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(we spend a lot of time on this, understand that operational processes require a lot of time and resources and are not research)

Operationalizing the use of ecosystem
information in Mid-Atlantic science
and management decisions

SCS8 Case Study
26 August 2024

Sarah Gaichas
Mid-Atlantic SSC and NOAA NMFS Northeast Fisheries Science Center

Thanks to Geret DePiper (MAFMC SSC, NEFSC) and Brandon Muffley (MAFMC)

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Operational: in use or ready for use

Components of an operational process:

  • Clear articulation of management objectives (use)

  • Standardized information updates on a regular reporting schedule

  • Feedbacks from the management system and adjustments to achieve management objectives

"Operationalizing" is an iterative process within a specific context

Mid Atlantic fishery management plans and species

MAFMC fishery management plans and species

Source: http://www.mafmc.org/fishery-management-plans

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(we spend a lot of time on this, understand that operational processes require a lot of time and resources and are not research)

Mid Atlantic operational use of ecosystem information

Stock assessments

ABC setting

EAFM: risk assessment → conceptual model → management strategy evaluation → operational management tool

Co-evolution of ecosystem reporting and operational use

Right: Bottom temperature time series covariate on recruitment in the 2024 operational black sea bass assessment

Image courtesy Emily Liljestrand, NEFSC

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  • Stock assessments (ESPs-->assessment structure; ecosystem covariates-->OFL projections)

  • ABC setting (data quality, model performance, and ecosystem information-->OFL CV-->ABC)

  • EAFM risk assessment (ecosystem information-->prioritize scoping and MSE)

In development, research coordinated with SSC

  • Ecosystem overfishing thresholds

Focus on the risk assessment and its operational use, process to bring new science in

Ecosystem and Socioeconomic Profiles (ESPs) in Assessments, and MAFMC ABC process

Pathways for scientific advice from the northeast ESP process

Bluefish ESP conceptual model

Images courtesy Abigail Tyrell, NEFSC

ABC proportion of OFL given OFL CV

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MAFMC Ecosystem Approach to Fisheries Management

EAFM Policy Guidance Doc Word Cloud

  • Mid-Atlantic EAFM framework:

    Mid-Atlantic EAFM framework

5 / 17

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.

(DePiper, et al., 2017) (Bastille, et al., 2021) (Muffley, et al., 2021) (Gaichas, et al., 2018) (DePiper, et al., 2021) (Gaichas, et al., 2016)

EAFM Risk Assessment: 2024 Update with new elements

Species level risk elements

Species Assess Fstatus Bstatus PreyA PredP FW2Prey Climate DistShift EstHabitat OffHab
Ocean Quahog lowest lowest lowest tbd tbd lowest highest modhigh lowest tbd
Surfclam lowest lowest lowest tbd tbd lowest modhigh modhigh lowest tbd
Summer flounder lowest highest lowmod tbd tbd lowest lowmod modhigh highest tbd
Scup lowest lowest lowest tbd tbd lowest lowmod modhigh highest tbd
Black sea bass lowest lowest lowest tbd tbd lowest modhigh modhigh highest tbd
Atl. mackerel lowest lowest highest tbd tbd lowest lowmod modhigh lowest tbd
Chub mackerel highest lowmod lowmod tbd tbd lowest na na lowest tbd
Butterfish lowest lowest lowmod tbd tbd lowest lowest highest lowest tbd
Longfin squid lowmod lowmod lowmod tbd tbd lowmod lowest modhigh lowest tbd
Shortfin squid highest lowmod lowmod tbd tbd lowmod lowest highest lowest tbd
Golden tilefish lowest lowest lowmod tbd tbd lowest modhigh lowest lowest tbd
Blueline tilefish highest highest modhigh tbd tbd lowest modhigh lowest lowest tbd
Bluefish lowest lowest lowmod tbd tbd lowest lowest modhigh highest tbd
Spiny dogfish lowest highest lowest tbd tbd lowest lowest highest lowest tbd
Monkfish highest lowmod lowmod tbd tbd lowest lowest modhigh lowest tbd
Unmanaged forage na na na tbd tbd lowmod na na na tbd
Deepsea corals na na na tbd tbd lowest na na na tbd
  • Mackerel and dogfish Fstatus risk reduced to low, Summer flounder risk increased to high. Spiny dogfish Bstatus risk decreased to low
  • Indicators in development for new Prey Availability, Predation Pressure, and Offshore Habitat elements

