Spatial Random Effects Improve the Predictions of Multispecies Distribution in a Marine Fish Assemblage

Species distribution patterns is one of the important topics in ecology and biological conservation. Although species distribution models have been intensively used in the research, the effects of spatial associations and spatial dependence have been rarely taken into account in the modeling processes. Recently, Joint Species Distribution Models (JSDMs) offer the opportunity to consider both environmental factors and interspecific relationships as well as the role of spatial structures. This study uses the HMSC (Hierarchical Modelling of Species Communities) framework to model the multispecies distribution of a marine fish assemblage, in which spatial associations and spatial dependence is deliberately accounted for. Three HMSC models were implemented with different structures of random effects to address the existence of spatial associations and spatial dependence, and the predictive performances at different levels of sample sizes were analyzed in the assessment. The results showed that the models with random effects could account for a larger proportion of explainable variance (32.8%), and particularly the spatial random effect model provided the best predictive performances (R_mean² = 0.31), indicating that spatial random effects could substantially influence the results of the joint species distribution. Increasing sample size had a strong effect (R_mean² = 0.24 – 0.31) on the predictive accuracy of the spatially-structured model than on the other models, suggesting that optimal model selection should be dependent on sample size. This study highlights the importance of incorporating spatial random effects for JSDM predictions and suggests that the choice of model structures should consider the data quality across species.