Patella ferruginea as a model to investigate how small sample sizes affect genetic diversity estimates. We show that small sample sizes do not always hamper the detection of genetic patterns in endangered marine species, even though caution is needed when genetic divergence among populations is weak.Knowledge of genetic diversity is crucial to improve conservation plans for endangered species and ensure their long-term persistence. However, it may be difficult to accurately estimate genetic diversity when it is unfeasible to obtain sufficiently large sample sizes, which is often the case for marine endangered species. Here, we use Patella ferruginea are used to assess the effect of sample size on metrics of within-and between-population genetic diversity by combining empirical and simulated data. Within-population metrics are slightly to moderately affected by small sample size, albeit the magnitude of the bias is proportional to the effective population size and gene flow. The power of detecting genetic differentiation among populations increases with sample size, albeit the gain of increasing the number of sampled individuals tends to be negligible between 30 and 50. Our results line up with those of previous studies and highlight that small sample sizes are not always a hindrance to investigating genetic patterns in endangered marine species. Caution is needed in interpreting genetic patterns based on small sample sizes when the observed genetic differentiation is weak. This study also highlights the importance of carrying out genetic monitoring in seemingly well-preserved but potentially isolated populations.Knowledge of Genetic diversity and its spatial distribution is crucial to improve conservation plans for endangered species. Genetic tools help ensure species’ long-term persistence by unraveling connectivity patterns and evolutionary trajectories of populations. Here, microsatellite genotypes of individuals from populations of  The amount of genetic diversity within populations, its spatial distribution, and population connectivity have been recognized as important components of reserve design . Indeed,Many coastal marine invertebrates are a noteworthy case in point as they are broadcast spawners with a sedentary lifestyle, which rely on larval stages for recruitment and dispersal. Thus, population connectivity and the spatial distribution of genetic diversity will depend on the interplay between species-specific life-history traits, nearshore oceanography, coastal topography, habitat availability and selection ,9,10,11.Patella ferruginea Gmelin, 1791 (Mollusca: Gastropoda) using the available set of microsatellite markers [Here, we focus on assessing the minimum sample size required to achieve reliable estimates of such genetic metrics in  markers . As prim markers ,22, as w markers . As with markers ,25. Over markers .Patella ferruginea, which is endemic to the Mediterranean Sea, fits this picture and is listed among the most threatened marine macro-invertebrates of the Mediterranean . Although the ‘strict protection’ status, the species is still subject to poaching and collection by fishermen as well as unaware tourists [P. ferruginea is fragmented and restricted to the north African shoreline, Spain, Sardinia, Corsica, and a few scattered sites along Italian coasts [P. ferruginea [tourists . The spetourists . The dectourists ,30. Nowan coasts ,32. Overn coasts ,34, whern coasts ,35, withn coasts . Thereforruginea ,41,42,43rruginea ,44,45. BP. ferruginea. Both mitochondrial and nuclear markers have been used to analyze genetic patterns at local and basin scales, yielding partly contrasting results [P. ferruginea and P. ulyssiponensis, the former had a lower potential for dispersal than the latter [Notwithstanding this issue, few studies have investigated patterns of genetic diversity in  results ,46,47,48 results . Moreovee latter . Fine-sce latter . This stP. ferruginea, which is crucial to assess the status of conservation and health of populations as well as the spatial genetic patterns over ecological time scales. Within this framework, it is paramount to investigate the power of assessing genetic diversity and its spatial distribution when the sample size per population is small, which is often the case with endangered and/or rare species. To reach this goal, empirical data were used as an illustrative example of the genetic patterns resulting from small sample sizes, and simulations were carried out to assess the minimum sample size required to obtain reliable genetic information to fulfill conservation purposes.The present study aimed at laying the ground for the genetic monitoring of To develop and test the multiplex, old genomic DNA obtained from previous studies ,50, whicUsing the singleplex PCR (Polymerase Chain Reaction) protocols as a starting point and the information on the allele size range of each locus outlined in a previous study , microsaTests on departure from Hardy-Weinberg proportions, linkage disequilibrium, genotyping errors , potential outliers for selection were not carried out in the present study as the loci were the same used in a previous study . In brieNA), Allelic richness (AR), Expected and observed heterozygosity (HE and HO), as well as the inbreeding coefficient (FIS). Genetic differentiation among populations was estimated using the function diffCalc to compute Weir and Cockerham’s FST estimator θ [G’ST [θ and G’ST. A Fisher’s exact test was further used to assess population differentiation among sites: the function chiCalc was used to test for genetic heterogeneity by carrying out 100,000 Montecarlo simulations. The joint probability of population differentiation was computed by combining single locus probabilities with Fisher’s method. Probability values at single loci were limited to p = 0.0001 to avoid a single locus having an overwhelming impact on the global probability [HE), which accounts for finite sample size, and the effective number of alleles (AE).Within the R 4.0.2 statistical environment , the pacimator θ  and Hedr θ [G’ST . A bootsbability . For paibability . Moreovebability  was usedP. ferruginea [Ne). Since BottleSim can use empirical genetic data, individual genotypes from Tavolara MPA [Ne = 500 individuals and differed for the intensity of the bottleneck: no population size reduction , moderate (five-fold) and severe (10-fold) population size reduction . Each scenario was run for 100 years and replicated 1000 times. The R-package PopGenKit 1.0 [The effect of sample size on within-population metrics of genetic diversity was investigated by simulating isolated populations that underwent bottlenecks of different intensities. Simulations were set up to mimic as close as possible the biological traits and the demographic history of rruginea . First, rruginea  to creatlara MPA  were usenKit 1.0  was usedThen, ten simulated datasets were randomly chosen from each scenario to assess the effect of sample size on allelic diversity: random sub-sampling was carried out using a customized version of Keenan’s R script: each dataset was resampled 100 times with replacement for each level of sampling intensity ranging from 4 to 48 individuals, and then diveRsity was used to compute the allelic richness for each locus in each resampled dataset. The mean and 95% confidence interval of allelic diversity were then computed over the resampled datasets for each sampling level.N), loci, alleles and sampling size (S) as the real dataset. Different levels of genetic differentiation (FST = 0.00–0.1) were simulated assuming constant population effective size (Ne = 1000) and varying the number of generations since divergence . The statistical power was evaluated by computing the fraction of both Χ2 and Fisher’s exact tests that successfully detected population differentiation out of 1000 replicates for each simulation. Three further sets of simulations were carried out by increasing the sample size . To account for different effective population sizes and some level of migration across demes, the connectivity module available in SpotG, an online simulation tool  was also used [S = 10, 20, 30, 50), effective population sizes  and migration rates  were combined to obtain 60 distinct scenarios, each of which was replicated 1000 times. Overall power was computed as the proportion of times out of 1000 iterations in which genetic differentiation was successfully inferred. All data and R scripts used to generate the analyses are available on FigShare at https://doi.org/10.6084/m9.figshare.14611407.v1.To assess the effect of sample size on the power of detecting population differentiation, we used a framework similar to that adopted by a previous study . Powsim lso used ,62,63. GP. ferruginea successfully amplified 173 out of 191 tested individuals. However, after keeping only those sites whose sample size exceeded five individuals, the final dataset consisted of 131 individuals, which were spread across 14 sites . Moreover, albeit the expected heterozygosity was below (HE = 0.65 ± 0.10), the unbiased heterozygosity reached the above threshold (uHE = 0.70 ± 0.11). In contrast to MVE, which showed the highest values, another site from a MPA, Punta Sabina, Asinara island (APS), showed the lowest values for these metrics . Most sites (9 out of 14) showed a slight excess of heterozygotes as evidenced by the small negative values of the inbreeding coefficient  were recorded at IPO (Isola dei Porri), MVE, PLF (Punta Li Francesi), and MAD . MVE also showed the largest value of the effective number of alleles (AE = 4.27 ± 0.71) but not for allelic richness: interestingly, an unprotected population located on the mainland, Coscia di Donna (CDN), showed the largest values for this metric (AR = 4.64 ± 0.65). As well, a site from Asinara MPA  displayed the lowest values for both metrics .Within population summary statistics for each site are reported in ent FIS, . The larp = 0.084). Both Weir and Cockerham’s θ and Hedrick’s G’ST were small, showing 95% confidence intervals that spanned zero . Although 21 and 34 out of 91 population pairs displayed pairwise values of genetic differentiation ≥ 0.01 for both θ and G’ST, respectively (p > 