SAFMC Ecosystem Options

For the South Atlantic Fishery Management Council

1 Executive Summary

Purpose: This report identifies practical ways for SAFMC to incorporate ecosystem information into its fishery management decisions, building on a prior cross-Council review of ecosystem data practices. It addresses two specific project objectives: (1) finding opportunities to weave ecosystem information into SAFMC’s management and inter-jurisdictional processes, and (2) identifying ways to expand cooperative/citizen-science data collection to support that work.

Background: East Coast fishery councils have been collaborating (via the East Coast Coordination Group) to make management more responsive to shifting ocean conditions and stock distributions. A cross-Council review found that every U.S. Council has some ecosystem approach in place, but common challenges include stakeholder concerns about increasing fishery restrictions, resource constraints, and the need to package complex science into “digestible” pieces tied clearly to decisions. SAFMC was found to have particular strengths to build on: integrated habitat/ecosystem policy (not siloed, as in some other regions), a mature Citizen Science program, an existing Ecosystem Status Report and climate vulnerability assessments, and a long-standing, SSC-endorsed regional food web model.

Methods: Five parallel analyses were conducted:

• A review of ~197 Council motions from March 2023–March 2026 to see where ecosystem information already enters (or could enter) decisions.
• Alignment of existing habitat/ecosystem policy documents with current ecosystem indicators.
• Evaluation of the Council’s ABC-setting (catch limit) process for opportunities to formally use ecosystem/climate-vulnerability indicators in risk rankings.
• Development of a prototype bottom temperature indicator from global ocean datasets, to test feasibility of producing spatially and seasonally resolved indicators.
• A scoping exercise for using the food web model in multispecies management strategy evaluation (MSE).

Key findings:

• 18% of Council motions over the past three years already had direct ecosystem linkages, and another 26% had potential linkages — concentrated in the Habitat and Ecosystem Committee, but also present in Snapper Grouper and other committees.
• Existing SAFMC policy documents (e.g., the Food Webs and Connectivity Policy) already align reasonably well with current indicators from the Ecosystem Status Report and climate vulnerability assessments, suggesting near-term “quick win” opportunities for using existing information.
• Existing risk factors for ABC setting can also be informed using food web and CVA results, with further opportunities to develop stock specific indicators
• A prototype habitat indicator was successfully developed, showing that spatially/seasonally resolved indicators are feasible for the region using existing global datasets and Northeast U.S. methods as a template.
• The Council’s food web model can support novel MSE work, testing multispecies management against single species strategies to balance complexity, yield, catch variability, and stock risk.

Recommendations:

An overarching recommendation to assist the Council with taking practical next steps is to develop a structured process for ecosystem-based decision making such as those outlined in the Pacific (Ecosystem initiatives), North Pacific (Action modules), Mid-Atlantic (EAFM decision process), Gulf (Fishery Ecosystem Initiatives), and Caribbean Council Fishery Ecosystem Plans (FEPs). SAFMC already has extensive FEPs, but these are more informational than action-oriented. Whether SAFMC wants to address overarching issues such as the impacts of climate change on the productivity of multiple stocks or specific issues such as the selection of a subset of indicators to monitor progress against particular policy goals, a structured process would help manage these options and ensure the SAFMC proceeds in a manner best suited to regional needs.

The report also lays out a menu of options for incorporating ecosystem information, ranging from “easy wins” to complex analyses:

• Continue and formalize what SAFMC already does (habitat/ecosystem policy alignment with indicators).
• Request information: add ecosystem Terms of Reference to stock assessments, and standardized ecosystem questions at stakeholder meetings.
• Use existing climate vulnerability and ecosystem indicators more formally in the SSC’s ABC risk-ranking process.
• Fill habitat data gaps with indicators derived from global datasets, validated via the Citizen Science program or a future study-fleet.
• Pursue MSE using the food web model to evaluate novel management options for data-limited multispecies complexes.

Integrating ecosystem information into management decisions can be achieved in a resource-limited environment by first agreeing on a structured process to address ecosystem issues, and then by developing a list of potential issues to address. For any ecosystem issues under consideration, the process will best use current resources by focusing on existing data products first, by selecting a small set of priority indicators tied explicitly to management objectives, and by expanding SAFMC’s Citizen Science infrastructure to validate new indicators.

2 Introduction

Federal Fishery Management Councils along the U.S. East Coast have been working towards improved management coordination and collaboration for the past several years under the East Coast Scenario Planning Initiative and subsequent East Coast Coordination Group. The purpose of the group is to improve management success in the face of changing ocean conditions and stock distributions that are outside historical bounds; in particular, across traditional management boundaries. A Potential Action Menu was developed for the Coordination Group to improve responsiveness and resilience in fishery management under changing conditions. A key Potential Action Menu item is identifying ecosystem information that can be used by Councils to evaluate changes in ecosystems and fishery resources, and ultimately to develop management that is robust to these changes. The South Atlantic Fishery Management Council (SAFMC) seeks to identify ecosystem data relevant to its region and managed resources, and to develop strategies to make the best use of ecosystem information in management.

2.1 Project Overview

Ecosystem information and data products are used in different ways across the U.S. Fishery Management Councils, depending on regional needs and information availability. To enhance opportunities for the SAFMC to use ecosystem information to support fishery management, an ecosystem information review was conducted to develop recommendations for ways to incorporate ecosystem information into SAFMC fisheries management processes. First, a comprehensive literature review was conducted and combined with structured interviews of key regional personnel to document ecosystem information sources, data products, and processes where ecosystem information is used for each U.S. Fishery Management Council, including SAFMC. This comprehensive review placed current SAFMC practice and products in the context of the experience of all other U.S. Councils, and set the stage to identify opportunities to use its existing ecosystem information resources, including an ecosystem status report, climate vulnerability analyses for fish and fishing communities, and citizen science program.

Based on the completed review, opportunities to use ecosystem information in SAFMC processes will now be identified, and practical steps for implementation will be outlined in the current report.

2.2 Objectives

This report addresses the second and third Project objectives:

2. Identify opportunities and methods for incorporating identified ecosystem information into SAFMC management processes, including inter-jurisdictional management decision-making processes, and identify practical requirements for successful implementation (e.g. data quality, frequency of information updates, regional council process consistency, implementation timelines); and

3. Identify opportunities to continue to expand cooperative, constituent-engaged data collection and research to improve the available ecosystem information in the South Atlantic region (e.g., study fleets, cooperative research, citizen science).

2.3 Review Results

Briefly, the All Council Ecosystem Data Review found a diversity of successful Council ecosystem approaches based on regional contexts and information availability, identified common management concerns, and highlighted potential ways forward for SAFMC that are further explored and detailed in this document. Common management concerns include food webs and climate change, with multiple Councils addressing forage fish management and all Councils experiencing rapid ecosystem changes. Even data-rich regions with long-term ecosystem reporting have experienced unexpected stock collapses, highlighting the need for climate-ready management approaches. Common challenges include the inherent complexity of ecosystem information, resource constraints, and stakeholder concerns with overly precautionary management. In interviews, Council staff emphasized the need to provide information in “digestible packets,” with clear linkages to management processes and decisions. Developing streamlined processes for both producing ecosystem information and for Council review and action were emphasized across regions; even data-rich regions identified capacity limitations for integrating ecosystem information into management. Finally, in some regions, stakeholders view ecosystem considerations as only increasing restrictions, rather than expanding opportunities.

Successful practices for the use of ecosystem information were identified across Council regions, all of which can be tailored to the South Atlantic context.

1. Iterative Co-Development: Ecosystem reports have been restructured with Council feedback in several regions, enhancing report utility. Most Fishery Ecosystem Plans (FEPs) emphasize collaborative development of problem statements, analyses, conceptual models, model scenarios, and indicators with fishers, managers, and scientists.

2. Risk-Based Frameworks: North Pacific, Pacific, New England, Mid-Atlantic, and Caribbean are developing or have implemented indicator-based risk assessments to adjust catch advice and/or to support strategic decisions. Risk policies and Acceptable Biological Catch (ABC) frameworks currently in development in the Pacific and New England emphasize a “two way street” of both increased precaution with increased ecosystem risk and higher risk tolerance/catch potential with decreased ecosystem risk.

3. Actionable FEPs: “Action modules” (North Pacific), “Initiatives” (Pacific), and “Fishery Ecosystem Issues” (Gulf) provide structured pathways from fishery ecosystem plans to management action for specific, Council-selected issues.

4. Multi-Scale Ecosystem Information: Regular reporting at the ecosystem scale provides context for decisions and familiarity with ecosystem data. Stock-specific ecosystem reports (Ecosystem and Socioeconomic Profiles, ESPs) link environmental drivers to individual stock productivity for use in stock assessment and catch specification. Streamlined processes can produce this information: a structured dialogue process between ecosystem and stock assessment scientists is producing stock-level ecosystem information in the Pacific similar to that found in ESP reports.

The South Atlantic Fishery Management Council stood out among Councils with unique strengths for further developing its ecosystem approaches, including habitat and ecosystem integration, an advanced citizen science program, existing ecosystem data products, and long term ecosystem modeling investments. First, SAFMC has always integrated habitat and ecosystem approaches, in contrast to many Councils where habitat and ecosystem efforts were described as “siloed.” SAFMC explicitly links EFH policies with ecosystem approaches through comprehensive policy statements. Second, SAFMC has a formal Citizen Science program in place to facilitate both participatory modeling [1] and data collection. Third, existing ecosystem data products are available in the region: a recent Ecosystem Status Report (ESR; [2]) and both fish [3,4] and fishing community [5] climate vulnerability assessments (CVAs) provide ready-to-use information. Fourth, SAFMC has invested in ecosystem modeling over the long term, and its regional food web model has been reviewed and endorsed by the Scientific and Statistical Committee (SSC), and is poised to address Council questions.

2.4 Initial Recommendations for SAFMC

The South Atlantic’s combination of habitat focus, existing ecosystem tools, and commitment to stakeholder engagement provides a strong foundation for advancing ecosystem-based fishery management in ways responsive to the region’s unique challenges and opportunities.

• Hybrid Approach: Given a mix of data-rich and data-limited stocks across diverse habitats, combining aspects of successful practices from different Councils has more potential than adopting a particular Council’s ecosystem approach

• Leverage Existing Products: Align indicators from the South Atlantic ESR with objectives in Essential Fish Habitat (EFH) policy documents and CVA results to evaluate whether an integrated risk assessment framework could be developed

• Formalize Action Process: Consider a process to develop explicit ecosystem initiatives or issues (similar to Pacific, North Pacific, Gulf) to move from planning to tangible management actions on priority topics

• Update Reporting Frequency: Work toward more regular ecosystem reporting focused on Council-derived objectives and associated indicators produced with streamlined automation processes developed for the Caribbean ESR

• Expand CVA Use: Consider climate vulnerability information in management processes where characterizing uncertainty is important (SSC ABC decisions)

• Explore Novel Approaches: Evaluate multispecies management strategies using the existing food web model, considering both commercial yield and recreational fishing opportunity objectives

In this report, multiple approaches are used to outline practical next steps for SAFMC’s use of ecosystem information. First, over three years of Council actions were systematically reviewed to identify current and potential pathways for the use of ecosystem information. Based on this review, management decisions were classified by data needs and process timelines to develop a prioritized list of processes linked to ecosystem information and recommend practical pathways for implementation, some of which could be supported by current or new citizen science projects. Given the Council emphasis on habitat issues, a second approach reviewed habitat policy documents along with current ecosystem data products to evaluate “quick wins” aligning objectives and current indicators, and identifying where data from citizen science projects might augment existing indicator data products. In the third approach, the current Council catch specification process, including SSC ABC decisions, was evaluated to determine whether products such as Ecosystem Socioeconomic Profiles would be useful and practical for SAFMC catch specification processes given data and staff resources, whether Climate Vulnerability Assessments might be useful for assessing uncertainty and risk in the SSC’s ABC determination, and what other approaches to integrating ecosystem information into catch specification would be feasible. A fourth approach evaluated the feasibility of developing ocean indicators from global and regional ocean physics datasets for SAFMC habitat or other decisions, using methods developed for the Northeast U.S. shelf. Potential citizen science projects for validating ocean model based indicators were identified based on existing programs in other regions. A fifth approach outlined potential use of the Council’s food web model for evaluating multispecies management strategies or harvest control rules, with decision points for Council consideration.

3 Approaches and Results

3.1 3+ Year Action Review

Council actions from March 2023-March 2026 meetings were reviewed based on posted committee reports and motion summaries for each meeting, with particular attention to directions to staff for agenda items. In addition, posted annual Council workplans and a current workplan provided by John Hadley were reviewed. Counts of actions by FMP were tallied and action types were classified into routine, tactical, occasional, and /or strategic decision types. In general, motions establishing catch limits and measures for stocks were considered tactical and routine, while motions considering planning or policy were considered strategic and occasional. The latter included setting research priorities, workplans, and terms of reference for assessments. Approving agendas for the Habitat and Ecosystem Advisory Panel was considered strategic (similar to setting priorities and workplans) and routine (because it happens annually). Establishing long term open or closed areas was considered tactical but occasional. The purpose of this was not to track changes in timelines for particular actions (which clearly happens) but to identify current and potential entry points for ecosystem information.

Each summary motions document (and committee report, if relevant) starting in March 2023 and ending in March 2026 was examined and the number of motions by committee tallied, along with comments on the action type and inclusion or potential for inclusion of ecosystem information (e.g. involving social, economic, biological, habitat, climate, environmental, or other ocean use issues, questions, or tradeoffs). Ecosystem information was considered to be included when explicitly mentioned in the motion or in the background material in the summary. Ecosystem information was considered to have the potential to be included in decisions regarding stock assessment and MSE terms of reference, assessment prioritization, evaluation of spawning closed areas or seasons, multispecies considerations, and allocation reviews. While nearly any decision could involve ecosystem information by asking “is this decision robust to future ocean conditions?”, this review applied expert judgement to highlight decisions that could benefit from specific ecosystem information or analyses. Therefore, the decisions highlighted are likely a minimum number where ecosystem information could be considered. Full Council Closed Session motions related to committee or working group nominations were excluded from analysis. This information was entered into a table (see Online Appendix Table).

In the period from March 2023 to March 2026, there were a total of 197 motions that did not involve closed session nominations. The Snapper Grouper Committee was responsible for more than half of Council Motions:

Table 3.1: Number and percent (%) of motions by committee, March 2023 - March 2026

Committee	n	%
Snapper Grouper	116	59
Council	21	11
Habitat and Ecosystem	17	9
Mackerel Cobia	13	7
Habitat and Ecosystem + Shrimp	12	6
SEDAR	10	5
Dolphin Wahoo	4	2
Citizen Science	2	1
Snapper Grouper + Mackerel Cobia + Dolphin Wahoo	2	1

While categorizing motions as tactical or strategic, routine or occasional required some judgement, the breakdown was as expected. Decisions are roughly evenly split between tactical/routine and strategic/occasional, with most staff tasking motions classified as both, and a small number of other combinations.

Table 3.2: Number and percent (%) of motions by category, March 2023 - March 2026

Category	Timing	n	%
strategic	occasional	74	38
tactical	routine	71	36
both	both	30	15
strategic	routine	11	6
tactical	occasional	11	6

More than half of motions had no obvious ecosystem pathway, but 18% addressed or included ecosystem issues, and 26% more had the potential for ecosystem linkages.

