inst/markdown/06_fishery_model_summary.md
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
title: "Snow crab fishery model"
keywords:
- snow crab fishery model results
abstract: |
Snow crab fishery model summary.
fontsize: 12pt
metadata-files:
- _metadata.yml
params:
year_assessment: 2024
year_start: 1999
data_loc: "~/bio.data/bio.snowcrab"
sens: 1
debugging: FALSE
model_variation: logistic_discrete_historical
#| eval: true
#| output: false
#| echo: false
#| label: setup
require(knitr)
knitr::opts_chunk$set(
root.dir = data_root,
echo = FALSE,
out.width="6.2in",
fig.retina = 2,
dpi=192
)
# things to load into memory (in next step) via _load_results.qmd
toget = c( "fishery_results", "fishery_model" )
{{< include _load_results.qmd >}}
Fishery model
$$\begin{aligned}
b_{t+1} & \sim N\left({{{b_{t}+r}b_{t}}{{({1-b_{t}})}-\mathit{Fishing}}{t}K^{-1},\sigma{p}}\right) \
Y_{t} & \sim N\left({q^{-1}\mathit{Kb}{t},\sigma{o}}\right) \
r & \sim N\left({1.0,0.1}\right) \
K & \sim N\left({\kappa,0.1\cdot\kappa}\right) \
q & \sim N\left({1.0,0.1}\right) \
\end{aligned}$$
$\kappa={\lbrack{5.0,60,1.25}\rbrack}$
${b=B}K^{-1}$, a real unobservable (latent) process
- MCMC (Julia/Turing):
- 4 chains, NUTS sampler (warmup: 10000, retain: 10000)
- target_acceptance_rate, max_depth, init_ϵ = 0.75, 9, 0.05
Surplus (Shaeffer) production
#| label: fig-logistic-surplus-production
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Surplus (Shaeffer) production. Each year is represented by a different colour."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = paste("plot_surplus_production_", regions, ".png", sep="" )
fns = file.path( fm_loc, fns )
include_graphics( fns )
Carrying capacity
#| label: fig-logistic-prior-posterior-K
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of carrying capacity (K; kt)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_K_", regions, ".png", sep="" ) )
include_graphics( fns )
Intrinsic rate of increase
#| label: fig-logistic-prior-posterior-r
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of th iIntrinsic rate of biomass increase (r)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_r_", regions, ".png", sep="" ) )
include_graphics( fns )
Catchability coefficient
#| label: fig-logistic-prior-posterior-q
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of the catchability coefficient (q)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_q1_", regions, ".png", sep="" ) )
include_graphics( fns )
Observation error
#| label: fig-logistic-prior-posterior-obserror
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of Observation error (kt)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_bosd_", regions, ".png", sep="" ) )
include_graphics( fns )
Model process error
#| label: fig-logistic-prior-posterior-processerror
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of Model process error (kt)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_bpsd_", regions, ".png", sep="" ) )
include_graphics( fns )
State space
#| label: fig-logistic-state-space
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "State space (kt): year vs year+1."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_state_space_", regions, ".png", sep="" ) )
include_graphics( fns )
Modelled Biomass (pre-fishery)
#| label: fig-logisticPredictions
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Fishable, posterior mean modelled biomass (pre-fishery; kt) are shown in dark orange. Light orange are posterior samples of modelled biomass (pre-fishery; kt) to illustrate the variability of the predictions. The biomass index (post-fishery, except prior to 2004) after model adjustment by the model catchability coefficient is in gray."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
loc = file.path( data_loc, "fishery_model", year_assessment, "logistic_discrete_historical" )
fns = file.path( loc, c(
"plot_predictions_cfanorth.png",
"plot_predictions_cfasouth.png",
"plot_predictions_cfa4x.png"
) )
include_graphics( fns )
#| label: fig-logisticPredictions2
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Fishable, posterior mean modelled biomass (post-fishery; kt) are shown in dark orange. Light orange are posterior samples of modelled biomass (post-fishery; kt) to illustrate the variability of the predictions. The biomass index (post-fishery, except prior to 2004) after model adjustment by the model catchability coefficient is in gray."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
loc = file.path( data_loc, "fishery_model", year_assessment, "logistic_discrete_historical" )
fns = file.path( loc, c(
"plot_predictions_postfishery_cfanorth.png",
"plot_predictions_postfishery_cfasouth.png",
"plot_predictions_postfishery_cfa4x.png"
) )
include_graphics( fns )
N-ENS: {r} round(B_north[t0], 2) t in {r} year_assessment
{r} round(B_north[t1], 2) t in {r} year_previous.
S-ENS: {r} round(B_south[t0], 2) t in {r} year_assessment
{r} round(B_south[t1], 2) t in {r} year_previous.
4X: {r} round(B_4x[t0], 2) t in {r} year_assessment-{r} year_assessment+1
{r} round(B_4x[t1], 2) t for the {r} year_previous-{r} year_assessment season.
Fishing Mortality
#| label: fig-logisticFishingMortality
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Time-series of modelled instantaneous fishing mortality from Model 1, for N-ENS (left), S-ENS (middle), and 4X (right). Samples of the posterior densities are presented, with the darkest line being the mean."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
odir = file.path( fishery_model_results, year_assessment, "logistic_discrete_historical" )
fns = file.path( odir, c(
"plot_fishing_mortality_cfanorth.png",
"plot_fishing_mortality_cfasouth.png",
"plot_fishing_mortality_cfa4x.png"
))
include_graphics( fns )
N-ENS: {r} round(FM_north[t0],3) (annual exploitation rate of {r} round(100*(exp(FM_north[t0])-1),2)%) in {r} year_assessment
- Up from the
{r} year_previous rate of {r} round(FM_north[t1],3) (annual exploitation rate of {r} round(100*(exp(FM_north[t1])-1),1)%)
S-ENS: {r} round(FM_south[t0],3) (annual exploitation rate of {r} round(100*(exp(FM_south[t0])-1),1)%) in {r} year_assessment
- Decreasing marginally from the
{r} year_previous rate of {r} round(FM_south[t1],3) (annual exploitation rate of {r} round(100*(exp(FM_south[t1])-1),1)%)
4X: {r} round(FM_4x[t0],3) (annual exploitation rate of {r} round(100*(exp(FM_4x[t0])-1),1)%) in {r} year_assessment-{r} year_assessment+1 season
- Decreasing from the
{r} year_assessment-1-{r} year_assessment season rate of {r} round(FM_4x[t1],3) (annual exploitation rate of {r} round(100*(exp(FM_4x[t1])-1),1)%)
Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished.
Reference Points
#| label: fig-ReferencePoints
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Harvest control rules for the Scotian Shelf Snow Crab fisheries."
fn = file.path( media_loc, "harvest_control_rules.png")
include_graphics( fn )
#| label: fig-logistic-hcr
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Reference Points (fishing mortality and modelled biomass) from the Fishery Model, for N-ENS (left), S-ENS (middle), and 4X (right). The large yellow dot indicates most recent year and the 95\\% CI. Not: the model does not account for illegal and unreported landings, and interspecific interactions."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
odir = file.path( fishery_model_results, year_assessment, "logistic_discrete_historical" )
fns = file.path( odir, c(
"plot_hcr_cfanorth.png" ,
"plot_hcr_cfasouth.png",
"plot_hcr_cfa4x.png"
) )
include_graphics( fns )
| | N-ENS | S-ENS | 4X |
|----- | ----- | ----- | ----- |
| | | | |
|q | r round(q_north, 3) (r round(q_north_sd, 3)) | r round(q_south, 3) (r round(q_south_sd, 3)) | r round(q_4x, 3) (r round(q_4x_sd, 3)) |
|r | r round(r_north, 3) (r round(r_north_sd, 3)) | r round(r_south, 3) (r round(r_south_sd, 3)) | r round(r_4x, 3) (r round(r_4x_sd, 3)) |
|K | r round(K_north, 2) (r round(K_north_sd, 2)) | r round(K_south, 2) (r round(K_south_sd, 2)) | r round(K_4x, 2) (r round(K_4x_sd, 2)) |
|Prefishery Biomass | r round(B_north[t0], 2) (r round(B_north_sd[t0], 2)) | r round(B_south[t0], 2) (r round(B_south_sd[t0], 2)) | r round(B_4x[t0], 2) (r round(B_4x_sd[t0], 2)) |
|Fishing Mortality | r round(FM_north[t0], 3) (r round(FM_north_sd[t0], 3)) | r round(FM_south[t0], 3) (r round(FM_south_sd[t0], 3)) | r round(FM_4x[t0], 3) (r round(FM_4x_sd[t0], 3)) |
Note: Values in parentheses are Posterior standard deviations.
