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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 It’s It’s It’s fishing climate (the PDO) everyone eating shrimp complicated… Walleye pollock, Theragra chalcogramma Adult diet shrimp euphausiids 4,000,000 stock assessment trawl survey Juvenile diet 2,000,000 copepods 1,000,000 euphausiids 2000 year 1990 1980 1970 0 1960 biomass (t) 3,000,000 Pacific cod, Gadus macrocephalus Adult diet pollock stock assessment 800,000 shrimp trawl survey Juvenile diet 600,000 400,000 shrimp 200,000 benthic amphipods year 2000 1990 1980 1970 0 1960 biomass (t) bairdi Pacific halibut, Hippoglossus stenolepis Adult diet pollock stock assessment 800,000 trawl survey 600,000 400,000 shrimp 200,000 year 2000 1990 1980 1970 0 1960 biomass (t) Juvenile diet hermit crabs Arrowtooth flounder, Atherestes stomias Adult diet pollock 2,000,000 capelin stock assessment trawl survey 1,500,000 1,000,000 capelin 500,000 euphausiids year 2000 1990 1980 1970 0 1960 biomass (t) Juvenile diet 4,000,000 Pollock P. cod Arrowtooth Halibut 1990-1993 snapshot 2,000,000 1,000,000 year 2000 1990 1980 1970 0 1960 biomass (t) 3,000,000 Mass balance to dynamic simulation Bioenergetics and mass accounting Population rates (total mortality is key) M2 GE M0 B P/B Q/B DC EE Catch BA q Vul (Bstart) Equilibrium built here, perturbed here Alternate stable states possible?? 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). Model structure: alternative myths Predator Production/Biomass • “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… 1 P/B (Age-structured von Bertalanffy) P/B (Ecosim foraging risk) 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 1 2 3 Predator Biomass 4 5 6 Alternative myths II Predator growth efficiency • 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. 0.6 Growth efficiency (Age-structured von Bertalanffy) Growth efficiency (Ecosim foraging risk) 0.5 0.4 0.3 0.2 0.1 0 0 1 2 3 4 5 6 Predator Biomass • 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.” Forcing alone (no fitting of Ecosim parameters) in the Northern California Current (Field 2004): This run is forced by NPZ output time series (19671998) and fishing mortality derived from catches and stock assessments Forcing: fishing only (big 4) Forcing (Fishery) Fit to Catch Fit to Biomass Fitting with fishing only, but add 60’s POP fishery Forcing (Fishery) Fit to Catch Fit to Biomass Fitting using fishing only—all GOA time series Fitting using fishing, pollock recruitment—all series Fitting using fishing and all recruitment—all series 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? 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? 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 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” Too complex—uncertainty overwhelms? • Each systematically added group adds constraints as well as data requirements, does one outweigh the other? GOA data pedigree Base arrowtooth trajectory baseGOA_1000 Arrowtooth_Adu 25 tons/km^2 20 15 10 5 0 2004 2014 2024 year 2034 2044 2054 1000% -200% Transient kille Sleeper sharks Salmon sharks Toothed whales Stellar sea lio Other rockfish Skates Seals Pacific halibut Macrouridae Arrowtooth fl. Pisc. birds Pacific cod Spiny dogfish Sculpins Cephalopods Baleen whales Sablefish Shortraker/roug Thornyheads pollock (all) other demersal POP/northern/du Jellyfish SMALL Pacific herring Salmon osmeridae Hexagrammidae Zoarcidae sandlance bathypelagics C. bairdi C. opilio King crab Shrimp EPIFAUNA LARGE ZOOP INFAUNA Benthic Amph. Copepods Ocean productio Phytoplankton 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... baseStd med 800% 600% 400% 200% 0% 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… Fish out arrowtooth “magically” magicATF_FisM_1000 Arrowtooth_Adu 12 10 tons/km^2 