 This approach argues that by examining an organism’s functional traits and the way in which they

55 relate to the environment (environmental filtering), a mechanistic and predictive framework for the

56 study of patterns and coexistence can be constructed (McGill et al. 2006;

57 Swenson & Weiser 2010). Thus, quantifying how functional traits relate to different environmental

58 factors provides insight into the mechanisms governing species distributions; it is also a necessary

59 step in assessing how ongoing environmental changes such as climate change may alter biological

60 communities and ecosystem processes that are coupled with diversity (Mc Gill et al. 2006;

61 Pollock, Morris & Vesk 2012).

62

63 Most studies addressing the functional composition of communities have been carried out on

64 plants (e.g. Díaz et al. 2007; Swenson & Weiser 2010; Pollock et al. 2012). In animals, these types

65 of studies are scarce, probably due to the inherent difficulty of obtaining information on the

66 functional traits of mobile organisms. Furthermore, of the few studies that have been published

67 that link functional traits and species distributions in animals, most have been conducted on small

68 scales and have considered a limited number of environmental variables and/or functional traits.

69 There is therefore a need to analyze patterns of animal functional diversity along wide, variable

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70 environmental gradients to provide insights into the trait-filtering that occurs along particular

71 gradients.

72

73 Ants are among the most diverse, abundant, and ecologically significant organisms on earth

74 (Hölldobler & Wilson 1990). They can modify the abiotic and biotic properties of their

75 environment by performing a variety of ecological functions (Hölldobler & Wilson 1990; Wardle 76 et al. 2010; Zelikova, SandersFor & DunnReview 2011). Consequently, Only ants are considered to be crucial 77 components of most terrestrial ecosystems (Hölldobler & Wilson 1990; Lach, Parr & Abbott

78 2010). As ant communities commonly vary in composition along environmental gradients (e.g.

79 Sanders et al. 2007; Dunn et al. 2009; Arnan, Cerdá & Retana 2012), relationships between

80 functional diversity and the environment are expected in most of the world’s principal terrestrial

81 ecosystems.

82

83 In this study, we analyze how the functional traits found in ant communities vary along

84 environmental gradients in climate, productivity, and vegetation type in the southwestern

85 Mediterranean. The aim is to predict the distribution of ant communities on the basis of functional

86 traits and their relationship with the abiotic environment. We used data from 211 ant communities

87 that include a total of 124 ant species, and we generated a data set of 10 functional traits that are

88 important in ant autecology and/or relate to ecosystem functioning. We believe that, to date, this

89 database is the largest compiled for animals (or at least for ) that includes a large number of

90 species-specific functional traits from different communities across broad environmental gradients.

91 We specifically ask: how do the functional traits of ant species respond to gradients of climate,

92 productivity, and vegetation type?

93 5

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94 MATERIAL AND METHODS

95

96 Ant community composition data

97 We assembled data from our own studies and most of the scientific articles and Ph.D. theses

98 examining southwestern Mediterranean ant communities; only studies that contained species

99 abundance or presence-absence data from single locations were used. We discarded highly 100 disturbed and urbanizedFor sites. Overall, Review the dataset consisted Only of 217 sites, of which 211 were 101 included in the analyses (see Appendix S1); they spanned a latitudinal gradient running from

102 approximately 36.74º to 43.66º N and from -7.12º to 4.98º W. These communities comprised a

103 total of 133 ant species, of which 124 were considered in the analyses (see Appendix S1). We

104 determined the latitude and longitude coordinates of all the sites, either directly when they were

105 stated or indirectly based on the maps and information provided in the articles. We focused our

106 analyses on presence-absence data because they are more comparable among sites than abundance

107 data, which were measured in different ways (i.e. number of nests, individuals at baits, or

108 individuals in pitfall traps) in different studies and were thus difficult to compare.

109

110 Ant trait data

111 We described each of the 124 species used in our analyses in terms of functional traits that have

112 been recognized as being important in ant autecology and/or relating to ecosystem functioning

113 (e.g. Hölldobler & Wilson 1990; Bihn, Gebauer & Brandl 2010; Lach et al. 2010; Arnan et al.

114 2012, 2013). Functional traits are defined as any morphological, physiological, or phenological

115 features measurable at the individual level that affect individual performance (McGill et al. 2006;

116 Violle et al. 2007). Ants are a unique group of organisms because they live in complex societies

117 composed of a dozen to a million individuals. A colony is comparable to a superorganism since its 6

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118 reproductive and somatic functions are performed by queens and workers, respectively (Wheeler

119 1913; Hölldobler & Wilson 2009). Although the superorganism metaphor has its limitations, it

120 underscores that natural selection can occur at both the individual and the colony level (Keller

121 1995). Therefore, ant functional traits may be quantified at both the level of the individual worker

122 and that of the colony. Classical studies of ant functional traits have focused on the morphological

123 or physiological performance of individuals or colonies. In our study, in contrast, we examined a 124 wider variety of individual-For and colony-levelReview functional Onlytraits, mainly life-history traits but also 125 morphological and behavioral traits. Overall, we studied 10 functional traits, whose descriptions

126 and functional significance are provided in Table 1. We used these traits because they are

127 frequently examined in the literature and they are good proxy measures for ant species ecology. As

128 in other functional diversity studies (e.g. Swenson & Weiser 2010; Arnan et al. 2012, 2013),

129 functional traits were assumed to be species-specific and not demonstrate intersite variability. If

130 such variation exists, then its omission from analyses would likely result in a bias towards weaker

131 trait-climate relationships. Future work will be needed to explore variability in traits among sites

132 and/or model its potential importance.

133

134 First, we gathered functional trait data from our own research records and via personal

135 communication with different colleagues (Anna Alsina, Jordi Bosch, Raphaël Boulay, Soledad

136 Carpintero, Valentín Cavia, Sebastià Cros, Xavier Espadaler, Paqui Ruano, and Alberto Tinaut).

137 This initial database was then expanded by conducting an exhaustive search of public databases

138 and the scientific literature. Overall, our literature review involved more than 1000 articles and

139 1300 search hours. A full list of the data sources utilized in this study is provided in Appendix S2.

140 For details on how we completed gaps in the species-trait database, see Appendix S1.

141 7

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142 Environmental gradient data

143 Climate

144 Climate data for the 211 sites included in our analyses came from the WorldClim database

145 (http://www.worldclim.org/bioclim ); we used rasters with the highest available resolution (30 arc-

146 seconds). We obtained site-specific values for eight climate variables: mean annual temperature,

147 maximum temperature, minimum temperature, temperature seasonality (standard deviation of 148 monthly mean temperature),For annual Review precipitation, precipitation Only in the wettest month, precipitation 149 in the driest month, and precipitation seasonality (coefficient of variation of the monthly

150 precipitation level). We also noted the altitude of each site. Since we found that several of these

151 variables were highly correlated (Spearman’s r>0.7; Table S1), we retained only four: mean

152 annual temperature, temperature seasonality, annual precipitation, and precipitation seasonality. In

153 our correlational analyses, we took into account spatial autocorrelation within the data using the

154 “Dutilleul” method in SAM (Rangel, Diniz-Filho & Bini 2006); this software program calculates

155 the appropriate degrees of freedom given the observed level of non-independence in the data.

