Merits of Multi-Indicator Precautionary Approach Management in a Male-Only Crab Fishery

Snow crab replaced groundfish fisheries as the primary focus of the Newfoundland and Labrador (NL) fishing industry in the mid-1990s. Over the past three decades, management philosophies in this male-only fishery have shifted from promoting industry expansion to promoting industry rationalization to implementing Precautionary Approach (PA) management. Until the mid-2010s, there were regionalized management strategies characterized by higher exploitation rates in northern than southern sub-stock units along the NL marine shelves. However, in the late 2010s, exploitation rates were permitted to elevate to high levels across the entire stock range in association with a large resource decline and evidence of biological harm through fishing emerged in chronically depleted areas. In 2019, when stock and fishery productivity were near historic lows, a multi-indicator PA system was informally introduced into management of the resource. This event coincided with anticipated improvements in stock and fishery performance. This paper examines the extent to which the multi-indicator PA management system, focused on promotion of both biological protection and maximization of fishing efficiency, may have contributed to recent improvements in stock and fishery status. A suite of indicators from areas implementing PA guidance into management in 2019 prior to formal adoption of the PA system in 2023 suggest the system has been highly beneficial in promoting rapid recovery, improving stock and fishery performance metrics to levels matching or exceeding historic levels under similar conditions. We discuss capacity of the system to better safeguard biological aspects of resource and fisheries productivity moving forward under a scenario of an expectant resource decline.

Keywords:

Subject: Biology and Life Sciences - Aquatic Science

1. Introduction

1.1. History of the Fishery

The collapse of groundfish stocks and shuttering of fisheries off Newfoundland and Labrador (NL), Canada (Figure 1) in the early 1990s are globally cited as an epitomical example of overfishing consequences (i.e., [1]). The closure of the Atlantic cod (Gadus morhua) fisheries in 1992 constituted the largest mass layoff in Canadian history with 30,000 – 40,000 directly affected workers [2,3]. These events would forever change the complexion of the fishery, the industry that had underpinned 500 years of colonization in these sparsely populated remote areas of the North Atlantic since European discovery of Newfoundland in 1497.

While a high level of scientific and social focus continues to be placed on factors leading to collapse and slow recovery of groundfish stocks, comparatively little focus occurs on fisheries that emerged post-collapse. The primary fisheries in the three decades since groundfish collapses have targeted snow crab (Chionoecetes opilio) and northern shrimp (Pandalus borealis), with the snow crab fishery overall dominant in supporting rural areas of the Province because the stock is distributed off all NL coasts, generally more feasible, and usually more lucrative. The NL snow crab fishery has represented the largest global supply for the species for the past three decades and enabled unprecedented economic prosperity for the NL fishing industry [4].

Changing philosophies regarding management of NL snow crab have partitioned the past three decades into distinct periods. Despite long-standing lucrative fisheries on snow crab in areas such as Alaska and the Gulf of St. Lawrence, little interest in fishing snow crab occurred in NL prior to groundfish collapses. Small-scale directed fishing has been documented back to 1968 [5], but as it became apparent that the snow crab stock was large enough to support a large-scale fishery in the wake of groundfish collapses, rapid fishery expansion ensued during 1992-1999 and the fleet ballooned from several hundred to nearly three thousand vessels. Herein, this is termed the Expansion Period. This period was characterized by competition for access and allocation rights [6] and ultimately led to the complex spatial management system characterized by many small management areas within the broad biological stock complex to control fleet dynamics (Figure 1). The stock is assessed at the Assessment Division (AD) level and managed at the Crab Management Area (CMA) level (Figure 1).

In 1999, the highest ever quota for NL snow crab (69,000 t) was set and captured. However, stock assessments immediately began documenting a resource decline and expressing concerns of forthcoming potential for overfishing [5,7]. Management philosophy shifted from one supporting rapid fishery expansion to one recognizing fleet over-capacity [8] and progressively implementing rationalization measures to remove vessels from the fishery [9]. This post-1999 period is termed the Rationalization Period in this analysis. It has no end point, although it can be viewed as nearing an end after approximately 2016 when the rate of vessel reductions slowed.

During 2000–2018, the fishery and management system progressed into what could initially be described as a maturation stage. The stock range was fully exploited with quotas being set based on a co-management system incorporating harvester recommendations alongside index-based stock assessments [10]. However, increased biological knowledge also accrued during this period and it became progressively clear that the ecosystem carrying capacity was dissipating and the stock was on a declining trajectory. In 2014, formal scientific warnings were given that a large decline in exploitable biomass was imminent and the industry should prepare [11,12]. However, despite lead time, overall fishery exploitation rates were enabled to further increase to unprecedented highs during 2016–2018, while the stock was in its lowest state. In 2016, the stock had shrunk to just 9% of its highest measured level in 1996, and males in areas chronically fished at high exploitation rates began to show declines in size-at-maturity (SaM; synonymous with size-at-terminal molt) in response to low population densities [13]. The fishery had progressed beyond a mature stage and into a senescent state and unprecedented quota cuts occurred.

In 2018, a multi-indicator Precautionary Approach (PA) management framework was introduced. This did not occur in response to the poor stock and fishery state, rather the timing was coincidental, with the system under development since 2012. Development of a PA management system occurred in response to fulfill both a processing sector request to meet eco-certification requirements, as well as a Fisheries and Oceans Canada (DFO) mandate to implement PA systems into management of major commercial fish stocks. The base system identified metrics for development of a holistic health score index as well as Limit Reference Points (LRPs) differentiating cautious and critical zones [14]. However, as per DFO process, the base system required collaborative work with industry to develop reference points to differentiate cautious from healthy zones and Harvest Control Rules (HCRs) to guide management decision-making. Furthermore, the unique multi-indicator approach required collaboration on development of a scoring matrix for the three health status metrics. Accordingly, the PA system was not implemented in 2018. The coincidental timing of development of a PA system to guide stock management decisions alongside a fishery under-going large quota cuts created a situation of considerable unrest within the industry. Extensive consultation occurred during 2019–2022 and during this time management adopted the approach of using “the spirit of the PA” to help guide decision-making while the system was finalized. In 2023, the PA was formally applied for the first time in management decisions. Herein, 2019–2022 is termed the Informal PA Period and post-2022 is termed the Formal PA Period.

A major difference regarding using the PA management system to guide harvest decisions is that it more stringently necessitates focus on survey biomass in setting quotas, as opposed to ancillary indicators such as fisheries CPUE, which often took precedent in the past [15].

1.2. Snow Crab Fishery Management

The focal biological feature underpinning snow crab management is sexual size dimorphism, which allows a sex-exclusive harvest of the larger males. This exploitation strategy alone is precautionary in principle by helping safeguard reproductive capacity while prosecuting fisheries [16,17,18]. A second biological safeguard is sperm storage by females, whereby multiple year’s egg clutches can be fertilized from a single season of mating. However, biological harm can still be induced through fishing only males, in particular through size-at-maturity effects [13] or sperm limitation [19] if harvests too severely deplete the density of large adults males. Siddeek et al. [20] estimated the fishing mortality rate producing the maximum sustainable yield (f_msy) in major snow crab stocks to be in the range of 26-42% annual harvest of the exploitable biomass of large males. Overall, species biology leads to fundamental approaches of using large-mesh traps to eliminate females from the catch and consistent application of low to moderate exploitation rates to maintain intraspecific competition in the population as the key regulatory strategies to promote a sustainable harvest.

A second management-related outcome affected by fishery exploitation rates is surplus discarding mortality. Components of males in the population discarded in the NL snow crab fishery are those that are either soft-shell (recently-molted) or sub-legal-size (legal-size is 95-mm carapace width (CW) in NL). Discard mortality rates on either group are not fully understood, but are likely affected by a myriad of factors including time out of water, air and water temperature, wind speed, sunlight, and individual size and shell condition [21,22,23,24,25]. Discard mortality rates are likely highest on soft-shell crab [26]. Soft-shell crab is most apt to appear in summer fisheries, following winter-spring molting, as recently molted crab become more mobile and competitive. An interaction of exploitation rates and seasonality governs incidence of soft-shell crab in the catch, with low exploitation rates and winter fishing most optimal in minimizing discards [26]. However, fisheries can maintain low levels of discards throughout the year, even summer, if exploitation rates on large males are consistently kept low to maintain sufficient competition to exclude less competitive crab from trapping.

1.3. Overview of NL Snow Crab PA System

PA systems focused on finfish overwhelmingly focus on spawning stock biomass toward maintaining reproductive capacity in fisheries targeting both sexes [27]. Typically, reference points to differentiate stock status are established within a range of historic spawning stock biomass levels. In snow crab, given the important role of large males in affecting insemination rates in females, sole focus on the exploitable biomass of males can be justified as the basis of PA management frameworks [28]. However, given the simplicity with which the major precautionary measure of a male-only harvest can be implemented, it is also convenient and appropriate to extend PA management systems on snow crab into ancillary population or fishery performance considerations toward applying a more holistic management system to supplement the central male-only harvest strategy [14].

The multi-indicator PA management system used for NL snow crab invokes an additional objective of maximizing fishing efficiency along with the typical objective of assuring biological protection of the resource as its over-arching philosophies [14,28]. The three metrics used to address these objectives are predicted fishery catch per unit of effort (pCPUE), predicted fishery discards (pDIS), and proportion of mature females carrying full clutches of viable eggs (EGG). The three metrics are weighted within a scoring matrix that produces a range of outcomes for an “integrated health score” ranging from 0 to 7. Maxima of 4, 2, and 1 health points are awarded for the pCPUE, pDIS, and EGG metrics respectively, depending on performance relative to reference points established for each metric. The scoring matrix was established collaboratively between industry, management, and science, and among other considerations weightings reflect the ability of management measures to directly affect outcomes of each metric (i.e., most direct effect on pCPUE), as well as dynamic data ranges within historic time series for each metric (i.e., most stability in EGG index). Upon summation, the integrated health score index is differentiated into healthy (5.5 to 7), cautious (2.5 to 5), and critical (0 to 2) zones and allowable exploitation rates consistent with f_msy are applied. The HCRs for exploitation rates are defined as ranges with minima of 0% and maxima of 42%, 35%, 20% for the healthy, cautious, and critical zones, respectively. Use of ranges to define HCRs for each zone rather than explicit levels allows flexibility for management and industry to consider a broader suite of factors (i.e., socio-economic) in formulating annual harvest rate strategies.

2. Materials and Methods

2.1. Fishery Data

Time series’ of the number of active vessels, fishery landings, and fishery effort for the four focal ADs of the NL shelf were examined in context of important periods of growth and decline and changes in management philosophies in the fishery. The number of active vessels was determined from DFO databases of commercial logbooks and landings offloads, identifying unique Vessel Registration Numbers (VRNs) present in the databases annually for each AD. Data on VRNs were incomplete during the rapid fleet expansion phase of 1995–1997, thus exact numbers of active vessels each year are unknown during that period.

Landings were enumerated by a dockside monitoring program and were summed by year for each AD. Fishery catch per unit of effort (CPUE) was calculated from commercial logbook data and standardized as per methods described in [29]. The standardization model is a linear mixed model [m1, in ‘lme4’ syntax [30] regressing square root transformed CPUE (sqrtcpue, catch/trap haul) against fixed effects of binned (5-day) time intervals (day) and gear soak time (soak, hours). It includes random slopes and intercepts which allows the y-intercept, and slope of the relationship between sqrtcpue and day to vary within each unique CMA within AD within year. Finally, the cumulative number of years fished within each 10’ x 10’ grid cell is used as a weighting term. Late season data (November–December) are not included in the model due to sporadic presence and entries of zero catch were omitted due to uncertainty in whether they represented real catch rates or practices such as dumping catches when quotas are subscribed.

sqrtcpue ~ (1+ day + soak + (1+day | year:AD:CMA)

(m1)

Total fishery effort estimates (# of trap hauls) for each AD and year were calculated as the quotient of the division of landings by fishery Catch Per Unit Effort (CPUE, [m1], measured in kilograms/trap. With little exception, logbook return rates have exceeded 80% of fishing trips in each of these ADs since 1996 [29]. Each fishery index (landings, CPUE, effort) was examined in context of periods of changes in management philosophy in the fishery.

Fishery discard rates were estimated from at-sea observer data, with in-season measurements of the catch beginning in 1999. Observer coverage in the fishery is typically low (i.e., < 5% of trips normally have an observer), with 0.05 – 0.6% of the catch measured in these ADs in any given year. Since 2019, all ADs have received particularly low coverage levels (i.e., ≤ 0.1% of the catch measured), thus the discard standardization model [m2] is only fit up to 2019 and subsequent estimates of fishery discards are projected based on 1999–2019 model fits [29]. It should be noted that the PA management system focuses on all discards, rather than any given subset population component (i.e., just soft-shell crab), toward protection of all crab that can contribute to both immediate and long-term resource productivity, with sub-legal-sized (<95 mm CW) adult males also active in mating.

In-line with low coverage levels, observer deployments in the fishery follow a random sampling design across time (weeks) and space (CMAs) toward collecting overall representative information. Observer measurements of sub-legal-size males, soft-shell males, and all female crab were considered discards while measurements of all other groups of crab were considered kept. We constructed a binomial generalized linear mixed model [m2] relating the number of crab discard occurrences (tdisc) out of the total number of discards plus kept crab (ttot) in any given sampling event to the same predictors in m1. ttot was used as a weighting term.

tdisc/ttot ~ 1+sday + ssoak + (1+sday | year:division:cma)

(m2)

2.2. Exploitable Biomass Estimation

The Exploitable Biomass Index (EBI) was examined in context of important periods of growth and decline and changes in management strategies in the fishery. The exploitable biomass is defined as the tonnage of male crab ≥ 95 mm CW. The index is derived from annual multispecies trawl surveys that occur in fall (September-December, beginning in 1995) in ADs 2HJ, 3K, and 3LNO, and in spring (April-May, beginning in 1996) in AD 3Ps. In the 3Ps time series, the multispecies surveys were incomplete in 2006 and fully missed in 2020 and 2023. In the fall time series, AD 3LNO had an incomplete survey in 2021 and surveys in all ADs (2HJ, 3K, 3LNO) were missing in 2022. This complete omission of fall surveys in 2022 was associated with comparative fishing experiments taking precedent over routine monitoring surveys with new trawlers replacing out-going trawlers and catch rate conversion factors being developed among two new and two old vessels [31].

Despite incomplete and missing multispecies trawl surveys, a cohesive time series EBI can be estimated for each AD. In the cases of AD 3Ps in 2006 and AD 3LNO 2021, biomass estimation based on spatiotemporal inference from historic captures of density by time, location, and depth were used to predict biomass in the R [32] package ‘sdmTMB’ [33], whereby generalized linear mixed models are estimated across spatial and temporal domains from geostatistical time series data. The efficacy of this approach was validated by comparison to an independent trap survey in AD 3LNO in 2021, which produced a near-identical result to the model-generated estimate for the trawl index. Moreover, in ADs 2HJ, 3K, and 3LNO in 2022, and AD 3Ps in 2023, the trawl data from the comparative fishing experiments were used in lieu of the routine survey data to estimate biomass using the model-based approach. Again, efficacy of these model predictions for trawl survey biomass were validated by similar biomass estimations from independent trap surveys in each AD. The trap surveys used for validation of model-based trawl survey biomass estimates only have usable time series since 2018 thus were not focused on in this analysis, but were used as the basis of the 2020 survey estimate for AD 3Ps, where there was a complete void of any trawl survey data to inform model predictions.

The routine monitoring multispecies trawl surveys are conducted within a depth-stratified sampling design of the NL marine shelves. Set allocation is proportional to areal extent of each survey stratum. The trawl itself is a Campelen 1800 rockhopper shrimp trawl [34] and is towed for 15 minutes duration at 3 knots, when conditions allow, with snow crab catch rates standardized to 15-minute tows. In most years, about 600–700 tows would cover the marine shelves of ADs 2HJ, 3K, and 3LNO in fall and about 150–175 tows would cover the AD 3Ps shelf in spring. In 2023, two new identical research vessels were introduced as the primary trawlers, and conversion factors developed for snow crab [31] were applied to the historic time series’ to bring historic data on abundance into the same scale as those derived from the new trawlers.

The Campelen trawl has a capture efficiency of less than 1 (i.e., fully efficient) for all sizes of snow crab [35], thus model-based biomass estimates are knowingly under-estimated. To compensate for this, annual EBIs are re-scaled based on comparison to another exploitable biomass estimate derived from DeLury catch rate depletion analysis [36] from fishery logbook data. This method estimates beginning of season exploitable biomass and relies on consistent depletion occurring throughout the fishing season, thus it cannot be estimated every year and is not useful for real-time assessment and management of the resource. To calculate a scalar for the trawl time series EBI, the ratio of the preceding survey to subsequent fishery biomass estimate is calculated wherever possible and a time-series median of the ratio is used as the scalar to adjust trawl survey EBIs into realistic values. Ultimately, this process results in the trawl survey shaping the EBI and the fishery-derived scalar determining the magnitude of the EBI. Accordingly, the EBI is deemed a realistic index but not an absolute estimation.

