부산대학교 도서관

Aim: The abundance and distribution of multiple species are interconnected through various mechanisms (e.g. biotic interactions or common responses to the environment) shaping communities. Joint species distribution models (jSDM) have been introduced as a potential tool to integrate these mechanisms when modelling multiple species distributions, by inferring a residual matrix of species associations that could inform on biotic interactions. However, the direct link between these residual associations and biotic interactions has been challenged. Here, we test how the data type, resolution and sampling size affect the species associations identified by jSDMs and their benefits for predicting species given the known state of others (i.e. conditional prediction). Location: France. Taxon: Birds. Method: Utilizing standardized co‐abundances of 40 common bird species, across 7040 monitoring sites, and eight environmental variables at high resolution (200 m), we compared jSDM residual associations across data types (abundance vs. occurrence), resolution and sampling size. Additionally, we investigated correlations between residual associations and species functional similarities (eight traits). We then assessed to what extent residual associations contain valuable information for conditional predictions. Results: Our results show that species associations identified by jSDM are greatly influenced by data resolution and sampling size rather than data types (abundance vs. occurrence). We find positive correlations between species associations and functional similarity that challenges the inference of negative biotic interactions expected from niche partitioning. However, retrieving these high‐resolution residual species associations for conditional predictions enhanced predictive quality for all species (+235% on average), potentially synthesizing missing variables difficult to capture in the field. Main Conclusions: We highlight that species associations identified by jSDM using fine‐resolution co‐abundance datasets do not retrieve biotic interactions expected from niche partitioning (i.e. positive correlation with functional similarity) but probably missing environmental variables. Nonetheless, these residual associations contain valuable information to enhance predictive performance through currently underutilized conditional predictions. [ABSTRACT FROM AUTHOR]