ebswp.qmd

---
title: "1. Stock assessment for eastern Bering Sea walleye pollock"
author: 
 -  name: James Ianelli 
    corresponding: true
    id: ji
    orcid: 0000-0002-7170-8677
    email: jim.ianelli@noaa.gov
    affiliation: 
      - name: Alaska Fisheries Science Center
        city: Seattle
        state: WA
        url: https://www.fisheries.noaa.gov/alaska/population-assessments/north-pacific-groundfish-stock-assessments-and-fishery-evaluation
 - name: Taina Honkalehto
 - name: Sophia Wassermann
 - name: Nathan Lauffenburger
 - name: Carey McGilliard
 - name: Elizabeth Siddon
keep-md: true
keep-tex: true
# csl: cjfas.csl
date: 11/03/2023
date-format: "MMMM YYYY"
bibliography: doc/references.bib
header-includes:
  - \usepackage{multirow}
  - \usepackage{hyperref}
  - \usepackage[capitalise,noabbrev]{cleveref}
fig-pos: H
tbl-pos: H
format: 
  pdf:
    number-pages: FALSE
    number-sections: TRUE
    number-offset: 1
    number-depth: 2
    includes:
      in_header: "doc/preamble.tex"
    extra_dependencies: rotating
editor_options: 
  chunk_output_type: console
---

```{r setup}
#| echo: false
#| warning: false

# source(here::here("R","prelims.R"))
library(here)
library(xtable)
library(gt)
# load(file="doc/septmod.rdata")
# load(file="doc/modtune.rdata")
# names(modlst) <-  c("Last year", "Diagonal cov BTS","GenGam","SSB0","SSB1","SSB2","AVO new","AVO full")

source(here("pm23.R"))
# load(file="doc/novmod.rdata")
# names(modlst) <-  c("Last year", "Diagonal cov BTS","GenGam","SSB0","SSB1","SSB2","AVO new","AVO full")
mod_names <- names(modlst)
knitr::opts_chunk$set(fig.pos = "H", out.extra = "")
# \label{tab:modcomp}
#        #\usepackage{fancyhdr}
# \pagestyle{fancy}
# \fancyhead[C]{Draft}
# \fancyfoot[C]{EBS pollock assessment}
```
```{=tex}
\begin{centering}
  Alaska Fisheries Science Center, National Marine Fisheries Service \\
  National Oceanic and Atmospheric Administration \\ 
  7600 Sand Point Way NE., Seattle, WA 98115-6349 \\ 
    \end{centering}
```


------------------------------------------------------------------------
This report may be cited as:  Ianelli, J. et al. 2023. Assessment of the 
eastern Bering Sea walleye pollock.  North Pacific Fishery Management Council, 
Anchorage, AK. Available [here](https://www.npfmc.org/library/safe-reports/).
------------------------------------------------------------------------

\thispagestyle{empty}

\newpage


# Executive summary
This chapter covers the Eastern Bering Sea (EBS) region---the Aleutian
Islands region (Chapter 1A) and the Bogoslof Island area (Chapter 1B)
are presented separately. A multi-species stock assessment is provided
separately and available
[here](https://apps-afsc.fisheries.noaa.gov/Plan_Team/2023/EBSmultispp.pdf).
A list of this document contents, including tables and figures is provided 
in @sec-contents.

### Summary of changes in assessment inputs
Relative to last year's BSAI SAFE report, the following substantive
changes have been made in the EBS pollock stock assessment. This
includes the `r thisyr` NMFS bottom-trawl survey (BTS) covering the EBS
and NBS. As before, these data were treated with a spatio temporal model
for index standardization. Age data from this survey effort was compiled
and included (also with an extensive spatio-temporal model treatment).
The NMFS acoustic-trawl survey (ATS) age composition data was revised from the
preliminary estimates developed in 2022.
The BTS chartered boats also collected acoustic data and the series was updated
this year (AVO). Explorations were presented in @ianelli2023.

#### Changes in the data
1.  Observer data for catch-at-age and average weight-at-age from the
    `r lastyr` fishery were finalized and included.

2.  Total catch as reported by NMFS Alaska Regional office was updated
    and included through `r thisyr`.

3.  In summer `r thisyr`, the AFSC conducted the bottom trawl survey in
    the EBS and extended into the NBS. A VAST model evaluation
    (including the cold-pool extent) was used as the main index.

4.  We refined estimates of weight-at-age data used to compute spawning
    biomass as presented to the Plan Team and SSC in September/October
    2023 (see @ianelli2023 for details).

5.  We applied a new time series from the acoustic data
    collected from the bottom trawl survey covering 2006-2023 (except
    for 2020) as presented in @ianelli2023.

6.  We applied updated age-composition from the 2022 ATS survey (last
    year a preliminary estimate was used based on the BTS age-length
    data plus a juvenile sample from the ATS.

### Changes in the assessment methods
The assessment method changes were presented in September 2023
(@ianelli2023). In that document we re-evaluated the relative weights
specified as input variances and sample sizes. We also applied different
tuning approaches which achieved a balance between the observation
errors and model process errors, specifically for the acoustic time
series. We examined additional model sensitivities under development that included
an exploration of using a generalized Gamma distribution for the bottom trawl index, 
age-determination errors, and simplified error structures for the bottom trawl index.
We adopted alternative estimates of weight-at-age applied to
the spawning biomass calculations. The modified estimates are intended
to reflect data available closest to the peak spawning season. The Plan Team and
SSC agreed with the data and model changes, and those present the new base case 
presented here.

## Summary of EBS pollock results
The results from the 2022 assessment have largely been confirmed: the
2018 year class appears to be one of the most abundant on record.
Nonetheless, the bottom-trawl survey was lower than expected (about 28%
below the long-term mean and the tenth lowest over the 41-year survey
period). The new AVO index (presented in September 2023) expanded the
area covered by acoustics and provided more precision (lower CV in the point estimates)
than in the  past. Ancilliary data indicate that the pollock in 2023 are substantially
skinnier than average given their length. The average weight-at-age was about 
average for the 2018 year class, but lighter for most other ages.

The following table is based on results from the selected model ("Model
23.0") based on changes presented in @ianelli2023. The ABC
recommendation is based on Tier 3 calculation as a proxy for Tier 1
because of the variability indicated by the very high value based on the
$F_{MSY}$ estimate and the large but uncertain 2018 year class.

```{=tex}
\begin{table}[ht]
\scalebox{0.95}{
\centering
\begin{tabular}{lrr|rr}
  \hline
       & \multicolumn{2}{c|}{As estimated or $\mathit{specified}$ } & \multicolumn{2}{c}{As estimated or $\mathit{recommended}$ }  \\
       & \multicolumn{2}{c|}{$\mathit{last}$ year for:}  & \multicolumn{2}{c}{$\mathit{this}$ year for: }               \\
        Quantity & `r thisyr`      &`r thisyr+1`   & `r thisyr+1`      &`r thisyr+2` \\ 
  \hline
  M            (natural mortality rate, ages 3+)    &   0.3 &   0.3 &   0.3 &   0.3 \\
  Tier         &    1a  &   1a  &   `r M$tier1ab1 ` & `r M$tier1ab2 ` \\
  Projected    total (age  3+) biomass (t)     & `r M$it$a1[1]` t & `r M$it$a2[1]` t & `r  M$age3plus1s` t & `r  M$age3plus2s` t \\
  Projected    female  spawning  biomass (t)   & `r M$it$b1[1]` t & `r M$it$b2[1]` t & `r  M$ssb1s`  t & `r  M$ssb2s`  t \\  
  $B_0$        & `r M$it$c1[1]` t & `r M$it$c2[1]` t & `r  M$b0s`,000  t & `r  M$b0s`,000  t \\          
  $B_{msy}$    & `r M$it$d1[1]` t & `r M$it$d2[1]` t & `r  M$bmsys`,000  t & `r  M$bmsys`,000  t \\          
  $F_{OFL}$    & `r M$it[1,8]` & `r M$it[2,8]` & `r  M$FOFL1s`  & `r  M$FOFL2s `  \\                  
  $maxF_{ABC}$ & `r M$it[1,9]` & `r M$it[2,9]` & `r  M$FABC1s `  & `r  M$FABC2s `  \\                  
  $F_{ABC}$    & `r M$it[1,10]`& `r M$it[2,10]`& `r  (M$fabc1)` & `r  (M$fabc2)` \\                  
  $OFL$        & `r M$it$e1[1]` t & `r M$it$e2[1]` t & `r  M$ofl1s`  t & `r  M$ofl2s`  t \\          
  $maxABC$     & `r M$it$f1[1]` t & `r M$it$f2[1]` t & `r  M$maxabc1s  ` t & `r  M$maxabc2s  ` t \\      
  $ABC$        & `r M$it$g1[1]` t & `r M$it$g2[1]` t & `r  M$abc1s ` t & `r  M$abc2s`  t \\        
  \hline
  Status                                  & `r lastyr-1`  & `r lastyr`&         `r lastyr`             &    `r thisyr`          \\
  \hline
  Overfishing                             & No          &   n/a       & No                         &    n/a                 \\
  Overfished                              & n/a         &   No        & n/a                        &    No                  \\
  Approaching overfished                  & n/a         &   No        & n/a                        &    No                  \\
  \hline
\end{tabular}
}
\end{table}
```

## Response to SSC and Plan Team comments
### SSC General groundfish stock assessment comments
The following are relevant SSC comments from their December 2022
minutes.

-   The SSC recommends that for future Tier 1-3 assessments some
    consideration be given as to how best to represent biomass estimates
    in the Executive Summary table for each stock (currently, model
    total biomass and spawning stock biomass are provided) so that the
    relationship of the biomass to the OFL and ABC in the stock status
    table is clear. - *We agree. Within the document we include biomass
    estimates that are outcomes for ABC and OFL calculations. However,
    the estimates involve an application of expected age-specific
    selectivity which can be variable.*

-   For all assessments using VAST, the SSC requests a figure comparing
    the VAST estimate used in the previous assessment to the current
    assessment (if new data are added), noting that VAST will refit the
    time series when additional data are added and the estimated extent
    and directionality of spatial correlation may change. - *We include
    model comparisons showing the impact of new (updated) VAST time
    series.*

**The SSC suggests that walleye pollock is a good candidate for
considering the impacts of highly variable recruitment on reference
points in the context of the Council's harvest control rules** (see
discussion on working groups in the JGPT report section). For example,
the SAFE authors suggested exploring an explicit harvest control rule
that maintains productivity at the level observed over recent decades
(p. 33). The SSC supports considerations of modified harvest control
rules, particularly for stocks with highly variable and uncertain
recruitment. If the Council chooses, this could include considerations
for stabilizing catches over time or including other economic
considerations in the harvest control rules.

-   *For the 2023 assessment we examine the variability of the
    biological reference points historically and note that there is
    general stability in the* $B_{MSY}$ estimates. The Tier 1 ABC/OFL
    calculations can result in highly variable estimates as the stock
    approaches $B_{MSY}$ and drops below that value (as happened in the
    2009-2010 period).

The SSC had the following additional recommendations for the authors:

-   Maturity and growth information from the NBS has not been examined
    yet. Given the possible importance of the NBS to walleye pollock and
    other species in the future, the SSC suggests this should be a high
    priority.

    -   *These data are being processed and this work is underway*

-   The SSC supports efforts to implement recent advances in improving
    the statistical treatment of compositional data using the Dirichlet
    distribution or other approaches.

    -   *Tradeoffs in data weighting were pursued in September 2023 and
        sought to find a balance between observation error and process
        errors. In general, trade-offs in data weighting appear
        consistent with sampling levels and include objective approaches
        to their specifications.*

-   The SAFE document lists a number of research recommendations (p.
    36/37). The SSC notes that some of these are at least in progress.
    The SSC generally supports these recommendations but requests that
    the authors update the list of priorities to clarify to what extent
    some of these priorities have been partially or fully addressed.

    -   *We updated the priorities and listed those that have been
        completed or are continued to be underway*

-   In particular, the SSC notes that genetic sample collection and
    analyses are listed as a research priority across all pollock stocks
    and that some work has been completed. The SSC highlights the
    importance of additional genetics work and would appreciate an
    update on the status of this work either as part of the assessment
    or separately.

    -   *We revisited the stock structure work attached as an appendix
        to the 2015 SAFE report chapter and are examining the extent
        that this work needs updating. Updated genetics work indicate
        that the Bering Sea pollock represent a distinct stock. This
        work indicates that GOA pollock seems to be similar to some
        Aleutian pollock but some AI pollock are distinct (Spies and
        Schaal, pers. comm.).*

-   The SSC appreciated the adjustments to weight-at-age in the survey
    that was included in this year's assessment and suggests that these
    changes may be substantial enough to warrant an examination of their
    impact on assessment results.

    -   *This publication has been completed and in the present
        assessment we evaluated the implication of alternative spawning
        biomass-at-age assumptions.*

-   With respect to the multi-species CEATTLE model, the SSC concurs
    with Plan Team recommendations to use the model to inform risk table
    discussions and to consider ways in which model outputs, in
    particular estimates of predation mortality, can inform
    single-species assessments.

    -   *We included some comments to this effect.*

-   The SSC encourages the authors to consider model-based solutions to
    uncertain recruitment estimates rather than ad-hoc adjustments. In
    particular, reductions in the assumed recruitment variance parameter
    may result in less extreme recruitment estimates. Other systematic
    approaches to addressing the uncertainty may also be considered.

    -   *We revisited applying the age-determination error matrix as a
        sensitivity as this can impact the recruitment variability and
        estimation uncertainty.*

-   The SSC suggests that authors include a plot to compare estimates of
    recent recruitments as they change over time similar to Fig 3.33
    (pg. 88) in the sablefish assessment.

    -   *We provide a figure of estimated recruitment by year class
        (1977 -- 2019) in number of age-1 fish (billions of fish) for
        the 2022 and 2023 models.*

-   The SSC supports the move across assessment from design-based
    estimates of survey biomass to VAST estimates. The SSC recommends
    that the design-based estimates be produced as a check on VAST
    estimates and as a fallback option if needed, although they may not
    need to be included in the assessment.

    -   *We provide a table showing the design-based estimates and
        conduct a model run with those estimates. This may be an
        approach to adopt so that bridging across assessment modeling
        platforms can be facilitated (most other assessment model
        platforms are unable to deal with index time series that have a
        covariance matrix)*

-   The SSC noted that a consideration of whether the observed sensitivity in the SRR to
    prior specification should constitute an increased risk level
    specification within the assessment or population dynamics related
    considerations should be considered. This could provide a clearer justification for the
    use of the Tier 3 calculation as the basis for harvest
    specification.

    -   *We evaluated factors affecting the Tier classification in the
        2020 assessment and showed that the priors used reflect the SRR
        curve were conservative and justified based on residual patterns
        near the origin (as opposed to alternatives that fit data on the
        descending slope of the Ricker SRR.*

-   The SSC recommends that if the assessment is considered in the
    appropriate Tier, buffers should be based on the use of the Risk
    Table rather than the continued use of Tier 3 calculations for a
    Tier 1 stock.

    -   *We agree.*

-   The SSC also notes that an alternative approach to consider for a
    buffer below the maximum permissible would be apply Tier 2 control
    rule. This tier uses the SR relationship for stock status and OFL,
    but uses the ratio of SPR rates for adjustments when the stock is
    below $B_{MSY}$.

    -   *An examination of Tier 2 as an option resulted in a value of
        `r M$Tier2_ABC1s` t (or a hybrid of Tier 1 and 2 of
        `r M$Tier1.5_ABC1s` t) for `r nextyr` ABC values. We note that
        selecting Tier 2 would require similar reliance on the
        underlying productivity estimates (via the stock-recruitment
        relationship) and how that affects the reference fishing rate
        (*$F_{MSY}$).

------------------------------------------------------------------------

**The SSC had a number of recommendations for additional research
supporting this assessment:**

**From previous requests:**

The SSC also looks forward to estimates of movement and abundance along
the US-Russia EEZ boundary based on echosounders fixed to moorings in
this area.

-   *The data evaluation from the moored sounders has been completed and
    initial results show that the flux of pollock back and forth over
    the maritime boundary is considerable, and appears to be a function
    of temperature conditions.*

\clearpage

# Introduction

## General

Walleye pollock (*Gadus chalcogrammus*; hereafter referred to as
pollock) are broadly distributed throughout the North Pacific with the
largest concentrations found in the Eastern Bering Sea. Also known as
Alaska pollock, this species continues to play important roles
ecologically and economically.

## Review of Life History

In the EBS pollock generally spawn during March-May and in
relatively localized regions during specific periods (@bailey2000 ).
Generally spawning begins nearshore north of Unimak Island in March and
April and later near the Pribilof Islands (@bacheler2010). Females are
batch spawners with up to 10 batches of eggs per female per year (during
the peak spawning period). Eggs and larvae of EBS pollock are planktonic
for a period of about 90 days and appear to be sensitive to
environmental conditions. These conditions likely affect their dispersal
into favorable areas (for subsequent separation from predators) and also
affect general food requirements for over-wintering survival (@gann2015,
@heintz2013, @hunt2011, @ciannelli2004). @duffy2016 provide a review of
the early life history of EBS pollock.

Throughout their range juvenile pollock feed on a variety of planktonic
crustaceans, including calanoid copepods and euphausiids. In the EBS
shelf region, one-year-old pollock are found throughout the water
column, but also commonly occur in the NMFS bottom trawl survey. Ages 2
and 3 year old pollock are rarely caught in summer bottom trawl survey
gear and are more common in the midwater zone as detected by mid-water
acoustic trawl surveys. Younger pollock are generally found in the more
northern parts of the survey area and appear to move to the southeast as
they age (@buckley2009). Euphausiids, principally *Thysanoessa inermis*
and *T. raschii*, are among the most important prey items for pollock in
the Bering Sea (@livingston1991; @lang2000; @brodeur2002;
@ciannelli2004; @lang2005). Pollock diets become more piscivorous with
age, and cannibalism has been commonly observed in this region. However,
@buckley2015 showed spatial patterns of pollock foraging varies by size of
predators. For example, the northern part of the shelf region between
the 100 and 200 m isobaths (closest to the shelf break) tends to be more
piscivorous than pollock found in more near-shore shallow areas.

## Stock structure
Stock structure for EBS pollock was evaluated in @ianelli2015. 
In that review past work on genetics (e.g., @bailey1999, @canino2005) 
provided insight on genetic differentiation.
The investigation also compared synchrony in year-classes and growth
patterns by region. Pollock samples from areas including Zhemchug Canyon, Japan,
Prince William Sound, Bogoslof, Shelikof, and the Northern Bering Sea
were processed and results presented in @ianelli2021.


A group of researchers at AFSC led by Drs. Ingrid Spies and Sara Schaal
have updated the recent genetics study and this is summarized here:

> Adult samples of walleye pollock were collected from 15 locations
> spanning Japan, the Bering Sea, and the Gulf of Alaska and were used
> for genetic analysis. Researchers performed low-coverage whole genome
> sequencing on 547 individuals from these sampling locations, which
> resulted in roughly 2 million polymorphic loci found throughout the
> genome. Although genetic differentiation is subtle in walleye pollock
> compared to other species, there are two strong and notable genetic
> breaks that highlight the genetic stock structure present. The first
> is between all the US samples and Japan (@fig-genetics: the split on
> PC axis 1) and the second is between the Bering Sea and the
> GOA/Aleutian Islands (@fig-genetics: the split on PC axis 2). This
> suggests that Bering Sea walleye pollock are genetically distinct from
> GOA/Aleutian Islands walleye pollock. Walleye pollock in the Aleutian
> Islands and Bogoslof show weak divergence with the GOA. Individuals
> caught in Adak and Atka comprise two genetic groups (@fig-genetics:
> dark green points). One group is not differentiated from the rest of
> the GOA (@fig-genetics : dark green points clustering behind the GOA
> points in pink) and the other shows some divergence along PC axis 1.
> This complex group warrants further investigation to understand the
> genomic regions that may be divergent between these two genetic groups
> present in the Adak and Atka. Additionally, individuals from Bogoslof
> show weak divergence from GOA samples (@fig-genetics: light green
> points). Walleye Pollock are currently managed as four distinct
> groups: 1) GOA, 2) Bering Sea, 3) Aleutian Islands and 4) Bogoslof.
> Our genetic groups mostly align with these delineations. However, the
> Aleutian Islands/Bogoslof stocks show subtle differentiation from the
> rest of the GOA.

For management purposes, the preliminary conclusions from these genetics
results are: 1) there is stock structure in pollock that appears to be
stable through time and 2) Some aspect of stock structure is
latitudinal---Bering Sea pollock appear distinct from fish collected
from the Gulf of Alaska and the Aleutian Islands. The results appear
strong enough that a GTseq panel could be designed in the future to
determine stock of origin of pollock, the scale of which may be
relatively large, such as "Bering Sea" or "GOA".

# Fishery

## Description of the directed fishery

Historically, EBS pollock catches were low until directed foreign
fisheries began in 1964. Catches increased rapidly during the late 1960s
and reached a peak in 1970--75 when they ranged from 1.3 to 1.9 million
t annually. Following the peak catch in 1972, bilateral agreements with
Japan and the USSR resulted in reductions. During a 10-year period,
catches by foreign vessels operating in the "Donut Hole" region of the
Aleutian Basin were substantial totaling nearly 7 million t
(\cref{tab:catch}). A fishing moratorium for this area was enacted in
1993 and only trace amounts of pollock have been harvested from the
Aleutian Basin region since then. Since the late 1970s, the average EBS
pollock catch has been about 1.2 million t, ranging from 0.810 million t
in 2009 to nearly 1.5 million t during 2002--2006 (\cref{tab:catch}).
United States vessels began fishing for pollock in 1980 and by 1988 the
fishery became fully domestic. The current observer program for the
domestic fishery formally began in 1991 and prior to that, observers
were deployed aboard the foreign and joint-venture operations since the
late 1970s. From the period 1991 to 2011 about 80% of the catch was
observed at sea or during dockside offloading. Since 2011, regulations
require that all vessels participating in the pollock fishery carry at
least one observer so nearly 100% of the pollock fishing operations are
monitored by scientifically trained observers. Historical catch
estimates used in the assessment, along with management measures (i.e.,
OFLs, ABCs and TACs) are shown in (\cref{tab:abctac}).

### Catch patterns

The "A-season" for directed EBS pollock fishing opens on January 20th
and fishing typically extends into early-mid April. During this season
the fishery targets pre-spawning pollock and produces pollock roe that,
under optimal conditions, can comprise over 4% of the catch in weight.
The summer, or "B-season" presently opens on June 10th and fishing
extends through noon on November 1st. The A-season fishery concentrates
primarily north and west of Unimak Island depending on ice conditions
and fish distribution. There has also been effort along the 100m depth
contour (and deeper) between Unimak Island and the Pribilof Islands. The
general pattern by season (and area) has varied over time with recent
B-season catches occurring in the southeast portion of the shelf (east
of 170$^\circ$W longitude; @fig-catch). 

Since 2011, regulations and
industry-based measures to reduce Chinook salmon bycatch have affected
the spatial distribution of the fishery and to some degree, the way
individual vessel operators fish (@stram2014). Comparing encounters of
bycatch relative to the effort (total duration of all tows) the pollock
fleet had a slight increase in the Chinook salmon bycatch rate
(@fig-fsh_psc_cpue). The nominal catch rate of sablefish in the pollock
fishery continue to be above historical averages (@fig-fsh_psc_cpue)
while for herring, the rate was low compared to 2020.

The catch estimates by sex for the seasons indicate that over time, the
number of males and females has been fairly equal but in the period
2017-2022 the A-season catch of females has been slightly higher and
conversely, in the B-season there has been a slightly higher number of
males taken (@fig-catch_sex). The pattern of catch numbers is impacted
by the magnitude of the quota (e.g., the drop in 2022 when the TAC was
lower) but also in the relative size of fish. For example, in 2020 estimated
absolute numbers of catch were relatively high because fish were smaller
(and younger) than average. 

The 2023 A-season fishery spatial pattern had a
relatively more catch around the Pribilof Islands compared to 
2021 (@fig-catch_distn_a). The amount of fishing near the Pribilof
Islands was lower than commonly observed in 2022. The 2023 A-season
nominal catch rates were near peak levels for all fleet sectors (middle panel,
@fig-fsh_cpue). Beginning in 2017, due to a regulatory
change, up to 45% of the TAC could be taken in the A-season (previously
only 40% of the TAC could be taken). This conservation measure was made
to allow greater flexibility to avoid Chinook salmon in the B-season.
The pollock fleet as a whole continues to take advantage of this
flexibility (@fig-prop_a_season). This figure shows that the proportion
of the TAC has been consistent over time. 
Pollock roe production remains at a low level but increased over 2022
(@fig-roe).

The summer-fall fishing conditions for 2023 were similar to 2022
(@fig-fsh_cpue). The number of hours the fleet required to catch the same tonnage of pollock
was also improved relative to 2020. 
In the B-season catches in the
northwestern area increased relative to the previous two years
(@fig-catch_distn_b). We updated our work on 
a measure of fleet dispersion: the relative distance or spread of
the fishery in space. Briefly, the calculation computes for a given day,
the distance between all trawl tows (within and across boats). These
distances are then averaged for year and season. Updated to this year,
results indicated that in the A-season dispersion increased slightly but for
the B-season in 2023, the fleet appeared to be less dispersed than
all the other years and since 2000  (@fig-fleet_dispersal).

We continued to investigate the tow specific mean weight of fish. These
provide a direct mean somatic mass (pollock body weight) for pollock
within a tow. The data arise from the sampled total weight (e.g., of
several baskets of pollock) divided by the enumerated number of fish in
that sample. Such records exist for each tow. Summing these by
extrapolated weight of the pollock catch within that tow, and binning by
weight increments (here by 50 gram intervals), allows us to obtain some
additional fine-scale information on the size trends in the pollock
fishery. The annual patterns of these data suggest that the 2023
A-season size was consistent with the expectation of the 2018 year class
predominating the catch (@fig-fsh_wt_freq). However, the 
2023 B-season pattern was smaller than expected.
Compiling the data by week we
show that the fish size was consistent with the pattern of fish being consistently
smaller than expected through the  B-season (@fig-fsh_wt_freq_week).

The catch of EBS pollock has averaged 1.26 million t in the period since
1979. The lowest catches occurred in 2009 and 2010 when the limits were
set to 0.81 million t due to stock declines (\cref{tab:abctac}). The
recent 5-year average (2019-2023) catch has been 1.304 million t.
Pollock catches that are retained or discarded (based on NMFS observer
estimates) in the Eastern Bering Sea and Aleutian Islands for
1991--`r thisyr` are shown in \cref{tab:catchdisc}. Since 1991,
estimates of discarded pollock have ranged from a high of 9.6% of total
pollock catch in 1991 to recent lows of around 0.6% to 1.2%. These low
values reflect the implementation of the NMFS' Improved Retention
/Improved Utilization program. Prior to the implementation of the
American Fisheries Act (AFA) in 1999, higher discards may have occurred
under the "race for fish" and pollock marketable sizes were caught
incidentally. Since implementation of the AFA, the vessel operators have
more time to pursue optimal sizes of pollock for market since the quota
is allocated to vessels (via cooperative arrangements). In addition,
several vessels have made gear modifications to avoid retention of
smaller pollock. In all cases, the magnitude of discards counts as part
of the total catch for management (to ensure the TAC is not exceeded)
and within the assessment. Bycatch of other non-target, target, and
prohibited species is presented in the section titled Ecosystem
Considerations below. In that section it is noted that the bycatch of
pollock in other target fisheries is more than double the bycatch of
other target species (e.g., Pacific cod) in the pollock fishery.

## Management measures

The EBS pollock stock is managed by NMFS regulations that provide limits
on seasonal catch. The NMFS observer program data provide near real-time
statistics during the season and vessels operate within well-defined
limits. In most years, the TACs have been set well below the ABC value
and catches have stayed within these constraints \cref{tab:abctac}).
Allocations of the TAC split first with 10% to western Alaska
communities as part of the Community Development Quota (CDQ) program and
the remainder between at-sea processors and shore-based sectors. For a
characterization of the CDQ program see @haynie2014. @seung2016 combined
a fish population dynamics model with an economic model to evaluate
regional impacts.

Due to concerns that groundfish fisheries may impact the rebuilding of
the Steller sea lion population, a number of management measures have
been implemented over the years. Some measures were designed to reduce
the possibility of competitive interactions between fisheries and
Steller sea lions. For the pollock fisheries, seasonal fishery catch and
pollock biomass distributions (from surveys) indicated that the apparent
disproportionately high seasonal harvest rates within Steller sea lion
critical habitat could lead to reduced sea lion prey densities.
Consequently, management measures redistributed the fishery both
temporally and spatially according to pollock biomass distributions.
This was intended to disperse fishing so that localized harvest rates
were more consistent with estimated annual exploitation rates. The
measures include establishing: 1) pollock fishery exclusion zones around
sea lion rookery or haulout sites; 2) phased-in reductions in the
seasonal proportions of TAC that can be taken from critical habitat; and
3) additional seasonal TAC releases to disperse the fishery in time.

Prior to adoption of the above management measures, the pollock fishery
occurred throughout each of the three major NMFS management regions of
the North Pacific Ocean: the Aleutian Islands (1,001,780 km$^2$ inside
the EEZ), the Eastern Bering Sea (968,600 km$^2$), and the Gulf of
Alaska (1,156,100 km$^2$). The marine portion of Steller sea lion
critical habitat in Alaska west of 150$^{\circ}$W encompasses 386,770
km$^2$ of ocean surface, or 12% of the fishery management regions.

From 1995--1999 84,100 km$^2$, or 22% of the Steller sea lion critical
habitat was closed to the pollock fishery. Most of this closure
consisted of the 10 and 20 nm radius all-trawl fishery exclusion zones
around sea lion rookeries (48,920 km$^2$, or 13% of critical habitat).
The remainder was largely management area 518 (35,180 km$^2$, or 9% of
critical habitat) that was closed pursuant to an international agreement
to protect spawning stocks of central Bering Sea pollock. In 1999, an
additional 83,080 km$^2$ (21%) of critical habitat in the Aleutian
Islands was closed to pollock fishing along with 43,170 km$^2$ (11%)
around sea lion haulouts in the GOA and Eastern Bering Sea. In 1998,
over 22,000 t of pollock were caught in the Aleutian Island region, with
over 17,000 t taken within critical habitat region. Between 1999 and
2004 a directed fishery for pollock was prohibited in this region.
Subsequently, 210,350 km$^2$ (54%) of critical habitat in the Aleutian
Islands was closed to the pollock fishery. In 2000 the remaining
phased-in reductions in the proportions of seasonal TAC that could be
caught within the BSAI Steller sea lion Conservation Area (SCA) were
implemented.

