The status of marine fish stocks needs to be assessed to ensure fishing practices that exploit the populations at sustainable levels. Stock assessments are based on multiple data types that include catch data, monitoring of fishery landings, biological observers and scientific research surveys. The latter provide critical fishery-independent information of fish stocks that is subsequently utilized for estimating key population demographic parameters such as biomass, abundance, fecundity, recruitment and mortality. These parameters are not only crucial to guarantee the effective management of the stocks but also to understand their recent evolutionary history (Swain, 2011; Kindsvater et al., 2016).

Despite their critical importance, scientific research surveys used to assess fish stocks present recognized shortfalls (Maunder and Piner, 2015). These include a slow progress coupled with high economic costs and complex logistics, that often results in sparse data with limited coverage in space and time (Stamatopoulos, 2002, Pennino et al., 2016). Moreover, the data obtained is not always accurate and the elevated costs prevent the assessment of many exploited stocks that remain data limited or, directly, unassessed, representing a major conservation concern.

The emergence of novel genomic techniques together with the plummeting sequencing costs have provided novel means to improve the cost-efficiency of fisheries research surveys, reduce bias and uncertainty of fish stocks assessments and expand the range of assessed species.

In this review, we focus particularly on the method of close-kin mark-recapture (CKMR), based on an idea founded nearly two decades ago (Nielsen et al., 2001; Skaug, 2001) that resurfaced recently as a promising tool to estimate key demographic parameters, through genotyping and the identification of close-kin using modern genomic methods (Bravington et al., 2016b). CKMR can be used to estimate abundance or biomass among other demographic parameters, which are especially challenging to infer in many marine species, characterized by large, highly mobile and dispersed populations.

This methodology has gathered considerable attention as it has important advantages over classical mark-recapture (MR), which requires capture, physical marking and recapture of individuals, who must remain alive, in multiple sampling events. In contrast, CKMR can be applied to samples collected during a unique sampling event as well as to dead specimens (Wacker et al., 2021). Besides, CKMR overcomes many of the challenges inherent to traditional MR modelling, as it does not suffer from the effects that can occur after initial capture, such as trap shyness or tag-loss (Marcy-Quay et al., 2020). Advances in high-throughput sequencing technologies, enable today the fast and accurate genotyping of large numbers of samples across many loci to identify close-kin with precision.

Despite its potential, the successful application of the method is yet very restricted, as it has been used to assess only a handful of species (Delaval et al., 2022). The broad applicability of CKMR to fish populations and, therefore, its usefulness for fisheries assessments has yet to be demonstrated. Here, we provide an overview of the method, together with guidance for its application, which includes crucial information that is scattered across grey-literature (mostly non-peer reviewed reports, including technical, workshop and project reports). Bravington et al. (2016b) lays the foundation of the method detailing the mathematical and statistical framework, assumptions and conditions needed for its application whereas Waples and Feutry (2022) compare the methodology with kin-based methods to estimate effective population size using analytical models. In contrast, here, we provide a deliberately oversimplified description of the statistical framework and use an accessible terminology to reach potential users alike – fisheries scientist and managers with no expertise in genomics and geneticists unfamiliar with modelling, respectively. We review the methodology in the context of fisheries assessments focusing on practical aspects and technical considerations. We analyze all CKMR studies applied to fish species published to date, discuss the readiness, viability, maturity and limitations of the method and issue recommendations for its uptake in stock assessments.

The method of CKMR offers great promise for the estimation of population parameters often difficult to infer by other methodologies. Nonetheless, few practical applications of CKMR across aquatic animals have been published to date. Here, we provide an overview of the most important aspects highlighted by studies involving teleosts and elasmobranchs (skates, rays and sharks) ( Table 1 ) and analyze their relevance in a framework of fisheries assessments.

