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Using Ecosystem Information in the
Stock Assessment and Advice Process

CFMC SSC EBFM-TAP
2 May 2023

Sarah Gaichas
Northeast Fisheries Science Center

With thanks to Brandon Muffley (MAFMC)

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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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Many options and entry points for a systematic ecosystem approach

Fishing icon made by EDAB       Fishing industry icon made by EDAB       Multiple drivers icon made by EDAB       Spiritual cultural icon made by EDAB       Protected species icon made by EDAB

Climate icon made by EDAB       Stock assessment icon made by EDAB       Ecosystem reorganization icon made by EDAB       Wind icon made by EDAB

Hydrography icon made by EDAB       Phytoplankon icon made by EDAB       Forage fish icon made by EDAB       Apex predators icon made by EDAB       Other human uses icon made by EDAB

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Goal: increased range of opportunities for relevant ecosystem information to be considered in management decision processes

Outline

  • What types of decisions do we need to make?

    • Single species catch limits
    • Allocations to fleets or areas
    • Coordination across boundaries and sectors
    • Multispecies and ecosystem level tradeoffs

  • How can ecosystem information support these decisions?

    • Key tools: ecosystem reporting, risk assessment, managment strategy evaluation
    • Developing decision processes along with products

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

EAFM Policy Guidance Doc Word Cloud

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Including ecosystem information at the species level: Ecosystem Socioeconomic Profiles (ESPs)

GOA pcod ESP conceptual model

Pacific cod example from ASFC: https://www.fisheries.noaa.gov/alaska/2021-alaska-fisheries-science-center-year-review and https://apps-afsc.fisheries.noaa.gov/refm/docs/2021/GOApcod.pdf

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ESP process is adaptable to many assessment situations

schematic of northeast ESP process

slide courtesy Scott Large

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Our ESP process was developed from the AFSC process, but we adjusted things slightly because of how our benchmarks are scheduled and because we are providing scientific advice to multiple Councils.

The ESP framework is an iterative cycle that complements the stock assessment cycle. First I will give you an overview of the ESP cycle, and then I will explain each step in more detail. The ESP begins with the development of the problem statement by identifying the topics that the assessment working group and ESP team want to assess. This process includes a literature review or other method of gathering existing information on the stock, such as reviewing prior assessments and research recommendations. Next, a conceptual model is created that links important processes and pressures to stock performance. From these linkages, we develop indicators that can be used to monitor the system conditions. Next, the indicators are analyzed to determine their status and the likely impacts on the stock. Some indicators may be tested for inclusion in assessment models. Finally, all of these analyses are synthesized into a report card to provide general recommendations for fishery management.

Bluefish ESP reviewed December 2022

GOA pcod ESP conceptual model

Reviewers commented that it was the most complete treatment of a stock assessment "ecosystem ToR" they had seen, and formed a good basis for integrating further ecosystem information into the stock assessment in the future.

The full ESP document is available as a working paper from the stock assessment data portal

Potential use of summary table in OFL CV or other decisions?

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The Bluefish Research Track ESP was presented December 7 2022, and was well received by CIE reviewers. Reviewers commented that it was the most complete treatment of a stock assessment "ecosystem ToR" they had seen, and formed a good basis for integrating further ecosystem information into the stock assessment in the future. The full ESP document is available as a working paper from the stock assessment data portal.

In addition to the conceptual model, a summary table was developed for bluefish ecosystem indicators. This type of summary could contribute to OFL CV decisions with further information on how these indicator levels affect uncertainty in assessment.

