class: right, bottom, my-title, title-slide .title[ # Assessing small pelagic fish trends in space<br /> and time using piscivore stomach contents ] .subtitle[ ## Quantfish <br /> 16 October 2023 ] .author[ ### Sarah Gaichas<span class="math inline"><sup>1</sup></span>, James Gartland<span class="math inline"><sup>2</sup></span>, Brian Smith<span class="math inline"><sup>1</sup></span>, Anthony Wood<span class="math inline"><sup>1</sup></span>, Elizabeth Ng<span class="math inline"><sup>3</sup></span>,<br /> Michael Celestino<span class="math inline"><sup>4</sup></span>, Katie Drew<span class="math inline"><sup>5</sup></span>, Abigail Tyrell<span class="math inline"><sup>1, 6</sup></span>, and James Thorson<span class="math inline"><sup>7</sup></span> ] .institute[ ### <span style="font-weight:normal;"><font size="-0.5"><span class="math inline"><sup>1</sup></span>NOAA NMFS Northeast Fisheries Science Center, Woods Hole, MA, USA; <span class="math inline"><sup>2</sup></span>Virginia Institute of Marine Science, Gloucester Point, VA, USA; <span class="math inline"><sup>3</sup></span>University of Washington, Seattle, WA, USA; <span class="math inline"><sup>4</sup></span>New Jersey Department of Environmental Protection, Port Republic, NJ, USA; <span class="math inline"><sup>5</sup></span>Atlantic States Marine Fisheries Commission, Arlington, VA, USA; <span class="math inline"><sup>6</sup></span>Ocean Associates Inc, Arlington, VA, USA; <span class="math inline"><sup>7</sup></span>NOAA NMFS Alaska Fisheries Science Center, Seattle, WA, USA</font></span> ] --- class: top, left background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/forageSeattleTimes.png"), url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/allcomponents.png") background-size: 590px, 550px background-position: top left, bottom right .right[ # Motivation Small pelagics as forage; Importance as a group How much? Where and when? Drive predator and fishery distributions ] ??? 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. --- # Does prey drive availability of bluefish? ## Bluefish, *Pomatomus saltatrix* .pull-left[  ] .pull-right[ .large[ "... it is perhaps the most ferocious and bloodthirsty fish in the sea, leaving in its wake a trail of dead and mangled mackerel, menhaden, herring, alewives, and other species on which it preys." <a name=cite-collette_bigelow_2002></a>([Collette, et al., 2002](#bib-collette_bigelow_2002)) "From Raritan Bay to Rockaway Inlet, we have had a phenomenal bluefish year with lots of bunker and other bait, ultimately leading to an abundance of bluefish." [Mid-Atlantic Bluefish Fishery Performance Report, 2021](https://www.mafmc.org/s/8_BF-FPR-2021.pdf) ] ] .center[ Can localized predator-prey observations scale to coastwide assessment and management? ] ??? <font size="-1"></font> 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](https://www.mafmc.org/s/8_BF-FPR-2021.pdf)). Therefore, spatial and temporal trends in the small pelagic prey of bluefish needed to be characterized to address this concern. --- ## Objective Create a “forage index” to evaluate changes in small pelagics over time and in space in the Northeast US continental shelf ecosystem. Two applications: 1. Addressing uncertainty in the stock assessment for a key predator species, bluefish (*Pomatomus saltatrix*) 1. Describing aggregate forage species trends for integrated ecosystem assessment .pull-left[  ] .pull-right[  ] --- ## Northeast US: How much? Where and when? .pull-left[ ## Stock assessments ([ASMFC](http://www.asmfc.org/species/atlantic-menhaden), [StockSmart](https://apps-st.fisheries.noaa.gov/stocksmart?app=homepage))  ] .pull-right[ ## Bottom trawl survey "planktivores" abundance estimates ([Northeast US Ecosystem Reports](https://www.fisheries.noaa.gov/new-england-mid-atlantic/ecosystems/state-ecosystem-reports-northeast-us-shelf)) <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-1-1.png" width="95%" /> ] ??? 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. --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/Ng2022dietvsassess.png"), url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/NgPredStomForageB2022.png") background-size: 800px, 400px background-position: right, left ## Fish stomach contents → Atlantic herring biomass estimates <a name=cite-ng_predator_2021></a>([Ng, et al., 2021](https://doi.org/10.1093/icesjms/fsab026)) <!--.pull-left-40[  ] .pull-right-60[]--> ??? --- ## Outline .pull-left-60[ 1. Vector Autoregressive Spatio-Temporal (VAST) modeling 1. Identify forage species: what are bluefish prey? 1. Can we use predators as "samplers"? Which predators? 