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Ecosystem Based Fishery Management
and Ecosystem Modeling

Rutgers Fisheries Course
19 July 2022

Sarah Gaichas
Northeast Fisheries Science Center

With thanks to Jason Link (NOAA), Gavin Fay (UMass), and Sean Lucey (NOAA)

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US Policy defines EBFM as:

A systematic approach to fisheries management in a geographically specified area that contributes to the resilience and sustainability of the ecosystem; recognizes the physical, biological, economic, and social interactions among the affected fishery-related components of the ecosystem, including humans; and seeks to optimize benefits among a diverse set of societal goals.

relating environment marine habitat and the marine community to human activities social systems and objectives

https://www.fisheries.noaa.gov/resource/document/ecosystem-based-fisheries-management-policy

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Levels of ecosystem-based management in relation to marine fisheries management

https://www.st.nmfs.noaa.gov/Assets/ecosystems/ebfm/EBFM%20Myths%20v7.pdf

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EBFM Guiding Principles

Five supporting EBFM steps to maintain resilient ecosystems

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An ecosystem approach implementation success story (in progress)

Diverse stakeholders agreed that an ecosystem approach was necessary. Developing and implementing an ecosystem approach to fishery management was done in collaboration between managers, stakeholders, and scientists.

Outline

  • Background: Fishery management in the United States

  • Mid-Atlantic Fishery Management Council Ecosystem Approach (EAFM)

  • Key tools: ecosystem reporting, risk assessment, MSE

  • Food web modeling introduction

  • Food web modeling exercise

Word cloud based on Mid-Atlantic Fishery Management Council EAFM Guidance Document

EAFM Policy Guidance Doc Word Cloud

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Fishery management in the US

Eight regional Fishery Management Councils establish plans for sustainable management of stocks within their jurisdictions. All are governed by the same law, but tailor management to their regional stakeholder needs.

US map highlighting regions for each fishery management council

More information: http://www.fisherycouncils.org/
https://www.fisheries.noaa.gov/topic/laws-policies#magnuson-stevens-act

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The Mid-Atlantic Fishery Management Council (MAFMC)

US East Coast map highlighting Mid-Atlantic council jurisdiction

MAFMC fishery management plans and species

Source: http://www.mafmc.org/fishery-management-plans
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Integrated Ecosystem Assessment and the MAFMC Ecosystem Approach

IEA process from goal setting to assessment to strategy evaluation with feedbacks

https://www.integratedecosystemassessment.noaa.gov/national/IEA-approach

Mid-Atlantic EAFM framework with full details in speaker notes

  • Direct link between ecosystem reporting and risk assessment
  • Conceptual model links across risk elements for fisheries, species
  • Management strategy evaluation includes key risks

    https://www.mafmc.org/eafm

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The Council’s EAFM framework has similarities to the IEA loop. It uses risk assessment as a first step to prioritize combinations of managed species, fleets, and ecosystem interactions for consideration. Second, a conceptual model is developed identifying key environmental, ecological, social, economic, and management linkages for a high-priority fishery. Third, quantitative modeling addressing Council-specified questions and based on interactions identified in the conceptual model is applied to evaluate alternative management strategies that best balance management objectives. As strategies are implemented, outcomes are monitored and the process is adjusted, and/or another priority identified in risk assessment can be addressed.

State of the Ecosystem (SOE) reporting

Improving ecosystem information and synthesis for fishery managers

2022 SOE Mid Atlantic Cover Page

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State of the Ecosystem summary 2022: Performance and Risks

State of the Ecosystem page 1 summary table

State of the Ecosystem page 2 risk bullets

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Implications: Climate change and managed species

Climate: 6 low, 3 low-mod, 4 mod-high, 1 high risk

Multiple drivers with different impacts by species

  • Seasonal estuarine conditions affect life stages of striped bass, blue crabs, summer flounder, black sea bass differently
    • Chesapeake summer hypoxia, temperature better than in past years, but worse in fall
    • Habitat improving in some areas (tidal fresh SAV, oyster reefs), but eelgrass declining
  • Ocean acidification impact on vulnerable surfclams
    • Areas of low pH identified in surfclam and scallop habitat
    • Lab work identified pH thresholds for surfclam growth
  • Warm core rings important to Illex availability. Fishing effort concentrates on the eastern edge of warm core rings, where upwelling and enhanced productivity ocurr

DistShift: 2 low, 9 mod-high, 3 high risk species

Shifting species distributions alter both species interactions, fishery interactions, and expected management outcomes from spatial allocations and bycatch measures based on historical fish and protected species distributions.

black sea bass survey distribution change over time from 2018 SOE

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EAFM Risk Assessment: 2022 Update

