Functional traits predict species co‐occurrence patterns in a North American Odonata metacommunity
Author(s)
Date Issued
2023
Type
article
Volume
14
Issue
12
Journal
Abstract
The probability of occurrence of a given species in a target locality andassemblage is conditioned not only by environmental/climatic variables butalso by the presence of other species (i.e., species co-occurrence). This frame-work, already complex in nature, becomes even more complicated if one con-siders the functional traits of species that, in turn, might influence thestructure of metacommunities in various ways. Depending on the ecologicaland environmental setting, functional similarity (i.e., convergence in morpho-logical and ecological traits) between species might either reduce theirco-occurrence due to high niche overlap driving negative interactions or pro-mote it if the similar traits are associated with local habitat suitability.Similarly, functional divergence might either promote species co-occurrenceby limiting negative interactions through niche separation or reduce it throughtrait mediated environmental filtering. Therefore, discriminating betweenthese alternative scenarios—predicting whether two species will tend toco-occur or not based on their traits—is extremely challenging. Here, wedevelop a novel protocol to tackle the challenge, and we demonstrate its effec-tiveness by showing that ecological species traits can predict speciesco-occurrence in a large dataset of North American Odonata. To this end, wefirst used the Hierarchical Modeling of Species Communities framework toquantify the pairwise species co-occurrence after controlling for environmentaland climatic factors. Then, we used machine learning to generate modelswhich proved capable of predict accurately the observed co-occurrence pat-terns from species functional traits. Our approach offers a generalizable analyt-ical framework with the potential to clarify long-standing ecological questions.