# Trait-mediated responses to aridity and experimental drought by springtail communities across Europe

Miquel Ferrín, Laura Márquez, Henning Petersen, Sandrine Salmon, Jean-François Ponge, Miquel Arnedo, Bridget Emmett, Claus Beier, Inger K. Kappel Schmidt, Albert Tietema, et al.

► To cite this version:

Miquel Ferrín, Laura Márquez, Henning Petersen, Sandrine Salmon, Jean-François Ponge, et al. Trait-mediated responses to aridity and experimental drought by springtail communities across Europe. Functional Ecology, 2023, 37 (1), pp.44-56. 10.1111/1365-2435.14036. mnhn-03902731

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# Trait-mediated responses to aridity and experimental drought by springtail communities across Europe

Miquel Ferrín<fnref n="1" />,<fnref n="2" />,*, Laura Márquez<fnref n="1" />,<fnref n="2" />, Henning Petersen<fnref n="3" />, Sandrine Salmon<fnref n="4" />, Jean-François Ponge<fnref n="4" />, Miquel Arnedo<fnref n="5" />, Bridget Emmett<fnref n="6" />, Claus Beier<fnref n="7" />, Inger K. Schmidt<fnref n="7" />, Albert Tietema<fnref n="8" />, Paolo de Angelis<fnref n="9" />, Dario Liberati<fnref n="9" />, Edit Kovács-Láng<fnref n="10" />, György Kröel-Dulay<fnref n="10" />, Marc Estiarte<fnref n="1" />,<fnref n="2" />, Mireia Bartrons<fnref n="11" />, Josep Peñuelas<fnref n="1" />,<fnref n="2" />, Guille Peguero<fnref n="1" />,<fnref n="2" />,<fnref n="12" />,*

<footnote n="1">¹ CSIC, Global Ecology Unit CREAF-CSIC-UAB, 08913 Bellaterra, Catalonia, Spain.</footnote>

<footnote n="2">² CREAF, 08913 Cerdanyola del Vallès, Catalonia, Spain.</footnote>

<footnote n="3">³ Natural History Museum, Mols Laboratory, Strandkaervej 6-8, Femmøller, DK8400 Ebeltoft, Denmark.</footnote>

<footnote n="4">⁴ Muséum National d'Histoire Naturelle, CNRS UMR 7179, 4 Avenue du Petit-Château, 91800 Brunoy, France.</footnote>

<footnote n="5">⁵ Department of Evolutionary Biology, Ecology and Environmental Sciences, and Biodiversity Research Institute (IRBio), Universitat de Barcelona, Avinguda Diagonal 643, 08028 Barcelona, Spain.</footnote>

<footnote n="6">⁶ Centre for Ecology and Hydrology, Environment Centre Wales, Deiniol Road, Bangor LL57 2UW, UK.</footnote>

<footnote n="7">⁷ Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23, 1958 Frederiksberg C, Denmark.</footnote>

<footnote n="8">⁸ Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 94240, 1090 GE Amsterdam, The Netherlands.</footnote>

<footnote n="9">⁹ Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via San Camillo de Lellis snc, 01100 Viterbo, Italy.</footnote>

<footnote n="10">¹⁰ Institute of Ecology and Botany, MTA Centre for Ecological Research, Alkotmany u. 2-4, 2163 Vacratot, Hungary.</footnote>

<footnote n="11">¹¹ Aquatic Ecology Group, Universitat de Vic-Universitat Central de Catalunya, Vic 08500, Barcelona, Spain.</footnote>

<footnote n="12">¹² Departament de Biologia Animal, Biologia Vegetal i Ecologia, Universitat Autònoma de Barcelona, 08193 Bellaterra, Spain</footnote>

<footnote>* Corresponding authors' e-mail: miquelferrin@gmail.com; guille.peguero@gmail.com</footnote>
**Acknowledgements**

All authors thank the efforts of the many people who helped with the fieldwork during
the VULCAN-INCREASE projects. The present study received the financial support
from the European Research Council through the Synergy grant

ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government grant PID2019-110521GB-I00, the Fundación Ramón Areces grant ELEMENTAL-CLIMATE, and the Catalan Government grant SGR 2017-1005. M.F is supported by an FPI grant from the Spanish Government.

**Conflicts of Interest:** Authors declare no conflicts of interest.

**AUTHOR CONTRIBUTIONS:** G.P., L.M. and J.P. designed the study. H.P., S.S., J-F.P., M.A., B.E., C.B., I.K.S., A.T., P. de A., D.L., E. K-L., G. K-D., M.E. and M.B. conducted field or laboratory work and/or contributed data. M.F., L.M. and G.P. analyzed the data. M.F. and G.P. wrote the first draft of the manuscript, with substantial inputs from J.P., and all authors contributed to revisions.

**Data availability:** The raw data supporting the results of this study will be available at a public repository upon the acceptance of the manuscript.
# Trait-mediated responses to aridity and experimental drought by springtail communities across Europe

## Abstract

1. The capacity to forecast the effects of climate change on biodiversity largely rely on identifying traits capturing mechanistic relationships with the environment through standardized field experiments distributed across relevant spatial scales. The effects of short-term experimental manipulations on local communities, however, may overlap with regional climate gradients that have been operating during longer time periods, though to the best of our knowledge, there are no studies simultaneously assessing such long-term macroecological drivers with climate manipulations.

2. We analyzed this hypothesis with springtails (subclass Collembola), a principal group of soil fauna, in a standardized climate manipulation experiment conducted across six European countries encompassing broad climate gradients. We crossed community data (near 20K specimens classified into 102 species) with 22 eco-morphological traits and reconstructed their phylogenetic relationships to track the evolution of adaptations to live at different soil depths, which is key to cope with dryness. We then applied joint species distribution models to investigate the combined effect of the regional aridity gradient with the local experimental treatment (drought and warming) over the assembly of springtail communities and tested for significant trait-environment relationships mediating their community-level responses.

3. Our results show: (1) a convergent evolution in all three major collembolan lineages of species adapted to inhabit at different soil strata; (2) a clear signature of aridity selecting traits of more epigeic species at a biogeographic scale, and (3) the association of short-term experimental drought with traits related to more edaphic life-forms.

4. The hemiedaphic condition is the plesiomorphic state for Collembola while the adaptations for an epigeic life would have been secondarily gained. Epigeic springtails are more resistant to drought but also have a higher dispersal capacity that allows them to seek more favorable micro-habitats after experiencing drier conditions. The observed relative edaphization of the springtail communities after short-term experimental drought may thus be a transient community response.
31 5. The disparity between macroecological trends and fast community-level responses
32 after climate manipulations highlights the need of simultaneously assessing long-term
33 and short-term drivers at broad spatial scales to adequately interpret trait-environment
34 relationships and better forecast biodiversity responses to climate change.

35 **Keywords:** climate change, collembola, functional biogeography, joint species
36 distribution models, shrublands, soil fauna
## 1. Introduction

Global biodiversity patterns are undergoing rapid shifts driven by on-going climate change (Blowes et al., 2019). Our capacity to forecast such biodiversity responses rely, however, on detailed knowledge of the processes behind species' population rates and the corresponding community reorganizations. Ecological theory predicts that a continued stress will filter out the species with the most vulnerable combinations of traits leading to changes on the functional structure of communities (Mouillot et al., 2013). This selection process may be either the result of the new abiotic conditions impairing the population growth of some species (strict environmental filtering) or the outcome of a reduced competitive performance under the new environmental conditions (Cadotte & Tucker, 2017). Traits, defined as any morphological, physiological, behavioural or phenological feature measurable at an individual level with impacts on species fitness via their effect on growth, reproduction and survival, are thus posited as the common currency for functional biogeography (McGill et al., 2006; Violle et al., 2014). Because trait selection generally precedes diversity loss, it is therefore fundamental to detect those traits capturing mechanistic relationships with stress factors such as climate change if we are to anticipate shifts on species composition and ultimately on ecosystem functioning (Berg et al., 2010; Haddad et al., 2008).

