In jae0/snowcrab: Snow crab assessment suite for aegis* and associated projects.
require(knitr)
knitr::opts_chunk$set(
root.dir = data_root,
echo = FALSE,
out.width="6.2in",
# dev.args = list(type = "cairo"),
fig.retina = 2,
dpi=192
)
# inits and data loading (front load all required data)
require(aegis)
year.assessment = params$year.assessment
year_previous = year.assessment - 1
p = bio.snowcrab::load.environment( year.assessment=year.assessment )
SCD = project.datadirectory("bio.snowcrab")
media_loc = params$media_loc
# fishery_model_results = file.path( "/home", "jae", "projects", "dynamical_model", "snowcrab", "outputs" )
fishery_model_results = file.path( SCD, "fishery_model" )
sn_env = snowcrab_load_key_results_to_memory( year.assessment, debugging=params$debugging, loc_dde=params$loc_dde, return_as_list=TRUE )
attach(sn_env)
# predator diet data
diet_data_dir = file.path( SCD, "data", "diets" )
require(data.table) # for speed
require(lubridate)
require(stringr)
require(gt) # table formatting
library(janitor)
require(ggplot2)
require(aegis) # map-related
require(bio.taxonomy) # handle species codes
# assimilate the CSV data tables:
# diet = get_feeding_data( diet_data_dir, redo=TRUE ) # if there is a data update
diet = get_feeding_data( diet_data_dir, redo=FALSE )
tx = taxa_to_code("snow crab")
# matching codes are
# spec tsn tx vern tx_index
#1 528 172379 BENTHODESMUS BENTHODESMUS 1659
#2 2522 98427 CHIONOECETES SPIDER QUEEN SNOW UNID 728
#3 2526 98428 CHIONOECETES OPILIO SNOW CRAB QUEEN 729
# 2 and 3 are correct
snowcrab_predators = diet[ preyspeccd %in% c(2522, 2526), ] # n=159 oservations out of a total of 58287 observations in db (=0.28% of all data)
snowcrab_predators$Species = code_to_taxa(snowcrab_predators$spec)$vern
snowcrab_predators$Predator = factor(snowcrab_predators$Species)
counts = snowcrab_predators[ , .(Frequency=.N), by=.(Species)]
setorderv(counts, "Frequency", order=-1)
# species composition
psp = speciescomposition_parameters( yrs=p$yrs, runlabel="1999_present" )
pca = speciescomposition_db( DS="pca", p=psp )
pcadata = as.data.frame( pca$loadings )
pcadata$vern = stringr::str_to_title( taxonomy.recode( from="spec", to="taxa", tolookup=rownames( pcadata ) )$vern )
# bycatch summaries
o_cfaall = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfaall" )
o_cfanorth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfanorth" )
o_cfasouth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfasouth" )
o_cfa4x = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfa4x" )
Ecosystem considerations
Connectivity: Oceanic currents {.c}
fn2=file.path( media_loc, "maritimes_currents.png" )
knitr::include_graphics( c(fn2) )
# \@ref(fig:movementtracks)
Connectivity: Movement {.c}
::: columns
:::: column
fn1=file.path( media_loc, "movement0.png" )
fn2=file.path( media_loc, "movement.png" )
knitr::include_graphics( c(fn1, fn2) )
# \@ref(fig:movementtracks)
::::
:::: column
fn1=file.path( media_loc, "snowcrab_movement_distances.png" )
fn2=file.path( media_loc, "snowcrab_movement_rates.png" )
knitr::include_graphics( c(fn1, fn2) )
# \@ref(fig:movement)
::::
:::
Bathymetry {.c}
::: columns
:::: column
bathydir = file.path( data_root, 'aegis', 'bathymetry', 'modelled', 'default', 'stmv', 'none_fft', 'z', 'maps', 'SSE' )
knitr::include_graphics( file.path( bathydir, 'bathymetry.z.SSE.png' ) )
::::
:::: column
bathydir = file.path( data_root, 'aegis', 'bathymetry', 'modelled', 'default', 'stmv', 'none_fft', 'z', 'maps', 'SSE' )
knitr::include_graphics( file.path( bathydir, 'bathymetry.b.sdSpatial.SSE.png' ) )
::::
:::
Substrate {.c}
::: columns
:::: column
substrdir = file.path( data_root, 'aegis', 'substrate', 'maps', 'canada.east.highres' )
knitr::include_graphics( file.path( substrdir, 'substrate.substrate.grainsize.canada.east.highres.png' ) )
::::
:::: column
substrdir = file.path( data_root, 'aegis', 'substrate', 'maps', 'canada.east.highres' )
knitr::include_graphics( file.path( substrdir, 'substrate.s.sdSpatial.canada.east.highres.png' ) )
::::
:::
Bottom Temperature {.c}
::: columns
:::: column
\vspace{12mm}
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 't.pdf') )
# \@ref(fig:bottom-temperatures-survey)
::::
:::: column
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'temperature_bottom.pdf') )
# \@ref(fig:bottom-temperatures)
::::
:::
Bottom Temperature ... {.c}
loc = file.path( data_root, 'aegis', 'temperature', 'maps', '1999_present' )
yrsplot = year.assessment + c(0:-10)
fn10 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[10], '-0.75', '.png', sep='') )
fn9 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[9], '-0.75', '.png', sep='') )
fn8 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[8], '-0.75', '.png', sep='') )
fn7 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[7], '-0.75', '.png', sep='') )
fn6 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[6], '-0.75', '.png', sep='') )
fn5 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[5], '-0.75', '.png', sep='') )
fn4 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[4], '-0.75', '.png', sep='') )
fn3 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[3], '-0.75', '.png', sep='') )
fn2 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[2], '-0.75', '.png', sep='') )
fn1 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[1], '-0.75', '.png', sep='') )
knitr::include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:bottom-temperatures-map)
# *Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September.*
Bottom Temperature ... {.c}
::: columns
:::: column
loc = file.path( data_root, 'aegis', 'temperature', 'maps', '1999_present' )
knitr::include_graphics( file.path( loc, 'Predicted_habitat_probability_persistent_spatial_effect.png') )
# \@ref(fig:bottom-temperatures-spatialeffect)
::::
:::: column
\vspace{12mm}
-
Persistent spatial gradient of $>1^\circ$C in bottom temperatures in the Maritimes Region.
