From cbdec03114ab2211465128463a1237377e7b059f Mon Sep 17 00:00:00 2001 From: Jim Ianelli Date: Tue, 10 Sep 2024 22:51:19 -0700 Subject: [PATCH] Carey's edits --- _quarto.yml | 11 + doc/references.bib | 16 + doc/sept.qmd | 219 +++--- docs/.DS_Store | Bin 6148 -> 0 bytes docs/doc/sept.docx | Bin 5690269 -> 5695595 bytes docs/doc/sept.html | 1654 +++++++++++++++++++++++++++++++------------- docs/doc/sept.pdf | Bin 1882144 -> 1889238 bytes docs/search.json | 24 +- docs/sitemap.xml | 2 +- 9 files changed, 1364 insertions(+), 562 deletions(-) delete mode 100644 docs/.DS_Store diff --git a/_quarto.yml b/_quarto.yml index 34e605e..d69319f 100644 --- a/_quarto.yml +++ b/_quarto.yml @@ -1,6 +1,8 @@ project: type: website output-dir: docs + title: "Eastern Bering Sea walleye pollock stock assessment" + render: - "doc/sept.qmd" - "!doc/DoubleLogistic.Rmd" @@ -58,6 +60,7 @@ website: lightbox: true format: html: + date: "`r format(Sys.time(), '%B %d, %Y %H:%M')`" theme: light: [cosmo, theme.scss] dark: [cosmo, theme-dark.scss] @@ -71,6 +74,14 @@ format: code-fold: true pdf: documentclass: scrreprt + papersize: letter + header-includes: + - \usepackage{fancyhdr} + - \pagestyle{fancy} + - \fancyhf{} + - \rfoot{\thepage} + - \lfoot{My Document} + docx: reference-docx: "template.docx" diff --git a/doc/references.bib b/doc/references.bib index 4a9578c..c784955 100644 --- a/doc/references.bib +++ b/doc/references.bib @@ -1,3 +1,19 @@ +@article{stock2021, + title = {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}, + shorttitle = {The {{Woods Hole Assessment Model}} ({{WHAM}})}, + author = {Stock, Brian C. and Miller, Timothy J.}, + year = {2021}, + month = aug, + journal = {Fisheries Research}, + volume = {240}, + pages = {105967}, + issn = {0165-7836}, + doi = {10.1016/j.fishres.2021.105967}, + urldate = {2024-09-11}, + abstract = {The rapid changes observed in many marine ecosystems that support fisheries pose a challenge to stock assessment and management predicated on time-invariant productivity and considering species in isolation. In single-species assessments, two main approaches have been used to account for productivity changes: allowing biological parameters to vary stochastically over time (empirical), or explicitly linking population processes such as recruitment (R) or natural mortality (M) to environmental covariates (mechanistic). Here, we describe the Woods Hole Assessment Model (WHAM) framework and software package, which combines these two approaches. WHAM can estimate time- and age-varying random effects on annual transitions in numbers at age (NAA), M, and selectivity, as well as fit environmental time-series with process and observation errors, missing data, and nonlinear links to R and M. WHAM can also be configured as a traditional statistical catch-at-age (SCAA) model in order to easily bridge from status quo models and test them against models with state-space and environmental effects, all within a single framework. We fit models with and without (independent or autocorrelated) random effects on NAA, M, and selectivity to data from five stocks with a broad range of life history, fishing pressure, number of ages, and time-series length. Models that included random effects performed well across stocks and processes, especially random effects models with a two dimensional (2D) first-order autoregressive, AR(1), covariance structure over age and year. We conducted simulation tests and found negligible or no bias in estimation of important assessment outputs (SSB, F, stock status, and catch) when the operating and estimation models matched. However, bias in SSB and F was often non-trivial when the estimation model was less complex than the operating model, especially when models without random effects were fit to data simulated from models with random effects. Bias of the variance and correlation parameters controlling random effects was also negligible or slightly negative as expected. Our results suggest that WHAM can be a useful tool for stock assessment when environmental effects on R or M, or stochastic variation in NAA transitions, M, or selectivity are of interest. In the U.S. Northeast, where the productivity of several groundfish stocks has declined, conducting assessments in WHAM with time-varying processes via random effects or environment-productivity links may account for these trends and potentially reduce retrospective bias.}, + keywords = {/unread,Environmental effects,Natural mortality,Random effects,Recruitment,State-space,Stock assessment,Template Model Builder (TMB),Time-varying} +} + @Manual{rpath, title = {Rpath: R implementation of Ecopath with Ecosim}, author = {Kerim Aydin and Sean Lucey and Sarah Gaichas}, diff --git a/doc/sept.qmd b/doc/sept.qmd index 49fc5c1..0a8958d 100644 --- a/doc/sept.qmd +++ b/doc/sept.qmd @@ -2,6 +2,8 @@ output: html_document title: "Eastern Bering Sea walleye pollock stock assessment" subtitle: September 2024 +author: "Jim Ianelli and Carey McGilliard" +date: "`r format(Sys.time(), '%B %d, %Y %H:%M')`" editor_options: chunk_output_type: console editor: @@ -63,31 +65,35 @@ CEATTLE model. ## Summary of Results Some research has indicated that the value of $\sigma_R$ can be reasonably well estimated -in both traditional stock assessment models and in state-space versions. Based on our -analysis which shows that estimates appear reasonable, we caution about the application -for management settings. This is because for EBS pollock, the SRR is heavily influenced by -data to the right of $B_{MSY}$ instead of nearer the origin (where measures of "steepness" -might most reliably be estimated). We show that the SRR is thus linked to the Tier 1 -fishing mortality recommendation in two ways: 1) by the assumption about $\sigma_R$ -(smaller values increase the "precision" of $F_{MSY}$) and 2) by having most of the data -far from the origin and hence having steepness affected observations that are distant from -the origin. We provided an illustration of this in an external simulation. In the 2023 -assessment, $\sigma_R$ was specified at 1.0 as a precautionary measure and seek guidance -on best practice given the direct management implication under Tier 1. +in both traditional stock assessment models and in state-space versions (e.g., +@stock2021). Based on our analysis, we confirm that estimates appear reasonable. However, +we caution about applying $\sigma_R$ estimates blindly in management settings. This is +because for EBS pollock, the SRR is heavily influenced by estimates of spawning biomass +being mainly larger than $B_{MSY}$ (to the right of $B_{MSY}$; far from the origin of the +SRR curve) instead of spawning biomass estimates that are much smaller than $B_{MSY}$ +(near the origin, which would better inform estimates