Coordinated and distributed multisite field experiments of climate manipulation are among the best tools available to infer these trait-environment relationships at relevant spatial scales under a scenario of rapid climate change (Halbritter et al., 2020). The effects of short-term experimental manipulations on local communities, however, overlap with regional climate gradients that have been operating during longer time periods which may lead to divergent ecosystem responses across such gradients that hinder our interpretations (Reinsch et al., 2017). This dependency of observed ecological patterns on multiple processes operating across a hierarchy of space-time scales is a classic issue in functional biogeography that strongly affects our predictive ability because general inferences and extrapolations are too often bogged down in local contingencies (Levin, 1992; Mouquet et al., 2015). Thus, for the development of the so-called predictive ecology, a key is to incorporate this complexity when assessing the performance of the analytical methods available in the macroecological toolkit (Mouquet et al., 2015).
Despite the growing concern over the impact of climate change on biodiversity, there has been a disproportionate focus on aboveground communities disregarding the key role of soil fauna on ecosystem functioning (Bardgett & Putten, 2014; Filser et al., 2016; Guerra et al., 2021). Among this neglected soil biodiversity there are the springtails (subclass Collembola), an abundant and diverse group of small arthropods whose communities show fast reductions in taxonomic, phylogenetic and functional richness due to drought, and additionally, are also a good proxy of ecosystem functions like litter decomposition (Peguero et al., 2019). Springtails present a diverse array of adaptations related to dwelling in specific soil layers (Figure 1). Smaller collembolan species with unpigmented bodies, shorter appendages, reduced sensory organs and mostly parthenogenetic reproduction are associated with more edaphic habitats (Chahartaghi et al., 2006; Salmon et al., 2014), while larger species with pigmented bodies, longer appendages, sexual reproduction and conspicuous mechanic and light receptors such as trichobothria and ocelli respectively, are typical from epigeic environments (Salmon et al., 2014). The species inhabiting the upper soil horizons have a greater drought resistance thanks to a lower cuticular permeability that allow them to resist desiccation (Kærsgaard et al., 2004). On the other hand, living deeper in the soil profile implies a lower exposure to variability in temperature and soil moisture, and additionally, soil-dwelling species also present biochemical, physiological and behavioral mechanisms to cope with drought (Holmstrup et al., 2001). Accordingly, a multi-site replicated experiment suggested that increasingly drier conditions may eventually favor edaphic over epigeic springtail species after short-term climate manipulations (Petersen, 2011). Even though, this relative edaphization may show idiosyncratic responses to short-term climate manipulations since in single-site studies at sub-arctic latitudes epigeic species seem to be favored by experimental warming (Bokhorst et al., 2012; Makkonen et al., 2011). Hence, potentially divergent community responses across large spatial scales may hamper our ability to detect general trends, and in particular to identify those traits behind the current climate-induced community reorganizations. This may be even more difficult if broad climate constraints have had time to differentially shape springtail assemblages across such environmental gradients, although this macroecological dimension, to the best of our knowledge, has never been included in studies assessing soil fauna responses to climate change.
The present study unfolds from a standardized climate manipulation experiment replicated across six natural shrublands encompassing the broad gradients of temperature and precipitation present in Europe (Beier et al., 2004). Here, we investigated the simultaneous effect of short-term experimental climate treatments (i.e., drought and warming) and the regional long-term climate gradient synthesized in the aridity index, an indicator based on mean annual temperature and precipitation, over the composition and trait distribution of springtail communities. First, we harnessed data from a comprehensive sampling of near 20 thousand springtail specimens sorted out in 102 species (Petersen, 2011) and then we crossed it with 22 eco-morphological springtail traits (Salmon et al., 2014) to track across the springtail phylogeny the evolution of the suite of adaptations to inhabit in specific soil layers (Table 1). Finally, we leveraged this community data to build joint species distribution models (JSDM) to test for significant associations between traits with climate manipulation treatments and aridity that could be mediating the assembly of collembolan communities. We specifically addressed the following hypotheses and predictions: (i) the adaptations that allow springtails to thrive at different layers of the soil matrix will show a pattern of repeated convergent evolution across the main lineages of Collembola; (ii) the long-term environmental forcing of the large-scale climatic gradient present across Europe has driven a selection of drought-tolerant springtail species with increasing aridity; (iii) the experimental short-term increase in local dryness and warming will be a major disturbance driving fast responses that eventually favor more edaphic springtail assemblages.

## 2. Materials and Methods

### 2.1 Study sites and climatic manipulations

The six shrublands studied comprised most of the European climatic regions (Figure S1 and Table S1). Mean annual precipitation (MAP) at the sites ranged from 544 to 1,263 mm, and mean annual temperature (MAT) ranged from 7.4 to 16.1 °C. The major types of shrublands present in the study area were: Atlantic heathland (UK, United Kingdom; NL, The Netherlands; DK, Denmark), continental forest steppe (HU, Hungary), and Mediterranean garrigue (SP, Spain; IT, Italy). In each study site, nine 20-m² plots were divided into three blocks and randomly assigned one plot at each block to a warming treatment, a drought treatment, or as a control. Warming plots were covered with reflective curtains during the night throughout the year inducing a passive night-time
134 warming, while drought plots were automatically covered by transparent polythene
135 curtains during precipitation events that retracted as soon as rain stopped thus avoiding
136 any warming effect (Beier et al., 2004). Manipulations of temperature and precipitation
137 were carried out in the same way in all sites, but the timing and duration of the
138 experimental drought were adjusted to the local climate regime (Table S2). The sites
139 were established in 1998 (UK, NL, DK, and SP) and 2001 (HU and IT), and on average
140 the warming treatment produced an increase of 0.5 °C of MAT and the drought
141 treatment a 35% reduction of soil moisture (Table S2).

142 **2.2 Springtail sampling, phylogeny and trait information**

143 Springtails (Subclass Collembola) were sampled sequentially (IT, SP, HU, DK, NL, and
144 UK) from April to July 2003 to equalize mean temperatures across sites. Five quadrats
145 of 1.25 m² representative of the most dominant plant species were chosen in each plot.
146 Springtails were sampled from plants and soil surface by suctioning with an adapted
147 vacuum cleaner connected to a fauna trap. Specimens dropped were also recovered by
148 placing small polythene boxes with aqueous benzoic acid beneath the plants. Soil
149 springtails were sampled in 10-cm deep soil cores with a surface of 25 cm² taken to the
150 lab and put through high-gradient extraction funnels (Gjelstrup & Petersen, 1987).
151 These procedures were slightly adapted in UK and IT due to an excess of soil moisture
152 and stones, respectively (Petersen, 2011). Springtails were identified to species level
153 and some dubious specimens were revised by Drs. L. Dányí, R. Jordana, and E. Mateos.
154 A total of 19,641 springtail specimens were collected and classified into 102 species-
155 level entities (Tables S3) and are conserved in glycerol in H. Petersen's personal
156 collection.

157 We reconstructed the phylogenetic relationships of springtails by means of sequence
158 data from two genes (cox1 and 28s) gathered from public repositories covering 75% of
159 the species found in our communities (accession numbers in Table S3). We applied
160 Maximum Likelihood and Bayesian procedures to obtain a highly-supported consensus
161 tree that agrees with previous systematic works integrating molecular and
162 morphological information (Yu et al., 2016). See supplementary methods and figure S2
163 for further details.

164 Additionally, we collated data of 22 eco-morphological traits from the ColTrait database
165 (Salmon et al., 2014) for 47 up to 64 of the species present in our study sites (Table 1
and Table S4). These traits encompass different dimensions of their ecological niche such as life history (e.g., reproduction strategy), dispersal ability (e.g., leg length) and biotic interactions (e.g., sensory organs and defensive structures). Besides, for each species having trait data available we calculated its Eco-Morphological Index (EMI, Parisi et al., 2005). This trait-based index provides a simple continuous metric ranging from 1 to 20 and indicates the level of adaptation of a given springtail species to a specific soil layer. Accordingly, they may be classified as atmobiotic (EMI < 2), epigeic (EMI < 6), hemiedaphic (EMI = [6-8]) or edaphic (EMI > 8, Figure 1).

## 2.3 Data analysis

First, we investigated the evolution of the adaptations that allow springtail species to live in specific soil layers by means of the reconstruction of the ancestral EMI score across the phylogenetic tree. This was done with the function *fastAnc* of the R package *phylosig* (Revell, 2012) and assuming a Brownian-Motion model of evolution of the suite of traits associated to the EMI. We also estimated the correlation between the similarity in EMI and species' evolutionary distance (i.e. the phylogenetic signal) through Pagel's $\lambda$. Then, we assessed how the evolution across Collembola of the suite of traits associated with the EMI may lead to specific patterns in the phylogenetic structure of springtail assemblages in response to the drought and warming treatments. Thus, we obtained the mean neighbour taxon distance (MNTD) and the mean pairwise distance (MPD) of each springtail assemblage standardized against 999 community randomizations with the R package *picante* (Kembel et al., 2010). By accounting for both indexes we explored possible non-random patterns occurring at different phylogenetic depths since MNTD can capture changes at the tips of the phylogeny (e.g. selection of sister species sharing specific adaptations) while MPD can detect shifts in the in-depth phylogenetic structure (e.g. selection of species belonging to specific lineages with highly conserved adaptations) (Cadotte & Davies, 2016).

Second, we assessed the role of aridity and the experimental climate manipulations on the trait-based assembly of springtail communities across Europe. To do so, we carried out a series of generalized linear latent variable models (GLLVM), as implemented in the R package *gllvm* (Niku et al., 2019). GLLVMs are a type of JSDM that extend the basic generalized linear mixed-effects model to multivariate abundance data incorporating a small number of latent variables accompanied by species-specific factor loadings to model correlations between response variables (i.e., all species in the
community matrix). These latent variables have a natural interpretation as ordination axes, but have also the capacity to predict new values controlling for known environmental factors using standard model selection tools (Niku et al., 2019).

Additionally, GLLVMs allow us to test for trait-environment associations and the potential effects of biotic interactions. Although the effects of the environment and biotic interactions cannot be teased apart, the inclusion of the latter are a major advance of JSDM compared with previous species distribution tools and ‘fourth corner’ models that do not include the co-variation of the abundances between all species within a series of sites or communities (Poggiato et al., 2021; Warton et al., 2015). Hence, we first built an unconstrained model (without environmental predictors) with two latent variables and a negative binomial distribution and a log link function. The selection of the best number of latent variables and distribution family followed the recommendations of Niku et al. (2019), which are based on goodness-of-fit and residual diagnostics. Afterwards, we added sequentially the aridity index and the local climate manipulation treatments as environmental constraints and performed likelihood-ratio tests (LRT) to assess the predictive improvement of these increasingly complex models, and if so, we computed the percentage of deviance explained by the best model. The aridity index of each site was calculated by means of a modified version of the inverse of the Gaussen Index (iGI) as: $iGI = 1/(MAP/(2*MAT))*100$ (Reinsch et al., 2017).