1. What affects the sampling process? Covariates 1. Which model is best? Selection 1. Index sensitivity 1. Stock assessment application 1. Discussion ] .pull-right-40[  .contrib[ https://xkcd.com/1138 ] ] Gaichas et al. in press, http://dx.doi.org/10.1139/cjfas-2023-0093 --- ## Vector Autoregressive Spatio-Temporal (VAST) modeling <a name=cite-thorson_comparing_2017></a><a name=cite-thorson_guidance_2019></a>([Thorson, et al., 2017](https://doi.org/10.1093/icesjms/fsw193); [Thorson, 2019](http://www.sciencedirect.com/science/article/pii/S0165783618302820)) VAST is a Geostatistical generalized linear mixed effects model (GLMM) that models two linear predictors for an index: 1. encounter rate, and 2. positive catch (amount in stomach) A full model for the first linear predictor `\(\rho_1\)` for each observation `\(i\)` can include: * fixed intercepts `\(\beta_1\)` for each category `\(c\)` and time `\(t\)`, * spatial random effects `\(\omega_1\)` for each location `\(s\)` and category, * spatio-temporal random effects `\(\varepsilon_1\)` for each location, category, and time, * fixed vessel effects `\(\eta_1\)` by vessel `\(v\)` and category, and * fixed catchability impacts `\(\lambda_1\)` of covariates `\(Q\)` for each observation and variable `\(k\)`: `$$\rho_1(i) = \beta_1(c_i, t_i) + \omega_1^*(s_i, c_i) + \varepsilon_1^*(s_i, c_i, t_i) + \eta_1(v_i, c_i) + \sum_{k=1}^{n_k} \lambda_1(k) Q(i,k)$$` The full model for the second linear predictor `\(\rho_2\)` has the same structure, estimating `\(\beta_2\)`, `\(\omega_2\)`, `\(\varepsilon_2\)`, `\(\eta_2\)`, and `\(\lambda_2\)` using the observations, categories, locations, times, and covariates. We modeled aggregate small pelagic prey as a single category, and apply a Poisson-link delta model to estimate expected prey mass per predator stomach as in ([Ng, et al., 2021](https://doi.org/10.1093/icesjms/fsab026)). VAST model code and documentation: https://github.com/James-Thorson-NOAA/VAST ??? 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. --- .pull-left-30[ ## Spatial estimation assumptions VAST has built-in "extrapolation grids" for many surveyed areas, including the Northeast US. Observations in space are used to define fixed locations ("knots") covering the full extent of the model. We assume constant variation within a timestep at a knot. Modelers specify the number of knots to group observation locations, to balance computation time and resolution. We used 500 knots: ] .pull-right-70[  ] ??? 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. --- ## Modeling spatial and spatio-temporal variation .pull-left-60[ Observations correlated in space and in space over time due to unmeasured processes are modeled as multivariate normal Gaussian Random Fields (GRF): `\(\omega_1\)` ~ MVN(0, `\(\mathbf R_1\)`); `\(\omega_2\)` ~ MVN(0, `\(\mathbf R_2\)`) `\(\varepsilon_1\)`(,t) ~ MVN(0, `\(\mathbf R_1\)`); `\(\varepsilon_2\)`(,t) ~ MVN(0, `\(\mathbf R_2\)`) Spatial and spatio-temporal correlation decay with increasing distance `\(\mathbf d\)` estimated as `\(\kappa\)` in a Matérn function with fixed smoothness `\(\nu\)` and geometric anisotropy `\(H\)` (directional correlation). Correlation function between locations `\(s\)` and `\(s'\)`: `$$\mathbf R_1(s, s') = \frac{1}{2^{\nu-1}\Gamma(\nu)} \times (\kappa_1 \lvert \mathbf d(s,s') \mathbf H \rvert)^\nu \times K_\nu (\kappa_1 \lvert \mathbf d(s,s') \mathbf H \rvert)$$` Estimation uses stochastic partial differential equation (SPDE) approximation. ] .pull-right-40[  ] --- ## Estimating density in space and the index .pull-left[ Poisson link delta model ([Ng, et al., 2021](https://doi.org/10.1093/icesjms/fsab026)) Probability of encounter Poisson: `\(p(i) = 1 - exp[-n(i)]\)` Mass of prey per stomach given encounter: `\(r(i) = \frac{n(i)}{p(i)}w(i)\)` Probability for weight `\(B\)` in stomach: $$ Pr[b(i) = B] = \begin{cases} 1-p(i), B = 0\\\\p(i) \times g[B|r(i), \sigma_b^2], B > 0 \end{cases},$$ where `\(g\)` is a Gamma function. ] .pull-right[ Density `\(b\)` at a location (knot) `\(s\)` for year `\(t\)` is then the predicted weight in a stomach (linear predictor for encounter * linear predictor for weight given encounter): `$$\hat{b}_{s,t} = \hat{n}_{s,t}\hat{w}_{s,t}$$` Index based on area `\(a\)` weighting for each of 500 knots (or subsets): `$$I_t = \sum_{s=1}^{500} a_s\hat{b}_{s,t}$$` Bias correction as in <a name=cite-thorson_implementing_2016></a>([Thorson, et al., 2016](https://www.sciencedirect.com/science/article/pii/S0165783615301399)) ] --- ## Defining bluefish prey from stomachs: a mix of managed and unmanaged small pelagics .pull-left-30[ <img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Squid-Longfin-v1-NOAAFisheries.png" width="45%" /><img src="https://objects.liquidweb.services/images/201909/robert_aguilar,_serc_48650727166_3ce8edc47d_b.jpg" width="45%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Butterfish-NOAAFisheries.png" width="45%" /><img