Students struggling with mathematics anxiety (MA) tend to show lower levels of mathematics self-efficacy and interest as well as lower performance. The current study addresses: (1) how MA relates to different aspects of mathematics attitudes (self-efficacy and interest), ability  and achievement (exam scores); (2) to what extent these observed relations are explained by overlapping genetic and environmental factors; and (3) the role of general anxiety in accounting for these associations. The sample comprised 3410 twin pairs aged 16–21 years, from the Twins Early Development Study. Negative associations of comparable strength emerged between MA and the two measures of mathematics attitudes, phenotypically (~ −0.45) and genetically (~ −0.70). Moderate negative phenotypic (~ −0.35) and strong genetic (~ −0.70) associations were observed between MA and measures of mathematics performance. The only exception was approximate number sense whose phenotypic (−0.10) and genetic (−0.31) relation with MA was weaker. Multivariate quantitative genetic analyses indicated that all mathematics-related measures combined accounted for ~75% of the genetic variance in MA and ~20% of its environmental variance. Genetic effects were largely shared across all measures of mathematics anxiety, attitudes, abilities and achievement, with the exception of approximate number sense. This genetic overlap was not accounted for by general anxiety. These results have important implications for future genetic research concerned with identifying the genetic underpinnings of individual variation in mathematics-related traits, as well as for developmental research into how children select and modify their mathematics-related experiences partly based on their genetic predispositions. MA is a widespread phenomenon: a recent large-scale investigation of 15-year-olds found that 30% of students across multiple countries member of the Organization for Economic Cooperation and Development (OECD) reported feeling anxious or incapable when solving a mathematics problem3. Due to the high incidence and hindering consequences for mathematics learning outcomes and experiences4, it is important to understand the etiology of the association between MA and the attitudinal and performance components of learning mathematics.Mathematics anxiety (MA) has been consistently linked to lower levels of engagement and motivation and poorer performance in mathematicsThe current study investigates the extent to which overlapping genetic and environmental factors underlie the associations between MA, attitudes towards mathematics, cognition and achievement. This work provides a foundation for the search for genetic variants linked to individual differences in MA, and mathematical learning. This study can also inform developmental research into how students select and modify their mathematics-related experiences, partly depending on their genetic predispositions. Moreover, identifying which aspects of performance and attitudes, if any, are more closely associated with anxiety, and the etiologies of these associations, will likely inform the focus of future interventions aimed at reducing MA and fostering mathematics learning.5. Moderate to strong negative associations between mathematics attitudes and anxiety are observed in student populations, as well as in samples of pre-service teachers—trainees working towards obtaining mathematics teaching qualifications—cross culturally6. The tendency to avoid situations involving mathematics, which covaries with MA is in line with observations of avoidance behaviour associated with general anxiety7, and might be related to holding negative beliefs about competence in the subject9. In line with this hypothesis, research found that mathematics self-efficacy, which describes individuals’ perception of their own competency10, mediated the negative association between MA and performance in high school students11. Students who achieved higher grades at the start of high school developed higher mathematics self-efficacy, which resulted in lower levels of MA at a two-year follow-up11. Additionally, self-efficacy was found to mediate the negative association between self-reported self-regulatory behaviour and MA: A greater capacity for self-regulation was positively associated with self-efficacy which was in turn negatively linked to MA in an adolescent sample12.Research has indicated a moderate negative association between MA and mathematics motivation and attitudes, including lower perseverance to learn and practice mathematics13 proposes that, as well as beliefs and expectancies, subjective task value is a crucial construct characterising motivated behaviour. However, few studies have focused on investigating the association between MA and other aspects of attitudes towards mathematics, beyond self-efficacy. One study found that MA relates to a similar degree to self-efficacy, interest, and importance attributed to mathematics 14. Similar results were obtained by two previous studies that examined the association bewteen MA and mathematics importance, interest and usefulness15 and bewteen MA and mathematics confidence, interest and importance in a sample of young children16. Yet, it remains unclear whether the same or distinct genetic and environmental influences underly the relations between MA and mathematics attitudes, such as self-efficacy and interest. The first goal of the present study is to address this gap in the literature.The expectancy-value theory of motivated behaviour17. A recent investigation found that, although children with developmental dyscalculia were more likely to show high levels of MA than neurotypical controls, 77% of children presenting with high levels of MA showed average or high performance in mathematics18. Nevertheless, research has found that students experiencing higher levels of MA on average show weaker mathematical performance. This negative association between MA and mathematics achievement remains significant and moderate after accounting for variation in general cognitive ability7. The association between MA and achievement has been observed at several developmental stages, from as early as primary school20. Longitudinal research in an adolescent sample has suggested that the stability of MA increases from moderate to strong during the course of adolescence21. This observed increase in the stability of MA is partly explained by stable levels of low achievement in mathematics, as achievement was