Highly variable species distribution models in a subarctic stream metacommunity: patterns, mechanisms and implications

• Main Authors: de Mendoza, Guillermo, Kaivosoja, Riikka, Grönroos, Mira, Hjort, Jan, Ilmonen, Jari, Kärnä, Olli-Matti, Paasivirta, Lauri, Tokola, Laura, Heino, Jani
• Other Authors: Géographie de l'environnement (GEODE), Centre National de la Recherche Scientifique (CNRS)-Université Toulouse - Jean Jaurès (UT2J), CEAB-CSIC, University of Oulu, Geography Reserach Unit, University of Helsinki, Department of Environmental Sciences, section of environmental ecology, Metasahallitus, Natural Heritage Services, Ruuhikoskenkatu, University of Oulu, Geography Research Unit, Finnish Environment Institute, Natural Environment Centre
• Format: Article in Journal/Newspaper
• Language: English
• Published: HAL CCSD 2018
• Subjects: beta regression; comparative analysis; insects; metacommunity theory; single-species 61 distribution models; stream macroinvertebrates; subarctic streams; envir; geo; Subarctic
• Online Access: https://hal-univ-tlse2.archives-ouvertes.fr/hal-02450949/file/deMendoza_etal_2018_FWB.pdf; https://hal-univ-tlse2.archives-ouvertes.fr/hal-02450949

International audience Metacommunity theory focuses on assembly patterns in ecological communities, originally exemplified through four different, yet non-exclusive, perspectives: patch dynamics, species sorting, source-sink dynamics, and neutral theory. More recently, three exclusive components have been proposed to describe a different metacommunity framework: habitat heterogeneity, species equivalence, and dispersal. Here, we aim at evaluating the insect metacommunity of a subarctic stream network under these two different frameworks. We first modelled the presence/absence of 47 stream insects in northernmost Finland using binomial generalised linear models (GLMs). The deviance explained by pure local environmental (E), spatial (S), and climatic variables (C) was then analysed across species using beta regression. In this comparative analysis, site occupancy, as well as taxonomic and biological trait vectors obtained from principal coordinate analysis, were used as predictor variables. Single-species distributions were better explained by in-stream environmental and spatial factors than by climatic forcing, but in a highly variable fashion. This variability was difficult to relate to the taxonomic relatedness among species or their biological trait similarity. Site occupancy, however, was related to model performance of the binomial GLMs based on spatial effects: as populations are likely to be better connected for common species due to their near ubiquity, spatial factors may also explain better their distributions. According to the classical four-perspective framework, the observation of both environmental and spatial effects suggests a role for either mass effects or species sorting constrained by dispersal limitation, or both. Taxonomic and biological traits, including the different dispersal capability of species, were scarcely important, which undermines the patch dynamics perspective, based on differences in dispersal ability between species. The highly variable performance of models makes the reliance ...