Small pelagics are widely recognized for their critical function as forage, supporting human populations as well as harvested fish and protected species in ecosystems worldwide. In some ecosystems, one or two small pelagic species may dominate as forage , while in others a wide variety of small pelagic species fill this role together. In both instances, understanding fluctuations in small pelagics is an important component of an ecosystem approach to management: low abundance of small pelagics can have implications for both directed small pelagic fisheries and management of their predators. Conversely, high aggregate abundance of small pelagics can provide a robust forage supply for generalist predators even if individual small pelagic species are depleted. Fish predators generally select the most abundant prey in the environment, so as individual prey populations vary, fish predators respond by switching prey.

In addition to abundance, spatial distribution of small pelagics has clear implications for both predators and fisheries. Central place foragers such as seabirds require small pelagics close to breeding colonies during the breeding season, while free ranging highly mobile fish predators can follow small pelagics if their distributions shift further offshore. Similarly, fisheries prosecuted on large vessels are better equipped to follow mobile predators offshore, while shore based artisanal and recreational fisheries may lose access to mobile predators that follow prey offshore. Changing spatial distribution can also impact stock assessments if changing availability of assessed fish cannot be incorporated into assessment models.

Changing distribution and abundance of small pelagics may drive changes in predator distributions, affecting predator availability to fisheries and surveys.Bluefish are medium-sized, rapidly growing pelagic piscivores known to prey on a wide variety of small pelagics and to target areas of dense prey. Participants in the US bluefish fishery have raised concerns that changes in prey distribution may change bluefish availability to surveys and recreational fisheries, creating uncertainty in stock assessments and subsequent fishery management (MAFMC Fishery performance report, 2021). Therefore, spatial and temporal trends in the small pelagic prey of bluefish needed to be characterized to address this concern.

Bottom trawls are not designed for efficient sampling of small pelagics. While we can estimate aggregate planktivore biomass for different ecoregions on the Northeast US shelf, estimates have high observation error. Black lines = NEFSC bottom trawl, Red lines = NEAMAP inshore bottom trawl. Orange line on GB Fall indicates significant increasing long term trend.

Many exploited small pelagic populations have a long history of scientific assessment, providing insight into long term and short term fluctuations relevant to both the fishery and the wider ecosystem. However, spatial shifts within a small pelagic stock’s range affecting different predator populations and distributions are difficult to track using conventional spatially aggregated stock assessment approaches. In addition, for ecosystems where small pelagics represent a mix of managed and unmanaged species, information on unmanaged species is often lacking, hindering assessment of the status of the full forage base supporting predators and other fisheries.

Spatial and spatio-temporal correlation decay with increasing distance estimated as $\kappa$ in a Matern function with fixed smoothness and geometric anisotropy (directional correlation, optionally estimated by the model).

Initial model selection consistently supported the inclusion of spatial and spatio-temporal random effects and anisotropy across all datasets: fall, spring, and annual.

Extrapolation grid:" area over which densities will be extrapolated to calculate derived quantities" encompassing survey area allows comparison with design-based estimates

Knots are defined that "minimize the average distance between samples and knots" using k-means clustering, which results in knots in proportion to sampling intensity across the area.

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.

The figure shows bluefish diet collection stations for fall surveys, 1985-2019.

NEAMAP survey stations with diet collections for piscivores (n = 3838) had a higher proportion with our defined bluefish prey (n = 2418, 63.0015633%).

All size classes of 50 fish predators captured in the NEFSC bottom trawl survey were grouped by diet similarity to identify the size classes of piscivore species with the most similar diet to bluefish in the region. Diet similarity analysis was completed using the Schoener similarity index (@schoener_nonsynchronous_1970; B. Smith, pers. comm.), and is available available via this link on the NEFSC food habits shiny app. The working group evaluated several clustering methods to develop the predator list (see this link with detailed cluster results).

Predators with highest diet similarity to Bluefish from the NEFSC diet database (1973-2020) include Atlantic cod, Atlantic halibut, buckler dory, cusk, fourspot flounder, goosefish, longfin squid, shortfin squid, pollock, red hake, sea raven, silver hake, spiny dogfish, spotted hake, striped bass, summer flounder, thorny skate, weakfish, and white hake. The NEAMAP survey operates closer to shore than the current NEFSC survey. The NEAMAP dataset includes predators sampled by the NEFSC survey and adds two species, Spanish mackerel and spotted sea trout, not captured by the NEFSC survey offshore but included based on working group expert judgement of prey similarity to bluefish. Predator size classes included are listed in Table 2 of the forage fish index working paper at this link.

Image credits: Weakfish and Spanish mackerel-- https://marinefishesofgeorgia.org ; spotted seatrout-- https://fishinginmiami.com ; Sea Raven photo 2/11/2019 11:07:56 AM, Photographer: Andrew J. Martinez, Location: Massachusetts, Stellwagen Bank NMS; all others NOAA Fisheries.

Diets from all 22 piscivores (including bluefish) were combined for the 20 forage fish (bluefish prey) groups at each surveyed location, and the mean weight of forage fish per predator stomach at each location was calculated. Data for each station included station ID, year, season, date, latitude, longitude, vessel, mean bluefish prey weight (g), mean piscivore length (cm), number of piscivore species, and sea surface temperature (degrees C). Because approximately 10% of survey stations were missing in-situ sea water temperature measurements, National Oceanic and Atmospheric Administration Optimum Interpolation Sea Surface Temperature (NOAA OI SST) V2 High Resolution Dataset [@reynolds_daily_2007] data provided by the NOAA PSL, Boulder, Colorado, USA, from their website at https://psl.noaa.gov were used to fill gaps. For survey stations with in-situ temperature measurements, the in-situ measurement was retained. For survey stations with missing temperature data, OI SST was substituted for input into VAST models.

Models were developed combining all data for the year ("Annual") and with separate data for "Spring" (collection months January - June) and "Fall" (collection months July-December) to align with assumptions used in the bluefish stock assessment. Modeled years included 1985-2021 to align with other data inputs in the bluefish stock assessment.

SST is also likely to affect prey distribution, but differently for each prey species. Therefore, SST is not modeled as a density covariate for aggregate small pelagics.

NEFSC survey strata definitions are built into the VAST northwest-atlantic extrapolation grid already. We defined additional new strata to address the recreational inshore-offshore 3 mile boundary. The area within and outside 3 miles of shore was defined using the sf R package as a 3 nautical mile (approximated as 5.556 km) buffer from a high resolution coastline from thernaturalearth R package. This buffer was then intersected with the current FishStatsUtils::northwest_atlantic_grid built into VAST and saved using code here. Then, the new State and Federal waters strata were used to split NEFSC survey strata where applicable, and the new full set of strata were used along with a modified function from FishStatsUtils::Prepare_NWA_Extrapolation_Data_Fn to build a custom extrapolation grid for VAST as described in detail here.

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

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.

aggregate forage important in this system with many forage species: Jason Link horrendogram

potential trends in aggregate forage to be compared with trends in zooplankton and environmental drivers

Potential risk criteria: