inst/markdown/04_ecosystem_summary.md
In jae0/snowcrab: Snow crab assessment suite using aegis* and associated projects and data streams.
title: "Snow crab ecosystem and life history"
keywords:
- snow crab ecosystem assessment
abstract: |
Snow crab ecosystem assessment 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", "ecosystem" )
{{< include _load_results.qmd >}}
$~$
Area of Interest: Scotian Shelf Ecosystem
#| label: fig-management-areas
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "The Scotian Shelf (NW Atlantic Ocean; NAFO Div. 4VWX). Shown are isobaths and major bathymetric features. Managed Crab Fishing Areas (CFAs; divided by dashed lines) include: NENS, SENS, 4X. SENS is further subdivided (dotted line) into 23 (NW) and 24 (SE)."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, "snowcrab_cfas.png" )
knitr::include_graphics( fn )
$~$
Life history
Photographs
#| label: fig-photos-snowcrab
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Snow Crab images. Note sexual dimorphism."
#| fig-subcap:
#| - "Pelagic zoea"
#| - "Male mature benthic form"
#| - "Mating pair - note sexual dimorphism, with a smaller female. Note epibiont growth on female which suggests it is an older female (multiparous)."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fns = file.path( media_loc, c(
"snowcrab_zoea.png",
"snowcrab_male.png" ,
"snowcrab_male_and_female.png"
) )
knitr::include_graphics( fns )
$~$
Life history stages
#| label: fig-lifehistory
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Life history patterns of snow crab and approximate timing of the main life history stages of snow crab and size (carapace width; CW mm) and instar (Roman numerals). Size and timings are specific to the area of study and vary with environmental conditions, food availability and genetic variability."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn1=file.path( media_loc, "life_history.png" )
knitr::include_graphics( fn1 )
Male growth stanzas
#| label: fig-growth-stanzas
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "The growth stanzas of the male component and decision paths to maturity and terminal moult. Black ellipses indicate terminally molted animals."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn1=file.path( media_loc, "life_history_male.png" )
knitr::include_graphics( fn1 )
Growth patterns inferred from size modes
#| label: fig-growth-modes
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Modes (CW, ln mm) identified from survey data using *Kernel Density Estmation* of local moving data windows. Legend: sex|maturity|instar"
#| fig-subcap:
#| - "Female modes"
#| - "Male modes"
fns = file.path( media_loc, c(
"density_f_imodes.png",
"density_m_imodes.png"
))
include_graphics( fns )
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 )
Spatial clustering
#| label: fig-aggregation
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Spider crab tend to cluster/aggregate in space."
#| fig-subcap:
#| - "Australian spider crab \\emph{Leptomithrax gaimardii} aggregation for moulting and migration."
#| - "Alaska red king crab \\emph{Paralithodes camtschaticus} aggregation in Alaska for egg release, migrations."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fns = file.path( media_loc, c(
"australian_leptomithrax_gaimardii.png",
"kingcrab_aggregation.png"
) )
knitr::include_graphics( fns )
-
Other crab species show "Mounding" for protection from predation (larval, moulting and females).
-
Narrow habitat preferences force them to move and cluster when environment is poor.
#| label: fig-aggregation2
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "High density locations of Snow Crab, approximately 1 per square meter."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path(p$project.outputdir, "maps", "map_highdensity_locations.png" )
knitr::include_graphics( fn )
Summary
- Sexual dimorphism
- Ontogenetic shifts (changes with age and stage) in habitat preferences:
- Pelagic in larval stages, with diurnal veritical migration
- Benthic in pre-adolescent and adult stages
- Complex substrates for small snow crab to muddy
- Adult females and smaller immature benthic crab have a preference for temperatures < 0 Celcius
- Adult males have a preference for < 4 Celcius
- Biannual, annual and biennial molts depending upon size/age and environmental conditions
- Terminal molt to maturity and survives for up to 5 years thereafter
- Total life span: up to 13 years (females) and 15 years (males)
- Cannibalism of immature crab by mature female snow crab are known
- Fecundity:
- Primiparous - 57.4 mm CW can produce between 35,000 to 46,000 eggs
- Multiparous - more than 100,000 eggs
- Eggs extruded February and April and brooded for up to two years
- more than 80% in the Area of Interest follow an annual cycle and hatched/released from April to June.
- Movement and connectivity
- pelagic and zoea stages -- no information: vertical migration related to temperature and light
- adult females -- no information: possibly related to food availability, temperature and predator avoidance
- adult males is long-tailed in distribution and possibly related to mate finding and food availablity
Ecosystem
Bathymetry
Data derived from multiple sources including Canadian Hydrographic Service, Snow crab surveys, Ground fish surveys, AZMP surveys. Modelled with stmv. Predicted surface and derived variables are used as covariates for habitat and abundance modelling.
#| label: fig-bathymetry
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Bathymetry (mean and standard deviation) in the Scotian Shelf region from an stmv analysis."
#| fig-subcap:
#| - "Predicted depth (log$_{10}$ m)"
#| - "Predicted slope (log$_{10}$ m / m)"
#| - "Predicted curvature (log$_{10}$ m / m$^2$)"
#| - "Obervation standard deviation (log$_{10}$ m)"
#| - "Spatial standard deviation (log$_{10}$ m)"
#| - "Spatial range (log$_{10}$ km)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
bdir = file.path(data_root, "aegis", "bathymetry", "modelled", "default", "stmv", "none_fft", "z", "maps", "canada.east.superhighres" )
fns = file.path( bdir, c(
"bathymetry.z.canada.east.superhighres.png",
"bathymetry.dZ.canada.east.superhighres.png",
"bathymetry.ddZ.canada.east.superhighres.png",
"bathymetry.b.sdObs.canada.east.superhighres.png",
"bathymetry.b.sdSpatial.canada.east.superhighres.png",
"bathymetry.b.localrange.canada.east.superhighres.png"
) )
knitr::include_graphics( fns)
$~$
Substrate
#| label: fig-substrate
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Substrate grain size log$_{10}$ (mm) variations (mean and standard deviation) in the Scotian Shelf region from a [carstm](https://github.com/jae0/carstm) analysis."
