Co‐occurrence is not evidence of ecological interactions

• Published in: Ecology letters Vol. 23; no. 7; pp. 1050 - 1063
• Main Authors: Blanchet, F. Guillaume, Cazelles, Kevin, Gravel, Dominique, Jeffers, Elizabeth
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.07.2020
• Subjects: Coexistence; Co‐occurrence analysis; co‐occurrence networks; Diamonds; ecological interactions; Food chains; Food webs; Geographical distribution; Information processing; observational studies; presence–absence data; probability; Statistical analysis; statistical inference; Co-occurrence analysis; statistical inference; ecological interactions; presence-absence data; co-occurrence networks
• DOI: 10.1111/ele.13525
• ISSN: 1461-023X; 1461-0248

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. We present seven arguments highlighting why co‐occurrence data is not, and should never be considered, evidence ecological interaction. These arguments are based on probability, sampling, food web and coexistence theories. We discuss appropriate interpretations of co‐occurrence, along with potential avenues to extract as much information as possible from co‐occurrence data. In addition, we present ideas to better describe, understand and predict ecological interactions.