class: right, middle, my-title, title-slide .title[ # Zooplankton indices as covariates in WHAM ] .subtitle[ ## Herring Research Track <br /> 2 October 2024 ] .author[ ### Sarah Gaichas, Jon Deroba, Adelle Molina ] --- class: top, left # Does food drive recruitment of Atlantic herring? ## Atlantic herring, *Clupea harengus* .pull-left[  ] .pull-right[ .large[ "The herring is a plankton feeder.... Examination of 1,500 stomachs showed that adult herring near Eastport were living solely on copepods and on pelagic euphausiid shrimps (*Meganyctiphanes norwegica*), fish less than 4 inches long depending on the former alone, while the larger herring were eating both." <a name=cite-collette_bigelow_2002></a>([Collette et al., 2002](#bib-collette_bigelow_2002)) ] ] ??? When first hatched, and before the disappearance of the yolk sac, the larvae (European) feed on larval snails and crustaceans, on diatoms, and on peridinians, but they soon begin taking copepods, and depend exclusively on these for a time after they get to be 12 mm. long, especially on the little Pseudocalanus elongatus. As they grow older they feed more and more on the larger copepods and amphipods, pelagic shrimps, and decapod crustacean larvae. --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/AherringConceptualMod.png") background-size: 1070px background-position: bottom ## Which indicators are potential covariates for recruitment? --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/AherringBRT_rec.png") background-size: 800px background-position: right ## Which indicators are potential covariates for recruitment? .pull-left-30[ Boosted regression tree (Molina 2024) investigated relationships between environmental indicators and Atlantic herring recruitment estimated in the assessment. Larval and juvenile food (zooplankton), egg predation, and temperature always highest influence ] .pull-right-70[ ] --- ## Spatial partitioning: zooplankton trends at multiple scales <div class="figure"> <img src="20241002_Zoopcovariates_Gaichas_files/figure-html/maps-1.png" alt="Maps of key areas for Herring assessment indices. The full VAST model grid is shown in brown." width="33%" /><img src="20241002_Zoopcovariates_Gaichas_files/figure-html/maps-2.png" alt="Maps of key areas for Herring assessment indices. The full VAST model grid is shown in brown." width="33%" /><img src="20241002_Zoopcovariates_Gaichas_files/figure-html/maps-3.png" alt="Maps of key areas for Herring assessment indices. The full VAST model grid is shown in brown." width="33%" /> <p class="caption">Maps of key areas for Herring assessment indices. The full VAST model grid is shown in brown.</p> </div> ??? 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). --- ## Exploratory zooplankton indices in the stock assessment .pull-left[ Use as a basis the new herring stock assessment in Woods Hole Assessment Model (WHAM) <a name=cite-stock_woods_2021></a>([Stock et al., 2021](https://www.sciencedirect.com/science/article/pii/S0165783621000953)). We are using the `devel` version of WHAM: https://github.com/timjmiller/wham/tree/devel Model [mm192](https://drive.google.com/drive/folders/1sQdDsfdnVbiiY4X7Rgr-fvegwT7Fa1Az?usp=drive_link) is our starting point. Zooplankton indices were explored as covariates on herring recruitment. Recruitment is modeled as deviations from the "recruitment scaling parameter", leaving one option for modeling effects of covariates on recruitment: "controlling". A "controlling" recruitment covariate results in a time-varying recruitment scaling parameter. ] .pull-right[ We explored indices with different zooplankton groups, seasons, and regions according to herring life history and results from the boosted regression tree: * Jan-Jun (Spring) large copepods in spring herring BTS strata with lag-0 to represent food for pre-recruit juveniles * Jul-Dec (Fall) small copepods in fall herring BTS strata with lag-1 to represent food for larvae in general * Sep-Feb small copepods in herring larval area with lag-1 to represent food for larvae more specifically * Combinations of large and small copepod covariates above ] ??? --- ## Implementing each index We evaluated * Options for covariate input (millions of cells vs. log(cells), VAST estimated SE vs. WHAM estimated SE) * Options for covariate observation model ("rw" vs. "ar1") * Options for recruitment link ("none" vs. "controlling-linear" with lag-0 for large copepods and lag-1 for small) Short story: * Models with covariates input on the log scale generally converged * Models with WHAM estimated covariate SE ("est_1") generally converged * Under the above conditions, most models with and without the recruitment link converged for all covariates * Models with the Jan-Jun (Spring) large copepods covariate also converged with input as millions of cells and VAST estimated SE * I'm still figuring out where to find all the diagnostics in WHAM, so "converged" may not be "a good model" [Way too much detail including false starts](https://noaa-edab.github.io/zooplanktonindex/WHAMcovariate_tests.html) --- ## Results: Spring large copepods covariate: model summary *Models with no covariates had slightly better AIC than comparable models with recruitment links* ``` Model ecov_process ecov_how ecovdat conv pdHess NLL dAIC AIC rho_R rho_SSB rho_Fbar m10 ar1 none logmean-est_1 TRUE TRUE -1793.611 0 -3325.2 0.8684 0.5901 -0.2314 m14 ar1 controlling-lag-0-linear logmean-est_1 TRUE TRUE -1794.325 0.6 -3324.6 0.8999 0.5844 -0.2296 m2 rw none logmean-est_1 TRUE TRUE -1790.837 3.5 -3321.7 0.8683 0.5901 -0.2314 m6 rw controlling-lag-0-linear logmean-est_1 TRUE TRUE -1791.227 4.7 -3320.5 0.8844 0.5846 -0.23 m11 ar1 none meanmil-logsigmil TRUE TRUE -1509.570 566.1 -2759.1 0.8682 0.5901 -0.2314 m15 ar1 controlling-lag-0-linear meanmil-logsigmil TRUE TRUE -1510.349 566.5 -2758.7 0.9081 0.592 -0.2379 m3 rw none meanmil-logsigmil TRUE TRUE -1506.709 569.8 -2755.4 0.8594 0.5907 -0.2362 m7 rw controlling-lag-0-linear meanmil-logsigmil TRUE TRUE -1507.424 570.4 -2754.8 0.8104 0.594 -0.2448 ``` --- ## Results: Spring large copepods covariate: logscale ar1 diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Spring large copepods covariate: logscale ar1 recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.797; lgCopeSpring2 beta_1 is -0.407, CI -1.063, 0.25 --- ## Results: Spring large copepods covariate: logscale rw diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Spring large copepods covariate: logscale rw recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.804; lgCopeSpring2 beta_1 is -0.45, CI -1.438, 0.538 --- ## Results: Spring large copepods covariate: natural scale ar1 diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Spring large copepods covariate: natural scale ar1 recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.791; lgCopeSpring2 beta_1 is -4.5\times 10^{-4}, CI -0.00128, 3.8\times 10^{-4} --- ## Results: Spring large copepods covariate: natural scale rw diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Spring large copepods covariate: natural scale rw recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.793; lgCopeSpring2 beta_1 is -4.3\times 10^{-4}, CI -0.00126, 4\times 10^{-4} --- ## Results: Fall small copepods covariate: model summary *Models with no covariates had slightly better AIC than the ar1 model with recruitment links* ``` Model ecov_process ecov_how ecovdat conv pdHess NLL dAIC AIC rho_R rho_SSB rho_Fbar m10 ar1 none logmean-est_1 TRUE TRUE -1797.177 0 -3332.4 0.8682 0.5901 -0.2314 m2 rw none logmean-est_1 TRUE TRUE -1796.175 0.1 -3332.3 0.8682 0.5901 -0.2314 m14 ar1 controlling-lag-1-linear logmean-est_1 TRUE TRUE -1797.931 0.5 -3331.9 0.8486 0.6001 -0.2333 m6 rw controlling-lag-1-linear logmean-est_1 TRUE FALSE -1794.370 --- --- --- --- --- ``` --- ## Results: Fall small copepods covariate: logscale ar1 diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Fall small copepods covariate: logscale ar1 recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.79; smcopeFall2 beta_1 is -1.013, CI -2.715, 0.689 --- ## Results: Sep-Feb small copepods in herring larval area covariate: model summary *Model with ar1 covariate had slightly better AIC than models without recruitment links* ``` Model ecov_process ecov_how ecovdat conv pdHess NLL dAIC AIC rho_R rho_SSB rho_Fbar m14 ar1 controlling-lag-1-linear logmean-est_1 TRUE TRUE -1791.900 0 -3319.8 0.814 0.5966 -0.2327 m2 rw none logmean-est_1 TRUE TRUE -1789.657 0.5 -3319.3 0.8683 0.5901 -0.2314 m10 ar1 none logmean-est_1 TRUE TRUE -1790.469 0.9 -3318.9 0.8683 0.5901 -0.2314 m6 rw controlling-lag-1-linear logmean-est_1 TRUE FALSE -1785.746 --- --- --- --- --- ``` --- ## Results: Sep-Feb small copepods in herring larval area covariate: logscale ar1 diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Sep-Feb small copepods in herring larval area covariate: logscale ar1 recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.77; smcopeSepFeb2 beta_1 is -0.85, CI -1.883, 0.182 --- ## Alternate: Sep-Feb small copepods in fall herring survey strata covariate *Both rw and ar1 models worked with covariates, rw better fit* ``` Model ecov_process ecov_how ecovdat conv pdHess NLL dAIC AIC rho_R rho_SSB rho_Fbar m6 rw controlling-lag-1-linear logmean-est_1 TRUE TRUE -1791.981 0.0 -3322.0 0.7592 0.6023 -0.2327 m14 ar1 controlling-lag-1-linear logmean-est_1 TRUE TRUE -1792.464 1.1 -3320.9 0.7943 0.5971 -0.2329 m2 rw none logmean-est_1 TRUE TRUE -1790.196 1.6 -3320.4 0.8682 0.5901 -0.2314 m10 ar1 none logmean-est_1 TRUE