-
Variable due to confluence:
-
Warm, high salinity Gulf Stream from the S-SE along the shelf edge
-
Cold, low salinity Labrador Current
-
Cold low salinity St. Lawrence outflow from the N-NE
-
Nearshore Nova Scotia current, running from the NE.
::::
:::
Species composition
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$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
Species composition PC1
spc_loc = file.path( data_root, 'aegis', 'speciescomposition', 'maps', '1999_present' )
fn1 = file.path( spc_loc, 'speciescomposition_pca1_spatial_effect.png')
fn2 = file.path( spc_loc, 'speciescomposition_pca2_spatial_effect.png')
ts_loc = file.path( data_root, 'aegis', 'speciescomposition', 'figures' )
fn3 = file.path( ts_loc, 'pca1_timeseries.png')
fn4 = file.path( ts_loc, 'pca2_timeseries.png')
knitr::include_graphics( c(fn1, fn3 ) )
# \@ref(fig:habitat3)
Species composition PC2
spc_loc = file.path( data_root, 'aegis', 'speciescomposition', 'maps', '1999_present' )
fn1 = file.path( spc_loc, 'speciescomposition_pca1_spatial_effect.png')
fn2 = file.path( spc_loc, 'speciescomposition_pca2_spatial_effect.png')
ts_loc = file.path( data_root, 'aegis', 'speciescomposition', 'figures' )
fn3 = file.path( ts_loc, 'pca1_timeseries.png')
fn4 = file.path( ts_loc, 'pca2_timeseries.png')
knitr::include_graphics( c(fn2, fn4 ) )
# \@ref(fig:habitat3)
Disease
include_graphics( file.path( SCD, 'output', 'bcd.png') )
Entanglements of large megafauna
region="cfaall"
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
oss = o$oss # subset for region of interest
# print("whale entaglements:")
whales = oss[ grep("whale", common, ignore.case=TRUE), ]
# print(whales[, .N, by=.(yr)] )
# print("leatherback entaglements:")
leatherback = oss[ grep("LEATHERBACK", common, ignore.case=TRUE), ]
# print(leatherback[, .N, by=.(yr)])
# print("basking sharks entaglements:")
basking_shark = oss[ grep("BASKING SHARK", common, ignore.case=TRUE), ]
# print(basking_shark[, .N, by=.(yr)])
plot(lat~-lon, oss, pch=".", col="lightgray", xlim=c(-65.2, -57), ylim=c(42.9,47) )
points(lat~-lon, whales, pch=19, cex=1.5, col="darkred" )
points(lat~-lon, leatherback, pch=18, cex=1.5, col="darkgreen" )
points(lat~-lon, basking_shark, pch=17, cex=1.5, col="slateblue" )
Bycatch Maritimes {.c}
o = o_cfaall
o$bycatch_table[ o$bycatch_table==0 ] = NA
o$bycatch_table[ is.na(o$bycatch_table) ] = "."
o$bycatch_table_catch[ o$bycatch_table_catch==0 ] = NA
o$bycatch_table_catch[ is.na(o$bycatch_table_catch) ] = "."
plot( o$spec ~ o$bct, xlab = "At sea observed catch rate in snow crab fishery (kg/trap)", ylab="Species", type="p", cex=0.9, pch=19, col="darkorange", xlim=c(0, max(o$bct, na.rm=TRUE)*1.4), yaxt="n" )
text( o$bct, o$spec, labels=o$species, pos=4, srt=0 , cex=0.5, col="darkslateblue")
text( max(o$bct, na.rm=TRUE)*0.88, 2.5, labels=paste( "Snow crab CPUE (At sea obs., mean): ", o$bct_sc, " kg/trap"), col="darkred", cex=1.0 )
Bycatch Maritimes ... {.c}
o = o_cfaall
lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern
xlab = paste("PC1 (", pca$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$vern, ignore.case=TRUE))
j = unique(j)
points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" )
text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
Bycatch N-ENS {.c}
\tiny
o = o_cfanorth
o$bycatch_table = o$bycatch_table[ which(o$bycatch_table$"Average/Moyen" > 10 ),]
o$bycatch_table$"Average/Moyen" = round(o$bycatch_table$"Average/Moyen")
o$bycatch_table[ o$bycatch_table==0 ] = NA
o$bycatch_table[ is.na(o$bycatch_table) ] = "."
gt(o$bycatch_table)
\normalsize
Bycatch N-ENS ... {.c}
o = o_cfanorth
lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern
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" )
Bycatch S-ENS {.c}
\tiny
o = o_cfasouth
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
o$bycatch_table = o$bycatch_table[ which(o$bycatch_table$"Average/Moyen" > 10 ),]
o$bycatch_table$"Average/Moyen" = round(o$bycatch_table$"Average/Moyen")
o$bycatch_table[ o$bycatch_table==0 ] = NA
o$bycatch_table[ is.na(o$bycatch_table) ] = "."
gt(o$bycatch_table)
\normalsize
Bycatch S-ENS ... {.c}
o = o_cfasouth
lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern
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" )
Bycatch 4X {.c}
\tiny
o = o_cfa4x
o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region )
o$bycatch_table = o$bycatch_table[ which(as.numeric(o$bycatch_table$"Average/Moyen") > 10 ),]
o$bycatch_table$"Average/Moyen" = round(o$bycatch_table$"Average/Moyen")
o$bycatch_table[ o$bycatch_table==0 ] = NA
o$bycatch_table[ is.na(o$bycatch_table) ] = "."
gt(o$bycatch_table)
\normalsize
Bycatch 4X ... {.c}
o = o_cfa4x
lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern
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" )
Prey
::: columns
:::: column
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$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
lookup= c( "echinoderm", "polychaete", "maldane", "nereis", "shrimp", "pandalus", "rock crab", "toad crab", "lesser toad crab", "quahog", "artica islandica", "mollusc", "mytilus", "modiolus", "hiatella", "starfish", "sea anemone", "brittle star", "sea star", "sea anemone", "ophiura", "ophiopholis", "edwardsia", "metridium", "euphasid" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$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" )
::::
:::: column
- 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).
