1. Using ecosystem modeling for fisheries management Cape Town, September 2006 IncoFish WP4 Workshop Villy Christensen

2. 2 Are ecosystem models useful for fisheries management?

3. 3 “One of those really smart quotes” “We believe the food web modelling approach is hopeless as an aid to formulating management advice; the number of parameters and assumptions required are enormous.” Hilborn and Walters (1992, p. 448) “We believe the food web modelling approach is hopeless as an aid to formulating management advice; the number of parameters and assumptions required are enormous.” Hilborn and Walters (1992, p. 448)

4. 4 Willie asked the right question... Why don’t the fish eat them all, dad?

5. 5

6. 6 Prey behavior limits predation (foraging arena assumptions) A key aspect of EwE modeling:

7. 7 Organisms are not chemicals! Ecological interactions are highly organized Big effects from small changes in space/time scale Reaction vat modelForaging arena model Prey eaten Prey density Prey eaten Prey density Prey behavior limits rate Predator handling limits rate

8. 8 Unavailable prey B-V Available prey, V vV Predator, P Foraging arena v = behavioral exchange rate (‘vulnerability’); predator-prey specific; based on foraging arena theory (Walters and Juanes, 1993) aVP v(B-V)

9. 9 Time predictions from an ecosystem model of the Georgia Strait, 1950-2000 With mass-action (Lotka-Volterra) interactions only: With foraging arena interactions:

10. 10 A critical parameter: vulnerability Top-down/bottom-up “control” & carrying capacity

11. 11 Predator abundance Predicted predation mortality ‘Traditional’ Ecosim Predation mortality: effect of vulnerability Bottom-up Top-Down High v Low v Carrying capacity 0 0 Ecopath baseline V = = 2

12. 12 So how do we get estimates of carrying capacity? Surveys Assessments –Stock reduction analysis Surveys Assessments –Stock reduction analysis Numbers (x 1000) Fin whales Blue whales Christensen, LB, 2006 Year

13. 13 Evaluation of simulations Can the model –replicate historic trends? –make plausible extrapolations to novel situations? Can the model –replicate historic trends? –make plausible extrapolations to novel situations?

14. 14 Fitting to time series: learning from ecosystem history A proliferation of ecosystem modeling activities has in recent years produced many apparently credible models that fit historical data well and make reasonable policy predictions

15. 15 Ecosystems where EwE models have been tested using historical trend data E Bering Sea Aleutian Islands W&C GoAlaska E GoAlaska W Vancouver Island Hecate Strait British Columbia Shelf Strait of Georgia NE Pacific CN & ET Pacific NWHI, Hawaii Gulf of California Central Chile E Bering Sea Aleutian Islands W&C GoAlaska E GoAlaska W Vancouver Island Hecate Strait British Columbia Shelf Strait of Georgia NE Pacific CN & ET Pacific NWHI, Hawaii Gulf of California Central Chile Bay of Quinte Oneida Lake Scotian Shelf Chesapeake Bay Tampa Bay S Brazil Bight Norwegian Sea North Sea Baltic S Benguela Gulf of Thailand South China Sea Bay of Quinte Oneida Lake Scotian Shelf Chesapeake Bay Tampa Bay S Brazil Bight Norwegian Sea North Sea Baltic S Benguela Gulf of Thailand South China Sea

16. 16 Formal estimation Ecosystem model (predation, competition, mediation, age structured) Climate Nutrient loading Nutrient loading Fishing Predicted C, B, Z, W, diets Observed C,B,Z,W, diets Observed C,B,Z,W, diets Log Likelihood ( B CC /B 0 ) (Diet 0 ) (Z 0 ) Habitat area Habitat area Error pattern recognition Choice of parameters to include in final estimation (e.g., climate anomalies) Judgmental evaluation Modeling process: fitting & drivers Search

17. 17 Confounding of fishery, environment, and trophic effects: monk seals in NWHI Initial Ecosim runs: fishing & trophic interactions together could not explain monk seal decline. Predicted lobster recovery Initial Ecosim runs: fishing & trophic interactions together could not explain monk seal decline. Predicted lobster recovery Satellite chlorophyll data indicate persistent ~40% decline in primary production around 1990. ‘Explains’ both continued monk seal decline and persistent low lobster abundance Satellite chlorophyll data indicate persistent ~40% decline in primary production around 1990. ‘Explains’ both continued monk seal decline and persistent low lobster abundance Low Chl Fishing effort: 19702000