Table 3.3: Number and percent (%) of motions with ecosystem linkages, March 2023 - March 2026

Ecosystem	n	%
no	111	56
maybe	51	26
yes	35	18

Ecosystem linkages are more commonly associated with strategic and occasional decisions. However there is potential to use ecosystem information to support routine tactical decisions as well.

Table 3.4: Number of motions with ecosystem linkages by decision type, March 2023 - March 2026

Category	Timing	no	maybe	yes
tactical	routine	58	13
strategic	occasional	35	22	17
both	both	8	13	9
strategic	routine	4	1	6
tactical	occasional	6	2	3

Motions with direct ecosystem linkages were most common in the Habitat and Ecosystem Committee (as might be expected), followed by the Snapper Grouper Committee.

Table 3.5: Number and percent (%) of motions with direct ecosystem linkages by Committee, March 2023 - March 2026

Committee	n	%
Habitat and Ecosystem	16	46
Snapper Grouper	6	17
Habitat and Ecosystem + Shrimp	5	14
SEDAR	3	9
Citizen Science	2	6
Council	2	6
Mackerel Cobia	1	3

Motions with direct ecosystem linkages included:

Table 3.6: Motions with direct ecosystem linkages, March 2023 - March 2026.

Committee	Topic	Species	How
Snapper Grouper	RegAmendment 35	RedSnapper	evaluate distribution change
Snapper Grouper	TimingTasks	AllSnapperGrouper	evaluate regime shifts, discard projections
Mackerel Cobia	TimingTasks	AllMackerelCobia	ABC control rule application, assessment prioritization
Habitat and Ecosystem	TimingTasks	All	EBM: beach policy, offshore wind
Council	TimingTasks	All	evaluate climate change management triggers and survey coordination to evaluate climate impacts
Habitat and Ecosystem	HabitatBlueprint	All	approve habitat blueprint
Habitat and Ecosystem	HEAP	All	approve HEAP job description
Habitat and Ecosystem	PolicyStatement	All	approve beach policy statement
Habitat and Ecosystem	HEAP	All	approve HEAP agenda: EFH, offshore wind, navy, space operations
Habitat and Ecosystem	TimingTasks	All	evaluate land discharge impacts on reef and fishery
SEDAR	SEDAR	SnowyGrouper;SpanishMackerel;Dolphin	evaluate distribution shifts or climate on Dolphin (only)
Citizen Science	ResearchPriorities	All	collect social, economic, biological, and environmental information
Habitat and Ecosystem	HEAP	All	approve HEAP agenda: EFH, offshore wind, river flow alteration, citizen science
Habitat and Ecosystem	TimingTasks	All	review and further develop coral amendments
Habitat and Ecosystem	HEAP	All	approve HEAP agenda: EFH, offshore wind, other EBM
Habitat and Ecosystem	TimingTasks	All	develop and consider coral and shrimp amendments
Habitat and Ecosystem	NewAmendment	Coral;Shrimp	coral and shrimp amendments
Council	TimingTasks	All	MOA with FL Keys NMS habitat collaboration
Habitat and Ecosystem + Shrimp	Coral Amendment 11/Shrimp Amendment 12	Coral;Shrimp	establish shrimp fishery access area at coral HAPC boundary
Habitat and Ecosystem	EFH	All	updates food web and connectivity policy and includes info on EFH
Habitat and Ecosystem	LivingShorelines	All	defines living shorelines as supporting ecological resilience
Habitat and Ecosystem	HEAP	All	HEAP agenda: climate readiness, food web policy into EFH, EBM
Habitat and Ecosystem	TimingTasks	All	discuss and develop IEA goals and objectives
Habitat and Ecosystem + Shrimp	Coral Amendment 11/Shrimp Amendment 12	Coral;Shrimp	balancing fishery and habitat objectives
Snapper Grouper	NewAmendment	AllSnapperGrouper	designate ecosystem component species
Habitat and Ecosystem	HEAP	All	approve EFH and EBM work
Habitat and Ecosystem + Shrimp	Coral Amendment 11/Shrimp Amendment 12	Coral;Shrimp	discussion of upwelling, sedimentation
Habitat and Ecosystem + Shrimp	TimingTasks	All	EBM: space industry info collection
Snapper Grouper	Amendment 61	AllSnapperGrouper	unmanaged landings report tracks ecosystem landings
Citizen Science	ResearchPriorities	All	collect social, economic, biological, and environmental information; cooperative research fleet mentioned
Habitat and Ecosystem + Shrimp	TimingTasks	Coral;Shrimp	update economic and social environment sections
Snapper Grouper	Amendment 61	AllSnapperGrouper	ecosystem management objectives acceptable as presented
Snapper Grouper	TimingTasks	AllSnapperGrouper	MSE, landings report unmanaged EC spp,
SEDAR	SEDAR	SpanishMackerel;KingMackerel;DolphinMSE	consider nonstationarity in Dolphin MSE, use citizen science data
SEDAR	SEDAR	All	expand scope include MSE and MP reviews

Motions with opportunities to include ecosystem linkages were found across the Snapper Grouper Committee, the full Council, SEDAR, and other committees.

Table 3.7: Number and percent (%) of motions with potential ecosystem linkages by Committee, March 2023 - March 2026

Committee	n	%
Snapper Grouper	31	61
Council	7	14
SEDAR	7	14
Mackerel Cobia	3	6
Habitat and Ecosystem + Shrimp	2	4
Dolphin Wahoo	1	2

Motions with potential ecosystem linkages included requesting ecosystem ToRs for assessments, evaluating multispecies reference points, and other topics:

Table 3.8: Motions with potential ecosystem linkages, March 2023 - March 2026. The How column reflects hypothetical potential linkages.

Committee	Topic	Species	How
SEDAR	SEDAR	BluelineTilefish	request ecosystem ToR
SEDAR	SEDAR	AtlanticTilefish	request ecosystem ToR
SEDAR	SEDAR	RedPorgy;Gag;KingMackerel	request ecosystem ToR
Dolphin Wahoo	TimingTasks	Dolphin	request MSE address distribution or productivity change
Snapper Grouper	TimingTasks	AllSnapperGrouper	EBM: evaluate space launch activity zones
Snapper Grouper	TimingTasks	AllSnapperGrouper	request ecosystem context during "What it means to me" video interviews
Mackerel Cobia	FrameworkAmendment 13	SpanishMackerel	evaluate ecosystem impacts on long term OY
Snapper Grouper	Amendment 48	Wreckfish	evaluate rationale for spawning season closure timing
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	evaluate ecological connections within new complex
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies MSY proxy
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies F reference point
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies MSST
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies rebuilding plan
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies ABC=ACL
Snapper Grouper	TimingTasks	All	include ecosystem context in stock status letters
SEDAR	SEDAR	Hogfish	request ecosystem ToR
Mackerel Cobia	PortMeetingsPlan	KingMackerel;SpanishMackerel	collect social, economic, biological, and environmental information
SEDAR	SEDAR	RedSnapper	request ecosystem ToR
SEDAR	SEDAR	YellowtailSnapper	request ecosystem ToR
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	evaluate whether spawning season timing shifts
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies trip limit
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies catch limits
Snapper Grouper	TimingTasks	AllSnapperGrouper	develop management strategies for discard reduction
Council	NewAmendment	All	request electronic reporting of ecosystem information
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	evaluate whether spawning season timing shifts
SEDAR	TimingTasks	SEDAR	consider ecosystem information in Key stock definition
Snapper Grouper	Amendment 55	Scamp;YellowmouthGrouper	multispecies OY
Snapper Grouper	TimingTasks	AllSnapperGrouper	consider ecosystem information in black sea bass effort and catch projections
Council	AllocationReview	Spadefish	consider social economic and ecosystem information in allocation updates
Council	AllocationReview	Jacks	consider social economic and ecosystem information in allocation updates
Council	TimingTasks	All	request electronic reporting of ecosystem information
Snapper Grouper	TimingTasks	AllSnapperGrouper	consider ecosystem information in outreach for safe catch and release (e.g water temperature)
Snapper Grouper	TimingTasks	AllSnapperGrouper	consider ecosystem interactions with SPR based MSY proxies
Habitat and Ecosystem + Shrimp	TimingTasks	Coral;Shrimp	consider ecosystem information in draft amendment
Mackerel Cobia	TimingTasks	AllMackerelCobia	include any ecosystem information from port meetings in summaries
Snapper Grouper	TimingTasks	AllSnapperGrouper	consider ecosystem information in shiny tool evaluating need for management, MSY proxies, recreational seasons
Council	TimingTasks	All	highlight ecosystem information when summarizing recent Dolphin journal articles
Habitat and Ecosystem + Shrimp	Coral Amendment 11/Shrimp Amendment 12	Coral;Shrimp	consider social, economic, and ecosystem information
Snapper Grouper	ReferencePoints	AllSnapperGrouper	consider ecosystem influence on steepness, SPR proxies
Snapper Grouper	Amendment 56	BlackSeaBass	consider ecosystem influence on SPR proxy and spawning season
Snapper Grouper	TimingTasks	AllSnapperGrouper	consider ecosystem influence in MSY best practices
Council	Process	Dolphin	consider ecosystem ToR
Snapper Grouper	MSE	AllSnapperGrouper	consider ecosystem change as uncertainty in MSE
Snapper Grouper	MSE	AllSnapperGrouper	consider species distribution shifts in MSE
Snapper Grouper	RegAmendment 37	BlackSeaBass	evaluate potential for spawning season change
Snapper Grouper	TimingTasks	AllSnapperGrouper	consider social economic and ecosystem information in allocation decision tool
Council	TimingTasks	All	indicator for nonharvest value in risk/value matrix
Snapper Grouper	NewFramework	AllSnapperGrouper	consider ecosystem information in revising spawning special managment zones
Snapper Grouper	Amendment 61	AllSnapperGrouper	alternatives for species that can be removed, managed as EC, and kept in FMP
Snapper Grouper	NewFramework	BlackSeaBass	consider ecosystem information for reopening nearshore closed areas and changing pot trip limit
Snapper Grouper	TimingTasks	AllSnapperGrouper	spawning special management zones, allocation review

Overall, it is clear the SAFMC already integrates ecosystem considerations in management, and there is scope for integrating more within the current range of decisions.

All results plus the full motions table are posted here.

3.2 Policy Objectives/Indicators Alignment

The Council has developed EFH policy statements to guide comment development for EFH consultations by describing habitat resources and potential threats to habitat, as well as actions that can be taken for EFH protection. EFH policy documents and current ESR indicators [2] were reviewed and aligned where possible, with gaps highlighted. Three key policy documents were reviewed in detail and aligned with existing indicators:

• South Atlantic Food Webs and Connectivity Policy – January 2025
  • Connects EFH and food web considerations, identifies research needs
  • Includes fish food habits data identifying top 10 prey by FMP
  • Includes food web model for the South Atlantic region
    
• Climate Variability and Fisheries – March 2018
  • Connects changing ocean conditions with fish distribution shifts and changing timing, identifies research needs
  • Notes these shifts present challenges for management and new fishery opportunities
  • Requests annual summaries of climate indicators tracking ecological, social and economic trends
  • Requests climate vulnerability analysis and management strategy evaluation focused on climate robustness
• Marine Submerged Aquatic Vegetation (SAV) Habitat Policy – June 2014
  • Describes ecosystem services provided by SAV and recent declines in this EFH
  • Outlines monitoring and research, planning, management, and education and enforcement to protect and restore SAV

Additional policies identify species with EFH and Habitat Areas of Particular Concern (HAPC) potentially impacted by each activity, and identify specific threats, best management practices and research needs. These were not reviewed for alignment with existing indicators due to time constraints:

• Beach Dredging and Filling, Beach Renourishment and Large-Scale Coastal Engineering – September 2023
  
• Energy Exploration, Development, Transportation and Hydropower Re-Licensing – November 2024
  
• Alteration of Riverine, Estuarine and Nearshore Flows Policy – June 2014
  
• Artificial Reef Habitat Policy Threats to EFH – September 2017
  
• Marine and Estuarine Non-Native and Invasive Species Policy – June 2014
  
• Interactions Between Essential Fish Habitats and Marine Aquaculture Policy – June 2014

Policy objectives without current indicators were identified. Information gaps that might be filled with Citizen Science projects were then identified, along with what those projects would need to deliver.

In tables below, columns summarize the policy name, a brief policy description, and any indicators outlined in the current policy. The Available Indicators column lists indicators currently in the ESR, the CVA, or from another known public source. The Potential Indicators column lists information that may not be currently available but would address the indicators outlined in the current policy. The Potential Indicator Feasibility column is based on the author’s expert judgement of the tools, resources, and personnel needed to obtain the Potential Indicators. High feasibility indicators are those that can be produced with current data and tools with minimal effort. Moderate feasibility indicators would require more work to produce because some tool modification, additional analysis or data synthesis would be needed to produce the indicator. Low feasibility indicators have sparse existing data and/or would require considerable effort to modify tools or synthesize data from multiple distinct sources. Unknown feasibility was assigned when information when data or tool sources and scope were unknown to the author.

In addition to policy objectives, the documents identify research needs. Listed research needs were also aligned with Available Indicators in tables below. Columns for Potential Council Actions suggest how the Council could make progress on or further refine the research needs using existing indicators or other available data. The Potential Action Feasibility column ranks actions similarly as above. High feasibility actions can apply existing indicators directly, Moderate feasibility actions require clarification from the Council prior to engaging in research, require more tool development or data synthesis or are otherwise are more resource intensive, or both. None of the potential actions suggested have low feasibility.

All policy documents were reviewed by the author. Initial matches with ESR indicators were taken from the ESR summary which used Claude Sonnet 4.5 to produce a draft summary and R code to compile indicator descriptions as described here. After initial matches were made, pertinent ESR sections were reviewed by the author to validate matches and provide indicator details.

3.2.1 Indicator Sources

Considerable work has been completed in the Southeast US region that forms an excellent starting point to evaluate how SAFMC policies align with available ecosystem indicators. The South Atlantic has an ESR [2] including indicators spanning climate drivers, physical and chemical pressures, habitat states, lower and upper trophic level status, ecosystem services, and human dimensions (Table 3.9).

Table 3.9: Indicators presented in the South Atlantic ESR, in order of appearance.

