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" )
Components of Snow Crab status in Maritimes Region {.c}
- Life history
- Ecosystem change
- Human interactions
- Fishery statistics
- Other interactions
- Scientific survey results
- Life history X Ecosystem change X Human interactions = Complexity
Life history {.c}
Life history {.c}
fn1=file.path( media_loc, "snowcrab_zoea.png" )
fn2=file.path( media_loc, "snowcrab_male.png" )
fn3=file.path( media_loc, "snowcrab_male_and_female.png" )
knitr::include_graphics( c(fn1, fn2, fn3) )
# \@ref(fig:photos)
Life history: stages{.c}
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" )
fn1=file.path( loc, "life_history.png" )
knitr::include_graphics( fn1 )
# \@ref(fig:lifehistory)
Life history: male growth stanzas {.c}
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" )
fn1=file.path( loc, "life_history_male.png" )
knitr::include_graphics( fn1 )
# \@ref(fig:lifehistory_male)
Life history: growth modes{.c}
loc = file.path( Sys.getenv("HOME"), "bio.data", "bio.snowcrab", "output" )
fn1=file.path( loc, "size_structure", "growth_summary.png" )
knitr::include_graphics( c(fn1) )
# \@ref(fig:lifehistory_male)
Life history: notable traits
- Sexual dimorphism
- Pelagic in larval stages; benthic in pre-adolescent and adult stages
- Biannual, annual molts depending upon size/age and environmental conditions
- Terminal molt to maturity and survives for up to 5 years
- Total life span: up to 15 years.
- Stenothermic (narrow) temperature requirements < 6 C
- Ontogenetic shifts in habitat preferences: warmer, complex substrates to mud.
- Cannibalism of immature crab by mature female snow crab are known
- Primiparous female (57.4 mm CW) produces between 35,000 to 46,000 eggs
- Multiparous females more fecund (>100,000 eggs)
- Eggs extruded February and April and brooded for up to two years (> 80% in SSE follow an annual cycle) and hatched/released from April to June.
Life history: movement (mark-recapture) {.c}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
fn1=file.path( media_loc, "movement0.png" )
fn2=file.path( media_loc, "movement.png" )
knitr::include_graphics( c(fn1, fn2) )
# \@ref(fig:movementtracks)
\end{column}
\begin{column}{.48\textwidth}
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)
\end{column}
\end{columns}
\end{small}
Life history: clustering {.c}
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
```r aggregation for moulting and migration and Alaska red king crab \emph{Paralithodes camtschaticus} aggregation in Alaska for egg release, migrations.' }
fn1=file.path( media_loc, "australian_leptomithrax_gaimardii.png" )
fn2=file.path( media_loc, "kingcrab_aggregation.png" )
knitr::include_graphics( c(fn1, fn2 ) )
\@ref(fig:aggregation)
- *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
\end{column}
\begin{column}{.48\textwidth}
```r
fn = file.path(p$project.outputdir, "maps", "map_highdensity_locations.png" )
knitr::include_graphics( fn )
# \@ref(fig:aggregation)
if (0) {
# high density locations directly from databases
M = snowcrab.db( DS="set.complete", p=p )
setDT(M)
i = which(M$totno.all > 2.5*10^5)
H = M[i, .( plon, plat, towquality, dist, distance, surfacearea, vessel, yr, z, julian, no.male.all, no.female.all, cw.mean, totno.all, totno.male.imm, totno.male.mat, totno.female.imm, totno.female.mat, totno.female.primiparous, totno.female.multiparous, totno.female.berried)]
H$log10density = log10(H$totno.all)
library(ggplot2)
cst = coastline_db( p=p, project_to=st_crs(pg) )
isodepths = c(100, 200, 300)
isob = isobath_db( DS="isobath", depths=isodepths, project_to=st_crs(pg))
isob$level = as.factor( isob$level)
plt = ggplot() +
geom_sf( data=cst, show.legend=FALSE ) +
geom_sf( data=isob, aes( alpha=0.1, fill=level), lwd=0.1, show.legend=FALSE) +
geom_point(data=H, aes(x=plon, y=plat, colour=log10density), size=5) +
coord_sf(xlim = c(270, 940 ), ylim = c(4780, 5200 )) +
theme(legend.position=c(0.08, 0.8))
png(filename=fn, width=1000,height=600, res=144)
(plt)
dev.off()
}
\begin{block}{Uncertainty}
Historical snow crab high density locations
\end{block}
\end{column}
\end{columns}
\end{small}
Ecosystem change
Ecosystem change: Predators {.c}
- 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) have significantly increased in abundance in the Region.
