jae0/snowcrab
Snow crab assessment suite for aegis* and associated projects.

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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" )

ABSTRACT

In the Scotian Shelf Ecosystem (SSE), Snow Crab (Chionoecetes opilio) has been a dominant macro-invertebrate since the decline of the groundfish in the 1990s. They are mostly observed in deep, soft-bottom substrates ranging from 60 to 300 m and at temperatures generally less than 6${}^\circ$ C. The SSE Snow Crab are in the southern-most extreme of their spatial distribution in the Northwest Atlantic Ocean and vulnerable to climate variability.

The assessment of 4VWX Snow Crab status (TOR #1; see Terms of reference, below) is based on fishery independent surveys with a focus upon indicators of abundance, reproductive potential, recruitment, and exploitation rates (TOR #2). Robust, Bayesian, Hurdle-type spatiotemporal models are used to incorporate habitat viability attributable to ecosystem influences (depth, species composition and bottom temperature variations, etc.) and so account for biases due to deficiencies of sample design and incomplete surveys. Models of fishery dynamics are used to infer historical abundance and a new model for forward projections is used to help evaluate the consequences of different harvest levels (TOR #3). Further, we highlight some of the bycatch in the survey that highlights potentially elevated predation-related mortality in some areas (TOR #4).

Commercial catch rates and other fishery statistics are reported. Overall, fishing effort was much reduced in 2022, especially in S-ENS and 4X. TACs were mostly caught, with the exception of 4X. Catch rates were at or near all-time highs in all areas, except for 4X. At-Sea-Observed information was sparse and so fishery bycatch and snow crab size distributions were not evaluated.

Survey indices suggest a downtrend in numerical densities of Snow Crab recruiting to fishable sizes, especially in N-ENS. Similarly, declines in numerical densities of mature females, especially in N-ENS, suggest potential declines in egg production and long term recruitment. Mature snow crab sex ratios remain close to balanced levels, except again in N-ENS. Crude geometric mean biomass densities of the fishable component have also declined in all areas, and for S-ENS and 4X, it was at an historical low. Adjustment by spacetime index modelling suggest that the biomass index is also low, but habitat variability, especially as they relate to bottom temperature variability was very significant.

Modelled inference (Model 1) closely follows the biomass index and suggests that the overall biomass of fishable Snow Crab has marginally declined over the last year and that fishing mortality has declined and are well below FMSY, with the exception of N-ENS. Model 1 places N- and S-ENS in the "healthy" zone and 4X in the "cautious" zone. Supplemental analysis using a more complex model (Model 2) supports these inferences. However, they suggest that Model 1's estimates of carrying capacity may be overly optimistic and as such fishing mortality estimates may be biased low. Fishery footprint, a sensitive index of how much a fishery activity is altering the dynamics of a population, is inferred to be lower than 50% in all areas. Assuming current ecosystem conditions persist, Model 2 suggest that a status quo TAC for 2023 would likely result in reductions in overall abundance and that the fishery footprint would go above 50% in N-ENS, upto 40% in S-ENS and in 4X. All regions have flexibility. However, due to a lower level of recruitment in N-ENS and potentially elevated predation mortality, care should be taken in 2023 until recruitment returns.

SUMMARY

BACKGROUND

Terms of Reference (TOR)

To provide an assessment of the:

  1. status of Snow Crabs
  2. relative abundance and exploitation rates
  3. evaluate consequences of different harvest levels upon abundance and exploitation rates
  4. bycatch in survey

Amendments of the Fisheries Act in 2022 (Fish Stock Provisions), have encoded management approaches and requirements relative to biological reference points. In the SSE Snow Crab Fishery, these biological reference points were originally defined heuristically with a phenomenological model (a descriptive, model without mechanism, herein "Model 1"; Choi 2023), with all participants to the management process being aware that it was simplistic at best and that it served as supplemental information to help provide context rather than be the tool used for management. As there is now a strong legal requirement that such reference points become a primary management tool, there is a need to define these reference point with greater care and evaluate their robustness and utility with other models. Here, we will provide some additional information towards this goal and present "Model 2" results (see Ecosystem considerations, below and Choi 2023), in order to obtain more context around the reference points and status (objectives 2 and 3 of the Terms of Reference).

Ecology

Snow Crab (Chionoecetes opilio; Figure \@ref(fig:crab-image)) are a circumpolar, subarctic species. In the Northwest Atlantic Ocean, they are found from northern Labrador to near the Gulf of Maine. The Scotian Shelf Ecosystem (SSE) represents the southern-most part of this distribution and so most influenced by environmental variability. In the SSE, habitat preference is generally for soft mud bottoms, at depths from 60 to 300 m and temperatures from -1 to 6${}^\circ$ C. Temperatures greater than 7${}^\circ$ C are known to be metabolically detrimental. Mature females and immature crab are found in slightly more complex habitats with shelter at marginally shallower depths (Choi et al. 2022). Their primary food items show ontogenetic shift. In their bottom dwelling phase, they rely upon: shrimp, fish, starfish, sea urchins, worms, detritus, large zooplankton, other crabs, molluscs, sea snails, and sea anemones. Their predators are primarily humans, Atlantic Halibut, Thorny Skate, Wolfish, Atlantic Cod, seals, American Plaice, squids, and other crabs. Crab in the size range of 3 to 30 mm carapace width (CW) are particularly vulnerable to predation, as are soft-shelled crab in the spring molting season. Cannibalism of immature crab by females have also been observed. More detailed information with regards to snow habitat requirements and spatiotemporal distributions of different life stages in the SSE are presented in Choi et al. (2022).

Precautionary Approach

A precautionary approach is a guiding principle for fisheries harvesting in Canada ("Fish Stock Provisions" Amendments of the Fisheries Act, 2022). As such it is worthwhile emphasizing the many existing measures and fishing practices in the SSE Snow Crab fishery that are inherently precautionary that are not always appreciated by the general public:

FISHERY STATISTICS

Fishing effort

Fishing effort in r year.assessment was r e_nens $\times 10^3$, r e_sens $\times 10^3$ and r e_4x $\times 10^3$ trap hauls in N-ENS, S-ENS and 4X, respectively. Relative to the previous year, these represent changes of r dt_e_nens %, r dt_e_sens % and r dt_e_4x %, respectively (Tables \@ref(tab:table-fishery-nens), \@ref(tab:table-fishery-sens), \@ref(tab:table-fishery-4x), Figure \@ref(fig:effort-timeseries)). Fishing effort was consistent 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 (Figure \@ref(fig:effort-map)). This is presumed to be related to, in part, warm water incursions into the area (see Ecosystem considerations, below).

Fishery landings and TACs

Landings across time are shown in Figure \@ref(fig:landings-timeseries). In r year.assessment, they were r l_nens, r l_sens and r l_4x t, in N-ENS, S-ENS and 4X (season ongoing), respectively. Relative to r year_previous, they represent changes of r dt_l_nens%, r dt_l_sens% and r dt_l_4x%, respectively (Tables \@ref(tab:table-fishery-nens), \@ref(tab:table-fishery-sens), \@ref(tab:table-fishery-4x)). Total Allowable Catches (TACs) for r year.assessment were r tac_nens t, r tac_sens t and r tac_4x t in N-ENS, S-ENS and 4X, respectively.