found to drive the development of subsequent MA21. These results point to the role of negative performance feedback in reinforcing the development of increasingly pervasive levels of MA in adolescence. However, another longitudinal study found reciprocal longitudinal links between negative emotions, including MA, and achievement in mathematics in a sample of secondary school students22. This is in line with the observation of reciprocal longitudinal links between MA and performance in a sample of primary school students, although effect sizes were observed to be greater for the link from earlier achievement to subsequent anxiety23. A further longitudinal investigation explored the emergence of the association between MA and achievement in a primary school sample24. The study found that while no direct longitudinal links between MA and achievement emerged, both constructs were associated with mathematics self-evaluation, suggesting a potential role of attitudes towards mathematics, and particularly self-efficacy, in the development of the link between MA and mathematics achievement24.Students experience MA across the entire distribution of mathematics ability25. Supporting this hypothesis, two studies have found that high levels of MA were associated with deficits in areas of basic numerical processing such as counting25 and a simple visual enumeration26. On the other hand, another investigation27 failed to find an association between MA and basic numerosity—the ability to discriminate between symbolic and non-symbolic numerical quantities at a first glance28. Using latent profile analysis, Hart et al. clustered students into different groups, based on their profile in MA, achievement and numerosity. They found that the link between MA and numerosity was weak across all identified groups27.A further line of investigation has explored the possibility that a deficit in lower-level numerical processing may be related to MA via its negative association with mathematics achievement29. This investigation, conducted in a twin sample, found that the association between MA and performance (measured as mathematics problem-solving ability) was mostly explained by common genetic influences. The second goal of the current study is to extend this research to explore the genetic and environmental overlap between MA and aspects of mathematics attitudes and performance.Despite the large number of studies on the phenotypic association between MA and mathematics cognition, at present, only one study has explored the association between MA and performance applying a genetically informative design30. However, the two anxieties are only moderately correlated (0.35)5, suggesting that they may be separate constructs. This is consistent with a number of studies that have observed an association between MA and performance beyond general anxiety33. In line with this, a recent twin study has shown that the genetic and environmental etiology of MA only partly overlap with that of general anxiety34. Crucially, Wang et al. reported that the partial etiological overlap between MA and general anxiety was independent from the etiology of the overlap between MA and mathematics performance in a problem verification task29. The third aim of the current study is to examine the extent to which individual differences in general anxiety account for the links between MA and mathematics attitudes, cognition and achievement.MA and general anxiety partly overlap in their physiological manifestations, which include increased heartbeat, rapid pulse and nervous stomach, as well as in cognitive and brain networks35 for additional information on the TEDS sample. The TEDS study received ethical approval from the King’s College London Ethics Committee. The present study focuses on data collected in a subsample of TEDS twins over two waves: age 16 and age 18–21.Participants were members of the Twins Early Development Study (TEDS), a longitudinal study of twins born in England and Wales between 1994 and 1996. The families in TEDS are representative of the British population in their socio-economic distribution, ethnicity and parental occupation. Informed consent was obtained from the twins prior to each collection wave. See Haworth et al.age 16, TEDS twins contributed data on mathematics ability and achievement  and mathematics self-efficacy and interest . At age 18–21, the twins contributed data on MA and general anxiety . All individuals with major medical, genetic or neurodevelopmental disorders were excluded from the dataset.At 36 was administered to assess MA. The AMAS asks participants to rate how anxious they would feel when facing several mathematics-related situations. The measure includes nine items that are rated on a 5-point scale, ranging from ‘not nervous at all’ to ‘very nervous’. Two items were adapted from the original version to make them age appropriate for the current sample34, these are: ‘Listening to a maths lecture’ and ‘Reading a maths book’. The AMAS showed excellent internal validity (α = 0.94) and test–retest reliability (r = 0.85)36.A modified version of the Abbreviated Math Anxiety Scale (AMAS)mathematics self-efficacy scale asked participants: ‘How confident do you feel about having to do the following mathematics tasks?’ The scale included eight items that participants had to rate on a 4-point scale, from 0 = not at all confident to 3 = very confident. Examples of items are: ‘Understanding graphs presented in newspapers’, and ‘Solving an equation like 3 × + 5 = 17’. The scale showed good internal validity (α = 0.90). The mathematics interest scale included three items that participants had to rate on a 4-point scale, from 1 = strongly disagree to 4 = strongly agree. The items were: ‘I look forward to my mathematics lessons’; ‘I do mathematics because I enjoy it’; and ‘I am interested in the things I learn in mathematics’. The scale showed good internal validity (α = 0.93).Two scales, adapted from the OECD Programme for International Student Assessment, measure mathematics self-efficacy and interest. The https://www.jcq.org.uk/examination-results/gcses/2017/gcse-full-course-results-summer-2017) and therefore unlikely to constitute a bias in the current study. For 7,367 twins, self- and parent-reported GCSE results were verified using data obtained from the National Pupil Database , yielding correlations of 0.98 for English, 0.99 for mathematics, and >0.95 for all sciences between self- and parent-reported grades and exam