- Elevated co-ocurrence in areal densities with snow crab trawl samples means greater encounter rates
Ecosystem change: Predators - Atlantic Halibut {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.30.pdf') )
# \@ref(fig:halibut-timeseries)
Ecosystem change: 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)
\begin{block}{Uncertainty}
Higher predation mortality seems likely (more encounters with warmer-water species)
\end{block}
Ecosystem change: Predators - Thorny skate {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.201.pdf') )
# \@ref(fig:thornyskate-timeseries)
Ecosystem change: Predators - Thorny skate ... {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.201' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.201', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.201', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.201', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.201', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:thornyskate-map)
\begin{block}{Uncertainty}
Higher predation mortality seems likely (more encounters with warmer-water species)
\end{block}
Ecosystem change: Co-occurring - Northern shrimp {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2211.pdf') )
# \@ref(fig:Shrimp-timeseries)
Ecosystem change: Co-occurring - 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)
Shrimp with similar habitat preferences have declined, possibly due to large-scaled habitat variations and predation.
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
Ecosystem change: species composition
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)
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
Ecosystem change: Bottom Temperature {.c}
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 't.pdf') )
# \@ref(fig:bottom-temperatures-survey)
\begin{block}{Uncertainty}
Sampling was incomplete in 2020 and 2022 in S-ENS.
\end{block}
Ecosystem change: Bottom Temperature ... {.c}
-
Average bottom temperatures observed in the 2022 Snow Crab survey were near or above historical highs in all areas
-
Temperatures are more stable in N-ENS than S-ENS; 4X exhibits the most erratic and highest annual mean bottom temperatures.
-
Observed temperatures in the 2022 Snow Crab survey for S-ENS increased well above the average.
-
Average temperature increased well beyond the $7^\circ$C threshold in 4X. N-ENS and S-ENS also continued to experience historical highs in bottom temperature and elevated spatial variability of bottom temperatures.
Ecosystem considerations: 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.*
\begin{block}{Uncertainty}
* 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.
\end{block}
Ecosystem considerations: Bottom Temperature ... {.c}
Persistent spatial gradient of almost $15^\circ$C in bottom temperatures in the Maritimes Region.
Variable due to confluence of the warm, high salinity Gulf Stream from the S-SE along the shelf edge; cold, low salinity Labrador Current; and cold low salinity St. Lawrence outflow from the N-NE, as well as a nearshore Nova Scotia current, running from the NE.
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)
Ecosystem considerations: Bottom Temperature ... {.c}
\begin{columns}
\begin{column}{.6\textwidth}
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'temperature_bottom.pdf') )
# \@ref(fig:bottom-temperatures)
\end{column}
\begin{column}{.4\textwidth}
\vspace{12mm}
\begin{footnotesize}
\textbf{Figure}: Temporal variations in bottom temperature estimated from a historical analysis of temperature data. Red horizontal line is at $7^\circ$C. Presented are 95\% Credible Intervals of spatial variability in temperature at each time slice, after adjustment for spatiotemporal autocorrelation.
\end{footnotesize}
\end{column}
\end{columns}
Human interactions
Human interactions: Every known population is exploited worldwide {.c}
- 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).
Human interactions: Management Approach
\begin{columns}
\begin{column}{.46\textwidth}
\begin{tiny}
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" )
fn1=file.path( loc, "area_map.png" )
knitr::include_graphics( fn1 )
# \@ref(fig:area_map)
\end{tiny}
\end{column}
\begin{column}{.52\textwidth}
\begin{footnotesize}
\begin{itemize}
\item Precautionary Approach, Fish Stock Provisions, 2022
\item Spatial refugia (slope edge, MPAs)
\item Temporal refugia (fishing seasons)
\item Biological refugia: most life stages protected
\begin{itemize}
\begin{scriptsize}
\item Conservative exploitation since mid-2000s
\item Spawning stock legally and completely protected
\item Market-driven protection for 10+ yrs
\end{scriptsize}
\end{itemize}
\item Evidence-based decision making: trawl survey, assessment
\item Distributed knowledge network: traditional, historical, scientific
\item Satellite VMS; biodegradeable mesh (ghost-fishing); weighted lines (entanglement), etc ...
