A spatially explicit method for evaluating accuracy of species distribution models

• Published in: Diversity and Distributions
• Main Authors: Smulders, Mary, Nelson, Trisalyn A., Jelinski, Dennis E., Nielsen, Scott E., Stenhouse, Gordon B.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2010
• Subjects: Canada; Ursus arctos
• Online Access: http://dx.doi.org/10.1111/j.1472-4642.2010.00707.x; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2Fj.1472-4642.2010.00707.x; https://onlinelibrary.wiley.com/doi/pdf/10.1111/j.1472-4642.2010.00707.x

Abstract Aim Models predicting the spatial distribution of animals are increasingly used in wildlife management and conservation planning. There is growing recognition that common methods of evaluating species distribution model (SDM) accuracy, as a global overall value of predictive ability, could be enhanced by spatially evaluating the model thereby identifying local areas of relative predictive strength and weakness. Current methods of spatial SDM model assessment focus on applying local measures of spatial autocorrelation to SDM residuals, which require quantitative model outputs. However, SDM outputs are often probabilistic (relative probability of species occurrence) or categorical (species present or absent). The goal of this paper was to develop a new method, using a conditional randomization technique, which can be applied to directly spatially evaluate probabilistic and categorical SDMs. Location Eastern slopes, Rocky Mountains, Alberta, Canada. Methods We used predictions from seasonal grizzly bear ( Ursus arctos ) resource selection functions (RSF) models to demonstrate our spatial evaluation technique. Local test statistics computed from bear telemetry locations were used to identify areas where bears were located more frequently than predicted. We evaluated the spatial pattern of model inaccuracies using a measure of spatial autocorrelation, local Moran’s I . Results We found the model to have non‐stationary patterns in accuracy, with clusters of inaccuracies located in central habitat areas. Model inaccuracies varied seasonally, with the summer model performing the best and the least error in areas with high RSF values. The landscape characteristics associated with model inaccuracies were examined, and possible factors contributing to RSF error were identified. Main conclusions The presented method complements existing spatial approaches to model error assessment as it can be used with probabilistic and categorical model output, which is typical for SDMs. We recommend that SDM accuracy assessments ...