Ecosystem information can be used in decisions even if not directly in the stock assessment

Pathways for scientific advice from the northeast ESP process

slide courtesy Scott Large

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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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Acceptable Biological Catch (ABC) determination: MAFMC approach

Council Risk Policy

ABC proportion of OFL given OFL CV

See MAFMC ABC control rule and update for 2020 MAFMC risk policy

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How to get the OFL CV? Characterizing scientific uncertainty

SSC evaluates 9 criteria

  1. Data quality
  2. Model appropriateness
  3. Retrospective analysis
  4. Comparison with simpler analysis
  5. Ecosystem factors
  6. Recruitment trends
  7. Prediction error
  8. Informative F
  9. Simulations/MSE

5. Informed by ecosystem factors or comparisons with other species

a. Stock-relevant ecosystem factors directly included in the assessment model, e.g.,:

  • Environmentally dependent growth or other population processes;
  • Factors limiting/enhancing stock productivity (habitat quality, etc.);
  • Predation, disease, or episodic environmental mortality (e.g., red tide);

b. Ecosystem factors outside the stock assessment affecting short term prediction

  • General measures of ecosystem productivity and habitat stability (e.g., primary production amount and timing, temperature trends, etc.);
  • Comparisons among related species; e.g., recruitment, growth, condition patterns across Mid Atlantic fish species stable, varying synchronously, or varying unpredictably;
  • Climate vulnerability or other risk assessment evaluation of potential for changing productivity under changing conditions.

See MAFMC SSC OFL CV Guidance Document

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Characterizing scientific uncertainty from ecosystem factors

Decsion Criteria Default OFL CV = 60% Default OFL CV = 100% Default OFL CV = 150%
Data quality One or more synoptic surveys over stock area for multiple years. High quality monitoring of landings size and age composition. Long term, precise monitoring of discards. Landings estimates highly accurate. Low precision synoptic surveys or one or more regional surveys which lack coherency in trend. Age and/or length data available with uncertain quality. Lacking or imprecise discard estimates. Moderate accuracy of landings estimates No reliable abundance indices. Catch estimates are unreliable. No age and/or length data available or highly uncertain. Natural mortality rates are unknown or suspected to be highly variable. Incomplete or highly uncertain landings estimates
... ... ... ...
Ecosystem factors accounted Assessment considered habitat and ecosystem effects on stock productivity, distribution, mortality and quantitatively included appropriate factors reducing uncertainty in short term predictions. Evidence outside the assessment suggests that ecosystem productivity and habitat quality are stable. Comparable species in the region have synchronous production characteristics and stable short-term predictions. Climate vulnerability analysis suggests low risk of change in productivity due to changing climate. Assessment considered habitat/ecosystem factors but did not demonstrate either reduced or inflated short-term prediction uncertainty based on these factors. Evidence outside the assessment suggests that ecosystem productivity and habitat quality are variable, with mixed productivity and uncertainty signals among comparable species in the region. Climate vulnerability analysis suggests moderate risk of change in productivity from changing climate. Assessment either demonstrated that including appropriate ecosystem/habitat factors increases short-term prediction uncertainty, or did not consider habitat and ecosystem factors. Evidence outside the assessment suggests that ecosystem productivity and habitat quality are variable and degrading. Comparable species in the region have high uncertainty in short term predictions. Climate vulnerability analysis suggests high risk of changing productivity from changing climate.
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Making improved use of ecosystem information in OFL CV decisions

MAFMC SSC Eco WG decisions

MAFMC SSC Ecosystem WG update

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SSC Ecosystem working group analyses in progress

  • Using explicit ecosystem drivers in OFL CV decision
    • For selected stocks representing a range of life history types
      • Which ecosystem factors affect uncertainty in current stock biomass and Fmsy?
      • Conceptual model mapping ecosystem factors to stock processes
      • Which current ecosystem indicators best match relevant ecosystem factors?
      • Simulation analysis: do changes in those indicators predictably change uncertainty?
  • Starting with existing summer flounder MSE framework, focusing on recruitment drivers
  • Evaluating both benefits of correct decision and costs of incorrect decision
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SSC Ecosystem working group analyses in progress

