Getting More by Asking for Less: Linking Species Interactions to Species Co-Distributions in Metacommunities

Abstract

One of the more difficult challenges in community ecology is inferring species interactions on the basis of patterns in the spatial distribution of organisms. At its core, the problem is that distributional patterns reflect the `realized niche', the net result of a complex interplay of processes involving dispersal, environmental, and interaction effects. Disentangling these effects can be difficult on at least two distinct levels. From a statistical point of view, splitting a population's variation into contributions from its interaction partners, abiotic environment and spatial proximity requires `natural experiments' where all three factors somehow vary independently from each other. On a more theoretical level, it is not even clear how to meaningfully separate these processes: for instance, species interactions could depend in many ways on the state of the environment, and these two processes may combine in highly non-additive ways. Here we show that these issues arise almost unescapably, even in a simple theoretical setting designed to minimize them. Using a model of metacommunity dynamics where direct species interactions are assumed to be context-independent, we show that inferring these interactions accurately from cross-species correlations is a major challenge under all but the most restrictive assumptions. However, we also find that it is possible to estimate the statistical moments (mean value and variance) of the species interactions distribution much more robustly, even if the precise values cannot be inferred. Consequently, we argue that study of multi-species spatial patterns can still be informative for theoretical approaches that build on statistical distributions of species parameters to predict macroscopic outcomes of community assembly.

Modeling tumors as species-rich ecological communities

“…The GLV system is extended to a network of M communities (the primary tumor and metastatic nodes), where the abundance of phenotype µ on node x, c x µ , depends on its GLV dynamics but also on the dispersal from and towards the rest of the nodes (Fig. 3B, [130]):…”

“…However, measuring the interactions and environmental impacts in vivo is a very difficult task. In this context, recent results show that species distribution patterns (in cancer, how different phenotypes are distributed along the primary tumor and its metastatic sites) can already predict certain properties of the niche differences and the community interactions at play [130]. Applying these results to cancer data could help unravel if phenotypic distributions, which sometimes show metastases that are very similar or different from the original tumor [128,131], can inform about the microenvironment at each metastases.…”

Europe-wide spatial trends in copper and imidacloprid sensitivity of macroinvertebrate assemblages

“…This approach cannot address second-order effects following the primary changes to the species composition or food web structure [30,86,129]. Such net effects of biotic interactions are context-dependent and currently defy accurate determination [6].…”