The first full application of the CKMR method was a study on the southern bluefin tuna (SBT), a heavily exploited marine species (FAO, 2022). This study, published in 2016, sparked great interests in the methodology (Bravington et al., 2016a). SBT fulfills a series of characteristics that facilitates the experimental design and sampling, making it an ideal target for close-kin studies. These, include an extensive biological knowledge of the species, absence of population structure, a single spawning site and availability of a large number of samples collected across several years. The stock is severely depleted after an intense exploitation for decades but a large uncertainty remains about its absolute abundance and the level of depletion of its reproductive component (Kolody et al., 2008; Kurota et al., 2010). Using CKMR, the authors assessed abundance of SBT, with 25 microsatellite markers that were used to genotype ~14,000 tissue samples of juvenile and adult tuna individuals. Kinship inference revealed 45 POPs that were subsequently used to infer absolute adult abundance and adult survival. Resulting estimates revealed a less depleted and more productive stock than previously suggested by traditional stock assessments. Interestingly, the methodology provided evidence of a strong nonlinear relationship between fecundity and weight. Big females have a much larger reproductive contribution than expected from their bodyweight, a highly relevant information for the productivity and resilience of the species, largely reported in the literature from life-history analyses and maternal effects (Green, 2008; Marshall, 2009). Thus, CKMR was able to provide an independent check of the assessment model in SBT, reducing the uncertainty and has been incorporated into current management procedures.

This CKMR study remains the more relevant in the context of fisheries assessments among those involving teleosts as most other applications to date have targeted much smaller populations. Ruzzante et al. (2019) analyzed seven populations of brook trout inhabiting coastal streams along the shore of Nova Scotia. These authors used in parallel CKMR and standard mark-recapture (MR) to compare their respective estimates of population abundance. Using 33 microsatellite markers, a number of POPs ranging from ≈8 to ≥ 50, depending on the population, were detected among 2,400 individuals. These kinship pairs were used to infer CKMR-derived abundances that proved to be statistically indistinguishable from those obtained by standard MR. This is the first study that validates CKMR estimates, but is important to emphasize that population structure and life history of this species is unusually simple. Moreover, confidence intervals of the estimations were wide in most populations, indicating the need of a significant increase of the sampling effort to achieve precision levels similar to those obtained for SBT (i.e., CV≈0.15) (Bravington et al., 2016a). Based on their results, the authors conclude that the method is unlikely to be applicable to stocks with tens of millions of individuals or larger due to the dependence of the sample size requirements on population size (Ruzzante et al., 2019). Another study of brook trout from the Honnedaga lake (New York) used 44 microsatellites to genotype 304 individuals, identifying 72 POPs that allowed the estimation of population abundance (Marcy-Quay et al., 2020). The authors compared the values obtained with abundance estimates from classical MR, finding a good agreement between both. The CKMR approach was also used to infer the abundance of the Arctic Grayling at the Lubbock system river in Yukon (Canada). A total of 967 specimens (split approximately equally between adults and juveniles), were collected and genotyped at 38 microsatellites, revealing 37 POPs that served to estimate adult population abundance with reasonable precision (CV ≈0.16) (Prystupa et al., 2021). In addition, Wacker et al. (2021) compared the estimates of spawner abundance of adult Atlantic salmon in a Norwegian river using three methods; conventional surveys, CKMR and CKMR combined with tagging. A total of 278 juveniles and 113 adults were genotyped at 164 SNP loci, revealing 80 POPs that allowed the estimation of CKMR abundances. CKMR estimates were considerably higher than those obtained in conventional surveys, and these, in turn, were smaller than those obtained by the combination of CKMR with tagging.

The results of these teleosts studies provide essential information for their management, however, they also highlight that most applications to date have targeted small populations inhabiting rivers, as this feature facilitates reaching the demanding requirements, in terms of sample sizes, needed to find relevant numbers of kinship pairs to produce estimates with reasonable precision. Also, most of these populations can be considered “closed” (low migration, high self-recruitment), making assumptions more straightforward. Furthermore, although few studies compare abundance estimates obtained from classical methods with those produced by CKMR, not all find a good agreement, highlighting the need for further studies to understand the biases that affect each of them.

