EAFM approach
EAFM approach
Visioning Project → Strategic Plan that developed the 2016 Ecosystem Approach to Fishery Management (EAFM) Policy Guidance document: http://www.mafmc.org/s/EAFM-Doc-Revised-2019-02-08.pdf
"A non-regulatory umbrella document intended to guide Council policy with respect to ecosystem considerations across existing Fishery Management Plans"
Details, including workshop presentations and white papers: http://www.mafmc.org/eafm
The Mid-Atlantic Council identified several theme areas from the visioning project as noted in the left panel of the workflow graphic: forage fish, species interactions, social and economic issues, climate and habitat. The Council held full day workshops during Council meetings where experts on the topics provided overviews and Council members asked questions and discussed the issues. Workships on Forage fish, Climate, Climate and Governance, Interactions (species and fleet), and Habitat were held between 2013 and 2015, resulting in white papers on Forage fish, Climate (and habitat), Interactions (species, fleet, climate, and habitat). Social and economic considerations were integrated in each workshop rather than looked at separately.
The Council’s EAFM framework has similarities to the IEA loop. It uses risk assessment as a first step to prioritize combinations of managed species, fleets, and ecosystem interactions for consideration. Second, a conceptual model is developed identifying key environmental, ecological, social, economic, and management linkages for a high-priority fishery. Third, quantitative modeling addressing Council-specified questions and based on interactions identified in the conceptual model is applied to evaluate alternative management strategies that best balance management objectives. As strategies are implemented, outcomes are monitored and the process is adjusted, and/or another priority identified in risk assessment can be addressed.
Ecosystem indicators linked to management objectives (DePiper, et al., 2017)
Open science emphasis (Bastille, et al., 2021)
Used within Mid-Atlantic Fishery Management Council's Ecosystem Process (Muffley, et al., 2021)
This element is applied at the ecosystem level. Revenue serves as a proxy for commercial profits.
Risk Level | Definition |
---|---|
Low | No trend and low variability in revenue |
Low-Moderate | Increasing or high variability in revenue |
Moderate-High | Significant long term revenue decrease |
High | Significant recent decrease in revenue |
Ranked moderate-high risk due to the significant long term revenue decrease for Mid-Atlantic managed species (red points)
Key: Black = Revenue of all species combined;
Red = Revenue of MAFMC managed species
This element is applied at the ecosystem level. Revenue serves as a proxy for commercial profits.
Risk Level | Definition |
---|---|
Low | No trend and low variability in revenue |
Low-Moderate | Increasing or high variability in revenue |
Moderate-High | Significant long term revenue decrease |
High | Significant recent decrease in revenue |
Ranked moderate-high risk due to the significant long term revenue decrease for Mid-Atlantic managed species (red points)
Key: Black = Revenue of all species combined;
Red = Revenue of MAFMC managed species
SOE Implications: Recent change driven by benthos. Monitor changes in climate and landings drivers:
Indicators: ocean currents, bottom and surface temperature, marine heatwaves
A marine heatwave is a warming event that lasts for five or more days with sea surface temperatures above the 90th percentile of the historical daily climatology (1982-2011).
Climate: 6 low, 3 low-mod, 4 mod-high, 1 high risk
Multiple drivers with different impacts by species
Chesapeake Bay warm winter, low freshwater flow
Ocean acidification impact on shellfish growth
DistShift: 2 low, 9 mod-high, 3 high risk species
Shifting species distributions alter both species interactions, fishery interactions, and expected management outcomes from spatial allocations and bycatch measures based on historical fish and protected species distributions.
