# Post-release survival of Whiteleg skate (Amblyraja taaf) caught as bycatch on demersal longlines in deep waters of the Southern Ocean (Crozet Archipelago)

Johanna Faure, Matthieu Authier, Félix Massiot-Granier, Floriane Plard, Clara Péron

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## To cite this version:

Johanna Faure, Matthieu Authier, Félix Massiot-Granier, Floriane Plard, Clara Péron. Post-release survival of Whiteleg skate (Amblyraja taaf) caught as bycatch on demersal longlines in deep waters of the Southern Ocean (Crozet Archipelago). ICES Journal of Marine Science, 2024, 81 (6), pp.1126-1136. 10.1093/icesjms/fsae073. hal-04979832

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# Post-release survival of Whiteleg skate (*Amblyraja taaf*) caught as bycatch on demersal longlines in deep waters of the Southern Ocean (Crozet Archipelago)

Johanna Faure<sup>1,*,</sup> Matthieu Authier<sup>1,2</sup>, Félix Massiot-Granier<sup>1</sup>, Floriane Plard<sup>2,3</sup>, Clara Péron<sup>1</sup>

## Abstract

Whiteleg skate (*Amblyraja taaf*) is a bycatch species commonly discarded in demersal longline fisheries targeting Patagonian toothfish around the Crozet archipelago (Southern Ocean). While there are global conservation issues on elasmobranchs, the post-release survival of this deep-sea species remains unknown. This study is the first tagging programme conducted on Whiteleg skates to investigate their fate after being released at sea. Almost 3000 skates were captured, double-tagged with conventional tags and released between 2020 and 2022 to assess survival using capture-mark-recapture (CMR) models. Our primary result was an estimated annual apparent survival rate higher than 92% for skates released in good condition (with 185 skates recaptured after up to 944 days at liberty). Low detection rate highlighted methodological challenges for CMR models. However, skate size and depth of capture had an effect on apparent survival probability with smaller individuals having lower values (<65 cm; 0.84 ± 0.04) as well as skates caught at shallower depths (<1200 m; 0.83 ± 0.04). Overall, this study suggests that the mandated release of skates in good condition is effective to limit fishing impact on skates in this fishery. To further limit this impact, we recommended changes in fishing practices (hauling speed and soak time).

**Keywords:** chondrichthyans; fishery management measures; fishing mortality; discards; Rajidae

## Introduction

Elasmobranchs (sharks, skates, and rays) are commonly captured in fisheries worldwide, mostly caught as bycatch (i.e. non-intentional catch in fisheries targeting other species; Stevens et al. 2000, Oliver et al. 2015). However, they are typically characterized by particular life-history traits such as long lifespan, slow growth, late maturity, and low fecundity, leading to low population growth rate and slow recovery after overexploitation (Stevens et al. 2000, Gedamke et al. 2007, Dulvy et al. 2014, Gallagher et al. 2014b, Barnes et al. 2016, Davidson et al. 2016). Given the recent conservation interest in elasmobranchs (Dulvy et al. 2021), various management measures have been introduced in the last 20 years to prevent the decline of populations, mostly focusing on avoiding areas of high density or promoting the return to the sea of bycatch species (Braccini and Waltrick 2019). While at-vessel mortality (occurring before or during the capture process) can be easily quantified, post-release mortality, which occurs after the release, remains unknown for many elasmobranch species (Dapp et al. 2016, Ellis et al. 2017). The subsequent underestimation of fishing mortality may lead to considerable ecological, management, and conservation risks, such that an understanding of post-release mortality is fundamental for effective management of bycatch in sustainable fisheries (Campana et al. 2009, Gilman et al. 2013, Barnes et al. 2016).

Assessment of post-release survival rates in elasmobranch field studies commonly relies on holding tank experiments (El-

lis et al. 2017). Such experiments can be effective to assess post-release survival (Enever et al. 2009, Benoît et al. 2012, Depestele et al. 2014, Knotek et al. 2018), but the set-up on-board fishing vessels is logistically challenging. Recently, the use of electronic tagging technologies has increased, allowing fate of released animals (i.e. returned to the sea alive) to be assessed (Musyl et al. 2011). However, due to the high cost of electronic devices, these are typically deployed on small number of individuals (Hammerschlag et al. 2011). In contrast, conventional tags, which are uniquely numbered nylon tags attached to fish, can be used in mark-recapture experiments to estimate natural and/or post-release mortalities (Julliard et al. 2001). Although it can be difficult to separate these different sources of mortality, the use of conventional tags may provide broader and longer-term information on larger sample size and can be favoured as an initial approach in limited-resource situations (Bird et al. 2020). Tagging data can be analysed through the use of capture-mark-recapture (CMR) models (King 2012), which are able to differentiate apparent survival (i.e. probability of an individual to be alive and still in the study area) from capture probabilities. However, CMRs often require multiple capture events of the same individual (Lebreton et al. 1992, King 2012, Neat et al. 2015), which can be inherently challenging to collect in the context of limited resources, low detectability, and sampling constraints (i.e. observation/capture tools). For these reasons, while conventional tagging experiments are

<footnote n="1"><sup>1</sup>Laboratoire de Biologie des Organismes et Ecosystème Aquatiques (BOREA), UMR 8067 – MNHN, CNRS, IRD, SU, UCN, UA, 43 rue Cuvier, 75005 Paris, France</footnote>

<footnote n="2"><sup>2</sup>Observatoire Pelagis, UAR 3462 CNRS – LR Univ, 5 allée de l’Océan, 17000 La Rochelle, France</footnote>

<footnote n="3"><sup>3</sup>Independent freelance researcher, Barraque de la Pinatelle, Tremoulet, 15500 Molompize, France</footnote>

<footnote>*Corresponding author. Laboratoire de Biologie des Organismes et Ecosystème Aquatiques (BOREA), UMR 8067 – MNHN, CNRS, IRD, SU, UCN, UA, 43 rue Cuvier, 75005 Paris, France E-mail: johanna.faure@mnhn.fr</footnote>
commonly used in fish stock assessment (Nowara et al. 2013, Bird et al. 2020), they are scarcely employed in survival estimations due to sparse data with low recapture rates (Ellis et al. 2017).