src="https://images.marinespecies.org/thumbs/31939_ammodytes.jpg?w=700" width="45%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Mackerel-Atlantic-NOAAFisheries.png" width="45%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Herring-Atlantic-NOAAFisheries.png" width="45%" /><img src="https://www.fishbase.se/images/thumbnails/jpg/tn_Etter_u2.jpg" width="45%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-09/640x427-Hake-Silver-NOAAFisheries.png" width="45%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2021-03/squid_illex_nb_w_0.png" width="45%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2021-01/640x427-Atlantic_Menhaden_NB_W.jpg" width="45%" /> .contrib[ Northeast Fisheries Science Center Diet Data Online: https://fwdp.shinyapps.io/tm2020/ ] ] .pull-right-70[ <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-3-1.png" width="756" /> ] ??? 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. --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/BluefishOnlyFallData_by_year.png") background-size: 650px background-position: right top ## Which predators: Bluefish stomachs only? .pull-left[ Due to uneven "sampling" of Atlantic herring by predators, ([Ng, et al., 2021](https://doi.org/10.1093/icesjms/fsab026)) recommended aggregating across predators to improve the diet-based Atlantic herring biomass index. Between 1985-2021 there were: .contrib[ * 25634 NEFSC survey stations with stomach collections. * 22751 NEFSC survey stations with piscivore stomachs. + 8869 piscivore stations with bluefish prey; + 39% of piscivore stations have bluefish prey. * 1814 NEFSC survey stations with bluefish stomachs. + 907 bluefish stations with bluefish prey; + 50% of bluefish stations have bluefish prey. * 3838 NEAMAP survey stations with piscivore stomachs + 2369 piscivore stations with bluefish prey; + 62% of piscivore stations have bluefish prey. ] For this index combining multiple small pelagic prey, aggregating across predators most similar to bluefish both increases sample size and reduces sampling variability due to different predator availability to surveys. ] .pull-right[ <!-- --> .center[ .footnote[ Bluefish diet collection stations, fall Northeast Fisheries Science Center surveys ] ] ] ??? 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%). --- ## Aggregating predators: diet similarity to bluefish .pull-left[ <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-4-1.png" width="504" /> ] .pull-right[ <img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Bluefish-NOAAFisheries.png" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-07/640x427-Cod-Atlantic-NOAAFisheries.png?itok=kNKcZ7iV" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Shark-SpinyDogfish-NOAAFisheries.png?itok=cdjTG3Hz" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-09/640x427-Monkfish-NOAAFisheries.png?itok=7JAgAz-u" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-09/640x427-Flounder-Summer-NOAAFisheries.png?itok=II2ii-Qw" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Pollock-Atlantic-NOAAFisheries.png?itok=ZFoDB-Qr" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-09/640x427-Hake-Red-NOAAFisheries.png?itok=SyCYEmmm" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Halibut-Atlantic-right-NOAAFisheries.png?itok=uPvRdIBx" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-StripedBass-NOAAFisheries.png?itok=4ZQoQM0S" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2020-12/640x427-Hake_White_NB_W.jpg?itok=yHy_AuTT" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-09/640x427-Hake-Silver-NOAAFisheries.png" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/dam-migration/640x427-cusk.jpg?itok=u0fw0hiv" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2022-08/640x427-Squid-Longfin-v1-NOAAFisheries.png" width="25%" /><img src="https://media.fisheries.noaa.gov/styles/original/s3/2021-03/squid_illex_nb_w_0.png" width="25%" /><img src="https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/weakfishASMFC.png" width="25%" /><img src="https://photolib.noaa.gov/Portals/0//GravityImages/36881/ProportionalFixedWidth/sanc100698384x800x800.jpg" width="25%" /> <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-6-1.png" width="25%" /><img src="https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/depositphotos_7476236-stock-photo-zeus-faber-fish.jpg" width="25%" /><img src="https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/spanishmackerelASMFC.png" width="25%" /><img src="https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/spottedseatroutASMFC.png" width="25%" /> ] ??? 