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Notice: The views expressed here are those of the individual authors and may not necessarily reflect the views and policies of the United States Environmental Protection Agency (EPA). Scientists in EPA have prepared the EPA sections, and those sections have been reviewed in accordance with EPA’s peer and administrative review policies and approved for presentation and publication. The EPA contributed funding to the construction of this website but is not responsible for it's contents. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.

Modeling And Multiobjective Risk Decision Tools Modeling And Multiobjective Risk Decision Tools for Ecosystem Managementfor Ecosystem Management

Benjamin F. Hobbs1

Richard Anderson2

Jong Bum Kim1

Joseph F. Koonce3

1. Dept. of Geography & Environmental Engineering,The Johns Hopkins University

2. National Oceanic & Atmospheric Administration3. Dept. of Biology,Case Western Reserve University

Goal of Presentation

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

Goal of Presentation

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

Introduce tools:Ecosystem modelMultiobjective tradeoff analysisBayesian evaluation of ecological research

I. Unresolved Problems in Lake Erie

Major decline of fisheries in 1990s Unknown effects of exotic species

Zebra Mussel invasion since 1988Round Goby increase in 1990sExpected invasion of Ruffe

I. Unresolved Problems in Lake Erie

Major decline of fisheries in 1990s Unknown effects of exotic species

Zebra Mussel invasion since 1988Round Goby increase in 1990sExpected invasion of Ruffe

Declining productivity caused by decrease in P loading

Uncertain role of habitat

Historical Variation in Fish Harvest and Environment in Lake ErieHarvest Trends

0.0E+00

2.0E+07

4.0E+07

6.0E+07

8.0E+07

1960 1970 1980 1990 2000

0.0E+00

2.0E+08

4.0E+08

6.0E+08

8.0E+08Walleye

Yellow Perch

Historical Variation in Fish Harvest and Environment in Lake Erie

P Loading (MT)

0

10000

20000

30000

1960 1970 1980 1990

Phosphorus Loading

Harvest Trends

0.0E+00

2.0E+07

4.0E+07

6.0E+07

8.0E+07

1960 1970 1980 1990 2000

0.0E+00

2.0E+08

4.0E+08

6.0E+08

8.0E+08Walleye

Yellow Perch

II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM)

Inedible Algae

Edible Algae

Zooplankton

Zoobenthos

Pred.

Fish

PO4

Prey

Fish


Inedible Algae

Edible Algae

Zooplankton

Zoobenthos

Pred.

Fish

Zebra Mussel

PO4

Prey

Fish


Stocking

Harvest

FishingMortality, Habitat

Inedible Algae

Edible Algae

Zooplankton

Zoobenthos

Pred.

Fish

Zebra Mussel

PO4

Prey

Fish


Stocking

Harvest

FishingMortality, Habitat

Inedible Algae

Edible Algae

Zooplankton

Zoobenthos

Pred.

Fish PCBZebra

Mussel

PO4

Prey

Fish

The Need for Multispecies Management: Effects of Species Interactions

on Max Sustained Yield

Optimal exploitation of predator varies with fishing rates of prey species

The Need for Multispecies Management: Effects of Species Interactions

on Max Sustained Yield

Optimal exploitation of predator varies with fishing rates of prey species

0.25 0.3 0.35 0.4

0.5

0.76500

6520

6540

6560

6580

Walleye MSY

Smelt Effort

YP Effort

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

P2P3

P4P5

W1W2

W3W4

W5

0.0

1.0

2.0

3.0

4.0

5.0

6.0

Total Fish

Biomass

P LoadingWalleye Effort

Implications of LEEM Studies

Fisheries and P Loading Jointly Determine Optimal Exploitation of Species

Implications of LEEM Studies

Fisheries and P Loading Jointly Determine Optimal Exploitation of Species

Derivation of Quotas for Single Species without Considering Interactions Can Lead to OverexploitationPrey and predators cannot be managed independently

III. Value of Research: III. Value of Research: Multiobjective Bayesian FrameworkMultiobjective Bayesian Framework

Information has value only if it can change decisions and improve outcomes“Value” is multidimensional!