The exploitable biomass is affected by changes in productivity, which is measured through recruitment, as well as fishing, which is measured by exploitation rates. Two dominant components are defined in the exploitable biomass based on shell condition of individuals, with shell condition representing a proxy for time since molting. Crab in a soft or new-shell condition are recently molted and constitute immediate recruitment into the biomass while crab in an intermediate or older-shell condition are assumed to have molted at least one year ago and represent the residual biomass. The proportion of the biomass comprised of residual crab was examined as an indicator of population health. Large, residual males are the most competitive crab in the population and help maintain hierarchical intrinsic competition, encourage growth of individuals to large sizes, and promote reproductive health of the stock [13,19]. From a fisheries perspective, large, residual males help offset incidence of soft-shell crab captures in the catch due to competition exclusion for access to baited pots [26].

A two-period moving average is applied to EBI for each AD to estimate fishery exploitation rates. This is done due to historic concerns of year effects in trawl survey time series [29]. Further, in context of the PA framework used to provide advice on outcomes of the forthcoming fishery, the predicted fishery CPUE metric maintains correlations to the EBI from multiple preceding surveys (i.e., lags of 1–3 years), not just the most-recent biomass estimate [14]. The Exploitation Rate Index (ERI) is calculated as fishery landings in a given year (y) divided by the two-period moving average estimate of EBI preceding the fishery (y-1) [m3].

ERI = landings (y) / EBI (y - 1)

(m3)

2.3. PA Management System Bases and Models

Like most PA management systems, one of the main features of the multi-indicator PA management system used in NL snow crab is that it imposes limits on fishery ERIs. Bases for the efficacy of doing this were examined through relationships of ERIs versus proportions of residual biomass in the population and soft-shell crab in the catch, contrasting rates of each metric across ADs. For each AD, cumulative ERIs, cumulative proportions of residual biomass, and cumulative proportions of soft-shell crab measured by observers were calculated for the 1999–2019 period. This period was chosen due to observer measurements not beginning until 1999 and poor levels of observer coverage and increased incidence of missing surveys after 2019. Slopes of linear regression models fit through the origin for each cumulative curve were used to quantify average rates of each metric over the time period and subsequently applied as data to examine relationships among the three variables in simple linear regression models.

The PA management system uses predicted fisheries CPUE (pCPUE) as a focal metric toward helping managers and harvesters plan for likely fishery performance outcomes in the forthcoming season under different catch scenarios. This approach capitalizes on a strong relationship between lagged fishery CPUE versus exploitable biomass. The pCPUE model is a generalized additive mixed model fit in the ‘mgcv’ package [37] in R [m3, in ‘mgcv’ syntax]. It regresses standardized fishery CPUE (st_cpue) against predictor variables of fishery ERI, the two-year moving average of exploitable biomass (BIO) from trawl surveys (1995–2017) or an average of trawl plus trap surveys (2018–2023), an index of the North Atlantic Oscillation (NAO) from seven years prior (NAO7), and random effects of AD and year, treated as a factor variable. NOA7 was included due to strong positive correlation with this climate system at lags of about 6–8 years [14]. A thin-plate smoothing spline was included on all fixed effects with the exception of ERI. The ERI term was simulated under various quota scenarios to predict outcomes of fishery CPUE under different management options.

st_cpue ~ ERI + s(BIO, k=6) + s(NAO7,k=6) + s(AD, bs='re') + s(year, bs='re')

(m4)

The second focal metric in the multi-indicator PA management system is predicted discards (pDIS), with the system aiming to minimize surplus fishing mortality caused by discarding. The pDIS model is a generalized additive mixed model fit in the ‘mgcv’ package [m5] that regresses the response variable of percentage of the catch discarded against predictor variables of pCPUE [m4], median fishing day (mFD), the ratio of exploitable to pre-recruit crab in the population in the previous year (EP1), and a random effect of AD. A thin-plate smoothing spline was included on all fixed effects. The pCPUE term extends the simulation of outcomes from different quota scenarios into predicting fishery discards, while the median fishing day is set to the previous year’s value to predict outcomes of discards under status quo fishery timing. The ratio of exploitable to pre-recruit crab is an index of the buffering capacity of the population to offset captures of small males or soft-shell crab, with an index favoring exploitable males beneficial. Pre-recruits are defined as 70–94 mm CW adolescent males and have the potential to be captured in the forthcoming fishery either in the form of under-sized males or soft-shell legal-size males depending on whether or not they molt and the size of the growth increment if they do molt.

pDIS ~ s(pCPUE) + s(mFD) + s(EP1) + s(AD, bs='re')

(m5)

The final focal metric in the multi-indicator PA management system is proportion of mature females in the population carrying full clutches of viable eggs (EGG). This metric serves as an index of reproductive capacity in light of only removing males through fishing. While the multispecies trawl surveys provide the only long-term time series of broad-scale survey data available for resource assessment processes such as biomass estimation, other survey series comprised of localized trap surveys conducted by DFO in inshore bays of Newfoundland, and trap surveys conducted through collaborations with industry groups throughout ADs 2HJ, 3K, 3LNO, and 3Ps, provide data useful for other purposes such as examination of fecundity in females. All trap surveys include small-mesh traps (1” mesh) that capture sub-legal-sized crab including females. Given the focus is on proportional (not absolute) levels of female fecundity, data from females captured in all trap and trawl surveys are used to quantify this metric. The assessment of relative clutch levels is based on visual examination of the abdomen, with incremental classifications used to quantify egg clutch fullness. Mature females assessed as having a full brood (i.e., eggs protruding beyond the abdominal flap) of healthy looking eggs (i.e., orange vs. brown or black in colour) or showing evidence of recently releasing healthy looking eggs (i.e., remnants of a clutch still apparent) are classed as having full clutches of viable eggs.

