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One model to rule them all? Talk is about current framework, context. Many slides of details, equations, set aside for discussion. Focus on groundfish, groundfish issues and predator/prey models on “whole ecosystem” scale and climate links to “whole ecosystem” models and ones that have been LINKED TO MANAGEMENT PROCESS with Statistical considerations. “Lessons learned”. (May discuss some IBMs following Al’s presentation). Groundfish-centric history of AFSC “ecosystem” models and issues • Laevastu 1970s – 1980s – Stock assessment focus: – Surplus production Limits on total fisheries production – (Indirectly) instrumental in developing Optimal Yield cap (imposed outside of model calculation) – Model next moves to Polovina to become ECOPATH. – Meanwhile… Groundfish-centric history of AFSC “ecosystem” models and issues • Stock assessments – Predation mortality (cannibalism or multispecies) key to unpredictable interactions (i.e. pollock). Several examples, beginning Honkalehto 1988. – Climate becomes an obvious player, incorporated (correlations, mechanisms) Groundfish-centric history of AFSC “ecosystem” models and issues • Then, all at once: EBFM, Steller Sea Lions, PSEIS • MSVPA (mainly for predation mortality of key species) JuradoMolina and Livingston • Pollock prediction models (FOCI) : most valuable species was least predictable, most eaten by everything. • Bormicon/Gadget spatial model (never advanced) Livingston • Ecosim started as S.S.L focused-investigation (EBS found limited effects on scale of model) Trites and Livingston Now Ecosim for EBS, GOA, ALEUTIANS used for PSEIS style analyses. • Multispecies Bycatch Model • Steller models and IBMs • MRM models: Cod/Crab. Atka/Cod/Pollock/SSL.Quinn et al. GOA Current issues • Programatic Supplemental Environment Impact Statement – Development of Ecosystem Considerations, designed to include trends but also model analyses. – Ecosystem Considerations are part of stock assessment process, regular reports to Council. • Modeling? – Include CUMULATIVE EFFECTS, OY analyses, etc. – Model policy tradeoffs: TAC EA and EIS – BiOp for Stellers •(Was a draft of a proposal for EcoFore, wasn’t submitted). Integration with management is key • Developing and integrating multiple models with different scales and different simplifying assumptions Insights gained • NEED CONTEXT: Integrating ecosystem and habitat information into current single species management process • Integrating ecosystem information with more spatial habitat information “Non believer” “Realist” Belief in model “Believer” Research survey Abundance data Biological data: Catch at age, size Life history Commercial fishery Catch data Stock assessment Ecosystem Stuff??? Plan Team Review Initial ABC OFL Scientific & Statistical Committee Final ABC OFL Advisory Panel Initial TAC Public input North Pacific Fishery Management Council Public input Final TAC specifications Ecosystem Considerations Organization I. – – – Ecosystem Assessment Summarize historical climate and fishing effects on BSAI and GOA Summarize possible future effects of climate and fishing on ecosystem structure and function (using multi-species and ecosystem models)–in progress MODELS for: what do the current indicators mean and what UNCOLLECTED indicators might be important and create testable hypotheses. II. Ecosystem Status Indicators – Historical and current status and trends III. Ecosystem-Based Mngt Indices – Early signals of direct human effects How will multispecies models affect a catch level? • Crises – BiOp (endangered, threatened) • Strategic – never done explicitly – Cumulative effects of harvest rates – Optimum Yield (2,000,000 mt?) – Tradeoffs between species • Single species – several avenues, none explict or mandated – – – – – – Currently part of “ecosystem considerations”, may affect Tiers. Reference points (F-40) Informational (qualitative MSE) Management Strategy Evaluation Addition of mortality to single species Adjustment of uncertainty Needed for ecosystem assessment • Issue driven or framework driven? (preparation for rapid scenario investigation in response to issues) • Many components or too many compoments? (140+ species, and not enough) • Precision or range of hypotheses? • Statistical preditions/fitting (must be “right”) • MSE operational models (must be inclusive of hypotheses) • Uncertainty, and also risk assessment • Data sensitivity Conclusions I: Forecasting and Contingency Planning • Ecosystem models provide – – – – Context of species relationships for current management Fuller evaluation of uncertainty than single species models alone Ability to simulate policy options under our control and Ecosystem change outside our control • Developing a suite of integrated models – Can address specific questions at appropriate scales – Can evaluate uncertainty across and between models • Current models are in place for contingency planning, and quicker response during “crisis management” – Natural disaster – Lawsuit, EIS analysis NOAA 5-year plan • Research milestone under “Scenario Development to Support Specific Management Actions and Decisions” “Develop the next generation of multi-species fisheries and food web production models (3-5 years).” • WHAT ABOUT NOW? – This talk focuses on operational or near-operational models (Alaska examples). – Operational = quantitative, for direct use in management context (either tactical or strategic). – Primarily predator prey or predator/prey/climate, stock or regional scale, limited spatial resolution. – MANY exploratory, in-development, not predictive in stock-assement context models exist or are in development. State of the art Similar predictive issues to stock assessment • Functional response / model error? • Similar to issues Ricker v. Beverton-Holt in singlespecies management, fitting and robust statistical techniques are part of the solution. • Recruitment, climate drivers, gear/bycatch changes, management/economic effects not predictable (but these models can be driven by such inputs if the scenarios are described). • Current solutions: test hypotheses with “external forcing”, test management responses for robustness. • Next improvements should explore these forcings with COUPLED models of proposed mechanisms. But needs careful consideration of WHAT is predictable and WHEN it’s predictable! (Flatfish may be, pollock sometimes, herring ever? Some key characteristics and tools shown later may define which will work right now). Model Definitions • Biomass dynamics – Ecopath with Ecosim – Elseas (AFSC version of Ecosim) – CIE Review – GEEM • Age-structured interactions – MSVPA –CIE Review – Bycatch Interaction Model • Individual interactions – IBMs (Hinckley et al.) – Bioenergetics – Minimum realistic models: Atka/Cod, Cod/crab, GOA (Terry Quinn) • Spatial interaction models (currently don’t exist) – Atlantis – GADGET • • Highlighed are most advanced “statistically” (e.g. in stock assessment sense) SLIDES CAN BE SHOWN AS NEEDED FOR SEVERAL Others OF THESE <-statistical rigor but model error SS -> MRM ->Bycatch -> MSVPA/MSM -> Ecosim -> Gadget -> Atlantis detail and hypothesis inclusiveness -> • All of the above could develop more statistically. • Do we need to choose one, or do we need a framework for uncertainty, to make blended model predictions? • Why general models? You can’t start building specific models in a crises, but can spawn them off a big one. “This Generation” Multispecies & multimulti-fisheries management Fisheries • Multispecies Bycatch Model – Gear interactions, age structured single species dynamics, simple bycatch, complex management scenarios – (no predator/prey links) • MSVPA/MSFOR/MSSAM – Multispecies age structured predator/prey for 7 target species, adds explicit predation effects to recruitment hindcasts – (no bycatch or full system effects) • Ecopath/Ecosim – Complex system effects on non-target and protected species biomass dynamics, gear interactions, simple management scenarios – ELSEAS: full age structure for key species (forward fitting). Multiple species/stocks • Current multispecies