::::
:::
Predators {.c}
::: columns
:::: column
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$vern, ignore.case=TRUE)
points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
lookup= c( "cod", "halibut", "sculpin", "skate", "plaice", "hake", "wolffish", "atlantic cod", "atlantic halibut", "longhorn sculpin", "thorny skate", "striped atlantic wolffish", "haddock", "american plaice", "smooth skate", "winter skate", "white hake", "shorthorn sculpin", "eelpout newfoundland", "squirrel or red hake", "sea raven", "ocean pout", "barndoor skate" )
j = NULL
for (k in lookup) j = c(j, grep( k, pcadata$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" )
::::
:::: column
kable( counts[1:11,], format="simple", row.names=FALSE)
::::
:::
Predators {.c}
::: columns
:::: column
ggplot() +
borders("world", fill = "lightgray", colour = "grey80") +
xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) +
geom_point(data=snowcrab_predators[year(timestamp) %in% c(2000:2010),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) +
labs(x="", y="", caption="2000-2010") +
theme(legend.position =c(0.16, 0.65), legend.title=element_blank(), legend.text=element_text(size=7.0)
)
::::
:::: column
ggplot() +
borders("world", fill = "lightgray", colour = "grey80") +
xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) +
geom_point(data=snowcrab_predators[year(timestamp) %in% c(2011:2020),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) +
labs(x="", y="", caption="2011-2020") +
theme(legend.position =c(0.16, 0.725), legend.title=element_blank(), legend.text=element_text(size=7.0) )
::::
:::
Predators - Atlantic cod ... {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.10' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.10', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.10', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.10', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.10', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:cod-map)
Predators - Atlantic cod {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.10.pdf') )
# \@ref(fig:cod-timeseries)
Predators - Atlantic Halibut {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.30' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.30', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.30', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.30', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.30', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:halibut-map)
Predators - Atlantic Halibut ... {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.30.pdf') )
# \@ref(fig:halibut-timeseries)
Competitors/Prey - Lesser toad crab ... {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2521' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.2521', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.2521', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.2521', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.2521', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:lessertoadcrab-map)
Competitor/Prey - Lesser toad crab {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2521.pdf') )
# \@ref(fig:lessertoadcrab-timeseries)
Competitor/Prey - Northern shrimp {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2211' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.2211', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.2211', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.2211', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.2211', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:Shrimp-map)
Competitor/Prey - Northern shrimp ... {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2211.pdf') )
# \@ref(fig:Shrimp-timeseries)
Stock status {.c}
Biomass Density {.c}
\begin{tiny}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'R0.mass')
yrsplot = setdiff(year.assessment + c(0:-9), 2020 )
fn6 = file.path( loc, paste( 'R0.mass', yrsplot[6], 'png', sep='.') )
fn5 = file.path( loc, paste( 'R0.mass', yrsplot[5], 'png', sep='.') )
fn4 = file.path( loc, paste( 'R0.mass', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'R0.mass', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'R0.mass', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'R0.mass', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
\end{tiny}
Biomass Density ... {.c}
\begin{tiny}
fn = file.path(SCD,'assessments', year.assessment, 'timeseries','survey','R0.mass.pdf')
include_graphics( c(fn) )
#\@ref(fig:fbGMTS)
\end{tiny}
Viable Habitat {.t}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
fn1=file.path( media_loc, "viable_habitat.png" )
knitr::include_graphics( c(fn1 ) )
# \@ref(fig:habitat)
\end{column}
\begin{column}{.48\textwidth}
fn2=file.path( media_loc, "viable_habitat_depth_temp.png" )
knitr::include_graphics( c( fn2 ) )
# \@ref(fig:habitat2)
\end{column}
\end{columns}s
\end{small}
Viable Habitat ... {.c}
loc = file.path( SCD, 'modelled', '1999_present_fb', 'predicted.presence_absence' )
yrsplot = year.assessment + c(0:-10)
fn10 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[10], '.png', sep='') )
fn9 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[9], '.png', sep='') )
fn8 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[8], '.png', sep='') )
fn7 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[7], '.png', sep='') )
fn6 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[6], '.png', sep='') )
fn5 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[5], '.png', sep='') )
fn4 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[4], '.png', sep='') )
fn3 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[3], '.png', sep='') )
fn2 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[2], '.png', sep='') )
fn1 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[1], '.png', sep='') )
include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:fb-habitat-map)
# *Figure XXX. Habitat viability (probability; fishable Snow Crab)*
Viable Habitat ... {.c}
loc = file.path( SCD, 'modelled', '1999_present_fb', 'aggregated_habitat_timeseries' )
include_graphics( file.path( loc, 'habitat_M0.png') )
# \@ref(fig:fb-habitat-timeseries)
Biomass Index (aggregate)
```r$(t/km$^2$) predicted from the Snow Crab survey.' }
loc = file.path( SCD, 'modelled', '1999_present_fb', 'predicted_biomass_densities' )
yrsplot = year.assessment + c(0:-10)
fn10 = file.path( loc, paste( 'biomass', yrsplot[10], 'png', sep='.') )
fn9 = file.path( loc, paste( 'biomass', yrsplot[9], 'png', sep='.') )
fn8 = file.path( loc, paste( 'biomass', yrsplot[8], 'png', sep='.') )
fn7 = file.path( loc, paste( 'biomass', yrsplot[7], 'png', sep='.') )
fn6 = file.path( loc, paste( 'biomass', yrsplot[6], 'png', sep='.') )
fn5 = file.path( loc, paste( 'biomass', yrsplot[5], 'png', sep='.') )
fn4 = file.path( loc, paste( 'biomass', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'biomass', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'biomass', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'biomass', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
## Biomass Index (aggregate) ... {.c}
\begin{tiny}
```r
include_graphics( file.path( SCD, 'modelled', '1999_present_fb', 'aggregated_biomass_timeseries' , 'biomass_M0.png') )
# \@ref(fig:fbindex-timeseries)
\end{tiny}
Fishery Model Biomass (pre-fishery){.c}
\begin{tiny}
loc = file.path( SCD, 'fishery_model', year.assessment, 'logistic_discrete_historical' )
fn1 = file.path( loc, 'plot_predictions_cfanorth.pdf' )
fn2 = file.path( loc, 'plot_predictions_cfasouth.pdf' )
fn3 = file.path( loc, 'plot_predictions_cfa4x.pdf' )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logisticPredictions)
\end{tiny}
Fishing Mortality {.c}
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" )
fn1 = file.path( odir, "plot_fishing_mortality_cfanorth.pdf" )
fn2 = file.path( odir, "plot_fishing_mortality_cfasouth.pdf" )
fn3 = file.path( odir, "plot_fishing_mortality_cfa4x.pdf" )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logisticFishingMortality)
Fishery Model Summary {.c}
| | 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)) |
\tiny
Note: Values in parentheses are Posterior standard deviations.
