Abstract
Joint species distribution models (JSDMs) are an important conservation tool for predicting ecosystem diversity and function under global change. The growing complexity of modern JSDMs necessitates careful model selection tailored to the challenges of community prediction under novel conditions (i.e., transferable models). Common approaches to evaluate the performance of JSDMs for community-level prediction are based on individual species predictions that do not account for the species correlation structures inherent in JSDMs. Here, we formalize a Bayesian model selection approach that accounts for species correlation structures and apply it to compare the community-level predictive performance of alternative JSDMs across broad environmental gradients emulating transferable applications. We connect the evaluation of JSDM predictions to Bayesian model selection theory under which the log score is the preferred performance measure for probabilistic prediction. We define the joint log score for community-level prediction and distinguish it from more commonly applied JSDM evaluation metrics. We then apply this community log score to evaluate predictions of 1,918 out-of-sample boreal forest understory communities spanning 39 species generated using a novel JSDM framework that supports alternative species correlation structures: independent, compositional dependence, and residual dependence. The best performing JSDM included all observed environmental variables and multinomial species correlations reflecting compositional dependence within modeled community data. The addition of flexible residual species correlations improved model predictions only within JSDMs applying a reduced set of environmental variables highlighting potential confounding between unobserved environmental conditions and residual species dependence. The best performing JSDM was consistent across successional and bio-climatic gradients regardless of whether interest was in species- or community-level prediction. Our study demonstrates the utility of the community log score to quantify differences in the predictive performance of complex JSDMs and highlights the importance of accounting for species dependence when interest is in community composition under novel conditions.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
The manuscript has received major revisions shifting its focus to Bayesian model selection for joint species distribution models when the goal is probabilistic prediction of community composition under global change. While the model methodology, data, and results remain the same, the manuscript has a new abstract, introduction, and modified methods (including a new subsection on log score calculation) and discussion sections. Alternative models remain the same, but have been given new, more intuitive names. Supplementary material has been updated to include new model names. Figures 4 and 5 in the original manuscript have been removed given that they are not directly related to the model selection results. Manuscript changes address comments and feedback received from colleagues and anonymous reviewers on the original manuscript.