On the EBS shelf, an estimate (based on observer at-sea data) of the
proportion of pollock caught in the SCA has averaged about 44% annually.
During the A-season, the average is also about 44%. Nonetheless, the
proportion of pollock caught within the SCA varies considerably,
presumably due to temperature regimes and the relative population age
structure. The annual proportion of catch has ranged from an annual low
of 11% in 2010 to high of 60% in 1998--the 2019 annual value was 58% and
quite high again in the A-season (68%). The higher values
in recent years were likely due to good fishing conditions close to the
main port. The recent transition from at-sea observer sampling of many
catcher vessels to a combination of at-sea electronic monitoring and
shore-based observer sampling has resulted in a temporary hiatus in to
associate catches with specific areas. Work has progressed to link the
position information to offloads so that haul records could be used to
evaluate fishing patterns.

The AFA reduced the capacity of the catcher/processor fleet and
permitted the formation of cooperatives in each industry sector by the
year 2000. Because of some of its provisions, the AFA gave the industry
the ability to respond efficiently to changes mandated for sea lion
conservation and salmon bycatch measures. Without such a catch-share
program, these additional measures would likely have been less effective
and less economical (@strong2014).

An additional strategy to minimize potential adverse effects on sea lion
populations is to disperse the fishery throughout more of the pollock
range on the Eastern Bering Sea shelf. While the distribution of fishing
during the A-season is limited due to ice and weather conditions, there
appears to be some dispersion to the northwest area
(@fig-catch_distn_a).

The majority (about 56%) of Chinook salmon caught as bycatch in the
pollock fishery originate from western Alaskan rivers. This was updated
at the June 2022 Council meeting and is activities are monitored and
reported closely at the Council ([at this
website](https://www.npfmc.org/fisheries-issues/bycatch/salmon-bycatch/)).
In summary, additional Chinook salmon bycatch management measures went into effect
in 2011 which imposed revised prohibited species catch (PSC) limits.
These limits, when reached, close the fishery by sector and season
(Amendment 91 to the BSAI Groundfish Fishery Management Plan (FMP)
resulting from the NPFMC's 2009 action). Previously, all measures for
salmon bycatch imposed seasonal area closures when PSC levels reached
the limit (fishing could continue outside of the closed areas). The
current program imposes a dual cap system by fishing sector and season.
A goal of this system was to maintain incentives to avoid bycatch at a
broad range of relative salmon abundance (and encounter rates).
Participants are also required to take part in an incentive program
agreement (IPA). These IPAs are approved and reviewed annually by NMFS
to ensure individual vessel accountability. The fishery has been
operating under rules to implement this program since January 2011.

Further measures to reduce salmon bycatch in the pollock fishery were
developed and the Council took action on Amendment 110 to the BSAI
Groundfish FMP in April 2015. These additional measures were designed to
add protection for Chinook salmon by imposing more restrictive PSC
limits in times of low western Alaskan Chinook salmon abundance. This
included provisions within the IPAs that reduce fishing in months of
higher bycatch encounters and mandate the use of salmon excluders in
trawl nets. These provisions were also included to provide more flexible
management measures for chum salmon bycatch within the IPAs rather than
through regulatory provisions implemented by Amendment 84 to the FMP.
The new measure also included additional seasonal flexibility in pollock
fishing so that more pollock (proportionally) could be caught during
seasons when salmon bycatch rates were low. Specifically, an additional
5% of the pollock can be caught in the A-season (effectively changing
the seasonal allocation from 40% to 45% (as noted above in the
discussion assosciated with @fig-prop_a_season). These measures are all
part of Amendment 110 and a summary of this and other key management
measures is provided in \cref{tab:mgt}.

There are three time/area closures in regulation to minimize herring PSC
impacts: *Summer Herring Savings Area 1* an area south of 57$^\circ$N
latitude and between 162$^\circ$W and 164$^\circ$W longitude from June
15 through July 1st. *Summer Herring Savings Area 2* an area south of
56$^\circ$ 30' N latitude and between 164$^\circ$W and 167$^\circ$W
longitude from July 1 through August 15. *Winter Herring Savings Area*
an area between 58$^\circ$ and 60$^\circ$N latitude and between
172$^\circ$W and 175$^\circ$W longitude from September 1st through March
1st of the next fishing year.

# Data

The following lists the data used in this assessment:

```{=tex}
\begin{table}[ht]
\centering
\label{tab:dataextent}
\begin{tabular}{p{1.5in}p{1.8in}p{2.8in}}
\hline
 Source & Type & Years \\
\hline
 Fishery & Catch biomass & 1964--`r thisyr` \\
 Fishery & Catch age composition & 1964--`r lastyr` \\
 Fishery & Japanese trawl CPUE & 1965--1976 \\
 EBS bottom trawl & Area-swept biomass and age-specific proportions & 1982--2019, 2021-`r thisyr ` \\ 
 Acoustic trawl survey & Biomass index and age-specific
 proportions & 1994, 1996, 1997, 1999, 2000, 2002, 2004, 2006--2010, 2012, 2014, 2016, 2018, 2020, 2022 \\
 Acoustic vessels of opportunity (AVO) & Biomass index & 2006--2019, 2021-`r thisyr` \\
\hline
\end{tabular}
\end{table}
```
*Note the 2020 acoustic survey data based on unmanned surface vessel
(USV) transects and age-specific proportions were unavailable in this
year*

## Fishery

### Catch

Biological sampling by scientifically trained observers form the basis
of a major data component of this assessment (as evaluated in Barbeaux
et al. 2005). The catch-at-age composition was estimated using the
methods described by @kimura1989 and modified by @dorn1992.
Length-stratified age data are used to construct age-length keys for
each stratum and sex. These keys are then applied to randomly sampled
catch length frequency data. The stratum-specific age composition
estimates are then weighted by the catch biomass within each stratum to
arrive at an overall age composition for each year. Data were collected
through shore-side sampling and at-sea observers (@barbeaux2005). The three strata for
the EBS were: i) January--June (all areas, but mainly east of
170$^\circ$W); ii) INPFC area 51 (east of 170$^\circ$W) from
July--December; and iii) INPFC area 52 (west of 170$^\circ$W) from
July--December. This method was used to derive the age compositions from
1991--`r lastyr` (the period for which all the necessary information is
readily available). Prior to 1991, we used the same catch-at-age
composition estimates as presented in @wespestad1996.

The catch-at-age estimation method uses a two-stage bootstrap
re-sampling of the data. Observed tows were first selected with
replacement, followed by re- sampling actual lengths and age specimens
given that set of tows. This method allows an objective way to specify
the starting values for the input sample size for fitting fishery age
composition data within the assessment model. In addition, estimates of
stratum-specific fishery mean weights-at-age (and variances) are
provided which are useful for evaluating general patterns in growth and
growth variability. For example, @ianelli2007 showed that seasonal
aspects of pollock condition factor could affect estimates of mean
weight-at-age. They showed that within a year, the condition factor for
pollock varies by more than 15%, with the heaviest pollock caught late
in the year from October-December (although most fishing occurs during
other times of the year) and the thinnest fish at length tending to
occur in late winter. They also showed that spatial patterns in the
fishery affect mean weights, particularly when the fishery is shifted
more towards the northwest where pollock tend to be smaller at age.
@gruss2021 showed cold-pool-extent impacts on the spatial map
of summer condition and relating environmental conditions to fish
condition continues to be an active area of research.

In 2011 the winter fishery catch consisted primarily of age 5 pollock
(the 2006 year class) and later in that year age 3 pollock (the 2008
year class) were present. In 2012--2016 the 2008 year class was
prominent in the catches with 2015 showing the first signs of the 2012
year-class as three year-olds in the catch (@fig-catage;
\cref{tab:fshage}). However, by 2017 the 2013 year-class began to be
also evident and surpassed the 2012 year-class in dominance and persist
through to 2021. The unusual pattern of switching adjacent year-classes
was examined in 2021 to see if there was a pattern of spatial
differences. There was a distinct spatial distribution of the different
year-classes. Having adjacent strong year-classes appears to be a new
characteristic of the stock. In 2020, an unusual presence of age-2
pollock appeared in the catch, along with some from the 2014 year-class
while the 2012 year-class was a smaller part of the catch (@fig-catage).
By 2021 and 2022, the predominance of 3- and 4-year olds in the catch confirms
the abundance year-class from 2018. We note that the center of locations
of the 2018 year-class, as plotted based on the locales of samples from that 
cohort, appears to be more oriented to the south east (by age) when compared to 
another abundant year-class (the 2008; @fig-fsh_cohort_locales).

The sampling effort for age determinations, weight-length measurements,
and length frequencies is shown in \cref{tab:fshmeas},
\cref{tab:lwmeas}, and \cref{tab:fshn}. Sampling for pollock lengths and
ages by area has been shown to be relatively proportional to catches.
The precision of total pollock catch biomass is considered high with
estimated CVs to be on the order of 1% (@miller2005).

Scientific research catches are reported to fulfill requirements of the
Magnuson-Stevens Fisheries Conservation and Management Act. The annual
estimated research catches (1963--2022) from NMFS surveys in the Bering
Sea and Aleutian Islands Region are given in (\cref{tab:rescatch}). 
Since these values represent extremely small
fractions of the total removals (about 0.02%) they are ignored for
assessment purposes.

## Surveys
### Bottom trawl survey (BTS)
Trawl surveys have been conducted annually by the AFSC to assess the
abundance of crab and groundfish in the Eastern Bering Sea since 1979
and since 1982 using standardized gear and methods. For pollock, this
survey has been instrumental in providing an abundance index and
information on the population age structure. This survey is complemented
by the acoustic trawl (AT) surveys that sample mid-water components of
the pollock stock. Between 1991 and 2023 the BTS biomass estimates
ranged from 2.28 to 8.39 million t (\cref{tab:btsbiom}) for the
design-based estimates). The values used for the assessment (VAST index,
see @sec-vast for details) are shown in @fig-bts_biom. In the
mid-1980s and early 1990s several years resulted in above-average
biomass estimates. The stock appeared to be at lower levels during
1996--1999 then increased moderately until about 2003 and since then has
averaged just over 4 million t (from the standard EBS region using
design-based estimators). 

These surveys also provide consistent
measurements of environmental conditions, such as the sea surface and
bottom temperatures. Large-scale zoogeographic shifts in the EBS shelf
documented during a warming trend in the early 2000s were attributed to
temperature changes (e.g., @mueter2008). However, after the
period of relatively warm conditions ended in 2005, the next eight years
were mainly below average, indicating that the zoogeographic responses
may be less temperature-dependent than they initially appeared (@kotwicki2013). 
Bottom temperatures increased in 2011 to about average
from the low value in 2010 but declined again in 2012--2013. In
the period 2014--2016, bottom temperatures increased and reached a new
high in 2016. In 2018 bottom temperatures were nearly as warm (after
2017 was slightly above average) but was highly unusual due to the
complete lack of "cold pool" (i.e., a defined area where water near
bottom was less than zero degrees. In 2019, the mean bottom temperature
was the warmest during the period the survey has occurred (since 1982;
@fig-bts_temp). In 2022 and 2023, the bottom temperatures have declined 
but remain above average.

The AFSC has expanded the area covered by the bottom trawl survey over
time. In 1987 the "standard survey area" comprising 6 main strata was
increased farther to the northwest and covered in all subsequent years.
These two northern strata have varied in estimated pollock abundance. In
2023 about 10% of the pollock biomass was found in these strata compared
to a long term average of 5% (\cref{tab:btsbiom}). Importantly, this
region is contiguous with the Russian border and the NBS region, and
treatment of the extent stock shifts between regions continues (e.g.,
@oleary2021).

After the increase in 2022, the 2023 survey estimate is similar to the 2021 value and is about
72% of the long term mean. The 2023 pollock density by station appeared to be lower overall
with some slight increases on the outer shelf area to the northwest (@fig-bts_3d).
The VAST model provides density-weighted population shifts in distribution. This
can be expressed in north-south and east-west trends over time. A representation of
such center of gravity estimates indicate that the stock has moved steadily north since the mid 2000s, 
but last year shifted south. This year it has shifted back north to some degree (@fig-cog). 
The stock center of gravity also moved east from 2010 to about 2017, then shifted west and seems about at it's long term mean. 

The BTS abundance-at-age estimates show variability in year-class
strengths with substantial consistency over time (@fig-bts_age). 
The abundance of 5-year old pollock (the 2018 year-class) dropped from 
2022, but still represents the most abundant year class. The abundance of age-1 pollock in 
2023 appears to be about average.

Pollock above 40 cm in length generally appear to be fully selected and in some
years, many 1-year olds occur on or near the bottom (with modal lengths
around 10--19 cm). Generally speaking, age 2 or 3 pollock (lengths
around 20--29 cm and 30--39 cm, respectively) are relatively rare in
this survey because they tend to be more pelagic as juveniles. Compared
to recent years, the size compositions were consistent with the
mid-range categories and consistent with the age data
(@fig-bts_lenfreq). 

Observed fluctuations in survey estimates may be attributed to a variety
of sources including unaccounted-for variability in natural mortality,
survey catchability, and **horizontal** migrations and **vertical
availability** (@monnahan2021; @oleary2022). As an example, some strong
year classes appear in the surveys over several ages (e.g., the 1989
year class) while others appear only at older ages (e.g., the 1992 and
2008 year class). Sometimes, initially strong year classes appear to wane
in successive assessments (e.g., the 1996 year class estimate (at age 1)
dropped from 43 billion fish in 2003 to 32 billion in 2007
(@ianelli2007)). Retrospective analyses (e.g., @parma1993) have also
highlighted these patterns, as presented in Ianelli et al. (2006, 2011).
@kotwicki2013 also found that the catchability of either the BTS or AT
survey for pollock is variable in space and time because it depends on
environmental variables, and is density-dependent in the case of the BTS
survey.

The 2023 survey age compositions were developed from age-structures
collected during the survey (June-July) and processed at the AFSC labs
within a few weeks after the survey was completed. The level of sampling
for lengths and ages in the BTS is shown in \cref{tab:btsn}. The
estimated numbers-at- age from the BTS for strata 1--9 (except for
1982--84 and 1986, when only strata 6 were surveyed) are presented in
\cref{tab:btsage} (based on the method in @kotwicki2014 and then using
VAST--see @sec-vast for those details). Compared to the previous
design-based age composition estimates, those derived from the
spatio-temporal model were generally very similar (@fig-bts_vast_age).

In the previous assessments, the BTS mean body mass-at-ages was computed
based on the sex-specific mean length-at-age in each year and converted
to weight using sex-specific length-weight parameters that were
estimated from data prior to 1999. In reconsidering this approach, data
on weight-at-age from intervening years have become available and some
new methods applied including those corrected by spatio-temporal
modeling (@indivero2023). This work was adopted in 2022
and values used are shown in \cref{tab:wtbts}. The
time series of BTS survey indices is shown in \cref{tab:btsabund}.

The NBS survey area was sampled in 2010, 2017, 2018 (limited to 49
stations), 2019, and 2021-2023. Given that the pollock
abundance was quite high in 2017 and 2018, a method for incorporating
this information as part of the standard survey was desired. One
approach for constructing a full time series that includes the NBS area
is to use observed spatial and temporal correlations. We used the
vector-autoregressive spatial temporal (VAST) model of @thorson2018b
together with the density-dependent corrected CPUE values from each
station (including stations where pollock were absent; 
\cref{tab:btsabund}). Please refer to the @sec-vast for further details
on the implementation. The appendix also includes results that indicate the
VAST model diagnostics are reasonable and provide consistent
interpretations relative to the observations. Notably, results indicate
increased uncertainty in years and areas when stations were missing. As
noted in past assessments, application of this index within the stock
assessment model required accounting for the time-series covariance
estimate.

To date, given other commitments, work on comparing the age-and-growth
from NBS samples has stalled. We hope to evaluate these data when they
become available in the near future to look at maturity and growth
conditions from this region.

### Acoustic trawl surveys
Acoustic trawl surveys are typically conducted every other year and are
designed to estimate the off- bottom component of the pollock stock
(compared to the BTS which are conducted annually and provide an
abundance index of the near-bottom pollock). Estimated pollock biomass
for the EBS shelf has averaged over 3.2 million t since the time-series
was revised to include the water column to 0.5 m (from the historical
midwater pollock index to 3 m off bottom) starting in 1994
(\cref{tab:btsabund}). The early 2000s (a relatively 'warm' period) were
characterized by low pollock recruitment, which was subsequently
reflected in lower pollock biomass estimates between 2006 and 2012 (a
'cold' period; @honkalehto2015). In 2014 and 2016 (another 'warm'
period) with the growth of the strong 2012 year class, AT biomass
estimates increased to over 4 million t (\cref{tab:btsabund}). The
number of trawl hauls, lengths, and ages sampled from the AT survey are
presented in \cref{tab:atsn}. These surveys have also provided insight
on the relative abundance of pollock in areas considered critical to
Steller sea lions (the "SCA"; \cref{tab:atsbiom}).

Pollock midwater abundance and distribution were last assessed in 2022. In addition 
to the traditional (core) survey area, a region north of most transects (the northern extension) 
was surveyed. Transect spacing, typically 20 nmi, ranged from 40 nmi in the east and middle 
shelf to 20 nmi in the western shelf due to ship staffing constraints and consequent 
survey schedule uncertainties.

The 2022 estimated amount of pollock in the core survey area was 9.67 billion fish with a 
biomass of 3.834 million metric tons (t), just over a 50% increase from the estimate of 
5.55 billion fish with a biomass of 2.497 million t in 2018. This was a 6% increase 
over the 3.617 million t estimated in 2020 by the acoustics-only Saildrone survey. 
Preliminary population age estimates from 2022 using BTS ages were revised in 2023 
using ages from the AT survey. Four-year-olds (2018 year class) dominated the pollock 
population numbers in the core survey area (\cref{tab:atsage}) comprising 71% of the core area 
biomass followed by 3-year-olds (2019 year class, 7.6 % of the core area biomass). 
Slightly more than one-half million t (0.539 million t) of pollock were observed 
distributed sparsely along the northern extension transects, 12% of the shelf-wide total. 
Eight year-olds (2014 year class) were the dominant aged pollock in the northern 
extension (29% by biomass), followed by 7 year-olds (17% of the northern extension biomass) 
and 4 year-olds (15% of the northern extension biomass).

<!-- mean fork length (FL)) composed 57% of the biomass in the core survey -->
<!-- area (@fig-at_age). Slightly more than one-half million t (0.539 million -->
<!-- t) of pollock were observed distributed sparsely along the northern -->
<!-- extension transects, 12% of the shelf-wide total. -->

Relative estimation errors for the total biomass were derived from a
one-dimensional (1D) geostatistical method, which accounts for observed
spatial structure for sampling along transects (@petitgas1993,
@walline2007, @williamson1996). The 2022 relative estimation error for
the core survey area was 0.068, slightly higher than the time series
mean of 0.043, likely due to increased transect spacing. As in previous
assessments, the other sources of error (e.g., target strength, trawl
selectivity) were accounted for by inflating the annual error estimates
to have an overall average CV of 20% for application within the
assessment model. This value was consistent with past model fitting procedures
(i.e., the standard deviation of the normalized residuals was very close to 1.0).

## Other time series used in the assessment
### Japanese fishery CPUE index
An available time series relating the abundance of pollock during the
period 1965--1976 was included. This series is based on Japanese fishery
catch rates which used the same size class of trawl vessels as presented
in @low1980. In lieu of an objective estimate, we applied a default coefficient of 
variation of 20% to these data

### Biomass index from Acoustic-Vessels-of-Opportunity (AVO)
Acoustic backscatter data (Simrad ES60, 38 kHz) were collected aboard two fishing vessels chartered for the AFSC summer 2023 bottom trawl surveys (F/V Alaska Knight, F/V Northwest Explorer). We have processed these Acoustic Vessels of Opportunity (AVO) data each year since 2006 to provide an index of age-1+ midwater pollock abundance. This is the first year implementing a new subsampling methodology (@levine2019) to generate a more spatially extensive AVO index. In developing the new index, we analyzed a 10% systematic subsample of the BTS backscatter data throughout the typical ATS geographic footprint. The new methods were applied to reanalyze years 2009, 2010, 2012, 2014-2019, and 2021-2023.  For the remaining 5 years of the time series, the original AVO index (@honkalehto2011, @stienessen2020) was rescaled to match the mean of the new AVO time series (@fig-avo).

Relative to the original index, the correlation between the AVO index and the AT survey biomass was higher (R^2^ = 0.9, compared to R^2^ = 0.6 for the same seven ATS-BTS years from the original index). The new and rescaled index trend dropped 15% relative to 2022 but still is 13% above the long-term mean (@fig-avo, \cref{tab:avo}; note that the relative error is based on a variance estimation from Petitgas (1993), while the final magnitude of the error term was ascertained based on other model components via an iterative re-weighting process as noted in Ianelli (2023)). The densest spatial distribution of pollock backscatter was predominantly measured along the southern portion of the EBS shelf in the northwest half of the index area (@fig-data_avomap). The three grid cells (20 by 20 nautical mile grids used for annual bottom-trawl survey stations) having the strongest pollock backscatter were 2^o^  south of St. Matthew Island close to (58^o^N, 172^o^W) and attributed to dense midwater pollock aggregations.


# Analytic approach
## General model structure
We used a statistical age-structured assessment model conceptually
outlined in @Fournier1982 and extended (e.g., @methot1990). This was
developed as an appendix to @wespestad1996 with current specifications
presented in the @sec-model (@ianelli1998).
The model was written in ADMB---a library for non-linear estimation and
statistical applications (@Fournier2012b). The data updated from last
year's analyses include:

-   The `r thisyr-1` fishery age composition data 

-   The catch biomass estimates through the current year

-   The `r thisyr` bottom-trawl survey index, weight, and age
    composition data 

-   The `r thisyr-1` acoustic-trawl age composition data were revised
    using only samples collected from that survey (previously the age
    compositions were estimated using the bottom-trawl survey age-length
    keys)

-   A completely revised time series of AVO backscatter data collected
    opportunistically from the bottom trawl survey.

A simplified version of the assessment (with mainly the same data and
likelihood-fitting method) is included as a supplemental multi-species
assessment model. As presented since 2016, it allows for trophic
interactions among key prey and predator species and for pollock, and it
can be used to evaluate age and time-varying natural mortality estimates
in addition to alternative catch scenarios and management targets (see
this volume: [EBS multi-species
model](https://apps-afsc.fisheries.noaa.gov/Plan_Team/2023/EBSmultispp.pdf)).

## Description of alternative models

In the 2019 assessment, the spatio-temporal model fit to BTS CPUE data
*including stations from the NBS* was expanded using the VAST methods
detailed in @thorson2018. This data treatment was included as a model
alternative and adopted for ABC/OFL specifications by the SSC in 2020
along with other modifications including a spatio-temporal treatment of
the age composition data. This year, we examined additional model and data
modifications as presented in @ianelli2023. 

By the SSC's numbering scheme, last year's model was designated Model 20.0, which
here we contrast with a revised model 23.0. As usual, we also provide an incrememntal
evaluation of the influence of new data introduced in 2023 (here as applied to Model 23.0).

```{=tex}
\begin{description}

    \item[m0--] The model selected last year (referred to as "Base")

    \item[m1--]  September revision but data only through 2022    

    \item[m2--]	as m1 but with 2023 catch and 2023 AVO data point included    
    
    \item[m3--] as m2 but with 2022 age composition data updated (very minor change)    
    
    \item[m4--] as m3 but addition of fishery catch-age to 2022    
    
    \item[m5--] as m4 but addition of VAST BTS biomass index through 2023    
    
    \item[m6--] as m5 but with BTS age compositions included through 2023    
    
    \item[m7--]	As m5 but with VAST age compositions included through 2023    
    
    \item[m8--]	As m7 but with Hulson et al. (2023) BTS input sample sizes    

\end{description}
```

As noted in @ianelli2023, we continue to provide some facility to
test different stock assessment software (as noted in @li2021). 


### Input sample size
Sample sizes for age-composition data were re-evaluated in @ianelli2023
and found to be consistent with the relative variability allowed for
selectivities and with the observation errors specified for the indices.
Principally, this work resulted in tuning the recent era (1991-present
year) to an average sample sizes of 350 for the fishery and then using
estimated values for the period 1978-1990 and as earlier
(\cref{tab:inputn}). As rationalized in earlier assessments, we found that
assuming average values of 100 and 50 for the BTS
and ATS data, respectively resulted in consistent model fits and were (relatively) appropriate
given the sampling levels among these surveys. The inter-annual variability reflects the
variability in the number of hauls sampled for ages in the ATS data. For the 
BTS data we adopted the results presesnted in @hulson2023.  We
re-evaluated tuning following @Francis2011
(equation TA1.8).

Recent work has shown ways to improve estimation schemes that deal with
the interaction between flexibility in fishery selectivity and
statistical properties of composition data sample size. Specifically,
the Dirichlet-multinomial using either Laplace approximation
(@thorson2015) or adnuts (@monnahan2018) should be implemented (e.g., as
shown by @xu2020). Progress in 2023 has lagged on this, but with the 
advent of some alternative three dimensional mixed-effects approaches to
weight-at-age and selectivity (@cheng2023), development of more elaborate approaches
are being pursued.

## Parameters estimated outside of the assessment model
### Natural mortality and maturity at age
The baseline model specification has been to use constant natural
mortality rates at age (M=0.9, 0.45, and 0.3 for ages 1, 2, and 3+
respectively (@wespestad1984). When predation was explicitly
considered estimates tended to be higher and more variable (Holsman et
al. *this volume*; @holsman2015; @livingston1998; @hollowed2000).
@clark1999 found that specifying a conservative (lower) natural
mortality rate may be advisable when natural mortality rates are
uncertain. More recent studies confirm this (e.g., @Johnson2014).

In the supplemental multi-species assessment model alternative values of
age and time-varying natural mortality are presented. As in past years
the estimates indicate higher values than used here. In the 2018
assessment we evaluated natural mortality, and it was noted that the
survey age compositions favored lower values of *M* while the fishery
age composition favored higher values. This is consistent with the
patterns seen in the BTS survey data as they show increased abundances
of "fully selected" cohorts. Hence, given the model specification
(asymptotic selectivity for the BTS age composition data), lower natural
mortality rates would be consistent with those data. Given these
trade-offs, structural model assumptions were held to be the same as
previous years for consistency (i.e., the mortality schedule presented
below).

Maturity-at-age values used for the EBS pollock assessment were
originally based on @smith1981 and were later reevaluated via histological methods
(e.g., @stahl2004; @Stahl2008, @ianelli2005). These studies found year-class effects and some
inter-annual variability but general consistency with the original schedule of
proportion mature at age.

With respect to assumptons about natural mortality, we evaluated applying results from an adjacent stock
(@ianelli2022bogoslof) in the 2022 assessment. We found the results were consistent with
past assumptions therefore again applyed the following age-specific values
for *M* (@smith1981) and maturity-at-age:

```{=tex}
\begin{table}[ht]
\centering
\label{tab:dataextent}
\scalebox{0.9}{
\begin{tabular}{rrrrrrrrrrrrrrrr}
\hline
Age&1&2&3&4&5&6&7&8&9&10&11&12&13&14&15 \\
\hline
$M$&0.90&0.45&0.30&0.30&0.30&0.30&0.30&0.30&0.30&0.30&0.30&0.30&0.30&0.30&0.30 \\
$P_{mat}$&0.00&0.008&0.29&0.64&0.84&0.90&0.95&0.96&0.97&1.00&1.00&1.00&1.00&1.00&1.00 \\
\hline
\end{tabular}
}
\end{table}
```

### Length and weight-at-age
Age determination methods have been validated for pollock (@kimura1992,
@kimura2006, and @kastelle2006). EBS pollock size-at-age show important
differences in growth with differences by area, year, and year class.
Pollock in the northwest area are typically smaller at age than pollock
in the southeast area. The differences in average weight-at-age are
taken into account by stratifying estimates of catch-at-age by year,
area, season, and weighting estimates proportional to catch.

The assessment model for EBS pollock accounts for numbers of individuals
in the population. As noted above, management recommendations are based
on allowable catch levels expressed as tons of fish. While estimates of
pollock catch-at-age are based on large data sets, the data are only
available up until the most recent completed calendar year of fishing
(e.g., 2022 for this year). Consequently, estimates of weight-at-age in
the current year are required to map total catch biomass (typically
equal to the quota) to numbers of fish caught (in the current year).
Therefore, if there are errors (or poorly accounted uncertainty) in the
current and future mean weight-at-age, this can translate directly into
errors between the expected fishing mortality and what mortality occurs.
For example, if the mean weight-at-age is biased high, then an ABC (and
OFL) value will result in greater numbers of fish being caught (and
fishing mortality being higher due to more fish fitting within the ABC).

As in previous assessments, we explored patterns in size-at-age and fish
condition. Using the NMFS fishery observer data on weight given length
we:

1.  extracted all data where non-zero measurements of pollock length and
    weight were available between the lengths of 35 and 60 cm for the
    EBS region

2.  computed the mean value of body mass (weight) for each cm length bin
    over all areas and time

3.  divided each weight measurement by that mean cm-specific value (the
    "standardization" step)

4.  plotted these standardized values by different areas, years, months
    etc. to evaluate condition differences (pooling over ages is
    effective as there were no size-specific biases apparent)

In the first instance, the overarching seasonal pattern in body mass
relative to the mean shows that as the winter progresses prior to peak
spawning, pollock are generally skinnier than average whereas in July,
the median is about average (@fig-fsh_lw_month). As the summer/fall
progresses, fish were at their heaviest given length
(@fig-fsh_lw_month). This is also apparent when the data are aggregated
by A- and B-seasons (and by east and west of 170$^\circ$W; referred to
as SE and NW respectively) when plotted over time
(@fig-fsh_lw_anom_str_yr_box, where stratum 1 = A season, stratum 2 = B
season SE, and stratum 3 = B season NW). Combining across seasons, the
fishery data shows that recent years were below average weight given
length (@fig-fsh_lw_anom_yr_box ; note that the anomalies are based on
the period 1991-2023).

Examining the weight-at-age, there are also patterns of variability that
vary due to environmental conditions in addition to spatial and temporal
patterns of the fishery. Based on the bootstrap distributions and large
sample sizes, the within-year sampling variability for pollock is small.
However, the between-year variability in mean weights-at-age is
relatively high (\cref{tab:wtage}). The coefficients of variation
between years are on the order of 6% to 9% (for the ages that are
targeted) whereas the sampling variability is generally around 1% or 2%.
The approach to account for the identified mean weight-at-age having
clear year and cohort effects was continued (e.g., @fig-fsh_wtage_comb).
Details were provided in appendix 1A of Ianelli et al. (2016). The
results from this method showed the relative variability between years
and cohorts and provide estimates for `r paste0(thisyr,"--",thisyr+2)`
(\cref{tab:wtage}). How these fishery weights-at-age estimates can be
supplemented using survey weights-at-age is further illustrated in
@fig-fish_wtage_data_pred.

In the 2020 and 2021 fishery, the average weight-at-age for ages 6-8
(the 2012-2014 year classes) was below the time series average. These
cohorts have fluctuated around their means in recent years
(@fig-fsh_wtage_comb). To examine this more closely, we split the
bootstrap results into area-season strata and were able to get an
overall picture of the pattern by strata (@fig-fsh_wtage_strata and
@fig-fsh_wtage_strata_yr). This showed that the mean weight-at-age is
higher in the the B-season in the area east of 170$^\circ$W compared to
the A-season and B-season in the area west of 170$^\circ$W.

## Parameters estimated within the assessment model

For the selected model, `r M$npar` parameters were estimated conditioned
on data and model assumptions. Initial age composition, subsequent
recruitment, and stock- recruitment parameters account for 80
parameters. This includes vectors describing the initial age composition
(and deviation from the equilibrium expectation) in the first year (as
ages 2--15 in 1964) and the recruitment mean and deviations (at age 1)
from 1964--`r thisyr` and projected recruitment variability (using the
variance of past recruitments) for five years
(`r paste0(nextyr,"--",nextyr+5)`). The two- parameter stock-recruitment
curve (see @sec-model) is included in addition to a term that allows the average
recruitment before 1964 (that comprises the initial age composition in
that year) to have a mean value different from subsequent years. Note
that the stock-recruit relationship is fit only to stock and recruitment
estimates from 1979 year-class through to the `r thisyr-2` year-class.

Fishing mortality is parameterized to be semi-separable with year and
age (selectivity) components. The age component is allowed to vary over
time; changes are allowed in each year. The mean value of the age
component is constrained to equal one and the last 5 age groups (ages
11--15) are specified to be equal. This latter specification feature is
intended to reduce the number of parameters while acknowledging that
pollock in this age-range are likely to exhibit similar life-history
characteristics (i.e., unlikely to change their relative availability to
the fishery with age). The annual components of fishing mortality result
in `r n<-thisyr-1963` `r n` parameters and the age-time selectivity
schedule forms a 10x`r paste0(n," matrix of ",10*n)` parameters bringing
the total fishing mortality parameters to `r n+n*10`. The rationale for
including time- varying selectivity has recently been supported as a
means to improve retrospective patterns (Szuwalski et al. 2017) and as
best practice (@martell2013).

For surveys and indices, the treatment of the catchability coefficient,
and interactions with age-specific selectivity require consideration.
For the BTS index, selectivity-at-age is estimated with a logistic curve
in which year specific deviations in the parameters is allowed. Such
time-varying survey selectivity is estimated to account for changes in
the availability of pollock to the survey gear and is constrained by
pre-specified variance terms. Presently, these variance terms have been
set based on balancing input data-based variances and are somewhat
subjective. For the AT survey, which originally began in 1979 (the
current series including data down to 0.5 m from bottom begins in 1994),
optional parameters to allow for age and time-varying patterns exist but
for this assessment and other recent assessments, ATS selectivity is
constant over time. Overall, four catchability coefficients were
estimated: one each for the early fishery catch-per-unit effort (CPUE)
data (from Low and Ikeda, 1980), the VAST combined bottom trawl survey
index, the AT survey data, and the AVO data. An uninformative prior
distribution is used for all of the indices. The selectivity parameters
for the 2 main indices (BTS and ATS) total 336 (the CPUE and AVO data
mirror the fishery and AT survey selectivities, respectively).

Additional fishing mortality rates used for recommending harvest levels
are estimated conditionally on other outputs from the model. For
example, the values corresponding to the $F_{40\%}$ $F_{35\%}$ and
$F_{MSY}$ harvest rates are found by satisfying the constraint that,
given age-specific population parameters (e.g., selectivity, maturity,
mortality, weight-at-age), unique values exist that correspond to these
fishing mortality rates. The likelihood components that are used to fit
the model can be categorized as:

-   Total catch biomass (log-normal, $\sigma=0.05$)
-   Log-normal indices of pollock biomass; bottom trawl surveys assume
    annual estimates of sampling error, as represented in @fig-bts_biom
    along with the covariance matrices (for the density-dependent and
    VAST index series); for the AT index the annual errors were
    specified to have a mean CV of 0.20; while for the AVO data, a value
    a mean CV was tuned for consistency with other data and resulted in a
    value of 23%).
-   Fishery and survey proportions-at-age estimates (multinomial with
    effective sample sizes presented \cref{tab:inputn}).
-   Age 1 index from the AT survey (CV set equal to 30% as in prior
    assessments).
-   Selectivity constraints: penalties/priors on age-age variability,
    time changes, and decreasing (with age) patterns.
-   Stock-recruitment: penalties/priors involved with fitting a
    stochastic stock-recruitment relationship within the integrated
    model.
-   "Fixed effects" terms accounting for cohort and year sources of
    variability in fishery mean weights-at-age estimated based on
    available data from 1991-`r lastyr` from the fishery (and
    1982-`r thisyr` for the bottom-trawl survey data) and externally
    estimated variance terms as described in Appendix 1A of Ianelli et
    al. (2016; see @fig-fish_wtage_data_pred).

Work evaluating temperature and predation-dependent effects on the
stock- recruitment estimates continues (@Spencer2016). This approach
modified the estimation of the stock-recruitment relationship by
including the effect of temperature and predation mortality. A
relationship between recruitment residuals and temperature was
noted (similar to that found in @mueter2011 and subsequently noted in
@thorson2020a) and lower pollock recruitment during warmer conditions
might be expected. Similar results relating summer temperature
conditions to subsequent pollock recruitment for recent years were also
found by @yasumiishi2015 where research suggests that summer warmth is
associated with earlier diapause of copepods (@thorson2020b), such that
a fall (but not spring) survey of copepod densities is also associated
with cold conditions and elevated recruitment (@eisner2020).

# Results
## Model evaluation
A sequential sensitivity of available new data showed that adding the
new data from 2023 had very minor changes and impact on the spawning biomass
estimates (@fig-mod_data; top panel). The largest effect on all the changes
arose from the revision to the mean body weight-at-age used for the spawning
biomass calculations (@fig-mod_data; bottom panel).
This was shown in @ianelli2023. 
Nonetheless, diagnostics of all the changes relative to model 
fits are given in \cref{tab:modfits} and a comparison of management quantities
for the final base model is given in \cref{tab:mgtquants}).

In the 2020 assessment, SRR evaluations related to Tier 1 classification
showed that dropping the influence of the 1978 year-class in the
estimation lowered the steepness of the curve and that when the
influence of the prior distribution was removed the residual pattern for
estimates near the origin was particularly bad (all below the curve).
From those results we conclude that the prior specification was
appropriate because we place priority on fitting estimated recruits near
the slope at the origin better. In the 2021 assessment we showed that
conditioning the SRR to fit the condition of having the "actual"
$F_{MSY}$ equal some $F_{MSY}$ proxies (e.g., equal $F_{35\%}$) 
resulted in more conservative ABCs due to shallower initial slopes. A
conclusion from these exercises was that the SPR proxy for $F_{MSY}$
implies a reasonable "shape" to the SRR.

The fit to the early Japanese fishery CPUE data (Low and Ikeda 1980) was
consistent with the estimated population trends for this period
(@fig-mod_cpue_fit). The model fits the fishery-independent index from
the 2006--2023 AVO data well through most of the period but the model
predicts lower biomass than the index data indicate in 2023
(@fig-mod_avo_fit). The model fits to the bottom-trawl survey biomass
(the density-dependent corrected series) were reasonable and within the
observation error bounds (@fig-mod_bts_biom). The model fit to the BTS
biomass index predicts fewer pollock than observed in the 2014 and 2015
survey but then varied in subsequent years (@fig-mod_bts_biom). The fit
to the acoustic-trawl survey biomass series (including the USV data from
2020) was consistent with the specified observation uncertainty
(@fig-mod_ats_biom).

The estimated parameters and standard errors are provided
[online](https://github.com/afsc-assessments/ebswp/blob/main/2023_runs/usehulsoniss/pm.std).
The code for the model (with dimensions and links to parameter names)
and input files are available [here](https://github.com/afsc-assessments/ebswp/tree/main).

The input sample size (as tuned in 2016 using "Francis Weights") can be
evaluated visually for consistency with expectations of mean annual age
for the different gear types (@fig-mod_mean_age; Francis 2011). The
estimated selectivity pattern changes over time and reflects to some
degree the extent to which the fishery is focused on particularly
prominent year-classes (@fig-mod_fsh_sel). The model fits the fishery
age-composition data quite well under this form of selectivity
(@fig-mod_fsh_age).

Bottom-trawl survey selectivity estimates are shown in @fig-mod_bts_sel.
The pattern of bottom trawl survey age composition data in recent years
shows a decline in the abundance of age 10+ pollock since 2011
(@fig-mod_bts_age). Through the time series of the available data, the
model predicted proportions of the 2012 and 2013 year classes varied in
terms of under- and over- estimates as the 2013 year-class became more
common in the data (@fig-mod_bts_age). The ATS selectivity varies slightly
among ages and years (@fig-mod_ats_sel). This enhances the fit to the
age composition data while still tracking the 
large year classes through the population (@fig-mod_ats_age).

As in past assessments, we evaluated the multivariate posterior
distribution using Monte-Carlo Markov chain (MCMC) simulation methods.
This year we adopted the no-uturn sampling approach from ADMB but
upgraded and packaged within R (adnuts, @monnahan2018). This allowed
thorough sampling diagnostics and was able to sample the posterior
efficiently within a few hours (or less). This new package also
demonstrated that the asymptotic parameter standard deviations were
reasonable approximations of the marginal densities from the integrated
posterior distribution (@fig-MLE_v_post). As before, we evaluated how
selected parameters relate by doing a pairwise (along with their
marginal distributions; @fig-mcmc_pairs). This illustrates how key
parameters relate to management parameters of interest. For example, the
stock recruitment steepness is negatively correlated to the resulting
$B_{MSY}$ estimate. We also compare the point estimates (highest
posterior density) with the mean of the posterior marginal distribution
of the `r thisyr` spawning biomass. This showed that the point estimate
was similar to the mean of the marginal posterior distribution
(@fig-mcmc_marg). As an additional part of the Tier 1 consideration, we
evaluated the posterior density of $F_{MSY}$ and is provided in
@fig-mcmc_marg_fmsy for reference.

We added code for producing posterior
predictive distributions (e.g., for the two acoustic indices in
@fig-acoustic_ppl. Additionally, we developed some preliminary
diagnostics to evaluate how the model's posterior components affect key
parameters of interest. For example, it is useful to know the relative
impact of the 2018 year-class on the next year's spawning biomass
(@fig-ssb_v_2018). Additionally, what different components (in negative
log-likelihood terms) conflict or interact with such a critical
parameter (@fig-post_profile)

### Retrospective analysis
Running the assessment model over a grid with progressively fewer years
included (going back to 10 years, i.e., assuming the data extent ended
in `r thisyr-10`) results in a fair amount of variability in spawning
biomass (@fig-mod_retro). Last year with the lower than expected survey
biomass estimate followed by an increase this year, the retrospective
pattern degraded with an average bias (Mohns $\rho$ equal to
`r round(rhoMohn10,3)` for the 10 year retrospective).

For the recruitment side, the retrospective pattern shows two key
results. First, the 2018 year-class (age 1 recruits in 2019) shows up as
a big estimate just this year (@fig-mod_retroR). Second, the
retrospective pattern shows how an equally abundant year-class occurred
from the 2012 year-class for three years (with data terminating in 2016,
2017, and 2018). Then, in 2019 and in subsequent years that estimate
dropped by over 10% and became the 2012 and the 2013 year-class. 
In the 2022 assessment we adjusted the value downwards to be equal to the 
mean of some earlier year classes. This year, we simply accepted the estimate
for projections given a better confirmation on the magnitude of the 2018 year class.

Related to this issue of consistency in year-class estimation, and in response to 
an SSC request, we evaluated how the influence 
of additional years of data affected year-class estimates. @fig-retrocohtyr and
@fig-retrocoh illustrate how year-class estimates can vary for retrospective 
analyses. These figures show some of the change in relative abundance between the 
2012 and 2013 year-classes and how the 2008 year estimate dissipated some as more
data became available.

In response to previous SSC requests to evaluate how selectivity is used
for ABC and catch advice, we used the retrospective runs to show how the
"projected" selectivity compared with subsequent estimates which had the
benefit of more data (@fig-retro_sel). To explain this figure, and
taking the 2023 panel as an example, the blue line in that panel
represents the projected estimate from the 2022 "peel" (the current
model projecting to 2023 using only data up until 2022). The dots
represent estimates from each "peel" and the dots in the 2022 panel are
based on this year's estimated selectivity. In general, the projected
selectivity conformed reasonably well with subsequent estimates. To
further summarize these results, we also computed a summary statistic as
the mean age of selection (independent of any age-specific stock size):

$\bar a =\frac{\sum{S_a a}}{\sum{S_a}}$

where $S_a$ is the selectivity at age (ages 1 to 11). This statistic
showed that recently the projection was biased towards younger pollock
but earlier on, the bias was toward older fish (@fig-retro_sel_mnage).

Since selectivity varies over time, and the fact that fishing mortality
rates for management advice depend on the assumed future selectivity, we
evaluate the pattern of $F_{MSY}$ rates given different selectivity
assumptions (i.e., @fig-mod_fsh_sel). In the 2020 and 2021 assessment,
because of the indications of small pollock being unusually present in
the fishery, we chose a selectivity pattern from history that reflected
tendency towards younger fish (specifically, that from 2005). Using the
statistic on mean selected age, we found that the corresponding
$F_{MSY}$ showed a correlation (@fig-fmsy_sel). This figure reveals how
shifts in the relative age of fish selected impact $F_{MSY}$ estimates.

## Time series results
The time series of begin-year biomass estimates (ages 3 and older)
suggests that the abundance of Eastern Bering Sea pollock remained at a
high level from 1982--88, with estimates ranging from 8 to 12 million t
(\cref{tab:biom3plus}). Historically, biomass levels increased from
1979 to the mid-1980s due to the strong 1978 and relatively strong 1982
and 1984 year classes recruiting to the fishable population. The stock
is characterized by peaks in the mid-1980s, the mid-1990s and again
appears to be increasing to a peak of more than 12 million t in 2016
following the low in 2008 of `r round(M$age3plus[45]/1000,2)` million t.
The estimate for `r thisyr` is trending downward and at
`r round(M$age3plus[length(M$age3plus)]/1000,2)` million t with
`r nextyr` estimated at `r round(M$age3plus1/1000,2)` million t.

The level of fishing relative to biomass estimates shows that the
spawning exploitation rate (SER, defined as the percent removal of egg
production in each spawning year) has been mostly below 20% since 1980
(@fig-mod_ser). During 2006 and 2007 the rate averaged more than 20%
and the average fishing mortality increased during the period of stock
decline. The estimate for 2009 through 2018 was below 20% due to the
reductions in TACs relative to the maximum permissible ABC values and
increases in the spawning biomass. The fishing mortality has fluctuated
since 2010-2015 but, unlike last year's upward trend, the improved
spawning biomass condition has held this rate tending toward lower
levels. Age specific fishing mortality rates reflect these patterns and
show some increases in the oldest ages from 2011--2013 but relatively
stable (@fig-mod_F). The estimates of age 3+ pollock biomass showed a
large drop last year compared to several of the earlier years but this
has reversed in the current assessment (@fig-mod_hist, 
\cref{tab:biom3plus}).

Estimated numbers-at-age are presented in (\cref{tab:estn}) and estimated
catch-at-age values are presented in (\cref{tab:estcatage}).
Estimated summary biomass (age 3+), female spawning biomass, and age-1
recruitment are given in (\cref{tab:biomssbrec}).

To evaluate past management and assessment performance it can be useful
to examine estimated fishing mortality relative to reference values. For
EBS pollock, we computed the reference fishing mortality from Tier 1
(unadjusted) and recalculated the historical values for $F_{MSY}$ (since
selectivity has changed over time). Since 1977 the current estimates of
fishing mortality suggest that during the early period, harvest rates
were above $F_{MSY}$ until about 1980. Since that time, the levels of
fishing mortality have averaged about 35% of the $F_{MSY}$ level
(@fig-mod_phase). Projections of spawning stock biomass given the
`r thisyr+1` estimate of fishing mortality rate given catches equal to
the `r thisyr` values shows a decline through 2021 and then an increase
after; albeit with considerable uncertainty due to uncertainty in
recruitment (@fig-proj_ssb).

### Recruitment
Model estimates indicate that the 2008, 2012, 2013, and now the 2018
year classes are above average (@fig-mod_rec). The 2018 year class is
nearly 4 times bigger than average with a CV of about 16%. The
stock-recruitment curve as fit within the integrated model shows the
variability of the estimated curve (@fig-mod_srr). Note that the 2021
and 2022 year classes (as age 1 recruits in 2022 and 2023) were excluded
from the stock-recruitment curve estimation as per convention and guidance
from NPFMC. Separate from fitting the stock-recruit relationship within the model, 
examining the estimated recruits-per-spawning biomass shows variability over time but seems to
lack trend and also is consistent with the Ricker stock- recruit
relationship used within the model (@fig-mod_rs).

Environmental factors affecting recruitment are considered important and
contribute to the variability. Previous studies linked strong Bering Sea
pollock recruitment to years with warm sea temperatures and northward
transport of pollock eggs and larvae (Wespestad et al. 2000; Mueter et
al. 2006). As part of the Bering-Aleutian Salmon International Survey
(BASIS) project research has also been directed toward the relative
density and quality (in terms of condition for survival) of
young-of-year pollock. For example, Moss et al. (2009) found age-0
pollock were very abundant and widely distributed to the north and east
on the Bering Sea shelf during 2004 and 2005 (warm sea temperature; high
water column stratification) indicating high northern transport of
pollock eggs and larvae during those years. Mueter et al. (2011) found
that warmer conditions tended to result in lower pollock recruitment in
the EBS. This is consistent with the hypothesis that when sea
temperatures on the eastern Bering Sea shelf are warm and the water
column is highly stratified during summer, age-0 pollock appear to
allocate more energy to growth than to lipid storage (presumably due to
a higher metabolic rate), leading to low energy density prior to winter.
This then may result in increased over-winter mortality (@swartzman2005, @winter2005). 
@ianelli2011) evaluated the
consequences of current harvest policies in the face of warmer
conditions with the link to potentially lower pollock recruitment and
noted that the current management system is likely to face higher
chances of ABCs below the historical average catches. Also, as part of
the evaluation of stationarity given periods of "regimes", we revisited
estimated mean recruitment during different periods previously
identified as being unique (@fig-mod_regimes). This shows that given the
revised estimate of the 2018 year class, the impact of the recent warm
conditions suggest that the recent period (2000-present) is similar to
the mean since 1977.

# Harvest recommendations

## Status summary

The estimate of $B_{MSY}$ is `r M$bmsys` kt (with a CV of
`r round(100*(M$bmsy.sd/M$bmsy),0)`%) which is
`r if(M$bmsy<M$nextyrssb) {"less"}else{"more"}` than the projected
`r nextyr` spawning biomass of `r M$nextyrssbs` kt; 
(\cref{tab:ressumm}). For `r thisyr+1`, the estimates put the stock in
Tier 1`r if(M$bmsy<M$curssb) {"a"}else{"b"}`. The corresponding maximum
permissible ABC would thus be `r M$maxabc1s` t with a fishable biomass
estimated at around `r M$ABC_biom1s` kt (\cref{tab:tier1proj}). For the
current year spawning biomass this corresponds to
`r round(M$curssb/M$bmsy*100,0)`% of the $B_{MSY}$ level. A diagnostic
(see @sec-model) on the impact of
fishing shows that the `r thisyr` spawning stock size is about
`r round(100*M$dynb0,0)`% of the predicted value had no fishing occurred
since 1978 (\cref{tab:ressumm}).

The probability that the current stock size is below 20% of $B_{0}$ (a
level important for additional management measures related to Steller
sea lion recovery) is \<0.1% for `r thisyr+1` and `r nextyr+1`.

In response to a past request from the SSC, we continued to include
results from projections based on Tier 2. 
We report the "standard" Tier 2 ABC calculation using
the point estimate (the mean of the posterior distribution) of
$F_{MSY}$. Therefore, for `r nextyr` the Tier
2`r if(M$bmsy<M$curssb) {"a"}else{"b"}` ABC would be `r M$Tier2_ABC1s`
t. Since we have estimates of the harmonic mean (from Tier 1
calculations) an alternative Tier 2 estimate using that in place of the
arithmetic mean $F_{MSY}$ results in an ABC of `r M$Tier1.5_ABC1s` t.

In summary, the criterion for Tier 1 depends on a reliable estimate of
$F_{MSY}$ and the uncertainty (the PDF). Tier 2 also requires a reliable
estimate of $F_{MSY}$ (without the PDF requirement). Given the seemingly
reasonable posterior marginal density for $F_{MSY}$, it seems if Tier 1
criterion is unmet, then so would the requirement for Tier 2. Adopting
Tier 3, while in principle may result in more conservative catch advice,
uses less information available about the stock productivity and
requires adopting more assumptions (i.e., that $F_{35\%}$ is a
reasonable proxy for $F_{MSY}$). As noted below in the section on risk
evaluations, there are reasons for increased concerns. However, these
seem to be unrelated to overall stock productivity as relates to the SRR
and estimates of $F_{MSY}$. Consequently, our overall analysis continues
to support the SSC's classification of this stock to be within Tier 1.

## Amendment 56 Reference Points
Amendment 56 to the BSAI Groundfish Fishery Management Plan (FMP)
defines overfishing level (OFL), the fishing mortality rate used to set
OFL (FOFL), the maximum permissible ABC, and the fishing mortality rate
used to set the maximum permissible ABC. The fishing mortality rate used
to set ABC ($F_{ABC}$) may be less than this maximum permissible level,
but not greater. Estimates of reference points related to maximum
sustainable yield (MSY) are currently available. However, we present
both reference points for pollock in the BSAI to retain the option for
consideration of either Tier 1, 2, or Tier 3 values from the harvest
control rules provided in Amendment 56. These Tiers require reference
point estimates for biomass level determinations. Consistent with other
groundfish stocks, the following values are based on recruitment
estimates from post-1976 spawning events (recognizing the the 1978 year
class is excluded from the MSY calculations but included in the SPR
calculations):

```{=tex}
\begin{table}[ht]
\begin{tabular}{lr}
$B_{MSY}$    &= `r M$bmsys` kt female spawning biomass    \\
$B_{0}$      &= `r M$b0s` kt female spawning biomass    \\
$B_{100\%}$  &= `r M$b100s` kt female spawning biomass   \\
$B_{40\%}$   &= `r M$b40s` kt female spawning biomass    \\
$B_{35\%}$   &= `r M$b35s` kt female spawning biomass    \\
\end{tabular}
\end{table}
```
## Specification of OFL and Maximum Permissible ABC

Under Amendment 56 of the [BSAI Groundfish
FMP](https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=&cad=rja&uact=8&ved=2ahUKEwjv3vTxvon0AhV1JTQIHYPrBbUQFnoECAMQAQ&url=https%3A%2F%2Fwww.npfmc.org%2Fwp-content%2FPDFdocuments%2Ffmp%2FBSAI%2FBSAIfmp.pdf&usg=AOvVaw0a9uMI-1r3Ylt4jQf1qImj),
the SSC qualified this stock as satisfying the Tier 1 conditions. As
such, the harmonic mean value of $F_{MSY}$ ---here computed as an
exploitation rate---is applied to the fishable biomass for computing ABC
levels. For details on the risk-averse properties of this approach see
Thompson (1996). For a future year, the fishable biomass is defined as
the sum over ages of predicted begin-year numbers multiplied by age
specific fishery selectivity and estimated mean body mass-at-age. The
uncertainty in the average weights-at-age projected for the fishery and
"future selectivity" has been demonstrated to affect the buffer between
ABC and OFL (computed as 1-ABC/OFL) for Tier 1 maximum permissible ABC
(Ianelli et al. 2015). The uncertainty in future mean weights-at-age had
a relatively large impact as did the selectivity estimation (see the
section above on retrospective behavior and @fig-fmsy_sel).

Since the `r nextyr` female spawning biomass is estimated to be
`r M$bmsystatus` the $B_{MSY}$ level (`r M$bmsys` kt) and
`r M$b40status` the $B_{40\%}$ value (`r M$b40s` kt) in `r nextyr` and
if the `r thisyr` catch is as specified above, then the OFL and maximum
permissible ABC values by the different Tier categorizations would be:

```{r abctab13}
#| output: asis
#| echo: false
df <- data.table::data.table(
  Tier = c(
    M$tier1ab1,
    M$tier1ab2,
    M$tier2ab1,
    M$tier2ab2,
    M$tier3ab1,
    M$tier3ab2
  ),
  Year = c(
    nextyr, nextyr + 1,
    nextyr, nextyr + 1,
    nextyr, nextyr + 1
  ),
  MaxABC = c(
    M$maxabc1s,
    M$maxabc2s,
    formatC(
      c(
        M$Tier2_ABC1 * 1000, M$Tier2_ABC2 * 1000,
        M$Tier3_ABC1 * 1000, M$Tier3_ABC2 * 1000
      ),
      digits = 0, format = "d", big.mark = ","
    )
  ),
  OFL = c(
    M$ofl1s,
    M$ofl2s,
    M$ofl1s,
    M$ofl2s,
    M$Tier3_OFL1s,
    M$Tier3_OFL2s
  )
)
tab <- xtable::xtable(df, digits = c(0, 0, 0, 0, 0), big.mark = ",", align = "ccrrr")
print(tab, floating = FALSE, include.rownames = F, big.mark = ",", hline.after = c(-1, 0, 2, 4, nrow(tab)), comment = FALSE)
```

Note that the values presented for 2024 assumed a catch of 1,300,000 t
in 2023.

## Standard Harvest Scenarios and Projection Methodology

A standard set of projections is required for each stock managed under
Tiers 1, 2, or 3 of Amendment 56 to the FMP. This set of projections
encompasses seven harvest scenarios designed to satisfy the requirements
of Amendment 56, the National Environmental Policy Act, and the
Magnuson-Stevens Fishery Conservation and Management Act (MSFCMA). While
EBS pollock is generally considered to fall within Tier 1, the standard
projection model requires knowledge of future uncertainty in $F_{MSY}$.
Since this would require a number of additional assumptions that presume
future knowledge about stock-recruit uncertainty, the projections in
this subsection are based on Tier 3.

For each scenario, the projections begin with the vector of `r thisyr`
numbers at age estimated in the assessment. This vector is then
projected forward to the beginning of `r nextyr` using the schedules of
natural mortality and selectivity described in the assessment and the
best available estimate of total (year-end) catch assumed for
`r thisyr`. In each subsequent year, the fishing mortality rate is
prescribed on the basis of the spawning biomass in that year and the
respective harvest scenario. Annual recruits are simulated from an
inverse Gaussian distribution whose parameters consist of maximum
likelihood estimates determined from the estimated age-1 recruits.
Spawning biomass is computed in each year based on the time of peak
spawning and the maturity and weight schedules described in the
assessment. Total catch is assumed to equal the catch associated with
the respective harvest scenario in all years. This projection scheme is
run 1,000 times to obtain distributions of possible future stock sizes
and catches under alternative fishing mortality rate scenarios.

Five of the seven standard scenarios support the alternative harvest strategies analyzed in the Alaska Groundfish Harvest Specifications Final Environmental Impact Statement. These five
scenarios, which are designed to provide a range of harvest alternatives
that are likely to bracket the final TAC for `r nextyr`, are as follows
("$maxFABC$" refers to the maximum permissible value of FABC under
Amendment 56):

```{=tex}
\begin{description}
\item[Scenario 1:]   
In all future years, $F$ is set equal to max$F_{ABC}$. (Rationale:  Historically,
TAC has been constrained by ABC, so this scenario provides a likely upper
limit on future TACs).

\item[Scenario 2:]   
In `r nextyr` and `r nextyr+1` the catch is set equal to 1.30 million t
and in future years $F$ is set equal to the Tier 3 estimate (Rationale: this 
has been about equal to the catch level in recent years).

\item[Scenario 3:]   
In all future years, $F$ is set equal to the `r thisyr-6;print("-");thisyr-1` average $F$. (Rationale:
For some stocks, TAC can be well below ABC, and recent average $F$ may provide a
better indicator of $F_{TAC}$ than $F_{ABC}$.)

\item[Scenario 4:]   
In all future years, $F$ is set equal to $F_{60\%}$. (Rationale:  This
scenario provides a likely lower bound on $F_{ABC}$ that still allows future
harvest rates to be adjusted downward when stocks fall below reference levels.

\item[Scenario 5:]   
In all future years, $F$ is set equal to zero. (Rationale:  In
extreme cases, TAC may be set at a level close to zero.)

\item[Scenario 6:]   
In all future years, F is set equal to $F_{OFL}$. (Rationale:  This scenario
determines whether a stock is overfished. If the stock is expected to be 
1) below its MSY level in `r thisyr` or 2) below half of its MSY level in `r thisyr` or below
its MSY level in `r thisyr +10` under this scenario, then the stock is overfished.)

\item[Scenario 7:]   
In `r nextyr` and `r nextyr+1`, F is set equal to $maxFABC$, and in all subsequent years, F
is set equal to $F_{OFL}$. (Rationale:  This scenario determines whether a stock is
approaching an overfished condition. If the stock is 1) below its MSY level in
`r nextyr+1` or 2) below 1/2 of its MSY level in `r nextyr+1` and expected to be below its MSY
level in `r thisyr+12` under this scenario, then the stock is approaching an
overfished condition).

\end{description}
```
The latter two scenarios are needed to satisfy the MSFCMA's requirement
to determine whether a stock is currently in an overfished condition or
is approaching an overfished condition (for Tier 3 stocks, the MSY level
is defined as $B_{35\%}$).

## Projections and status determination

For the purposes of these projections, we present results based on
selecting the $F_{40\%}$ harvest rate as the $F_{ABC}$ value and use
$F_{35\%}$ as a proxy for $F_{MSY}$. Scenarios 1 through 7 were
projected 14 years from `r thisyr` (\cref{tab:tier3SSB} for Model
23.0--including the 1978 year-class as is convention for Tier 3
estimates). Under catches set to Tier 3 ABC estimates, the expected
spawning biomass is well above $B_{35\%}$ and is expected to be drop
below $B_{40\%}$ by 2026 (given mean recruitment; @fig-tier3_proj and
assuming catches \>2 million t in 2025).

Any stock that is below its minimum stock size threshold (MSST) is
defined to be overfished. Any stock that is expected to fall below its
MSST in the next two years is defined to be approaching an overfished
condition. Harvest scenarios 6 and 7 are used in these determinations as
follows:

Is the stock overfished? This depends on the stock's estimated spawning
biomass in `r thisyr`:

-   If spawning biomass for `r thisyr` is estimated to be below 1/2
    $B_{35\%}$ the stock is below its MSST.

-   If spawning biomass for `r thisyr` is estimated to be above
    $B_{35\%}$, the stock is above its MSST.

-   If spawning biomass for `r thisyr` is estimated to be above 1/2
    $B_{35\%}$ but below $B_{35\%}$, the stock's status relative to MSST
    is determined by referring to harvest scenario 6 (\cref{tab:tier3C} 
    through \cref{tab:tier3SSB}). If the mean spawning
    biomass for `r thisyr + 10` is below $B_{35\%}$, the stock is below
    its MSST. Otherwise, the stock is above its MSST.

