class: right, middle, my-title, title-slide .title[ # Georges Bank multispecies model <br />keyrun project ] .subtitle[ ## Overview <br />ICES WGSAM review, 10 and 14 October 2022 ] .author[ ### Sarah Gaichas, Andy Beet, Kiersten Curti, Gavin Fay, Robert Gamble, <br />Ron Klasky, Sean Lucey, Maria Cristina Perez, and Howard Townsend ] --- class: top, left background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/ecomods.png") background-size: contain ??? Alt text: Fisheries models range from single and multispecies models to full ecosystem models --- ## Why include species interactions? *Ignore predation at your peril: results from multispecies state-space modeling * <a name=cite-trijoulet_performance_2020></a>([Trijoulet, et al., 2020a](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.13515)) >Ignoring trophic interactions that occur in marine ecosystems induces bias in stock assessment outputs and results in low model predictive ability with subsequently biased reference points. .pull-left-40[  EM1: multispecies state space EM2: multispecies, no process error EM3: single sp. state space, constant M EM4: single sp. state space, age-varying M *note difference in scale of bias for single species!* ] .pull-right-60[  ] ??? This is an important paper both because it demonstrates the importance of addressing strong species interactions, and it shows that measures of fit do not indicate good model predictive performance. Ignoring process error caused bias, but much smaller than ignoring species interactions. See also Vanessa's earlier paper evaluating diet data interactions with multispecies models --- # EBFM Objectives in the Northeast US * EBFM Objective 1: what happens with all the species in the region under a certain management regime? + Apply a full system model to assess "side effects" of target species management + Ability to implement fishing and biological scenarios + Hypothesis testing and MSE framework desirable * EBFM Objective 2: how well do multispecies models perform for assessment? + Consider alternative model structures + Biomass dynamics + Size structured + Age structured + Evaluate data availability for each structure + Evaluate estimation performance of each structure + Evaluate uncertainty and sensitivity + Evaluate feasibility of developing and using multi-model inference ??? MS-Keyrun model development and testing objectives are based on general ecosystem based management questions as well as specific discussions regarding EBFM development in New England. We will use this as an opportunity to address questions about the effects of management on the broader ecosystem, and about performance of assessment tools. --- background-image: url("https://raw.githubusercontent.com/NOAA-EDAB/presentations/master/docs/EDAB_images/EPU_Designations_Map.jpg") background-size: 650px background-position: right ## Place-based approach .pull-left-40[ "Place-based" means a common spatial footprint based on ecological production, which contrasts with the current species-based management system of stock-defined spatial footprints that differ by stock and species. The medium blue area in the map is Georges Bank as defined by NEFSC trawl survey strata. SOE = State of the Ecosystem report *The input data for this project differs from the input data for most current stock assessments, and the results of these multispecies assessments are not directly comparable with current single species assessments.* ] .pull-right-60[ <!