This index captures the large-scale gradient of aridity currently present across Europe and that can be traced back to the beginning of the Holocene about 10,000 years ago after the last Younger Dryas cold spell came to an end (Hewitt, 1999). Finally, to see whether specific traits were selected by aridity and by the climate manipulation treatments favouring more edaphic or epigeic springtail species, we built a series of GLLVMs including or not the interaction between a given trait and these two environmental variables and tested their significance via LRT (Niku et al., 2019).

## 3. Results

Comparative analyses of the EMI revealed multiple independent evolutions of the collembolan traits associated to live in specific soil layers (Figure 2). In all the major lineages of subclass Collembola there are groups of species showing either an epigeic habitus (Symphipleona and Entomobryomorpha) or, to a lesser extent, a more edaphic habitus (Poduromorpha), apparently as a result of a convergent adaptive evolution of a distinctive suite of eco-morphological traits as depicted in Figure 1. The high and
significant phylogenetic signal of the EMI (Pagel's $\lambda = 0.757$) pointed out, however, that once fixed phenotypic reversions in these set of traits within a clade are rare and only in a few cases sister species show contrasting soil layer preferences (e.g *Tomocerus minor* and *Oncopodura crassicornis*; TOMMIN and ONCCRAS respectively in Figure 2). The analysis of the phylogenetic structure of springtail assemblages showed that under the experimental drought there was a relative increase in phylogenetic clustering according to the MNTD, i.e. the mean distance of the more closely related species in the assemblage was smaller under drought relative to control conditions (*P* < 0.01). However, the MPD showed no differences across climatic treatments (*P* = 0.37), suggesting that under all experimental conditions there were rather similar phylogenetic community structure.

The unconstrained ordination of springtail communities displayed a clear grouping of all springtail assemblages according to the country of origin (Figure 3a). This clustered pattern captured the species turnover among our experimental sites, but additionally, the two latent variables implicitly highlighted the steep aridity gradient that pervades the European geography by grouping together the Spanish assemblages close to those from the Italian and the Hungarian sites. Accordingly, this pattern disappeared when the aridity index was included as a constraining predictor (Figure 3b), thus confirming water availability and mean annual temperature as major drivers of springtail communities across Europe. Indeed, the aridity index alone captured 70.6% of the whole compositional variability (LRT *P* < 0.05). Regarding the interaction between traits and the environment, the structuring power of aridity was stronger than that of the short-term effect of the experimental climate manipulations (Figure 4). Among the 22 traits tested for trait-environment associations, 8 showed significant (*P* < 0.05) relationships with aridity, 6 with the experimental drought and only 3 with the warming treatment based on LRTs between equivalent models with and without the corresponding trait-environment interaction (see Table 2 for a complete list of all significant trait-environment relationships).

At a biogeographic scale, all traits showing significant interactions with aridity indicated a clear trend towards more epigeic springtail assemblages as mean annual temperatures increased and precipitation decreased. Indeed, species with a predominance of sexual reproduction over parthenogenesis, higher numbers of ocelli and longer furcula and antennae were positively selected by increasing aridity (Figure
265 266 267 268 269 270 271
4). On the other hand, the lower presence and abundance of pigmented species and with photoreceptors (ocelli) in the plots under the experimental drought, along with the increase of species with pseudocelli and with a greater minimum and maximum number of vesicles in this typically edaphic defensive organ pointed out to a relative edaphization of the springtail assemblages after this short-term experimental treatment. Warming only reduced the presence of species with parthenogenesis and slightly increased the minimum and maximum number of ocelli in the springtail assemblages.

272 **4. Discussion**

273 274 275 276 277 278 279 280 281 282 283 284 285 286 287
The capacity to forecast the effects of climate change on biodiversity largely rely on identifying those traits capturing mechanistic relationships with the environment through standardized field experiments distributed across macro-ecologically relevant spatial scales (Halbritter et al., 2020; Mouquet et al., 2015). Our results demonstrate, however, that the effects of local climate manipulations on communities of springtails overlap with the regional gradients that have been operating during longer time periods. The broad aridity gradient present across Europe promoted assemblages of species with more epigeic traits as water availability decreases and temperature increases. Despite this long-term and strong aridity driver, the effect of the drought treatment led to a relative edaphization of springtail communities. We discuss below how this disparity of community responses against short versus long-term climate restrictions result from the trait differences between epigeic and more edaphic springtails and why this also warns out that our interpretations of the trait-environment relationships behind the current biodiversity reorganizations of soil fauna may change or even be reversed as climate change continues.

288 **4.1 Evolutionary convergence of edaphic traits in Collembola**

289 290 291 292 293 294 295 296 297
Springtail species adapted to inhabit at specific soil strata typically share a common suite of eco-morphological traits and, to some extent, they even share a similar trophic niche irrespective of their phylogenetic affiliation (Ponge, 2000; Potapov et al., 2016; Salmon et al., 2014). Our results complement these earlier findings demonstrating that this is the result of an adaptive convergence that has repeatedly happened in the lineages of the three major Collembolan orders. The ancestral ecology of springtails has been of particular interest in light of the still unknown terrestrialization path followed by the subphylum Hexapoda (Ghilarov, 1958; van Straalen, 2021). According to our dataset, the hemiedaphic condition would be the plesiomorphic state for Collembola while the
298 adaptations for a truly epigeic life would have been secondarily gained, particularly by
299 Entomobryomorpha and Symphypleona. This conclusion agrees with previous
300 morphological assessments (D'Haese, 2003), thus bridging together the cladistics
301 approach with our phylogenetic analysis.

302 The development of an epigeic habitus is associated with larger and pigmented bodies
303 with longer appendages, sexual reproduction and more developed sensory organs, while
304 the specialized edaphic life-form typically imply a smaller unpigmented body, shorter
305 appendages, reduced or absent sensory organs along with the development of the ability
306 to reproduce through parthenogenesis (Chahartaghi et al., 2006; Salmon et al., 2014).
307 Despite the repeated convergent evolution of these traits at a broad phylogenetic scale,
308 the transitions between epigeic and edaphic life-forms, however, are rarely reversed
309 within a clade so that sister species usually share a similar habitus and hence soil layer
310 preferences. A previous study at a smaller spatial scale has shown that evolutionary
311 close species tend to co-occur due to their akin ecological preferences and competitive
312 exclusion of sister species may be rare notwithstanding (Ponge & Salmon, 2013). This
313 suggests that the relative phylogenetic clustering observed under the experimental
314 drought according to the MNTD metric may mirror springtail assemblages where sister
315 species are coexisting. The apparent discrepancy with the MPD, which points out to a
316 lack of phylogenetic clustering due to the climate manipulations, may actually arise by
317 the fact that in all major springtail lineages there are species relatively specialized to
318 either an epigeic or edaphic lifestyle. Thus, the presence of species from all
319 evolutionary lineages under all experimental treatments results in similar in-depth
320 phylogenetic community structure and may preclude any phylogenetic imbalance in the
321 resulting assemblages. Therefore, this adaptive convergence may explain why in
322 Collembola phylogenetic diversity measures are less sensitive to species losses or
323 community shifts due to warming and drought than functional richness or other trait-
324 based diversity metrics (Peguero et al., 2019).

325 **4.2 Long-term climate gradients versus short-term climate manipulations**

326 The current climate gradients present in Europe date back to around 10000 years BP and
327 by 6000 year BP the vegetation patterns already resembled that of today (Hewitt, 1999).
328 Here we demonstrate that the increase in aridity across Europe nowadays is coupled
329 with springtail assemblages where species with typically epigeic traits are better
330 represented. These positively selected traits at a macroecological scale include longer
331 furculas and antennas, well-developed light and mechanic sensory organs, and a
332 predominance of sexual reproduction over parthenogenesis, all of them being
333 characteristic adaptations to live in the soil surface or its uppermost horizons (Salmon et
334 al., 2014). These atmobiotic and epigeic species have a higher resistance to drought and
335 thermal stress than specialized soil-dwelling springtails thanks to biochemical and
336 physiological mechanisms that include a lower cuticular permeability, the production of
337 sugars and polyols to regulate internal osmolality and a greater plasticity in the fatty
338 acid composition of their cellular membranes (Dooremalen et al., 2013; Kærsgaard et
339 al., 2004). But additionally, their larger body size, longer appendages and fully
340 functional visual organs provide these epigeic species with a greater dispersal capacity
341 (Ojala & Huhta, 2001), which allows them to migrate to avoid adverse conditions and
342 choose more favorable micro-habitats within their home range (Chauvat et al., 2014;
343 Ponge et al., 2006), ultimately making them more resilient against local disturbances
344 (Lindberg & Bengtsson, 2006; Malmström, 2012).