Additional carry forward allowance was implemented by DFO Fisheries Management of up to 25% of the 2020 quota to the 2021 season for N-ENS and S-ENS Snow Crab fishery. These were a response to COVID-19 related uncertainties with safe fishing activity. The carry-forward amounts were: 11.2 t in N-ENS, and 217.4 t in S-ENS (Tables \@ref(tab:table-fishery-nens), \@ref(tab:table-fishery-sens)). In 2022, landings in all areas were below respective TACs.

The landings in N-ENS for 2022 and 2021 were similar in their spatial patterns (Figure \@ref(fig:sse-map)). In S-ENS, landings, as with fishing effort, shifted slightly inshore and away from the area 23-24 boundary (Figure \@ref(fig:landings-map)). There were no landings on the continental slope areas of S-ENS in 2022 which continues to serve as a "reserve" for Snow Crab from fishing. The landings in 4X for 2022 as with 2021, were primarily in the area just south of Sambro, bordering onto area 24. In N-ENS, most landings occured in the spring.

Fishery catch rates

Non-standardized fishery catch rates in r year.assessment were r c_nens, r c_sens and r c_4x kg/trap haul in N-ENS, S-ENS and 4X, respectively. This represents a change of respectively, r dt_c_nens %, r dt_c_sens % and r dt_c_4x % (season ongoing) relative to the previous year (Tables \@ref(tab:table-fishery-nens), \@ref(tab:table-fishery-sens), \@ref(tab:table-fishery-4x), Figures \@ref(fig:cpue-timeseries)). Though the spatial extent of exploitation was smaller, many of the exploited area show elevated catch rates (Figure \@ref(fig:cpue-map)).

At-Sea-Observed information

Carapace condition of the fished component is determined from At-Sea-Observed catches. In 2021, both N-ENS and 4X were not sampled by At-Sea-Observers. In 2022, 4X was not sampled by At-Sea-Observers. Estimates of carapace condition since 2020 are unreliable as they represent only small areas of the fishing grounds and short time periods relative to the whole fishing season (Figure \@ref(fig:observer-locations-map)).

In the exploited fraction of Snow Crab, Carapace Condition (CC) is an index of the approximate time since the last molt so describes the relative development and subsequent decay of the carapace. CC1 signifies a newly molted crab, soft-shelled, with no epibiont (e.g., barnacles) growth. CC2 crab have begun to harden, but is still considered to be soft and of no commercial value. CC3 and CC4 represent ideal commercial crab. The oldest carapace condition (CC5) signifies extensive shell decay with no expectation of survival into the next year.

Commercial catches of soft-shelled crab were r cc_soft_nens% (low sampling), r cc_soft_sens% (low sampling) and r cc_soft_4x% (no sampling; season ongoing) in N-ENS, S-ENS and 4X, respectively for r year.assessment. In r year_previous, it was r cc_soft_nens_p% (no sampling), r cc_soft_sens_p% (low sampling) and r cc_soft_4x_p% (no sampling), respectively. Generally, higher soft-shell indicates incoming recruitment to the fishery and their handling potential and unnecessary handling/discard mortality.

In 2022, CC5 crab levels were higher in N- and S-ENS than in previous years, though again low and inconsistent sampling effort (space, time aliasing) makes this uncertain.

Bycatch in the Snow Crab fishery is also monitored from the At-Sea-Observed catches. Due to a paupacy of consistent data, very little can be said of bycatch after 2017. Historically, bycatch in the Snow Crab fishery has been minimal (Tables 4, 5) with increasing levels as a function of increasing water temperature: there are higher by catch in warmer conditions, primarily other Crustacea (crab and lobster), especially when viable habitat is low and animals with divergent habitat requirements find themselves next to each other. Low bycatch has been attributed to trap design (top entry conical traps), the large mesh size (5.25 inches, knot to knot) and the passive nature of the gear (Hebert et al., 2001).

SURVEY INDICES

Survey catch rates are confounded by numerous factors that vary across space and time. This is because distributions of Snow Crab and variables that influence these distributions vary across space and time, while survey effort does not (they are mostly fixed stations, in time and space). Survey catch rates depend upon seasonality, bottom temperatures, predator distributions, food availability, reproductive behavior, substrate/shelter availability, relative occurrence of soft and immature crab, species co-occurrence, vessel/captain experience, gear configuration, ambient currents, etc. These factors are taken into account where possible with CARSTM (Conditional AutoRegressive SpatioTemporal Models; Choi 2020, Choi et al. 2022; and references therein), a robust extension of Bayesian, Hurdle-type generalized linear random effects models to spatially and temporally connected units.

The survey was not conducted in 2020 due to Covid-19 related to health and safety uncertainties. In 2022, the survey did not complete due to mechanical issues with the survey vessel. Inshore areas of S-ENS were most affected (Figure \@ref(fig:survey-locations-map)).

Size and carapace condtion

Geometric mean size of the mature male component of Snow Crab has varied between 83 to 108 mm CW (Figure \@ref(fig:meansize-male-mat)) in the historical record. Changes in size can be caused by poor environmental conditions that encourage early maturation, size-selective predation, loss of the largest individuals due to fishing and the start or end of a recruitment pulse. N-ENS and 4X have seen the greatest volatility. S-ENS has been stable. In 2022 it has declined in S-ENS (though this may be an result of incomplete surveys) and 4X (death of the largest crab possibly due to warm water incursions) while increasing in N-ENS (low recruitment).

The carapace condition of mature male crab captured by the Snow Crab survey are shown in Figure (\@ref(fig:sizefeq-male-survey-cc)). The relative distributions are mostly comparable across time. However, there has been a slight increase in the proportion of CC5 crab in 2022 for S-ENS. An increase was also seen in At-sea-observed fishery data and so could be indicative of accelerated ageing associated with high temperature conditions (see below). However, the incomplete sampling in the inshore areas of S-ENS and in the At-Sea-Observed data makes inference uncertain.

Recruitment

Quantitative determination of recruitment levels into the fishable component is confounded by a number of factors. These include terminal molt (the timing offset of molting in spring and the survey in the fall), the inability to age crab, the inability to predict the age that male crab will terminally molt and the focus of the survey upon the fishable component which results in a biased under-representation of recruitment. As habitat requirements/preferences of adolescent and larval components are divergent to those of the fishable component (Choi et al 2022), their estimation is challenging.

Based on size-frequency histograms of the male Snow Crab population, little to no recruitment is expected for the next 1-3 years in N-ENS (Figure \@ref(fig:sizefeq-male)). In S-ENS, continued moderate levels of recruitment are expected. In 4X, low to moderate levels of recruitment are expected for 2 years.

Reproduction

In all areas, there was substantial and continued recruitment of female crab into the mature (egg-bearing) segment of the population from 2016-2022 (Figure \@ref(fig:sizefeq-female)). However, in N-ENS for 2022, a decline in numerical densities was observed as well as low densities of adolescent females. Egg and larval production is expected to be moderate to high in the next year in all areas except N-ENS. Mature female abundance (Figure \@ref(fig:fmat-timeseries)) distributions are heterogeneous and often in shallower areas.