----> ] ??? The project currently implements several place-based multispecies assessment models and one food web model. "Place-based" means a common spatial footprint based on ecological production, which contrasts with the current species-based management system of stock-defined spatial footprints that differ by stock and species. (See <a href="stockAreas.html"> stock area comparisons</a>.) Therefore, the input data for this project differs from the input data for most current stock assessments, and the results of these multispecies assessments are not directly comparable with current single species assessments. However, similar processes can be applied to evaluate these models. Georges Bank as defined for this project uses the NEFSC bottom trawl survey strata highlighted in medium blue below, which corresponds to the spatial unit for survey-derived ecosystem indicators in the Northeast Fisheries Science Center (NEFSC) New England State of the Ecosystem (SOE) report. Orange outlines indicate the ten minute square definitions for Ecological Production Units defined by a previous analysis. --- # Objective 1: evaluate system responses to management ## [Rpath](https://github.com/NOAA-EDAB/Rpath) <a name=cite-lucey_conducting_2020></a>([Lucey, et al., 2020a](http://www.sciencedirect.com/science/article/pii/S0304380020301290)) with MSE capability <a name=cite-lucey_evaluating_2021></a>([Lucey, et al., 2021](http://www.sciencedirect.com/science/article/pii/S0165783620302976)) .pull-left[  ] .pull-right[  ] --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/Balance_concept.png") background-size: 700px background-position: right ## Food web: [Rpath](https://github.com/NOAA-EDAB/Rpath) in collaboration with AFSC .pull-left[ Species interactions: - Full predator-prey: Consumption leads to prey mortality and predator growth - Static and dynamic model components Static model: For each group, `\(i\)`, specify: Biomass `\(B\)` (or Ecotrophic Efficiency `\(EE\)`) Population growth rate `\(\frac{P}{B}\)` Consumption rate `\(\frac{Q}{B}\)` Diet composition `\(DC\)` Fishery catch `\(C\)` Biomass accumulation `\(BA\)` Im/emigration `\(IM\)` and `\(EM\)` Solving for `\(EE\)` (or `\(B\)`) for each group: `$$B_i\Big(\frac{P}{B}\Big)_i*EE_i+IM_i+BA_i=\sum_{j}\Big[ B_j\Big (\frac{Q}{B}\Big)_j*DC_{ij}\Big ]+EM_i+C_i$$` ] .pull-right[ ] ??? Predation mortality `$$M2_{ij} = \frac{DC_{ij}QB_jB_j}{B_i}$$` Fishing mortality `$$F_i = \frac{\sum_{g = 1}^n (C_{ig,land} + C_{ig,disc})}{B_i}$$` Other mortality `$$M0_i = PB_i \left( 1 - EE_i \right)$$` --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/ForagingArena.png") background-size: 380px background-position: right ## Food web: [Rpath](https://github.com/NOAA-EDAB/Rpath) in collaboration with AFSC .pull-left-70[ Dynamic model (with MSE capability): `$$\frac{dB_i}{dt} = \left(1 - A_i - U_i \right) \sum_{j} Q \left(B_i, B_j \right) - \sum_{j} Q \left( B_j, B_i \right) - M0_iB_i - C_m B_i$$` Consumption: `$$Q \left( B_i, B_j \right) = Q_{ij}^* \Bigg( \frac{V_{ij} Ypred_j}{V_{ij} - 1 + \left( 1 - S_{ij} \right) Ypred_j + S_{i} \sum_k \left( \alpha_{kj} Ypred_k \right)} \Bigg) \times \\\Bigg( \frac{D_{ij} Yprey_i^{\theta_{ij}}}{D_{ij} - 1 + \big( \left( 1 - H_{ij} \right) Yprey_i + H_{i} \sum_k \left( \beta_{ik} Yprey_k \right) \big)^{\theta_{ij}}} \Bigg)$$` Where `\(V_{ij}\)` is vulnerability, `\(D_{ij}\)` is “handling time” accounting for predator saturation, and `\(Y\)` is relative biomass which may be modified by a foraging time multiplier `\(Ftime\)`, `$$Y[pred|prey]_j = Ftime_j \frac {B_j}{B_j^*}$$` ] .pull-right-30[ ] ??? The parameters `\(S_{ij}\)` and `\(H_{ij}\)` are flags that control whether the predator density dependence `\(S_{ij}\)` or prey density dependence `\(H_{ij}\)` are affected solely by the biomass levels of the particular predator and prey, or whether a suite of other species’ biomasses in similar roles impact the relationship. For the default value for `\(S_{ij}\)` of 0 (off), the predator density dependence is only a function of that predator biomass and likewise for prey with the default value of 0 for `\(H_{ij}\)`. Values greater than 0 allow for a density-dependent effects to be affected by a weighted sum across all species for predators, and for prey. The weights `\(\alpha_{kj}\)` and `\(\beta_{kj}\)` are normalized such that the sum for each functional response (i.e. `\(\sum_k\alpha_{kj}\)` and `\(\sum_k\beta_{kj}\)` for the functional response between predator *j* and prey *i*) sum to 1. The weights are calculated from the density-independent search rates for each predator/prey pair, which is equal to `\(2Q_{ij}^*V_{ij} / (V_{ij} - 1)B_i^*B_j^*\)`. --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/modeling_study.png") background-size: 380px background-position: right bottom # Objective 2: evaluate multispecies assessment tools ## Multispecies production <a name=cite-gamble_analyzing_2009></a>([Gamble, et al., 2009a](http://linkinghub.elsevier.com/retrieve/pii/S0304380009003998)) ## Multispecies catch at length <a name=cite-gaichas_combining_2017></a>([Gaichas, et al., 2017a](https://academic.oup.com/icesjms/article/74/2/552/2669545/Combining-stock-multispecies-and-ecosystem-level)) (and eventually) ## Multispecies catch at age <a name=cite-curti_evaluating_2013></a>([Curti, et al., 2013a](http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2012-0229)) --- ## Multispecies production simulation: [Kraken](https://github.com/NOAA-EDAB/Kraken) and estimation [FIT: MSSPM](https://nmfs-ecosystem-tools.github.io/MSSPM/) Species interactions: - Predation: Top down (predation decreases population growth of prey, predator population growth independent of prey) - Competition: Within and between species groups *Based on Shaefer and Lotka-Volterra population dynamics and predation equations* - Species have intrinsic population growth rate `\(r_i\)` - Full model has - Carrying capacity `\(K\)` at the species group level `\(K_G\)` and at the full system level `\(K_\sigma\)` - Within group competition `\(\beta_{ig}\)` and between group competition `\(\beta_{iG}\)` slow population growth near `\(K\)` - Predation `\(\alpha_{ip}\)` and harvest `\(H_i\)` reduce population `$$\frac{dN_i}{dt} = r_iN_i \bigg(1 - \frac{N_i}{K_G} - \frac{\sum_g \beta_{ig}N_g}{K_G} - \frac{\sum_G \beta_{iG}N_G}{K_\sigma - K_G} \bigg) - N_i\sum_p\alpha_{ip}N_p - H_iN_i$$` - Simpler version used in most applications has interaction coefficient `\(\alpha\)` that incorporates carrying capacity `$$B_{i,t+1}=B_{i,t} + r_iB_{i,t} - B_{i,t}\sum_j\alpha_{i,j}B_{j,t} - C_{i,t}$$` ??? Interaction coefficients `\(\alpha_{i,j}\)` can be positive or negative `\(C\)` can be a Catch time series, an exploitation rate time series `\(B_{i,t}*F_{i,t}\)` or an `\(qE\)` (catchability/Effort) time series. Environmental covariates can be included on growth or carrying capacity (in the model forms that have an explicit carrying capacity). --- ## Multispecies catch at length simulation model: [Hydra](https://github.com/NOAA-EDAB/hydra_sim) Species interactions: - Predation: Top down only (predators increase M of prey, predators grow regardless of prey) *Based on standard structured stock assessment population dynamics equations, Same MSVPA predation equation as MSCAA (but length based), same dependencies and caveats* - First, split `\(M\)` for species `\(i\)` size `\(j\)` into components: `$$M_{i,j,t} = M1_i + M2_{i,j,t}$$` - Calculate `\(M2\)` with MSVPA predation equation, which applies a predator consumption:biomass ratio to the suitable prey biomass for that predator. - Suitability, `\(\rho\)`, of