The generally smaller population sizes of elasmobranchs compared to teleosts together with a poor conservation status and a lack of any abundance estimates affecting many species has made them also a frequent choice in CKMR studies. The first study that applied a CKMR framework to estimate the population of an elasmobranch targeted white sharks in eastern Australia and New Zealand (Hillary et al., 2018). This analysis was based solely on HSPs due to the unfeasibility of sampling adults in useful numbers. Genetic sampling of juveniles is easier as there are known aggregation sites. In this study, a total of 75 juveniles were genotyped using 2,186 SNPs, revealing 20 HSPs used to estimate population size. Kinship also served to produce estimates of adult survival rates and to inform of adult sex ratios. Despite the low precision of the results, acknowledged by the authors, mainly due to the small sample size and the limited range of sampled cohorts, the estimates obtained are highly valuable, given the lack of data for this population, as they provide crucial data for assessing its status. This data deficiency is common in elasmobranchs, with many populations considered severely depleted and endangered worldwide (Juan-Jordá et al., 2022), urging the application of methodologies like CKMR to assess their conservation status and protect them.

The abundance of the thornback ray in the Bay of Biscay was assessed by genotyping over 6,500 specimens using 3,668 SNPs. The analysis revealed 73 “usable” POPs and 431 FSPs that could not be considered for abundance calculations due to the lack of biological data (particularly, age) and the limited knowledge on population structure available for the species. The large number of specimens analyzed still permitted the estimation of adult abundance with reasonable precision (CV=0.19). The authors provide a good overview of practical lessons learned from the application of CKMR to their case study, worth to be considered by anyone intending to apply the CKMR method for abundance estimation (Trenkel et al., 2022).

A CKMR study of the blue skate in the Celtic Sea assessed its abundance using over 6,000 SNPs to genotype 662 specimens collected across four years (Delaval et al., 2022). The results revealed 15 cross-cohort half-sibling pairs while no POPs were detected, possibly due to a limitation in the number of cohorts sampled as samples mostly comprised young adults. The authors inferred abundance and annual adult survival rate but due to the small number of kinship pairs detected the estimates involved a large uncertainty. Interestingly, the authors compared the CKMR estimated abundance with CPUE-based estimates. Although the latter reflects the abundance of all the individuals in a population, not just the breeders, a good general agreement was found. Both approaches indicated a stable, possibly expanding population of blue skate in the Celtic Sea during the analyzed period.

Finally, Patterson et al. (2022) used CKMR to assess abundance and connectivity in a critically endangered euryhaline elasmobranch, the speartooth shark (Glyphis glyphis). The analysis involved only juveniles as finding mature individuals is extremely difficult, similarly to other elasmobranchs. With only three encounters of adults in over seven years in Australian waters, the abundance was unknown. A total of 226 juveniles collected in two Australian river systems across four years were genotyped using 1,400 SNPs revealing the presence of 21 and 41 full- and half-siblings, respectively. The authors also used information from the mitogenomes to determine whether the inferred HSPs share the mother or the father. These relationships served to calculate the total adult abundance of the whole population and at each river system but also to estimate other parameters, including sex-specific connectivity and annual survival as well as to infer several aspects of the reproductive dynamics of this critically endangered species. This study demonstrates that the method is capable of providing essential parameters in a short period of time for a long-lived species, even when the collection of auxiliary data (age) involved a substantial uncertainty (Patterson et al., 2022). Nonetheless, it also highlights a remarkable complication that derives from the difficulties in assigning age accurately in elasmobranchs, which often requires lethal sampling to obtain vertebral growth readings. As this is not possible for many protected species, the potential of sample collection is extremely reduced but the alternative non-lethal sampling entails a high uncertainty in cohort assignment that can significantly lower the number of usable kinship pairs. Thus, even when using a large number of markers for genotyping that allows the inclusion of second-degree relationships (HSP), lack of accurate biological ancillary data can undermine the power of a CKMR study. Nonetheless, these elasmobranch studies, also reveal the potential of the methodology for inferring abundance in species spanning a large variety of life-histories, even when sampling and background biological knowledge are scarce, conditional on the model being tailored to each species-specific characteristics (Rodriguez-Ezpeleta et al., 2020).