  • Using explicit ecosystem drivers in OFL CV decision
    • For selected stocks representing a range of life history types
      • Which ecosystem factors affect uncertainty in current stock biomass and Fmsy?
      • Conceptual model mapping ecosystem factors to stock processes
      • Which current ecosystem indicators best match relevant ecosystem factors?
      • Simulation analysis: do changes in those indicators predictably change uncertainty?
  • Starting with existing summer flounder MSE framework, focusing on recruitment drivers
  • Evaluating both benefits of correct decision and costs of incorrect decision
  • Outlining multispecies and system level advice: identify initial priorities in collaboration with Council
    • Where are there multispecies/multifleet tradeoffs linking to economic and social outcomes?
    • Are there multi-indicator thresholds suggesting when FMP level management needs to change?
    • Are there changes in ecosystem productivity that imply standardized approaches for
      • Setting reference points?
      • Developing rebuilding plans?
      • Other analyses requiring short-term projections?
  • More direct SSC involvement in ecosystem reporting priorities MAFMC EAFM
  • Evaluate current multispecies indicators for common signals
  • Evaluate proposed ecosystem-level reference points and thresholds for regional ecosystems
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Integrated Ecosystem Assessment and the MAFMC Ecosystem Approach

Diverse stakeholders agreed that an ecosystem approach was necessary. Developing and implementing EAFM is done in collaboration between managers, stakeholders, and scientists. https://www.mafmc.org/eafm

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
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(Gaichas et al., 2016) 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.

Northeast US State of the Ecosystem (SOE) reporting

Improving ecosystem information and synthesis for fishery managers

2023 SOE Mid Atlantic Cover Page

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State of the Ecosystem Summary 2023:

Performance relative to management objectives

Seafood production decreasing arrow icon below average icon icon

Profits decreasing arrow icon below average icon icon

Recreational opportunities: Effort increasing arrow icon above average icon icon; Effort diversity decreasing arrow icon below average icon icon

Stability: Fishery no trend icon near average icon icon; Ecological mixed trend icon near average icon icon

Social and cultural, trend not evaluated, status of:

  • Fishing engagement and reliance by community
  • Environmental Justice (EJ) Vulnerability by community

Protected species:

  • Maintain bycatch below thresholds mixed trend icon meeting objectives icon
  • Recover endangered populations (NARW) decreasing arrow icon below average icon icon
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State of the Ecosystem Summary 2023:

Risks to meeting fishery management objectives

Climate: warming and changing oceanography continue

  • Heat waves and Gulf Stream instability
  • Estuarine, coastal, and offshore habitats affected, with range of species responses
  • Distribution shifts complicate management
  • Multiple fish with poor condition, declining productivity

Other ocean uses: offshore wind development

  • Current revenue in proposed areas
    • 1-34% by port (some with EJ concerns)
    • up to 17% by managed species
  • Different development impacts for species preferring soft bottom vs. hard bottom
  • Overlap with important right whale foraging habitats, increased vessel strike and noise risks
  • Rapid buildout in patchwork of areas
  • Scientific survey mitigation required

 
 
     
 

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Implications: Climate change and Mid-Atlantic 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

  • Ocean acidification approaching sensitivity thresholds in commercial shellfish habitats

  • Gulf stream warm core rings more abundant, important to shortfin squid availability

 
 
     

*Climate vulnerability and Distribution Shift risk levels from climate vulnerability analysis (Hare et al., 2016)

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

  • Managed species shifts already impacting allocation discussions

New Indicator: protected species shifts

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State of the Ecosystem → MAFMC Risk assessent example: Commercial revenue

This element is applied at the ecosystem level. Revenue serves as a proxy for commercial profits.

Risk Level Definition
Low No trend and low variability in revenue
Low-Moderate Increasing or high variability in revenue
Moderate-High Significant long term revenue decrease
High Significant recent decrease in revenue

Ranked moderate-high risk due to the significant long term revenue decrease

Key: Black = Revenue of all species combined; Red = Revenue of MAFMC managed species

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State of the Ecosystem → MAFMC Risk assessent example: Commercial revenue

This element is applied at the ecosystem level. Revenue serves as a proxy for commercial profits.

