Rich Zabel NOAA Fisheries Seattle, WA

1. Population viability analysis of Snake River chinook: What do we learn by including climate variability? Rich Zabel NOAA Fisheries Seattle, WA

2. Population Viability Analyses Count-based PVA (Dennis et al. 1991) Based on time series of abundance ln(Nt) t

3. Population Viability Analyses Count-based PVA (Dennis et al. 1991) Based on time series of abundance ln(Nt) t

4. Population Viability Analyses Count-based PVA → l (mean annual growth rate) → Prob [falling below a threshold] → Salmon Example: McClure et al. 2003

5. Population Viability Analyses Demographic PVA (Leslie 1945) Typically based on short-term demographic data. Demographic Rates are fixed.

6. Population Viability Analyses Demographic PVA → l (Mean annual growth rate) → Sensitivity analysis: How does l change in response to changes in demographic rates? → Kareiva et al. 2000, Wilson 2003

7. Population Viability Analyses But climate effects notably absent from most PVAs PVAs are data-driven, and considerable data are required to characterize climate effects

8. Population Viability Analyses Spawners Parr Ocean Smolts Early ocean Estuary

9. Population Viability Analyses Spawners Parr Ocean Climate Smolts Early ocean Estuary

10. Population Viability Analyses “Mechanistic” PVA Relate variability in specific demographic rates to intrinsic (population density) or extrinsic (environmental) factors

11. Population Viability Analyses “Mechanistic” PVA → More realism by capturing important drivers → Combination of count-based and demographic PVA, thus can produce viability measures of both

12. Population Viability Analyses “Mechanistic” PVA → Snake River spring summer chinook → Long-term data at several life stages → Important drivers: 1) Ocean conditions upon entry 2) Density dependence in freshwater productivity

13. General Question • How does adding complexity to the models enhance our understanding of population dynamics, and hence our ability to manage populations?

14. Snake River spring/summer Chinook Listed as a threatened ESU Meta-population with 31 identified sub-populations

15. Migratory Route in the Snake and Columbia Rivers

16. Migration of Adult Snake River Spring Chinook In the Pacific Ocean

17. Age-structured Life Cycle Model for Snake River spring/summer chinook F5(n) b4·F4(n) 1 2 3 4 5 s2 s3(t) so·(1-b4) so

18. Age-structured Life Cycle Model for Snake River spring/summer chinook F5(n) b4·F4(n) 1 2 3 4 5 s2 s3(t) so·(1-b4) so Survival

19. Age-structured Life Cycle Model for Snake River spring/summer chinook Propensity to breed F5(n) b4·F4(n) 1 2 3 4 5 s2 s3(t) so·(1-b4) so

20. Age-structured Life Cycle Model for Snake River spring/summer chinook Fertility F5(n) b4·F4(n) 1 2 3 4 5 s2 s3(t) so·(1-b4) so

21. Age-structured Life Cycle Model for Snake River spring/summer chinook F5(n) b4·F4(n) 1 2 3 4 5 s2 s3(t) so·(1-b4) so Related to Ocean Conditions

23. 3 year olds 4 year olds 5 year olds

24. Smolts per spawner Freshwater productivity Smolt-to-Adult Ocean Survival

25. Third-year survival and Climate Effects • Back-calculate from Smolt-to-Adult data (and estimates of riverine survival, ocean survival, harvest, age composition) 2) Relate to Monthly Pacific Decadal Oscillation Index (PDO)

26. Third-year survival and Climate Effects OCT SEP AUG NOV Monthly PDO Indices JUL DEC Estuary Entry JUN JAN FEB MAY APR MAR

27. Third-year survival and Climate Effects OCT SEP AUG NOV Monthly PDO Indices JUL DEC JUN Estuary Entry JAN FEB MAY APR MAR

28. Third-year survival and Climate Effects

29. R2 = 0.768 Third-year Survival Year Fit of Third-Year survival to Climate Data

30. R2 = 0.768 Third-year Survival Year Fit of Third-Year survival to Climate Data Data were autocorrelated, Residuals were not

31. Predicted Third-Year survival (and 95% CI) over the 100 year PDO record Predicted Third-Year Survival

32. Freshwater Density-Dependent Recruitment

33. Beverton-Holt fit to freshwater productivity a = density-independent slope a/b = carrying capacity

34. R2 = 0.776

35. Putting it all together: Sample Model Output

36. Effects of Ocean Conditions

37. Effects of Ocean Conditions Four climate scenarios: 1900-2002 “Historic” “Recent” 1964-2002 “Bad” 1977-1997 “None” mean and variance from 1964-2002

38. “Historic” Ocean “Bad” Ocean Effects of Ocean Conditions

39. Climate Produces Autocorrelation…

40. Climate Produces Autocorrelation…

41. Effects of Ocean Conditions

42. Effects of Ocean Conditions Quasi extinction defined as < 3100 spawners

43. Effects of Ocean Conditions

44. Interactions between ocean conditions and freshwater productivity?

45. Sensitivity Analysis

46. Sensitivity Analysis Sensitivity of l to 20% increase in DD-independent Survival Sensitivity of l to 20% increase in Carrying Capacity l(t) Year

47. Sensitivity Analysis Sensitivity of l to 20% increase in DD-independent Survival r = -0.70 Sensitivity of l to 20% increase in Carrying Capacity r = 0.95 l(t) Year

48. In other words… • In time of favorable ocean conditions → More important to increase freshwater Carrying Capacity • In times of unfavorable ocean conditions → More important to increase DD-independent Survival

49. Future Directions • Meta-population structure • Other drivers: Freshwater climate effects,seawater Density dependent effects • Next step: How can we incorporate fish condition into viability models?

50. Conclusions • Very useful to relate important drivers to the specific life stages upon which they act • Climate clearly important factor for viability, both good versus bad and autocorrelation.