\end{itemize}
\end{footnotesize}
\end{column}
\end{columns}
Human interactions: Fishing effort {.c}
Similar between r year.assessment and r year_previous in terms of spatial distribution. In S-ENS, there was, however, a minor spatial contraction to inshore areas and away from the area 23-24 boundary.
\begin{tiny}
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "effort" )
fn1 = file.path( loc0, paste( "effort", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "effort", year.assessment, "png", sep=".") )
include_graphics( c(fn1, fn2) )
# \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishing effort ... {.c}
\begin{tiny}
fn1=file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "effort.ts.pdf" )
knitr::include_graphics( fn1 )
# \@ref(fig:effort-timeseries)
\end{tiny}
Human interactions: Fishery landings and TACs {.c}
-
The landings in N-ENS for 2022 and 2021 were similar in their spatial patterns.
-
The landings in 4X for 2022 were spatially more contracted than 2021.
\begin{tiny}
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "landings" )
fn1 = file.path( loc0, paste( "landings", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "landings", year.assessment, "png", sep=".") )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishery landings and TACs ... {.c}
In 2022, landings in all areas were below respective TACs.
\begin{tiny}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "landings.ts.pdf" ) )
# \@ref(fig:landings-timeseries)
\end{tiny}
Human interactions: Fishery catch rates
-
Generally, the spatial extent of exploitation was smaller, many of the exploited area show elevated catch rates,
-
In 4X catch rates were lower in 2022.
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "cpue" )
fn1 = file.path( loc0, paste( "cpue", year_previous, "png", sep=".") )
fn2 = file.path( loc0, paste( "cpue", year.assessment, "png", sep=".") )
knitr::include_graphics( c(fn1, fn2 ) )
# \@ref(fig:cpue-map)
Human interactions: Fishery catch rates ...
\begin{tiny}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "cpue.ts.pdf" ) )
# \@ref(fig:cpue-timeseries)
\end{tiny}
Human interactions: At-Sea-Observed information
-
Target: 5% of landings
-
In 2021, both N-ENS and 4X were not sampled by At-Sea-Observers.
-
In 2022, ~ 0.8 % of landings in 4X were sampled by At-Sea-Observers
\begin{tiny}
loc = file.path( SCD, "output", "maps", "observer.locations" )
yrsplot = year.assessment + c(0:-4)
fn4 = file.path( loc, paste( "observer.locations", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "observer.locations", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "observer.locations", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "observer.locations", yrsplot[1], "png", sep=".") )
include_graphics( c( fn4, fn3, fn2, fn1) )
# \@ref(fig:observer-locations-map)
\end{tiny}
\vspace{5mm}
Bycatch: last assessment was in 2017 and levels were << 1% by weight.
Stock status {.c}
Stock status: survey {.c}
-
No survey in 2020 (Covid-19) and incomplete 2022 (mechanical issues).
-
Inshore areas of S-ENS were most affected.
-
N-ENS and CFA 4X were not affected.
\begin{tiny}
loc = file.path( SCD, "output", "maps", "survey.locations" )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn6 = file.path( loc, paste( "survey.locations", yrsplot[6], "png", sep=".") )
fn5 = file.path( loc, paste( "survey.locations", yrsplot[5], "png", sep=".") )
fn4 = file.path( loc, paste( "survey.locations", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "survey.locations", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "survey.locations", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "survey.locations", yrsplot[1], "png", sep=".") )
include_graphics( c(fn3, fn2, fn1) )
# \@ref(fig:survey-locations-map)
\end{tiny}
Stock status: 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)*
Stock status: 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)
Stock status: Sex ratios (proportion female, mature)
\begin{small}
\begin{columns}
\begin{column}{.48\textwidth}
\vspace{2mm}
\begin{itemize}
\item Mostly male-dominated: larger size may be protective against predation?