results obtained from NPD37.The General Certificate of Secondary Education (GCSE) grades provided a measure of mathematics exam grade. The GCSE exams are taken nationwide at the end of compulsory education, usually when students are 16-years-old. As mathematics is one of the core subjects in the UK educational curriculum, taking the mathematics GCSE exam is a compulsory requirement for all students. Mathematics GCSE scores were collected by questionnaires sent to the twins or their parents by post, via email, or through a phone interview. The GCSE grades, which are given in letters from A* (similar to A+) to G, were re-coded on a scale from 11, corresponding to the highest grade (A*) to 4 corresponding to the lowest pass grade (G). No information about ungraded or unclassified results was available. However, these constitute a small proportion of all pupils in the UK  booklets 8 to 14. The test asked participants to solve 18 mathematics problems arranged in ascending level of difficulty. Questions were presented in multiple formats, ranging from equations to problems. Participants were asked either to type a numerical response into a box or to select one or multiple correct responses out of a set of possible options. An example of one of the difficult items is ‘Denise has thought of two numbers. The numbers added together make 23. The smaller number subtracted from twice the larger number makes 22. What are Denise’s numbers?’ with numbers 8 and 15 being correct. Each correct answer was allocated 1 point, resulting in a maximum score of 18. The test showed good reliability in the present sample (α = 0.90).The understanding numbers test39 presented participants with a series of mathematics equations appearing for 10 s on a computer screen. Participants responded to each equation , by pressing the corresponding keys on the computer keyboard. If they timed out, they were automatically redirected to the following equation. The PVT included 48 items. Examples of items are ‘32–16 = 14’; and ‘2/6 = 3/9’. Each correct response was allocated the score of 1 and other responses and non-responses the score of 0, for a maximum score of 48. The test showed good reliability in the current sample (α = 0.85).The problem verification test (PVT)28 included 150 trials displaying arrays of yellow and blue dots, varying in size. Each trial lasted 400 ms and included a different number of blue and yellow dots presented on the screen. Participants were required to judge whether there were more yellow or blue dots on the screen for each trial . Each correct answer was allocated the score of 1 and the final score was calculated as the number of correct trials. The final accuracy score correlated strongly  with the alternative score calculated using the Weber fraction41 for which a smaller score indicates better performance.The approximate number sense test42 assessed general anxiety. The scale includes 7 items asking participants to rate on a scale from 1 = not at all to 4 = nearly every day ‘How often in the past month have you been bothered by the following problems?’ Examples of items are ‘Not being able to control worrying’, and ‘Feeling afraid as if something awful might happen’. As well as measuring generalised anxiety disorder, the GAD-7 has been validated and is considered a reliable measure of anxiety in the general population. The GAD-7 is characterised by good internal validity (α = 0.89) and test–retest reliability r = 0.6442.The Generalised Anxiety Disorder Scale (GAD-7)Descriptive statistics and ANOVAs were conducted on data from one randomly selected twin out of each pair in order to control for sample dependency (i.e. the fact that the children in the study were twins). Measures were residualised for age and sex and standardised prior to analyses.43. Comparing how similar MZ and DZ twins are for a given trait (intraclass correlations), it is possible to estimate the relative contribution of genes and environments to variation in that trait. Heritability, the amount of variance in a trait that can be attributed to genetic variance (A), is intuitively calculated as double the difference between the MZ and DZ twin intraclass correlations44. The ACE model further partitions the variance into shared environment (C), which describes the extent to which twins raised in the same family resemble each other beyond their shared genetic variance, and non-shared environment (E), which describes environmental variance that does not contribute to similarities between twin pairs.The twin method allows for the decomposition of individual differences in a trait into genetic and environmental sources of variance by capitalizing on the genetic relatedness between monozygotic twins (MZ), who share 100% of their genetic makeup, and dizygotic twins (DZ), who share on average 50% of the genes that differ between individuals. The method is further grounded in the assumption that both types of twins who are raised in the same family share their rearing environments to approximately the same extent45. While additive genetic factors (A) are the sum of the effects of all alleles at all loci contributing to the variation in a trait or to the co-variation between traits, non-additive genetic effects (D) describe interactions between alleles at the same locus (dominance) and at different loci (epistasis). The classic twin design, comparing MZ and DZ twins does not allow us to estimate all four sources of influence  within one univariate model, as it only includes two coefficients of relatedness46. Therefore, with the classic twin design it is possible to partition the variance into three sources of influences: A, E, and either C or D.An alternative to the ACE model is the ADE model, which partitions the variance into additive genetic (A), non-additive (or dominant) genetic (D) and non-shared environmental (E) effects. This model is fitted in cases when intraclass correlations for DZ twins are below 50% of the MZ intraclass correlation—indicating non additive genetic influencesACE models were fitted for mathematics GCSE, understanding numbers, and mathematics problems verification test. For these measures, intraclass correlations for DZ pairs were more than half of those for MZ pairs, suggesting that environmental factors contributed to the similarity between twins beyond their genetic similarity.ADE models were fitted for MA, general anxiety, mathematics interest, mathematics self-efficacy, and number sense. For these measures, the DZ intraclass