models – “Stock assessment” scale. – Calibrated from annual or quarterly data, may be subregional parameter partitioning (but no “true” spatial movement). – Main units are “stocks” on a “management/survey region” scale. • IBM or NEMURO/Fish: Explicit spatial movement, no population closure. Council areas First: Information requirements Standard stock assessment data – Biomass or abundance index – Productivity information Fishery observation – Commercial catch – Incidental catch and discards Food habits collections – Multiple species and trophic levels – Multiple seasons Spatial resolution of data Why Multispecies? SHIFTING BASELINES Biomass at F0 (change from reference B2002) 70% Single Species Ecosim MSFOR 60% % change 50% 40% 30% 20% 10% 0% -10% pollock cod g turbot yf sole r sole herring at flounder -20% Species Key to uncertainty: this is MEDIAN % change over multiple climate scenarios and error range for inputs (error bars not shown). MSM Estimates of M2 (age-1 pollock) Predation mortality (t-1) 0.6 0.5 0.4 0.3 0.2 0.1 0 1975 1980 1985 1990 Year 1995 2000 2005 Population-scale: dB/dt = ??? Top-down control Bottom-up control vs. prey switching Life-history: Age structure and energetics Life-history: Recruitment (younger ages than SS) MSFOR Set by age structure 100% prey switching Explicit age-structure, energetics shift with agestructure External, S-R relationship possible. Ecosim Set by foraging parameter 100% bottomup Juvenile/adult to year class for some species, energetic bias of “current” age structure for others “Emergent” from parameter apportioning food to growth vs. fecundity Blended dynamics/ bioenergetics Age structure proxy plus prey selectivity parameters. Set by satiation parameter. Explicit age-structure, energetics shift with agestructure External, S-R relationship possible. GEEM Economic choice model produces Type II and Type III as emergent properties of energy maximization. Biomass dynamics only, no age structure. No explicit recruitment. ALL RESPONSES ARE NONLINEAR, HARD TO SIMPLFY. Only way to evaluate these is to fit historical data, or test results for robustness over a range of parameter values. SENSITIVITY RESULTS Ecopath food webs (EBS, GOA, AI) EBS food web Quantifying uncertainty Data quality Results for use in stock assessments Web-based format for results • Example: The ecosystem role of Pacific cod varies between systems, especially in the AI (not in proportion to biomass). – Quantifying impacts and uncertainties from removal: Pacific cod example. – Quantifying factors influencing target species: Pacific cod example. Forage fish v. squid Common data issues •“Tier 4-6” resolution of individual stocks OBSERVATION •Need continued food habits input (update ~5 years). •Need off-survey food habits data, especially late summer/early fall. •Need bycatch reconstruction, updating. Bycatch in non-NMFS fisheries? PROCESS •Recruitment and climate drivers. •Euphausiid variability/uncertainty is a key bottom-up effect in all systems. •Deepwater food web (sablefish, grenadier, squid) poorly understood. •Squids are key, poorly understood component. Not all species are equally predicable! (Why did we start with pollock??) Scales differ: (switch driven species by local studies, longer life-histories by correlative studies, etc.) LEADS TO PREDICTABILITY STUDIES… Gulf of Alaska groundfish 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 Fitting using fishing, pollock recruitment—all series Predictability 1: hypotheses testing? • GOA – all have support – Final AIC under review, each fit in “different ways”, each give wildly different “best” functional response types and fits (ecosystem states). – Climate link to primary production (Ecosim) – Climate link to pollock recruitment (Elseas) – Fishing with arrowtooth recovery from POP fishery (assumes present-day bycatch q in that fishery) • EBS has food data available to do functional fitting and distingusih hypotheses, GOA data is limited for this purpose. Food web networks GOA 2001: 307 nodes 1011 links EBS 2000: 277 nodes 1027 links