Risk Level Definition
Low No trend and low variability in revenue
Low-Moderate Increasing or high variability in revenue
Moderate-High Significant long term revenue decrease
High Significant recent decrease in revenue

Ranked moderate-high risk due to the significant long term revenue decrease

Key: Black = Revenue of all species combined; Red = Revenue of MAFMC managed species

Risk element: CommRev, unchanged

SOE Implications: Recent change driven by benthos. Monitor changes in climate and landings drivers:

  • Climate risk element: Surfclams and ocean quahogs are sensitive to ocean warming and acidification.
  • pH in surfclam summer habitat is approaching, but not yet at, pH affecting surfclam growth
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EAFM Risk Assessment: 2023 Update (all methods to be reviewed/revised this year)

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 highest 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 lowmod lowest lowest lowest lowest modhigh highest
Spiny dogfish lowest highest 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
  • RT assessment decreased Spiny dogfish Assess, risk to low and increased Fstatus risk to high
  • RT assessment decreased bluefish Bstatus risk from high to low-moderate
  • RT assessment increased Illex Assess risk from low-moderate to high

Ecosystem level risk elements

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

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
  • Management section not updated--to be revised this year
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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).

MSE results: can improve on current management, but distribution shifts lower expectations

Summer flounder MSE results by OM

August MAFMC briefing materials and full results on the web

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  • Linked recreational demand and population dynamics model
  • Alternative operating model included northward distribution shift as change in availability by state
  • Rank order of management options maintained, but degraded performance when considering ecosystem change

System level decisions: Are we observing multispecies shifts?

Indicator: fish condition

Indicator: fish productivity anomaly →

Implications: Species in the MAB had mixed condition in 2022. Fish productivity based on surveys and assessments has been below average.

Black line indicates sum where there are the same number of assessments across years.

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System level decisions: Ecosystem Overfishing Indicators

Operational thresholds for management?

Reviewed new information and agreed with proposals:

  • Updated data inputs (menhaden)
  • Discard estimation still in progress
  • Comparisons of data sources
  • Calculate new thresholds using regional ecosystem productivity
  • Simulation test thresholds within ecosystem model Atlantis

Previous SSC discussion:

  • Thresholds define a "safe operating space"
    • Define the bounds where fishing causes poor system performance
    • Identify tradeoffs across species within the safe zone
  • Provide advice on options to correct ecosystem overfishing
  • Use social benefits to measure outcomes

What information would be most useful in decision making?

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Declining commercial and recreational landings can be driven by many interacting factors, including combinations of ecosystem and stock production, management actions, market conditions, and environmental change. While we cannot evaluate all possible drivers at present, here we evaluate the extent to which ecosystem overfishing (total landings exceeding ecosystem productive capacity), stock status, and system biomass trends may play a role.

Entry points for ecosystem information in management decisions: where to start?

Management decisions

  1. What are our issues and goals?
  2. Current decisions
    • Stock assessments
    • Advice on catch levels
    • Harvest control rules
  3. New (current) decisions
    • Habitat change or restoration
    • Changing species distribution and interactions
    • Tradeoffs between fisheries
    • Tradeoffs between ocean use sectors

Methods and tools

  1. Stakeholder engagement, surveys, strategic planning
  2. Add information to current process
    • Ecosystem ToRs, overviews, ESP, SOE
    • Risk or uncertainty assessments
    • Management strategy evaluation
  3. Integrate across current processes
    • Risk assessment
    • Conceptual models
    • Scenario planning
    • MSE (again)

Focus on developing decision processes that are able to use ecosystem information

  • Collaborative, iterative process between scientists, managers, stakeholders
  • Multispecies and system level indicators of productivity change or overexploitation

State of the Ecosystem data on github https://github.com/NOAA-EDAB/ecodata

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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).

Gaichas, S. K. et al. (2016). "A Framework for Incorporating Species, Fleet, Habitat, and Climate Interactions into Fishery Management". In: Frontiers in Marine Science 3. ISSN: 2296-7745. DOI: 10.3389/fmars.2016.00105. URL: https://www.frontiersin.org/articles/10.3389/fmars.2016.00105/full (visited on Apr. 29, 2020).

Hare, J. A. et al. (2016). "A Vulnerability Assessment of Fish and Invertebrates to Climate Change on the Northeast U.S. Continental Shelf". In: PLOS ONE 11.2, p. e0146756. ISSN: 1932-6203. DOI: 10.1371/journal.pone.0146756. URL: http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0146756 (visited on Mar. 01, 2016).

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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