\normalsize
Reference Points {.c}
include_graphics( file.path( params$media_loc, 'harvest_control_rules.png') )
# \@ref(fig:ReferencePoints)
Reference Points ... {.c}
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" )
fn1 = file.path( odir, 'plot_hcr_cfanorth.pdf' )
fn2 = file.path( odir, 'plot_hcr_cfasouth.pdf' )
fn3 = file.path( odir, 'plot_hcr_cfa4x.pdf' )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logistic-hcr)
- N-ENS is in the "healthy" zone
- S-ENS is in the "healthy" zone
- 4X is in the "critical" zone
Conclusions
The ESS ecosystem is still experiencing a lot of volatility and prudence is wise:
- Rapid ecosystem change (groundfish collapse in 1980s and 1990s)
- Climatic variability: very warm period from 2012-2023
- Snow crab:
- Can persist if extreme conditions are episodic by shifting spatial distributions
- 4X was possibly too long and did not have the habitat space
- Climate variability can induce juxtaposition of warmer water species (prey and predators) with snow crab
Conclusions: N-ENS
- Viable habitat stable near historical mean and marginally improved relative to 2022.
- Female larval production peaked in 2017 and has since declined.
- Recruitment continues at low levels, a noticible gap in recruitment to the fishery persists.
- Predation mortality (Cod, Halibut, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass continues a decreasing trend.
- PA template suggest "healthy zone".
- A more careful harvest strategy would enable bridging this coming recruitment gap.
- A reduced TAC is prudent.
Conclusions: S-ENS
- Viable habitat marginally improved relative to a historic low in 2022.
- Female larval production peaked in 2023/2024.
- Recruitment to the fishery from a strong year class has begun, full entry by 2025.
- Predation mortality (Halibut, Plaice, Sculpin, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass is at a historical low.
- PA template suggest "healthy zone".
- Flexiblity in harvest strategy exists due to strong recuitment.
- A status quo TAC is prudent until confirmation of recruitment.
Conclusions: 4X
- Viable habitat has been at historical lows since 2015.
- Female larval production peaked in 2022.
- Recruitment to the fishery in the near-term is not evident. There is a pulse 4-5 years away.
- Predation mortality (Halibut) and competition with other Crustacea is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has declined to negligible levels since a peak in 2019.
- Fishable biomass is at a historical low.
- PA template suggest "critical zone".
- Stock status is extremely poor.
- Fishery closure until recovery is prudent.
References
\begin{tiny}
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.
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.
\end{tiny}
References ...
\begin{tiny}
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{tiny}
End
Supplemental Information
Stock status {.c}
Geometric mean density by size, sex and maturity
::: columns
:::: column
include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "male.denl.png" ) )
# \@ref(fig:sizefeq-male)
::::
:::: column
include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "female.denl.png" ) )
# \@ref(fig:sizefeq-male)
::::
:::
Mature female maps
Distributions are heterogeneous and often in shallower areas.
```r$(no/km$^2$) from the Snow Crab survey.' }
loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "totno.female.mat" )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( "totno.female.mat", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "totno.female.mat", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "totno.female.mat", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "totno.female.mat", yrsplot[1], "png", sep=".") )
include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:fmat-map)
## Mature female timeseries
```r$(no/km$^2$) from the Snow Crab survey.' }
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "totno.female.mat.pdf") )
# \@ref(fig:fmat-timeseries)
jae0/snowcrab documentation built on Feb. 27, 2024, 2:42 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.
