# Mountain soil multitrophic networks shaped by the interplay between habitat and pedoclimatic conditions

Irene Calderón-Sanou, Marc Ohlmann, Tamara Münkemüller, Lucie Zinger, Mickael Hedde, Clément Lionnet, Camille Martinez-Almoyna, Amélie Saillard, Julien Renaud, Nicolas Le Guillarme, et al.

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## ► To cite this version:

Irene Calderón-Sanou, Marc Ohlmann, Tamara Münkemüller, Lucie Zinger, Mickael Hedde, et al. Mountain soil multitrophic networks shaped by the interplay between habitat and pedoclimatic conditions. Soil Biology and Biochemistry, 2024, 190, pp.109282. 10.1016/j.soilbio.2023.109282. hal-04409236

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1 **Mountain soil multitrophic networks shaped by the interplay between habitat and**
2 **pedoclimatic conditions**

3

4 Irene Calderón-Sanou¹,²*§, Marc Ohlmann¹§†, Tamara Münkemüller¹, Lucie Zinger³,⁴,

5 Mickael Hedde⁵, Clément Lionnet¹, Camille Martinez-Almoyna¹,⁶, Amélie Saillard¹, Julien

6 Renaud¹, Nicolas Le Guillarme¹, Ludovic Gielly¹, Orchamp Consortium & Wilfried Thuiller¹

7

8 § Joint first co-authors

9 †Deceased shortly before submission (17/06/2023).

10 1. Univ. Grenoble Alpes, Univ. Savoie Mont Blanc, CNRS, LECA, F-38000 Grenoble, France

11 2. German Centre for Integrative Biodiversity Research (iDiv) Halle-Jena-Leipzig, Leipzig, Germany

13 3. Institut de Biologie de l'ENS (IBENS), Département de biologie, École normale supérieure,
14 CNRS, INSERM, Université PSL, 75005 Paris, France

15 4. Naturalis Biodiversity Center, 2300 RA Leiden, The Netherlands

16 5. Eco&Sols, INRAE - IRD - CIRAD - Institut Agro, Montpellier, France

17 6. Univ. Grenoble Alpes, LESSEM, INRAE, Grenoble, France

18 *Corresponding author: irecalsa@gmail.com

19

20

21 **Number of words:**

22 -abstract: 148

23 -main text: 5169

24 Number of references: 92

25 Number of figures: 5

26 Number of tables: 1

27

28 **Supplementary information:**

29 Supplementary Figures and Tables

30 Appendix 1 & 2

31

32
**Statement of authorship:** WT, TM, MO and IC-S conceptualised the study. AS and WT led the field work, with participation of MO, CM-A, JR, CL, LG, IC-S and several members of the Orchamp consortium. LG executed eDNA extractions and all laboratory samples processing. CL and IC-S processed and cleaned the sequenced eDNA data. JR and CM-A provided the code for extracting the environmental data. IC-S, LZ, MH, CM-A, MO and NLG participated in the assignment of the taxa to trophic groups and the construction of the metaweb. IC-S and MO ran the analyses. All co-authors participated in the writing led by IC-S, MO, WT and TM.

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**Data accessibility statement:** Should the manuscript be accepted, the data supporting the results will be archived in an appropriate public repository (Dryad, Figshare or Hal) and the data DOI will be included at the end of the article.

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**Acknowledgments:**

This project was possible thanks to the many people that participated in the field work and in the lab procedures, including students, interns, postdoctoral fellows, field assistants, park managers and rangers. We thank Cindy Arnoldi for conducting the soil physicochemical analyses.

This research was funded by the French Agence Nationale de la Recherche (ANR) through the GlobNet & EcoNet projects (ANR-16-CE02-0009, ANR-18-CE02-0010-01), from two 'Investissement d'Avenir' grant (Idex UGA: ANR-15-IDEX-02; Montane: OSUG@2020: ANR-10-LAB-56, MIAI@Grenoble Alpes: ANR-19-P3IA-0003), the METRO Grenoble Alpes, the Isere departement and the Office Français de la Biodiversité (OFB). Support was also received from the Lautaret Garden-UMS 3370 (Univ. Grenoble Alpes, CNRS, SAJF, 38000 Grenoble, France), member of AnaEE-France (ANR-11-INBS-0001AnaEE-Services, Investissements d'Avenir frame) and of the LTSER Zone Atelier Alpes, a member of the eLTER-Europe network.

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60 **Abstract**

61
62 Our knowledge of the factors influencing the distribution of soil organisms is limited to specific
63 taxonomic groups. Consequently, our understanding of the drivers shaping the entire soil
64 multitrophic network is constrained. To address this gap, we conducted an extensive soil
65 biodiversity monitoring program in the French Alps, using environmental DNA to obtain multi-
66 taxon data from 418 soil samples. The spatial structure of resulting soil multitrophic networks
67 varied significantly between and within habitats. From forests to grasslands, we observed a
68 shift in the abundance of trophic groups from fungal to bacterial feeding channels, reflecting
69 different ecosystem functioning. Furthermore, forest soil networks were more strongly
70 spatially structured which could only partly be explained by abiotic conditions. Grassland soil
71 networks were more strongly driven by plant community composition and soil characteristics.
72 Our findings provide valuable insights into how climate and land-use changes may
73 differentially affect soil multitrophic networks in mountains.