Ecosystem level risk elements

System EcoProd CommVal RecVal FishRes1 FishRes2 ComDiv RecDiv Social ComFood RecFood
Mid-Atlantic lowmod modhigh lowmod lowest modhigh lowest tbd lowmod modhigh modhigh
  • Recreational value risk increased from low to low-moderate
  • Recreational diversity added, risk criteria in development

Species and Sector level risk elements

Species FControl Interact OSW1 OSW2 OtherUse RegComplex Discards Allocation
Ocean Quahog-C lowest lowest tbd tbd tbd lowest modhigh lowest
Surfclam-C lowest lowest tbd tbd tbd lowest modhigh lowest
Summer flounder-R lowmod lowest tbd tbd tbd highest modhigh highest
Summer flounder-C lowmod lowmod tbd tbd tbd lowmod modhigh lowest
Scup-R highest lowest tbd tbd tbd highest modhigh highest
Scup-C lowest lowmod tbd tbd tbd lowmod modhigh lowest
Black sea bass-R highest lowest tbd tbd tbd highest modhigh highest
Black sea bass-C lowmod lowmod tbd tbd tbd lowmod highest lowest
Atl. mackerel-R lowmod lowest tbd tbd tbd lowmod lowmod lowest
Atl. mackerel-C lowest lowmod tbd tbd tbd highest lowmod lowest
Butterfish-C lowest lowmod tbd tbd tbd modhigh modhigh lowest
Longfin squid-C lowest modhigh tbd tbd tbd modhigh modhigh lowest
Shortfin squid-C lowmod lowmod tbd tbd tbd modhigh lowest lowest
Golden tilefish-R na lowest tbd tbd tbd lowest lowest lowest
Golden tilefish-C lowest lowest tbd tbd tbd lowest lowest lowest
Blueline tilefish-R lowest lowest tbd tbd tbd lowmod lowest lowest
Blueline tilefish-C lowmod lowest tbd tbd tbd lowest lowest lowest
Bluefish-R lowmod lowest tbd tbd tbd modhigh lowmod highest
Bluefish-C lowest lowest tbd tbd tbd lowmod lowmod lowest
Spiny dogfish-R lowest lowest tbd tbd tbd lowest lowmod lowest
Spiny dogfish-C lowest modhigh tbd tbd tbd highest lowmod lowest
Chub mackerel-C lowest lowmod tbd tbd tbd lowest lowest lowest
Unmanaged forage lowest lowest tbd tbd tbd lowest lowest lowest
Deepsea corals na na tbd tbd tbd na na na
  • Management fully updated for existing elements
  • Offshore wind (OSW) risks split into 2 new elements in development, non-OSW uses added
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How is MAFMC using the risk assessment?

  • Risk based prioritization: the Council selected summer flounder for conceptual modeling

  • Council completed management strategy evaluation (MSE) addressing recreational fishery discards based on conceptual modeling

  • Stakeholder driven MSE coupled population and recreational demand models

  • Results: potential improvement in angler welfare with low risk to stock status
7 / 17

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).

Operational multispecies recreational demand model sets specifications

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Updating the risk assessment: add recreational and cross-sectoral risks, static → dynamic indicators

Example: Evaluate risks posed by prey availability to achieving OY for Council managed species

9 / 17

Council and Advisory Panel members recommended new elements addressing human dimensions (recreational access equity), new elements addressing cross-sectoral impacts (offshore wind impacts on biology and ecosystem as well as fishery access and scientific sampling), and transitions from static ecosystem indicators to time series indicators (prey availability, predation pressure, and fishing community vulnerability). New ecosystem science was required to support these requests. The process included development of new indicators of prey availability based on spatio-temporal modeling using ecological datasets (stomach contents, zooplankton), and new spatial analyses of habitat, revenue, and surveys relative to wind energy development areas. Development of potential risk criteria is ongoing; thresholds between low, moderate, and high risk that are essential to operational use are developed collaboratively with Council and Advisory Panel members.

The slide shows a higher risk example (black sea bass, low recent condition correlated with recently declining prey) and a lower risk example (bluefish, despite a long term decline in forage fish prey. recent condition has been good)

(Gaichas, et al., 2023)

Evolution of ecosystem reporting: from physical time series to specific management risks

2016-2023 Reports: Climate Section

2024 Report: Climate/Ecosystem Risks

Risks to Spatial Management/Allocation

  • Indicators and potential drivers of distribution shifts

Risks to Seasonal Management/Timed Closures

  • Indicators and potential drivers of changing timing (phenology)