345 In contrast to epigeic springtails, the species adapted to live deeper in the soil profile
346 typically migrate downwards to escape from dryness (Detsis, 2000; Hopkin, 1997). This
347 behavioral difference may be behind the observed relative edaphization of springtail
348 assemblages under the experimental drought. Atmobiotic and epigeic species may have
349 dispersed seeking for better patches nearby (Chauvat et al., 2014; Ojala & Huhta, 2001;
350 Ponge et al., 2006) after experiencing between 2 up to 4 years of drought, while edaphic
351 species were still there likely retreated below and perhaps migrating across the soil
352 profile tracking the daily variation in moisture (Detsis, 2000; Hopkin, 1997). Indeed, the
353 soil matrix provides a remarkable buffering capacity against environmental variation.
354 However, the edaphic species are generally more vulnerable to desiccation and thermal
355 stress than epigeic collembolans (Dooremalen et al., 2013; Kærsgaard et al., 2004) in
356 spite of their biochemical and physiological adaptations (Holmstrup et al., 2001). The
357 effectiveness of this vertical migration strategy may therefore be limited if the drought
358 episode lasts too long. The response to the warming treatments did not lead to major
359 changes in the functional structure of the communities. Our warming treatment was
360 rather mild just increasing from 0.2 to 0.9 °C the MAT of the experimental plots (Table
361 S2). Additionally, previous research suggests that the diversity and composition of
362 springtail communities may be fairly resistant and resilient to moderate warming
363

(Alatalo et al., 2015; Holmstrup et al., 2013, 2018; Peguero et al., 2019; Petersen,
2011).

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5. Conclusions

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There is a solid consensus around the fact that the responses of species to withstand
climate change can be grouped around two major strategies: to disperse or to adapt
(Berg et al., 2010; Jump & Penuelas, 2005). Springtails, and most likely all soil fauna,
are no exception and when experiencing increasingly adverse environmental conditions
may either ‘move or change’ (Ponge, 2020). To move implies from local dispersion up
to distribution range shifts. The effectiveness of this strategy relies, however, first on
the buffering capacity of the local microhabitats, and secondly on the mismatch (or not)
between compositional changes of communities tracking environmental suitability and
the velocity of climate change itself (Devictor et al., 2012). Dispersal estimates for
collembolan species are scarce but lie in the range of few (~5) centimeters per week
during the favorable season, so that springtail assemblages, at a community level, are
expected to select habitats by active movement within circles up to 200 meters (Chauvat
et al., 2014; Qjala & Huhta, 2001; Ponge & Salmon, 2013; Ponge, 2020). Taking into
account that birds and butterflies, which both have notably high dispersal capacities, are
already experiencing climatic debts of 212 and 135 km respectively (Devictor et al.,
2012), we may have serious doubts about the ability of Collembola to keep up with
climate change through their active dispersal. Like other organisms such as plants that
are unlikely to migrate fast enough to track the rapidly changing climate, adaptation
must play an increasingly important role (Jump & Penuelas, 2005; Ponge, 2020). There
are hopeful examples of soil fauna showing rapid evolutionary changes in response to
climate change (Bataillon et al., 2016), and although the only example with a springtail
species reported some degree of ontogenetic plasticity (i.e. steeper reaction norms of
developmental rates of juveniles than those of adults), there was almost no sign of local
adaptation to geothermal warming (Kutcherov et al., 2020). If so, this calls for urgent
studies assessing the adaptive potential of springtails, and more generally tracking
population dynamics and functional trait shifts at a community level (Bardgett & Putten,
2014; Berg et al., 2010; Guerra et al., 2021), specially taking into account that for
conservation purposes collembolan functional diversity indices may outperform
phylogenetic metrics and better correlate with ecosystem functioning (Peguero et al.,
2019).
Finally, our study also warns out that our interpretations of the trait-environment relationships behind the current biodiversity reorganizations of soil fauna may change or even be reversed as climate change continues. As pointed out by previous studies (Alatalo et al., 2015; Holmstrup et al., 2013, 2018b), our work also suggests that the observed shifts of springtail communities to climate manipulations may be transient in time, with epigeic species showing faster responses probably due to their higher vagility. Thus, the resultant relative edaphization we have observed in the springtail communities of our experimentally drought plots could change and even be reversed after some time, in light of the clear and strong selection towards more epigeic assemblages that the aridity gradient has exerted at a larger spatiotemporal scale. To the best of our knowledge there are no studies simultaneously assessing long-term macroecological drivers with short-term climate manipulations at relevant spatial scales and this study demonstrates how important this is if we are to adequately forecast soil fauna responses to climate change.

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613
614 TABLES

<table><caption>Table 1. List of Collembolan traits analysed.</caption><thead><tr><th>Type</th><th>Trait</th><th>Type of variable</th><th>Values</th><th>Species<sup>1</sup></th></tr></thead><tbody><tr><td rowspan="2">Body aspect</td><td>Length</td><td>Continuous</td><td>0.18 – 6.5 mm</td><td>60</td></tr><tr><td>Shape</td><td>Categorical</td><td>Spherical<br>Cylindrical<br>Wide-cylindrical</td><td>61</td></tr><tr><td>Reproductive<br>strategy</td><td>Reproduction</td><td>Categorical</td><td>Parthenogenesis<br>Sexual<br>Mixed</td><td>47</td></tr><tr><td rowspan="3">Dispersal<br>capacity</td><td>Furcula<sup>2</sup></td><td>Discrete Binomial</td><td>0 – 1</td><td>61</td></tr><tr><td>Furcula length<sup>2</sup></td><td>Semi-quantitative</td><td>0 – 4</td><td>53</td></tr><tr><td>Leg-body ratio</td><td>Continuous</td><td>0.11 – 0.69</td><td>52</td></tr><tr><td rowspan="7">Sensory<br>organs</td><td>Antennal length</td><td>Semi-quantitative</td><td>1 – 3</td><td>55</td></tr><tr><td>Antenna-head ratio</td><td>Continuous</td><td>0.40 – 3.50</td><td>51</td></tr><tr><td>Ocelli</td><td>Discrete Binomial</td><td>0 – 1</td><td>62</td></tr><tr><td>Ocelli number</td><td>Discrete</td><td>0 – 8</td><td>62</td></tr><tr><td>Post Antennal Organ (PAO)</td><td>Discrete Binomial</td><td>0 – 1</td><td>58</td></tr><tr><td>PAO number</td><td>Discrete</td><td>0 – 190</td><td>56</td></tr><tr><td>Trichobothria</td><td>Discrete Binomial</td><td>0 – 1</td><td>60</td></tr><tr><td rowspan="4">Protective<br>features</td><td>Scales</td><td>Discrete Binomial</td><td>0 – 1</td><td>61</td></tr><tr><td>Pigmentation</td><td>Discrete Binomial</td><td>0 – 1</td><td>60</td></tr><tr><td>Pseudocelli</td><td>Discrete Binomial</td><td>0 – 1</td><td>64</td></tr><tr><td>Pseudocelli number</td><td>Discrete</td><td>0 – 56</td><td>64</td></tr></tbody></table>

615

616

<footnote n="1">¹ Number of species with trait data available.</footnote>

<footnote n="2">² Note that furcula as presence/absence and its length are also protective features related with defensive evasion.</footnote>