correlation was less than half that of MZ, indicating non additive genetic effects.The twin method can be extended to the exploration of the covariance between two or more traits . Multivariate genetic analysis allows for the decomposition of the covariance between multiple traits into genetic and environmental sources of variance, by modelling the cross-twin cross-trait covariances. Cross-twin cross-trait covariances describe the association between two variables, with twin 1 score on variable 1 correlated with twin 2 score on variable 2, which are calculated separately for MZ and DZ twins.47. A Cholesky decomposition can be interpreted similarly to a hierarchical regression analysis, as the independent contribution of a predictor variable to the dependent variable is estimated after accounting for the variance it shares with other predictors previously entered in the model. The current study applies Cholesky decompositions to the investigation of the genetic and environmental overlap between MA, mathematics motivation and performance.One way of partitioning the genetic and environmental covariation between two or more traits is to conduct a multivariate Cholesky decomposition. The Cholesky decomposition allows us to examine the overlapping and independent genetic (A), shared (C) (or non-additive D), and non-shared (E) environmental effects on the variance in two or more traits48 and performance49, we firstly tested for sex differences in all measures using univariate ANOVAs  and strong negative genetic correlations (rA ranging between −0.67 and −0.75) were observed between MA and all other mathematics-related variables. The only exception was the association between MA and approximate number sense, which was weak phenotypically (r = −0.10) and modest genetically (rA = −0.31). Measures of mathematics attitudes shared a positive moderate to strong phenotypic association (r ranging between 0.38 and 0.56) and strong genetic association (rA ranging between 0.57 and 0.82) with measures of mathematics performance. Phenotypic and genetic correlations across measures of mathematics performance were strong. Again, an exception was approximate number sense, which was only moderately related to other mathematical measures.Figure 1,3 paths in Fig. We conducted two Cholesky decomposition analyses to explore the unique genetic and environmental overlap between each measure of mathematics self-efficacy and interest and MA. Following the rationale of hierarchical regression, in order to explore the unique genetic and environmental variance shared between self-efficacy and MA after accounting for interest, we entered interest as the first variable in the model, followed by self-efficacy and MA Fig. . The att1,2 and e1,3 paths linking self-efficacy and interest to MA  by the total non-shared environmental variance in MA (0.63).A degree of specificity was observed in the non-shared environmental overlap between the measures, as ~10% of the non-shared environmental variance in MA overlapped with self-efficacy independently of the other ~10% of the variance it shared with interest. These can be calculated by dividing the effect size of the standardised eTwo additional Cholesky decompositions explored the association between MA and all mathematics-related traits. The first decomposition Fig.  exploredThe second multivariate model Fig.  includedThe Cholesky decomposition presented in Fig. The present investigation was the first to adopt a genetically informative framework to explore the genetic and environmental overlap between anxiety, self-efficacy, interest and performance in the domain of mathematics, and the role of general anxiety in accounting for the observed associations. The results showed a substantial genetic overlap between all mathematics-related traits. This shared genetic variance was largely independent from general anxiety.The first aim of the study was to explore whether MA was similarly associated with different measures of mathematics attitudes. Results indicated that similar effect sizes characterised the negative associations between MA and mathematics self-efficacy and interest. More than one third of the genetic variance in MA overlapped with mathematics self-efficacy and interest. In contrast, environmental effects across MA, and attitudes towards mathematics were mostly specific to each measure.These results show that a high degree of generality characterizes the genetic overlap between MA, interest and self-efficacy, as largely overlapping genetic effects were found to contribute to variation in all constructs. On the other hand, the environmental links between MA and interest and self-efficacy were found to be largely specific to each construct, and to include individual-specific, or stochastic processes (including measurement error), which are encompassed by non-shared environmental variance, rather than family-wide characteristics which are subsumed under shared environmental effects. In fact, the majority of the non-shared environmental links were specific to the pairwise associations between MA and self-efficacy and MA and interest, and not shared across the three constructs.51. Future research is needed to identify the environmental factors that link MA to self-efficacy but not interest, and vice versa.Different environmental experiences, such as different classrooms, teachers, peers, life events, or perception of parental involvement and socio-economic status, could all play a role in explaining these observed non-shared environmental associations. Evidence of an overlap between environmental factors across measures of mathematics attitudes and anxiety is consistent with research showing that the classroom learning environment is similarly associated with both MA and mathematics self-efficacy53, is moderately heritable, while the heritability of mathematics performance is more substantial. Longitudinal studies in genetically informative samples (e.g.54) are needed to investigate causal directions between constructs.The second aim of the study was to explore the common genetic and environmental variance across MA and multiple measures of mathematics attitudes and performance. Common genetic factors were observed to characterize the etiology of all mathematics-related traits. MA accounted for more than one third of the genetic variance in mathematics attitudes and between one third and half of the genetic variance in mathematics performance. In turn, measures of mathematics performance accounted