0.05). Moreover, all pairwise comparisons displayed large confidence intervals that spanned zero for both metrics of genetic differentiation  underwent a loss of genetic diversity. On average, about 18% of alleles were lost by genetic drift; conversely, expected and observed heterozygosity showed a negligible decrease as both metrics dropped to 99 and 98.5% of their starting value, respectively. The number of effective alleles decreased only by 5%, from 4.53 ± 1.07 to 4.29 ± 0.95. In contrast, the number of alleles dropped to 48 and 61% of the starting value when the population effective size underwent a five- and ten-fold reduction, respectively. Noteworthy, whilst a population with Ne = 50 lose three times more alleles than an unbottlenecked one (Ne = 500), the decrease of effective alleles was roughly seven times larger in the small (AE = 2.89 ± 1.07) compared to the large population. Even bigger was the relative loss of HE and HO related to an unbottlenecked population, which dropped to 87 and 84% of their starting values, respectively (The change in genetic diversity over time is illustrated in ectively .S). The three randomly selected populations, which are depicted in S = 50) was inversely proportional to the effective population size. For instance, in a population with a small effective size (Ne = 50), <10 individuals are sufficient to detect 71% of the allelic diversity that was assessed with S = 50 . in a large population (Ne = 500), a similar proportion was obtained by sampling twice the individuals . These differences affected the outcome of random subsampling, which outlined as allelic diversity  increased at increasing sample sizes . The bigger the sample size, the weaker the level of genetic divergence at which population differentiation was detected with power ≥ 95% .Small sample sizes did not always preclude the chance of successfully detecting population differentiation even when genetic divergence was weak. er ≥ 95% . For insNe) and migration rates (m). The combination of these demographic parameters with different sampling efforts resulted in 60 simulated scenarios  successfully detected population differentiation when genetic divergence was weak to moderate . Among these, only one scenario successfully detected population differentiation when the sample size was small (S = 10): this simulation combined a large effective population size (Ne = 1000) with a small migration rate (m = 0.001), resulting in an FST = 0.023. However, another scenario with S = 10 detected genetic differentiation showing a good success rate (Power = 91%): this simulation resulted in an overall FST = 0.022 by combining Ne = 100 and m= 0.01. Disregarding the magnitude of genetic differentiation, two further points need to be stressed. First, small sample sizes per se did not always jeopardize the success of detecting population differentiation: half of the scenarios simulated at S = 10 showed good to high power . Power < 90% included scenarios characterized by large Ne and/or high m  and high migration rate (m = 0.1). However, the increase of power with sample size was not always linear as it can be shown in S was twice or five-fold larger than the smallest one.The results outlined above, based on the assumption that genetic drift was the only evolutionary force driving genetic divergence, were compared to those of a simple symmetric finite island model with varying effective population sizes  ratio will fall close to the upper bound (Ne/NC ratio = 10−2–10−6) usually observed in broadcast spawning marine species [Ne at Asinara and Tavolara MPAs indicate that Ne/NC ratio could be even higher than 10−2 in P. ferruginea [Ne/NC ratios may depend on gene flow among populations, for which local population sizes can be closer to the global metapopulation size [Ne.Clues in on the effective population sizes that populations of rganisms . Moreoven coasts . In this species . Howeverrruginea , which irruginea . Furtherion size , resultiP. ferruginea. This choice ensures individuals behave as either males or females, thus mimicking the sex change mode of P. ferruginea: depending on environmental conditions, individuals may switch from males to females and back more times during their lifespan [Ne, it cannot be ruled out that loss of genetic diversity could be larger in real than in simulated populations, all else being equal. The maximum duration of lifespan was set at 20 years, which is partly a subjective choice. Determining the age of limpets based on their shell size is not straightforward as it depends on the growth rate and the used method [Bottlenecks have been simulated assuming overlapping generations and a monoecious mating system without selfing as a proxy for the protandric sequential hermaphroditism of lifespan ,38,40,42lifespan . Moreovelifespan ,34,38. Ad method ,30. Hencd method .P. ferruginea. Yet, both models are suitable to assess the power of detecting population differentiation, even if they do not exhaustively account for asymmetric dispersal patterns, overlapping generations, and varying population sizes [Ne with weak to moderate migration rates show a degree of genetic divergence that is twice up to more than a factor larger than observed in real populations. Such a discrepancy should not be interpreted as evidence that populations considered in the present study are characterized by larger than expected Ne and/or m. Population genetic models compute genetic divergence measures expected at migration-drift equilibrium, assuming that both Ne and m are constant over generations. This picture is unlikely in most natural populations and in those of P. ferruginea as well, which probably faced a gradual decline until the end of the 19th century, followed by the most severe size reductions during the last century [The framework used to model genetic differentiation relies upon simple population genetic models (pure genetic drift and symmetric finite island model) relative to seascape dispersal patterns and the demographic history of on sizes . We conson sizes . Remarka century . TherefoAR) as the probability of missing rare alleles increases. Hence, this metric is smaller than that reported in other limpets and gasteropods [AR estimates that were computed on the same MPAs but using large sample sizes [AR is as large as, or even larger than values observed in other coastal gasteropods. Nevertheless, the effect of sample size on AR cannot be generalized to all sites, as exploitation might influence this metric as well. For instance, harvested limpet populations from the Macaronesian archipelago, P. candei and P. aspera, show levels of allelic richness that are comparable to those reported in the present study [Clearly, small sample sizes affect the magnitude of mean allelic richness , which is not surprising as the contribution of rare alleles to this metric is downweighed by their relative frequency. Hence, unsampled rare alleles will have a negligible impact on HE. Consistent with this picture, HE values are similar to those reported for other gasteropods [P. ferruginea does not show a heterozygote deficit as the magnitude of observed heterozygosity (HO) is as big as that of HE. this outcome might partly be a side-effect of small sampling effort, as HO is slightly smaller than HE when large samples can be analyzed [HE < HO, FIS < 0) by increasing the rate at which rare alleles are lost [Conversely, small sample sizes do not affect the expected heterozygosity  or unprotected/exploited populations , further highlights some interesting remarks. A small sample size does not preclude the comparison of allelic diversity between genetically diverse (populations with no history of bottlenecks because of enforced protection or inaccessibility) and depauperated populations (which suffer bottlenecks because of exploitation). Although allelic diversity is underestimated throughout all scenarios, large-sized populations always show a mean allelic richness that is higher than that of small-sized ones at all levels of sampling. For instance, The patterns outlined above are well depicted by simulations that represent the effect of population size reductions on genetic diversity . As expeP. ferruginea showcased in Ne = 100) or large (Ne = 500), even more individuals are needed (S ≥ 40) to include the maximum value of allelic diversity within the confidence interval of random sub-samples. Nonetheless, even sample sizes commonly used in microsatellite datasets  could not be sufficient to include the true value of allelic diversity in the confidence interval when Ne is large. Empirical data on populations of P. ferruginea, whose Ne likely ranged between 100 and 500 individuals, further highlight this point [Simulated data of is point , thus cois point .S ≥ 16) to safely detect differences in allelic diversity between a large sized-population and one that is undergoing a five-fold population size reduction, relative to the former : as FST will tend to be small in large populations, sufficiently large sample sizes are needed to increase the precision and accuracy of genetic differentiation estimates. Conversely, if FST is large as it could be in small populations, small sample sizes  could be sufficient to obtain precise and accurate estimates of genetic differentiation [Ne and levels of gene flow), small samples can successfully detect population differentiation even when FST ≥ 0.01 . First, as FST depends on both historical and contemporary gene flow [FST equilibrium values after the onset of isolation [P. ferruginea, which has been recently investigated by breeding individuals in aquaculture tanks, disclosed life-history traits that may underpin a dispersal potential of at least tens of km [FST, the reverse does not hold true: when genetic connectivity is restored, footprints of genetic structuring are quickly erased in the lapse of a few generations [Although low statistical power because of the small sample size is the more likely explanation for the absence of genetic structure, two alternative scenarios cannot be ruled out, based on simulated data  are unavoidable. Our results line up with former studies and indicate that metrics estimating genetic diversity within populations are still informative