Asymmetric Biotic Interactions Cannot Be Inferred Without Accounting for Priority Effects

• Published in: Ecology letters Vol. 27; no. 9; pp. e14509 - n/a
• Main Authors: Powell‐Romero, Francisca, Wells, Konstans, Clark, Nicholas J.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 01.09.2024
• Subjects: asymmetric interactions; biotic interactions; Community ecology; Computer applications; Computer Simulation; Ecological effects; Ecosystem; Geographical distribution; Models, Biological; Performance prediction; priority effects; Sampling; Sampling designs; Skewed distributions; Software; species interactions; biotic interactions; priority effects; species interactions; asymmetric interactions; community ecology
• DOI: 10.1111/ele.14509
• ISSN: 1461-023X; 1461-0248

ABSTRACT Understanding biotic interactions is a crucial goal in community ecology and species distribution modelling, and large strides have been made towards improving multivariate computational methods with the aim of quantifying biotic interactions and improving predictions of species occurrence. Yet, while considerable attention has been given to computational approaches and the interpretation of these quantitative tools, the importance of sampling design to reveal these biotic interactions has received little consideration. This study explores the influential role of priority effects, that is, the order of habitat colonisation, in shaping our ability to detect biotic interactions. Using a simple set of simulations, we demonstrate that commonly used cross‐sectional co‐occurrence data alone cannot be used to make reliable inferences on asymmetric biotic interactions, even if they perform well in predicting the occurrence of species. We then show how sampling designs that consider priority effects can recover the asymmetric effects that are lost when priority effects are ignored. Based on these findings, we urge for caution when drawing inferences on biotic interactions from cross‐sectional binary co‐occurrence data, and provide guidance on sampling designs that may provide the necessary data to tackle this longstanding challenge. This study explores the influential role of priority effects, that is, the order of habitat colonisation, in shaping our ability to detect asymmetric biotic interactions. Using a simple set of simulations, we demonstrate that common approaches to detect interactions from cross‐sectional binary co‐occurrence data alone cannot make reliable inferences, even if they perform well in predicting the occurrence of species. Based on these findings, we urge for caution when drawing inferences on biotic interactions from cross‐sectional binary co‐occurrence data, and provide guidance on sampling designs that may provide the necessary data to tackle this longstanding challenge.