WhaleWatch: a dynamic management tool for predicting blue whale density in the California Current

• Published in: Journal of Applied Ecology
• Main Authors: Hazen, Elliott L., Palacios, Daniel M., Forney, Karin A., Howell, Evan A., Becker, Elizabeth, Hoover, Aimee L., Irvine, Ladd, DeAngelis, Monica, Bograd, Steven J., Mate, Bruce R., Bailey, Helen
• Other Authors: Singh, Navinder, National Aeronautics and Space Administration, U.S. Geological Survey, National Park Service, U.S. Fish and Wildlife Service, Smithsonian Institution, National Oceanic and Atmospheric Administration, Oregon State University
• Format: Article in Journal/Newspaper
• Language: English
• Published: Wiley 2016
• Subjects: Pacific; Balaenoptera musculus; Blue whale
• Online Access: http://dx.doi.org/10.1111/1365-2664.12820; https://api.wiley.com/onlinelibrary/tdm/v1/articles/10.1111%2F1365-2664.12820; https://onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2664.12820; https://onlinelibrary.wiley.com/doi/full-xml/10.1111/1365-2664.12820; http://api.wiley.com/onlinelibrary/chorus/v1/articles/10.1111%2F1365-2664.12820; https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/1365-2664.12820

Summary Management of highly migratory species is reliant on spatially and temporally explicit information on their distribution and abundance. Satellite telemetry provides time‐series data on individual movements. However, these data are underutilized in management applications in part because they provide presence‐only information rather than abundance information such as density. Eastern North Pacific blue whales are listed as threatened, and ship strikes have been suggested as a key factor limiting their recovery. Here, we developed a satellite‐telemetry‐based habitat model in a case–control design for Eastern North Pacific blue whales Balaenoptera musculus that was combined with previously published abundance estimates to predict habitat preference and densities. Further, we operationalize an automated, near‐real‐time whale density prediction tool based on up‐to‐date environmental data for use by managers and other stakeholders. A switching state‐space movement model was applied to 104 blue whale satellite tracks from 1994 to 2008 to account for errors in the location estimates and provide daily positions (case points). We simulated positions using a correlated random walk model (control points) and sampled the environment at each case and control point. Generalized additive mixed models and boosted regression trees were applied to determine the probability of occurrence based on environmental covariates. Models were used to predict 8‐day and monthly resolution, year‐round density estimates scaled by population abundance estimates that provide a critical tool for understanding seasonal and interannual changes in habitat use. The telemetry‐based habitat model predicted known blue whale hot spots and had seasonal agreement with sightings data, highlighting the skill of the model for predicting blue whale habitat preference and density. We identified high interannual variability in occurrence emphasizing the benefit of dynamic models compared to multiyear averages. Synthesis and applications . This ...