inst/markdown/snowcrab_presentation_advisory_sens.md
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
title: "Snow Crab Advisory SENS"
subtitle: "Maritimes Region"
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
todo: [fishery_results,fishery_model,ecosystem]
#| eval: true
#| output: false
#| echo: false
#| label: setup
require(knitr)
knitr::opts_chunk$set(
root.dir = data_root,
echo = FALSE,
out.width="6.2in",
fig.retina = 2,
dpi=192
)
{{< include _load_results.qmd >}}
::: {.landscape}
Terms of Reference
-
Fishery performance
-
Bycatch of non-target species
-
Environmental and climate change considerations
-
Stock status and trends
-
Major sources of uncertainty, where applicable.
Fishery performance
Fishery statistics
- TAC: no change
- Landings: declined marginally (0.4%)
- Effort: increased 27%
- Catch rates: decreased 21%
#| label: tbl-fishery-performance-S-ENS
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "Fishery performance statistics: S-ENS"
r=2
reg = regions[r]
REG = reg_labels[r]
oo = dt[ which(dt$Region==reg), c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
names(oo) = c( "Year", "Licenses", "TAC (t)", "Landings (t)", "Effort (1000 th)", "CPUE (kg/th)" )
gt::gt(oo) |> gt::tab_options(table.font.size = 20, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
Effort
#| label: fig-effort-timeseries
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 8
#| fig-cap: "Temporal variations in fishing effort."
if (params$sens==1) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery" )
} else if (params$sens==2) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery", "split" )
}
include_graphics( file.path( ts_dir, "effort.ts.png" ) )
#| label: fig-effort-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 8
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab fishing effort from fisheries logbook data for previous and current years (no X 10$^3$ per 10 km X 10 km grid)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc0 = file.path( data_loc, "output", "maps", "logbook", "snowcrab", "annual", "effort" )
yrsplot = year_assessment + c(0:-3)
fn = file.path( loc0, paste( "effort", yrsplot, "png", sep=".") )
include_graphics( fn )
Landings
#| label: fig-landings-timeseries
#| eval: true
#| output: true
#| fig-cap: "Landings (t) of Snow Crab on the SSE. For 4X, the year refers to the starting year of the season. Inset is a closeup view of the timeseries for N-ENS and 4X."
#| fig-dpi: 144
#| fig-height: 8
if (params$sens==1) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery" )
} else if (params$sens==2) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery", "split" )
}
include_graphics( file.path( ts_dir, "landings.ts.png" ) )
#| label: fig-landings-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab landings from fisheries logbook data for previous and current years (tons per 10 km x 10 km grid)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc0 = file.path( data_loc, "output", "maps", "logbook", "snowcrab", "annual", "landings" )
yrsplot = year_assessment + c(0:-3)
fn = file.path( loc0, paste( "landings", yrsplot, "png", sep=".") )
include_graphics( fn )
Catch rates
#| label: fig-cpue-timeseries
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Temporal variations in crude catch rates of Snow Crab (kg/trap haul)."
if (params$sens==1) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery" )
} else if (params$sens==2) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery", "split" )
}
include_graphics( file.path( ts_dir, "cpue.ts.png" ) )
#| label: fig-cpue-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab crude catch rates on the Scotian Shelf for previous and current years. Units are kg/trap haul per 10 km x 10 km grid."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc0= file.path( data_loc, "output", "maps", "logbook", "snowcrab", "annual", "cpue" )
yrsplot = year_assessment + c(0:-3)
fn = file.path( loc0, paste( "cpue", yrsplot, "png", sep=".") )
include_graphics( fn )
Bycatch of non-target species
At sea observed trips
#| label: tbl-observed-summary
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 5
#| echo: false
#| layout-ncol: 1
#| tbl-cap: "Table of observed data coverage"
fns = c(
"observersummary2.png"
)
include_graphics( file.path( media_supplementary, fns ) )
#| label: fig-map-observer-locations
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab At-sea-observer locations."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc = file.path( data_loc, "output", "maps", "observer.locations" )
yrsplot = year_assessment + c(0:-3)
fns = paste( "observer.locations", yrsplot, "png", sep="." )
fn = file.path( loc, fns )
include_graphics( fn )
Discard of (all) soft-shell crab map
#| label: fig-observed-softshell-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Map of observed soft shell locations."