for three quarters of the genetic variance in MA. These differences in the proportion of heritability accounted for by MA and mathematics performance likely reflect the difference in heritability between the measures. MA, as it is often observed for self-reported constructs27. Moreover, our findings suggest that MA is particularly linked to numerical tasks that involve learned symbolic representations of discrete quantities, rather than approximate representations40. The lack of a shared genetic etiology between measures of mathematics affect and approximate number sense suggests that basic approximate numerical skills are unlikely to function as a cognitive precursor of the negative association observed between MA and performance.A significant genetic association between mathematics attitudes, particularly self-efficacy, and performance beyond MA was observed. The only exception was approximate number sense, which shared a very small proportion of its genetic variance with all measures of mathematics affect. The negligible association between approximate number sense and MA is in line with previous evidenceThe third aim of the present study was to explore whether the association between MA, attitudes and performance was domain specific, or whether general anxiety accounted for part of their association. Although general anxiety and MA shared ~20% of their genetic variance, general anxiety did not account for the association between MA and measures of mathematics attitudes and performance; in fact, it was mostly unrelated to variation in mathematics interest, self-efficacy and performance.29 and suggest that the common etiology of the association between MA, self-efficacy, interest and cognition may be partly specific to the domain of mathematics. Our results are consistent with evidence showing genetic and environmental specificity for general anxiety and measures of anxiety in the mathematics and spatial domains34. Research integrating measures of anxiety and performance in other domains, such as for example second language learning, will be able to further test the hypothesis of domain-specific factors linking affect and cognition in the field of mathematics.These findings extend the line of evidence provided by Wang et al.46 for a detailed description), the models employed in the current investigation do not specifically account for gene–environment interplay. One possibility is that the observed genetic association between MA, attitudes and performance may operate via environmental effects that are correlated or interact with genetic predisposition. For example, children with a genetic predisposition towards experiencing difficulties with mathematics may develop a greater vulnerability to negative social influences in the context of mathematics, such as negative feedback received from teachers or parents on their effort and performance, which in turn may lead to greater feelings of anxiety towards mathematics55. This has the potential to generate a negative feedback loop7 between performance, motivation and anxiety—that is potentially a product of interacting inherited and environmental factors. The present investigation, including one time point for each measure of MA, attitudes and performance does not allow us to establish the direction of causality between the observed associations. Longitudinal genetically informative studies, integrating multiple measures of mathematics attitudes, anxiety and performance are therefore needed.The current investigation presents some limitations. As well as relying on the methodological assumptions of twin design (see ref. 21, attitudes and performance56, which suggests that the links between this two-year time lapse capture the majority of the processes involved.A further limitation of the present investigation is that the measure of MA was not collected at the same time as the measures of mathematics performance and motivation. However, longitudinal investigations found moderate to strong phenotypic and genetic stability of MAThe present investigation was the first to examine the genetic and environmental overlap between MA and several aspects of mathematics attitudes and performance. Our findings of a shared, likely domain-specific, etiology between these mathematics-related traits provide a seminal step for future genetic research aimed at identifying the specific genes implicated in variation in the cognitive and non-cognitive factors of mathematics. Our results suggest that the majority of genetic variants implicated in individual differences across MA, attitudes and performance are unlikely to be implicated in variation in general anxiety. The current findings also provide a starting ground for developmental research to delve deeper into the observed common genetic links, examining how the experiential processes through which children select, shape and modify their mathematical experiences interact with genetic predispositions to produce variation in MA, attitudes and performance.Supplementary Information


Etheostoma caeruleum) in which we evaluated the importance of genetic variation on adaptive potential and determined responses to extreme thermal stress. We compared fine‐scale patterns of morphological and thermal tolerance differentiation across eight sites, including a unique lake habitat. We also inferred contemporary population structure using genomic data and characterized the relationship between individual genetic diversity and stress tolerance. We found site‐specific variation in thermal tolerance that generally matched local conditions and morphological differences associated with lake‐stream divergence. We detected patterns of population structure on a highly local spatial scale that could not be explained by isolation by distance or stream connectivity. Finally, we showed that individual thermal tolerance was positively correlated with genetic variation, suggesting that sites with increased genetic diversity may be better at tolerating novel stress. Our results highlight the importance of considering intraspecific variation in understanding population vulnerability and stress response.Adaptation to environmental change requires that populations harbor the necessary genetic variation to respond to selection. However, dispersal‐limited species with fragmented populations and reduced genetic diversity may lack this variation and are at an increased risk of local extinction. In freshwater fish species, environmental change in the form of increased stream temperatures places many cold‐water species at‐risk. We present a study of rainbow darters ( Although rainbow darters have a widespread distribution, studies of other darter species have shown that populations potentially suffer from decreased connectivity and reductions in habitat quality  from each site using a combination of seine nets and hand nets for a total of 156 individuals. We included both males and females in our sampling, but avoided sampling juveniles, defined as individuals with standard length < 25mm. During sampling, individuals were held streamside in plastic buckets filled with site water until completion of sampling. We transported individuals from each site in Nalgene® containers filled with site water to the Experimental Pond Lab at W.K. Kellogg Biological Station in Hickory Corners, Michigan. We collected water temperature data from each site using a YSI Pro20 (YSI Inc.) on the date of sample collection. Additionally, we obtained the estimated average maximum air temperature for each site from June–August using daily values from the Daymet dataset, which extrapolates temperatures at a 1 km2 scale using weather station data  and then were resampled in 2018 to account for potential temporal variability in phenotypes. The only individuals from resampled sites in 2018 that were included in the genomic data collection were those from Gull Lake, to increase sample size at this site.2.2Immediately upon arrival to the laboratory, each fish was anesthetized in a dilute solution of tricaine mesylate (MS‐222), weighed, measured (standard length), and photographed. We took photographs under fluorescent lighting of each individual spread laterally behind a measured background in water using a Canon EOS Rebel T3 digital camera (Canon U.S.A. Inc.). Rainbow darters from the same site were housed in 20‐gallon group tanks with a maximum of 10 individuals per tank. We prepared these tanks identically, including filters, water circulation devices, aquarium gravel, and bricks and plastic plants for refugia. Water was dechlorinated, denitrified, treated for ammonia and heavy metals, and boosted with aquatic microbiota  before use in tanks. We fed fish daily, alternating between frozen brine shrimp and bloodworms with amounts based on the number of individuals per tank. The room was lit with natural light from windows, following daily light/dark cycles. All fish were acclimated to a similar ambient water temperature of 24.5 ± 3°C.max). We defined CTmax as the temperature threshold at which loss of equilibrium or organized locomotion occurs, signaling individuals have reached their physiological limit prior to death  of room temperature, dechlorinated, and denitrified water treated for ammonia and heavy metals. Two aeration devices were used to prevent a decline in dissolved oxygen content due to increasing temperatures. A 2.8 watt Rio® Plus 90 Aqua Pump (TAAM Inc.) generated a current to maintain temperature equilibrium throughout the cooler, while two immersion heaters connected to a voltage transformer (PHC Enterprise Inc.) were placed on opposite ends of the cooler to facilitate gradual temperature increase. Reusable coffee filter containers covered with mesh netting were used to contain individuals during trials. Individuals were acclimated in the water for 10 min before beginning trials. We tested one fish per container, with a maximum of four individuals tested per trial. During trials, temperature increase was recorded using a YSI Pro20, and individuals were monitored for visual indications of reaching CTmax. Upon reaching CTmax, individuals were removed from the trial and given a recovery period to ensure survival. A single fish in our study failed to recover, likely due to surpassing its CTmax. This individual was not included in subsequent CTmax analyses. After each CTmax trial, we used sterile scissors to clip one anal fin from all recovered individuals. Tissues were stored within 1.5 ml tubes containing 100% ethanol for subsequent genomic analyses. After recovery was confirmed, we euthanized individuals using a lethal dose of buffered MS‐222.Prior to trials, individuals were starved for 24 hr. An insulated, rectangular (48 cm × 34 cm × 18 cm) Coleman2.3tpsUtil v1.76  versus lake (N = 24)). In order to determine the effect of sex, we also carried out a DFA between males (N = 70) and females (N = 84) for all sites. For each DFA, a permutation test with 10,000 iterations was used to assess significance between groups, while a cross‐validation analysis determined likely individual group membership. Wireframe graphs were used for visualizing the direction of morphological change associated with each canonical variate and discriminant function axis.To test for differences in morphology among sites and habitat, we conducted geometric morphometric analyses. We compiled JPEG images from 154 individuals into a TPS file using 6 Rohlf, . Each im0 Rohlf, . We used0 Rohlf, , with th2.4® M‐280 Streptavidin beads for a series of four washes, after which the DNA was liberated using the SbfI restriction enzyme. Preparation for sequencing followed the protocol outlined in the NEBNext Ultra DNA Library Prep Kit for Illumina with no modifications (New England Biolabs). Six base pair length adapter sequences were added to identify replicate individual adapter sequences between the two libraries. Agencourt® AMPure XP (Beckman Coulter) bead size selection was performed based on the DNA concentration obtained from a Qubit 3.0 fluorimeter. The final libraries were submitted to the RTSF Genomics Core at Michigan State University and were pooled for sequencing in one lane with paired‐end 150 base pair reads on an Illumina HiSeq 4000.Total genomic DNA was extracted from fin clips using a DNeasy Blood and Tissue Kit, according to manufacturer protocols (QIAGEN). After extraction, individual DNA concentration was quantified using either a Qubit 3.0 fluorimeter dsDNA HS Assay kit (Thermo Fisher Scientific) or by Quant‐iT™ PicoGreen™ assay (Thermo Fisher Scientific). Library preparation followed a modified restriction site‐associated DNA sequencing (RADseq) protocol described by Ali et al. . Briefly2.5Flip2BeRad (https://github.com/tylerhether/Flip2BeRAD). We then used stacks v2.0 . Combining both libraries, the ref_map.pl function in stacks was used to identify single nucleotide polymorphisms (SNPs), calling only the first SNP per RAD locus. We only kept SNPs that were found in all eight sites, were present