The impact of varying spatiotemporal scales on different joint species distribution models: A case study of pelagic fish species in the northwest Pacific Ocean

Joint Species Distribution Models (JSDMs) have become a critical tool in community ecology research, with a wide scope of application that is continuously expanding. However, inferring interspecies relationships from co-occurrence data remains a challenge. This study examined the impact of varying spatiotemporal scales on JSDMs, with a focus on model stability and the evaluation of interspecies relationships. We compared the performance of the models across 32 different spatiotemporal scales. Our results indicate that the spatiotemporal scale significantly affects the performance of JSDMs, with notable differences among the models. As both temporal and spatial scales increased, model simulation and prediction performance improved, and stability increased. Moreover, spatial scale has a substantial impact on the evaluation of interspecies relationships, with finer spatial scales identifying weaker positive relationships and stronger negative relationships. Among the models, HMSC demonstrated better balancing performance, while the Boral model showed the least stability. Overall, the optimal JSDM identified was the HMSC model with an annual temporal and 0.25° spatial scale.

Methods
To comprehensively evaluate the impact of varying spatiotemporal scales on JSDMs, this study was designed using two temporal scales (monthly and annual), four spatial scales (0.25°, 0.5°, 1°, and 2°), and four different JSDMs (Bayescomm, HMSC, Boral, and Gjam). Using three economically important pelagic fish species from the northwest Pacific Ocean as examples—Japanese sardine (Sardinops melanostictus), chub mackerel (Scomber japonicus), and neon flying squid (Ommastrephes bartramii)—we compared the performance of the models across 32 different spatiotemporal scales.