#| fig-subcap:
#| - "CFA23"
#| - "CFA24"
# #| - "N-ENS"
fns = c(
# "nens_soft_crab_positions_68.png" #
"cfa23_soft_crab_positions_68.png",
"cfa24_soft_crab_positions_68.png"
)
include_graphics( file.path( media_supplementary, fns ) )
Discard of non-target species ("Bycatch")
#| label: tbl-fishery-discard-effort-sens
#| eval: true
#| output: true
#| echo: false
#| tbl-cap: "Bycatch (kg) estimated from fisheries effort. Dots indicate values less than 10 kg/year. Where species exist in a list but there is no data, this indicates some historical bycatch. The overall average is from 2004 to present."
r = 2
reg = regions[r]
REG = reg_labels[r]
o = BC[[reg]]
oo = o$bycatch_table_effort
oo[ oo==0 ] = NA
oo[ is.na(oo) ] = "."
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
- Average of 0.03% of landings; primarily other Crustacea (crab and lobster)
Environmental and climate change considerations
Bottom temperatures
-
Average bottom temperatures observed in the 2024 Snow Crab survey have returned to historical ranges, after worrisome highs in 2022.
-
Bottom temperatures are more stable in N-ENS than S-ENS; 4X exhibits the most erratic and highest annual mean bottom temperatures.
#| label: fig-bottom-temperatures-timeseries
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Annual variations in bottom temperature observed during the Snow Crab survey. The horizontal (black) line indicates the long-term, median temperature within each subarea. Error bars represent standard errors."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
tloc = file.path( data_loc, "assessments", year_assessment, "timeseries" )
fns = c(
file.path("survey", "t.png")
)
include_graphics( file.path( tloc, fns) )
#| label: fig-figures-temperature-bottom-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Bottom temperature ($^\\circ$C) observed during the Snow Crab survey."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab", "annual" )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, "t", paste( "t", map_years, "png", sep="." )
) )
include_graphics( fn )
#| label: fig-bottom-temperatures-timeseries-modelled
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Temporal variations in bottom temperature from a historical reanalysis of temperature data. Red horizontal line is at $7^\\circ$C. Presented are 95% Credible Intervals of spatial variability in temperature at each time slice after adjustment for spatiotemporal autocorrelation."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
tloc = file.path( data_loc, "assessments", year_assessment, "timeseries" )
fns = c(
"temperature_bottom.png"
)
include_graphics( file.path( tloc, fns) )
#| label: fig-figures-temperature-bottom-map-modelled
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Bottom temperature ($^\\circ$C) estimated from a historical analysis of temperature data for 1 September."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( data_root, 'aegis', 'temperature', 'modelled', 'default', 'maps' )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, paste( 'predictions.', map_years, '.0.75', '.png', sep='')
) )
include_graphics( fn )
Interspecific interactions
- Assumption: Elevated co-ocurrence in areal densities with snow crab trawl samples means greater potential encounter rates
#| label: fig-speciescomposition-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species ordination (PCA: eigenanalysis of correlation matrices). PC1 is associatd with bottom temperatures. PC2 is associated with depth. Snow crab is shown as an orange dot."
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.7, col="slateblue" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
Predators
#| label: tbl-predators
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "Main predators based upon frequency of occuence of snow crab in finfish stomach samples, unadjusted for sampling effort."
gt::gt(counts[1:11,])
#| label: fig-predator-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Main predators of snow crab on Scotian Shelf of Atlantic Canada (1999-2020). Relative location of snow crab predators (green) in the species composition ordination. Snow crab in orange. Of 58,287 finfish stomach samples, 159 had snow crab (0.28%). There is no information on snow crab diet in the database."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential predators:
lookup= c( "cod", "halibut", "sculpin", "skate", "plaice", "hake", "wolffish", "atlantic cod", "atlantic halibut", "longhorn sculpin", "thorny skate", "striped atlantic wolffish", "haddock", "american plaice", "smooth skate", "winter skate", "white hake", "shorthorn sculpin", "eelpout newfoundland", "squirrel or red hake", "sea raven", "ocean pout", "barndoor skate", "Squid" )
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.75, col="slategrey" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
-
Overall, Atlantic Halibut densities have increased rapidly since 2010 (The Gully, Slope Edge and near Sable Island).
-
Thorny skate densities have been increasing (especially in N-ENS and along the margins of Banquereau Bank)
-
Striped Atlantic Wolffish densities have been high (declining in N-ENS since 2007, peaking in Laurentian Channel).
-
Potential predators: in warmer and deeper (left and bottom of Snow Crab).
Prey
- echinoderms
- polychaete worms (Maldane, Nereis), worm-like animals
- detritus (dead organic matter)
- large zooplankton, shrimp
- juvenile crab (Rock Crab; Toad Crab; Lesser Toad Crab)
- Ocean Quahog (Artica islandica), bivalve molluscs (Mytilus sp, Modiolus, Hiatella)
- brittle stars (Ophiura, Ophiopholis)
- sea anemones (Edwardsia, Metridium).