Unbiased and precise estimates of demographic parameters are essential to ensure the sustainable exploitation of fish stocks. Reliable estimates of abundance and life history traits (e.g., fecundity, mortality) are central components of population models, but often extremely difficult to obtain, as they must rely on data that is often scarce and suffer from recognized biases (Bradshaw et al., 2007; Schmidt et al., 2015; Hoenig et al., 2016).

Close‐kin mark–recapture (CKMR) has recently emerged as a promising technique for estimating some of these parameters in animal populations from the frequency, and the distribution in space and time of kinship relationships observed in genetic samples (Davies et al., 2015). This alternative has potentially several advantages over traditional methods, such as the use of data from CPUE, scientific surveys or tagging experiments, as it does not suffer from many of the potential sources of bias affecting these (Rodriguez-Ezpeleta et al., 2020). CKMR can be applied to tissue samples from live or dead animals and although it could be theoretically used with any set of individuals that can be divided into “parents” and “juveniles, most applications have shown the importance of specimen´s ancillary data and sampling design considerations (e.g. Trenkel et al., 2022). Life histories of most species determines the coexistence of several cohorts for most fish stocks, requiring a precise assignment of the time of birth to take full advantage of the method. In some cases, accurate length estimates can be used for this purpose but most case studies highly benefit from accurate age information of the specimens (Coggins et al., 2013; Patterson et al., 2022). If age data is available, it becomes possible to extend the basic method (POPs) to more distant kinship (HSP) and use a short-term, cross-sectional sample of a population to produce reliable estimations, opposite to traditional methods that require decades-long datasets (Bravington et al., 2016b).

Therefore, CKMR might have the potential to become more informative, robust and cost-efficient than current methods for estimating demographic parameters. Nonetheless, the use of this new genetic tool in fisheries science is still in its infancy. The theory and promise of close‐kin analysis beyond the target species of CKMR studies published so far - mostly characterized by relatively small population sizes and high uncertainty in abundance estimation by traditional means - still needs to be demonstrated and validated by further studies (Friedman et al., 2022). In this regard, it is crucial to validate the method in stocks with good quality estimations of abundance based in well-established stock assessment models.

The method in its current form has several limitations. First, it is not suited to all fish populations as it relies on being able to differentiating parental and offspring generations and also requires the independent sampling of the different cohorts. To fulfill these requirements, knowledge of the biology of the species, especially life-history strategies and reproductive biology as well as of the patterns of social structure and habitat-use, is needed, but these parameters are unknown for a vast number of species.

Even when an in-deep biological knowledge is accessible, there are some aquatic organisms for which CKMR is unlikely to produce reliable estimations. These, include species with very long lifespans and those displaying semelparity or facultative parthenogenesis (Bravington et al., 2016b). The latest has been described in, at the minimum, six species of sharks and rays, which have shown the ability to reproduce asexually in the absence of males (hammerhead shark (Chapman et al., 2007), blacktip shark (Chapman et al., 2008), zebra shark (Robinson et al., 2011), bamboo shark (Feldheim et al., 2010), white tip reef shark (Portnoy et al., 2014), small tooth sawfish (Fields et al., 2015) and spotted eagle ray (Harmon et al., 2016)). Although it is unknown how frequently parthenogenesis occurs in elasmobranchs in the wild, it has been proposed to be facultative in situations where females have difficulty encountering suitable mates. As elasmobranchs numbers decline worldwide the difficulty of finding mates increases, possibly promoting parthenogenesis (Bernal et al., 2015). This should be taken into account when applying CKMR to this group of animals, preferentially targeted in the studies published so far due to their low population densities that are, in many cases, exacerbated by overexploitation (e.g. Hillary et al., 2018; Patterson et al., 2022). It is essential to pay special attention to low genetic diversity and significant homozygosity all across the genome in order to avoid wrong estimations of abundance with CKMR.