even when sample sizes are fairly small. For instance, unbiased expected heterozygosity seems to estimate genetic diversity with good precision even when the sample size is smaller than 10 individuals, as observed in songbird populations . AllelicIn contrast, assessing genetic differentiation could be a really difficult issue related to assessing population connectivity or the genetic uniqueness of populations. Results based on simulations show it is possible to infer population differentiation using small sample sizes, as well as it is unlikely to detect genetic structuring when it is absent. Therefore, when moderate to strong genetic differentiation is assessed, it should not be dismissed as unreliable just because of the small sample size, as recommended by Björklund and Bergek . HoweverNe = 500 individuals) does not always ensure the maintenance of allelic diversity over ecological timescales as nearly 20% of alleles are lost over 100 years. This trend further supports the dismissal of the 50/500 rule in favor of the revised 100/1000 rule [Ne = 50 to 100) and maintain the evolutionary potential in the long-term (Ne = 500 to 1000). The use of the revised rule in future studies on P. ferruginea, however, should not disregard the following caveat: simulations concerned completely isolated populations. The global effective size of a network of populations connected by gene flow may be large enough to ensure both the short- and the long-term persistence of local populations, despite the local effective population sizes being small [Our results also stress the importance of carrying genetic monitoring programs on well-preserved populations of endangered species. Our simulations show that large effective population size (000 rule , which sng small .P. ferruginea in which large samples can be easily obtained without harming the whole population. Indeed, when the goal is focused on the conservation and health status of populations, estimating levels of recruitment, contemporary effective population size, or assessing recent bottlenecks, one should strive for sample sizes larger than 30 individuals, if it is possible.As a last remark, the multiplex tool here developed is best suited for the genetic monitoring of those populations of Patella ferruginea, results can be expanded to other marine species with similar life-histories. Overall, the present study outlines that valuable genetic information can be obtained even when sample sizes are small, depending on the species’ biology and demographic history. This outcome is important for decision-making in conservation plans dealing with endangered, rare, or elusive species, for which it is neither advisable nor feasible to achieve sufficiently large sample sizes. The following guidelines may be useful for conservation practitioners interested in embedding genetic information into conservation plans. First, high population connectivity should be inferred with caution when sample sizes are small and the species’ life-history traits are poorly known. Second, both expected heterozygosity and allelic richness should be used in conservation frameworks as these metrics give complementary information on patterns of genetic diversity. Finally, even seemingly well-preserved but potentially isolated populations may need genetic monitoring in order to prevent the loss of species’ adaptive and evolutionary potential over time.The present study combines both empirical and simulated data to investigate how small sample sizes influence the detection of genetic patterns in marine endangered species. Although the study is focused on


Policy regulations of ethically controversial genetic technologies should, on the one hand, be based on ethical principles. On the other hand, they should be socially acceptable to ensure implementation. In addition, they should align with ethical theory. Yet to date we lack a reliable and valid scale to measure the relevant ethical judgements in laypeople. We target this lacuna.We developed a scale based on ethical principles to elicit lay judgments: the Genetic Technologies Questionnaire (GTQ). In two pilot studies and a pre-registered main study, we validated the scale in a representative sample of the US population.The final version of the scale contains 20 items but remains highly reliable even when reduced to five. It also predicts behaviour; for example, ethical judgments as measured by the GTQ predicted hypothetical donations and grocery shopping. In addition, the GTQ may be of interest to policymakers and ethicists because it reveals coherent and ethically justified judgments in laypeople. For instance, the GTQ indicates that ethical judgments are sensitive to possible benefits and harms (in line with utilitarian ethics), but also to ethical principles such as the value of consent-autonomy.The GTQ can be recommended for research in both experimental psychology and applied ethics, as well as a tool for ethically and empirically informed policymaking.The online version contains supplementary material available at 10.1186/s12910-022-00792-x. Genetic technologies have recently been at the heart of much discussion in both academic and non-academic circles –3. This In democracies, ethical judgments of laypeople inform regulations of genetic technologies , 6. HoweOur work targets this lacuna. We created and validated a scale to measure ethical judgements about genetic technologies by joining forces from ethics and cognitive sciences. The present paper reports first results from a study in a representative sample. In addition to gaining meaningful insights into laypeople’s ethical views about issues raised by genetic technologies, which align largely with ethical theory, we also find that our scale is predictive of real-world behaviour and differentiates between individual characteristics. In what follows, we first outline the ethical issues arising from genetic technologies “” sectionGenetic technologies raise ethical concerns in a vast range of domains such as the moral status of the organisms affected by genetic technologies , privacy , and social justice . In what follows, we take these domains in turn.Intrusively observing or interfering with nature may be, per se, ethically problematic –10. It mHaving the relevant cognitive capacities to a fuller extent or greater degree grounds a higher moral status, other things being equal. For example, ethicists commonly ascribe a lower moral status to living beings that are not or to a lesser degree conscious, sentient, autonomous, or self-aware. In this vein, most animals enjoy a higher moral status than most plants , and altThe ethical prohibition against interfering with a living being that has moral status may be overruled or outweighed. Depending on the reasons that outweigh or the rights that trump the prohibition to interfere with a being of moral status, it is less or more permissible to violate it. For instance, the reason that your life is threatened and your right to self-defense may arguably trump the prohibition against shooting a tiger; yet your enjoyment of hunting or your desire for a trophy do not.In the context of genetic technology, a strong reason overriding the prohibition to interfere with a being that has moral status is a person’s consent. Consent is, roughly, the voluntary agreement given by a fully informed and decision-capacitated agent . In mostThe ethical permissibility to interfere also varies with the severity or nature of the interference. For example, taking photos of a living being with moral status is, ceteris paribus, less severe than killing or torturing it. Accordingly, the latter is less permissible than the former. The same is true for genetic technologies: the more profoundly they interfere with a living being, the less ethically permissible it is to do so, other things being equal. For one thing, genetic testing is a less severe interference than genetic editing, and thus the former is more ethically permissible than the latter.In sum, to determine the degree to which it is ethically permissible, if at all, to use genetic technologies in a given case, it is necessary to consider the moral status of the living beings affected by it, the potential benefits or reasons promoted by it, and the severity of the interference. Approaches that take several or all of these considerations into account often employ cost–benefit analyses or a weighing of moral reasons or goods. For example, from a utilitarian perspective, different options of various dimensions are commensurable with respect to the potential benefits and harms they bring about , 22, 24.That privacy is ethically valuable has been noted at least since Aristotle , whoGenetic technologies raise ethical concerns about privacy, and in particular about the privacy of genetic data. Collecting or sharing a human being’s genetic data is pro tanto ethically impermissible –32. GeneAlthough definitions of “justice” vary greatly, many capture the core idea that justice consists in rendering each their due . RoughlBecause just principles, practices, and institutions include regulations and usage of genetic technologies, the ethics of genetic technologies is in part concerned with issues of social justice. For example, principles of social justice require that, ceteris paribus, everyone have fair and equal access to public goods like genetic technologies, or that individuals are treated fairly and justly in light of different genetic profiles.If laypeople’s moral judgments align with the ethical theory as outlined in the previous section, empirical evidence can be expected to support the following hypotheses  to assess the sensitivity of laypeople’s moral judgments to the moral status of the living being affected, data privacy concerns, and issues of social justice. As one clearly distinguishable feature of moral status, we chose membership of the human or a non-human species. As another salient feature, we categorically varied the severity of the interference with a being that has moral status: genetic testing versus genome editing. Thus, we developed questionnaire items covering six domains: genetic testing of humans, genetic testing of non-humans, genome editing of humans, genome