Ficetola, Gentile Francesco, et al. “Estimating patterns of reptile biodiversity in remote regions.” Journal of Biogeography 40.6 (2013): 1202-1211.
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The authors aim to predict species richness of reptiles (turtles, amphisbaenians, and lizards) in the Western Palaearctic using Bayesian autoregressive models (BCA) and spatial eigenvector mapping. They used these methods to account for the significant amount of spatial autocorrelation in their predictor variables. They considered accesssibility (travel time to nearest city with population > 50000 people), density of protected areas, and environmental covariates as their predictors of species richness, where richness was count data with an assumed Poisson error distribution. They argued that accessibility was a layer that got at sampling and collection biases. Further, they argued that spatial resolution could influence predictions, so they predict at 1 degree squared and 2 degree squared resolution. Further, they transformed nearly all of the predictor variables, which I can’t really tell if they needed to based on the modeling approach. Models were validated by comparing predicted species richness to data from exhaustive sampling on a subset of the spatial cells. Accuracy was computed by comparing outputs of 3 linear models of predicted versus actual richness values (1. actual ~ predicted, 2. actual ~ recorded richness until 2008, 3. actual ~ intercept). BCA models were qualitative similar to the spatial eigenvector approach, and the BCA models predicted species richness with high accuracy.