<footnote>Trait data collated from COLTRAIT database (Salmon et al., 2014). Some continuous or discrete numerical traits (e.g. Length, Ocelli, PAO number, Pseudocelli) have minimum and maximum values recorded separately. See Table S4 for further information on the traits included in this study.</footnote>
<table><caption>**Table 2.** Statistically significant interactions between springtail traits and environmental conditions.</caption><thead><tr><th>Trait</th><th>Drought</th><th>Warming</th><th>Aridity</th><th>P-value</th><th>Nº of species</th></tr></thead><tbody><tr><td>Parthenogenesis</td><td>-0.63±0.67</td><td>-1.19±0.66 *</td><td>-2.44±1.20 *</td><td>0.0087</td><td>46</td></tr><tr><td>Sexual reproduction</td><td>0.05±0.43</td><td>0.53±0.43 *</td><td>0.87±0.81 *</td><td>0.0087</td><td>46</td></tr><tr><td>Furcula length (1)</td><td>0.44±1.53</td><td>-1.53±1.72</td><td>5.76±3.43 *</td><td>0.0000</td><td>53</td></tr><tr><td>Furcula length (2)</td><td>-0.75±1.47</td><td>-0.71±1.44</td><td>5.02±2.85 *</td><td>0.0000</td><td>53</td></tr><tr><td>Furcula length (4)</td><td>-0.05±0.69</td><td>0.44±0.66</td><td>7.78±1.77 *</td><td>0.0000</td><td>53</td></tr><tr><td>Antennal length (2)</td><td>-0.47±3.45</td><td>-0.26±3.79</td><td>7.21±6.10 *</td><td>0.0002</td><td>55</td></tr><tr><td>Antennal length (3)</td><td>-0.42±0.56</td><td>0.26±0.50</td><td>3.07±1.34 *</td><td>0.0002</td><td>55</td></tr><tr><td>Antenna-head ratio (min.)</td><td>0.01±0.38</td><td>0.24±0.34</td><td>2.05±0.76 *</td><td>0.0000</td><td>51</td></tr><tr><td>Antenna-head ratio (max.)</td><td>0.07±0.35</td><td>0.40±0.32</td><td>2.12±0.71 *</td><td>0.0000</td><td>51</td></tr><tr><td>Ocelli</td><td>-0.65±0.62 *</td><td>0.58±0.61</td><td>0.61±1.57</td><td>0.0199</td><td>61</td></tr><tr><td>Ocelli number (min.)</td><td>-0.16±0.23</td><td>0.36±0.27 *</td><td>3.18±0.86 *</td><td>0.0018</td><td>62</td></tr><tr><td>Ocelli number (max.)</td><td>-0.09±0.28</td><td>0.44±0.27 *</td><td>1.73±0.75 *</td><td>0.0001</td><td>62</td></tr><tr><td>Post Antennal Organ (PAO)</td><td>0.27±0.51</td><td>-0.31±0.52</td><td>-2.54±1.35 *</td><td>0.0024</td><td>58</td></tr><tr><td>PAO number (min.)</td><td>0.53±0.54</td><td>0.26±0.44</td><td>1.30±0.51 *</td><td>0.0000</td><td>56</td></tr><tr><td>PAO number (max.)</td><td>0.46±0.41 *</td><td>0.20±0.36</td><td>1.15±0.44 *</td><td>0.0002</td><td>56</td></tr><tr><td>Trichobothria</td><td>0.07±0.48</td><td>0.50±0.45</td><td>1.46±1.19 *</td><td>0.0828</td><td>60</td></tr><tr><td>Pigmentation</td><td>-0.60±0.59 *</td><td>0.53±0.58</td><td>-0.09±1.35</td><td>0.0055</td><td>60</td></tr><tr><td>Pseudocelli</td><td>1.28±0.67 *</td><td>-0.31±0.93</td><td>-0.82±3.67</td><td>0.0031</td><td>63</td></tr><tr><td>Pseudocelli number (min.)</td><td>0.53±0.28 *</td><td>-0.01±0.24</td><td>-0.04±0.58</td><td>0.0002</td><td>63</td></tr><tr><td>Pseudocelli number (max.)</td><td>0.53±0.28 *</td><td>-0.01±0.24</td><td>0.01±0.55</td><td>0.0002</td><td>63</td></tr></tbody></table>

Each line corresponds to a fourth corner model based on Generalized Linear Latent Variable Models. Effect estimates are followed by its 95% Confidence Interval. Significant and marginal interactions are represented by "*" and ",", respectively. P-values show the result of a LRT against an equivalent model without the corresponding trait-environment interaction. Last column indicates the number of species with data available for each trait and thus included in the models.
618 FIGURE CAPTIONS

619 **Figure 1.** Schematic representation of the morphological and functional adaptations to
620 living in different soil layers by the main phylogenetic lineages of Collembola (Order
621 Entomobryomoprha, Symphypleona and Poduromorpha) along with the corresponding
622 range for the Eco-Morphological Index (EMI). Based on Parisi et al. (2005), Potapov et
623 al. (2016) and Salmon et al. (2014).

624 **Figure 2.** Phylogenetic reconstruction of the Eco-Morphological Index (EMI) across the
625 clade Collembola. Red to yellow indicate lower EMI values associated to atmo
626 and epigeic species, while green to blue denote higher EMI values related to
627 hemiedaphic and edaphic species. Drawings are placed along with the corresponding
628 species or its closest relative to illustrate their morphological and function
629 See the Supplementary Methods and Table S3 in the Supporting Information for further
630 details about the phylogeny and for the species abbreviations.
631

631 **Figure 3.** Ordination based on a generalized linear latent variable model fitted to the
632 collembolan community data (102 species) without environmental predictors (a) and
633 after controlling for the effects of the aridity index (b). Symbol shape correspond to the
634 countries included in the study (DK, Denmark; HU, Hungary; IT, Italy; NL,
635 Netherlands; SP, Spain and UK, United Kingdom), and colour refer to the experimental
636 climate manipulation applied to each plot. Taxa with the largest factor loadings are
637 included as indicator species. See Table S3 in the Supporting Information for species
638 abbreviations. Inset in panel b shows the result of a likelihood-ratio test between the
639 unconstrained and the constrained model along with the increase in variance explained
640 by the aridity index.
641

641 **Figure 4.** Level plot for the empirical significant interactions between collembolan
642 traits and short-term experimental drought and the long-term large-scale aridity gradient
643 present in Europe according to generalized linear latent variable models (GLLVM).
644 Each trait-environment interaction was modelled with a separate GLLVM maximizing
645 the number of species with trait data available (47 to 64 species). See Table 2 in the
646 Supporting Information for a complete list of interactions and model outputs. Statistical
647 significance was assessed by means of likelihood-ratio tests between equivalent models
648 with or without the trait-environment interaction. Coloured squares show significant
649 interactions (*P* < 0.05) with (·) denoting marginal significance (*P* < 0.1). The colour
650 scale offers an indication of the sign and magnitude of the selection exerted over each
651 trait in the collembolan communities.
652

652
<figure><img src="image_1.png" /><figcaption>653 FIGURE 1</figcaption></figure>

<table><thead><tr><th>Entomobryomorpha</th><th>Symphypleona</th><th>Poduromorpha</th><th>Description</th><th>EMI value</th></tr></thead><tbody><tr><td></td><td></td><td></td><td>Medium/large size<br>Complex pigmentation<br>Long appendages<br>Full set of ocelli<br>Trichobotrium and/or<br>scales</td><td>&lt;2</td></tr><tr><td></td><td></td><td></td><td>Medium/large size<br>Uniform pigmentation<br>Less developed<br>appendages<br>6+6 or 8+8 sets of ocelli</td><td>2-6</td></tr><tr><td></td><td></td><td></td><td>Medium/small size<br>Dispersed pigmentation<br>Scarcely developed<br>appendages<br>Reduced ocelli</td><td>6-8</td></tr><tr><td></td><td></td><td></td><td>Medium/small size<br>Absent pigmentation<br>Reduced/absent<br>appendages and ocelli<br>Presence of pseudocelli<br>and postantennal organs</td><td>&gt;8</td></tr></tbody></table>

<figure><img src="image_2.png" /></figure>

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /><figcaption>654
655</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>656 FIGURE 2</figcaption></figure>

657

658
659 FIGURE 3

<figure><img src="image_14.png" /></figure>

660
661
662 FIGURE 4

<figure><img src="image_15.png" /></figure>

663
SUPPORTING INFORMATION

# Trait-mediated responses to aridity and experimental drought by springtail communities across Europe
FIGURE S1

Location of the climatic manipulation experiments in Europe. Arrows depict broad-scale gradients of temperature and precipitation that result in an aridity gradient crossing Europe from North-West to South-East. DK, Denmark; HU, Hungary; IT, Italy; NL, the Netherlands; SP, Spain; UK, United Kingdom.

<figure><img src="image_16.png" /></figure>
<table><caption>TABLE S1</caption><caption>Table S1. Characteristics of the study sites.</caption><thead><tr><th>Site code</th><th>UK</th><th>NL</th><th>DK</th><th>HU</th><th>SP</th><th>IT</th></tr></thead><tbody><tr><th>Country</th><td>United Kingdom</td><td>The Netherlands</td><td>Denmark</td><td>Hungary</td><td>Spain</td><td>Italy</td></tr><tr><th>Site name</th><td>Clocaenog</td><td>Oldebroek</td><td>Mols</td><td>Kiskunság</td><td>Garraf</td><td>Capo Caccia</td></tr><tr><th>Coordinates</th><td>53°03′N<br>3°28′W</td><td>52°24′N<br>5°55′E</td><td>56°23′N<br>10°57′E</td><td>46°53′N<br>19°23′E</td><td>41°18′N<br>1°49′E</td><td>40°36′N<br>8°9′E</td></tr><tr><th>Soil type (FAO)</th><td>Peaty Podzol</td><td>Haplic Arenosol</td><td>Sandy Podzol</td><td>Calcaric Arenosol</td><td>Petrocalcic Calcixerepts</td><td>Luvisol and Leptosol</td></tr><tr><th>MAT (°C)</th><td>7.4</td><td>8.9</td><td>8.7</td><td>10.5</td><td>15.2</td><td>16.1</td></tr><tr><th>MAP (mm)</th><td>1263</td><td>1005</td><td>669</td><td>558</td><td>559</td><td>544</td></tr><tr><th>Growing season</th><td>April-September</td><td>April-October</td><td>April-September</td><td>April-September</td><td>January-May<br>October-December</td><td>January-May<br>October-December</td></tr><tr><th>Dominant species</th><td>Calluna vulgaris</td><td>Calluna vulgaris</td><td>Calluna vulgaris<br>Deschampsia flexuosa</td><td>Populus alba<br>Festuca vaginata</td><td>Erica multiflora<br>Globularia alypum</td><td>Cistus monspeliensis<br>Helichrysum italicum<br>Dorycnium pentaphyllum</td></tr></tbody></table>

MAT, mean annual temperature; MAP, mean annual precipitation.

MATs and MAPs apply to the study period (see Table 2).

