Evaluating ecological uniqueness over broad spatial extents using species distribution modelling

Local contributions to beta diversity (LCBD) can be used to identify sites with high ecological uniqueness and exceptional species composition within a region of interest. Yet, these indices are typically used on local or regional scales with relatively few sites, as they require information on complete community compositions difficult to acquire on larger scales. Here, we investigated how LCBD indices can be predicted over broad spatial extents using species distribution modelling and examined the effect of scale changes on beta diversity quantification. We used Bayesian additive regression trees (BARTs) to predict warbler species distributions in North America based on observations recorded in the eBird database. We then calculated LCBD indices for observed and predicted data and compared the site-wise difference using direct comparison, a spatial association test and generalized linear regression. We also examined the relationship between LCBD values and species richness in different regions and at various spatial extents. Our results showed that species distribution models provided uniqueness estimates highly correlated with observed data. The form and variance of the LCBD-richness relationship varied according to the region and the total extent size. The relationship was also affected by the proportion of rare species in the communities. Therefore, sites identified as unique over broad spatial extents may vary according to regional characteristics. These results show that species distribution modelling can be used to predict ecological uniqueness over broad spatial extents, which could help identify beta diversity hotspots and important targets for conservation purposes in unsampled locations.

Automated summary

This study used species distribution modelling to predict local contributions to beta diversity (LCBD) indices over broad spatial extents and examined the effect of scale changes on beta diversity quantification. The results showed that species distribution models provided uniqueness estimates highly correlated with observed data.