# Fast attrition of springtail communities by experimental drought and richness-decomposition relationships across Europe

Guille Peguero, Daniel Sol, Miquel Arnedo, Henning Petersen, Sandrine Salmon, Jean-François Ponge, Joan Maspons, Bridget Emmett, Claus Beier, Inger Schmidt, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Guille Peguero, Daniel Sol, Miquel Arnedo, Henning Petersen, Sandrine Salmon, et al. Fast attrition of springtail communities by experimental drought and richness-decomposition relationships across Europe. Global Change Biology, 2019, 25 (8), pp.2727-2738. 10.1111/gcb.14685. hal-02173203

HAL Id: hal-02173203

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Submitted on 4 Jul 2019

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# Fast attrition of springtail communities by experimental drought and richness-decomposition relationships across Europe

Guille Peguero, Daniel Sol, Miquel Arnedo, Henning Petersen, Sandrine Salmon, Jean-François Ponge, Joan Maspons, Bridget Emmett, Claus Beier, Albert Tietema, et al.

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

## ► To cite this version:

Guille Peguero, Daniel Sol, Miquel Arnedo, Henning Petersen, Sandrine Salmon, et al. "Fast attrition of springtail communities by experimental drought and richness-decomposition relationships across Europe." Global Change Biology, Wiley, 2019, 10.1111/gcb.14685. hal-02173203

HAL Id: hal-02173203

https://hal.archives-ouvertes.fr/hal-02173203

Submitted on 4 Jul 2019

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.
<figure><img src="image_3.png" /></figure>

Fast attrition of springtail communities by experimental drought and richness-decomposition relationships across Europe

<table><tr><td>Journal:</td><td>Global Change Biology</td></tr><tr><td>Manuscript ID</td><td>GCB-19-0067.R1</td></tr><tr><td>Wiley - Manuscript type:</td><td>Primary Research Articles</td></tr><tr><td>Date Submitted by the Author:</td><td>04-Apr-2019</td></tr><tr><td>Complete List of Authors:</td><td>Peguero, Guille; CSIC, Global Ecology Unit CREAF-CSIC-UAB, 08913 Cerdanyola del Vallès, Catalonia, Spain<br>Sol, Daniel; Universitat Autonoma de Barcelona Centre de Recerca Ecologica i Aplicacions Forestals<br>Arnedo, Miquel; Universitat de Barcelona, Department of Evolutionary Biology, Ecology and Environmental Sciences, and Biodiversity Research Institute (IRBio)<br>Petersen, Henning; Natural History Museum, Mols Laboratory Salmon, Sandrine; MNHN, MECDEV<br>Ponge, Jean-François; Museum National d'Histoire Naturelle, Adaptations du Vivant<br>Maspons, Joan; Universitat Autonoma de Barcelona Centre de Recerca Ecologica i Aplicacions Forestals<br>Emmett, Bridget; Centre for Ecology and Hydrology Bangor<br>Beier, Claus; University of Copenhagen, Department of Geosciences and Natural Resources Management; University of Copenhagen<br>Schmidt, Inger; University of Copenhagen, Department of Geosciences and Natural Resources Management<br>Tietema, Albert; Universiteit van Amsterdam Institute for Biodiversity and Ecosystem Dynamics<br>de Angelis, Paolo; Universita degli Studi della Tuscia, Department for Innovation in Biological, Agro-food and Forest systems<br>Kovács-Láng, Edit; MTA Centre for Ecological Research, Institute of Ecology and Botany<br>Kroel-dulay, Gyorgy; MTA Centre for Ecological Research, Estiarte, Marc; CREAF,<br>Bartrons, Mireia; Universitat de Vic - Universitat Central de Catalunya, Aquatic Ecology Group<br>Holmstrup, Martin; Aarhus Universitet, Bioscience<br>Janssens, Ivan; University of Antwerp, Biology<br>Penuelas, Josep; CREAF-CSIC, Global Ecology</td></tr><tr><td>Keywords:</td><td>Biodiversity-Ecosystem Functioning, Climate Change, Collembola, Drought, Litter Decomposition, Shrublands, Soil Fauna</td></tr><tr><td>Abstract:</td><td>Soil fauna play a fundamental role on key ecosystem functions like organic matter decomposition, although how local assemblages are responding to climate change and whether these changes may have consequences to ecosystem functioning is less clear. Previous studies</td></tr></table>
have revealed that a continued environmental stress may result in poorer communities by filtering out the most sensitive species. However, these experiments have rarely been applied to climate change factors combining multi-year and multi-site standardized field treatments across climatically contrasting regions, which has limited drawing general conclusions. Moreover, other facets of biodiversity such as functional and phylogenetic diversity, potentially more closely linked to ecosystem functioning, have been largely neglected. Here, we report that the abundance, species richness, phylogenetic diversity and functional richness of springtails (Subclass Collembola), a major group of fungivores and detritivores, decreased within four years of experimental drought across six European shrublands. The loss of phylogenetic and functional richness were higher than expected by the loss of species richness, leading to communities of phylogenetically similar species sharing evolutionary conserved traits. Additionally, despite the great climatic differences among study sites, we found that taxonomic, phylogenetic and functional richness of springtail communities alone were able to explain up to 30% of the variation in annual decomposition rates. Altogether, our results suggest that the forecasted reductions in precipitation associated with climate change may erode springtail communities and likely other drought-sensitive soil invertebrates, thereby retarding litter decomposition and nutrient cycling in ecosystems.
1

Fast attrition of springtail communities by experimental drought and
richness-decomposition relationships across Europe

2

3

*Running title: Decline of springtail richness by drought*

4

Guille Peguero¹,², Daniel Sol³,⁴, Miquel Arnedo⁵, Henning Petersen⁶, Sandrine Salmon⁷,

5

Jean-François Ponge⁷, Joan Maspons³, Bridget Emmett⁸, Claus Beier⁹, Inger Schmidt, Albert Tietema¹⁰,

6

Paolo De Angelis¹¹, Edit Kovács-Láng¹², György Kröel-Dulay¹², Marc Estiarte²,³, Mireia

7

Bartrons²,¹³, Martin Holmstrup¹⁴,¹⁵, Ivan A. Janssens¹, Josep Peñuelas²,³

8

¹Research Group of Plant and Vegetation Ecology, Department of Biology, University of Antwerp, 2610

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Wilrijk, Belgium.