Nonetheless, the major drawback of the method affects generally all species and is linked to the number of samples required for the analysis, which is proportional to the square root of the true population abundance (Bravington et al., 2013; Bravington et al., 2014). In large populations, entails the collection of a substantial number of samples to ensure the detection of sufficient numbers of kinship pairs, implying high effort and high costs (Casey et al., 2016). A simulation exercise (ICES, 2017) estimated that, for a species with an estimated adult population of ~1.5 million individuals, a sample size of ~17 000 individuals and 70 POPs would be required to obtain reliable estimates of abundance (CV =10%). Many commercially relevant fish species exceed these abundances by several orders of magnitude and thus, adhering to these standards would be economically daunting even without considering the sampling costs (ICES, 2017). Super-abundant species, such as krill, would possibly never be a candidate for CKMR studies as they require colossal sample sizes, even taking into account that this number is proportional to N a d u l t rather than to N_adult itself (Bravington et al., 2016b). On the other hand, applying CKMR to very small populations would imply little cost but requires the collection of an impractically high fraction of the population to yield a useful number of kin pairs, particularly in marine ecosystems that are characterized by few physical barriers to dispersal (Bravington et al., 2016b).

The method has other important drawbacks for its application in the context of fisheries. CKMR is not able to provide precise estimates of abundance-at-age for the whole population, a required parameter to be used in age-structured stock assessment models, widely used to provide scientific advice in fisheries management. Another important limitation is that it provides information only for the adult component of the stock, as is not able to produce any estimates on recruitment or juvenile abundance. This prevents to forecast stock abundance at mid- and long-term. More importantly, the large economic and sampling requirements of CKMR are too demanding to produce annual estimates of stock abundance to be used in stock assessment models (irrespective of the models being age-structured or not) for most exploited species. In its current state, CKMR cannot replace the estimation of abundance based in research surveys and analytical stock assessment models. Another technical challenge is that close-kin mark-recapture analysis requires specialized knowledge that is usually outside of the experience of population geneticists who have the skills to generate and analyze the genetic data. Besides the population genetics knowledge, expertise in statistics and mathematics to develop statistical mark-recapture and population-dynamic models is considered essential to perform this type of studies (Ovenden et al., 2015; Bravington et al., 2016a). It is imperative to build bridges across these disciplines to promote advances in the methodology.

Despite the challenges, we believe that the method can bring enormous advancements to the field of fisheries management. We foresee several topics where CKMR may contribute importantly in scientific advice.

First, creating or modifying Biological Reference Points (BRP). These are standardized stock indicators used to compare stock status and inform fisheries managers about stock’s status relative to various management objectives. Therefore, they are key components in scientific advice and management as they establish limits - mostly based in mortality and/or biomass - above or below which stock sustainability is jeopardized. The estimation of BRPs is subject to important uncertainties very much depending on the model used to estimate them, but often leading to a loss of fishing yield to avoid the risk of overexploitation (Mildenberger et al., 2022). CKMR outputs, particularly abundance and mortality, should help in understanding how accurate are the defined BRPs, even if CKMR is not applied annually, and contribute to adjust current BRPs or to build new ones during stock benchmarking. For example, the first CKMR study on bluefin tuna (Bravington et al., 2016a) revealed a significantly higher abundance of adult abundance than that estimated by the Operating Model (OM) used to assess this stock. The method was subsequently implemented and has been used to observe trends in abundance in combination with other indexes based on surveys or fisheries CPUE. The abundance estimates from CKMR should allow to define a new BRP or modify the target reference points used in many stock assessments. This is a field that should be further explored and it is very much linked with the need of establishing dynamic reference points that cope with ecosystem changes (Berger, 2019).

