Co‐occurrence is not evidence of ecological interactions

TLDR

Co‑occurrence data have long been used to infer community assembly, with null models, networks, and joint species distribution models developed to detect ecological interactions, yet the extent to which such associations reflect true interactions remains uncertain. This study critically evaluates whether co‑occurrence patterns can serve as reliable proxies for ecological interactions. Using probability, sampling, food‑web, and coexistence theory, the authors argue that spatial associations are poor proxies for interactions and outline how co‑occurrence data can still be interpreted and leveraged.

Abstract

Abstract There is a rich amount of information in co‐occurrence (presence–absence) data that could be used to understand community assembly. This proposition first envisioned by Forbes (1907) and then Diamond (1975) prompted the development of numerous modelling approaches (e.g. null model analysis, co‐occurrence networks and, more recently, joint species distribution models). Both theory and experimental evidence support the idea that ecological interactions may affect co‐occurrence, but it remains unclear to what extent the signal of interaction can be captured in observational data. It is now time to step back from the statistical developments and critically assess whether co‐occurrence data are really a proxy for ecological interactions. In this paper, we present a series of arguments based on probability, sampling, food web and coexistence theories supporting that significant spatial associations between species (or lack thereof) is a poor proxy for ecological interactions. We discuss appropriate interpretations of co‐occurrence, along with potential avenues to extract as much information as possible from such data.