Region	Year	Section	Indicator
South Atlantic	2021	Climate Drivers	Atlantic Multidecadal Oscillation (AMO)
South Atlantic	2021	Climate Drivers	North Atlantic Oscillation (NAO)
South Atlantic	2021	Climate Drivers	El Niño Southern Oscillation (ENSO)
South Atlantic	2021	Climate Drivers	North Atlantic Sea Surface Temperature Tripole
South Atlantic	2021	Climate Drivers	Atlantic Warm Pool (AWP)
South Atlantic	2021	Physical and Chemical Pressures	Sea surface temperature
South Atlantic	2021	Physical and Chemical Pressures	Bottom temperature
South Atlantic	2021	Physical and Chemical Pressures	Decadal temperature
South Atlantic	2021	Physical and Chemical Pressures	Florida Current transport
South Atlantic	2021	Physical and Chemical Pressures	Gulf Stream position
South Atlantic	2021	Physical and Chemical Pressures	Upwelling
South Atlantic	2021	Physical and Chemical Pressures	Coastal salinity
South Atlantic	2021	Physical and Chemical Pressures	Stream flow
South Atlantic	2021	Physical and Chemical Pressures	Nutrient loading
South Atlantic	2021	Physical and Chemical Pressures	Precipitation and drought
South Atlantic	2021	Physical and Chemical Pressures	Sea level rise
South Atlantic	2021	Physical and Chemical Pressures	Storms and hurricanes
South Atlantic	2021	Physical and Chemical Pressures	Ocean acidification
South Atlantic	2021	Habitat States	Wetlands and forests
South Atlantic	2021	Habitat States	Submerged aquatic vegetation (SAV)
South Atlantic	2021	Habitat States	Oyster reefs
South Atlantic	2021	Habitat States	Coral demographics
South Atlantic	2021	Habitat States	Coral bleaching
South Atlantic	2021	Lower Trophic Level States	Primary productivity
South Atlantic	2021	Lower Trophic Level States	Zooplankton
South Atlantic	2021	Lower Trophic Level States	Ichthyoplankton diversity and abundance
South Atlantic	2021	Lower Trophic Level States	Forage fish abundance
South Atlantic	2021	Upper Trophic Level States	Nearshore demersal fish diversity and abundance
South Atlantic	2021	Upper Trophic Level States	Offshore hard bottom fish diversity and abundance
South Atlantic	2021	Upper Trophic Level States	Coastal shark diversity and abundance
South Atlantic	2021	Upper Trophic Level States	Coral reef fish diversity and abundance
South Atlantic	2021	Upper Trophic Level States	Mean trophic level
South Atlantic	2021	Upper Trophic Level States	Life history parameters
South Atlantic	2021	Ecosystem Services	Biomass of economically important species
South Atlantic	2021	Ecosystem Services	Recruitment of economically important species
South Atlantic	2021	Ecosystem Services	Commercial landings and revenue
South Atlantic	2021	Ecosystem Services	Recreational landings and effort
South Atlantic	2021	Ecosystem Services	Estuarine shrimp, crab, and oyster landings
South Atlantic	2021	Ecosystem Services	Status of federally managed stocks
South Atlantic	2021	Ecosystem Services	Marine bird abundance
South Atlantic	2021	Ecosystem Services	Marine mammal strandings
South Atlantic	2021	Ecosystem Services	Sea turtle nest counts
South Atlantic	2021	Human Dimensions	Human population
South Atlantic	2021	Human Dimensions	Coastal and urban land use
South Atlantic	2021	Human Dimensions	Total ocean economy
South Atlantic	2021	Human Dimensions	Social connectedness

In addition, a CVA for 71 fish and invertebrate species [3,4] is complete (Fig. 3.1), as well as analysis for South Atlantic and Gulf fishing communities [5]. Marine mammal (108 stocks) and highly migratory fish (58 stocks) climate vulnerability has also been assessed for the entire Atlantic Coast [6,7]. These documents contain indicators that represent a starting point towards meeting the needs identified in the EFH policy documents. In addition, the fish CVA might inform the climate portion of the p* process used to set ABC for stocks in the Dolphin-Wahoo, Golden Crab, and Snapper-Grouper FMPs (see section below).

Figure 3.1: South Atlantic Fish and Invertebrate Climate Vulnerability from Craig et al. 2025

3.2.2 South Atlantic Food Webs and Connectivity Policy

The Food Webs and Connectivity Policy document outlines 9 policies and 9 research and information needs.

Research needs are broader and so considered separate from policies here. Both are aligned with existing indicators in the following tables.

Overall, 5 of the 9 food web policies have potential indicators with high feasibility, 2 have moderate feasibility, and only 2 have low or unknown feasibility. While two food web policy related indicators are already reported in the 2021 ESR, at least 3 additional policies can be assessed with indicators derived from the existing food web model. Two others could potentially be addressed with more labor intensive updates to the existing food web model by integrating spatial capabilities and existing literature. Some literature information on invasive species diet is available [8,9]. Contaminant studies are not included in the ESR, perhaps due to limited work in the South Atlantic region [10–12].

Table 3.10: SAFMC Food Web and Connectivity Policy Objectives and Current Indicators

Policy	Brief Policy Description	Indicators Outlined in the Policy	Available Indicators	Potential Indicators	Potential Indicator Feasibility
Forage Fisheries	Consider forage impacts on predator productivity when setting catch limits	Forage stock abundance and dynamics for important forage by FMP (Appendix A)	Forage fish (menhaden) abundance and estuarine crab and shrimp landings	Add food web estimated trends for aggregate forage	High: use current food web model
Prey Importance	Classify important prey as ecosystem component species	Mass, occurrence, and degree of overlap among multiple predators	Average percent prey in diet by FMP (Appendix A)	Diet analysis to estimate prey mass, occurrence, and degree of overlap among multiple predators	Unknown, dependent on quality of existing food habits data
Food Web Indicators	Consider food web targets and thresholds for management action	Not listed	Mean Trophic Level	Estimate from food web model	High: use current food web model
Food Web Connectivity	Account for migratory species interactions across otherwise separate food webs	Not listed	None	Potentially from spatial food web model	Moderate: requires spatial update to food web model
Trophic Pathways	Maintain diverse (bottom up and top down) energy pathways	Diversity of energy pathways	None	Estimate from food web model	High: use current food web model
Food Web Models	Use food web models in multiple decision contexts where appropriate	Not listed	None	Identify desirable food web states and estimate from food web model	High: use current food web model, pending Council ecosystem objectives
Ecosystem Component Species	Definition: otherwise unmanaged species important to achieving ecosystem management objectives	Not listed	None	Use indicators for Prey Importance	High: aggregate indicators available
Invasive Species	Account for invasive species impacts in management actions	Lionfish predation on and competition with other reef species	None	Analysis of lionfish diet, distribution and abundance, potentially add to food web model	Moderate: published lionfish diet studies exist, requires abundance data and model update
Contaminants	Consider human health and food web impacts	Not listed	None	Potentially from seafood safety monitoring	Low: published contaminant studies have limited spatial scope

Research and information needs are already partially addressed by existing CVA [3,4] and ESR indicators, and by current MSE efforts [13]. Collaboration between the Council and NOAA/other partners to identify clear priorities and objectives for research is a practical next step. Many current ESR indicators have the potential to address specific Council needs; collaboration to tailor the indicators for specific uses is recommended. A structured decision process to identify and analyze ecosystem issues (similar to Pacific FEP Initiatives, North Pacific Action Modules, or Gulf Fishery Ecosystem Issues) could help achieve these goals.

Table 3.11: SAFMC Food Web and Connectivity Research Needs and Current Indicators

Research	Available Indicators	Potential Council Actions	Potential Action Feasibility
Climate Impacts on Productivity	CVA, Recruitment of Economically Important Species, Coral Bleaching	Prioritize and tailor to species based on CVA	Moderate: resource intensive depending on priority species
Offshore Habitats for Estuarine Species	Surface and Bottom Temperature, FL Current Transport, Gulf Stream Position, Upwelling, Primary Productivity, Ocean Acidification	Prioritize and tailor existing indicators to species based on CVA	Moderate: resource intensive depending on priority species
Role of Forage Species	Forage fish (menhaden) abundance and estuarine crab and shrimp landings	Habitat specific managed species diet	Unknown, dependent on quality of existing food habits data
Fix Data Gaps	None	Ensure full use of ESR and CVA data, prioritize data for Citizen Science collection	Mixed: will vary by application
Overarching Risks	CVA, Community CVA, Sea Level Rise, FL Current Transport, Gulf Stream Position, Upwelling	Consider FMP level risks to refine and align existing indicators with management	High: existing CVA and ESR applied by FMP
Species Risk Assessments	CVA	Clarify additional factors to build on CVA	Moderate: depends on additional factors needed
MSEs	Peterson et al 2025	List/provide model code, inputs, and outputs across analyses	High: central repository for existing models
Ecosystem Reference Points	None	Develop objectives before reference points	Moderate: requires Council objectives to test literature ELRPs
Essential Fish Habitat	Wetlands and Forests, SAV, Oyster Reefs, Coral Demographics and Bleaching, Nearshore, Offshore Hard Bottom, and Coral Reef Fish Diversity and Abundance	Prioritize and tailor existing indicators to EFH and HAPC	High: many ESR indicators align with habitats

Additional reference material:

• Published lionfish studies

3.2.3 Climate Variability and Fisheries

The Climate Variability and Fisheries Policy document describes historical and projected ocean conditions in the South Atlantic, climate effects on fish, habitats, and fisheries, information gaps and research priorities, and relationships with SAFMC management. Here, stated management policies and research priorities are aligned with existing indicators. This policy was last updated in 2018, before the 2021 ESR and 2023 CVA were released.

Many current ESR indicators directly address Climate Variability and Fisheries policy statements. Current gaps in ESR indicators related to species distribution shifts and forecast conditions could be filled with existing information. DisMAP indicators summarizing species distribution from the SEAMAP survey are available. Additional species distribution indicators and management tools are being developed in the Mid-Atlantic which may be useful. Regional ocean model forecasts are available for the South Atlantic region, but resources are required to process, evaluate, and translate these outputs into indicators for management review. Some Councils already receive reports of unmanaged species landings compiled by NOAA Regional Offices to flag potential emerging fisheries and species distribution changes; a report for the South Atlantic is currently in progress. However, these reports focus on commercial landings because there are few information sources for recreational landings of unmanaged species.

Table 3.12: SAFMC Climate Variability and Fisheries Policy Objectives and Current Indicators

Policy	Brief Policy Description	Indicators Outlined in the Policy	Available Indicators	Potential Indicators	Potential Indicator Feasibility
Collaboration	Work across jurisdictions with multiple organizations and stakeholders	Species distribution shifts	None	Latitudinal and depth shifts for all or individual species	High: available on DisMAP website
Climate Indicators	Develop and present climate indicators annually	Climate, ecological, social, and economic trends and status, environmentally driven fishery trends	5 Climate, 13 Physical, 5 Habitat, 4 Low and 6 Upper Trophic Level, 8 Fishery, Social, and Economic indicator categories	Review and refine existing before considering additional indicators	High: prioritize available indicators for refinement
Tradeoffs	Consider increased uncertainty and changing productivity in management	Uncertainty and stock productivity	Biomass and Recruitment of Commercially Important Species, Shrimp, Crab, and Oyster landings	Short term ocean forecasts including uncertainty, MSE with changing productivity	Moderate: resources required to integrate regional ocean forecasts and uncertainty in MSE
Precautionary	Apply precautionary principle under uncertain future climate conditions	Not listed	None	Short term ocean forecasts including uncertainty	Moderate: resources required to process regional ocean forecasts
New Fisheries	Manage new fishery development to avoid negative EFH impacts	Not listed	None	Landings of currently unmanaged species	Moderate: resources required to compile information from multiple sources

There are multiple ways to move forward with Climate Variability and Fisheries policies and research priorities given existing indicators and analyses. An initial step is a collaborative Council and NOAA review of current indicators to prioritize a subset for refinement and annual updates. The CVA results could help identify a subset of species or a representative species for each FMP for initial focus. Considerable effort towards understanding the social and economic context and community climate vulnerability already exists [5,14] and can contribute to these efforts. Note that the last research priority here is identical to the second research priority identified in the Food Webs and Connectivity Policy.

Note that an example of indicator development for the “3D Estuarine-Coastal-Ocean Observations” research priority is included in the Prototype Indicator Development section below.

Table 3.13: SAFMC Climate Variability and Fisheries Research Needs and Current Indicators

Research	Available Indicators	Potential Council Actions	Potential Action Feasibility
Climate Impacts on Productivity	CVA combined with many ESR indicators	Prioritize and tailor existing indicators to species based on CVA	Moderate: resource intensive depending on priority species
Climate Impacts in Assessments	ESR indicators need refinement for assessment application	Prioritize and tailor existing ESR indicators to assessments based on CVA	Moderate: resource intensive depending on priority species
3D Estuarine-Coastal-Ocean Observations	Surface and Bottom Temperature, FL Current Transport, Gulf Stream Position, Upwelling	Add ocean model reanalysis products (GLORYS) to existing indicators	High: apply methods and code developed in other regions
Climate Robust MSE	Peterson et al 2025	Consider standard climate scenarios in all MSEs	High: develop simple climate scenarios as robustness tests
Socioeconomic Impacts and Fishery Responses	CVA, Community CVA, Sea Level Rise, FL Current Transport, Gulf Stream Position, Upwelling	Prioritize and tailor existing indicators	High: existing CVA, Community CVA, and ESR indicators
Offshore Habitats for Estuarine Species	Surface and Bottom Temperature, FL Current Transport, Gulf Stream Position, Upwelling, Primary Productivity, Ocean Acidification	Prioritize and tailor existing indicators to species based on CVA	Moderate: resource intensive depending on priority species

3.2.4 Marine Submerged Aquatic Vegetation

Submerged Aquatic Vegetation (SAV) or seagrass is an important habitat in the South Atlantic, found primarily in North Carolina and Florida coastal waters. SAFMC has designated SAV to be EFH for managed fish (snapper-grouper species and cobia) and invertebrates (Penaeid shrimp and spiny lobster), as well as a Habitat Area of Particular Concern (HAPC) for snapper-grouper species.

The Council does not directly manage many of the activities and conditions that affect SAV. The overall policy goal in the Marine Submerged Aquatic Vegetation Policy document is to support actions that protect and restore SAV throughout the South Atlantic. These actions require collaboration with the four South Atlantic states: North Carolina, South Carolina, Georgia, and Florida, as well as local authorities.

Existing ESR indicators for SAV, coastal habitat attributes, land use and ocean economy align with SAV policy. Additional indicators of SAV benefits to dependent managed species could be derived from current indicators for commercial and recreational landings and value. However, comprehensive SAV indicators and research would require dedicated effort from multiple partners.

Table 3.14: SAFMC Submerged Aquatic Vegetation Policy Objectives and Current Indicators

Policy	Brief Policy Description	Indicators Outlined in the Policy	Available Indicators	Potential Indicators	Potential Indicator Feasibility
Monitoring	Monitoring is needed for assessment and management	SAV distribution and shifts, water quality	SAV extent and % change	Same indicator with more comprehensive data in space and time	Low-Moderate: depends on multiple states resources and coordination
Planning	Establish goals, objectives, measures of success	Not listed	SAV extent and % change, Coastal salinity	Change in SAV distribution; habitat depth, sediment, light penetration, salinity, and wave energy	Mixed: depends on water quality information availability
Management	Review existing human activity rules, water quality standards, and restoration guidelines	Not listed	Coastal salinity, Stream flow, Nutrient loading, Coastal and Urban land use	Habitat disturbance indicators (dredging, construction, bottom contact from boating and fishing)	Low-Moderate: resource intensive to collate information from many local sources
Education and Enforcement	Analyze and communicate benefits of SAV protection, evaluate current enforcement	Not listed	Total ocean economy	Commercial and Recreational landings and value for SAV dependent species	High: subset of information from current indicators

In general, research priorities for SAV require more coordination and collaboration with state and local partners relative to the priorities above for food web connectivity and climate fisheries interactions. However, the research objectives are clearly stated and well focused. Initial steps could include evaluation and prioritization of ESR indicators for relevance to SAV extent and % change. A longer term project could consider habitat-climate vulnerability analysis as was done for Northeast US Atlantic habitats [15], which would apply not just to SAV but to all Council designated EFH and HAPC.