#| fig-subcap:
#| - "Mean prediction"
#| - "Standard deviation"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
substrdir = file.path( data_root, "aegis", "substrate", "modelled", "default", "maps" )
fns = file.path( substrdir, c(
"predictions.png",
"space_re_total.png"
) )
knitr::include_graphics( fns)
Rapid climate change
#| label: fig-rapid-climate-change
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Global surface (2 meter) air temperature. Source: [The Crisis Report](https://richardcrim.substack.com/p/the-crisis-report-99) and [James E. Hansen](https://www.columbia.edu/~jeh1/mailings/2024/ICJ.PressBriefing.09December2024.pdf). Note 2023 was and El Nino year."
#| fig-subcap:
#| - "Anomalies relative to pre-industrial baseline."
#| - "Seasonal variations by year."
#| - "Annual temperatures."
fn = file.path( media_loc, c(
"gst_anomaly.png",
"gst_seasonal.png",
"gst_ts.png"
) )
knitr::include_graphics( fn )
$~$
Ocean productivity: Chlorophyll-a (satellite-based)
#| label: fig-ocean-productivity-chla
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Chlorphyll-a in the NW Atlantic. Source: [Copernicus Marine Service](https://marine.copernicus.eu/access-data/ocean-monitoring-indicators/chlorophyll-and-primary-production)"
#| fig-subcap:
#| - "Full timeseries of surface Chl-a estimated from satellite imagery."
#| - "Trends in surface Chl-a over time."
fn = file.path( media_loc, c(
"copernicus_chla.png",
"copernicus_chla_map.png"
) )
knitr::include_graphics( fn )
$~$
Ocean currents
A visualization of The Thermohaline Circulation - The Great Ocean Conveyor Belt.
#| label: fig-ocean-currents
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Ocean currents in the Maritimes. Source: [DFO](https://www.dfo-mpo.gc.ca/oceans/publications/soto-rceo/2018/atlantic-ecosystems-ecosystemes-atlantiques/index-eng.html)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, c(
"maritimes_currents.png"
) )
knitr::include_graphics( fn )
$~$
Strength of the Atlantic Meridional Overturning Circulation (AMOC):
#| label: fig-ocean-currents-amoc-strength
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Strength of the Atlantic Meridional Overturning Circulation (AMOC). Top is full time-eries. Bottom is the annual average. Source: [Copernicus Marine Service](https://marine.copernicus.eu/access-data/ocean-monitoring-indicators/atlantic-meridional-overturning-circulation-amoc-timeseries)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, c(
"GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries-hq.png"
) )
knitr::include_graphics( fn )
$~$
Expectations if the Atlantic Meridional Overturning Circulation (AMOC) collapses:
#| label: fig-ocean-currents-amoc-expectation
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Expectations if the Atlantic Meridional Overturning Circulation (AMOC) collapses. Source: [Icelandic Met Office](https://en.vedur.is/media/ads_in_header/AMOC-letter_Final.pdf)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, c(
"amoc_letter.png"
) )
knitr::include_graphics( fn )
$~$
Bottom Temperature
#| label: fig-bottom-temperatures-ts
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Bottom temperatures"
#| fig-subcap:
#| - "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."
#| - "Posterior densities of predicted average bottom temperatures. Red horizontal line is at $7^\\circ$C."
#| 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"),
"temperature_bottom.png"
)
knitr::include_graphics( file.path( tloc, fns) )
#| label: fig-bottom-temperatures-map
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September for specified years."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_root, "aegis", "temperature", "modelled", "default", "maps" )
yrsplot = year_assessment + c(0:-9)
fns = file.path( loc, paste( "predictions.", yrsplot, ".0.75", ".png", sep="") )
knitr::include_graphics( fns )
- Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain.
- Snow crab surveys were not conducted in 2020, and incomplete in 2022 for S-ENS.
$~$
Species composition
This is a Principle Component Analysis. That is, an eigen-decomposition of relative abundance on standard deviation scale or "Z-score", with additional constraint of being positive valued after a log transformation and including zero-values.
Ordination
#| 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" )
Time series
#| label: fig-speciescomposition-ts
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species composition in time. Primary gradient (PC1) is related to bottom temperatures; second (PC2) to depth. Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain. Snow crab surveys were not conducted in 2020, and incomplete in 2022 in S-ENS."
#| fig-subcap:
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
pc = c(1, 2)
spc_loc = file.path( data_root, "aegis", "speciescomposition", "modelled", "default" )
fnpr = file.path( spc_loc, "figures", paste("pca", pc, "_time.png", sep="" ) )
knitr::include_graphics( fnpr )
Maps: PC1
#| label: fig-speciescomposition-map-pc1
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species composition (PC1) in space. Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain. Snow crab surveys were not conducted in 2020, and incomplete in 2022 in S-ENS."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
yrsplot = year_assessment + c(0:-3)
vn = "pca1"
spc_loc = file.path( data_root, "aegis", "speciescomposition", "modelled", "default", "maps" )
fns = file.path( spc_loc, paste( vn, "predictions", yrsplot, "png", sep=".") )
knitr::include_graphics( fns )
Maps: PC2
#| label: fig-speciescomposition-map-pc2
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species composition (PC2) in space. Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain. Snow crab surveys were not conducted in 2020, and incomplete in 2022 in S-ENS."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
yrsplot = year_assessment + c(0:-3)
vn = "pca2"
spc_loc = file.path( data_root, "aegis", "speciescomposition", "modelled", "default", "maps" )
fns = file.path( spc_loc, paste( vn, "predictions", yrsplot, "png", sep=".") )
knitr::include_graphics( fns )
Predators
Most predators of snow crab are species associated with warmer water conditions (PC1 left of snow crab; exception: Skates, Scuplins and Ocean Pout) and deeper in distribution.