Second, stock-recruitment relationship is a cornerstone in fisheries management. It is the basis to define some of the BRPs used in fisheries management and is also used to predict future recruitment and forecast stock abundance for different management options (catch projections). However, stock-recruitment relationships are normally poor, impeding the ability to predict recruitment. One of the reasons for this is the use of spawning stock biomass (SSB), which does not consider variations in the stock reproductive potential (Saborido-Rey and Trippel, 2013) and specifically patterns of variation in individual reproductive success, i.e. the probability that an adult will produce offspring (recruitment) differs within and across-years. CKMR based on HSPs can inform about the effective number of breeders per year, N_b (Waples and Feutry, 2022), and has the potential to improve stock-recruitment relationships, especially when combined with CKMR adult abundance estimations. Although the importance of N_b in fisheries management has been diminished (Hillary et al., 2018), it is actually a relevant parameter.

Finally, there is a range of situations where CKMR estimations of abundance can be very valuable. For many populations, bottom trawl research surveys are not able to provide realistic estimates of abundance due to low catchability, either because of low abundance, or to low/null accessibility (e.g. coastal and littoral species, species inhabiting rocky areas, river streams or vulnerable ecosystems). The vast majority of these are data-limited stocks that lack complex stock assessments or forecasts (Rosenberg et al., 2014). The stock size of many pelagic species is assessed based on acoustic surveys, which still contain uncertainty in spite of the huge advances in technology. CKMR may contribute to reduce uncertainty, although the size of some stocks (especially small pelagic stocks like sardines, anchovies, herrings, mackerels) would require extremely large sample sizes. However, for widely distributed stocks (redfishes, i.e. Atlantic Sebastes spp.), or large pelagic fish stocks (sharks, tunas, billfishes) the application of CKMR seems to be more feasible and might provide valuable information as estimations of abundance by traditional research surveys are limited. The method has the potential to offer as well a better understanding of stocks productivity, the relative importance of different age classes to total stock reproductive potential of stocks and more generally, a better understanding of the biology of exploited species (e.g. Bravington et al., 2016a, Patterson et al., 2022). Moreover, CKMR can also inform about parameters often ignored in stock assessments, like connectivity and migration in iteroparous species (Akita, 2022; Patterson et al., 2022). This information is normally not included due to the lack of data but can lead to biased estimates of population parameters (Van Beveren et al., 2019) and is essential to understand population dynamics as well as to identify appropriate management units (Goethel et al., 2012).

The major impediment on the application of CKMR outputs as stock indicators is still the quantification of the uncertainty and its impact in scientific advice and the subsequent management options that can easily lead to stock overexploitation. However, in stocks where there is lack of data to build proper stock indicators (as BRPs), CKMR outputs may provide valuable information for stock benchmarking if used with caution.

Integrating the collection of tissues (finclips) for genetic analysis into the regular scientific fisheries surveys has little cost, and would be a framework that provides, at the same time, the specimen´s biological information (e.g.: age from the otoliths, sex or maturity stage) needed for the application of CKMR. Pilot studies could start in parallel with regular assessments to maintain the long time-series datasets and facilitate validation of the CKMR methodology. The implementation of CKMR into assessment programs will clearly depend on its capacity to reduce uncertainty and contribute to establishing or modifying BRPs but its potential uptake would also be promoted if fisheries scientist become familiar with the methodology. Finally, CKMR can be crucial to determine the status of data-limited stocks since a single CKMR study can be adequate to estimate abundance and other population parameters. If an appropriate sampling scheme can be maintained over time, the time series of abundance could be used to establish harvest rates in species that lack a full stock assessment (Maunder et al., 2021).

The information and views set out in this manuscript are based on scientific data and information collected under Service Contract “Improving cost-efficiency of fisheries research surveys and fish stocks assessments using next-generation genetic sequencing methods EMFF/2018/015” signed with the European Climate, Infrastructure and Environment Executive Agency CINEA and funded by the European Union. The information and views set out in this publication are those of the authors and do not necessarily reflect the official opinion of CINEA or of the European Commission. Neither CINEA nor the European Commission can guarantee the accuracy of the scientific data/information collected under the above Specific Contract or the data/information included in this publication. Neither CINEA nor the European Commission or any person acting on their behalf may be held responsible for the use which may be made of the information contained therein.

All authors contributed to the article and approved the submitted version.