of steepness). We show that the SRR +is thus linked to the Tier 1 fishing mortality recommendation in two ways: 1) by the +assumption about $\sigma_R$ (smaller values increase the "precision" of $F_{MSY}$) and 2) +by having most of the data far from the origin means estimating steepness becomes less +certain and possibly unreliable. We provided an illustration of this in an external +simulation. In the 2023 assessment, $\sigma_R$ was specified at 1.0 as a precautionary +measure. We seek guidance on $\sigma_R$ approaches and general treatment of the SRR given +the direct management implication under Tier 1. Incorporating the natural mortality at age and year from CEATTLE had a modest impact on -the SRR because the recruitment scales higher. +the SRR because the recruitment scales were higher. The model code was updated to work as an operating model so that a full-feedback simulation loop could be used to test different management procedures. Presently the ecosystem factors that affect the pollock TAC are mostly related to the constraint due to the 2-Mt cap. We attempt to elaborate more directly on the ecosystem -aspect by developing a parallel catch guideline that is intended to have some aspect of -the role pollock plays in the ecosystem. We contrast that with historical patterns of -catch. Additionally, we consider multi-species mass-balance projections under scenarios -where catch projections closer to the maximum permissible are used. This was done using -"ecosense" within the package Rpath (courtesy Andy Whitehouse). +aspect by developing a parallel catch guideline that is intended to take into account the +role pollock plays in the ecosystem. We contrast this alternative guideline (in the form +of "advice") with historical patterns of catch. Additionally, we consider multi-species +mass-balance projections under scenarios where catch projections closer to the maximum +permissible are used. This was done using "ecosense" within the package Rpath (@rpath; +courtesy Andy Whitehouse). ## Responses to SSC and Plan Team Comments @@ -145,7 +151,7 @@ where catch projections closer to the maximum permissible are used. This was don sensitivity test should be done to evaluate the effects of data removal on the assessment. - - *We also investigated that in this report* + - *We investigated this and report on it here.* - Document the method used for determining the selectivity to use in the forward projections and continue to evaluate projection variability due to selectivity. The @@ -153,10 +159,12 @@ where catch projections closer to the maximum permissible are used. This was don helpful to limit the comparison to the projection used in each year against only the most recent (best) estimate of selectivity for that year. - - *We have been clear about projection assumptions used for selectivity and will - provide an updated retrospective presentation for this year's assessment. In this - report, we evaluated the sensitivity of Tier 1 ABC given different selectivity - estimtates from historical annual values.* + - *We will be clearer about projection assumptions especially for selectivity. In + this report we show the sensitivity of selectivity assumption and evaluated the + sensitivity of Tier 1 ABC estimates given different selectivity estimates from + historical annual values. In the next draft we will update how different + selectivity assumptions perform based on an updated retrospective presentation for + this year's assessment.* - The SSC supports the use of posterior predictive distributions, an underutilized tool in fisheries science, but common in other fields. To fully implement this approach to @@ -294,12 +302,11 @@ plot_srr(srrlst[c(2, 4)], alpha = .2, xlim = c(0, 4200), ylim = c(0, 85000), siz An alternative SRR conditioning exercise was conducted where the year range for the conditioning of the curve was dropped in successive years. This was intended to show how sensitive the curve is to the years included in the analysis. We expect that it should -revert to the prior as fewer years are included in the conditioning of the SRR curve. -To consider the variability of the SRR estimates due to additional years of -data, we changed the window of years used within the assessment model. -Results showed given the prior and other assumptions, the SRR estimates -were relatively stable (@fig-yearlop2 and @fig-trans). - +revert to the prior as fewer years are included in the conditioning of the SRR curve. To +consider the variability of the SRR estimates due to additional years of data, we changed +the window of years used within the assessment model. Results showed given the prior and +other assumptions, the SRR estimates were relatively stable (@fig-yearlop2 and +@fig-trans). ```{r SRR_yearlop} #| echo: false @@ -405,7 +412,6 @@ p1 <- plot_srr(M, color = "red", size = 1) ; p1 ``` - ## Removing the impact of the prior on the stock-recruitment relationship As we have done in past years, we evaluated the impact of the prior on the SRR. Results @@ -458,10 +464,11 @@ knitr::include_graphics("../doc/figs/srr_sim.png") ## Beverton-Holt stock-recruitment relationship -As part of the review of different approaches for modeling the stock-recruit relationship, -we compared last year's model with different forms, priors, and periods using the -Beverton-Holt curve. The shorter and longer periods with the Beverton-Holt curve (using -the same priors) were similar to the Ricker curve, but had higher uncertainty +Traditionally the SRR form has been parameterized as a "Ricker" due to the presence of +cannibalism. As part of the review of different approaches for modeling the stock-recruit +relationship, we compared last year's model with different forms, priors, and periods +using the Beverton-Holt curve. The shorter and longer periods with the Beverton-Holt curve +(using the same priors) were similar to the Ricker curve, but had higher uncertainty (@fig-bholt). Removing the prior on the Beverton-Holt curve resulted in estimates of steepness near 1.0 and higher precision (@fig-bholt2). @@ -513,9 +520,13 @@ For this case we evaluated the impact of the $\sigma_R$ prior on the ABC estimat Results show that the assessment model as configured indicates a relatively low value is favored (@fig-sigmRplot1). The assumption about a fixed value of this parameter (set to 1.0 in the 2023 assessment) shows that this can impact the ABC estimate (@fig-sigmR_abc). -This figure also shows that the problem persists in Tier 2 (and a candidate "Tier 1.5" -from an earlier assessment) but that Tier 3 is relatively insensitive. The value of -$\sigma_R$ affects the shape of the curve as well (@fig-sigrplot1 and @fig-sigrplot2). +This figure also shows that the problem persists in Tier 2 along with a candidate hybrid +Tier 2 approach from the 2021 assessment (here labeled as "Tier 1.5")[^1] but Tier 3 is +insensitive. We note that the value of $\sigma_R$ affects the shape of the curve as well +(@fig-sigrplot1 and @fig-sigrplot2). + +[^1]: This modification of Tier 2 simply applies the harmonic mean of $F_{MSY}$ instead of + the arithmetic mean to make it slightly more precautionary. ```{r sigmaRprofile2} #| echo: false @@ -637,15 +648,18 @@ To examine the assumptions about fishery selectivity variability we ran alternat configurations where selectivity variability was contrasted. In one configuration it was constrained such that the fishing mortality was considered completely separable with respect to age and one where there was limited constraint on the selectivity. This later -model is similar in nature to traditional VPA models where the catch at age is assumed -known precisely. The resulting selectivity patterns are shown in @fig-selex. Results -showed that very little difference between a freely specified selectivity model -(@fig-separable_ssb_r_vpa) Results comparing the constrained selectivity differed -substantially from last year's configuration (@fig-separable_ssb_r). +model is similar in nature to traditional VPA models where the catch-at-age is assumed +to be known precisely. The resulting selectivity patterns are shown in @fig-selex. +Results showed minor differences between the model with selectivity allowed +to be freely variable and the 2023 assessment (@fig-separable_ssb_r_vpa). +Results comparing the constrained ("separable") selectivity differed +substantially from last year's configuration (@fig-separable_ssb_r; see @fig-selex for +differences in selectivity patterns). The large differences due to assuming separability also impacts the estimates of the stock -recruitment relationship (@fig-separable_srr). This figure also dipicts the different -magnitude of the recent recruitment and an increased uncertainty. In particular, the 2018 +recruitment relationship (@fig-separable_srr). This figure also depicts the different +magnitude of the recent recruitment and the corresponding +increased uncertainty in recent recruitment. In particular, the 2018 year class is estimated to be much larger in the separable model. Another form of evaluating the selectivity estimates was to simply apply each of the @@ -653,7 +667,8 @@ annual selectivity estimates (or partial Fs) from the past 20 years. We note tha result used the mean selectivity over the recent 2 years; specifically, the mean selectivity for 2021 and 2022 for the 2023 terminal year assessment. -Results show that the srr +Results show that the SRR also varied slightly when a constant selectivity was assumed +(@fig-separable_srr). ```{r selex} #| echo: false @@ -733,15 +748,16 @@ plot_srr(sellst[c(1, 2)], alpha = .2, xlim = c(0, 6400), ylim = c(0, 115000), si ``` For another set of experiments, we evaluated the SRR curve given the past 20 years of -selectivity estimates. The model has an option for which specific year to use for "future" selectivity -and for the number of years over which an average selection pattern could be used. - -Initial results showed that there could be large and variable impacts on the ABC estimates -under the current Tier 1 and for different Tiers (@fig-sel_ABC_plot). This was -presumably due to the fact that the selectivity changes can shift to younger or older ages in some years. -However, the SRR curve was insensitive to the selectivity estimate (@fig-selsrrplot) because -the future selectivity pattern is separate from the SRR estimation. +selectivity estimates. The model has an option for which specific year to use for "future" +selectivity and for the number of years over which an average selection pattern could be +used. +Initial results showed that there could be large and variable impacts on the ABC estimates +under the current Tier 1 and for different Tiers (@fig-sel_ABC_plot). This was presumably +due to the fact that the selectivity changes can shift to younger or older ages in some +years. However, the SRR curve was insensitive to the selectivity estimate +(@fig-selsrrplot) because the future selectivity pattern is separate from the SRR +estimation. ```{r sel_ABC_plot} #| echo: false @@ -807,8 +823,8 @@ M <- modlst[[1]] # .OVERLAY=TRUE plot_srr(modlst[c(1, 4, 12, 18)], alpha = .2, xlim = c(0, 6400), ylim = c(0, 85000), sizeout = 2, sizein = 4, yrsin = c(1977, 1979:2021)) ``` -## Conditioning the stock-recruitment relationship to have $F_{MSY}$ equal to $F_{35\%}$ +## Conditioning the stock-recruitment relationship to have $F_{MSY}$ equal to $F_{35\%}$ As another consideration for Tier 1 and Tier 3, we recognized that the SPR rate for the 2023 assessment that corresponded to a value of about $F_{32\%}$. For contrast we used a @@ -817,8 +833,6 @@ constraint that out $F_{35\%}$ = $F_{MSY}$." Results from this run shows that th are quite similar (@fig-F35). This suggests that at least given the current model from 2023, the $F_{35\%}$ is a reasonable proxy for $F_{MSY}$. - - ```{r F35} #| echo: false #| warnings: FALSE @@ -837,10 +851,12 @@ In past assessments we have mentioned that a commonly adopted approach for stock also included in multispecies trophic interaction models (@trijoulet2020) it is considered best practice to include the estimates of natural mortality-at-age over time within the assessment model. We developed an option to include the 2023 CEATTLE estimates of natural -mortality (@fig-Mmatrix_matrix). This resulted slightly higher recruitment but lower +mortality (@tbl-Mmatrix; @fig-Mmatrix_matrix). This resulted slightly higher recruitment but lower spawning biomass in the near term (@fig-Mmatrix). This is due to the higher natural mortality for most ages and years compared to the base 2023 model. + + ```{r Mmatrix_matrix} #| echo: false #| warnings: FALSE @@ -893,6 +909,20 @@ p2<- ggplot(data = Mdf |> filter(Age<3) |> mutate(Year=Year+1963), aes(x = Age, p2+p1 + plot_layout(widths=c(2,12), guides='collect') ``` +```{r Mmatrix_table} +#| echo: false +#| warnings: FALSE +#| messages: FALSE +#| label: tbl-Mmatrix +#| tbl-cap: "Natural mortality-at-age used over ages and time based on the CEATTLE +#| 2023 model." +m_matrix <- as_tibble(M$M)[,1:10] +#dim(m_matrix) +names(m_matrix) <- 1:10 +rownames(m_matrix) <- 1964:2023 +gt::gt(m_matrix, rownames_to_stub = T) |> gt::fmt_number( columns = 1:10, decimals = 3) +``` + ```{r Mmatrix} #| echo: false #| warnings: FALSE @@ -964,8 +994,7 @@ data included in the fitting the the SRR between 1978 and 1991 being downweighte #| see the impact of model differences." p1 <- plot_recruitment(modlst[c(1, 6)], xlim = c(1963.5, 1980.5), alpha = .6, fatten=.2, ylab="Age-1 recruits") + - ggthemes::theme_few(base_size = 9) #+ - scale_x_continuous(breaks=seq(1964, 1980, by = 4)) + ggthemes::theme_few(base_size = 9) #+ scale_x_continuous(breaks=seq(1964, 1980, by = 4)) p2 <- plot_ssb(modlst[c(1, 6)], xlim = c(1963.5, 2024.5), breaks = seq(1964, 2024, by = 10), alpha = .2, ylab="SSB") + ggthemes::theme_few(base_size = 9) @@ -994,7 +1023,7 @@ rates in the Russian zone requires some additional developments. Such a research evaluation has begun but it more work is neede before it can be included in the assessment. -# Further considerations of pollock and ecosystem role +# Further considerations of pollock and its role in the ecosystem As noted, above, the SSC requested an evaluation of the ecosystem function as part of the SRR consideration and Tier 1 control rules within the FMP. Their comment was: @@ -1040,20 +1069,20 @@ million t. As a point of curiosity, we considered inverting the SRR productivity estimate by posing the question "what SRR would give a long-term expected MSY of 1.3 million t?" Additionally, would our estimates of uncertainty in the SRR curve overlap in a manner that -would provide context for the management advice. +would provide context for the management advice? We thus added a feature of the model where one can provide a condition that the SRR be consistent with a specified MSY value.\ As an experiment, we conditioned the SRR curve to have the MSY value set to 1.75 and 1.3 -million t. We then compared those curves with the 2023 model specificaitons -(@fig-srrplot_cond). When overplotted, the fit comparisons indicate somewhat worse fit to -the available years of data (post 1977; @fig-srrplot_cond2) but reasonable within the -estimates of uncertainty. Here we conclude that the management advice (under Tier 1) is -sensitive to relatively small apparent perturbations in the shape of the stock-recruitment -curves. Such sensitivity is expected given the sections above. Furthermore, we know that -non-stationarity in the SRR is likely, especially given the uncertainties of climate -change. An alternative ABC-setting rule may be preferred that stabilizes the advice while -adhering to the NS1 guidelines. +million t. We then compared those curves with the 2023 model specifications +(@fig-srrplot_cond). When overplotted, the fit comparisons indicate somewhat worse fits to +the available years of data (post 1977; @fig-srrplot_cond2). However, the fits were +reasonable within the estimates of uncertainty. Here we conclude that the management +advice (under Tier 1) is sensitive to relatively small apparent perturbations in the shape +of the stock-recruitment curves. Such sensitivity is expected given the sections above. +Furthermore, we know that non-stationarity in the SRR is likely, especially given the +uncertainties of climate change. An alternative ABC-setting rule may be preferred that +stabilizes the advice while adhering to the NS1 guidelines. ```{r srrplot_cond} #| echo: false @@ -1081,7 +1110,7 @@ p1 / p2 / p3 + plot_layout(axis_titles = "collect") #| warnings: FALSE #| messages: FALSE #| label: fig-srrplot_cond2 -#| fig.cap: 'SRR curves as estimated in the 2023 assessment overlain with those conditioned +#| fig.cap: 'SRR curves as estimated in the 2023 assessment overlaid with those conditioned #| on alternative Fmsy assumptions.' ### How sensitive is the curve to the selectivity assumption @@ -1095,20 +1124,21 @@ p1 ### Evaluating patterns in historical TACs relative to stock status -In thinking about the role of pollock in the ecosystem and relation to management advice -within the fisheries management plan (FMP) and Tier system, we considered pollock as a key -part of the forage base (say of 1-3 yr old pollock). A management goal with an explicit -consideration of ecosystem function might be to avoid low levels. For example, if the -forage base appeared to be close to say the lower 20th percentile from historical -estimates, then a management might include an adjustment that would occur then to avoid -any further declines (e.g., @fig-quant_prey). +In thinking about the role of pollock in the ecosystem and in relation to management +advice within the fisheries management plan (FMP) and Tier system, we considered pollock +as a key part of the forage base (say of 1-3 yr old pollock). A management goal with an +explicit consideration of ecosystem function might be to avoid low levels of forage base. +For example, if the forage base appeared to be close to say the lower 20th percentile from +historical estimates, then a management might include an adjustment that would occur then +to avoid any further declines (e.g., @fig-quant_prey). While conceptually appealing, in practical terms including an explicit adjustment might be -difficult. For example, information on the abundance of those age classes groups would be +difficult. For example, information on the abundance of those age classes would be limited. The historical abundance of the prey base compared to spawning biomass shows that -the relationship is poorly determined (@fig-ssb_prey). However, the forage component does -tend to decline at lower spawning biomass levels. This suggests that the practice of -conserving spawning biomass may be linked to downstream impacts of the main forage ages. +the relationship between the two is poorly determined (@fig-ssb_prey). However, the forage +component does tend to decline at lower spawning biomass levels. This suggests that the +practice of conserving spawning biomass may be linked to downstream impacts of the main +forage ages. The FMP for the BSAI is subject to the categorization of the stock assessment to obtain the maximum permissible ABC and OFL. For pollock, this falls on the determination of the @@ -1121,10 +1151,10 @@ for pollock in most years (in 18 out of the past 23 years from 2001-2023). For t period, the total pollock catch was 99.4% of the TACs and the average ABC was 1.737 Mt compared to the mean TAC of 1.295 Mt. The TACs being lower than the ABCs is due to the 2-million t overarching limit on all BSAI groundfish TACs. One of the reasons for the -limit is to account for ecosystem sustainability (@low1983). We examine a historical -pattern of pollock ABCs and catches and present a model for future -catches that could be more reflective of objectives related specifically to pollock within -the ecosystem (compared to TAC setting within the 2 million t cap constraint). +limit is to account for ecosystem sustainability (@low1983). We examine a historical +pattern of pollock ABCs and catches and present a model for future catches that could be +more reflective of objectives related specifically to pollock within the ecosystem +(compared to TAC setting within the 2 million t cap constraint). Such a simple rule would begin with incremental catch advice based on the recent catches and the spawning