Unveiling the food webs of tetrapods across Europe through the prism of the Eltonian niche

Abstract

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.

TETRA‐EU 1.0: A species‐level trophic metaweb of European tetrapods

“…For each species, we gathered information on species‐specific trophic links plus a set of 11 trophic items: mushrooms, mosses/lichens, algae, detritus, seeds–nuts–grains, fruits, other plant parts, invertebrates, fish, domestic animals and faeces. These diet items could then be used as basal food items in the network (see Braga et al, 2019; O’Connor et al, 2020).…”

“…This could yield false negatives in our dataset (i.e., interactions that exist in nature but that we characterized as non-existent because the literature fails to document those interactions). Using the approach proposed by Morales-Castilla et al (2015), it is possible to use data on species' ecology (e.g., habitat preferences) and distribution (for example considering the data presented in Maiorano et al 2013) to distinguish potential trophic links (what we reported) from trophic links actually occurring (see Braga et al 2019 andO'Connor et al 2020).…”

“…Wherever possible, we considered literature sources focusing specifically on trophic interactions of species measured or inferred in our study area. This dataset has recently been used to investigate the environmental drivers of local network structure in Europe (e.g., connectance; Braga et al, 2019) and the functional structure of the different trophic groups and their spatial structure (O’Connor et al, 2020). In the Supporting Information, we have added an example of the type of analysis that could be carried out with our dataset and the associated R script (Supporting Information Appendices S1–S3).…”

A Roadmap Toward Predicting Species Interaction Networks (Across Space and Time)

“…Because we almost can, and because we definitely should. A better understanding of species interactions, and the networks they form, would help unify the fields of community, network, and spatial ecology; improve the quantification of the functional relationships between species (Dehling and Stouffer 2018;O'Connor et al 2020); re-evaluate metacommunities in light of network structure (Guzman et al 2019); and enable a new line of research into the biogeography of species interactions (Massol et al 2017;Braga et al 2019) which incorporates a synthesis of both Eltonian and Grinnellian niche (Gravel et al 2019). Further, the ability to reliably predict and forecast species interactions would inform conservation efforts for protecting species, communities, and ecosystems.…”

Clustering Species With Residual Covariance Matrix in Joint Species Distribution Models

“…Choosing the prior knowledge that we want to give to the residual covariance matrix is tricky, but feasible. For instance, in a species-rich foodweb, there are a fair amount of species that share the same preys and predators with others, forming what is usually called trophic groups (O'Connor et al, 2020). If they are known, or inferred with a specific approach like a stochastic block model (Lee and Wilkinson, 2019), the number of trophic groups can be used as a prior to reduce the residual correlation matrix.…”