----------- The text will need to be updated ------------------
- N-ENS is in the "cautious" zone
- S-ENS is in the "healthy" zone
- 4X is in the "critical" zone
Conclusions
The ESS ecosystem is still experiencing a lot of volatility and prudence is wise:
- Rapid ecosystem change (groundfish collapse in 1980s and 1990s)
- Rapid climatic change: variability high, very warm period from 2012-2024
- Snow crab:
- Can persist if extreme conditions are episodic by shifting spatial distributions
- 4X was possibly too long and did not have the habitat space
- Climate variability can induce juxtaposition of warmer water species (prey and predators) with snow crab
- Some shifts in spatial distribution towards cooler and deeper waters
Modelled solutions:
- A few of many possible views
- Over-emphasis of any one view and associated Reference Points is not precautionary.
Conclusions: N-ENS
- Viable habitat stable near historical mean and marginally improved relative to 2022.
- Female larval production peaked in 2017 and has since declined.
- Recruitment continues at low levels, a noticible gap in recruitment to the fishery persists.
- Predation mortality (Cod, Halibut, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass continues a decreasing trend.
- PA template suggest "healthy zone".
- A more careful harvest strategy would enable bridging this coming recruitment gap.
- A reduced TAC is prudent.
In N-ENS, though recruitment continues at low levels,
a gap in future recruitment to the fishery is expected for the next 1-3 years
bridging this coming recruitment gap. A reduced TAC is prudent.
Conclusions: S-ENS
- Viable habitat marginally improved relative to a historic low in 2022.
- Female larval production peaked in 2023/2024.
- Recruitment to the fishery from a strong year class has begun, full entry by 2025.
- Predation mortality (Halibut, Plaice, Sculpin, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass is at a historical low.
-
PA template suggest "healthy zone".
-
Flexiblity in harvest strategy exists due to strong recuitment.
- A status quo TAC is prudent until confirmation of recruitment.
recruitment to the fishery is likely to continue at a moderate rate for the upcoming season
Conclusions: 4X
- Viable habitat has been at historical lows since 2015.
- Female larval production peaked in 2022.
- Recruitment to the fishery in the near-term is not evident. There is a pulse 4-5 years away.
- Predation mortality (Halibut) and competition with other Crustacea is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has declined to negligible levels since a peak in 2019.
- Fishable biomass is at a historical low.
- PA template suggest "critical zone".
- Stock status is extremely poor.
- Fishery closure until recovery is prudent.
low to moderate levels of recruitment are expected for 2 years.
4X exists in the "cautious zone".
habitat has been depressed for many years. A reduced TAC is prudent.
jae0/snowcrab documentation built on March 5, 2025, 4:44 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "Snow crab fishery model" keywords: - snow crab fishery model results abstract: | Snow crab fishery model summary. fontsize: 12pt metadata-files: - _metadata.yml params: year_assessment: 2024 year_start: 1999 data_loc: "~/bio.data/bio.snowcrab" sens: 1 debugging: FALSE model_variation: logistic_discrete_historical
#| eval: true
#| output: false
#| echo: false
#| label: setup
require(knitr)
knitr::opts_chunk$set(
root.dir = data_root,
echo = FALSE,
out.width="6.2in",
fig.retina = 2,
dpi=192
)
# things to load into memory (in next step) via _load_results.qmd
toget = c( "fishery_results", "fishery_model" )
{{< include _load_results.qmd >}}
Fishery model
$$\begin{aligned} b_{t+1} & \sim N\left({{{b_{t}+r}b_{t}}{{({1-b_{t}})}-\mathit{Fishing}}{t}K^{-1},\sigma{p}}\right) \ Y_{t} & \sim N\left({q^{-1}\mathit{Kb}{t},\sigma{o}}\right) \ r & \sim N\left({1.0,0.1}\right) \ K & \sim N\left({\kappa,0.1\cdot\kappa}\right) \ q & \sim N\left({1.0,0.1}\right) \ \end{aligned}$$
$\kappa={\lbrack{5.0,60,1.25}\rbrack}$
${b=B}K^{-1}$, a real unobservable (latent) process
- MCMC (Julia/Turing):
- 4 chains, NUTS sampler (warmup: 10000, retain: 10000)
- target_acceptance_rate, max_depth, init_ϵ = 0.75, 9, 0.05
Surplus (Shaeffer) production
#| label: fig-logistic-surplus-production
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Surplus (Shaeffer) production. Each year is represented by a different colour."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = paste("plot_surplus_production_", regions, ".png", sep="" )
fns = file.path( fm_loc, fns )
include_graphics( fns )
Carrying capacity
#| label: fig-logistic-prior-posterior-K
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of carrying capacity (K; kt)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_K_", regions, ".png", sep="" ) )
include_graphics( fns )
Intrinsic rate of increase
#| label: fig-logistic-prior-posterior-r
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of th iIntrinsic rate of biomass increase (r)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_r_", regions, ".png", sep="" ) )