#| label: fig-diet-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Relative location of snow crab prey (green) in the species composition ordination. Snow crab in orange. Most of the potental prey are found to the right of snow crab (i.e. colder-water species) at a variety of depths."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential food items:
lookup= c( "echinoderm", "polychaete", "maldane", "nereis", "shrimp", "pandalus", "rock crab", "toad crab", "lesser toad crab", "quahog", "artica islandica", "mollusc", "mytilus", "modiolus", "hiatella", "starfish", "sea anemone", "brittle star", "sea star", "sea anemone", "ophiura", "ophiopholis", "edwardsia", "metridium", "euphasid" )
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.75, col="slategrey" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
- Potential prey: in colder (right of Snow Crab).
Competitors
#| label: fig-competitor-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Potential competitors of snow crab on Scotian Shelf of Atlantic Canada (1999-2020). Relative location of snow crab predators (green) in the species composition ordination. Snow crab in orange."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential predators:
lookup= c( "pandalus", "Jonah Crab", "Atlantic Rock Crab" , "Toad Crab", "Hyas Coarctatus", "Northern Stone Crab" ) # add more here: most are not direct compeitors as they have slightly different depth/temp preferences
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.75, col="slategrey" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
-
Northern shrimp co-occur as they share similar habitat preferences. Numerical densities have declined after a peak in 2011, especially in S-ENS.
-
Lesser toad crab is a co-occurring species and potential competitor. Their numbers have declined to low levels throughout, after a peak in densities in 2007 and 2016 in N-ENS.
Viable habitat
#| label: fig-fb-habitat-timeseries
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Habitat viability (probability; fishable Snow Crab). Means and 95\\% Credible Intervals are presented."
loc = file.path( data_loc, "modelled", "default_fb", "aggregated_habitat_timeseries" )
include_graphics( file.path( loc, "habitat_M0.png") )
#| label: fig-fb-habitat-map
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Habitat viability (probability; fishable Snow Crab)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_loc, "modelled", "default_fb", "predicted_habitat" )
vn = "habitat."
yrsplot = year_assessment + c(0:-9)
fns = file.path( loc, paste( vn, yrsplot, ".png", sep="") )
include_graphics( fns )
-
Nouanced: not just temperature, but joint distribution of temperature and other factors (substrate, depths, co-occuring species, ...)
-
SSE is variable Warm-Water incursions resulted in predators co-occupying Snow Crab habitats while simultaneously losing access to cold water preferring prey.
-
S-ENS: Viable habitat is highest even though temperatures are more stable and cooler in N-ENS. Currently, declined to near historical lows (peaks in 2012). Peaks near Sable and Missaine.
Stock status and trends
Data sources: Trawl Survey
Marine Protected Areas (St Ann's, Gully) were not sampled
- S-ENS: 282 stations completed
#| label: fig-survey-locations-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Survey locations."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc = file.path( data_loc, "output", "maps", "survey.locations" )
years = year_assessment + c(0:-3)
fn = check_file_exists( file.path( loc, paste( "survey.locations", years, "png", sep=".") ))
include_graphics( fn )
Growth patterns inferred from modes
#| label: fig-growth-modes-growth
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Inferred growth derived from *Kernel Mixture Models* (priors)."
#| fig-subcap:
#| - "Female growth trajectory"
#| - "Male growth trajectory"
fns = file.path( media_loc, c(
"plot_growth_female.png",
"plot_growth_male.png"
))
include_graphics( fns )
Recruitment: males
#| label: fig-sizefeq-male
#| eval: true
#| output: true
#| fig-cap: "Size-frequency (areal density; no/km$^2$) histograms by carapace width of male Snow Crab. The vertical line represents the legal size (95 mm). Immature animals are shown with light coloured bars, mature with dark."
#| fig-dpi: 144
#| fig-height: 10
if (params$sens==1) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey")
} else if (params$sens==2) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey", "split")
}
include_graphics( file.path( sf_outdir, "male.denl.png" ) )
- S-ENS: strong and stable size structure suggestive of a stable size/age structure; recruitment expected in 2025 and elevated soft-shell capture will require care
Reproduction (longer term)
#| label: fig-sizefeq-female
#| eval: true
#| output: true
#| fig-cap: "Size-frequency (areal density; no/km$^2$) histograms by carapace width of female Snow Crab. Immature animals are shown with light coloured bars, mature with dark."
#| fig-dpi: 144
#| fig-height: 10
if (params$sens==1) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey")
} else if (params$sens==2) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey", "split")
}
fn = file.path( sf_outdir, "female.denl.png" )
include_graphics( fn )
S-ENS:
- increase since 2021
- egg and larval production is expected to be high in the next year
#| label: fig-totno-female-mat-timeseries
#| eval: true
#| output: true
#| fig-cap: "The crude, unadjusted geometric mean of mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey. Error bars represent 95\\% Confidence Intervals. Note the absence of data in 2020. Prior to 2004, surveys were conducted in the Spring."