biomass relative to the historical mean. I.e., if the catch in the @@ -1135,18 +1165,20 @@ of the mean value, the recommendation would be $1.2 \times \sqrt{0.75} = 1.039$ If the SSB stayed at 75% of the mean, then the following year would be $1.039 \times \sqrt{0.75} = 0.8998$ million t. More formally, +```{=tex} \begin{align} A_{y+1} &= TAC_y \times \sqrt{\frac{B_{y+1}}{\bar{B}}} \\&=TAC_y \times [\frac{B_{y+1}}{\bar{B}}]^\lambda \end{align} - +``` where $A_{y+1}$ is the "advice" on catch, $B_{y+1}$ is the spawning biomass projected in -the coming year, $\bar{B}$ is the mean spawning biomass and $\lambda$ is a responsiveness parameter (set to 0.5 in this example). +the coming year, $\bar{B}$ is the mean spawning biomass and $\lambda$ is a responsiveness +parameter (set to 0.5 in this example). Results indicate that the combination of historical ABC and TAC decisions can be distilled -to a simple formula for providing catch advice (@fig-mp_hist). -This advice is intended to focus on the conservation goal of maintaining spawning -biomass and provide ecosystem stability--similar to the over-arching principle of the 2 Mt -catch limit for the BSAI region. +to a simple formula for providing catch advice (@fig-mp_hist). This advice is intended to +focus on the conservation goal of maintaining spawning biomass and provide ecosystem +stability--similar to the over-arching principle of the 2 Mt catch limit for the BSAI +region. Too evaluate performance of alternative pollock fishing at the ecosystem level, we examined results from a mass-balance ecosystem model (e.g., @whitehouse2020). This model @@ -1242,7 +1274,7 @@ df |> ggplot(aes(x = Year, y = SSB)) + #| fig.cap: "Results from applying a simple catch-advice rule given historical #| spawning biomass projections (SSB.projected) compared to the actual catch (as a function #| of historical ABCs and TACs). A single value for -#| responsiveness was used ($/lambda$=0.5)." +#| responsiveness was used ( $/lambda =0.5$ )." #| df1 <- NULL for (resp in c(0.05, 0.5, 0.95)) { @@ -1508,6 +1540,9 @@ data added, and with a newly developed 3-parameter double logistic parameterizat logistic) ```{r like_table, echo=FALSE} +#| messages: FALSE +#| eval: FALSE +#| # Convert list of lists to tibble df<- cbind(am_run$lst[grep("Like_Comp",names(am_run$lst))[2]] , as_tibble(am_run$lst[grep("Like_Comp",names(am_run$lst))[1]]), diff --git a/docs/.DS_Store b/docs/.DS_Store deleted file mode 100644 index 9b786ce52aa00e90336160999621dda530c39633..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 6148 zcmeH~J!%6%427R!7lt%jrkutH$PET#pTHLg*pSB9U`XmYdY*ooY+RcqJc0B^niaeI z6+0^cw);B20~3G^-4$C8Gc)EZoN>eH`*^=zZr4v%yb8QT%#4)@v;EqZh=2%)fCz|y z2rP&|p5oZNF6f!`C?X&N%OK$2hemhpr6V;y9Sku7P}eMnaUHV+wRwTsOGhdzG^^>s zs?}l)@p`mVTV2;nM{2giYWT3av-uQ5vuuYoCN%3I3L+o^GXks3CqMrW^hfjmtVO8^ 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- - - - + + + + + + + + + + + - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + - - - - + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
NLL Componentbasedbl_logisticcpue12345678910
catch_like5.80.05.819640.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19650.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19660.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19670.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19680.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19690.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19700.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19710.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19720.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19730.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19740.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19750.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19760.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19770.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19780.9510.3220.3100.3050.3010.3020.3010.3010.3020.3001
19790.6030.3160.3090.3040.3010.3020.3010.3010.3010.3001
19800.8440.3150.3070.3040.3010.3010.3010.3010.3010.3001
19810.9440.3170.3070.3040.3010.3010.3010.3010.3010.3001
19821.2880.3330.3140.3070.3020.3030.3020.3010.3020.3001
19831.0760.3310.3120.3070.3020.3020.3020.3010.3030.3001
19841.2840.3390.3160.3100.3030.3030.3030.3030.3030.3001
19851.3380.3450.3200.3110.3030.3040.3040.3030.3050.3001
19861.4610.3480.3220.3130.3030.3040.3050.3030.3070.3001
19871.6770.3630.3290.3190.3050.3050.3070.3050.3090.3001
19881.6280.3740.3340.3190.3060.3070.3060.3060.3060.3001
19891.5730.3940.3430.3250.3080.3080.3070.3070.3090.3001
19901.1610.3600.3290.3180.3050.3050.3060.3050.3080.3001
19911.2310.3600.3290.3180.3050.3060.3070.3050.3090.3001
19921.1980.3460.3230.3150.3040.3040.3050.3040.3070.3001
19931.2250.3270.3130.3070.3020.3020.3020.3020.3030.3001
19941.5500.3410.3180.3110.3030.3030.3030.3030.3040.3001
age_like_fsh0.0434.60.719951.4480.3440.3180.3100.3030.3040.3030.3030.3040.3001
length_like_fsh0.00.00.019961.2610.3420.3180.3100.3030.3030.3030.3030.3040.3001
sel_like_fsh0.0342.91.019971.1340.3410.3180.3100.3030.3030.3030.3020.3040.3001
ind_like0.037.94.019981.4540.3550.3240.3140.3040.3050.3050.3040.3060.3001
age_like_ind0.081.30.219991.2930.3430.3180.3100.3020.3040.3030.3020.3050.3001
length_like_ind0.00.00.020001.2090.3440.3200.3100.3030.3030.3030.3030.3050.3001
sel_like_ind41.90.041.920011.1610.3390.3170.3080.3020.3030.3020.3020.3030.3001
rec_like0.011.14.820021.1770.3370.3150.3090.3020.3030.3020.3020.3030.3001
fpen0.00.00.020031.3320.3450.3180.3100.3030.3030.3030.3020.3040.3001
post_priors_indq0.00.20.020041.2840.3570.3250.3120.3030.3030.3030.3030.3050.3001
post_priors0.00.00.020051.3150.3640.3310.3150.3040.3050.3050.3040.3060.3001
residual0.00.00.020061.2180.3640.3330.3160.3030.3040.3040.3030.3060.3001
total0.0860.410.720071.3010.3700.3380.3170.3040.3050.3050.3040.3070.3001
20081.3900.3620.3270.3160.3030.3040.3040.3030.3060.3001
20091.2060.3550.3250.3140.3040.3050.3040.3030.3050.3001
20101.1230.3410.3170.3090.3020.3030.3030.3020.3030.3001
20111.2260.3430.3180.3100.3030.3030.3030.3020.3040.3001
20121.2170.3510.3210.3100.3030.3040.3020.3020.3030.3001
20130.8090.3230.3100.3050.3010.3020.3010.3010.3020.3001
20141.4850.3470.3210.3100.3030.3030.3030.3020.3040.3001
20151.6260.3480.3200.3100.3020.3030.3030.3020.3040.3001
20161.9480.3690.3280.3150.3040.3040.3040.3030.3050.3001
20171.6850.3740.3330.3150.3040.3050.3040.3030.3050.3001
20181.7260.3880.3430.3200.3050.3060.3060.3040.3080.3001
20191.2150.3480.3240.3120.3030.3040.3040.3030.3050.3001
20201.4600.3470.3220.3130.3030.3040.3040.3030.3060.3001
20211.5620.3530.3240.3140.3040.3040.3040.3040.3060.3001
20221.5620.3610.3280.3140.3040.3050.3040.3030.3060.3001
20231.1950.3390.3200.3110.3030.3040.3030.3030.3030.3001
@@ -8196,6 +8591,332 @@