include_graphics( fns )
Catchability coefficient
#| label: fig-logistic-prior-posterior-q
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of the catchability coefficient (q)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_q1_", regions, ".png", sep="" ) )
include_graphics( fns )
Observation error
#| label: fig-logistic-prior-posterior-obserror
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of Observation error (kt)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_bosd_", regions, ".png", sep="" ) )
include_graphics( fns )
Model process error
#| label: fig-logistic-prior-posterior-processerror
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of Model process error (kt)."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_prior_bpsd_", regions, ".png", sep="" ) )
include_graphics( fns )
State space
#| label: fig-logistic-state-space
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "State space (kt): year vs year+1."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
fns = file.path( fm_loc, paste("plot_state_space_", regions, ".png", sep="" ) )
include_graphics( fns )
Modelled Biomass (pre-fishery)
#| label: fig-logisticPredictions
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Fishable, posterior mean modelled biomass (pre-fishery; kt) are shown in dark orange. Light orange are posterior samples of modelled biomass (pre-fishery; kt) to illustrate the variability of the predictions. The biomass index (post-fishery, except prior to 2004) after model adjustment by the model catchability coefficient is in gray."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
loc = file.path( data_loc, "fishery_model", year_assessment, "logistic_discrete_historical" )
fns = file.path( loc, c(
"plot_predictions_cfanorth.png",
"plot_predictions_cfasouth.png",
"plot_predictions_cfa4x.png"
) )
include_graphics( fns )
#| label: fig-logisticPredictions2
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Fishable, posterior mean modelled biomass (post-fishery; kt) are shown in dark orange. Light orange are posterior samples of modelled biomass (post-fishery; kt) to illustrate the variability of the predictions. The biomass index (post-fishery, except prior to 2004) after model adjustment by the model catchability coefficient is in gray."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
loc = file.path( data_loc, "fishery_model", year_assessment, "logistic_discrete_historical" )
fns = file.path( loc, c(
"plot_predictions_postfishery_cfanorth.png",
"plot_predictions_postfishery_cfasouth.png",
"plot_predictions_postfishery_cfa4x.png"
) )
include_graphics( fns )
N-ENS: {r} round(B_north[t0], 2) t in {r} year_assessment
{r} round(B_north[t1], 2)t in{r} year_previous.
S-ENS: {r} round(B_south[t0], 2) t in {r} year_assessment
{r} round(B_south[t1], 2)t in{r} year_previous.
4X: {r} round(B_4x[t0], 2) t in {r} year_assessment-{r} year_assessment+1
{r} round(B_4x[t1], 2)t for the{r} year_previous-{r} year_assessmentseason.
Fishing Mortality
#| label: fig-logisticFishingMortality
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Time-series of modelled instantaneous fishing mortality from Model 1, for N-ENS (left), S-ENS (middle), and 4X (right). Samples of the posterior densities are presented, with the darkest line being the mean."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
odir = file.path( fishery_model_results, year_assessment, "logistic_discrete_historical" )
fns = file.path( odir, c(
"plot_fishing_mortality_cfanorth.png",
"plot_fishing_mortality_cfasouth.png",
"plot_fishing_mortality_cfa4x.png"
))
include_graphics( fns )
N-ENS: {r} round(FM_north[t0],3) (annual exploitation rate of {r} round(100*(exp(FM_north[t0])-1),2)%) in {r} year_assessment
- Up from the
{r} year_previousrate of{r} round(FM_north[t1],3)(annual exploitation rate of{r} round(100*(exp(FM_north[t1])-1),1)%)
S-ENS: {r} round(FM_south[t0],3) (annual exploitation rate of {r} round(100*(exp(FM_south[t0])-1),1)%) in {r} year_assessment
- Decreasing marginally from the
{r} year_previousrate of{r} round(FM_south[t1],3)(annual exploitation rate of{r} round(100*(exp(FM_south[t1])-1),1)%)
4X: {r} round(FM_4x[t0],3) (annual exploitation rate of {r} round(100*(exp(FM_4x[t0])-1),1)%) in {r} year_assessment-{r} year_assessment+1 season
- Decreasing from the
{r} year_assessment-1-{r} year_assessmentseason rate of{r} round(FM_4x[t1],3)(annual exploitation rate of{r} round(100*(exp(FM_4x[t1])-1),1)%)
Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished.
Reference Points
#| label: fig-ReferencePoints
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Harvest control rules for the Scotian Shelf Snow Crab fisheries."
fn = file.path( media_loc, "harvest_control_rules.png")
include_graphics( fn )
#| label: fig-logistic-hcr
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Reference Points (fishing mortality and modelled biomass) from the Fishery Model, for N-ENS (left), S-ENS (middle), and 4X (right). The large yellow dot indicates most recent year and the 95\\% CI. Not: the model does not account for illegal and unreported landings, and interspecific interactions."