Sex ratios

The sex ratios (proportion female) of the mature component is particularly important as locally unbalanced ratios can impact upon encounter rates and ultimately reproductive success (Figure \@ref(fig:sexratio-mature)). In the ESS, there is generally a lack of females, in contrast to, for example, the Gulf of St-Lawrence where the reverse is often the case. Higher sex ratios are usually found in inshore and bottom slope areas (Figure \@ref(fig:sexratio-map)). A decline in sex ratios has been observed since 2017 in N-ENS (Figure \@ref(fig:sexratio-mature)). In S-ENS the sex ratio increased from 20% in 2021 to just under 35% in 2022. In 4X, mature sex ratios are more balanced and currently near the 50% level.

Biomass Density

The fishable component is defined as Snow Crab that are male, mature, and larger than 95 mm CW. The crude biomass density (that is, unadjusted, geometric mean fishable per unit swept area by the trawl) of the fishable component, sampled by the Snow Crab survey is shown in Figures \@ref(fig:fbGMTS), and \@ref(fig:fbgeomean-map). A peak in crude biomass density was observed in 2009 to 2014 and has since been declining in all areas. Note that high and low biomass density areas fluctuate with time (Figure \@ref(fig:fbgeomean-map)). Biomass density, however, does not equate to total biomass as the areas occupied by crab can contract, expand and shift with environmental conditions and ecosystem variability.

Biomass Index

The fishable biomass index is a statistically modelled estimate after adjustment for ecosystem indices and spatiotemporal autocorrelation; Figure \@ref(fig:fbindex-map)). More specifically, it was computed using conditional auto-regressive spatio-temporal models (Choi 2020) of probability of observation, numerical abundance of positive valued occurrence and mean size with environmental covariates of depth, substrate, bottom temperature, two axes of species composition ordinations. Further, the biomass index model infers (that is, imputes) the spatiotemporal distribution of the biomass density of the fishable component (Figure \@ref(fig:fbindex-map)). The locations with no sampling from 2022 are also imputed. Upon aggregation of the solutions, we see that the overall biomass has had several cycles (Figure \@ref(fig:fbindex-timeseries)). Note, however, the more elevated uncertainty for the imputed 2020 and partially imputed 2022 estimates (Figure \@ref(fig:fbindex-timeseries)).

The magnitudes of the biomass index are optimistically high as the spatial expansion uses areal units with large surface areas, larger than the patchiness of Snow Crab distributions (spatial autocorrelation length is <20 km, on average; Choi 2020). As such, it should only be seen as a spatially and temporally comparable relative index of abundance. As the size of areal units decreases to scales smaller than the spatial autocorrelation length, the solutions will converge to reality; but the costs in time and resources of achieving this are prohibitive as sampling intensity required to achieve such a result would require at least another two orders of magnitude increase in sampling effort (in time and space).

The spatial distribution of the biomass index has been consistent over the past six years, with a peak in overall biomass index in 2019 and 2020 (Figure \@ref(fig:fbindex-map)). Since then, a reduction was observed throughout the region, with the exception of the core areas. A contraction of spatial range in 4X and the western parts of S-ENS were also evident in 2021 to 2022. Upon aggregation, the biomass index declined marginally in all areas (Figure \@ref(fig:fbindex-timeseries)).

Modelled Biomass

The biomass index along with fishery removals are used to fit a Logistic Biomass Dynamics Model (Model 1; Choi 2023) to determine fishable modelled biomass (biomass estimated from the fisheries model; Figure \@ref(fig:logisticPredictions)) and relevant biological reference points (i.e., carrying capacity and fishing mortality at maximum sustainable yield, or F~MSY~). In N-ENS, the modelled biomass (pre-fishery) of Snow Crab in r year.assessment was r round(B_north[t0], 2) t, relative to r round(B_north[t1], 2) t in r year_previous. In S-ENS, the r year.assessment modelled biomass (pre-fishery) was r round(B_south[t0], 2) t, relative to r round(B_south[t1], 2) t in r year_previous. In 4X, the modelled biomass (pre-fishery) for the r year.assessment-r year.assessment+1 season was r round(B_4x[t0], 2) t, relative to r round(B_4x[t1], 2) t for the r year_previous-r year.assessment season.

Fishing Mortality

In N-ENS, the r year.assessment fishing mortality is estimated to have been r round(FM_north[t0],3) (annual exploitation rate of r round(100*(exp(FM_north[t0])-1),2)%), up from the r year_previous rate of r round(FM_north[t1],3) (annual exploitation rate of r round(100*(exp(FM_north[t1])-1),1)%; Figure \@ref(fig:logisticFishingMortality)).

In S-ENS, the r year.assessment fishing mortality is estimated to have been r round(FM_south[t0],3) (annual exploitation rate of r round(100*(exp(FM_south[t0])-1),1)%), decreasing marginally from the r year_previous rate of r round(FM_south[t1],3) (annual exploitation rate of r round(100*(exp(FM_south[t1])-1),1)%; Figure \@ref(fig:logisticFishingMortality)). Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished (e.g., continental slope areas and western, inshore areas of CFA 24).

In 4X, the r year.assessment-r year.assessment+1 season (ongoing), fishing mortality is estimated to have been r round(FM_4x[t0],3) (annual exploitation rate of r round(100*(exp(FM_4x[t0])-1),1)%), decreasing from the r year.assessment-1-r year.assessment season rate of r round(FM_4x[t1],3) (annual exploitation rate of r round(100*(exp(FM_4x[t1])-1),1)%; Figure \@ref(fig:logisticFishingMortality)). Localized exploitation rates are likely higher, as not all areas for which biomass is estimated are fished.

Reference Points

Reference points are used to guide harvest strategies (Canada Gazette 2022; DFO 2013; Figures \@ref(fig:logistic-hcr)). More specifically, the state of the fishery relative to the following "Reference Points" are assessed. In terms of modelled biomass, Lower and Upper Stock Reference are 25% and 50% of carrying capacity which delineate "critical", "cautious" and "healthy" zones. In terms of exploitation rates, the Upper Removal Reference is the exploitation rate that we try not to go beyond; it is defined in term of the fishing mortality associated with Maximum Sustainable Yield (FMSY). In the biomass dynamics model, FMSY = $r /2$. As $r \approx 1$ for snow crab, FMSY $\approx 0.5$ is expected.

The operational target exploitation changes depending upon the "zone" in which a population lands. When in the "healthy" zone, the rule of thumb has been to keep annual exploitation rates between 10% to 32% of the available biomass ($F = 0.11, 0.36$, respectively). In the "cautious" zone, the rule of thumb has been to keep annual exploitation rates between 0% to 20% ($F = 0, 0.22$, respectively). In the "critical" zone, fishery closure is considered until recovery is observed, where recovery indicates at a minimum, modelled biomass > LSR. Other biological and ecosystem considerations such as recruitment, spawning stock (female) biomass, size structure, sex ratios and environmental and ecosystem conditions, provide additional guidance within each range.

