Spatial Autocorrelation in Species Distribution Models: Simultaneous Incorporation of Multiple Scales of Influence Using a Bayesian Framework
نویسندگان
چکیده
Spatial autocorrelation (SAC), defined as the positive association between sample similarity and spatial proximity, is pervasive in ecological data. Several methods exist for the incorporation of SAC into statistical models, but not until recently have these methods been applied to species distribution data. When incorporated into species distribution models, SAC has been found to significantly alter model coefficients, subsequently changing the statistical inference of the model. Models without a SAC component run the risk of overestimating the relationship between environmental variables and the presence or abundance of a species, potentially resulting in poor predictive ability of the model. Numerous studies have demonstrated the improvement in species distribution model performance after the incorporation of SAC suggesting inclusion of this spatial component is essential for developing statistical models that accurately predict species distributions in novel environments.
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