2.4. PA Management System Performance Assessment

The performance of the PA management system was judged during the 2019–2023 period. System performance was assessed based on trends in the three focal PA metrics, pCPUE, pDIS, and EGG, along with trends in two ancillary metrics of the proportion of the exploitable biomass comprised of residual crab as well as an index of male SaM. The residual biomass proportions metric was fit with loess regression curves to partially account for missing or different (i.e., comparative fishing) survey types in some years. For investigation of “stock-level” analyses of fishery CPUE, ERIs, and residual biomass proportions, boxplots were used to investigate contrast across ADs while data for each AD were additively pooled to form annual point estimates of each index.

The SaM performance metric was included because of the downward shift in SaM concurrent with the heavy exploitation preceding the Informal PA Implementation Period [13]. With the omission of trawl surveys in recent years, this index had not been updated, thus it was unknown if the PA system was having any influence on the phenomenon. The model used to estimate proportions of mature crab (pMAT) was a generalized additive model fit in the ‘mgcv’ package [m6]. It was fit to recently-molted males (soft or new-shell crab), with sizes binned to 10-mm CW increments. The model simply incorporated the interaction of carapace width (CW) and year as predictor variables and the shape of the non-linear relationship (ie. Smooth) was allowed to vary separately for each AD. The interaction term was constructed as a tensor product smoother, and a weighting term of size-specific sample size was included to offset influences of small samples sizes in AD-year specific size bin groupings. The model family was quasibinomial to help control for influences of 0 and 1 values in the binomial form data.

pMAT ~ te(CW, year, k = 11, by=AD)

(m6)

The performance metric was expressed as the size at which 50% of individuals were predicted to become mature in any given AD and year, with this “mat50” index commonly used in fisheries resource assessments.

3. Results

3.1. Fishery Data

The number of active vessels increased from 36 to 430 between 1988 and 1991before substantial fishery expansion occurred to reach a maximum level of about 2,700 active vessels by 1998–1999 during the Expansion Period (Figure 2). Both the pre- and post- Expansion Period fleet sizes were largest in AD 3LNO, representing the stock area off southeastern Newfoundland (Figure 1), where about 1,200 vessels were active in 1998. Rationalization of the fleet has progressively occurred in all ADs since 1998-1999, with a total of 1,328 vessels active in 2023 and approximately 600 of those in AD 3LNO.

Overall fishing landings increased by a factor of 4 from 1992 (16,199 t) to 1999 (67,503 t) in ADs 2HJ, 3K, 3LNO, and 3Ps, with AD 3LNO accounting for 40–50% of the landings each year (Figure 2). Since the 1999 peak, landings trends have differed markedly across ADs, with most contrast across ADs 2HJ and 3LNO. AD 2HJ has undergone a prolonged progressive decline to just 885 t in 2023, while expansion of landings in AD 3LNO to 34,945 t in 2015 occurred following the initial adjustment to 26, 816 t in 2000. Since the Informal PA Period began, landings in ADs 3LNO and 3Ps have increased to near historic highs and recovered back to near the long-term average, approximating 10,000 t in AD 3K. Meanwhile, they have reached post-Expansion Period lows in AD 2HJ. Overall, landings about doubled from 26,187 t in 2019 to 51,169 t in 2023. Trends in effort have loosely conformed to landings, although peak levels occurred later than 1999 (2003-2004) in all ADs. The overall peak level of 7.03 million trap hauls in 2004 contrasts a post-expansion era low of 2.71 million trap hauls in 2020 and a near-average level of 3.92 million trap hauls in 2023. The overall fishery CPUE levels in 2021–2023 (range 12.3 kg/trap to 13.6 kg/trap) are the three highest levels recorded since the 1996–2001 period (range 11.6 kg/trap to 14.1 kg/trap).

The overall high fishery CPUE levels of 1997–2001 reflected sporadic or sustained incidence of high catch rates in all ADs and were associated with historic highs in the EBI (Figure 3). The overall pattern in the EBI, which averaged 325 kt from 1996 to 2001, is primarily driven by AD 3LNO in all years, with that AD accounting for an average of 72% of the index (range 63–86%) in any given year throughout the time series. The overall EBI reached a historic low of just 42,176 t in 2016 and has since recovered to near the long-term average, approximating 170 kt in 2023. Trends in fishery CPUE have generally reflected the EBI at a lag of 1-2 years in all ADs since the fishery Rationalization Phase began.

Overall ERIs have been higher in ADs 2HJ and 3K than in 3LNO and 3Ps (Figure 3). During 2000–2018, after rationalization began and before the PA management system was developed, the ERI averaged 60%, 55%, 30%, and 37% annually in ADs 2HJ, 3K, 3LNO, and 3Ps, respectively. Since the Informal PA Period began, ERIs have been maintained at or near historic low levels, associated with allowable harvest rates for the cautious and critical zones, in all but AD 2HJ where the ERI only dropped to below 42% and within the allowable range of the PA management system in 2023, when it was formally implemented. At 35% harvest rate, the 2023 AD 2HJ ERI was set at the maximum allowable limit associated with its designation of being in the cautious zone. Overall, with data pooled, the stock-level ERI was unusually high in 2016–2018, reaching a peak of 64% in 2017. However, the index dropped substantially after 2018 and has remained steady near 25% per annum in the past four years. When considering across-AD comparisons, the boxplot median levels of about 25–35% ERI occurring in the fishery in the past 4 years are the lowest levels occurring since the fishery Rationalization Period began.

