• Allocations to fleets or areas
  • Coordination across boundaries and sectors

Our ESP process was developed from the AFSC process, but we adjusted things slightly because of how our benchmarks are scheduled and because we are providing scientific advice to multiple Councils.

The ESP framework is an iterative cycle that complements the stock assessment cycle. First I will give you an overview of the ESP cycle, and then I will explain each step in more detail. The ESP begins with the development of the problem statement by identifying the topics that the assessment working group and ESP team want to assess. This process includes a literature review or other method of gathering existing information on the stock, such as reviewing prior assessments and research recommendations. Next, a conceptual model is created that links important processes and pressures to stock performance. From these linkages, we develop indicators that can be used to monitor the system conditions. Next, the indicators are analyzed to determine their status and the likely impacts on the stock. Some indicators may be tested for inclusion in assessment models. Finally, all of these analyses are synthesized into a report card to provide general recommendations for fishery management.

The Bluefish Research Track ESP was presented December 7 2022, and was well received by CIE reviewers. Reviewers commented that it was the most complete treatment of a stock assessment "ecosystem ToR" they had seen, and formed a good basis for integrating further ecosystem information into the stock assessment in the future. The full ESP document is available as a working paper from the stock assessment data portal.

In addition to the conceptual model, a summary table was developed for bluefish ecosystem indicators. This type of summary could contribute to OFL CV decisions with further information on how these indicator levels affect uncertainty in assessment.

Changing distribution and abundance of small pelagics may drive changes in predator distributions, affecting predator availability to fisheries and surveys. However, small pelagic fish are difficult to survey directly, so we developed a novel method of assessing small pelagic fish aggregate abundance via predator diet data. We used piscivore diet data collected from multiple bottom trawl surveys within a Vector Autoregressive Spatio-Temporal (VAST) model to assess trends of small pelagics on the Northeast US shelf. The goal was to develop a spatial “forage index” to inform survey and/or fishery availability in the bluefish (Pomatomus saltatrix) stock assessment. Using spring and fall surveys from 1973-2020, 20 small pelagic groups were identified as major bluefish prey using the diet data. Then, predators were grouped by diet similarity to identify 19 piscivore species with the most similar diet to bluefish in the region. Diets from all 20 piscivores were combined for the 20 prey groups at each surveyed location, and the total weight of small pelagic prey per predator stomach at each location was input into a Poisson-link delta model to estimate expected prey mass per predator stomach. Best fit models included spatial and spatio-temporal random effects, with predator mean length, number of predator species, and sea surface temperature as catchability covariates. Spring and fall prey indices were split into inshore and offshore areas to reflect changing prey availability over time in areas available to the recreational fishery and the bottom trawl survey, and also to contribute to regional ecosystem reporting

Using NEFSC bottom trawl survey diet data from 1973-2021, 20 small pelagic groups were identified as major bluefish prey with 10 or more observations (in descending order of observations): Longfin squids (Doryteuthis formerly Loligo sp.), Anchovy family (Engraulidae), bay anchovy (Anchoa mitchilli), Atlantic butterfish, (Peprilus triachanthus), Cephalopoda, (Anchoa hepsetus), red eye round herring (Etrumeus teres), Sandlance (Ammodytes sp.), scup (Stenotomus chrysops), silver hake (Merluccius bilinearis), shortfin squids (Illex sp.), Atlantic herring (Clupea harengus), Herring family (Clupeidae), Bluefish (Pomatomus saltatrix), silver anchovy (Engraulis eurystole), longfin inshore squid (Doryteuthis pealeii), Atlantic mackerel (Scomber scombrus), flatfish (Pleuronectiformes), weakfish (Cynoscion regalis), and Atlantic menhaden (Brevoortia tyrannus).

Prey categories such as fish unidentified, Osteichthyes, and unidentified animal remains were not included in the prey list. Although unidentified fish and Osteichthyes can comprise a significant portion of bluefish stomach contents, we cannot assume that unidentified fish in other predator stomachs represent unidentified fish in bluefish stomachs.

Image credits: Striped and bay anchovy photo--Robert Aguilar, Smithsonian Environmental Research Center; redeye round herring photo--https://diveary.com ; sandlance photo--Virginia Institute of Marine Science; all others NOAA Fisheries.

WHAM is a state space stock assessment model framework: https://timjmiller.github.io/wham/

The Bigelow index fit with the fall forage fish index did not improve the model fit (AIC), was slightly worse fit and gave identical results The Albatross index fit with the fall forage fish index did not converge or hessian was not positive definite for any of the models (even when how = 0 for some of them). The MRIP index fit with the annual forage fish index did not converge or hessian was not positive definite for any of the models

SSC Ecosystem working group analyses in progress

• Outlining multispecies and system level advice: identify initial priorities in collaboration with Council
  • Where are there multispecies/multifleet tradeoffs linking to economic and social outcomes?
  • Are there multi-indicator thresholds suggesting when FMP level management needs to change?
  • Are there changes in ecosystem productivity that imply standardized approaches for
    • Setting reference points?
    • Developing rebuilding plans?
    • Other analyses requiring short-term projections?
• More direct SSC involvement in ecosystem reporting priorities MAFMC EAFM
• Evaluate current multispecies indicators for common signals
• Evaluate proposed ecosystem-level reference points and thresholds for regional ecosystems

(Gaichas et al., 2016) 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.

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).

• Linked recreational demand and population dynamics model
• Alternative operating model included northward distribution shift as change in availability by state
• Rank order of management options maintained, but degraded performance when considering ecosystem change

Declining commercial and recreational landings can be driven by many interacting factors, including combinations of ecosystem and stock production, management actions, market conditions, and environmental change. While we cannot evaluate all possible drivers at present, here we evaluate the extent to which ecosystem overfishing (total landings exceeding ecosystem productive capacity), stock status, and system biomass trends may play a role.