in 75% of individuals within each site, and with a minimum minor allele frequency threshold of 0.05. We further filtered the data to remove individuals with more than 75% missing data, excluding eight individuals, using VCFtools v0.1.15 . We carried out ten independent runs with two genetic clusters (k = 2). Results from these ten runs were visualized using the pophelper package in R  in the R package Jombart, . This meFrancis,  and consST between sites, calculated from stacks, was used as a metric of genetic distance. A Mantel test was carried out in the ade4 package in R . For both IBD and StreamTree, we visualized and tested for a significant correlation between distance metrics using the JGR package . Geographic distances between sites were collected by following stream networks on Google Earth Table . Pairwis2.7max by testing for a correlation between individual observed heterozygosity and CTmax using a Spearman's rank correlation coefficient. In order to determine whether local environmental temperature at a given sample site or observed heterozygosity better explained individual variation in CTmax, we carried out a linear mixed‐effects model , G (heterozygosity), E x G (interaction between site and heterozygosity), S (sex), and W (weight). We also included C (container position within cooler), B , and Y (year) as random effects. All models included weight and sex as fixed effects and container position, trial number, and year as random effects. In total, we tested five models: the null (neither site nor heterozygosity), only site (E), only heterozygosity (G), both site and heterozygosity (E + G), and both effects plus their interaction (E + G + E x G). We fit each model using maximum likelihood and used a likelihood ratio test to determine significance between models with different fixed effect terms. Plots of residuals were used to determine whether assumptions of normality and homoscedasticity were met for all models. The Akaike information criterion (AIC) was used in model selection to determine the model with the greatest explanatory power  model using the emmeans . Body shape differences between the two groups are characterized by greater body depth and a terminal mouth position in stream individuals, while lake individuals display reduced body depth and a shift toward a superior mouth position. The cross‐validation analysis assigned eight lake individuals with the stream group (~33%), and 16 stream individuals with the lake group (~12%). We also found significant differences in body shape between sexes. Generally, sex differences were associated with subtle differences in body depth, with males having greater body depth. The cross‐validation analysis was less accurate, assigning 33 females as males (~39%) and 21 males as females (~30%). The Procrustes ANOVA for general differences in body shape found significant differences across sites =4.28, p < .001), and between sexes =3.19, p = .003). Despite significant differences indicated by the Procrustes ANOVA, the canonical variate analysis (CVA) between sites revealed a significant overlap in body shape among sites  and Lower and Upper Wabascon Creek sites , and lowest in Upper Augusta Creek  and Lower Gull Creek Table . GLD andFST) was generally low, ranging from 0.02 (between UAC and LGC) to 0.07 (Table FST between UAC and the paired downstream KFB site (0.06) was higher than the genetic distance between KFB and all other sites. The discriminant analysis of principal components (DAPC) was primarily defined by the first linear discriminant differentiating sites  and a nonsignificant Mantel test (p = .9911). Genetic divergence between sites was not explained by stream connectivity, with differentiation between sites being either greater or lower than expected based upon the genetic distance of each stream segment . Lack of support for the StreamTree model reinforced the absence of IBD, indicating a structuring of these sites not owing to stream connectivity (sites have either higher or lower observed genetic distance than is expected from cumulative stream genetic distance).We found no evidence for isolation by distance (IBD), indicated by the nonsignificant correlation between genetic and geographic distance  and expected  heterozygosity, we found a positive trend, in which sites with higher heterozygosity also had higher CTmax  and the model including only site (E) =2.92, p = .087); both models explained a significant proportion of variability in CTmax .Heterozygosity showed a significant correlation with CTx Figure . The topE), we found significant pairwise differences in critical thermal maximum (CTmax), generally among tributaries, but also found within‐tributary differences in Gull Creek  and between average maximum air temperature and CTmax . Cooler tributaries such as Augusta Creek displayed lower CTmax, while warmer tributaries such as Wabascon Creek had higher CTmax. We also found a significant, positive correlation between water temperature and average maximum air temperature for each site . Although we did not detect any variance in our model associated with year as a random effect, we resampled a subset of 2017 sites to determine whether yearly variation in CTmax existed. KFB was the only site to display significant differences in CTmax between years (t(21) = −3.4052, p = .003). Excluding resamples, a positive correlation still existed between site water temperature  and CTmax, and average maximum air temperature  and CTmax.In the site only model  and intrinsic (genetic diversity) factors point to the importance of characterizing intraspecific variation and suggest population‐level differences in vulnerability in a common, yet sensitive species.In the face of anthropogenic change and novel stress, populations may persist if they have high plasticity and/or the necessary genetic variation to facilitate evolutionary rescue  occupying both lakes and streams show marked differences in body size, life‐history traits, and other morphological characteristics  individuals have reduced body depth and superior mouth positions compared to stream darters, are consistent with other examples of intraspecific ecotypic divergences. For example, threespine stickleback  that occurs when individuals are faced with acute high temperature stress , a population found in a nuclear reactor effluent pond with near‐lethal temperatures had higher heterozygosity than surrounding natural populations  with recent histories of gene flow had higher CTmax compared