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Modeling And Multiobjective Risk Decision Tools for Ecosystem Management Benjamin F. Hobbs 1 Richard Anderson 2 Jong Bum Kim 1 Joseph F. Koonce 3 1. Dept. of Geography & Environmental Engineering, The Johns Hopkins University 2. National Oceanic & Atmospheric Administration 3. Dept. of Biology,Case Western Reserve University

Goal of Presentation u Demonstrate how ecosystem-based fisheries management can be joined with ecological risk analysis under multiple management objectives

Goal of Presentation u Demonstrate how ecosystem-based fisheries management can be joined with ecological risk analysis under multiple management objectives u Introduce tools: Ecosystem model Multiobjective tradeoff analysis Bayesian evaluation of ecological research

I. Unresolved Problems in Lake Erie u Major decline of fisheries in 1990s u Unknown effects of exotic species Zebra Mussel invasion since 1988 Round Goby increase in 1990s Expected invasion of Ruffe

I. Unresolved Problems in Lake Erie u Major decline of fisheries in 1990s u Unknown effects of exotic species Zebra Mussel invasion since 1988 Round Goby increase in 1990s Expected invasion of Ruffe u Declining productivity caused by decrease in P loading u Uncertain role of habitat

Historical Variation in Fish Harvest and Environment in Lake Erie Harvest Trends

Historical Variation in Fish Harvest and Environment in Lake Erie Phosphorus Loading Harvest Trends

II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM) Inedible Algae Edible Algae Zooplankton Zoobenthos Pred. Fish PO 4 Prey Fish

II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM) Inedible Algae Edible Algae Zooplankton Zoobenthos Pred. Fish Zebra Mussel PO 4 Prey Fish

II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM) Stocking Harvest Fishing Mortality, Habitat Inedible Algae Edible Algae Zooplankton Zoobenthos Pred. Fish Zebra Mussel PO 4 Prey Fish

II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM) Stocking Harvest Fishing Mortality, Habitat Inedible Algae Edible Algae Zooplankton Zoobenthos Pred. Fish PCB Zebra Mussel PO 4 Prey Fish

The Need for Multispecies Management: Effects of Species Interactions on Max Sustained Yield u Optimal exploitation of predator varies with fishing rates of prey species

The Need for Multispecies Management: Effects of Species Interactions on Max Sustained Yield u Optimal exploitation of predator varies with fishing rates of prey species

Interaction of Walleye Harvest & Phosphorus Loading Walleye abundance/harvest has a greater influence on total fish biomass than P loading

Interaction of Walleye Harvest & Phosphorus Loading Walleye abundance/harvest has a greater influence on total fish biomass than P loading P2 P3 P4 P5 W1 W2 W3 W4 W Total Fish Biomass P Loading Walleye Effort

Implications of LEEM Studies u Fisheries and P Loading Jointly Determine Optimal Exploitation of Species

Implications of LEEM Studies u Fisheries and P Loading Jointly Determine Optimal Exploitation of Species u Derivation of Quotas for Single Species without Considering Interactions Can Lead to Overexploitation Prey and predators cannot be managed independently

III. Value of Research: Multiobjective Bayesian Framework u Information has value only if it can change decisions and improve outcomes “Value” is multidimensional!

III. Value of Research: Multiobjective Bayesian Framework u Information has value only if it can change decisions and improve outcomes “Value” is multidimensional! u Necessary elements: 1. Management context: Alternatives, objectives, decision rule

III. Value of Research: Multiobjective Bayesian Framework u Information has value only if it can change decisions and improve outcomes “Value” is multidimensional! u Necessary elements: 1. Management context: Alternatives, objectives, decision rule 2. What we know now: “States of nature” (hypotheses, parameter distributions), and confidence in each (“prior probabilities”)

III. Value of Research: Multiobjective Bayesian Framework u Information has value only if it can change decisions and improve outcomes “Value” is multidimensional! u Necessary elements: 1. Management context: Alternatives, objectives, decision rule 2. What we know now: “States of nature” (hypotheses, parameter distributions), and confidence in each (“prior probabilities”) 3. Research: Information options (monitoring, modeling, experiments), and what might be learned

III. Value of Research: Multiobjective Bayesian Framework u Information has value only if it can change decisions and improve outcomes “Value” is multidimensional! u Necessary elements: 1. Management context: Alternatives, objectives, decision rule 2. What we know now: “States of nature” (hypotheses, parameter distributions), and confidence in each (“prior probabilities”) 3. Research: Information options (monitoring, modeling, experiments), and what might be learned 4. System response: LEEM, expert judgment

III. Value of Research: Multiobjective Bayesian Framework u Information has value only if it can change decisions and improve outcomes “Value” is multidimensional! u Necessary elements: 1. Management context: Alternatives, objectives, decision rule 2. What we know now: “States of nature” (hypotheses, parameter distributions), and confidence in each (“prior probabilities”) 3. Research: Information options (monitoring, modeling, experiments), and what might be learned 4. System response: LEEM, expert judgment 5. Integrating framework: A way of determining how information affects our knowledge and choices: Decision trees, Bayes’ rule

Problem Structure Two decision stages – Research project; e h – P loading and fisheries management; a s ={a s1, a s2, a s3, a s4 }

Problem Structure Two decision stages Uncertainties θ – Research project; e h – Lower trophic level; – P loading and fisheries – Other uncertainties; –management; a s ={a s1, a s2, a s3, a s4 }

Problem Structure Two decision stages Uncertainties θ Outcomes – Research project; e h – Lower trophic level; – 10 Attributes X, combined – P loading and fisheries – Other uncertainties; using additive utility management; a s ={a s1, a s2, a s3, a s4 } function U(X)