3.2. PA Management System Performance Assessment

The proportion of the exploitable biomass comprised of residual crab has varied both across and within ADs over the time series (Figure 3). Overall, residual biomass proportions are lowest in AD 2HJ, where the loess regression model fit varied between 0.25 to 0.50 in most years. The loess index in AD 3K was relatively stable at about 0.4 until 2014 before dropping to a level closer to 0.25 from 2015 to 2018. It recovered to about the 0.4 level from 2019 to 2021 but has increased further to a level of 0.67-0.68 in the 2022 and 2023. In AD 3LNO, the loess index of residual crab proportions in the exploitable biomass has varied between 0.35-0.55 throughout the time series. Overall, highest variability has occurred in AD 3Ps, where the loess index of residual crab proportions in the biomass has fluctuated from 0.3 to 0.8. When data from all ADs are pooled, a pattern of increasing residual biomass proportions from 0.25 in 1995 to 0.5 in 1999–2000 occurred before a downward oscillation to a level of about 0.3 in 2016-2018. The index has increased every year since 2018 with the values of greater than 0.5 in the past two years producing a historic high in the loess model fit to the data.

The linear regression slopes of cumulative fishery ERI during 1999–2019, when observer data allowed for comparisons to soft-shell crab incidence in the catch, were 0.51 and 0.55 in ADs 2HJ and 3K respectively and 0.35 and 0.26 in ADs 3Ps and 3LNO (Figure 4). These levels were associated with lower regression slopes for proportions of residual biomass in the population in ADs 2HJ (0.35) and 3K (0.41) than in 3LNO (0.46) and 3Ps (0.58). Moreover, the higher average ERIs in ADs 2HJ and 3K were associated with much higher proportions of soft-shell crab measured in the catch in these ADs. The slopes of linear regressions fit to soft-shell crab proportions were 0.15 and 0.11 in ADs 2HJ and 3K respectively, while they were just 0.03-0.04 in ADs 3LNO and 3Ps. The inverse direction of the relationship of ERI with residual biomass proportions in the population (R²=0.39, slope=-0.45) versus soft-shell crab proportions in the catch (R²=0.81, slope=0.38) are consistent in depicting how high exploitation rates strip away the residual biomass and enable soft-shell incidence in the catch to become more problematic. In-turn, a negative relationship between proportions of residual biomass in the population and soft-shell crab proportions in the catch occurs (R²=0.68, slope=-0.49).

Overall stock health status was poorest in 2017 when ADs 2HJ and 3LNO were in the cautious zone of the PA management system health assessment and ADs 3K and 3Ps were both in the critical zone (Figure 5). Relatively abrupt improvements occurred thereafter in ADs 3K, 3LNO, and 3Ps, with each entering the healthy zone by 2021 and being maintained there since. Conversely, AD 2HJ has remained in the cautious zone since 2017, although the health score of 5 occurring from 2021 to 2024 is higher than the level of 4 achieved during 2017–2020 and suggests improvements may be occurring in the population in that AD. Since 2018, rapid improvements in predicted and realized CPUE occurred in ADs 3K, 3LNO, and 3Ps, and each AD has sustained pCPUEs in the healthy zone since 2020. In AD 3Ps, fishery CPUE tripled from 5.8 kg/trap in 2018 to 19.0 kg/trap in 2022. Moreover, predicted fishery discard levels were at or near historic lows or below healthy levels in each of these three ADs in 2017–2018, but have been sustained either in the healthy zone or near time-series lows in each AD since, with the exception of AD 3LNO in 2023 which was predicted to be at a cautious level.

The overall abrupt improvements in ADs 3K, 3LNO, and 3Ps stock status are associated with low ERIs in recent years, while the more gradual improvements in AD 2HJ are associated with a more gradual decline in ERIs (Figure 4). In AD 2HJ, pDIS has been gradually decreasing in recent years and were below 20% in 2023, while pCPUE had remained at about 7.5 kg/trap during 2015–2021, but the rate of improvements in predictions has increased in the past two years. Under status-quo removals in 2024, AD 2HJ discards would be predicted to be in the healthy zone while under low-moderate ERI (i.e., <20%) and pCPUE would enter the healthy zone for the first time in sixteen years. Egg clutch fullness levels cannot be as cleanly ascribed to events associated with PA development and implementation, with long-term progressive increases in trend improvement in AD 2HJ and recoveries from low levels occurring in ADs 3K and 3Ps prior to 2018. However, all ADs have coincidentally maintained very high (> 0.9) proportions of mature females carrying full clutches of viable eggs since the Informal PA Period, with no other period in the time series sustaining consistency in this performance metric across all ADs for this long (5 years).

Comparison of long-term trends in mat50 in males across ADs suggests fluctuations reaching levels as low as about 80 or 85 mm CW are a normal feature in the stock populations in these ADs (Figure 6). However, the abrupt decreases to levels of about 65–70 mm CW from 2015 to 2019 in AD 2HJ was unusual. There have been improvements since 2019 to annual levels of about 75–80 mm CW, suggesting declining ERIs may be beneficial, but the concerning phenomenon persists. In AD 3K, reversal of a prolonged decrease in the metric from 2005 to 2018 is coincidental with PA development, while high levels of mat50 (>90 mm CW) have been maintained in ADs 3LNO and 3Ps since the Informal PA Period.