#| fig-dpi: 144
#| fig-height: 4
if (params$sens==1) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey")
} else if (params$sens==2) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey", "split")
}
fn = file.path( ts_outdir, paste("totno.female.mat", "png", sep=".") )
include_graphics( fn )
#| label: fig-totno-female-mat-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual" )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, "totno.female.mat", paste( "totno.female.mat", map_years, "png", sep="." )
) )
include_graphics( fn )
Fishable biomass density
- S-ENS: densities have increased; increases mostly offshore
#| label: fig-R0-timeseries
#| eval: true
#| output: true
#| fig-cap: "The crude, unadjusted geometric mean fishable biomass density log$_{10}$(t/km$^2$) from the Snow Crab survey. Error bars represent 95\\% Confidence Intervals. Note the absence of data in 2020. Prior to 2004, surveys were conducted in the Spring."
#| fig-dpi: 144
#| fig-height: 4
if (params$sens==1) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey")
} else if (params$sens==2) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey", "split")
}
fn = file.path( ts_outdir, paste("R0.mass", "png", sep=".") )
include_graphics( fn )
#| label: fig-R0-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab survey fishable component biomass density log$_{10}$(t/km$^2$)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual" )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, "R0.mass", paste( "R0.mass", map_years, "png", sep="." )
) )
include_graphics( fn )
Fishable biomass index
- S-ENS: three peaks; increased marginally; increase offshore (marginally, north of Sable)
#| label: fig-fbindex-timeseries
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "The fishable biomass index (t) predicted by CARSTM of Snow Crab survey densities. Error bars represent Bayesian 95\\% Credible Intervals. Note large errors in 2020 when there was no survey."
fn = file.path( data_loc, "modelled", "default_fb", "aggregated_biomass_timeseries" , "biomass_M0.png")
include_graphics( fn )
#| label: fig-fbindex-map
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Biomass index log~10(t/km$^2$) predicted from the Snow Crab survey."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_loc, "modelled", "default_fb", "predicted_biomass_densities" )
yrsplot = year_assessment + c(0:-9)
fns = file.path( loc, paste( "biomass", yrsplot, "png", sep=".") )
include_graphics( fns )
Modelled pre-fishery fishable biomass
- S-ENS: 41.5t, relative to 40.6kt in the previous year
#| 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 )
Posterior estimates of fishing mortality
-
S-ENS: F=0.17 (annual exploitation rate of 18.6%), F=0.18 (annual exploitation rate of 19%) in the previous year.
-
Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished (e.g., continental slope areas and western, inshore areas of CFA 24, 4X).
#| 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 )
Reference Points
- Limit Reference Point (LRP) = K/4
- Upper Stock Reference (USR) = K/2
-
Removal Reference (RR) = F_{MSY} = r/ 2
-
S-ENS is in the “healthy” zone, though with someoverlap with other zones
| | 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)) |
: Reference points from the logistic biomass dynamics fishery model. K is Carrying capacity (kt); and r is Intrinsic rate of increase (non-dimensional). Note that FMSY (fishing mortality associated with 'Maximum Sustainable Yield') is r/2. Similarly, BMSY (biomass associated with 'Maximum Sustainable Yield') is K/2. SD is posterior Standard deviations.* {#tbl-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. Prefishery."
# #| 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 )
Major sources of uncertainty
-
Capture of soft-shell Snow Crab (handling mortality)
-
Bycatch of Snow Crab in other fisheries (use as bait)
-
Illegal, unreported, and unregulated fishing activities
-
Marine Protected Areas (MPAs) act as a refuge from fishing activities. However, positive effects upon other organisms (predators or prey) can have counter-balancing indirect effects.
Conclusions
The SSE continues to experience rapid ecosystem and climatic variations. Under such conditions, it is prudent to be careful.
S-ENS:
- Recruitment to the fishery continues at a sustainable rate matching total mortality.
- Remains in the “healthy” zone.
Acknowledgements
-
Innovative Capital Investments Inc.
-
Captain Coalie D’Eon and crew of the FV Journey II for their expertise and provision of a safe and hospitable environment for the conduct of the survey.
-
Snow Crab license holders and fishers of the SSE a stellar model of a collaborative, sustainable and precautionary co-management of a fishery
END
:::
jae0/snowcrab documentation built on June 13, 2025, 3:51 p.m.
R Package Documentation
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title: "Snow Crab Advisory SENS" subtitle: "Maritimes Region" 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 todo: [fishery_results,fishery_model,ecosystem]
#| 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
)
{{< include _load_results.qmd >}}
::: {.landscape}
Terms of Reference
-
Fishery performance
-
Bycatch of non-target species
-
Environmental and climate change considerations
-
Stock status and trends
-
Major sources of uncertainty, where applicable.