\item Imbalance indicates differential mortality: predation, competition and fishing
\item In 4X, sex ratios are balanced.
\end{itemize}
\end{column}
\begin{column}{.48\textwidth}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "sexratio.mat.pdf") )
# \@ref(fig:sexratio-mature)
\end{column}
\end{columns}
\end{small}
yrsplot = setdiff( year.assessment + c(0:-4), 2020)
loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "sexratio.mat" )
fn4 = file.path( loc, paste( "sexratio.mat", yrsplot[4], "png", sep=".") )
fn3 = file.path( loc, paste( "sexratio.mat", yrsplot[3], "png", sep=".") )
fn2 = file.path( loc, paste( "sexratio.mat", yrsplot[2], "png", sep=".") )
fn1 = file.path( loc, paste( "sexratio.mat", yrsplot[1], "png", sep=".") )
include_graphics( c( fn3, fn2, fn1 ) )
# \@ref(fig:sexratio-map)
Stock status: Biomass Density {.c}
Note that high and low biomass density areas fluctuate with time
\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}
Stock status: Biomass Density ... {.c}
- A peak in 2009 to 2014 and has since been declining in all areas.
\begin{tiny}
fn = file.path(SCD,'assessments', year.assessment, 'timeseries','survey','R0.mass.pdf')
include_graphics( c(fn) )
#\@ref(fig:fbGMTS)
\end{tiny}
Stock status: Biomass Index (aggregate)
A contraction of spatial range in 4X and the western parts of S-ENS were also evident in 2021 to 2022.
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) )
Stock status: Biomass Index (aggregate) ... {.c}
\begin{tiny}
include_graphics( file.path( SCD, 'modelled', '1999_present_fb', 'aggregated_biomass_timeseries' , 'biomass_M0.png') )
# \@ref(fig:fbindex-timeseries)
\end{tiny}
Stock status: Modelled 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}
Stock status: Model 2 {.c}
- Embed habitat viability variations directly into a model
odir = file.path( fishery_model_results, year.assessment, "size_structured_dde_normalized" )
fn1 = file.path( odir, "plot_predictions_everything_cfanorth.pdf" )
fn2 = file.path( odir, "plot_predictions_everything_cfasouth.pdf" )
fn3 = file.path( odir, "plot_predictions_everything_cfa4x.pdf" )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:dde-predictions-everything)
Stock status: 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)
Stock status: Fishery Footprint {.c}
odir = file.path( fishery_model_results, year.assessment, "size_structured_dde_normalized" )
fn1 = file.path( odir, "plot_trace_footprint_projections_cfanorth__1.0__.pdf" )
fn2 = file.path( odir, "plot_trace_footprint_projections_cfasouth__1.0__.pdf" )
fn3 = file.path( odir, "plot_trace_footprint_projections_cfa4x__1.0__.pdf" )
include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:dde-fisheryfootprint)
Stock status: Reference Points {.c}
include_graphics( file.path( params$media_loc, 'harvest_control_rules.png') )
# \@ref(fig:ReferencePoints)
Stock status: 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 "cautious" zone
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
jae0/snowcrab documentation built on Feb. 27, 2024, 2:42 p.m.
R Package Documentation
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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" )
Components of Snow Crab status in Maritimes Region {.c}
- Life history
- Ecosystem change
- Human interactions
- Fishery statistics
- Other interactions
- Scientific survey results
- Life history X Ecosystem change X Human interactions = Complexity
Life history {.c}
Life history {.c}
fn1=file.path( media_loc, "snowcrab_zoea.png" ) fn2=file.path( media_loc, "snowcrab_male.png" ) fn3=file.path( media_loc, "snowcrab_male_and_female.png" ) knitr::include_graphics( c(fn1, fn2, fn3) ) # \@ref(fig:photos)
Life history: stages{.c}
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" ) fn1=file.path( loc, "life_history.png" ) knitr::include_graphics( fn1 ) # \@ref(fig:lifehistory)
Life history: male growth stanzas {.c}
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" ) fn1=file.path( loc, "life_history_male.png" ) knitr::include_graphics( fn1 ) # \@ref(fig:lifehistory_male)
Life history: growth modes{.c}
loc = file.path( Sys.getenv("HOME"), "bio.data", "bio.snowcrab", "output" ) fn1=file.path( loc, "size_structure", "growth_summary.png" ) knitr::include_graphics( c(fn1) ) # \@ref(fig:lifehistory_male)
Life history: notable traits
- Sexual dimorphism
- Pelagic in larval stages; benthic in pre-adolescent and adult stages
- Biannual, annual molts depending upon size/age and environmental conditions
- Terminal molt to maturity and survives for up to 5 years
- Total life span: up to 15 years.
