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H. Bailey , B. Mate, L. Irvine, S. Bograd, D. Palacios, E. Hazen, K. Forney and E. Howell

WhaleWatch : Using Satellite Data and Habitat Models to Assist Management in Reducing Human Impacts on Whales. H. Bailey , B. Mate, L. Irvine, S. Bograd, D. Palacios, E. Hazen, K. Forney and E. Howell. Outline. Goals Baseline Information Progress on: Satellite telemetry data analysis

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H. Bailey , B. Mate, L. Irvine, S. Bograd, D. Palacios, E. Hazen, K. Forney and E. Howell

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  1. WhaleWatch: Using Satellite Data and Habitat Models to Assist Management in Reducing Human Impacts on Whales H. Bailey, B. Mate, L. Irvine, S. Bograd, D. Palacios, E. Hazen, K. Forney and E. Howell

  2. Outline • Goals • Baseline Information • Progress on: • Satellite telemetry data analysis • Identification of core areas • Habitat-based density modeling • Transitioning process • Next steps

  3. Goals • Use satellite data to develop habitat models that will allow us to identify large whale hotspots and provide a tool for estimating occurrence in the California Current System. This will assist management efforts to mitigate against human impacts. • Just over half-way through 3-year project

  4. Blue whale baseline • NOAA Cetacean and Sound Mapping Project • http://www.st.nmfs.noaa.gov/cetsound/cda.html Blue whale Average model-based density 1991-2005 July to November December Blue whales Downloaded from Cetacean & Sound Mapping Project. Modeled densities originated from Barlow et al. 2009 and Becker et al. 2012. Records of occurrence from OBIS-SEAMAP.

  5. Whale Satellite Telemetry Data Blue (104) Fin (2) Humpback (21) Gray (35) Telemetry data from: Bruce Mate, OSU

  6. Approach • Apply a state-space model to provide regularized daily positions from whale satellite telemetry data • Identify core area hotspots • Extract environmental data • Develop habitat preference models using remotely sensed environmental data • Develop a tool predicting whale densities based on the current environmental conditions.

  7. Step 1: Standardize positions • Applied a state-space model • “A time series model that predicts the future state of a system from its previous states probabilistically” (Patterson et al. 2008 TREE)

  8. Measurement equation Transition equation Mean turning angle dt dt-1 Transiting Foraging Model method Autocorrelation in speed and direction From Vincent et al. (2002) From Jonsen et al. (2007)

  9. Step 2: Identify core areas Irvine et al. in prep.

  10. Step 3: Integrate whale positions with environmental data Blue whale daily locations color-coded by the value of the remotely sensed environmental variables. Palacios et al. in prep.

  11. Step 4: Habitat-based model • Fit a generalized additive mixed model (GAMM). • Response variable: Presence (real whale location)/Absence (correlated random walk positions as pseudo-absences). • Explanatory variables e.g. SST, CHL, meridional winds, depth. • Model provides estimates of probability of occurrence. • Density calculation: Scale predictions to sum to 1 and then multiply by population abundance estimate of 2,497 individuals. Hazen et al. in prep.

  12. Proof of Concept for Densities July to November From Barlow et al. 2009 and Becker et al. 2012. Hazen et al. in prep.

  13. Expanding months of the year July to November December From NOAA Cetacean Mapping Project Hazen et al. in prep.

  14. Transitioning process • Our partner is invited to participate in our monthly conference calls. • June 2012: Meeting and presentation at NOAA/NMFS Southwest Regional Office to discuss baseline information and benefits WhaleWatch project could provide. • January 2013: Presentation at NOAA/NMFS Southwest Regional Office, which included representatives from Maersk shipping company, Marine Exchange and U.S. Coastguard. Feedback on our preliminary products. • April 2013: Inquiry from Boeing regarding status of WhaleWatch in relation to flight testing and sonobuoy deployments off Washington and Oregon. • Early 2014: WhaleWatch tool ready for testing and feedback by our partner, Monica DeAngelis, at the NOAA/NMFS Southwest Regional Office. • Mid 2014: Final refinements to tool and hosting on publicly accessible NOAA website.

  15. Next steps • Blue whales: • Refine and validate habitat-based density model • Humpback whales: • Identify high-use areas for the autumn/winter so it can be related to fisheries entanglement risk • Gray whales: • Estimate speeds to refine stratified density model and expansion into Alaskan waters • Identify core areas and foraging behavior for whales feeding off U.S. west coast.

  16. Acknowledgements • Funding was provided under the interagency NASA, USGS, National Park Service, US Fish and Wildlife Service, Smithsonian Institution Climate and Biological Response program, Grant Number NNX11AP71G. • Dave Foley (NOAA Coastwatch) assisted with the extraction of the remotely sensed environmental data. • NASA data served by ERDDAP at the Environmental Research Division • The support of field crews was essential to the success of the tagging operations. Tagging was supported by private donors to the MMI Endowment at OSU, as well as the support from ONR and the Sloan, Packard and Moore foundations to the TOPP program.

  17. Thank you!E-mail: hbailey@umces.eduhttp://www.umces.edu/cbl/whalewatch Photo courtesy of the Marine Mammal Institute, OSU.

  18. WhaleWatch Benefits • For all four whale species: • Maps of whale occurrence from standardized positions • Identification of core areas and migration routes • Identification of foraging areas • Improve spatio-temporal match with anthropogenic activities to identify risk and inform marine spatial planning and dynamic management. • Focus species: Blue whales • Expand on the months of the year for which habitat-based density estimates are available • Create near real-time tool that estimates densities based on latest environmental data

  19. Next Steps • Refine and complete application of state-space model to gray whale satellite tracks (spring 2013). • Complete and submit manuscript on blue whale core areas (summer 2013). • Refine and validate blue whale habitat-based density model (end of 2013). • Run and complete blue whale habitat-based foraging model (end of 2013). • Create automatically updating tool from the blue whale habitat-based model for the website (early 2014).

  20. WhaleWatch Benefits • Products • Maps of standardized daily whale locations • Identification of home range, core areas, and migration routes. • Habitat-based probability of occurrence • Habitat-based density models • Habitat-based foraging models • Focus species: Blue whales • Expand on the months of the year for which habitat-based density estimates are available • Create near real-time tool that estimates densities based on latest environmental data

  21. Whale Satellite Telemetry Data Telemetry data from: Bruce Mate, OSU

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