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²CSIC, Global Ecology Unit CREAF-CSIC-UAB, 08913 Bellaterra, Catalonia, Spain.

11

³CREAF, 08913 Cerdanyola del Vallès, Catalonia, Spain.

12

⁴CSIC, 08193 Cerdanyola del Vallès, Catalonia, Spain.

13

⁵Department of Evolutionary Biology, Ecology and Environmental Sciences, and Biodiversity Research

14

Institute (IRBio), Universitat de Barcelona, Avinguda Diagonal 643, 08028 Barcelona, Spain.

15

⁶Natural History Museum, Mols Laboratory, Strandkaervej 6-8, Femmøller, DK8400 Ebeltoft, Denmark.

16

⁷Muséum National d'Histoire Naturelle, CNRS UMR 7179, 4 Avenue du Petit-Château, 91800 Brunoy, France.

17

⁸Centre for Ecology and Hydrology, Environment Centre Wales, Deiniol Road, Bangor LL57 2UW, UK.

18

⁹Department of Geosciences and Natural Resource Management, University of Copenhagen, Rolighedsvej 23,

19

1958 Frederiksberg C, Denmark.

20

¹⁰Institute for Biodiversity and Ecosystem Dynamics, University of Amsterdam, 94240, 1090 GE Amsterdam,

21

The Netherlands.

22

¹¹Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via San

23

Camillo de Lellis snc, 01100 Viterbo, Italy.

24

¹²Institute of Ecology and Botany, MTA Centre for Ecological Research, Alkotmany u. 2-4, 2163 Vacratot,

25

Hungary.

26

¹³BETA Technological Centre (Tecnio), Aquatic Ecology Group, University of Vic-Central University of

27

Catalonia, Vic 08500, Barcelona, Spain.

28

¹⁴Department of Bioscience, Aarhus University, Silkeborg, Denmark;

29

¹⁵Aarhus Institute of Advanced Studies, Aarhus University, Aarhus C, Denmark;

30

*Correspondence author e-mail and telephone number: guille.peguero@gmail.com (+34 93 5814667)
31 **Abstract**

32 Soil fauna play a fundamental role on key ecosystem functions like organic matter 33 decomposition, although how local assemblages are responding to climate change and 34 whether these changes may have consequences to ecosystem functioning is less clear.
35 Previous studies have revealed that a continued environmental stress may result in poorer 36 communities by filtering out the most sensitive species. However, these experiments have 37 rarely been applied to climate change factors combining multi-year and multi-site 38 standardized field treatments across climatically contrasting regions, which has limited 39 drawing general conclusions. Moreover, other facets of biodiversity such as functional and 40 phylogenetic diversity, potentially more closely linked to ecosystem functioning, have been 41 largely neglected. Here, we report that the abundance, species richness, phylogenetic 42 diversity and functional richness of springtails (Subclass Collembola), a major group of 43 fungivores and detritivores, decreased within four years of experimental drought across six 44 European shrublands. The loss of phylogenetic and functional richness were higher than 45 expected by the loss of species richness, leading to communities of phylogenetically similar 46 species sharing evolutionary conserved traits. Additionally, despite the great climatic 47 differences among study sites, we found that taxonomic, phylogenetic and functional richness 48 of springtail communities alone were able to explain up to 30% of the variation in annual 49 decomposition rates. Altogether, our results suggest that the forecasted reductions in 50 precipitation associated with climate change may erode springtail communities and likely 51 other drought-sensitive soil invertebrates, thereby retarding litter decomposition and nutrient 52 cycling in ecosystems.
53

54 **Keywords:** Biodiversity-Ecosystem Functioning, Climate Change, Collembola, Drought,
55 Litter Decomposition, Shrublands, Soil Fauna
## 56 Introduction

57 Climate change is considered a major threat for biodiversity (Urban, 2015), potentially 58 eroding biological communities and altering their fundamental functions (Peñuelas et al., 59 2013). Ecological theory predicts that a continued stress, such as increased drought and 60 warming, may result in poorer assemblages by filtering out the most sensitive species (Chase, 61 2007), either because the new abiotic regime precludes their population growth (strict 62 environmental filtering) or because it decreases their competitive performance (Cadotte & 63 Tucker, 2017; Kraft et al., 2015). By selectively removing species with traits poorly fitted to 64 the new environmental conditions, an enduring stress might also erode functional richness 65 and even cause a shift in the occupation of the functional space (Mouillot, Graham, Villéger, 66 Mason, & Bellwood, 2013). If the functional traits that provide sensitivity to the stress are 67 phylogenetically conserved, then these sustained environmental pressures might also result in 68 simplified communities populated by closely-related species (Helmus et al., 2010). Since 69 fundamental processes such as productivity and decomposition are functionally linked with 70 community properties like taxonomic, phylogenetic and functional richness (Hooper et al., 71 2012; Kardol, Fanin, & Wardle, 2018; Tilman, Isbell, & Cowles, 2014), these climate change 72 impacts on biological diversity may have important consequences on ecosystem functioning 73 and thus on nutrient cycling.