- Stenothermic (narrow) temperature requirements < 6 C
- Ontogenetic shifts in habitat preferences: warmer, complex substrates to mud.
- Cannibalism of immature crab by mature female snow crab are known
- Primiparous female (57.4 mm CW) produces between 35,000 to 46,000 eggs
- Multiparous females more fecund (>100,000 eggs)
- Eggs extruded February and April and brooded for up to two years (> 80% in SSE follow an annual cycle) and hatched/released from April to June.
Life history: movement (mark-recapture) {.c}
\begin{small} \begin{columns} \begin{column}{.48\textwidth}
fn1=file.path( media_loc, "movement0.png" ) fn2=file.path( media_loc, "movement.png" ) knitr::include_graphics( c(fn1, fn2) ) # \@ref(fig:movementtracks)
\end{column}
\begin{column}{.48\textwidth}
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)
\end{column} \end{columns} \end{small}
Life history: clustering {.c}
\begin{small} \begin{columns} \begin{column}{.48\textwidth} ```r aggregation for moulting and migration and Alaska red king crab \emph{Paralithodes camtschaticus} aggregation in Alaska for egg release, migrations.' } fn1=file.path( media_loc, "australian_leptomithrax_gaimardii.png" ) fn2=file.path( media_loc, "kingcrab_aggregation.png" ) knitr::include_graphics( c(fn1, fn2 ) )
\@ref(fig:aggregation)
- *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
\end{column}
\begin{column}{.48\textwidth}
```r
fn = file.path(p$project.outputdir, "maps", "map_highdensity_locations.png" )
knitr::include_graphics( fn )
# \@ref(fig:aggregation)
if (0) {
# high density locations directly from databases
M = snowcrab.db( DS="set.complete", p=p )
setDT(M)
i = which(M$totno.all > 2.5*10^5)
H = M[i, .( plon, plat, towquality, dist, distance, surfacearea, vessel, yr, z, julian, no.male.all, no.female.all, cw.mean, totno.all, totno.male.imm, totno.male.mat, totno.female.imm, totno.female.mat, totno.female.primiparous, totno.female.multiparous, totno.female.berried)]
H$log10density = log10(H$totno.all)
library(ggplot2)
cst = coastline_db( p=p, project_to=st_crs(pg) )
isodepths = c(100, 200, 300)
isob = isobath_db( DS="isobath", depths=isodepths, project_to=st_crs(pg))
isob$level = as.factor( isob$level)
plt = ggplot() +
geom_sf( data=cst, show.legend=FALSE ) +
geom_sf( data=isob, aes( alpha=0.1, fill=level), lwd=0.1, show.legend=FALSE) +
geom_point(data=H, aes(x=plon, y=plat, colour=log10density), size=5) +
coord_sf(xlim = c(270, 940 ), ylim = c(4780, 5200 )) +
theme(legend.position=c(0.08, 0.8))
png(filename=fn, width=1000,height=600, res=144)
(plt)
dev.off()
}
\begin{block}{Uncertainty}
Historical snow crab high density locations
\end{block}
\end{column}
\end{columns}
\end{small}
Ecosystem change
Ecosystem change: Predators {.c}
- 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) have significantly increased in abundance in the Region.