Risks to Quota Management/Rebuilding

  • Indicators and potential drivers of changing productivity

2024 Report: Days at stressful scallop temperature

scallop stress bottom temp

Image courtesy Joseph Caracappa, NEFSC

10 / 17

Conclusions

  • Ecosystem information is being used operationally to support decisions in multiple processes

    • Assessment, ABC setting, EAFM
    • Contextual information is valued--used--by the Councils
    • Align information with objectives
  • Risk assessment frameworks are one useful basis for operational use of ecosystem information

    • Alaska assessment risk tables
    • New England developing indicator-based risk policy
  • Management Strategy Evaluation is needed to identify robust operational approaches

  • Collaborative iteration is recommended into the future

11 / 17

THANK YOU! Assessment and Ecosystem Working Groups

Black Sea Bass Assessment
Emily Liljestrand (NEFSC) - MTA lead
Anna Mercer (NEFSC) - RTA chair
Kiersten Curti (NEFSC) - RTA lead
Julia Beaty (MAFMC)
Gavin Fay (UMassD)
Marissa McMahan (Manomet)
Jason McNamee (RIDEM)
Tim Miller (NEFSC)
Sam Truesdell (MADMF/NEFSC)
Ricky Tabendera (NEFSC/HCRI)
Alex Hansell (NEFSC)
Andy Jones (NEFSC)
Jeff Brust (NJDEP)
Lisa Chong (MSU)
Scott Large (NEFSC)
Abby Tyrell (NEFSC)
Andie Painten (UMassD)
Maria Cristina Perez (UMassD)
Hannah Verkamp (CFRF)
John Wiedenmann (Rutgers)
Paula Fratantoni (NEFSC)
Gary Shephard (NEFSC - retired)
EAFM Products
Greg Ardini
Fred Akers
Leah Barton
Kimberly Bastille
Bob Beal
Rick Bellavance
Mark Binsted
Eleanor Bochenek
Bonnie Brady
Jeff Brust
Andrew Carr-Harris
Paul Caruso
Jessica Coakley
Dustin Colson-Leaning
Jonathan Cummings
Kiley Dancy
Maureen Davidson
Jeff Deem
Peter deFur
Neil Delanoy
John DePerseniaire
Jonathan Deroba
Greg DiDomenico
Michelle Duval
G. Warren Elliott
Gavin Fay
Jeremy Firestone
Emily Gilbert
Willy Goldsmith
EAFM Products
Zachary Greenberg
Kaili Gregory
Joseph Grist
Pam Lyons Gromen
Jeremy Hancher
Jay Hermsen
Peter Himchak
Rich Hittinger
Jorge Holzer
Fiona Hogan
Jeff Kaelin
LCDR Matt Kahley
Emily Keiley
Jeff Kipp
Meghan Lapp
Scott Lenox
Douglas Lipton
Carl LoBue
Sean Lucey
Jason McNamee
Adam Nowalsky
Mike Oppegaard
Robert O’Reilly
Danielle Palmer
Charles Perretti
Michael Plaia
Kirby Rootes-Murdy
Scott Rubow
EAFM Products
Robert Ruhle
Tom Schlichter
Robin Scott
Rich Seagraves
Philip Simon
Annabelle Stanley
Mark Terceiro
George Topping
Michael Waine
Judith Weis
Michael Wilberg
Sara Winslow
Greg Wojcik
Harvey Yenkinson

12 / 17


EAFM: Conceptual Model
Greg Ardini NEFSC
Mark Terceiro NEFSC
Michael Wilberg UMD
Douglas Lipton NMFS
Kiley Dancy MAFMC
Jessica Coakley MAFMC
Kirby Rootes-Murdy ASMFC,