Model 1 estimates key Reference Points as shown in Table 6 and Figure \@ref(fig:ReferencePoints). The related PA thresholds can be computed as:

Model 1 suggests the current state of the fishable components to be (Figure \@ref(fig:logistic-hcr)):

It should be emphasized that using these parameters assumes that the population dynamics are well described by the fishery model. This is, of course, not true. For example, the observation of fisheries landings is assumed to be known without error. This is not true as illegal and unreported exploitation occurs. These and other unaccounted factors (recruitment strength, environmental variability, predation intensity, disease) can easily bias parameter estimates. As such, caution is required in using these reference points. Other contextual reference points must be used in conjunction:

We turn to some these additional factors in the next section.

Ecosystem Considerations

Bottom Temperature

Average bottom temperatures observed in the 2022 Snow Crab survey were near or above historical highs in all areas (Figure \@ref(fig:bottom-temperatures-survey)). A general warming trend has been observed in the Snow Crab survey since the early 1990s on the Scotian Shelf (Choi et al. 2022). Temperatures are more stable in N-ENS than S-ENS; 4X exhibits the most erratic and highest annual mean bottom temperatures. Of particular note, the observed temperatures in the 2022 Snow Crab survey for S-ENS increased well above the average. This is expected to be a source of bias and certainty for S-ENS abundance predictions as it is outside the range normally encountered by the statistical models. Furthermore, the Groundfish surveys did not operate over Snow Crab grounds in 2020 and 2022 and so temperature data is also very sparse for the area of interest.

Upon aggregation and modelling of historical temperature data, the average temperature is found to have 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 (Figure \@ref(fig:bottom-temperatures)). In particular, the spike observed in S-ENS (above) was less extreme and so likely due to a short term influx of warm water.

Overall, since 1999, there has been a persistent spatial gradient of almost $15^\circ$C in bottom temperatures in the Maritimes Region (Figure \@ref(fig:bottom-temperatures-spatialeffect)). This large gradient in a spatially complex and temporally dynamic area makes assessment a particular challenge for a stenothermic organism such as Snow Crab (Choi et al. 2022).

Viable habitat

Snow Crab being cold water stenotherms, stability of environmental conditions is critical for their survival. The Maritimes Region being at the confluence of many oceanic currents renders the area highly variable. Rapid climate change and uncertainty exacerbates this situation. The viable habitat estimated for each area across time has shown some variations (Figures \@ref(fig:fb-habitat-timeseries), \@ref(fig:fb-habitat-map)) in the historical record. As can be seen, 4X showed a significantly lower average viable habitat levels relative to the N-ENS and S-ENS. A peak in average probability of observing fishable snow crab ("viable habitat") was observed in 2010 for 4X, 2011 for N-ENS and 2012 for S-ENS. Since 2015, the average viable habitat has declined to historical lows and remained so in 2022.

Viable habitat in a stage-dependent model

The Model 1 representations of Snow Crab populations are imperfect at best. It is a naive phenomenological model that fits a pattern rather than biological processes (see Choi 2023). Amongst its' difficulties are:

In light of these issues, we present the results of another complementary modelling approach: a Delay Differential Stage Structured Model (Model 2). The rationale for Model 2 is developed and described in detail elsewhere (Choi 2023). It is a six-component system (five male stages from instar 9 [40.9-55.1mm], 10 [55.1-74.4mm], 11 [74.4-95mm], immature [95mm+], mature [95mm+] and mature females) delay differential continuous model, where each component has a molt and/or birth from a previous stage in balance with a constant (first order) background death rate; a second-order death rate associated with varying levels of viable habitat, similar to the logistic form; and fishing mortality, assumed to be known without error.

It must be emphasized that Model 2, though promising, is still in the early stages of development. Most notably, there are many more processes (parameters) being estimated; the more complex parameter space results in many local-optima. Though the use of the NUTS sampler in a Bayesian context permits some measure of robustness from becoming stuck in such local-optima, this slows down computations. Further, missing from the model are: predation mortality and movement. These latter processes will need to be parameterized for the model system to be more robust and complete. However, as it is already computationally quite expensive taking up to several days to complete one area, additional model complexity will need to be added carefully.

As Model 2 is continuous in form, we can infer greater detail with respects to the time evolution and in particular the intensity of fishing vs the usually slower recovery that depends upon recruitment levels. This creates the sawtooth pattern seen in Figure (\@ref(fig:dde-predictions-everything), orange lines). This also demonstrates why it is so difficult to model the dynamics of snow crab as temporal aliasing can be large. Even small changes in the timing of surveys or fishing can alter our understanding of the dynamics of the population. Further, the volatility of the environment in conjunction with the small population size (especially in 4X), renders a robust understanding of population dynamics a significant challenge.

Note also, the diminished overall magnitudes of the biomass (Tables 6, 7) as the relative scale of fisheries activity, variability in viable habitat, and dynamics between components informs Model 2 to more reasonable bounds. Model 2 still had difficulty tracking the fishable biomass index in N-ENS (2016 to present), S-ENS (all years) and 4X (2016-2022). There are other factors that are not captured by the model and/or the data. We hypothesize that one such process is subarea dynamics with sub-legal components that are out of phase and internal structure (inshore-offshore and southwest-northeast decoherence) causing an overall dampening effect. Predation and movement are the missing processes. But the survey itself taking up to 4 months to complete, can be seen to be problematic as well, in that temporal and spatial aliasing is introduced.

Fishing mortality estimates from Model 2 (Figure \@ref(fig:dde-fishing-mortality)) have an overall form that is similar to those of Model 1 (Figure \@ref(fig:logisticFishingMortality)). As with Model 1, Model 2 suggests peak fishing mortality likely occurred in N-ENS in 2005 and earlier. After strong and difficult reductions, it has slowly increased over time. In S-ENS, it has been more stable and conservative throughout the timeseries. In 4X, prior to formal assessments beginning in 2005, fishing mortality was likely to have been high, but has since declined to a range consistent with the other regions.

The important difference between the two models is that Model 2 suggests fishing mortality rates are higher in magnitude than those of Model 1. Model 2 estimates of fishing mortality rate for N-ENS in the r year.assessment was r round(ddeFM_north[t0],2) (annual exploitation rate of r round(100*(exp(ddeFM_north[t0])-1),2)%), up from the r year_previous rate of r round(ddeFM_north[t1],1) (annual exploitation rate of r round(100*(exp(ddeFM_north[t1])-1),1)%). In S-ENS, the r year.assessment fishing mortality was r round(ddeFM_south[t0],2) (annual exploitation rate of r round(100*(exp(ddeFM_south[t0])-1),1)%), down slightly from the r year_previous rate of r round(ddeFM_south[t1],2) (annual exploitation rate of r round(100*(exp(ddeFM_south[t1])-1),1)%). In 4X, the r year.assessment-r year.assessment+1 season (ongoing) fishing mortality was r round(ddeFM_4x[t0],2) (annual exploitation rate of r round(100*(exp(ddeFM_4x[t0])-1),1)%), down from the r year_previous-r year.assessment season rate of r round(ddeFM_4x[t1],2) (annual exploitation rate of r round(100*(exp(ddeFM_4x[t1])-1),1)%).

There is no concept of FMSY in Model 2 as there is no stable solution to such an externally perturbed system (viable habitat). One can, however, accept Model 1's assertion that FMSY should be somewhere close to 0.5. Using this an approximate landmark, N-ENS may have come close to this threshold in 2022. The trajectory of stock status (Figure \@ref(fig:dde-hcr)), would suggest that all areas are in "healthy" states, though 4X is very close to the border of the "cautious" zone. The important difference in interpretation is that carrying capacity estimates may be generally lower. In particular, for S-ENS, dynamics may have been moving quite close to carrying capacity levels and sometimes even surpassing it when good environmental conditions are followed rapidly by average conditions. Overall, reality is likely somewhere in between the two Models inferences.

Further, assuming there is no fishing in the next year, there is a prediction of increased abundance for N-ENS and S-ENS and a decrease in abundance in 4X (Figure (\@ref(fig:dde-predictions-everything)), orange lines after 2022 converge towards the green). In contrast, Model 1 will always project an increase due to its naive assumptions (\@ref(fig:logisticPredictions)).

An alternative, and perhaps more intuitive index of fishing effect is to express it not in terms of an instantaneous rate nor interval approximations which are bound to parameters that can be biased (such as K, r, FMSY), but rather, how much fishing has moved the dynamics of a population away from what it may have been if there had been no fishing. This fishery footprint (Figure \@ref(fig:dde-footprint-trace)) represented by the degree of divergence between the orange lines (with fishing) and the green lines (no fishing). This fishery footprint has been about 25% to 50% in N-ENS since 2010. In S-ENS, the fisheries footprint has been stable and low between 10% to 30%. Area 4X fisheries footprint has declined from over 50% in the pre-2010 period to now a value of less than 25%. This suggests fisheries exploitation in 2022 was reasonably precautionary, with the exception of N-ENS.

By assuming that:

we can project slightly more mechanistic expectations of future time trends for the fishable component (Figure \@ref(fig:projections-fb)) for varying levels of catch. All scenarios considered (80%, 100% and 120% of r year.assessment's TACs) lead to declining trends in the fishable biomass, with steeper declines with higher harvest scenarios.

Exact probabilities could also be computed from the posterior distributions, but the approach would have a high risk of failure due to the large number of assumptions. Rather than presuming that we have captured the true dynamics and the associated uncertainties, we instead, focus upon the effects of harvest scenarios upon the fishery footprint, which is more robust to errors of parameterizations as it is internally coherent and so perhaps a more utilitarian device to assess sub-component dynamics and integrate them to judge the directionality the fishery at different levels of exploitation (fishery footprint; Figure \@ref(fig:projections-footprint)).

These results from Model 2 are supplementary in nature to provide additional context. Their direct use in defining reference point thresholds is not recommended at this point.

Predators, preys, competitors

Being long-lived, the influence of predators can be significant. Especially important are predators of the smaller immature and female Snow Crab. Increasing predation not only lowers the abundance and recruitment, it can also reduce the reproductive potential of Snow Crab and therefore long-term population cycles. N-ENS and S-ENS are well known to have skewed sex ratios with few mature females for extended periods of time, quite possibly linked to differential predation mortality (mature females being much smaller and full of fat-rich gonads and eggs).

Based on stomach sampling, Atlantic Halibut, Atlantic Wolffish, Thorny Skate, and other skate species are known predators of Snow Crab. Large Atlantic Halibut with mature female Snow Crab in their stomachs have been reported. Anecdotal information of some seals having fed upon Snow Crab are also known.

Some of these predators (e.g., Halibut; DFO 2018) have significantly increased in abundance in the Region. However, for all, the abundance and encounter rates in areas overlapping with snow crab habitat is more important, but this is not known. We do know from the bycatch in the Snow Crab survey that there are elevated areal densities with many snow crab trawl samples. This means that encounter rates will also likely increase and so too potentially predation mortality. However, high density does not equate to high abundance nor high total predation mortality; but this remains unknown and requires further analysis. The following presents information of areal density of co-occuring species; they are potential predators, competitors and prey.

Atlantic Halibut densities have increased rapidly since 2010 (Figure \@ref(fig:halibut-timeseries); DFO 2018) on Snow Crab grounds. Most of these increases were towards The Gully, Slope Edge and near Sable Island (\@ref(fig:halibut-map)).

Thorny skate densities have been increasing as well (Figure \@ref(fig:thornyskate-timeseries)), especially in N-ENS and along the margins of Banquereau Bank (Figure \@ref(fig:thornyskate-map)). A minor decline in the densities have been seen in 4X.

Striped Atlantic Wolffish densities have been high, though declining in N-ENS since 2007 (Figure \@ref(fig:Wolffish-timeseries)). Highest densities were towards the Laurentian Channel (Figure \@ref(fig:Wolffish-map)).

Northern shrimp co-occur as they share similar habitat preferences and are also potential prey items of Snow Crab. Their numerical densities have declined after a peak in 2011, especially in S-ENS (Figures \@ref(fig:Shrimp-timeseries), \@ref(fig:Shrimp-map)).

Lesser toad crab is a co-occurring species and potential competitor. Their numbers have declined to low levels throughout, after a peak in densities in 2007 and 2016 in N-ENS (Figures \@ref(fig:lessertoadcrab-timeseries), \@ref(fig:lessertoadcrab-map)).

Overall, higher predation mortality seems likely in N- and S-ENS and lower in 4X. Further shrimp with similar habitat preferences have declined, possibly due to large-scaled habitat variations and predation.

Other sources of uncertainty

All of the above uncertainties are human induced and unlike the larger scaled climatic and ecosystemic uncertainties, there is some measure of human intervention possible. To remain adaptive in the face of these and other as yet unknown uncertainties including the climatic and ecosystemic uncertainties of which we are already aware, is the true challenge. It requires a balance of both resilience and robustness (see, Choi and Patten 2001). The Precautionary Approach as practiced by the snow crab fishers in Maritimes Region represents a unique model of such an adaptive approach. It values qualitity information and the communication and discussion of approaches in a distributed, collective information network that goes well beyond the simplistic and potentially maladaptive rubric of a purely "reference-points" based approach. Continued vigilence is necessary.

CONCLUSIONS

The ESS ecosystem is still experiencing a lot of volatility driven by rapid ecosystem and climatic variations. Under such conditions, it is prudent to be careful. Further, the overall indications of population status suggest that Snow Crab are still able to persist under extreme conditions if they are episodic, albeit, with some shifts in spatial distribution towards cooler and deeper waters.

The modelled solutions represent a few of many possible views of the dynamics of snow crab. Over-emphasis of any one of these modelled solutions and associated Reference Points in determining a strategy for fisheries management is not prudent and certainly not precautionary.

REFERENCES

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.

DFO. 2013. Integrated Fisheries Management Plan for Eastern Nova Scotia and 4X Snow Crab (Chionoecetes Opillio.)