Species level risk elements

Species Assess Fstatus Bstatus FW1Pred FW1Prey FW2Prey Climate DistShift EstHabitat
Ocean Quahog lowest lowest lowest lowest lowest lowest highest modhigh lowest
Surfclam lowest lowest lowest lowest lowest lowest modhigh modhigh lowest
Summer flounder lowest lowest lowmod lowest lowest lowest lowmod modhigh highest
Scup lowest lowest lowest lowest lowest lowest lowmod modhigh highest
Black sea bass lowest lowest lowest lowest lowest lowest modhigh modhigh highest
Atl. mackerel lowest highest highest lowest lowest lowest lowmod modhigh lowest
Chub mackerel highest lowmod lowmod lowest lowest lowest na na lowest
Butterfish lowest lowest lowmod lowest lowest lowest lowest highest lowest
Longfin squid lowmod lowmod lowmod lowest lowest lowmod lowest modhigh lowest
Shortfin squid lowmod lowmod lowmod lowest lowest lowmod lowest highest lowest
Golden tilefish lowest lowest lowmod lowest lowest lowest modhigh lowest lowest
Blueline tilefish highest highest modhigh lowest lowest lowest modhigh lowest lowest
Bluefish lowest lowest highest lowest lowest lowest lowest modhigh highest
Spiny dogfish lowmod lowest lowmod lowest lowest lowest lowest highest lowest
Monkfish highest lowmod lowmod lowest lowest lowest lowest modhigh lowest
Unmanaged forage na na na lowest lowmod lowmod na na na
Deepsea corals na na na lowest lowest lowest na na na
  • Chub mackerel were added to the table

Ecosystem level risk elements

System EcoProd CommRev RecVal FishRes1 FishRes4 FleetDiv Social ComFood RecFood
Mid-Atlantic lowmod modhigh lowmod lowest modhigh lowest lowmod highest modhigh
  • Recreational value risk decreased from high to low-moderate

Species and Sector level risk elements

Species MgtControl TecInteract OceanUse RegComplex Discards Allocation
Ocean Quahog-C lowest lowest lowmod lowest modhigh lowest
Surfclam-C lowest lowest lowmod lowest modhigh lowest
Summer flounder-R modhigh lowest lowmod modhigh highest highest
Summer flounder-C lowmod modhigh lowmod modhigh modhigh lowest
Scup-R lowmod lowest lowmod modhigh modhigh highest
Scup-C lowest lowmod modhigh modhigh modhigh lowest
Black sea bass-R highest lowest modhigh modhigh highest highest
Black sea bass-C highest lowmod highest modhigh highest lowest
Atl. mackerel-R lowmod lowest lowest lowmod lowest lowest
Atl. mackerel-C lowest lowmod modhigh highest lowmod highest
Butterfish-C lowest lowmod modhigh modhigh modhigh lowest
Longfin squid-C lowest modhigh highest modhigh highest lowest
Shortfin squid-C lowmod lowmod lowmod modhigh lowest highest
Golden tilefish-R na lowest lowest lowest lowest lowest
Golden tilefish-C lowest lowest lowest lowest lowest lowest
Blueline tilefish-R lowmod lowest lowest lowmod lowest lowest
Blueline tilefish-C lowmod lowest lowest lowmod lowest lowest
Bluefish-R lowmod lowest lowest lowmod modhigh highest
Bluefish-C lowest lowest lowmod lowmod lowmod lowest
Spiny dogfish-R lowest lowest lowest lowest lowest lowest
Spiny dogfish-C lowest modhigh modhigh modhigh lowmod lowest
Chub mackerel-C lowest lowmod lowmod lowmod lowest lowest
Unmanaged forage lowest lowest modhigh lowest lowest lowest
Deepsea corals na na modhigh na na na
  • 4 Allocation risks decreased from high to low
  • 4 Regulatory complexity risks decreased, 2 increased
  • Management control risk increased for blueline tilefish fisheries to low-moderate
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Changes: Recreational value decreased from high to low-mod Allocation risk decreased for 4 fisheries from high to low (intermediate rankings not applied) Black sea bass regulatory complexity risk decreased from highest to moderate-high

Potential new indicators from new SOE sections on climate risk, habitat vulnerability, offshore wind

How is MAFMC using the risk assessment?

