Aims: Species distributions are hypothesized to be underlain by a complex association of processes that span multiple spatial scales including biotic interactions, dispersal limitation, fine-scale resource gradients and climate. Species disequilibrium with climate may reflect the effects of non-climatic processes on species distributions, yet distribution models have rarely directly considered non-climatic processes. Here, we use a Joint Species Distribution Model (JSDM) to investigate the influence of non-climatic factors on species co-occurrence patterns and to directly quantify the relative influences of climate and alternative processes that may generate correlated responses in species distributions, such as species interactions, on tree co-occurrence patterns.
Location: US Rocky Mountains.
Methods: We apply a Bayesian JSDM to simultaneously model the co-occurrence patterns of ten dominant tree species across the Rocky Mountains, and evaluate climatic and residual correlations from the fitted model to determine the relative contribution of each component to observed co-occurrence patterns. We also evaluate predictions generated from the fitted model relative to a single-species modelling approach.
Results: For most species, correlation due to climate covariates exceeded residual correlation, indicating an overriding influence of broad-scale climate on co-occurrence patterns. Accounting for covariance among species did not significantly improve predictions relative to a single-species approach, providing limited evidence for a strong independent influence of species interactions on distribution patterns.
Conclusions: Overall, our findings indicate that climate is an important driver of regional biodiversity patterns and that interactions between dominant tree species contribute little to explain species co-occurrence patterns among Rocky Mountain trees.
Copenhaver-Parry, Paige E. and Bell, David M., "Species Interactions Weakly Modify Climate-Induced Tree Co-Occurrence Patterns" (2017). Faculty Publications - Department of Biological & Molecular Science. 133.