Fishery performance
Fishery statistics
- TAC: no change
- Landings: declined marginally (0.4%)
- Effort: increased 27%
- Catch rates: decreased 21%
#| label: tbl-fishery-performance-S-ENS
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "Fishery performance statistics: S-ENS"
r=2
reg = regions[r]
REG = reg_labels[r]
oo = dt[ which(dt$Region==reg), c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
names(oo) = c( "Year", "Licenses", "TAC (t)", "Landings (t)", "Effort (1000 th)", "CPUE (kg/th)" )
gt::gt(oo) |> gt::tab_options(table.font.size = 20, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
Effort
#| label: fig-effort-timeseries
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 8
#| fig-cap: "Temporal variations in fishing effort."
if (params$sens==1) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery" )
} else if (params$sens==2) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery", "split" )
}
include_graphics( file.path( ts_dir, "effort.ts.png" ) )
#| label: fig-effort-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 8
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab fishing effort from fisheries logbook data for previous and current years (no X 10$^3$ per 10 km X 10 km grid)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc0 = file.path( data_loc, "output", "maps", "logbook", "snowcrab", "annual", "effort" )
yrsplot = year_assessment + c(0:-3)
fn = file.path( loc0, paste( "effort", yrsplot, "png", sep=".") )
include_graphics( fn )
Landings
#| label: fig-landings-timeseries
#| eval: true
#| output: true
#| fig-cap: "Landings (t) of Snow Crab on the SSE. For 4X, the year refers to the starting year of the season. Inset is a closeup view of the timeseries for N-ENS and 4X."
#| fig-dpi: 144
#| fig-height: 8
if (params$sens==1) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery" )
} else if (params$sens==2) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery", "split" )
}
include_graphics( file.path( ts_dir, "landings.ts.png" ) )
#| label: fig-landings-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab landings from fisheries logbook data for previous and current years (tons per 10 km x 10 km grid)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc0 = file.path( data_loc, "output", "maps", "logbook", "snowcrab", "annual", "landings" )
yrsplot = year_assessment + c(0:-3)
fn = file.path( loc0, paste( "landings", yrsplot, "png", sep=".") )
include_graphics( fn )
Catch rates
#| label: fig-cpue-timeseries
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Temporal variations in crude catch rates of Snow Crab (kg/trap haul)."
if (params$sens==1) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery" )
} else if (params$sens==2) {
ts_dir = file.path( data_loc, "assessments", year_assessment, "timeseries", "fishery", "split" )
}
include_graphics( file.path( ts_dir, "cpue.ts.png" ) )
#| label: fig-cpue-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab crude catch rates on the Scotian Shelf for previous and current years. Units are kg/trap haul per 10 km x 10 km grid."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc0= file.path( data_loc, "output", "maps", "logbook", "snowcrab", "annual", "cpue" )
yrsplot = year_assessment + c(0:-3)
fn = file.path( loc0, paste( "cpue", yrsplot, "png", sep=".") )
include_graphics( fn )
Bycatch of non-target species
At sea observed trips
#| label: tbl-observed-summary
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 5
#| echo: false
#| layout-ncol: 1
#| tbl-cap: "Table of observed data coverage"
fns = c(
"observersummary2.png"
)
include_graphics( file.path( media_supplementary, fns ) )
#| label: fig-map-observer-locations
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab At-sea-observer locations."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc = file.path( data_loc, "output", "maps", "observer.locations" )
yrsplot = year_assessment + c(0:-3)
fns = paste( "observer.locations", yrsplot, "png", sep="." )
fn = file.path( loc, fns )
include_graphics( fn )
Discard of (all) soft-shell crab map
#| label: fig-observed-softshell-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Map of observed soft shell locations."
#| fig-subcap:
#| - "CFA23"
#| - "CFA24"
# #| - "N-ENS"
fns = c(
# "nens_soft_crab_positions_68.png" #
"cfa23_soft_crab_positions_68.png",
"cfa24_soft_crab_positions_68.png"
)
include_graphics( file.path( media_supplementary, fns ) )
Discard of non-target species ("Bycatch")
#| label: tbl-fishery-discard-effort-sens
#| eval: true
#| output: true
#| echo: false
#| tbl-cap: "Bycatch (kg) estimated from fisheries effort. Dots indicate values less than 10 kg/year. Where species exist in a list but there is no data, this indicates some historical bycatch. The overall average is from 2004 to present."
r = 2
reg = regions[r]
REG = reg_labels[r]
o = BC[[reg]]
oo = o$bycatch_table_effort
oo[ oo==0 ] = NA
oo[ is.na(oo) ] = "."
gt::gt(oo) |> gt::tab_options(table.font.size = 12, data_row.padding = gt::px(1),
summary_row.padding = gt::px(1), grand_summary_row.padding = gt::px(1),
footnotes.padding = gt::px(1), source_notes.padding = gt::px(1),
row_group.padding = gt::px(1))
- Average of 0.03% of landings; primarily other Crustacea (crab and lobster)
Environmental and climate change considerations
Bottom temperatures
-
Average bottom temperatures observed in the 2024 Snow Crab survey have returned to historical ranges, after worrisome highs in 2022.
