A new method for faster and more accurate inference of species associations from novel community data
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Joint Species Distribution models (jSDMs) explain spatial variation in community composition by contributions of the environment, biotic interactions, and possibly spatially structured residual variance. They show great promise as a general analysis method for community ecology and macroecology, but current jSDMs scale poorly on large datasets, limiting their usefulness for novel community data, such as datasets generated using metabarcoding and metagenomics. Here, we present sjSDM, a novel method for estimating jSDMs that is based on Monte-Carlo integration of the joint likelihood. We show that our method, which can be calculated on CPUs and GPUs, is orders of magnitude faster than existing jSDM algorithms and can be scaled to very large datasets. Despite the dramatically improved speed, sjSDM produces the same predictive error and more accurate estimates of species association structures than alternative jSDM implementations. We provide our method in an R package to facilitate its applicability for practical data analysis.
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