  • Based on risk assessment, the Council selected summer flounder as high-risk fishery for conceptual modeling

Mid-Atlantic EAFM framework

  • Council proceeding with management strategy evaluation (MSE) addressing recreational fishery discards using information from conceptual modeling.
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In this interactive circular graph visualization, model elements identified as important by the Council (through risk assessment) and by the working group (through a range of experience and expertise) are at the perimeter of the circle. Elements are defined in detail in the last section of this page. Relationships between elements are represented as links across the center of the circle to other elements on the perimeter. Links from a model element that affect another element start wide at the base and are color coded to match the category of the element they affect.Hover over a perimeter section (an element) to see all relationships for that element, including links from other elements. Hover over a link to see what it connects. Links by default show text for the two elements and the direction of the relationship (1 for relationship, 0 for no relationship--most links are one direction).For example, hovering over the element "Total Landings" in the full model shows that the working group identified the elements affected by landings as Seafood Production, Recreational Value, and Commercial Profits (three links leading out from landings), and the elements affecting landings as Fluke SSB, Fluke Distributional Shift, Risk Buffering, Management Control, Total Discards, and Shoreside Support (6 links leading into Total Landings).

aa with management with management strategy evaluation (MSE)strategy evaluation (MSE)

  • Working group of habitat, biology, stock assessment, management, economic and social scientists developed:

    • draft conceptual models of high risk elements, linkages
    • dataset identification and gap analysis for each element and link
    • draft questions that the Council could persue with additional work

  • Final conceptual model and supporting information at December 2019 Council meeting

Implementing the Ecosystem Approach: lessons learned

  • Collaborative, iterative process between scientists, managers, stakeholders
  • Ecosystem reporting, risk and vulnerablity assessment
  • Establish national policy based in current legal mandates
  • Integrated strategic planning for advice

https://spo.nmfs.noaa.gov/sites/default/files/TMSPO184.pdf

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  • Within the science community – diverse expertise is needed
    • Between science and management – understanding what information is needed and important to management, providing tools to management to understand ecosystem linkages and implications
    • Between science and stakeholders – need to build trust, open dialogue (everyone is heard), and sharing data and observations (on water and with information)
    • Between management and stakeholders – listening to/acting on stakeholder priorities and feedback, process not out to add more uncertainty but provide for more informed decisions

What is an ecosystem model?

ecosystem model spectrum

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Ecosystem modelling approaches: What questions can we address?

Why build a model? Identify the problem

whybuild

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Why build a model? Identify the problem

whybuild

  1. Develop clearly specified questions and objectives

    • What does the model have to output/predict?
    • What does the user need to be able to change?

  2. Evaluate data availability (appropriate temporal/spatial scales)

  3. THEN build a model, or better, model(s)

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Today's objective

Primary: Think about your research question

  • What data do you have to address it?
  • Would a model be useful to address your research question?
  • If so, what would you want it to do?
  • Advanced: How might you build it?

Secondary: Understand what a food web model is

  • Know what questions to ask about how it was built
  • Know what it can and cannot do
  • Experiment: perturb the model in different ways
    • What do you observe?
    • Why might this be? (including model limitations)
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What is a food web model?

A system of linear equations

For each group, i, specify:

Biomass B [or Ecotrophic Efficiency EE ]
Population growth rate PB
Consumption rate QB
Diet composition DC
Fishery catch C
Biomass accumulation BA
Im/emigration IM and EM

Solving for EE [or B ] for each group:

toyfoodweb

Bi(PB)i∗EEi+IMi+BAi=∑j[Bj(QB)j∗DCij]+EMi+Ci

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Mathematically identical to an "input-output" model in economics

How do you build one?

Specify most parameters for each species; can solve for one

How? Observations from:

  1. Fishery independent surveys

    • Biomass estimates
    • Diet compositions
    • Growth and reproduction information

  2. Catch/landings/discard data from fishery managers

  3. Literature, historical information, general life history theory

  4. Other transparent, reproducible processes/sources

workinprogress

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What can you do with one? Lab overview

Anchovy Bay food web model

  1. Orientation using simple food web invented for illustration

  2. Tradeoffs between fishing fleets targeting different species

Gulf of Maine food web model

  1. More complex food web model based on a real system

  2. System reaction to different scenarios

    1. Changing fishing effort (combined fleet)
    2. Changing mortality for individual species
    3. Changing predator-prey dynamics
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The Northeast US shelf food web

GOM food web 2002

Link, J. 2002. Does food web theory work for marine ecosystems? Marine ecology progress series, 230: 1–9.
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The Gulf of Maine food web model

GOM food web

Link, J., Overholtz, W., O’Reilly, J., Green, J., Dow, D., Palka, D., Legault, C., et al. 2008. The Northeast U.S. continental shelf Energy Modeling and Analysis exercise (EMAX): Ecological network model development and basic ecosystem metrics. Journal of Marine Systems, 74: 453–474.

Link, J., Col, L., Guida, V., Dow, D., O’Reilly, J., Green, J., Overholtz, W., et al. 2009. Response of balanced network models to large-scale perturbation: Implications for evaluating the role of small pelagics in the Gulf of Maine. Ecological Modelling, 220: 351–369.

