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Splitting Historical Blue Whale Catches using Spatial GAMs

This study investigates the historical catches of North Pacific blue whales (B. musculus) from 1905 to 1971, focusing on the eastern (ENP) and western (WNP) populations. Utilizing spatial Generalized Additive Models (GAMs) and incorporating acoustic data, we aim to differentiate stock identities amidst uncertainties related to date and location. Our findings suggest that better data and sensitivity analyses could refine ecological assumptions and enhance assessments of ENP and potentially WNP populations in the future, addressing the long-standing issue of conflated catch data.

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Splitting Historical Blue Whale Catches using Spatial GAMs

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  1. Splitting Historical Blue Whale Catches using Spatial GAMs Mizrochet al. 1984 Cole Monnahan 1/16/2012

  2. Background/Motivation • North Pacific Blue whales (B. musculus) were exploited commercially from 1905-1971 • Endangered, but no assessment due in part to conflated catches Themes: • 2 Stocks: the eastern (ENP) and western (WNP) based on morphological¹ and acoustic² analyses • Uncertainty: date, location, statistical, ecological ¹ Gilpatrick & Perryman 2008, ² Stafford et al. 2001

  3. Ecology WNP ENP

  4. Land Stations

  5. Catches Before 1950

  6. Floating Factories Photo: A. Berzin

  7. Catches After 1950

  8. Catches from 1905-1971

  9. Acoustic Data Locations Acoustic Hydrophones Goal: Infer stock identity of catches (using the acoustic data)

  10. Acoustic Data Dates 1999/2001 GoA 1995/1996 NP 1995/1996 NP 1996/1997 Eastern Tropical Pacific (ETP)

  11. Acoustic Spectrograms Image from: Stafford 2003, Two types of blue whale calls recorded in the Gulf of Alaska

  12. Acoustic Data

  13. Western population only in western Aleutians Eastern population only in ETP

  14. Models • A priori predictors: Lat/Long and Month cover the spatial and temporal stock movements • Generalized Additive Models (GAM) • Over-dispersion is expected, so a beta-binomial distribution is used • Jackknifing bootstrapping to quantify statistical uncertainty of predictions

  15. Binomial vs. Beta-Binomial Point: accounts for over-dispersion

  16. Over-dispersed Binomial

  17. GAM Models • AICsupports beta-binomial and GAM • GAMLSS [Rigby & Stasinopoulos 2002] in R • Separate models for ENP and WNP of form: The model predicts: the probability of observing a call at a given location/month in an hour.

  18. Residuals for ENP Models

  19. Eastern Model Surface

  20. Western Model Surface

  21. Final Model The final probability of catch being ENP is then: Ecological Assumptions: • Populations call at the same rates • Relative population sizes stable over decades • Movement patterns stable over many decades • AB song distribution reflects all demographic groups

  22. Ecological Uncertainty Deviations from α=1 lead to unexplained uncertainty Vary α to explore sensitivity to assumptions

  23. Probability of Eastern (α=1)

  24. Statistical Uncertainty: Jackknifing

  25. Jackknifed Errors: May

  26. Probability of Eastern (α=1)

  27. Results: Best Estimate

  28. Results: Min ENP

  29. Results: Max ENP

  30. Final Results

  31. Conclusions • This approach splits the catches and tries to accurately quantify the uncertainty • With more data, the ecological assumptions could be tested. But sensitivity analyses are best option. • It allows assessment of ENP (chapter 2!)…and maybe WNP in the future

  32. Acknowledgements • Trevor Branch: Advice and funding • International Whaling Commission (IWC): Provided annual catch and individual database. • Kate Stafford (UW Applied Physics Lab): Raw acoustic data on call types (+ advice). • YuliaIvashchenko(NMML): Providing Soviet catch data • Andre Punt (UW SAFS): Suggesting the beta-binomial GAM And of course….

  33. Questions?Comments?Advice?

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