prey species `\(m\)` size `\(n\)` for a given predator species `\(i\)` size `\(j\)` a function of size preference and vulnerability {0,1}. - Food intake `\(I\)` for each predator-at-size is temperature dependent consumption rate times mean stomach content weight. - Also sensitive to "other food" `\(\Omega\)`. `$$M2_{m,n,t} = \sum_i \sum_j I_{i,j,t} N_{i,j,t} \frac{\rho_{i,j,m,n}}{\sum_a \sum_b \rho_{i,j,a,b} W_{a,b} N_{a,b} + \Omega}$$` ??? But: - Covariates on growth, maturity, recruitment possible; intended for environmental variables - So could hack in prey-dependent growth but making it dynamic is difficult We specify 'preferred' predator-prey weight ratio (log scale) `\(\Psi_j\)` and variance in predator size preference `\(\sigma_j\)` to compare with the actual predator-prey weight ratio `\((w_n / w_j)\)` to get the size preference `\(\vartheta\)`. `$$\vartheta_{n,j} = \frac{1}{(w_n / w_j)\sigma_j \sqrt{2\pi}} e^{-\frac{[log_e(w_n / w_j) - \Psi_j]}{2\sigma_j^2}}$$` Food intake is `$$I_{i,j,t} = 24 [\delta_j e^{\omega_i T}]\bar{C}_{i,j,k,t}$$` --- background-image: url("https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/NRCmodelLifeCycle.png") background-size: 650px background-position: right # Model life cycle ##<a name=cite-nrc_chapter_2007></a>([NRC, 2007](https://www.nap.edu/read/11972/chapter/6)) What makes a good model? * Differs by life stage * Each builds on the next * Common themes --- ## This week: constructed model (framework) review For each model, reviews should evaluate: 1. Spatial and temporal resolution 1. Algorithm choices 1. Assumptions (scientific basis, computational infrastructure; adequacy of conceptual model) 1. Data availability/software tools 1. Quality assurance/quality control (code testing) 1. Test scenarios 1. Corroboration with observations 1. Uncertainty/sensitivity analysis 1. Peer review (previous) --- ## Common attributes across models A common dataset for 10 Georges Bank species has been developed, as well as a simulated dataset for model performance testing. The [`mskeyrun`]() data package holds both datasets. All modeling teams used these datasets. Group decisions on data are also documented [online](). .pull-left-40[ **Years:** 1968-2019 **Area:** Georges Bank (previous map) **Species:** Atlantic cod (*Gadus morhua*), Atlantic herring (*Clupea harengus*), Atlantic mackerel (*Scomber scombrus*), Goosefish (*Lophius americanus*), Haddock (*Melanogrammus aeglefinus*), Silver hake (*Merluccius bilinearis*), Spiny dogfish (*Squalus acanthias*), Winter flounder (*Pseudopleuronectes americanus*), Winter skate (*Leucoraja ocellata*), and Yellowtail flounder (*Limanda ferruginea*) ] .pull-right-60[  ] --- # Datasets: real and simulated .pull-left[ Real data from NEFSC databases via R packages [`survdat`](https://noaa-edab.github.io/survdat/), [`comlandr`](https://noaa-edab.github.io/comlandr/), [`mscatch`](https://noaa-edab.github.io/mscatch/)  ] .pull-right[ Simulated data from Norwegian Barents Sea Atlantis model via R package [`atlantisom`](https://sgaichas.github.io/poseidon-dev/atlantisom_landingpage.html) **Norwegian-Barents Sea** [Hansen et al. 2016](https://www.imr.no/filarkiv/2016/04/fh-2-2016_noba_atlantis_model_til_web.pdf/nn-no), [2018](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210419) .center[  ] ] --- ## [ms-keyrun real biomass and catch data](https://noaa-edab.github.io/ms-keyrun/articles/GBsurveycatchviz.html) <iframe width="1212" height="682" src="https://noaa-edab.github.io/ms-keyrun/articles/GBsurveycatchviz.html" title="MS-keyrun real biomass and catch data" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" style="width:100%; height:100%;" allowfullscreen></iframe> --- ## [ms-keyrun