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](https://fwdp.shinyapps.io/tm2020/#4_DIET_OVERLAP_AND_TROPHIC_GUILDS). The working group evaluated several clustering methods to develop the predator list (see [this link with detailed cluster results](https://sgaichas.github.io/bluefishdiet/PreySimilarityUpdate.html)). 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](https://sgaichas.github.io/bluefishdiet/VASTcovariates_forageindex_WP.html). 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. --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/AllPiscivoresFallData_by_year.png") background-size: 600px background-position: right top .pull-left[ ## "Catchability" covariates for aggregate predator samplers at a location Number of predator species → likely to affect *encounter rate* Mean size of predators → likely to affect *amount of prey* ([Ng, et al., 2021](https://doi.org/10.1093/icesjms/fsab026)) Sea surface temperature (SST) → likely to affect predator activity and feeding rate *encounter rate and amount of prey* * Many missing SST measurements for surveys before 1991 * NOAA OI SST V2 High Resolution Dataset <a name=cite-reynolds_daily_2007></a>([Reynolds, et al., 2007](https://journals.ametsoc.org/view/journals/clim/20/22/2007jcli1824.1.xml)) filled gaps Model selection consistently included number of predator species, mean predator size, and SST as catchability covariates using fall, spring, and annual datasets ] .pull-right[ ] .footnote[ All piscivore diet collection stations, fall NEFSC and NEAMAP surveys → ] ??? 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. --- ## Spatial partitioning: examining small pelagics trends at multiple scales <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/maps-1.png" alt="Maps of key areas for Bluefish assessment indices. The full VAST model grid is shown in brown." width="33%" /><img src="20231015_Quantfish_Gaichas_files/figure-html/maps-2.png" alt="Maps of key areas for Bluefish assessment indices. The full VAST model grid is shown in brown." width="33%" /><img src="20231015_Quantfish_Gaichas_files/figure-html/maps-3.png" alt="Maps of key areas for Bluefish assessment indices. The full VAST model grid is shown in brown." width="33%" /> <p class="caption">Maps of key areas for Bluefish assessment indices. The full VAST model grid is shown in brown.</p> </div> Indices for aggregate small pelagics from piscivore stomachs can be calculated for any subset of the full model domain. Bias correction of the resulting indices is then applied ([Thorson, et al., 2016](https://www.sciencedirect.com/science/article/pii/S0165783615301399)). ??? 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 the`rnaturalearth` R package. This buffer was then intersected with the current `FishStatsUtils::northwest_atlantic_grid` built into VAST and saved using code [here](https://github.com/sgaichas/bluefishdiet/blob/main/VASTcovariates_updatedPreds_sst_3mi.Rmd#L49-L94). 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](https://sgaichas.github.io/bluefishdiet/VASTcovariates_finalmodbiascorrect_3misurvstrat.html). --- background-image: url("https://github.com/NOAA-EDAB/forageindex/raw/main/pyindex/allagg_fall_500_lennosst_ALLsplit_biascorrect/ln_density-predicted.png") background-size: 560px background-position: right top ## Results: Fall Forage Index .pull-left[ <img src="20231015_Quantfish_Gaichas_files/figure-html/fallall-1.png" width="504" /> VAST estimated Fall forage biomass density → ] .pull-right[ ] ??? --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/cropped_ln_density-predicted.png") background-size: 600px background-position: right ## Comparisons: Spatial .pull-left[ Forage fish habitat occupancy, <a name=cite-friedland_forage_2023></a>([Friedland, et al., 2023](https://onlinelibrary.wiley.com/doi/abs/10.1002/mcf2.10230))  .contrib[ Fig 4. Mean occupancy habitats at the 20% (light blue) and 80% (dark blue) quantile thresholds across forage species; gray shows the model extent. Taxa with autumn models include (D) Round Herring, (E) longfin inshore squid, (F) Atlantic Chub Mackerel, (G) Spanish Sardine, (H) Butterfish, and (I) Atlantic Thread Herring. Offshore wind lease areas are outlined in red. The dashed line marks the 100-m depth contour. ] ] .pull-right[ ] --- ## Comparisons: Design based survey aggregate forage .pull-left[ <img src="20231015_Quantfish_Gaichas_files/figure-html/tscomp-1.png" width="504" /> ] .pull-right[ <img src="20231015_Quantfish_Gaichas_files/figure-html/corr-1.png" width="504" /> ] --- ## What drives differences? .pull-left[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/sppprop-1.png" alt="Comparison of raw proportional species composition from all predator stomachs combined (source = stomach, red) to the raw proportional species composition from bottom trawl survey sampling (source = survey, black) in the model domain forall regions and seasons from 1985-2021." width="504" /> <p class="caption">Comparison of raw proportional species composition from all predator stomachs combined (source = stomach, red) to the raw proportional species composition from bottom trawl survey sampling (source = survey, black) in the model domain forall regions and seasons from 1985-2021.