#| 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$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
#| label: tbl-predators
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "Main predators based upon frequency of occuence of snow crab in finfish stomach samples, unadjusted for sampling effort."
gt::gt(counts[1:11,])
Prey
Prey items in the literature include:
- echinoderms
- polychaete worms (Maldane, Nereis), worm-like animals
- detritus (dead organic matter)
- large zooplankton, shrimp
- juvenile crab (Rock Crab; Toad Crab; Lesser Toad Crab)
- Ocean Quahog (Artica islandica), bivalve molluscs (Mytilus sp, Modiolus, Hiatella)
- brittle stars (Ophiura, Ophiopholis)
- sea anemones (Edwardsia, Metridium).
Most of the potental prey are found to the right of snow crab (i.e. colder-water species) at a variety of depths.
#| label: fig-diet-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Relative location of snow crab prey (green) in the species composition ordination. Snow crab in orange."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential food items:
lookup= c( "echinoderm", "polychaete", "maldane", "nereis", "shrimp", "pandalus", "rock crab", "toad crab", "lesser toad crab", "quahog", "artica islandica", "mollusc", "mytilus", "modiolus", "hiatella", "starfish", "sea anemone", "brittle star", "sea star", "sea anemone", "ophiura", "ophiopholis", "edwardsia", "metridium", "euphasid" )
xlab = paste("PC1 (", pca$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$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
Competitors
Some potential competition from other detritivorous species such as shrimp are possible. However, they tend to have slightly colder-water preferences, with the exception of Pandalus borealis (Northern Shrimp). Other large crab are potential competitors: Jonah Crab, Atlantic Rock Crab, Toad Crab, Hyas Coarctatus, Northern Stone Crab, however, they also have slightly different depth and temperature preferences.
#| label: fig-competitor-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Potential competitors of snow crab on Scotian Shelf of Atlantic Canada (1999-2020). Relative location of snow crab predators (green) in the species composition ordination. Snow crab in orange."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential predators:
lookup= c( "pandalus", "Jonah Crab", "Atlantic Rock Crab" , "Toad Crab", "Hyas Coarctatus", "Northern Stone Crab" ) # add more here: most are not direct compeitors as they have slightly different depth/temp preferences
xlab = paste("PC1 (", pca$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" )
Disease
#| label: tbl-bcd
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "[Bitter Crab Disease in Maritimes Region](https://www.dfo-mpo.gc.ca/science/aah-saa/diseases-maladies/hematcb-eng.html) is a dinoflagellate (*Hematodinium*) that causes muscle degeneration. They are widespread (Alaska, NW Atlantic, Greenland) and usually found in warm-water, physiologically stressful conditions. In th Maritimes, it seems to be a low level background infection, found everywhere in the fishing grounds.."
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 10
include_graphics( file.path( data_loc, "output", "bcd.png") )
#| label: fig-bcd-map
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Bitter crab disease observations since 2008"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fns = file.path( media_loc, c(
"BCD_map.png"
) )
knitr::include_graphics( fns )
$~$
Connectivity: Movement
#| label: fig-movement-tracks
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Snow Crab movement"
#| fig-subcap:
#| - "Tracks from 1996-2004"
#| - "Tracks from 2004 - present"
#| - "Distance between mark and recapture (km)"
#| - "Minimum speed for each mark-recapture event (km/month)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| layout: [[100], [100], [50,50] ]
fns = file.path( media_loc, c(
"movement0.png",
"movement.png" ,
"snowcrab_movement_distances.png",
"snowcrab_movement_rates.png"
) )
knitr::include_graphics( fns )
$~$
Ecosystem changes: summary
- SSE: many species changes, snow crab are long-lived so interact with many of them
- Stomach samples: Atlantic Halibut, Atlantic Wolffish, Thorny Skate as primary predators
- Skewed sex ratios (few mature females): possibly linked to differential predation mortality (mature females being much smaller and full of fat-rich gonads and eggs).
-
Halibut (DFO 2018)
-
Increased in abundance in the Region
- Elevated co-ocurrence in areal densities with snow crab trawl samples means greater encounter rates
Human interactions
Every known population is exploited worldwide
- Human consumption (males; > 95mm CW) due to higher meat yield and sexual dimorphism
- Bait in other fisheries (illegal in Canada, but still present)
- Fertilizer in agriculture
- Chitosan: glucosamine polysaccharide derived from chitin
- agent to slow bleeding from wounds (Zhang et al. 2015, Moghadas et al. 2016)
- agriculturally as natural fungicides and bactericides (Linden & Stoner 2007)
- plastic replacement (Tampieri et al. 2003)
- battery electrolyte (Poosapati et al. 2021).