-
Bottom temperatures are more stable in N-ENS than S-ENS; 4X exhibits the most erratic and highest annual mean bottom temperatures.
#| label: fig-bottom-temperatures-timeseries
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Annual variations in bottom temperature observed during the Snow Crab survey. The horizontal (black) line indicates the long-term, median temperature within each subarea. Error bars represent standard errors."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
tloc = file.path( data_loc, "assessments", year_assessment, "timeseries" )
fns = c(
file.path("survey", "t.png")
)
include_graphics( file.path( tloc, fns) )
#| label: fig-figures-temperature-bottom-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Bottom temperature ($^\\circ$C) observed during the Snow Crab survey."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab", "annual" )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, "t", paste( "t", map_years, "png", sep="." )
) )
include_graphics( fn )
#| label: fig-bottom-temperatures-timeseries-modelled
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Temporal variations in bottom temperature from a historical reanalysis of temperature data. Red horizontal line is at $7^\\circ$C. Presented are 95% Credible Intervals of spatial variability in temperature at each time slice after adjustment for spatiotemporal autocorrelation."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
tloc = file.path( data_loc, "assessments", year_assessment, "timeseries" )
fns = c(
"temperature_bottom.png"
)
include_graphics( file.path( tloc, fns) )
#| label: fig-figures-temperature-bottom-map-modelled
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Bottom temperature ($^\\circ$C) estimated from a historical analysis of temperature data for 1 September."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( data_root, 'aegis', 'temperature', 'modelled', 'default', 'maps' )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, paste( 'predictions.', map_years, '.0.75', '.png', sep='')
) )
include_graphics( fn )
Interspecific interactions
- Assumption: Elevated co-ocurrence in areal densities with snow crab trawl samples means greater potential encounter rates
#| label: fig-speciescomposition-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species ordination (PCA: eigenanalysis of correlation matrices). PC1 is associatd with bottom temperatures. PC2 is associated with depth. Snow crab is shown as an orange dot."
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.7, col="slateblue" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
Predators
#| label: tbl-predators
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "Main predators based upon frequency of occuence of snow crab in finfish stomach samples, unadjusted for sampling effort."
gt::gt(counts[1:11,])
#| label: fig-predator-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Main predators of snow crab on Scotian Shelf of Atlantic Canada (1999-2020). Relative location of snow crab predators (green) in the species composition ordination. Snow crab in orange. Of 58,287 finfish stomach samples, 159 had snow crab (0.28%). There is no information on snow crab diet in the database."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential predators:
lookup= c( "cod", "halibut", "sculpin", "skate", "plaice", "hake", "wolffish", "atlantic cod", "atlantic halibut", "longhorn sculpin", "thorny skate", "striped atlantic wolffish", "haddock", "american plaice", "smooth skate", "winter skate", "white hake", "shorthorn sculpin", "eelpout newfoundland", "squirrel or red hake", "sea raven", "ocean pout", "barndoor skate", "Squid" )
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.75, col="slategrey" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
-
Overall, Atlantic Halibut densities have increased rapidly since 2010 (The Gully, Slope Edge and near Sable Island).
-
Thorny skate densities have been increasing (especially in N-ENS and along the margins of Banquereau Bank)
-
Striped Atlantic Wolffish densities have been high (declining in N-ENS since 2007, peaking in Laurentian Channel).
-
Potential predators: in warmer and deeper (left and bottom of Snow Crab).
Prey
- echinoderms
- polychaete worms (Maldane, Nereis), worm-like animals
- detritus (dead organic matter)
- large zooplankton, shrimp
- juvenile crab (Rock Crab; Toad Crab; Lesser Toad Crab)
- Ocean Quahog (Artica islandica), bivalve molluscs (Mytilus sp, Modiolus, Hiatella)
- brittle stars (Ophiura, Ophiopholis)
- sea anemones (Edwardsia, Metridium).
#| label: fig-diet-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Relative location of snow crab prey (green) in the species composition ordination. Snow crab in orange. Most of the potental prey are found to the right of snow crab (i.e. colder-water species) at a variety of depths."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential food items:
lookup= c( "echinoderm", "polychaete", "maldane", "nereis", "shrimp", "pandalus", "rock crab", "toad crab", "lesser toad crab", "quahog", "artica islandica", "mollusc", "mytilus", "modiolus", "hiatella", "starfish", "sea anemone", "brittle star", "sea star", "sea anemone", "ophiura", "ophiopholis", "edwardsia", "metridium", "euphasid" )
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.75, col="slategrey" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
- Potential prey: in colder (right of Snow Crab).