</p> </div> ] .pull-right[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/silver-1.png" alt="Proportion silver hake in stomach contents data (red) and survey biomass sampling (black)." width="504" /> <p class="caption">Proportion silver hake in stomach contents data (red) and survey biomass sampling (black).</p> </div> ] --- ## What drives differences? .pull-left[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/sandlance-1.png" alt="Proportion sandlance species in stomach contents data (red) and survey biomass sampling (black)." width="504" /> <p class="caption">Proportion sandlance species in stomach contents data (red) and survey biomass sampling (black).</p> </div> ] .pull-right[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/unmanagedprop-1.png" alt="Proportion unmanaged forage groups in stomach contents (red) and survey sampling (black)." width="504" /> <p class="caption">Proportion unmanaged forage groups in stomach contents (red) and survey sampling (black).</p> </div> ] --- ## Prey exclusion sensitivities: minor prey (left) and major prey (herring, silver hake, makerel; right) .pull-left[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/preycut1-1.png" alt="Fall forage index trends using different prey cut-offs." width="504" /> <p class="caption">Fall forage index trends using different prey cut-offs.</p> </div> ] .pull-right[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/noassessedprey1-1.png" alt="Fall forage index trends using different prey groups." width="504" /> <p class="caption">Fall forage index trends using different prey groups.</p> </div> ] --- ## Predator exclusion sensitivities: low sample predators (left) vs high sample predators (right) .pull-left[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/comptrend1-1.png" alt="Trend comparison between fall forage indices using different predator groups: exclude predators with low sampling (fourspot flounder, longfin squid) vs. full predator list (current method)." width="504" /> <p class="caption">Trend comparison between fall forage indices using different predator groups: exclude predators with low sampling (fourspot flounder, longfin squid) vs. full predator list (current method).</p> </div> ] .pull-right[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/comptrend3-1.png" alt="Trend comparison between fall forage indices using different predator groups: exclude predators with high sampling (white hake, spiny dogfish) vs. full predator list (current method)." width="504" /> <p class="caption">Trend comparison between fall forage indices using different predator groups: exclude predators with high sampling (white hake, spiny dogfish) vs. full predator list (current method).</p> </div> ] --- ## Integrating into stock assessment .pull-left[ A new bluefish stock assessment was implemented using the Woods Hole Assessment Model (WHAM) <a name=cite-stock_woods_2021></a>([Stock, et al., 2021](https://www.sciencedirect.com/science/article/pii/S0165783621000953)). WHAM is a state space stock assessment model framework: https://timjmiller.github.io/wham/ Forage fish indices were explored as covariates on catchability for the fishery independent bottom trawl surveys (R/V Bigelow), but did not improve the assessment. However, the application of the forage fish index to the recreational catch per angler catchability was successful when implemented as an autoregressive process over the time-series with WHAM estimating the standard error. ] .pull-right[ <div class="figure"> <img src="20231015_Quantfish_Gaichas_files/figure-html/WHAMq-1.png" alt="Fall state waters forage index fit as a catchabilty covariate within the bluefish assessment model (top), with resulting catchability, q, for the recreational fishery catch per unit effort (MRIP) index (bottom)." width="504" /> <p class="caption">Fall state waters forage index fit as a catchabilty covariate within the bluefish assessment model (top), with resulting catchability, q, for the recreational fishery catch per unit effort (MRIP) index (bottom).</p> </div> ] ??? 