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






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; eh

– P loading and fisheries

management; a s ={as1, as2, as3, as4}

Problem Structure

Two decision stages Uncertainties θ– Research project; eh – Lower trophic level;

– P loading and fisheries – Other uncertainties;

–management; a s ={as1, as2, as3, as4}

Problem Structure

Two decision stages Uncertainties θ Outcomes – Research project; eh – Lower trophic level; – 10 Attributes X, combined

– P loading and fisheries – Other uncertainties; using additive utility

management; a s ={as1, as2, as3, as4} function U(X)

Problem Structure

No Research ,e0

Research, e1

P(Z 1)

ResearchDecision E

Outcomes from Research, eh

Decision A

UtilityOutcome

U(X(as, θ ))

as

Chance node for θ

θ)P(

P(θ |Z1)

as

Research, eh

P(Z h)

Two decision stages Uncertainties θ Outcomes – Research project; eh – Lower trophic level; – 10 Attributes X, combined

– P loading and fisheries – Other uncertainties; using additive utility

management; a s ={as1, as2, as3, as4} function U(X)

Management LeversManagement Levers

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

Management LeversManagement Levers

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

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 KnowledgePresent State of Knowledge

Prior probabilitiesUncertainties in LEEM parameters


Prior probabilitiesUncertainties in LEEM parametersHypotheses 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)


Prior probabilitiesUncertainties in LEEM parametersHypotheses 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)

Disregarding uncertainties may result in inappropriate, nonrobust decisions

Options for Gathering InformationOptions for Gathering Information Characteristics of research

Cost & timeReliability of outcomes

Options for Gathering InformationOptions for Gathering Information Characteristics of research

Cost & timeReliability of outcomes

Estimating the value of researchResearch revises prior probabilities “Posterior

probabilities” (new state of knowledge)New knowledge may influence management decisionsCalculate value by simulating decisions with and

without new information

Multiple Objective Framework for Risk Analysis

Recreation

Consumption

Social

Productivity Structure

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-planktivorebiomass ratio

Self-sustaining

Native species

X7: Biomass ratio nativespecies to total

Self-sustaining

Ecological

Economically important species

X8: commercial walleye biomassX9: commercial yellow perch biomassX10: commercial smelt biomass

Economic

Ecosystem Health & Human Well-being

Bayesian Analysis ResultsBayesian Analysis Results Priorities for objectives can affect decisions

All participants prefer High trawling; most High sport harvestSplit on Gill net effort



Ignoring uncertainty can change decisionsTrue for 2 of 6 participantsUncertainty about Zoobenthos productivity effects of zebra

mussels most important (perfect information changes decisions)

Uncertainties about Zooplankton productivity and Zebra mussel recycling also important



Ignoring uncertainty can change decisionsTrue for 2 of 6 participantsUncertainty about Zoobenthos productivity effects of zebra mussels most

important (perfect information changes decisions)Uncertainties about Zooplankton productivity and Zebra mussel recycling also

important The value of research stems (in part) from its effect on decisions.

Research has value for 5 of 6 participantsTwo projects most valuable:

Goby predation on musselsLakewide estimates of productivity

Worth: 101 - 104 tons/yr equivalent of Walleye sport harvest

SummarySummary• 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.



• “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:

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

Could be applied to MAIA or any region to support ecosystem management

Acknowledgments

Research support provided by the International Joint Commission and US Environmental Protection Agency (STAR R82-5150)

US and Canadian environmental & natural resources managers and stakeholders for participating in modeling workshops and providing data and guidance in model development

Documents

Notice: The views expressed here are those of the individual authors and may not necessarily reflect the views and policies of the United States Environmental