Publication Date
2021
Journal or Book Title
FRONTIERS IN MARINE SCIENCE
Abstract
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 the monitoring range of acoustic receiver arrays, making this a valuable tool for marine ecologists and resource managers. Here, we apply RSFs using machine learning to an acoustic telemetry dataset of four shark species to explore and predict species-specific habitat selection within a marine protected area. In addition, we also apply this RSF-machine learning approach to investigate predator-prey relationships by comparing and averaging tiger shark relative selection values with the relative selection values derived for eight potential prey-species. We provide methodological considerations along with a framework and flexible approach to apply RSFs with machine learning algorithms to acoustic telemetry data and suggest marine ecologists and resource managers consider adopting such tools to help guide both conservation and
DOI
https://doi.org/10.3389/fmars.2021.631262
Volume
8
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
License
UMass Amherst Open Access Policy
Funder
Puerto Rico Sea Grant [R-101-2-14]; New England Aquarium's Marine Conservation Action Fund; Atlantic White Shark Conservancy; National Geographic SocietyNational Geographic Society; Allen Family Foundation; USGS Natural Resource Protection Program; USGS Ecosystems Program
Recommended Citation
Griffin, Lucas P.; Casselberry, Grace A.; Hart, Kristen M.; Jordaan, Adrian; Becker, Sarah L.; Novak, Ashleigh J.; DeAngelis, Bryan M.; Pollock, Clayton G.; Lundgren, Ian; and Hills-Star, Zandy, "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" (2021). FRONTIERS IN MARINE SCIENCE. 462.
https://doi.org/10.3389/fmars.2021.631262