Competitors
#| label: fig-competitor-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Potential competitors of snow crab on Scotian Shelf of Atlantic Canada (1999-2020). Relative location of snow crab predators (green) in the species composition ordination. Snow crab in orange."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential predators:
lookup= c( "pandalus", "Jonah Crab", "Atlantic Rock Crab" , "Toad Crab", "Hyas Coarctatus", "Northern Stone Crab" ) # add more here: most are not direct compeitors as they have slightly different depth/temp preferences
xlab = paste("PC1 (", pca$variance_percent[1], "%)", sep="" )
ylab = paste("PC2 (", pca$variance_percent[2], "%)", sep="" )
plot( PC2 ~ PC1, pcadata, type="n", xlab=xlab, ylab=ylab )
text( PC2 ~ PC1, labels=vern, data=pcadata, cex=0.75, col="slategrey" )
i = grep("Snow crab", pcadata$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
-
Northern shrimp co-occur as they share similar habitat preferences. Numerical densities have declined after a peak in 2011, especially in S-ENS.
-
Lesser toad crab is a co-occurring species and potential competitor. Their numbers have declined to low levels throughout, after a peak in densities in 2007 and 2016 in N-ENS.
Viable habitat
#| label: fig-fb-habitat-timeseries
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Habitat viability (probability; fishable Snow Crab). Means and 95\\% Credible Intervals are presented."
loc = file.path( data_loc, "modelled", "default_fb", "aggregated_habitat_timeseries" )
include_graphics( file.path( loc, "habitat_M0.png") )
#| label: fig-fb-habitat-map
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Habitat viability (probability; fishable Snow Crab)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_loc, "modelled", "default_fb", "predicted_habitat" )
vn = "habitat."
yrsplot = year_assessment + c(0:-9)
fns = file.path( loc, paste( vn, yrsplot, ".png", sep="") )
include_graphics( fns )
-
Nouanced: not just temperature, but joint distribution of temperature and other factors (substrate, depths, co-occuring species, ...)
-
SSE is variable Warm-Water incursions resulted in predators co-occupying Snow Crab habitats while simultaneously losing access to cold water preferring prey.
-
S-ENS: Viable habitat is highest even though temperatures are more stable and cooler in N-ENS. Currently, declined to near historical lows (peaks in 2012). Peaks near Sable and Missaine.
Stock status and trends
Data sources: Trawl Survey
Marine Protected Areas (St Ann's, Gully) were not sampled
- S-ENS: 282 stations completed
#| label: fig-survey-locations-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Survey locations."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
loc = file.path( data_loc, "output", "maps", "survey.locations" )
years = year_assessment + c(0:-3)
fn = check_file_exists( file.path( loc, paste( "survey.locations", years, "png", sep=".") ))
include_graphics( fn )
Growth patterns inferred from modes
#| label: fig-growth-modes-growth
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Inferred growth derived from *Kernel Mixture Models* (priors)."
#| fig-subcap:
#| - "Female growth trajectory"
#| - "Male growth trajectory"
fns = file.path( media_loc, c(
"plot_growth_female.png",
"plot_growth_male.png"
))
include_graphics( fns )
Recruitment: males
#| label: fig-sizefeq-male
#| eval: true
#| output: true
#| fig-cap: "Size-frequency (areal density; no/km$^2$) histograms by carapace width of male Snow Crab. The vertical line represents the legal size (95 mm). Immature animals are shown with light coloured bars, mature with dark."
#| fig-dpi: 144
#| fig-height: 10
if (params$sens==1) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey")
} else if (params$sens==2) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey", "split")
}
include_graphics( file.path( sf_outdir, "male.denl.png" ) )
- S-ENS: strong and stable size structure suggestive of a stable size/age structure; recruitment expected in 2025 and elevated soft-shell capture will require care
Reproduction (longer term)
#| label: fig-sizefeq-female
#| eval: true
#| output: true
#| fig-cap: "Size-frequency (areal density; no/km$^2$) histograms by carapace width of female Snow Crab. Immature animals are shown with light coloured bars, mature with dark."
#| fig-dpi: 144
#| fig-height: 10
if (params$sens==1) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey")
} else if (params$sens==2) {
sf_outdir = file.path( p$annual.results, "figures", "size.freq", "survey", "split")
}
fn = file.path( sf_outdir, "female.denl.png" )
include_graphics( fn )
S-ENS: - increase since 2021 - egg and larval production is expected to be high in the next year
#| label: fig-totno-female-mat-timeseries
#| eval: true
#| output: true
#| fig-cap: "The crude, unadjusted geometric mean of mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey. Error bars represent 95\\% Confidence Intervals. Note the absence of data in 2020. Prior to 2004, surveys were conducted in the Spring."