Table 3.15: SAFMC Submerged Aquatic Vegetation Research Needs and Current Indicators

Research	Available Indicators	Potential Council Actions	Potential Action Feasibility
Standardize Mapping Protocols	None	Collaborate with partners to design and implement regional surveys	Moderate: resource intensive process
EFH GIS Database	SAV areal coverage and % change based on existing data	Expand Council EFH and HAPC maps	Moderate: resource intensive process
Evaluate Water Quality	Stream Flow, Nutrient Loading, Precipitation and Drought	Prioritize existing indicators for further refinement	Moderate-High: may need additional indicators for water clarity
Drivers of SAV Loss	SAV, Stream Flow, Nutrient Loading, Precipitation and Drought, Sea Level Rise, Storms and Hurricanes, Primary Productivity, Human Population, Coastal Land Use	Prioritize existing indicators for further refinement	High: many potential indicators for SAV drivers exist
Restoration Efficacy	None	Collaborate with partners	Moderate: resource intensive process
Climate Impacts on SAV	SAV, Surface and Bottom Temperature, Primary Productivity, Ocean Acidification	Consider habitat climate vulnerability assessment	Moderate: resource intensive process

Overall, there are many existing indicators that align with current SAFMC policy statements, and there is high feasibility for developing tailored indicators from existing resources for 8 of the 18 policy statements examined here.

3.3 Indicator Opportunities for SSC ABC Setting

For catch advice, the project evaluated whether products such as Ecosystem Socioeconomic Profiles (ESPs, [16–18]) would be useful and practical for SAFMC catch specification processes given data and staff resources, whether Ecosystem Status Report (ESR, [2]) indicators or Climate Vulnerability Analysis (CVA, [3,4]) might be useful for assessing uncertainty and risk in adjusting catch levels, and what other approaches to integrating ecosystem information into catch specification would be feasible.

The current SAFMC risk policy and ABC approach were updated for three FMPs (Snapper Grouper, Dolphin Wahoo, and Golden Crab) in 2023. The policy assigns a different probability of overfishing, p*, based on stock biomass status combined with multiple risk attributes spanning biological sensitivity, human dimensions, and environmental conditions.

Risk tolerance is generally highest for stocks with biomass above B_MSY, so they can have higher p*. Risk tolerance is intermediate for stocks at or below B_MSY but above the halfway point between B_MSY and MSST, and low for stocks below the midpoint (Fig. 3.2).

The magnitude of p* across these stock status categories changes depending on an overall risk ranking from low to high, which is determined by the Council after reviewing risk rankings assigned by the AP and the SSC using the attributes and criteria described below.

Figure 3.2: SAFMC Risk Policy, 2023

The Council can deviate, up or down, from default p* in the figure above by up to 10%, not to exceed p* = 0.50.

Specific attributes that can inform risk of overfishing:

• Biological:
  • Estimated natural mortality
  • Age at maturity
• Human Dimension:
  • Ability to regulate fishery
  • Potential for discard losses
  • Annual commercial value
  • Recreational desirability
  • Social concerns
• Environmental:
  • Ecosystem importance
  • Climate change
  • Other environmental variables

Risk evaluation could include an indicator-based approach. While some Councils use information from annual stock level ESPs or full system ESRs to evaluate risk when specifying catch levels, others are developing a more streamlined process based on structured discussions between stock assessors and ecosystem scientists. A mix of approaches will be explored for the South Atlantic context given existing resource levels.

Experience with applying the current risk framework and potential approaches for incorporating ecosystem information were discussed with the SSC at their April 2026 meeting to evaluate feasibility. Questions for the SSC and the answers are listed below. The SSC also provided many helpful comments regarding the All Council Ecosystem Data Review in its April 2026 meeting report. As a result of the SSC review, new information has been incorporated into that report (see Section 4.2.9.2) as well as in this section, the MSE section, and in the Discussion section of this report.

1. Is the characterization of the ABC control rule above correct?

The SSC verified that the characterization of the ABC control rule described above is correct.

2. How many times has the SSC applied the rule?

The rule and risk scoring procedure have been used for six species in the Snapper Grouper FMP to date.

3. For which species has the SSC filled out the risk table? (example: Black Sea Bass from April 2025)

This approach has been applied in 2024 (from Snapper Grouper Committee December 2024 Council Report):

Stock Risk Ratings for Golden Tilefish, Blueline Tilefish, Red Snapper, Mutton Snapper, and Yellowtail Snapper

Under the ABC Control Rule, the Council incorporates an evaluation of how much risk of overfishing it would be willing to accept based on biological, fishery (human interaction), and environmental factors affecting each stock. The Committee reviewed recommended scores from the AP and SSC and related information for golden tilefish, blueline tilefish, red snapper, mutton snapper, and yellowtail snapper. The Committee developed stock risk ratings for use in application of the ABC Control Rule to each of these species and rated each of these stocks as High Risk.

The approach was also applied in 2025 (from Snapper Grouper Committee June 2025 Council Report):

Black Sea Bass (Amendment 56)

Council staff presented the stock risk rating matrix from the ABC Control Rule for Black Sea Bass and the preliminary scores and comments made by the Advisory Panel and SSC. The Council determined the stock risk rating of black sea bass to be “high.”

4. What is the SSC’s experience in filling out the risk table?

• Which portions have high agreement?
• Which portions generate much discussion and disagreement?
• What information does the SSC have when filling out the risk table?

The SSC discussed how the risk table is operationally applied. When a new assessment is coming forward, the SSC, AP, and Council scores risk for all of the categories. In general there have been only minor differences between SSC AP and Council scores, so there is not a lot of disagreement between the groups to date. The ABC Control Rule and Risk Table approach was established by FMP amendment, so changes to the table also require FMP amendments.

When first applying the framework, the SSC had difficulties with some of the risk categories, mostly related to economic scoring and ecosystem scoring, because it wasn’t always clear how to interpret the text. There was a general consensus that indicators for biological and human dimensions sections are currently well specified, but the ecosystem factors lacked consistent data. Biological risk was sometimes adjusted from previous Productivity Susceptibility Analysis (PSA) scores if that information was considered too old. The table itself specifies criteria for each risk level of the human dimensions indicators.

5. It appears that quantitative indicators are available for the Biological and Human Dimensions Attributes, but not the Environmental Attributes

• Could the Ecosystem Importance attribute be informed by the SAFMC Food Web Model?
• Could the Climate Change attribute be informed by the South Atlantic Climate Vulnerability Assessment [4]?
• What information is most often used to evaluate the Other Environmental Variables attribute?

The SSC noted in the meeting minutes that “Clearer guidance is needed to define how ecosystem indicators should inform risk table scoring and ABC-setting decisions” and that “Effective integration of climate information requires consideration of both species vulnerability and environmental exposure (i.e., observed or projected changes), as either component alone is insufficient to inform risk.” The three sections below expand on these ideas. The SSC further noted that “Initial implementation should prioritize a small, targeted set of indicators that can be clearly linked to existing decision frameworks (e.g., risk tables), with iterative refinement over time rather than attempting comprehensive integration at the outset.”

The SSC briefly discussed the SAFMC food web model and its capabilities to inform ecosystem importance in the risk table. This is expanded with an example of potential use below.

The SSC also discussed how information in the South Atlantic Climate Vulnerability Assessment [4] might be applied to the climate change attribute in the risk table. This is also expanded in an example below.

Finally, The SSC noted that it has been particularly difficult to score the environmental factors risk. This was originally intended to capture relevant factors that may not be the same across stocks; lacking specific environmental drivers, trends in recruitment or regime shifts would be appropriate indicators for this risk. Some potential pathways for approaching this risk are also outlined below.

3.3.1 Using food web model results to inform ecosystem importance

The criterion to determine high ecosystem importance in the stock risk rating process is “Does this species significantly affect other species, e.g. as a keystone predator, primary prey, habitat builder etc.?” Food web models can be used to characterize important prey and predators of species by summing biomass flows into and out of each species. Influential prey and predators can be identified for the entire system without the need for dynamic simulation. This information can be useful to provide context for stock assessments in multispecies systems [19], as well as directly inform ecosystem importance for risk assessment.

The SAFMC food web model is an Ecopath with Ecosim model with over 20 years of development. The original model [20] included over 200 functional groups, but has since been modified into simpler more aggregated versions to address particular issues, including spatial issues. Contractors from the Florida Fish and Wildlife Research Institute and the University of Florida are leading current model development, in close collaboration with SAFMC. This model has been endorsed by the SSC in 2020, and developed recently to evaluate questions such as predation on juvenile black sea bass by red snapper and development for MSE applications.

In this simple example, results from a draft Mid-Atlantic food web model are used to identify key prey and mortality sources for summer flounder, as well as predation impacts by summer flounder on other managed species (Fig. 3.3). A similar analysis can be performed with the SAFMC food web model for South Atlantic species. Blue shaded prey and predators are Mid-Atlantic managed species, while other prey and mortality from fisheries or non-MAFMC managed species are shaded in gray. Managed squids are an important diet item for Summer Flounder. Summer flounder is also an important predator of squids, causing more than 10% of mortality on Illex squids. Fisheries cause most of the mortality on Summer Flounder.

Figure 3.3: Summer flounder diet and predation mortality on Illex squid and Summer flounder based on a draft Mid-Atlantic food web model

Thresholds of diet or mortality proportion could be further discussed and standardized by the SSC, AP, and Council to determine risk from species interactions. These thresholds could be tailored to a species, fishery, or FMP depending on the ecosystem role of the species or primary concerns of the Council and stakeholders. These thresholds can also be refined over time. For example, the Mid-Atlantic Fishery Management Council (MAFMC) established thresholds in its original ecosystem risk assessment that evaluated whether a particular MAFMC managed species had more than 50% of their diet from other MAFMC managed species. In addition, risk thresholds for food web interactions were defined differently between MAFMC managed species and protected species. Criteria for that food web interaction defined risk as the MAFMC managed species as “important” prey if they contributed more than 30% of the protected predator diet, with risk increasing across the number of protected predators where the MAFMC managed species was considered important [21]. The risk criteria for MAFMC managed species risk from food web interactions have since evolved to evaluate broader prey availability and predation pressure risks for each species, reflecting the changing needs of MAFMC. In all cases, the criteria for each risk level and indicators for use have been clearly and quantitatively specified using a collaborative process between the Council and ecosystem scientists.

3.3.2 Using climate vulnerability assessment (CVA) results to inform climate change

The SSC pointed out that while the CVA could inform the climate change risk attribute, they would also need indicators of the conditions that species are sensitive to in order to best characterize risk. The Risk Table criterion for High Risk is “Is this species likely to experience/be experiencing negative stock impacts due to climate change?” The CVA can provide a starting point to both prioritize species likely to see negative impacts of climate and to identify key sensitivities for those species.

One way to inform the SSC ABC process might be to use the CVA directionality scores [3,4] (Fig. 3.4). When considering the climate change risk criterion for a particular species, the SSC could first look up whether the direction of climate impact was positive, neutral, or negative for that species using CVA results. Species with positive impacts could be assigned lowest risk, those with neutral impacts could be assigned the default risk, and those with negative impacts could be assigned a higher risk overall in the absence of other information.

Many snapper species are in the positive impacts bin, suggesting that introducing a way to account for “climate winners” in the risk table might be useful.

Figure 3.4: South Atlantic Fish and Invertebrate Climate Impact Directional Effects from Craig et al. 2025

For species predicted to have negative impacts from climate change, the sensitivity and exposure for that species could be considered to develop additional indicators of risk. For example, some species are more sensitive to ocean acidification, while others are more sensitive to temperature. Appropriate indicators can then be evaluated to determine whether risk may remain the same for the species or may be increasing or decreasing given current indicator trends.

For example, Nassau grouper (Epinephelus striatus) were identified as a species with a very high vulnerability ranking and a negative impact directionality in the CVA (Fig. 3.5). Individual species sensitivity data shows high vulnerability to both temperature and ocean acidification. The spawning cycle is the sensitivity attribute with the highest sensitivity score, followed by habitat specificity.

Figure 3.5: Nassau Grouper Species Narrative, from Craig et al. 2025 Supplemental information

If this species were assessed and required scoring, it could get a high risk score simply from the negative directional impact from the CVA. However, indicators of temperature and ocean acidification could also be evaluated during spawning seasons and in key habitats to further determine whether the high climate vulnerability and negative direction is likely to be realized in the near future. Spatial bottom temperature indicators, such as the one developed in the next section, can contribute information on conditions in specific areas of the South Atlantic inhabited by species of interest. If multiple indicators are used, correlations among the indicators could be evaluated to ensure that each indicator is giving distinct information. Regional ocean model projections could also be used to develop projected indicators appropriate to a species based on CVA results if adequate resources are available.

3.3.3 Approaches for identifying and evaluating other environmental variables

The SSC noted that evaluating the risk from other environmental variables has been difficult. The criterion is phrased broadly in order to capture a wide variety of potential stock impacts: “Are other environmental variables causing negative effects on this stock, e.g. in the form of regime shifts, recruitment failure, etc.?” The implication is that these variables should be something other than the food web effects or climate impacts evaluated in the previous two risk attributes. There was a general consensus that stock specific information and indicators would be useful here, rather than general ecosystem level indicators of the type provided in the ESR. A direct linkage between environmental driver and stock response would be ideal, although observations of changes in population dynamics such as an unexpected series of low recruitments (that are not already accounted for in stock projections) or a clear environmental regime shift could be used to indicate high risk.

Ecosystem and Socio-economic Profiles (ESPs), an ecosystem status report tailored to an individual stock, were invented in the North Pacific region [18] and have since been applied in multiple other regions. These reports start with a conceptual model of the species life history that identifies key transitions between life stages and habitats as well as environmental tolerances and any known mechanistic linkages to environmental drivers at each stage (e.g, Fig. 3.6 from [22]). The conceptual model, developed from literature review and expert knowledge (which can include scientists and fishers), forms the basis for both qualitative risk statements and quantitative indicator development tailored to the priority stock.

Figure 3.6: Atlantic herring conceptual model, from Molina et al. 2025

A preliminary list of ecosystem factors to be considered has been developed for US Pacific fisheries and could be modified for the South Atlantic (quoted from Appendix A of the California Current IEA (CCIEA) Team Report on Fisheries Ecosystem Initiative 4):

• Food webs (e.g., predator abundance or productivity, competitor abundance or productivity, spatial overlap with predators/competitors, prey abundance) – this closely matches the SAFMC “ecosystem importance” risk attribute so should not be reproduced under “other environmental variables”
• Habitat availability (e.g., do current or near-term predictions of ocean temperatures and oxygen match known optimal ranges for [priority species] life history stages, closed area regulations) – this may be similar to the SAFMC “climate impact” risk attribute so should not be reproduced under “other environmental variables”
• Changes in ecosystem productivity (e.g., upwelling indices, chlorophyll a, abundance of low trophic level organisms) – related to regime shifts
• Physiological indices (e.g., growth, condition, pathogens/parasites, stomach weight/fullness) – possible drivers of recruitment trends
• Reproductive indices (e.g., maturity, fecundity, spawn timing and duration) – possible drivers of recruitment trends

The last three points on this list may be useful to clarify “other environmental variables” because they can result in regime shifts and recruitment trends, but unlike recruitment trends may not already be accounted for in the stock assessment.