- Elevated co-ocurrence in areal densities with snow crab trawl samples means greater encounter rates
Ecosystem change: Predators - Atlantic Halibut {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.30.pdf') ) # \@ref(fig:halibut-timeseries)
Ecosystem change: 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)
\begin{block}{Uncertainty} Higher predation mortality seems likely (more encounters with warmer-water species) \end{block}
Ecosystem change: Predators - Thorny skate {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.201.pdf') ) # \@ref(fig:thornyskate-timeseries)
Ecosystem change: Predators - Thorny skate ... {.c}
loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.201' ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn4 = file.path( loc, paste( 'ms.no.201', yrsplot[4], 'png', sep='.') ) fn3 = file.path( loc, paste( 'ms.no.201', yrsplot[3], 'png', sep='.') ) fn2 = file.path( loc, paste( 'ms.no.201', yrsplot[2], 'png', sep='.') ) fn1 = file.path( loc, paste( 'ms.no.201', yrsplot[1], 'png', sep='.') ) include_graphics( c( fn3, fn2, fn1) ) # \@ref(fig:thornyskate-map)
\begin{block}{Uncertainty} Higher predation mortality seems likely (more encounters with warmer-water species) \end{block}
Ecosystem change: Co-occurring - Northern shrimp {.c}
include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2211.pdf') ) # \@ref(fig:Shrimp-timeseries)
Ecosystem change: Co-occurring - 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)
Shrimp with similar habitat preferences have declined, possibly due to large-scaled habitat variations and predation.
\begin{block}{Uncertainty} Sampling was incomplete in 2020 and 2022 in S-ENS. \end{block}
Ecosystem change: species composition
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)
\begin{block}{Uncertainty} Sampling was incomplete in 2020 and 2022 in S-ENS. \end{block}
Ecosystem change: Bottom Temperature {.c}
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 't.pdf') ) # \@ref(fig:bottom-temperatures-survey)
\begin{block}{Uncertainty} Sampling was incomplete in 2020 and 2022 in S-ENS. \end{block}
Ecosystem change: Bottom Temperature ... {.c}
-
Average bottom temperatures observed in the 2022 Snow Crab survey were near or above historical highs in all areas
-
Temperatures are more stable in N-ENS than S-ENS; 4X exhibits the most erratic and highest annual mean bottom temperatures.
-
Observed temperatures in the 2022 Snow Crab survey for S-ENS increased well above the average.
-
Average temperature increased well beyond the $7^\circ$C threshold in 4X. N-ENS and S-ENS also continued to experience historical highs in bottom temperature and elevated spatial variability of bottom temperatures.
Ecosystem considerations: 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.*
\begin{block}{Uncertainty} * 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. \end{block}
Ecosystem considerations: Bottom Temperature ... {.c}
Persistent spatial gradient of almost $15^\circ$C in bottom temperatures in the Maritimes Region.
Variable due to confluence of the warm, high salinity Gulf Stream from the S-SE along the shelf edge; cold, low salinity Labrador Current; and cold low salinity St. Lawrence outflow from the N-NE, as well as a nearshore Nova Scotia current, running from the NE.
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)
Ecosystem considerations: Bottom Temperature ... {.c}
\begin{columns} \begin{column}{.6\textwidth}
knitr::include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'temperature_bottom.pdf') ) # \@ref(fig:bottom-temperatures)
\end{column} \begin{column}{.4\textwidth}
\vspace{12mm}
\begin{footnotesize} \textbf{Figure}: Temporal variations in bottom temperature estimated from a historical analysis of temperature data. Red horizontal line is at $7^\circ$C. Presented are 95\% Credible Intervals of spatial variability in temperature at each time slice, after adjustment for spatiotemporal autocorrelation. \end{footnotesize} \end{column} \end{columns}
Human interactions
Human interactions: Every known population is exploited worldwide {.c}
- 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).
Human interactions: Management Approach
\begin{columns} \begin{column}{.46\textwidth} \begin{tiny}
loc = file.path( Sys.getenv("HOME"), "projects", "dynamical_model", "snowcrab", "media" ) fn1=file.path( loc, "area_map.png" ) knitr::include_graphics( fn1 ) # \@ref(fig:area_map)
\end{tiny}
\end{column}
\begin{column}{.52\textwidth}
\begin{footnotesize}
\begin{itemize}
\item Precautionary Approach, Fish Stock Provisions, 2022
\item Spatial refugia (slope edge, MPAs)
\item Temporal refugia (fishing seasons)
\item Biological refugia: most life stages protected
\begin{itemize}
\begin{scriptsize}
\item Conservative exploitation since mid-2000s
\item Spawning stock legally and completely protected
\item Market-driven protection for 10+ yrs
\end{scriptsize}
\end{itemize}
\item Evidence-based decision making: trawl survey, assessment
\item Distributed knowledge network: traditional, historical, scientific
\item Satellite VMS; biodegradeable mesh (ghost-fishing); weighted lines (entanglement), etc ...
\end{itemize}
\end{footnotesize}
\end{column}
\end{columns}
Human interactions: Fishing effort {.c}
Similar between r year.assessment and r year_previous in terms of spatial distribution. In S-ENS, there was, however, a minor spatial contraction to inshore areas and away from the area 23-24 boundary.
\begin{tiny}
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "effort" ) fn1 = file.path( loc0, paste( "effort", year_previous, "png", sep=".") ) fn2 = file.path( loc0, paste( "effort", year.assessment, "png", sep=".") ) include_graphics( c(fn1, fn2) ) # \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishing effort ... {.c}
\begin{tiny}
fn1=file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "effort.ts.pdf" ) knitr::include_graphics( fn1 ) # \@ref(fig:effort-timeseries)
\end{tiny}
Human interactions: Fishery landings and TACs {.c}
-
The landings in N-ENS for 2022 and 2021 were similar in their spatial patterns.
-
The landings in 4X for 2022 were spatially more contracted than 2021.
\begin{tiny}
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "landings" ) fn1 = file.path( loc0, paste( "landings", year_previous, "png", sep=".") ) fn2 = file.path( loc0, paste( "landings", year.assessment, "png", sep=".") ) knitr::include_graphics( c(fn1, fn2 ) ) # \@ref(fig:landings-map)
\end{tiny}
Human interactions: Fishery landings and TACs ... {.c}
In 2022, landings in all areas were below respective TACs.
\begin{tiny}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "landings.ts.pdf" ) ) # \@ref(fig:landings-timeseries)
\end{tiny}
Human interactions: Fishery catch rates
-
Generally, the spatial extent of exploitation was smaller, many of the exploited area show elevated catch rates,
-
In 4X catch rates were lower in 2022.
loc0= file.path( SCD, "output", "maps", "logbook", "snowcrab", "annual", "cpue" ) fn1 = file.path( loc0, paste( "cpue", year_previous, "png", sep=".") ) fn2 = file.path( loc0, paste( "cpue", year.assessment, "png", sep=".") ) knitr::include_graphics( c(fn1, fn2 ) ) # \@ref(fig:cpue-map)
Human interactions: Fishery catch rates ...
\begin{tiny}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery", "cpue.ts.pdf" ) ) # \@ref(fig:cpue-timeseries)
\end{tiny}
Human interactions: At-Sea-Observed information
-
Target: 5% of landings
-
In 2021, both N-ENS and 4X were not sampled by At-Sea-Observers.
-
In 2022, ~ 0.8 % of landings in 4X were sampled by At-Sea-Observers
\begin{tiny}
loc = file.path( SCD, "output", "maps", "observer.locations" ) yrsplot = year.assessment + c(0:-4) fn4 = file.path( loc, paste( "observer.locations", yrsplot[4], "png", sep=".") ) fn3 = file.path( loc, paste( "observer.locations", yrsplot[3], "png", sep=".") ) fn2 = file.path( loc, paste( "observer.locations", yrsplot[2], "png", sep=".") ) fn1 = file.path( loc, paste( "observer.locations", yrsplot[1], "png", sep=".") ) include_graphics( c( fn4, fn3, fn2, fn1) ) # \@ref(fig:observer-locations-map)
\end{tiny}
\vspace{5mm}
Bycatch: last assessment was in 2017 and levels were << 1% by weight.
Stock status {.c}
Stock status: survey {.c}
-
No survey in 2020 (Covid-19) and incomplete 2022 (mechanical issues).
-
Inshore areas of S-ENS were most affected.