74 Despite the growing concern over the effects of climate change on biological communities, it 75 is still uncertain how biodiversity will respond by the rise of temperatures and the increase in 76 the frequency and severity of droughts. Most previous research has examined shifts in species 77 abundance and richness, generally finding evidence for declines (Urban, 2015). However, 78 there has been a disproportionate focus on aboveground communities, particularly vertebrates 79 and plants, overlooking that belowground thrives an extremely rich diversity of soil
invertebrates that are key for ecosystem functioning and are at high risk (Bardgett & van der Putten, 2014; Eisenhauer, Bonn, & A. Guerra, 2019). Moreover, it is increasingly appreciated that changes in species abundance and richness provide an incomplete picture of the connection between biodiversity and ecosystem functioning because they may be lost at different rates than functional and phylogenetic diversity (Tilman et al., 2014). Because anticipation of responses is the basis to build realistic biodiversity scenarios, these gaps in knowledge limit our ability to develop conservation efforts and future planning to mitigate the impact of climate change.

Here, we investigate changes in springtail communities (Subclass Collembola) in response to climate manipulations in a standardized field experiment replicated at six natural shrubland sites across Europe (Fig. 1; Table 1). Although logistically challenging, combining multi-year and multi-site standardized field experimental approaches across climatically contrasting regions is crucial to draw conclusions that are realistic and apply across large regions (Kröel-Dulay et al., 2015). Springtails are a highly diverse and abundant group of soil fauna involved in many key ecosystem functions such as leaf-litter decomposition and nutrient cycling (Bardgett & van der Putten, 2014; Filser et al., 2016; Handa et al., 2014).

Notwithstanding, our current knowledge of whether and how their phylogenetic and functional diversity will be altered by climate change is still poor despite recent advances with some biodiversity metrics and in particular ecosystem types (Alatalo, Jägerbrand, & Čuchta, 2015; Blankinship, Niklaus, & Hungate, 2011; Holmstrup et al., 2013, 2017, 2018; Kardol, Reynolds, Norby, & Classen, 2011; Lindberg, Bengtsson, & Persson, 2002; Makkonen et al., 2011; Petersen, 2011). To simulate climate change, we applied two types of treatments on natural vegetation plots: 1) a drought treatment, using transparent plastic coverings during rain events to decrease rainfall; and 2) a warming treatment, increasing heat retention by means of reflective curtains extended between sunset and sunrise throughout the
year. At each study site, these treatments were replicated in three randomly selected plots, and compared to three adjacent control plots with the same scaffolding but no treatment.

Between 1 to 4 years since the onset of the climate manipulations, springtail communities were comprehensively sampled from topsoil as well as soil and vegetation surface. To investigate how our warming and drought treatments affected taxonomic, phylogenetic and functional richness, we identified all recorded specimens to the species level, reconstructed their phylogenetic relationships with two molecular markers, and compiled published information on 28 relevant functional traits. As diversity loss may alter ecosystem functioning (Hooper et al., 2012; Kardol et al., 2018; Tilman et al., 2014), we also assessed rates of decomposition by means of a simultaneous litterbag experiment with leaf-litter of the dominant local plant species, and tested whether variation in this key ecosystem function correlated with these newly gathered biodiversity measures. We hypothesized that warming and drought treatments would reduce springtail abundance as well as taxonomic, phylogenetic and functional richness of springtail assemblages. Additionally, if the traits analyzed are phylogenetically conserved, and the phylogenetic structure of springtail communities in warmed and dried plots is relatively clustered compared to those in control plots, this could suggest a non-random loss of springtail species. Finally, springtails are involved in leaf-litter decomposition through multiple direct and indirect mechanisms (Filser et al., 2016). However, to the best of our knowledge, direct assessments of the relationship between springtail richness and decomposition are lacking. Therefore, we hypothesized that if there is a diversity-decomposition relationship and springtails are to some extent a good proxy of soil biodiversity, then we should find significant correlations between local springtail richness and the rates of leaf-litter decomposition.

## 128 Materials and Methods
**Study sites.** The six shrublands studied comprised most of the broad-scale European climatic regions (Fig. 1). Mean annual temperature (MAT) at the sites ranged from 7.4 to 16.1 °C, and mean annual precipitation (MAP) ranged from 544 to 1263 mm (Table 1). The major types of shrubland present in temperate Europe were included: Atlantic heathland (UK - United Kingdom, NL - The Netherlands, DK - Denmark), continental forest steppe (HU - Hungary), and Mediterranean garriga/machia (SP - Spain and IT - Italy). The sites were established in 1998 (UK, NL, DK, and SP) and 2001 (HU and IT). Climatic data were recorded in the control plots to obtain the characteristics of each experimental site (Table 1).

Figure 1 Location of the climatic manipulation experiments in Europe. Arrows depict broad-scale gradients of temperature and precipitation. DK, Denmark; HU, Hungary; IT, Italy; NL, The Netherlands; SP, Spain; UK, United Kingdom.

<figure><img src="image_4.png" /><figcaption>**Table 1. Characteristics of the study sites.**</figcaption></figure>

<table><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></tbody></table>
<table><thead><tr><th>Soil type<br>(FAO)</th><th>Peaty Podzol</th><th>Haplic<br>Arenosol</th><th>Sandy Podzol</th><th>Calcaric<br>Arenosol</th><th>Petrocalcic Calcixerept</th><th>Luvisol and Leptosol</th></tr></thead><tbody><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<br>season</th><td>April-<br>September</td><td>April-<br>October</td><td>April-<br>September</td><td>April-<br>September</td><td>January-May<br>October-December</td><td>January-May<br>October-December</td></tr><tr><th>Dominant<br>species</th><td>Calluna<br>vulgaris</td><td>Calluna<br>vulgaris</td><td>Calluna vulgaris<br>Deschampsia<br>flexuosa</td><td>Populus alba<br>Festuca<br>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).