TRUE -1790.734 2.5 -3319.5 0.8683 0.5901 -0.2314 ``` --- ## Results: Sep-Feb small copepods in herring larval area covariate: logscale rw diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Sep-Feb small copepods in herring larval area covariate: logscale rw recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.762; smcopeSepFeb2 beta_1 is -1.01, CI -2.11, 0.089 --- ## Results: Sep-Feb small copepods in herring larval area covariate: logscale ar1 diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Sep-Feb small copepods in herring larval area covariate: logscale ar1 recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.757; smcopeSepFeb2 beta_1 is -0.976, CI -2.044, 0.093 --- ## Results: Both Spring large copepods and Sep-Feb small copepods in herring larval area: model summary *Models with and without ar1 covariates linked to recruitment had the same AIC* ``` Model ecov_process ecov_how ecovdat conv pdHess NLL dAIC AIC rho_R rho_SSB rho_Fbar m2 ar1 none logmean-est_1 TRUE TRUE -1771.328 0.0 -3272.7 0.8682 0.5901 -0.2314 m4 ar1 controlling-lag-0/1-linear logmean-est_1 TRUE TRUE -1773.347 0.0 -3272.7 0.8529 0.5922 -0.2313 m1 rw none logmean-est_1 TRUE TRUE -1767.742 3.2 -3269.5 0.8683 0.5901 -0.2314 m3 rw controlling-lag-0/1-linear logmean-est_1 TRUE TRUE -1769.627 3.4 -3269.3 0.8001 0.5976 -0.2321 ``` --- ## Results: Both Spring large copepods and Sep-Feb small copepods: logscale rw diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Both Spring large copepods and Sep-Feb small copepods: logscale rw diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Both Spring large copepods and Sep-Feb small copepods: logscale rw recruitment .pull-left[  ] .pull-right[  ] Without covariates, recruitment variance is 0.823, and with is 0.755; smcopeSepFeb2 beta_1 is -0.88, CI -1.929, 0.169 ??? --- ## Implications for reference points: Sep-Feb small copepods in herring larval area .pull-left[ <img src="20241002_Zoopcovariates_Gaichas_files/figure-html/unnamed-chunk-11-1.png" width="504" /> ] .pull-right[ <img src="20241002_Zoopcovariates_Gaichas_files/figure-html/unnamed-chunk-12-1.png" width="504" /> ] --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/IEA/HerringRT_2025/WHAM/mm192_smcopeSepFeb2/compare_rel_status_kobe.png") background-size: 800px background-position: right ## Implications for reference points Sep-Feb small copepods m14 Same pattern by area * herring larval * fall survey --- ## Results: Duration of optimal larval temperature, Sept-Dec: model summary ``` Model ecov_process ecov_how ecovdat conv pdHess NLL dAIC AIC rho_R rho_SSB rho_Fbar m4 rw controlling-lag-1-linear logmean-est_1 TRUE TRUE -1827.543 0 -3393.1 0.6543 0.5984 -0.2323 m3 rw none logmean-est_1 TRUE TRUE -1826.370 0.4 -3392.7 0.8682 0.5901 -0.2314 m8 ar1 controlling-lag-1-linear logmean-est_1 TRUE TRUE -1826.627 3.8 -3389.3 0.6714 0.598 -0.2324 m7 ar1 none logmean-est_1 TRUE TRUE -1825.511 4.1 -3389 0.8682 0.5901 -0.2314 m2 rw controlling-lag-1-linear mean-est_1 TRUE TRUE -1650.465 354.2 -3038.9 0.6473 0.5976 -0.2321 m1 rw none mean-est_1 TRUE TRUE -1649.265 354.6 -3038.5 0.8594 0.5907 -0.2362 m6 ar1 controlling-lag-1-linear mean-est_1 TRUE TRUE -1649.607 357.9 -3035.2 0.6643 0.5971 -0.2322 m5 ar1 none mean-est_1 TRUE FALSE -1644.890 --- --- --- --- --- ``` --- ## Results: Duration of optimal larval temperature, Sept-Dec: logscale rw diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Duration of optimal larval temperature, Sept-Dec: logscale rw recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.79; LarvalTempDuration beta_1 is 2.066, CI -0.657, 4.789 --- ## Results: Duration of optimal larval temperature, Sept-Dec: logscale ar1 diagnostics .pull-left[  ] .pull-right[  ] --- ## Results: Duration of optimal larval temperature, Sept-Dec: logscale ar1 recruitment .pull-left[  ] .pull-right[  ] Without covariate, recruitment variance is 0.823, and with is 0.791; LarvalTempDuration beta_1 is 2.032, CI -0.711, 4.775 --- ## Implications for reference points: Duration of optimal larval temperature, Sept-Dec .pull-left[ <img src="20241002_Zoopcovariates_Gaichas_files/figure-html/unnamed-chunk-15-1.png" width="504" /> ] .pull-right[ <img src="20241002_Zoopcovariates_Gaichas_files/figure-html/unnamed-chunk-16-1.png" width="504" /> ] --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/IEA/HerringRT_2025/WHAM/mm192_LarvalTempDuration/compare_png/compare_rel_status_kobe.png") background-size: 800px background-position: right ## Implications for reference points --- # Discussion? Thoughts? ## 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-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). ] .footnote[ Slides available at https://noaa-edab.github.io/presentations Contact: <Sarah.Gaichas@noaa.gov> ]