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75 **Keywords:** network dissimilarity, soil biodiversity, trophic interactions, environmental DNA
76 metabarcoding, ecological filters, metaweb.

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# 1. Introduction

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Soil biodiversity encompasses a complex network of interactions among functionally and trophically diverse organisms, playing a vital role in supporting ecosystem functions and services such as carbon sequestration, organic matter decomposition, and enhancing plant performance and resistance to pests and stress (Bardgett and van der Putten, 2014; Delgado-Baquerizo et al., 2020). Over the past decades, our understanding of the spatial distribution of soil taxa has improved, particularly for specific groups like bacteria (Fierer and Jackson, 2006; Delgado-Baquerizo et al., 2018) and fungi (Tedersoo et al., 2014; Davison et al., 2015). However, our current knowledge primarily relies on a few specific taxa, while the broader ecological processes shaping the whole soil communities and their organisation into interaction networks remain poorly understood. This knowledge gap stem from challenges associated with capturing the intricate spatial heterogeneity inherent to the soil environment (Ettema and Wardle, 2002), and the methodological complexities of sampling and analysing the vast diversity of the soil biota (Geisen et al., 2019). These limitations have hindered the application of existing theoretical frameworks on soil biota (Thakur et al., 2020). Advancing research in this direction promises to unravel the ecological processes that structure soil biodiversity and to predict the impacts of global change on terrestrial ecosystems (Soliveres et al., 2016; Eisenhauer et al., 2021).

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Gaining insights into the organization of soil communities necessitates studying spatial, environmental, and biotic drivers, and this poses challenges (Decaëns, 2010; Münkemüller et al., 2020; Eisenhauer et al., 2022). First, capturing the full complexity of soil systems requires accounting for their inherent spatial heterogeneity from landscape to local scales (Ettema and Wardle, 2002; Thakur et al., 2020). At the landscape scale, dominant habitat types, such as grasslands or forests, are crucial for soil taxonomic and functional composition (Fiore-Donno et al., 2020; Arribas et al., 2021; Sepp et al., 2021). Consequently, the heterogeneity between habitats within a landscape can give rise to mosaic patterns of soil communities. Moreover, the spatial connectivity of habitats and the varying dispersal capacities of soil organisms can influence the local composition and structure of soil communities (Arribas et al., 2021). But even at the local scale of a few meters, variable abiotic conditions, including microclimate and soil physico-chemical properties, along with stochastic processes, can lead to pronounced differences in soil communities (Ramirez et al., 2014; O'Brien et al., 2016; Zinger et al., 2019). The structure of the vegetation within an habitat or its taxonomy or functional composition can
also affect the abundance and diversity of different soil taxa or trophic groups (Ganault et al., 2021; Noguerales et al., 2021; Calderón-Sanou et al., 2022). Effectively disentangling the effects of spatial, environmental and biotic processes necessitates a sampling design that encompasses multiple spatial scales, ranging from the heterogeneity between habitats to small-scale soil variations.

Second, comprehensive sampling and cross-taxa analysis are required to capture the high diversity in soils. Recent advancements in monitoring techniques, such as environmental DNA metabarcoding (eDNA) (Taberlet et al., 2012), offer cost-effective means to obtain extensive data on soil biota at large spatial extent (Taberlet et al., 2018; Geisen et al., 2019). To more concisely present the different taxa they can be classified into trophic groups that share similar resources or prey (Eltonian niche; Elton 1927), e.g. fungivorous nematodes or photoautotrophic bacteria (Louca et al., 2016; Potapov et al., 2022). To better comprehend the complex interactions, we can construct multitrophic networks that consider multiple trophic groups (nodes) and their linkages across trophic levels (links) (Thompson et al., 2012; Gravel et al., 2019). However, constructing multitrophic networks from large eDNA datasets is not without challenges. It entails handling and classifying thousands of sequences into trophic groups, and subsequently linking these groups based on known interactions. Obstacles include the resolution limitations of DNA markers for species-level assignment, the incompleteness of sequence reference databases, inconsistent terminology in trophic ecology across soil taxa (Hedde et al., 2022), and the sparse trophic information available for soil organisms in literature and repositories (but see Potapov et al., 2022). Nevertheless, the application of artificial intelligence tools now offers the potential for automating classification tasks (Compson et al., 2018; Le Guillarme and Thuiller, 2023).