18. 18 Are seals causing fish declines in the Georgia Strait? Is it fishing? Is it environ- mental change? Or, is it all three? 1950 2000

19. 19 Strait of Georgia EwE PP & Index of Fraser River runoff (March- April salinity at two measuring stations) Dave Preikshot, UBC FC

20. 20 BC Shelf biomass changes Dave Preikshot, UBC FC

21. 21 BC shelf: Upwelling index in May, June, and July. ≥10 year period Dave Preikshot, UBC FC

22. 22 Northeast Pacific biomass changes Dave Preikshot, UBC FC

23. 23 Northeast Pacific: PDO index (Pacific Decadal Oscillation), April to July. 50 year period Dave Preikshot, UBC FC

24. 24 Why have Steller sea lions declined? Guenette, Heymans, Christensen & Trites (CJFAS Nov 2006)

25. Competitive Interactions Fishing Ocean Climate Change Predation Alaska Aleutian Islands Guénette, Heymans, Christensen & Trites (MS) 196019802000 0 10,000 20,000 30,000 40,000 Abundance Competitive Interactions Predation Fishing

26. General finding: multiple factors impact ecosystem resources (in all but the easiest cases)

27. 27 Evaluating trends 1.Fishing pressure 2.Trophic impact, including competition 3.Environmental impact 4.Nutrient loading As a rule: All of the above contribute 1.Fishing pressure 2.Trophic impact, including competition 3.Environmental impact 4.Nutrient loading As a rule: All of the above contribute

28. 28 It’s beginning to look like it; We can with some credibility describe agents of mortality and trophic interdependencies; Evaluation of relative impact of fisheries and environmental factors is progressing; As a rule we need to invoke fisheries and environmental drivers to fit models. It’s beginning to look like it; We can with some credibility describe agents of mortality and trophic interdependencies; Evaluation of relative impact of fisheries and environmental factors is progressing; As a rule we need to invoke fisheries and environmental drivers to fit models. Are we finally able to develop useful predictive models for ecosystem management? When we have a model that can replicate development over time we can (with some confidence) use it for ecosystem-based policy exploration.

29. 29 Report card: Using models to address ecosystem management questions CONCERNGRADECOMMENT Bycatch impactsA- We are not bad at predicting direct effect of fishing in general Top-down effects (of predator culling or protection) C Trophic effects of fishing can be classified as ‘top down’ or ‘bottom up’ with respect to where management controls are exerted - on valued preyB Changes in M for prey species already subject to assessment - on ‘rare’ preyF Outbreaks of previously rare species

30. 30 Modeling report card (cont.) CONCERNGRADE COMMENT Bottom-up effects (effects of prey harvesting on predator stocks) C Uncertainty here is about flexibility of predators to find alternative food sources when prey are fished Multiple stable states B ‘Cultivation-depensation’ mechanism appears to be main mechanism that could cause ‘flips’ Habitat damageD Lack of understanding about real habitat dependencies, bottlenecks Selective fishing practices/policies F We have not yet looked closely at options in this area! Production regime changes B Models look good when fitted to data, but have not stood test of time Regime shiftsC Policy adjustments in response to ecosystem-scale productivity change

31. 31 So are ecosystem models actually used for fisheries management?

32. 32 Use of EM for fisheries management Multispecies models –Estimating predation mortality for stock assessment; –Limit harvest of prey species to meet consumer demands; –Impact of changing mesh size, North Sea roundfish; –Minke whale and harp seal culling? –Environmental Impact Assessment (EIA), Alaska groundfish; –Target species response to TACs, Bering Sea. Multispecies models –Estimating predation mortality for stock assessment; –Limit harvest of prey species to meet consumer demands; –Impact of changing mesh size, North Sea roundfish; –Minke whale and harp seal culling? –Environmental Impact Assessment (EIA), Alaska groundfish; –Target species response to TACs, Bering Sea.

33. 33 Use of EM for fisheries management EwE –Evaluate impact of shrimp trawling, GoCalifornia; –Evaluate impact of bycatch, GoCalifornia; –Evaluate impact of predators on shrimp, GoMexico; –Demonstrate ecological role of species, GoMexico; –Impact of proposed fisheries interventions, Namibia? –EIA of proposed fisheries interventions, Bering Sea; –EIA of alternative TAC’s, Bering Sea and GoAlaska; –Target species response to TACs, Bering Sea –Closed area sizing, Great Barrier Reef, Australia –Valuation of cormorant impact, Ortobello, Italy –South Africa pelagic fisheries: in progress. EwE –Evaluate impact of shrimp trawling, GoCalifornia; –Evaluate impact of bycatch, GoCalifornia; –Evaluate impact of predators on shrimp, GoMexico; –Demonstrate ecological role of species, GoMexico; –Impact of proposed fisheries interventions, Namibia? –EIA of proposed fisheries interventions, Bering Sea; –EIA of alternative TAC’s, Bering Sea and GoAlaska; –Target species response to TACs, Bering Sea –Closed area sizing, Great Barrier Reef, Australia –Valuation of cormorant impact, Ortobello, Italy –South Africa pelagic fisheries: in progress.

34. 34 So why aren’t ecosystem models used more for management? Lack of experience using ecosystem models for predictive purposes; Ecosystem modeling is for strategic management, and supplements the tactical single species assessment; Fisheries management process is trapped in tactical management; Strategic decisions are virtually non-existing. Lack of experience using ecosystem models for predictive purposes; Ecosystem modeling is for strategic management, and supplements the tactical single species assessment; Fisheries management process is trapped in tactical management; Strategic decisions are virtually non-existing.

35. 35 We need longer-term data than typical in assessments to avoid shifting baselines, e.g., 1950-present; –Data mining is required; –There is much more information out there: Catches, CPUE, w, … Assessments should be expanded back in time: –Stock Reduction Analysis; Biggest information gaps for: –Mid-TL forage fishes; –Novel conditions (vampires in the basement) –Estimates of mortality rates. Data gap for modeling

36. 36 Our empirical knowledge is limited Habitat and environmental changes (including those caused by fishing) and intensive fishery removals are creating novel situations, which we can only handle with difficulty: –We do not to understand the ‘mechanics’ of ecological response well enough to be able to predict all important responses to these novel situations; –Make models one can play with; Habitat and environmental changes (including those caused by fishing) and intensive fishery removals are creating novel situations, which we can only handle with difficulty: –We do not to understand the ‘mechanics’ of ecological response well enough to be able to predict all important responses to these novel situations; –Make models one can play with;

37. 37 Our capability to provide advice about large-scale dynamics is limited We cannot resolve uncertainty about how ecosystems change based on models and time-series data only;

38. 38 Predictive approaches are uncertain, for some obvious reasons Lack of long-term monitoring data on non-target species and life stages; Concentration of interaction effects (trophic, habitat) on early life stages (recruitment) that are difficult to monitor; Confounding of fishery, environmental, and trophic effects in historical data; Failure to anticipate new problems (‘vampires in the basement’) due to unpredictable changes in system structure, (exotic invasions, fisheries inventions); Unpredictable pre-adaptations to habitat alterations. Lack of long-term monitoring data on non-target species and life stages; Concentration of interaction effects (trophic, habitat) on early life stages (recruitment) that are difficult to monitor; Confounding of fishery, environmental, and trophic effects in historical data; Failure to anticipate new problems (‘vampires in the basement’) due to unpredictable changes in system structure, (exotic invasions, fisheries inventions); Unpredictable pre-adaptations to habitat alterations.

39. 39 Ecosystem modeling for adaptive management requires a very different approach to assessment Modelers must attempt to uncover alternative models that equally well explain historical data but imply different policy choices: –Environmental vs. fisheries vs. trophic effects; Policy options would include diagnostic management experiments to distinguish between the alternative models: –Spatial closures to test recovery predictions; –Ecosystem modification to test trophic interaction effects. Modelers must attempt to uncover alternative models that equally well explain historical data but imply different policy choices: –Environmental vs. fisheries vs. trophic effects; Policy options would include diagnostic management experiments to distinguish between the alternative models: –Spatial closures to test recovery predictions; –Ecosystem modification to test trophic interaction effects.

40. 40 Models are not like religion –you can have more than one

41. 41 The new Ecopath with Ecosim Four year project funded through Lenfest Ocean Program Lenfest Ocean Futures Project: –New generation of EwE to be released Sep 07 –Single-player game version 2008 –Multi-player game version 2009 Customized versions facilitated User Ownership Customized versions facilitated User Ownership