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+Figure 21: Model results comparing last year’s selected model with one where the natural mortality matrix estimated from CEATTLE is used. +
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+Figure 22: Model results comparing last year’s selected model with one where the natural mortality matrix estimated from CEATTLE is used. +
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4 Omitting early CPUE data and foreign fishery data

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The SSC requested a model run where the early CPUE data were excluded. This was done and showed that the model was insensitive to the early CPUE data (Figure 23).

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+Figure 23: Model results comparing last year’s selected model with one where the early CPUE data are excluded. +
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The SSC also noted “Catch-at-age data provided by foreign fishing agencies in the pre-Magnuson era were not produced using the same aging criteria as the AFSC age-and-growth program. Consideration should be given to removal of these data from the assessment. A sensitivity test should be done to evaluate the effects of data removal on the assessment.”

+

While these data are already downweighted by the effective sample size, we ran the model with the foreign catch-at-age data removed by setting the sample size to 0.01 which effectively removed the impact of the data on the model. Results showed that the model was sensitive to the removal of the foreign catch-at-age data for the early period but had little impact on near-term trends (Figure 24). Interestingly, the stock-recruit relationship was sensitive to the removal of the early period, presumably because of the data included in the fitting the the SRR between 1978 and 1991 being downweighted (Figure 25).

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+Figure 24: Model results comparing last year’s selected model with one where the early CPUE data and the early age compositions are downweighted (effectively removed). Note that for the recruitment plot (top) the year-range is shifted to see the impact of model differences. +
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+Figure 25: Model results of the stock-recruit relationships comparing last year’s selected model with one where the early CPUE data and the early age compositions are downweighted (effectively removed). +
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5 Pollock movement issues

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In the 2011 EBS pollock assessment we compiled all of the available pollock survey biomass estimates from the Navarin/Anadyr region and found a modest positive relationship with bottom temperatures from the summer bottom trawl survey. Subsequent analyses provides additional support using moored sea-floor echo-sounders (Levine et al. (2024)). Connecting these studies with future temperature scenarios together with alternative fishing mortality rates in the Russian zone requires some additional developments. Such a research model evaluation has begun but it more work is neede before it can be included in the assessment.

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6 Further considerations of pollock and its role in the ecosystem

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As noted, above, the SSC requested an evaluation of the ecosystem function as part of the SRR consideration and Tier 1 control rules within the FMP. Their comment was:

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“The SSC would prefer not to make a risk table adjustment based on the difference from Tier 1 to Tier 3 again during the 2024 assessment cycle. The SSC requests that the next stock assessment bring back a new approach that may include development of a constant buffer based on factors extrinsic to the stock assessment (ecosystem function), or a better representation of the uncertainty in the Tier 1 and control rule calculations such that a reduction from maximum ABC is not needed every year.”

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National Standard 1 (NS1) of the Magnuson-Stevens Act states that: “Conservation and management measures shall prevent overfishing while achieving, on a continuing basis, the optimum yield from each fishery for the United States fishing industry.” This standard involves balancing the competing policy objectives of preventing overfishing and achieving the optimum yield (OY). The specification of reference points such as maximum sustainable yield (MSY), OY, overfishing limit (OFL), acceptable biological catch (ABC), and annual catch limit (ACL) are central to U.S. fisheries management. The NS1 guidelines provide guidance on the specification of these reference points and the control rules used to establish limit and target catch levels. The NS1 guidelines require that each Fishery Management Council specify within their fishing management plans an ABC control rule that accounts for scientific uncertainty in the OFL and for the Council’s risk policy. The ABC cannot exceed the OFL. Beyond that, the guidelines provide flexibility in how ABC control rules can be specified. Many Councils have developed tiered ABC control rules. And many ABC control rules have risk policies that use the P* approach, where ABC is based on scientific uncertainty around the OFL and an acceptable probability of overfishing (P). The choice of P is often explicitly based on the status of the stock and other biological and ecological factors. Risk policies also include an element of policy choice between being risk adverse or risk tolerant, and implicit in this are social and economic considerations. This presentation will discuss the NS1 guidance on ABC control rules, highlight some of the flexibilities, and provide a few examples of how those flexibilities have been applied in practice.

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The concern over the SSCs adjustment to the maximum permissible ABCs for EBS pollock stem from (in general) the magnitude of the ABCs and OFLs–they often exceed the 2 million t catch limit for the BSAI for all groundfish species combined. The over-arching TAC limit thus moderates the variability in advice from the Council to the Department of Commerce. As noted above, the SRR estimate is the main driver of the single-species ABCs. For context, the estimate for the long-term MSY is on the order of 2.2 million t of pollock catch. Over the past 4 decades, the actual EBS pollock catches have averaged about 1.3 million t.

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As a point of curiosity, we considered inverting the SRR productivity estimate by posing the question “what SRR would give a long-term expected MSY of 1.3 million t?” Additionally, would our estimates of uncertainty in the SRR curve overlap in a manner that would provide context for the management advice?

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We thus added a feature of the model where one can provide a condition that the SRR be consistent with a specified MSY value.
+As an experiment, we conditioned the SRR curve to have the MSY value set to 1.75 and 1.3 million t. We then compared those curves with the 2023 model specifications (Figure 26). When overplotted, the fit comparisons indicate somewhat worse fits to the available years of data (post 1977; Figure 27). However, the fits were reasonable within the estimates of uncertainty. Here we conclude that the management advice (under Tier 1) is sensitive to relatively small apparent perturbations in the shape of the stock-recruitment curves. Such sensitivity is expected given the sections above. Furthermore, we know that non-stationarity in the SRR is likely, especially given the uncertainties of climate change. An alternative ABC-setting rule may be preferred that stabilizes the advice while adhering to the NS1 guidelines.

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+Figure 26: SRR curves as estimated in the 2023 assessment (top) and conditioned on alternative Fmsy assumptions (middle and bottom). +
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+Figure 27: SRR curves as estimated in the 2023 assessment overlaid with those conditioned on alternative Fmsy assumptions. +
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6.0.1 Evaluating patterns in historical TACs relative to stock status

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In thinking about the role of pollock in the ecosystem and in relation to management advice within the fisheries management plan (FMP) and Tier system, we considered pollock as a key part of the forage base (say of 1-3 yr old pollock). A management goal with an explicit consideration of ecosystem function might be to avoid low levels of forage base. For example, if the forage base appeared to be close to say the lower 20th percentile from historical estimates, then a management might include an adjustment that would occur then to avoid any further declines (e.g., Figure 28).

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While conceptually appealing, in practical terms including an explicit adjustment might be difficult. For example, information on the abundance of those age classes would be limited. The historical abundance of the prey base compared to spawning biomass shows that the relationship between the two is poorly determined (Figure 29). However, the forage component does tend to decline at lower spawning biomass levels. This suggests that the practice of conserving spawning biomass may be linked to downstream impacts of the main forage ages.