Third, if we are to understand soil biodiversity, not as a group of independent taxa, but rather as multitrophic networks, we need the appropriate methods and frameworks to investigate how ecological processes act on soil trophic groups and their interactions. The mono-trophic community assembly framework (Keddy, 1992; Thuiller et al., 2013) can be extended to multitrophic networks using the metaweb concept (Dunne, 2006). At the regional scale, the metaweb represents the potential ecological network encompassing all species (or trophic groups) from the regional pool and their potential interactions (Fig. 1c). At the local scale, realised local networks emerge as subsets of the metaweb due to ecological filtering of species (Bauer et al., 2022). This filtering process can be influenced by spatial filters like landscape
barriers, limiting dispersal (Peay et al., 2010; Gobbi et al., 2022; Blasbichler et al., 2023), abiotic factors filtering out species lacking physiological adaptations (Maaß et al., 2015; Glassman et al., 2017; Steinwandter and Seeber, 2023), and biotic interactions such as mutualisms, herbivory and predation (Vályi et al., 2016). By using the metaweb concept, we can evaluate the effects of ecological filters not only on the taxonomic composition of the local community but also on its network structure (i.e. composition of interactions). Important biotic interactions in the soil are via plant-soil feedbacks, where the taxonomic or functional composition of plant communities influences soil multitrophic networks and vice-versa (Kardol and De Long, 2018; Kardol et al., 2018). Moreover, trophic interactions within the network can elucidate its local structure and composition (Thakur and Geisen, 2019). We anticipate that interactions between taxa or groups within the network should result in co-variation across environmental and spatial gradients. For instance, environments characterised by decomposer communities dominated by bacteria (rather than fungi) should promote the dominance of bacteria consumers and related higher-level consumers within the multitrophic network (Moore and de Ruiter, 1991), see e.g. Martinez-Almoyna et al. 2022. Null models can be used to test the significance of such co-variation in the assembly of ecological communities (Caruso et al., 2022).

Here, we analysed the spatial variation in soil multitrophic network structure, encompassing trophic group and interaction composition. We used data from all over the French Alps including lowland forests and high-elevation grassland ecosystems. To represent their spatial heterogeneity, we employed a stratified and nested sampling design, consisting of 24 elevation gradients with multiple plots at varying elevations (Fig. 1a). We measured soil biodiversity in 418 soil samples, using environmental DNA (eDNA). Using an ontology-based data integration pipeline, combining multiple databases with existing knowledge on the trophic habits of soil organisms (Le Guillarme and Thuiller, 2023; Le Guillarme et al., 2023), we constructed a metaweb comprising 55 soil trophic groups with 383 potential trophic interactions (Fig. 1b). Finally, we applied the metaweb framework to characterise the importance of ecological filters on soil multitrophic networks (Fig. 1c), using a combination of network diversity indices based on Hill numbers (Ohlmann et al., 2019) and associated null models. We quantified local variations in soil multitrophic network structure both between and within habitats, addressing three specific questions: (Q1) Do different habitats differ in soil multitrophic networks? (Q2) Do trophic interactions contribute to variations in group abundances between habitats?, and,
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(Q3) How do abiotic conditions, spatial factors, and plant communities shape soil multitrophic networks within habitats?

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## 2. Material and Methods

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### 2.1. Study site

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The data come from the Orchamp long-term biodiversity observatory (www.orchamp.osug.fr, Appendix 1). It encompasses 24 elevation gradients (at the time of this study) across the French Alps sampled between 2016 and 2020 (Fig. 1a). These gradients represent diverse climatic, vegetation and soil conditions. Each gradient consists of four to nine 30 x 30 m permanent plots, spaced approximately 200 m apart in elevation. The elevation range covered by each gradient varies between 585 and 1380 m (Fig. S1). We worked with 127 plots, ranging in total from 280 m and 2780 m above sea level.

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Habitat type (forest, grassland, shrubland) was determined on-site. Most gradients covered all three types of habitats, with some exceptions where only one or two types were present (e.g. grassland and shrubland in high-elevation mountains). Plant species abundances were estimated at the peak of the vegetation along a linear transect traversing each plot, using the pin-point method (Jonasson, 1988). Soil sampling was performed at the end of the summer season in three subplots (2 x 2 m) located along a separate 4 m wide transect within each plot. In each subplot, we collected ten soil cores, 5 cm in diameter and ~15 cm in depth (excluding litter), pooled together to a biological sample. Some elevation gradients (37 plots) were sampled twice (i.e. in different years).

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### 2.2. Soil biota sampling

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Soil biodiversity was measured using environmental DNA (eDNA) metabarcoding from the soil samples. In the field, we extracted eDNA from a 15 g aliquot following Taberlet *et al.* (2012, 2018). The remaining soil was sieved at 2 mm and used to measure soil physico-chemical properties (Table 1).

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We employed six DNA markers, including two universal markers (Euka02 for eukaryote, Bact01 for bacteria) and four clade-specific markers (Fung02 for fungi, Inse01 for insect, Olig01 for oligochaeta, and Coll02 for collembola) (Taberlet et al., 2018). Details regarding the markers and molecular analyses are detailed in Appendix 2. A standardised bioinformatic pipeline was applied to curate the retrieved sequences from contaminants and errors (Calderón-Sanou et al., 2020), using the OBITools software (Boyer et al., 2016) and the 'metabaR' R package (R Core Team, 2020; Zinger et al., 2021). Sequences were clustered into Molecular

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Operational Taxonomic Unit (MOTU) with SUMACLUST (Mercier et al., 2013) using a clustering threshold of 97% for Euka02, Fung01 and Bacte01, 85% for Coll01, 88% for Olig01 and 95% for Inse01, following the recommendations in Bonin *et al.* (2023). Taxonomic annotations were performed on MOTUs with the SILVAngs pipeline (Quast et al., 2013) (SILVA version 138.1) for ribosomal universal markers. For clade specific markers, we used the ecotag program from the OBITools, and marker-specific databases built with the ecoPCR program (Ficetola et al., 2010), from the EMBL database version 136. Metazoan taxa not registered in the European region were removed from the data (<1% of total reads) using the GBIFfilter tool (https://github.com/nleguillarme/gbif-filter-python).