require(knitr) knitr::opts_chunk$set( root.dir = data_root, echo = FALSE, out.width="6.2in", # dev.args = list(type = "cairo"), fig.retina = 2, dpi=192 ) # inits and data loading (front load all required data) require(aegis) year.assessment = params$year.assessment year_previous = year.assessment - 1 p = bio.snowcrab::load.environment( year.assessment=year.assessment ) SCD = project.datadirectory("bio.snowcrab") media_loc = params$media_loc # fishery_model_results = file.path( "/home", "jae", "projects", "dynamical_model", "snowcrab", "outputs" ) fishery_model_results = file.path( SCD, "fishery_model" ) sn_env = snowcrab_load_key_results_to_memory( year.assessment, debugging=params$debugging, loc_dde=params$loc_dde, return_as_list=TRUE ) attach(sn_env) # predator diet data diet_data_dir = file.path( SCD, "data", "diets" ) require(data.table) # for speed require(lubridate) require(stringr) require(gt) # table formatting library(janitor) require(ggplot2) require(aegis) # map-related require(bio.taxonomy) # handle species codes # assimilate the CSV data tables: # diet = get_feeding_data( diet_data_dir, redo=TRUE ) # if there is a data update diet = get_feeding_data( diet_data_dir, redo=FALSE ) tx = taxa_to_code("snow crab") # matching codes are # spec tsn tx vern tx_index #1 528 172379 BENTHODESMUS BENTHODESMUS 1659 #2 2522 98427 CHIONOECETES SPIDER QUEEN SNOW UNID 728 #3 2526 98428 CHIONOECETES OPILIO SNOW CRAB QUEEN 729 # 2 and 3 are correct snowcrab_predators = diet[ preyspeccd %in% c(2522, 2526), ] # n=159 oservations out of a total of 58287 observations in db (=0.28% of all data) snowcrab_predators$Species = code_to_taxa(snowcrab_predators$spec)$vern snowcrab_predators$Predator = factor(snowcrab_predators$Species) counts = snowcrab_predators[ , .(Frequency=.N), by=.(Species)] setorderv(counts, "Frequency", order=-1) # species composition psp = speciescomposition_parameters( yrs=p$yrs, runlabel="1999_present" ) pca = speciescomposition_db( DS="pca", p=psp ) pcadata = as.data.frame( pca$loadings ) pcadata$vern = stringr::str_to_title( taxonomy.recode( from="spec", to="taxa", tolookup=rownames( pcadata ) )$vern ) # bycatch summaries o_cfaall = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfaall" ) o_cfanorth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfanorth" ) o_cfasouth = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfasouth" ) o_cfa4x = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region="cfa4x" )
Ecosystem considerations
Connectivity: Oceanic currents {.c}
fn2=file.path( media_loc, "maritimes_currents.png" ) knitr::include_graphics( c(fn2) ) # \@ref(fig:movementtracks)
Connectivity: Movement {.c}
::: columns :::: column
fn1=file.path( media_loc, "movement0.png" ) fn2=file.path( media_loc, "movement.png" ) knitr::include_graphics( c(fn1, fn2) ) # \@ref(fig:movementtracks)
:::: :::: column
fn1=file.path( media_loc, "snowcrab_movement_distances.png" ) fn2=file.path( media_loc, "snowcrab_movement_rates.png" ) knitr::include_graphics( c(fn1, fn2) ) # \@ref(fig:movement)
:::: :::
Bathymetry {.c}
::: columns
:::: column
bathydir = file.path( data_root, 'aegis', 'bathymetry', 'modelled', 'default', 'stmv', 'none_fft', 'z', 'maps', 'SSE' ) knitr::include_graphics( file.path( bathydir, 'bathymetry.z.SSE.png' ) )
:::: :::: column
bathydir = file.path( data_root, 'aegis', 'bathymetry', 'modelled', 'default', 'stmv', 'none_fft', 'z', 'maps', 'SSE' ) knitr::include_graphics( file.path( bathydir, 'bathymetry.b.sdSpatial.SSE.png' ) )
:::: :::
Substrate {.c}
::: columns
:::: column
substrdir = file.path( data_root, 'aegis', 'substrate', 'maps', 'canada.east.highres' ) knitr::include_graphics( file.path( substrdir, 'substrate.substrate.grainsize.canada.east.highres.png' ) )
:::: :::: column
substrdir = file.path( data_root, 'aegis', 'substrate', 'maps', 'canada.east.highres' ) knitr::include_graphics( file.path( substrdir, 'substrate.s.sdSpatial.canada.east.highres.png' ) )
:::: :::
Bottom Temperature {.c}
::: columns
:::: column \vspace{12mm}
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 't.pdf') ) # \@ref(fig:bottom-temperatures-survey)
::::
:::: column
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'temperature_bottom.pdf') ) # \@ref(fig:bottom-temperatures)
:::: :::
Bottom Temperature ... {.c}
loc = file.path( data_root, 'aegis', 'temperature', 'maps', '1999_present' ) yrsplot = year.assessment + c(0:-10) fn10 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[10], '-0.75', '.png', sep='') ) fn9 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[9], '-0.75', '.png', sep='') ) fn8 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[8], '-0.75', '.png', sep='') ) fn7 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[7], '-0.75', '.png', sep='') ) fn6 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[6], '-0.75', '.png', sep='') ) fn5 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[5], '-0.75', '.png', sep='') ) fn4 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[4], '-0.75', '.png', sep='') ) fn3 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[3], '-0.75', '.png', sep='') ) fn2 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[2], '-0.75', '.png', sep='') ) fn1 = file.path( loc, paste( 'Bottom-temperature-', yrsplot[1], '-0.75', '.png', sep='') ) knitr::include_graphics( c( fn3, fn2, fn1) ) # \@ref(fig:bottom-temperatures-map) # *Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September.*
Bottom Temperature ... {.c}
::: columns :::: column
loc = file.path( data_root, 'aegis', 'temperature', 'maps', '1999_present' ) knitr::include_graphics( file.path( loc, 'Predicted_habitat_probability_persistent_spatial_effect.png') ) # \@ref(fig:bottom-temperatures-spatialeffect)
::::
:::: column
\vspace{12mm}
-
Persistent spatial gradient of $>1^\circ$C in bottom temperatures in the Maritimes Region.
-
Variable due to confluence:
-
Warm, high salinity Gulf Stream from the S-SE along the shelf edge
-
Cold, low salinity Labrador Current
-
Cold low salinity St. Lawrence outflow from the N-NE
-
Nearshore Nova Scotia current, running from the NE.