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The FMP for the BSAI is subject to the categorization of the stock assessment to obtain the maximum permissible ABC and OFL. For pollock, this falls on the determination of the appropriateness of the \(F_{MSY}\) and the probability distribution of that value (i.e., the uncertainty estimates). Factors affecting this include the selectivity, the SRR, and future weight-at-age (\(F_{MSY}\) applies to numbers of fish, but ABC is in biomass). Within the FMP, the SSC can set the ABC below the maximum permissible value and for management, the Council can set the TAC below the ABC. In practice, the TAC has been set below the ABC for pollock in most years (in 18 out of the past 23 years from 2001-2023). For that same period, the total pollock catch was 99.4% of the TACs and the average ABC was 1.737 Mt compared to the mean TAC of 1.295 Mt. The TACs being lower than the ABCs is due to the 2-million t overarching limit on all BSAI groundfish TACs. One of the reasons for the limit is to account for ecosystem sustainability (Low (1983)). We examine a historical pattern of pollock ABCs and catches and present a model for future catches that could be more reflective of objectives related specifically to pollock within the ecosystem (compared to TAC setting within the 2 million t cap constraint).

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Such a simple rule would begin with incremental catch advice based on the recent catches and the spawning biomass relative to the historical mean. I.e., if the catch in the current year is say 1.2 million t, and the SSB next year is 30% above the mean, then with a regulator to dampen change, next year’s recommendation would be \(1.2 \times \sqrt{1.30} = 1.368\) million t. Similarly, if the SSB next year was only 75% of the mean value, the recommendation would be \(1.2 \times \sqrt{0.75} = 1.039\) million t. If the SSB stayed at 75% of the mean, then the following year would be \(1.039 \times \sqrt{0.75} = 0.8998\) million t. More formally,

+\[\begin{align} +A_{y+1} &= TAC_y \times \sqrt{\frac{B_{y+1}}{\bar{B}}} \\&=TAC_y \times [\frac{B_{y+1}}{\bar{B}}]^\lambda +\end{align}\] +

where \(A_{y+1}\) is the “advice” on catch, \(B_{y+1}\) is the spawning biomass projected in the coming year, \(\bar{B}\) is the mean spawning biomass and \(\lambda\) is a responsiveness parameter (set to 0.5 in this example).

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Results indicate that the combination of historical ABC and TAC decisions can be distilled to a simple formula for providing catch advice (Figure 31). This advice is intended to focus on the conservation goal of maintaining spawning biomass and provide ecosystem stability–similar to the over-arching principle of the 2 Mt catch limit for the BSAI region.

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Too evaluate performance of alternative pollock fishing at the ecosystem level, we examined results from a mass-balance ecosystem model (e.g., Whitehouse and Aydin (2020)). This model is based on the extensive diet composition data and is a version of Ecosim implemented using the software “Rpath” (Aydin, Lucey, and Gaichas (2024)). One scenario involved increasing pollock TACs to approximate Tier 1 ABC values. Another maintained fishing at status quo levels and a third removed the pollock fishing mortality altogether. Preliminary results indicated that under the increased fishing mortality scenario (to approximate fishing at Tier 1 ABC) there were relatively large changes in other species in the system. The runs with pollock fishing altogether removed had relatively minor changes to other species except that rockfish biomass tended to decrease (presumably because of increased competition for prey). In the status quo scenario, unlike the other two scenarios, the trends in other ecosystem components, while all highly uncertain, tracked well with patterns from the historical period.

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In summary, within the current structure of the FMP, we suggest that aligning advice with historical conditions could help with communicating stock conditions and avoid over-reliance on managing the stock based on the stock-recruitment relationship (as required under Tiers 1 and 2). Accepting that the SRR is reliably estimated implicitly assumes the SRR to be non-stationary. Additionally, as shown above, the number of observations near the origin affects the ability to reliably estimate productivity. As such, continued reliance on the SRR for tactical management might best be abandoned. We suggest that management recommendations might be better derived from decades of observed conditions. Adjustments to the tactical advice can then become more transparent and implicitly include ecosystem conditions and fishing communities that rely on them.

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The next steps for formalizing catch advice would likely require amending the FMP. Doing so should take on a full management strategy evaluation (MSE) approach. Consequently, we updated the pollock model so that if can behave as an operating model. This required some simplifications on how the bottom-trawl survey covariance structure was applied, in addition to a couple of other issues (e.g., the random-effects derived estimates of weight-at-age). The ability to test alternatives should also be able to accommodate plausible (and non-stationary) aspects of the stock-recruitment relationship. Additionally, the model should be able to accommodate movement patterns and other ecosystem considerations. Specifically, the interaction of potentially warming conditions and distribution changes that extend further into the Russian zone.

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+Figure 28: Historical age-1 to age-3 pollock abundance as estimated from the assessment model. +
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+Figure 29: Historical spawning biomass and ‘prey’ abundance for pollock as estimated from the assessment model. +
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+Figure 30: Historical spawning biomass relative to the mean for pollock as estimated from the assessment model. Red horizontal line is the mean value. +
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+Figure 31: Results from applying a simple catch-advice rule given historical spawning biomass projections (SSB.projected) compared to the actual catch (as a function of historical ABCs and TACs). A single value for responsiveness was used ( \(/lambda =0.5\) ). +
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7 Bayesian diagnostics

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In response to the SSC’s request to provide documentation on the convergence properties and other aspects of Bayesian integrations, we start by following the advice of Monnahan (2024). This involved performing multiple “no-U-turn sampler” (NUTS) chains and note that that the potential scale reduction \(̂\hat{R}\) was <1.01 and the effective sample sizes were >400 for all parameters. We also report on the presence of divergent transitions as part of the diagnostics and investigated how to implement the Pareto-smoothed importance sampling leave-one-out cross-validation (PSIS-LOO) method. We hope to have this for future analysis to assess model fit.

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7.1 Steps for judging model performance based on the posterior predictive distributions

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As in past years, we used posterior predictive checks to validate models by confirming simulated data were consistent with the observations. Investigations on how to produce standard figures we highlight those as follows.

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Process error variances can be estimated jointly with random effects and other parameters when desired, and should be for important model components. After obtaining posterior samples, let’s denote these samples as \(\theta_1\), \(\theta_2\), \(\ldots\), \(\theta_N\), where N is the number of posterior samples. Next is to generate predictive samples. For each posterior sample \(\theta_i\), generate a predictive sample \(y_i^{\text{pred}}\) from the likelihood function \(p(y \mid \theta_i)\). These predictive samples form the posterior predictive distribution. This step is repeated for each posterior sample, resulting in a collection of predicted values.