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

\newpage

Tables

ii = which(dt$Region=="cfanorth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")] 
kable( oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in N-ENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th).")
# \@ref(tab:table-fishery-nens)

ii = which(dt$Region=="cfasouth")
oo = dt[ii, c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")] 
kable( oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in S-ENS. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th).")
# \@ref(tab:table-fishery-nens)

ii = which(dt$Region=="cfa4x")
oo = dt[ii,c("Year", "Licenses", "TAC", "Landings", "Effort", "CPUE")]
kable(oo, format="simple", row.names=FALSE, align="cccccc",
caption = "Fishery performance statistics in 4X. Units are: TACs and Landings (tons, t), Effort ($\\times 10^3$ trap hauls, th) and CPUE (kg/th). There were no landings or TACs in 2018/2019 due to indications of low abundance. The 2022 season is ongoing.")
# \@ref(tab:table-fishery-4x)

Table 4. Bycatch (kg) estimates from the N-ENS and S-ENS Snow Crab fishery. Estimates are extrapolated from At-Sea-Observed bycatch and biomass of catch (bycatch = [observed biomass of bycatch species / observed landings of Snow Crab] X total landings of Snow Crab). Reliable species specific data beyond 2017 are currently unavailable.

| Species | 2015 | 2016 | 2017 |
| :---------------------: | :-------: | :-------: |:-------: |
|Rock Crab | 19 | 0 | 0 | |Cod | 187 | 84 | 353 | |Jonah Crab | 19 | 854 | 0 | |Northern Stone Crab | 0 | 670 | 18 | |Toad Crab | 0 | 84 | 35 | |Soft Coral | 0 | 0 | 18 | |Basket Star | 0 | 0 | 18 | |Sea Urchin | 0 | 33 | 18 | |Sand Dollars | 0 | 17 | 0 | |Purple Starfish | 0 | 0 | 35 | |Sea Cucumbers | 19 | 50 | 495 | |Whelk | 0 | 17 | 0 | |Winter Flounder | 0 | 0 | 35 | |Eelpout | 0 | 0 | 35 | |Redfish | 75 | 50 | 247 | |Sea Raven | 37 | 33 | 0 | |Skate | 0 | 67 | 18 | |Northern Wolffish | 112 | 17 | 0 | |Spotted Wolffish | 0 | 0 | 194 | |Striped Wolffish | 149 | 100 | 371 | |Total Bycatch | 617 | 2076 | 1890 |

Table 5. Bycatch (kg) estimates from the 4X Snow Crab fishery. Estimates are extrapolated from At-Sea-Observed bycatch and biomass of catch (bycatch = [observed biomass of bycatch species / observed landings of Snow Crab] X total landings of Snow Crab). Reliable species specific data beyond 2017 are currently unavailable.

| Species | 2015 | 2016 | 2017 |
| :---------------------: | :-------: | :-------: |:-------: |
|American Lobster | 98 | 48 | 55 |
|Cod | 0 | 16 | 0 |
|Jonah Crab | 0 | 16 | 14 | |Rock Crab | 0 | 0 | 14 |
|Lumpfish | 11 | 0 | 0 |
|Northern Stone Crab | 130 | 81 | 82 |
|Redfish | 0 | 0 | 14 | |Sea Raven | 239 | 0 | 41 |
|Total Bycatch | 478 | 161 | 219 |

Table 6. Reference points from the logistic biomass dynamics fishery model (Model 1): K is Carrying capacity (kt); and r is Intrinsic rate of increase (non-dimensional). Note that FMSY (fishing mortality associated with 'Maximum Sustainable Yield') is r/2. Similarly, BMSY (biomass associated with 'Maximum Sustainable Yield') is K/2. SD is posterior Standard deviations.

| | $K$ [SD] | $r$ [SD] | | :---: | :----: | :---: | | N-ENS | r K_north [r K_north_sd] | r r_north [r r_north_sd] | | S-ENS | r K_south [r K_south_sd] | r r_south [r r_south_sd] | | 4X | r K_4x [r K_4x_sd] | r r_4x [r r_4x_sd] |

Table 7. Reference points from the six-component fishery model (Model 2): K is Carrying capacity (numbers converted to biomass, kt, using averge body weight of the fishable component); and b is "birth" rate of mature females (non-dimensional).

| | $K$ [SD] | $b$ [SD] | | :---: | :----: | :---: | | N-ENS | r Kdde_north [r Kdde_north_sd] | r bdde2_north [r bdde2_north_sd] | | S-ENS | r Kdde_south [r Kdde_south_sd] | r bdde2_south [r bdde2_south_sd] | | 4X | r Kdde_4x [r Kdde_4x_sd] | r bdde2_4x [r bdde2_4x_sd] |

\clearpage

\newpage

Figures

include_graphics( file.path( params$media_loc, "snowcrab_image.png" ) )
# \@ref(fig:crab-image)

include_graphics( file.path (params$media_loc, "area_map.png" ) )
# \@ref(fig:sse-map)

fn1=file.path( SCD, "assessments", year.assessment, "timeseries", "fishery",   "effort.ts.pdf" )
knitr::include_graphics( fn1 ) 
# \@ref(fig:effort-timeseries)  

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) 

include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery",   "landings.ts.pdf" ) )
# \@ref(fig:landings-timeseries)

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)  

include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "fishery",   "cpue.ts.pdf" ) ) 
# \@ref(fig:cpue-timeseries)  

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)  

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)  
#*Figure XXX. Snow Crab observer locations.*

  loc = file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "observer")
  fn1 = file.path( loc, paste( "size.freqcfanorth", (year_previous), ".pdf", sep="" ) )
  fn2 = file.path( loc, paste( "size.freqcfanorth", (year.assessment  ), ".pdf", sep="" ) )
  fn3 = file.path( loc, paste( "size.freqcfasouth", (year_previous), ".pdf", sep="" ) )
  fn4 = file.path( loc, paste( "size.freqcfasouth", (year.assessment  ), ".pdf", sep="" ) )
  fn5 = file.path( loc, paste( "size.freqcfa4x", (year_previous), ".pdf", sep="" ) )
  fn6 = file.path( loc, paste( "size.freqcfa4x", (year.assessment  ), ".pdf", sep="" ) )
  include_graphics(  c(fn1, fn2, fn3, fn4, fn5, fn6) )
# \@ref(fig:observer-CC)  
# *Figure XXX. Size frequency distribution of Snow Crab sampled by At-Sea-Observers, broken down by Carapace Condition (CC). Left side are for `r year_previous` and right side for `r year.assessment`. Top row is N-ENS, middle row is S-ENS, and bottom row is 4X. For 4X, the year refers to the starting year of the season; the current season is ongoing. Vertical lines indicate 95 mm Carapace Width, the minimum legal commercial size.*