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 --- .pull-left[ ## Do prey affect bluefish availability? Yes, for the recreational CPUE index <template id="05b09eee-bf11-41a1-b6ab-6cc5efd9bc95"><style> .tabwid table{ border-spacing:0px !important; border-collapse:collapse; line-height:1; margin-left:auto; margin-right:auto; border-width: 0; display: table; margin-top: 1.275em; margin-bottom: 1.275em; border-color: transparent; } .tabwid_left table{ margin-left:0; } .tabwid_right table{ margin-right:0; } .tabwid td { padding: 0; } .tabwid a { text-decoration: none; } .tabwid thead { background-color: transparent; } .tabwid tfoot { background-color: transparent; } .tabwid table tr { background-color: transparent; } </style><div class="tabwid"><style>.cl-57112fc0{}.cl-56e24b42{font-family:'Helvetica';font-size:11pt;font-weight:normal;font-style:normal;text-decoration:none;color:rgba(0, 0, 0, 1.00);background-color:transparent;}.cl-56e2641a{margin:0;text-align:left;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-56e26424{margin:0;text-align:right;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);padding-bottom:5pt;padding-top:5pt;padding-left:5pt;padding-right:5pt;line-height: 1;background-color:transparent;}.cl-56e2a010{width:54pt;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-56e2a01a{width:54pt;background-color:transparent;vertical-align: middle;border-bottom: 0 solid rgba(0, 0, 0, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-56e2a01b{width:54pt;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-56e2a024{width:54pt;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 0 solid rgba(0, 0, 0, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-56e2a02e{width:54pt;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 2pt solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}.cl-56e2a02f{width:54pt;background-color:transparent;vertical-align: middle;border-bottom: 2pt solid rgba(102, 102, 102, 1.00);border-top: 2pt solid rgba(102, 102, 102, 1.00);border-left: 0 solid rgba(0, 0, 0, 1.00);border-right: 0 solid rgba(0, 0, 0, 1.00);margin-bottom:0;margin-top:0;margin-left:0;margin-right:0;}</style><table class='cl-57112fc0'><caption class="Table Caption"> Bluefish stock assessment model fit and retrospective diagnostics with (model = ecov_on) and without (model = ecov_off) the fall StateWaters forage index included as a catchability covariate on the recreational fishery catch per unit effort index. Mohn's rho values (rho) indicate retrospective performance for recruitment (R), spawning stock biomass (SSB) and fully selected fishing mortality (Fbar). </caption><thead><tr style="overflow-wrap:break-word;"><td class="cl-56e2a02f"><p class="cl-56e2641a"><span class="cl-56e24b42">model</span></p></td><td class="cl-56e2a02e"><p class="cl-56e26424"><span class="cl-56e24b42">dAIC</span></p></td><td class="cl-56e2a02e"><p class="cl-56e26424"><span class="cl-56e24b42">AIC</span></p></td><td class="cl-56e2a02e"><p class="cl-56e26424"><span class="cl-56e24b42">rho_R</span></p></td><td class="cl-56e2a02e"><p class="cl-56e26424"><span class="cl-56e24b42">rho_SSB</span></p></td><td class="cl-56e2a02e"><p class="cl-56e26424"><span class="cl-56e24b42">rho_Fbar</span></p></td></tr></thead><tbody><tr style="overflow-wrap:break-word;"><td class="cl-56e2a01a"><p class="cl-56e2641a"><span class="cl-56e24b42">ecov_on</span></p></td><td class="cl-56e2a010"><p class="cl-56e26424"><span class="cl-56e24b42">0.0</span></p></td><td class="cl-56e2a010"><p class="cl-56e26424"><span class="cl-56e24b42">3,230.6</span></p></td><td class="cl-56e2a010"><p class="cl-56e26424"><span class="cl-56e24b42">-0.0136</span></p></td><td class="cl-56e2a010"><p class="cl-56e26424"><span class="cl-56e24b42">0.0880</span></p></td><td class="cl-56e2a010"><p class="cl-56e26424"><span class="cl-56e24b42">-0.0641</span></p></td></tr><tr style="overflow-wrap:break-word;"><td class="cl-56e2a024"><p class="cl-56e2641a"><span class="cl-56e24b42">ecov_off</span></p></td><td class="cl-56e2a01b"><p class="cl-56e26424"><span class="cl-56e24b42">5.6</span></p></td><td class="cl-56e2a01b"><p class="cl-56e26424"><span class="cl-56e24b42">3,236.2</span></p></td><td class="cl-56e2a01b"><p class="cl-56e26424"><span class="cl-56e24b42">0.0104</span></p></td><td class="cl-56e2a01b"><p class="cl-56e26424"><span class="cl-56e24b42">0.1301</span></p></td><td class="cl-56e2a01b"><p class="cl-56e26424"><span class="cl-56e24b42">-0.0962</span></p></td></tr></tbody></table></div></template> <div class="flextable-shadow-host" id="48bc8269-5428-403a-907d-563c6356194c"></div> <script> var dest = document.getElementById("48bc8269-5428-403a-907d-563c6356194c"); var template = document.getElementById("05b09eee-bf11-41a1-b6ab-6cc5efd9bc95"); var caption = template.content.querySelector("caption"); if(caption) { caption.style.cssText = "display:block;text-align:center;"; var newcapt = document.createElement("p"); newcapt.appendChild(caption) dest.parentNode.insertBefore(newcapt, dest.previousSibling); } var fantome = dest.attachShadow({mode: 'open'}); var templateContent = template.content; fantome.appendChild(templateContent); </script> ] .pull-right[ *Inclusion of the forage fish index improved model fit.