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## 2.3. Multitrophic network construction

**Trophic groups and classes:** We assigned the retrieved taxonomically annotated MOTUs to 10 trophic classes including autotrophs, decomposers, detritivores, phytophages or phytoparasites, plant mutualists, bacterivores, fungivores, omnivores, predators and zooparasites. We then refined those trophic classes by subdividing them into 55 trophic groups in total. These were defined by separating phylogenetic distant groups that could have a different set of prey/predators (e.g. bacterivorous mites vs. bacterivorous nematodes) and groups differing in their resources acquisition strategy (e.g. different types of mycorrhiza and saprotrophs). The taxonomic rank used to delimitate phylogenetic distant groups comprised Bacteria, Fungi, Protista, the different phyla within Metazoa, and the different classes or orders within Arthropoda and Annelida. (Appendix 2).

To facilitate the assignment of the taxonomically annotated MOTUs to trophic classes, we first built a knowledge graph integrating information about the trophic interactions and feeding habits of soil-associated consumers from multiple data sources using the ontology-based data integration pipeline described in Le Guillarme & Thuiller (2023). This trophic knowledge graph uses the NCBITaxon ontology and the Soil Food Web Ontology (SFWO) (Le Guillarme et al., 2023) as global schemas for reconciling taxonomic and semantic heterogeneities between the data sources. It provides a unified access to multisource trophic information across taxonomic groups and trophic levels. The list of data sources and details on the assignment criteria can be found in Appendix 2.

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**Building the metaweb:** A metaweb is a potential network containing all trophic groups and their potential interactions (Dunne, 2006). We added three basal resources to construct our metaweb: light (or chemical energy), plants, and organic matter. Trophic links were then added
249 between groups based on their main feeding preferences. Therefore, plant mutualists and
250 phytoparasites were linked with the plant resource, detritivores and decomposers were linked
251 with the organic matter resource and autotrophs were associated with the light resource.
252 Bacterivores were linked with all trophic groups containing bacteria, and fungivores with all
253 trophic groups containing fungi. The trophic interactions of omnivores, predators and
254 zooparasites, were determined through a literature review of dietary preferences of taxa
255 representing over 90% of the read abundance of the group (Appendix 2).
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257 **Local networks:** From the metaweb, the composition and structure of the local soil multitrophic
258 networks were deduced based on locally present trophic groups, i.e. local networks are
259 subgraphs of the metaweb. This assumes that co-occurring groups interact locally as defined
260 in the metaweb. We approximated local relative abundance of each trophic group using a
261 double-transformation, where first, total read counts were transformed into proportions within
262 the sample, and second, the resulting proportions were standardised by the largest proportion
263 observed across samples for each trophic group ('eDNA index' in Kelly et al. 2019). The
264 relative abundance of each trophic group varied thus between 0 (absent) to 1 (largest observed
265 proportion), allowing to have a comparable measure across trophic groups. Abundance values
266 of the basal resources (light, plant, organic matter) were set to non-null (0.001) to not affect
267 diversity measurements. For trophic class abundance estimates, we summed group relative
268 abundances.
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270 **2.4. Network analyses**

271 With the aim of analysing spatial variation in soil multitrophic network structure, we used beta
272 diversity metrics that account for compositional changes in the network across samples. We
273 used a set of network diversity indices based on the generalisation of Hill numbers proposed
274 by Ohlmann et al. (2019). These indices allow to quantify diversity in trophic groups and
275 trophic interactions, varying the weight of the relative abundance of groups and links. Here,
276 we focussed on Shannon diversity (q=1, see Calderón-Sanou et al. 2020) but also replicated
277 the analyses using species richness (q=0) and Simpson diversity (q=2) to assess the extent to
278 which the observed changes are compositional versus structural. Since interactions in the
279 metaweb are binary (either presence or absent), we approximated the abundance of interactions
280 as the product of the relative abundances of the interacting groups. These diversity indices can
281 be further decomposed into alpha, gamma, and β-diversity (βT), and be used to calculate the
282 pairwise dissimilarities for both groups (βpP) and links (βpL). β-diversity (βT) quantifies the
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effective number of different communities, based on shared groups (βTP) or interactions (βTL), ranging from 1 (indicating identical group or interaction abundance distribution) to the total number of communities, when networks do not share any common group or interaction. The diversity metrics were computed using the R package 'econetwork' (Miele et al., 2021).

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To quantify changes in the spatial structure of soil multitrophic networks across habitats, we calculated pairwise dissimilarity measures for both groups (βpP) and interactions (βpL) among all pairs of local networks. Subsequently, we used UMAP, a non-linear dimension reduction algorithm, to visualize the local networks in a two-dimensional space based on their group and interaction dissimilarities (McInnes et al., 2020). Given the strong structural differences observed in local soil multitrophic networks between forests and grasslands (Fig. 2, S3), and the high variability in network composition in shrublands due their intermediate state along the elevation gradient, our analysis focused exclusively on grasslands and forests for further comparison. We compared the relative abundances of trophic groups and classes between forests and grasslands with a Wilcoxon test and used the ‘group-TL-tsne’ network layout from the R package ‘metanetwork’ (Ohlmann et al., 2023) together with edge bundling provided by ‘edgebundle’ R package (Schoch, 2022) to represent the difference network between average networks in forests vs. grasslands.