:::: :::
Species composition
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$vern, ignore.case=TRUE) points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" )
Species composition PC1
spc_loc = file.path( data_root, 'aegis', 'speciescomposition', 'maps', '1999_present' ) fn1 = file.path( spc_loc, 'speciescomposition_pca1_spatial_effect.png') fn2 = file.path( spc_loc, 'speciescomposition_pca2_spatial_effect.png') ts_loc = file.path( data_root, 'aegis', 'speciescomposition', 'figures' ) fn3 = file.path( ts_loc, 'pca1_timeseries.png') fn4 = file.path( ts_loc, 'pca2_timeseries.png') knitr::include_graphics( c(fn1, fn3 ) ) # \@ref(fig:habitat3)
Species composition PC2
spc_loc = file.path( data_root, 'aegis', 'speciescomposition', 'maps', '1999_present' ) fn1 = file.path( spc_loc, 'speciescomposition_pca1_spatial_effect.png') fn2 = file.path( spc_loc, 'speciescomposition_pca2_spatial_effect.png') ts_loc = file.path( data_root, 'aegis', 'speciescomposition', 'figures' ) fn3 = file.path( ts_loc, 'pca1_timeseries.png') fn4 = file.path( ts_loc, 'pca2_timeseries.png') knitr::include_graphics( c(fn2, fn4 ) ) # \@ref(fig:habitat3)
Disease
include_graphics( file.path( SCD, 'output', 'bcd.png') )
Entanglements of large megafauna
region="cfaall" o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region ) oss = o$oss # subset for region of interest # print("whale entaglements:") whales = oss[ grep("whale", common, ignore.case=TRUE), ] # print(whales[, .N, by=.(yr)] ) # print("leatherback entaglements:") leatherback = oss[ grep("LEATHERBACK", common, ignore.case=TRUE), ] # print(leatherback[, .N, by=.(yr)]) # print("basking sharks entaglements:") basking_shark = oss[ grep("BASKING SHARK", common, ignore.case=TRUE), ] # print(basking_shark[, .N, by=.(yr)]) plot(lat~-lon, oss, pch=".", col="lightgray", xlim=c(-65.2, -57), ylim=c(42.9,47) ) points(lat~-lon, whales, pch=19, cex=1.5, col="darkred" ) points(lat~-lon, leatherback, pch=18, cex=1.5, col="darkgreen" ) points(lat~-lon, basking_shark, pch=17, cex=1.5, col="slateblue" )
Bycatch Maritimes {.c}
o = o_cfaall o$bycatch_table[ o$bycatch_table==0 ] = NA o$bycatch_table[ is.na(o$bycatch_table) ] = "." o$bycatch_table_catch[ o$bycatch_table_catch==0 ] = NA o$bycatch_table_catch[ is.na(o$bycatch_table_catch) ] = "." plot( o$spec ~ o$bct, xlab = "At sea observed catch rate in snow crab fishery (kg/trap)", ylab="Species", type="p", cex=0.9, pch=19, col="darkorange", xlim=c(0, max(o$bct, na.rm=TRUE)*1.4), yaxt="n" ) text( o$bct, o$spec, labels=o$species, pos=4, srt=0 , cex=0.5, col="darkslateblue") text( max(o$bct, na.rm=TRUE)*0.88, 2.5, labels=paste( "Snow crab CPUE (At sea obs., mean): ", o$bct_sc, " kg/trap"), col="darkred", cex=1.0 )
Bycatch Maritimes ... {.c}
o = o_cfaall lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern xlab = paste("PC1 (", pca$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$vern, ignore.case=TRUE)) j = unique(j) points( PC2 ~ PC1, pcadata[j,], pch=19, cex=2.0, col="lightgreen" ) text( PC2 ~ PC1, labels=vern, data=pcadata[j,], cex=0.75, col="darkgreen" )
Bycatch N-ENS {.c}
\tiny
o = o_cfanorth o$bycatch_table = o$bycatch_table[ which(o$bycatch_table$"Average/Moyen" > 10 ),] o$bycatch_table$"Average/Moyen" = round(o$bycatch_table$"Average/Moyen") o$bycatch_table[ o$bycatch_table==0 ] = NA o$bycatch_table[ is.na(o$bycatch_table) ] = "." gt(o$bycatch_table)
\normalsize
Bycatch N-ENS ... {.c}
o = o_cfanorth lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern 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" )
Bycatch S-ENS {.c}
\tiny
o = o_cfasouth o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region ) o$bycatch_table = o$bycatch_table[ which(o$bycatch_table$"Average/Moyen" > 10 ),] o$bycatch_table$"Average/Moyen" = round(o$bycatch_table$"Average/Moyen") o$bycatch_table[ o$bycatch_table==0 ] = NA o$bycatch_table[ is.na(o$bycatch_table) ] = "." gt(o$bycatch_table)
\normalsize
Bycatch S-ENS ... {.c}
o = o_cfasouth lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern 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" )
Bycatch 4X {.c}
\tiny
o = o_cfa4x o = observer.db( DS="bycatch_summary", p=p, yrs=p$yrs, region=region ) o$bycatch_table = o$bycatch_table[ which(as.numeric(o$bycatch_table$"Average/Moyen") > 10 ),] o$bycatch_table$"Average/Moyen" = round(o$bycatch_table$"Average/Moyen") o$bycatch_table[ o$bycatch_table==0 ] = NA o$bycatch_table[ is.na(o$bycatch_table) ] = "." gt(o$bycatch_table)
\normalsize
Bycatch 4X ... {.c}
o = o_cfa4x lookup = bio.taxonomy::taxonomy.recode( from="spec", to="taxa", tolookup=o$specid )$vern 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" )
Prey
::: columns :::: column
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$vern, ignore.case=TRUE) points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" ) lookup= c( "echinoderm", "polychaete", "maldane", "nereis", "shrimp", "pandalus", "rock crab", "toad crab", "lesser toad crab", "quahog", "artica islandica", "mollusc", "mytilus", "modiolus", "hiatella", "starfish", "sea anemone", "brittle star", "sea star", "sea anemone", "ophiura", "ophiopholis", "edwardsia", "metridium", "euphasid" ) j = NULL for (k in lookup) j = c(j, grep( k, pcadata$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" )
:::: :::: column - 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). :::: :::
Predators {.c}
::: columns :::: column
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$vern, ignore.case=TRUE) points( PC2 ~ PC1, pcadata[i,], pch=19, cex=3.0, col="darkorange" ) lookup= c( "cod", "halibut", "sculpin", "skate", "plaice", "hake", "wolffish", "atlantic cod", "atlantic halibut", "longhorn sculpin", "thorny skate", "striped atlantic wolffish", "haddock", "american plaice", "smooth skate", "winter skate", "white hake", "shorthorn sculpin", "eelpout newfoundland", "squirrel or red hake", "sea raven", "ocean pout", "barndoor skate" ) j = NULL for (k in lookup) j = c(j, grep( k, pcadata$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" )