Indicators: climate sensitive species life stages mapped to climate vulnerable habitats
See EAFM risk assessment for example species narratives
Species level risk elements
Species | Assess | Fstatus | Bstatus | FW1Pred | FW1Prey | FW2Prey | Climate | DistShift | EstHabitat |
---|---|---|---|---|---|---|---|---|---|
Ocean Quahog | lowest | lowest | lowest | lowest | lowest | lowest | highest | modhigh | lowest |
Surfclam | lowest | lowest | lowest | lowest | lowest | lowest | modhigh | modhigh | lowest |
Summer flounder | lowest | lowest | lowmod | lowest | lowest | lowest | lowmod | modhigh | highest |
Scup | lowest | lowest | lowest | lowest | lowest | lowest | lowmod | modhigh | highest |
Black sea bass | lowest | lowest | lowest | lowest | lowest | lowest | modhigh | modhigh | highest |
Atl. mackerel | lowest | highest | highest | lowest | lowest | lowest | lowmod | modhigh | lowest |
Butterfish | lowest | lowest | lowmod | lowest | lowest | lowest | lowest | highest | lowest |
Longfin squid | lowmod | lowmod | lowmod | lowest | lowest | lowmod | lowest | modhigh | lowest |
Shortfin squid | lowmod | lowmod | lowmod | lowest | lowest | lowmod | lowest | highest | lowest |
Golden tilefish | lowest | lowest | lowmod | lowest | lowest | lowest | modhigh | lowest | lowest |
Blueline tilefish | highest | highest | modhigh | lowest | lowest | lowest | modhigh | lowest | lowest |
Bluefish | lowest | lowest | highest | lowest | lowest | lowest | lowest | modhigh | highest |
Spiny dogfish | lowmod | lowest | lowmod | lowest | lowest | lowest | lowest | highest | lowest |
Monkfish | highest | lowmod | lowmod | lowest | lowest | lowest | lowest | modhigh | lowest |
Unmanaged forage | na | na | na | lowest | lowmod | lowmod | na | na | na |
Deepsea corals | na | na | na | lowest | lowest | lowest | na | na | na |
Ecosystem level risk elements
System | EcoProd | CommRev | RecVal | FishRes1 | FishRes4 | FleetDiv | Social | ComFood | RecFood |
---|---|---|---|---|---|---|---|---|---|
Mid-Atlantic | lowmod | modhigh | highest | lowest | modhigh | lowest | lowmod | highest | modhigh |
Species and Sector level risk elements
Species | MgtControl | TecInteract | OceanUse | RegComplex | Discards | Allocation |
---|---|---|---|---|---|---|
Ocean Quahog-C | lowest | lowest | lowmod | lowest | modhigh | lowest |
Surfclam-C | lowest | lowest | lowmod | lowest | modhigh | lowest |
Summer flounder-R | modhigh | lowest | lowmod | modhigh | highest | highest |
Summer flounder-C | lowmod | modhigh | lowmod | modhigh | modhigh | highest |
Scup-R | lowmod | lowest | lowmod | modhigh | modhigh | highest |
Scup-C | lowest | lowmod | modhigh | modhigh | modhigh | highest |
Black sea bass-R | highest | lowest | modhigh | modhigh | highest | highest |
Black sea bass-C | highest | lowmod | highest | modhigh | highest | highest |
Atl. mackerel-R | lowmod | lowest | lowest | lowest | lowest | lowest |
Atl. mackerel-C | lowest | lowmod | modhigh | highest | lowmod | highest |
Butterfish-C | lowest | lowmod | modhigh | highest | modhigh | lowest |
Longfin squid-C | lowest | modhigh | highest | highest | highest | lowest |
Shortfin squid-C | lowmod | lowmod | lowmod | lowmod | lowest | lowest |
Golden tilefish-R | na | lowest | lowest | lowest | lowest | lowest |
Golden tilefish-C | lowest | lowest | lowest | lowest | lowest | lowest |
Blueline tilefish-R | lowest | lowest | lowest | modhigh | lowest | highest |
Blueline tilefish-C | lowest | lowest | lowest | modhigh | lowest | highest |
Bluefish-R | lowmod | lowest | lowest | lowmod | modhigh | highest |
Bluefish-C | lowest | lowest | lowmod | lowmod | lowmod | highest |
Spiny dogfish-R | lowest | lowest | lowest | lowest | lowest | lowest |
Spiny dogfish-C | lowest | modhigh | modhigh | modhigh | lowmod | lowest |
Chub mackerel-C | lowest | lowmod | lowmod | lowmod | lowest | lowest |
Unmanaged forage | lowest | lowest | modhigh | lowest | lowest | lowest |
Deepsea corals | na | na | modhigh | na | na | na |
Changes: Butterfish B status risk increased from lowest to low-mod (below Bmsy) Allocation risk decreased for 4 fisheries from high to low (intermediate rankings not applied) Black sea bass regulatory complexity risk decreased from highest to moderate-high
Potential new indicators from new SOE sections on climate risk, habitat vulnerability, offshore wind
Habitat vulnerability analysis writeups--comments?
Working group of habitat, biology, stock assessment, management, economic and social scientists developed:
Final conceptual model and supporting information at December 2019 Council meeting
In this interactive circular graph visualization, model elements identified as important by the Council (through risk assessment) and by the working group (through a range of experience and expertise) are at the perimeter of the circle. Elements are defined in detail in the last section of this page. Relationships between elements are represented as links across the center of the circle to other elements on the perimeter. Links from a model element that affect another element start wide at the base and are color coded to match the category of the element they affect.Hover over a perimeter section (an element) to see all relationships for that element, including links from other elements. Hover over a link to see what it connects. Links by default show text for the two elements and the direction of the relationship (1 for relationship, 0 for no relationship--most links are one direction).For example, hovering over the element "Total Landings" in the full model shows that the working group identified the elements affected by landings as Seafood Production, Recreational Value, and Commercial Profits (three links leading out from landings), and the elements affecting landings as Fluke SSB, Fluke Distributional Shift, Risk Buffering, Management Control, Total Discards, and Shoreside Support (6 links leading into Total Landings).