Is the stock approaching an overfished condition? This is determined by
referring to harvest Scenario 7:

-   If the mean spawning biomass for `r thisyr` is below 1/2 $B_{35\%}$,
    the stock is approaching an overfished condition.

-   If the mean spawning biomass for `r thisyr` is above $B_{35\%}$, the
    stock is not approaching an overfished condition.

-   If the mean spawning biomass for `r nextyr+1` is above 1/2
    $B_{35\%}$ but below $B_{35\%}$, the determination depends on the
    mean spawning biomass for `r nextyr+11`. If the mean spawning
    biomass for `r nextyr+11` is below $B_{35\%}$, the stock is
    approaching an overfished condition. Otherwise, the stock is not
    approaching an overfished condition.

For scenarios 6 and 7, we conclude that pollock is above MSST for the
year `r thisyr`, and it is expected to be above the "overfished
condition" based on Scenario 7 (the mean spawning biomass in `r thisyr`
is between the 1/2 $B_{35\%}$ and $B_{35\%}$ estimate but by
`r nextyr+11` the stock is above $B_{35\%}$; (\cref{tab:tier3SSB}). 
Based on this, the EBS pollock stock is being
fished below the overfishing level and is not approaching an overfished
condition.

To fulfill reporting requirements for [NOAA's Species Information
System](https://www.fisheries.noaa.gov/resource/data/species-information-system),
we computed the average fishing mortality rate corresponding to the specified
OFL for the last complete year (`r thisyr-1`). This hypothetical
`r thisyr-1` $F_{OFL}$ from this year's model was estimated to be 0.262
for EBS pollock (assuming this year's estimated 2022 selectivity and
weight-at-age).

## ABC Recommendation

ABC levels are affected by estimates of $F_{MSY}$ which depend
principally on the estimated stock-recruitment steepness parameter,
demographic schedules such as selectivity-at-age, maturity, and growth.
The current stock size (both spawning and fishable) is estimated to be
`r M$bmsystatus` average levels and projections indicate the potential
for further declines. Updated data and analysis result in an estimate of
`r thisyr` spawning biomass (`r M$curssbs` kt) which is about
`r round(100*M$curssb/M$bmsy,0)`% of $B_{MSY}$ (`r M$bmsys` kt). This
follows a short period of decline from 2017-2020 followed by a
previously unexpected increase due to revised estimates of the 2018 year
class. Treating all new data the same way as in the past, this estimate
suggests that it would be the biggest year-class on record
(`r M$yc2018s` age 1 numbers), but with considerable uncertainty.

Given the same estimated aggregate fishing effort as in 2022 and given
the estimated stock trend, the constant-F scenario would yield about 1.3
million t. To obtain 2023 catches on the order of 1.45 million t, given
the base model estimates, would require about 18% more effort than what
was estimated from 2022.

### Should the ABC be reduced below the maximum permissible ABC?

The SSC in its September 2018 minutes recommended that assessment
authors and Plan Teams use the risk table below when determining whether
to recommend an ABC lower than the maximum permissible. The details of
the risk table are provided below. Given the concerns listed there, we
recommend reducing the ABC to the value provided under Tier 3
projections.

\pagebreak

```{=tex}
\begin{table}[ht]
\scalebox{0.88}{
\begin{tabular}{p{0.7in}p{1.5in}p{1.5in}p{1.5in}p{1.5in}}
\hline
 &\multicolumn{4}{c}{\textbf{Considerations}} \\
\hline
 &\textbf{Assessment-related} & \textbf{ Population dynamics} & \textbf
 {Environmental \& ecosystem }    & Fishery performance \\
\hline
Level 1 No concern   
& Typical to moderately increased uncertainty \& minor unresolved issues in assessment       
& Stock trends are typical for the stock; recent recruitment is within normal range.    
& No apparent environmental \& ecosystem concerns  
&  No apparent fishery/resource-use performance and/or behavior concerns \\ 
Level 2 Major Concern                    
& Major problems with the stock assessment, very poor fits to data, high level of 
uncertainty, strong retrospective bias. 
& Stock trends are highly unusual; very rapid changes in stock abundance, or highly atypical recruitment patterns.                           
& Multiple indicators showing consistent adverse signals a) across the same trophic level, 
and/or b) up or down trophic levels (i.e., predators and prey of stock) 
& Multiple indicators showing consistent adverse signals a) across different sectors, 
and/or b) different gear types
\\
Level 3 Extreme concern                  
& Severe problems with the stock assessment, severe retrospective bias. Assessment considered unreliable.          
& Stock trends are unprecedented. More rapid changes in stock abundance than have ever been seen previously, or a very long stretch of poor recruitment compared to previous patterns. 
& Extreme anomalies in multiple ecosystem indicators that are highly likely to impact the stock. Potential for cascading effects on other ecosystem components    
& Extreme anomalies in multiple performance indicators that are highly like to
impact the stock.
\\
\hline
\end{tabular}
}
\end{table}
```
The table is applied by evaluating the severity of four types of
considerations that could be used to support a scientific recommendation
to reduce the ABC from the maximum permissible. Examples of the types of
concerns that might be relevant include the following (as identified by
the work-group):

1.  Assessment considerations\

    -   *Data-inputs:* biased ages, skipped surveys, lack of
        fishery-independent trend data\
    -   *Model fits:* poor fits to fits to fishery or survey data,
        inability to simultaneously fit multiple data inputs.\
    -   *Model performance:* poor model convergence, multiple minima in
        the likelihood surface, parameters hitting bounds.\
    -   *Estimation uncertainty:* poorly-estimated but influential year
        classes.\
    -   Retrospective bias in biomass estimates.

2.  Population dynamics considerations---decreasing biomass trend, poor
    recent recruitment, inability of the stock to rebuild, abrupt
    increase or decrease in stock abundance.

3.  Environmental/ecosystem considerations--trends in
    environmental/ecosystem indicators, ecosystem model results,
    decreases in ecosystem productivity, decreases in prey abundance or
    availability, increases or increases in predator abundance or
    productivity.

4.  Fisheries considerations--fishery CPUE is showing a contrasting
    pattern from the stock biomass trend, unusual spatial pattern of
    fishing, changes in the percent of TAC taken, changes in the
    duration of fishery openings."

#### Assessment considerations

The EBS pollock assessment model has appeared to track the stock from
year-to-year based on retrospective analysis in previous assessments.
In 2022 the surveys showed an increase from the relatively low observation from 2021. 
This affected the retrospective analyses which last year indicated a tendency to over
estimate the stock trend. This year the model tracks the available data reasonably
well and fishery data confirm an apparently very strong 2018 year class.
The trend towards fishing being below average in body weight so far has been limited, despite
the observation that the condition (weight-given length) in 2023 seems to be the lowest on record.
**We therefore rated the assessment-related concern as
Level 1, No Concern.**

#### Population dynamics considerations

The age structure of EBS pollock has exhibited some peculiarities over
time. On the positive side, some strong year-classes appear to have
increased in abundance based on the bottom-trawl survey data (e.g., the
1992, 2012, 2013 and 2018 year classes). Conversely, the period from
2000--2007 had relatively poor year-class strengths which resulted in
declines in stock below ${B_{msy}}$ and reduced TACs due to lower ABC
values. Given new support for the  strong year-class strength from 2018, it appears that
the mean recruitment since 2000 has been nearly average but with greater
variability than earlier years (@fig-mod_regimes). The stock is
estimated to be above ${B_{msy}}$ at present, and projections indicate a
increases given recent catch levels. Recruitment in the near term is
about average and highly uncertain. Additional age-specific
aspects of the spawning population indicate that the stock has increased
from a low diversity of ages (for both the population and the mean age
of the spawning stock weighted by spawning output @fig-age_diversity).
**We therefore rated the population-dynamics concern as level 1, No Concern**

#### Environmental/Ecosystem considerations

**Summary for Environmental/Ecosystem considerations**
The following summarizes “Environmental/Ecosystem” considerations (see @sec-ecorisk
for details):

*  	September 2022 - August 2023 oceanographic conditions, based on sea surface and bottom temperatures, sea ice, and cold pool extent, were near respective time series averages.

*  	The 2023 cold pool spatial extent was near its time series average (1982-2023). 

*  	2023 chlorophyll-a biomass was among the lowest in the time series

*  	2023 coccolithophore index was among the highest ever observed in the timeseries

*  	pH and Ωaragonite remain near threshold levels of biological significance

*  	Zooplankton were dominated by small copepods. Large copepods and euphausiids had low lipid content; abundances increased to the north with hot spots around St. Lawrence Island

*  	Age-0 pollock condition (length-weight residual, % lipid, and energy density residual) was below average in 2023 and continued decreasing trends

*  	Juvenile pollock (100-250mm) condition was below average in the SEBS and NBS in 2021 and has decreased since 2021

*  	Adult pollock (>250mm) condition was below average in the SEBS and has decreased since 2019, while condition was above average in the NBS and has increased since 2021

*  	 Trends in potential competitors are mixed over the shelf, with increases in jellyfish over the northern shelf, increases in herring biomass, mixed trends in salmon run strengths and juvenile salmon condition (negative over the southern shelf, positive over the northern shelf), and a continued decline in the biomass of the pelagic forager biomass. 

*  	In 2023, with an average cold pool extent, predation pressure from cannibalism may have been mitigated as adult pollock avoided the cold pool and their center of distribution shifted north and west.

*  	Northern fur seal pup production at St. Paul Island in 2022 continued a declining trend since 1998.

*  	Trends in other potential predators are mixed over the shelf, especially in the NBS, with increased jellyfish abundance and decreased chum salmon abundance. 

Together, the most recent data available suggest an ecosystem risk of
**Level 2 – “Multiple indicators showing consistent adverse signals a) across the same 
trophic level as the stock, and/or b) up or down trophic levels (i.e., predators and 
prey of the stock).”** Multiple indicators of primary and secondary productivity show 
adverse signals borne out in continued declining trends in juvenile and adult fish condition.

#### Fishery performance
As noted above, the 2023 fishery again experienced good nominal 
fishing rates that were improved over 2020 and 2021 conditions.
The 2023 fishery seemed to have generally smaller-than-expected
pollock in the fishery and there was indications that the fish were 
unusually skinny given their length. The
fleet dispersion (the relative distance or spread of the fishery in
space) as shown in the past has indicated that the seasonal dispersion
levels increased slightly but was still relatively  low 
(indicating relatively good fishing; @fig-fleet_dispersal).

The CPUE of PSC species and other bycatch declined in 2023. Sablefish,
herring and Chinook salmon bycatch rates (per hour of fishing) all
decreased from 2021 (except for a slight increase in herring CPUE during
the B season from low levels; @fig-fsh_psc_cpue).

The way the ABC control rule interacts with actual fishing is worth
considering. Specifically, given the 2 million t OY cap for all of
groundfish, when the EBS pollock stock is above target levels, the
fishing effort is lower (a lower *F*). As it approaches the target
($B_{MSY}$), it increases and then when it drops below, the fishing
mortality rate is ratcheted downwards rapidly. This can be exacerbated
when there are sudden unanticipated changes in survey estimates (like
what was apparently the case in 2021 which caused the retrospective
pattern to degrade). The mean weight-at-age for the 2021 B-season was
near average, but in general, pollock were skinny given their length.
However, concerns over the amount of 2-year old pollock in the 2020
fishery data has been ameliorated with continued positive signs of that
year-class which is projected to be an abundant number of 5-year olds in
2023. For this reason, we **conclude that that the fishery performance
warrants a score of 1, No Concern.**

These results are summarized as:

```{=tex}
\begin{table}[ht]
\begin{tabular}{p{1.5in}p{1.5in}p{1.5in}p{1.5in} }
\hline
 \multicolumn{4}{c}{Considerations}   \\
Assessment-related & 
Population dynamics  & 
Environmental ecosystem  & 
Fisheries\\
\hline
Level 1: No concern  &
Level 1: No concern & 
Level 2: Major concern & 
Level 1: No concern  \\
\hline
\end{tabular}
\end{table}
```
Having a score at level 2 suggests that adjustments to the ABC may be
prudent. In the past, the SSC has considered factors similar to those
presented above and selected an ABC based on Tier 3 estimates. Last year
the SSC requested examining Tier 2 values as an alternative. Unlike Tier
3, using Tier 2 would have a constant buffer relative to the Tier 1
value (at about 11%). Setting the ABC to Tier 3 levels provides a very
large buffer but one that could be warranted given that the impact on
subsequent spawning biomass levels will be much more variable and have a
high probability of dropping below the target stock size and result in
much reduced future ABCs under the current FMP. It is worth noting that
fishing at the full Tier 1 ABC would imply a more than doubling of
effort and well exceed the 2 million t groundfish catch limit. Even
fishing at a full Tier 3 ABC shows there is a relatively high
probability of falling below $B_{MSY}$ values or proxies thereof. Under
our standard scenarios, Alternative 3 shows trajectories if fishing
effort is held equal to the recent 5-year average. It is noteworthy that
this provides stock sizes that have a good probability of being above
targets and avoiding drastic reductions in yeild (lower overall
variability in ABC/yields; @fig-tier3_proj_alt3).

The SSC has requested "an explicit set of concerns that explain the ABC
adjustment." In response, we direct attention to the decision table
(\cref{tab:dectable}) and the fact that the biological basis for the
continued stock productivity has most to do with the OY constraint which
has effectively maintained fishery production at around 1.3 million t
since 1990. Demonstrations that would allow fishing to near $F_{MSY}$
catch quantities would show that catch variability would be extremely
high (and unrealistic given current capacity and OY limits for combined
BSAI groundfish; Ianelli 2005). Furthermore, the frequency of being at
much lower spawning stock sizes would be much higher, and would likely
be riskier and fishing effort would need to be much higher. While the
biological basis for ABC setting is founded in sound conservation of
spawning biomass, the history of the current fishery productivity should
inform desirable biomass. In only 6 of the 41 years since 1981 has the
stock been below the $B_{MSY}$ level (15% of the years). The mean
spawning biomass over this period has averaged about
`r round(100*(mean(M$SSB[M$SSB[,1]>1980,2])/M$bmsy-1),0)`% higher than
the estimated $B_{MSY}$. In terms of an actual "management target", Punt
et al. (2013) developed some robust estimators for $B_{MEY}$ (Maximum
Economic Yield) noting that a typical target would be
1.2$\times B_{MSY}$ or about
`r round((mean(M$SSB[M$SSB[,1]>1980,2])/(1.2*M$bmsy)-1)*100,0)`% lower
than the mean value or a target female spawning biomass at
`r round(1.2*M$bmsy/1000,3)` million t. It therefore seems worth
considering developing an explicit harvest control rule that achieves
the level of productivity observed over the past 30 years.

In recent years when the pollock biomass was estimated to be well above
average, the catch was constrained by other factors. Specifically, the 2
million t BSAI groundfish catch limit and bycatch avoidance measures has
an impact on the potential for large increases in catch. As the stock is
presently estimated to be below $B_{MSY}$, the maximum permissible ABC
under the FMP can become the limiting factor for TAC specification.
Unfortunately, this ABC can ratchet down quickly because as the stock
declines further below this target stock size, the ABC fishing mortality
rate is adjusted downwards nearly proportionately. This part of the FMP
control rule can create high variability in the TAC. Less variability in
the catch, accordingly, would also result in less spawning stock
variability and reduce risks to the fishery should the period of poor
recruitment continue.

To more fully evaluate these considerations performance indicators as
modified from Ianelli et al. (2012) were developed to evaluate some
near-term risks given alternative `r nextyr` catch values. These
indicators and rationale for including them are summarized in 
\cref{tab:dectabledesc}). Model 23 (the "base") results for these
indicators are provided in \cref{tab:dectable}. Each column of this
table uses a fixed `r nextyr` catch and assumes the same effort for the
four additional projection years (`r nextyr+1`--`r nextyr+4`). Given
this specification, there is a low probability that any of the catches
shown in the first row would exceed the $F_{MSY}$ level. Also, in the
near term it appears unlikely that the spawning stock will be below
$B_{MSY}$ (rows 3 and 4). Relative to the historical mean spawning
biomass, by `r nextyr` it is more likely than not that the spawning
biomass will be lower than the historical mean (fifth row). The range of
catches examined have relatively small or no impact on the age diversity
indicators. 
The table indicates that for the  2024 catch to equal the 2023 value, 
about the same level of fishing effort would be required.
In terms of catch advice, the results presented in
the decision table indicates that catches above 1.3 million t will very
likely result in `r nextyr+1` spawning stock estimates being below the
long term mean (but above $B_{MSY}$).

In the past, another approach/rationale for stabilizing effort by
setting the fishing mortality equal to the current year. Doing so this
year suggests setting the fishing mortality to `r thisyr` levels results
in a catch of `r M$abc1constFs` t. Given the revisions to last year's
model results and the positive increases in stock size, maintaining a
constant fishing mortality rate seems unnecessary at this time.

# Additional ecosystem considerations {#Additional_ecosystem}

In general, a number of key issues for ecosystem conservation and
management can be highlighted. These include:

-   Preventing overfishing;

-   Avoiding habitat degradation;

-   Minimizing incidental bycatch;

-   Monitoring bycatch and the level of discards; and

-   Considering multi-species trophic interactions relative to harvest
    policies.

For the case of pollock in the Eastern Bering Sea, the NPFMC and NMFS
continue to manage the fishery on the basis of these issues in addition
to the single- species harvest approach (Hollowed et al. 2011). The
prevention of overfishing is clearly set out as the main guideline for
management. Habitat degradation has been minimized in the pollock
fishery by converting the industry to pelagic-gear only. Bycatch in the
pollock fleet is closely monitored by the NMFS observer program and
managed on that basis. Discard rates of many species have been reduced
in this fishery and efforts to minimize bycatch continue.

In comparisons of the Western Bering Sea (WBS) with the Eastern Bering
Sea using mass-balance food-web models based on 1980--85 summer diet
data, @aydin2002 found that the production in these two systems
is quite different. On a per-unit-area measure, the western Bering Sea
has higher productivity than the EBS. Also, the pathways of this
productivity are different with much of the energy flowing through
epifaunal species (e.g., sea urchins and brittlestars) in the WBS
whereas for the EBS, crab and flatfish species play a similar role. In
both regions, the keystone species in 1980--85 were pollock and Pacific
cod. This study showed that the food web estimated for the EBS ecosystem
appears to be relatively mature due to the large number of
interconnections among species. In a more recent study based on 1990--93
diet data, pollock remain in a central role in the ecosystem. The diet
of pollock is similar between adults and juveniles with the exception
that adults become more piscivorous (with consumption of pollock by
adult pollock representing their third largest prey item).

Regarding specific small-scale ecosystems of the EBS, Ciannelli et al.
(2004a, 2004b) presented an application of an ecosystem model scaled to
data available around the Pribilof Islands region. They applied
bioenergetics and foraging theory to characterize the spatial extent of
this ecosystem. They compared energy balance, from a food web model
relevant to the foraging range of northern fur seals and found that a
range of 100 nautical mile radius encloses the area of highest energy
balance representing about 50% of the observed foraging range for
lactating fur seals. This has led to a hypothesis that fur seals depend
on areas outside the energetic balance region. This study develops a
method for evaluating the shape and extent of a key ecosystem in the EBS
(i.e., the Pribilof Islands). Furthermore, the overlap of the pollock
fishery and northern fur seal foraging habitat has been identified (see
@sterling2004, @zeppelin2006).

A brief summary of these two perspectives (ecosystem effects on pollock
stock and pollock fishery effects on ecosystem) is given in (\cref{tab:ecosystab}). 
Unlike the food-web models discussed above,
examining predators and prey in isolation may overly simplify
relationships. This table serves to highlight the main connections and
the status of our understanding or lack thereof.

## Ecosystem effects on the EBS pollock stock
The pollock stock trends appear to be responding to ecosystem conditions
in the EBS. The conditions on the shelf during 2008 apparently affected
age-0 northern rock sole due to cold conditions and apparently
unfavorable currents that retain them into the over- summer nursery
areas (@cooper2014). It may be that such conditions favor pollock
recruitment. @hollowed2012 provided an extensive review of
habitat and density for age-0 and age-1 pollock based on survey data.
They noted that during cold years, age-0 pollock were distributed
primarily in the outer domain in waters greater than 1$^\circ$C and
during warm years, age-0 pollock were distributed mostly in the middle
domain. This temperature relationship, along with interactions with
available food in early-life stages, appears to have important
implications for pollock recruitment success (@coyle2011).

A separate section presented again this year updates a multispecies
model with more recent data and is presented as a supplement to the BSAI
SAFE report. This approach incorporates a number of simplifications for
the individual species data and fisheries processes (e.g., constant
fishery selectivity and the use of design-based survey indices for
biomass). However, that model mimics the biomass levels and trends with
the single species reasonably well. It also allows specific questions to
be addressed regarding pollock TACs. For example, since predation (and
cannibalism) is explicitly modeled, the impact of relative stock sizes
on subsequent recruitment to the fishery can be now be directly
estimated and evaluated (in the model presented here, cannibalism is
explicitly accounted for in the assumed Ricker stock-recruit
relationship).

## EBS pollock fishery effects on the ecosystem.

Since the pollock fishery is primarily pelagic in nature, the bycatch of
non- target species is small relative to the magnitude of the fishery
(\cref{tab:nontargbycatch}). Jellyfish represent the largest component
of the bycatch of non-target species and had averaged around 5--6 kt per
year but more than doubled in 2014, then dropping again in 2015. The
2018 value was high, dropped and then was again high in 2021. The data
on non-target species shows a high degree of inter-annual variability,
which reflects the spatial variability of the fishery and high
observation error. This variability may reduce the ability to detect
significant trends for bycatch species.

The catch of other target species in the pollock fishery (defined as any
trawl set where the catch represents more than 80% of the catch)
represents about 1% of the total pollock catch. Incidental catch of
Pacific cod has varied but after a period of low catch levels it
increased to over 9,000 t in 2020 and 2021 but in 2022 was under 4
thousand t (\cref{tab:fmpbycatch}). There has been a marked increase in
the incidental catch of Pacific ocean perch in the since 2014 with a
peak just under 8 thousand t in 2019. The incidental catch of sablefish
peaked in 2020 at about 3.5 thousand t but was less that 300 t in 2022.
The incidental catch of pollock in other target fisheries is more than
double the bycatch of target species in the pollock fishery with the
largest pollock catches in the yellofin sole and Pacific cod fisheries
(\cref{tab:pollbycatch}).

The number of non-Chinook salmon (nearly all made up of chum salmon)
taken incidentally varies considerably over time. The bycatch increased since 2014
with the 2017 number in excess of 465 thousand fish, the third highest
non-Chinook salmon bycatch that's been observed since 1991. Since then,
7 of the top 10 highest bycatch years have occurred with nearly 550
thousand taken in 2021 (\cref{tab:pscbycatch}). Chinook salmon bycatch
has varied (42% CV since 2011) and averaged just under 19 thousand fish
from 2011-2023 (\cref{tab:pscbycatch}). After a recent high bycatch of
over 32,000 fish in 2020, the 2022 and 2023 bycatch was 6,415 and 11,750 Chinook
salmon, respectively. @ianelli2014 provided estimates of the bycatch impact on Chinook
salmon runs to the coastal west Alaska region and found that the peak
bycatch levels exceeded 7% of the total run return. Since 2011, the
impact has been estimated to be below 2%. Updated estimates given new
genetic information and these levels of PSC as provided to the Council
continue to suggest that the impact is low.

# Data gaps and research priorities
The available data for EBS pollock are extensive yet many processes
behind the observed patterns continue to be poorly understood.
The recent patterns of abundance observed in the northern Bering Sea
provide an example. As such, we recommend the following research
priorities:

-   Support developing a team of analysts to evaluate all aspects of the
    current model against alternatives (e.g., Rceattle, WHAM, Stock
    Synthesis, etc.). *This work has progressed and presently, developments 
    on the Gulf of Alaska assessment adopting WHAM appears promising.*

-   Continue to investigate using spatial processes for estimation
    purposes (e.g., combining acoustic and bottom trawl survey data).
    The application of the geostatistical methods seems like a
    reasonable approach to statistically model disparate data sources
    for generating better abundance indices. Also, examine the potential
    to use pelagic samples from the BASIS survey to inform recruitment
    and subsequent spatial patterns. *Work on developing data from the 
    BASIS survey to inform recruitment has not been pursued. The work
    to refine the AVO data has helped with information used in this assessment.*

-   Develop methods to use spatio-temporal models to estimate
    composition information (specifically, weight-at-age in the survey).
    *Two papers, (@indivero2023) and (@cheng2023) have been published 
    targeting this type of activity. The former is presently used in this 
    assessment while the  latter has yet to be applied.*

-   Study the relationship between climate and recruitment and trophic
    interactions of pollock within the ecosystem. This would be useful
    for improving ways to evaluate the current and alternative fishery
    management systems. In particular, a careful re-evaluation of the
    current FMP harvest control rule should be undertaken.
    *As part of the ACLIM program, progress is being made. However, a full
    evaluation of the FMP control rules is pending.*

-   Apply new technologies (e.g., bottom-moored echosounders) to
    evaluate pollock movement between regions and supplement this work
    with analytical approaches. *The data have been processed completely and
    a manuscript is being submitted. Next steps is to use this information
    to develop scenarios for flux over the maritime boundary and evaluate
    relative fishing effort impacts on either side.*

-   Expand genetic sample collections for pollock (and process available
    samples) and apply high resolution genetic tools for stock structure
    analyses. *Additional analyses have been completed and are expected to lead
    to a publication in 2024.*

# Acknowledgments
We thank the scientifically trained observers and the staff of the
Fisheries and Monitoring Division at AFSC for their hard work. The
diligence of survey staff who contribute immensely in collecting
samples, especially given these times is exceptional. The AFSC
age-and-growth department is thanked for their continued excellence in
promptly processing the samples used in this assessment. We thank the
many colleagues who provided edits and suggestions to improve this
document, in particular, the timely review done by Dr. Melissa Haltuch.

# References

::: {#refs}
:::

\clearpage

# Tables

```{=tex}
\renewcommand\thetable{\arabic{table}} 
\setcounter{table}{0}
```
<!-- ```{r caswed} -->

<!-- #| label: tbl-catch -->

<!-- #| output: asis -->

<!-- #| echo: false -->

<!-- #| tbl-cap: "Goodness-of-fit measures to primary data for different assessment model configurations. RMSE=root-mean square log errors, NLL=negative log-likelihood (may not be comparable across model configurations), SDNR=standard deviation of normalized residuals, Eff. N=effective sample size for composition data)" -->

<!-- # Try to do table from raw data instead... -->

<!-- catch <- read_csv("doc/data/catch_tab.csv") -->

<!-- catch <- catch %>% mutate( -->

<!--   NW=Northwest_A+Northwest_B, -->

<!--   SE=Southeast_A+Southeast_B, -->

<!--   A=Northwest_A+Southeast_A, -->

<!--   B=Northwest_B+Southeast_B, -->

<!--   Total=A+B -->

<!-- ) -->

<!--   catch <- catch %>%mutate(Year=as.integer(Year)) -->

<!-- #glimpse(catch) -->

<!-- ncol=length(catch[1,]) -->

<!-- cap <- tabcap[1] -->

<!-- lab <- tablab[1] -->

<!-- tab <- xtable(catch, caption = cap, label=paste0("tab:",tablab[1]), -->

<!--   digits=0, auto=TRUE, big.mark=",",align=rep("r",(ncol+1)) ) -->

<!-- print(tab, format.args=list(big.mark=","),caption.placement = "top",include.rownames = FALSE, sanitize.text.function = function(x){x}, -->