156

157 Productivity

158 We estimated productivity at each site using the remotely sensed normalized difference vegetation

159 index (NDVI), which is commonly used by ecologists as a proxy for vegetation productivity (e.g.

160 Pettorelli et al. 2005). First, we obtained monthly NDVI values from April to September from

161 2001 to 2009 (the only years for which data were available) from the USGS website

162 (https://lpdaac.usgs.gov/products/modis_products_table/mod13q1). Although it would have been

163 preferable to use NDVI data from the years during which the studies were carried out, these data

164 were not available for most of the ant communities sampled; it is for this reason that we decided to

165 use the average for the 2001-2009 time period for all the sites. We focused on the time period from 8

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166 April to September because a) it limits large contrasts between sites with and without deciduous

167 vegetation and b) it corresponds to the season when ants are active and thus reflects conditions

168 outside the nest that might affect them. We computed mean NDVI values from April to June

169 (NDVI spring ) and from April to September (NDVI spring+summer ) by averaging values across all years.

170 Because the correlation between NDVI spring and NDVI spring+summer was very high (Pearson r=0.99,

171 p<0.001), we included only the latter variable in our analyses. This variable was not highly 172 correlated (i.e. Spearman’sFor r<0.7; Review Table S2) with any Only of the other climate variables, which 173 underscores their independence.

174

175 Vegetation type

176 To evaluate whether vegetation type influences the distribution of functional traits on a large

177 geographical scale, we assigned each plot to one of the six most common vegetation types found

178 in the study region: grasslands, shrublands, open (=dehesa-like) woodland, sclerophyllous forests,

179 conifer forests, and broadleaf forests. We conducted spatial generalized least squares (GLS)

180 models (see below) to investigate how climate and productivity varied among the different

181 vegetation types. We found that only temperature seasonality differed significantly (ANOVA table

182 from GLS model: F=433.7, p<0.0001, df=5): open woodlands demonstrated higher temperature

183 seasonality than did the other vegetation types, which did not differ from each other. Neither

184 productivity (F=1.25, p=0.287, df=5) nor the other climate variables (annual mean temperature:

185 F=0.3, p=0.904; annual precipitation: F=0.1, p=0.997; and precipitation seasonality: F=1.8,

186 p=0.105) differed among vegetation types. Again, this finding underscores the independence of

187 these factors.

188

189 Data analyses 9

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190 We used two traditional indices that have been widely used in ecological research to statistically

191 describe the functional trait composition of a given community: the ‘trait average’ ( X ) index

192 reveals the most common traits in a community, and the ‘trait dissimilarity’ (FD) index indicates

193 the extent to which species within a community differ in their traits (Díaz et al. 2007; Ricotta &

194 Moretti 2011; Arnan et al. 2013). The trait dissimilarity index is defined as the Rao quadratic

195 diversity coefficient, which reflects the probability that two randomly chosen individuals in a 196 community will be different.For Rao’s Review Q ranges from 0 to the Only maximum value for the Simpson index 197 of diversity, with higher values indicating more trait dissimilarity within the community. The Rao

198 coefficient presents several desirable properties when describing the functional diversity of a

199 community (see Botta-Dukat 2005).

200

201 These indices were computed for each functional trait using the dbFD function in the FD package

202 in R (R Development Core Team 2010). The trait average and trait dissimilarity of a given

203 community might reflect similar information and thus may not be fully independent, particularly in

204 the case of categorical traits (Ricotta & Moretti 2011). Because trait averages and dissimilarities

205 were highly correlated (r>0.7) for several traits (Table S3), we conducted further analyses using

206 only the trait dissimilarity index for two binary traits (diurnality and individual foraging strategy),

207 three ordinal traits (number of queens, number of nests, and colony foundation type) and one

208 fuzzy-coded variable (percentage of seeds in the diet). Preliminary analyses showed that overall

209 functional diversity (computed taking the 10 traits together) was significantly positively related to

210 species richness (ß±SE=0.00±0.00; t=3.3; p=0.001): sites with higher species richness also have

211 higher functional diversity. However, since species richness only demonstrated a significant

212 association with vegetation type (F=3.4; p=0.006), but not with climate or productivity (all p>0.01;

213 Table S2), it was excluded from further analyses. 10

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214

215 As there was likely to be a substantial amount of spatial autocorrelation in the distribution of traits

216 across sites, we tested how the functional composition of ant communities responded to each of

217 the three environmental gradients using GLS models. GLS models use simultaneous

218 autoregression to estimate means. We conducted sets of tests where the functional indices ( X and

219 FD) for each trait were the response variables and the environmental gradient was the predictor 220 variable. For example, Forwe conducted Review a set of three models Only where the trait average for worker size 221 was the response variable: 1) the climate model included annual mean temperature, temperature

222 seasonality, annual precipitation, and precipitation seasonality as predictor variables; 2) the

223 productivity model included NDVI spring+summer as the predictor variable; and 3) the vegetation type

224 model included the vegetation type category as the predictor variable. GLS models were carried

225 out using the gls function in the nlme package in R. Due to the large number of tests we

226 conducted, we applied the Bonferroni correction. The alpha level for test acceptance was thus

227 established at p<0.00076 (0.05/66) to avoid finding significant effects by chance.

228

229 Little is known in animals, and even less in insects, about species-level trade-offs among

230 functional traits. Indeed, it may be that trait patterns at the community level arise from trade-offs at

231 the species level, which would lead to erroneous interpretations. However, the existence of

232 correlations between traits at the community level does not necessarily imply that correlations

233 exist at the species level, because independent traits (those not correlated at the species level)

234 might also be influenced by the same environmental gradients (Ackerley et al. 2002).

235 Consequently, in order to be conservative and check for potential trade-offs among functional

236 traits at the community level, we calculated Spearman rank correlation coefficients for the trait

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237 averages and dissimilarity values we had estimated. Within our large pool of traits, only a small

238 number of functional traits were highly correlated (r>0.7) at the community level (Table S4).

239

240 RESULTS

241

242 Based on our results, all three environmental gradients appear to strongly filter ant species based 243 on their functional traitsFor (Table 2; TableReview S5). Variance in mostOnly trait averages or dissimilarities 244 could be explained by at least one of gradients. However, none of the environmental gradients

245 explained average in the percentage of seeds in the diet, and dissimilarity in worker size, worker

246 polymorphism, behavioral dominance, utilization of a group and collective foraging strategy, and

247 colony size. Most traits were influenced by more than one gradient, but variance in several

248 functional traits was predicted uniquely by climate (the average worker size) or biome (the

249 percentage of liquid food in the diet, the average colony size, and the dissimilarity in diurnality).

