A multiregion community model for inference about geographic variation in species richness. Issue 7 (25th February 2016)

• Record Type: Journal Article
• Authors: Sutherland, Chris; Brambilla, Mattia; Pedrini, Paolo; Tenan, Simone
• Editors: Isaac, Nick
• Is Part Of: Methods in ecology and evolution. Volume 7:Issue 7(2016:Jul.)
• Page Start: 783
• Page End: 791
• Publication Date: 2016-02-25
• Subjects: Bayesian analysis -- biodiversity -- biogeography -- community structure -- data augmentation -- geographic variation -- site occupancy models -- species richness; Ecology -- Periodicals; Evolution -- Periodicals; 577
• DOI: 10.1111/2041-210X.12536
• Languages: English
• ISSNs: 2041-210X

Abstract

Summary: An enduring challenge in ecology is to understand what drives spatial variation in the size and structure of communities. The ability to count the number of species present at a location is hindered by the fact that not all species are equally detectable, and invariably some go completely undetected. This makes comparing species richness across distinct spatial units (or regions) problematic as sources of error are usually unaccounted for in simple enumerations of species. Multi‐species occupancy models explicitly incorporate a model for this observation uncertainty and provide a framework for estimating community size when detection is imperfect. Currently, however, the model is restricted to estimating the number of species at only a single region of interest. In this paper we extend the multi‐species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes). We report improved model performance of the joint multiregion approach when compared to the more traditional two‐stage approach of modelling spatial variation in species richness using simulations. Then, applying the model to data collected from eight avian communities in northern Italy, we demonstrate how species richness can be modeled as a spatially varying function of habitat complexity. Extending the multi‐species occupancy model to accommodate data collected across multiple regions of interest (e.g., reserves or biomes) allows for joint estimation of region‐specific community size and permits species richness to be modeled as a function of region‐specific covariates. Our approach provides a mechanism for testing hypotheses about why and how species richness varies across space.