Management Approach
- Precautionary Approach
- Industry - DFO lead since 1996
- Formal PA and Havest Control Rules in IFMP, since 2012
- Fish Stock Provisions, 2022
- Spatial refugia (slope edge, MPAs)
- Temporal refugia (fishing seasons)
-
Biological refugia: most life stages protected
-
Conservative exploitation since mid-2000s
- Spawning stock legally and completely protected
-
Market-driven protection for 10+ yrs
-
Evidence-based decision making: trawl survey, assessment
- Distributed knowledge network: traditional, historical, scientific
- Soft-shell Protocol
- Satellite VMS; biodegradeable mesh (ghost-fishing); weighted lines (entanglement), etc.
Snow crab fishery summary
See the Fishery performance and status in fishery summary for more detailed information.
Science survey
Stock status of snow crab is based largely upon a directed trawl survey that is conducted annually in the autumn. See the Survey result summary for more information of collected data and basic statisitics.
Synthesis
Viable Habitat
Temperature and depth
#| label: fig-temp-depth
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Habitat preferences associated with depth and temperature."
fn2=file.path( media_loc, "viable_habitat_depth_temp.png" )
knitr::include_graphics( c( fn2 ) )
Spatial random effects
#| label: fig-viable-habitat-persistent
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Persistent habitat, independent of temperature, time, etc."
fn1 = file.path( media_loc, "viable_habitat.png" )
knitr::include_graphics( c(fn1 ) )
Predicted habitat maps
#| 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 )
Predicted habitat timeseries
#| 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") )
Biomass density
Biomass density maps
Note that high and low biomass density areas fluctuate with time
#| label: fig-fbgeomean-map
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Snow Crab survey fishable component biomass density log~10(t/km$^2$). Note, there is no data in 2020."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_loc, "output", "maps", "survey", "snowcrab", "annual", "R0.mass")
yrsplot = setdiff(year_assessment + c(0:-9), 2020 )
fns = file.path( loc, paste( "R0.mass", yrsplot, "png", sep=".") )
include_graphics( fns )
Biomass density timeseries
- A peak in 2009 to 2014 and has since been declining in all areas.
#| label: fig-fbGMTS
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| 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."
fn = file.path(data_loc, "assessments", year_assessment, "timeseries","survey","R0.mass.png")
include_graphics( fn )
Biomass Index maps
A contraction of spatial range in 4X and the western parts of S-ENS were also evident in 2021 to 2022.
#| 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 )
Biomass Index (aggregate)
#| label: fig-fbindex-timeeries
#| 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 )
Fishery modelled results and advise
To be reviewed by CSAS (Feb 2025).
References
Banerjee, S., Carlin, B. P., and Gelfand, A. E.. 2004. Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability. Chapman and Hall/CRC.
Besag, Julian. 1974. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society Series B (Methodological) 1974: 192-236.
Canada Gazette. 2022. Regulations Amending the Fishery (General) Regulations. Part II, Volume 156, Number 8.
Canada Gazette. 2016. St. Anns Bank Marine Protected Area Regulations. Canada Gazette, Part I, Vol 150, Issue 51: 4143-4149.
Choi, J.S. 2020. A Framework for the assessment of Snow Crab (Chioneocete opilio) in Maritimes Region (NAFO Div 4VWX) . DFO Can. Sci. Advis. Sec. Res. Doc. 2020/nnn. v + xxx p.
Choi, J.S. 2022. Reconstructing the Decline of Atlantic Cod with the Help of Environmental Variability in the Scotian Shelf of Canada. bioRxiv. [https://doi.org/10.1101/2022.05.05.490753](https://doi.org/10.1101/2022.05.05.490753].
Choi, J. S., and B. C. Patten. 2001. Sustainable Development: Lessons from the Paradox of Enrichment. Ecosystem Health 7: 163–77.
Choi, Jae S., B. Cameron, K. Christie, A. Glass, and E. MacEachern. 2022. Temperature and Depth Dependence of the Spatial Distribution of Snow Crab. bioRxiv. https://doi.org/10.1101/2022.12.20.520893.
Choi, Jae S. 2023. A Multi-Stage, Delay Differential Model of Snow Crab Population Dynamics in the Scotian Shelf of Atlantic Canada. bioRxiv. https://doi.org/10.1101/2023.02.13.528296.
DFO. 2018. Stock Status Update of Atlantic Halibut (Hippoglossus hippoglossus) on the Scotian Shelf and Southern Grand Banks in NAFO Divisions 3NOPs4VWX5Zc. DFO Can. Sci. Advis. Sec. Sci. Resp. 2018/022.
Hebert M, Miron G, Moriyasu M, Vienneau R, and DeGrace P. Efficiency and ghost fishing of Snow Crab (Chionoecetes opilio) traps in the Gulf of St. Lawrence. Fish Res. 2001; 52(3): 143-153. 10.1016/S0165-7836(00)00259-9
Riebler, A., Sørbye, S.H., Simpson D., and Rue, H. 2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical methods in medical research 25: 1145-1165.
Simpson, D., Rue, H., Riebler, A., Martins, T.G., and Sørbye, SH. 2017. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors. Statist. Sci. 32: 1-28.
END
jae0/snowcrab documentation built on Feb. 20, 2025, 2:27 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
title: "Snow crab ecosystem and life history" keywords: - snow crab ecosystem assessment abstract: | Snow crab ecosystem assessment 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", "ecosystem" )
{{< include _load_results.qmd >}}
$~$
Area of Interest: Scotian Shelf Ecosystem
#| label: fig-management-areas
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "The Scotian Shelf (NW Atlantic Ocean; NAFO Div. 4VWX). Shown are isobaths and major bathymetric features. Managed Crab Fishing Areas (CFAs; divided by dashed lines) include: NENS, SENS, 4X. SENS is further subdivided (dotted line) into 23 (NW) and 24 (SE)."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, "snowcrab_cfas.png" )
knitr::include_graphics( fn )
$~$
Life history
Photographs
#| label: fig-photos-snowcrab
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Snow Crab images. Note sexual dimorphism."
