Patterns in biological productivity in the North Pacific Ocean

1. Patterns in biological productivity in the North Pacific Ocean Megan Stachura and Nathan Mantua University of Washington School of Aquatic and Fishery Sciences September 8, 2012

2. Synchrony in the Northeast Pacific • Synchrony of biological time series • Ecosystem regime shifts • Correspond with climatic shifts Leading principal component scores from principal component analysis of 69 North Pacific biological time series (Hare and Mantua, 2000)

3. Common patterns of variability • Positive correlation within groundfish taxonomic group and region, negative between (Hollowed et al. 1987, Mueter et al. 2007) • Grouping based on life history (Spencer and Collie, 1997) BSAI groundfish stock-recruit residuals and cluster dendogram based on these (Mueter et al., 2007)

4. Hypothesis • Organisms with related life history and habitat characteristics will show similar exposure to environmental variables and exhibit common patterns of recruitment/abundance

5. Data • Recruitment • Number of fish surviving to enter the fishery or some specified age • We have survey and fishing data of adult stages but don’t know a lot about what happens during early life • Age of recruitment varies by species • GOA walleye pollock: age-2 • GOA Pacific cod: age-0 • GOA arrowtooth flounder: age-3 • Lag time series to line up by age-0 • Estimated in stock assessment models

6. (Cooper, 2006)

7. Data • Marine fish recruitment • 13 Eastern Bering Sea and Aleutian Islands (BSAI) stocks • 15 Gulf of Alaska (GOA) stocks • 31 California Current (CC) stocks • Russian saffron cod

8. Data • Salmon • Ruggerone et al., 2010 • Wild pink, chum, and sockeye salmon • 12 regions in North America and Asia • Catch + Escapement • Lagged to year of ocean entry

9. Russia Mainland & Islands Western AK Map of regional salmon stock groups (Ruggerone et al., 2010)

10. Data • Crabs • Recruitment estimates from stock assessments • Lagged to age-0 • 6 Eastern Bering Sea stocks • 1 Gulf of Alaska stock • Other • Eastern Bering Sea zooplankton biomass • Eastern Bering Sea jellyfish biomass • 106 biological time series total

11. Stock-recruitment relationship • Account for influences of spawning stock biomass on recruitment • May be impacted by fishing • Beverton Holt • Ricker • Constant • Residuals from best model GOA arrowtoothflounder recruitment and spawning stock biomass time series and Beverton-Holt model fit

12. Multivariate analysis • Reduce these 106 time series into a smaller number of uncorrelated time series • Three methods • Principal component analysis • Non-metric multidimensional scaling • Dynamic factor analysis • Similar results for all- only presenting PCA results

13. PC 1: 12.5% of variance PC 2: 9.6% of variance

14. EBS flatfish GOA marine fish NW Pacific salmon

15. Correlation with physical variables • PC 1 positively correlated with: • Arctic Oscillation Index (r=0.40) • Multivariate El Nino-Southern Oscillation Summer Index (r=0.42) • Pacific Decadal Oscillation Summer Index (r=0.50) • PC 2 positively correlated with • Pacific Decadal Oscillation Summer Index (r=0.63)

16. PC 1 Correlation with SST and SLP Winter SST Summer SST Winter SLP Summer SLP

17. PC 2 Correlation with SST and SLP Winter SST Summer SST Winter SLP Summer SLP

18. Conclusions • Common patterns are exhibited by marine species throughout the North Pacific • Similar to those identified by Hare and Mantua (2000) • Loadings on these patterns seem related to region and life history • Large scale physical forces coordinate environment/ecosystem changes important to many stocks using different habitats across large distances • We hypothesize this is a consequence of multiple mechanisms operating in different places at the same time • A mixed-bag of sometimes similar and sometimes different mechanisms at play for different stocks within and between large marine ecosystems

19. Extra slides

20. Gulf of Alaska

21. California Current

22. Eastern Bering Sea and Aleutian Islands