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Possible exercises: work in teams

Anchovy Bay:

  1. Describe impacts of different fishing scenarios

  2. Describe potential catch and biomass objectives

  3. Can you engineer any scenarios to achieve them?

Gulf of Maine:

  1. Describe impacts of different fishing scenarios

  2. Describe impacts of individual species mortality scenarios

  3. Describe the impacts of "as prey" scenario

  4. Can you suggest mechanisms for any observed differences?

Both models

  1. Describe your Ecosystem Based management objective

    • Can you achieve it by changing sliders?
    • Are there any side effects (tradeoffs) of achieving your objective?
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The next level: uncertainty

How would you address this?

Biological parameters:

  1. Biomass

  2. Production rate

  3. Consumption rate

  4. Diet composition

  5. Predator-prey interactions

Management needs:

  1. Species/group resolution

  2. Fishing fleet resolution

  3. Spatial resolution

  4. Changing stakeholder questions

  5. Limited time

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Herring MSE food web model results: ecosystem tradeoffs

Tradeoffs between forage groups and mixed impacts to predators apparent when multiple species and full predator prey interaction feedbacks can be included

  • Rpath (Lucey, et al., 2020) Ecosense functions evaluate parameter uncertainty within a scenario

  • Now we have MSE closed loop possibilities in Rpath (Lucey, et al., 2021)

  • Can implement HCRs with predator prey interactions

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Herring MSE food web model results: ecosystem uncertainty?

Compare 10% change (green, same as previous slide gray boxes) with more extreme "herring" biomass:

  • 50% increase from base herring biomass (red)
  • 50% decrease from base herring biomass (blue)

More system uncertainty with increased herring biomass?

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Additional resources

References

Bastille, K. et al. (2021). "Improving the IEA Approach Using Principles of Open Data Science". In: Coastal Management 49.1. Publisher: Taylor & Francis _ eprint: https://doi.org/10.1080/08920753.2021.1846155, pp. 72-89. ISSN: 0892-0753. DOI: 10.1080/08920753.2021.1846155. URL: https://doi.org/10.1080/08920753.2021.1846155 (visited on Apr. 16, 2021).

DePiper, G. S. et al. (2017). "Operationalizing integrated ecosystem assessments within a multidisciplinary team: lessons learned from a worked example". En. In: ICES Journal of Marine Science 74.8, pp. 2076-2086. ISSN: 1054-3139. DOI: 10.1093/icesjms/fsx038. URL: https://academic.oup.com/icesjms/article/74/8/2076/3094701 (visited on Mar. 09, 2018).

DePiper, G. et al. (2021). "Learning by doing: collaborative conceptual modelling as a path forward in ecosystem-based management". In: ICES Journal of Marine Science. ISSN: 1054-3139. DOI: 10.1093/icesjms/fsab054. URL: https://doi.org/10.1093/icesjms/fsab054 (visited on Apr. 15, 2021).

Gaichas, S. K. et al. (2018). "Implementing Ecosystem Approaches to Fishery Management: Risk Assessment in the US Mid-Atlantic". In: Frontiers in Marine Science 5. ISSN: 2296-7745. DOI: 10.3389/fmars.2018.00442. URL: https://www.frontiersin.org/articles/10.3389/fmars.2018.00442/abstract (visited on Nov. 20, 2018).

Lucey, S. M. et al. (2021). "Evaluating fishery management strategies using an ecosystem model as an operating model". En. In: Fisheries Research 234, p. 105780. ISSN: 0165-7836. DOI: 10.1016/j.fishres.2020.105780. URL: http://www.sciencedirect.com/science/article/pii/S0165783620302976 (visited on Dec. 09, 2020).

Lucey, S. M. et al. (2020). "Conducting reproducible ecosystem modeling using the open source mass balance model Rpath". En. In: Ecological Modelling 427, p. 109057. ISSN: 0304-3800. DOI: 10.1016/j.ecolmodel.2020.109057. URL: https://www.sciencedirect.com/science/article/pii/S0304380020301290 (visited on Apr. 16, 2021).

Muffley, B. et al. (2021). "There Is no I in EAFM Adapting Integrated Ecosystem Assessment for Mid-Atlantic Fisheries Management". In: Coastal Management 49.1. Publisher: Taylor & Francis _ eprint: https://doi.org/10.1080/08920753.2021.1846156, pp. 90-106. ISSN: 0892-0753. DOI: 10.1080/08920753.2021.1846156. URL: https://doi.org/10.1080/08920753.2021.1846156 (visited on Apr. 16, 2021).

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Questions?

Thank you

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US Policy defines EBFM as:

A systematic approach to fisheries management in a geographically specified area that contributes to the resilience and sustainability of the ecosystem; recognizes the physical, biological, economic, and social interactions among the affected fishery-related components of the ecosystem, including humans; and seeks to optimize benefits among a diverse set of societal goals.

relating environment marine habitat and the marine community to human activities social systems and objectives

https://www.fisheries.noaa.gov/resource/document/ecosystem-based-fisheries-management-policy

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