editing of non-humans, data privacy, and social justice. All items described, as far as possible, real-life applications of genetic technologies. For example, they concerned the actual use of genetic technologies on crops or farm animals, in reproductive medicine, or in scientific research.We also used language and topics that laypersons could understand without further information and explanation. Items and response options were formulated in a neutral way to avoid framing effects or other unwanted influences of wording—an issue that  criticizMoreover, we were interested in ethical judgements about genetic technologies specifically, not about technology more generally or incidental features of contexts in which genetic technologies are used. Therefore, we employed a contrastive design (described in further detail below).We refined the questionnaire in two consecutive pilot studies and validated it in a representative sample of the US population. The aim of the pilot studies was to generate a set of candidate items, explore the relevant themes in laypeople’s moral judgment of genetic technologies, and select the most suitable items. While the literature in applied ethics guided item generation, we were not committed to preserving the six domains outlined above in the final version of the scale, let alone establishing sub-scales. Because genetic technologies are complex, we expected that judgments about them would be sensitive to a multitude of different aspects. Our goal was to develop one coherent general scale for ethical judgments about genetic technologies that would be behaviourally relevant.Changing the genome of farm animals in order to improve their wellbeing is….We employed a contrastive design following a well-established tradition in experimental philosophy  and bioeChanging the hormones of farm animals in order to improve their wellbeing is….Participants completed the claim by choosing a response ranging from “morally bad” to “morally good” on a six-point Likert scale. The corresponding contrast item read:Comparing responses to target and contrast item allows to attribute differences in ethical judgments more straightforwardly to the difference between genetic versus conventional technologies, rather than to, say, ethical judgments about farming or animal wellbeing. In addition to the GTQ, we thus also used a matched Conventional Technologies Questionnaire (CTQ). The Ethics Committee of the University of Konstanz approved all studies . Materials and data are available on the OSF.2M = 27.0, SD = 12.0, range 12–68).We administered a questionnaire with candidate items for the GTQ as part of a more extensive survey of teenagers attending an outreach event at the German Cancer Research Centre, which uses genetic technologies to develop new cancer treatments. The study was explained in detail to the teenage participants, and they and their parents gave informed consent to participation. We also randomly assigned participants at an interdisciplinary summer school for Ph.D. students and postdocs to fill out a questionnaire with the GTQ or CTQ candidate items. To increase statistical power, we pooled the data from both samples. Fifty-two participants from the outreach event and 70 participants from the summer school returned complete questionnaires, resulting in a total sample size of 122  to strongly agree (6). All items were coded so that high values indicate a positive attitude towards the respective technologies.We used a contrastive design with two 35-item versions of the questionnaire (the GTQ and the CTQ). The items were statements about the ethical permissibility of using genetic or conventional technologies in various contexts. The response format was a five-point scale ranging from M = 3.47, SD = 0.54) was significantly higher than that of the contrast items from the CTQ , t(76.9) = 2.42, p = 0.018. However, the group that filled out the CTQ differed from the one that filled out the GTQ, as the latter but not the former included the entire teenager sample. Therefore, we also calculated a mixed linear model with the questionnaire type as fixed effect and participants and items as random effects. This yielded a significant effect of questionnaire type, i.e., participants judged genetic technologies as ethically more permissible than conventional technologies, β = 0.32, SEβ = 0.11, t(120) = 2.97, p = 0.003), even when variation between participants was accounted for in the model. However, when age was entered as a fixed effect in addition to the questionnaire type into a mixed model controlling for participant and item, the effect of questionnaire type was no longer significant, β = 0.18, SEβ = 0.12, t(119) = 1.47, p = 0.143. Instead, age was a significant predictor, β = − 0.01, SEβ < 0.01, t(120) = 2.30, p = 0.022.We found that the mean ethical judgment for the target items from the GTQ  to “morally bad” (6).M = 3.15) and the GTQ (M = 3.70), t(24.4) = − 2.66, p = 0.014. To account for interindividual differences, we also calculated a mixed linear model that predicted the ethical judgment by group and controlled for random effects of participants and items. This yielded a significant effect of questionnaire type, β = 0.67, t(28.0) = 3.33, p = 0.002, indicating genetic technologies were seen as ethically worse than conventional technologies, over and above differences between participants and items.To compare whether our contrastive design detected differences in ethical judgments of genetic versus conventional questionnaires, we calculated the mean rating of the CTQ . The corresponding contrast items make up the conventional technologies questionnaire (CTQ30).We validated the final version of the 30-items GTQ  and the contrast (CTQ30) conditions. Thus, the expected sample size of participants responding to the GTQ was 150. The statistical power to detect a sufficient internal consistency (Cronbach’s α = 0.80) with this sample compared to the null hypothesis that the internal consistency would be lower (Cronbach’s α = 0.70) was 1 − β = 0.95, calculated as described by . Finally, a scale consisting of the top five items (GTQ5) still had an acceptable internal consistency, α = 0.89, CI [0.87–0.92]. Table The overall internal consistency of the 30-items scale (GTQ30) as measured by Cronbach’s alpha was p > 0.05). Nonetheless, a trend suggests that conservative participants tend to judge genetic technologies as morally less permissible, r = − 0.13, p = 0.118. The correlation between the GTQ and SES was significant and positive, r = 0.25, p = 0.002. The SES indicator is a composite of income and education. As the correlation of the GTQ with education  is stronger than with income , education seems to be the primary driver of the SES effect. An exploratory follow-up regression analysis predicting the GTQ-score based on both income and education found that education was a significant predictor, β = 0.20, SEβ = 0.09, t(142) = 2.31, p = 0.023, while income was not, β = 0.05, SEβ = 0.04, t(142) = 1.10, p = 0.272. A nonsignificant trend suggested that female participants tended to rate genetic technologies as morally less permissible  than male participants , t(128.74) = − 1.69, p = 0.093. In addition, age was negatively related to the GTQ rating of moral permissibility, r = − 0.28, p < 0.001. For an item by item summary of correlations with demographic variables see the Additional file Table p = 0.133), their actual knowledge was: the more participants actually knew about genetic technologies, the better they rated them morally, β = 0.17, p = 0.036.We also analysed whether presumed and actual knowledge about genetic technologies was related to bioethical judgments about those technologies. That is, we asked participants to report how much they knew about genetic technologies, and we tested their actual knowledge in this domain. While participants’ presumed knowledge was no significant predictor of the GTQ score (p = 0.418), purity values on the moral foundation questionnaire (p = 0.264), prior personal exposure to genetic testing (p = 0.387), or religiosity (p = 0.360).We found no statistically significant evidence for links between bioethical judgments in the GTQ and openness to experience  = 14.63, p < 0.001. To detect possible effects of participants’ ethical judgments on hypothetical donation decisions, we performed a linear regression, with the share of donations participants allocated on the hypothetical allocation questions to charities supporting the use of genetic technologies as the dependent variable and the GTQ score as the predictor. Morally favourable views of genetic technologies on the GTQ were related to a higher share of hypothetical donations given to those charities in favour of genetic technologies, β = 6.94, SEβ = 1.56, t(145) = 4.44, p < 0.001. In the third-party dictator game, the higher the participants’ GTQ score, the more money they allocated descriptively to another participant who was also in favour of genetic technologies, although this trend was not statistically significant, β = 1.89, SEβ = 1.58, t(145) = 1.20, p = 0.233.The GTQ score was highly correlated with participants’ general endorsement of genetic technologies asked in a single item, β = 0.38, SEβ = 0.08, t(140.8) = 4.56, p < 0.001.To detect a possible relationship between participants’ ethical judgments and their reported shopping behaviour, we set up a regression model that predicted the frequency of buying genetically modified food by the mean GTQ score. We found that the better participants judged genetic technologies on average, the more often they reportedly bought genetically modified food, r = 0.77, t(145) = 14.46, p < 0.001. Still, participants regarded genetic testing  as morally better than genome editing , t(281.4) = 4.92, p < 0.001.Examining bioethical judgments, we first performed a sanity check and determined whether ethical judgments about genetic testing (items GT1 to GT8 of the GTQ30) correlated positively with ethical judgments about genome editing (items GT15 to GT30). We found a strong correlation between the two, M = 3.72, SD = 0.98, scale midpoint = 3.5, 6 = good), but ratings were worse than those of conventional technologies as rated by participants who