1. GOA Retrospective analysis Model use: hypothesis testing The system, the stories, and the “data” The model: Elseas; like Ecosim but more flexible for our purposes The simple and clear hypotheses: what drives species trends in the GOA? –It’s fishing –It’s climate (the PDO) –It’s everyone eating shrimp –It’s complicated…

2. Walleye pollock, Theragra chalcogramma euphausiids copepods euphausiids shrimp Juvenile diet Adult diet

3. Pacific cod, Gadus macrocephalus pollock benthic amphipods shrimp bairdi shrimp Juvenile diet Adult diet

4. pollock Juvenile diet shrimp hermit crabs Pacific halibut, Hippoglossus stenolepis Adult diet

5. Arrowtooth flounder, Atherestes stomias euphausiids pollock Juvenile diet Adult diet capelin

6. 1990-1993 snapshot

7. Mass balance to dynamic simulation B P/B Q/B DC EE Catch BA Bioenergetics and mass accounting M2 GE M0 q Vul (B start ) Population rates (total mortality is key) Equilibrium built here, perturbed here Alternate stable states possible??

8. Modeling Recruitment –A delay-difference equation with juveniles divided into monthly pools: Fixed age at recruitment Adjustable relationship between food intake and fecundity –Knife edge recruitment to fishery, spawning, and ontogenetic diet switch. –Spawning biomass is not directly comparable to stock assessments (because stock assessments vary).

9. “Surplus” production (compensation) is an absolute requirement for sustainable single-species fishing. As biomass decreases, production per biomass must increase. This can happen in more than one way… Model structure: alternative myths

10. In von Bertalanffy (vB) models (MSVPA, single species), fishing compensation comes from increasing growth rates (conversion efficiency) of relatively younger fish in a fished population. In Ecosim, compensation comes from increased per-capita consumption: all at the expense of other species. By definition, in Ecosim there is no true energetic “surplus,” it all comes from other species. Conversely, in vB models there is no “bottom up control.” Alternative myths II

11. Forcing alone (no fitting of Ecosim parameters) in the Northern California Current (Field 2004): This run is forced by NPZ output time series (1967- 1998) and fishing mortality derived from catches and stock assessments

12. Forcing: fishing only (big 4) Forcing (Fishery)Fit to CatchFit to Biomass

13. Fitting with fishing only, but add 60’s POP fishery Forcing (Fishery)Fit to Catch Fit to Biomass

14. Fitting using fishing only—all GOA time series

15. Fitting using fishing, pollock recruitment—all series

16. Fitting using fishing and all recruitment—all series

17. Summary… Can’t explain system dynamics (species trends) –with fishing alone (unlike in other, more heavily fished systems) –with simple climate (PDO) forcing of primary production Reproducing “known” groundfish dynamics –OK when forcing with stock assessment “data” Recruitment variability dominates this system?

19. Predictive Process: predict, communicate, use Between Prediction and Use –What ought to be predicted? –How are predictions actually used? Between Prediction and Communication –What does the prediction mean in operational terms? –How reliable is the prediction, and how is uncertainty conveyed? Between Use and Communication –What information is needed by the decision maker? –What content or form of communication leads to the desired response?

20. Predictive Potential Single Species Stock Assessment Model –Unknown parameters fit using data, updated annually –Predict direct effects of fishing on target populations –Quantitative prediction, 1-2 years out Ecosystem Model –Predict direct effects of fishing on nontarget species –Predict indirect effects of fishing mediated by trophic interactions –Predict consequences of ecosystem changes not related to fishing, therefore beyond our control –Qualitative predictions, must incorporate uncertainty

21. Data requirements in a simple food web Biomass (B) Population growth rate or Production (P/B) Consumption (Q/B) Diet comp (DC) For ALL groups!! Alternative: solve for B assuming a fixed proportion of production is used in the system: “top down balance”

22. Each systematically added group adds constraints as well as data requirements, does one outweigh the other? Too complex—uncertainty overwhelms?

23. GOA data pedigree

24. Base arrowtooth trajectory

25. Results: “Base trophic uncertainty” Bars show 95% confidence interval for year-50 biomasses in accepted ecosystems; symbols show varied assumptions of functional responses Limited confidence of exactly where system will be in 50 years, but patterns do emerge...

26. Predicting trophically mediated fishing effects (and level of control in a system): Try to fish out arrowtooth? What effect would a “magic” arrowtooth reduction have? What might a real increase in targeting of arrowtooth look like? Different tradeoffs…

27. Fish out arrowtooth “magically”

28. Scenario difference from base

29. Fish out arrowtooth “magically” (F on arrowtooth increases with no bycatch)

30. Fish out arrowtooth “realistically” (increase flatfish fishery q for arrowtooth)

31. Predicting fishing effects on nontarget species Can we use knowledge of some system components to learn about effects of fishing on nontarget species? Apply the same method to “small” Gulf of Alaska model… Perturbations are new: stop fishing, increase fishing on all, increase target fishing to MSY levels for major groundfish

32. No fishing (top), 2xF (bottom)

33. Predicting effects beyond our control Changes in species or group production Evaluate system structure, relative predictability

34. Conclusions Predictive potential? –Most powerful when considering uncertainty –Error bars incorporate both data quality and predictability –Direction of change a robust indicator –The GOA and the EBS may have different levels of predictive potential—useful information for management Implications for policy –Keep active policy options for changing fishing mortality –Explore new policy options for preparing for the unexpected (system change will happen)

35. Discussion: What controls recruitment variability? Ideas: –The difference between the single species models’ recruitment predictions and the ecosystem model’s may reflect the effect of predation –So, these models can measure the proportion of recruitment variability due to trophic effects Next step: –Fit to series of diet composition to identify prey switching, quantify mortality due to predation –Time series of low trophic level production would help—output from NPZ model as in NCC

36. Discussion: When does fishing matter? Is there a threshold where dynamics switch from “recruitment dominated” to “fishing dominated”? –How much fishing, and on whom? –Is threshold dependent on system characteristics? The tradeoff: –Cross the line, and you can explain dynamics –Stay below it but live with low predictive power –Either way you may have less fish!! The policy implications…