Much effort has been devoted to better understanding the effects of environment and biodiversity on ecosystem functioning. However, few studies have moved beyond measuring biodiversity as species richness of a single group and/or focusing on a single ecosystem function. While there is a growing recognition that along environmental gradients, the compositional turnover of multiple trophic groups influences not only productivity but multiple ecosystem functions, we do not know yet which components of multi‐trophic β‐diversity influence which ecosystem functions. Here, we captured the biodiversity found in soils using environmental DNA to study total soil multi‐trophic β‐diversity (between all taxa regardless of their trophic group association), horizontal β‐diversities (β‐diversities within trophic groups) and vertical β‐diversity (β‐diversity across trophic groups) along a 1,000 m elevational gradient in the French Alps. Using path analyses, we quantified how these β‐diversity components mediate the effects of environmental turnover on the turnover of multiple ecosystem functions (i.e. productivity, N‐cycling, N‐leaching) and overall multifunctionality. While we found a strong direct effect of soil properties on the turnover of multiple ecosystem functions, we also found an indirect effect of climate and soil properties through multi‐trophic β‐diversity. More specifically, only total multi‐trophic β‐diversity and the horizontal β‐diversity of saprophytic fungi were strongly related to the turnover of multifunctionality and, to a lower extent, the turnover of productivity and N‐cycling. Our results suggest that decomposition processes and resulting nutrient availability are key to understand how ecosystem functions change along soil properties and climatic gradients in alpine ecosystems. By demonstrating how saprophytic fungi and their associated trophic groups can offset the direct responses of multiple ecosystem functions to environmental change, our study highlights the paramount importance of multi‐trophic diversity for better understanding ecosystem multifunctionality in a changing world. A free Plain Language Summary can be found within the Supporting Information of this article.

Aim: Despite recent calls for integrating interaction networks into the study of large-scale biodiversity patterns, we still lack a basic understanding of the functional characteristics of large interaction networks and how they are structured across environments. Here, building on recent advances in network science around the Eltonian niche concept, we aim to characterize the trophic groups in a large food web, and understand how these trophic groups vary across space. Location: Europe and Anatolia.Taxon: Tetrapods (1,136 species). Methods:We combined an expert-based metaweb of all European tetrapods with their spatial distributions and biological traits. To understand the functional structure of the metaweb, we first used a stochastic block model to group species with similar Eltonian niches, and then analysed these groups with species' functional traits and network metrics. We then combined these groups with species distributions to understand how trophic diversity varies across space, in function of the environment, and between the European ecoregions. Results:We summarized the 1,136 interacting species within the metaweb into 46 meaningful trophic groups of species with a similar role in the metaweb. Specific aspects of the ecology of species, such as their activity time, nesting habitat and diet explained these trophic groups. Across space, trophic diversity was driven by both biotic and abiotic factors (species richness, climate and primary productivity), and the representation of trophic groups differed among European ecoregions. Main conclusions:We have characterized the Eltonian niche of species in a large food web, both in terms of species interactions and functional traits, and then used this to understand the spatial variation of food webs at a functional level, thus bringing together network science, functional ecology and biogeography. Our results highlight the need to integrate multiple aspects of species ecology in global change research.Further, our approach is strongly relevant for conservation biology as it could help predict the impact of species translocations on trophic diversity.

While soil ecosystems undergo important modifications due to global change, the effect of soil properties on plant distributions is still poorly understood. Plant growth is not only controlled by soil physico-chemistry but also by microbial activities through the decomposition of organic matter and the recycling of nutrients essential for plants. A growing body of evidence also suggests that plant functional traits modulate species' response to environmental gradients. However, no study has yet contrasted the importance of soil physico-chemistry, microbial activities and climate on plant species distributions, while accounting for how plant functional traits can influence speciesspecific responses. Using hierarchical effects in a multi-species distribution model, we investigate how four functional traits related to resource acquisition (plant height, leaf carbon to nitrogen ratio, leaf dry matter content and specific leaf area) modulate the response of 44 plant species to climatic variables, soil physico-chemical properties and microbial decomposition activity (i.e. exoenzymatic activities) in the French Alps. Our hierarchical trait-based model allowed to predict well 41 species according to the TSS statistic. In addition to climate, the combination of soil C/N, as a measure of organic matter quality, and exoenzymatic activity, as a measure of microbial decomposition activity, strongly improved predictions of plant distributions. Plant traits played an important role. In particular, species with conservative traits performed better under limiting nutrient conditions but were outcompeted by exploitative plants in more favorable environments. We demonstrate tight associations between microbial decomposition activity, plant functional traits associated to different resource acquisition strategies and plant distributions. This highlights the importance of plant-soil linkages for mountain plant distributions. These results are crucial for biodiversity modelling in a world where both climatic and soil systems are undergoing profound and rapid transformations.