The structured conversation between ecosystem and stock assessment scientists described in Appendix A of the CCIEA document could be a useful starting point for developing the conceptual model. This process is more streamlined than developing a full ESP report and therefore may be more feasible in a resource limited context.

In a full ESP, indicator development and testing follows from the conceptual model. Indicators represent the hypothesized ecosystem drivers at different life stages, areas, and times of year. Priority should be given to developing indicators for processes or life history stages contributing to high assessment or projection uncertainty. Considerable environmental information is available in the ESR that could be tailored to priority species life stages (for example, surface temperature in a particular area for particular months rather than annual basin-wide averages reported in the ecosystem overview). The indicator development process should document the source, availability, and timeliness of data for alignment with operational stock assessments as well as hypothesized mechanistic connections to stock dynamics.

Once stock specific indicators are developed, they are tested to ensure that they are tied to changes in the stock and that they are uncorrelated with each other, providing unique information. An understanding of the variance or uncertainty in indicators is as important as the mean or trend.

Indicators could be used outside the assessment model to inform a risk table, as part of the assessment process to support decisions regarding parameterization or timeframes for calculating reference points, or within an assessment as a covariate or driver of a particular process if evidence supports this use. In each case, decisions and rationale would require clear documentation.

The SSC also pointed out that environmental drivers may affect fisheries as well as the stocks themselves. Environmental indicators above the surface of the ocean–wind and waves, bad weather days, etc. could affect fishing effort, and also the likelihood of catch reaching ABC. Indicators could be tailored to specific fisheries to address changes in effort.

Overall, a prioritization process for developing stock specific indicators would be required to make the best use of limited resources. Indicators of general risk for use in the ABC risk table may prove to be more feasible than quantitative indicators to be incorporated directly into stock assessments.

3.3.4 Considering positive ecosystem effects?

A new Pacific Council risk table allows for positive, neutral, and negative ecosystem effects to be used in ABC setting (Table 3.16). Although this type of approach would require an FMP amendment in the South Atlantic to make additional categories of ecosystem risk beyond the current “High” category, it may be worth considering to get a full picture of ecosystem effects, positive and negative, on the risk of overfishing.

The Pacific Council SSC is evaluating risk tables in progress for stock assessments and ABC decisions, where risk tables are reframed as uncertainty tables using IPCC “confidence” language on degree of agreement of indicators and robustness of evidence. This approach is patterned on the use of risk tables in NPFMC harvest specification, but is tailored to the p* process used in PFMC. The ecosystem team tested options and recommended one where ecosystem and climate risks would alter the sigma applied to characterize scientific uncertainty in the OFL (sigma is equivalent to the MAFMC SSC OFL CV). PFMC sigmas are 0.5 for high certainty assessments, 1.0 for data moderate assessments, and 2.0 for data limited assessments, with additional increases from a baseline sigma as time passes since the most recent assessment. Ecosystem and climate risks could further inform sigma, increasing or decreasing it as these factors increase or decrease uncertainty.

Operationally, a prototype process has ecosystem and stock scientists participate in a structured conversation as noted above, to identify key uncertainties in the assessment and evaluate ecosystem drivers of the stock (that are not already included in the assessment), and to fill out a table indicating whether ecosystem conditions are favorable, neutral, or unfavorable for the stock. This draws on previous literature and the indicators reported in the ESR. Information from the CVA for each stock is also included in this discussion. The structured discussion template is included in the CCIEA team’s 2024 report. For groundfish stock assessments conducted in 2025, pilot risk tables were developed for five full/benchmark assessments: yellowtail rockfish [23], California quillback rockfish [24], chilipepper rockfish [25], rougheye and blackspotted rockfishes [26], and sablefish [27].

Table 3.16: Pacific Pilot Risk Levels and Indicator Criteria.

Risk Level	Ecosystem and Environmental Conditions	Assessment Data Inputs	Assessment Model Fits and Structural Uncertainty
Level 1: Favorable	Indicators not used in the stock assessment show medium to high level of agreement and moderate to strong evidence supporting high species productivity.	...	...
Level 2: Neutral	Majority of indicators show no notable trends and/or no apparent environmental and ecosystem concerns.	...	...
Level 3: Unfavorable	Majority of indicators show medium to high level of agreement and moderate to strong evidence supporting low species productivity	...	...

Potential Action:
Favorable Conditions → Decrease Risk Buffer (Higher Catch Recommendation)
Neutral Conditions → Keep Standard Risk Buffer (Standard Catch Recommendation)
Unfavorable Conditions → Increase Risk Buffer (Lower Catch Recommendation)

Source: CCIEA Risk Table Report

Overall, there are multiple options to address the challenges for including ecosystem information in assessment and management identified by the SSC, by providing standardized guidance for interpreting and scoring ecosystem and environmental components using existing quantitative environmental indicators for direct inclusion in risk tables, and for incorporating environmental drivers into stock assessment and catch frameworks.

3.4 Prototype Indicator Development

Ocean data is available for the Southeast US coast, from multiple sources. Regional ocean models and global ocean models, including the NOAA MOM6 model [28] and Copernicus products including the Global Oceanic Reanalysis (GLORYS, [29]) already provide both hindcasts and forecasts of ocean temperature and salinity, as well as other ocean variables, for the South Atlantic.

An ocean reanalysis consists of an ocean physics model that has incorporated (“assimilated”) observational data collected from many instruments measuring the ocean. The model adjusts to match observational data where it exists, and fills in areas without observational data. While the general patterns in GLORYS compare well with independently collected ocean data, there can be differences at fine scales and in highly dynamic areas. Therefore, GLORYS is more appropriate for some applications than others, and comparisons with in situ measurements are encouraged. Validation of a model-based temperature indicator could be achieved with an expanded Citizen Science project or by establishment of a formal Study Fleet such as that in the Northeast US [30].

A prototype habitat indicator characterizing bottom temperature in space over time was developed using GLORYS data with a reproducible workflow based on workflows used in the Northeast US by NEFSC. The objective is to produce maps and time series indicating where and when bottom temperature conditions approach or exceed stressful levels relevant to particular species ranging from corals to targeted fish species. Stressful temperature levels for key species would need to be determined from literature or other studies if unknown.

This indicator can be used to compare seasonal spatial changes in temperature across years, and the same information can be expressed relative to a thermal tolerance threshold. For example, the number of days above a certain temperature for a given year can be visualized in space to determine whether certain areas have become too warm for the species in the area, and how long these conditions lasted.

The workflow is outlined here and all code will be provided so that this can be reproduced for other available information in the GLORYS datasets (salinity, currents, etc). The steps are first to pull the GLORYS data product of choice for the South Atlantic region from the Copernicus website, then to process the pulled data into indicator datasets, and finally to plot the indicators. This section outlines several interim steps and final results.

3.4.1 Pull GLORYS bottom temperature

The workflow pulls daily mean bottom temperature from the Copernicus website using code here. There are separate files for each day of each year. The code for downloading is courtesy Joseph Caracappa, NOAA NMFS NEFSC, and was modified for the South Atlantic. Pulling 6 years of data took a laptop and home internet connection about 8 hours. All of the files pulled for the South Atlantic are found here in folders for each year 2020-2025.

As an example of the daily information, we plot a South Atlantic file (Fig. 3.7):

Figure 3.7: Example GLORYS mean daily bottom temperature for December 31, 2025 in the South Atlantic region.

3.4.2 Process GLORYS bottom temperature

Joe Caracappa, NEFSC, wrote nearly all of the code used to produce these indicators for the Northeast US State of the Ecosystem (SOE) reports. Code was modified from publicly available repositories (READ-EDAB_GLORYS and READ_EDAB_Utilities) maintained by NEFSC on GitHub. R [31] packages used in this workflow include tidyverse [32] for general data organization and visualization, terra [33] and ncdf4 [34] for processing netCDF files downloaded from Copernicus, and knitr [35–37], rmarkdown [38–40], and bookdown [41,42] for producing reports in both .html and .pdf formats.

Here the focus is on both spatial bottom temperature by season and the spatial indicator of days with bottom temperature over a certain threshold in a given year. These indicators are called [1bottom_temp_model_gridded](https://noaa-edab.github.io/catalog/bottom_temp_model_gridded.html) and [thermal_habitat_gridded](https://noaa-edab.github.io/catalog/thermal_habitat_gridded.html), respectively, in the SOE. Functions to plot indicators are found in theecodata` [43] package and in the NOAA-EDAB ecodata GitHub repository.

Finally, a shape file is required to run the code for the South Atlantic. For this demonstration, SA_EEZ_off_states was used. The entire South Atlantic region was used in the shapefile by removing the parts of code that mapped to subareas, but this can be modified if there are subsets of the South Atlantic defined in a shape file that need separate indicators.

3.4.3 Visualize prototype bottom temperature indicators

The spatial bottom temperature by season indicator was successfully produced for the South Atlantic following the same approach used in ecodata for the Northeast US. The plots below show the seasonal and spatial bottom temperature indicator data for all years pulled, 2020-2025 (Figs. 3.8 - 3.13).

Figure 3.8: South Atlantic average bottom temperature by season: 2025

Figure 3.9: South Atlantic average bottom temperature by season: 2024

Figure 3.10: South Atlantic average bottom temperature by season: 2023

Figure 3.11: South Atlantic average bottom temperature by season: 2022

Figure 3.12: South Atlantic average bottom temperature by season: 2021

Figure 3.13: South Atlantic average bottom temperature by season: 2020

The next plot visualizes the thermal habitat indicator for a single year looking at different temperature thresholds (Fig. 3.14). The indicator is the number of days with bottom temperature above the temperature listed at the top of each plot for the year in the plot title. This can be an indicator of thermal stress for different species, depending on their temperature tolerance. Temperatures are in degrees C but can be converted to F if desired.

If there were 0 days with bottom temperature above the given temperature, nothing appears on the plot. This is why the higher temperatures have less of the shelf covered. This approach can also be modified to fill with a neutral color instead.

Figure 3.14: Number of days with South Atlantic bottom temperatures above six thermal thresholds in 2020. Thermal thresholds are above the top of each plot and range from 5 to 30 degrees C.

These plots compare the number of days exceeding 25 degrees C or 77 degrees F for all the years pulled (Figs. 3.15 - 3.16).

Figure 3.15: Number of days with South Atlantic bottom temperatures above a 25 degree C thermal threshold, 2020-2022.

Figure 3.16: Number of days with South Atlantic bottom temperatures above a 25 degree C thermal threshold, 2023-2025.

Maps can be zoomed in to show more detailed areas as well. For example, the number of days with bottom temperature over 25 degrees C or 77 degrees F was greater in 2020 than in other years towards the outer edge of the shelf off Wilmington NC (Fig. 3.17).

Figure 3.17: Number of days with South Atlantic bottom temperatures off Wilmington NC above a 25 degree C thermal threshold, 2020-2025.

The indicators include data for all temperatures from 0 to 30 degrees C in 1 degree increments. Another indicator showing the proportion of area for different depth ranges above a given bottom temperature threshold was also produced. This plot compares proportions of each depth range above any given threshold 2020-2025 (Fig. 3.18).

Figure 3.18: Proportion of South Atlantic area in each depth range above a given bottom temperature threshold, 2020-2025. Bold line indicates 2025.

All of the South Atlantic indicator datasets are found here with a folder for each indicator.

This was a proof of concept, demonstrating that information is available for the South Atlantic region from global databases and can be processed with available code. Because code was already available, this took approximately one week of work (with the computer occasionally working at night to do long downloads or calculations).

A full indicator workflow complete with code and false starts is here.

3.5 Multispecies Management Potential: MSE

The SAFMC has several MSEs in progress (e.g., Snapper Grouper, Dolphin), and is therefore familiar with the MSE steps outlined here. These steps include defining objectives and performance metrics related to achieving the objectives, identifying potential management measures to achieve the objectives, identifying key uncertainties, developing an operating model (that can can include the key uncertainties, implement the alternative management measures, and output the performance metrics), simulating system dynamics with the operating model, calculating performance metrics for alternative management measures, and comparing the results including identifying tradeoffs between objectives associated with each alternative management measure [44].

Current SAFMC MSE objectives focus primarily on developing and testing management procedures for individual species, or for a small group of individually modeled species. The Dolphin MSE is developing empirical management procedures for a single, highly productive but difficult to assess species, intended to balance multiple stakeholder objectives that vary regionally (see the general overview and [45]). The Snapper-Grouper MSE centers on red snapper recreational releases but also includes gag grouper and black sea bass in a multi-stock operating model that includes common fishing fleets for all three species, and correlated recruitment deviations for use in projections (see the general overview and Building Base Case Operating Models). The two additional stocks included in the Snapper-Grouper MSE were specifically chosen both for commercial and recreational importance and because they both have age structured stock assessments.

Management of large multispecies complexes in a resource-limited context was a key concern identified by SAFMC staff in interviews conducted for this project. Resource limitations mean fewer and lower frequency of stock assessment reports in the South Atlantic relative to other regions; for the Snapper-Grouper FMP there are 15 assessments but 55 species under management. To improve management of multispecies complexes, investigation and testing of multispecies complex-level reference points that can account for species relationships and habitat across all species in an FMP was recommended. Staff recognized single species MSY as unachievable for all individual species in a large complex, so reference points considering tradeoffs between species as well as inherent data limitations are needed. This section provides an outline of what a complex-level multispecies MSE could look like given existing issues identified by SAFMC in the All Council Ecosystem Data Review.

There is considerable scientific literature on potential multispecies reference points and harvest control rules [46–51]. Some of these approaches may be useful in the South Atlantic context, where large numbers of species are included in FMPs and fisheries catch diverse species. However, testing these approaches using traditional single species models designed for stock assessment is inherently difficult, if not impossible. The SAFMC’s ecosystem model may be suited to evaluating the performance of multispecies reference points and harvest strategies. While the SAFMC model is implemented in Ecopath with Ecosim, which is designed for implementing general fishing scenarios rather than testing dynamic harvest control rules, the model can be transferred into Rpath [52], which has the capability to test harvest control rules [53].

The current South Atlantic EwE model is a model of intermediate complexity focused on reef fish and parameterized using the Southeast reef fish trap and camera survey. The level of aggregation in the current model may or may not permit the type of multispecies reference point testing that would be most useful. The first step in the analysis will be to determine which species and species complexes are needed for testing multispecies management. Information from the full South Atlantic model may be needed to achieve the appropriate level of species complexity for analysis. Once appropriately aggregated and implemented in Rpath, a set of multispecies harvest control rules can be developed with stakeholder and Council input and tested. Performance metrics for each rule can be compared. Each MSE step along with key decisions is expanded below.