A Climate Fisheries Initiative within NOAA aims to improve regional ocean modeling capabilities and develop interdisciplinary communities of practice--including managers--that will be essential to operational ecological forecasts. Finally, the iterative, collaborative IEA process itself provides opportunities to prioritize which ecological forecasts to develop.
The New England and Mid-Atlantic SOEs made possible by (at least) 52 contributors from 10 institutions
Andy Beet
Kimberly Bastille
Ruth Boettcher (Virginia Department of Game and Inland Fisheries)
Mandy Bromilow (NOAA Chesapeake Bay Office)
Zhuomin Chen (Woods Hole Oceanographic Institution)
Joseph Caracappa
Doug Christel (GARFO)
Patricia Clay
Lisa Colburn
Jennifer Cudney (NMFS Atlantic HMS Management Division)
Tobey Curtis (NMFS Atlantic HMS Management Division)
Geret DePiper
Emily Farr (NMFS Office of Habitat Conservation)
Michael Fogarty
Paula Fratantoni
Kevin Friedland
Sarah Gaichas
Ben Galuardi (GARFO)
Avijit Gangopadhyay (School for Marine Science and Technology, University of Massachusetts Dartmouth)
James Gartland (Virginia Institute of Marine Science)
Glen Gawarkiewicz (Woods Hole Oceanographic Institution)
Sean Hardison
Kimberly Hyde
John Kosik
Steve Kress (National Audubon Society’s Seabird Restoration Program)
Young-Oh Kwon (Woods Hole Oceanographic Institution)
Scott Large
Andrew Lipsky
Sean Lucey
Don Lyons (National Audubon Society’s Seabird Restoration Program)
Chris Melrose
Shannon Meseck
Ryan Morse
Kimberly Murray
Chris Orphanides
Richard Pace
Charles Perretti
CJ Pellerin (NOAA Chesapeake Bay Office)
Grace Roskar (NMFS Office of Habitat Conservation)
Grace Saba (Rutgers)
Vincent Saba
Chris Schillaci (GARFO)
Angela Silva
Emily Slesinger (Rutgers University)
Laurel Smith
Talya tenBrink (GARFO)
Bruce Vogt (NOAA Chesapeake Bay Office)
Ron Vogel (UMD Cooperative Institute for Satellite Earth System Studies and NOAA/NESDIS Center for Satellite Applications and Research)
John Walden
Harvey Walsh
Changhua Weng
Mark Wuenschel
Bastille, K. et al. (2021). "Improving the IEA Approach Using Principles of Open Data Science". In: Coastal Management 49.1. Publisher: Taylor & Francis _ eprint: https://doi.org/10.1080/08920753.2021.1846155, pp. 72-89. ISSN: 0892-0753. DOI: 10.1080/08920753.2021.1846155. URL: https://doi.org/10.1080/08920753.2021.1846155 (visited on Apr. 16, 2021).
DePiper, G. S. et al. (2017). "Operationalizing integrated ecosystem assessments within a multidisciplinary team: lessons learned from a worked example". En. In: ICES Journal of Marine Science 74.8, pp. 2076-2086. ISSN: 1054-3139. DOI: 10.1093/icesjms/fsx038. URL: https://academic.oup.com/icesjms/article/74/8/2076/3094701 (visited on Mar. 09, 2018).
DePiper, G. et al. (2021). "Learning by doing: collaborative conceptual modelling as a path forward in ecosystem-based management". In: ICES Journal of Marine Science. ISSN: 1054-3139. DOI: 10.1093/icesjms/fsab054. URL: https://doi.org/10.1093/icesjms/fsab054 (visited on Apr. 15, 2021).
Gaichas, S. K. et al. (2018). "Implementing Ecosystem Approaches to Fishery Management: Risk Assessment in the US Mid-Atlantic". In: Frontiers in Marine Science 5. ISSN: 2296-7745. DOI: 10.3389/fmars.2018.00442. URL: https://www.frontiersin.org/articles/10.3389/fmars.2018.00442/abstract (visited on Nov. 20, 2018).
Muffley, B. et al. (2021). "There Is no I in EAFM Adapting Integrated Ecosystem Assessment for Mid-Atlantic Fisheries Management". In: Coastal Management 49.1. Publisher: Taylor & Francis _ eprint: https://doi.org/10.1080/08920753.2021.1846156, pp. 90-106. ISSN: 0892-0753. DOI: 10.1080/08920753.2021.1846156. URL: https://doi.org/10.1080/08920753.2021.1846156 (visited on Apr. 16, 2021).
EAFM approach
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