#| fig-dpi: 144
#| fig-height: 4
if (params$sens==1) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey")
} else if (params$sens==2) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey", "split")
}
fn = file.path( ts_outdir, paste("totno.female.mat", "png", sep=".") )
include_graphics( fn )
#| label: fig-totno-female-mat-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Mature female density log$_{10}$(no/km$^2$) from the Snow Crab survey."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual" )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, "totno.female.mat", paste( "totno.female.mat", map_years, "png", sep="." )
) )
include_graphics( fn )
Fishable biomass density
- S-ENS: densities have increased; increases mostly offshore
#| label: fig-R0-timeseries
#| eval: true
#| output: true
#| fig-cap: "The crude, unadjusted geometric mean fishable biomass density log$_{10}$(t/km$^2$) from the Snow Crab survey. Error bars represent 95\\% Confidence Intervals. Note the absence of data in 2020. Prior to 2004, surveys were conducted in the Spring."
#| fig-dpi: 144
#| fig-height: 4
if (params$sens==1) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey")
} else if (params$sens==2) {
ts_outdir = file.path( p$annual.results, "timeseries", "survey", "split")
}
fn = file.path( ts_outdir, paste("R0.mass", "png", sep=".") )
include_graphics( fn )
#| label: fig-R0-map
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Snow Crab survey fishable component biomass density log$_{10}$(t/km$^2$)."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
map_outdir = file.path( p$project.outputdir, "maps", "survey", "snowcrab","annual" )
map_years = year_assessment + c(0:-3)
fn = check_file_exists( file.path(
map_outdir, "R0.mass", paste( "R0.mass", map_years, "png", sep="." )
) )
include_graphics( fn )
Fishable biomass index
- S-ENS: three peaks; increased marginally; increase offshore (marginally, north of Sable)
#| label: fig-fbindex-timeseries
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "The fishable biomass index (t) predicted by CARSTM of Snow Crab survey densities. Error bars represent Bayesian 95\\% Credible Intervals. Note large errors in 2020 when there was no survey."
fn = file.path( data_loc, "modelled", "default_fb", "aggregated_biomass_timeseries" , "biomass_M0.png")
include_graphics( fn )
#| label: fig-fbindex-map
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Biomass index log~10(t/km$^2$) predicted from the Snow Crab survey."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_loc, "modelled", "default_fb", "predicted_biomass_densities" )
yrsplot = year_assessment + c(0:-9)
fns = file.path( loc, paste( "biomass", yrsplot, "png", sep=".") )
include_graphics( fns )
Modelled pre-fishery fishable biomass
- S-ENS: 41.5t, relative to 40.6kt in the previous year
#| 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 )
Posterior estimates of fishing mortality
-
S-ENS: F=0.17 (annual exploitation rate of 18.6%), F=0.18 (annual exploitation rate of 19%) in the previous year.
-
Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished (e.g., continental slope areas and western, inshore areas of CFA 24, 4X).
#| 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 )
Reference Points
- Limit Reference Point (LRP) = K/4
- Upper Stock Reference (USR) = K/2
-
Removal Reference (RR) = F_{MSY} = r/ 2
-
S-ENS is in the “healthy” zone, though with someoverlap with other zones
| | 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)) |
: Reference points from the logistic biomass dynamics fishery model. K is Carrying capacity (kt); and r is Intrinsic rate of increase (non-dimensional). Note that FMSY (fishing mortality associated with 'Maximum Sustainable Yield') is r/2. Similarly, BMSY (biomass associated with 'Maximum Sustainable Yield') is K/2. SD is posterior Standard deviations.* {#tbl-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. Prefishery."
# #| 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 )
Major sources of uncertainty
-
Capture of soft-shell Snow Crab (handling mortality)
-
Bycatch of Snow Crab in other fisheries (use as bait)
-
Illegal, unreported, and unregulated fishing activities
-
Marine Protected Areas (MPAs) act as a refuge from fishing activities. However, positive effects upon other organisms (predators or prey) can have counter-balancing indirect effects.
Conclusions
The SSE continues to experience rapid ecosystem and climatic variations. Under such conditions, it is prudent to be careful.
S-ENS: - Recruitment to the fishery continues at a sustainable rate matching total mortality. - Remains in the “healthy” zone.
Acknowledgements
-
Innovative Capital Investments Inc.
-
Captain Coalie D’Eon and crew of the FV Journey II for their expertise and provision of a safe and hospitable environment for the conduct of the survey.
-
Snow Crab license holders and fishers of the SSE a stellar model of a collaborative, sustainable and precautionary co-management of a fishery
END
:::
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