filled out the CTQ , t(273.1) = 3.47, p < 0.001. To test for this difference while also accounting for by-subject and by-item variability, we calculated a mixed linear model and compared the mean ethical judgments of our target questionnaire with those of the contrastive questionnaire. The CTQ group had significantly higher scores than the GTQ group, β = 0.35, SEβ = 0.10, t(298) = 3.48, p < 0.001.Overall, the participants who filled out the GTQ rated genetic technologies as morally good  as better than in human embryos , t(291.8) = 3.85, p < 0.001.Ethical judgments varied with the moral status of the being on and the purpose for which genetic technologies were deployed. For instance, participants judged genome editing in human adults  as morally better than genetic technologies used to improve taste , t(290) = 4.26, p < 0.001. They also rated using genetic technologies against poverty  as morally better than using them to improve taste, t(290) = 4.26, p < 0.001. Genome editing was rated as morally better when used to prevent a fatal disease  rather than influenza, which need not be fatal , t(292) = 2.56, p = 0.011.Participants also judged genetic technologies as morally better when used to fight world poverty or improve the nutritional value of foods rather than their taste. More precisely, they rated genetic technologies used to improve nutritional value  than the sharing of nutrition data , t(298.0) = 2.58, p = 0.012. As an example of the latter, participants rated the use of public health funds for gene therapies as morally worse  than their use for chemotherapies , t(285.9) = 6.72, p < 0.001.Ethical judgments also reflected concerns about data privacy and social justice. As an example of the former, participants rated a doctor’s sharing of genetic data as morally worse , which is also highly reliable, covers the ethical domains homogeneously, and may thus be of interest for both experimentalists and moral philosophers. Finally, we also developed a matched contrast questionnaire about conventional technologies (CTQ). These questionnaires are available in Table 5Our scale was highly reliable in a representative sample of the US population, correlated meaningfully with individual characteristics, and showed good construct validity in predicting behaviour. In line with prior research, we found that ethical ratings about genetic technologies as measured by our scale correlated positively with income , 39, eduA novel result is that we find bioethical judgments to correlate with behavioural decision-making: measures on our scale predicted allocations in a dictator game, self-reported shopping behaviour, and hypothetical donations. Note that only the third-party dictator game, in which we measured if like-minded participants would be treated favourably, measured real-life behaviour, and that all other decisions concerned self-reported and hypothetical behaviour. These results indicate that our scale may measure ethical judgments that align with social discourse and actions. Consequently, it could prove a valuable tool to predict behaviour in experimental studies on the one hand and to gauge support for and implementation of policy regulations on the other hand.Overall, we found that participants rated genetic technologies as morally good, thereby replicating previous findings from the literature , 37–39. The questionnaire and our study sheds light on two strands of research in ethics. First, our scale elicits laypeople’s judgments about core ethical issues arising from genetic technologies, which pertain to moral status, data privacy, and social justice. Second, we find ethical judgments as measured by our scale to be coherent and sound. Experimental and empirically-informed philosophers have long worried that lay and expert judgements, as well as real-life behaviour, do not conform to norms of morality, rationality, and epistemology –57. Our More specifically, we found that ethical judgments were sensitive to the moral status of the beings on which they were deployed and the severity of the reasons for which the moral status of those beings was compromised, thus converging with ethical theory. For instance, it is pro tanto ethically impermissible to interfere with beings that are not yet or temporarily unable to give their informed consent; this is in accordance with our finding that participants judged genome editing in human adults as better than in human embryos, in line with previous research .Moreover, participants rated genetic technologies as morally better when used to fight world poverty or to improve the nutritional value of foods rather than their taste. Genome editing was rated morally better when used to prevent a fatal disease rather than influenza, which may but need not be fatal. These findings align with cost–benefit analyses in applied ethics and converge with utilitarian theory more generally. That is, participants seemed to trade off potential risks of genetic technologies against their potential benefits. They also distinguished prudential goods like an improved taste from altruistic or moral goods such as prosocial benefits like improved healthcare, rating the latter as morally better.Participants also seemed to take issues of data privacy and social justice into account in their ethical judgments. For instance, in their view, it was morally worse for a doctor to share a patient’s genetic disposition rather than their nutrition with a third party, which aligns with philosophical views that genetic data is particularly ethically sensitive –32. SimiThe work presented in this paper is limited in at least two ways. First, we did not find empirical support for some of our preregistered hypotheses and thus failed to replicate some findings reported in previous research. In particular, we did not find significant evidence for hypothesised correlations of bioethical judgments and individual moral values , openness to experience (as measured by a subset of Big Five items), religiosity (as measured by self-assessment and reported frequency of attending religious services), and prior exposure to genetic testing. We had expected that individuals would rate genetic technologies as ethically better the less they considered purity and sanctity as important moral values , the morA second limitation of our paper is that it only reports main results from a representative sample of the US population, which may or may not generalise to other cultures and countries. Bioethical judgments may be susceptible to idiosyncratic views of particular cultures or policy regulations in different legislations. For example, survey respondents from the European economic area (EEA) have been found to be more opposed to the use of genetic technology in adults and embryos than those from the US , and whiOverall, our work may provide experimentalists with a reliable and valid tool to quantify ethical judgments about genetic technologies. Moreover, it may be of interest to ethicists and moral psychologists in that it presents judgments by a representative sample of the US population on the full range of ethical concerns about genetic technologies. Finally, not only do we hope that our scale and findings will further foster interdisciplinary research, but we also aspire to contribute to public discourse and policymaking concerned with genetic technologies in particular and ethical issues in general.Additional file 1: Supplemental Material.


Octopus vulgaris  is a cephalopod species with great economic value. In western Asturias (northwest of Spain), O. vulgaris artisanal fisheries are relatively well monitored and conditionally eco-labeled by the Marine Stewardship Council (MSC). Despite this, the Asturian octopus stocks have not been genetically assessed so far. In order to improve the current fishery plan and contrast the octopus eco-label validity in Asturias, 539 individuals from five regions of the O. vulgaris geographic distribution, including temporal samplings in Asturias, were collected and genotyped at thirteen microsatellite loci. All the samples under analysis were in agreement with Hardy–Weinberg expectations. Spatial levels of genetic differentiation were estimated using F-statistics, multidimensional scaling, and Bayesian analyses. Results suggested that the O. vulgaris consists of at least four genetically different stocks coming from two ancestral lineages. In addition, temporal analyses showed stability in terms of genetic variation and high NE (> 50) for several generations in different localities within Asturias, pointing out to indeed sustainable fishery exploitation levels. Even though, the current Asturias fishery plan shows no significant genetic damages to the stocks, the regional-specific management plans need systematic genetic monitoring schemes as part of an efficient and preventive regional fishery regulation strategy. Although overfishing is undoubtedly the greatest threat to marine biodiversity4, it is clear that the depletion of the world’s fish stocks cannot be attributed solely to fishing. Habitat destruction6, pollution8, anthropogenic climate change9 or invasive species10 also have an impact on fish populations. However, the high economic growth observed in recent years has triggered a global increase in consumption, which in turn has had a damaging effect on the natural environment11 and the subsequent loss of the ocean biodiversity12. Fortunately, over the past few decades there has been an increasing trend in global awareness regarding this emerging issue12, prompting several proposed programs for establishing sustainable fishery plans15 as well as the development of tools that can educate consumers about the impact of products on the natural environment throughout their life cycle, but which at the same time can also provide producers with the opportunity to inform consumers about the benefits of their products11.Marine biological resources are limited, yet the global increasing demand for seafood products has led to the overexploitation of fisheries, which is highlighted as one of the current main threats to marine species. It has been argued that the fraction of fish stocks that are within biologically sustainable levels decreased by more than a 