Species with relative cover above 10% in the control plots during the study period are listed as dominant species.
<table><caption>TABLE S2</caption><caption>Table S2. Experimental manipulations at the study sites.</caption><thead><tr><th>Site code</th><th>UK</th><th>NL</th><th>DK</th><th>HU</th><th>SP</th><th>IT</th></tr></thead><tbody><tr><td>Start of the experiment<br>(pre-treatment year)</td><td>1998</td><td>1998</td><td>1998</td><td>2001</td><td>1998</td><td>2001</td></tr><tr><td>First treatment year</td><td>1999</td><td>1999</td><td>1999</td><td>2002</td><td>1999</td><td>2002</td></tr><tr><td colspan="7"><strong>Drought</strong></td></tr><tr><td>Timing</td><td>May-September</td><td>April-July</td><td>May-July</td><td>May-June</td><td>May-June<br>October-November</td><td>April-October</td></tr><tr><td>Precipitation excluded<br>(% of yearly total)</td><td>25</td><td>19</td><td>18</td><td>22</td><td>49</td><td>16</td></tr><tr><td>Reduction in soil moisture (% of control, 0-20 cm)</td><td>45</td><td>43</td><td>41</td><td>23</td><td>28</td><td>27</td></tr><tr><td colspan="7"><strong>Warming</strong></td></tr><tr><td>Timing</td><td>Year-round</td><td>Year-round</td><td>Year-round</td><td>Year-round</td><td>Year-round</td><td>Year-round</td></tr><tr><td>Increase in MAT (C)</td><td>0.2</td><td>0.3</td><td>0.9</td><td>0.4</td><td>0.6</td><td>0.4</td></tr></tbody></table>

DK, Denmark; HU, Hungary; IT, Italy; MAT, mean annual temperature; NL, Netherlands; SP, Spain; UK, United Kingdom.

Drought and warming effects are averages during the study period.

Soil moisture reduction applies to the end of the experimental drought period.
<caption>TABLE S3</caption>

List of the 102 springtail species identified. See Petersen (2011) for further information. Accession numbers for the molecular markers (28S rDNA and cox1 mtDNA) used to infer their evolutionary relationships and the availability of trait data are also shown.

