PER‐SIMPER—A new tool for inferring community assembly processes from taxon occurrences. Issue 3 (2nd December 2018)

• Record Type: Journal Article
• Authors: Gibert, Corentin; Escarguel, Gilles
• Editors: Lyons, Kathleen
• Is Part Of: Global ecology & biogeography. Volume 28:Issue 3(2019)
• Page Start: 374
• Page End: 385
• Publication Date: 2018-12-02
• Subjects: community assembly process -- dispersal -- niche -- null model -- SIMPER analysis; Ecology -- Periodicals; Biogeography -- Periodicals; Biodiversity -- Periodicals; Macroevolution -- Periodicals; 577
• DOI: 10.1111/geb.12859
• Languages: English
• ISSNs: 1466-822X

Abstract

Abstract: Aim: Understanding how ecosystem functioning and evolution shape taxonomic assemblages is a lively debate basically involving two major opposite views: the niche‐ and dispersal‐assembly hypotheses. Here, we introduce a new method allowing for the identification of the first‐order process of assembly underlying a set of taxonomic assemblages. Methods: Building on Clarke's SIMPER (for "similarity percentage") analysis of a taxon/locality occurrence data set, we develop a permutation‐based algorithm named PER‐SIMPER, allowing for the identification of the first‐order process—either niche‐ or dispersal‐assembly—that drives species distribution within two or more groups of assemblages. We demonstrate the reliability and robustness of the method through cellular automaton‐like simulations generating niche‐assembled and/or dispersal‐assembled species occurrence data sets. Sensitivity analysis further allows evaluation of its accuracy and robustness to sampling effort, including reduced numbers of sampled localities and/or species. Main conclusions: Niche‐ and/or dispersal‐assembled communities generate very different SIMPER profiles, which, in turn, allow for the accurate and consistent identification of the first‐order process of assembly operating within two or more groups of species assemblages through a threefold randomization procedure named PER‐SIMPER. The PER‐SIMPER method appears robust to varying sampling efforts that may affect the number of sampled localities and/or species, especially when one of the two processes of assembly dominates the other. The PER‐SIMPER analysis can be achieved on any empirical occurrence data set using a dedicated R function available as Supporting Information.