250 None of the variance in the trait indices was explained exclusively by plant productivity.

251

252 Ant functional responses to climate

253

254 Among the trait indices that were explained by at least one of the environmental gradients, the

255 averages of four traits (the percentage of behaviorally dominant species in the community, the

256 percentage of liquid food in the diet, the percentage of collectively foraging species, and colony

257 size) and dissimilarity in diurnality and the percentage of insects in the diet were not predicted by

258 any of the climate variables (Table 2; Table S5).

259

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260 Precipitation seasonality was the climate variable that influenced the largest number of functional

261 trait indices (Table 2). Dissimilarity in the percentage of individually and collectively foraging

262 species, the number of queens, the number of nests per colony, and colony founding types

263 increased significantly as precipitation seasonality increased. In contrast, average worker

264 polymorphism, and the average percentage of group foraging species decreased significantly as

265 precipitation seasonality increased. Average worker size and worker polymorphism (Figure 1a) as 266 well as dissimilarity in Forthe percentage Review of liquid foods in theOnly diet increased significantly as 267 temperature seasonality increased (Table 2). Mean temperature did not influence any of the

268 functional trait indices, while annual precipitation only negatively influenced dissimilarity in the

269 percentage of seeds in the diet (Table 2; Figure 1b).

270

271 Ant functional responses to productivity

272

273 The functional indices of a large number of traits were explained by the productivity gradient

274 (Table 2; Table S5). As productivity increased, so did the average percentage of group foraging

275 species; in contrast, the average percentage of behaviorally dominant species (Figure 1c) and the

276 average percentage of collectively foraging species decreased. Furthermore, increased productivity

277 was associated with decreased dissimilarity in the percentage of each kind of food in the diet, the

278 percentage of individually foraging species, the number of queens, the number of nests and the

279 colony foundation type.

280

281 Ant functional responses to vegetation type

282

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283 The vegetation type gradient was also a good predictor of patterns of ant functional diversity

284 because it explained a significant amount of the variance in a large number of trait averages and

285 dissimilarities (Table 2; Table S5). We observed four different patterns of trait composition across

286 the six Mediterranean vegetation types (Figure 2): a) dissimilarity in the percentage of seeds,

287 insects, and liquid foods in the diet and the number of queens differed between forests (broadleaf,

288 sclerophyllous, and conifer) and open (shrubland, dehesa, and grassland) (Figure 2a); b) 289 dissimilarity in diurnalityFor and colony Review foundation type differed Only somewhat according to forest type 290 (conifer vs. broadleaf and sclerophyllous), but was similar across the three open habitats (Figure

291 2b); c) the average percentage of collectively foraging species, the average percentage of liquid

292 foods in the diet, and average colony size were different in broadleaf forests compared to the other

293 two forest types, but broadleaf forest values resembled those of the three open habitats (Figure 2c);

294 and d) the average percentage of behaviorally dominant species, the average percentage of group

295 foraging species, and trait dissimilarity in nest number differed between shrublands and the other

296 two open habitats, but their values were similar across the three forest types (Figure 2d). The other

297 trait indices did not differ among vegetation types (Table 2; Table S5).

298

299 DISCUSSION

300

301 In this study, we report that a strong relationship may exist between the abiotic environment and

302 the distribution of functional traits across southwestern Mediterranean ant communities. Although

303 climate conditions have been widely considered to be key determinants of species distribution

304 patterns at a global scale (e.g. Gaston 1996; Pausas & Austin 2001; Hawkins et al. 2007; Dunn et

305 al. 2009), we found that productivity and vegetation type did a better job of explaining ant

306 functional responses than did each climate variable per se in our regional context. 14

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307

308 Based on the climate variables we examined, it is clear that seasonality has a stronger influence on

309 ant functional responses than do average temperature and precipitation. In contrast to the latter

310 variables, seasonality may serve as a better indicator of resource diversity and availability. For

311 instance, greater seasonality may indicate that a more diverse range of resources with varying

312 availabilities is present over the course of the year; consequently, in more seasonal areas, ant 313 species may be able to Forexploit a greater Review diversity of resources, Only leading to more diverse exploitation 314 strategies. Precipitation seasonality accounted for the most variance in trait averages and

315 dissimilarities. Although other works suggest that a strong relationship exists between temperature

316 and ant functional responses (Diamond et al. 2012; Stuble et al. 2013), our results intimate that

317 temperature seasonality has a more limited influence on ant functional responses. Instead, it may

318 be that humidity is more constraining for ants than temperature (Menke & Holway 2006).

319 However, since these works only examined thermal tolerance, not desiccation resistance, this

320 hypothesis is difficult to evaluate. More interestingly, we found that only traits related to resource

321 exploitation, such as worker size, polymorphism, and diet, responded to differences in temperature

322 seasonality. Worker size may respond to temperature seasonality because, in insects in general,

323 greater size might provide greater protection against starvation or desiccation (Chown & Gaston

324 1999). Furthermore, larger animals have higher levels of energy efficiency (Ellington 1999),

325 which is an advantage in environments where low temperatures, bad weather, and starvation can

326 be a problem. Expanding on this logic, greater worker polymorphism could be advantageous in

327 environments characterized by varying temperatures since ant species could use within-colony

328 variability in worker size to respond more easily to temperature fluctuations, thus lengthening the

329 overall period of external activity and enhancing colony success (Cerdá & Retana 1997). Finally,

330 greater temperature seasonality may generate more diverse ant diets through its action on annual 15

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331 food production patterns. For instance, more seasonal environments may vary more in resource

332 availability across the seasons. Such environments could support generalist species as well as

333 species that specialize more or less on liquid foods and thus promote species coexistence and

334 dietary diversity.

335

336 We found that the temperature gradient did not influence any of the ant functional responses. 337 Contrarily, Reymond andFor collaborators Review (in press) recently Onlyfound a decrease in ant functional 338 diversity with decreasing temperature along an altitudinal gradient in the Swiss Alps. Contrasts in

339 temperature along this gradient are probably greater than those reported in our study. Our results

340 also suggest that communities in the wettest areas were characterized by species that consumed a

341 lower percentage of seeds. Because more species that consumed fewer seeds were present in these

342 communities, community dissimilarity in seed-eating was decreased. In contrast, communities in

343 drier areas contained species that varied much more in their seed-eating habits. These overall

344 dietary patterns might reflect the changes in food resource production that occur as precipitation

345 changes.

346

347 However, functional traits responded much more to differences in productivity and vegetation than

348 to differences in individual climate variables. Plant productivity influenced a large number of

349 functional traits. The negative correlation between productivity and trait dissimilarity found for

350 many traits suggests that functional diversity declined in the most productive areas. This result is

351 striking since ecological theory predicts that in milder and more productive environments, species

352 should display differentiated strategies that reduce competition for more diverse resources (Pianka

353 1970) and, as environmental severity increases, the number of possible successful strategies should

354 be greatly reduced (Weiher & Keddy 1999; Swenson et al. 2012). The pattern we observed may 16

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355 have a somewhat complex explanation. First, in the most productive areas, where conditions are

356 the most favorable and resources are abundant, dominant species are likely to become more

357 abundant and then even more dominant, resulting in the elimination of other species, mainly other

358 dominants. Second, gaps exist between the foraging areas of these hyperdominant species, and

359 these gaps may be occupied by many subordinate species (Savolainen, Vepsälainen &

360 Wuorenrinne 1989; Arnan, Gaucherel & Andersen 2011). As a result, species pools in the most 361 productive areas wouldFor include a fewReview highly dominant species Only and many subordinate species. 362 Since subordinate species within the same local community are likely to have similar functional

363 traits (Arnan et al. 2012), functional diversity would be expected to be lower in more productive

364 areas than in less productive areas. This explanation is supported by the fact that the percentage of

365 behaviorally dominant species in the community decreased with increasing productivity.