4. Discussion

4.1. PA Management System Timing

The development of a multi-indicator PA management system for NL snow crab and associated invocation of Informal PA management beginning after 2018 occurred at a pivotal time for the NL snow crab resource. The EBI was at historic lows and fishery ERIs were enabled to elevate to historic highs. By most operationalized definitions of stock collapse, at just 9% of its historic maximum in 2016, this stock would be classified as collapsed preceding informal PA implementation. However, given severe depletion had not occurred for multiple decades and some recovery potential was evident [29], the poor state of resource in the late 2010s does not constitute a collapse by standardized criteria [38]. Nonetheless, interpretation of exploitation rates suggest that resource conservation was not the principal priority in the late 2010s while the stock was in a vulnerable state [39]. Accordingly, the pivotal informal change in management strategy for the resource after 2018 is best contextualized as occurring at a sensitive time and outcomes of application of PA philosophy into resource management are best judged by the extent to which they helped enable recovery in context of resource state and ecosystem carrying capacity.

4.2. PA Management System Impacts

Exploitable biomass levels in NL snow crab are strongly affected by both climate and fishing. There is strong climatic control of resource productivity through mechanisms such as water temperature, atmospheric forcing, and sea ice [40]. Despite warm conditions helping promote large individual size, overall, cold conditions are most favourable for sustained high productivity of this cold water specialist [41]. NL snow crab are predominately 9–11 years old when they reach legal-size, with crab in the exploitable biomass typically 9–13 years of age [42]. Thus, positive impacts of cold conditions during early ontogeny can become evident at lags of a decade or more, and mid-life benefits of cold conditions can exacerbate realized outcomes [40]. In extension of this, climate forcings along the NL marine shelf tend to oscillate at roughly decadal time scales [43], thus oscillating pattens in resource trajectory can be predictable using long- and mid-term ontogenetic climate variables, such as occurred preceding the forecasted mid-late 2010s decline [11,12]. However, top-down fishing effects are also important in regulating the exploitable biomass, as became particularly obvious during the unusually heavy exploitation period of the mid-2010s, with the downward shift in male SaM indicative of biological harm through fishing, a novel observation on the global scale.

One important aspect of PA fishery management is that it imposes explicit maximum limits on exploitation rates. This represents an important departure from harvest strategies immediately preceding 2019 as the fishery transitioned from a mature state into a senescent state. For example, during 2016–2018, the overall ERIs were 43%, 62%, and 52% annually, the highest levels ever enabled on the stock, all exceeding f_msy, and beyond those consciously applied to any other known major snow crab stock. Most indicators examined herein suggest that stricter limits on ERIs abruptly invoked in the Informal PA Period, which were near historic lows in all but AD 2HJ, led to improvements beyond what would have been anticipated if exploitation rates had been enabled to remain higher. For example, most AD-specific and overall fisheries CPUE were at levels approximating historic highs and not seen since the EBI was at a level of double or more of what it was in recent years (i.e., 1996–2001). In extension of this, discard rates in the fishery have become coincidentally low everywhere and the stock-level residual biomass has been at or near historically high proportions in 2022 and 2023.

In conjunction with the Informal PA Period, the NL snow crab fishery has become overall more efficient in resource extraction than it has been for virtually all of its post-Expansion Period history, with recent broad-scale consistency in minimization of wastage and increased survival of recruits. This outcome is at-least partially attributable to informal implementation of the PA management system. Ancillary supporting indicators of coincidental improvements associated with the shift in management philosophy in 2019 are development of the longest sustained period of broad-scale high (>90% full clutches) fecundity rates in females seen to-date and reversal of a SaM decline in males in AD 3K. Both phenomena have been sustained since onset of the Informal PA Period. As per its intent, the available evidence is showing that the multi-indicator PA management system is benefitting multiple stock health and fishery performance indicators. Moreover, contrast across ADs reinforce the benefits of invoking this multi-indicator PA management system, with positive changes being realized abruptly and substantially in all ADs where ERIs were immediately curtailed (3K, 3LNO, 3Ps) versus AD 2HJ, where stock improvements have been more gradual in association with gradual reductions in ERI after 2018.

Overall, the suite of circumstantial evidence suggests that this new management approach has equaled or exceeded historic fishery and resource performance outcomes during periods of similar conditions. There appear few biological deterrents associated with the adoption of this multi-indicator PA management system into resource use decision-making, particularly when considering performance indicators at the broad stock-level. Historically, management strategies for the stock have differed across ADs, generally characterized by higher exploitation rates in the northern ADs than in the southern ADs. However, in the Formal PA Period, all ADs will become more consistent in management strategies due to analogous fishery and resource performance indicators and formalized HCRs. History has shown that the lower exploitation rate strategy in the southern ADs is more effective from both biological protection and fishing efficiency perspectives. The shift to aligning future management strategies in the northern ADs to become more analogous to historic practices in the southern ADs is likely to be of broad benefit to the entire stock, with AD 2HJ representing the most upstream extent of the NL snow crab stock range and prevailing ocean currents flowing south. Accordingly, better protection of the upstream stock components should help safeguard reproductive capacity in downstream ADs with southerly flowing larval drift. Ultimately, homogenous management across the entire stock range reduces risks of fracturing biological connectivity by exploiting sub-stock components too aggressively.

4.3. Looking Forward

The informal invocation of the multi-indicator PA management was well-timed to help the NL snow crab stock recover from a historically poor state in the late 2010s, with evidence suggesting it has helped promote fishery extraction that is more efficient than occurred for most of the post-expansion period. Increased extraction efficiency helps foster increased sustainability in fisheries management and is advantageous in a scenario of a resource decline. In this regard, it is important to highlight that a decline in the EBI is anticipated in the short-term, with the most recent assessment showing declines in pre-recruit crab [44] and a long-term prognosis is for reduced habitat as greenhouse effect warming ensues [40].

