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)
The IEA Loop1
2016 Ecosystem Approach to Fishery Management (EAFM) Policy Guidance document: http://www.mafmc.org/s/EAFM-Doc-Revised-2019-02-08.pdf
Mid-Atlantic EAFM framework:
The Council’s EAFM framework has similarities to the IEA loop on slide 2. 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.
Objective Categories | Indicators reported here |
---|---|
Provisioning and Cultural Services | |
Seafood Production | Landings; commercial total and by feeding guild; recreational harvest |
Profits | Revenue decomposed to price and volume |
Recreation | Days fished; recreational fleet diversity |
Stability | Diversity indices (fishery and ecosystem) |
Social & Cultural | Community engagement/reliance status |
Protected Species | Bycatch; population (adult and juvenile) numbers, mortalities |
Supporting and Regulating Services | |
Biomass | Biomass or abundance by feeding guild from surveys |
Productivity | Condition and recruitment of managed species, Primary productivity |
Trophic structure | Relative biomass of feeding guilds, Zooplankton |
Habitat | Estuarine and offshore habitat conditions |
Characterizing ecosystem change for fishery management
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 in top plot)
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 in top plot)
Key: Black = Revenue of all species combined;
Red = Revenue of MAFMC managed species
Indicators: Commercial and recreational landings
Key: Black = Landings of all species combined;
Red = Landings of MAFMC managed species
Multiple drivers: ecosystem and stock production, management, market conditions, and environment
Is biomass driving?
Key: Black = NEFSC survey;
Red = NEAMAP survey
Key: Orange background = Tipping point overfishing threshold, Link and Watson 2019 Green background = Optimal range, Link and Watson 2019
One change: Butterfish Bstatus
Key: Black = Landings of all species combined;
Red = Landings of MAFMC managed species
Drivers:
market dynamics affecting commercial landings of surfclams and ocean quahogs
other drivers affecting recreational landings: shark fishery management, possibly survey methodology
Monitor:
Because ecosystem overfishing seems unlikely, stock status is mostly acceptable, and aggregate biomass trends appear stable, the decline in commercial landings is most likely driven by market dynamics affecting the landings of surfclams and ocean quahogs, as quotas are not binding for these species.
Climate change also seems to be shifting the distribution of surfclams and ocean quahogs, resulting in areas with overlapping distributions and increased mixed landings. Given the regulations governing mixed landings, this could become problematic in the future and is currently being evaluated by the Council.
]
Indicators: development timeline, revenue in lease areas, survey overlap (full map)
Implications:
Current plans for rapid buildout of offshore wind in a patchwork of areas spreads the impacts differentially throughout the region
2-24% of total average revenue for major Mid-Atlantic commercial species in lease areas could be displaced if all sites are developed. Displaced fishing effort can alter fishing methods, which can in turn change habitat, species (managed and protected), and fleet interactions.
Right whales may be displaced, and altered local oceanography could affect distribution of their zooplankton prey.
Scientific data collection surveys for ocean and ecosystem conditions, fish, and protected species will be altered, potentially increasing uncertainty for management decision making.
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?
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).
Spatial scale
A glossary of terms (Memo 5), detailed technical methods documentation and indicator data are available online.
Key to figures
Trends assessed only for 30+ years: more information
Orange line = significant increase
Purple line = significant decrease
No color line = not significant or < 30 yearsGrey background = last 10 years
Indicators: climate sensitive species life stages mapped to climate vulnerable habitats
See EAFM risk assessment for example species narratives
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)
The IEA Loop1
Keyboard shortcuts
↑, ←, Pg Up, k | Go to previous slide |
↓, →, Pg Dn, Space, j | Go to next slide |
Home | Go to first slide |
End | Go to last slide |
Number + Return | Go to specific slide |
b / m / f | Toggle blackout / mirrored / fullscreen mode |
c | Clone slideshow |
p | Toggle presenter mode |
t | Restart the presentation timer |
?, h | Toggle this help |
Esc | Back to slideshow |