<!--        scalebox=.85) #|> gt::gt() -->

<!-- #library(xtable) -->

<!-- ``` -->

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[1]` }
\label{tab:`r tablab[1]`}
\scalebox{0.81}{
\begin{tabular}{lrrrrrrrrr}
\hline
Year & Northwest A & Northwest  B & Southeast A & Southeast B & NW      & SE        & A       & B       & Total     \\
\hline
1991 & 12,191       & 529,897      & 297,202      & 356,353      & 542,088 & 653,555   & 309,393 & 886,250 & 1,195,643 \\
1992 & 101,597      & 458,143      & 454,947      & 375,612      & 559,740 & 830,559   & 556,544 & 833,755 & 1,390,299 \\
1993 & 93,013       & 139,160      & 475,143      & 619,287      & 232,173 & 1,094,430 & 568,156 & 758,447 & 1,326,603 \\
1994 & 11,026       & 165,751      & 586,105      & 566,470      & 176,777 & 1,152,575 & 597,131 & 732,221 & 1,329,352 \\
1995 & 3,631        & 88,310       & 600,499      & 571,807      & 91,941  & 1,172,306 & 604,130 & 660,117 & 1,264,247 \\
1996 & 1,804        & 104,135      & 549,297      & 537,545      & 105,939 & 1,086,842 & 551,101 & 641,680 & 1,192,781 \\
1997 & 2,363        & 302,180      & 557,106      & 262,784      & 304,543 & 819,890   & 559,469 & 564,964 & 1,124,433 \\
1998 & 1,508        & 131,007      & 476,214      & 410,353      & 132,515 & 886,567   & 477,722 & 541,360 & 1,019,082 \\
1999 & 4,139        & 202,558      & 414,030      & 368,953      & 206,697 & 782,983   & 418,169 & 571,511 & 989,680   \\
2000 & 19,363       & 274,170      & 439,023      & 400,154      & 293,533 & 839,177   & 458,386 & 674,324 & 1,132,710 \\
2001 & 108,013      & 317,207      & 444,565      & 517,412      & 425,220 & 961,977   & 552,578 & 834,619 & 1,387,197 \\
2002 & 36,693       & 283,747      & 561,061      & 599,272      & 320,440 & 1,160,333 & 597,754 & 883,019 & 1,480,773 \\
2003 & 151,465      & 406,123      & 446,152      & 487,039      & 557,588 & 933,191   & 597,617 & 893,162 & 1,490,779 \\
2004 & 50,661       & 339,883      & 556,469      & 533,538      & 390,544 & 1,090,007 & 607,130 & 873,421 & 1,480,551 \\
2005 & 120,320      & 560,548      & 476,970      & 325,184      & 680,868 & 802,154   & 597,290 & 885,732 & 1,483,022 \\
2006 & 18,789       & 642,034      & 585,507      & 241,700      & 660,823 & 827,207   & 604,296 & 883,734 & 1,488,030 \\
2007 & 62,504       & 563,748      & 505,306      & 222,943      & 626,252 & 728,249   & 567,810 & 786,691 & 1,354,501 \\
2008 & 44,801       & 463,079      & 358,936      & 123,762      & 507,880 & 482,698   & 403,737 & 586,841 & 990,578   \\
2009 & 81,575       & 370,957      & 247,944      & 110,307      & 452,532 & 358,251   & 329,519 & 481,264 & 810,783   \\
2010 & 199,011      & 356,061      & 124,670      & 130,443      & 555,072 & 255,113   & 323,681 & 486,504 & 810,185   \\
2011 & 102,252      & 348,897      & 401,755      & 346,128      & 451,149 & 747,883   & 504,007 & 695,025 & 1,199,032 \\
2012 & 104,926      & 481,418      & 380,373      & 238,497      & 586,344 & 618,870   & 485,299 & 719,915 & 1,205,214 \\
2013 & 94,763       & 480,331      & 415,014      & 280,652      & 575,094 & 695,666   & 509,777 & 760,983 & 1,270,760 \\
2014 & 50,465       & 388,712      & 469,973      & 388,267      & 439,177 & 858,240   & 520,438 & 776,979 & 1,297,417 \\
2015 & 258,574      & 366,752      & 267,095      & 429,153      & 625,326 & 696,248   & 525,669 & 795,905 & 1,321,574 \\
2016 & 81,531       & 104,078      & 458,044      & 709,028      & 185,609 & 1,167,072 & 539,575 & 813,106 & 1,352,681 \\
2017 & 37,730       & 143,430      & 544,028      & 633,993      & 181,160 & 1,178,021 & 581,758 & 777,423 & 1,359,181 \\
2018 & 3,842        & 326,753      & 598,533      & 450,160      & 330,595 & 1,048,693 & 602,375 & 776,913 & 1,379,288 \\
2019 & 2,649        & 304,532      & 614,949      & 487,217      & 307,181 & 1,102,166 & 617,598 & 791,749 & 1,409,347 \\
2020 & 85,717       & 421,102      & 560,438      & 299,978      & 506,819 & 860,416   & 646,155 & 721,080 & 1,367,235 \\
2021 & 56,779       & 295,470      & 554,774      & 469,234      & 352,249 & 1,024,008 & 611,553 & 764,704 & 1,376,257 \\
2022 & 9,018 & 197,610 & 487,216 & 411,573 & 206,628 & 898,789 & 496,234 & 609,183 & 1,105,417 \\ 
2023 & 11,335 & 408,832 & 568,447 & 306,154 & 420,167 & 874,601 & 579,782 & 714,986 & 1,294,768 \\ 
\hline
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[2]`}
\label{`r paste0("tab:",tablab[2])`}
\scalebox{0.80}{
\begin{tabular}{rrrrrrr}
\hline
Year    &   Catch   &   Year    &   OFL &   ABC &   TAC &   Catch   \\
\hline                                                  
1964    &   174,792 &   1977    &   -   &   950,000 &   950,000 &   978,370 \\
1965    &   230,551 &   1978    &   -   &   950,000 &   950,000 &   979,431 \\
1966    &   261,678 &   1979    &   -   &   1,100,000   &   950,000 &   935,714 \\
1967    &   550,362 &   1980    &   -   &   1,300,000   &   1,000,000   &   958,280 \\
1968    &   702,181 &   1981    &   -   &   1,300,000   &   1,000,000   &   973,502 \\
1969    &   862,789 &   1982    &   -   &   1,300,000   &   1,000,000   &   955,964 \\
1970    &   1,256,565   &   1983    &   -   &   1,300,000   &   1,000,000   &   981,450 \\
1971    &   1,743,763   &   1984    &   -   &   1,300,000   &   1,200,000   &   1,092,055   \\
1972    &   1,874,534   &   1985    &   -   &   1,300,000   &   1,200,000   &   1,139,676   \\
1973    &   1,758,919   &   1986    &   -   &   1,300,000   &   1,200,000   &   1,141,993   \\
1974    &   1,588,390   &   1987    &   -   &   1,300,000   &   1,200,000   &   859,416 \\
1975    &   1,356,736   &   1988    &   -   &   1,500,000   &   1,300,000   &   1,228,721   \\
1976    &   1,177,822   &   1989    &   -   &   1,340,000   &   1,340,000   &   1,229,600   \\
    &       &   1990    &   -   &   1,450,000   &   1,280,000   &   1,455,193   \\
    &       &   1991    &   -   &   1,676,000   &   1,300,000   &   1,195,664   \\
    &       &   1992    &   1,770,000   &   1,490,000   &   1,300,000   &   1,390,299   \\
    &       &   1993    &   1,340,000   &   1,340,000   &   1,300,000   &   1,326,602   \\
    &       &   1994    &   1,590,000   &   1,330,000   &   1,330,000   &   1,329,352   \\
    &       &   1995    &   1,500,000   &   1,250,000   &   1,250,000   &   1,264,247   \\
    &       &   1996    &   1,460,000   &   1,190,000   &   1,190,000   &   1,192,781   \\
    &       &   1997    &   1,980,000   &   1,130,000   &   1,130,000   &   1,124,433   \\
    &       &   1998    &   2,060,000   &   1,110,000   &   1,110,000   &   1,102,159   \\
    &       &   1999    &   1,720,000   &   992,000 &   992,000 &   989,680 \\
    &       &   2000    &   1,680,000   &   1,139,000   &   1,139,000   &   1,132,710   \\
    &       &   2001    &   3,536,000   &   1,842,000   &   1,400,000   &   1,387,197   \\
    &       &   2002    &   3,530,000   &   2,110,000   &   1,485,000   &   1,480,776   \\
    &       &   2003    &   3,530,000   &   2,330,000   &   1,491,760   &   1,490,779   \\
    &       &   2004    &   2,740,000   &   2,560,000   &   1,492,000   &   1,480,552   \\
    &       &   2005    &   2,100,000   &   1,960,000   &   1,478,500   &   1,483,022   \\
    &       &   2006    &   2,090,000   &   1,930,000   &   1,485,000   &   1,488,031   \\
    &       &   2007    &   1,640,000   &   1,394,000   &   1,394,000   &   1,354,502   \\
    &       &   2008    &   1,440,000   &   1,000,000   &   1,000,000   &   990,578 \\
    &       &   2009    &   977,000 &   815,000 &   815,000 &   810,784 \\
    &       &   2010    &   918,000 &   813,000 &   813,000 &   810,206 \\
    &       &   2011    &   2,450,000   &   1,270,000   &   1,252,000   &   1,199,041   \\
    &       &   2012    &   2,474,000   &   1,220,000   &   1,200,000   &   1,205,293   \\
    &       &   2013    &   2,550,000   &   1,375,000   &   1,247,000   &   1,270,827   \\
    &       &   2014    &   2,795,000   &   1,369,000   &   1,267,000   &   1,297,849   \\
    &       &   2015    &   3,330,000   &   1,637,000   &   1,310,000   &   1,322,317   \\
    &       &   2016    &   3,910,000   &   2,090,000   &   1,340,000   &   1,353,686   \\
    &       &   2017    &   3,640,000   &   2,800,000   &   1,345,000   &   1,359,367   \\
    &       &   2018    &   4,797,000   &   2,592,000   &   1,364,341   &   1,379,301   \\
    &       &   2019    &   3,914,000   &   2,163,000   &   1,397,000   &   1,409,235   \\
    &       &   2020    &   4,085,000   &   2,043,000   &   1,425,000   &   1,325,792   \\
    &       &   2021    &   2,594,000   &   1,626,000   &   1,375,000   &   1,339,000   \\
    &       &   2022    &   1,469,000   &   1,111,000   &   1,111,000   &   1,105,677   \\
    &       &   2023    &   3,381,000   &   1,910,000   &   1,300,000   &   1,259,161   \\
\hline                                                  
    \multicolumn{3}{c}{1977--2023   mean}           &   2,468,438   &   1,495,681   &   1,221,247   &   1,203,410   \\
\hline                                                  
\end{tabular}                                                   
}
\end{table}
```
\clearpage

<!-- Retained and discarded pollock -->

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[5]`}
\label{tab:`r tablab[5]`}
\begin{tabular}{lrrrrrr}
\hline 
Year & NW Discarded & SE Discarded & NW Retained & SE Retained & Total     & Discarded  \% \\
\hline 
1991 & 48,257       & 66,792       & 493,852      & 586,763      & 1,195,664 & 9.6\%       \\
1992 & 57,578       & 71,194       & 502,163      & 759,365      & 1,390,299 & 9.3\%       \\
1993 & 26,100       & 83,986       & 206,073      & 1,010,443    & 1,326,602 & 8.3\%       \\
1994 & 16,084       & 88,098       & 160,693      & 1,064,476    & 1,329,352 & 7.8\%       \\
1995 & 9,715        & 87,492       & 82,226       & 1,084,814    & 1,264,247 & 7.7\%       \\
1996 & 4,838        & 71,368       & 101,100      & 1,015,474    & 1,192,781 & 6.4\%       \\
1997 & 22,557       & 71,032       & 281,986      & 748,858      & 1,124,433 & 8.3\%       \\
1998 & 1,581        & 14,291       & 130,934      & 957,098      & 1,103,903 & 1.4\%       \\
1999 & 1,912        & 26,912       & 204,786      & 756,071      & 989,680   & 2.9\%       \\
2000 & 1,942        & 19,678       & 291,591      & 819,499      & 1,132,710 & 1.9\%       \\
2001 & 2,450        & 14,874       & 422,770      & 947,103      & 1,387,197 & 1.2\%       \\
2002 & 1,441        & 19,430       & 319,002      & 1,140,904    & 1,480,776 & 1.4\%       \\
2003 & 2,959        & 13,795       & 554,629      & 919,397      & 1,490,779 & 1.1\%       \\
2004 & 2,781        & 20,380       & 387,763      & 1,069,628    & 1,480,552 & 1.6\%       \\
2005 & 2,586        & 14,838       & 678,282      & 787,316      & 1,483,022 & 1.2\%       \\
2006 & 3,677        & 11,877       & 657,147      & 815,330      & 1,488,031 & 1.0\%       \\
2007 & 3,769        & 12,334       & 622,484      & 715,915      & 1,354,502 & 1.2\%       \\
2008 & 1,643        & 5,968        & 506,237      & 476,730      & 990,578   & 0.8\%       \\
2009 & 1,936        & 4,014        & 450,596      & 354,238      & 810,784   & 0.7\%       \\
2010 & 1,270        & 2,490        & 553,802      & 252,623      & 810,186   & 0.5\%       \\
2011 & 1,376        & 3,444        & 449,773      & 744,438      & 1,199,031 & 0.4\%       \\
2012 & 1,190        & 4,080        & 585,154      & 614,791      & 1,205,214 & 0.4\%       \\
2013 & 1,225        & 4,084        & 573,869      & 691,582      & 1,270,760 & 0.4\%       \\
2014 & 1,786        & 12,556       & 437,391      & 845,684      & 1,297,417 & 1.1\%       \\
2015 & 2,418        & 7,055        & 622,907      & 689,193      & 1,321,574 & 0.7\%       \\
2016 & 1,036        & 8,124        & 184,574      & 1,158,948    & 1,352,681 & 0.7\%       \\
2017 & 1,356        & 6,848        & 179,803      & 1,171,173    & 1,359,181 & 0.6\%       \\
2018 & 2,005        & 9,170        & 328,590      & 1,039,523    & 1,379,288 & 0.8\%       \\
2019 & 1,979        & 7,126        & 305,202      & 1,095,040    & 1,409,346 & 0.6\%       \\
2020 & 2,450        & 9,364        & 504,370      & 851,053      & 1,367,236 & 0.9\%       \\
2021 & 1,534        & 12,379       & 350,715      & 1,011,629    & 1,376,258 & 1.0\%       \\
2022	&	3,538	&	9,925	&	203,091	&	889,124	&	1,105,677	&	1.2\%	\\
2023	&	 3,660 	&	 8,820 	&	 405,467 	&	 841,214 	&	1,259,161	&	1.0\%	\\
\hline 
Mean	&	 7,292 	&	 24,964 	&	 386,031 	&	 846,225 	&	 1,264,512 	&	2.1\%	\\
\hline 
\end{tabular}
\end{table}
\clearpage
```
<!-- Management -->

