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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 for ecosystem management

Modeling And Multiobjective Risk Decision Tools for 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 Administration

3. Dept. of Biology,Case Western Reserve University

goal of presentation
Goal of Presentation
  • Demonstrate how ecosystem-based fisheries management can be joined with ecological risk analysis under multiple management objectives
goal of presentation4
Goal of Presentation
  • Demonstrate how ecosystem-based fisheries management can be joined with ecological risk analysis under multiple management objectives
  • Introduce tools:
    • Ecosystem model
    • Multiobjective tradeoff analysis
    • Bayesian evaluation of ecological research
i unresolved problems in lake erie
I. Unresolved Problems in Lake Erie
  • Major decline of fisheries in 1990s
  • Unknown effects of exotic species
    • Zebra Mussel invasion since 1988
    • Round Goby increase in 1990s
    • Expected invasion of Ruffe
i unresolved problems in lake erie6
I. Unresolved Problems in Lake Erie
  • Major decline of fisheries in 1990s
  • Unknown effects of exotic species
    • Zebra Mussel invasion since 1988
    • Round Goby increase in 1990s
    • Expected invasion of Ruffe
  • Declining productivity caused by decrease in P loading
  • Uncertain role of habitat
ii modeling tradeoffs among productivity exotics fisheries lake erie ecological model leem
II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM)

PO

4

Zooplankton

Edible Algae

Pred.

Prey

Fish

Fish

Inedible Algae

Zoobenthos

ii modeling tradeoffs among productivity exotics fisheries lake erie ecological model leem10
II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM)

PO

4

Zooplankton

Edible Algae

Zebra

Pred.

Prey

Mussel

Fish

Fish

Inedible Algae

Zoobenthos

ii modeling tradeoffs among productivity exotics fisheries lake erie ecological model leem11
II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM)

Stocking

PO

Fishing

Mortality, Habitat

4

Zooplankton

Edible Algae

Zebra

Pred.

Prey

Mussel

Fish

Fish

Inedible Algae

Zoobenthos

Harvest

ii modeling tradeoffs among productivity exotics fisheries lake erie ecological model leem12
II. Modeling Tradeoffs among Productivity, Exotics, & Fisheries: Lake Erie Ecological Model (LEEM)

Stocking

PO

Fishing

Mortality, Habitat

4

Zooplankton

Edible Algae

Zebra

Pred.

Prey

PCB

Mussel

Fish

Fish

Inedible Algae

Zoobenthos

Harvest

the need for multispecies management effects of species interactions on max sustained yield
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 yield14
The Need for Multispecies Management: Effects of Species Interactions on Max Sustained Yield
  • Optimal exploitation of predator varies with fishing rates of prey species
interaction of walleye harvest phosphorus loading
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 loading16

Total Fish

Biomass

6.0

5.0

4.0

3.0

2.0

1.0

0.0

W5

W4

P5

P4

W3

P3

W2

P2

W1

Walleye Effort

P Loading

Interaction of Walleye Harvest & Phosphorus Loading

Walleye abundance/harvest has a greater influence on total fish biomass than P loading

implications of leem studies
Implications of LEEM Studies
  • Fisheries and P Loading Jointly Determine Optimal Exploitation of Species
implications of leem studies18
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 Overexploitation
    • Prey and predators cannot be managed independently
iii value of research multiobjective bayesian framework
III. Value of Research: Multiobjective Bayesian Framework
  • Information has value only if it can change decisions and improve outcomes
    • “Value” is multidimensional!
iii value of research multiobjective bayesian framework20
III. Value of Research: Multiobjective 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

iii value of research multiobjective bayesian framework21
III. Value of Research: Multiobjective 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”)

iii value of research multiobjective bayesian framework22
III. Value of Research: Multiobjective 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

iii value of research multiobjective bayesian framework23
III. Value of Research: Multiobjective 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

iii value of research multiobjective bayesian framework24
III. Value of Research: Multiobjective 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