real diet data](https://noaa-edab.github.io/ms-keyrun/articles/GBdietcomp.html) <iframe width="1212" height="682" src="https://noaa-edab.github.io/ms-keyrun/articles/GBdietcomp.html" title="MS-keyrun real diet data" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" style="width:100%; height:100%;" allowfullscreen></iframe>" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" style="width:100%; height:100%;" allowfullscreen></iframe> --- ## [ms-keyrun simulated data](https://noaa-edab.github.io/ms-keyrun/articles/SimData.html) <iframe width="1212" height="682" src="https://noaa-edab.github.io/ms-keyrun/articles/SimData.html" title="MS-keyrun simulated data" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" style="width:100%; height:100%;" allowfullscreen></iframe> --- .center[ # Modelers discuss overview of results ] --- ## References .contrib[ <a name=bib-curti_evaluating_2013></a>[Curti, K. L. et al.](#cite-curti_evaluating_2013) (2013a). "Evaluating the performance of a multispecies statistical catch-at-age model". En. In: _Canadian Journal of Fisheries and Aquatic Sciences_ 70.3, pp. 470-484. ISSN: 0706-652X, 1205-7533. DOI: [10.1139/cjfas-2012-0229](https://doi.org/10.1139%2Fcjfas-2012-0229). URL: [http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2012-0229](http://www.nrcresearchpress.com/doi/abs/10.1139/cjfas-2012-0229) (visited on Jan. 13, 2016). <a name=bib-gaichas_combining_2017></a>[Gaichas, S. K. et al.](#cite-gaichas_combining_2017) (2017a). "Combining stock, multispecies, and ecosystem level fishery objectives within an operational management procedure: simulations to start the conversation". In: _ICES Journal of Marine Science_ 74.2, pp. 552-565. ISSN: 1054-3139. DOI: [10.1093/icesjms/fsw119](https://doi.org/10.1093%2Ficesjms%2Ffsw119). URL: [https://academic.oup.com/icesjms/article/74/2/552/2669545/Combining-stock-multispecies-and-ecosystem-level](https://academic.oup.com/icesjms/article/74/2/552/2669545/Combining-stock-multispecies-and-ecosystem-level) (visited on Oct. 18, 2017). <a name=bib-gamble_analyzing_2009></a>[Gamble, R. J. et al.](#cite-gamble_analyzing_2009) (2009a). "Analyzing the tradeoffs among ecological and fishing effects on an example fish community: A multispecies (fisheries) production model". En. In: _Ecological Modelling_ 220.19, pp. 2570-2582. ISSN: 03043800. DOI: [10.1016/j.ecolmodel.2009.06.022](https://doi.org/10.1016%2Fj.ecolmodel.2009.06.022). URL: [http://linkinghub.elsevier.com/retrieve/pii/S0304380009003998](http://linkinghub.elsevier.com/retrieve/pii/S0304380009003998) (visited on Oct. 13, 2016). <a name=bib-lucey_evaluating_2021></a>[Lucey, S. M. et al.](#cite-lucey_evaluating_2021) (2021). "Evaluating fishery management strategies using an ecosystem model as an operating model". En. In: _Fisheries Research_ 234, p. 105780. ISSN: 0165-7836. DOI: [10.1016/j.fishres.2020.105780](https://doi.org/10.1016%2Fj.fishres.2020.105780). URL: [http://www.sciencedirect.com/science/article/pii/S0165783620302976](http://www.sciencedirect.com/science/article/pii/S0165783620302976) (visited on Dec. 09, 2020). <a name=bib-lucey_conducting_2020></a>[Lucey, S. M. et al.](#cite-lucey_conducting_2020) (2020a). "Conducting reproducible ecosystem modeling using the open source mass balance model Rpath". En. In: _Ecological Modelling_ 427, p. 109057. ISSN: 0304-3800. DOI: [10.1016/j.ecolmodel.2020.109057](https://doi.org/10.1016%2Fj.ecolmodel.2020.109057). URL: [http://www.sciencedirect.com/science/article/pii/S0304380020301290](http://www.sciencedirect.com/science/article/pii/S0304380020301290) (visited on Apr. 27, 2020). <a