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[4]`} 
\label{tab:`r tablab[4]`} 
\scalebox{.80}{
\begin{tabular}{p{0.5in}p{5.5in}}
\hline
Year    &   Management  \\
\hline 
1977    &   Preliminary BSAI FMP implemented with several closure areas \\
1982    &   FMP implement for the BSAI  \\
1982    &   Chinook salmon bycatch limits established for foreign trawlers  \\
1984    &   2 million t groundfish OY limit established \\
1984    &   Limits on Chinook salmon bycatch reduced    \\
1990    &   New observer program established along with data reporting  \\
1992    &   Pollock CDQ program commences   \\
1994    &   NMFS adopts minimum mesh size requirements for trawl codends    \\
1994    &   Voluntary retention of salmon for foodbank donations    \\
1994    &   NMFS publishes individual vessel bycatch rates on internet  \\
1995    &   Trawl closures areas and trigger limits established for chum and Chinook salmon \\
1998    &   Improved utilization and retention in effect (reduced discarded pollock)    \\
1998    &   American Fisheries Act (AFA) passed \\
1999    &   The AFA was implemented for catcher/processors  \\
1999    &   Additional critical habitat areas around sea lion haulouts in the GOA and Eastern Bering Sea are closed.    \\
2000    &   AFA implemented for remaining sectors (catcher vessel and motherships)  \\
2001    &   Pollock industry adopts voluntary rolling hotspot program for chum salmon   \\
2002    &   Pollock industry adopts voluntary rolling hotspot program for Chinook salmon    \\
2005    &   Rolling hotspot program adopted in regulations to exempt fleet from triggered time/area closures for Chinook and chum salmon    \\
2011    &   Amendment 91 enacted, Chinook salmon management under hard limits   \\
2015    &   Amendment 110 (BSAI) Salmon prohibited species catch management in the Bering Sea pollock fishery (additional measures that change limits depending on Chinook salmon run-strength indices) and includes additional provisions for reporting requirements (see https://alaskafisheries.noaa.gov/fisheries/chinook-salmon-bycatch-management for update and general information) \\
2016    &   Measures of amendment 110 go into effect for 2017 fishing season; Chinook salmon runs above the 3-run index value so bycatch limits stay the same   \\
2017    &   Due to amendment 110 about 45\% of the TAC is taken in the A-season (traditionally only 40\% was allowed). \\
2018    &   In-river estimates of Chinook salmon (three river index) fell below the threshold and therefore a lower PSC limit applies (from a performance standard of 47,491 to 33,318 and a PSC limit from 60,000 to 45,000 Chinook salmon overall). Additionally, squid have been recategorized as an ecosystem component.  \\
2019    &   Some pollock sectors experienced high bycatch levels for chum and Chinook salmon and also for sablefish.\\
2020    &   Bycatch rates unusually high again for sablefish. Herring PSC occurred in the A season and triggered area closures that will persist into 2021. Salmon bycatch rates (relative to hours fished) was lower than last year for both chum and Chinook. \\
2021    &   Bycatch rates for sablefish and herring moderate (but above average). Chinook salmon bycatch rates (relative to hours fished) was lower than last year but there was a marked increase in the rate for chum salmon (2nd highest since 1991). In-river estimates of Chinook salmon (three river index) fell below the threshold and therefore a lower PSC limit applies (from a performance standard of 47,491 to 33,318 and a PSC limit from 60,000 to 45,000 Chinook salmon overall).  \\
2022    &   Chum and Chinook salmon bycatch dropped by more than half of the 2021 levels while the rate (salmon per ton of pollock) were about 59\% of the 2021 rates.  \\
2023    & The Council initiated and review preliminary analyses on the feasibility of proposed alternatives for additional management measures to minimize chum salmon bycatch in the Bering Sea.  \\
\hline
\end{tabular}
}
\end{table}
```
\clearpage

<!-- fishery catch-age -->

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[10]`} 
\label{tab:`r tablab[10]`} 
\scalebox{.8}{
\begin{tabular}{crrrrrrrrrrrrrrr}
\hline
1979 & 101.4 & 543   & 719.8    & 420.1    & 392.5    & 215.5 & 56.3  & 25.7  & 35.9  & 27.5  & 17.6  & 7.9  & 3    & 1.1  & 2,567 \\
1980 & 9.8   & 462.2 & 822.9    & 443.3    & 252.1    & 210.9 & 83.7  & 37.6  & 21.7  & 23.9  & 25.4  & 15.9 & 7.7  & 3.7  & 2,421 \\
1981 & 0.6   & 72.2  & 1,012.70 & 637.9    & 227      & 102.9 & 51.7  & 29.6  & 16.1  & 9.3   & 7.5   & 4.6  & 1.5  & 1    & 2,175 \\
1982 & 4.7   & 25.3  & 161.4    & 1,172.20 & 422.3    & 103.7 & 36    & 36    & 21.5  & 9.1   & 5.4   & 3.2  & 1.9  & 1    & 2,004 \\
1983 & 5.1   & 118.6 & 157.8    & 312.9    & 816.8    & 218.2 & 41.4  & 24.7  & 19.8  & 11.1  & 7.6   & 4.9  & 3.5  & 2.1  & 1,745 \\
1984 & 2.1   & 45.8  & 88.6     & 430.4    & 491.4    & 653.6 & 133.7 & 35.5  & 25.1  & 15.6  & 7.1   & 2.5  & 2.9  & 3.7  & 1,938 \\
1985 & 2.6   & 55.2  & 381.2    & 121.7    & 365.7    & 321.5 & 443.2 & 112.5 & 36.6  & 25.8  & 24.8  & 10.7 & 9.4  & 9.1  & 1,920 \\
1986 & 3.1   & 86    & 92.3     & 748.6    & 214.1    & 378.1 & 221.9 & 214.3 & 59.7  & 15.2  & 3.3   & 2.6  & 0.3  & 1.2  & 2,041 \\
1987 & -     & 19.8  & 111.5    & 77.6     & 413.4    & 138.8 & 122.4 & 90.6  & 247.2 & 54.1  & 38.7  & 21.4 & 28.9 & 14.1 & 1,379 \\
1988 & -     & 10.7  & 454      & 421.6    & 252.1    & 544.3 & 224.8 & 104.9 & 39.2  & 96.8  & 18.2  & 10.2 & 3.8  & 11.7 & 2,192 \\
1989 & -     & 4.8   & 55.1     & 149      & 451.1    & 166.7 & 572.2 & 96.3  & 103.8 & 32.4  & 129   & 10.9 & 4    & 8.5  & 1,784 \\
1990 & 1.3   & 33    & 57       & 219.5    & 200.7    & 477.7 & 129.2 & 368.4 & 65.7  & 101.9 & 9     & 60.1 & 8.5  & 13.9 & 1,746 \\
1991 & 1     & 111.6 & 43.5     & 85.1     & 156.1    & 184.5 & 500.5 & 76.2  & 289.2 & 28    & 139.5 & 18.3 & 93.6 & 76.9 & 1,804 \\
1992 & 1.1   & 84.6  & 675.1    & 129.9    & 79.5     & 108.6 & 133.6 & 253.4 & 102.2 & 146.9 & 57.9  & 46.3 & 13.4 & 78.2 & 1,911 \\
1993 & 0.1   & 7.4   & 260.3    & 1,145.50 & 102.9    & 66.1  & 66.3  & 56.4  & 86.1  & 21.1  & 32.7  & 12.3 & 13.5 & 22.9 & 1,893 \\
1994 & 0.7   & 30.2  & 55.1     & 360.8    & 1,058.60 & 175.5 & 53.5  & 19.1  & 13.1  & 20.1  & 9.7   & 9.4  & 7.5  & 12.3 & 1,826 \\
1995 & -     & 0.5   & 72.8     & 146.6    & 395.1    & 760.3 & 136.1 & 34.5  & 12.3  & 7.5   & 17.5  & 5    & 5.8  & 10.6 & 1,605 \\
1996 & -     & 21.6  & 48       & 71.7     & 160.8    & 361.5 & 481.2 & 184.5 & 33.6  & 13.4  & 7.9   & 8.8  & 4.3  & 11.1 & 1,409 \\
1997 & 1     & 77.6  & 40.3     & 118.9    & 454.7    & 288.7 & 256.1 & 198.4 & 64    & 13.3  & 6     & 4.6  & 2.9  & 13.9 & 1,540 \\
1998 & 0.3   & 42    & 84.4     & 70.4     & 153.2    & 702.1 & 199.4 & 131.6 & 110.6 & 27.8  & 6.1   & 5.6  & 2.6  & 7    & 1,543 \\
1999 & 0.2   & 10.3  & 298.4    & 224.8    & 102.9    & 156.9 & 469.3 & 130.9 & 56.4  & 33.1  & 4     & 2.2  & 0.9  & 2.5  & 1,493 \\
2000 & -     & 16.1  & 82.4     & 428.1    & 346.2    & 106.6 & 168.2 & 357.4 & 84.8  & 29.7  & 22    & 5.2  & 1.4  & 1.6  & 1,650 \\
2001 & -     & 3.2   & 42.7     & 154.3    & 580.5    & 414.6 & 137   & 128.9 & 157.1 & 57.8  & 33.6  & 16.2 & 5.5  & 5    & 1,736 \\
2002 & 0.8   & 47    & 107.9    & 217.6    & 287.3    & 605.7 & 267.7 & 98.4  & 85.8  & 93.8  & 34.6  & 14.4 & 11   & 4.8  & 1,877 \\
2003 & -     & 14.5  & 411.4    & 323.8    & 360      & 301.2 & 337.3 & 158.4 & 49.4  & 39.2  & 35.7  & 22.9 & 6.6  & 6.8  & 2,067 \\
2004 & -     & 0.5   & 89.5     & 830.3    & 480.2    & 236.6 & 169.1 & 156.1 & 64.9  & 16.1  & 17    & 25.2 & 9.4  & 12.8 & 2,108 \\
2005 & -     & 4.8   & 52.1     & 392.5    & 862.9    & 484.1 & 159.3 & 68    & 66.6  & 30.1  & 10    & 9.1  & 3.2  & 5.9  & 2,149 \\
2006 & -     & 9.9   & 84.1     & 295.5    & 619      & 597   & 278.3 & 107.2 & 48    & 38.3  & 17.7  & 8.2  & 8.3  & 12.5 & 2,124 \\
2007 & 1.7   & 15.7  & 59.1     & 139      & 389      & 511.5 & 300.5 & 136.9 & 47.6  & 27.5  & 21.8  & 8.9  & 6.5  & 14.2 & 1,680 \\
2008 & -     & 25.2  & 58.8     & 79.1     & 146.9    & 309.4 & 242   & 148.6 & 84.2  & 22.2  & 17.5  & 14.4 & 8.6  & 15.4 & 1,172 \\
2009 & -     & 1.3   & 175.3    & 200.4    & 82.5     & 114.3 & 124.2 & 104.2 & 66.6  & 40.2  & 23.5  & 7.6  & 7.5  & 11.4 & 959   \\
2010 & 1.1   & 26.4  & 31.8     & 558.8    & 220.3    & 54.7  & 43    & 57.6  & 51.7  & 31.8  & 15.9  & 8.6  & 6    & 9.5  & 1,117 \\
2011 & 0.4   & 10.3  & 193.1    & 115.3    & 808.4    & 284.4 & 63.5  & 37.5  & 38.5  & 41.4  & 25.8  & 12.5 & 1.8  & 8.3  & 1,641 \\
2012 & -     & 22.2  & 116.6    & 945.8    & 172.6    & 432.1 & 141.4 & 36.6  & 17.4  & 14.6  & 15.9  & 13.5 & 7.4  & 9.5  & 1,946 \\
2013 & 1.8   & 1     & 63.9     & 342.1    & 954.9    & 194.2 & 156.4 & 69.9  & 20.7  & 12.7  & 12.7  & 10.8 & 7.8  & 10.5 & 1,859 \\
2014 & -     & 39.2  & 31       & 167.6    & 398.9    & 751   & 210   & 86.1  & 29.8  & 9     & 4.5   & 4.5  & 4.6  & 8.8  & 1,745 \\
2015 & -     & 15.5  & 631.8    & 196.2    & 228.3    & 383.9 & 509.6 & 88.7  & 42.1  & 17.6  & 2.9   & 2.1  & 3.1  & 3.9  & 2,126 \\
2016 & -     & 0.5   & 90.5     & 1,388.90 & 159.7    & 174.3 & 174.6 & 224.5 & 34    & 13.8  & 8     & 0.5  & 1.2  & 1.7  & 2,272 \\
2017 & -     & 2.2   & 28.1     & 548.5    & 898.1    & 215.3 & 147.4 & 122   & 97.2  & 21.7  & 7.2   & 5.6  & 0.5  & 0.4  & 2,094 \\
2018 & -     & 1.3   & 13.8     & 114.9    & 1,214.90 & 506.1 & 104.7 & 81.9  & 60.6  & 25.9  & 4.3   & 1.1  & 0.4  & 1.1  & 2,131 \\
2019 & 0.7   & 10.9  & 12.3     & 18.3     & 157.4    & 915.9 & 422   & 93.1  & 52.1  & 52.9  & 10    & 2.9  & 0.8  & -    & 1,749 \\
2020 & 3.7   & 245.9 & 85.6     & 99.2     & 134.1    & 548.5 & 598.3 & 126.6 & 53.0  & 37.8  & 27.0  & 6.9  & 1.7  & 1.2  & 1,970 \\
2021 & 0.0   & 111.3 & 1295.7   & 144.0    & 110.0    & 107.1 & 309.3 & 295.8 & 72.1  & 26.5  & 16.1  & 8.5  & 2.0  & 0.4  & 2,499 \\
2022 & 0.0   & 64.6  & 174.7    & 1037.4   & 182.1    & 70.8  & 80.1  & 139.1 & 80.7  & 16.7  & 13.4  & 4.7  & 6.8  & 1.0  & 1,872 \\
\hline
Mean & 5.8   & 58.0  & 218.7    & 369.2    & 385.4    & 333.1 & 217.2 & 117.8 & 65.1  & 33.0  & 22.1  & 10.9 & 7.5  & 10.5 & 1,852 \\
\hline
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[11]`} 
\label{tab:`r tablab[11]`} 
\scalebox{.8}{
\begin{tabular}{crrrrrrr}
\hline
\multicolumn{8}{c}{ Length Frequency samples } \\
\hline
        & \multicolumn{2}{c}{ A Season } &\multicolumn{2}{c}{B Season SE } &\multicolumn{2}{c}{B Season NW} &   \\
Year    &   Males   &   Females &   Males   &   Females &   Males   &   Females &   Total   \\
\hline
1977    &   26,411  &   25,923  &   4,301   &   4,511   &   29,075  &   31,219  &   121,440 \\
1978    &   25,110  &   31,653  &   9,829   &   9,524   &   46,349  &   46,072  &   168,537 \\
1979    &   59,782  &   62,512  &   3,461   &   3,113   &   62,298  &   61,402  &   252,568 \\
1980    &   42,726  &   42,577  &   3,380   &   3,464   &   47,030  &   49,037  &   188,214 \\
1981    &   64,718  &   57,936  &   2,401   &   2,147   &   53,161  &   53,570  &   233,933 \\
1982    &   74,172  &   70,073  &   16,265  &   14,885  &   181,606 &   163,272 &   520,273 \\
1983    &   94,118  &   90,778  &   16,604  &   16,826  &   193,031 &   174,589 &   585,946 \\
1984    &   158,329 &   161,876 &   106,654 &   105,234 &   243,877 &   217,362 &   993,332 \\
1985    &   119,384 &   109,230 &   96,684  &   97,841  &   284,850 &   256,091 &   964,080 \\
1986    &   186,505 &   189,497 &   135,444 &   123,413 &   164,546 &   131,322 &   930,727 \\
1987    &   373,163 &   399,072 &   14,170  &   21,162  &   24,038  &   22,117  &   853,722 \\
-       &   --      &   --      &   --      &   --      &   --      &   --      &   --     \\
1991 & 155,876 & 143,625 & 148,385 & 132,539 & 123,516 & 122,241 & 826,182 \\
1992 & 152,566 & 148,024 & 150,829 & 152,003 & 93,073  & 94,701  & 791,196 \\
1993 & 136,211 & 126,635 & 145,280 & 137,384 & 24,797  & 26,057  & 596,364 \\
1994 & 138,179 & 146,067 & 154,311 & 148,497 & 26,431  & 26,380  & 639,865 \\
1995 & 128,719 & 125,847 & 175,115 & 150,323 & 16,142  & 16,327  & 612,473 \\
1996 & 147,992 & 139,905 & 193,493 & 149,814 & 18,101  & 18,288  & 667,593 \\
1997 & 123,454 & 102,619 & 114,846 & 106,001 & 58,492  & 51,498  & 556,910 \\
1998 & 135,136 & 109,119 & 205,282 & 174,676 & 31,968  & 39,475  & 695,656 \\
1999 & 36,035  & 32,407  & 38,229  & 35,084  & 16,258  & 18,321  & 176,334 \\
2000 & 64,430  & 58,030  & 63,746  & 41,027  & 40,839  & 39,105  & 307,177 \\
2001 & 79,190  & 75,491  & 54,037  & 51,179  & 44,232  & 45,766  & 349,895 \\
2002 & 71,502  & 69,467  & 65,299  & 64,243  & 37,661  & 39,285  & 347,457 \\
2003 & 74,902  & 77,533  & 49,307  & 52,899  & 51,764  & 53,435  & 359,840 \\
2004 & 75,208  & 75,811  & 63,146  & 61,957  & 47,261  & 44,220  & 367,603 \\
2005 & 75,784  & 68,665  & 43,271  & 33,917  & 68,831  & 63,022  & 353,490 \\
2006 & 72,543  & 63,349  & 35,378  & 27,939  & 77,620  & 67,219  & 344,048 \\
2007 & 66,533  & 63,969  & 38,104  & 29,558  & 76,755  & 70,504  & 345,423 \\
2008 & 51,303  & 46,296  & 23,467  & 20,462  & 64,126  & 60,678  & 266,332 \\
2009 & 43,476  & 41,540  & 17,343  & 16,148  & 45,418  & 47,926  & 211,851 \\
2010 & 41,019  & 39,495  & 20,577  & 19,194  & 40,914  & 40,449  & 201,648 \\
2011 & 62,295  & 58,481  & 65,057  & 60,208  & 48,055  & 50,927  & 345,023 \\
2012 & 57,946  & 53,557  & 46,942  & 45,024  & 53,243  & 49,968  & 306,680 \\
2013 & 62,148  & 51,984  & 44,582  & 37,307  & 49,649  & 49,161  & 294,831 \\
2014 & 58,066  & 55,954  & 51,743  & 46,568  & 46,067  & 46,642  & 305,040 \\
2015 & 56,419  & 55,646  & 43,601  & 46,853  & 41,183  & 45,117  & 288,819 \\
2016 & 58,915  & 57,478  & 69,654  & 72,973  & 9,015   & 10,264  & 278,299 \\
2017 & 64,693  & 55,965  & 65,982  & 70,285  & 14,125  & 15,871  & 286,921 \\
2018 & 64,628  & 57,156  & 49,653  & 56,243  & 32,796  & 35,811  & 296,287 \\
2019 & 64,665  & 49,191  & 54,927  & 59,416  & 27,753  & 34,955  & 290,907 \\
2020 & 65,609  & 60,018  & 47,791  & 53,161  & 48,459  & 53,985  & 329,023 \\
2021 & 75,561  & 59,580  & 53,205  & 61,208  & 27,641  & 30,833  & 308,028 \\
2022 & 57,459  & 46,843  & 43,299  & 47,450  & 16,811  & 18,006  & 229,868 \\
\hline
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{\label{tab:lwmeas} Number of EBS pollock measured for weight and length by sex and strata as collected by the NMFS observer program, 1977-`r thisyr-1`}
\label{tab:lwmeas} 
\scalebox{.8}{
\begin{tabular}{crrrrrrr}
\hline
\multicolumn{8}{c}{ Weight-length samples } \\
\hline
&\multicolumn{2}{c}{A Season}   &\multicolumn{2}{c}{B Season SE} & \multicolumn{2}{c}{B Season NW}   &        \\
         & Males  & Females     & Males  & Females     & Males & Females & Total  \\
\hline
1977    &   1,222   &   1,338   &   137 &   166 &   1,461   &   1,664   &   5,988   \\
1978    &   1,991   &   2,686   &   409 &   516 &   2,200   &   2,623   &   10,425  \\
1979    &   2,709   &   3,151   &   152 &   209 &   1,469   &   1,566   &   9,256   \\
1980    &   1,849   &   2,156   &   99  &   144 &   612 &   681 &   5,541   \\
1981    &   1,821   &   2,045   &   51  &   52  &   1,623   &   1,810   &   7,402   \\
1982    &   2,030   &   2,208   &   181 &   176 &   2,852   &   3,043   &   10,490  \\
1983    &   1,199   &   1,200   &   144 &   122 &   3,268   &   3,447   &   9,380   \\
1984    &   980 &   1,046   &   117 &   136 &   1,273   &   1,378   &   4,930   \\
1985    &   520 &   499 &   46  &   55  &   426 &   488 &   2,034   \\
1986    &   689 &   794 &   518 &   501 &   286 &   286 &   3,074   \\
1987    &   1,351   &   1,466   &   25  &   33  &   72  &   63  &   3,010   \\
-       &   --      &   --      &   --      &   --      &   --      &   --      &   --     \\
1991     &   2,893   &   2,791   &   1,209   &   1,116   &   2,536   &   2,408   &   12,953     \\
1992     &   1,605   &   1,537   &   556     &   600     &   2,003   &   1,940   &   8,241  \\
1993     &   1,278   &   1,205   &   451     &   437     &   1,412   &   1,459   &   6,242  \\
1994     &   1,638   &   1,553   &   174     &   166     &   1,591   &   1,584   &   6,706  \\
1995     &   1,258   &   1,220   &   232     &   223     &   1,352   &   1,331   &   5,616  \\
1996     &   2,165   &   2,117   &   -       &   -       &   1,393   &   1,421   &   7,096  \\
1997     &   629     &   630     &   552     &   536     &   674     &   620     &   3,641  \\
1998     &   1,958   &   1,865   &   357     &   335     &   936     &   982     &   6,433  \\
1999     &   4,813   &   5,337   &   3,767   &   3,546   &   7,182   &   7,954   &   32,599     \\
2000     &   11,346      &   12,457      &   7,736   &   7,991   &   7,800   &   12,463      &   59,793     \\
2001     &   14,411      &   14,965      &   9,064   &   8,803   &   10,460      &   10,871      &   68,574     \\
2002     &   13,564      &   14,098      &   7,648   &   7,213   &   13,004      &   12,988      &   68,515     \\
2003     &   15,535      &   14,857      &   10,272      &   10,031      &   10,111      &   9,437   &   70,243     \\
2004     &   7,924   &   7,742   &   4,318   &   4,617   &   6,868   &   6,850   &   38,319     \\
2005     &   7,039   &   7,428   &   6,426   &   6,947   &   4,114   &   5,139   &   37,093     \\
2006     &   6,566   &   7,381   &   6,442   &   7,406   &   3,045   &   4,006   &   34,846     \\
2007     &   6,640   &   6,695   &   7,081   &   7,798   &   3,202   &   4,305   &   35,721     \\
2008     &   4,501   &   4,865   &   5,855   &   6,264   &   2,236   &   2,624   &   26,345     \\
2009     &   4,033   &   4,382   &   4,655   &   4,511   &   1,723   &   1,934   &   21,238     \\
2010     &   4,258   &   4,536   &   3,883   &   4,125   &   2,012   &   2,261   &   21,075     \\
2011     &   5,845   &   6,388   &   4,954   &   4,647   &   5,929   &   6,456   &   34,219     \\
2012     &   5,494   &   5,979   &   4,923   &   5,346   &   4,507   &   4,774   &   31,023     \\
2013     &   5,689   &   6,525   &   4,844   &   4,920   &   3,599   &   4,313   &   29,890     \\
2014     &   5,675   &   5,871   &   4,785   &   4,652   &   4,753   &   5,180   &   30,916     \\
2015     &   5,310   &   5,323   &   4,648   &   4,194   &   4,365   &   4,064   &   27,904     \\
2016     &   5,312   &   5,725   &   1,077   &   909     &   6,872   &   6,635   &   26,530     \\
2017     &   5,238   &   6,047   &   1,586   &   1,343   &   6,575   &   6,254   &   27,043     \\
2018     &   5,583   &   6,174   &   3,430   &   3,172   &   5,506   &   4,850   &   28,715     \\
2019     &   4,513   &   6,086   &   3,594   &   2,953   &   5,809   &   5,499   &   28,454     \\
2020     &   6,116   &   6,846   &   5,325   &   4,815   &   5,376   &   4,900   &   33,378     \\
2021     &   5,852   &   7,368   &   6,247   &   5,468   &   2,886   &   2,698   &   30,519 \\
2022     &   4,862   &   5,817   &   2,240   &   1,858   &   531     &   506     &   15,814 \\
\hline
\end{tabular}
}
\end{table}
```
```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[12]`} 
\label{tab:`r tablab[12]`} 
\scalebox{.8}{
\begin{tabular}{crrrrrrr}
\hline
&\multicolumn{2}{c}{A Season}   &\multicolumn{2}{c}{B Season SE} & \multicolumn{2}{c}{B Season NW}   &        \\
         & Males  & Females     & Males  & Females     & Males & Females & Total  \\
\hline
1977 & 1,229 & 1,344 & 137 & 166 & 1,415 & 1,613 & 5,904  \\
1978 & 1,992 & 2,686 & 407 & 514 & 2,188 & 2,611 & 10,398 \\
1979 & 2,647 & 3,088 & 152 & 209 & 1,464 & 1,561 & 9,121  \\
1980 & 1,854 & 2,158 & 93  & 138 & 606   & 675   & 5,524  \\
1981 & 1,819 & 2,042 & 51  & 52  & 1,620 & 1,807 & 7,391  \\
1982 & 2,030 & 2,210 & 181 & 176 & 2,865 & 3,062 & 10,524 \\
1983 & 1,200 & 1,200 & 144 & 122 & 3,249 & 3,420 & 9,335  \\
1984 & 980   & 1,046 & 117 & 136 & 1,272 & 1,379 & 4,930  \\
1985 & 520   & 499   & 46  & 55  & 426   & 488   & 2,034  \\
1986 & 689   & 794   & 518 & 501 & 286   & 286   & 3,074  \\
1987 & 1,351 & 1,466 & 25  & 33  & 72    & 63    & 3,010  \\
-       &   --      &   --      &   --      &   --      &   --      &   --      &   --     \\
1991     &   439     &   431     &   367     &   349     &   263     &   289     &   2,138  \\
1992     &   399     &   396     &   178     &   180     &   391     &   375     &   1,919  \\
1993     &   476     &   445     &   122     &   124     &   496     &   507     &   2,170  \\
1994     &   201     &   200     &   142     &   132     &   574     &   571     &   1,820  \\
1995     &   313     &   299     &   131     &   123     &   420     &   439     &   1,725  \\
1996     &   465     &   479     &   -       &   -       &   436     &   443     &   1,823  \\
1997     &   437     &   434     &   343     &   341     &   313     &   286     &   2,154  \\
1998     &   663     &   595     &   237     &   222     &   311     &   316     &   2,344  \\
1999     &   506     &   541     &   298     &   308     &   748     &   750     &   3,151  \\
2000     &   629     &   667     &   293     &   254     &   596     &   847     &   3,286  \\
2001     &   563     &   603     &   205     &   178     &   697     &   736     &   2,982  \\
2002     &   672     &   663     &   247     &   202     &   890     &   839     &   3,513  \\
2003     &   653     &   588     &   274     &   262     &   701     &   671     &   3,149  \\
2004     &   547     &   561     &   221     &   245     &   698     &   600     &   2,872  \\
2005     &   599     &   617     &   420     &   422     &   490     &   614     &   3,162  \\
2006     &   528     &   609     &   507     &   568     &   367     &   459     &   3,038  \\
2007     &   627     &   642     &   552     &   568     &   485     &   594     &   3,468  \\
2008     &   513     &   497     &   538     &   650     &   342     &   368     &   2,908  \\
2009     &   404     &   484     &   440     &   432     &   240     &   299     &   2,299  \\
2010     &   545     &   624     &   413     &   466     &   418     &   505     &   2,971  \\
2011     &   581     &   808     &   404     &   396     &   582     &   660     &   3,431  \\
2012     &   517     &   571     &   485     &   579     &   480     &   533     &   3,165  \\
2013     &   666     &   703     &   525     &   568     &   401     &   518     &   3,381  \\
2014     &   609     &   629     &   413     &   407     &   475     &   553     &   3,086  \\
2015     &   653     &   642     &   511     &   493     &   508     &   513     &   3,320  \\
2016     &   488     &   599     &   157     &   125     &   929     &   969     &   3,267  \\
2017     &   604     &   778     &   179     &   163     &   777     &   753     &   3,254  \\
2018     &   569     &   662     &   366     &   358     &   621     &   591     &   3,167  \\
2019     &   552     &   778     &   387     &   332     &   558     &   531     &   3,138  \\
2020     &   757     &   899     &   405     &   420     &   450     &   408     &   3,339  \\
2021     &   760     &   910     &   588     &   542     &   270     &   256     &   3,326  \\
2022     &   608     &   776     &   616     &   558     &   209     &   211     &   2,978  \\
\hline
\end{tabular}
}
\end{table}
```
```{=tex}
\clearpage
\begin{table}[ht]
\centering
\caption{`r tabcap[13]`}
\label{tab:`r tablab[13]`}
\begin{tabular}{crrrrr}
\hline
Year & Bering Sea & Year & Bering Sea & Year & Bering Sea \\
\hline
1964 &    0    &    1983 &    508  &    2002 &    440  \\
1965 &    18   &    1984 &    208  &    2003 &    285  \\
1966 &    17   &    1985 &    435  &    2004 &    363  \\
1967 &    21   &    1986 &    163  &    2005 &    87   \\
1968 &    7    &    1987 &    174  &    2006 &    251  \\
1969 &    14   &    1988 &    467  &    2007 &    333  \\
1970 &    9    &    1989 &    393  &    2008 &    168  \\
1971 &    16   &    1990 &    369  &    2009 &    156  \\
1972 &    11   &    1991 &    465  &    2010 &    226  \\
1973 &    69   &    1992 &    156  &    2011 &    1322 \\
1974 &    83   &    1993 &    221  &    2012 &    219  \\
1975 &    197  &    1994 &    267  &    2013 &    183  \\
1976 &    122  &    1995 &    249  &    2014 &    308  \\
1977 &    35   &    1996 &    206  &    2015 &    256  \\
1978 &    94   &    1997 &    262  &    2016 &    198  \\
1979 &    458  &    1998 &    121  &    2017 &     363      \\
1980 &    139  &    1999 &    299  &    2018 &     269      \\
1981 &    466  &    2000 &    313  &    2019 &     338      \\
1982 &    682  &    2001 &    241  &    2020 &     13  \\
     &         &         &         &    2021 &     898  \\
     &         &         &         &    2022 &     246  \\
\hline
\end{tabular}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[15]`}
\label{tab:`r tablab[15]`}
\scalebox{0.93}{
\begin{tabular}{crrrr}
\hline
     & \multicolumn{3}{c}{Survey biomass}                       &     \\
Year & Strata 1-6              & Strata 8-9  &   Total           & \%NW \\
\hline                                            
1982	&	2,854,067	&	54,333	&	2,908,400	&	1.9\%	\\
1983	&	5,910,351	&	-	&	5,910,351	&	\\	
1984	&	4,538,253	&	-	&	4,538,253	&	\\	
1985	&	4,543,845	&	1,388,121	&	5,931,966	&	23.4\%	\\
1986	&	4,830,239	&	-	&	4,830,239	&	\\	
1987	&	5,101,495	&	386,312	&	5,487,807	&	7.0\%	\\
1988	&	6,988,785	&	179,531	&	7,168,316	&	2.5\%	\\
1989	&	5,891,474	&	642,812	&	6,534,286	&	9.8\%	\\
1990	&	7,086,231	&	189,055	&	7,275,286	&	2.6\%	\\
1991	&	5,060,594	&	62,291	&	5,122,885	&	1.2\%	\\
1992	&	4,310,567	&	208,971	&	4,519,538	&	4.6\%	\\
1993	&	5,196,757	&	98,257	&	5,295,014	&	1.9\%	\\
1994	&	4,974,941	&	49,563	&	5,024,503	&	1.0\%	\\
1995	&	5,408,305	&	68,377	&	5,476,682	&	1.2\%	\\
1996	&	2,988,107	&	143,394	&	3,131,501	&	4.6\%	\\
1997	&	2,866,779	&	692,854	&	3,559,633	&	19.5\%	\\
1998	&	2,141,303	&	550,487	&	2,691,790	&	20.5\%	\\
1999	&	3,592,006	&	199,345	&	3,791,350	&	5.3\%	\\
2000	&	4,981,435	&	118,285	&	5,099,720	&	2.3\%	\\
2001	&	4,141,748	&	51,030	&	4,192,778	&	1.2\%	\\
2002	&	4,744,887	&	197,356	&	4,942,242	&	4.0\%	\\
2003	&	8,107,657	&	285,661	&	8,393,318	&	3.4\%	\\
2004	&	3,745,935	&	118,370	&	3,864,305	&	3.1\%	\\
2005	&	4,723,494	&	137,326	&	4,860,820	&	2.8\%	\\
2006	&	2,842,370	&	199,328	&	3,041,698	&	6.6\%	\\
2007	&	4,152,074	&	179,550	&	4,331,625	&	4.1\%	\\
2008	&	2,829,351	&	188,832	&	3,018,183	&	6.3\%	\\
2009	&	2,226,322	&	51,057	&	2,277,379	&	2.2\%	\\
2010	&	3,542,594	&	186,598	&	3,729,191	&	5.0\%	\\
2011	&	2,942,823	&	166,349	&	3,109,172	&	5.4\%	\\
2012	&	3,280,469	&	205,701	&	3,486,170	&	5.9\%	\\
2013	&	4,286,060	&	276,788	&	4,562,848	&	6.1\%	\\
2014	&	6,549,316	&	876,186	&	7,425,501	&	11.8\%	\\
2015	&	5,943,128	&	449,089	&	6,392,217	&	7.0\%	\\
2016	&	4,698,980	&	211,474	&	4,910,454	&	4.3\%	\\
2017	&	4,690,053	&	125,651	&	4,815,704	&	2.6\%	\\
2018	&	3,016,181	&	97,024	&	3,113,204	&	3.1\%	\\
2019	&	4,968,072	&	483,937	&	5,452,009	&	8.9\%	\\
2020	&	&	&	&	\\				
2021	&	 2,694,658 	&	 336,330 	&	3,030,988	&	11.1\%	\\
2022	&	 4,006,148 	&	 147,823 	&	4,153,971	&	3.6\%	\\
2023	&	 3,029,110 	&	 125,558 	&	3,154,668	&	4.0\%	\\
\hline									
Average	&	4,400,658	&	266,553	&	4,647,706	&	5.7\%	\\
\hline
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[16]`}
\label{tab:`r tablab[16]`}
\begin{tabular}{crrrrrrrr}
\hline
Year & Number of & Lengths & Aged &  & Year & Number of & Lengths & Aged \\
     & Hauls &  &  &  &  & Hauls &  &  \\
\hline
1982 & 329 & 40,001 & 1,611 &  & 1999 & 373 & 32,532 & 1,385 \\
1983 & 354 & 78,033 & 1,931 &  & 2000 & 372 & 41,762 & 1,545 \\
1984 & 355 & 40,530 & 1,806 &  & 2001 & 375 & 47,335 & 1,641 \\
1985 & 434 & 48,642 & 1,913 &  & 2002 & 375 & 43,361 & 1,695 \\
1986 & 354 & 41,101 & 1,344 &  & 2003 & 376 & 46,480 & 1,638 \\
1987 & 356 & 40,144 & 1,607 &  & 2004 & 375 & 44,102 & 1,660 \\
1988 & 373 & 40,408 & 1,173 &  & 2005 & 373 & 35,976 & 1,676 \\
1989 & 373 & 38,926 & 1,227 &  & 2006 & 376 & 39,211 & 1,573 \\
1990 & 371 & 34,814 & 1,257 &  & 2007 & 376 & 29,679 & 1,484 \\
1991 & 371 & 43,406 & 1,083 &  & 2008 & 375 & 24,635 & 1,251 \\
1992 & 356 & 34,024 & 1,263 &  & 2009 & 375 & 24,819 & 1,342 \\
1993 & 375 & 43,278 & 1,385 &  & 2010 & 376 & 23,142 & 1,385 \\
1994 & 375 & 38,901 & 1,141 &  & 2011 & 376 & 36,227 & 1,734 \\
1995 & 376 & 25,673 & 1,156 &  & 2012 & 376 & 35,782 & 1,785 \\
1996 & 375 & 40,789 & 1,387 &  & 2013 & 376 & 35,908 & 1,847 \\
1997 & 376 & 35,536 & 1,193 &  & 2014 & 376 & 43,042 & 2,099 \\
1998 & 375 & 37,673 & 1,261 &  & 2015 & 376 & 54,241 & 2,320 \\
     &     &        &       &  & 2016 & 376 & 50,857 & 1,766 \\
     &     &        &       &  & 2017 & 376 & 47,873 & 1,623 \\
     &     &        &       &  & 2018 & 376 & 48,673 & 1,486 \\
     &     &        &       &  & 2019 & 376 & 42,382 & 1,519 \\
     &     &        &       &  & --   & --  & --     &    -- \\
     &     &        &       &  & 2021 & 376 & 53,545 & 1,528 \\
     &     &        &       &  & 2022 & 376 & 36,687 & 1,578 \\
     &     &        &       &  & 2023 & 376 & xx,xxx & x,xxx \\
\hline
\end{tabular}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[17]`}
\label{tab:`r tablab[17]`}
\scalebox{0.83}{
\begin{tabular}{crrrrrrrrrrrrrrrr}
\hline
Year    &   1   &   2   &   3   &   4   &   5   &   6   &   7   &   8   &   9   &   10  &   11  &   12  &   13  &   14  &   15  &   Total   \\
\hline                                                                                                                                  
1982 & 1,131 & 2,750 & 3,291 & 4,544 & 1,516 & 217   & 147   & 71    & 47    & 25    & 13    & 10  & 3   & 1   & 0   & 13,765 \\
1983 & 451   & 656   & 1,619 & 3,060 & 6,727 & 1,979 & 367   & 193   & 75    & 69    & 53    & 18  & 9   & 7   & 2   & 15,286 \\
1984 & 340   & 332   & 453   & 1,563 & 1,811 & 4,535 & 897   & 203   & 87    & 32    & 20    & 8   & 5   & 6   & 3   & 10,294 \\
1985 & 2,825 & 686   & 3,172 & 1,421 & 4,743 & 2,406 & 1,720 & 355   & 87    & 68    & 19    & 6   & 7   & 1   & 0   & 17,516 \\
1986 & 2,256 & 344   & 399   & 1,989 & 1,092 & 2,010 & 1,641 & 1,388 & 472   & 77    & 32    & 15  & 0   & 3   & -   & 11,720 \\
1987 & 305   & 630   & 893   & 583   & 5,633 & 1,141 & 1,369 & 519   & 1,671 & 235   & 88    & 30  & 4   & 2   & 2   & 13,105 \\
1988 & 754   & 697   & 1,460 & 3,750 & 1,481 & 5,329 & 1,701 & 1,307 & 728   & 1,598 & 161   & 86  & 15  & 31  & 11  & 19,111 \\
1989 & 792   & 332   & 655   & 2,147 & 4,770 & 1,054 & 3,986 & 582   & 749   & 284   & 727   & 136 & 107 & 61  & 69  & 16,452 \\
1990 & 1,714 & 349   & 103   & 938   & 1,630 & 5,951 & 1,315 & 3,325 & 361   & 567   & 100   & 767 & 57  & 68  & 53  & 17,299 \\
1991 & 3,102 & 1,021 & 132   & 77    & 641   & 594   & 1,945 & 740   & 1,684 & 410   & 568   & 116 & 313 & 45  & 32  & 11,419 \\
1992 & 1,394 & 436   & 2,171 & 501   & 516   & 810   & 629   & 795   & 375   & 863   & 312   & 371 & 159 & 111 & 98  & 9,541  \\
1993 & 1,637 & 305   & 1,137 & 3,972 & 684   & 511   & 305   & 466   & 629   & 394   & 352   & 255 & 165 & 109 & 131 & 11,050 \\
1994 & 1,091 & 585   & 566   & 1,702 & 4,242 & 817   & 200   & 207   & 194   & 358   & 207   & 306 & 106 & 91  & 156 & 10,828 \\
1995 & 1,465 & 98    & 445   & 1,955 & 2,748 & 4,630 & 1,634 & 349   & 285   & 240   & 314   & 148 & 224 & 80  & 111 & 14,726 \\
1996 & 1,497 & 354   & 144   & 378   & 1,006 & 1,338 & 1,293 & 392   & 105   & 92    & 77    & 147 & 40  & 85  & 90  & 7,038  \\
1997 & 2,535 & 312   & 194   & 229   & 3,129 & 1,348 & 864   & 1,026 & 154   & 58    & 67    & 80  & 94  & 32  & 115 & 10,238 \\
1998 & 660   & 628   & 252   & 204   & 465   & 2,610 & 720   & 465   & 345   & 88    & 30    & 11  & 27  & 25  & 59  & 6,590  \\
1999 & 1,064 & 948   & 854   & 1,016 & 591   & 966   & 2,833 & 765   & 424   & 349   & 144   & 46  & 21  & 27  & 82  & 10,131 \\
2000 & 1,090 & 389   & 379   & 1,744 & 1,720 & 1,050 & 758   & 2,773 & 1,003 & 548   & 205   & 162 & 30  & 16  & 83  & 11,949 \\
2001 & 1,675 & 954   & 511   & 542   & 1,311 & 1,490 & 568   & 307   & 969   & 715   & 252   & 208 & 71  & 30  & 76  & 9,679  \\
2002 & 761   & 425   & 729   & 1,063 & 1,286 & 1,661 & 921   & 473   & 571   & 1,158 & 552   & 250 & 149 & 43  & 42  & 10,083 \\
2003 & 412   & 162   & 684   & 2,063 & 2,201 & 1,995 & 2,479 & 1,369 & 576   & 888   & 1,875 & 759 & 283 & 103 & 69  & 15,918 \\
2004 & 367   & 267   & 156   & 1,250 & 1,439 & 1,049 & 612   & 669   & 317   & 201   & 197   & 365 & 155 & 35  & 28  & 7,106  \\
2005 & 383   & 145   & 195   & 1,028 & 2,747 & 2,210 & 1,206 & 447   & 382   & 276   & 74    & 149 & 267 & 95  & 90  & 9,693  \\
2006 & 866   & 43    & 45    & 370   & 990   & 1,269 & 843   & 391   & 227   & 198   & 98    & 56  & 85  & 113 & 110 & 5,704  \\
2007 & 2,338 & 40    & 101   & 450   & 1,461 & 1,735 & 1,281 & 902   & 390   & 166   & 148   & 146 & 61  & 82  & 146 & 9,450  \\
2008 & 505   & 93    & 65    & 147   & 530   & 1,163 & 861   & 597   & 402   & 150   & 129   & 99  & 48  & 28  & 143 & 4,963  \\
2009 & 793   & 138   & 400   & 458   & 236   & 362   & 530   & 446   & 350   & 159   & 103   & 35  & 32  & 18  & 69  & 4,130  \\
2010 & 548   & 119   & 148   & 2,902 & 1,396 & 414   & 364   & 378   & 386   & 270   & 228   & 85  & 48  & 27  & 65  & 7,377  \\
2011 & 1,113 & 101   & 243   & 364   & 1,788 & 908   & 257   & 165   & 244   & 235   & 186   & 152 & 59  & 30  & 80  & 5,924  \\
2012 & 1,173 & 197   & 380   & 3,199 & 744   & 1,282 & 411   & 193   & 123   & 165   & 143   & 122 & 107 & 38  & 64  & 8,340  \\
2013 & 1,204 & 89    & 133   & 958   & 4,953 & 1,171 & 737   & 259   & 82    & 79    & 101   & 79  & 71  & 37  & 47  & 10,000 \\
2014 & 2,238 & 517   & 106   & 382   & 1,691 & 6,261 & 3,210 & 737   & 380   & 149   & 56    & 68  & 62  & 37  & 78  & 15,971 \\
2015 & 1,167 & 771   & 2,166 & 603   & 1,181 & 2,283 & 4,471 & 1,324 & 314   & 140   & 22    & 18  & 39  & 15  & 30  & 14,543 \\
2016 & 734   & 375   & 646   & 3,258 & 1,341 & 936   & 1,338 & 1,909 & 372   & 142   & 47    & 10  & 8   & 5   & 4   & 11,125 \\
2017 & 850   & 249   & 415   & 2,401 & 3,299 & 1,399 & 1,096 & 1,082 & 1,354 & 406   & 165   & 49  & 8   & 10  & 7   & 12,790 \\
2018 & 1,027 & 447   & 161   & 361   & 2,609 & 1,510 & 501   & 373   & 375   & 281   & 91    & 13  & 2   & -   & 4   & 7,754  \\
2019 & 2,545 & 659   & 303   & 487   & 1,464 & 5,477 & 2,241 & 561   & 426   & 244   & 132   & 56  & 23  & 14  & -   & 14,633 \\
2020 & -     & -     & -     & -     & -     & -     & -     & -     & -     & -     & -     & -   & -   & -   & -   & -      \\
2021 & 906   & 522   & 1,251 & 668   & 554   & 435   & 1,174 & 1,359 & 355   & 134   & 107   & 72  & 14  & 10  & 3   & 7,563  \\
2022 & 904   & 292   & 500   & 3,218 & 1,571 & 694   & 763   & 1,088 & 859   & 254   & 101   & 88  & 39  & 14  & -   & 10,386 \\
2023 & 1,384 & 329   & 258   & 627   & 2,681 & 630   & 325   & 315   & 554   & 387   & 97    & 42  & 44  & 8   & 11  & 7,693  \\
\hline
Mean & 1,219 & 458   & 681   & 1,429 & 2,015 & 1,845 & 1,232 & 763   & 475   & 321   & 205   & 138 & 75  & 39  & 55  & 10,948 \\
\hline                                                                                                                                  
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[18]`}
\label{tab:`r tablab[18]`}
\scalebox{0.8}{
\begin{tabular}{crrrrrrrrrrrrrrr}
\hline
\normalsize  Year & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14 & 15+  \\
\hline
1982 &    0.032     &    0.077     &    0.181     &    0.340     &    0.411     &    0.777     &    1.051     &    1.188     &    1.401     &    1.567     &    2.235     &    1.990     &    1.904     &    1.524     &    2.946     \\
1983 &    0.021     &    0.101     &    0.209     &    0.342     &    0.536     &    0.774     &    1.016     &    1.453     &    1.406     &    1.667     &    1.529     &    1.577     &    2.013     &    2.127     &    1.834     \\
1984 &    0.017     &    0.084     &    0.215     &    0.314     &    0.432     &    0.603     &    0.926     &    1.313     &    1.268     &    1.473     &    1.985     &    1.688     &    1.905     &    1.426     &    2.122     \\
1985 &    0.031     &    0.093     &    0.229     &    0.368     &    0.481     &    0.715     &    0.910     &    1.213     &    1.723     &    1.436     &    1.532     &    1.777     &    2.038     &    1.652     &    2.634     \\
1986 &    0.019     &    0.073     &    0.169     &    0.310     &    0.414     &    0.608     &    0.767     &    1.018     &    1.304     &    1.650     &    1.276     &    1.381     &    1.983     &    2.233     &    2.223     \\
1987 &    0.023     &    0.123     &    0.253     &    0.332     &    0.422     &    0.547     &    0.718     &    0.849     &    1.008     &    1.261     &    1.581     &    1.612     &    2.202     &    2.045     &    2.395     \\
1988 &    0.019     &    0.133     &    0.281     &    0.330     &    0.446     &    0.494     &    0.590     &    0.814     &    0.908     &    1.040     &    1.230     &    1.285     &    1.571     &    0.686     &    1.740     \\
1989 &    0.022     &    0.082     &    0.169     &    0.277     &    0.371     &    0.549     &    0.662     &    0.837     &    1.027     &    1.000     &    1.115     &    1.014     &    1.260     &    1.144     &    1.103     \\
1990 &    0.029     &    0.097     &    0.187     &    0.356     &    0.478     &    0.545     &    0.614     &    0.735     &    1.029     &    0.979     &    1.023     &    1.188     &    0.855     &    1.390     &    1.714     \\
1991 &    0.030     &    0.145     &    0.201     &    0.333     &    0.565     &    0.650     &    0.777     &    0.856     &    1.016     &    1.105     &    1.287     &    1.370     &    1.347     &    1.733     &    1.691     \\
1992 &    0.030     &    0.134     &    0.255     &    0.400     &    0.464     &    0.570     &    0.756     &    0.771     &    0.929     &    1.008     &    1.138     &    1.520     &    1.539     &    1.426     &    1.563     \\
1993 &    0.016     &    0.098     &    0.250     &    0.409     &    0.464     &    0.548     &    0.662     &    0.783     &    0.987     &    0.999     &    1.149     &    1.285     &    1.509     &    1.576     &    1.909     \\
1994 &    0.025     &    0.117     &    0.212     &    0.401     &    0.536     &    0.672     &    0.648     &    1.046     &    1.166     &    1.107     &    1.219     &    1.241     &    1.367     &    1.436     &    1.451     \\
1995 &    0.018     &    0.105     &    0.169     &    0.363     &    0.478     &    0.648     &    0.624     &    0.785     &    0.911     &    1.281     &    1.222     &    1.254     &    1.399     &    1.422     &    1.740     \\
1996 &    0.021     &    0.112     &    0.151     &    0.300     &    0.487     &    0.583     &    0.758     &    0.820     &    0.979     &    1.023     &    1.350     &    1.461     &    1.487     &    1.639     &    1.964     \\
1997 &    0.017     &    0.095     &    0.189     &    0.277     &    0.382     &    0.530     &    0.674     &    0.776     &    0.996     &    0.966     &    1.211     &    1.465     &    1.090     &    1.566     &    1.951     \\
1998 &    0.016     &    0.081     &    0.213     &    0.332     &    0.446     &    0.519     &    0.811     &    0.887     &    1.078     &    1.293     &    1.592     &    1.415     &    1.516     &    1.669     &    1.905     \\
1999 &    0.020     &    0.097     &    0.216     &    0.351     &    0.392     &    0.526     &    0.616     &    0.882     &    1.038     &    1.008     &    1.279     &    1.132     &    1.666     &    1.760     &    2.192     \\
2000 &    0.017     &    0.085     &    0.219     &    0.398     &    0.470     &    0.521     &    0.722     &    0.759     &    0.925     &    1.035     &    1.236     &    1.338     &    1.782     &    1.626     &    2.048     \\
2001 &    0.022     &    0.092     &    0.201     &    0.356     &    0.617     &    0.729     &    0.750     &    1.000     &    0.982     &    1.031     &    1.278     &    1.419     &    1.474     &    1.766     &    1.539     \\
2002 &    0.025     &    0.107     &    0.269     &    0.402     &    0.544     &    0.685     &    0.713     &    0.904     &    1.006     &    1.053     &    1.010     &    1.139     &    1.474     &    1.410     &    2.108     \\
2003 &    0.032     &    0.109     &    0.342     &    0.419     &    0.648     &    0.710     &    0.886     &    0.869     &    1.124     &    1.239     &    1.269     &    1.285     &    1.369     &    1.747     &    1.780     \\
2004 &    0.035     &    0.202     &    0.284     &    0.516     &    0.595     &    0.750     &    0.895     &    0.932     &    1.119     &    1.027     &    1.279     &    1.552     &    1.537     &    2.375     &    1.712     \\
2005 &    0.034     &    0.112     &    0.231     &    0.394     &    0.539     &    0.698     &    0.859     &    0.931     &    0.993     &    1.223     &    1.383     &    1.229     &    1.401     &    1.504     &    1.682     \\
2006 &    0.010     &    0.087     &    0.179     &    0.457     &    0.605     &    0.679     &    0.789     &    0.866     &    1.058     &    1.171     &    1.279     &    1.336     &    1.669     &    1.519     &    1.704     \\
2007 &    0.015     &    0.100     &    0.294     &    0.493     &    0.637     &    0.810     &    0.928     &    1.060     &    1.002     &    1.315     &    1.309     &    1.268     &    1.417     &    1.367     &    1.385     \\
2008 &    0.019     &    0.059     &    0.220     &    0.491     &    0.601     &    0.730     &    0.857     &    0.946     &    0.987     &    1.154     &    1.640     &    1.372     &    1.708     &    1.540     &    1.682     \\
2009 &    0.019     &    0.070     &    0.241     &    0.509     &    0.688     &    0.815     &    1.010     &    1.068     &    1.121     &    1.359     &    1.449     &    1.769     &    1.767     &    2.049     &    2.486     \\
2010 &    0.023     &    0.069     &    0.244     &    0.496     &    0.657     &    0.804     &    1.097     &    1.140     &    1.260     &    1.376     &    1.190     &    1.389     &    1.629     &    2.161     &    2.221     \\
2011 &    0.018     &    0.081     &    0.216     &    0.511     &    0.653     &    0.785     &    0.907     &    1.066     &    1.159     &    1.246     &    1.358     &    1.419     &    1.365     &    1.496     &    2.016     \\
2012 &    0.017     &    0.076     &    0.285     &    0.410     &    0.592     &    0.738     &    0.865     &    1.008     &    1.354     &    1.203     &    1.340     &    1.424     &    1.500     &    1.711     &    1.981     \\
2013 &    0.020     &    0.067     &    0.228     &    0.516     &    0.577     &    0.721     &    0.973     &    1.173     &    1.265     &    1.461     &    1.513     &    1.404     &    1.717     &    1.822     &    1.965     \\
2014 &    0.019     &    0.113     &    0.393     &    0.445     &    0.567     &    0.693     &    0.737     &    0.978     &    1.136     &    1.336     &    1.534     &    1.484     &    1.638     &    1.638     &    2.012     \\
2015 &    0.020     &    0.091     &    0.347     &    0.442     &    0.566     &    0.675     &    0.742     &    0.864     &    1.064     &    1.270     &    1.545     &    1.455     &    1.446     &    1.482     &    1.596     \\
2016 &    0.022     &    0.090     &    0.278     &    0.524     &    0.574     &    0.688     &    0.764     &    0.795     &    0.883     &    0.919     &    1.193     &    1.846     &    1.244     &    1.228     &    1.393     \\
2017 &    0.026     &    0.096     &    0.242     &    0.488     &    0.621     &    0.649     &    0.740     &    0.782     &    0.889     &    0.923     &    0.998     &    1.013     &    1.323     &    1.020     &    1.813     \\
2018 &    0.024     &    0.101     &    0.210     &    0.442     &    0.575     &    0.665     &    0.756     &    0.750     &    0.845     &    0.886     &    0.722     &    0.838     &    0.876     &    1.074     &    0.964     \\
2019 &    0.026     &    0.112     &    0.292     &    0.505     &    0.642     &    0.715     &    0.823     &    0.899     &    0.901     &    0.989     &    0.986     &    1.043     &    1.068     &    1.120     &    1.390     \\
2020 &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   &    --   \\
2021 &    0.023     &    0.148     &    0.278     &    0.440     &    0.590     &    0.696     &    0.774     &    0.854     &    0.960     &    1.233     &    1.023     &    1.350     &    1.307     &    0.927     &    1.389     \\
2022 &    0.020     &    0.104     &    0.347     &    0.452     &    0.583     &    0.668     &    0.764     &    0.853     &    0.945     &    0.972     &    1.122     &    1.474     &    1.242     &    0.895     &    1.247     \\
2023	&	0.020	&	0.104	&	0.315	&	0.418	&	0.530	&	0.642	&	0.784	&	0.925	&	0.979	&	1.024	&	1.166	&	1.137	&	1.445	&	1.845	&	1.428	\\
\hline																															
Avg	&	0.022	&	0.101	&	0.242	&	0.406	&	0.534	&	0.662	&	0.798	&	0.938	&	1.076	&	1.178	&	1.312	&	1.381	&	1.513	&	1.556	&	1.820	\\
\hline
\end{tabular}
}
\end{table}
```
\clearpage

```{r indices,results="asis",echo=FALSE}
#| output: asis
#| echo: false
# biom <- read_csv("doc/data/Indices.csv")
biom <- read.csv("doc/data/Indices.csv")
cap <- tabcap[14]
lab <- paste0("tab:", tablab[14])
tabindices <- xtable(biom, caption = cap, big.mark = ",", label = lab, digits = 0, align = rep("r", (length(biom[1, ]) + 1)))
print(tabindices,
  caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
    x
  },
  scalebox = .76, format.args = list(big.mark = ","), comment = FALSE
)
```