#| fig-subcap:
#| - "Pelagic zoea"
#| - "Male mature benthic form"
#| - "Mating pair - note sexual dimorphism, with a smaller female. Note epibiont growth on female which suggests it is an older female (multiparous)."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fns = file.path( media_loc, c(
"snowcrab_zoea.png",
"snowcrab_male.png" ,
"snowcrab_male_and_female.png"
) )
knitr::include_graphics( fns )
$~$
Life history stages
#| label: fig-lifehistory
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Life history patterns of snow crab and approximate timing of the main life history stages of snow crab and size (carapace width; CW mm) and instar (Roman numerals). Size and timings are specific to the area of study and vary with environmental conditions, food availability and genetic variability."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn1=file.path( media_loc, "life_history.png" )
knitr::include_graphics( fn1 )
Male growth stanzas
#| label: fig-growth-stanzas
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "The growth stanzas of the male component and decision paths to maturity and terminal moult. Black ellipses indicate terminally molted animals."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn1=file.path( media_loc, "life_history_male.png" )
knitr::include_graphics( fn1 )
Growth patterns inferred from size modes
#| label: fig-growth-modes
#| eval: true
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| echo: false
#| layout-ncol: 2
#| fig-cap: "Modes (CW, ln mm) identified from survey data using *Kernel Density Estmation* of local moving data windows. Legend: sex|maturity|instar"
#| fig-subcap:
#| - "Female modes"
#| - "Male modes"
fns = file.path( media_loc, c(
"density_f_imodes.png",
"density_m_imodes.png"
))
include_graphics( fns )
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 )
Spatial clustering
#| label: fig-aggregation
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Spider crab tend to cluster/aggregate in space."
#| fig-subcap:
#| - "Australian spider crab \\emph{Leptomithrax gaimardii} aggregation for moulting and migration."
#| - "Alaska red king crab \\emph{Paralithodes camtschaticus} aggregation in Alaska for egg release, migrations."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fns = file.path( media_loc, c(
"australian_leptomithrax_gaimardii.png",
"kingcrab_aggregation.png"
) )
knitr::include_graphics( fns )
-
Other crab species show "Mounding" for protection from predation (larval, moulting and females).
-
Narrow habitat preferences force them to move and cluster when environment is poor.
#| label: fig-aggregation2
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "High density locations of Snow Crab, approximately 1 per square meter."
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path(p$project.outputdir, "maps", "map_highdensity_locations.png" )
knitr::include_graphics( fn )
Summary
- Sexual dimorphism
- Ontogenetic shifts (changes with age and stage) in habitat preferences:
- Pelagic in larval stages, with diurnal veritical migration
- Benthic in pre-adolescent and adult stages
- Complex substrates for small snow crab to muddy
- Adult females and smaller immature benthic crab have a preference for temperatures < 0 Celcius
- Adult males have a preference for < 4 Celcius
- Biannual, annual and biennial molts depending upon size/age and environmental conditions
- Terminal molt to maturity and survives for up to 5 years thereafter
- Total life span: up to 13 years (females) and 15 years (males)
- Cannibalism of immature crab by mature female snow crab are known
- Fecundity:
- Primiparous - 57.4 mm CW can produce between 35,000 to 46,000 eggs
- Multiparous - more than 100,000 eggs
- Eggs extruded February and April and brooded for up to two years
- more than 80% in the Area of Interest follow an annual cycle and hatched/released from April to June.
- Movement and connectivity
- pelagic and zoea stages -- no information: vertical migration related to temperature and light
- adult females -- no information: possibly related to food availability, temperature and predator avoidance
- adult males is long-tailed in distribution and possibly related to mate finding and food availablity
Ecosystem
Bathymetry
Data derived from multiple sources including Canadian Hydrographic Service, Snow crab surveys, Ground fish surveys, AZMP surveys. Modelled with stmv. Predicted surface and derived variables are used as covariates for habitat and abundance modelling.
#| label: fig-bathymetry
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Bathymetry (mean and standard deviation) in the Scotian Shelf region from an stmv analysis."
#| fig-subcap:
#| - "Predicted depth (log$_{10}$ m)"
#| - "Predicted slope (log$_{10}$ m / m)"
#| - "Predicted curvature (log$_{10}$ m / m$^2$)"
#| - "Obervation standard deviation (log$_{10}$ m)"
#| - "Spatial standard deviation (log$_{10}$ m)"
#| - "Spatial range (log$_{10}$ km)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
bdir = file.path(data_root, "aegis", "bathymetry", "modelled", "default", "stmv", "none_fft", "z", "maps", "canada.east.superhighres" )
fns = file.path( bdir, c(
"bathymetry.z.canada.east.superhighres.png",
"bathymetry.dZ.canada.east.superhighres.png",
"bathymetry.ddZ.canada.east.superhighres.png",
"bathymetry.b.sdObs.canada.east.superhighres.png",
"bathymetry.b.sdSpatial.canada.east.superhighres.png",
"bathymetry.b.localrange.canada.east.superhighres.png"
) )
knitr::include_graphics( fns)
$~$
Substrate
#| label: fig-substrate
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Substrate grain size log$_{10}$ (mm) variations (mean and standard deviation) in the Scotian Shelf region from a [carstm](https://github.com/jae0/carstm) analysis."