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We tested the imprint of trophic interactions on community assembly with null models. Firstly, we constructed an average soil multitrophic network for each habitat by using the average relative abundances of trophic groups per habitat. We then calculated the β-diversity (βT) for both group and link interactions between the two habitats. Next, we built the null model. Since group and interaction diversities are strongly correlated, we aimed at keeping group diversity constant in the null model while modifying interaction diversity. This was achieved by permuting the node labels within the metaweb. Consequently, group β-diversity remains unchanged as this diversity index is invariant to label permutation. However, interaction β-diversity was affected since it relies on the product of relative abundances of interacting groups. If dominant interacting groups differ between the two habitats, the observed interaction β-diversity would be higher than expected under a random distribution of group abundances. To evaluate the significance of our findings, we conducted 3000 permutations and compared the observed diversities with the corresponding null distribution.

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Finally, to quantify the relative importance of environmental and spatial distances in explaining network dissimilarity within habitats (i.e. grasslands and forests), we used Generalised Dissimilarity Models (GDM) (Ferrier et al., 2007). We built two GDM per habitat using trophic group turnover (βpP) and interaction turnover (βpL) as response variables, with the R package 'gdm' (Fitzpatrick et al., 2022). For the environmental predictors we selected a set of weakly correlated variables (Pearson's *r* < 0.5, Fig. S2) representing climatic, soil and vegetation conditions that could influence soil organisms (Table 1). The spatial coordinates of the samples and all the selected environmental variables were used as predictors. The environmental distance between samples was directly calculated by the 'gdm' package, but for plant composition, we used our own dissimilarity indices to represent changes in vegetation composition from both a taxonomic and functional point of view (Table 1). Plant taxonomic dissimilarity between plots was calculated using the *beta pair* function from the R package 'betapart' (Baselga et al., 2022). To estimate plant functional dissimilarity we retrieved trait values for species of which cumulative abundances represented at least 90% of the plot coverage. Missing values (<15% of the total data) were estimated using the imputation method offered by the R package 'mice' (van Buuren and Groothuis-Oudshoorn, 2011). Plant functional dissimilarity was estimated using the *beta.fd.multidim* function from the R package 'mFD' (Magneville et al., 2021). In the analysis, soil samples from the same subplot collected in different years (i.e. 95 of the 418 samples) were treated as separate samples as they differed in environmental conditions, except for climate and NDVI that were averaged across a period of 10 years. Their spatial dependency was considered through the spatial coordinates of the plot.

## 3. Results

The metaweb constructed for the French Alps region using the taxa identified in 418 soil samples consisted of 55 trophic groups, three basal resources and a total of 383 potential interactions (Fig. S3). On average, local networks were composed of 41 ± 4 trophic groups (and resources), with 206 ± 37 interactions. All 10 trophic classes were present in all local networks, with a single exception where no group from the fungivore class was detected. The mean dissimilarity between local soil multitrophic networks was primarily influenced by changes in the dominance of trophic groups, rather than by changes in the presence or absence of groups (as indicated by increasing dissimilarity when increasing parameter *q*, which represents the weight of trophic group abundance and interaction, Fig. S4).
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Overall, we found that multitrophic network composition varied across the environmental range covered in this study. The habitat type, particularly when comparing grassland and forest habitats, strongly structured the network composition, both in terms of trophic group and interaction composition (Q1) (Fig. 2, Fig. S5). The influence of habitat type on interaction composition was more pronounced compared to trophic group composition (Table S1). When examining shrubland areas, which represent ongoing shifts from grassland to forest, we found highly variable network structures (Fig. 2). These structures exhibited similarities to both forest and grassland networks, potentially due to the spatial proximity of shrubland sampling sites to forests or grasslands, or the specific stage of succession in the shrubland areas. By contrasting the average networks of forests and grasslands, we identified compositional differences in the abundance of trophic groups and classes between the two habitats (Fig. 3). Forest soil multitrophic networks were enriched in ectomycorrhizal fungi, litter and wood saprotrophic fungi, macro-detritivores like earthworms and diplopods at basal level, fungivores (particularly eudaphic collembola and protura), soil top predators including predatory mites, centipedes, predatory coleoptera and pseudoscorpions, as well as zooparasites. In contrast, grassland soil multitrophic networks had a higher proportion of decomposers and detritivores, particularly soil saprotrophic fungi, enchytraeids and coprophagous coleoptera (e.g. dung beetles), arbuscular mycorrhizal fungi and root endophytes, and most groups of plant phytoparasites and autotrophs. Grasslands also exhibited a higher proportion of bacterivores and omnivores compared to forests, notably bacterivorous nematodes, rotifers and epigeic collembola, at higher trophic levels. We further examined the β-diversity of interactions between habitats and compared it to null expectations derived from randomising trophic group abundances while preserving trophic group diversity and network structure (see methods). The observed β-diversity was significantly higher than expected by chance (Fig. 4), indicating that trophic groups enriched in each habitat were not randomly distributed in the network, but instead formed pairs of trophically linked groups. This suggests that differences in trophic group abundance between habitats (Fig. 3) are influenced by the structure of trophic links within the network (Q2).