:::: :::: column
kable( counts[1:11,], format="simple", row.names=FALSE)
:::: :::
Predators {.c}
::: columns :::: column
ggplot() + borders("world", fill = "lightgray", colour = "grey80") + xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) + geom_point(data=snowcrab_predators[year(timestamp) %in% c(2000:2010),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) + labs(x="", y="", caption="2000-2010") + theme(legend.position =c(0.16, 0.65), legend.title=element_blank(), legend.text=element_text(size=7.0) )
::::
:::: column
ggplot() + borders("world", fill = "lightgray", colour = "grey80") + xlim(c( -65.5, -57.1)) + ylim(c(42.1, 47.1)) + geom_point(data=snowcrab_predators[year(timestamp) %in% c(2011:2020),], aes(x=slongdd, y=slatdd, colour=Predator ), size=2.5 ) + labs(x="", y="", caption="2011-2020") + theme(legend.position =c(0.16, 0.725), legend.title=element_blank(), legend.text=element_text(size=7.0) )
:::: :::
Predators - Atlantic cod ... {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.10' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.10', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.10', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.10', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.10', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) ) # \@ref(fig:cod-map)
Predators - Atlantic cod {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.10.pdf') ) # \@ref(fig:cod-timeseries)
Predators - Atlantic Halibut {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.30' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.30', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.30', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.30', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.30', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) ) # \@ref(fig:halibut-map)
Predators - Atlantic Halibut ... {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.30.pdf') ) # \@ref(fig:halibut-timeseries)
Competitors/Prey - Lesser toad crab ... {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2521' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.2521', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.2521', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.2521', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.2521', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) ) # \@ref(fig:lessertoadcrab-map)
Competitor/Prey - Lesser toad crab {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2521.pdf') ) # \@ref(fig:lessertoadcrab-timeseries)
Competitor/Prey - Northern shrimp {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.2211' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.2211', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.2211', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.2211', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.2211', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) ) # \@ref(fig:Shrimp-map)
Competitor/Prey - Northern shrimp ... {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2211.pdf') ) # \@ref(fig:Shrimp-timeseries)
Stock status {.c}
Biomass Density {.c}
\begin{tiny}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'R0.mass') yrsplot = setdiff(year.assessment + c(0:-9), 2020 ) fn6 = file.path( loc, paste( 'R0.mass', yrsplot[6], 'png', sep='.') ) fn5 = file.path( loc, paste( 'R0.mass', yrsplot[5], 'png', sep='.') ) fn4 = file.path( loc, paste( 'R0.mass', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'R0.mass', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'R0.mass', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'R0.mass', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
\end{tiny}
Biomass Density ... {.c}
\begin{tiny}
fn = file.path(SCD,'assessments', year.assessment, 'timeseries','survey','R0.mass.pdf') include_graphics( c(fn) ) #\@ref(fig:fbGMTS)
\end{tiny}
Viable Habitat {.t}
\begin{small} \begin{columns} \begin{column}{.48\textwidth}
fn1=file.path( media_loc, "viable_habitat.png" ) knitr::include_graphics( c(fn1 ) ) # \@ref(fig:habitat)
\end{column} \begin{column}{.48\textwidth}
fn2=file.path( media_loc, "viable_habitat_depth_temp.png" ) knitr::include_graphics( c( fn2 ) ) # \@ref(fig:habitat2)
\end{column} \end{columns}s \end{small}
Viable Habitat ... {.c}
loc = file.path( SCD, 'modelled', '1999_present_fb', 'predicted.presence_absence' ) yrsplot = year.assessment + c(0:-10) fn10 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[10], '.png', sep='') ) fn9 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[9], '.png', sep='') ) fn8 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[8], '.png', sep='') ) fn7 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[7], '.png', sep='') ) fn6 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[6], '.png', sep='') ) fn5 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[5], '.png', sep='') ) fn4 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[4], '.png', sep='') ) fn3 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[3], '.png', sep='') ) fn2 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[2], '.png', sep='') ) fn1 = file.path( loc, paste( 'Predicted_presence_absence_', yrsplot[1], '.png', sep='') ) include_graphics( c( fn3, fn2, fn1) ) # \@ref(fig:fb-habitat-map) # *Figure XXX. Habitat viability (probability; fishable Snow Crab)*
Viable Habitat ... {.c}
loc = file.path( SCD, 'modelled', '1999_present_fb', 'aggregated_habitat_timeseries' ) include_graphics( file.path( loc, 'habitat_M0.png') ) # \@ref(fig:fb-habitat-timeseries)
Biomass Index (aggregate)