* <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-11-1.png" width="504" /> ] The recreational index is important in scaling the biomass results, and the lower availability at the end of the time-series led to <span style="color:#cd5c5c;">higher biomass estimates from the assessment including forage fish.</span> --- ## Including ecosystem information for fish stocks: Alaska Ecosystem and Socioeconomic Profiles .pull-left[  Ecosystem and Socioeconomic Profiles (ESPs) ] .pull-right[  ] .footnote[ Pacific cod example from Alaska: https://www.fisheries.noaa.gov/alaska/2021-alaska-fisheries-science-center-year-review and https://apps-afsc.fisheries.noaa.gov/refm/docs/2021/GOApcod.pdf ] ??? 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. --- ## Northeast US Bluefish *Pomatomus saltatrix* ESP, reviewed December 2022 .pull-left[  .contrib[Figure and table by Abigail Tyrell] ] .pull-right[  ] The full ESP document is available as a working paper from the [stock assessment data portal](https://apps-nefsc.fisheries.noaa.gov/saw/sasi_files.php?year=2022&species_id=32&stock_id=6&review_type_id=5&info_type_id=5&map_type_id=&filename=WP%2001%20Tyrell%20etAl%202022%20-%20ESP.pdf) ## Ecosystem considerations can apply to observation as well as population processes ??? --- ## What have we learned? New England Atlantic Herring as forage <a name=cite-deroba_dream_2018></a>([Deroba, et al., 2018](http://www.nrcresearchpress.com/doi/10.1139/cjfas-2018-0128)). .pull-left-30[ Complex food web, generalist predators - Weak individual predator response to many herring harvest control rules - (Stronger predator response to changing herring growth) - Herring is one of several important prey (36-40 in plot) - Assessing multiple prey together will likely show stronger effects on predators ] .pull-right-70[  .contrib[The original "horrendogram" <a name=cite-link_does_2002></a>([Link, 2002](https://www.int-res.com/abstracts/meps/v230/p1-9/))] ] --- ## Extensions: Can forage indices link zooplankton and fish productivity? .pull-left[ <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-12-1.png" width="504" /> .left[ <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-13-1.png" width="90%" /> ] ] .pull-right[ #### MAB .center[ <img src="20231015_Quantfish_Gaichas_files/figure-html/MAforagebio-1.png" width="70%" /> ] #### NE <img src="20231015_Quantfish_Gaichas_files/figure-html/NEforagebio-1.png" width="100%" /> ] ??? 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 --- ## Mid-Atlantic Council Ecosystem Risk Assessment: New indices **Food web (Council-managed predators): change to "Food web: Prey availability"** .contrib[ This element is applied at the species level. Fish stocks and protected species stocks are managed using single species approaches, but fish and protected species stocks exist within a food web of predator and prey interactions. This element is one of two separating food web risks to achieving OY for Council managed species from two sources. This first element assesses prey availability for each species, and the second food web risk element assesses predation pressure on each species (see next element). ] **Proposed definition**: Risk of not achieving OY for Council managed species due to availability of prey. Indicators: .pull-left[ <img src="20231015_Quantfish_Gaichas_files/figure-html/unnamed-chunk-14-1.png" width="120%" /> ] .pull-right[ <img src="20231015_Quantfish_Gaichas_files/figure-html/MAforageind-1.png" width="504" /> ] ??? Potential risk criteria: | Risk Level | Definition | |:-------------------|:--------------------------------------------------------------------| | Low | Prey availability high (not limiting) and/or good fish condition past 5 years | | Low-Moderate | Aggregate prey available for this species has stable or increasing trend, moderate condition | | Moderate-High | Aggregate prey available for this species has significant decreasing trend, poor condition | | High | Managed species highly dependent on prey with limited and declining availability, poor condition | --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/SOE-MA-draft-03.16.23_Page_3.png") background-size: 500px background-position: right ## Next steps Update for 2024 ecosystem reporting Benthic index for food web modeling, risk assessment Improvements/exploration: * Add predator functional response? * Multivariate model tracking prey groups? * Your ideas here! --- ## Thank you! References .contrib[ <a name=bib-collette_bigelow_2002></a>[Collette, B. B. et al.](#cite-collette_bigelow_2002) (2002). _Bigelow and Schroeder's Fishes of the Gulf of Maine, Third Edition_. 