The composition of forest and grassland soil multitrophic networks was influenced by distinct ecological filters (Q3). In both habitats, predictors associated with these filters accounted for approximately 10% of the variance in soil multitrophic network dissimilarity (Fig. 5, Fig. S6). In forests, spatial and environmental predictors played a more important role, particularly soil
384 C/N ratio (related to organic matter degradability), pH, and frozen degree days (FDD) as
385 indicators of abiotic stress (Fig. 5a). Dissimilarities in trophic groups and interactions were
386 more pronounced between sites separated by large spatial distances and remained consistent
387 along the gradients of FDD and pH (Fig. 5b). Changes in soil C/N ratio primarily influenced
388 interaction dissimilarity, particularly at higher values of the C/N ratio gradient (>20).

389 In contrast, grassland soil multitrophic networks were predominantly driven by plant
390 taxonomic and functional composition, as well as soil C/N ratio (Fig. 5a). Soil C/N ratio and
391 plant taxonomic/functional composition better explained interaction dissimilarity (Fig. 5c),
392 while plant taxonomic composition was more effective in predicting trophic group
393 dissimilarity. Dissimilarities in trophic groups and interactions remained relatively constant
394 along the plant dissimilarity gradient but were more pronounced in the lower part of the FDD
395 gradient (> 40 cumulative degrees) and the upper part of the C/N ratio gradient (>15).

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4. Discussion
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398

399 Our study employed high-resolution data spanning contrasted habitats and extensive
400 environmental gradients to quantify the variability of soil multitrophic network structure. This
401 approach revealed new insights into how the environment, space and biotic interactions shape
402 soil multitrophic network assembly in the French Alps. We observed significant differences in
403 dominant trophic groups between habitats, that was associated with the structure of the trophic
404 links in the network (i.e. the abundance of trophically linked groups co-vary across habitats).

405 Furthermore, we uncovered notable discrepancies in the relative importance of the ecological
406 filters between forests and grasslands.

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4.1. Habitat filtering in soil multitrophic networks
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409 This study encompassed diverse climatic regions in the French Alps (e.g. alpine, continental,
410 mediterranean) and an elevation range of 2500m, resulting in significant environmental
411 variability within and across habitat. As anticipated, habitat filtering strongly influenced soil
412 multitrophic network structure, especially when comparing forests and grasslands (Crotty et
413 al., 2014; Arribas et al., 2021; Sepp et al., 2021). Differences in abiotic conditions and resource
414 availability between habitats could explain the observed differences in the proportions of
415 trophic groups. Grassland soils exhibit lower acidity, higher P and N availability and a lower
416 C/N ratio (Joimel et al. 2016, Fig. S1), and have a higher below-ground plant biomass, which
417 is more easily decomposed, leading to significant inputs of readily decomposable organic
matter into the soil (Mason and Zanner, 2005; Heděnec et al., 2022). These factors likely
enhanced resource availability for detritus-based trophic groups, resulting in the higher
dominance of copiotroph decomposers (e.g. saprophytic fungi and bacteria), micro- or meso-
detritivores (i.e. Enchytraeidae) and microbivores in grasslands. In contrast, plant detritus in
forests, which is more challenging to decompose, serves as a direct resource for litter and wood
saprotrophic fungi and provide a habitat for macro-detritivores, which feed on plant litter and
associated fungi (David and Handa, 2010; Zuo et al., 2014). Light availability in grasslands
may favour the presence of photoautotrophic bacteria that use light as their primary resource,
and facilitate interactions, such as arbuscular mycorrhiza (Konvalinková and Jansa, 2016).
Moreover, grasslands had a higher proportion of phytoparasites, likely due to the higher
biomass of fine roots in this habitat (Jackson et al., 1997; Heděnec et al., 2022), which are more
easy to colonise by symbionts, while forests had a higher proportion of zooparasites, benefiting
from a greater abundance of potential hosts, predominantly arthropods, molluscs or
earthworms, for the taxa belonging to our zooparasite groups. These findings align with our
results showing that trophic interactions within the soil multitrophic network contributed to
explain network dissimilarities between habitats (Fig. 4) and are consistent with previous
studies examining changes in the trophic composition of protists (de Araujo et al., 2018; Fiore-
Donno et al., 2020) and nematodes (Zhao and Neher, 2014) across habitats. The distinct
identities of soil antagonists in the soil multitrophic networks from grassland to forests warrant
further investigation as they could provide insights for land management strategies in mountain
ecosystems (Wall et al., 2015).