Among elasmobranch, Rajiforms received less attention than other orders (Ellis et al. 2017), and most studies focused on survival of neritic or coastal species (<200 m; Benoît et al. 2013, Knotek et al. 2020). In neritic waters, post-release survival was variable and dependent on species, body size, depth of capture, and gear type (Benoît et al. 2013, Knotek et al. 2020). Most studies on post-release survival of deep-sea (i.e. >200 m) skates were conducted in the Southern Ocean and provided limited evidence of survival due to very low tag-recapture rates in longline demersal fisheries: <2% for the Antarctic starry skate (*Amblyraja georgiana*) in the Ross Sea and for *Bathyraja* species at Heard and McDonald Islands. The first study, which revealed an effect of depth of capture on survival of *Amblyraja* spp., was carried out in deep waters of South Georgia Island. The proportion of skates surviving up to 12 h in a holding tank (on bottom trawlers) varied from 24% in deep waters (>1500 m) to 75% in waters <1300 m (Endicott and Agnew 2004).

In the Southern Ocean, a demersal longline fishery occurs in the exclusive economic zone (EEZ) of the Crozet sub-antarctic islands (southern Indian Ocean). This fishery, targeting Patagonian toothfish (*Dissostichus eleginoides*), commonly catches as bycatch the Whiteleg skate (*Amblyraja taaf*), a deep-water species found in the range of fishing depths (500–2300 m). This fishery is managed by the Administration of the French Overseas Territories, *Terres australes et antarctiques françaises* (hereafter “TAAF”), within a National Natural Reserve and the general framework of the Convention for the Conservation of Antarctic Marine Living Resources (Hureau 2011). In contrast with other toothfish fisheries, there is no bycatch limit for skates in the Crozet EEZ. However, other management measures have been implemented, such as movement rules: the requirement for a vessel to move a minimum distance when a particular level of bycatch is encountered during a fishing event (Auster et al. 2011), and the allocation of fishing quotas based on skate bycatch level (higher quota for vessel with the lowest skate bycatch rate). Moreover, fishers are provided with maps of high bycatch rates, accessible through their navigation system, for spatial avoidance. When capture of skates cannot be avoided and to ensure limited effect of fishing on skate population dynamics, skates must be released at sea following a procedure named “cut-off.” In such cases, when a skate is caught, the hauler has to be stopped, the snood cut, and the hook retrieved by a member of the fishing crew, which carries out a visual assessment of the skate condition. Consequently, skates in good condition (alive with minor injuries) must be immediately released. Otherwise, skates are retained (i.e. kept onboard) and often discarded (i.e. stored in offal tanks and washed before being discarded at sea) in the absence of a viable market.

The high proportion of skates caught in good condition results in most skates being released at sea. However, physical trauma and physiological stress caused by hooking and hauling as well as the interaction with predators after the release can result in acute and chronic effects on animal health and survival. Consequently, the effectiveness of the requirement to release skates at sea (“cut-off” practice) depends on the survival of these skates (Chin et al. 2015, Dapp et al. 2016, Womersley et al. 2021). Yet, the proportion of skates released and

their fate have never been studied in the Crozet toothfish fishery, which means that we may underestimate fishing mortality with potential implications on fishery sustainability and biodiversity conservation. Moreover, since *A. taaf* is considered as “data-deficient” by the IUCN red list (Skomal 2007, Polom et al. 2020), assessing skate mortality in this fishery is a high priority.

In this study, we investigated for the first time the at-vessel mortality of the Whiteleg skate caught in the Crozet longline fishery. By addressing several challenging methodological issues in a data-poor context, we also estimated apparent survival of *A. taaf* after being released (defined as the combination of natural and post-release survivals), using CMR analyses in a Bayesian framework. In addition, we explored the influence of body size and depth of capture on skate survival estimates. The ultimate goals of this study were to discuss the effectiveness of the cut-off management measure to reduce fishing pressure and conclude on fishery sustainability and conservation issues regarding skate bycatch.

# Materials and Methods

## Fishing technique

This study was carried out on the fleet of longliners that were operating in the Crozet EEZ between 2020 and 2023 ($n = 7$ in 2020–2022 and $n = 8$ in 2023). All commercial fishing vessels used integrated weight autolines with thousands of hooks (Fiskevegn® Eagle or Eagle Claw hooks of nominal sizes 14/0 and 15/0), each attached to a snood (~40 cm) laying on the seafloor. Hooks were baited using an automated machine with pieces of mackerel (*Scomber scombrus*) and occasionally squids. At Crozet, longlines set were composed of 6500 ± 2450 hooks (mean ± standard deviation) and soaking time (time during which the gear is fishing) was 33 ± 18 h during the study period (between April 2020 and March 2023, $n = 12$ 157). Longlines were always set at night to avoid seabird bycatch while hauling can occur both night and day. Two vessels of the fleet had a moonpool (i.e. a system to haul the line inside the vessel) while other vessels were hauling fish on one side of the vessel (bunker system). Gear characteristics (hook model and distance between hooks) and baits were consistent across vessels, seasons, and years during the study period (2020–2023). All fishing vessels carried a fishery observer who was responsible for monitoring the catches and enforcing fishing regulations during each fishing trip for a period of 3 months.