\clearpage

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(fn6, fn5, fn4, fn3, fn2, fn1) )
# \@ref(fig:survey-locations-map)  
# *Figure XXX. Snow Crab survey locations.*

include_graphics(  file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "cw.male.mat.mean.pdf" )  )
# \@ref(fig:meansize-male-mat)

  odir = file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "carapacecondition" )
  fn1 = file.path( odir, "sizefreq.cfanorth.2019.pdf" ) 
  fn2 = file.path( odir, "sizefreq.cfasouth.2019.pdf" ) 
  fn3 = file.path( odir, "sizefreq.cfa4x.2019.pdf" ) 
  fn4 = file.path( odir, "sizefreq.cfanorth.2021.pdf" ) 
  fn5 = file.path( odir, "sizefreq.cfasouth.2021.pdf" ) 
  fn6 = file.path( odir, "sizefreq.cfa4x.2021.pdf" ) 
  fn7 = file.path( odir, "sizefreq.cfanorth.2022.pdf" ) 
  fn8 = file.path( odir, "sizefreq.cfasouth.2022.pdf" ) 
  fn9 = file.path( odir, "sizefreq.cfa4x.2022.pdf" ) 
  include_graphics(c(fn1, fn2, fn3, fn4, fn5, fn6, fn7, fn8, fn9) )
# \@ref(fig:sizefeq-male-survey-cc)

include_graphics(  file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey",  "male.denl.png" )  )
# \@ref(fig:sizefeq-male)

include_graphics(  file.path( SCD, "assessments", year.assessment, "figures", "size.freq", "survey",  "female.denl.png" )  )
# \@ref(fig:sizefeq-female)

\clearpage

```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)

```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)  

include_graphics( file.path( SCD, "assessments", year.assessment, "timeseries", "survey", "sexratio.mat.pdf") )
# \@ref(fig:sexratio-mature)

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)

\clearpage

fn = file.path(SCD,'assessments',year.assessment,'timeseries','survey','R0.mass.pdf')
include_graphics( c(fn) )
#\@ref(fig:fbGMTS)

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(fn6, fn5, fn4, fn3, fn2, fn1) )
# \@ref(fig:fbgeomean-map)  

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( fn8, fn7, fn6, fn5, fn4, fn3, fn2, fn1) )

include_graphics( file.path( SCD, 'modelled', '1999_present_fb', 'predicted_biomass_densities', 'biomass_M0.png') )
# \@ref(fig:fbindex-timeseries)

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' ) 
knitr::include_graphics(c(fn1, fn2, fn3) )
# \@ref(fig:logisticPredictions)

  odir = file.path( SCD, "fishery_model", 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)

include_graphics( file.path( params$media_loc, 'harvest_control_rules.png') ) 
# \@ref(fig:ReferencePoints)

  odir = file.path( SCD, 'fishery_model', 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)

\clearpage

include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 't.pdf') )
# \@ref(fig:bottom-temperatures-survey)

include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'temperature_bottom.pdf') )
# \@ref(fig:bottom-temperatures)

loc = file.path( data_root, 'aegis', 'temperature', 'maps', '1999_present' )
include_graphics( file.path( loc, 'Predicted_habitat_probability_persistent_spatial_effect.png') )
# \@ref(fig:bottom-temperatures-spatialeffect)

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='') )
include_graphics( c( fn6, fn5, fn4, fn3, fn2, fn1) )
# \@ref(fig:bottom-temperatures-map)
# *Spatial variations in bottom temperature estimated from a historical analysis of temperature data for 1 September.*

\clearpage

loc = file.path( SCD, 'modelled', '1999_present_fb', 'aggregated_habitat_timeseries' )
include_graphics( file.path( loc, 'habitat_M0.png') )
# \@ref(fig:fb-habitat-timeseries)

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( fn8, fn7, fn6, fn5, fn4, fn3, fn2, fn1) )
# \@ref(fig:fb-habitat-map)  
# *Figure XXX. Habitat viability (probability; fishable Snow Crab)* 

  odir = file.path( SCD, "fishery_model", 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)

  odir = file.path( SCD, "fishery_model", year.assessment, "size_structured_dde_normalized" )
  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:dde-fishing-mortality)

  odir = file.path( SCD, "fishery_model", year.assessment, "size_structured_dde_normalized" )
  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:dde-hcr)

  odir = file.path( SCD, "fishery_model", year.assessment, "size_structured_dde_normalized" )
  fn1 = file.path( odir, "plot_hcr_footprint_cfanorth.pdf" ) 
  fn2 = file.path( odir, "plot_hcr_footprint_cfasouth.pdf" ) 
  fn3 = file.path( odir, "plot_hcr_footprint_cfa4x.pdf" ) 
  include_graphics(c(fn1, fn2, fn3) )
  # \@ref(fig:dde-hcr-footprint)

  odir = file.path( SCD, "fishery_model", year.assessment, "size_structured_dde_normalized" )
  fn1 = file.path( odir, "plot_footprint_trace_cfanorth.pdf" ) 
  fn2 = file.path( odir, "plot_footprint_trace_cfasouth.pdf" ) 
  fn3 = file.path( odir, "plot_footprint_trace_cfa4x.pdf" ) 
  include_graphics(c(fn1, fn2, fn3) )
  # \@ref(fig:dde-footprint-trace)

  odir = file.path( SCD, "fishery_model", year.assessment, "size_structured_dde_normalized" )
  fn1 = file.path( odir, "plot_trace_projections_cfanorth__0.8__.pdf" ) 
  fn2 = file.path( odir, "plot_trace_projections_cfasouth__0.8__.pdf" ) 
  fn3 = file.path( odir, "plot_trace_projections_cfa4x__0.8__.pdf" ) 
  fn4 = file.path( odir, "plot_trace_projections_cfanorth__1.0__.pdf" ) 
  fn5 = file.path( odir, "plot_trace_projections_cfasouth__1.0__.pdf" ) 
  fn6 = file.path( odir, "plot_trace_projections_cfa4x__1.0__.pdf" ) 
  fn7 = file.path( odir, "plot_trace_projections_cfanorth__1.2__.pdf" ) 
  fn8 = file.path( odir, "plot_trace_projections_cfasouth__1.2__.pdf" ) 
  fn9 = file.path( odir, "plot_trace_projections_cfa4x__1.2__.pdf" ) 
  include_graphics(c(fn1, fn2, fn3, fn4, fn5, fn6, fn7, fn8, fn9) )
  # \@ref(fig:projections-fb)

  odir = file.path( SCD, "fishery_model", year.assessment, "size_structured_dde_normalized" )
  fn1 = file.path( odir, "plot_trace_footprint_projections_cfanorth__0.8__.pdf" ) 
  fn2 = file.path( odir, "plot_trace_footprint_projections_cfasouth__0.8__.pdf" ) 
  fn3 = file.path( odir, "plot_trace_footprint_projections_cfa4x__0.8__.pdf" ) 
  fn4 = file.path( odir, "plot_trace_footprint_projections_cfanorth__1.0__.pdf" ) 
  fn5 = file.path( odir, "plot_trace_footprint_projections_cfasouth__1.0__.pdf" ) 
  fn6 = file.path( odir, "plot_trace_footprint_projections_cfa4x__1.0__.pdf" ) 
  fn7 = file.path( odir, "plot_trace_footprint_projections_cfanorth__1.2__.pdf" ) 
  fn8 = file.path( odir, "plot_trace_footprint_projections_cfasouth__1.2__.pdf" ) 
  fn9 = file.path( odir, "plot_trace_footprint_projections_cfa4x__1.2__.pdf" ) 
  include_graphics(c(fn1, fn2, fn3, fn4, fn5, fn6, fn7, fn8, fn9) )
  # \@ref(fig:projections-footprint)