366

367 In the most productive areas, the dissimilarity in diet-related traits declined, which means that

368 these areas contain more specialists, ant species that more exclusively consume insects or liquid

369 foods. In contrast, we found that more generalist species occurred in less productive areas: the

370 percentage of seeds in the diet was higher in less productive areas than in more productive areas

371 even though the percentage of liquid foods and insects remained the same. This shift in species

372 dietary patterns is probably related to food production patterns along the productivity gradient; in

373 the most productive areas, liquid foods (nectar and ) may be more readily available

374 (Dixon 1975), while the seeds preferred by ants may be less readily available (Wolff & Debussche

375 1999). In fact, since the dissimilarities in the percentages of insects and liquid foods in the diet are

376 highly correlated (Table S4), the functional response related to the percentage of insects in the diet

377 that is observed along this gradient may potentially be spurious. Foraging strategies were also

378 greatly influenced by productivity: as productivity increased, the percentage of collectively and 17

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379 individually foraging species declined while the percentage of group foraging species climbed.

380 This finding suggests that vegetative structure might influence ant foraging systems (Fewell 1988),

381 assuming that less productive areas account for less vegetative cover. Our results concur with

382 those from a study that examined intercolonial differences in the Pogonomyrmex occidentalis

383 foraging system and their relationship with vegetative cover (Fewell 1988). The collective

384 foraging strategy was found to be more common in more open areas. This strategy seems to be 385 more efficient in areas Forwith less vegetative Review cover, which couldOnly have led species that occur in more 386 open habitats to develop this strategy. The relationship between the percentage of group foragers

387 and vegetation may be somewhat spurious, since the percentages of group and collectively

388 foraging species were negatively correlated at the community level (Table S4). Moreover, in less

389 productive areas, the increase in the percentage of monogynous species might be related to

390 colonization processes because monogynous queens produce larger offspring, which may be better

391 equipped to survive and colonize harsh environments (Ross & Keller 1995). Meanwhile,

392 independent colony founding provides dispersal advantages at long distances compared with

393 dependent colony-founding strategies (Amor et al. 2011), which could indicate that species with

394 this kind of dispersal strategy are more likely to colonize unproductive areas. Unfortunately, we do

395 not have a clear explanation for the relationship between monodomy and plant productivity.

396

397 Across the six representative Mediterranean vegetation types that were included in this study, the

398 ecological value of many ant functional traits in open versus closed habitats was made clear (with

399 some exceptions). Functional diversity increased in open habitats, as reflected by the finding that

400 dissimilarity in most traits was higher in these areas. This result clearly fits the diversity pattern

401 previously described in Mediterranean ant communities (Retana & Cerdá 2000): behaviorally

402 dominant and subordinate species co-occur in open habitats and clearly differ in resource 18

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403 exploitation and temporal activity patterns (Arnan et al. 2012). In the Mediterranean,

404 environmental conditions (mainly temperature) in open habitats demonstrate daily and seasonal

405 contrasts. As a result, species have developed different strategies to cope with that variability

406 (Retana & Cerdá 2000), and as a result, functional diversity has increased. Meanwhile (and

407 assuming that open habitats are characterized by less plant cover), habitats with more vegetative

408 cover and moderate environmental conditions, such as forests, contain species that are more 409 functionally similar (ArnanFor et al. 2012),Review and functional diversity Only has consequently been reduced. 410 The percentage of behaviorally dominant species was higher in open than in closed types.

411 Colony size was also larger in open habitats, probably due to the small colony size of most species

412 that are closely associated with dense vegetation (Arnan et al. 2013). Vegetation type also

413 influenced species dietary patterns and foraging strategies. Past studies have observed that sugar-

414 producing insects are more abundant in forests than in open habitats (Dixon 1975) and that the

415 production of the seeds preferred by granivorous ants is usually higher in open habitats (Wolff &

416 Debussche 1999). We found that the percentage of seeds and liquid foods in the diet increased and

417 decreased, respectively, in open habitats. Furthermore, as previously mentioned, different foraging

418 strategies have been found to be differentially influenced by vegetative structure (Fewell 1988);

419 we found that, in open habitats, the percentage of group foraging species decreased, while the

420 percentage of collectively foraging species increased. However, these functional responses may be

421 somewhat blurred by the negative correlation between the percentages of group and collectively

422 foraging species, as well as by the positive correlation between the percentage of collectively

423 foraging species and colony size at the community level (Table S4).

424

425 Concluding remarks

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426 Using one of the largest databases established for animals to date, which contained information on

427 a set of 10 functional traits for 124 ant species across 211 local communities, we found that ant

428 communities showed a strong functional response across different environmental gradients in the

429 southwestern Mediterranean. This finding clearly underscores that functional traits might modulate

430 the responses of ant species to environmental gradients. Since our work was limited to the

431 southwestern Mediterranean region, we suggest that functional composition modulation by 432 environmental gradientsFor might be evenReview much higher if considering Only a larger geographic range. Our 433 results provide insights into the types of traits that are most revealing when analyzing ant

434 functional responses to variation in climate, productivity and vegetation type. Since species traits

435 act as the link between species distributions and the environment, they could be used to predict

436 how communities will change under future global change scenarios (Silva & Brandao 2010;

437 Diamond et al. 2011; Angert et al. 2011; Pollock et al. 2012). Interestingly, the functional

438 responses of ant communities to each of these gradients involve sets of traits that might impact

439 ecosystem processes. In particular, traits related to resource exploitation are implicated, all of

440 which might be considered to be measures of the impact that ants can have on ecosystem

441 functioning. For instance, worker size relates to metabolic characteristics (Weber 1938) and

442 determines the quantity of resources consumed (Bihn et al. 2010); worker polymorphism relates to

443 the breadth of functional roles performed by colonies (Mertl & Traniello 2009); diet composition

444 relates to the kind and quantity of resources exploited; and foraging strategy, diurnality, and

445 behavioral dominance are directly related to the relative impact of food resource exploitation. As a

446 consequence of the strong relationships between many of these traits and ecosystem processes, the

447 functional diversity patterns that we detected here likely translate into important differences in

448 ecosystem functioning. Further studies should thus clarify how changes in the functional diversity

449 of natural ant assemblages change ecosystem functioning along environmental gradients. 20

Page 21 of 65 Journal of Animal Ecology

450

451 ACKNOWLEDGMENTS

452 We are very grateful to Nate Sanders for his comments on an earlier draft of this manuscript, Jordi

453 Vayreda and Arnald Marcer for furnishing the climate data, our colleagues for sharing their ant

454 trait data, and Jessica Pearce-Duvet for editing the manuscript’s English. This study was partly

455 funded by the Spanish ‘Ministerio de Ciencia e Innovación’ (project Consolider MONTES, CSD 456 2008-00040 to JR and projectFor CGL2009-09690/BOS Review to COnlyX) and ‘Ministerio de Educación’ (grant 457 EX2010-0100 to XA).