*EAFM: Conceptual Model Cont'd*<br>
Jason McNamee RI DEM<br>
Jeff Brust NJ DEP<br>
Danielle Palmer GARFO<br>
Emily Gilbert GARFO<br>
Robert O’Reilly EOP & VMRC<br>
G. Warren Elliott EOP Chair<br>
Charles Perretti NEFSC<br>
Geret DePiper NEFSC<br>
Sarah Gaichas NEFSC<br>
Brandon Muffley MAFMC<br>
<br>
EAFM: MSE Technical Group*<br>
Andrew Carr-Harris/NEFSC<br>
Dustin Colson-Leaning/ASMFC<br>
Jonathan Cummings/USFWS<br>
Kiley Dancy/MAFMC<br>
Geret DePiper/NEFSC<br>
Jon Deroba/NEFSC<br>
Gavin Fay/UMass Dartmouth<br>
Sarah Gaichas/NEFSC<br>
Kaili Gregory/Cornell<br>
Jorge Holzer/U. Maryland<br>
Emily Keiley/GARFO<br>
Jeff Kipp/ASMFC<br>
Doug Lipton/NOAA Fisheries<br>
Annabelle Stanley/Cornell<br>
Mark Terceiro/NEFSC<br>
Mike Wilberg/U. Maryland<br>
Greg Wojcik/CT DEEP,
EAFM: MSE Stakeholder Group*<br>
Leah Barton/Shore<br>
Rick Bellavance/Charter Boat<br>
Eleanor Bochenek/Academic<br>
Neil Delanoy/Party Boat<br>
John DePersenaire/National Recreational Org.<br>
Greg DiDomenico/Commercial<br>
Paul Haertel/Private Boat<br>
Rich Hittinger/Private Boat<br>
Mike Oppegaard/Charter Boat<br>
Michael Plaia/Charter Boat<br>
Harvey Yenkinson/Private Boat<br>
Mike Waine/Rec. Secondary Market<br>
<br>
EAFM: MSE Council Advisory Group*<br>
Adam Nowalsky<br>
Justin Davis<br>
Tony DeLernia<br>
Peter deFur<br>
<br>
Recreational Fishery Decision Support Tool*<br>
Kim Bastille (NEFSC)<br>
Andrew Carr-Harris (NEFSC)<br>
Geret DePiper (NEFSC)<br>
Scott Steinback (NEFSC)]

]

THANK YOU! Ecosystem reporting made possible by (at least) 80 contributors from 20+ institutions

Kimberly Bastille
Aaron Beaver (Anchor QEA)
Andy Beet
Brandon Beltz
Ruth Boettcher (Virginia Department of Game and Inland Fisheries)
Mandy Bromilow (NOAA Chesapeake Bay Office)
Baoshan Chen (Stony Brook University)
Zhuomin Chen (U Connecticut)
Joseph Caracappa
Doug Christel (GARFO)
Patricia Clay
Lisa Colburn
Jennifer Cudney (NMFS Atlantic HMS Management Division)
Tobey Curtis (NMFS Atlantic HMS Management Division)
Art Degaetano (Cornell U)
Geret DePiper
Dan Dorfman (NOAA-NOS-NCCOS)
Hubert du Pontavice
Emily Farr (NMFS Office of Habitat Conservation)
Michael Fogarty
Paula Fratantoni
Kevin Friedland
Marjy Friedrichs (Virginia Institute of Marine Science)
Sarah Gaichas
Ben Galuardi (GARFO)
Avijit Gangopadhyay (School for Marine Science and Technology UMass Dartmouth)
James Gartland (Virginia Institute of Marine Science)
Lori Garzio (Rutgers University)
Glen Gawarkiewicz (Woods Hole Oceanographic Institution)
Sean Hardison
Dvora Hart
Kimberly Hyde
John Kocik
Steve Kress (National Audubon Society’s Seabird Restoration Program)
Young-Oh Kwon (Woods Hole Oceanographic Institution)
Scott Large
Gabe Larouche (Cornell U)
Daniel Linden
Andrew Lipsky
Sean Lucey
Don Lyons (National Audubon Society’s Seabird Restoration Program)
Chris Melrose
Shannon Meseck
Ryan Morse
Ray Mroch (SEFSC)
Brandon Muffley (MAFMC)
Kimberly Murray
David Moe Nelson (NCCOS)
Janet Nye (University of North Carolina at Chapel Hill)
Chris Orphanides
Richard Pace
Debi Palka
Tom Parham (Maryland DNR)
Charles Perretti
CJ Pellerin (NOAA Chesapeake Bay Office)
Kristin Precoda
Grace Roskar (NMFS Office of Habitat Conservation)
Jeffrey Runge (U Maine)
Grace Saba (Rutgers)
Vincent Saba
Sarah Salois
Chris Schillaci (GARFO)
Amy Schueller (SEFSC)
Teresa Schwemmer (Stony Brook University)
Dave Secor (CBL)
Angela Silva
Adrienne Silver (UMass/SMAST)
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
Dave Wilcox (VIMS)
Timothy White (Environmental Studies Program BOEM)
Sarah Wilkin (NMFS Office of Protected Resources)
Mark Wuenschel
Qian Zhang (U Maryland)
13 / 17

References

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

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

DePiper, G. et al. (2021). "Learning by doing: collaborative conceptual modelling as a path forward in ecosystem-based management". In: ICES Journal of Marine Science 78.4, pp. 1217-1228. ISSN: 1054-3139. DOI: 10.1093/icesjms/fsab054. URL: https://doi.org/10.1093/icesjms/fsab054 (visited on Aug. 08, 2022).