## Skate bycatch estimation

Captains have to declare their catches of Patagonian toothfish and all other species caught as bycatch, including skates. They must declare (i) the number of skates released at sea (“cut-off”) and (ii) for skates retained, their number and weight (irrespective of whether they are, ultimately, discarded or landed). *A. taaf* being the main skate species caught in this fishery, the risk of misidentification is considered negligible.

Since skates released at sea were not weighed, their gross weight was estimated by multiplying the number of individuals released by the average weight of an individual over the current season. The average weight was calculated for each fishing season by dividing the weight of retained skates by their numbers at the scale of the fleet.
<figure><img src="image_3.png" /><figcaption>**Figure 1.** Experimental area for tagging in the Crozet fishing area shown by the rectangle (a) with the dashed line (45°S) representing the upper limit of the Convention for the Conservation of Antarctic Marine Living Resources (CCAMLR) area. Spatial distributions of (b) tagged skates (n = 2974) and (c) recaptured skates (n = 185) are represented with circles. Dots represent location of fishing hauls between April 2020 and March 2023. Solid lines correspond to isobaths (500 m; 1000 m; 1500 m and 2000m).</figcaption></figure>

## Tagging protocol

Skate tagging was carried out during commercial operations onboard the fishing vessel "Ile de la Réunion II" (63-m longliner with a moonpool system) between April and June in 2020, 2021, and 2022. Each year, a dedicated scientist was in charge of this protocol for which the vessel was allocated an extra fishing quota (3.5 tonnes of Patagonian toothfish/year) by the TAAF administration. This protocol consisted of setting lines in a well-defined tagging area of ~12 000 km² (Fig. 1a) to capture and tag skates. The size and location of this area were chosen to optimize tagging efficiency (number of tagged skates per day) and tag recapture rate since the latter was expected to be low. To this aim, we selected an area where bycatch of skates were roughly constant over space and time and relatively high (20 skates/1000 hooks on average during the period 2014–2019, J. Faure pers. comm.) compared to other fishing grounds in the Crozet EEZ. Following this experimental design, we tagged ~1000 skates during a period of 5–9 consecutive days (19 longlines deployed on average) each year. Longlines set for the experiment were composed of 6086 ± 874 hooks and soak time was 33 ± 12 h, comparable with fishing practices of the fleet.

After being hauled and handled by crew as per normal practice, skates were tagged by the trained scientist with two

uniquely numbered dart tags in case of tag-shedding. Tags were provided by Hallprint (Hindmarsh Valley, Australia) in 2020 and 2021, and because of supply issues, they were provided by Floytag (Seattle, WA, USA) for the last tagging session in 2022 (Supplementary Fig. S1). We assumed no difference in tagging effect (same application method), tag detection, or tag loss (similar design) between both tag models. Upon capture, condition of skates (presence and type of injuries) was assessed by the trained scientist following a defined protocol (Supplementary Table S1). Due to fishing process, severely injured or dead individuals were processed by the fishing crew without consistently being recorded by the scientist. All individuals found alive with no injury "uninjured" or with mild injuries and considered in "good condition" were tagged and released. A tag was attached externally into the dorsal side of each wing musculature and tag numbers were recorded along with sex, total length (TL in cm), weight (in kg), and wingspan measurements (in cm). Data characterizing fishing operations (date, latitude, longitude, and depth) were also systematically recorded for each tagged individual. To minimize the potential impact of tagging procedure on post-release fate, skates were processed as quickly as possible (1–6 min). If the skate abundance was too high for individuals to be processed quickly, they were placed in a well-oxygenated seawater tank for a
maximum of 20 min. When skate abundance was too high for the skates to be tagged in a limited time, skates were released without tags.

This protocol resulted in a total number of 2974 double-tagged individuals at the end of the 3-year tagging programme (Fig. 1b).

## Recapture protocol

Tag recaptures occurred on all vessels of the fishing fleet from January 2021 to March 2023 during commercial operations. As low recapture rate was expected and because skates were expected to display limited movements in the tagging area (Nowara et al. 2013, Soeffker et al. 2018, Kneebone et al. 2020), the fishery manager (TAAF) required each vessel of the fleet to deploy a minimum of 125 000 hooks per year in the tagging area to maximize tag recapture chances. This effort corresponded to ~20% of the total annual fishing effort (in number of hooks) and was comparable to other areas of similar size where fishing can be concentrated. Since the fleet is operating along the slopes of the Crozet archipelago (Fig. 1a), fishing efforts were not limited to the tagging area, and recaptures were possible outside the tagging area. Tagged skates were detected by crew members during hauling, retained, and then presented to fishery observers in charge of data reporting. No reward, beyond an extra quota (1.3 tonnes of Patagonian toothfish), was provided to fishers for tag recapture. At recapture, tag IDs, skate condition, and injury types, along with sex, TL, weight, wingspan measurement, and data associated with fishing operations were recorded by fishery observers. The absence of a dedicated scientist onboard fishing vessels during the skate recapture period (outside tagging period) prevented multiple releases and recaptures of the same individual. All recaptured skates were thus retained, and tags were read and kept by the fishery observer for validation at the French National Museum of Natural History (MNHN) using photo and tag reclaim. All data were extracted from “PECHEKER” database hosted and managed by the MNHN (Martin et al. 2021).

## Modelling approach

A capture-recapture history matrix (1: captured; 0: not captured) was generated using time intervals of three months as sampling units. This time duration was chosen to limit the number of sampling units without fishing effort. The recapture history matrix resulted in 12 successive sampling units. Among those, three periods, including one without fishing effort, had seen no recapture. Recaptures that occurred within 3 months of tagging ($n = 8$) were excluded from the analysis due to the definition of the sampling unit.

