(we spend a lot of time on this, understand that operational processes require a lot of time and resources and are not research)
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
Source: http://www.mafmc.org/fishery-management-plans
(we spend a lot of time on this, understand that operational processes require a lot of time and resources and are not research)
Right: Bottom temperature time series covariate on recruitment in the 2024 operational black sea bass assessment
Image courtesy Emily Liljestrand, NEFSC
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
Focus on the risk assessment and its operational use, process to bring new science in
Images courtesy Abigail Tyrell, NEFSC
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)
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 |
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 |
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 |
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
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).
Example: Evaluate risks posed by prey availability to achieving OY for Council managed species
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)
2016-2023 Reports: Climate Section
2024 Report: Climate/Ecosystem Risks
Risks to Spatial Management/Allocation
Risks to Seasonal Management/Timed Closures
Risks to Quota Management/Rebuilding
2024 Report: Days at stressful scallop temperature
Image courtesy Joseph Caracappa, NEFSC
Ecosystem information is being used operationally to support decisions in multiple processes
Risk assessment frameworks are one useful basis for operational use of ecosystem information
Management Strategy Evaluation is needed to identify robust operational approaches
Collaborative iteration is recommended into the future
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)]
]
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).
SSC evaluates 6 criteria
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.,:
c. Ecosystem factors outside the stock assessment affecting short term prediction can inform uncertainty, e.g.,:
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).
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. |
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
Source: http://www.mafmc.org/fishery-management-plans
(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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