inst/markdown/06_assessment_summary.md

In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.

title: "Snow crab stock assessment" author: - name: given: Snow Crab Unit family: DFO Science # orcid: 0000-0003-3632-5723 # email: [e-mail] # email: [e-mail] # corresponding: true affiliation: - name: Bedford Institute of Oceanography, Fisheries and Oceans Canada city: Dartmouth state: NS # url: www.bio.gc.ca keywords: - snow crab stock status assessment abstract: | Snow crab stock status assessment. 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 >}}

Stock status

• Survey timing changed from Spring to Autumn in 2004.

• No survey in 2020 (Covid-19) and incomplete 2022 (mechanical issues).

• Inshore areas of S-ENS were most affected.

• N-ENS and CFA 4X were not affected.

Size structure

Factors: early maturation, size-selective predation, fishing of largest individuals, start or end of a recruitment pulse, timing of survey.

Recruitment

• Little to no recruitment is expected for the next 1-3 years in N-ENS.
• Moderate levels of recruitment are expected in S-ENS.
• Low to moderate levels of recruitment are expected for 2 years in 4X.

Reproduction

• All areas had recruitment of female crab into the mature (egg-bearing) segment of the population from 2016-2022.
• In N-ENS for 2022, a decline in numerical densities, and low densities of adolescent females.
• Egg and larval production is expected to be moderate to high in the next year in all areas except N-ENS.

Reproduction (mature females)

Distributions are heterogeneous and often in shallower areas.

Sex ratios (proportion female, mature)

• Mostly male-dominated: larger size may be protective against predation?
• Imbalance indicates differential mortality: predation, competition and fishing
• In 4X, sex ratios are balanced.

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 )

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 needs 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.

Surplus production ("Shaeffer form") {.c}

#| label: fig-logistic-surplus-production
#| results: asis
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Surplus production. Each year is represented by a different colour."


for (r in 1:nregions){
  reg = regions[r]
  REG = reg_labels[r] 
  cat("#### ", REG, "\n")
  fn = file.path( fm_loc, paste("plot_surplus_production_", reg, ".png", sep="" ) ) 
  show_image(check_file_exists(fn)) 
  cat("\n\n")
} 

Prior-posterior comparsons: Carrying capacity (K; kt) {.c}

#| label: fig-logistic-prior-posterior-K
#| results: asis
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of carrying capacity (K; kt)."

for (r in 1:nregions){
  reg = regions[r]
  REG = reg_labels[r] 
  cat("#### ", REG, "\n")
  fn = file.path( fm_loc, paste("plot_prior_K_", reg, ".png", sep="" ) ) 
  show_image(check_file_exists(fn)) 
  cat("\n\n")
} 

Prior-posterior comparsons: Intrinsic rate of biomass increase (r) {.c}

#| label: fig-logistic-prior-posterior-r
#| results: asis
#| 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)."


for (r in 1:nregions){
  reg = regions[r]
  REG = reg_labels[r] 
  cat("#### ", REG, "\n")
  fn = file.path( fm_loc, paste("plot_prior_r_", reg, ".png", sep="" ) ) 
  show_image(check_file_exists(fn)) 
  cat("\n\n")
} 

Prior-posterior comparsons: Catchability coefficient (q) {.c}

#| label: fig-logistic-prior-posterior-q
#| results: asis
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of the catchability coefficient (q)."


for (r in 1:nregions){
  reg = regions[r]
  REG = reg_labels[r] 
  cat("#### ", REG, "\n")
  fn = file.path( fm_loc, paste("plot_prior_q1_", reg, ".png", sep="" ) ) 
  show_image(check_file_exists(fn)) 
  cat("\n\n")
} 

Prior-posterior comparsons: Observation error {.c}

#| label: fig-logistic-prior-posterior-obserror
#| results: asis
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of Observation error (kt)."

for (r in 1:nregions){
  reg = regions[r]
  REG = reg_labels[r] 
  cat("#### ", REG, "\n")
  fn = file.path( fm_loc, paste("plot_prior_bosd_", reg, ".png", sep="" ) ) 
  show_image(check_file_exists(fn)) 
  cat("\n\n")
} 

Prior-posterior comparsons: Model process error {.c}

#| label: fig-logistic-prior-posterior-processerror
#| results: asis
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Prior-posterior comparisons of Model process error (kt)."


for (r in 1:nregions){
  reg = regions[r]
  REG = reg_labels[r] 
  cat("#### ", REG, "\n")
  fn = file.path( fm_loc, paste("plot_prior_bpsd_", reg, ".png", sep="" ) ) 
  show_image(check_file_exists(fn)) 
  cat("\n\n")
} 

State space {.c}

#| label: fig-logistic-state-space
#| results: asis
#| echo: false
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "State space (kt): year vs year+1."


for (r in 1:nregions){
  reg = regions[r]
  REG = reg_labels[r] 
  cat("#### ", REG, "\n")
  fn = file.path( fm_loc, paste("plot_state_space_", reg, ".png", sep="" ) ) 
  show_image(check_file_exists(fn)) 
  cat("\n\n")
} 

jae0/snowcrab documentation built on Feb. 20, 2025, 2:27 p.m.