<table><thead><tr><th>Abbreviation</th><th>Order</th><th>Family</th><th>Species</th><th>Authority</th><th>28S</th><th>cox1</th><th>Trait_data</th></tr></thead><tbody><tr><td>ARR</td><td>Symphypleona</td><td>Arrhopalitidae</td><td>Arrhopalites_sericus</td><td>Gisin, 1947</td><td>AF483417.1</td><td>MHCLB233-09</td><td>Yes</td></tr><tr><td>ARRPRI</td><td>Symphypleona</td><td>Arrhopalitidae</td><td>Arrhopalites_principalis</td><td>Stach, 1945</td><td>AY239037.1</td><td>MHCLB073-09</td><td>Yes</td></tr><tr><td>BOURHOR</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Bourletiella_hortensis</td><td>Fitch, 1863</td><td></td><td></td><td>No</td></tr><tr><td>BOURPIG</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Bourletiellidae_g_sp</td><td></td><td>FJ411426.1</td><td>BBCCS308-10</td><td>No</td></tr><tr><td>BOURVIR</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Bourletiella_viridescens</td><td>Stach, 1920</td><td>FJ411426.1</td><td>KM617234</td><td>Yes</td></tr><tr><td>BRAC</td><td>Poduromorpha</td><td>Brachystomellidae</td><td>Brachystomella_parvula</td><td>Schäffer, 1896</td><td>AF483360.1</td><td>SMTPP1428-15</td><td>Yes</td></tr><tr><td>CER</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Ceratophysella_sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>CERDEN</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Ceratophysella_denticulata</td><td>Bagnall, 1941</td><td>KF684424.1</td><td>KU374568.1</td><td>Yes</td></tr><tr><td>CERGRAN</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Ceratophysella_granulata</td><td>Stach, 1949</td><td>KF684413.1</td><td>HM399009.1</td><td>Yes</td></tr><tr><td>CRYP</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Cryptopygus_sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>CRYPALB</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Cryptopygus_albaredai/delamarei</td><td>Selga, 1962/ Poinsot, 1970</td><td></td><td></td><td>No</td></tr><tr><td>CRYPSP</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Cryptopygus_sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>CRYPTH</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Cryptopygus_thermophilus</td><td>Axelson, 1900</td><td>HQ592746.1</td><td>KJ419039.1</td><td>Yes</td></tr><tr><td>CYPALB</td><td>Entomobryomorpha</td><td>Cyphoderidae</td><td>Cyphoderus_albinus</td><td>Nicolet, 1842</td><td>KM978338.1</td><td>KM617184</td><td>Yes</td></tr><tr><td>DEUPAL</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Deuterosminthurus_pallipes Cf</td><td>Bourlet, 1843</td><td>FJ411426.1</td><td>MHCLB277-09</td><td>Yes</td></tr><tr><td>DIC</td><td>Symphypleona</td><td>Dicyrtomidae</td><td>Dicyrtoma_sp</td><td></td><td>LK024302.1</td><td>KM621738</td><td>No</td></tr><tr><td>DICFUSC</td><td>Symphypleona</td><td>Dicyrtomidae</td><td>Dicyrtoma_fusca</td><td>Lubbock, 1893</td><td>LK024301.1</td><td>KT808355.1</td><td>Yes</td></tr><tr><td>DICORN</td><td>Symphypleona</td><td>Dicyrtomidae</td><td>Dicyrtomina_ornata</td><td>Nicolet, 1841</td><td>LK024305.1</td><td>KM623796.1</td><td>Yes</td></tr><tr><td>DICSAU</td><td>Symphypleona</td><td>Dicyrtomidae</td><td>Dicyrtomina_saundersi</td><td>Lubbock, 1862</td><td>EF199974.2</td><td>BBCCS138-10</td><td>Yes</td></tr><tr><td>DICSP</td><td>Symphypleona</td><td>Dicyrtomidae</td><td>Dicyrtomina_sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>EHAND</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya_handschini</td><td>Stach, 1922</td><td>KC236317.1</td><td>KR119034.1</td><td>Yes</td></tr><tr><td>ELAN</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya_lanuginosa</td><td>Nicolet, 1842</td><td>AF483423.1</td><td>JN970907.1</td><td>Yes</td></tr><tr><td>EMAR</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya marginata</td><td>Tullberg, 1871</td><td>LK024312.1</td><td>GENHP1210-12</td><td>Yes</td></tr><tr><td>EMULT</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya_multifasciata</td><td>Tullberg, 1871</td><td>KC236316.1</td><td>KM978392.1</td><td>Yes</td></tr></tbody></table>
<table><tr><td>ENIC</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya_nicoleti</td><td>Lubbock, 1868</td><td>KC236319.1</td><td>KM617653</td><td>Yes</td></tr><tr><td>ENIV</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya_nivalis</td><td>Linné, 1758</td><td>LK024318.1</td><td>KT707112.1</td><td>Yes</td></tr><tr><td>ENT</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya_sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>ENTQUI</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobrya_quinquelineata Cf</td><td>Börner, 1901</td><td>LK024308.1</td><td>KM618447</td><td>Yes</td></tr><tr><td>ENTSL</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Entomobryoides_myrmecophilus Cf</td><td>Reuter, 1886</td><td>KC236315.1</td><td>KR114833</td><td>Yes</td></tr><tr><td>FASANG</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Fasciosminthurus Angulipunctatus</td><td>Loksa & Bogojevic, 1970</td><td></td><td></td><td>No</td></tr><tr><td>FASBED</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Fasciosminthurus Bedosae Cf</td><td>Nayrolles, 1994</td><td></td><td></td><td>No</td></tr><tr><td>FASSP</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Fasciosminthurus Cassagnauai/ cugnyi Cf</td><td>Nayrolles, 1994</td><td></td><td></td><td>No</td></tr><tr><td>FBREV</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Folsomia brevicauda</td><td>Agrrell, 1939</td><td>LK024329.1</td><td>COLNO068-09</td><td>Yes</td></tr><tr><td>FLAW</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Folsomia Litsteri</td><td>Bagnall, 1939</td><td>LK024328.1</td><td>COLNO067-09</td><td>Yes</td></tr><tr><td>FMAN</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Folsomia Manolachei</td><td>Bagnall, 1939</td><td>LK024327.1</td><td>COLNO069-09</td><td>Yes</td></tr><tr><td>FOLPAR</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Folsomides Parvulus</td><td>Stach, 1922</td><td>JN981041.1</td><td>JN981069</td><td>Yes</td></tr><tr><td>FOLPOR</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Folsomides Portulacensis Cf</td><td>Gama, 1961</td><td></td><td></td><td>No</td></tr><tr><td>FPEN</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Folsomia Penicula</td><td>Bagnall, 1939</td><td>JN981049.1</td><td>GBCO2496-14</td><td>Yes</td></tr><tr><td>FQOC</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Folsomia quadrioculata</td><td>Tullberg, 1871</td><td>JN981050.1</td><td>KU373191.1</td><td>Yes</td></tr><tr><td>FRIMIR</td><td>Poduromorpha</td><td>Neanuridae</td><td>Friesea Mirabilis</td><td>Tullberg, 1871</td><td>AF483426.1</td><td>GENHP1269-12</td><td>Yes</td></tr><tr><td>FRITRU</td><td>Poduromorpha</td><td>Neanuridae</td><td>Friesea Truncata</td><td>Cassagnau, 1958</td><td>AF483427.1</td><td>GENHP1270-12</td><td>Yes</td></tr><tr><td>HETBIL</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Heterosminthurus Bilineatus</td><td>Bourlet, 1842</td><td>FJ411426.1</td><td>JF884240</td><td>Yes</td></tr><tr><td>HETCLA</td><td>Symphypleona</td><td>Bourletiellidae</td><td>Heterosminthurus Claviger</td><td>Gisin, 1958</td><td></td><td></td><td>No</td></tr><tr><td>HETMAJ</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Heteromurus Major</td><td>Moniez, 1889</td><td>JX261654.1</td><td>HM397736.1</td><td>Yes</td></tr><tr><td>IANGVIR</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Isotoma viridis sl</td><td>Bourlet, 1839</td><td>JN981061.1</td><td>KT808360.1</td><td>Yes</td></tr><tr><td>ICAER</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Isotoma Caerulea</td><td>Bourlet, 1839</td><td></td><td></td><td>No</td></tr><tr><td>IMIN</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Isotomiella Minor</td><td>Schäffer, 1896</td><td>JN981062.1</td><td>KJ186386</td><td>Yes</td></tr><tr><td>IMOD</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Isotomodes sp</td><td></td><td>JN981042.1</td><td>JN981070</td><td>No</td></tr><tr><td>INOT</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Parisotoma Notabilis</td><td>Schäffer, 1896</td><td>KJ792158.1</td><td>KT808351.1</td><td>Yes</td></tr><tr><td>ISMUR</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Isotomurus graminis Cf</td><td>Fjellberg, 2007</td><td>JN981063.1</td><td>JN981085</td><td>No</td></tr><tr><td>LCYAN</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Lepidocyrtus Cyaneus</td><td>Tullberg, 1871</td><td>KC236291.1</td><td>KM623832.1</td><td>Yes</td></tr><tr><td>LEPAP</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Lepidocyrtus Apicalis</td><td>Mateos & Petersen 2012</td><td></td><td></td><td>No</td></tr><tr><td>LEPLAN</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Lepidocyrtus Lanuginosus</td><td>Gmelin, 1790</td><td>KC236289</td><td>JQ801596.1</td><td>Yes</td></tr></table>
<table><tr><td>LEPLYS</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Lepidocyrtus lignorum cf</td><td>Fabricius, 1793</td><td>LK024362.1</td><td>KM623980.1</td><td>Yes</td></tr><tr><td>LEPSEL</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Lepidocyrtus selvaticus cf</td><td>Arbea & Ariza, 2007</td><td>KC236329</td><td>KT707770.1</td><td>No</td></tr><tr><td>MEG</td><td>Neelipleona</td><td>Neelidae</td><td>Megalothorax minimus</td><td>Willem, 1900</td><td>KC900207.1</td><td>KC900195.1</td><td>Yes</td></tr><tr><td>MES</td><td>Poduromorpha</td><td>Tullbergiidae</td><td>Mesaphorura sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>MIC</td><td>Poduromorpha</td><td>Neanuridae</td><td>Micranurida pygmaea</td><td>Börner, 1901</td><td>AF483435.1</td><td>GENHP1168-12</td><td>Yes</td></tr><tr><td>MICRAN</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Micranurophorus musci</td><td>Bernard, 1977</td><td>LK024298.1</td><td>HM893776</td><td>Yes</td></tr><tr><td>MMAC</td><td>Poduromorpha</td><td>Tullbergiidae</td><td>Mesaphorura macrochaeta</td><td>Rusek, 1976</td><td>AF483375.1</td><td>COLNO048-09</td><td>Yes</td></tr><tr><td>NEAN</td><td>Poduromorpha</td><td>Neanuridae</td><td>Neanura muscorum</td><td>Templeton, 1835</td><td>AJ251733.2</td><td>KT808329.1</td><td>Yes</td></tr><tr><td>NEANSP</td><td>Poduromorpha</td><td>Neanuridae</td><td>Neanura sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>NEOTUL</td><td>Poduromorpha</td><td>Tullbergiidae</td><td>Neotullbergia ramicuspis</td><td>Gisin, 1953</td><td>AF483434.1</td><td>COLNO042-09</td><td>Yes</td></tr><tr><td>ONCCRAS</td><td>Entomobryomorpha</td><td>Oncopoduridae</td><td>Oncopodura crassicornis</td><td>Shoebotham, 1911</td><td>DQ016581.1</td><td>KR117530</td><td>Yes</td></tr><tr><td>ONYGSP</td><td>Poduromorpha</td><td>Onychiuridae</td><td>Onychiurus sp2</td><td>without anal spines</td><td></td><td></td><td>No</td></tr><tr><td>ONYSP</td><td>Poduromorpha</td><td>Onychiuridae</td><td>Onychiurus sp1</td><td>with anal spines</td><td>AF483442.1</td><td>HQ732075.1</td><td>No</td></tr><tr><td>ORCC</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Orchesella cincta</td><td>Linné, 1758</td><td>LK024377.1</td><td>KM623648.1</td><td>Yes</td></tr><tr><td>ORCHSP</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Orchesella sp1</td><td></td><td></td><td></td><td>No</td></tr><tr><td>PFLAV</td><td>Entomobryomorpha</td><td>Tomoceridae</td><td>Pogonognathellus flavescens</td><td>Tullberg, 1871</td><td>EU376053.2</td><td>KT808376.1</td><td>Yes</td></tr><tr><td>PLON</td><td>Entomobryomorpha</td><td>Tomoceridae</td><td>Pogonognathellus longicornis</td><td>Müller, 1776</td><td>KF592009.1</td><td>KT808381.1</td><td>Yes</td></tr><tr><td>PROQUER</td><td>Poduromorpha</td><td>Onychiuridae</td><td>Protaphorura quercetana cf</td><td>Mateos & Arbea, 1986</td><td>LK024409.1</td><td>GENHP1278-12</td><td>No</td></tr><tr><td>PROTAPH</td><td>Poduromorpha</td><td>Onychiuridae</td><td>Protaphorura armata s.l.</td><td>Tullberg, 1869</td><td>HQ731965.1</td><td>HQ732078</td><td>Yes</td></tr><tr><td>PSAC</td><td>Poduromorpha</td><td>Neanuridae</td><td>Pseudachorudina subcrassus cf</td><td>Tullberg, 1871</td><td>LK024411.1</td><td>LK024514</td><td>No</td></tr><tr><td>PSALB</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Pseudosinella alba sp_group</td><td>Packard, 1873</td><td>KC236295.1</td><td>COLNO062-09</td><td>No</td></tr><tr><td>PSEUDAC</td><td>Poduromorpha</td><td>Neanuridae</td><td>Pseudachorudina meridionalis cf</td><td>Bonet, 1929</td><td>X90680.1</td><td>HG422637.2</td><td>Yes</td></tr><tr><td>PSISOT</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Pseudisotoma sensibilis</td><td>Tullberg, 1876</td><td>JN981065.1</td><td>JN981086.1</td><td>Yes</td></tr><tr><td>PSPAR</td><td>Poduromorpha</td><td>Neanuridae</td><td>Pseudachorutes parvulus</td><td>Börner, 1901</td><td>HQ731966.1</td><td>KR117034</td><td>Yes</td></tr><tr><td>SCAPAR</td><td>Poduromorpha</td><td>Tullbergiidae</td><td>Scaphaphorura arenaria</td><td>Petersen, 1965</td><td>AF483434.1</td><td>HQ732072</td><td>Yes</td></tr><tr><td>SCHOET</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Schoettella ununguiculata</td><td>Tullberg, 1869</td><td>HQ731967.1</td><td>HQ732079.1</td><td>Yes</td></tr><tr><td>SEIDOM</td><td>Entomobryomorpha</td><td>Entomobryidae</td><td>Seira domestica</td><td>Nicolet, 1841</td><td>KC236298.1</td><td>KM978395.1</td><td>Yes</td></tr><tr><td>SMAC</td><td>Symphypleona</td><td>Sminthuridae</td><td>Sminthurus maculatus</td><td>Tömösvary, 1883</td><td></td><td></td><td>No</td></tr><tr><td>SMEL</td><td>Symphypleona</td><td>Katiannidae</td><td>Sminthurinus elegans</td><td>Fitch, 1863</td><td>LK024418.1</td><td>JQ909220</td><td>Yes</td></tr></table>
<table><tr><td>SMIDPAR</td><td>Symphypleona</td><td>Sminthurididae</td><td>Sminthurides_parvulus</td><td>Krausbauer, 1898</td><td>DQ016590.1</td><td>GENHP1309-12</td><td>Yes</td></tr><tr><td>SMIGSPS</td><td>Symphypleona</td><td>Sminthuridae</td><td>Sminthuridae_g.sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>SMINBOUR</td><td>Symphypleona</td><td>Sminthuridae</td><td>Sminthurus_bourgeois_i Cf</td><td>Nayrolles, 1995</td><td></td><td></td><td>No</td></tr><tr><td>SMNAU</td><td>Symphypleona</td><td>Katiannidae</td><td>Sminthurinus_aureus</td><td>Lubbock, 1862</td><td>LK024419.1</td><td>KU374396.1</td><td>Yes</td></tr><tr><td>SMNSP</td><td>Symphypleona</td><td>Katiannidae</td><td>Sminthurinus_alpinus Cf</td><td>Gisin, 1953</td><td>LK024422.1</td><td>KR117243</td><td>No</td></tr><tr><td>SNIG</td><td>Symphypleona</td><td>Katiannidae</td><td>Sminthurinus_niger</td><td>Lubbock, 1867</td><td>LK024423.1</td><td>KT808361.1</td><td>Yes</td></tr><tr><td>SNIGMAC</td><td>Symphypleona</td><td>Sminthuridae</td><td>Sminthurus_nigromaculatus</td><td>Tullberg, 1872</td><td>EF199973.2</td><td>JN970939</td><td>Yes</td></tr><tr><td>SPAFLA</td><td>Symphypleona</td><td>Sminthuridae</td><td>Spatulosminthurus_flaviceps</td><td>Tullberg, 1871</td><td></td><td></td><td>No</td></tr><tr><td>SPUM</td><td>Symphypleona</td><td>Sminthurididae</td><td>Sphaeridia_pumilis</td><td>Krausbauer, 1898</td><td>EF192443.1</td><td>HM424143</td><td>Yes</td></tr><tr><td>STEVIOL</td><td>Symphypleona</td><td>Sminthurididae</td><td>Stenacidia_violacea Cf</td><td>Reuter, 1878</td><td>AF483455.1</td><td>GENHP1309-12</td><td>Yes</td></tr><tr><td>TETRAC</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Tetracanthella_sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>TETRHYG</td><td>Entomobryomorpha</td><td>Isotomidae</td><td>Tetracanthella_hygropetrica Cf</td><td>Cassagnau, 1954</td><td>JN981043.1</td><td>HM366594</td><td>Yes</td></tr><tr><td>TOMMIN</td><td>Entomobryomorpha</td><td>Tomoceridae</td><td>Tomocerus_minor</td><td>Lubbock, 1862</td><td>KF592037.1</td><td>KT808378.1</td><td>Yes</td></tr><tr><td>TULGSP</td><td>Poduromorpha</td><td>Tullbergiidae</td><td>Tullbergiinae_gspit</td><td></td><td></td><td></td><td>No</td></tr><tr><td>TULLGSP</td><td>Poduromorpha</td><td>Tullbergiidae</td><td>Tullbergiinae_gspsp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>WIL</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Willemia_sp</td><td></td><td></td><td></td><td>No</td></tr><tr><td>WILAN</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Willemia_anophthalma</td><td>Börner, 1901</td><td>HQ731973.1</td><td>KF642103.1</td><td>Yes</td></tr><tr><td>XEN</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Xenylla.maritima</td><td>Tullberg, 1869</td><td>LK024444.1</td><td>KT808347.1</td><td>Yes</td></tr><tr><td>XENLOD</td><td>Poduromorpha</td><td>Odontellidae</td><td>Xenyllodes_sp</td><td></td><td>AF483469.1</td><td>HQ732089</td><td>No</td></tr><tr><td>XENMED</td><td>Poduromorpha</td><td>Hypogastruridae</td><td>Xenylla_mediterranea Cf</td><td>Gama, 1964</td><td>LK024443.1</td><td>SMTPF8667-14</td><td>No</td></tr></table>
<caption>TABLE S4</caption>