30% from 1974 to 2017, whereas the percentage of stocks fished at biologically unsustainable levels increased, especially in the late 1970s and 1980s, from 10% in 1974 to 34.2% in 201716. The FAO recognized that eco-labels could contribute to improved fisheries management and convened a technical consultation in 1998, which led to the development of the “Guidelines for the Ecolabelling of Fish and Fishery Products from Marine Capture Fisheries”17. Since then, numerous programes have been proposed for eco-labeling seafood products in an effort to encourage fisheries managers to create sustainable fisheries16. These initiatives aim to provide a market-based incentive for sustainable fisheries management. Processors, wholesalers, and retailers who purchase products from these accredited fisheries can acquire the right to affix an eco-label, informing consumers that the product has been caught in a sustainable fishery. Hypothetically, if there were a demand for environmental quality, consumers would respond by purchasing those products with an eco-label, thereby reducing demand for those without and causing price devaluation on unlabeled products. This may result in fishermen putting pressure on fisheries managers to achieve sustainability accreditation and thus receive a higher percentage of the price16. One of the most recognized and prestigious ecolabels in use today is the Marine Stewardship Council (MSC).Eco-labels are “seals of approval” given to products that are deemed to have low or no negative impacts on the environment18. Effective and sustainable management plans for artisanal fisheries also need to be established to reduce potential threats to marine resources and ensure environmental protection and sustainability20. Unfortunately, the vast majority of fished artisanal stocks are unassessed22, and their status, although highly uncertain, is generally considered worse than that of data-rich populations24.Designing sustainable fishery management requires deep understanding of the current conservation status of species and valid biological data. Stock assessment is crucial, but it can be challenging due to methodology difficulties, financial cost and intensive data requirements, so its application is usually limited to industrial fisheries26. Defining stock boundaries for fisheries is fundamental and it is linked to the central idea that each stock has a harvestable surplus, and fisheries that comply with this limit will not compromise the stock’s natural perpetuation. Stocks that are scientifically assessed are usually in better condition than stocks that are unassessed27, being either exploited at sustainable rates or being re-built. Since January 2014, the reformed Common Fisheries Policy (CFP) of the European Union prescribes the end of overfishing and the rebuilding of all stocks above levels that can produce maximum sustainable yields (MSY). Although the global number of sustainable fishery products is increasing, there is reported evidence that the types of data upon which most assessments are established can be insufficient and misleading29. In this matter, there is an emerging need for radical changes in monitoring and collecting precise biological data from marine stocks; moreover, there is a direct link between fishing pressure and loss of species gene pools31. Genetic factors play an important role in fishery resource conservation because the latter are the product of their genes, the environment, and the interactions between the two32. Failure to detect biological characteristics of a stock within a population can lead to genomics changes and the subsequent loss of populations genetic diversity33.Stock has been historically defined as an intraspecific group of randomly mating individuals with temporal and spatial integrity35. However, genetics offers a diverse collection of versatile and useful tools to inform fisheries management on issues that have a biological basis36. Despite this, genetics has not yet been effectively integrated into fishery management schemes mainly due to the existing gap between managers, decision-makers, fisheries scientists and geneticists, resulting in less application of fisheries genetics37. Significant progress and findings in the field of marine genomics as well as the proven importance and validity of molecular genetic data, have made them vital tools for species identification; fisheries stock structure; resolving mixed-stock fisheries; age biomarkers; ecosystem monitoring; estimating harvest rates and abundance; genetic diversity, population abundance, and resilience; evolutionary responses to fishing; genetic effect of stock enhancement; detection of pathogens and invasive species; and product provenance and fisheries surveillance36.Traditionally, fisheries conservation and management have been based on abundance data, productivity estimates, and information on stock dynamics38. However, there is a clear species bias where marine invertebrates continue to lack genetic and genomic resources compared to other widely studied groups such as fish39. The importance of accurate genetic data is even more tangible when it comes to marine resources with huge market interest such as Octopus vulgaris Cuvier, 1797.The use of molecular genetic techniques in fisheries research has increased dramatically in the last few decades. Microsatellites and mitochondrial DNA markers have been used at an increasing rate in fisheries and today the application of new techniques to fisheries research, such as mass sequencing-derived markers, has grown significantlyO. vulgaris, also known as common octopus, is a cephalopod species with a very strong market interest in Europe, and specifically in Spain, the current major supplier of octopus in the global seafood market41. The great commercial interest in O. vulgaris along with the growing demand for this species, increase the potential risk of fundamental changes in its population structure, which can lead to stock collapse due to overexploitation. Recent studies have reported a significant decrease in octopus global captures, mostly caused by unmonitored fishing activities in some regions42. Particularly, the information obtained from the analysis of genetic markers in this species can be useful in assessing the level of the marine stock healthiness, and subsequently in framing more sustainable fishery plans43. It is vitally important that this valuable species is sustainably managed, as it is not only an important food but also provides a significant source of income to local communities and families31.44. The management measures include limiting entry to licensed boats from any of the eight legally recognized fishers’ associations, seasonal closures , type and number of gears and boat regulations, minimum landing octopus weight of 1 kg, and a maximum catch per season45. In the last two fishery seasons, Asturias management set the annual global catch as equal to the mean latent productivity (instead of the Maximum Sustainable Yield (MSY) criteria) minus two times the standard error of the estimate as a precautionary, sustainable and economically viable annual harvest rate46.A co-management system was established for the small-scale fishery (SSF) of octopuses with baited traps in Asturias (NW Spain) from 2000 to 2001 covering from the Eo estuary to the Nalon estuary47. Having passed the first 5 years of its initial certification, and with explicit use of a precautionary annual harvest rate46, the octopus fishery of western Asturias has achieved MSC recertification in 2021. In the case of the western Asturias octopus fishery, the eco-label validated the fishery as sustainable and environmentally friendly since it has a minimum impact on the marine ecosystem48.The artisanal octopus fishing with traps in western Asturias was certified with the Marine Stewardship Council (MSC) eco-certification in 2016, which made it the world’s first cephalopod fishery with this accreditation. However, the BUREAU VERITAS IBERIA (the certifying body) noted some weaknesses when recommending the MSC sustainability certificate for this fishery. They reported that “biological information on the resource was still scarce”, explicitly recommending that “information on the knowledge of octopus populations need to be improved”O. vulgaris population using DNA markers51. However, the genetic status of O. vulgaris stocks in the southern area of the Bay of Biscay is still poorly studied. In this work, the main objectives have been to determine, both spatially and temporally, the levels and characteristics of genetic variation in octopus samples from areas localized within the MSC-certified octopus management plan in western Asturias, in comparison with other areas of the species geographic distribution, as well as to assess the stability of gene frequencies and carry out the first estimations of effective population sizes for this valuable species. All this data can address questions of direct relevance for the sustainable management of the species.Over the past twenty years, some research has been conducted on studying the Atlantic O. vulgaris samples from several areas of the species’ geographic distribution  and including localities where the MSC-certified western Asturias octopus fishery is established  of 7.03 (ranging from 1.36 to 15.65)  and HS = 0.06 for the same locus, to HO = 0.89  and HS = 0.92 . The global averages for observed heterozygosity and within-population gene diversity were 0.49 and 0.51, respectively (Table FIS = 0.113 p < 0.05), showed significant deviations from the Hardy–Weinberg equilibrium (13 loci average FIS = 0.04 p < 0.05) whereas the other twelve loci (92%) agreed with Hardy Weinberg expectations  and four of them showed higher and significant FST values: Vulg15 ; Vulg12 ; Vulg13 ; Ovul08    to AR = 7.621  and from HO = 0.444  to HO = 0.571  (Table E = 0.613) whereas Cudillero  showed the lowest ones (HE = 0.461) (Table O. vulgaris). That was the case of Puerto de Vega  and Olhão  and the same non-significant result was also obtained for the time range between 2018 and 2021 for the Ribadesella, Cudillero, Puerto de Vega, and Tapia de Casariego populations (Asturias); reflecting stable levels of genetic variation for the stocks in these locations for the mentioned period  showing the highest values for allelic richness 7.621) while Cudillero (Asturias) showed the highest number of private alleles for the 2020–2021 fishery season (Ap = 7)  but none of the samples disagreed with Hardy Weinberg expectations Table . The popO. vulgaris samples  Bay of Biscay (including Basque Country and Asturias) and also the northern Atlantic samples from Galicia; (2) Southern Portugal; (3) Canary Islands and 4) Catalonia , showing a significant differentiated genetic pattern with the rest of the samples  pattern.A significant spatial structuring pattern was found in this work for les Fig. . The paiGalicia;  Southernortugal;  Canary IGalicia;  SouthernFST values . No significant temporal differences were found between 2018 and 2021 (2 generations) for the rest of the locations, except for the samples from Puerto de Vega collected between 2007 and 2018, with respect to the last sampling collected during 2020–2021 in the same locality (21PV)  = 733.240)  = ∞ ; NE-Puerto de Vega(2007–2021) = 182.3 (27.8–∞) and NE- Olhão(2007–2021) = 1651.3 (292.7–∞) for approx. 7 to 9 generations (2007–2018–2021). The shorter period from 2018 to 2021 (approx. 2 generations) revealed the following estimates in Asturias NE-Ribadesella(2018–2021) = ∞ (248.3–∞); NE-Cudillero(2018–2021) = ∞ (261.5–∞); NE-Puerto de Vega(2018–2021) = 46.6 (8.0–∞) and finally NE-Tapia de Casariego(2018–2021) = 137.6 (54.6–∞).Effective population sizes were analyzed with NeEstimator 2.1Octopus vulgaris, a species of great commercial interest, and on the efficiency of the current O. vulgaris management plan in Asturias, northern Spain.This study aims to shed light on the genetic status of the populations of 60. In this work, the levels of genetic variability found when using a set of 13 microsatellite markers on samples from 2007, 2018, and 2021  did not show significant temporal changes. The reported values were just slightly lower than those previously reported by De Luca et al.43  for Mediterranean O. vulgaris samples. Melis et al.42  and Cabranes et al.50  reported higher genetic variation values for Mediterranean and Atlantic populations, respectively; but using a very low number of genetic markers. Significant loss of genetic variation will lead to loss of species’ evolutionary potential61. It is well established that overfishing is seen as the major threat to the loss of marine populations genetic diversity within populations and it has the potential (when fishing is highly selective) to permanently change the characteristics within a population, usually in directions of less economic value63. Negative consequences of losing genetic diversity will not only affect the fishing industry, but also disturb the respective predator–prey populations and eventually the entire marine ecosystem64.Genetic diversity is the key element for maintaining a species’ potential to adapt to environmental changes, when there is undeniable evidence that the environment is changing mainly due to anthropogenic activity65. None of the populations in our study showed significant deviations from the Hardy–Weinberg Equilibrium (HWE), and at the same time, no signals of recent bottlenecks were found in those localities under study. These results could suggest that those populations  are at rest and not perturbed in a significant way.The Hardy–Weinberg principle depends on a number of assumptions namely simple Mendelian inheritance in a diploid organism with discrete generations, random mating, an infinite population, and no mutation, migration, or selectionFST and genetic distances56, our results remain the same when using the ENA correcting method. Previous studies on genetic structuring in O. vulgaris have been carried out based on mitochondrial and nuclear DNA datasets in the Mediterranean67 and Atlantic areas51. Using both types of markers, De Luca et al.43 found a pronounced differentiation of the Atlantic and Sicilian specimens supporting the isolation of the biota within the Strait of Messina and significant differentiation within the Mediterranean Sea as was previously suggested by Casu et al.66. Moreover, Melis et al.42 highlighted high variability and low but significant genetic differentiation among populations in a very small geographic scale in the Mediterranean, where samples clustered into four groups in the coasts of Sardinia. In the Atlantic area, significant subpopulation structure was also identified using only five microsatellites, consistent with an isolation-by-distance (IBD) model for Atlantic populations, but the genetic differences between pairs of samples separated by < 200 km were not significant50. Recently, Quinteiro et al.51 used mitochondrial DNA and suggested a significant differentiation in their study including insular and continental samples from the Galicia and Morocco coasts, with the exception of pairwise comparisons for samples from Madeira and the Canaries populations. These results pointed out the existence of genetically differentiated octopus populations in the Mediterranean and Atlantic areas and the necessity of local regulations for the appropriate management of octopus stocks. The octopus stocks of the Bay of Biscay had not been genetically monitored for more than fifteen years until the present study.Our data includes a representation of the eastern Atlantic and Mediterranean area octopus populations . The Bayesian analyses suggested two ancestral gene pools . Contemporary genetic heterogeneity is evident at smaller geographic scales among Portugal, Canary Islands, and Mediterranean samples. Some samples (i.e. Puerto de Vega 2020–2021 in Asturias) showed peculiarities that revealed punctual genetic heterogeneity within Galicia and the Bay of Biscay samples analyses at very small distance scales  in a capture-recapture experiment conducted by Mereu et al.70 where high site fidelity and the necessity for creating small no-take areas were suggested. While it seems that planktonic O. vulgaris paralarvae have an oceanic strategy in upwelling systems rather than the coastal-shelf strategy of other neritic species (Loliginidae and Sepiidae families)71; pre-recruits usually are distributed in specific and differential grounds, as has been found in Portugal (8 pre-recruit grounds), where its western zone adjacent to Ria Formosa lagoon (southern coast) was identified as the main recruitment ground for O. vulgaris along the Portuguese coast72. Our Portuguese samples were coming from this area (Algarve) and as it is mentioned above, they showed high levels of genetic diversity. It seems also that environmental factors are fundamental to the behavior of the dispersal ability of paralarvae in O. vulgaris73 and wide inter-annual fluctuations in O. vulgaris abundances have been described in distinct geographic areas in connection with environmental drivers . In Galicia, it seems that a large fraction of the annual variability in catch is due the impact of upwelling on the survival of planktonic life stages74. Characterizing possible local paralarvae retentions in the Asturian coast as a consequence of hydrographic conditions as well as identifying recruitment grounds are still pending. Moreover, it had been recently argued that the Asturias O. vulgaris stock presents a rich dynamic that results from intrinsic properties of the stock, as well as from small perturbations from a combination of moderate fishing removals and possibly environmental forces that determine strong density-dependent and overcompensations that cause fluctuations in O. vulgaris stocks abundance46. These facts can explain punctual genetic heterogeneity within the Bay of Biscay area, where we have found a global pattern of genetic homogeneity  which is congruent with previous studies on the area . Despite this, periodic genetic monitoring on these exploited and fluctuating stocks seems to be advisable. Deeper studies using genomic tools (i.e.: SNP studies covering wider areas within the octopus genome) will help in the future to re-assess structuring and management units in this small geographic area.Our study area is also located in the Canaries-Iberian upwelling system. Even when upwelling was early viewed as dispersive environments for the larval stages, these systems are now considered more retentive areas than previously thought, where the larvae are capable to regulate their transport by exploiting the circulation patterns and later recruiting close to their natal habitats59. Moreover, sampling adult population over generations and looking for statistically significant shifts in allelic frequencies that cannot be explained by evolutionary forces, such as mutation, selection, migration, or varying year-class strengths in populations, help to identify Sweepstakes Reproductive Success (SRS) patterns, common in marine species, where extremely large variance in individual reproductive success is due to sweepstakes-like chances of matching reproductive activity with oceanographic conditions75. In addition, temporal replicates allow estimating of effective population sizes, which is a relevant parameter that gives clues about the health state of an endangered/exploited population determining the rate of loss of genetic diversity, fixation of deleterious alleles, and the efficiency of natural selection at maintaining beneficial alleles76.In this work, we have found stable levels of genetic variation for the localities where temporal analyses were possible . It is worth mentioning that temporal replicates were all in HWE. Temporal analyses are a powerful tool in population genetics and in its application to relevant problems, such as assessments of fishery stock status. Temporal studies allow to discerning between real genetic signals and noise artifacts, meaning that genetic patterns that are consistent through time are unlikely to be sampling artifacts78. O. vulgaris stock assessment is difficult due to distinct life history features such as short life cycles, semelparous reproduction, high natural mortality rates, rapid growth, and complex population structures79. A keystone in any attempt at species conservation and/or management is the