```r$(t/km$^2$) predicted from the Snow Crab survey.' } loc = file.path( SCD, 'modelled', '1999_present_fb', 'predicted_biomass_densities' ) yrsplot = year.assessment + c(0:-10) fn10 = file.path( loc, paste( 'biomass', yrsplot[10], 'png', sep='.') ) fn9 = file.path( loc, paste( 'biomass', yrsplot[9], 'png', sep='.') ) fn8 = file.path( loc, paste( 'biomass', yrsplot[8], 'png', sep='.') ) fn7 = file.path( loc, paste( 'biomass', yrsplot[7], 'png', sep='.') ) fn6 = file.path( loc, paste( 'biomass', yrsplot[6], 'png', sep='.') ) fn5 = file.path( loc, paste( 'biomass', yrsplot[5], 'png', sep='.') ) fn4 = file.path( loc, paste( 'biomass', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'biomass', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'biomass', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'biomass', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) )
## Biomass Index (aggregate) ... {.c}
\begin{tiny}
```r
include_graphics( file.path( SCD, 'modelled', '1999_present_fb', 'aggregated_biomass_timeseries' , 'biomass_M0.png') )
# \@ref(fig:fbindex-timeseries)
\end{tiny}
Fishery Model Biomass (pre-fishery){.c}
\begin{tiny}
loc = file.path( SCD, 'fishery_model', year.assessment, 'logistic_discrete_historical' ) fn1 = file.path( loc, 'plot_predictions_cfanorth.pdf' ) fn2 = file.path( loc, 'plot_predictions_cfasouth.pdf' ) fn3 = file.path( loc, 'plot_predictions_cfa4x.pdf' ) include_graphics(c(fn1, fn2, fn3) ) # \@ref(fig:logisticPredictions)
\end{tiny}
Fishing Mortality {.c}
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" ) fn1 = file.path( odir, "plot_fishing_mortality_cfanorth.pdf" ) fn2 = file.path( odir, "plot_fishing_mortality_cfasouth.pdf" ) fn3 = file.path( odir, "plot_fishing_mortality_cfa4x.pdf" ) include_graphics(c(fn1, fn2, fn3) ) # \@ref(fig:logisticFishingMortality)
Fishery Model Summary {.c}
| | 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)) |
\tiny Note: Values in parentheses are Posterior standard deviations. \normalsize
Reference Points {.c}
include_graphics( file.path( params$media_loc, 'harvest_control_rules.png') ) # \@ref(fig:ReferencePoints)
Reference Points ... {.c}
odir = file.path( fishery_model_results, year.assessment, "logistic_discrete_historical" ) fn1 = file.path( odir, 'plot_hcr_cfanorth.pdf' ) fn2 = file.path( odir, 'plot_hcr_cfasouth.pdf' ) fn3 = file.path( odir, 'plot_hcr_cfa4x.pdf' ) include_graphics(c(fn1, fn2, fn3) ) # \@ref(fig:logistic-hcr)
- N-ENS is in the "healthy" zone
- S-ENS is in the "healthy" zone
- 4X is in the "critical" zone
Conclusions
The ESS ecosystem is still experiencing a lot of volatility and prudence is wise:
- Rapid ecosystem change (groundfish collapse in 1980s and 1990s)
- Climatic variability: very warm period from 2012-2023
- Snow crab:
- Can persist if extreme conditions are episodic by shifting spatial distributions
- 4X was possibly too long and did not have the habitat space
- Climate variability can induce juxtaposition of warmer water species (prey and predators) with snow crab
Conclusions: N-ENS
- Viable habitat stable near historical mean and marginally improved relative to 2022.
- Female larval production peaked in 2017 and has since declined.
- Recruitment continues at low levels, a noticible gap in recruitment to the fishery persists.
- Predation mortality (Cod, Halibut, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass continues a decreasing trend.
- PA template suggest "healthy zone".
- A more careful harvest strategy would enable bridging this coming recruitment gap.
- A reduced TAC is prudent.
Conclusions: S-ENS
- Viable habitat marginally improved relative to a historic low in 2022.
- Female larval production peaked in 2023/2024.
- Recruitment to the fishery from a strong year class has begun, full entry by 2025.
- Predation mortality (Halibut, Plaice, Sculpin, Skate) is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has been increasing since 2019.
- Fishable biomass is at a historical low.
- PA template suggest "healthy zone".
- Flexiblity in harvest strategy exists due to strong recuitment.
- A status quo TAC is prudent until confirmation of recruitment.
Conclusions: 4X
- Viable habitat has been at historical lows since 2015.
- Female larval production peaked in 2022.
- Recruitment to the fishery in the near-term is not evident. There is a pulse 4-5 years away.
- Predation mortality (Halibut) and competition with other Crustacea is likely high and causing reduced recruitment to the fishery.
- Fishing mortality has declined to negligible levels since a peak in 2019.
- Fishable biomass is at a historical low.
- PA template suggest "critical zone".
- Stock status is extremely poor.
- Fishery closure until recovery is prudent.
References
\begin{tiny}
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.
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\end{tiny}
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\begin{tiny}
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\end{tiny}
End
Supplemental Information
Stock status {.c}
Geometric mean density by size, sex and maturity
::: columns :::: column
include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "male.denl.png" ) ) # \@ref(fig:sizefeq-male)
:::: :::: column
include_graphics( file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey", "female.denl.png" ) ) # \@ref(fig:sizefeq-male)
:::: :::
Mature female maps
Distributions are heterogeneous and often in shallower areas.
```r$(no/km$^2$) from the Snow Crab survey.' } loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "totno.female.mat" ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( "totno.female.mat", yrsplot[4], "png", sep=".") ) fn3 = file.path( loc, paste( "totno.female.mat", yrsplot[3], "png", sep=".") ) fn2 = file.path( loc, paste( "totno.female.mat", yrsplot[2], "png", sep=".") ) fn1 = file.path( loc, paste( "totno.female.mat", yrsplot[1], "png", sep=".") ) include_graphics( c( fn3, fn2, fn1) )
\@ref(fig:fmat-map)
## Mature female timeseries ```r$(no/km$^2$) from the Snow Crab survey.' } include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "totno.female.mat.pdf") ) # \@ref(fig:fmat-timeseries)
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