Growing season refers to the period of vegetation growth.

Species with relative cover above 10% in the control plots during the study period are listed as dominant species.

140

141 **Experimental climatic manipulations.** In each study site, we defined nine 20-m² plots

142 divided in three blocks and randomly assigned one plot at each block to 1) drought treatment,

143 2) warming treatment or 3) control. Drought plots were covered with a transparent plastic

144 roof during rain events in a pre-specified experimental drought period. The roof was

145 automatically withdrawn after the rain, thus avoiding any warming effect (Beier et al., 2004).

146 Warming plots were covered with reflective curtains between sunset and sunrise throughout

147 the year, inducing a passive night-time warming (Beier et al., 2004, see picture S1 at the

148 Supporting Information). All experimental methods to manipulate climate have specific

149 strengths and weaknesses due to their particular unrealistic and/or unintended effects (Beier

150 et al., 2004; Harte et al., 1995). Notwithstanding, a comparison of different passive and active

151 experimental warming methods concluded that passive night time warming is among the

152 most realistic and applicable (Aronson & McNulty, 2009), agreeing with evidence in the

153 ongoing global warming that there is a higher rate of heating during the night (Alward,

154 Detling, & Milchunas, 1999). Warming obtained with this method is greatest at night, but

155 there is also some carry-over effect during the day (Bruhn et al., 2013). Control plots were

156 equipped with the same metallic scaffolding but no treatment was applied (cf. Harte et al.,

157 1995). While the same technology was used for the climatic manipulations (warming,
158 drought, control) at each study site, timing and duration of the experimental drought were

159 adjusted to the local conditions according to climatic predictions (Table 2).

160

<table><caption>**Table 2. 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 (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>Drought*</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>Timing</td><td>May-September</td><td>May-August</td><td>May-July</td><td>May-June</td><td>May-June<br>October-Nov.</td><td>April-October</td></tr><tr><td>Precipitation excluded (% of Control, 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>Warming**</td><td></td><td></td><td></td><td></td><td></td><td></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><tr><td>Increase air temperature (C) (month of fauna sampling)</td><td>+1.4</td><td>+1.0</td><td>+1.0</td><td>+0.9</td><td>+0.9</td><td>+0.5</td></tr></tbody></table>

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

*Drought treatment effects are average changes for the sampling year (2003).

**Increase in MAT are averages during the 1998-2012 period (Kröel-Dulay et al., 2015) and the increase of air temperature refer to the sampling year (2003).

161

162 Taxon sampling. Springtails (Subclass Collembola) were sampled from April to July 2003

163 sequentially (i.e. IT, SP, HU, DK, NL and UK) to equalize mean temperatures across sites

164 (Petersen, 2011). Five quadrats (1.25 m² each) representative of the most dominant plant

165 species were selected within each experimental plot. Springtails from vegetation were

166 suctioned with an adapted vacuum cleaner connected to a fauna trap. Specimens dropped

167 were recovered through polythene boxes with aqueous benzoic acid placed beneath the

168 plants. Ground-dwelling springtails were also suctioned with the same method for the same

169 area and, subsequently, 10-cm deep soil cores with a surface of 25 cm² were taken to the lab

170 to recover springtails from the soil through high-gradient extraction (10 days of gradually

171 increasing temperature from 25 to 60°C, Gjelstrup & Petersen, 1987). The sampling methods

172 were slightly adapted in the UK and IT due to an excess of soil moisture and stones,
respectively (see Petersen, 2011). Once extracted and sorted, springtails were conserved in glycerol and identified to the species level following references cited in (Petersen, 2011).

Some dubious specimens were kindly revised by Drs. L. Dányí, R. Jordana and E. Mateos.

This sampling resulted in 19641 springtail specimens that were classified into 102 species-level entities (Table S7). An initial assessment of these data can be found in Petersen (2011) and the specimens are deposited in his personal collection.

**Phylogenetic data.** The phylogeny of springtails was constructed with sequence data of two genes (*cox1* and *28s*), obtained from public repositories (Table S7). The tree obtained was consistent with previous systematic works integrating molecular and morphological information (Yu et al., 2016). See figure S4 and supplementary methods for a detailed description about the procedures.

**Trait data.** We obtained data of 28 traits for the 64 species that were present in our study sites from the ColTrait database (Salmon & Ponge, 2012). This springtail database is the most comprehensive to date and the traits collated encompass different dimensions of ecological and functional niche (Table S8), including abiotic components of habitat (e.g. preferred strata, temperature and precipitation range), dispersal ability (e.g. locomotory appendages, furcula), features involved in biotic interactions (e.g. specific sensory organs and defensive features) and life history strategies (e.g. parthenogenetic, sexual or mixed). These traits have shown high potential to explain springtails distribution and community composition (Salmon et al., 2014; Salmon & Ponge, 2012).

**Litterbag decomposition experiment.** Leaf litter from the dominant ericaceous plant species at each site (i.e. *Calluna vulgaris* and *Erica vulgaris*, Table 1) was collected in DK, NL, UK and SP between September-November 1999. After air drying and sorting to obtain homogeneous samples, 3 g of dried leaves and shoots were placed in nylon bags with a mesh
size of 1 mm for *Calluna vulgaris* and 0.71 mm for *Erica vulgaris* due to the smaller size of the leaves of this species. This protocol allows the entrance of medium-sized decomposers, including all juveniles and the adults of almost all springtail species as well as other invertebrate fauna (Handa et al., 2014). The bags were incubated over a period of 2 years divided in 10-12 time-periods. Three replicates for each time-period were placed either randomly (UK), beneath *Calluna* plants (DK), stratified beneath *Calluna* plants and open areas (NL) or beneath *Erica* plants (SP) due to the specific characteristics of vegetation cover at each site. Litterbags were then sequentially retrieved and sorted to remove non-target material and then oven-dried. We calculated the annual decomposition rates as the annual fractional weight loss (k) for each plot using the following equation: $M_t = M_0 \cdot e^{-kt}$, where $M_t$ is leaf-litter mass loss at time t and $M_0$ is the initial mass (Emmett et al., 2004). HU and IT were not included in the litterbag experiment due to the delay in the onset of the climatic manipulations in both sites (Table 2). A previous analysis of these decomposition experiment can be found in Emmett *et al.* (2004).