#| fig-subcap:
#| - "Mean prediction"
#| - "Standard deviation"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
substrdir = file.path( data_root, "aegis", "substrate", "modelled", "default", "maps" )
fns = file.path( substrdir, c(
"predictions.png",
"space_re_total.png"
) )
knitr::include_graphics( fns)
Rapid climate change
#| label: fig-rapid-climate-change
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Global surface (2 meter) air temperature. Source: [The Crisis Report](https://richardcrim.substack.com/p/the-crisis-report-99) and [James E. Hansen](https://www.columbia.edu/~jeh1/mailings/2024/ICJ.PressBriefing.09December2024.pdf). Note 2023 was and El Nino year."
#| fig-subcap:
#| - "Anomalies relative to pre-industrial baseline."
#| - "Seasonal variations by year."
#| - "Annual temperatures."
fn = file.path( media_loc, c(
"gst_anomaly.png",
"gst_seasonal.png",
"gst_ts.png"
) )
knitr::include_graphics( fn )
$~$
Ocean productivity: Chlorophyll-a (satellite-based)
#| label: fig-ocean-productivity-chla
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Chlorphyll-a in the NW Atlantic. Source: [Copernicus Marine Service](https://marine.copernicus.eu/access-data/ocean-monitoring-indicators/chlorophyll-and-primary-production)"
#| fig-subcap:
#| - "Full timeseries of surface Chl-a estimated from satellite imagery."
#| - "Trends in surface Chl-a over time."
fn = file.path( media_loc, c(
"copernicus_chla.png",
"copernicus_chla_map.png"
) )
knitr::include_graphics( fn )
$~$
Ocean currents
A visualization of The Thermohaline Circulation - The Great Ocean Conveyor Belt.
#| label: fig-ocean-currents
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Ocean currents in the Maritimes. Source: [DFO](https://www.dfo-mpo.gc.ca/oceans/publications/soto-rceo/2018/atlantic-ecosystems-ecosystemes-atlantiques/index-eng.html)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, c(
"maritimes_currents.png"
) )
knitr::include_graphics( fn )
$~$
Strength of the Atlantic Meridional Overturning Circulation (AMOC):
#| label: fig-ocean-currents-amoc-strength
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Strength of the Atlantic Meridional Overturning Circulation (AMOC). Top is full time-eries. Bottom is the annual average. Source: [Copernicus Marine Service](https://marine.copernicus.eu/access-data/ocean-monitoring-indicators/atlantic-meridional-overturning-circulation-amoc-timeseries)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, c(
"GLOBAL_OMI_NATLANTIC_amoc_max26N_timeseries-hq.png"
) )
knitr::include_graphics( fn )
$~$
Expectations if the Atlantic Meridional Overturning Circulation (AMOC) collapses:
#| label: fig-ocean-currents-amoc-expectation
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Expectations if the Atlantic Meridional Overturning Circulation (AMOC) collapses. Source: [Icelandic Met Office](https://en.vedur.is/media/ads_in_header/AMOC-letter_Final.pdf)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fn = file.path( media_loc, c(
"amoc_letter.png"
) )
knitr::include_graphics( fn )
$~$
Bottom Temperature
#| label: fig-bottom-temperatures-ts
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Bottom temperatures"
#| fig-subcap:
#| - "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."
#| - "Posterior densities of predicted average bottom temperatures. Red horizontal line is at $7^\\circ$C."
#| 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"),
"temperature_bottom.png"
)
knitr::include_graphics( file.path( tloc, fns) )
#| label: fig-bottom-temperatures-map
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 4
#| fig-cap: "Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September for specified years."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_root, "aegis", "temperature", "modelled", "default", "maps" )
yrsplot = year_assessment + c(0:-9)
fns = file.path( loc, paste( "predictions.", yrsplot, ".0.75", ".png", sep="") )
knitr::include_graphics( fns )
- Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain.
- Snow crab surveys were not conducted in 2020, and incomplete in 2022 for S-ENS.
$~$
Species composition
This is a Principle Component Analysis. That is, an eigen-decomposition of relative abundance on standard deviation scale or "Z-score", with additional constraint of being positive valued after a log transformation and including zero-values.
Ordination
#| 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" )
Time series
#| label: fig-speciescomposition-ts
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species composition in time. Primary gradient (PC1) is related to bottom temperatures; second (PC2) to depth. Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain. Snow crab surveys were not conducted in 2020, and incomplete in 2022 in S-ENS."
#| fig-subcap:
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
pc = c(1, 2)
spc_loc = file.path( data_root, "aegis", "speciescomposition", "modelled", "default" )
fnpr = file.path( spc_loc, "figures", paste("pca", pc, "_time.png", sep="" ) )
knitr::include_graphics( fnpr )
Maps: PC1
#| label: fig-speciescomposition-map-pc1
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species composition (PC1) in space. Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain. Snow crab surveys were not conducted in 2020, and incomplete in 2022 in S-ENS."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
yrsplot = year_assessment + c(0:-3)
vn = "pca1"
spc_loc = file.path( data_root, "aegis", "speciescomposition", "modelled", "default", "maps" )
fns = file.path( spc_loc, paste( vn, "predictions", yrsplot, "png", sep=".") )
knitr::include_graphics( fns )
Maps: PC2
#| label: fig-speciescomposition-map-pc2
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Species composition (PC2) in space. Groundfish surveys were not conducted in 2020 and 2022 in the snow crab domain. Snow crab surveys were not conducted in 2020, and incomplete in 2022 in S-ENS."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
yrsplot = year_assessment + c(0:-3)
vn = "pca2"
spc_loc = file.path( data_root, "aegis", "speciescomposition", "modelled", "default", "maps" )
fns = file.path( spc_loc, paste( vn, "predictions", yrsplot, "png", sep=".") )
knitr::include_graphics( fns )
Predators
Most predators of snow crab are species associated with warmer water conditions (PC1 left of snow crab; exception: Skates, Scuplins and Ocean Pout) and deeper in distribution.