List of the springtail functional traits included in the study. Completeness is the proportion of species with data for a given trait relative to the entire species pool.

<table><thead><tr><td>Trait</td><td>Definitions and related functions</td><td>No. species with data</td></tr></thead><tbody><tr><td>reproduction</td><td>sexual vs. parthenogenetic or mixed (reproductive strategy)</td><td>47</td></tr><tr><td>body shape</td><td>spherical vs. cylindrical or wide_cylindrical (body aspect)</td><td>61</td></tr><tr><td>length.min</td><td>minimum body length (morphology)</td><td>60</td></tr><tr><td>length.max</td><td>maximum body length (morphology)</td><td>60</td></tr><tr><td>furcula</td><td>presence or absence of furcula (evasive organ)</td><td>61</td></tr><tr><td>furc.length</td><td>furcula length, semi-quantitative scale (escaping ability)</td><td>53</td></tr><tr><td>legbody</td><td>leg/body length ratio (locomotive skills)</td><td>52</td></tr><tr><td>scales</td><td>presence or absence (defensive feature)</td><td>62</td></tr><tr><td>pigmentation</td><td>presence or absence (protection)</td><td>60</td></tr><tr><td>pseudocelli</td><td>presence or absence of pseudocelli (defensive structure)</td><td>64</td></tr><tr><td>pseudo.min</td><td>minimum number of vesicles in pseudocelli</td><td>64</td></tr><tr><td>pseudo.max</td><td>maximum number of vesicles in pseudocelli</td><td>64</td></tr><tr><td>ocelli</td><td>presence or absence of ocelli (sensory organ, photoreception)</td><td>61</td></tr><tr><td>ocelli.min</td><td>minimum number of ocelli</td><td>61</td></tr><tr><td>ocelli.max</td><td>maximum number of ocelli</td><td>61</td></tr><tr><td>ant</td><td>antennal length, semi-quantitative scale (sensory organ, chemoreception)</td><td>54</td></tr><tr><td>antratio.min</td><td>antenna/head minimum length</td><td>51</td></tr><tr><td>antratio.max</td><td>antenna/head maximum length</td><td>51</td></tr><tr><td>post-antennal organ</td><td>presence or absence of PAO (sensory organ, unknown function)</td><td>58</td></tr><tr><td>pao.min</td><td>minimum number of PAO vesicles</td><td>56</td></tr><tr><td>pao.max</td><td>maximum number of PAO vesicles</td><td>56</td></tr><tr><td>trichobotria</td><td>presence or absence (sensory organ, mechanoreception)</td><td>60</td></tr></tbody></table>
**Supplementary Methods**

**Phylogenetic data.** The phylogeny of springtails was constructed with sequence data of two genes (*cox1* and *28s*), obtained from public repositories (GenBank and BOLD). We found information for 77 species (75.5% of all species recorded), although in some cases we had to use sequences for the closest taxa within genus or family level (Table S3). The two genes were aligned separately with CLUSTAL W (Larkin et al., 2007) and then concatenated with GENEIOUS v10.1 (Kearse et al., 2012). The best partition scheme and evolutionary model was selected with PartitionFinder v1.1 (Lanfear, Calcott, Ho, & Guindon, 2012) using the BIC criterion. The optimal partition scheme assigned different models to the three codon positions of the *cox1* and to the *28s* genes (SYM+I+G, GTR+I+G, TM+G+I, GTR+G models, respectively). The best Maximum Likelihood (ML) tree was inferred with the RAxML v.7.2.8 (Stamatakis, 2006) with the help of the RAxMLGUI v1.1 (Silvestro & Michalak, 2012) interface. Each partition was assigned a GTR+CAT model to speed up computation and the best tree and bootstrap support obtained using the ML+ rapid bootstrap option and the ‘autoMRE’ approach to select the optimal number of bootstrap replicates (Pattengale, Alipour, Bininda-Emonds, Moret, & Stamatakis, 2010). Bayesian best trees were inferred with MrBayes v.3.2 (Ronquist et al., 2012), implementing the preferred partition scheme and evolutionary models and running two independent chains for 10 million generations. The correct mixing and the burn-in were evaluated with the help of the program TRACER v1.6 (Rambaut, 2009). Finally, we inferred an ultrametric tree with the program BEAST v1.8.1 (Drummond, Suchard, Xie, & Rambaut, 2012). The bristletail *Dilta littoralis* and the dipluran *Lepidocampa weberi* were used as outgroups, i.e. a group of organisms that allow determining the phylogenetic root of a clade or ingroup with which they are closely related but of which they are not members. Outgroups were removed from the matrix before the analyses and the root of the ingroup was assigned based on the results of the previous phylogenetic analyses. A single relaxed lognormal clock to all partitions was preferred due to the large number of incomplete sequences. The Birth Death model was selected as tree prior. Because the goal of the analyses was to obtain an ultrametric tree, we assigned an arbitrary rate of 1 to the ‘ucld.mean’ parameter of the relaxed clock. The accompanying programs LOGCOMBINER and TREEANNOTATOR were used to remove the burn-in generations, to combine the results of three independent chains, and to select the optimal distribution of posterior premasters and tree values. Despite the inherent topological uncertainty of any phylogenetic reconstruction, the tree
we obtained was consistent with previous systematic works integrating molecular and morphological information, even in correctly placing the Family Tomoceridae closer to Neelipleona and Poduromorpha than to Entomobriyoidae (Yu et al., 2016) (Figure S2).

<caption>FIGURE S2</caption>

Ultrametric Bayesian consensus tree of the springtails sampled obtained with BEAST under the assumption of lognormal relaxed molecular clock and substitution rates arbitrarily set to 1. Outgroups were removed before the analysis and root was assigned based on previous analyses conducted with maximum likelihood and Bayesian inference including one bristletail and one dipluran as outgroups. Numbers at the nodes are the posterior probabilities, which are correlated with branch color with red denoting the maximum support for a node. See Supplementary Table S3 for interpretation of the species abbreviations.

<figure><img src="image_17.png" /></figure>