**Data analysis.** All metric estimations and statistical analyses we describe here were carried out with R v3.4.3 (R Core Team, 2016). To assess if taxonomic, phylogenetic and functional trait-based dimensions of springtail richness responded in the same way to experimental climate change treatments, we characterized springtail assemblages in terms of: (i) abundance (total number of individuals of all species per plot); (ii) species richness (SR, number of species per plot); (iii) Faith's phylogenetic diversity index (PD, sum of branch lengths connecting all species in an assemblage (Faith, 1992)); (iv) mean neighbor taxon distance (MNTD, mean phylogenetic shortest distance for each species from an assemblage (Kembel et al., 2010)); (v) variance in nearest taxonomic distance (VNTD, variance of phylogenetic shortest distance for each species from the distance matrix of an assemblage (Tucker et al., 2016)); (vi) functional richness (FR, the multidimensional volume occupied by all species in
an assemblage within a functional space, where the axes are functional traits along which species placed according to their trait values (Mouillot et al., 2013; Villéger, Mason, & Mouillot, 2008)); (vii) functional divergence (FD, divergence in the distribution of abundance within the volume or functional space occupied by a community (Villéger et al., 2008)); and (viii) functional evenness (FE, regularity in the distribution of abundance within the volume or functional space occupied by a community (Villéger et al., 2008)). These metrics were selected following the framework proposed by Tucker et al. (Tucker et al., 2016; Tucker, Davies, Cadotte, & Pearse, 2018), according to which PD vs FR, MNTD vs FD and VNTD vs FE are phylogenetic and functional equivalent indices associated to richness (i.e. the sum of accumulated phylogenetic and functional differences among taxa in an assemblage), divergence (i.e. the mean phylogenetic and functional relatedness among taxa in an assemblage), and regularity (i.e. the variance in differences among taxa, representing how regular are the phylogenetic and functional differences between taxa in an assemblage) dimensions of biological communities, respectively.

Metrics for the phylogenetic community structure were calculated with the package *picante* (Kembel et al., 2010) using the reference ML-Bayesian consensus phylogeny. To account for baseline differences in species richness among sites and treatments, we standardized phylogenetic measures against 999 community randomizations using ‘richness’ and ‘independent swap’ as null model specifications, i.e. constraining the randomized communities only to observed species richness or also to the frequency of species occurrence. This was done by means of the built-in functions ‘ses.pd’ and ‘ses.mntd’ and with a modified version of the latter for VNTD. The standardized effect size was then calculated as the difference between the value observed in the community and the mean value of the null communities divided by the standard deviation of the distances in the null data. The same approach was applied to FR, FD and FE, which were calculated with the package *FD*
247 (Laliberte & Legendre, 2010) and standardized with our own script applying the same
248 constraints as with PD, MNTD and VNTD. Additionally, we estimated the phylogenetic
249 signal of trait data, i.e. the correlation between trait similarity and species' evolutionary
250 distance. We used the D-statistic and Pagel's λ for discrete and continuous traits, respectively
251 (Fritz & Purvis, 2010; Pagel, 1999) through the packages *caper* and *phylosig* (Orme, 2013;
252 Revell, 2012). Values near or below 0 and close to 1 mean strong phylogenetic signal for the
253 D-statistic and for Pagel's λ, respectively. The existence of phylogenetic signal suggests that
254 ecological similarity is linked to phylogenetic relatedness (Losos, 2008).

255 We investigated how taxonomic, phylogenetic and functional metrics of springtail
256 assemblages changed due to climatic manipulation by means of linear mixed-effects models,
257 as implemented in the R-package *lme4* (Bates, Mächler, Bolker, & Walker, 2014). Our
258 response variables (i.e. each of the above diversity metrics estimated at a community level)
259 were modelled separately as a function of the experimental treatments (coded as a fixed
260 effect) and study site (coded as a random intercept factor to cope with the heterogeneity in
261 abiotic conditions across sites.). We estimated the *P*-values for fixed-effects by means of a
262 Satterthwaite approximation to the number of degrees of freedom with *lmerTest* (Kuznetsova,
263 Brockhoff, & Christensen, 2018). To assess for potential interactions between the effect of
264 climatic treatments and sites, we also built models where site, treatment and their interactions
265 were included as fixed-effects terms. We used variation in second-order Akaike's
266 Information Criterion (ΔAICc), marginal and conditional coefficients of determination ($R^2_m$)
267 and $R^2_c$) and residuals diagnostic plots to compare model performance and assess model
268 assumptions. For visualization, the observed changes in abundance, SR, PD and FR are
269 represented as proportions relative to control plots (Emmett et al., 2004). Additionally, to
270 validate the consistency of the community patterns detected, we carried out a series of
271 sensitivity analyses. First, to discard that SR differences were only due to variation in
abundance, we repeated the analyses with SR estimated after rarefying communities to median, first and third quartile of the observed abundance. Second, since we did not have molecular and trait information for all the species pool, we assessed the potential effect of missing species repeating all the analyses restricting the community datasets to only those species present in the phylogeny and to only those with trait data available. Third, the reconstruction of the phylogenetic relationships of a given set of species necessarily implies a degree of uncertainty around the topology, and thereby of the derived metrics extracted from the trees. Hence, to assess the effect of phylogenetic uncertainty we iterated all the preceding phylogenetic measures and the corresponding linear models with 1000 trees drawn from the Bayesian posterior distribution.