3.5.1 Objectives

Actual MSE objectives would be determined prior to beginning any analyses, along with roles and responsibilities for stakeholders, the modeling team, the SSC, and Council. For example, similar roles and responsibilities as used in the the Dolphin MSE (page 7) could be used, where stakeholders primarily determine management objectives for evaluating multispecies reference points and management measures. An example objective could be to simplify management and regulations for a multispecies fishery while maintaining stocks at sustainable levels under the prevailing ecosystem conditions and maintaining fishing mortality for the full multispecies fishery within sustainable bounds. Additional objectives as specified by stakeholders could relate to timing and location of access, maintaining a broad range of opportunities, ensuring availability of larger fish, etc. A specific multispecies fishery and its component stocks, habitats, and participants would need to be identified for analysis. For example, all stocks in a single FMP could be included, or all stocks captured by certain fisheries within a particular region (across multiple FMPs) could be included.

3.5.2 Performance Metrics

Actual performance metrics would be determined prior to beginning any analyses. Example performance metrics associated with the example objective above could include the frequency and number of Council actions and regulatory updates required to manage the multispecies fishery, the frequency, number, and type of scientific assessments required to manage the multispecies fishery, the number of stocks in the multispecies fishery at sustainable levels, and the level of multispecies fishing mortality. Additional performance metrics would relate to stakeholder goals identified, such as season length, size of fish caught, diversity of fishing opportunities, complexity of regulations, etc.

3.5.3 Potential Management Measures

The actual list of management measures would be determined prior to beginning any analyses. Example management measures could include status quo management (single species reference points, assessments, and management measures for some species in the multispecies fishery) and a range of options for multispecies management. One option for multispecies management could combine a limit on removals for the entire multispecies complex based on a multispecies fishing mortality reference point with minimum biomass thresholds at the species level for assessed species in the multispecies fishery, similar to the measures proposed for New England FEP [48]. A range of stock complex level assessments and management measures were simulation tested in New England in a prototype MSE project (Fig. 3.19). Additional management measures could be adapted from single species to multispecies use. For example, previous MSEs have evaluated simplified management procedures at the stock level in the South Atlantic region [13].

Figure 3.19: New England prototype MSE structure, reprinted from https://d23h0vhsm26o6d.cloudfront.net/pMSE-Final-Report2.pdf

3.5.4 Uncertainties

Actual uncertainties would be determined prior to beginning any analyses according to the roles and responsibilities specified in the first step. Often, stakeholders and the Council prioritize uncertainties to be addressed in MSE. Example uncertainties could include changes in the prevailing environmental conditions, changes in species interactions, availability of stock level productivity information, and availability of timely and correct fishery information. Uncertainty due to information availability and quality is routinely addressed in MSE analyses and would be particularly appropriate for evaluating management of multispecies complexes with variable data quality among species.

3.5.5 Operating Model

The existing SAFMC full food web model (South Atlantic Region (SAR) EwE Model) is an excellent starting point for an operating model, because it already includes species interactions, and versions of it have already been reviewed and endorsed by the SAFMC SSC. This model serves as the basis for the SAR MICE (model of intermediate complexity) focused on the Snapper Grouper complex (SAFMC EwE MICE Model Technical Report June 2022, and SSC Report October 2021). The SAFMC SSC noted that “current applications of the food web model are primarily focused on hypothesis testing and scenario exploration. Additional work is needed to translate model outputs into management-relevant metrics and integrate them into ABC-setting or risk evaluation frameworks.” The SAR EwE model could be modified as outlined below to serve as an operating model for estimating and evaluating management-relevant multispecies reference points for different species complexes.

A first step is to focus the model structure for the issue. This is achieved by aggregating the full model’s 140 trophic groups to a smaller number by grouping species that are non-essential for the desired analysis into general biomass pools. Species involved in the multispecies fishery of interest, as well as gear types involved in the fishery would be kept separate in the operating model, as well as any critical species linked to the focal managed complex as predators or prey. For example, the current SAR MICE model is aggregated to 41 components to focus on key assessed species in the Snapper Grouper FMP.

The next step would be to add any necessary detail to the modeled fisheries to enable simulation of fishing patterns associated with the management measures (e.g. characterize key commercial and recreational “fleets”). There are three commercial (commercial handline, longline, and other) and two recreational (headboat and other) fleets in the SAR MICE model. Depending on the management measures tested, this type of fleet structure may be adequate or may require modification.

Another consideration is which environmental drivers, if any, would be needed to address uncertainties. Decisions are then required on how to implement the drivers. A complex physical ocean model is not necessary to implement environmentally driven uncertainties; these can be addressed simply using the food web model to inform potential performance of management measures. For example, if distributions for some stocks are expected to shift outside the region, this could be forced in the model using declining biomass within the model bounds over time as one scenario. Another example might be to force an increase in productivity over time for “climate winners” identified in the South Atlantic CVA and to force a corresponding decrease in productivity for “climate losers.” These uncertainties could be considered separately and together in the simulations to indicate how management measures would perform under each scenario as well as with cumulative impacts.

While some MSEs focus on the performance of stock assessment models by incorporating a version of the full assessment within the operating model, this level of detail is unnecessary to evaluate multispecies reference points and management procedures. In the SAR food web operating model, the assessment can be represented by a “shortcut” method that adds uncertainty or bias to the true operating model dynamics. More uncertainty or bias can be added to simulate poor assessment performance due to data availability, model misspecification, or a combination of factors in the MSE, and different uncertainty can be added for different species. However, it is important to note that this operating model structure for the MSE does not rule out the use of stock assessments for tactical management under multispecies reference points. For operational use, combining food web model-based multispecies reference points with a detailed single species stock assessment has been successful for Atlantic menhaden [54–57].

Finally, although the EwE software does not easily facilitate the closed loop simulation testing required for MSE, the aggregated and adjusted operating model can be converted into the Rpath format for streamlined MSE testing [52,53]. Rpath provides functions for importing EwE model parameter sets (see https://noaa-edab.github.io/Rpath/articles/Convert_EwE_to_Rpath.html). The resulting operating model would be based on the same parameters as used in all other SAFMC food web model analyses, and could also start at the “current year” endpoint of any fitted SAFMC run using historic data. MSE projections can start from that endpoint using fitted parameters in Rpath.

3.5.6 Simulations

The actual specifications of simulations would be determined according to the agreed roles and responsibilities and to output the required performance metrics. The simulations could address both short term and long term performance of the range of multispecies reference points and management procedures relative to status quo management. Conducting the MSE in Rpath allows the possibility to include model parameter uncertainty in the simulations for any given scenario [58,59]. Given the uncertainty in some food web model input parameters, this capability can help determine the sensitivity of alternative management procedures to our understanding of food web structure and general stock productivity.

3.5.7 Tradeoffs

An important outcome of the MSE testing will be identifying tradeoffs between performance metrics and associated management objectives. These tradeoffs can be shown for each alternative management approach. For example, the New England pMSE project highlighted tradeoffs between fleet-specific yield, full ecosystem yield, interannual variability in catch, the proportion of stocks below a biomass limit, and underutilized quota (Fig. 3.20). For the example objectives above, a South Atlantic food web based set of tradeoffs could include similar metrics and add metrics regarding the number of management actions or assessments required for each approach.

Figure 3.20: New England protoype MSE results, reprinted from Fig. 2 in https://d23h0vhsm26o6d.cloudfront.net/pMSE-Final-Report2.pdf. Relative performance of eight key metrics between the five management alternatives. Points closer to the exterior edge indicate a better performance for the metric than points closer to the center.

Results can also be compared across traditional single species management and proposed multispecies management. In the New England analysis, the multispecies complexes outperformed single species management for yield and unused quota while having similar results for variation in catch and stock status (Fig. 3.21). In the figure, reprinted from slide 8 of the pMSE summary presentation, colors indicate different scenarios for initial conditions: red for High biomass, olive for High predators, green for High prey, blue for Low biomass, and magenta for Price-based fishing mortality.

Figure 3.21: New England protoype MSE results.

Overall, an MSE approach using the SAFMC’s food web model with the MSE capabilities in Rpath would reduce resources needed for operating model development, while providing information on the performance of novel alternatives to the current species-based management for the Council to consider.

4 Discussion

Based on the review of all U.S. Fishery Management Council’s ecosystem data and current approaches, including SAFMC’s, three conclusions can be drawn. First, all US Fishery Management Councils have some kind of ecosystem approach already embedded in management or in progress, regardless of the level of ecosystem data available. Second, interviewed Council staff noted that Councils want to use ecosystem information, but are often overwhelmed by it. Third, collaboration between managers and scientists is required for operational use of ecosystem information. This collaboration will allow managers to prioritize needs and for scientists to provide the right information at the right time to make a decision. Best practices identified across all Councils included:

• Iterative co-production of processes and products: Learn by doing, refine and adjust over time
• Risk-based methods: Adaptable to many decision processes
• Science communication: Councils need ecosystem information in “digestible packets”
• Actionable ecosystem plans: Address management issues in digestible packets too!

The suggestions for integrating ecosystem information into SAFMC decisions identified in this document follow these general principles, and present a menu of options for the Council to consider ranging from “easy wins” to more substantial investments.

4.1 Potential pathways

An overarching recommendation to assist the Council with taking practical next steps is to develop a structured process for ecosystem-based decision making such as those outlined in the Pacific (Ecosystem initiatives), North Pacific (Action modules), Mid-Atlantic (EAFM decision process), Gulf (Fishery Ecosystem Initiatives), and Caribbean Council Fishery Ecosystem Plans (FEPs). SAFMC already has extensive FEPs, but these are more informational than action-oriented. Whether SAFMC wants to address overarching issues such as the impacts of climate change on the productivity of multiple stocks, or specific issues such as the selection of a subset of indicators to monitor progress against particular policy goals, a structured process would help manage these options and ensure the SAFMC proceeds in a manner best suited to regional needs.

Based on the analyses described above, the SAFMC has many options for integrating more ecosystem information into management decisions. These are outlined here and detailed below.

1. SAFMC already addresses ecosystem issues and requests ecosystem information in its decision making process. Further opportunities were identified for SAFMC to request or use ecosystem information its decision making process. These include assessment prioritization, defining assessment and MSE Terms of Reference, and collecting stakeholder observations of ecosystem, economic, and social conditions, as well as more complex processes.

2. Existing SAFMC habitat and ecosystem policies align with existing ecosystem indicators, facilitating use of these indicators to support policy-related decisions.

3. The SAFMC ABC setting process (used in 3 FMPs) already has a framework where existing ecosystem indicators could be used to support risk rankings. A range of simpler to more complex approaches can be used to evaluate risks of overfishing.

4. Information is available to develop spatially and seasonally resolved habitat indicators for the South Atlantic region from global ocean model data products. These indicators can be ground-truthed using both existing scientific surveys and by further developing existing Citizen Science priority research areas.

5. SAFMC’s multispecies complexes combined with its long term investment in food web modeling create an opportunity to evaluate multispecies management and reference points while accounting for species interactions. Management strategy evaluation could include climate scenarios as well as food web interactions to test the robustness of any multispecies management measures.

First, it is notable that 18% of decisions (motions) over the past 3 years already involve ecosystem issues or information. This is because SAFMC has a long history of integrating habitat and ecosystem issues in management, as noted in the introduction. The currently active SAFMC Habitat and Ecosystem Committee and Habitat and Ecosystem Advisory Panel already develop policy and planning for cross-sectoral ecosystem issues (Ecosystem Based Management) as well as fishery-ecosystem issues such as balancing fishery access with habitat protection (Ecosystem Based Fisheries Management). Policies already developed by the HEAP and Habitat and Ecosystem Committee align well with many existing ecosystem indicators and could be addressed further with the existing SAFMC ecosystem model. Gaps were identified as well as the feasibility of filling them with current resources. Next steps could include a review of policies with existing indicators and or high feasibility of indicator development to determine if the Council would like to use the indicators to monitor progress towards policy objectives.

The analysis of SAFMC motions for the past 3 years identified another 26% of motions where ecosystem information could be requested or used to inform management. Potential ecosystem actions that the Council could take range from relatively simple to complex:

Simpler

• Prioritize assessments based on ecosystem concerns
• Request ecosystem ToRs in assessments and MSEs
• Request ecosystem information at stakeholder meetings

Moderate; consider climate robustness while

• Evaluating spawning seasonal or spatial closures
• Evaluating single species and multispecies reference points
• Evaluating catch, effort, and discard projections
• Evaluating allocations

Complex; not currently addressed but more manageable with a dedicated process

• Address productivity changes across multiple stocks
• Evaluate and address recreational fishing objectives
• Develop options to manage discards across stocks

Simpler actions entail a Council request, for example to include an ecosystem Term of Reference (ToR) in priority stock assessments. At present, a specific ToR for addressing ecosystem interactions was found in 3 (Mutton snapper, Gray triggerfish and Red snapper) of 10 South Atlantic SEDAR ToRs since 2021. All three included a ToR for evaluating ecosystem information at the Data workshop, while only Mutton Snapper included an explicit Assessment ToR to attempt including environmental covariates in the model.

The Council could also request specific reporting of ecosystem information, even if it is not ultimately included quantitatively in the assessment. This could assist with other Council processes while requiring minimal additional effort to reformat assessment reports. For example, despite having an explicit ecosystem ToR for both the data and assessment workshops, the Mutton snapper assessment report does not directly address these ToRs in dedicated sections. It was noted under the Data workshop review that “no attempt was made to evaluate episodic types of natural mortality (red tides, cold kills, oil spills, etc.) because there were no data on which to base such modifications to the M parameter.” However, potential indicators are identified in that section: cold stuns and kills may occur in waters 15 C or lower; SAV habitats important for age-0 snapper has been declining and may in combination with hurricanes account for some low abundances. In the spawning season section “It is important to note that all reproductive data used here were more than 10 years old. Because reproductive timing in spring and summer-spawning fish is tightly coupled to temperature (Lowerre-Barbieri et al, 2011) spawning seasonality may have changed with climate change. In addition, the data are suggestive of potential migration through SE Florida to Keys spawning grounds and possibly even a second spawning season. Understanding these processes will be critical to estimating annual fecundity in this species.”

This ecosystem information, while not considered of quantitative value for assessment, could still contribute to more qualitative risk assessments if it were made available for those processes. Including a generic “ecosystem” ToR would facilitate systematic examination of ecosystem drivers for priority stocks and could provide dedicated indicators for use either within assessments or in the risk scoring for the ABC process. Reporting a clear response to each ToR in dedicated report sections enables communication of this information outside the assessment process. An example ToR included in some Northeast US assessments provides linkages from data to the other assessment ToRs (Research Track ToR 1):

Identify relevant ecosystem and climate influences on the stock. Characterize the uncertainty in the relevant sources of data and their link to stock dynamics. Consider findings, as appropriate, in addressing other TORs. Report how the findings were considered under impacted TORs.

This information has been included in multiple northeast US assessments since adding the ToR. Assessment reports in the northeast US include specific responses to each ToR. In some cases, addressing ToR 1 was achieved with full Ecosystem and Socioeconomic Profiles (ESPs). These have been developed with associated stock specific indicators for American plaice [60,61], Atlantic cod [62–64], Atlantic herring [22], black sea bass [65], bluefish [66], golden tilefish [67], longfin inshore squid (in progress as of October 2025), and shortfin squid [68]. Ecosystem factors were also considered in the 2025 Atlantic Scallop research track assessment, highlighting that developing a full ESP is not required to address the ToR to consider these factors within an assessment or within subsequent risk analyses.