name=bib-nrc_chapter_2007></a>[NRC](#cite-nrc_chapter_2007) (2007). "Chapter 4. Model Evaluation". En. In: _Models in Environmental Regulatory Decision Making_. Washington D.C.: The National Academies Press, pp. 104-169. DOI: [10.17226/11972](https://doi.org/10.17226%2F11972). URL: [https://www.nap.edu/read/11972/chapter/6](https://www.nap.edu/read/11972/chapter/6) (visited on Aug. 29, 2019). <a name=bib-trijoulet_performance_2020></a>[Trijoulet, V. et al.](#cite-trijoulet_performance_2020) (2020a). "Performance of a state-space multispecies model: What are the consequences of ignoring predation and process errors in stock assessments?" En. In: _Journal of Applied Ecology_ n/a.n/a. ISSN: 1365-2664. DOI: [10.1111/1365-2664.13515](https://doi.org/10.1111%2F1365-2664.13515). URL: [https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.13515](https://besjournals.onlinelibrary.wiley.com/doi/abs/10.1111/1365-2664.13515) (visited on Dec. 04, 2019). ] ## Additional resources .pull-left[ * [`mskeyrun` R data package](https://noaa-edab.github.io/ms-keyrun/index.html) * [Georges Bank keyrun project overview](https://noaa-edab.github.io/ms-keyrun/articles/mskeyrun.html) * [Rpath](https://github.com/NOAA-EDAB/Rpath) * [FIT: MSSPM](https://nmfs-ecosystem-tools.github.io/MSSPM/) ] .pull-right[ *Hydra-Associated GitHub repositories* * [hydra-sim (Simulation Model Wiki)](https://github.com/NOAA-EDAB/hydra_sim/wiki) * [hydra-sim (estimation fork)](https://github.com/thefaylab/hydra_sim) * [hydradata (estimation fork)](https://github.com/thefaylab/hydradata) * [hydra-diag (diagnostics)](https://github.com/thefaylab/hydra_diag) ] .footnote[ Slides available at https://noaa-edab.github.io/presentations Contact: <Sarah.Gaichas@noaa.gov> ] --- ## Hydra: past use as simulation model .pull-left[ *2018 CIE for Ecosystem Based Fishery Management Strategy* * [EBFM strategy review](https://www.nefsc.noaa.gov/program_review/) * Multispecies model simulations <img src="https://github.com/NOAA-EDAB/presentations/raw/master/docs/EDAB_images/EBFM HCR.png" width="75%" /> ] .pull-right[  ] --- ## Next: Multispecies catch at age estimation model: *seeking catchy name* [FIT: MSCAA](https://nmfs-ecosystem-tools.github.io/MSCAA/) Species interactions: - Predation: Top down only (predators increase M of prey, predators grow regardless of prey) *Based on standard age structured stock assessment population dynamics equations* - First, split `\(M\)` for species `\(i\)` age `\(a\)` into components: `$$M_{i,a,t} = M1_i + M2_{i,a,t}$$` - Calculate `\(M2\)` with MSVPA predation equation, which applies a predator consumption:biomass ratio to the suitable prey biomass for that predator. - Suitability is a function of predator size preference (based on an age-specific predator:prey weight ratio) and prey vulnerability (everything about the prey that isn't size related). - Also sensitive to "other food" `$$M2_{i,a,t} = \frac{1}{N_{i,a,t}W_{i,a,t}}\sum_j \sum_b CB_{j,b} B_{j,b,t} \frac{\phi_{i,a,j,b,t}}{\phi_{j,b,t}}$$` ??? Size preference is `$$g_{i,a,j,b,t}=\exp\bigg[\frac{-1}{2\sigma_{i,j}^2}\bigg(\ln\frac{W_{j,b,t}}{W_{i,a,t}}-\eta_{i,j}\bigg)^2\bigg]$$` Suitability, `\(\nu\)` of prey `\(i\)` to predator `\(j\)`: `$$\nu_{i,a,j,b,t}=\rho_{i,j}g_{i,a,j,b,t}$$` Scaled suitability: `$$\tilde\nu_{i,a,j,b,t}=\frac{\nu_{i,a,j,b,t}}{\sum_i \sum_a \nu_{i,a,j,b,t} + \nu_{other}}$$` Suitable biomass of prey `\(i\)` to predator `\(j\)`: `$$\phi_{i,a,j,b,t}=\tilde\nu_{i,a,j,b,t}B_{i,a,t}$$` Available biomass of other food, where `\(B_other\)` is system biomass minus modeled species biomass: `$$\phi_{other}=\tilde\nu_{other}B_{other,t}$$` Total available prey biomass: `$$\phi_{j,b,t}=\phi_{other} + \sum_i \sum_a \phi_{i,a,j,b,t}$$`