Finally, we tested if our climate manipulations affected annual decomposition rates and whether the richness of springtail communities correlated with this key ecosystem function using linear mixed-effects models. Rather than trying to establish purely causal relationships, which is not possible with the design of our litterbag experiment, we aimed to assess the predictive power of springtail richness over litter decomposition, that is the amount of variance in litter decomposition that each richness metric is able to explain. So, we built different models with decomposition rate as response variable varying the inclusion of climate manipulation treatments, SR, PD and FR and their interaction as fixed effect terms. We additionally tested if these models differed when site was included as a random intercept term (i.e. the variation in large-scale abiotic conditions across sites may determine different baseline rates of decomposition), or additionally, by letting each covariate (SR, PD and FR) have a random slope correlated with each site (group) intercept (i.e. the potential diversity-function relationship may differ between sites due to the contrasting abiotic conditions). The inclusion of site as a random intercept term allowed to cope with the heterogeneity in abiotic conditions across sites. By letting each covariate (i.e. SR, PD and FR) to have a different
slope at each site, the model also accounted for the possibility that the same large-scale
abiotic controls may modulate the potential relationship between local richness-related
metrics with decomposition. See Bradford et al. for a similar analytical approach (Bradford et
al., 2014). To assess these distinct model structures, we compared both types of models by
means of likelihood ratio tests and evaluated their predictive ability through R²_m and R²_c with
the package *MuMln* (Barton, 2018), which capture the explanatory power of both fixed and
random terms. We estimated *P*-values for the fixed terms through a Satterthwaite
approximation to the number of degrees of freedom and used residuals diagnostic plots to
assess model performance and assumptions. For model visualization we used the *visreg*
package, which allows to plot best-fitted models focusing on the variables of interest and
holding the other factors constant (i.e. partial residuals regression plots) (Breheny &
Burchett, 2016).

## 309 Results

The experimentally warmed plots did not differ from the control plots in any of the diversity
metrics, despite an average increase of 0.43° C in mean annual temperature (MAT, Table 2 and
Table S1). In contrast, the drought treatment, which on average reduced annual precipitation
by 22% and soil moisture by 36%, considerably altered springtail communities. This resulted
in a decline on average of 22 ± 10% (mean ± 1 standard error, hereinafter) of the species
richness compared to control plots (*P* < 0.01; Fig. 2). The drought treatment also reduced on
average 44 ± 7% the number of springtails per plot (*P* < 0.01; Fig. 2), raising the possibility
that the observed species loss was a random consequence of a community downsizing.
However, the decline of species richness with the drought was consistent even when
communities were rarefied to equalize total abundances (always *P* < 0.05 when rarefied to
median, first and third quartile, see Table S2). The ‘drought effect’ on both abundance and
321

species richness was also robust to the exclusion of species for which no information was
available on functional traits and/or phylogenetic relationships (Table S2).

322

323

Figure 2 Change of springtail communities in response to climatic manipulation. We used linear mixed-effects models to assess the change of springtail communities after 1 to 4 years of drought and warming in an experiment replicated at six sites across Europe. Values are treatment means ± 1 standard error. Phylogenetic diversity and functional richness are based on standardized measures using community randomizations (see text for further details).

324

325

326

327

328

Asterisks denote statistically significant differences of drought communities relative to control:

* P < 0.05; ** P < 0.01.

329

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

330

331

In parallel to the loss of species, the drought plots exhibited significant reductions in phylogenetic diversity and functional richness compared to control plots (*P* < 0.05; Fig. 2).

332

333

Both metrics are known to decrease with species loss, a pattern also detected in our analyses.
However, the standardized effect sizes of phylogenetic diversity and functional richness were still significantly lower in drought plots than in control plots (*P* < 0.05; Table S1), indicating that the reduction did not only reflect a decline in species richness. Although the study sites largely differed in species composition due to their large geographic spread and broad-scale climatic gradients, the local drought-induced responses were remarkably consistent across regions (Fig. S1).

The drought treatment eroded the phylogenetic and functional richness of springtail assemblages apparently without important shifts in the distribution of species abundances or in the regularity of these abundances within the functional trait space. Indeed, functional evenness and divergence showed no sign of change in springtail communities exposed to climatic manipulations (Table S1). However, the species persisting the drought were on average more closely related to each other than expected by chance (Fig. 3 and Table S1).

This pattern was robust to phylogenetic uncertainty and the type of null model used for community randomizations (Fig. S2 and Table S3), and it also exhibited a remarkable consistency across study sites (Fig. S1). It is also important to note that almost all springtail traits studied here were strongly phylogenetically conserved (Table S4), which imply that phylogenetic distance is to some extent related with trait distance between springtail species.

Figure 3 Change in the phylogenetic structure of springtail communities in response to climatic manipulations. We used linear mixed-effects models to evaluate the change on the phylogenetic structure of springtail communities after 1 to 4 years of experimental drought and warming. Values are treatment means ± 1 standard error of the standardized effect size (SES) of the mean neighbor taxon distance (MNTD). Lower MNTD values denote communities populated by species more closely related. Asterisks denote significantly lower MNTD values in drought communities relative to control: ** P < 0.01.
358

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

359

360

The loss of biodiversity due to drought might, if sustained, amplify the consequences of climate change to leaf-litter decomposition, a process in which springtails are known to play an important role (Bardgett & van der Putten, 2014; Handa et al., 2014). Consistent with this possibility, taxonomic, phylogenetic and functional richness-related metrics all showed highly significant correlations with annual decomposition rates (Fig. 4 and Table S5a and 5b): The greater the richness of the local springtail community, the higher the rate of leaf-litter decomposition. The strength of this association was fundamentally regular across sites irrespective of the random effects structure included in the models (Table S6), and it was particularly high for taxonomic and functional richness (Fig. 4 and Table S5a and 5b).