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4.2. Impact of ecological filters within habitats

Our study design aimed to investigate the impact of ecological processes on soil multitrophic
network structure across elevation gradients and diverse environmental conditions. It is
important to note that the studied mountain system included various forest types (e.g. low-
elevation deciduous, high-elevation coniferous) and grassland types (e.g. subalpine, alpine).
To capture this ecological heterogeneity within habitats, our research incorporated variations
in abiotic conditions, plant composition, and geographic distance, offering a comprehensive
analysis of the complex environmental variation characterising our mountainous systems.
Although the GDM explained a relatively low percentage of variance when quantifying
changes in the structure of local networks (Fig. 5a), it is important to note that typical deviance
explained for models of compositional dissimilarity ranges from 20% to 50%, and this
percentage can decrease with an increasing number of sites, which was relatively high in our
452

study (Ferrier et al., 2007; Mokany et al., 2022). Furthermore, the resolution of our soil
multitrophic networks using trophic groups instead of species led to low average dissimilarities
(0.27 and 0.48 in forests, and 0.30 and 0.53 in grasslands for groups and interactions
respectively), reducing model predictability (Mokany et al., 2022). Importantly, we found that
trophic link structure significantly influenced changes in trophic groups abundance
composition (Fig. 4). However, current models cannot account for this factor, and
incorporating network structure in diversity models may enhance predictability but requires
further implementation (Poggiato et al., 2022).

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Spatial processes, environmental filtering, and changes in plant community composition explained variation in soil multitrophic networks within habitats. In forests, spatial distance played a significant role, explaining a substantial portion of trophic group (31%) and interaction dissimilarity (58%), even when accounting for confounding environmental changes. Limited dispersal opportunities in closed forest habitats, particularly for organisms dispersed through wind (anemochory), might influence soil multitrophic network structure in mountain forests. Conversely, open grassland habitats subject to strong winds (Tackenberg and Stöcklin, 2008) and high levels of herbivory, both domestic and wild, can facilitate the dispersal of soil organisms. Furthermore, mountain massifs and large distances between forested areas (up to 250 km) can impact meso- and macrofauna, susceptible to ecological drift due to their limited dispersal capacity or smaller population sizes (Arribas et al., 2021; Kang et al., 2022). This is in line with our results showing that network dissimilarity increased with greater spatial distances (Fig. 5b). Yet, the spatial distance used in the model may not fully capture the complexity of the spatial structure, and further investigation using experimental setups to clarify spatial connectivity and dispersal limitation effects is required.

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Environmental filtering played a significant role in both habitats, primarily influenced by soil
edaphic properties. In the French Alps, the soil C/N ratio emerged as the most influential abiotic
factor in shaping soil multitrophic network composition. It reflects organic matter
decomposability and resource availability, as discussed above, impacting decomposer groups,
their consumers, and the entire feeding channel. Conversely, variables related to total energy
or resources, such as the NDVI, GDD and SOM, had a smaller impact on soil multitrophic
network dissimilarity. While this contrasts with previous studies emphasizing the role of these
variables in diversity patterns of soil groups in mountains (Zinger et al., 2011; Calderón-Sanou
et al., 2022; Steinwandter and Seeber, 2023), it highlights the emergence of different ecological

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drivers at different levels of biodiversity organization. In our study, resource quality, particularly litter quality, seemed to dominate network composition rather than quantity. Additionally, environmental factors associated with harsh conditions, such as pH and frozen degree days, known drivers of soil taxonomic diversity and composition (Fierer and Jackson, 2006; Decaëns, 2010; Gobbi et al., 2022), also played a major role in shaping soil multitrophic networks.

Plant-soil interactions likely play a crucial role in shaping grasslands soil multitrophic networks in the French Alps. Although we did not directly measure these interactions, the taxonomic and/or functional composition of plant communities had a major impact on soil multitrophic network structure. Mountain grasslands exhibit high variability in plant species richness and the presence of functionally important taxa such as Fabaceae, Brassicaceae or Poaceae across different elevations and massifs. Changes in plant diversity can influence the composition of soil multitrophic networks (Eisenhauer et al., 2013), and altering plant identity can affect different soil taxa (Zinger et al., 2011). Interestingly, taxonomic dissimilarity had a larger effect than plant functional dissimilarity, possibly because above-ground traits used in our study may have a lesser role in plant-soil interactions compared to root traits (Kardol and De Long, 2018; Wilschut et al., 2019). Surprisingly, in forests, plant composition had a minor impact on soil multitrophic network structure. Soil variables like pH and C/N ratio may better capture variations in forest type (e.g. broadleaf vs coniferous forests), directly influencing soil multitrophic networks.

## 5. Conclusions

This study enhances our understanding of how ecological processes operate in below-ground communities and networks, thereby extending our knowledge of ecological communities as a whole. Furthermore, our research offers valuable insights into the impacts of global change on soil biodiversity and terrestrial ecosystem functioning, particularly in vulnerable mountainous systems experiencing climate change and land-use modifications. Land management decisions, such as forestry, agriculture, and tourism, can alter local land-use and habitats, necessitating consideration of their effects on soil multitrophic networks and ecosystem functioning. However, further investigations are needed to explore the underlying mechanisms driving spatial changes in network structure. Additionally, studying the recovery and resilience of soil multitrophic networks to land-use changes is essential, considering the dynamic nature of ecosystems in the face of a constantly changing climate. These future studies will enhance our
520 understanding of soil multitrophic networks and their responses to environmental changes,

521 aiding informed land management decisions and sustainable practices.

