Incorporating spatial autocorrelation in species distribution models
|This is an ongoing project.||
Contact DetailsJennifer Miller
The main outcome of this project will be a framework that can be used to guide all aspects of model conceptualization and development (sampling strategy, statistical method(s) used, appropriate spatial scale) when using binary data with spatial autocorrelation. This research will make an important contribution to a greater understanding of how spatial autocorrelation affects inductive models used with binary response data. Beyond predicting species distributions, these models have become an important and widely used decision-making tool for a variety of biogeographical applications, such as studying the effects of climate change, identifying potential protected areas, determining locations potentially susceptible to invasion, and mapping vector-borne disease spread and risk. Outside of biogeography, similar binary response models are used in medical/health applications (e.g., diagnostic tests) and economic and social sciences (e.g. labor market status, credit scoring, voting behavior).
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