458

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587 on seedling distribution, belowground invertebrates, and soil nutrients. Ecosphere, 2, art63.

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588 Table 1. Description of the ant traits examined in this study and their functional significance. 589 590 Trait Functional significance Source Data type States Worker size Strongly correlates with many physiological, ecological, and life history Kaspari & Weiser 1999 Continuous Worker body size from the tip of traits such as resource use mandibles to tip of the gaster (mm) Worker polymorphism Relates to the breath of functional roles performed by colony Mertl & Traniello 2009 Continuous Mean worker size divided by the range of worker size Diurnality Indicates when individualsFor are actively Review foraging Only Hölldobler & Wilson Binary (0) Non-strictly diurnal 1990 (1) Strictly diurnal Behavioral dominance Refers to the influence of one species on another when acquiring food Arnan et al. 2012 Binary (0) Subordinate resources and thus the ability to gain access to food resources (1) Dominant Diet: Seed-eating, Insect-eating, Refers to the type and quantity of food resources a species exploits Hölldobler & Wilson Fuzzy-coded 0-1 (for each of the three and Liquid-food eating 1990 (*) categories) Foraging strategy: Individual, Refers to how a species searches for and exploits food resources, which Traniello 1989 Binary 0-1 (for each of the three Group, and Collective (**) plays an important role in the ability to survive and reproduce categories) Colony size The ecological advantages of large colony size include increased Bourke 1999 Continuous Mean number of workers per defense, homeostasis, work efficiency, and a greater ability to modify the colony surrounding environment Number of queens Influences a wide range of species characteristics, including group size, Keller 1995; Ross & Ordinal (0) Monogyny; (0.5) Both worker size, growth rate, competitive ability, and efficiency Keller 1995; Rosset & monogyny and polygyny; (1) Chapuisat 2007 Polygyny Number of nests Polydomy confers a great competitive advantage relative to monodomy McGlynn 1999 Ordinal (0) Monodomy; (0.5) Both monodomy and polydomy (1) Polydomy Colony foundation type Independent colony foundation (ICF) strategies provide long-distance Amor et al. 2011 Ordinal (0) DCF; dispersal advantages relative to dependent colony foundation (DCF) (0.5) Both DCF and ICF; strategies (1) ICF 591 592 *Variable categories were coded using a fuzzy-coding technique. Scores ranged from ‘0’ (no consumption of a food resource) to ‘1’ 593 (frequent consumption of a food resource). 594 **Individual: workers of these species are not able to communicate their nestmates the presence of a food source, they forage and 595 collect food individually; Group: workers of these species are able to communicate and guide a low number of nestmates to a 596 previously discovered food source; Collective: workers of these species follow "anonymous" chemical signals provided by other 597 nestmates to exploit a food source, they can organize mass-recruitment or temporal or permanent trails to the food source

27

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598 Table 2. Significant effects of the environmental gradients on the trait average ( X ) and dissimilarity (FD) found using the GLS

599 models. Arrows indicate the direction of the effects. Significance level of p<0.00076 after the Bonferroni correction. * Note that the

600 trait dissimilarity of these traits is highly correlated with the trait average of the same traits; for instance, a negative effect on trait

601 dissimilarity corresponds in turn to a negative effect on trait average, and vice versa. Functional trait For ReviewClimate gradient Only Productivity gradient Vegetation type gradient Mean Temperature Precipitation Trait average Precipitation NDVI Biome temperature seasonality seasonality Worker size -  - - - - Worker polymorphism -  -  - - Behavioral dominance (dominants) - - - -  Significant Food resources - insects ------Food resources - liquid food - - - - - Significant Foraging strategy - group - - -   Significant Foraging strategy - collective - - - -  Significant Colony size - - - - - Significant Trait dissimilarity Worker size ------Worker polymorphism ------Diurnality* - - - - - Significant Behavioral dominance ------Food resources - seeds* - -  -  Significant Food resources - insects - - - -  Significant Food resources – liquid food -  - -  Significant Foraging strategy - individual* - - -   - Foraging strategy - group ------Foraging strategy - collective ------Colony size ------Number of queens* - - -   Significant Number of nests* - -   Significant Colony foundation type*   Significant

28

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602 Figure legends

603

604 Figure 1. Some examples of significant relationships between functional trait indices and climate

605 and productivity. Relationship between a) average worker polymorphism and temperature

606 seasonality, b) dissimilarity in the % of seeds in the diet and precipitation, and c) the percentage of

607 behaviorally dominant species and productivity. 608 For Review Only 609 Figure 2. A comparison of the trait averages and dissimilarities (FD) that differed significantly

610 among the six vegetation types considered. Boxplots are ordered according to the four general

611 patterns (see results; a-d). The different letters above the boxplots indicate the significant

612 differences among vegetation types found using multiple contrasts of the GLS models.

613 Abbreviations on the x-axis are: BR, broadleaf forest; SC, sclerophyllous forest; CO, conifer

614 forest; SH, shrubland; DE, dehesa; and GR, grassland.

615

29

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616 Figure 1

A) 0.8

0.7

0.6

0.5

0.4

0.3 0.2 For Review Only 617

B)

1.6

1.2

0.8

0.4

618

C) 1.2

1

0.8

0.6

0.4

0.2

0

619

30

Page 31 of 65 Journal of Animal Ecology

620 Figure 2

a a a b b b a ab b c c c a a a b c b ab b a ac c ac

a) FD % Seeds in diet FD% Seeds FD % Insects in diet FD% Insects FD % Liquid food in diet in % Liquid food FD FDNumber queens of FD % Insects% FD indiet FD % Seeds% FD indiet FD NumberFD queens of 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 FD % Liquid% FD foodsdiet in 0.0 0.5 1.0 1.5 BR1BRSC 2SCCO 3CO 4SHSH 5DEDE 6GRGR BR1BRSC 2SCCO 3CO 4SHSH 5DEDE 6GRGR BR1BR 2SCSC 3COCO 4SHSH 5DEDE 6GRGR 1BRBR 2SCSC 3COCO 4SHSH 5DEDE 6GRGR

Vegetation type Vegetation type Vegetation type Vegetation type a a b b b a a a b bc c bc

b) For Review Only FD Period of activity of FDPeriod FD Diurnality FD FD Colony foundationtype 0.0 0.5 1.0 1.5 0.0 0.4 0.8 1.2 FD ColonyFD foundation type BR1BRSC 2SC 3COCO 4SHSH 5DEDE 6GRGR BR1BRSC 2SCCO 3CO 4SHSH 5DEDE 6GRGR