522

523
<table><caption>524 Table 1. Environmental variables used in this study.</caption><thead><tr><th></th><th>Environmental variable</th><th>Abbr.</th><th>Description</th><th>Temporal range</th><th>Resolution</th><th>Source</th></tr></thead><tbody><tr><td rowspan="3">Climate</td><td>Growing degree days</td><td>GDD</td><td>Annual sum of degree-days over a 0°C soil temperature</td><td rowspan="3">Average across 2009-2019</td><td rowspan="3">Plot</td><td rowspan="3">SAFRAN- SURFEX/ISBA-Crocus-MEPRA reanalysis (Vernay et al., 2021)</td></tr><tr><td>Annual precipitation</td><td>AP</td><td>The sum of all the monthly precipitation estimates</td></tr><tr><td>Frost degree days</td><td>FDD</td><td>Annual sum of average daily degrees below zero in soil temperature. Summarizes the duration and intensity of ground freezing events.</td></tr><tr><td rowspan="3">Soil</td><td>Soil pH</td><td>pH</td><td>Top-soil wahter pH</td><td rowspan="3">Same year than the sample was collected (2016-2020)</td><td>Sample</td><td rowspan="3">Measured in the lab</td></tr><tr><td>Soil Organic Matter</td><td>SOM</td><td>Soil organic matter content</td><td>Sample</td></tr><tr><td>Soil C/N</td><td>C/N</td><td>Soil carbon to nitrogen ration in organic matter</td><td>Sample</td></tr><tr><td rowspan="3">Vegetation</td><td>Plant taxonomic dissimilarity</td><td></td><td>Jaccard pairwise dissimilarity index between subplots.</td><td rowspan="3">Same year than the sample was collected (2016-2020)</td><td>Plot</td><td>Botanical surveys</td></tr><tr><td>Plant functional dissimilarity</td><td></td><td>Jaccard-like functional index. Traits: specific leaf area (SLA), leaf C/N, root depth (ordered variable), vegetative plant height, and woodyness index (categorical variable ranging from herbaceous to woody).</td><td>Plot</td><td>Botanical surveys and our own trait measurement values for species (median values across individuals). Root depth was, extracted from (Landolt et al., 2010).</td></tr><tr><td>Normalised Difference Vegetation Index</td><td>NDVI</td><td>Estimated from the surface spectral reflectance at a resolution of 250 m. Raw NDVI times series were pre-processed following (Choler, 2015), and we kept the mean yearly sum of NDVI greater than 0.2</td><td>Average across 2010-2020</td><td>Plot</td><td>MODIS (Moderate Resolution Imaging Spectroradiometer), available online: https://lpdaac.usgs.gov/produ cts/mod09q1v006/.</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Study design to investigate soil multitrophic network structure in the French Alps (a) Study sites across the French Alps were selected using a stratified sampling design across elevations and nested design with subplots within gradients. The collected 418 soil samples were distributed across nested spatial distances ranging from 8 m to 250 km. (b) eDNA and bioinformatic analysis were used to assign detected organisms to taxonomic and functional information. (c) The metaweb framework was used to quantify spatial changes in the structure of soil multitrophic networks across the study area and examine their associations with environmental and spatial factors.</figcaption></figure>
<figure><img src="image_4.png" /><figcaption>**Figure 2.** 2-D plane representing the dissimilarity between soil multitrophic networks based on the composition of trophic groups and trophic interactions with a colour scale representing the habitat type. Each dot is a local network from a single sub-plot. Dots that are close to each other in the 2D plane have similar trophic group compositions or interactions.</figcaption></figure>

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<figure><img src="image_5.png" /><figcaption>Figure 3. Differences in trophic group and class abundances between soil multitrophic networks in forests and grasslands. Colours indicate the significance of Wilcoxon tests calculated for the difference in abundances of each group between habitats (p<0.05) while node sizes are proportional to significant abundance differences. A Wilcoxon test was also applied to compare differences in trophic class abundances (dashed circles) between habitats. Abundance of trophic classes correspond to the sum of trophic groups relative abundances.</figcaption></figure>

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<figure><img src="image_6.png" /><figcaption>Figure 4. Null distribution of the β-diversity of interactions (βTL) at fixed trophic group diversity.</figcaption></figure>

The values of the histogram show the βTL between the average soil multitrophic networks of forest and grassland computed using 3000 permutations of trophic group abundances at fixed network structure, leading to various βTL values while keeping trophic group β-diversity constant. The vertical line shows the observed βTL across habitats. The value of βTL varies from one when the average network of forest and grassland have identical interaction abundances distribution, to two, when the networks do not share any common group or interaction.
<figure><img src="image_7.png" /><figcaption>**Figure 5.** Main predictors of soil multitrophic network dissimilarity within forest and grassland habitats. The relative importance of each predictor was estimated using Generalised Dissimilarity Models (a). It is measured as the sum of the coefficients of the three I-splines of the focus predictor and represents the total amount of change along the predictor gradient. Partial curves for three of the most important predictors of network dissimilarity in forest (b) and grassland (c). Each panel shows the network dissimilarity as a function of an environmental predictor when holding all other variables to their mean. The slope at any point on the curve indicates the rate of network dissimilarity at that position along the environmental gradient (x-axis), while the total height reached by the function indicates the total amount of network dissimilarity due to that environmental predictor. SOM: Soil Organic Matter, FDD: Frost Degree Days, GDD: Growing Degree Days, AP: Annual Precipitation, NDVI: Normalised Difference Vegetation Index.</figcaption></figure>

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