Aim Although soil biodiversity is extremely rich and spatially variable, both in terms of species and trophic groups, we still know little about its main drivers. Here, we contrast four long‐standing hypotheses to explain the spatial variation of soil multi‐trophic diversity: energy, physiological tolerance, habitat heterogeneity and resource heterogeneity. Location French Alps. Methods We built on a large‐scale observatory across the French Alps (Orchamp) made of seventeen elevational gradients (~90 plots) ranging from low to very high altitude (280–3,160 m), and encompassing large variations in climate, vegetation and pedological conditions. Biodiversity measurements of 36 soil trophic groups were obtained through environmental DNA metabarcoding. Using a machine learning approach, we assessed (1) the relative importance of predictors linked to different ecological hypotheses in explaining overall multi‐trophic soil biodiversity and (2) the consistency of the response curves across trophic groups. Results We showed that predictors associated with the four hypotheses had a statistically significant influence on soil multi‐trophic diversity, with the strongest support for the energy and physiological tolerance hypotheses. Physiological tolerance explained spatial variation in soil diversity consistently across trophic groups, and was an especially strong predictor for bacteria, protists and microfauna. The effect of energy was more group‐specific, with energy input through soil organic matter strongly affecting groups related to the detritus channel. Habitat and resource heterogeneity had overall weaker and more specific impacts on biodiversity with habitat heterogeneity affecting mostly autotrophs, and resource heterogeneity affecting bacterivores, phytophagous insects, enchytraeids and saprotrophic fungi. Main Conclusions Despite the variability of responses to the environmental drivers found across soil trophic groups, major commonalities on the ecological processes structuring soil biodiversity emerged. We conclude that among the major ecological hypotheses traditionally applied to aboveground organisms, some are particularly relevant to predict the spatial variation in soil biodiversity across the major soil trophic groups.

In recent years, simulation methods such as approximate Bayesian computation have extensively been used to infer parameters of population genetic models where the likelihood is intractable. We describe an alternative approach, summary likelihood, that provides a likelihood-based analysis of the information retained in the summary statistics whose distribution is simulated. We provide an automated implementation as a standard R package, Infusion, and we test the method, in particular for a scenario of inference of population-size change from genetic data. We show that the method provides confidence intervals with controlled coverage independently of a prior distribution on parameters, in contrast to approximate Bayesian computation. We expect the method to be applicable for at least six-parameter models and discuss possible modifications for higher-dimensional inference problems.

Aim Plant–soil interactions can be major driving forces of community responses to environmental changes in terrestrial ecosystems. These interactions can leave signals in aboveground plant functional traits and belowground microbial activities and these signals can manifest in observed covariations. However, we know little about how these plant–soil linkages vary in response to environmental conditions at biogeographic scales for which experiments are impossible. Here, we investigate patterns of direct and indirect linkages between plant functional traits, soil microbial activities and environmental conditions in mountain grasslands along elevational gradients. Location The French Alps. Taxon Vascular plants and soil microbiota. Methods We analysed observational grassland data sampled along 14 elevational gradients across the entire French Alps (between 1500 and 2800 m of elevation). Using Graphical Lasso, we inferred a partial correlation network to tease apart direct and indirect plant–soil linkages without defining the direction of interactions a priori. Results We found tight spatial associations of plant traits with microbial activities, climate driving the former and soil properties the latter. In these plant–soil linkages, the dominance of specific plant traits was more important than their diversity. We then showed that in sites with conservative plant traits and reduced organic matter quality, soil microbes invested strongly in nutrient acquisition. Main conclusions By investigating plant–soil linkages along elevational gradients in the French Alps, we showed that plant functional traits and belowground microbial activity are tightly linked and how they depend on environmental conditions. Overall, we demonstrated how soil functioning can be integrated in studies of ecosystem shifts under environmental change at large spatial scales.

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Although soil ecology has benefited from recent advances in describing soil organism trophic traits, large scale reconstruction of soil food webs is still impeded by (1) the dissemination of most data about trophic interactions and diets into distributed, heterogeneous repositories, (2) no well-established terminology for describing feeding preferences, processes, and resource types, and (3) much heterogeneity in the classification of different soil groups, or absence of such classifications. Soil trophic ecology could therefore benefit from standardisation efforts. Here, we propose the Soil Food Web Ontology as a new formal framework for representing knowledge on trophic ecology of soil organisms. This ontology captures the semantics of trophic concepts, including consumer-resource interactions, feeding preferences and processes, and provides a formalisation of trophic group definitions. The ontology can be used to add semantic annotations to trophic data, thus facilitating the integration of heterogeneous datasets. It also provides lexical resources that can support the development of information extraction tools to facilitate literature-based datasets creation. Finally, it enables automatic and consistent classification of soil organisms based on their trophic relationships. We argue that, by harmonising the terminology and underlying concepts of soil trophic ecology, our ontology allows for better use of available information on the feeding habits of soil organisms and sounder classifications, thus facilitating the reconstruction of soil food webs and making food web research more accessible, reusable and reproducible.