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

Objectives Develop products for our partner, NOAA/NMFS West Coast Regional Office, and other stakeholders. –Where are the whales? –Can we predict where the whales are now, how many and their behavior? –What is the ecological function of different areas? –Can we predict the whale species based on the environmental characteristics of a location?

WhaleWatch Outcomes Whale species Daily locations Migratory/ foraging areas Habitat preference Model predictions Multi- species model Blue N=104 ✔✔✔ Occurrence and foraging ✔ Probability of occurrence and density ✔ Species occurrence related to SST Humpback N=15 ✔✔✔ Relative use ✔ Relative use Gray N=35 ✔✔✔ PCFG Fin N=2 ✔✔

Approach 1.Apply a state-space model to provide regularized daily positions of whale satellite telemetry data. 2.Extract environmental data for the time and location of each whale position. 3.Develop habitat preference models using remotely sensed environmental data. 4.Develop a tool predicting whale densities based on the current environmental conditions. + Whale Locations Ocean Environment Habitat Model Density Predictions

Blue whale habitat-based model State-Space Modeled Whale Positions Examine Environmental Characteristics Telemetry- Based Habitat Model Near Real-Time Predictions of Occurrence Survey Data for Validation Simulate Correlated Random Walk Tracks Hazen et al. in prep.

Model results – Summer/Autumn Generalized additive mixed models Hazen et al. in prep.

Model results – Winter/Spring Generalized additive mixed models Hazen et al. in prep.

Predictions of occurrence March 2009 July 2009 Sept 2009 Dec 2009 Hazen et al. in prep.

Predictions of density Hazen et al. in prep. Number of blue whales per 25 x 25 km grid cell

Comparison with Sightings Line transect surveys shown as gray lines and sightings as black circles (NOAA/NMFS/SWFSC, image by K. Forney and adapted from Becker et al. 2012).

Automated Data Processing Extraction of Environmental Variables Run Variables through Models Create Visualization of Predictions Send Map to Appropriate Servers Call SWFSC’s ERDDAP data server Data input into GAMM models Collate mean and SE of model outputs Filter and consolidate variables Build map from model output Generate high-quality image file for web upload NOAA/SWFSC server and link to Regional Office website Automatically update map at desired time By A. Hoover and E. Howell

Step 4: Prediction tool Preliminary outline. Final tool expected to be available online by Autumn 2015.

Humpback whales Relative use within 0.25° grid cells using weighting scheme by Block et al. (2011). Application of state-space model and integration with environmental data completed.

Humpback Whale Habitat Model Generalized additive model Response: Relative use Deviance explained=60.6% Smoother term edfP-value SST2.861<0.001 Log(CHL)2.600<0.001 SSH2.958<0.001 Ekman Upwel.2.996<0.001 Depth2.327<0.001 Bailey et al. in prep.

Model Prediction Dec 2004 Dec 2005 Bailey et al. in prep. Jul-Nov Becker et al. 2012

Gray whales Eastern North Pacific (ENP) population. Tagged in March 2005 off Mexico (n=17). Pacific Coast Feeding Group (PCFG). Tagged in Sept-Dec 2009 off Oregon (n=18).

Gray whale speeds Model by M. DeAngelis Migration speeds All speeds By H. Bailey

PCGF Foraging Grounds Significant relationship between probability of foraging behavior and the environmental variables water depth and SST. Foraging more likely to occur in shallower depths and colder temperatures. Generalized linear mixed model by H. Bailey

Fin whale and the environment Track days

Multi-species comparison Examined SST distributions for whale positions off U.S. West Coast and then scaled by their population abundance to estimate probability of occurrence. By H. Bailey Jan-May

Summer/Autumn Jun-Dec

Species Probabilities This could be used to identify likely species if, for example, an unidentified species is reported entangled. X Species11- 12°C °C 13-14°C Gray98.3%99.0%98.2% Humpback1.6%0.7%1.6% FinNA Blue0.1%0.3%0.2% Species11-12°C12-13°C13- 14°C Gray20.0%10.1%2.1% Humpback58.8%66.6%47.7% Fin0%8.6%40.8% Blue21.2%14.7%9.4% Jan-MayJun-Dec

Acknowledgements We are grateful to Monica DeAngelis for her advice and support of the project. We thank Jessica Wingfield for her assistance. 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. NASA data served by ERDDAP at the NOAA/NMFS 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.

Thank you! Photo courtesy of the Marine Mammal Institute, OSU.