3rd ed. edition. Washington, DC: Smithsonian Books. ISBN: 978-1-56098-951-6. <a name=bib-deroba_dream_2018></a>[Deroba, J. J. et al.](#cite-deroba_dream_2018) (2018). "The dream and the reality: meeting decision-making time frames while incorporating ecosystem and economic models into management strategy evaluation". In: _Canadian Journal of Fisheries and Aquatic Sciences_. ISSN: 0706-652X. DOI: [10.1139/cjfas-2018-0128](https://doi.org/10.1139%2Fcjfas-2018-0128). URL: [http://www.nrcresearchpress.com/doi/10.1139/cjfas-2018-0128](http://www.nrcresearchpress.com/doi/10.1139/cjfas-2018-0128) (visited on Jul. 20, 2018). <a name=bib-friedland_forage_2023></a>[Friedland, K. D. et al.](#cite-friedland_forage_2023) (2023). "Forage Fish Species Prefer Habitat within Designated Offshore Wind Energy Areas in the U.S. Northeast Shelf Ecosystem". En. In: _Marine and Coastal Fisheries_ 15.2. \_ eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mcf2.10230, p. e10230. ISSN: 1942-5120. DOI: [10.1002/mcf2.10230](https://doi.org/10.1002%2Fmcf2.10230). URL: [https://onlinelibrary.wiley.com/doi/abs/10.1002/mcf2.10230](https://onlinelibrary.wiley.com/doi/abs/10.1002/mcf2.10230) (visited on Aug. 07, 2023). <a name=bib-link_does_2002></a>[Link, J.](#cite-link_does_2002) (2002). "Does food web theory work for marine ecosystems?" En. In: _Marine Ecology Progress Series_ 230, pp. 1-9. ISSN: 0171-8630, 1616-1599. DOI: [10.3354/meps230001](https://doi.org/10.3354%2Fmeps230001). URL: [https://www.int-res.com/abstracts/meps/v230/p1-9/](https://www.int-res.com/abstracts/meps/v230/p1-9/) (visited on Nov. 04, 2022). <a name=bib-ng_predator_2021></a>[Ng, E. L. et al.](#cite-ng_predator_2021) (2021). "Predator stomach contents can provide accurate indices of prey biomass". In: _ICES Journal of Marine Science_ 78.3, pp. 1146-1159. ISSN: 1054-3139. DOI: [10.1093/icesjms/fsab026](https://doi.org/10.1093%2Ficesjms%2Ffsab026). URL: [https://doi.org/10.1093/icesjms/fsab026](https://doi.org/10.1093/icesjms/fsab026) (visited on Sep. 01, 2021). <a name=bib-reynolds_daily_2007></a>[Reynolds, R. W. et al.](#cite-reynolds_daily_2007) (2007). "Daily High-Resolution-Blended Analyses for Sea Surface Temperature". EN. In: _Journal of Climate_ 20.22. Publisher: American Meteorological Society Section: Journal of Climate, pp. 5473-5496. ISSN: 0894-8755, 1520-0442. DOI: [10.1175/2007JCLI1824.1](https://doi.org/10.1175%2F2007JCLI1824.1). URL: [https://journals.ametsoc.org/view/journals/clim/20/22/2007jcli1824.1.xml](https://journals.ametsoc.org/view/journals/clim/20/22/2007jcli1824.1.xml) (visited on Aug. 01, 2022). <a name=bib-stock_woods_2021></a>[Stock, B. C. et al.](#cite-stock_woods_2021) (2021). "The Woods Hole Assessment Model (WHAM): A general state-space assessment framework that incorporates time- and age-varying processes via random effects and links to environmental covariates". En. In: _Fisheries Research_ 240, p. 105967. ISSN: 0165-7836. DOI: [10.1016/j.fishres.2021.105967](https://doi.org/10.1016%2Fj.fishres.2021.105967). URL: [https://www.sciencedirect.com/science/article/pii/S0165783621000953](https://www.sciencedirect.com/science/article/pii/S0165783621000953) (visited on May. 26, 2021). <a name=bib-thorson_guidance_2019></a>[Thorson, J. T.](#cite-thorson_guidance_2019) (2019). "Guidance for decisions using the Vector Autoregressive Spatio-Temporal (VAST) package in stock, ecosystem, habitat and climate assessments". En. In: _Fisheries Research_ 210, pp. 143-161. ISSN: 0165-7836. DOI: [10.1016/j.fishres.2018.10.013](https://doi.org/10.1016%2Fj.fishres.2018.10.013). URL: [http://www.sciencedirect.com/science/article/pii/S0165783618302820](http://www.sciencedirect.com/science/article/pii/S0165783618302820) (visited on Feb. 24, 2020). <a name=bib-thorson_comparing_2017></a>[Thorson, J. T. et al.](#cite-thorson_comparing_2017) (2017). "Comparing estimates of abundance trends and distribution shifts using single- and multispecies models of fishes and biogenic habitat". In: _ICES Journal of Marine Science_ 74.5, pp. 1311-1321. ISSN: 1054-3139. DOI: [10.1093/icesjms/fsw193](https://doi.org/10.1093%2Ficesjms%2Ffsw193). URL: [https://doi.org/10.1093/icesjms/fsw193](https://doi.org/10.1093/icesjms/fsw193) (visited on Nov. 04, 2021). <a name=bib-thorson_implementing_2016></a>[Thorson, J. T. et al.](#cite-thorson_implementing_2016) (2016). "Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples". En. In: _Fisheries Research_ 175, pp. 66-74. ISSN: 0165-7836. DOI: [10.1016/j.fishres.2015.11.016](https://doi.org/10.1016%2Fj.fishres.2015.11.016). URL: [https://www.sciencedirect.com/science/article/pii/S0165783615301399](https://www.sciencedirect.com/science/article/pii/S0165783615301399) (visited on Jul. 29, 2022). ] .footnote[ Slides available at https://noaa-edab.github.io/presentations Contact: <Sarah.Gaichas@noaa.gov> ]