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

Gaichas, S. K. et al. (2023). "Assessing small pelagic fish trends in space and time using piscivore stomach contents". En. In: Canadian Journal of Fisheries and Aquatic Sciences, pp. cjfas-2023-0093. ISSN: 0706-652X, 1205-7533. DOI: 10.1139/cjfas-2023-0093. URL: https://cdnsciencepub.com/doi/10.1139/cjfas-2023-0093 (visited on Feb. 15, 2024).

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

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).

Resources

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Extra

15 / 17

How to get the OFL CV? Characterizing scientific uncertainty

SSC evaluates 6 criteria

  1. Data quality (Tier 1)
  2. Model appropriateness (Tier 1)
  3. Retrospective analysis (Tier 1)
  4. Comparison with simpler analysis (Tier 2)
  5. Ecosystem factors (Tier 2)
  6. Recruitment trends (Tier 2)

5. Informed by ecosystem factors or comparisons with other species

a. Ecosystem factors considered may reduce or increase uncertainty; simply considering an ecosystem factor does not automatically decrease uncertainty;

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

  • Environmentally dependent growth or other population processes;
  • Environmentally dependent availability or other observation processes;
  • Factors limiting/enhancing stock productivity (habitat quality, etc.);
  • Predation, disease, or episodic environmental mortality (e.g., red tide);
  • Time varying inputs such as empirical weight at age or stanzas of growth not explicitly tied to ecosystem factors are considered under criterion #2, not here. Stanzas of recruitment not explicitly tied to ecosystem factors are considered under criterion #6;

c. Ecosystem factors outside the stock assessment affecting short term prediction can inform uncertainty, e.g.,:

  • General measures of ecosystem productivity and habitat stability (e.g., primary production amount and timing, temperature trends, and other MAFMC EAFM risk assessment indicators at the stock or ecosystem level);
  • Climate vulnerability or other risk assessment evaluation of potential for changing productivity under changing conditions;
  • Acute ecosystem events potentially affecting stock dynamics across the stock range over the short term (e.g., marine heat waves, acidification or hypoxia events, harmful algal blooms);

d. Comparisons among related species; e.g., recruitment, growth, condition patterns across Mid Atlantic fish species that are: stable (low uncertainty), varying synchronously (supports common environmental driver, lower uncertainty), or varying unpredictably (higher uncertainty).

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Characterizing scientific uncertainty from ecosystem factors

Decsion Criteria Default OFL CV = 60% Default OFL CV = 100% Default OFL CV = 150%
Ecosystem factors accounted Assessment considers habitat and ecosystem effects on stock productivity, distribution, mortality and quantitatively includes appropriate factors reducing uncertainty in short term predictions. And/or evidence outside the assessment suggests that ecosystem productivity and habitat quality are stable or accountable. And/or ecosystem events affecting stock in the short term are absent. And/or comparable species in the region have synchronous production characteristics and stable short-term predictions. And/or climate vulnerability analysis suggests low risk of change in productivity due to changing climate. Assessment considers habitat/ecosystem factors but does not demonstrate either reduced or inflated short-term prediction uncertainty based on these factors. And/or evidence outside the assessment suggests that ecosystem productivity and habitat quality are variable. And/or acute ecosystem events are likely to have a low to moderate risk of affecting the stock in the short term. And/or mixed productivity and uncertainty signals among comparable species in the region. And/or climate vulnerability analysis suggests moderate risk of change in productivity from changing climate. Assessment either demonstrates that including appropriate ecosystem/habitat factors increases short-term prediction uncertainty, or does not consider habitat and ecosystem factors. And/or evidence outside the assessment suggests that ecosystem productivity and habitat quality are variable and degrading. And/or acute ecosystem events are likely to have a high risk of affecting the stock in the short term. And/or comparable species in the region have high uncertainty in short term predictions. And/or climate vulnerability analysis suggests high risk of changing productivity from changing climate.
17 / 17

Operational: in use or ready for use

Components of an operational process:

  • Clear articulation of management objectives (use)

  • Standardized information updates on a regular reporting schedule

  • Feedbacks from the management system and adjustments to achieve management objectives

"Operationalizing" is an iterative process within a specific context

Mid Atlantic fishery management plans and species

MAFMC fishery management plans and species

Source: http://www.mafmc.org/fishery-management-plans

2 / 17

(we spend a lot of time on this, understand that operational processes require a lot of time and resources and are not research)

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