Problem Structure No Research,e 0 Research, e 1 P(Z 1 ) Research Decision E Outcomes from Research, e h Decision A Utility Outcome U(X(a s, θ )) asas Chance node for θ θ)θ) P( P(θ |Z 1 ) asas Research, e h P(Z h ) Two decision stages Uncertainties θ Outcomes – Research project; e h – Lower trophic level; – 10 Attributes X, combined – P loading and fisheries – Other uncertainties; using additive utility management; a s ={a s1, a s2, a s3, a s4 } function U(X)

Management Levers u Phosphorus Loads: 5K, 10K (as is), and 15K ton/yr

Management Levers u Phosphorus Loads: 5K, 10K (as is), and 15K ton/yr u Exploitation effort: A measure of the number of boats or the time they spend fishing Exploitation: Trawl, Gill Nets, and Sport Harvest Base = historical exploitation level Vary exploitation by + 50%

Present State of Knowledge u Prior probabilities Uncertainties in LEEM parameters

Present State of Knowledge u Prior probabilities Uncertainties in LEEM parameters Hypotheses presented at 1999 IAGLR Modeling Summit and Lake Erie Millenium Conference  Changes in structure of lower trophic level (e.g., Zoobenthos production efficiency )  The role of zebra mussels in Lake Erie energy and nutrient flows (e.g., Zebra mussel recycling nutrients; Primary productivity as function of P loading)

Present State of Knowledge u Prior probabilities Uncertainties in LEEM parameters Hypotheses presented at 1999 IAGLR Modeling Summit and Lake Erie Millenium Conference  Changes in structure of lower trophic level (e.g., Zoobenthos production efficiency )  The role of zebra mussels in Lake Erie energy and nutrient flows (e.g., Zebra mussel recycling nutrients; Primary productivity as function of P loading) u Disregarding uncertainties may result in inappropriate, nonrobust decisions

Options for Gathering Information u Characteristics of research Cost & time Reliability of outcomes

Options for Gathering Information u Characteristics of research Cost & time Reliability of outcomes u Estimating the value of research Research revises prior probabilities  “Posterior probabilities” (new state of knowledge) New knowledge may influence management decisions Calculate value by simulating decisions with and without new information

Multiple Objective Framework for Risk Analysis Recreation Consumption Social ProductivityStructure Function Self- sustaining Native species Self-sustaining Ecological Economically important species = Economic Ecosystem Health & Human Well-being

Multiple Objective Framework for Risk Analysis Recreation X1: density of walleye X2: annual mean walleye sport Consumption X3: PCB conc in small mouth bass Social Productivity X4: total biomass Structure X5: walleye-percid biomass ratio Function X6: piscivore-planktivore biomass ratio Self- sustaining Native species X7: Biomass ratio native species to total Self-sustaining Ecological Economically important species X8: commercial walleye biomass X9: commercial yellow perch biomass X10: commercial smelt biomass Economic Ecosystem Health & Human Well-being

Bayesian Analysis Results u Priorities for objectives can affect decisions All participants prefer High trawling; most High sport harvest Split on Gill net effort

Bayesian Analysis Results u Priorities for objectives can affect decisions All participants prefer High trawling; most High sport harvest Split on Gill net effort u Ignoring uncertainty can change decisions True for 2 of 6 participants Uncertainty about Zoobenthos productivity effects of zebra mussels most important (perfect information changes decisions) Uncertainties about Zooplankton productivity and Zebra mussel recycling also important

Bayesian Analysis Results u Priorities for objectives can affect decisions All participants prefer High trawling; most High sport harvest Split on Gill net effort u Ignoring uncertainty can change decisions True for 2 of 6 participants Uncertainty about Zoobenthos productivity effects of zebra mussels most important (perfect information changes decisions) Uncertainties about Zooplankton productivity and Zebra mussel recycling also important u The value of research stems (in part) from its effect on decisions. Research has value for 5 of 6 participants Two projects most valuable:  Goby predation on mussels  Lakewide estimates of productivity Worth: tons/yr equivalent of Walleye sport harvest

Summary Heuristic application of LEEM can lead to multi-fishery rules that recognize uncertainties (P, invasions, habitat)

Summary “Ecosystem Health” can be operationalized E.g., Lake Erie stakeholders compared alternative futures using fuzzy cognitive maps and multiobjective analysis. Value judgments combined diverse “health” attributes, such as productivity, aesthetics, & community structure.

Summary Heuristic application of LEEM can lead to multi-fishery rules that recognize uncertainties (P, invasions, habitat) “Ecosystem Health” can be operationalized E.g., Lake Erie stakeholders compared alternative futures using fuzzy cognitive maps and multiobjective analysis. Value judgments combined diverse “health” attributes, such as productivity, aesthetics, & community structure. Multiobjective Bayesian analysis can include ecological uncertainties in management, and quantify the value of research E.g., fish managers made value and probability judgments for a risk analysis, & showed that intensive monitoring of lower trophic level productivity could improve fisheries management

Take Home Message: u Methods to model the decision-making process itself (multiobjective tradeoff analysis, decision trees, Bayesian risk analysis) provide an important complement to science intended to develop indicators of ecosystem health u Could be applied to MAIA or any region to support ecosystem management

Acknowledgments u Research support provided by the International Joint Commission and US Environmental Protection Agency (STAR R ) u US and Canadian environmental & natural resources managers and stakeholders for participating in modeling workshops and providing data and guidance in model development