A machine-learning approach for extending classical wildlife resource selection analyses.

ABSTRACT: Resource selection functions (RSFs) are tremendously valuable for ecologists and resource managers because they quantify spatial patterns in resource utilization by wildlife, thereby facilitating identification of critical habitat areas and characterizing specific habitat features that are selected or avoided. RSFs discriminate between known-use resource units (e.g., telemetry locations) and available (or randomly selected) resource units based on an array of environmental features, and in their standard form are performed using logistic regression. As generalized linear models, standard RSFs have some notable limitations, such as difficulties in accommodating nonlinear (e.g., humped or threshold) relationships and complex interactions. Increasingly, ecologists are using flexible machine-l

SUBMITTER: Shoemaker KT 

PROVIDER: S-EPMC5869366 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature