The status of marine fish stocks needs to be evaluated regularly to ensure fishing practices are kept at sustainable levels, through a process called “stock assessment”. This process involves collecting, analyzing, and reporting demographic information to determine changes in the abundance of fishery stocks in response to fishing and, to the extent possible, predict future trends of stock abundance (NMFS, 2001). It is based on different data types, for example, fishery catches, landings, biological information recorded by onboard observers and fishery-independent data coming from research surveys. In the European Union (EU), the Common Fisheries Policy (CFP) established in Regulation (EU) No. 1380/2013 is supported by the fisheries data collected by Member States (MS). This regulation sets out the rules for managing European fishing fleets in a sustainable way and protecting marine resources exploited in European fisheries. According to the CFP, MS shall “collect biological, environmental, technical, and socio-economic data necessary for fisheries management”, enabling “the assessment of: (a) the state of exploited marine biological resources; (b) the level of fishing and the impact that fishing activities have on the marine biological resources and on the marine ecosystems; and (c) the socio-economic performance of the fisheries, aquaculture and processing sectors within and outside Union waters”.

To do this, a Data Collection Framework (DCF) was established in the EU and is currently set out in Regulation (EU) 2017/1004 and the Commission Delegated Decision (EU) 2021/1167, that establishes the multiannual Union programme for the collection and management of biological, environmental, technical and socioeconomic data in the fisheries and aquaculture sectors from 2022 (EU MAP). The DCF provides for the EU MAP that details the requirements for data collection by MS and a list mandatory scientific surveys at sea. Under EU MAP, a research survey at sea is defined as: “trips carried out on a research vessel, or a vessel dedicated to scientific research for stock and ecosystem monitoring, and designated for this task by the body in charge of the implementation of the national work plan established in accordance with Article 21 of Regulation (EU) No 508/2014” (Commission Delegated Decision (EU) 2019/910). The current list of mandatory surveys is established in Commission Implementing Decision (EU) 2021/1168, and includes 51 surveys that are carried out in the Baltic Sea, the North Sea and eastern Arctic, the North Atlantic and the Mediterranean and Black Sea.

In research surveys, samples are taken across the distribution range of the targeted fish species employing standardized fishing gears (e.g., trawls and seines), hydroacoustics and other devices (remotely operated vehicles, towed cameras, etc.). Survey data are important in stock assessment because they provide indices that help tuning the stock assessment models (e.g., fish abundance indices, usually the number or weight of fish caught per unit of effort). For example, the Baltic International Trawl Survey (BITS) provides two indices (one from the BITS-Q1 and another from the BITS-Q4) used in the stock assessment of cod in the eastern Baltic Sea. Surveys also provide key information about fish stocks such as the size and age distributions, the size-age relationships, the proportion of fish mature at each age, and information on reproductive performance of the stocks. Species’ diet and trophic relationships can also be determined by sampling stomach contents in research surveys (Cooper, 2006). However, the sampling programs in the surveys also provide information on various ecosystem components such as hydrography, geochemistry of seawater and sediment, benthic epifauna and infauna, zooplankton, phytoplankton, and other aspects such as marine litter (ICES, 2019). In fact, the sampling design used in surveys takes into account the distribution of many different species, the overall community and the environmental characteristics. In the recent years, budget and time-dependent efforts have enabled the development of a full ecosystem monitoring programme without disrupting the fisheries time-series (ICES, 2019).

Nevertheless, traditional methods to assess the state of fish stocks through scientific surveys have experienced a very slow progress and present recognized shortcomings (Maunder and Piner, 2015). Research surveys involve complex logistics and are costly. The consequence of this is that the data collected are sparse in space and time. In addition, a long time is needed to process and analyze the collected data (Stamatopoulos, 2002). Moreover, some important parameters in fish stock assessment cannot be estimated using traditional methodologies at present. For example, the sex of juveniles cannot be determined using traditional methods (e.g., observation of the gonads), despite the importance of sex ratios to evaluate the status of exploited stocks. Similarly, difficulties have been faced when determining the age of individuals of some species, an essential parameter for growth estimates, population dynamic studies and for optimizing the harvesting time (Gursoy et al., 2005). Otoliths of some commercially important fish species such as European hake (Merluccius merluccius) or cod (Gadus morhua), for instance, have proved unreliable due to the presence of false rings, lack of definition of rings or deposition at irregular intervals (Morales-Nin et al., 1998; Hüssy et al., 2010; Ligas et al., 2011). In this context, High-throughput sequencing (HTS) genomic methods can offer the possibility to resolve some of these hurdles and enhance the data collected for fisheries assessment. In recent years, several genomic methods have been developed and applied to study different aspects of marine organisms and biodiversity. For example, Close-Kin Mark-Recapture (CKMR) is a method that allows estimating abundance and other demographic parameters (e.g., mortality rates and connectivity) from kinship relationships determined from genetic samples (Bravington et al., 2016a). Until now, it has been used for few fish species, such as bluefin tuna (Bravington et al. 2016b), white sharks (Hillary et al., 2018), brook trout (Ruzzante et al., 2019) and thornback ray (Trenkel et al., 2022), but CKMR is being considered for several more species (e.g., Maunder et al., 2021). Another genomic method that could potentially provide an indicator of stock abundance and/or biomass is environmental DNA (eDNA). eDNA is DNA that is collected from an environmental sample (e.g., water, sediment and air) rather than directly from an organism. eDNA can originate in cells from the body or waste products of organisms (Ficetola et al., 2008; Taberlet et al., 2012). This genomic tool has been mostly used to determine the presence and distribution of a species, but recent evidence suggests the concentration of eDNA could also be used to estimate abundance and/or biomass for stock assessments in a fast and cost-effective way (Rourke et al., 2022 and references therein). Regarding age estimation, epigenetic age determination (Horvath and Raj, 2018) could provide an accurate alternative method for aging fish and overcome the limitations of the current methods (e.g., otolith readings). This genomic method analyses changes in DNA methylation associated with ageing and allows constructing epigenetic clocks to predict age, such as the one recently developed for sea bass (Dicentrarchus labrax) (Anastasiadi and Piferrer, 2020). Genomic methods could be used as well to improve knowledge on stock structure and substructure, which is also relevant to stock assessment. For example, restriction site-associated DNA sequencing (RAD-seq) could be used to analyze genome-wide diversity of fish populations using thousands of single nucleotide polymorphisms (SNPs). Then, for example, differences of RAD-Seq‐derived SNP frequencies within stocks could be analyzed to define their substructure (e.g., Ceballos et al., 2021); or screening of SNPs could be used to search for sex markers that would allow sexing fish individuals and determining the sex structure of the stock (e.g., Palaiokostas et al., 2013; Gamble, 2016; Feron et al., 2021).

Considering this, we carried out a review of the bottom trawl research surveys in the EU with the purpose of: 1) identifying the current approaches for monitoring fishery resources and the ecosystem and 2) determining how genomic techniques can be used to improve survey data, taking into account the needs of current and future stock assessment in Europe.

Currently, there are 19 mandatory bottom trawl surveys that are carried out by EU MS and other European countries (see tables in Supplementary Materials S1, S2). These surveys are carried out in several regions: The Baltic Sea, the North Sea (ICES areas 1 and 2), the North Atlantic (ICES Areas 5-14 and the NAFO areas) and the Mediterranean and Black Sea (Figure 1). Thus, different organizations are responsible for coordinating and standardizing these surveys. The main management bodies involved in planning and coordinating research surveys in Europe are the following:

Mandatory bottom trawl surveys are diverse. Primarily, this is a consequence of the different regions covered by each survey, which means that different ecosystems with different species of marine organisms are surveyed. Regarding methodological aspects, bottom trawl surveys have different characteristics related to the fishing gear used, the survey duration, the distance from the shore, and the international participation (i.e., number of countries contributing to a particular survey). There are two types of fishing gears used in bottom trawl surveys in Europe, the otter trawl (e.g., Northeast Atlantic IBTS) and the beam trawl (e.g., North Sea BTS). Otter trawls are generally used in surveys targeting a wide variety of demersal species and that cover large areas, many of them located off-shore. Beam trawls are used mostly in surveys targeting flatfish species. In beam trawl surveys, there is no standardized gear across surveys, as the width and rigging of the beam trawls depends on the local circumstances and the vessel’s capacity. The setup of the gear is mainly determined by seafloor conditions. For instance, in the beam trawl survey carried out by the Netherlands (part of the BTS survey) the gear is rigged with a flip-up rope in the central and western North Sea to avoid rocks from entering the net. In the south-eastern North Sea, no flip-up rope is used because there are no rocks in that area (ICES, 2019). Regarding distance, surveys can be divided into in-shore surveys and off-shore surveys. In-shore surveys such as the DYFS and SNS_NLD in the North Sea cover small regions close to the coast. On the other hand, off-shore surveys are carried out further away from the coast and may cover extensive areas (e.g., IBTS and MEDITS). Among off-shore surveys, those that are carried out in NAFO areas can be considered long-distance (i.e., GGS, FCGS and 3LNO). Survey duration is very variable. For example, the Sole Net in-shore survey lasts for 8 or 9 days, while others like the IBTS or MEDITS last more than 250 days (when the effort from all participating countries is considered). In addition to these differences, some surveys are focused in only a few species (for example, the Sole Net Survey in the North Sea, that targets 0-4 group sole, plaice and turbot) while others target dozens of species (e.g., MEDITS or western IBTS) (see table in Supplementary Material S1). Regarding international participation, the number of countries contributing to a particular survey also differs among surveys. About half of the surveys are carried out by one or two countries but there are surveys, such as MEDITS, the North Sea IBTS and the western IBTS, that involve many countries.

Regarding data collection, biological parameters such as length, weight, sex, maturity and age are determined in all surveys for target species. For the rest of species, taxonomic identification is carried out and, in many cases, length of the specimens is recorded. Additional information is collected on other biological components of the ecosystem (e.g., marine mammals, birds, benthic invertebrates and plankton) as input to an ecosystem approach to fisheries. Oceanographic data such as temperature and salinity and marine litter data are recorded as well. The type of data that are collected in each bottom trawl survey is shown in the table in Supplementary Material S3.

A stock assessment is “the process of collecting, analyzing, and reporting demographic information to determine changes in the abundance of fishery stocks in response to fishing and, to the extent possible, predict future trends of stock abundance” (NMFS, 2001). Figure 2 shows the general process of stock assessment, including in this case, sampling for genomic methods within “Data Collection”. Stock assessments are used to provide scientific advice to resource managers and policy makers about the present health and expected trends of a particular fish stock. Certainly, stock assessments are the technical basis for establishing annual fishery harvest levels (e.g., catch limits and quotas) and other management measures for the fishery (NOAA, 2012). These assessments are usually carried out by fitting mathematical models to the available information to produce simplified representations of population and fishery dynamics. Stock assessment methods have progressed over time, from descriptive models to complex statistical models with many estimated parameters and formal approaches to evaluating uncertainty (Cadrin and Dickey-Collas, 2015).

Stock assessment models require different sources of information depending on their assumptions. In general, the data used in assessments includes commercial catches and landings, effort, biological information recorded by onboard observers (e.g., age and length composition data) and fishery-independent data coming from research surveys. As already mentioned, survey data are important in stock assessment because they provide indices that help tuning the stock assessment models (e.g., fish abundance indices, usually the number or weight of fish caught per unit of effort). For example, survey abundance indices are used in analytical stock assessment models such as Stock synthesis models (Methot and Wetzel, 2013), in State-space assessment models (SAM, Nielsen and Berg, 2014; Berg and Nielsen, 2016) or in Stochastic surplus production model in continuous time (SPiCT, Pedersen and Berg, 2017). Equally important, surveys provide information about fish stocks such as the size and age distributions, the size-age relationships, the proportion of fish mature at each age, and information on their reproductive performance. For instance, the eastern Baltic cod (cod in ICES subdivisions 24-32) is currently assessed using an age-length based Stock synthesis model, where abundance indices from the Baltic International Trawl Surveys (BITS Q1 and Q4) are used. In this model, age-length keys are based on otolith readings from these surveys as well, and maturity and weight-length data are provided by the BITS Q1 survey (ICES, 2022b). In the case of the northern European hake (ICES subareas 4,6 and 7, and in divisions 3a, 8ab) the assessment is done using a length-based and sex-disaggregated Stock Synthesis model. In this model, several survey indices are used, including those from the western IBTS survey (i.e., from the French Southern Atlantic Bottom trawl survey (EVHOE-WIBTS-Q4), the Spanish Porcupine Bottom Trawl Survey (SpPGFS-WIBTS-Q3), the Irish Groundfish Survey (IGFS-WIBTS-Q4), the French surveys in the Bay of Biscay (FR-RESSGACQ)) and from the Irish Anglerfish and Megrim Survey (IAMS_IRL) (ICES, 2022c). For other species (e.g., data-limited species such as stocks in the ICES stock category 3), management advice relies solely on a survey trend-based assessment. Such assessments are “restricted to qualitative advice about whether the stock is trending up, down or is stable, and on whether the stock is approaching a possible trigger for management action (e.g., the lowest point in the abundance index time series)”. For this reason, these assessments cannot be used to provide advice on the absolute level of the fish stock or the direct effect of fishing on it. (ICES, 2012). For example, for the beaked redfish in Division 14b, the assessment is based only on the Greenland Groundfish Survey (GGS) abundance indices (ICES, 2022a).

The assessments are carried out by several expert working groups within ICES, NAFO, GFCM and the European Commission’s Scientific Technical and Economic Committee on Fisheries (STECF). A list of these groups can be found in Table 1.

At present, there are several genomic methods that could be used to estimate key parameters of fish populations, and possibly resolve some of the current difficulties and enhance the data collected for fisheries assessment. In this section, an overview of the most relevant methods is presented, namely, CKMR, eDNA, epigenetic age determination and other approaches, such as RAD-Seq.

Stock assessments are continuously improving, mainly due to methodological and computational advances (Punt et al., 2020), but also due to improvements in data collection that allow better estimates of stock parameters. In this context, the information coming from genomic methods, such as abundance estimates (CKMR, eDNA), mortality rates (CKMR), age (epigenetics) and sex (RAD-Seq) determination and stock substructure (RAD-Seq) has the potential to improve stock assessments. In general, the use of genomic approaches can improve stock assessments by allowing, facilitating or improving estimation of key parameters and by providing insights of stock structure and connectivity. However, this does not necessarily mean that these approaches will end up substituting traditional data collection. The set of traditional methods is the outcome of a long process of adaptation to the goals and needs of stock assessments, while genomic methodologies are scientific developments which still need to follow further innovation steps for fitting them with the stock assessment specific needs. It is expected that genomic methods, once fully developed and tuned, will be able to provide more accurate data on their fields of application than traditional methods. Nevertheless, the improved accuracy of the genomic methods regarding traditional approaches is yet to be demonstrated in a variety of scenarios. Data obtained from genomic methods cannot be implemented in stock assessment if their accuracy is lower than that from traditional methods. In such a case, its implementation would incorporate a great uncertainty in the stock assessment. Thus, thorough research on genomic accuracy and precision in comparison with traditional methods is required for each of the stocks where the genomic methods can be expected to be implemented.

Also, the type of data that can be collected by using the different approaches varies. For example, the size structure of a fish population can be determined in a traditional survey by measuring the length of fish samples, however, no genomic method is capable of determining size structure because the length of a fish cannot be determined by genomic analysis. When comparing the type of data that can be obtained using traditional methodology and genomic methods (see Table 2), it is evident that genomic methods do not provide all the parameters that traditional methods are able to provide for the stock assessment of targeted species. Traditional surveys, in addition, provide information for monitoring the general conditions of the marine environment (e.g., marine litter and pollutants). Most notably, genomic methods do not provide information on size structure and maturity, and there are difficulties for estimating abundance-at-age.

It is clear that traditional methods show a number of difficulties in relation to data collection and stock assessment that genomic methods may help to overcome. But at present, substituting traditional methods with genomic methods would lead to a loss of information, and both methodologies seem to be rather complementary than substitutes. For instance, the few assessments based on the CKMR methodology (e.g., Bravington et al., 2016a; Ruzzante et al., 2019; Marcy-Quay et al., 2020) also required traditional parameters (e.g., length-based estimated age or sex).

From the above, it stands out that data collection, in terms of quantity and quality, would benefit from complementing traditional surveys with genomic methods. The next question that arises is: How can genomic methods be implemented into the already existing traditional surveys? To answer this, two options could be considered: the first would be to implement directly genomic methods into existing surveys. This would mean adding the collection of tissue and water/sediment samples required by genomic methods on top of the already existing activities in the survey, but without altering the survey program (i.e., survey duration, number of sampled stations, etc.). The second option would be to re-structure the surveys, for example, by reducing sampling effort using traditional methodology and giving genomic methods their own space in the survey.

The first option can be considered for surveys with sufficient resources —mainly, scientific staff and storage capacity, given that the extraction of samples for the genomic analysis does not require significant additional equipment and/or consumables onboard. In these cases, integrating the tissue sample collection for genetic analysis into the regular survey work can provide a solution to this without increasing costs significantly. However, even if costs do not increase during the survey itself, the processing and analysis of samples using genomic methods is still costly and would add up to the already expensive survey. The second option (restructuring the survey) could be considered when data collection for genomic methods cannot be directly implemented on an existing survey or if cost reduction is necessary. In that case, the survey would need to be restructured, so that dedicated resources (e.g., scientific/technical staff time) can be reorganized to gather samples for both traditional and genomic methods. For example, in the case of a particular survey which could be reduced from 30 days to 20 days, the first 5 days could be dedicated to collect eDNA samples and the remaining 15 days to collect biological samples (for genomics, size structure, age, sex and maturity) but from fewer stations (due to fewer days available). This could reduce the total survey cost, but not without several consequences regarding the quantity and quality of the data obtained. According to ICES (2020), reductions in survey effort (i.e., number of sampled stations, tow duration, survey frequency) can have consequences on many aspects of the information produced from surveys. These consequences not only may affect stock assessments, but would also have an impact on fisheries management, ecosystem indicators, and fisheries research (e.g., loss of data on ecosystem indicators, loss of non-target fish and invertebrates’ abundance data, loss of food habits information needed for multispecies ecosystem models, loss of platforms for novel studies, etc.).

Another possibility worth exploring is complementing traditional surveys with genomic data obtained using commercial fishing vessels. For example, Russo et al. (2021) have investigated the possibility of assessing catch composition of single hauls carried out by trawlers by applying eDNA metabarcoding to the dense water draining from fishing nets just after the end of hauling operations (i.e., slush). In that study, the authors demonstrate that the fish assemblages identified using eDNA in the slush reflected those determined by visual inspection of net content (approx. 71% of species and 86% of families of fish) and detected a strong relationship between read counts and species abundances in the catch. Thus, this approach could be upscaled to serve as a powerful source of information on the structure of demersal assemblages and the impact of fisheries. Following that line, Maiello et al. (2022) designed a customized low-cost 3D-printed plastic probe that, placed inside the trawl net, serves as a container for rolls of gauze that are positioned to capture DNA from the during fishing operations. Their results strengthen the idea that eDNA-based biomonitoring can become embedded in fishery-dependent surveys, at negligible additional cost and effort, to study catch composition and the broader faunal features of the ecosystems that sustain commercial fishing.

As already mentioned, fish stock assessments have been evolving over time due to several reasons, for example, due to improvements in computational approaches, advances in methods for fitting models to data, and the need to not only provide best estimates of model parameters and outputs but also to quantify the uncertainties associated with the estimates (Punt et al., 2020). However, the incorporation of genomic data into these methods will necessarily mean that assessment methods must be also adapted to be able to handle these data. In fact, some experts already consider that one of the necessary improvements for a next-generation stock assessment package in the next 5-15 years is to ensure its ability for handling genetic data, and in particular, to be able to use close-kin mark-recapture data (Punt et al., 2020).

In conclusion, in the short and medium-term, it does not seem feasible to implement directly genomic tools into data collection and stock assessment, given that stock assessment accuracy requires a certain length and stability of the data time series. However, given the growing evidence of the utility of genomic tools in the stock assessment context, it is necessary to devise a roadmap to implement such tools in the future. The guideline for future implementation could be based on the evolution of the cost-efficiency and on further evidence of precision and accuracy gains.

RR-M and FS-R: conceived the article. RR-M: wrote the article. FS-R: reviewed the article. All authors contributed to the article and approved the submitted version.