8 6 4 2 0 2004 2014 2024 year 2034 2044 2054 Scenario difference from base magicATF_FisM_1000lessBase Arrowtooth_Adu 0 2004 2014 2024 2034 Percent change -20 -40 -60 -80 -100 -120 year 2044 2054 -1 Pandalidae Shortspine Thorns_Juv Fish Larvae NP shrimp Dusky Rock N. Fur. Seal_Juv Mysid N. Fur. Seal_Adu W. Pollock_Juv Offal Capelin Pacific Grenadier Giant Grenadier Shortraker Rock Dover Sole Rougheye Rock Other Macruids Scypho Jellies Prickle squish deep Sea Star Central S.S.L._Adu Other sculpins Other Sebastes Central S.S.L._Juv Herring_Juv Salmon Sharks P. Halibut_Adu Greenlings P. Cod_Adu West S.S.L_Adu West S.S.L_Juv Eelpouts Resident seals Arrowtooth_Juv Sleeper Sharks Atka_Adu Sablefish_Adu Arrowtooth_Adu W. Pollock_Adu Herring_Adu Fish out arrowtooth “magically” (F on arrowtooth increases with no bycatch) 5 Median 4 3 2 1 0 -1 Sea Star 4 Bathyraja Aleutica (Aleutian skate) 5 Central S.S.L._Adu Central S.S.L._Juv Giant Grenadier Shortspine Thorns_Juv Other Macruids Other Sebastes Salmon Sharks P. Halibut_Adu Herring_Juv King Crab P. Cod_Adu Raja binoculata (Big skate) Greenlings Rougheye Rock Shortraker Rock Bathyraja interupta (Bering skate) Prickle squish deep West S.S.L_Juv West S.S.L_Adu Rex Sole Offal Raja rhina (Longnosed skate) Bathyraja maculata (Whiteblotched) Resident seals Eelpouts Arrowtooth_Juv Shortspine Thorns_Adu Sleeper Sharks S. Rock sole Sablefish_Adu Misc. Flatfish Atka_Adu Dover Sole N. Rock sole Arrowtooth_Adu W. Pollock_Adu Discards Herring_Adu Fish out arrowtooth “realistically” (increase flatfish fishery q for arrowtooth) Median 3 2 1 0 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 -200% 200% 100% 0% -400% Transient kille Sleeper sharks Salmon sharks Toothed whales Stellar sea lio Other rockfish Skates Seals Pacific halibut Macrouridae Arrowtooth fl. Pisc. birds Pacific cod Spiny dogfish Sculpins Cephalopods Baleen whales Sablefish Shortraker/roug Thornyheads pollock (all) other demersal POP/northern/du Jellyfish SMALL Pacific herring Salmon osmeridae Hexagrammidae Zoarcidae sandlance bathypelagics C. bairdi C. opilio King crab Shrimp EPIFAUNA LARGE ZOOP INFAUNA Benthic Amph. Copepods Ocean productio Phytoplankton 1000% Transient kille Sleeper sharks Salmon sharks Toothed whales Stellar sea lio Other rockfish Skates Seals Pacific halibut Macrouridae Arrowtooth fl. Pisc. birds Pacific cod Spiny dogfish Sculpins Cephalopods Baleen whales Sablefish Shortraker/roug Thornyheads pollock (all) other demersal POP/northern/du Jellyfish SMALL Pacific herring Salmon osmeridae Hexagrammidae Zoarcidae sandlance bathypelagics C. bairdi C. opilio King crab Shrimp EPIFAUNA LARGE ZOOP INFAUNA Benthic Amph. Copepods Ocean productio Phytoplankton No fishing (top), 2xF (bottom) noFlessBase median 800% 600% 400% 200% 0% 2FlessBase median -100% -200% -300% -50% Species affected by a 10% increase in W. Pollock production Species affected by a 10% increase in W. Pollock production EBS Shortspine Thorns_Juv Grenadiers Misc. fish shallow Greenlings Shortspine Thorns FH. Sole Shortraker Rock Rougheye Rock -30% Offal -20% Steller Sea Lion_Juv -10% Resident seals 0% Steller Sea Lion 10% P. Halibut 20% W. Pollock_Juv 40% W. Pollock 30% Percent change in equilibruim production 50% P. Cod Steller Sea Lion_Juv Steller Sea Lion Kamchatka fl._Juv Herring Wintering seals W. Pollock Kamchatka fl. Resident Killers Shortraker Rock Other skates Sablefish Offal P. Halibut Alaska skate Percent change in equilibrium production Predicting effects beyond our control • Changes in species or group production • Evaluate system structure, relative predictability 50% 40% GOA 30% 20% 10% 0% -10% -20% -30% -40% -40% -50% 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) 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 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…