This multi-indicator PA system cannot stop a resource decline, but there are measures in place to prohibit excessive exploitation, such as occurred under reduced biomass in the late 2010s, if and when the resource does decline. Historic data shows that by keeping ERIs low, sufficient intraspecific competition should be maintained in the population to help safeguard against deleterious outcomes exacerbated by overfishing during periods of naturally dampened population density. In particular, under the assumption that resource status will fall into cautious or even critical zones at some point in the future, the system assures that ERIs will be maintained at levels at a maximum of 35% annual harvest rate or lower, which we showed maintains a relatively strong residual biomass and minimizes discards. The direct incorporation of additional metrics of discards and female fecundity into stock health scoring provides additional ability to react to factors other than fishing rates that may become problematic during reduced biomass conditions. Any offsetting gains realized through maximizing fishing efficiency during a resource decline are also advantageous from an economic sense as help keep fishery CPUE high and thus reduce costs associated with effort inputs. Finally, continued ability of the multi-indicator PA management system to accurately predict fishery outcomes during resource declines should be an asset to help harvesters and managers plan for necessary adjustments. Conceptually, the use of both survey biomass and climate variables to predict fishery outcomes should help bolster the ability to the system to maintain accurate predictions.

5. Synopsis

Our analysis shows that like all male-only crab fisheries, exploitation strategies of NL snow crab must adjust to stock size changes as they occur. Recent history from this fishery demonstrates problems resulting from overfishing and contrast of historic practices across ADs demonstrate biological and fishery benefits of consistently applying low-moderate exploitation rates. The NL snow crab fishery teaches us that male-only harvest strategies alone are not sufficient for resource management of this or other male-only crab fisheries without conservation-oriented limits on fisheries exploitation rates. Leading inferences from outcomes on invocation of a multi-indicator PA management system for this vital marine resource suggest that achievement of additional beneficial outcomes stemming from maximizing extraction efficiency are indeed achievable ancillary goals for resource management in male-only crab fisheries.

Author Contributions

Conceptualization, D.M.; methodology, D.M., and K.B.; software, D.M., K.B., J.P., and K.L.; validation, W.C., K.L., and K.M.; formal analysis, D.M.; investigation, D.M., K.B., and J.P.; resources, D.M., J.P., and K.L.; data curation, W.C., K.L., and K.M.; writing—original draft preparation, D.M.; writing— review and editing, K.B., J.P., W.C., K.L., and K.M.; visualization, D.M.; supervision, D.M.; K.B.; J.P.; and W.C.; project administration, D.M.; All authors have read and agreed to the published version of the manuscript.

Funding

Please add: This research received no external funding

Data Availability Statement

The data used in this study are available on request from the corresponding author.

Acknowledgments

We wish to say thank-you to our DFO Resource Management Colleagues who were instrumental in developing and implementing the snow crab PA system with us.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Map of Newfoundland & Labrador snow crab Assessment Divisions (AD) 2HJ, 3K, 3LNO, and 3Ps used in the analysis. Black lines show Crab Management Areas (CMAs).

Figure 2. Number of active vessels (left panels), recorded landings (middle panels) and estimated trap hauls (right panels) in the snow crab fishery by year and Assessment Division. All ADs pooled in bottom panels. Vertical lines show starts of groundfish moratorium (solid red), Rationalization Period (dashed red), Informal PA Period (dashed blue), and Formal PA Period (solid blue).

Figure 3. Trends in fishery CPUE (left panels), exploitable biomass indices (left-middle panels), exploitation rate indices (right-middle panels) and proportion of residual crab in the exploitable biomass (right panels) in the snow crab fishery and resource by year and Assessment Division. All ADs pooled in bottom panels. Loess regression lines fit to proportions of residual biomass data (brown). In exploitation rate plots (right-middle panels) horizontal green, yellow, and red lines show maximum allowable ERIs for healthy, cautious, and critical zones within the PA framework. Fishery CPUE and exploitation rate index plots in the All ADs (bottom) row boxplots show levels across ADs and solid lines show indices based on pooled data. For proportion of residual biomass in the All ADs row (bottom right panel) boxplots show levels across ADs while loess regression line is fit to pooled data. Vertical lines show starts of groundfish moratorium (solid red), Rationalization Period (dashed red), Informal PA Period (dashed blue) and Formal PA Period (solid blue).

Figure 4. Left panels show cumulative proportions of annual exploitation rate indices (top row), residual biomass proportions (middle row), and soft-shell crab incidence (bottom row) by AD from 1999 to 2019. Numbers show slopes of linear regression models fit through the origins for each metric by AD. Right panels show linear regression models fit to scatter plots with point data represented by slopes of cumulative proportions linear regressions for each metric and AD.

Figure 5. Precautionary Approach framework for NL snow crab. Rows represent ADs. Left panels show pCPUE metric, with solid lines showing predicted values and points showing response data (standardized CPUE). Left-middle panels show pDIS metric, with solid lines showing predicted values and points showing response data (standardized discards). Right-middle panels show proportions of females carrying full clutches of viable eggs and right panels show integrated stock health scores in context of healthy (green), cautious (white), and critical (red) zones. Orange dots in 2024 in pCPUE (left) and pDIS (middle-left) metrics show predicted values under status quo landings with 2023, with vertical dots showing predictions under a range of 5% (light blue) to 40% (dark blue) ERI levels. 2024 values in integrated stock health score (right) panels show predictions under status quo landings with 2023. Horizontal solid lines pCPUE, pDIS, and EGG panels show reference points differentiating cautious from healthy (green) and cautious from critical (red) zones. Vertical lines show Informal PA Period (dashed blue) and Formal PA Period (solid blue).

Figure 6. Carapace widths at which 50% of males are predicted to mature (terminally-molted) by year within each AD. Loess regression lines fit to time series point estimates in each AD. Vertical lines show starts of groundfish moratorium (solid red), Rationalization Period (dashed red), Informal PA Period (dashed blue), and Formal PA Period (solid blue).

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