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

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

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 tele...

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Bibliographic Details
Main Authors: 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 Hillis-Starr, Andy J. Danylchuk, Gregory B. Skomal
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-04-01
Series:Frontiers in Marine Science
Subjects:
resource selection
space use
acoustic telemetry
machine learning
random forest
marine protected area
Online Access:https://www.frontiersin.org/articles/10.3389/fmars.2021.631262/full

Internet

https://www.frontiersin.org/articles/10.3389/fmars.2021.631262/full

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