Recent studies are fueling relevant genetic diversity metrics and protocols (e.g., Hoban et al., 2023; Clarke et al., 2024). Candidate parameters for implementing genetic reference points (GRPs) should be reliable indicators of population genetic diversity for intergeneration comparisons. The population genetic effective size N _e​ is a parameter representing the theoretical minimum number of spawners (but not a rate of genetic change) of an idealized population whose random mating would yield the observed genetic diversity of the actual fishery. To effectively assess the impact of genetic drift, both fishing-induced and stochastic, the effective population size (N_e ) should first be normalized within a species. This normalization characterizes the species maximum genetic resilience and allows for the long-term tracking of changes in its adaptive potential. (e.g., Keller et al., 1994). The strength of comprehensive scores of N_e ​ or N_e ​/N _SSB lies primarily in assessing past population dynamics and current fishery genetic status. However, their standardization to predict fisheries genetic architecture is challenging, and likely requires advanced AI models capable of integrating historical N_e or N_e ​/N_SSB trends, species reproductive dynamics, and ecological/demographic factors (Waples, 2024).

The population effective genetic mortality rate (Ze​ or Z__LDNe ​), derived from historical genetic data of European hake (Merluccius merluccius), quantifies the instantaneous reduction in allele frequency dispersion due to genetic drift (Fernández-Míguez et al., 2023). Thus, Z_e ​ measures the rate of effective population size (N_e ​) decline driven by fishing pressure. This transcends demographic mortality by focusing on the genetic consequences of removals, including fishing-induced selection favoring certain genetic variants. Fernández-Míguez et al. (2023) demonstrated a direct computation of Z_LDN_e ​ between two infinitesimal LDN_e ​ moments provided estimates of N_e ​ are available at an initial (t ₀​) and at a subsequent (t) time point (Equation 1), i.e.

The effective number of genetic deaths ( D _ L D N e t ) is a parameter to estimate the reduction in the effective size LDN_e in period t (Equation 2) and can be put as,

the substitution of Equation 1 in Equation 2 allows to afford the effective number of genetic deaths   D _ L D N e t (Equation 3) as follows:

Similarly, the reduction in the reproductive census size N_SSB in a period t can be put (Equation 5) as,

A way to express the number of genetic deaths ( D _ L D N e t ) relative to the initial population effective size L D N e t 0 , i.e., the proportion of N_e eroded at an effective post harvesting mortality rate Z_LDN_et , is through the Genetic Resilience Index (GRI) as deduced after Equation 4, i.e.,

This Genetic Resilience Index (GRI) quantifies the proportional reduction in effective genetic size relative to the initial effective genetic size over a period t, effectively representing the net change in (LDN_e ​) between two moments of the fishery. The expected behavior of the GRI was simulated according to expression 1 by varying Z_LDN_e ​ in the range [-20, 50] and initializing LDN_e in the range [0.001, 10] x 10⁶. Furthermore, the simulated behavior of GRI was modeled using expression [6], by randomizing Z_LDN_e ​ within 21 deciles (1-210)% in the Z_LDN_e ​ range [-1, 10] using an excel spreadsheet.

Genetic Reference Points (GRPs) are benchmark values of genetic indicators used to assess a fish stock’s genetic status and to guide management for maintaining or restoring its genetic diversity and adaptive potential. GRPs aim to prevent anthropogenic activities from eroding genetic diversity crucial for long-term population viability, encompassing adaptive responses to stressors, disease resistance, and sustained productivity (e.g., Gibson and Nguyen, 2021). GRPs aim to capture critical aspects of a population’s genetic status relevant to long-term persistence, quantified by genetic metrics (e.g., N_e ​), metapopulation structure (e.g., F _ST​), and other parameters. Thus, GRP-based assessments can inform management interventions like adjusting fishing pressure, implementing genetically informed stock enhancement, or managing habitat connectivity. Analogous to biological FRPs (e.g., B_MSY​ or F_lim ​), genetic diversity-based Target Genetic Reference Points (TGRPs) specify optimal genetic status to avoid rare allele loss, maintain genome-wide diversity (e.g., Allelic Richness), retain adaptive variation, and limit the global inbreeding (F _IT​). The theoretical genetic diversity spectrum ranges from zero to the N_SSB -dependent maximal evolutionary diversity, representing maximum genetic resilience. Also, maintaining metapopulation structure, i.e., a minimum gene flow (Nm​) or acceptable levels of genetic differentiation (F _ST) can be another crucial TGRP. Limit Genetic Reference Points (LGRPs) establish thresholds not to be exceeded to prevent genetic degradation (e.g., minimum N_e ​). The following GRPs are based on demographic analogs and the historical genetics of southern European hake, and provide an example of guidelines dependent on each species genetic status and its metapopulation structure.

The southern European hake (Merluccius merluccius) stock, a fishery distributed across the Cantabrian Sea and the Atlantic Iberian Peninsula (ICES Management Divisions VIIIc and IXa, respectively), exhibits a lack of significant genetic structuring based on multiple genetic and geochemical markers (e.g., Tanner et al., 2014). A key challenge in the genetic management of this fishery lies in accurately assessing genetic diversity following periods of overfishing to ensure its long-term sustainability. Prior genetic investigations of this stock utilizing microsatellites revealed a post-fishing reduction in both the effective population size (N_e​) (a 43-fold decrease) and its spawning stock biomass (N_SSB , an 80% loss) (Pita et al., 2017). These findings suggested that the genetic consequences of fishing were more profound than a simple decline in population abundance, e.g., that genetic assessment indicated that this stock experienced a genetic bottleneck with an N_e ​≈300 in the early 2000s, followed by a recovery to N_e ​≈3200 in 2007. While acknowledging the potential role of environmental facilitation in this population rebound (Díez et al., 2012), the EU regulatory measures implemented on this fishery appeared to have been effective later on (European Commission Council Reg, 2005). However, the recovery of N_e ​ from its historical minimum likely involved a synergistic effect of both demographic and genetic enrichment from the neighboring northern hake stock, as well as the maintenance of its genetic status above a minimum evolutionary N_e ​ threshold, thereby safeguarding the stock’s resilience to overfishing (Pita et al., 2017).

Subsequent research on this stock highlighted a temporal dissociation between demographic and genetic metrics (Fernández-Míguez et al., 2023). Specifically, the peak of population demographic mortality (Z_N_SSB ) (1986–1990) preceded both the peak of effective genetic mortality (Z_LDN_e ​) (1991–1995) and the peak of the official cohort-based mortality (Z_ICES​) (1996–2000). This temporal decoupling between demographic and genetic indicators (see Waples, 2005), implies that a) genetic impacts exhibit delayed responses or recovery trajectories compared to changes in population size, and b) official cohort analyses were insufficiently aligned with assessing the true biological status of the southern European hake fishery to adequately inform about sustainability-oriented recommendations. In this study, we apply the candidate metric termed the effective genetic death number D _ L D N e t between two time points of the fishery, relative to the effective genetic size at an initial time point ( L D N e t 0 ), to calculate the proportion of N_e​ eroded at a post-harvest effective genetic mortality rate (Z_LDN_e ​). This ratio, D _ L D N e t ​/ L D N e t 0 ​ (termed the GRI index or Genetic Resilience Index), expresses the net variation of LDN_e​ between two time points in the fishery and can provide enhanced insights into genetic fluctuations compared to the absolute value of N_e ​ which still lacks a species-specific quantitative or qualitative reference scale.

The rationale for the Genetic Resilience Index (GRI) is to provide a composite metric that summarizes a fish population capacity to maintain, lose, or recover genetic diversity in a timeframe between two points under environmental stressors (including fishing). It extends beyond the absolute value of N_e ​ to offer a more nuanced and robust assessment of the population rate of genetic change for management applications. Randomization of the effective mortality rate Z_LDN_e ​ in the range [-3, 20] showed that GRI decreases exponentially with increasing effective mortality, approaching its minimum value (-1) when Z_LDN_e ​> 1 ( Figure 1 ).

The GRI index ranges from negative (up to -1) when genetic mortality occurs, to zero when there is no genetic mortality, and turns positive (theoretically unbounded) when there is a recovery of LDN_e ​ relative to the previous time point. Simulations of the GRI index under varying Z_LDN_e indicate that GRI spans the range [-0.63, 1.72] when LDN_e​ fluctuates smoothly around its initial value ( L D N e t 0 ​), specifically under moderate Z_LDN_e values [-1, 1], which corresponds to LDN_e being approximately 3-fold less or 3-fold higher than L D N e t 0 ​. However, GRI rapidly approaches its minimum (-1) or becomes exceptionally large when Z_LDN_e ​>1 or Z_LDN_e ​<−1, respectively ( Figure 2 ).

In the European hake case study, a comparison between the number of genetic deaths ( D _ L D N e t ​) and the number of demographic deaths ( D _ N S S B t ​) reveals a temporal disparity between the intergenerational demographic impact and its corresponding genetic impact. Specifically, the highest demographic death occurred during the lustrum 1986–1990 ( Figure 3 ), consistent with the maximum mortality rate (Z) observed during that period (see Table 3 in Fernández-Míguez et al., 2023). However, the number of genetic deaths ( D _ L D N e t ​) was negligible and even negative in samples from that same period ( Figure 3 ). This apparent mismatch likely arises because the genetic status of that generation is assessed on samples from the extant fished population which carries all the genetic diversity inherited from the preceding generation. Conversely, the fish biomass depleted during the lustrum 1986–1990 experiences a loss of genetic diversity that becomes quantifiable approximately one generation later, which in this case study corresponds to lustrum 1991–1995 ( Figure 3 ). While the effective population size (LDN_e ​) cannot be negative, its change can be negative under genetic erosion, or positive as reflecting inter-stock migration, mutation, reduction in reproductive variance, favorable reproductive conditions, or any combination thereof occurring alongside low mortality rates (natural and/or fishing-induced).

The Genetic Resilience Index (GRI) calculated for this fishery illustrates the behavior of D _ L D N e t ( Figure 3 ) relative to L D N e t 0 ( Figure 4 ). The differentiallity is that while D _ L D N e t ​ quantifies the absolute change in N_e​ (positive, zero, or negative), GRI provides information on the genetic impact of the effective genetic mortality Z_LDN_e ​ during period t, representing the net N_e ​ change in the population between two pre- and post-harvesting time points. Current lustrum-based LDN_e ​ estimates derived from relatively small European hake samples (n ≈ 40) (Fernández-Míguez et al., 2023) may indicate an accelerated loss of genetic diversity. However, the downward bias inherent in LDN_e ​ estimates from small sample sizes is expected to be minimized by the GRI indicator, as it is a proportion calculated within the same population (e.g., Jamieson and Allendorf, 2012). Metric GRI generally fluctuated within the interval [-1, 1] during the examined hake fishery period, specifically [-0.927, 0.786], with the exception of lustrum 1996–2000 when GRI reached 3.572, reflecting a substantial LDN_e ​ recovery. While acknowledging the potential influence of its large census size and migration from the northern hake stock (Pita et al., 2014), a compensatory effect cannot be ruled out given the depleted N_SSB levels during that period.

Genetic Reference Points (GRPs) derived from historical genetic data of the European hake establish the Basal Genetic Reference Point (BGRP) around 1976, when LDN_e ≈12000. Sustained overharvesting led to its subsequent erosion, with the stock entering a zone of critically low GD levels after the second half of 1980s ( Figure 5 ), falling below the theoretically conservative LGRP = 0.30·BGRP. Previous studies estimated a bottleneck with N_e ≈300 in the early 2000s for this southern hake stock (Pita et al., 2017), suggesting that this GD level might represent a critical threshold below which the stock resilience to environmental challenges would be compromised, increasing the risk of demographic non-recovery (e.g., Frankham et al., 2014).

Despite the debated effectiveness of EU fishing regulations in recovering the southern hake stock based on its depleted N_SSB figures (European Commission Council Reg, 2005), a significant fishery N_SSB rebound was documented in lustrum 7 (2006–2010) (e.g., JRC (Joint Research Centre) et al., 2010), with N_e also rebounding to 3200 in 2006–2010 ( Figure 5 ). Although the precise mechanisms of this rebound remain poorly elucidated in the literature, the synergistic effects of EU fishing regulations, a large population census size, the strong connectivity observed with the northern hake stock (Pita et al., 2014), and a compensatory effect on N_e (reduction of reproductive variance) (Pita et al., 2017) constitute the most plausible scenario given the historically depleted N_SSB levels at that time. However, LDN_e only re-approached the herein proposed Limit Genetic Reference Point (LGRP) in lustrum 8 (2011–2014) ( Figure 5 ), following its positive trend from the preceding lustrum and the initiative-taking management implementation of EU fishing regulations (e.g., JRC (Joint Research Centre) et al., 2010) as should be pertinent once a species approaches the tGRP reference zone.

The Genetic Structural Reference Point (GSRP) is essential for the genetic delineation of fisheries (Pita et al., 2016a) and for assessing range shifts or stock collapses within a species metapopulation structure (e.g., Palacios-Abrantes et al., 2022). GSRP implementation is not feasible in this case study due to the steady genetic homogeneity observed in the southern hake stock (Pita et al., 2017). Nevertheless, the genetic structure of the Atlantic hake metapopulation exhibited connectivity among Atlantic stocks with variable directionality, intensity, and periodicity, and F _ST values ranging from 0.0001 to 0.024 during period 2000–2010 (Pita et al., 2016b). Opposite, temporal information on current structural genetic metrics from the two subpopulations in the European hake range (Atlantic and Mediterranean) can help identifying changes within their gene pools (e.g., Smedbol and Wroblewski, 2002) supporting their management distinction.

By adhering to a structured roadmap, fisheries management can progress towards the species-specific implementation of GRPs, thereby fostering more resilient and sustainable fisheries. However, the development and implementation of species-specific GRPs for fisheries is a multistage process necessitating collaboration among geneticists, fisheries scientists, managers, and stakeholders. A tentative roadmap aims to delineate the steps involved in establishing operational GRPs for fisheries ( Table 2 ). We believe that setting validated GRPs in fisheries assessment can be far operational than the classical 50/500 rule (Franklin, 1980; Soulé, 1980) which proposes a minimum viable population (MVP) of 50 individuals to avoid inbreeding and 500 to mitigate genetic drift. That universal MVP rule is now considered too simplistic and less relevant as many species survive below its thresholds, and extinction risk varies greatly among species (see Jamieson and Allendorf, 2012; Frankham et al., 2013, 2014; Rosenfeld, 2014; Hoban et al., 2020). Species-specific MVPs estimated using Population Viability Analysis (PVA) models are more accurate (Shaffer, 2005). Such PVA models have evolved to handle complex scenarios and can identify factors significantly impacting extinction probability, such as habitat loss, disease, and inbreeding. Importantly, PVA can now incorporate genetic data to determine the minimum genetic diversity needed for a viable population (e.g., Zilko et al., 2021).

The first step to implement GRPs in concerned fisheries involves a clear categorization of fish stocks (e.g., ICES, 2021), i.e., stock category 1 encompasses fisheries with existing records of historical gene diversity (BGRP) and metapopulation structure (GSRP), or with the potential to reconstruct these through various means (e.g., museum collections, preserved tissue samples, otoliths, scales, bones, scientific data). Stock category 2 comprises fisheries where the reconstruction of either their historical genetic diversity or their spatiotemporal metapopulation structure (GSRP) is not feasible. In this last category, the first genetic assessment of the fishery should serve as a basis for subsequent assessments.

The second step involves developing the genetic tools using tissue samples of the species. Noteworthy, optimized purification methods now facilitate the extraction of DNA from subfossil material (e.g., Muschick et al., 2023), enabling the application of high-throughput sequencing and subsequent characterization of thousands of SNPs.

The third step entails the acquisition of high-resolution GD data from the baseline population using standardized methodologies. This GD data can be used to parameterize both the Basal Genetic Reference Point (BGRP) by applying a genetic metric (e.g., N_e ) and the Genetic Structural Reference Point (GSRP) using an inter-stock genetic distance measure such as the inter-subpopulation fixation index (F _ST​).

The fourth step involves annual genetic monitoring on non-invasive samples (e.g., from directed commercial sampling and oceanographic missions) of selected fisheries, e.g., those identified upon commercial, economic or ecological criteria, to track the genetic diversity indexes used to work out its specific GRPs. It is important to note that a spatio-temporal sampling design based on the species-specific life cycle is crucial to minimize bias in GRI and GRP estimates (based on the accuracy of N_e ) and to produce meaningful data for comparing the genetic structural reference point (GSRP) of the metapopulation with the actual one. While sampling by direct fishing methods carried out by oceanographic institutes (scientific campaigns) already accounts for the species life cycle, sampling of new species of conservation concern necessitates an appropriate sampling strategy upon its life cycle (e.g. Harris et al., 2013).

At this stage, the trends and ratios of GD established through interannual data allow for the estimation of the post-harvesting rate of genetic erosion (e.g., GRI index) and the definition of the target (TGRP), the trigger GRP (tGRP), and the limit (LGRP) Genetic Reference Point based on field estimates of GD (e.g., N_e ​).

The fifth step consists of GRP testing to appraise error, uncertainty, and robustness of genetic estimates used to define GRPs for category 1 stocks, where comprehensive data knowledge is attainable. This task includes the validation and modeling of GRPs behavior and the assessment of their operational interest in fishery assessment (e.g., Kell et al., 2021).

The sixth step involves substantiating the biological relevance of GRPs in fishery management (through both laboratory and field-based studies, where feasible) to elucidate the relationships between GD and fitness-related traits such as growth rate, reproductive success, survival, and disease resistance in the target species. This task can be undertaken at any time, provided that large phenotypic and genetic datasets are available, e.g., enabling Genome-Wide Association Studies (GWAS).

The seventh step consists on implementing adaptive management strategies to modulate GRPs and management actions over time. This entails adjusting the observed GRPs in response to specific management interventions (e.g., fishing quotas, temporal or spatial closures, fishing gears, or genetic enhancement programs). In this context, the application of simulation modeling allows for the exploration of potential consequences of various fishing scenarios on GD and the evaluation of the effectiveness of modulating potential GRP values.

The eight step consists on integrating qualitative and quantitative GD, and structural (metapopulation) criteria into assessment process and fisheries management frameworks. The integration of genetic metrics into ecological models seems to be relatively straightforward, enabling the evaluation of extinction risks stemming from genetic factors and improving the precision of estimating fishing-induced genetic erosion rates (e.g., Yang et al., 2025; Shan et al., 2025). Its incorporation into fishery assessment algorithms and fishery management presents greater challenges, such as inertia, corporativism and across-agencies assumption of methodological novelty.

A final, yet crucial, step is capacity building to strengthen the sustainability of the genetic assessment of fisheries. Implementing GRPs necessitates a long-term commitment to research, monitoring, and adaptive management. The genetic dimension must be integrated into the scientific culture alongside traditional assessments and socio-economic considerations for holistic fisheries management. In this regard, the implementation of training programs for fishery scientists, managers, and enforcement agencies on the principles of fishery genetics, the interpretation of genetic data, and the application of GRPs becomes consequential. Within the socio-economic public domain, the engagement of stakeholders, including fishers, industry representatives, conservation organizations, and policymakers, in the development and implementation of GRPs is also a priority, as it facilitates co-participation in understanding the rationale behind GRPs and their potential benefits for the long-term sustainability of fisheries.