We used a Cormack-Jolly-Seber (CJS) model to estimate apparent survival from CMR data. Estimate of apparent survival probability ($\phi_i$) gives the probability of an individual $i$ that was first captured at time $t-1$, to survive over a 3-month period. $\phi_i$ also depends on recapture probabilities ($p_i$), the probability for an individual first captured before period $t$ to be recaptured at period $t$, knowing that it is alive. Homogeneity of recapture probabilities among individuals (similar detection probability of individuals of different sizes, e.g.) and independence of individuals are assumed in this study (Lebreton et al. 1992).

CJS model is generally used to estimate recapture and survival probabilities from data with an initial capture (and tag-

ging) of individuals and multiple recaptures of the same individuals released after each recapture event (King 2012). Multiple capture events of the same individual are favoured when running CJS models because it increases the dataset size and allow survival to be estimated as a function of time and thus, separate natural mortality from mortality due to fishing. However, in our study involving a single recapture event, we were not able to disentangle natural and post-release mortalities, so our estimate of apparent survival is a combination of both. We were also expecting more uncertainty in parameter estimates compared to multi-recapture studies due to their short duration (3 years), the low recapture rate of deep-sea species caught on longline fishing gear, and the experimental design. To help quantify uncertainties in model parameters, we fitted CJS models using Bayesian statistical methods (Rivot et al. 2008, King 2012).

A useful feature of the Bayesian approach is the possibility to incorporate relevant prior information directly into inferences about quantities of interest (Mackey et al. 2008). Based on a published study about post-release survival estimates of Rajidae and theoretical relationship between longevity and natural survival (Dureuil et al. 2021), we expected high survival of uninjured or mildly injured Whiteleg skates. We therefore constructed an informative prior for the apparent survival probability using a $\beta$ distribution centred on 0.90 with a 95% confidence interval spanning the values 0.84–0.97:

$$ \phi_i \sim \beta (60.84, 5.52). \quad (1) $$

Detection probability during a time period is expected to be positively correlated with the number of recapture opportunities that have occurred over the said period, which varies significantly among the study periods (Table 1). Fishing effort was therefore added as a covariate to model the recapture probabilities as follows:

$$ \begin{aligned} \operatorname{logit}(p_t) &= \mu_p + \beta_p \times \operatorname{Effort}_t \\ \mu_p &\sim \mathcal{N}(0, 1.5) \\ \beta_p &\sim \mathcal{N}(0, 1), \end{aligned} \quad (2) $$

where $\mu_p$ is the mean detection probability and $\beta_p$ is the covariate effect of $\operatorname{Effort}_t$ (expressed in number of hooks set per time period $t$) on a logit scale. $\mathcal{N}(\mu, \sigma)$ denotes the normal distribution with location ($\mu$) and scale ($\sigma$) parameters. The inverse logit of the resulting values yielded recapture probabilities bounded between 0 and 1.

The model [(1) and (2)] was considered as a baseline model (Model 0). We also tested three additional different models to test our hypotheses regarding the effect of skate body size and/or depth of capture on apparent survival probability. To this aim, two categorical variables ($SIZE_i$ and $DEPTH_i$) were constructed based on TL and depth at first capture, respectively.

Model 1: Size effect model with a size-dependent survival probability such that $\phi_i = \phi_{SIZE_i}$, where tagged skates $i$ were classified in three size categories ($SIZE_i$): small ($TL \le 65$ cm, $n = 590$), mid-size ($65$ cm < $TL \le 80$ cm, $n = 1$ 196), and large ($TL > 80$ cm, $n = 910$).

Model 2: Depth effect model with a depth-dependent survival probability such that $\phi_i = \phi_{DEPTH_i}$, where depth categories were as follows: shallow ($\text{depth} \le 1200$ m, $n = 325$), mid-depth ($1200 < \text{depth} \le 1500$ m, $n = 936$), and deep ($\text{depth} > 1500$ m, $n = 1$ 435).
<table><caption>**Table 1.** Summary of the tag-recapture experiment with fishing effort (in number of hooks set), tagging effort, and number of recaptures in the tagging area for each 3-month period used in our CJS models.</caption><thead><tr><th>Time step</th><th>Time period</th><th>Number of hooks set</th><th>Number of skates tagged</th><th>Number of skates recaptured</th></tr></thead><tbody><tr><td>1</td><td>Apr–June 2020</td><td>192 200</td><td>928</td><td>–</td></tr><tr><td>2</td><td>July–Sep 2020</td><td>0</td><td>–</td><td>–</td></tr><tr><td>3</td><td>Oct–Dec 2020</td><td>40 040</td><td>–</td><td>0</td></tr><tr><td>4</td><td>Jan–March 2021</td><td>649 260</td><td>–</td><td>12</td></tr><tr><td>5</td><td>Apr–June 2021</td><td>210 400</td><td>1 046</td><td>2</td></tr><tr><td>6</td><td>July–Sep 2021</td><td>59 000</td><td>–</td><td>2</td></tr><tr><td>7</td><td>Oct–Dec 2021</td><td>127 200</td><td>–</td><td>0</td></tr><tr><td>8</td><td>Jan–March 2022</td><td>825 560</td><td>–</td><td>26</td></tr><tr><td>9</td><td>Apr–June 2022</td><td>466 640</td><td>1 000</td><td>60</td></tr><tr><td>10</td><td>July–Sep 2022</td><td>97 730</td><td>–</td><td>4</td></tr><tr><td>11</td><td>Oct–Dec 2022</td><td>77 420</td><td>–</td><td>8</td></tr><tr><td>12</td><td>Jan–March 2023</td><td>1 035 850</td><td>–</td><td>71</td></tr><tr><td>Total</td><td>–</td><td>–</td><td>2 974</td><td>185</td></tr></tbody></table>

Model 3: Combined effect model with an interaction between size and depth such that $\phi_i = \phi_{(SIZE \times DEPTH)}$. The Combined effect model was constructed by creating an indicator variable with nine levels, representing each possible combination of size and depth for each individual (Supplementary Table S2).

For models 1–3, we used a less informative prior, corresponding to an *a priori* expected survival $E[\phi]$ of 0.5 with a medium variance [$\beta(5, 5)$], so that extremely high or low value for apparent survival is assumed unlikely. The rationale for using this prior is related to the precautionary principle. Skate condition and injury types were not used in modelling analysis because of non-compliance with protocol (Supplementary Table S1) by one of the three scientists dedicated to skate tagging who classified 100% of the skates as “Injured.”

Apparent survival probabilities, $\phi_i$, were quarterly values estimated for each individual and the mean of the posterior distribution was calculated for each category of individuals. Recapture probabilities, $p_t$, were estimated for each time period regardless of the skate size or depth of capture.

Over the 2 966 tagged individuals, 270 individuals had not been measured at first capture; they were removed from all analyses, resulting in 2 696 skates included in the 4 competing models. To compare the different models and assess predictive performance, we computed the Watanabe-Akaike information criterion (wAIC; Gelman et al. 2013) on the four models.

We used the freely available Nimble software in R (version 0.13.1; de Valpine et al. 2022) that implements Markov Chain Monte Carlo (MCMC) algorithms to sample from the posterior distribution conditional on the observed data (de Valpine et al. 2017). For each model, a double-chain MCMC run was used to assess the convergence of the MCMC runs, and inference was based on 450 000 iterations after parameter convergence was assessed using the rhat statistics (rhat < 1.1; Gelman and Rubin 1992).

# Results

## Skate bycatch and fate

During the three fishing seasons (September–August) of our study, skate bycatch represented a total of 93 566 individuals (353.3 tonnes) (Table 2). A large majority of the skates were released at sea (>90%), as shown in Table 2.

## Overall tagging results

Body size of tagged skates varied from 34.4 to 105.0 cm TL (mean and standard deviation: 76.3 ± 12.4 cm TL; Supplementary Fig. S2). They were caught at depths ranging from 544 to 1750 m (1399 ± 234 m). These size and depth ranges were consistent with skate body size and fishing depths during commercial activities in other fishing grounds of the Crozet EEZ. At first capture, 37% of tagged skates were visually assessed as uninjured and 63% were categorized as being in good condition (Supplementary Table S1). Most skates in “good condition” had minor mouth injuries (83%) while other injuries, included minor intestine prolapse (1.4%) or bleeding from gill slits (1.2%), e.g.

A total of 185 skates were recaptured from January 2021 to March 2023, resulting in an average recapture rate of 6% (Supplementary Table S3). All skates were recaptured within the tagging area, at an average distance of 8.8 km (±9.1 km) from their released location (minimum strait-line distance). The distance between release and recapture locations varied from 0.2 to 49.7 km for skates at liberty for 371–728 days, respectively. No recapture has been reported outside the tagging area despite fishing occurring all along the slope of the Crozet EEZ (Fig. 1c). Time at liberty ranged between 3 and 944 days (470 days in average). All individuals were recaptured with both tags, except one.

## CJS model results

Overall, mean posterior estimates of $\phi_i$ and $p_t$ were characterized by extreme values (very low detection and very high survival probabilities). The Combined effect model had the lowest wAIC value (Table 3), indicating significant improvement of fit when including combined effect of size and depth. All models converged (rhat < 1.1).

### (a) Detection probability

A total of 12 recapture probabilities were estimated with $p_2 = 0$ due to the absence of fishing effort and recapture possibility during the second time period (July–September 2020; Fig. 2). Altogether, estimates of recapture probabilities ($p_t$) were highly variable through time because of strong positive correlation to fishing effort. Across all models, $\beta_p$ had positive values, indicating an increase of detection probability with higher fishing effort (Table 3). Maximum values of detec-
<table><caption>**Table 2.** Summary of skate bycatch as declared by captains (in green weight and number). Retained individuals were in poor condition (dead or severely injured) and either discarded or landed. Landed skates represent <1 tonne every year.</caption><thead><tr><th rowspan="2">Fishing seasons</th><th colspan="2">Retained</th><th colspan="2">Released (cut-off)</th><th colspan="2">Total</th><th rowspan="2">% Released (cut-off)</th></tr><tr><th>Weight (tonnes)</th><th>No. of ind</th><th>Estimated weight (tonnes)</th><th>No. of ind</th><th>Estimated weight (tonnes)</th><th>No. ind</th></tr></thead><tbody><tr><td>2020–2021</td><td>5.6</td><td>1 653</td><td>57.1</td><td>16 844</td><td>62.7</td><td>18 497</td><td>91.1</td></tr><tr><td>2021–2022</td><td>9.6</td><td>2 307</td><td>143.5</td><td>34 481</td><td>153.1</td><td>36 788</td><td>93.7</td></tr><tr><td>2022–2023</td><td>7.1</td><td>1 976</td><td>130.4</td><td>36 305</td><td>137.5</td><td>38 281</td><td>94.8</td></tr></tbody></table>