\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[19]`}
\label{tab:`r tablab[19]`}
\scalebox{0.8}{
\begin{tabular}{crrrrrrrrrrrrrrrr}
\hline
    &\multicolumn{4}{c}{Hauls} & \multicolumn{4}{c}{Lengths} & \multicolumn{4}{c}{Otoliths} &       \multicolumn{4}{c}{Number aged} \\
Year  & E  & W  & US  & RU      & E      & W      & US     & RU       & E     & W     & US    & RU          & E     & W     & US    & RU  \\
\hline
1979  &    &    & 25  &         &        &        & 7,722  &          &       &       & 0     &             &       &       & 2,610 &     \\
1982  & 13 & 31 & 48  &         & 1,725  & 6,689  & 8,687  &          & 840   & 2,324 & 3,164 &             & 783   & 1,958 & 2,741 &     \\
1985  &    &    & 73  &         &        &        & 19,872 &          &       &       & 2,739 &             &       &       & 2,739 &     \\
1988  &    &    & 25  &         &        &        & 6,619  &          &       &       & 1,471 &             &       &       & 1,471 &     \\
1991  &    &    & 62  &         &        &        & 16,343 &          &       &       & 2,062 &             &       &       & 1,663 &     \\
1994  & 25 & 51 & 76  & 19      & 4,553  & 21,011 & 25,564 & 8,930    & 1,560 & 3,694 & 4,966 & 1,270       & 612   & 932   & 1,770 & 455 \\
1996  & 15 & 42 & 57  &         & 3,551  & 13,273 & 16,824 &          & 669   & 1,280 & 1,949 &             & 815   & 1,111 & 1,926 &     \\
1997  & 25 & 61 & 86  &         & 6,493  & 23,043 & 29,536 &          & 966   & 2,669 & 3,635 &             & 936   & 1,349 & 2,285 &     \\
1999  & 41 & 77 & 118 &         & 13,841 & 28,521 & 42,362 &          & 1,945 & 3,001 & 4,946 &             & 946   & 1,500 & 2,446 &     \\
2000  & 29 & 95 & 124 &         & 7,721  & 36,008 & 43,729 &          & 850   & 2,609 & 3,459 &             & 850   & 1,403 & 2,253 &     \\
2002  & 47 & 79 & 126 &         & 14,601 & 25,633 & 40,234 &          & 1,424 & 1,883 & 3,307 &             & 1,000 & 1,200 & 2,200 &     \\
2004  & 33 & 57 & 90  & 15      & 8,896  & 18,262 & 27,158 & 5,893    & 1,167 & 2,002 & 3,169 & 461         & 798   & 1,192 & 2,351 & 461 \\
2006  & 27 & 56 & 83  &         & 4,939  & 19,326 & 24,265 &          & 822   & 1,871 & 2,693 &             & 822   & 1,870 & 2,692 &     \\
2007  & 23 & 46 & 69  & 4       & 5,492  & 14,863 & 20,355 & 1,407    & 871   & 1,961 & 2,832 & 319         & 823   & 1,737 & 2,560 & 315 \\
2008  & 9  & 53 & 62  & 6       & 2,394  & 15,354 & 17,748 & 1,754    & 341   & 1,698 & 2,039 & 177         & 338   & 1,381 & 1,719 & 176 \\
2009  & 13 & 33 & 46  & 3       & 1,576  & 9,257  & 10,833 & 282      & 308   & 1,210 & 1,518 & 54          & 306   & 1,205 & 1,511 & 54  \\
2010  & 11 & 48 & 59  & 9       & 2,432  & 20,263 & 22,695 & 3,502    & 653   & 1,868 & 2,521 & 381         & 652   & 1,598 & 2,250 & 379 \\
2012  & 17 & 60 & 77  & 14      & 4,422  & 23,929 & 28,351 & 5,620    & 650   & 2,045 & 2,695 & 418         & 646   & 1,483 & 2,129 & 416 \\
2014  & 52 & 87 & 139 & 3       & 28,857 & 8,645  & 37,502 & 747      & 1,739 & 849   & 2,588 & 72          & 845   & 1,735 & 2,580 & 72  \\
2016  & 37 & 71 & 108 &         & 10,912 & 24,134 & 35,046 &          & 880   & 1,514 & 2,394 &             & 876   & 1,513 & 2,388 &     \\
2018	 & 	 36 	 & 	 64 	 & 	 100 	 & 		 & 	 11,031 	 & 	 21,173 	 & 	 32,204 	 & 		 & 	 1,105 	 & 	 1,750 	 & 	 2,855 	 & 		 & 	 1,100 	 & 	 1,743 	 & 	 2,843 	 & 		\\
2022	 & 	 26 	 & 	 36 	 & 	 62 	 & 		 & 	 4,954 	 & 	 11,994 	 & 	 16,948 	 & 		 & 	 671 	 & 	 1,691 	 & 	 2,362 	 & 		 & 	 668 	 & 	 1,688 	 & 	 2,356 	 & 		\\
\hline
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[20]`}
\label{tab:`r tablab[20]`}
\scalebox{1}{
\begin{tabular}{ccrrrrrrr}
\hline
    &   &   Area    &   \multicolumn{4}{c}{Biomass}& & \\                                                  
Year	&	Date					&	(nmi)$^2$	&	SCA	&	E170-SCA	&	W170	&	0.5m Total	&	Rel. Error	&	CV	\\
\hline																					
1994	&	9	Jul	-	19	Aug	&	78,251	&	0.378	&	0.656	&	2.595	&	3.629	&	0.040	&	19\%	\\
1996	&	20	Jul	-	30	Aug	&	93,810	&	0.272	&	0.49	&	2.182	&	2.944	&	0.032	&	15\%	\\
1997	&	17	Jul	-	4	Sept	&	102,770	&	0.274	&	0.853	&	2.463	&	3.59	&	0.029	&	14\%	\\
1999	&	7	Jun	-	5	Aug	&	103,670	&	0.323	&	0.758	&	3.06	&	4.141	&	0.044	&	20\%	\\
2000	&	7	Jun	-	2	Aug	&	106,140	&	0.457	&	0.717	&	2.452	&	3.626	&	0.028	&	13\%	\\
2002	&	4	Jun	-	30	Jul	&	99,526	&	0.755	&	0.946	&	2.605	&	4.306	&	0.027	&	12\%	\\
2004	&	4	Jun	-	29	Jul	&	99,659	&	0.550	&	0.918	&	2.528	&	3.996	&	0.031	&	14\%	\\
2006	&	3	Jun	-	25	Jul	&	89,550	&	0.147	&	0.340	&	1.387	&	1.874	&	0.033	&	16\%	\\
2007	&	2	Jun	-	30	Jul	&	92,944	&	0.136	&	0.245	&	1.899	&	2.280	&	0.038	&	18\%	\\
2008	&	2	Jun	-	31	Jul	&	95,374	&	0.122	&	0.087	&	1.196	&	1.404	&	0.056	&	26\%	\\
2009	&	9	Jun	-	7	Aug	&	91,414	&	0.156	&	0.058	&	1.117	&	1.331	&	0.069	&	32\%	\\
2010	&	5	Jun	-	7	Aug	&	92,849	&	0.098	&	0.193	&	2.345	&	2.636	&	0.054	&	25\%	\\
2012	&	7	Jun	-	10	Aug	&	96,852	&	0.196	&	0.319	&	1.764	&	2.279	&	0.034	&	16\%	\\
2014	&	12	Jun	-	13	Aug	&	94,361	&	0.571	&	1.458	&	2.714	&	4.743	&	0.034	&	16\%	\\
2016	&	12	Jun	-	17	Aug	&	100,674	&	0.542	&	1.268	&	3.028	&	4.838	&	0.019	&	9\%	\\
2018	&	12	Jun	-	22	Aug	&	92,283	&	0.234	&	0.510	&	1.753	&	2.497	&	0.039	&	18\%	\\
2020	&	4	Jul	-	20	Aug	&	102,320	&	0.398	&	0.531	&	2.688	&	3.617	&	0.096	&	45\%	\\
2022	&	1	Jun	-	5	Aug	&	103,942	&	0.538	&	0.826	&	2.470	&	3.834	&	0.068	&	32\%	\\
\hline
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[21]`} 
\label{tab:`r tablab[21]`} 
\scalebox{0.9}{
\begin{tabular}{crrrrrrrrrrrr}
\hline
& \multicolumn{10}{c}{Age}                                                          & Age    &       \\
\hline
Year    &   1   &   2   &   3   &   4   &   5   &   6   &   7   &   8   &   9   &   10+ &   2+  &   Total   \\
\hline                                                                                                  
1979    &   69,110  &   41,132  &   3,884   &   413 &   534 &   128 &   30  &   4   &   28  &   161 &   46,314  &   115,424 \\
1982    &   108 &   3,401   &   4,108   &   7,637   &   1,790   &   283 &   141 &   178 &   90  &   177 &   17,805  &   17,913  \\
1985    &   2,076   &   929 &   8,149   &   898 &   2,186   &   1,510   &   1,127   &   130 &   21  &   15  &   14,965  &   17,041  \\
1988    &   11  &   1,112   &   3,586   &   3,864   &   739 &   1,882   &   403 &   151 &   130 &   414 &   12,280  &   12,292  \\
1991    &   639 &   5,942   &   967 &   215 &   224 &   133 &   120 &   39  &   37  &   53  &   7,730   &   8,369   \\
1994    &   1,140   &   4,969   &   1,424   &   1,819   &   2,252   &   389 &   109 &   96  &   56  &   221 &   11,335  &   12,475  \\
1996    &   1,800   &   567 &   552 &   2,741   &   915 &   634 &   585 &   142 &   39  &   165 &   6,338   &   8,139   \\
1997    &   13,227  &   2,881   &   440 &   536 &   2,330   &   546 &   313 &   290 &   75  &   220 &   7,633   &   20,860  \\
1999    &   607 &   1,780   &   3,717   &   1,810   &   652 &   398 &   1,548   &   526 &   180 &   249 &   10,859  &   11,466  \\
2000    &   460 &   1,322   &   1,230   &   2,588   &   1,012   &   327 &   308 &   950 &   278 &   252 &   8,266   &   8,726   \\
2002    &   796 &   4,944   &   3,385   &   1,295   &   661 &   935 &   538 &   140 &   162 &   493 &   12,554  &   13,351  \\
2004    &   83  &   313 &   1,217   &   3,123   &   1,634   &   567 &   288 &   283 &   121 &   265 &   7,811   &   7,894   \\
2006    &   525 &   217 &   291 &   654 &   783 &   659 &   390 &   145 &   75  &   171 &   3,386   &   3,910   \\
2007    &   5,775   &   1,041   &   345 &   478 &   794 &   729 &   407 &   241 &   98  &   135 &   4,267   &   10,042  \\
2008    &   71  &   2,915   &   1,047   &   166 &   161 &   288 &   235 &   136 &   102 &   120 &   5,169   &   5,240   \\
2009    &   5,197   &   816 &   1,734   &   281 &   77  &   94  &   129 &   111 &   77  &   114 &   3,433   &   8,630   \\
2010    &   2,568   &   6,404   &   984 &   2,295   &   446 &   73  &   33  &   37  &   38  &   91  &   10,400  &   12,968  \\
2012    &   177 &   1,989   &   1,693   &   2,710   &   280 &   367 &   113 &   36  &   25  &   103 &   7,315   &   7,492   \\
2014    &   4,751   &   8,655   &   969 &   1,161   &   1,119   &   1,770   &   740 &   170 &   79  &   99  &   14,762  &   19,513  \\
2016    &   174 &   1,038   &   4,496   &   4,476   &   715 &   348 &   392 &   420 &   96  &   64  &   12,046  &   12,220  \\
2018    &   450 &   517 &   249 &   621 &   2,268   &   944 &   198 &   112 &   107 &   104 &   5,120   &   5,570   \\
2022	&	142	&	332	&	975	&	6,578	&	819	&	211	&	133	&	239	&	166	&	79	&	9,533	&	9,674	\\
\hline																									
Mean	&	4,995	&	4,237	&	2,066	&	2,107	&	1,018	&	601	&	376	&	208	&	95	&	171	&	10,878	&	15,873	\\
Median	&	623	&	1,551	&	1,224	&	1,553	&	789	&	394	&	298	&	144	&	85	&	148	&	8,900	&	10,754	\\
\hline                                                                                                  
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[22]` }
\label{tab:`r tablab[22]`}
\begin{tabular}{crrrr}
\hline
Year    &   AT  scaled  biomass index   &   AVO index   &   Relative error  &   $CV_{AVO}$  \\
\hline                                                  
2006    &   1.8729      &   1.741   &   0.0510  &   23\%    \\
2007    &   2.2779      &   2.002   &   0.0865  &   40\%    \\
2008    &   1.4056      &   0.992   &   0.0643  &   30\%    \\
2009    &   1.3248      &   0.695   &   0.1222  &   56\%    \\
2010    &   2.6423      &   1.922   &   0.0656  &   30\%    \\
2011    &   $-no\,  survey-$    &   1.704   &   0.0572  &   26\%    \\
2012    &   2.2958      &   1.521   &   0.0532  &   24\%    \\
2013    &   $-no\,  survey-$    &   2.178   &   0.0390  &   18\%    \\
2014    &   4.73        &   3.077   &   0.0411  &   19\%    \\
2015    &   $-no\,  survey-$    &   3.593   &   0.0246  &   11\%    \\
2016    &   4.829       &   2.832   &   0.0291  &   13\%    \\
2017    &   $-no\,  survey-$    &   2.263   &   0.0305  &   14\%    \\
2018    &   2.4994      &   2.084   &   0.0266  &   12\%    \\
2019    &   $-no\,  survey-$    &   2.829   &   0.0489  &   22\%    \\
2020    &   3.62        &       &   &   \\      
2021    &   $-no\,  survey-$    &   2.410   &   0.0479  &   22\%    \\
2022    &   3.834       &   2.903   &   0.0341  &   16\%    \\
2023    &   $-no\,  survey-$    &   2.481   &   0.0294  &   14\%    \\
\hline                                          
Mean    &   2.848       &   2.190   &   5\% &   23\%    \\
\hline                                          
\end{tabular}
\end{table}
```

\clearpage

```{r inputn,results="asis",echo=FALSE}
#| output: asis
#| echo: false
```
\clearpage
<!-- sample size -->
\begin{table}[ht]
\centering
\caption{\label{tab:inputn}Pollock sample sizes assumed for the age-composition data likelihoods from the fishery, bottom-trawl survey, and AT surveys, 1964--`r thisyr`. Note fishery sample size for 1964--1977 was fixed at 10.}
\label{tab:inputn}
\scalebox{.85}{
\begin{tabular}{rrrr}
  \hline
Year & Fishery & BTS & ATS \\
  \hline
1978	&	39	&	&	\\		
1979	&	39	&	&	\\		
1980	&	39	&	&	\\		
1981	&	39	&	&	\\		
1982	&	39	&	77	&	\\	
1983	&	39	&	24	&	\\	
1984	&	39	&	112	&	\\	
1985	&	39	&	31	&	\\	
1986	&	39	&	73	&	\\	
1987	&	39	&	71	&	\\	
1988	&	39	&	62	&	\\	
1989	&	39	&	95	&	\\	
1990	&	39	&	83	&	\\	
1991	&	259	&	65	&	\\	
1992	&	227	&	41	&	\\	
1993	&	343	&	43	&	\\	
1994	&	285	&	52	&	43	\\
1995	&	256	&	40	&	\\	
1996	&	188	&	111	&	32	\\
1997	&	318	&	42	&	49	\\
1998	&	353	&	100	&	\\	
1999	&	474	&	91	&	67	\\
2000	&	481	&	80	&	70	\\
2001	&	327	&	110	&	\\	
2002	&	485	&	117	&	72	\\
2003	&	439	&	80	&	\\	
2004	&	389	&	89	&	51	\\
2005	&	493	&	94	&	\\	
2006	&	504	&	139	&	47	\\
2007	&	498	&	78	&	39	\\
2008	&	522	&	106	&	35	\\
2009	&	419	&	67	&	26	\\
2010	&	547	&	87	&	34	\\
2011	&	725	&	118	&	\\	
2012	&	605	&	126	&	44	\\
2013	&	751	&	149	&	\\	
2014	&	604	&	148	&	79	\\
2015	&	818	&	203	&	\\	
2016	&	702	&	174	&	61	\\
2017	&	605	&	234	&	\\	
2018	&	665	&	99	&	50	\\
2019	&	698	&	151	&	\\	
2020	&	563	&		&	\\	
2021	&	815	&	129	&	\\	
2022	&	639	&	163	&	52	\\
2023	&		&	148	&		\\
\hline
\end{tabular}
}
\end{table}


\clearpage

```{r fshwtage,results="asis",echo=FALSE}
#<!-- fishery wtage -->
df <- data.frame(Year = 1964:thisyr, wt = round(M$wt_fsh, 3))
names(df) <- c("Year", 1:15)
df <- df[df$Year >= 1989, ]
sdw <- apply(df[, -1], 2, sd)
nr <- dim(df)[1] - 2
df[1, 1] <- "1964-"
df[1, 2:16] <- " "
df[2, 1] <- "1990"
mnw <- (colMeans(as.matrix(M$wt_fsh[1:nr, ])))
cvw <- (sdw / mnw)
cvw <- c("CV", "-", "-", paste0(round(cvw[3:15] * 100, 0), "\\%"))
df <- rbind(df, c(nextyr, "-", "-", round(M$wt_next, 3)), c(nextyr + 1, "-", "-", round(M$wt_yraf, 3)), c("Mean", round(mnw, 3)))
df <- rbind(df, cvw)
cap <- tabcap[24]
lab <- paste0("tab:", tablab[24])
tab <- xtable(df, caption = cap, label = lab, , digits = 3)
hlines <- c(-1, 0, nrow(tab) - 2, nrow(tab))
print(tab,
  scalebox = 0.8,
  hline.after = hlines, comment = FALSE,
  caption.placement = "top", align = rep("r", (length(df[1, ]) + 1)), digits = 3, include.rownames = FALSE, sanitize.text.function = function(x) {
    x
  }
)
```

\clearpage

```{r modfits, results = "asis",echo=FALSE}
#| output: asis
#| echo: false
# Fit to data-------------------
# Comparison among variance constraints for RW in survey selectivity/q
# fnprof  = c("r_1","r_2","r_3","r_4","r_5")
# prof_names  = c("CV70\\%","CV50\\%","CV20\\%","CV10\\%","CV05\\%")
# fn       <- paste0("../runs/q_sens/prof/",fnprof)
# proflst   <- lapply(fn, read_admb)
# names(proflst) <- prof_names
# nmods <- length(prof_names)
# for (i in 1:nmods) {
#  proflst[[i]] <- c(proflst[[i]],get_vars(proflst[[i]]))
# }
# df <- tab_fit(M=proflst,1:nmods)
# tab <- xtable(df, caption = "Goodness of fit to primary data used for assessment model parameter estimation profiling over different constraints on the extent bottom-trawl survey selectivity/availability is allowed to change;  EBS pollock.", label = "tab:mod_prof_fits")#, digits = 3,align="llrrrrr")
# print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x){x})
#

# Fit to data-------------------

df <- tab_fit(modlst[c(2:9)])
tab <- xtable(df,
  caption = "\\label{tab:modfits}Goodness-of-fit measures to primary data for different assessment model configurations.
RMSE=root-mean square log errors, NLL=negative log-likelihood, SDNR=standard deviation of normalized residuals,
Eff. N=effective sample size for composition data). See text for incremental model descriptions.",
  label = "tab:modfits", digits = 2, align = paste0("ll", paste0(rep("r", length(df) - 1), collapse = ""))
)
hlines <- c(-1, 0, 4, 7, 10, 17, nrow(tab) - 2, nrow(tab))
print(tab,
  caption.placement = "top",
  hline.after = hlines, comment = FALSE,
  include.rownames = FALSE, sanitize.text.function = function(x) {
    x
  }
)
```

```{r mgtquants, results = "asis",echo=FALSE}
#| output: asis
#| echo: false
# Mgt quants by model
mod_names <- c(
  "Last year",
  "m1", "m2", "m3", "m4", "m5", "m6", "m7",
  "Model 23.0"
)
# names(modlst[9])
df <- NULL
mod_scen <- c(1, 9)
for (ii in mod_scen) {
  x <- modlst[[ii]]
  ssb <- x$nextyrssbs
  names(ssb) <- paste0("${B}_{", nextyr, "}$")
  ssbcv <- round(x$nextyrssb.cv, 2)
  names(ssbcv) <- paste0("$CV_{B_{", nextyr, "}}$")
  Bmsy <- x$bmsys
  names(Bmsy) <- "$B_{MSY}$"
  Bmsycv <- round(x$bmsy.cv, 2)
  names(Bmsycv) <- "$CV_{B_{MSY}}$"
  spr <- paste0(round(x$sprmsy * 100, 0), "\\%")
  names(spr) <- "SPR rate at $F_{MSY}$"
  b35 <- x$b35s
  names(b35) <- "$B_{35\\%}$"
  pbmsy <- paste0(round(100 * x$ssb1 / x$bmsy, 0), "\\%")
  names(pbmsy) <- paste0("${B}_{", nextyr, "}/B_{MSY}$")
  bzero <- x$b0s
  names(bzero) <- "$B_0$"
  dynb0 <- x$dynb0s
  names(dynb0) <- paste0("Est. $B_{", thisyr, "} / B_{", thisyr, ",no fishing}$")
  b_bmsy <- paste0(round(x$curssb / x$bmsy * 100, 0), "\\%")
  names(b_bmsy) <- paste0("$B_{", thisyr, "} / B_{MSY}$")
  steep <- round(x$steep, 2)
  names(steep) <- "Steepness"
  v <- c(ssb, ssbcv, Bmsy, Bmsycv, pbmsy, bzero, b35, spr, steep, dynb0, b_bmsy)
  df <- cbind(df, v)
}
df <- data.frame(rownames(df), df, row.names = NULL)
names(df) <- c("Component", mod_names[mod_scen])

tab <- xtable::xtable(df, caption = tabcap[27], label = "tab:mgtquants", digits = 3, align = paste0("ll", strrep("r", length(mod_scen))))
print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
  x
}, comment = FALSE)
```

\clearpage

```{r biom_3plus,results="asis",echo=FALSE}
#| output: asis
#| echo: false
cap <- tabcap[32]
biom <- read.csv("doc/data/Age3plus.csv", header = T, as.is = TRUE)
# dim(biom);(M$age3plus);biom[,2:13];biom[,1]
# biom <- read.csv("doc/data/Age3history.csv",header=T,as.is=TRUE)
t3 <- cbind(biom[, 1], M$age3plus, M$age3plus.sd / M$age3plus, biom[, 2:15])
names(t3) <- c("Year", "Current", "CV", "2022", "CV", "2021", "CV", "2020", "CV", "2019", "CV", "2018", "CV", "2017", "CV", "2016", "CV") # ,"2014","CV")

for (i in seq(2, length(t3[1, ]), 2)) {
  t3[, i] <- formatC(t3[, i], format = "d", big.mark = ",", digits = 0)
  t3[, i + 1] <- formatC(t3[, i + 1] * 100, format = "d", digits = 0, big.mark = ",")
}

tab <- xtable(t3, caption = cap, label = paste0("tab:", tablab[32]), digits = 0, auto = TRUE, align = rep("r", (length(t3[1, ]) + 1)))
print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
  x
}, scalebox = .69, comment = FALSE)
```

\clearpage

```{r Nage, results="asis",echo=FALSE}
#| output: asis
#| echo: false
tmp <- cbind(M$Yr, M$N[, 1:9] / 1000, rowSums(M$N[, 10:15]) / 1000)
colnames(tmp) <- c("Year", 1:9, "10+")
tab <- xtable(tmp,
  digits = 2, auto = TRUE, caption = "\\label{tab:estn}Estimated billions of EBS pollock at age (columns 2--11) from the current assessment model.",
  label = "tab:estn"
)
print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
  x
}, scalebox = 0.7, comment = FALSE)
```

\clearpage

```{r Cage,results="asis",echo=FALSE}
#| output: asis
#| echo: false
tmp <- cbind(M$Yr, M$C[, 1:9], rowSums(M$C[, 10:15]))
colnames(tmp) <- c("Year", 1:9, "10+")
tab <- xtable(tmp,
  digits = 2, auto = TRUE, caption = "\\label{tab:estcatage}Estimated millions of EBS pollock caught at age (columns 2--11) from the current assessment model.",
  label = "tab:estcatage"
)
print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
  x
}, scalebox = 0.7, comment = FALSE)
```

\clearpage