5.Integrating framework: A way of determining how information affects our knowledge and choices: Decision trees, Bayes’ rule

problem structure
Problem Structure
  • Two decision stages
  • Research project; eh
  • P loading and fisheries
  • management;as ={as1, as2, as3, as4}
problem structure26
Problem Structure
  • Two decision stagesUncertainties θ
  • Research project; eh –Lower trophic level;
  • P loading and fisheries – Other uncertainties;
  • management;as ={as1, as2, as3, as4}
problem structure27
Problem Structure
  • Two decision stagesUncertainties θ Outcomes
  • Research project; eh –Lower trophic level; – 10 Attributes X, combined
  • P loading and fisheries – Other uncertainties; using additive utility
  • management;as ={as1, as2, as3, as4} function U(X)
problem structure28

θ)

P(

Problem Structure
  • Two decision stagesUncertainties θ Outcomes
  • Research project; eh –Lower trophic level; – 10 Attributes X, combined
  • P loading and fisheries – Other uncertainties; using additive utility
  • management;as ={as1, as2, as3, as4} function U(X)

U(X(as, θ))

as

No Research ,e0

P(θ |Z1)

Research, e1

as

P(Z1)

Research, eh

P(Zh)

Research

Decision E

Outcomes

from Research, eh

Decision A

Chance node

for θ

Utility

Outcome

management levers
Management Levers
  • Phosphorus Loads:
    • 5K, 10K (as is), and 15K ton/yr
management levers30
Management Levers
  • Phosphorus Loads:
    • 5K, 10K (as is), and 15K ton/yr
  • Exploitation effort: A measure of thenumber 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
Present State of Knowledge
  • Prior probabilities
    • Uncertainties in LEEM parameters
present state of knowledge32
Present State of Knowledge
  • 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 knowledge33
Present State of Knowledge
  • 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)

  • Disregarding uncertainties may result in inappropriate, nonrobust decisions
options for gathering information
Options for Gathering Information
  • Characteristics of research
    • Cost & time
    • Reliability of outcomes
options for gathering information35
Options for Gathering Information
  • Characteristics of research
    • Cost & time
    • Reliability of outcomes
  • 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

Ecosystem Health & Human Well-being

Ecological

Economic

Social

Recreation

Consumption

Self-sustaining

Economically important species

=

Self-

sustaining

Native

species

Productivity

Structure

Function

Multiple Objective Framework for Risk Analysis
multiple objective framework for risk analysis37

Ecosystem Health & Human Well-being

Ecological

Economic

Social

Recreation

Consumption

Self-sustaining

Economically important species

X1: density of walleye

X3: PCB conc in

small mouth bass

X8: commercial walleye biomass

X9: commercial yellow perch biomass

X10: commercial smelt biomass

X2: annual mean

walleye sport

X7: Biomass ratio native

Self-

sustaining

Native

species

species to total

Productivity

Structure

Function

X4: total biomass

X5: walleye-percid

X6: piscivore-planktivore

biomass ratio

biomass ratio

Multiple Objective Framework for Risk Analysis
bayesian analysis results
Bayesian Analysis Results
  • Priorities for objectives can affect decisions
    • All participants prefer High trawling; most High sport harvest
    • Split on Gill net effort
bayesian analysis results39
Bayesian Analysis Results
  • Priorities for objectives can affect decisions
    • All participants prefer High trawling; most High sport harvest
    • Split on Gill net effort
  • 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 results40
Bayesian Analysis Results
  • Priorities for objectives can affect decisions
    • All participants prefer High trawling; most High sport harvest
    • Split on Gill net effort
  • 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
  • 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: 101 - 104 tons/yr equivalent of Walleye sport harvest
summary
Summary
  • Heuristic application of LEEM can lead to multi-fishery rules that recognize uncertainties (P, invasions, habitat)
summary42
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.

summary43
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
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
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