Predictability 2: Structural type Biological Regime: “Climate” forcing experiment -0.4 0.00 0.05 Power (cv) 0.10 0.15 forcing anomaly -0.2 0.0 0.2 0.20 0.4 0.25 • Band-pass filtered (white noise at annual and higher timescales) for all species 0 20 40 60 years 80 100 0.2 0.5 2.0 5.0 20.0 Period (years) 50.0 200.0 0 20 40 60 Time (years) 80 100 0 20 40 60 Time (years) 80 100 Structural type Biological Regime: The Punchline 7 GOA 0 0 1 10 Density (t/km^2) 2 3 4 5 Density (t/km^2) 20 30 6 40 EBS 40 60 Time (years) 80 100 0 20 40 60 Time (years) 45 16 40 14 35 80 100 12 30 10 25 8 20 6 15 4 10 2 5 1999 1994 1989 1984 1979 1974 1969 2000 1996 1992 1988 1984 1980 1976 1972 1964 - 0 1968 20 1964 0 EBS EBS GOA GOA 0.00 0.05 Power 0.10 (cv)0.15 0.20 0.2 0.4 0.6 0.8 1.0 1.2 Bottom-up Combined Power (cv) Juv. pollock 1 1 2 2 5 5 10 20 50 10 20 50 Period Period(years) (years) 100 200 100 200 500 500 10 20 50 Period (years) 100 500 Adu. pollock 0.0 Power (cv) 0.5 1.0 GOA food web is less predictable than EBS for adult pollock: this is emergent property. SCALE for data-needs differs and is species and ecosystem specific! Combined 1.5 EBS GOA 1 2 5 200 Linking models: Ecosim and scale?? SCALE doesn’t work for our purposes (linking ecosystems, investigating climate interactions in species with strong seasonal, migratory, or bottleneck dynamics). Ecosim on its own works well for stock-scale top-down perturbations (fishing) on relatively closed populations, NOT for mechanisms: in particular, VERY poor for Pacific salmon. Latitude (°N) NEMURO vs. ECOSIM Alaska Gyre Alaska Stream Alaska Current 'OSP' Subarctic Current Subarctic Frontal Zone California Current Longitude (°W) Biomass/Average Biomass 2.5 Copepods 2 Microzoop. Large phyto. 1.5 Small phyto. NEMURO output drives seasonality in primary and secondary production in Ecosim 1 0.5 0 J F M A M J J A S O N D J F M A M J J A S O N D J Month Biomass/Average Biomass 2.5 NEMURO Pred. zoop Ecosim Euphausiids 2 1.5 RESULT: NEMURO predatory zooplanktion vs. Ecosim Euphausiids 1 0.5 0 J F M A M J J A S O N D J Month F M A M J J A S O N D J Latitude (°N) NEMURO vs. ECOSIM Alaska Gyre Alaska Stream Alaska Current 'OSP' Subarctic Current Subarctic Frontal Zone California Current Longitude (°W) 2.5 Biomass/Average Biomass Micronek. squid Mesopel. fish Lg. Jellyfish 2 Ctenophores Salps 1.5 1 Ecosim gelatinous zooplankton and forage fish 0.5 0 J F M A M J J A S O N D J F M A M J J A S O N D J Month Biomass/Average Biomass 2.5 Sharks Flying squid 2 Pink Coho 1.5 1 Large predators, and salmon (???) 0.5 0 J F M A M J J A S O N D J Month F M A M J J A S O N D J Didn’t capture salmon well, so made model linkages Pink salmon bioenergetics model, predicts daily pink salmon growth and numerical mortality based on input ration. Consumption and mortality rates for Pink salmon based on predator and prey biomass. Pink salmon body weight and numbers used to set Ecosim biomass for predator and prey equations in next timestep. Ecosim (ecosystem biomass dynamics model), run on a daily timestep. No direct feedback to NEMURO: Ecosim parameters for predatory zooplankton (euphausiids) tuned to match NEMURO predictions for same species. Daily biomass density of phytoplankton, microzooplankton, large zooplankton (copepods). NEMURO (nutrient-phytoploankton-zooplanktondetritus): 1-dimensional water column model integrated on an hourly timestep. Pink salmon growth 1800 1600 Body N Body weight (g) 1400 weight (g) s.d. (million s) N/k m2 t/km2 Jul 31.8 5.8 1,159 320 0.010 600 Aug 39.5 27.5 945 261 0.010 400 Sep 58.1 25.5 770 213 0.012 Oct 138.7 40.7 628 173 0.024 Nov 145.7 38.5 512 141 0.021 Dec 172.9 81.4 418 115 0.020 Jan 154.9 36.7 340 94 0.015 Feb 318.5 93 278 77 0.024 Mar 408.3 153.7 226 62 0.026 Apr 584.5 184.8 185 51 0.030 May 777.6 232.3 150 42 0.032 Jun 919.0 252.3 123 34 0.031 Jul 1128.9 322.5 Aug 1320.6 336.6 Sep 1523.1 397.6 1200 Month 1000 800 200 0 Jul Aug Sep Oct Nov Dec (Ishida et al. 1998) Jan Feb Mar Apr May Jun Jul Aug Additional data 16 14 OSP temperature SST (°C) 12 10 8 6 4 2 Month Prey