The SAFMC ABC setting process (used in 3 FMPs) already has a framework where existing ecosystem information can be used to support risk rankings. As noted above, the Pacific Council has developed a structured discussion process to identify ecosystem factors relevant to each stock for use in a risk table based approach to ABC setting. Information from the CVA could be used more formally in the SAFMC SSC’s risk table process as noted above, but adding an assessment ToR would expand the possibilities of developing the stock-specific indicators that the SSC would prefer. The ToR could also be phrased to elicit this information using a modified version of the Pacific Council’s ecosystem risk factors:

• Food webs (e.g., predator abundance or productivity, competitor abundance or productivity, spatial overlap with predators/competitors, prey abundance) – addresses the SAFMC “ecosystem importance” risk attribute
• Habitat availability (e.g., do current or near-term predictions of ocean temperatures and oxygen match known optimal ranges for [priority species] life history stages, closed area regulations) – addresses the SAFMC “climate impact” risk attribute
• Changes in ecosystem productivity (e.g., upwelling indices, chlorophyll a, abundance of low trophic level organisms) – related to regime shifts under the “other environmental variables” risk attribute
• Physiological indices (e.g., growth, condition, pathogens/parasites, stomach weight/fullness) – possible drivers of recruitment trends under the “other environmental variables” risk attribute
• Reproductive indices (e.g., maturity, fecundity, spawn timing and duration) – possible drivers of recruitment trends under the “other environmental variables” risk attribute

SAFMC could also use ecosystem (including socioeconomic) information to prioritize which assessments are conducted in a resource-limited situation, as well as which assessments would benefit from an ecosystem ToR.

Other relatively simple modifications to Council decisions could be included during “direction to staff” motions. One opportunity could be to gather ecosystem observations during stakeholder meetings about particular fisheries. A standard set of questions for use in stakeholder meetings could systematically collect ecosystem information of interest to the Council, and or to highlight the key ecosystem concerns for use in other processes such as filling out the ABC risk tables or developing MSEs. Sample questions that could be asked during meetings could be modified from those asked for Fishery Performance Reports:

Environmental, Ecological, and Habitat:

• Do you perceive that the abundance of [species] has changed over the past ten years? If so, how has it changed?
• When/where are the fish available, and has this changed? For instance, has there been any shift in catch (annually/seasonally) inshore/offshore or north/south? If so, please describe.
• Has the size of the fish that you typically encounter changed? If so, could you briefly describe the trend?
• Have you noticed any unique effects of environmental conditions on [species]? If so, please describe.
• What are your observations on the timing and length of the [species] spawning season in your area (time periods when fish are observed with large ovaries or eggs spilling out externally or while venting)?
• What do you see now in terms of recruitment? Where are the small fish? Are large and small fish found in the same locations?
• Have you observed changes in catch depth or apparent bottom type fished on?
• How have sea conditions (monthly/seasonally) affected fishable days?
• Have you noticed any change in the species caught with [species] over the years or seasonally?

This information could be used to identify or fill data gaps if it is collected systematically during stakeholder meetings or other processes. The current SAFMC Citizen Science program research priorities highlight the collection of oceanographic conditions, habitat characterization, and movement and migration, which could be supplemented with such a systematic approach. More importantly, establishing this dialogue with stakeholders across many contexts fosters two-way communication about ecosystem issues and may provide opportunities to tailor the scientific information provided during Council decision processes to address stakeholder concerns.

Council decisions about assessment ToRs and staff tasking for stakeholder meetings may represent the most straightforward pathways for incorporating ecosystem information into current processes. Changes to assessment ToRs could also provide more stock specific indicators for use in the current ABC risk assessment process. More involved processes, data, and resources would be needed to assess the potential impact of ecosystem change on management measures such as spawning closures in space and time, or on discard projections, or allocation processes. These more complex decisions may be more appropriately addressed using combinations of dedicated research and MSE of the options for including ecosystem information. The MSE outlined above to evaluate multispecies reference points and management is a tangible example of a way forward to address a more complex set of decisions by capitalizing on the SAFMC’s continued investment in maintaining the South Atlantic Regional food web model. The more complex the decision, including those spanning FMPs and stakeholder groups, the more value there is in developing a structured decision process for ecosystem based decision making.

4.2 Balancing resources

Existing ecosystem data products for the South Atlantic must be consulted before initiating new indicator or product development. The ESR and CVA contain a wealth of useful information that aligns well with existing SAFMC policy, as demonstrated above. A small set of priority indicators should be identified collaboratively with the Council for regular updating, maintenance, and presentation, while other indicators could be tailored to specific assessments or decisions as need and resources allow. All selected indicators should be directly linked to a management objective or decision. The objective or decision could be to inform the Council on whether ecosystem conditions are “normal” or “novel” as context for further decision making. Presenting a small set of core indicators would also facilitate the delivery of potentially complex information in “digestible packets” where SAFMC has selected the indicators and uses them for specific reasons.

Despite the gaps in data in the South Atlantic region, this project has demonstrated that information is available to develop spatially and seasonally resolved habitat indicators from global ocean model data products. By adapting existing methods and code used in the Northeast US, a prototype bottom temperature indicator was developed. This process can be extended to derive priority ocean indicators at different spatial and temporal scales for the South Atlantic region. Producing the indicators is relatively easy, but having a way to validate them may require more resources. The SAFMC Citizen Science program is well positioned to consider ocean data collection by fishery participants to validate these ocean model indicators. Multiple cooperative research programs alredy exist in the Southeast.

One potential extension of existing South Atlantic cooperative data collection could be ocean temperature observations for comparison with regional or global ocean data products such as MOM6 and GLORYS. Comparisons of vessel-collected temperature with model assimilated temperature can identify where improvements can be made to models as well as provide more direct information for management in near-real time. The Northeast U.S. eMOLT (Environmental Monitors on Lobster Traps and Large Trawlers) program regularly compares bottom temperature data collected aboard fishing vessels with regional ocean model bottom temperature predictions, highlighting both areas of agreement and areas where measurements and model predictions diverge.

Figure 4.1: Comparison of electronic monitoring on lobster trap and trawlers (eMOLT) data with modeled ocean bottom temperature from 13 March 2026. Source: weekly eMOLT update email, George Maynard, eMOLT coordinator

A more formal study fleet such as the one in the Northeast US could also be established to collect data for ecosystem indicators. Data collected by vessels participating in the NEFSC study fleet program have been used in species distribution modeling, biological studies, evaluation of potential ocean industry user conflicts, and as supporting data for stock assessment. This program also collects oceanographic indicators which are directly associated with haul level catch data, allowing analysis of habitat associations. Fishery-collected information often includes seasons and regions outside the scope of scientific surveys, providing key information at relatively low cost.

ESPs are extremely useful to inform risk assessments and even to bring ecosystem information directly into stock assessments, but they are resource intensive. A facilitated structured discussion among ecosystem and assessment experts, as used in the Pacific region, may provide a more feasible path forward to outline the highest priority ecosystem drivers, risks, and potential indicators for individual stocks. This could also be extended to include information collected from stakeholder workshops or citizen science initiatives to fill data gaps for specific stocks.

MSEs are likewise useful and resource intensive. SAFMC already has experience with multiple MSEs and that experience should inform the design of any multispecies reference point MSE. Although MSE can be resource intensive, it may also be the best way to address uncertainty in future conditions under resource limitations. MSE could be more broadly conducted to address whether management is likely to be robust across unexpected ecosystem conditions [69]. While predictive capability for ecosystem conditions is improving in several US regions, including in the Atlantic [28], this capability may not predict all variables important to all managed fisheries equally well. In addition, models may not predict specific short term ecosystem changes driven by local conditions. Rapid changes in conditions comparable to those observed in Alaska can be implemented within an MSE simulation framework to develop and test resilient management measures. Simpler management procedures can also be tested using MSE to allow more informed management of stocks without regular assessments, and to evaluate the use of ecosystem information to enhance management procedures.

4.3 Potential next steps

A key decision is which of these options to consider and how to develop actionable management using ecosystem information. A structured decision process such as those outlined in the Pacific (Ecosystem initiatives), North Pacific (Action modules), Mid-Atlantic (EAFM decision process), Gulf (Fishery Ecosystem Initiatives), and Caribbean Council FEPs (Fig. 4.2; [70]) could help manage these options and ensure the SAFMC proceeds in a manner best suited to regional needs. While ecosystem decisions can be taken outside a process like this, the experience of other Councils is that opportunities for addressing systemic or cross-FMP issues can be limited without a dedicated process, because they don’t fit well within stock or FMP-specific committee structures. Therefore, the first step is likely to outline this type of actionable process for the SAFMC.

Figure 4.2: The FEP loop is an interpretation of adaptive management as applied to Fishery Ecosystem Planning. Reprinted from Levin et al. 2018, Fig. 1

There are several examples of how versions of this decision process have been used operationally, as noted in the All Council Ecosystem Data Review portion of this project. The Pacific Council FEP specifies goals and objectives as well as “ecosystem initiatives” that focus on priority actions. The Council annually reviews and updates its list of potential actions. PFMC’s first FEP Initiative resulted in regulatory change after evaluating data and ecosystem science related to forage species. Initiatives 2 and 3 were more procedural in nature, while the current initiative 4 focuses on developing more operational uses of ecosystem information in the catch specification process.

• FEP Initiative 1 prohibited directed fishing on currently unexploited, unmanaged forage fish in the region
  
• FEP Initiative 2 reviewed the ESR and made recommendations to better inform the Council of both ecosystem processes and the potential use of this information in management
• FEP Initiative 3 evaluated potential impacts of climate change and identified ways to improve flexibility and responsiveness of management
• FEP Initiative 4 Ecosystem and Climate Information for Species, Fisheries, and FMPs, is developing the risk-based framework for use in the Council’s catch specification process

PFMC’s Initiative 4 developed conceptual models of catch specification processes under the Coastal Pelagics, Groundfish, and Salmon FMPs, highlighting the timing of each step in the process and specific points where ecosystem information could be brought in. The structured process for developing the stock assessment risk table (noted above) has enhanced communication between ecosystem and assessment scientists, and is highlighted as a key success of FEP Initiative 4.

Another example is the Mid-Atlantic Council’s EAFM approach outlined in its 2019 EAFM Policy Document. The process starts with prioritization of a fishery or topic based on a broad ecosystem level risk assessment, refines issues identified in the risk assessment by relating them in a conceptual model and identifying available information (and data gaps) for key relationships, outlines they key question, and then addresses it with MSE [71]. The initial risk assessment [21] was used to prioritize the summer flounder fishery. Conceptual modeling provided the Council with a set of key interactions around the summer flounder fishery [72], and the MAFMC chose to evaluate recreational discards from an ecosystem perspective in a stakeholder-driven MSE [73]. MAFMC updates the Risk Assessment every year, and recently reviewed, updated, and added risk elements and indicators. The MSE resulted in multiple options for improving angler welfare without changing stock status, as well as a multispecies recreational demand model now used to set recreational fishery specifications.

A process tailored to the SAFMC could start by using existing expertise within the Habitat and Ecosystem Committee and HEAP, with additional membership as needed. Initiating and maintaining an ecosystem decision process could follow these general steps:

1. Outline a structured process for SAFMC ecosystem decisions
   • Adapt a current SAFMC decision process, another Council’s or use the Levin et al 2018 template and modify
   • Build in iterative co-production and risk based methods
   • Fully integrate the Citizen Science program
2. Develop a list of potential action items
   • Review cross-FMP issues and concerns?
   • Review 2021 ESR to prioritize indicators for annual reporting?
   • Develop assessment ecosystem ToRs?
   • Develop ecosystem objectives (in progress)?
   • … other items from this report or elsewhere
3. Prioritize an action item and apply the decision process
   • Something that needs to be done anyway?
   • Something that is a high priority for stakeholders?
   • Something that could provide a “quick win”? (recommended)
   • Something that is inherently complex?
   • … starting somewhere and iterating is key to progress
4. Revisit the list of action items regularly
5. Review FEP process and adjust

Once this process is established and adjusted for SAFMC needs, the menu of options outlined here could be considered in a systematic and organized manner, taking both priorities and resources into account.

References

1.

McPherson M. Participatory modeling of dolphin and wahoo fisheries in the U.S. South Atlantic: Final report from a workshop series. 2022 [cited 25 Jun 2025]. doi:10.25923/7EG6-9856

2.

Craig JK, Kellison GT, Binion-Rock SM, Regan SD, Karnauskas M, Lee S-K. Ecosystem Status Report for the U.S. South Atlantic Region. 2021 [cited 25 Jun 2025]. doi:https://doi.org/10.25923/qmgr-pr03

3.

Burton ML, Muñoz RC, Quinlan JA, Nelson MW, Bacheler NM, Runde BJ, et al. A Climate vulnerability assessment for fish and invertebrates in the United States South Atlantic large marine ecosystem. 2023 [cited 25 Jun 2025]. doi:https://doi.org/10.25923/f90h-1z90

4.

Craig JK, Runde BJ, Bacheler NM, Burton ML, Muñoz RC, Quinlan JA, et al. Climate vulnerability assessment of fish and invertebrates in the U.S. South Atlantic large marine ecosystem. PLOS Climate. 2025;4: e0000543. doi:10.1371/journal.pclm.0000543

5.

Seara T, Jepson M, McPherson M. Community Climate Change Vulnerability in the South Atlantic, Florida Keys and Gulf of Mexico. 2022 [cited 25 Jun 2025]. doi:https://doi.org/10.25923/0wqe-3511

6.

Lettrich MD, Asaro MJ, Borggaard DL, Dick DM, Griffis RB, Litz JA, et al. Vulnerability to climate change of United States marine mammal stocks in the western North Atlantic, Gulf of Mexico, and Caribbean. PLOS ONE. 2023;18: e0290643. doi:10.1371/journal.pone.0290643

7.

Loughran TC, Cudney JL, Crear DP, Crawford LM, Curtis BJ, Gutierrez EM, et al. A climate vulnerability assessment for U.S. Highly migratory fishes in the Atlantic Ocean. PLOS Climate. 2025;4: e0000530. doi:10.1371/journal.pclm.0000530

8.

Peake J, Bogdanoff AK, Layman CA, Castillo B, Reale-Munroe K, Chapman J, et al. Feeding ecology of invasive lionfish (Pterois volitans and Pterois miles) in the temperate and tropical western Atlantic. Biological Invasions. 2018;20: 2567–2597. doi:10.1007/s10530-018-1720-5

9.

Sancho G, Kingsley-Smith PR, Morris JA, Toline CA, McDonough V, Doty SM. Invasive Lionfish (Pterois volitans/miles) feeding ecology in Biscayne National Park, Florida, USA. Biological Invasions. 2018;20: 2343–2361. doi:10.1007/s10530-018-1705-4

10.

Fair PA, White ND, Wolf B, Arnott SA, Kannan K, Karthikraj R, et al. Persistent organic pollutants in fish from Charleston Harbor and tributaries, South Carolina, United States: A risk assessment. Environmental Research. 2018;167: 598–613. doi:10.1016/j.envres.2018.08.001

11.

Barbieri MV, González MP, Araya Piqué V, Blasco J, Eljarrat E. Contaminants in fish and seafood from the marine environment: A global overview of current status and future perspective. Marine Pollution Bulletin. 2025;219: 118319. doi:10.1016/j.marpolbul.2025.118319

12.