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365

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368

369

370

371

372

373

374

375

376

377

When comparing the effect of climatic manipulations between treated and control plots the changes in leaf-litter decomposition were rather modest (Fig. S3). Litterbags in drought plots were decomposed at slightly lower rates on average whereas in warmed plots decomposition was marginally accelerated (-3.5 in drought versus +2.6 g y⁻¹ in warming, with associated *P*-values < 0.05 and < 0.1 compared to control plots, Fig. S3 and Table S5). Although moderate, these effects were consistent despite the great among-site variation in annual decomposition rates (the variance explained by the model increased from 0.07 up to 0.89 when including sites' random intercept effect, Fig. S3 and Table S5), mirroring the broad-scale gradients in abiotic conditions across Europe (Fig. 1 and Table 1). When taxonomic,
378 phylogenetic or functional richness of the springtail community were accommodated as

379 covariates in their respective models (Fig. S3 and Table S5), the effects of the climatic

380 manipulations turned out non-significant masked by the effects of the local springtail richness

381 metrics.

382 Figure 4 Relationships between annual rates of leaf-litter decomposition and species richness

383 (A), phylogenetic diversity (B) and functional richness (C) of springtail communities across

384 Europe. We used linear mixed-effects models to assess the correlations allowing each site to

385 have its own random intercept to capture large-scale climatic differences among sites. We

386 used marginal and conditional coefficients of determination (R2m and R2c) to assess the

387 predictive ability of fixed alone and fixed + random terms, respectively. Partial residuals

388 regression plots of the best-fitted models show the relationships between response and

389 explanatory variables holding the random term constant.
A

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

B

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

C

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

390

391

392
393 **Discussion**

394 Our experiment replicated on a large-scale showed that the number of species and the size of
395 the communities of springtails declined within four years of simulating the drought induced
396 by climate change. Similar declines in both abundance and species richness of soil fauna after
397 experimental droughts have also been documented in arctic and temperate ecosystems
398 (Kardol et al., 2011; Lindberg et al., 2002; Makkonen et al., 2011), although in some cases
399 the response was rather weak (Eisenhauer, Cesarz, Koller, Worm, & Reich, 2012; Holmstrup
400 et al., 2013). Our results, however, extend these previous findings in three important
401 directions. We first demonstrate that the richness contraction after drought was
402 fundamentally consistent across climatically contrasting sites, thereby generalizing at a
403 continental scale the effects of climate change upon a major soil animal group. The
404 robustness of the results is remarkable considering the notable differences in springtail
405 diversity found across the study sites.

406 Second, we provide evidence that drought not only reduced species richness (Petersen, 2011)
407 but also caused a disproportionate loss of phylogenetic diversity, resulting in increasingly
408 phylogenetically clustered springtail assemblages. A continued stress is expected to increase
409 the phylogenetic similarity of species in a community whenever their sensitivity to a
410 particular environmental factor is phylogenetically conserved (Helmus et al., 2010).
411 Accumulating evidence suggests that species filtering by stress disturbance, like sustained
412 drought, is partly driven by species' traits (Chase, 2007; Mouillot et al., 2013). However, it is
413 yet unclear whether the changes in phylogenetic structure arise from strict environmental
414 filtering, from a modification of species' competitive performance due to the new abiotic
415 regime, or from a combination of both processes (Cadotte & Tucker, 2017; Kraft et al.,
416 2015).
Finally, we report that the drought-induced impoverishment of springtail communities also resulted in a fast reduction of the overall functional trait space. Again, according to community randomizations the decline was higher than simply expected by the loss of species. The drought eroded the phylogenetic and functional richness of springtail assemblages apparently without important shifts in functional evenness and divergence. This result may reflect that our trait dataset did not fully capture those features more associated to drought sensitivity in Collembola. Further research, perhaps including phenological and trophic characteristics, should allow to establish the linkages between particular traits, the environment and the functions performed by springtails as well as other groups of soil invertebrates (Pey et al., 2014).

In addition to providing evidence for a non-random drought-induced attrition of a major soil fauna group, our results also warn over the possibility that diversity loss due to climate change may have a negative amplifying consequence to ecosystem functioning (Peñuelas et al., 2013). Our experiments yielded clear evidence that the annual rate of leaf-litter decomposition, a key ecosystem process in which springtails are involved through multiple direct and indirect mechanisms (Filser et al., 2016), increases with their taxonomic, phylogenetic and functional richness, a relationship that deserves attention beyond a purely phenomenological consideration. Certainly, most of the variation in decomposition was associated with differences among our study sites likely caused by the great disparity in temperature and soil moisture regime along the broad environmental gradient included (Emmett et al., 2004; Reinsch et al., 2017). This highlights the importance of large-scale abiotic controls on leaf-litter decomposition (García-Palacios, Maestre, Kattge, & Wall, 2013). However, the fact that up to 30% of the variation in annual decomposition rates was explained by local variation in taxonomic phylogenetic and functional richness of springtails underscores the role of soil fauna modulating the effect of these large-scale abiotic factors
442 (García-Palacios et al., 2013; Handa et al., 2014). In fact, recent estimations pinpoint that 443 invertebrates enhance leaf-litter decomposition by 37% at a global scale (García-Palacios et 444 al., 2013), which implies that reducing its functional diversity will necessarily lessen the 445 cycling of carbon and nitrogen in terrestrial and aquatic ecosystems (Handa et al., 2014). But 446 additionally, these results provide further evidence that local-scale biotic factors can reach a 447 great explanatory power, so that they must be explicitly incorporated in Earth-system models 448 if we are to adequately forecast how decomposition will respond to climate change at a global 449 scale (Bradford et al., 2014, 2017).