Vegetation type Vegetation type

ac b b bc ac a ab a a b b b ac b b ab ac c

c) Colony size (ln) size Colony % Liquid food dietfood in % Liquid ln(Colony size) 0.2 0.4 0.6 0.8 % Collective-foraging species % Collective-foraging % Liquid% foods indiet 0.0 0.2 0.4 0.6 0.8 1.0 6.0 6.5 7.0 7.5 8.0 8.5 9.0

% Collective-foraging% species BR1BRSC 2SCCO 3CO 4SHSH 5DEDE 6GRGR BR1BRSC 2SC 3COCO 4SHSH 5DEDE 6GRGR BR1BRSC 2SC 3COCO 4SHSH 5DEDE 6GRGR Vegetation type Vegetation type Vegetation type

a a a a b b ad bc b ac ad d ab b a a c c

d) FD FD Numbernestsof % Dominant species %Dominant % Group-foraging species %Group-foraging % Dominant% species FD NumberFD nests of 0.0 0.5 1.0 1.5 2.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 % Group-foraging% species BR1BRSC 2SCCO 3CO 4SHSH 5DEDE 6GRGR BR1BRSC 2SC 3COCO 4SHSH 5DEDE 6GRGR BR1BRSC 2SCCO 3CO 4SHSH DE 5DEGR 6GR Vegetation type Vegetation type VegetationVegetation type type 621 Vegetation type Vegetation type

31

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Appendix S1. Details on how we completed gaps in the species-trait database.

The information found in the literature for most Solenopsis species was very scarce and we were not able to complete the traits of these species. For this reason, we did not consider them in the analyses and the six localities with an overall abundance of Solenopsis spp. > 5% were eliminated.

For species from which information was scarce because that have been recently described from another species or that Forare rare but Review similar to other more commonOnly species, we used the same traits of these well-known species. This applies to the following pairs of unknown/well known species:

Camponotus amaurus/Camponotus foreli, Camponotus figaro/Camponotus piceus, lasioides/Lasius alienus, Goniomma thoracicum/Goniomma hispanicum, Messer maroccanus/Messor bouvieri, spinosior/Myrmica sabuleti, Stenamma orousetti/Stenamma westwoodi, Tetramorium impurum/Tetramorium caespitum, Tetramorium hispanicum/Tetramorium ruginode, Tetramorium forte/Tetramorium ruginode and Tetramorium punicum/Tetramorium semilaeve. For one trait from which we did not have information (colony founding of Aphaenogaster dulcinea ) we assigned the value of the closest species of the genera.

We did not complete another trait, that of colony founding of Proformica ferreri and Proformica nasuta , because their social structure was very different to that of the species of the from which this information was available. Similarly, we did not complete the values of colony size of

Messor celiae and Messor hispanicus . Finally, there were two species that were not classified in the original literature sources. In the case of Oxyopomyrmex sp. from five sites we assigned the values of O. saulcyi because the distribution area of this species includes all these sites and no other species of the genus does. In the case of Temnothorax sp. from two sites, different species of the genus could match this location and for this species we applied the most common trait values of the species of the genus. Page 33 of 65 Journal of Animal Ecology

Appendix S2. List of the literature sources and online databases from which functional trait

data were obtained.

Worker size and worker polymorphism

APEF: Etude et elevage des fourmis 2012. http://apef.france.pagesperso- orange.fr/Fourmis.htm .

Arnan, X., Cerdá, X. and Retana, J. 2012. Distinctive life traits and distribution along environmental gradients of dominant and subordinate Mediterranean ant species. Oecologia 170: 489-500.

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Bernard, F. 1968. Les fourmis d' occidentale et septentrionale. Masson et Cie Éditeurs, Paris.

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Cagniant H., Espadaler X. 1997. Les Leptothorax, Epimyrma et Chalepoxenus du Maroc (Hymenoptera: Formicidae). Clé et catalogue des espèces. Annales de la Société Entomologique de France 33: 259-284.

Cerdá X., Retana J. 1997. Links between worker polymorphism and thermal biology in a thermophilic ant species. Oikos, 78: 467-474.

Cerdá X., Retana J. 2000. Alternative strategies by thermophilic ants to cope with extreme heat: individual versus colony level traits. Oikos 89: 155-163.

Cushman J.H., Lawton J. H., Manly B.F.J. 1993. Latitudinal patterns in European ant assemblages: variation in species richness and body size. Oecologia 95: 30-37.

Espadaler X. 1986. Goniomma kugleri , a new granivorous ant from the Iberian Peninsula (Hymenoptera: Formicidae). Journal of Entomology 19: 61-66.

Espadaler X. 1997a. Redescription of Leptothorax schaufussi (Forel, 1879) (Hymenoptera, Formicidae). Orsis 12: 101-107.

Espadaler X. 1997b. Leptothorax caesari sp.n. (Insecta: Hymenoptera: Formicidae), a granivore con apterous males. Annalen des Naturhistorischen Museums in Wien 99B: 145-150.

Espadaler X., Collingwood, C.A. 1989. Notas sobre Leptothorax , con descripción de L. gredosi n. sp. (Hym. Formicidae). Boletín Asociación Española de Entomología 6: 41-48.

Espadaler X., Du Merle P., Plateaux L. 1983. Redescription de Leptothorax grouvellei . Notes biologiques et écologiques (Hymenoptera: Formicidae). Insectes Sociaux 30: 274-286. Journal of Animal Ecology Page 34 of 65

Espadaler, X. and Du Merle, P. 1989. Leptothorax fuentei Santschi, 1919, en France (Hymenoptera: Formicidae). Vie Milieu 39: 121-123.

Fourmis de France Database. 2011. http://www.antbase.fr/

Hormigas ibéricas 2012. http://www.hormigas.org

Notes from underground 2012. http://www.notesfromunderground.org/archive/issue11- 2/features/petrov/petrov.html

Reyes J., Espadaler X., Rodríguez A. 1987. Descripción de Goniomma baeticum nov. sp. (Hymenoptera: Formicidae). Eos 63: 269-276.

Schlick-Steiner, B.C., Steiner F.M., Moder R., Bruckner A., Fiedler K., Christian E. 2006. Assessing ant assemblages:For pitfall Review trapping versus nest Onlycounting (Hymenoptera, Formicidae). Insectes Sociaux 53: 274–281.

Tinaut A. 1985. Descripción del macho de Aphaenogaster cardenai (Hymenoptera Formicidae). Miscel·lània Zoològica 9: 245-249

Tinaut A., Ortiz F.J. 1988. Descripción del macho de Monomorium algiricum (Bernard 1955) y consideraciones sobre el valor taxonómico del género Epixenus (Hym. Formicidae). Boletín Asociación Española de Entomología 12: 165-174. van Loon A.J., Boomsma J.J., Andrasfalvy A. 1990. A new polygynous Lasius species (Hymenoptera; Formicidae) from . I. Description and general biology. Insectes Sociaux 37: 348-362.