Thornton AL, Chung KW, Key PB, Pisarski EC, Tanabe P, DeLorenzo ME. Multistressor Interaction of Perfluorooctane Sulfonate (PFOS) and Temperature in Two Estuarine Fish Species, Red Drum (Sciaenops ocellatus) and Sheepshead Minnow (Cyprinodon variegatus). Environmental Toxicology. 2026;n/a. doi:10.1002/tox.70058

13.

Peterson CD, Klibansky N, Vincent MT, Walter JF III. Climate-readiness of fishery management procedures with application to the southeast US Atlantic. ICES Journal of Marine Science. 2025;82: fsae154. doi:10.1093/icesjms/fsae154

14.

Jepson M, Colburn LL. Development of Social Indicators of Fishing Community Vulnerability and Resilience in the US Southeast and Northeast Regions. NOAA Technical Memorandum NMFS-F/SPO-129 (US Dept Commerce, 2013). 2013. Available: http://www.nmfs.noaa.gov/sfa/management/councils/training/2014/r_h3_fishing_community_vulnerability.pdf

15.

Farr ER, Johnson MR, Nelson MW, Hare JA, Morrison WE, Lettrich MD, et al. An assessment of marine, estuarine, and riverine habitat vulnerability to climate change in the Northeast U.S. PLOS ONE. 2021;16: e0260654. doi:10.1371/journal.pone.0260654

16.

Behan J, Kerr L, Hart A, Hansell A, Paklovitch T, Cadrin S. Ecosystem profile of American plaice. 2022 [cited 26 Jun 2025]. doi:https://doi.org/10.25923/7kj5-1155

17.

Shotwell SK, Pirtle JL, Watson JT, Deary AL, Doyle MJ, Barbeaux SJ, et al. Synthesizing integrated ecosystem research to create informed stock-specific indicators for next generation stock assessments. Deep Sea Research Part II: Topical Studies in Oceanography. 2022;198: 105070. doi:10.1016/j.dsr2.2022.105070

18.

Shotwell SK, Blackhart K, Cunningham C, Fedewa E, Hanselman D, Aydin K, et al. Introducing the Ecosystem and Socioeconomic Profile, a Proving Ground for Next Generation Stock Assessments. Coastal Management. 2023;51: 319–352. doi:10.1080/08920753.2023.2291858

19.

Gaichas SK, Aydin KY, Francis RC. Using food web model results to inform stock assessment estimates of mortality and production for ecosystem-based fisheries management. Canadian Journal of Fisheries and Aquatic Sciences. 2010;67: 1493–1506.

20.

Okey TA, Pugliese R. A PRELIMINARY ECOPATH MODEL OF THE ATLANTIC CONTINENTAL SHELF ADJACENT TO THE SOUTHEASTERN UNITED STATES. Vancouver, BC; 2001 pp. 167–181.

21.

Gaichas SK, DePiper GS, Seagraves RJ, Muffley BW, Sabo M, Colburn LL, et al. Implementing Ecosystem Approaches to Fishery Management: Risk Assessment in the US Mid-Atlantic. Frontiers in Marine Science. 2018;5. doi:10.3389/fmars.2018.00442

22.

Molina A, Gaichas S, Owen S. Atlantic herring ecosystem and socioeconomic profile. Northeast Fisheries Science Center (U.S.); 2025. Available: https://apps-nefsc.fisheries.noaa.gov/saw/sasi.php

23.

Oken KL, Taylor IG, Feddern ML, Whitman AD. Status of the Yellowtail Rockfish Stock off the U.S. West Coast North of 40°10’ in 2025. Pacific Fisheries Management Council, Portland, Oregon. 2025.

24.

Langseth BJ, Monk MH, Coates JH. Status of Quillback Rockfish in U.S. Waters off California in 2025. Pacific Fisheries Management Council, Portland, Oregon. 2025.

25.

Dick EJ, Field JC, Grunloh N, Rogers T. The Status of Chilipepper Rockfish (Sebastes goodei) in U.S. Waters off California, Oregon, and Washington in 2025. Pacific Fisheries Management Council, Portland, Oregon. 2025.

26.

Cope JM, Gertseva V, Rosemond RC, Whitman AD, Caltabellotta FP. Status of the Rougheye and Blackspotted Rockfishes stock off the U.S. West Coast in 2025. Pacific Fisheries Management Council, Portland, Oregon. 2025.

27.

Wetzel CR, Berger AM, Barnes C, Zahner JA, Tolimieri N, Ward EJ, et al. Status of the sablefish (Anoplopoma fimbria) off the U.S. West Coast in 2025. Pacific Fisheries Management Council, Portland, Oregon. 2025 p. 258.

28.

Ross AC, Stock CA, Koul V, Delworth TL, Lu F, Wittenberg A, et al. Dynamically downscaled seasonal ocean forecasts for North American east coast ecosystems. Ocean Science. 2024;20: 1631–1656. doi:10.5194/os-20-1631-2024

29.

Jean-Michel L, Eric G, Romain B-B, Gilles G, Angélique M, Marie D, et al. The Copernicus Global 1/12° Oceanic and Sea Ice GLORYS12 Reanalysis. Frontiers in Earth Science. 2021;9. doi:10.3389/feart.2021.698876

30.

Jones AW, Burchard KA, Mercer AM, Hoey JJ, Morin MD, Gianesin GL, et al. Learning From the Study Fleet: Maintenance of a Large-Scale Reference Fleet for Northeast U.S. Fisheries. Frontiers in Marine Science. 2022;9. Available: https://www.frontiersin.org/articles/10.3389/fmars.2022.869560

31.

R Core Team. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical Computing; 2023. Available: https://www.R-project.org/

32.

Wickham H, Averick M, Bryan J, Chang W, McGowan LD, François R, et al. Welcome to the tidyverse. Journal of Open Source Software. 2019;4: 1686. doi:10.21105/joss.01686

33.

Hijmans RJ. Terra: Spatial Data Analysis. 2025. Available: https://CRAN.R-project.org/package=terra

34.

Pierce D. ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files. 2025. Available: https://CRAN.R-project.org/package=ncdf4

35.

Xie Y. Knitr: A General-Purpose Package for Dynamic Report Generation in R. 2025. Available: https://yihui.org/knitr/

36.

Xie Y. Dynamic Documents with R and knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC; 2015. Available: https://yihui.org/knitr/

37.

Xie Y. Knitr: A Comprehensive Tool for Reproducible Research in R. In: Stodden V, Leisch F, Peng RD, editors. Implementing Reproducible Computational Research. Chapman; Hall/CRC; 2014.

38.

Allaire JJ, Xie Y, Dervieux C, McPherson J, Luraschi J, Ushey K, et al. Rmarkdown: Dynamic Documents for R. 2024. Available: https://github.com/rstudio/rmarkdown

39.

Xie Y, Allaire JJ, Grolemund G. R Markdown: The Definitive Guide. Boca Raton, Florida: Chapman; Hall/CRC; 2018. Available: https://bookdown.org/yihui/rmarkdown

40.

Xie Y, Dervieux C, Riederer E. R Markdown Cookbook. Boca Raton, Florida: Chapman; Hall/CRC; 2020. Available: https://bookdown.org/yihui/rmarkdown-cookbook

41.

Xie Y. Bookdown: Authoring Books and Technical Documents with R Markdown. 2025. Available: https://github.com/rstudio/bookdown

42.

Xie Y. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC; 2016. Available: https://bookdown.org/yihui/bookdown

43.

Beltz B, Beet A, Bastille K, Hardison S. Ecodata: Documentation of Ecosystem Indicator Reporting. 2025. Available: https://noaa-edab.github.io/ecodata/

44.

Punt AE, Butterworth DS, Moor CL de, De Oliveira JAA, Haddon M. Management strategy evaluation: Best practices. Fish and Fisheries. 2016;17: 303–334. doi:10.1111/faf.12104

45.

Peterson C, Karnauskas M, McPherson M, Hadley J, Blake S, Byrd J. Report of the South Atlantic dolphinfish management strategy evaluation stakeholder workshops. Southeast Fisheries Science Center (U.S.); 2024. doi:10.25923/f628-n174

46.

Collie JS, Gislason H. Biological reference points for fish stocks in a multispecies context. Canadian Journal of Fisheries and Aquatic Sciences. 2001;58: 2167–2176. doi:10.1139/f01-158

47.

Appeldoorn RS. Transforming reef fisheries management: Application of an ecosystem-based approach in the USA Caribbean. Environmental Conservation. 2008;35: 232–241. doi:10.1017/S0376892908005018

48.

Fogarty MJ. The art of ecosystem-based fishery management. Canadian Journal of Fisheries and Aquatic Sciences. 2014;71: 479–490. doi:10.1139/cjfas-2013-0203

49.

Thorpe RB. What is multispecies MSY? A worked example from the North Sea. Journal of Fish Biology. 2019;94: 1011–1018. doi:10.1111/jfb.13967

50.

Fulton EA, Sainsbury K, Noranarttragoon P, Leadbitter D, Staples DJ, Porobic J, et al. Shifting baselines and deciding on the desirable form of multispecies maximum sustainable yield. ICES Journal of Marine Science. 2022;79: 2138–2154. doi:10.1093/icesjms/fsac150

51.

Karp WA, Melnychuk MC, Forrest RE, Little LR, McQuaw K, Demarest C, et al. Quota use in mixed-stock fisheries. Fish and Fisheries. 2024;25: 251–267. doi:10.1111/faf.12806

52.

Lucey SM, Gaichas SK, Aydin KY. Conducting reproducible ecosystem modeling using the open source mass balance model Rpath. Ecological Modelling. 2020;427: 109057. doi:10.1016/j.ecolmodel.2020.109057

53.

Lucey SM, Aydin KY, Gaichas SK, Cadrin SX, Fay G, Fogarty MJ, et al. Evaluating fishery management strategies using an ecosystem model as an operating model. Fisheries Research. 2021;234: 105780. doi:10.1016/j.fishres.2020.105780

54.

Anstead KA, Drew K, Chagaris D, Schueller AM, McNamee JE, Buchheister A, et al. The Path to an Ecosystem Approach for Forage Fish Management: A Case Study of Atlantic Menhaden. Frontiers in Marine Science. 2021;8. doi:10.3389/fmars.2021.607657

55.

Drew K, Cieri M, Schueller AM, Buchheister A, Chagaris D, Nesslage G, et al. Balancing Model Complexity, Data Requirements, and Management Objectives in Developing Ecological Reference Points for Atlantic Menhaden. Frontiers in Marine Science. 2021;8. doi:10.3389/fmars.2021.608059

56.

Chagaris D, Drew K, Schueller A, Cieri M, Brito J, Buchheister A. Ecological Reference Points for Atlantic Menhaden Established Using an Ecosystem Model of Intermediate Complexity. Frontiers in Marine Science. 2020;7. doi:10.3389/fmars.2020.606417

57.

Howell D, Schueller AM, Bentley JW, Buchheister A, Chagaris D, Cieri M, et al. Combining Ecosystem and Single-Species Modeling to Provide Ecosystem-Based Fisheries Management Advice Within Current Management Systems. Frontiers in Marine Science. 2021;7. doi:10.3389/fmars.2020.607831

58.

Whitehouse GA, Aydin KY. Assessing the sensitivity of three Alaska marine food webs to perturbations: An example of Ecosim simulations using Rpath. Ecological Modelling. 2020;429: 109074. doi:10.1016/j.ecolmodel.2020.109074

59.

Whitehouse GA, Aydin KY, Hollowed AB, Holsman KK, Cheng W, Faig A, et al. Bottom–Up Impacts of Forecasted Climate Change on the Eastern Bering Sea Food Web. Frontiers in Marine Science. 2021;8. Available: https://www.frontiersin.org/articles/10.3389/fmars.2021.624301

60.

Behan J, Kerr L, Hart A, Hansell A, Paklovitch T, Cadrin S. Ecosystem profile of American plaice. NOAA National Marine Fisheries Service; 2022. doi:10.25923/7kj5-1155

61.

Behan J, Kerr L. Environmental influences on American Plaice stock dynamics. NOAA National Marine Fisheries Service; 2022. doi:10.25923/yvhh-4288

62.

Behan J, Tyrell A, Hart A, Large S, Hansell A, Lankowicz K, et al. Profile of Ecosystem and Climate Influences on Atlantic Cod. NOAA National Marine Fisheries Service; 2023. doi:10.25923/c267-zd92

63.

Behan J, Kerr L. Environmental influences on Atlantic Cod (Gadus morhua) stock dynamics. NOAA National Marine Fisheries Service; 2023. doi:10.25923/y195-qy63

64.

Tyrell A, Large S. Development of ecosystem indicators for Northwest Atlantic Cod. NOAA National Marine Fisheries Service; 2023. doi:10.25923/dvhr-w337

65.

Tabandera R, Tyrell A, McMahan M, Perez P, Hankowsky K, Large S. Black sea bass ecosystem considerations and indicator development. NOAA National Marine Fisheries Service; 2024. doi:10.25923/ez9g-af05

66.

Tyrell A, Celestino M, Cisneros K, Drew K, Gaichas S, Gartland J, et al. Bluefish ecosystem and socioeconomic profile. NOAA National Marine Fisheries Service; 2022. doi:10.25923/s3xc-ta06

67.

Salois S, Owen S, Molina A, Jones A, Hyde K. Golden Tilefish ecosystem and socioeconomic profile. NOAA National Marine Fisheries Service; 2024. doi:10.25923/8g2c-0447

68.

Salois SL, Hyde KJW, Silver AM, Gangopadhyay A, Gawarkiewicz G, Mercer A, et al. Oceanographic indicators for Northern shortfin squid (Illex illecebrocus) in the Northwest Atlantic. NOAA National Marine Fisheries Service; 2022. doi:10.25923/f87x-mj55

69.

Walter JFIII, Peterson CD, Marshall K, Deroba JJ, Gaichas S, Williams BC, et al. When to conduct, and when not to conduct, management strategy evaluations. ICES Journal of Marine Science. 2023; fsad031. doi:10.1093/icesjms/fsad031

70.

Levin PS, Essington TE, Marshall KN, Koehn LE, Anderson LG, Bundy A, et al. Building effective fishery ecosystem plans. Marine Policy. 2018;92: 48–57. doi:10.1016/j.marpol.2018.01.019

71.

Gaichas SK, Seagraves RJ, Coakley JM, DePiper GS, Guida VG, Hare JA, et al. A Framework for Incorporating Species, Fleet, Habitat, and Climate Interactions into Fishery Management. Frontiers in Marine Science. 2016;3. doi:10.3389/fmars.2016.00105

72.

DePiper G, Gaichas S, Muffley B, Ardini G, Brust J, Coakley J, et al. Learning by doing: Collaborative conceptual modelling as a path forward in ecosystem-based management. ICES Journal of Marine Science. 2021;78: 1217–1228. doi:10.1093/icesjms/fsab054

73.

Muffley B, Gaichas S, DePiper G, Seagraves R, Lucey S. There Is no I in EAFM Adapting Integrated Ecosystem Assessment for Mid-Atlantic Fisheries Management. Coastal Management. 2021;49: 90–106. doi:10.1080/08920753.2021.1846156