-
N-ENS and CFA 4X were not affected.
\begin{tiny}
loc = file.path( SCD, "output", "maps", "survey.locations" ) yrsplot = setdiff( year.assessment + c(0:-9), 2020) fn6 = file.path( loc, paste( "survey.locations", yrsplot[6], "png", sep=".") ) fn5 = file.path( loc, paste( "survey.locations", yrsplot[5], "png", sep=".") ) fn4 = file.path( loc, paste( "survey.locations", yrsplot[4], "png", sep=".") ) fn3 = file.path( loc, paste( "survey.locations", yrsplot[3], "png", sep=".") ) fn2 = file.path( loc, paste( "survey.locations", yrsplot[2], "png", sep=".") ) fn1 = file.path( loc, paste( "survey.locations", yrsplot[1], "png", sep=".") ) include_graphics( c(fn3, fn2, fn1) ) # \@ref(fig:survey-locations-map)
\end{tiny}
Stock status: 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)*
Stock status: 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)
Stock status: Sex ratios (proportion female, mature)
\begin{small} \begin{columns} \begin{column}{.48\textwidth}
\vspace{2mm}
\begin{itemize}
\item Mostly male-dominated: larger size may be protective against predation?
\item Imbalance indicates differential mortality: predation, competition and fishing
\item In 4X, sex ratios are balanced.
\end{itemize}
\end{column}
\begin{column}{.48\textwidth}
include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "sexratio.mat.pdf") ) # \@ref(fig:sexratio-mature)
\end{column} \end{columns} \end{small}
yrsplot = setdiff( year.assessment + c(0:-4), 2020) loc = file.path( SCD, "output", "maps", "survey", "snowcrab", "annual", "sexratio.mat" ) fn4 = file.path( loc, paste( "sexratio.mat", yrsplot[4], "png", sep=".") ) fn3 = file.path( loc, paste( "sexratio.mat", yrsplot[3], "png", sep=".") ) fn2 = file.path( loc, paste( "sexratio.mat", yrsplot[2], "png", sep=".") ) fn1 = file.path( loc, paste( "sexratio.mat", yrsplot[1], "png", sep=".") ) include_graphics( c( fn3, fn2, fn1 ) ) # \@ref(fig:sexratio-map)
Stock status: Biomass Density {.c}
Note that high and low biomass density areas fluctuate with time
\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}
Stock status: Biomass Density ... {.c}
- A peak in 2009 to 2014 and has since been declining in all areas.
\begin{tiny}
fn = file.path(SCD,'assessments', year.assessment, 'timeseries','survey','R0.mass.pdf') include_graphics( c(fn) ) #\@ref(fig:fbGMTS)
\end{tiny}
Stock status: Biomass Index (aggregate)
A contraction of spatial range in 4X and the western parts of S-ENS were also evident in 2021 to 2022.
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) )
Stock status: Biomass Index (aggregate) ... {.c}
\begin{tiny}
include_graphics( file.path( SCD, 'modelled', '1999_present_fb', 'aggregated_biomass_timeseries' , 'biomass_M0.png') ) # \@ref(fig:fbindex-timeseries)
\end{tiny}
Stock status: Modelled 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}
Stock status: Model 2 {.c}
- Embed habitat viability variations directly into a model
odir = file.path( fishery_model_results, year.assessment, "size_structured_dde_normalized" ) fn1 = file.path( odir, "plot_predictions_everything_cfanorth.pdf" ) fn2 = file.path( odir, "plot_predictions_everything_cfasouth.pdf" ) fn3 = file.path( odir, "plot_predictions_everything_cfa4x.pdf" ) include_graphics(c(fn1, fn2, fn3) ) # \@ref(fig:dde-predictions-everything)
Stock status: 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)
Stock status: Fishery Footprint {.c}
odir = file.path( fishery_model_results, year.assessment, "size_structured_dde_normalized" ) fn1 = file.path( odir, "plot_trace_footprint_projections_cfanorth__1.0__.pdf" ) fn2 = file.path( odir, "plot_trace_footprint_projections_cfasouth__1.0__.pdf" ) fn3 = file.path( odir, "plot_trace_footprint_projections_cfa4x__1.0__.pdf" ) include_graphics(c(fn1, fn2, fn3) ) # \@ref(fig:dde-fisheryfootprint)
Stock status: Reference Points {.c}
include_graphics( file.path( params$media_loc, 'harvest_control_rules.png') ) # \@ref(fig:ReferencePoints)
Stock status: 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 "cautious" zone
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
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