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Forecasting the trajectories of Southern Resident Killer Whales with stochastic continuous-time movement models

Resource type
Thesis type
(Project) M.Sc.
Date created
2023-11-29
Authors/Contributors
Abstract
The Southern Resident Killer Whale (SRKW) is an endangered population of killer whales that is present in the Salish Sea. This fish-eating predator has been heavily impacted by human activities in the region, particularly by commercial vessels in shipping lanes that traverse federally-designated SRKW critical habitat. Forecasting the movement trajectories of these whales would help provide early warning alerts to slow down or reroute commercial vessels, and reduce the risks of ships overlapping with whale presence. In this study, we develop a stochastic animal movement model that is guided by a historical database of sighting records of SRKW. Specifically, we make use of a continuous-time Ornstein-Uhlenbeck (O- U) velocity process that provides the basis for a movement forecast system and simulates realizations of SRKW velocities and trajectories given initial conditions. However, if the forecast system were to simply rely on the O-U velocity process alone, it would steer simulated whale trajectories to areas where SRKWs are rarely found. To address this, we propose a direction blending scheme to project the simulated velocities in more realistic directions. It makes use of historical directional information along with the O-U velocity process to create more probable pathways consistent with observed SRKW movement patterns. By integrating the simulated trajectories generated from the simulated velocities, we establish a dynamic probability-based forecast scheme that demonstrates skill in forecasting SRKW trajectories on a time-scale that aligns with the time to slow and reroute commercial vessels.
Document
Extent
47 pages.
Identifier
etd22791
Copyright statement
Copyright is held by the author(s).
Permissions
This thesis may be printed or downloaded for non-commercial research and scholarly purposes.
Supervisor or Senior Supervisor
Thesis advisor: Joy, Ruth
Language
English
Download file Size
etd22791.pdf 7.46 MB

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