<table><caption>**Table 3.** Comparison of detection probability parameters (posteriors means and sd), sample size (n), model performance (wAIC) for the four competing CJS models, and differences between the best model (3) and others.</caption><thead><tr><th>Model</th><th>μ<sub>p</sub></th><th>β<sub>p</sub></th><th>n</th><th>wAIC</th><th>ΔwAIC</th></tr></thead><tbody><tr><td>0: Baseline model</td><td>-4.43 ± 0.08</td><td>0.28 ± 0.09</td><td>2696</td><td>1921</td><td>99</td></tr><tr><td>1: Size effect model</td><td>-4.31 ± 0.09</td><td>0.29 ± 0.09</td><td>2696</td><td>1882</td><td>39</td></tr><tr><td>2: Depth effect model</td><td>-4.30 ± 0.09</td><td>0.29 ± 0.09</td><td>2696</td><td>1877</td><td>55</td></tr><tr><td>3: Combined effect model</td><td>-4.14 ± 0.09</td><td>0.30 ± 0.09</td><td>2696</td><td>1822</td><td>-</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Figure 2.** Detection probability *P* estimated for each time period (3 months from 1: April–June 2020 to 12: January–March 2023) for the Combined effect model.</figcaption></figure>

tion probability were reached at the 4th, 8th, and 12th time periods (Fig. 2 and Supplementary Fig. S3).

In the *Combined effect* model, estimates of detection probability (*p*<sub>t</sub>) varied between 0.010 (± 0.001) in the 3rd time period and 0.024 (± 0.006) in the 12th (and last) time period (Fig. 2).

### (b) Survival probability

Apparent survival probability estimate was high for all 4 models, with the *Baseline* model presenting the highest estimate across all models (φ = 0.98 ± 0.006), resulting in an apparent annual survival rate of 92% (i.e. an annual survival probability of 0.92; Supplementary Fig. S4a). Overall, while recapture rates varied from 3.3 to 9.3% for the group “mid-size × mid-depth” (Supplementary Table S2), the *Combined effect* model revealed that smaller skates (≤65 cm)

had lower apparent survival (φ<sub>i</sub> ≤ 0.92) than mid-size and large-size individuals, whatever the depth at which they were caught (Fig. 3). This pattern was also highlighted by single-effect models (Supplementary Figs S4B and S4C). However, the group “large size * shallow depth” had the lowest apparent survival (φ<sub>7</sub> = 0.79 ± 0.06). The group “mid-size* shallow depth” had much lower value (φ<sub>4</sub> = 0.49 ± 0.15), but we considered that this result was mostly driven by the prior distribution, and not informed by the too small sample size of this group (n = 10). In contrast, the group “mid-size × mid-depth” had the highest survival estimate (φ<sub>5</sub> = 0.95 ± 0.02).

## Discussion

Our study quantified for the first time the proportion of Whiteleg skates (*A. taaf*) released alive from demersal longlines in the Southern Ocean and estimated their apparent survival after release. We found that a high proportion of skates caught were considered in good condition (>90%) and released at sea, suggesting a limited at-vessel mortality. Using a conventional tagging experiment, our study also revealed that Whiteleg skates released at sea in good condition have high annual apparent survival rates (>92%), with body size and depth of capture having an effect. As expected, the low detection probability (<2%) of deep-sea skates was challenging when applying CMR methods but sensitivity analyses indicated that the dataset was sufficient to inform model outputs (Appendix 1).

## Detection probability

Our tagging experiment led to higher tag recapture rate (6%) than previous tagging experiments carried out on skates in the Southern Ocean (<2% in HIMI and the Ross Sea; McCully et al. 2013, Nowara et al. 2013, Finucci and Moore 2022). This relatively high recapture rate was made possible by our study design (limited spatial extent and high fishing effort following tagging). Tag recapture rate remained low, however, compared to other fisheries with less selective gears such as trawls and/or higher fishing effort, such as in the English Channel (North Atlantic Ocean), where recapture rates may be up to 35% for Rajidae (McCully et al. 2013, Simpson et al. 2020).
<figure><img src="image_5.png" /><figcaption>**Figure 3.** Estimates of survival probabilities of skates, *φ*, for the Combined effect model depending on depth categories: shallow (≤1200 m), medium (> 1200 m and ≤1500 m), and deep (> 1500 m) and skate body size categories: small size (≤65 cm; panel a); mid-size (> 65 cm and ≤80 cm; panel b); and large size (> 80 cm; panel c) individuals. The sceptical prior is represented in each panel.</figcaption></figure>

Detection probabilities were low (<0.1) and could be
caused by poor gear selectivity (tagged skates not being cap-
tured even though they are present in the area fished), skate
abundance (skates are tagged in low number compared to the
total number and are consequently less detectable), or a com-
bination of these two reasons. As a consequence, maximum
detection probabilities (up to 0.024 ± 0.006) were obtained
for time periods from January to March, when fishing effort
peaked in Crozet EEZ.

Considering the duration of the study (3 years) and the
low observed tag-shedding rate (all 185 recaptured individ-
uals had their two tags except for one that had shed one tag),
we assumed negligible tag-shedding effect in our models. This
also meant that our tagging procedure was suitable for deep-
water skates.

None of the recaptures took place outside of the tagging
area despite significant effort nearby and all around the slope
of the Crozet archipelago (Fig. 1a). The average distance be-
tween release and recapture locations was 8 km (±1.1 km),
indicating limited movements of tagged skates. Site fidelity
pattern suggested by our study is consistent with previous
studies performed on other skate species (Nowara et al. 2013,
Kneebone et al. 2020). However, we cannot exclude move-
ments outside the study area, particularly outside the fishing
grounds.