#| fig-subcap:
#| - "N-ENS"
#| - "S-ENS"
#| - "4X"
odir = file.path( fishery_model_results, year_assessment, "logistic_discrete_historical" )
fns = file.path( odir, c(
"plot_hcr_cfanorth.png" ,
"plot_hcr_cfasouth.png",
"plot_hcr_cfa4x.png"
) )
include_graphics( fns )
| | N-ENS | S-ENS | 4X |
|----- | ----- | ----- | ----- |
| | | | |
|q | r round(q_north, 3) (r round(q_north_sd, 3)) | r round(q_south, 3) (r round(q_south_sd, 3)) | r round(q_4x, 3) (r round(q_4x_sd, 3)) |
|r | r round(r_north, 3) (r round(r_north_sd, 3)) | r round(r_south, 3) (r round(r_south_sd, 3)) | r round(r_4x, 3) (r round(r_4x_sd, 3)) |
|K | r round(K_north, 2) (r round(K_north_sd, 2)) | r round(K_south, 2) (r round(K_south_sd, 2)) | r round(K_4x, 2) (r round(K_4x_sd, 2)) |
|Prefishery Biomass | r round(B_north[t0], 2) (r round(B_north_sd[t0], 2)) | r round(B_south[t0], 2) (r round(B_south_sd[t0], 2)) | r round(B_4x[t0], 2) (r round(B_4x_sd[t0], 2)) |
|Fishing Mortality | r round(FM_north[t0], 3) (r round(FM_north_sd[t0], 3)) | r round(FM_south[t0], 3) (r round(FM_south_sd[t0], 3)) | r round(FM_4x[t0], 3) (r round(FM_4x_sd[t0], 3)) |
Note: Values in parentheses are Posterior standard deviations.
----------- The text will need to be updated ------------------
- N-ENS is in the "cautious" zone
- S-ENS is in the "healthy" zone
- 4X is in the "critical" zone
Conclusions
The ESS ecosystem is still experiencing a lot of volatility and prudence is wise:
- Rapid ecosystem change (groundfish collapse in 1980s and 1990s)
- Rapid climatic change: variability high, very warm period from 2012-2024
- Snow crab:
- Can persist if extreme conditions are episodic by shifting spatial distributions
- 4X was possibly too long and did not have the habitat space
- Climate variability can induce juxtaposition of warmer water species (prey and predators) with snow crab
- Some shifts in spatial distribution towards cooler and deeper waters
Modelled solutions:
- A few of many possible views
- Over-emphasis of any one view and associated Reference Points is not precautionary.
Conclusions: N-ENS
- Viable habitat stable near historical mean and marginally improved relative to 2022.
- Female larval production peaked in 2017 and has since declined.
- Recruitment continues at low levels, a noticible gap in recruitment to the fishery persists.
- Predation mortality (Cod, Halibut, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass continues a decreasing trend.
- PA template suggest "healthy zone".
- A more careful harvest strategy would enable bridging this coming recruitment gap.
- A reduced TAC is prudent.
In N-ENS, though recruitment continues at low levels, a gap in future recruitment to the fishery is expected for the next 1-3 years
bridging this coming recruitment gap. A reduced TAC is prudent.
Conclusions: S-ENS
- Viable habitat marginally improved relative to a historic low in 2022.
- Female larval production peaked in 2023/2024.
- Recruitment to the fishery from a strong year class has begun, full entry by 2025.
- Predation mortality (Halibut, Plaice, Sculpin, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass is at a historical low.
-
PA template suggest "healthy zone".
-
Flexiblity in harvest strategy exists due to strong recuitment.
- A status quo TAC is prudent until confirmation of recruitment.
recruitment to the fishery is likely to continue at a moderate rate for the upcoming season
Conclusions: 4X
- Viable habitat has been at historical lows since 2015.
- Female larval production peaked in 2022.
- Recruitment to the fishery in the near-term is not evident. There is a pulse 4-5 years away.
- Predation mortality (Halibut) and competition with other Crustacea is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has declined to negligible levels since a peak in 2019.
- Fishable biomass is at a historical low.
- PA template suggest "critical zone".
- Stock status is extremely poor.
- Fishery closure until recovery is prudent.
low to moderate levels of recruitment are expected for 2 years. 4X exists in the "cautious zone".
habitat has been depressed for many years. A reduced TAC is prudent.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.