Data from: PER-SIMPER - a new tool for inferring community assembly processes from taxon occurrences

Aim: Understanding how ecosystem functioning and evolution shape taxonomic
as- semblages 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-as- sembled
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 dif- ferent
SIMPER profiles, which, in turn, allow for the accurate and consistent
identifica- tion 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.