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To judge model performance, compare the observed data \(y_{\text{obs}}\) with the posterior predictive distribution. A common approach is to use the percentile (or quantile) of the observed data in the posterior predictive distribution.

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For each observed data point \(y_{\text{obs}}\), calculate its percentile in the corresponding posterior predictive distribution. This is done by determining the proportion of predictive samples that are less than or equal to the observed value.

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Given M predictive samples \(y_1^{\text{pred}}\), \(y_2^{\text{pred}}\), \(\ldots\), \(y_M^{\text{pred}}\). The percentile p of the observed value \(y_{\text{obs}}\) can be computed as:

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\(p = \frac{\sum_{j=1}^M \mathbb{I}(y_j^{\text{pred}} \leq y_{\text{obs}})}{M}\)

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Here, \(\mathbb{I}(\cdot)\) is an indicator function that returns 1 if the condition inside is true, and 0 otherwise. This gives the count the predictive samples are less than or equal to the observed value divided by the total number of samples.

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Under a well-calibrated model, the observed data should fall uniformly across the range of the posterior predictive distribution. This implies that the percentiles should be roughly uniformly distributed between 0 and 1. A histogram of these percentiles can be evaluated relative to a uniform distribution. Significant deviations from uniformity may indicate model misspecification.

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Posterior predictive checks can also be facilitated by visualizing the posterior predictive distribution. This can includes comparing the observed data’s summary statistics (e.g., mean, variance) relative to those of the posterior predictive distribution.

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Updating the 2023 model for MCMC runs using ADNUTS, we were able achieve reasonable statistics but some divergent transitions remained. Investigations showed that they were reasonable. Figure 32 shows some of the summary figures related to the MCMC sampling and Figure 33 shows the relationship of the slowest mixing parameters. The result from sampling showed that for the 1340 parameters, there was 1,000 iterations over 8 chains with an average run time per chain of 12 minutes. The minimum effective sample size was 2,756 (36.26%) and and the maximum \(\hat{R}\) was 1.004 with 21 divergences.

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+Figure 32: Diagnostic output for ADNUTS sampling for the 2023 EBS pollock model. +
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+Figure 33: Diagnostic output showing the slow mixing parameters of the 2023 EBS pollock model posterior sampling. +
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8 Alternative software platforms

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There is continued interest in using alternative software platforms for this assessment. A repository was developed for these alternatives here. The main reason for this is to provide options for upgrading the base software and providing some of the trade-offs between tailored assessments and general packages. In addition to the current model used for EBS pollock, alternaive platforms considered were:

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  • Stock Synthesis 3: A very popular software platform

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  • GOA pollock model: A customized program convertible between ADMB and TMB

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  • SAM: A state-space model for age-structured assessments

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  • AMAK: A general model assessment model developed to have flexible number of fisheries, indices etc.

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  • WHAM: The Woods Hole Assessment Model (written in TMB…withdrawn from this presentation due to limits on time)

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Each platform was intended to include as much of the configuration and baseline data from the pollock model as possible. Very little effort was made to do fine-scale bridging.

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In subsequent sections we compare how the selectivity estimates compare, along with spawning biomass and recruitment.

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8.1 Comparing base results over different platforms

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Our first pass at comparing models involved examining how selectivity could be specified and fit with the different platforms. The success in coming close to matching the pattern estimated in the present assessment varied among different platforms (Figure 34). An alternative presentations shows the differences by age over time (Figure 35). These preliminary runs over different platforms showed important differences in spawning biomass and recruits with the current assessment coming in below expectations (Figure 36). The differences for the SRR were also noteworthy (Figure 37).

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+Figure 34: Comparison of the time series of selectivity estimates over different modeling platforms. +
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+Figure 35: Comparison of the time series of selectivity-at-age estimates over different modeling platforms. +
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+Figure 36: Comparison of the time series of age-1 recruitment (top) and spawning biomass (bottom) estimates over different modeling platforms. +
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+Figure 37: Comparison of the stock-recruitment relationships between different modeling platforms. +
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8.2 Additional AMAK runs

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In an attempt to get different platforms closer to eachother, some tuning of the model specification for AMAK was undertaken. This included some comparisons with the early CPUE data added, and with a newly developed 3-parameter double logistic parameterizations:

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  • base: selectivity at age allowed to vary (sigma penalty=0.7)

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  • cpue: As base but with the early CPUE data included

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  • dbl_logistic: selectivity at age with TV selectivity parameters (3-parameter logistic)

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Figure 39: Comparison of the selectivity-at-age estimates between different modeling specifications in AMAK @@ -8228,7 +8949,7 @@

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Figure 40: Comparison of the selectivity-at-age estimates between different modeling specifications in AMAK @@ -8241,7 +8962,7 @@

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Figure 41: Comparison of the fit to indices between different modeling specifications using the AMAK software platform. @@ -8302,7 +9023,7 @@

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9 References

Aydin, Kerim, Sean Lucey, and Sarah Gaichas. 2024. Rpath: R Implementation of Ecopath with Ecosim. https://github.com/NOAA-EDAB/Rpath. @@ -8337,13 +9059,21 @@

Monnahan, Cole C. 2024. “Toward Good Practices for Bayesian Data-Rich Fisheries Stock Assessments Using a Modern Statistical Workflow.” Fisheries Research 275: 107024. https://doi.org/https://doi.org/10.1016/j.fishres.2024.107024.

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+Stock, Brian C., and Timothy J. Miller. 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.” Fisheries Research 240 (August): 105967. https://doi.org/10.1016/j.fishres.2021.105967. +
Trijoulet, Vanessa, Gavin Fay, and Timothy J. Miller. 2020. “Performance of a State-Space Multispecies Model: What Are the Consequences of Ignoring Predation and Process Errors in Stock Assessments?” Journal of Applied Ecology 57 (1): 121–35. https://doi.org/https://doi.org/10.1111/1365-2664.13515.
Whitehouse, George A., and Kerim Y. Aydin. 2020. “Assessing the Sensitivity of Three Alaska Marine Food Webs to Perturbations: An Example of Ecosim Simulations Using Rpath.” Ecological Modelling 429: 109074. https://doi.org/https://doi.org/10.1016/j.ecolmodel.2020.109074.
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Footnotes

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  1. This modification of Tier 2 simply applies the harmonic mean of \(F_{MSY}\) instead of the arithmetic mean to make it slightly more precautionary.↩︎

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