450 Although we cannot deduce a specific mechanistic explanation, the association between the 451 richness of local springtail communities and the rates of litter decomposition reported here 452 may have a causal origin. Generally, higher biodiversity levels enhance interspecific 453 complementarity and nutrient-cycling feedbacks that increase nutrient stores and supply rates 454 over the long term throughout food-webs (Tilman et al., 2014). So, springtails could be a 455 reliable proxy of soil biodiversity particularly sensitive to increasingly drying conditions 456 (Holmstrup et al., 2018; Kærsgaard, Holmstrup, Malte, & Bayley, 2004). Additionally, it is 457 possible that drought treatments have also affected other decomposers besides springtails. 458 Indeed, previous work has shown declines associated with droughts in a variety of 459 decomposers, including microbial communities (Sowerby et al., 2005; Yuste et al., 2011), 460 enchytraeid earthworms and oribatid mites (Holmstrup et al., 2012; Lindberg et al., 2002), as 461 well as in fungivorous springtails and other invertebrates known to top-down regulate 462 microbial communities (Crowther, Boddy, & Jones, 2011). Therefore, the observed 463 contraction of springtail richness may represent the effect of drought throughout the entire 464 detritus-based food web. Moreover, increasingly warmer and drier conditions may reduce 465 feeding of soil detritivores (Thakur et al., 2018), amplifying the effects of the treatments on 466 decomposition rates through a drop in soil fauna activity. Future experiments that
467 simultaneously manipulate species richness and environmental factors concurrently are
468 therefore warranted to disentangle the relative importance of biotic and abiotic factors in litter
469 decomposition (Boyero, Cardinale, Bastian, & Pearson, 2014).

470 The community shifts in springtail assemblages induced by drought contrast with the general
471 lack of effects of the warming treatment. The interaction between experimental warming and
472 soil moisture are long known (Harte et al., 1995). Likely, the intensity of the warming applied
473 was moderate not only for collembolans (although see Petersen, 2011, for a further discussion
474 on the modest warming effects found in some of the sites), but also for plant communities
475 and ecosystem functions like net primary productivity and respiration, which were also rather
476 insensitive to the warming treatment (Kröel-Dulay et al., 2015; Reinsch et al., 2017). The
477 night-time warming treatment resulted in a moderate increase of MAT (range 0.2-0.9 K,
478 Table 2), which matches past changes recorded at a multi-decadal (50 years) time scale
479 (Christensen et al., 2007). Recent findings have nonetheless revealed that springtails, and in
480 general soil fauna, may be quite resistant to increases in temperature (Alatalo et al., 2015;
481 Holmstrup et al., 2017, 2018). Moreover, a recent meta-analysis has identified reduced
482 precipitation as the most threatening global change driver to soil biodiversity (Blankinship et
483 al., 2011) because many soil invertebrates, like springtails, are essentially freshwater
484 organisms in physiological terms (Kærsgaard et al., 2004).

485 Most of our current understanding of the responses of soil fauna to climate change comes
486 from controlled microcosm experiments with unnatural low-diversity levels (Boyero et al.,
487 2014; Cragg & Bardgett, 2001; Heemsbergen et al., 2004). Studies like ours that manipulate
488 abiotic conditions in natural communities exposed to contrasting climatic regimes are rare,
489 although they are essential to build realistic scenarios of the impact of climate change on
490 biodiversity and its consequences for ecosystem functioning. Our analyses demonstrate that
491 under such realistic conditions, climate change has a great potential to alter the abundance,
492 species richness, phylogenetic diversity and functional richness of springtail communities.

493 The analyses also suggest that these springtail declines, if sustained, may be linked to
494 reductions on litter decomposition that could dwindle nutrient cycling and ultimately the
495 productivity of terrestrial ecosystems. We cannot completely discard that the fast responses to
496 drought reported here are in part a transient state within the resilience space of natural
497 ecosystems, as within the wealth of soil organisms contributing to decomposition processes
498 some groups like oribatid mites, millipedes and isopods may be more resistant in the long-
499 term (Holmstrup et al., 2012; Maraldo et al., 2010). Likewise, it cannot be dismissed that fast
500 evolutionary adaptations could counteract increasingly stressful conditions due to climate
501 change (Hoffmann & Sgro, 2011). However, a previous study with an enchytraeid species
502 showed a limited adaptive ability to drought (Maraldo, Schmidt, Beier, & Holmstrup, 2008)
503 and on-going research suggests that this would also be the case with springtails (Kutcherov et
504 al. unpublished). In any case, our finding that soil biodiversity loss embraces multiple
505 biodiversity facets and is non-random with respect to functional traits supports the view that a
506 reduction in precipitation may result in a lasting attrition of springtail communities. If similar
507 effects occur in other drought-sensitive soil organisms, this may carry critical consequences
508 for ecosystem functioning such as slow-downs on litter decomposition rates.

509 Acknowledgements

510 All authors thank the efforts of the many people that helped with the field-work during the
511 VULCAN-INCREASE projects, the statistical advice of Roberto Molowny-Horas and the
512 comments of the editor and three anonymous that greatly improved the manuscript. The
513 authors acknowledge the financial support from the European Research Council Synergy
514 grant ERC-SyG-2013-610028 IMBALANCE-P, the Spanish Government grant CGL2016-
515

79835-P and the Catalan Government grant SGR 2017-1005. G.P was supported by a Ramon

516

Areces' Foundation postdoctoral fellowship.

517

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