Bringing Elton and Grinnell together: a quantitative framework to represent the biogeography of ecological interaction networks

Abstract

Biogeography has traditionally focused on the spatial distribution and abundance of species. Both are driven by the way species interact with one another, but only recently community ecologists realized the need to document their spatial and temporal variation. Here, we call for an integrated approach, adopting the view that community structure is best represented as a network of ecological interactions, and show how it translates to biogeography questions. We propose that the ecological niche should encompass the effect of the environment on species distribution (the Grinnellian dimension of the niche) and on the ecological interactions among them (the Eltonian dimension). Starting from this concept, we develop a quantitative theory to explain turnover of interactions in space and time -i.e. a novel approach to interaction distribution modeling. We apply this framework to host-parasite interactions across Europe and find that two aspects of the environment (temperature and precipitation) exert a strong imprint on species co-occurrence, but not on species interactions. Even where species co-occur, interaction proves to be stochastic rather than deterministic, adding to variation in realized network structure. We also find that a large majority of host-parasite pairs are never found together, thus precluding any inferences regarding their probability to interact. This first attempt to explain variation of network structure at large spatial scales opens new perspectives at the interface of species distribution modeling and community ecology.

Spatial analyses of multi‐trophic terrestrial vertebrate assemblages in Europe

“…We defined local food webs by intersecting the metaweb with local community composition (Gravel et al, 2019). In the few cases where a given species was present in a cell but had no available prey or did not share a common habitat type with any of its prey, the species was considered absent in that particular location (i.e., assuming a false positive in the distribution data; Gravel, Massol, Canard, Mouillot, & Mouquet, 2011).…”

Towards a predictive model of species interaction beta diversity

“…; Gravel et al . , ; Poisot et al . 2015b; Weinstein & Graham ,b) to model interactions as probability distributions using a hierarchical Bayesian framework (Wells & O'Hara ; Coblentz et al .…”

“…We build from recent developments Gravel et al 2013Gravel et al , 2016Poisot et al 2015b;Weinstein & Graham 2017a,b) to model interactions as probability distributions using a hierarchical Bayesian framework (Wells & O'Hara 2013;Coblentz et al 2017). This approach differentiates the predictors of interactions from the probability of detecting an interaction, and assesses the variation in emergent network properties using out-of-sample prediction.…”

Introduction: Special issue on species interactions, ecological networks and community dynamics – Untangling the entangled bank using molecular techniques

“…Much of community ecology to date has assumed that each particular interaction is a fixed feature: If two species interact at one point in space, then they are expected to do so wherever they meet. However, recent research forces us to revisit this assumption, and add emphasis on just why interactions vary (Gravel et al, ; Pellissier et al, ; Poisot, Stouffer, & Gravel, ). Two papers in this issue show how molecular tools can reveal where, and under what circumstances, specific interactions occur.…”

“…However, recent research forces us to revisit this assumption, and add emphasis on just why interactions vary (Gravel et al, 2018;Pellissier et al, 2017;Poisot, Stouffer, & Gravel, 2015). Among predators of a different type, Littlefair et al (2019) find an imprint of elevation on the prey community of carnivorous Sarracenia pitcher plants.…”