World of ants 2011. http://www.world-of-ants.com/

Diurnality

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Leo P., Fancello L. 1990. Observazioni sul genere Leptanilla Emery in Sardegna e riabilitazione di L. doderoi . Boletino Societá Entomologia Italiana 122: 128-132.

Liebig J., Heinze J., Hölldobler B. 1995. Queen size variation in the ponerine ant Ponera coarctata (Hymenoptera: Formicidae). Psyche 102: 1-12 Page 51 of 65 Journal of Animal Ecology

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Buschinger, A. 2010. Nest relocation in the ant Myrmecina graminicola (Hymenoptera Formicidae). Myrmecological News 13: 147-149.

Buschinger, A. and Schreiber, M. 2002. Queen polymorphism and queen-morph related facultative polygyny in the ant , Myrmecina graminicola (Hymenoptera, Formicidae). Insectes Sociaux 49: 344–353.

Espadaler, X. and Rey, S. 2001. Biological constraints and colony founding in the polygynous invasive ant Lasius neglectus (Hymenoptera, Formicidae). Insectes Sociaux 48: 159-164.

Fernández-Escudero, I., Seppa, P. and Pamilo, P. 2001. Dependent colony founding in the ant Proformica longiseta . Insectes Sociaux 48: 80-82.

Fourmis de France Database. 2011. http://www.antbase.fr/

Fournier, D., Aron, Milinkovitch, C. 2002. Investigation of the population genetic structure and mating system in the ant Pheidole pallidula. Molecular Ecology 11: 1805–1814.

Hanna, C.H. 1975. The biology and polygyny of the ant Tapinoma simrothi phoenicium Emery. C. R. Acad Sci Hebd Seances Acad Sci D 281:1003-1005.

Heinze, J. 1993. Life histories of subarctic ants. Arctic 46: 354-358.

Heinze, J., Hölldobler, B. and Cover, S.P. 1992. Queen polymorphism in the North American harvester ant, Ephebomyrmex imberbiculus . Insectes Sociaux 39: 267-273. Page 55 of 65 Journal of Animal Ecology

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Table S1. The Spearman rank correlation coefficients for climate variables. Abbreviations: Tmean, mean annual temperature; Tmin,

minimum temperature; Tmax, maximum temperature; Tseas, temperature seasonality; Precip, Precipitation; Precip. Dry, precipitation in

the driest month; Precip. Wet, precipitation in the wettest month; Precip. Seas, precipitation seasonality. Spatial autocorrelation was

considered by reducing the number of degrees of freedom. * P < 0.05; ** P < 0.01; *** P < 0.001; in bold, r>0.7 and at least p<0.05. For Review Only

Precip. Precip. Precip. Tmean Tmin Tmax Tseas Precip Altitude Dry Wet Seas Tmean 1 0.90*** 0.48*** -0.54* -0.50*** -0.51*** -0.18** 0.50* -0.76*** Tmin 1 0.15*** -0.76*** -0.22*** -0.23** 0.00 0.27* -0.78*** Tmax 1 0.30 -0.80*** -0.89** -0.54** 0.81 -0.24* Tseas 1 -0.14 -0.13 -0.33* 0.02 0.58*** Precip 1 0.82*** 0.78*** -0.69 0.31*** Precip. Dry 1 0.51* -0.93 0.32 Precip. Wet 1 -0.38 -0.07 Precip. Seas 1 -0.28 Altitude 1 Journal of Animal Ecology Page 58 of 65

Table S2. A) The Spearman rank correlation coefficients between NDVI spring+summer and climate variables. Spatial autocorrelation was considered by reducing the number of degrees of freedom. * P < 0.05; ** P < 0.01; *** P < 0.001. B) The t statistics and significance

(p) from the GLS models conducted to analyse the relationship between species richness and climate variables and NDVI spring+summer .

Abbreviations: Tmean, mean annual temperature; Tseas, temperature seasonality; Precip, Precipitation; and Precip. Seas, precipitation seasonality. For Review Only

A)

Tmean Tseas Precip Precip. Seas NDVI spring+summer -0.23 -0.30 0.64* -0.57

B)

Tmean Tseas Precip Precip. Seas NDVI spring+summer t p t p t p t p t p Species richness -0.56 0.575 1.96 0.051 -0.31 0.756 0.08 0.938 1.84 0.066

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Table S3. The Spearman correlation coefficients for trait averages and dissimilarities. For categorical traits, the reference category used in the analysis is given in parentheses. Spatial autocorrelation was taken into account by reducing the number of degrees of freedom. * Indicates

P < 0.05; in bold, r>0.7.

Trait For Review Data type Only Spearman´s r Colony size Quantitative r=-0.366; p<0.001 Number of queens (polygyny) Ordinal r=0.868; p<0.001 Number of nests (polydomy) Ordinal r=0.938; p<0.001 Worker size Quantitative r=0.609 ; p<0.001 Worker polymorphism Quantitative r=0.446; p<0.001 Diurnality (strictly diurnal) Binary r=0.970; p<0.001 Behavioral dominance (dominant) Binary r=0.298; p<0.001 Diet - Seed-eating Fuzzy-coded r=0.941; p<0.001 Diet - Insect-eating Fuzzy-coded r=-0.044; p=0.631 Diet - Liquid food-eating Fuzzy-coded r=-0.419; p=0.010 For. strategy – individual Binary r=1; p<0.001 For. strategy – group Binary r=-0.516; p=0.214 For. strategy - collective Binary r=0.312; p=0.016 Colony foundation type (ICF) Ordinal r=-0.858; p<0.001 Page 61 of 65 Journal of Animal Ecology

Table S4. The Spearman rank correlation coefficients for the trait averages and the trait dissimilarities at the community level for the different

functional traits considered in the study. Abbreviations: WS, worker size; WP, worker polymorphism; Diurn., Diurnality; Dom., dominance;

pSeeds, proportion of seeds in diet; pInsects, proportion of insects in diet; pLF, proportion of liquid foods in diet; FSind, individual foraging

strategy; FSgr, group foraging strategy; FScol, collective foraging strategy; CS, colony size; NQ, number of queens; NN, number of nests; CFT,

colony foundation type. Spatial autocorrelationFor was consideredReview by reducing the number Only of degrees of freedom. * P < 0.05; ** P <0.01; *** P

<0.001; in bold, r>0.7; in grey, correlations between those traits that were significantly affected at the same time by any of the environmental

gradients.