Apparent survival estimates

Apparent survival probability of Whiteleg skates across all
individuals released in good condition was high (annual ap-
parent survival rate = 92%). Due to our modelling design
(survival estimated over several months and single recapture
event), apparent survival estimated through CMR analysis
was natural and post-release survival combined. Although it
was not possible to separate the two survivals in this study, our
survival estimate was comparable to short-term post-release
survival of other skate species (e.g. the winter skate (*Leuco-
raja ocellata*) caught by trawls in the Gulf of St. Lawrence—
Benoît et al. 2010a, Mandelman et al. 2013) or other gear

types (e.g. Depestele et al. 2014—beam trawl; Knotek et al. 2018—scallop dredge; Sulikowski et al. 2018—gillnet). High apparent survival probability suggests Whiteleg skate being particularly robust to demersal longline capture with high post-release survival. If natural mortality had been known, it would have been possible to use it compared to apparent survival estimate to assess the post-release survival. Comparison with previous studies conducted on *Amblyraja* spp., which is a common bycatch species in the South Georgia toothfish longline fishery, is not possible because we did not consider individuals with major injuries—unlike other studies (Endicott and Agnew 2004, Ellis et al. 2017). The presence of scars assumed to be related to previous capture events were sometimes reported by fishery observers in our study, which indicates good healing capacities characterizing elasmobranchs (Benoît et al. 2013, Chin et al. 2015).

Factors affecting survival estimates

Adding body size and depth of capture as covariates to de-
scribe the survival process (*Combined effect model*) allowed
a better fit to the data. Estimates of φᵢ and pᵢ of the *Com-
bined effect* model were less extreme but consistent with the
*Baseline* model and single effect models (*Size effect* and *Depth
effect*). Both size and depth influenced apparent survival prob-
ability, which is in accordance with the literature (Ellis et al.
2017). Overall apparent survival probabilities were lower for
small individuals, across all depth classes, than for other size
categories. The size effect highlighted in this study matched re-
sults obtained from studies using holding tank experiments on
other skate species and could be related to lower healing ca-
capacities of juveniles (Benoît et al. 2013, Campbell et al. 2018).

In contrast with Endicott and Agnew (2004), who showed
decreasing survival of skates with increasing trawling depths
due to changes in pressure or injuries associated with the haul-
ing process on South Georgia toothfish fishery, our study re-
vealed nonlinear effect of depth. Apparent survival was the
lowest at shallow depths and the highest at mid-depth (across
all size classes, Fig. 3). At greater depths (> 1500 m), the appar-
ent survival probability increased with increasing body size, which was also found in previous studies (Benoît et al. 2013, Dapp et al. 2016, Campbell et al. 2018). In Benoît et al. (2013), lower survival probability of small fish (including skates) was explained by greater susceptibility to hypoxia due to their higher metabolic rate and energy cost for breathing. In contrast, all skate size classes exhibited their lowest overall apparent survival when caught at shallower depths (<1200 m). This relationship was also highlighted for the little skate (*Leucoraja eurinacea*, captured in dredge fishery <50 m) in northwest Atlantic (Knotek et al. 2018). In the latter, it was assumed that shallower depths were associated with different fishing behaviours, intensifying physical interactions with captured individuals. In demersal longline fisheries, lower survival in shallow depths could be a consequence of higher hauling speeds in shallow depths (as compared with deeper waters), causing increased risk of barotrauma and jaw injury (Rodríguez-Cabello and Sánchez 2017). Conversely to trawling, in longline fisheries, hauling speed varies depending on the length of the line and depths; shorter lines often deployed at shallower depths being hauled faster than longer lines at greater depths. In the Crozet EEZ in particular, hauling speed may be significantly higher than normal to limit interactions with cetaceans (Tixier et al. 2015, Janc et al. 2021), potentially increasing the severity of skate injuries due to the pressure exerted by the hook on the jaws. However, little is known about the effect of hauling speed on skate injuries due to the difficulty of measuring this parameter accurately.

## Limitations and future work

Survival and detection probabilities were found to be extreme values, one being very high (survival) and the other being very low (detection). With a data-poor situation and uneven fishing effort through time to ensure recaptures, patterns observed in our results may have arisen from modelling choices. In some instances, prior assumptions, the strength of Bayesian analyses, can force recapture and survival estimates to extreme values in absence of sufficiently informative data (Zeh et al. 2002). This is unlikely in our study because we used either informative prior based on current knowledge to implement soft constraints (model 0) or sceptical prior giving less weight to values at the boundary of the parameter space (models 1–3). We also performed a sensitivity analysis using different priors, including non-informative priors that confirmed that our data had sufficient information on survival (Appendix 1).

Heterogeneity in detection probabilities between fishing vessels/fishing crews was not accounted for in our models because of small sample sizes (n = 185 recaptures). This heterogeneity could arise with differences in involvement of the crew in the recapture programme (attention paid to the presence of tags before immediate release at sea) or the distribution of fishing effort within the tagging area (Fenaughty 2008). Our experimental design aimed at limiting the effect of the latter effect by focusing tagging and fishing effort in a small area (~12 000 km²). However, given the limited movements of Whiteleg skates, tagging individuals within a smaller area at fixed stations that vessels would revisit could have led to higher and more homogeneous detection probabilities between vessels.

All tagging sessions were carried out on a vessel where longlines are hauled from a “moonpool” while the toothfish fleet

is also composed of vessels hauling at the bunker, more exposed to weather conditions than “moonpool” vessels. On one hand, the “moonpool” conditions could have inflated survival estimates because skates are protected from wind desiccation, freezing, and potential fresh water exposure with rain or snow. On the other hand, air exposure is longer on “moonpool” vessels compared to bunker vessels because the skate has to be carried from the moonpool to the release trap by a crew member, which could have negative effect on survival estimates (Benoît et al. 2010b). As the cut-off is extremely fast on bunker vessels and air exposure on “moonpool” vessels must be limited to a few seconds, we consider the vessel effect to be limited.

The effect of water tanks used for skates during tagging (occasionally) is difficult to predict because it can either inflate survival allowing skates to recover, limiting effects of predation occurring after the release or reduce survival due to confinement stress (Portz et al. 2006, Fenaughty 2008).