\clearpage

include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.30.pdf') )
# \@ref(fig:halibut-timeseries)

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)

\clearpage

include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.201.pdf') )
# \@ref(fig:thornyskate-timeseries)

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)

\clearpage

include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.50.pdf') )
# \@ref(fig:Wolffish-timeseries)

loc = file.path( SCD, 'output', 'maps', 'survey', 'snowcrab', 'annual', 'bycatch', 'ms.no.50' )
yrsplot = setdiff( year.assessment + c(0:-9), 2020)
fn4 = file.path( loc, paste( 'ms.no.50', yrsplot[4], 'png', sep='.') )
fn3 = file.path( loc, paste( 'ms.no.50', yrsplot[3], 'png', sep='.') )
fn2 = file.path( loc, paste( 'ms.no.50', yrsplot[2], 'png', sep='.') )
fn1 = file.path( loc, paste( 'ms.no.50', yrsplot[1], 'png', sep='.') )
include_graphics( c( fn3, fn2, fn1) )
# \@ref(fig:Wolffish-map)

\clearpage

include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2211.pdf') )
# \@ref(fig:Shrimp-timeseries)

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)

\clearpage

include_graphics( file.path( SCD, 'assessments', year.assessment, 'timeseries', 'survey', 'ms.no.2521.pdf') )
# \@ref(fig:lessertoadcrab-timeseries)

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)

Appendix Counts

 yr region Nstation         vessel Ntemp Nsp Ncrab

1: 1996 S-ENS 24 OPILIO 24 34 2972 2: 1997 N-ENS 26 GLACE BAY LADY 26 40 3216 3: 1997 S-ENS 124 GLACE BAY LADY 124 46 11365 4: 1998 N-ENS 56 MARCO BRITTANY 56 47 3881 5: 1998 S-ENS 157 MARCO BRITTANY 157 56 13272 6: 1999 N-ENS 59 MARCO BRITTANY 59 44 4091 7: 1999 S-ENS 215 MARCO BRITTANY 215 55 10305 8: 2000 N-ENS 69 MARCO BRITTANY 69 47 1968 9: 2000 S-ENS 253 MARCO BRITTANY 253 60 11718 10: 2001 N-ENS 99 MARCO BRITTANY 99 60 1995 11: 2001 N-ENS 99 MARCO MICHEL 99 60 1995 12: 2001 S-ENS 257 MARCO MICHEL 257 71 10674 13: 2001 S-ENS 257 MARCO BRITTANY 257 71 10674 14: 2001 S-ENS 257 DEN C. MARTIN 257 71 10674 15: 2001 4X 3 DEN C. MARTIN 3 16 20 16: 2002 N-ENS 177 MARCO MICHEL 177 60 2539 17: 2002 S-ENS 231 MARCO MICHEL 231 63 6917 18: 2002 4X 21 MARCO MICHEL 21 33 110 19: 2003 N-ENS 79 MARCO MICHEL 79 47 2035 20: 2003 S-ENS 274 MARCO MICHEL 274 65 8368 21: 2003 4X 3 MARCO MICHEL 3 12 5 22: 2004 N-ENS 61 GENTLE LADY 61 68 1850 23: 2004 S-ENS 292 GENTLE LADY 292 100 13877 24: 2004 4X 26 GENTLE LADY 26 48 246 25: 2005 N-ENS 63 GENTLE LADY 63 76 4284 26: 2005 S-ENS 294 GENTLE LADY 294 111 18736 27: 2005 4X 32 GENTLE LADY 32 51 782 28: 2006 N-ENS 63 GENTLE LADY 63 65 8676 29: 2006 S-ENS 281 GENTLE LADY 281 90 26337 30: 2006 4X 30 GENTLE LADY 30 56 2083 31: 2007 N-ENS 65 GENTLE LADY 65 74 9027 32: 2007 S-ENS 284 GENTLE LADY 284 108 23573 33: 2007 4X 29 GENTLE LADY 29 57 1320 34: 2008 N-ENS 71 GENTLE LADY 71 82 7953 35: 2008 S-ENS 300 GENTLE LADY 300 117 22278 36: 2008 4X 34 GENTLE LADY 34 72 1084 37: 2009 N-ENS 71 GENTLE LADY 71 81 4360 38: 2009 S-ENS 303 GENTLE LADY 303 114 22430 39: 2009 4X 33 GENTLE LADY 33 72 2377 40: 2010 N-ENS 70 GENTLE LADY 70 80 4339 41: 2010 S-ENS 306 GENTLE LADY 306 102 23671 42: 2010 4X 31 GENTLE LADY 31 65 3188 43: 2011 N-ENS 72 GENTLE LADY 72 78 3141 44: 2011 S-ENS 310 GENTLE LADY 310 100 20464 45: 2011 4X 32 GENTLE LADY 32 65 1247 46: 2012 N-ENS 71 GENTLE LADY 71 75 2081 47: 2012 S-ENS 304 GENTLE LADY 304 97 17144 48: 2012 4X 32 GENTLE LADY 32 63 1130 49: 2013 N-ENS 71 GENTLE LADY 71 72 3262 50: 2013 S-ENS 304 GENTLE LADY 304 106 18003 51: 2013 4X 33 GENTLE LADY 33 67 450 52: 2014 N-ENS 65 MISS JESSIE 65 83 4399 53: 2014 S-ENS 281 MISS JESSIE 281 100 18768 54: 2014 4X 13 MISS JESSIE 13 59 345 55: 2015 N-ENS 71 MISS JESSIE 71 90 4226 56: 2015 S-ENS 308 MISS JESSIE 308 112 14967 57: 2015 4X 34 MISS JESSIE 34 66 556 58: 2016 N-ENS 70 MISS JESSIE 70 79 4605 59: 2016 S-ENS 307 MISS JESSIE 307 119 15620 60: 2016 4X 34 MISS JESSIE 34 63 469 61: 2017 N-ENS 70 MISS JESSIE 70 83 3637 62: 2017 S-ENS 266 MISS JESSIE 266 109 11573 63: 2017 4X 28 MISS JESSIE 28 59 243 64: 2018 N-ENS 66 MISS JESSIE 66 76 3212 65: 2018 S-ENS 298 MISS JESSIE 298 111 11635 66: 2018 4X 20 MISS JESSIE 20 51 583 67: 2019 N-ENS 68 MISS JESSIE 68 80 5262 68: 2019 S-ENS 342 MISS JESSIE 342 108 21602 69: 2019 4X 20 MISS JESSIE 20 49 971 70: 2021 N-ENS 68 MISS JESSIE 68 81 3717 71: 2021 S-ENS 293 MISS JESSIE 293 131 21411 72: 2021 4X 19 MISS JESSIE 19 50 778 73: 2022 N-ENS 68 R.S.JOURNEY II 68 114 1466 74: 2022 S-ENS 214 R.S.JOURNEY II 214 140 16203 75: 2022 4X 20 R.S.JOURNEY II 20 69 726



jae0/snowcrab documentation built on Feb. 27, 2024, 2:42 p.m.

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