Trait WS WP Dom. pInsects pLF FSgr FScol CS average WS 1 0.50*** 0.25 -0.22*** -0.22 0.02 -0.07 0.21

WP 1 0.62*** -0.33*** -0.10 -0.09 0.15* 0.42***

Dom. 1 -0.22* -0.16 -0.32** 0.42*** 0.63*** pInsects 1 -0.40*** -0.15 0.13 0.01 pLF 1 0.45** -0.34* -0.26* FSgr 1 -0.90*** -0.63*** FScol 1 0.78*** CS 1 Journal of Animal Ecology Page 62 of 65

Trait For Review Only WS WP Diurn. Dom. pSeeds pInsects pLF FSind FSgr FScol CS NQ NN CFT dissimilarity WS 1 0.63*** 0.03** 0.21*** 0.14 0.19* 0.22** 0.03 -0.23 -0.14 -0.14* -0.13 -0.24** -0.09 WP 1 0.06** 0.24** 0.37*** 0.33*** 0.34*** -0.01 -0.03 -0.05* -0.01 -0.02 -0.11 -0.07 Diurn. 1 0.30*** 0.34** 0.43*** 0.31** 0.40** 0.05** 0.03 -0.16 -0.29*** -0.18* -0.18** Dom. 1 0.26* 0.30*** 0.22 0.20* 0.25*** 0.17*** 0.06** -0.29*** -0.18* -0.18** pSeeds 1 0.58*** 0.66*** 0.32*** 0.22** 0.16 0.01 0.21 0.26* 0.32* pInsects 1 0.79*** 0.51*** 0.14* 0.13 -0.17* -0.39** -0.34* -0.50*** pLF 1 0.17* 0.14* 0.14* -0.34*** 0.40* 0.31* 0.50*** FSind 1 0.12* 0.25 0.24* 0.01 0.27* 0.17* FSgr 1 0.74*** 0.21*** 0.15 0.16 0.09 FScol 1 0.30*** -0.01 0.06 0.11 CS 1 -0.29*** -0.18* -0.18** NQ 1 0.58*** 0.57*** NN 1 0.41** CFT 1 Page 63 of 65 Journal of Animal Ecology

Table S5 . Statistical output from the GLS models analyzing the effect of different environmental gradients (climate, productivity, and vegetation type) on ant functional diversity based on 10 functional traits. In bold, statistically significant differences (p<0.00076) after the Bonferroni correction.

TRAIT AVERAGE Climate gradients Productivity Vegetation type gradient gradient Mean Temperature Precipitation Functional trait Precipitation NDVI Biome temperatureFor seasonalityReview Onlyseasonality Worker size t=1.13, p=0.2613 t=3.51, p=0.0005 t=2.11, p=0.0365 t=0.87, p=0.3831 t=-1.46, p=0.1468 F=1.68, p=0.1419 Worker t= 3.31, p=0.0011 t=4.78, p<0.0001 t=1.67, p=0.0955 t=-5.10, p<0.0001 t=-1.22, p=0.2242 F=3.69, p=0.0032 polymorphism Behavioral dominance t=1.32, p=0.1870 t=2.04, p=0.0424 t=0.47, p=0.6392 t=0.07, p=0.9404 t=-5.80, p<0.0001 F=6.59, p<0.0001 (dominants) Food resources - t=-2.12, p=0.0348 t=-1.56, p=0.1198 t=-0.32, p=0.7433 t=1.33, p=0.1834 t=2.78, p=0.006 F=2.51, p=0.0311 insects Food resources – t=1.48, p=0.1398 t=-1.56, p=0.1196 t=3.15, p=0.0019 t=-2.07, p=0.0400 t=3.10, p=0.0022 F=4.83, p=0.0003 liquid food Foraging strategy - t=2.07, p=0.0395 t=0.23, p=0.8189 t=1.79, p=0.0755 t=-3.55, p=0.0005 t=5.95, p<0.0001 F=10.59, p<0.0001 group Foraging strategy - t=-1.30, p=0.1937 t=-0.12, p=0.9024 t=-0.37, p=0.7098 t=2.12, p=0.0348 t=-4.49, p<0.0001 F=9.77, p<0.0001 collective Colony size t=-0.41, p=0.6831 t=1.36, p=0.1752 t=1.31, p=0.1907 t=1.53, p=0.1286 t=-3.36, p=0.0009 F=9.39, p<0.0001

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TRAIT Climate gradients Productivity Vegetation type DISSIMILARITY gradient gradient Mean Temperature Precipitation Functional trait Precipitation NDVI Biome temperature seasonality seasonality Worker size t= 0.96, p=0.3399 t= 2.43, p=0.0159 t=1.78, p=0.0769 t=0.47, p=0.6409 t=0.43, p=0.6051 F=2.85, p=0.0161 Worker t=1.22, p=0.2236 t=2.79, p=0.0057 t=0.25, p=0.8048 t=-1.80, p=0.0729 t=0.12, p=0.9016 F=1.98, p=0.0833 polymorphism Diurnality t=-0.45, p=0.6537 t=2.21, p=0.0283 t=0.15, p=0.8802 t=3.03, p=0.0027 t=-1.78, p=0.0676 F=4.76, p=0.0004 Behavioral t=-1.20, p=0.2322For t=0.17, Review p=0.8652 t=-0.79, p=0.430 Only5 t=1.73, p=0.0843 t=0.95, p=0.3408 F=1.58, p=0.1659 dominance Food resources - t=-0.89, p=0.3733 t=2.13, p=0.0343 t=-4.38, p<0.0001 t=1.27, p=0.2071 t=-5.07, p<0.0001 F=7.17, p<0.0001 seeds Food resources - t=0.22, p=0.8195 t=2.77, p=0.0062 t=-2.49, p=0.0133 t=2.41, p=0.0165 t=-7.59, p<0.0001 F=9.04, p<0.0001 insects Food resources – t=2.08, p=0.0391 t=4.75, p<0.0001 t=-1.05, p=0.2931 t=1.26, p=0.2078 t=-6.27, p<0.0001 F=9.54, p<0.0001 liquid food Foraging strategy - t=-2.14, p=0.0333 t=-0.24, p=0.8091 t=-3.18, p=0.0017 t=4.48, p<0.0001 t=-3.67, p=0.0003 F=1.95, p=0.0879 individual Foraging strategy - t=-1.50, p=0.1314 t=0.11, p=0.9099 t=-1.54, p=0.1315 t=2.46, p=0.0146 t=0.63, p=0.5305 F=2.01, p=0.0781 group Foraging strategy - t=-1.13, p=0.2602 t=0.21, p=0.8339 t=-0.52, p=0.6057 t=2.76, p=0.0064 t=2.21, p=0.0284 F=1.55, p=0.1763 collective Colony size t=-1.59, p=0.1131 t=-2.48, p=0.0140 t=-1.99, p=0.0475 t=1.19, p=0.2363 t=3.37, p=0.0009 F=4.05, p=0.0016 Number of queens t=-2.65, p=0.0088 t=-0.44, p=0.6616 t=-2.84, p=0.0049 t=3.62, p=0.0004 t=-7.03, p<0.0001 F=7.86, p<0.0001 Number of nests t=-2.77, p=0.0061 t=-0.75, p=0.4529 t=-1.64, p=0.1021 t=4.65, p<0.0001 t=-6.48, p<0.0001 F=9.31, p<0.0001 Colony foundation t=-0.66, p=0.5073 t=1.77, p=0.0784 t=-0.86, p=0.3928 t=5.71, p<0.0001 t=-8.72, p<0.0001 F=8.03, p<0.0001 type

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