#| 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$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
#| label: tbl-predators
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "Main predators based upon frequency of occuence of snow crab in finfish stomach samples, unadjusted for sampling effort."
gt::gt(counts[1:11,])
Prey
Prey items in the literature include:
- echinoderms
- polychaete worms (Maldane, Nereis), worm-like animals
- detritus (dead organic matter)
- large zooplankton, shrimp
- juvenile crab (Rock Crab; Toad Crab; Lesser Toad Crab)
- Ocean Quahog (Artica islandica), bivalve molluscs (Mytilus sp, Modiolus, Hiatella)
- brittle stars (Ophiura, Ophiopholis)
- sea anemones (Edwardsia, Metridium).
Most of the potental prey are found to the right of snow crab (i.e. colder-water species) at a variety of depths.
#| label: fig-diet-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Relative location of snow crab prey (green) in the species composition ordination. Snow crab in orange."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential food items:
lookup= c( "echinoderm", "polychaete", "maldane", "nereis", "shrimp", "pandalus", "rock crab", "toad crab", "lesser toad crab", "quahog", "artica islandica", "mollusc", "mytilus", "modiolus", "hiatella", "starfish", "sea anemone", "brittle star", "sea star", "sea anemone", "ophiura", "ophiopholis", "edwardsia", "metridium", "euphasid" )
xlab = paste("PC1 (", pca$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$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
Competitors
Some potential competition from other detritivorous species such as shrimp are possible. However, they tend to have slightly colder-water preferences, with the exception of Pandalus borealis (Northern Shrimp). Other large crab are potential competitors: Jonah Crab, Atlantic Rock Crab, Toad Crab, Hyas Coarctatus, Northern Stone Crab, however, they also have slightly different depth and temperature preferences.
#| label: fig-competitor-biplot
#| eval: true
#| echo: false
#| output: true
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 6
#| fig-cap: "Potential competitors of snow crab on Scotian Shelf of Atlantic Canada (1999-2020). Relative location of snow crab predators (green) in the species composition ordination. Snow crab in orange."
#| fig-subcap:
#| - "Total random effect (space and space-time) - PC1"
#| - "Total random effect (space and space-time) - PC1"
#| - "Mean annual PC1 score."
#| - "Mean annual PC2 score."
#| layout-ncol: 1
# potential predators:
lookup= c( "pandalus", "Jonah Crab", "Atlantic Rock Crab" , "Toad Crab", "Hyas Coarctatus", "Northern Stone Crab" ) # add more here: most are not direct compeitors as they have slightly different depth/temp preferences
xlab = paste("PC1 (", pca$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" )
Disease
#| label: tbl-bcd
#| echo: false
#| eval: true
#| output: true
#| tbl-cap: "[Bitter Crab Disease in Maritimes Region](https://www.dfo-mpo.gc.ca/science/aah-saa/diseases-maladies/hematcb-eng.html) is a dinoflagellate (*Hematodinium*) that causes muscle degeneration. They are widespread (Alaska, NW Atlantic, Greenland) and usually found in warm-water, physiologically stressful conditions. In th Maritimes, it seems to be a low level background infection, found everywhere in the fishing grounds.."
#| fig.show: hold
#| fig-dpi: 144
#| fig-height: 10
include_graphics( file.path( data_loc, "output", "bcd.png") )
#| label: fig-bcd-map
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Bitter crab disease observations since 2008"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
fns = file.path( media_loc, c(
"BCD_map.png"
) )
knitr::include_graphics( fns )
$~$
Connectivity: Movement
#| label: fig-movement-tracks
#| eval: true
#| echo: false
#| output: true
#| fig-cap: "Snow Crab movement"
#| fig-subcap:
#| - "Tracks from 1996-2004"
#| - "Tracks from 2004 - present"
#| - "Distance between mark and recapture (km)"
#| - "Minimum speed for each mark-recapture event (km/month)"
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| layout: [[100], [100], [50,50] ]
fns = file.path( media_loc, c(
"movement0.png",
"movement.png" ,
"snowcrab_movement_distances.png",
"snowcrab_movement_rates.png"
) )
knitr::include_graphics( fns )
$~$
Ecosystem changes: summary
- SSE: many species changes, snow crab are long-lived so interact with many of them
- Stomach samples: Atlantic Halibut, Atlantic Wolffish, Thorny Skate as primary predators
- Skewed sex ratios (few mature females): possibly linked to differential predation mortality (mature females being much smaller and full of fat-rich gonads and eggs).
-
Halibut (DFO 2018)
-
Increased in abundance in the Region
- Elevated co-ocurrence in areal densities with snow crab trawl samples means greater encounter rates
Human interactions
Every known population is exploited worldwide
- Human consumption (males; > 95mm CW) due to higher meat yield and sexual dimorphism
- Bait in other fisheries (illegal in Canada, but still present)
- Fertilizer in agriculture
- Chitosan: glucosamine polysaccharide derived from chitin
- agent to slow bleeding from wounds (Zhang et al. 2015, Moghadas et al. 2016)
- agriculturally as natural fungicides and bactericides (Linden & Stoner 2007)
- plastic replacement (Tampieri et al. 2003)
- battery electrolyte (Poosapati et al. 2021).