group cal/g wet weight species and range Copepods 700 Neocalanus cristatus, 627-748 Euphausiids 1000 Thysanoessa spp., Euphausia spp., 840-1050 Pteropods 650 Limacina helicina; 624-940 Amphipods 800 Parathemisto pacifica; 852-1010 Ctenophores 50 Beroe sp., 47 Salps 36 Salpa sp., 36 Chaetognaths 450 Sagitta elegans; 455-488 pelagic forage fish 1200 mesopelagic forage fish 2000 Stenobrachius leucopsarus; Tarletonbeania crenularis; Leuroglossus schmidti; 2041-2365 (Bering Sea) Micron. squid 1500 Berryteuthis anonychus; 1307-1737 (increases with increasing mantle length). Gasterosteus aculeatus; 1166-1533 Prey quality Dec Nov Oct Sep Aug Jul Jun May Apr Mar Feb Jan 0 Results 2.5 Euphausiids, amphipods and pteropods Copepod Micron. squid biomass/reference biomass 2 1.5 1 0.5 biomass/reference biomass 0 1.2 A S O N D J F M A M J J A S O N D J F M A M J J A month 1 0.8 0.6 0.4 Pink salmon biomass 0.2 0 pink salmon body weight (g) 1400 A S Empirical O N 1200 D J F M A M J J A S O N D J F M A M J J A O N D J F M A M J J A month Modelled 1000 800 600 400 200 0 A S O N D J F M A M J J A month S Matching growth rates Run calibrated for “fast growth” (fits base growth rate to later data points) Original run pink salmon body weight (g) 1400 Empirical 1200 Modelled 1000 800 600 400 200 0 A S O N D J F M A M J J A month S O N D J F M A M J J A Sensitivity analysis Original run pink salmon body weight (g) 1400 Empirical 1200 Modelled 1000 800 600 400 200 0 A S O N D J F M A M J J A S O N D J F M month % change in input variable Parameter Base +10% -10% Salmon entry day 228 (Julian day) -0.8% +1.1% Starting body weight 39.5 g +3.8% -4.1% Average water temperature 8.68 °C -2.8% +1.1% Seasonal temperature amplitude 3.16 °C -1.2% +1.1% Prey cal/gram wet weight Varies by diet item +24.3% -22.1% % change in final pink salmon body weight A M J Diet switching? 100% Diet % by weight 90% 80% Other 70% 60% Fish 50% Squid Amphipods 40% 30% Pteropods Euphausiids 20% Copepods 10% 0% 625 (n=3) 875 1125 1375 1625 1875 2125 (n=65) (n=92) (n=125) (n=137) (n=66) (n=26) 2375 2500 (n=10) (n=5) Body weight category 1 Proportion squid in diet 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 500 1000 1500 2000 Body weight (by category) 2500 3000 Finally: mixed layer depth Original run pink salmon body weight (g) 1400 Mixed-layer depth Empirical 1200 Fast growth Modelled 1000 800 600 400 200 0 A S O N D J F dependence M A M J Density J A S O N D J F M A M J J month Concentration of prey density inversely proportional to mixed-layer depth. A Model WHAT NEXT? Biogeography: N/S or E/W?? Circulation connecting gyres (squid and other species). More detailed salmon models with more detailed density-dependence: MULTIPLE BOTTLENECKS model. More realistic climate scenarios (eg. changes in mixed-layer depth -> NEMURO -> food webs in coupled models). What made this successful: Key feature: Can swap out components Some specific modeling data needs (example) • Movement (float tracking, etc) for development of spatial models (Stockhausen in progress). • Local scale studies (hotspots, especially for pollock and other forage). • Interaction matrices (diets, bycatch, economics). • The rest are issue specific (analyses can be provided). • Ecosystem Considerations needs, especially climate/fish links (e.g. flatfish) (come back/talk to Boldt). Collaboration Lesson • Explicit between management/decision-bodies as well as scientists: REFM, NMML, AK, Fish and Wildlife • Data SYSTEM • RESULT Framework for nesting and feedback of nesting (getting together data within the center takes a long time). • Scientists from multiple fields • Workshop-driven construction processes are a good thing, multiple people at table. PSEIS mandate has driven GOOD integration and team-building within groundfish community, models have helped focus that quite a bit. But it was a painful process (time). Next level, integration with marine mammal management is limited by different mandates and cultures. Framework needs • Predictive framework • Results exchange/feedback • Data framework