effective size of a population (NE). Fifty individuals (NE = 50) have been considered necessary for a population’s immediate survival avoiding inbreeding depression80. In the Algarve, Portugal, high effective population sizes NE-OL(2018–2021) = 1651.3 (292.7–∞) were found. In three out of the four Asturias locations temporally sampled in this work, our data revealed also high effective population sizes (NE-Ribadesella (2018–2021) = ∞ (248.3–∞); NE-Cudillero (2018–2021) = ∞ (261.5–∞); and NE-Tapia de Casariego (2018–2021) = 137.6 (54.6–∞)). The decrease in the effective population size in the case of Puerto de Vega is the only noticeable case, as in the time range between 2007 and 2018 the value is infinite (NE-PV(2007–2018) = ∞ (1042.5–∞)) while from 2018 to 2021, it drops sharply to 46.4 (NE-PV(2018–2021) = 46.6 (8.0–∞). It has been said that octopus fisheries in Asturias exercise a low pressure on the stability and renewal capacity of the stocks47. During the first few years of the management plan (2000–2001 to 2007–2008), total annual landings averaged 180 tons, and total annual effort normally exceeded 3000 days of O. vulgaris fishing, but later (2008–2009 to 2018–2019), landings decreased, averaging 102 tons, and effort decreased as well to less than 2000 days46. Recently, Roa-Ureta et al.46 estimated abundances for octopus’ stocks within the Asturias management plan using depletion models and reported densities ranging from 1250 to nearly 5000 per km2 considering the area of the fishing grounds off in Asturias (228 to 397 km2). Moreover, they predicted recruitment from spawning abundance observations for female spawning stock and recruitment dynamics in O. vulgaris in this area. These temporal analyses indicate significant oscillations in fishing efforts and recruitments . Moreover, taking into account the entire available time range (2007–2021), the NE-PV(2007–2021) = 182.3 (27.8–∞) is still high. The case of Puerto de Vega can be punctual, or an indicator of restricted gene flow81, or a signal about current environmental and/or fishery pressures on the specific zone perturbing the octopus populations. Discerning this will need more studies, including replication, to be evaluated.In the Portuguese samples , the temporal replicates were genetically homogeneous and showed high diversities. Besides being a relevant recruitment zone, it seems that a relevant co-management plan has been reported in the area and has shown promising results for the sustainable use of fishery resourcesO. vulgaris management plan in Asturias, as well as of the healthiness levels of these fishery stocks. The Atlantic O. vulgaris populations show significant genetic structuring at a large geographical scale that fits with a classical isolation by distance model, where the probability of individuals mating with one another is restricted and local retention of paralarvae makes populations small in comparison to the total species distribution. That reinforces the necessity of local and regional plans to guarantee long-term sustainability. Results from our work can provide a baseline for further genomic studies on Asturian common octopus and therefore for sustainable exploitation. To the best of our current knowledge the Asturias O. vulgaris fishery plan seems to be currently adequate, since our data is not detecting recent harms to the fished stock and, accordingly, the validity of the MSC label seems to be rational. More data will be needed to assess if the Asturias management plan may require a more specific regional approach including smaller spatial scales.Findings from this study can give a better vision of the spatial and temporal distributions of genetic variation in common octopus in the Atlantic area, and of the efficiency of the current No use of live animals was required for this study. All samples used for the present study came from animals fished for commercial purposes or from the collections of other research centers. For more information, please see the Acknowledgements section.O. vulgaris individuals were collected in nine localities across the Bay of Biscay , Portuguese waters , Macaronesia  and the Mediterranean sea  during the last fishery seasons (2020–2021) Moreover, samples from the fishery campaign 2006–2007 from Portugal and Asturias (Puerto de Vega) and from the season 2017–2018 in Asturias  were also available for analyses. All the procedures were conducted using preserved or freshly small tissues from specimens collected by local fishermen and fixed in pure ethanol (100%). No use of live animals was required for this study.82 according to dye colors and expected amplicon sizes. Microsatellite amplifications were carried out by combining 13 loci (previously tested in single PCRs) in two multiplex PCR reactions . Approximately 25 mg from octopus tissues were cut and chopped into small pieces, put into 1.5 ml microcentrifuge tubes and processed following the manufacturer’s instructions. The resulting DNA was visualized on agarose gel 1% and stored in a − 20 °C freezer for further applications. Thirteen microsatellite loci were amplified reliably and arranged into two multiplex PCRs using Multiplex Manager 1.2 software83 and FreeNA56, with the number of replicates fixed to 10,000. The data set corrected for null alleles was used as a final input file for further statistical analysis. Allele frequencies, the number of alleles per locus (k), the mean number of alleles (NA) per locus, the observed heterozygosity, within-population gene diversity and the overall gene diversity , were calculated using “adegenet”84, “pegas”85 and “hierfstat”86 packages implemented in R version 4.1.2 through RStudio 2021.09.2 + 382 “Ghost Orchid” Release. The “PopGenReport” package87 in R was used to calculate the allelic richness (AR) per locus and per population and also the number of private alleles (AP) within populations. Spatial and temporal comparisons for levels of genetic variation were conducted using a two-sided statistical analysis included in the FSTAT 2.94 software88 for several statistics .Microsatellite genotyping was conducted locus per locus by two different and independent readers using the automatic procedure implemented in Geneious Prime® 2020.2 and manually corrected. Possible genotyping errors and null allele frequency estimates were determined using Micro-Checker 2.2.3F statistics following Weir and Cockerham55 and possible deviations from expected proportions in Hardy Weinberg’s equilibrium for each locus and population were assessed using FSTAT 2.94 software88. Significance levels of FIS were estimated by permutating alleles between genotypes within samples 10,000 times, and adjusted following a Bonferroni correction89. Additionally, FST (ENA) values were estimated using FreeNA, which estimated the unbiased FST following the ENA method56. The bottleneck hypothesis was investigated using the program BOTTLENECK v 1.2.02 under the two-phased model of mutation (TPM)90, taking into account 90% single stepwise mutations with a variance of 12. The “Wilcoxon sign-rank test” was used to determine whether a population exhibits a significant number of loci with heterozygosity excess91. Pairwise FST values between samples and corresponded p-values were calculated using FSTAT 2.94 software where for significance levels of FST, multi-locus genotypes were randomized between pairs of samples  and calculated after Bonferroni correction89. Comparisons between regions and between fishery seasons were conducted using a two-sided statistical analysis included in the FSTAT software for several statistics .The 84, was conducted to cluster the samples in groups. Besides this, the population structure was also assessed with Bayesian clustering population structure analysis in STRUCTURE 2.3.492. In order to fasten the procedure, structure analysis was performed through “ParallelStructure” package93 in R. Structure analysis was run among all nine populations, without taking temporal data into account. The settings used were an admixture model from K = 1 to K = 18 in 20 runs95. Assignment clusters were made with a length burn-in period of 20,000 and 200,000 Markov Chain Monte Carlo repetitions. The most likely value of K was chosen using the delta K statistic94, using the STRUCTURE HARVESTER software96. A Neighbour‐Joining (NJ) tree based upon pairwise Nei's genetic distance DA57 was constructed with the software POPTREEW97 using 10,000 bootstraps and visualized in The Interactive Tree of Life (https://itol.embl.de)98.A discriminant analysis of principal components (DAPC) using the R working package “adegenet”99 was used to perform a partial Mantel test to study if the observed patterns of genetic structure found here conformed to the isolation by distance model (IBD) explaining genetic isolation between populations100. The geographic distances between each pair of sampling localities (Kms) were calculated on the basis of a spherical earth , using the haversine formula59 and they were related with Edwards' distance101. The software BayeScan 2.1.102 was used to identify candidate loci deviating from neutral expectations from genetic data, using differences in allele frequencies between populations. Twenty pilot runs of 5000 iterations each, followed by an additional burn-in of 50,000 iterations, and then 5000 samplings with a thinning interval of 10 were conducted. Loci with α-value significantly > 0 and q-values < 0.05 were defined as “outliers”—i.e., loci putatively under directional selection. Loci with α-value significantly < 0 were considered putatively under balancing selection. The remaining loci were classified as neutral.The Package “ade4”E) using the Temporal Method, Nei/Tajima, Plan II103 with the NeEstimator software58. The lifespan of O. vulgaris from several regions in the Atlantic frequently exceeds 1 year and may reach a maximum of nearly 2 years105. A generation interval of 1.5 years for O. vulgaris was used for NE estimations, consequently, the 2007 fishing season was considered as the starting year (generation 0), while 2018 was considered generation 7 and 2021 generation 9.Finally, temporal change in allelic frequencies was used for estimating the effective population sizes (N