Environmental Sciences Commons^™

A Novel Framework To Predict Relative Habitat Selection In Aquatic Systems: Applying Machine Learning And Resource Selection Functions To Acoustic Telemetry Data From Multiple Shark Species, Lucas P. Griffin, Grace A. Casselberry, Kristen M. Hart, Adrian Jordaan, Sarah L. Becker, Ashleigh J. Novak, Bryan M. Deangelis, Clayton G. Pollock, Ian Lundgren, Zandy Hills-Star

Environmental Conservation Faculty Publication Series

Resource selection functions (RSFs) have been widely applied to animal tracking data to examine relative habitat selection and to help guide management and conservation strategies. While readily used in terrestrial ecology, RSFs have yet to be extensively used within marine systems. As acoustic telemetry continues to be a pervasive approach within marine environments, incorporation of RSFs can provide new insights to help prioritize habitat protection and restoration to meet conservation goals. To overcome statistical hurdles and achieve high prediction accuracy, machine learning algorithms could be paired with RSFs to predict relative habitat selection for a species within and even outside …