```{r ssb,results="asis",echo=FALSE}
#| output: asis
#| echo: false
# tmp <- data.frame(Year=M$SSB[,1], SSB=M$SSB[,2], CVSSB=round(100*M$SSB[,3]/M$SSB[,2],0), Recruit=M$R[,2], CVR=round(100*M$R[,3]/M$R[,2],0),
# Age3plus =M$age3plus,CV_B=round(100*M$age3plus.sd/M$age3plus,0))
tmp <- data.frame(
  Year = M$SSB[, 1], SSB = M$SSB[, 2], "CV.SSB" = round(100 * M$SSB[, 3] / M$SSB[, 2], 0), Recruit = M$R[, 2], "CV.Rec" = round(100 * M$R[, 3] / M$R[, 2], 0),
  "Age.3+.B" = M$age3plus, "CV %" = round(100 * M$age3plus.sd / M$age3plus, 0)
)
for (i in c(2, 4, 6)) {
  tmp[, i] <- formatC(tmp[, i], format = "d", digits = 0, big.mark = ",")
}
tab <- xtable(tmp, caption = tabcap[31], label = paste0("tab:", tablab[31]), digits = 0, auto = TRUE, align = rep("r", (length(tmp[1, ]) + 1)))
print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
  x
}, scalebox = .66, comment = FALSE)
```

\clearpage

```{r res_summ, results = "asis",echo=FALSE}
#| output: asis
#| echo: false
# Mgt quants by final
df <- NULL
mod_scen <- c(9)
for (ii in mod_scen) {
  x <- modlst[[ii]]
  ssb <- x$nextyrssbs
  names(ssb) <- paste0("${B}_{", nextyr, "}$")
  ssbcv <- round(x$nextyrssb.cv, 2)
  names(ssbcv) <- paste0("$CV_{B_{", nextyr, "}}$")
  Bmsy <- x$bmsys
  names(Bmsy) <- "$B_{MSY}$"
  Bmsycv <- round(x$bmsy.cv, 2)
  names(Bmsycv) <- "$CV_{B_{MSY}}$"
  spr <- paste0(round(x$sprmsy * 100, 0), "\\%")
  names(spr) <- "SPR rate at $F_{MSY}$"
  b35 <- x$b35s
  names(b35) <- paste0("$B_{35\\%}$")
  pbmsy <- paste0(round(100 * x$ssb1 / x$bmsy, 0), "\\%")
  names(pbmsy) <- paste0("${B}_{", nextyr, "}/B_{MSY}$")
  bzero <- x$b0s
  names(bzero) <- paste0("$B_0$")
  dynb0 <- x$dynb0s
  names(dynb0) <- paste0("Est. $B_{", thisyr, "} / B_{", thisyr, ",no fishing}$")
  b_bmsy <- paste0(round(x$curssb / x$bmsy * 100, 0), "\\%")
  names(b_bmsy) <- paste0("$B_{", thisyr, "} / B_{MSY}$")
  steep <- round(x$steep, 2)
  names(steep) <- "Steepness"
  v <- c(ssb, ssbcv, Bmsy, Bmsycv, pbmsy, bzero, b35, spr, steep, dynb0, b_bmsy)
  df <- cbind(df, v)
}
df <- data.frame(rownames(df), df, row.names = NULL)
names(df) <- c("Component", mod_names[mod_scen])
tab <- xtable(df, caption = "\\label{tab:ressumm}Summary of model results and the stock condition for EBS pollock. Biomass units are thousands of t.", label = "tab:ressumm", digits = 3, align = paste0("ll", strrep("r", length(mod_scen))))
# tab <- xtable(df, caption = tabcap[28], digits = 3,align=paste0("ll",strrep("r",length(mod_scen))) )
print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
  x
}, comment = FALSE)
```

```{r tier1proj, results = "asis",echo=FALSE}
#| output: asis
#| echo: false
# Tier 1 projections
df <- NULL
mod_scen <- c(9)
for (ii in mod_scen) {
  x <- modlst[[ii]]
  abcbiom <- paste0(x$ABC_biom1s, ",000")
  names(abcbiom) <- paste0(nextyr, " fishable biomass (GM)")
  msybiom <- paste0(x$bmsyrs, ",000")
  names(msybiom) <- paste0("Equilibrium fishable biomass at MSY")
  msyr <- round(x$harmeanF, 3)
  names(msyr) <- ("MSY R (HM)")
  tier1abc <- x$maxabc1s
  names(tier1abc) <- paste(nextyr, "Tier 1 ABC")
  fofl <- x$arithmeanF
  names(fofl) <- paste(nextyr, "Tier 1 $F_{OFL}$ unadjusted")
  ofl <- x$ofl1s
  names(ofl) <- paste(nextyr, "Tier 1 OFL")
  recabc <- x$abc1s
  names(recabc) <- "Recommended ABC"
  v <- c(abcbiom, msybiom, msyr, tier1abc, fofl, ofl, recabc)
  df <- cbind(df, v)
}
df <- data.frame(rownames(df), df, row.names = NULL)
names(df) <- c("Component", mod_names[mod_scen])
tab <- xtable(df, caption = tabcap[28], label = paste0("tab:", tablab[28]), digits = 3, align = paste0("ll", strrep("r", length(mod_scen))))
print(tab, caption.placement = "top", include.rownames = FALSE, sanitize.text.function = function(x) {
  x
}, comment = FALSE)
```

\clearpage

```{r projtab, results = "asis",echo=FALSE}
#| output: asis
#| echo: false
# See "print_tier3_tables in R folder
# 40,tier3_C,"Tier 3 projections of EBS pollock catch for the 7 scenarios.
# 41,tier3_ABC,"Tier 3 projections of EBS pollock ABC (given catches in Table \ref{tab:tier3_C}) for the 7 scenarios.
# 42,tier3_F,"Tier 3 projections of EBS pollock fishing mortality for the 7 scenarios.
# 43,tier3_SSB,"Tier 3 projections of EBS pollock spawning biomass (kt) for the 7 scenarios. Note that the values for B100\%, B40\%, and B35\% are `r b100s`, `r b40s`, `r b35s`, thousand t, respectively."

print_Tier3_tables(thismod)

# print_Tier3_tables(3)
```

\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{\label{tab:dectabledesc}Details and explanation of the decision table factors selected in response to the Plan
Team requests (as originally proposed in the 2012 assessment).  } 
\label{tab:dectabledesc}
\scalebox{0.80}{
\begin{tabular}{p{1.5in}p{3in}p{3in}}
  \hline

 Term                                                & Description                                                                                                                                      & Rationale                                                                                                                                                                         \\
 \hline
 $P\left[F_{`r nextyr`}>F_{MSY}\right]$              & Probability that the fishing mortality in `r nextyr` exceeds $F_{MSY}$                                                                           & OFL definition is based on $F_{MSY}$                                                                                                                                              \\
 $P\left[B_{`r nextyr+1` }<B_{MSY}\right]$           & Probability that the spawning biomass in `r nextyr+1` is less than $B_{MSY}$                                                                     & $B_{MSY}$ is a reference point target and biomass in 2021 provides an indication of the impact of `r thisyr+1` fishing                                                            \\
 $P\left[B_{`r nextyr+2` }<B_{MSY}\right]$           & Probability that the spawning biomass in `r nextyr+2` is less than $B_{MSY}$                                                                     & $B_{MSY}$ is a reference point target and biomass in 2024 provides an indication of the impact of fishing in `r thisyr+1` and `r thisyr+2`                                        \\
 $P\left[B_{`r nextyr+2` }<\bar{B}\right]$           & Probability that the spawning biomass in `r nextyr+1` is less than the 1978--`r thisyr` mean                                                     & To provide some perspective of what the stock condition might be relative to historical estimates after fishing in `r thisyr+1`.                                                  \\
 $P\left[B_{`r nextyr+4` }<\bar{B}\right]$           & Probability that the spawning biomass in `r nextyr+4` is less than the long term mean                                                            & To provide some perspective of what the stock condition might be relative to historical estimates after fishing in `r thisyr+1`.                                                  \\
 $P\left[B_{`r nextyr+4` }<B_{`r thisyr+1` }\right]$ & Probability that the spawning biomass in `r nextyr+4` is less than that estimated for `r nextyr`                                                 & To provide a medium term expectation of stock status relative to `r thisyr+1` levels                                                                                              \\
 $P\left[B_{`r nextyr+2` }<B_{20\%}\right]$          & Probability that the spawning biomass in `r nextyr+2` is less than $B_{20\%}$                                                                    & $B_{20\%}$ had been selected as a Steller Sea Lion lower limit for allowing directed fishing                                                                                      \\
 $P\left[p_{a_5,`r nextyr+2` }>\bar{p}_{a_5}\right]$ & Probability that in `r nextyr+2` the proportion of age 1--5 pollock in the population exceeds the long-term mean                                 & To provide some relative indication of the age composition of the population relative to the long term mean.                                                                      \\
 $P\left[D_{`r nextyr+1` }<D_{1994}\right]$          & Probability that the diversity of ages represented in the spawning biomass (by weight) in `r nextyr+1` is less than the value estimated for 1994 & To provide a relative index on the abundance of different age classes in the `r thisyr+2` population relative to 1994 (a year identified as having low age composition diversity) \\
 $P\left[D_{`r nextyr+4` }<D_{1994}\right]$          & Probability that the diversity of ages represented in the spawning biomass (by weight) in `r nextyr+4` is less than the value estimated for 1994 & To provide a medium-term relative index on the abundance of different age classes in the population relative to 1994 (a year identified as having low age composition diversity)  \\
 $P\left[E_{`r nextyr`}>E_{`r thisyr`}\right]$       & Probability that the theoretical fishing effort in `r nextyr` will be greater than that estimated in `r thisyr`.                                 & To provide the relative effort that is expected (and hence some idea of costs).                                                                                                   \\
 \hline
\end{tabular}
}
\end{table}
```
\clearpage

```{r dectable,results="asis",echo=FALSE}
#| output: asis
#| echo: false
rn <- c(
  "$P\\left[F_{2024}>F_{MSY}\\right]$",
  "$P\\left[B_{2024}<B_{MSY}\\right]$ ",
  "$P\\left[B_{2025}<B_{MSY}\\right]$ ",
  "$P\\left[B_{2024}<\\bar{B}\\right]$ ",
  "$P\\left[B_{2027}<\\bar{B}\\right]$ ",
  "$P\\left[B_{2027}<B_{2023}\\right]$ ",
  "$P\\left[B_{2025}<B_{20\\%}\\right]$",
  "$P\\left[p_{a_5,2024}>\\bar{p}_{a_5}\\right]$ ",
  "$P\\left[D_{2024}<D_{1994}\\right]$  ",
  "$P\\left[D_{2027}<D_{1994}\\right]$  ",
  "$P\\left[E_{2024}>E_{2023}\\right]$  "
)
m <- rbind(
  round(M$pfcur_fmsy * 100, 1),
  round(M$pbcur_bmsy * 100, 1),
  round(M$pbcur2_bmsy * 100, 1),
  round(M$pbcur_bmean * 100, 1),
  round(M$pbcur3_bmean * 100, 1),
  round(M$pbcur3_bcur * 100, 1),
  round(M$pbcur2_b20 * 100, 1),
  round(M$plta1_5r * 100, 1),
  round(M$pmatdiv1 * 100, 1),
  round(M$pmatdiv2 * 100, 1),
  round(M$prel_effort * 100, 1)
)
colnames(m) <- round(M$catch_dec_tab, 0)
rownames(m) <- rn
# cap<-"\\label{tab:dec_table} "
cap <- paste("\\label{tab:dectable} Outcomes of decision (expressed as chances out of 100) given different", 1 + thisyr, "catches (first row, in kt). Note that for the ", thisyr - 2, "and later year-classes average values were assumed. Constant Fs based on the", 1 + thisyr, "catches were used for subsequent years. ")
## kable(m,caption=cap,format="pandoc")
tab <- xtable(m, caption = cap, label = "tab:dectable", digits = 0, auto = TRUE, align = rep("r", (length(m[1, ]) + 1)))
print(tab, caption.placement = "top", include.rownames = TRUE, sanitize.text.function = function(x) {
  x
}, scalebox = 1, comment = FALSE)
```

\clearpage
```{r byc3,results="asis",echo=FALSE}
#| output: asis
#| echo: false
byc <- read.csv("doc/data/byc_nontarg.csv", header = T)
for (i in 2:length(byc[1, ])) {
  byc[, i] <- formatC(byc[, i], format = "d", big.mark = ",")
}
cap <- tabcap[36]
tab <- xtable::xtable(byc, caption = cap, label = paste0("tab:", tablab[36]), digits = 2, auto = TRUE, align = rep("r", (length(byc[1, ]) + 1)))
print(tab,
  caption.placement = "top", include.rownames = FALSE, rotate.colnames = FALSE, sanitize.text.function = function(x) {
    x
  },
  scalebox = .90, format.args = list(decimal.mark = "."), comment = FALSE
)
```
\clearpage

```{r byc1,results="asis",echo=FALSE}
#| output: asis
#| echo: false
byc <- read.csv("doc/data/byc_in_pollock.csv", header = T, as.is = TRUE)
for (i in 2:length(byc[1, ])) {
  byc[, i] <- formatC(byc[, i], format = "d", big.mark = ",")
}
# cap <- paste0("\\label{tab:",tablab[37],"}"tabcap[37])
cap <- tabcap[37]
lab <- tablab[37]
tab <- xtable::xtable(byc, caption = cap, label = paste0("tab:", tablab[37]), digits = 2, auto = TRUE, align = rep("r", (length(byc[1, ]) + 1)))
# cap <- "Bycatch estimates (t) of FMP species caught in the BSAI directed pollock fishery, 1997--2019 based on then NMFS Alaska Regional Office reports from observers (2019 data are preliminary). "
# tab <- xtable(byc, caption = cap, label="tab:fmpbycatch",digits=0, auto=TRUE, align=rep("r",(length(byc[1,])+1)) )
print(tab,
  caption.placement = "top", include.rownames = FALSE, rotate.colnames = FALSE,
  sanitize.text.function = function(x) {
    x
  }, scalebox = .85, comment = FALSE
)
``` 

```{r byc2,results="asis",echo=FALSE}
#| output: asis
#| echo: false
# byc <- read_csv("../data/fishery/pollock_catch.csv")[,c(1,16,19,22,27,28)]
byc <- read.csv("../ebs_main/data/fishery/pollock_catch.csv")[, c(1, 6, 9, 4, 10, 11)]

names(byc) <- c("Year", "target", "gear", "NMFS_area", "Catch", "wed")
byc <- byc %>% filter(NMFS_area < 540)
ntmp <- unique(byc$target)
poll <- ntmp[c(1, 5)]
pcod <- ntmp[c(2)]
rsole <- ntmp[c(6)]
yfsole <- ntmp[c(13)]
oflat <- ntmp[c(3, 7, 9, 15, 16, 17, 18)]
ospp <- ntmp[c(4, 8, 11, 12, 14)]

byc <- byc %>%
  filter(!target %in% poll) %>%
  mutate(
    target =
      ifelse(target %in% pcod, "P.cod",
        ifelse(target %in% rsole, "NRock sole",
          ifelse(target %in% yfsole, "Yellowfin sole",
            ifelse(target %in% ospp, "Other spp",
              ifelse(target %in% oflat, "Other flats", target)
            )
          )
        )
      )
  ) %>%
  group_by(Year, target) %>%
  summarise(Catch = sum(Catch), .groups = "drop")
byc <- byc %>% pivot_wider(names_from = target, values_from = Catch)
byc <- data.frame(byc)

# byc <- read.csv("data/byc_of_pollock.csv",header=T)
for (i in 2:length(byc[1, ])) {
  byc[, i] <- formatC(byc[, i], format = "d", digits = 0, big.mark = ",")
}
cap <- tabcap[38]
lab <- tablab[38]
tab <- xtable::xtable(byc, caption = cap, label = paste0("tab:", tablab[38]), digits = 0, auto = TRUE, align = rep("r", (length(byc[1, ]) + 1)))
print(tab,
  caption.placement = "top", include.rownames = FALSE, rotate.colnames = FALSE, sanitize.text.function = function(x) {
    x
  },
  scalebox = .90, format.args = list(decimal.mark = "."), comment = FALSE
)
``` 


```{=tex}
\begin{table}[ht]
\centering
\caption{\label{tab:pscbycatch}Bycatch estimates of prohibited species caught in the BSAI directed pollock fishery, 1997--2023 based on the AKFIN (NMFS Regional Office) reports from observers. Herring and halibut units are in t, all others represent numbers of individuals caught. Data for 2023 are preliminary.} 
\label{tab:pscbycatch}
\scalebox{0.78}{
\begin{tabular}{rrrrrrrrrrr}
  \hline
Year & Bairdi& Chinook& Halib & Halib.M& Herring & NonChin& Snow Crab & Red King & Blue King& Goldn King\\
  \hline
  1991 & 1,397,836 & 36,348 & 2,155 & NA & 3,158 & 28,657 & 4,378,007 & 17,777 & NA & NA \\ 
  1992 & 1,500,764 & 33,672 & 2,220 & NA & 646 & 40,186 & 4,569,662 & 43,873 & NA & NA \\ 
  1993 & 1,649,086 & 36,615 & 1,326 & NA & 527 & 241,971 & 738,250 & 58,139 & NA & NA \\ 
  1994 & 371,213 & 31,880 & 963 & 688 & 1,626 & 91,764 & 811,733 & 42,360 & NA & NA \\ 
  1995 & 153,992 & 13,403 & 491 & 397 & 904 & 17,754 & 206,651 & 4,644 & NA & NA \\ 
  1996 & 89,415 & 55,467 & 382 & 320 & 1,241 & 77,173 & 63,398 & 5,933 & NA & NA \\ 
  1997 & 17,046 & 44,312 & 257 & 200 & 1,134 & 65,414 & 216,152 & 137 & NA & NA \\ 
  1998 & 57,036 & 51,244 & 352 & 278 & 800 & 60,676 & 123,400 & 14,286 & NA & NA \\ 
  1999 & 2,397 & 10,381 & 153 & 124 & 799 & 44,610 & 15,829 & 90 & NA & NA \\ 
  2000 & 1,484 & 4,242 & 110 & 90 & 482 & 56,866 & 6,480 & 0 & NA & NA \\ 
  2001 & 5,060 & 30,933 & 242 & 199 & 225 & 53,901 & 5,653 & 105 & NA & NA \\ 
  2002 & 2,112 & 32,381 & 165 & 137 & 108 & 77,167 & 2,697 & 16 & NA & NA \\ 
  2003 & 732 & 43,095 & 88 & 74 & 967 & 179,987 & 608 & 52 & 8 & NA \\ 
  2004 & 1,091 & 48,799 & 96 & 81 & 1,095 & 441,188 & 640 & 26 & 4 & 1 \\ 
  2005 & 601 & 66,208 & 119 & 100 & 593 & 703,076 & 2,016 & 0 & NA & 1 \\ 
  2006 & 1,288 & 80,915 & 132 & 111 & 433 & 305,793 & 2,567 & 288 & NA & 3 \\ 
  2007 & 1,465 & 116,329 & 312 & 269 & 351 & 86,380 & 3,033 & 7 & NA & 3 \\ 
  2008 & 9,025 & 20,602 & 373 & 311 & 127 & 15,119 & 8,894 & 670 & 8 & 33 \\ 
  2009 & 6,155 & 12,284 & 541 & 436 & 64 & 45,960 & 7,312 & 1,136 & 19 & NA \\ 
  2010 & 12,787 & 9,833 & 335 & 267 & 348 & 13,728 & 9,444 & 1,122 & 28 & NA \\ 
  2011 & 10,973 & 25,499 & 459 & 378 & 376 & 193,754 & 6,493 & 577 & 25 & NA \\ 
  2012 & 5,620 & 11,343 & 462 & 388 & 2,352 & 22,297 & 6,189 & 343 & NA & NA \\ 
  2013 & 12,426 & 13,091 & 333 & 271 & 958 & 125,525 & 8,605 & 507 & 34 & 107 \\ 
  2014 & 12,521 & 15,135 & 239 & 199 & 159 & 219,837 & 19,454 & 368 & NA & NA \\ 
  2015 & 8,872 & 18,329 & 152 & 130 & 1,487 & 237,776 & 8,339 & 0 & NA & NA \\ 
  2016 & 2,295 & 22,204 & 121 & 102 & 1,431 & 343,208 & 1,166 & 439 & NA & 26 \\ 
  2017 & 7,269 & 30,078 & 97 & 88 & 963 & 467,750 & 3,406 & 202 & 0 & 67 \\ 
  2018	&	 2,249 	&	 13,726 	&	 75 	&	 62 	&	 474 	&	 295,818 	&	 5,143 	&	 565 	&	 -   	&	 53 	 \\ 
  2019	&	 3,146 	&	 25,038 	&	 134 	&	 113 	&	 1,102 	&	 348,631 	&	 6,228 	&	 453 	&	 99 	&	 445 	 \\ 
  2020	&	 10,749 	&	 32,204 	&	 128 	&	 102 	&	 3,861 	&	 343,625 	&	 40,005 	&	 479 	&	 1 	&	 522 	 \\ 
  2021	&	 8,417 	&	 13,852 	&	 145 	&	 131 	&	 1,708 	&	 546,472 	&	 4,668 	&	 52 	&	 -   	&	 115 	 \\ 
  2022	&	 4,758 	&	 6,415 	&	 170 	&	 156 	&	 1,708 	&	 242,375 	&	 1,952 	&	 311 	&	 59 	&	 88 	 \\ 
  2023	&	 11,978 	&	 11,750 	&	 84 	&	 67 	&	 3,087 	&	 112,445 	&	 4,100 	&	 54 	&	 -   	&	 132 	 \\ 
   \hline
\end{tabular}
}
\end{table}
```
\clearpage

```{=tex}
\begin{table}[ht]
\centering
\caption{`r tabcap[45]`}
\label{tab:`r tablab[45]`}
\scalebox{0.80}{
\begin{tabular}{p{1.5in}p{1.5in}p{1.5in}p{1.5in}}
\hline
Indicator                                                   & Observation                                                              & Interpretation                                                                                                      & Evaluation                                                                                            \\
\hline
\multicolumn{4}{c}{\textbf{Ecosystem effects on EBS pollock}}                                                                          \\
\hline
\multicolumn{4}{l}{\textit{Prey availability or abundance trends}} \\
Zooplankton                                                 & Stomach contents, AT and ichthyoplankton surveys, changes mean wt-at-age & Data improving, indication of increases from 2004--2009 and subsequent decreasees (for euphausiids in 2012 and 2014) & Variable abundance-indicates important recruitment (for prey)  \\
\hline
\multicolumn{4}{l}{\textit{Predator population trends}} \\
Marine mammals                                              & Fur seals declining, Steller sea lions increasing slightly               & Possibly lower mortality on pollock                                                                                 & Probably no concern                                                                                   \\
Birds                                                       & Stable, some increasing some decreasing                                  & Affects young-of-year mortality                                                                                     & Probably no concern                                                                                   \\
Fish (Pollock, Pacific cod, halibut)                        & Stable to increasing                                                     & Possible increases to pollock mortality                                                                             &                                                                                                       \\
\hline
\multicolumn{4}{l}{\textit{Changes in habitat quality}} \\
Temperature regime                                          & Cold years pollock distribution towards NW on average                    & Likely to affect surveyed stock                                                                                     & Some concern, the distribution of pollock availability to different surveys may change systematically \\
Winter-spring environmental conditions                      & Affects pre-recruit survival                                             & Probably a number of factors                                                                                        & Causes natural variability                                                                            \\
Production                                                  & Fairly stable nutrient flow from upwelled BS Basin                       & Inter-annual variability low                                                                                        & No concern                                                                                            \\
\hline
\multicolumn{4}{c}{\textbf{Fishery effects on ecosystem}} \\
\hline
\multicolumn{4}{l}{\textit{Fishery contribution to bycatch}} \\
Prohibited species                                          & Stable, heavily monitored                                                & Likely to be safe                                                                                                   & No concern                                                                                            \\
Forage (including herring, Atka mackerel, cod, and pollock) & Stable, heavily monitored                                                & Likely to be safe                                                                                                   & No concern                                                                                            \\
HAPC biota                                                  & Likely minor impact                                                      & Likely to be safe                                                                                                   & No concern                                                                                            \\
Marine mammals and birds                                    & Very minor direct-take                                                   & Safe                                                                                                                & No concern                                                                                            \\
Sensitive non-target species                                & Likely minor impact                                                      & Data limited, likely to be safe                                                                                     & No concern                                                                                            \\
Fishery concentration in space and time                     & Generally more diffuse                                                   & Mixed potential impact (fur seals vs Steller sea lions)                                                             & Possible concern                                                                                      \\
Fishery effects on amount of large size target fish         & Depends on highly variable year-class strength                           & Natural fluctuation                                                                                                 & Probably no concern                                                                                   \\
Fishery contribution to discards and offal production       & Decreasing                                                               & Improving, but data limited                                                                                         & Possible concern                                                                                      \\
Fishery effects on age-at-maturity and fecundity            & Maturity study (gonad collection) continues                              & NA                                                                                                                  & Possible concern                                                                                      \\
\hline
\end{tabular}
}
\end{table}
```
\pagebreak


\clearpage

<!-- ```{r modfits} -->
<!-- #| label: tbl-modfits -->
<!-- #| tbl-cap: "Goodness-of-fit measures to primary data for different assessment model configurations. RMSE=root-mean square log errors, NLL=negative log-likelihood (may not be comparable across model configurations), SDNR=standard deviation of normalized residuals, Eff. N=effective sample size for composition data)" -->
<!-- #| echo: false -->
<!-- #names(modlst[5:6]) =  -->
<!-- df <- tab_fit(modlst[c(2:9)]) -->
<!-- df |> gt::gt() |>  gt::fmt_number( rows = c(8:21),decimals=0) -->
<!-- ``` -->

<!-- \clearpage -->

<!-- ```{r tbl-modref} -->
<!-- #| label: tbl-modref -->
<!-- #| tbl-cap:  "Summary of model results and the stock condition for EBS pollock. Biomass units are thousands of t." -->
<!-- #| echo: false -->
<!-- df <- tab_ref(modlst[c(1,7:9)]) -->
<!-- df |> gt::gt() |>  gt::fmt_markdown() -->
<!-- ``` -->

<!-- \clearpage -->

# Figures

```{r fshfigs}
#| output: asis
#| echo: false
printfig("genetics.pdf", 85)
printfig("catch.pdf", 1)
printfig("fsh_psc_cpue.png", 67)
printfig("catch_sex.pdf", 2)
printfig("catch_distn_a.png", 3)
printfig("fsh_cpue.png", 4)
printfig("catchp.png", 5)
printfig("roe.pdf", 8)
printfig("catch_distn_b.png", 6)
# printfig("cpue_bseason.png",7)
printfig("fleet_dispersal.png", 9)
printfig("fsh_wt_freq.png", 72)
printfig("fsh_wt_freq_week.png", 73)
printfig("fsh_lenfreq.pdf", 82)

printfig("catage.png", 10)
printfig("fsh_cohort_locales_0818.pdf", 84)
```

\clearpage

```{r mainfigs}
#| echo: false
#| output: asis
i <- 11
printfig("bts_biom.png", i)
i <- i + 1
printfig("bts_temp.pdf", i)
i <- i + 1
# printfig("bts_temp_cpue.pdf",i);
i <- i + 1
printfig("bts_3d.png", i)
i <- i + 1
printfig("cog.png", 100)
printfig("bts_age_comp.png", i)
i <- i + 1
printfig("bts_lenfreq.png", 83)
printfig("vastage_vs_db.pdf", 27)
# printfig("wtage_bts.pdf",94);
printfig("at_age.png", 74)
printfig("AVO_Series_change.png", 99)
printfig("avo_map_yr.pdf", 16)
printfig("fsh_lw_month.png", 23)
printfig("fsh_lw_anom_str_yr_box.pdf", 24)
printfig("fsh_lw_anom_yr_box.pdf", 25)
printfig("fsh_wtage_comb.pdf", 20)
printfig("fsh_wtage_data_pred.pdf", 88)
printfig("fsh_wtage_strata.pdf", 21)
printfig("fsh_wtage_strata_yr.pdf", 22)
```

```{r modfigs}
#| output: asis
#| echo: false
printfig("mod_data.pdf", 30)
printfig("mod_cpue_fit.pdf", 38)
printfig("mod_avo_fit.pdf", 39)
printfig("mod_bts_biom.pdf", 33)
printfig("mod_ats_biom.pdf", 34)
printfig("mod_mean_age.pdf", 35)
printfig("mod_fsh_sel.pdf", 36)
printfig("mod_fsh_age.pdf", 37)
```

```{r diag}
#| output: asis
#| echo: false
printfig("mod_bts_sel.pdf", 40)
printfig("mod_bts_age.pdf", 41)
printfig("mod_ats_sel.pdf", 42)
printfig("mod_ats_age.pdf", 43)
printfig("MLE_v_post.png", 98)
printfig("mcmc_pairs.png", 55)
printfig("mcmc_marg.pdf", 56)
printfig("mcmc_marg_fmsy.pdf", 87)
printfig("acoustic_ppl.png", 93)
printfig("ssb_v_2018.pdf", 96)
printfig("post_profile.pdf", 95)

printfig("mod_retro.pdf", 52)
printfig("mod_retroR.pdf", 97)
printfig("retro_cohort_termyr.pdf", 101)
printfig("retro_cohort.pdf", 102)
# printfig("retro_sel.pdf",89)
printfig("retro_sel_mnage.pdf", 90)
printfig("fmsy_sel", 92)

printfig("mod_ser.pdf", 44)
printfig("mod_F.pdf", 45)
printfig("mod_hist.pdf", 46)
printfig("mod_phase.pdf", 47)
printfig("proj_ssb.pdf", 65)
printfig("mod_rec.pdf", 48)
printfig("mod_srr.pdf", 49)
printfig("mod_rs.pdf", 50)
printfig("mod_regimes.pdf", 86)
printfig("tier3_proj.pdf", 53)
printfig("tier3_proj_alt3.pdf", 54)
printfig("age_diversity.pdf", 51)
# printfig("sa_density.pdf",61)
# printfig("sel_comp_vast.pdf",61)
# printfig("poll_cod_rec.png",62)
```

\clearpage

```{r, results='asis'}
#| echo: false
a <- knitr::knit_child("doc/model.Rmd", quiet = T)
cat(a, sep = "\n")
```

\clearpage

```{r, results='asis'}
#| echo: false
a <- knitr::knit_child("doc/envecorisk.qmd", quiet = T)
cat(a, sep = "\n")
```
\clearpage

```{r, results='asis'}
#| echo: false
a <- knitr::knit_child("doc/vast.qmd", quiet = T)
cat(a, sep = "\n")
```


\clearpage
# List of contents, tables and figures {#sec-contents}
```{=tex}
 \tableofcontents

\clearpage
 \listoftables

\clearpage
 \listoffigures
```