Other methods have also been used to assess post-release mortality in other fisheries, such as qualitative health assessments (Benoît et al. 2010b, 2013) and blood-physiology studies (Skomal 2007, Gallagher et al. 2014a, Campbell et al. 2018, Whitney et al. 2021), but they may not be necessarily relevant to quantify post-release survival estimates (Ellis et al. 2017) since they only rely on immediate assessment although death can occur few hours, weeks, or months after releases. However, including health assessment or blood physiology as covariates in CMR models could help identify physical or physiological proxies of post-release survival. Another factor that could be investigated in future studies is air exposure duration since it is known to affect post-release mortality (Musyl et al. 2011, Benoît et al. 2013, Poisson et al. 2019, Mura et al. 2021).

Post-release survival could also be further investigated by using electronic tags (pop-up satellite and acoustic tags or accelerometers). While acknowledging potential limitations of CMR models in a data-poor context, our study is the first attempt to quantify post-released survival using cost-effective CMR approaches informing on an important aspect of fisheries management.

## Implications for conservation and fishery management

Our first important result for fishery management is that at-vessel mortality of Whiteleg skate, caught by catch in Crozet EEZ, is low since >90% of skates are released in good condition. It indicates that demersal longlines and associated fishing practices (hauling speed, soak time, cut-off, etc.) rarely cause severe external injuries on skates. However, this outcome depends on the crew following instructions for skate handling and condition assessment. The role of the fishery observer in controlling “cut-off” practice on each vessel is thus crucial for the effectiveness of this management measure.

The second important result is the high apparent survival rate of Whiteleg skates released in good condition by the crew (>92%). This value being comparable to expected natural survival, we can hypothesize that long-term post-release survival is also high in this species and that capture process does not have long-term negative impact on survival. This highlights the benefits of maintaining the “cut-off” management measure for this species.
Nevertheless, the combined effect of depth and size on apparent post-release survival showed that small and large skates were less likely to survive when they were brought up from shallow depths (<1200 m). Further investigations could be undertaken to test whether the hauling speed, known to be higher at shallowest depths, could have a negative effect on apparent post-release survival. Small and medium-sized individuals had reduced apparent post-release survival at greater depths (>1500 m). Longer hauling time and potentially soaking time at greater depths could explain this result (Dapp et al. 2016, Braccini and Waltrick 2019). Future studies are needed before making recommendations about changes in fishing practices.

Our findings raise the question of the post-release survival of more severely injured individuals who are currently retained but may have a chance of survival. In many European fisheries, elasmobranchs caught as bycatch are returned to the sea regardless of their physical condition (exemption of the landing obligation) because they are considered to be resilient towards capture-and-releasing processes (Borges and Penas Lado 2019).

Estimating post-release survival can be challenging, in particular for deep-sea species, as our study shows. However, this should not prevent this approach from being applied to other fisheries with similar challenges. For example, a low tag-recapture rate was observed in Kerguelen waters for two species of *Bathyraja* sp. (<1%), under similar sampling design. If recapture rates do not allow survival estimation models to be applied, the use of tag-recapture rates as a proxy of post-release survival may be desirable as part of a precautionary approach. According to tag-recapture rate comparisons, post-release survival of *Bathyraja* sp. may be much lower than for *A. taaf*. Morphological differences between species of the genus *Amblyraja* and *Bathyraja* may explain such tag-recapture rate differences since *A. taaf* is sturdy, with a thick skin covered with thorns (family Rajidae, also named "hard-nose" skates), while *Bathyraja* species (family Arhynchobatidae, also named "soft-nose" skates), with smoother and thinner skin potentially more prone to suffer severe injuries.

All together, these results represent a first step towards assessing fishing mortality in skate caught in the Crozet fishery. The next step is to estimate total abundance of Whiteleg skate population through models of population dynamics, but basic information on life history traits (growth, longevity, and length frequency distributions) is still lacking.

## Acknowledgements

We thank Marc Leménager, Julie Caquelard, and Camille Biechy for carrying out the tagging experiments of skates and the many people who helped in handling skates, including fishery observers, crew members, and captain of the fishing vessel *Ile de la Réunion II*. We also thank the shipowner, COMATA, for supporting this work and the staff of the Administration of the Terres Australes et Antarctiques Françaises (TAAF), including Simon Fournier and Sylvain Lenoir, for technical co-ordination. We are grateful to Samuel Iglésias (MNHN) for his assistance providing alternative tags and all other people reacting to the delayed arrival of our own tags (François Garren, Sylvain Bonhommeau, Thomas Barreau, Jaimie Cleeland, Tim Lamb, and Scott Cooper). We thank Charlotte Chazeau and Nicolas Gasco for their help with data cura-

tion. We are also grateful to the three reviewers for their comments and suggestions that have significantly improved this study.

## Author contributions

J.F.: conceptualization, data curation, formal analysis, investigation, visualization, and writing—original draft; M.A.: methodology, resources, supervision, validation, and writing—review and editing; F.M.-G.: formal analysis, investigation, methodology, and writing—review and editing; F.P.: methodology, resources, validation, and writing—review and editing; C.P.: conceptualization, funding acquisition, investigation, supervision, and writing—review and editing.

## Supplementary data

Supplementary data is available at *ICES Journal of Marine Science* online.

*Conflict of interest:* The authors have no conflict of interest to declare.

## Funding

This work was financially supported by the administration of the Terres australes et antarctiques françaises (TAAF), Fondation d'entreprises des mers australes, and the Ocean Stewardship Fund (OSF) programme of Marine Stewardship Council.

## Data availability

The data underlying this article will be shared on reasonable request to the corresponding author.

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