Management Approach
- Precautionary Approach
- Industry - DFO lead since 1996
- Formal PA and Havest Control Rules in IFMP, since 2012
- Fish Stock Provisions, 2022
- Spatial refugia (slope edge, MPAs)
- Temporal refugia (fishing seasons)
-
Biological refugia: most life stages protected
-
Conservative exploitation since mid-2000s
- Spawning stock legally and completely protected
-
Market-driven protection for 10+ yrs
-
Evidence-based decision making: trawl survey, assessment
- Distributed knowledge network: traditional, historical, scientific
- Soft-shell Protocol
- Satellite VMS; biodegradeable mesh (ghost-fishing); weighted lines (entanglement), etc.
Snow crab fishery summary
See the Fishery performance and status in fishery summary for more detailed information.
Science survey
Stock status of snow crab is based largely upon a directed trawl survey that is conducted annually in the autumn. See the Survey result summary for more information of collected data and basic statisitics.
Synthesis
Viable Habitat
Temperature and depth
#| label: fig-temp-depth
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Habitat preferences associated with depth and temperature."
fn2=file.path( media_loc, "viable_habitat_depth_temp.png" )
knitr::include_graphics( c( fn2 ) )
Spatial random effects
#| label: fig-viable-habitat-persistent
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Persistent habitat, independent of temperature, time, etc."
fn1 = file.path( media_loc, "viable_habitat.png" )
knitr::include_graphics( c(fn1 ) )
Predicted habitat maps
#| 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 )
Predicted habitat timeseries
#| 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") )
Biomass density
Biomass density maps
Note that high and low biomass density areas fluctuate with time
#| label: fig-fbgeomean-map
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| fig-cap: "Snow Crab survey fishable component biomass density log~10(t/km$^2$). Note, there is no data in 2020."
#| fig-subcap:
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| - ""
#| layout: [[100], [100], [50,50], [50,50], [50,50], [50,50]]
loc = file.path( data_loc, "output", "maps", "survey", "snowcrab", "annual", "R0.mass")
yrsplot = setdiff(year_assessment + c(0:-9), 2020 )
fns = file.path( loc, paste( "R0.mass", yrsplot, "png", sep=".") )
include_graphics( fns )
Biomass density timeseries
- A peak in 2009 to 2014 and has since been declining in all areas.
#| label: fig-fbGMTS
#| eval: true
#| echo: false
#| output: true
#| fig-dpi: 144
#| fig-height: 4
#| fig.show: hold
#| 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."
fn = file.path(data_loc, "assessments", year_assessment, "timeseries","survey","R0.mass.png")
include_graphics( fn )
Biomass Index maps
A contraction of spatial range in 4X and the western parts of S-ENS were also evident in 2021 to 2022.
#| 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 )
Biomass Index (aggregate)
#| label: fig-fbindex-timeeries
#| 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 )
Fishery modelled results and advise
To be reviewed by CSAS (Feb 2025).
References
Banerjee, S., Carlin, B. P., and Gelfand, A. E.. 2004. Hierarchical Modeling and Analysis for Spatial Data. Monographs on Statistics and Applied Probability. Chapman and Hall/CRC.
Besag, Julian. 1974. Spatial interaction and the statistical analysis of lattice systems. Journal of the Royal Statistical Society Series B (Methodological) 1974: 192-236.
Canada Gazette. 2022. Regulations Amending the Fishery (General) Regulations. Part II, Volume 156, Number 8.
Canada Gazette. 2016. St. Anns Bank Marine Protected Area Regulations. Canada Gazette, Part I, Vol 150, Issue 51: 4143-4149.
Choi, J.S. 2020. A Framework for the assessment of Snow Crab (Chioneocete opilio) in Maritimes Region (NAFO Div 4VWX) . DFO Can. Sci. Advis. Sec. Res. Doc. 2020/nnn. v + xxx p.
Choi, J.S. 2022. Reconstructing the Decline of Atlantic Cod with the Help of Environmental Variability in the Scotian Shelf of Canada. bioRxiv. [https://doi.org/10.1101/2022.05.05.490753](https://doi.org/10.1101/2022.05.05.490753].
Choi, J. S., and B. C. Patten. 2001. Sustainable Development: Lessons from the Paradox of Enrichment. Ecosystem Health 7: 163–77.
Choi, Jae S., B. Cameron, K. Christie, A. Glass, and E. MacEachern. 2022. Temperature and Depth Dependence of the Spatial Distribution of Snow Crab. bioRxiv. https://doi.org/10.1101/2022.12.20.520893.
Choi, Jae S. 2023. A Multi-Stage, Delay Differential Model of Snow Crab Population Dynamics in the Scotian Shelf of Atlantic Canada. bioRxiv. https://doi.org/10.1101/2023.02.13.528296.
DFO. 2018. Stock Status Update of Atlantic Halibut (Hippoglossus hippoglossus) on the Scotian Shelf and Southern Grand Banks in NAFO Divisions 3NOPs4VWX5Zc. DFO Can. Sci. Advis. Sec. Sci. Resp. 2018/022.
Hebert M, Miron G, Moriyasu M, Vienneau R, and DeGrace P. Efficiency and ghost fishing of Snow Crab (Chionoecetes opilio) traps in the Gulf of St. Lawrence. Fish Res. 2001; 52(3): 143-153. 10.1016/S0165-7836(00)00259-9
Riebler, A., Sørbye, S.H., Simpson D., and Rue, H. 2016. An intuitive Bayesian spatial model for disease mapping that accounts for scaling. Statistical methods in medical research 25: 1145-1165.
Simpson, D., Rue, H., Riebler, A., Martins, T.G., and Sørbye, SH. 2017. Penalising Model Component Complexity: A Principled, Practical Approach to Constructing Priors. Statist. Sci. 32: 1-28.
END
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