with neighboring populations with strong genetic drift and no gene flow , experimentally bottlenecked populations having undergone declines in genetic diversity were less likely to evolve an adaptive response (increased CTmax) when compared to nonbottlenecked populations  and genetic (intrinsic) factors likely explains the described variation in CTmax with some degree of phenotypic expression likely due to plasticity. We found evidence for plastic responses in one site , where individuals sampled in 2018 had significantly higher CTmax than the previous year. Conversely, UAC, in the same tributary, did not display a plastic CTmax response across years despite temperature variability. This site also had the lowest genetic diversity and highest relatedness, suggesting a possible constraint on plastic responses. Although no study has directly tested the interplay between CTmax, genetic diversity, and plasticity in darters, seasonal variation in CTmax has been shown in darters across temporal ranges . Importantly, plastic mechanisms can also promote evolutionary change as well, through the accommodation or assimilation of traits in response to selection pressures (Kelly, max.When comparing models that incorporated effects of both local thermal regime and heterozygosity on CTs Kelly, . In ordeContemporary population structure can impact the spread of adaptive genetic variation through evolutionary processes such as gene flow or genetic drift Lande, . Despitemax suggestive of adaptive differentiation. At the same time, sites with lower genetic variation and increased relatedness appear to be more vulnerable to abrupt stress, suggesting that maintaining high genetic variation, possibly through gene flow, could be an important buffer against environmental change. Therefore, variation in dispersal capacity and gene flow could impact the spread of adaptive phenotypic and genetic variation that is critical for buffering against stress and enabling population persistence in this system.Taken together, results from our study highlight the importance of understanding how ecological and evolutionary processes interact to affect intraspecific variation, adaptive potential, and vulnerability to environmental stress. Most darter species, including rainbow darters, are comprised of locally patchy populations with limited dispersal Craig, . Fine‐scmax. Broadly, adaptive potential is consequential for populations to withstand novel stressors resulting from anthropogenic defaunation (Frankham, Our study adds to the body of literature highlighting the link between genome‐wide diversity and stress tolerance. Furthermore, this is to our knowledge the only study in a darter species to specifically link individual genome‐wide diversity to CTSupplementary MaterialClick here for additional data file.


Carex gayana, and then assessed whether genetic variation of candidate climate-selected loci better predicted their genome-wide responses than genetic variation of non-candidate loci. Contrary to this expectation, genomic responses of climate-linked loci only related significantly to environmental variables and genetic diversity of non-candidate loci. The effects of genome-wide genetic diversity detected in this study may be a result of either the combined influence of small effect variants or neutral and demographic factors altering the adaptive evolutionary potential of C. gayana populations. Regardless of the processes involved, our results redeem genome-wide genetic diversity as a potentially useful indicator of population adaptive evolutionary potential.Assessing population evolutionary potential has become a central tenet of conservation biology. Since adaptive responses require allelic variation at functional genes, consensus has grown that genetic variation at genes under selection is a better surrogate for adaptive evolutionary potential than neutral genetic diversity. Although consistent with prevailing theory, this argument lacks empirical support and ignores recent theoretical advances questioning the very concept of neutral genetic diversity. In this study, we quantified genome-wide responses of single nucleotide polymorphism loci linked to climatic factors over a strong latitudinal gradient in natural populations of the high Andean wetland plant,  Because genetic variation defines opportunities for adaptive evolution6, it is universally recognized as a crucial population characteristic indicative of adaptive potential9. Nevertheless, there is some debate as to whether traditional estimates of genetic variation are suitable for this purpose10. Many studies have resorted to neutral markers for their convenience8. However, neutral genetic diversity has increasingly been dismissed as a useful proxy for adaptive potential13, since it is not directly targeted by selection and covaries only weakly with quantitative genetic variation14.In the current context of unprecedented biodiversity loss16. For instance, successful adaptations to new selective pressures are more likely in large populations than in small ones; this is because large populations hold more absolute copies of potentially favorable alleles, have a lower risk of losing those alleles by drift, and have greater potential for mutation, including beneficial de novo mutations5. Gene flow can also either constrain or favor adaptation by causing fitness declines resulting from the introduction of maladapted individuals18 or, in contrast, by introducing new beneficial genetic variants20. Thus, in populations where neutral and demographic processes significantly alter adaptive evolutionary capacity, neutral genetic diversity could provide useful insights into adaptive potential. On the other hand, growing evidence indicates that neutral genetic diversity can be influenced by selection at linked loci23 and that a significant proportion of genetic variants within genomes, originally thought to be neutral, could in fact be under selection and functional as a result of the combined genome-wide influence of small effect variants26. Thus, genome-wide genetic diversity could also reflect adaptive processes and, as such, be a useful predictor of population adaptive potential.Dismissing traditional neutral genetic diversity estimates as useful indicators of adaptive potential both negates the possibility that neutral and demographic processes play a role in populations’ capacity to respond to selective pressures and assumes that neutral genetic diversity is well characterized and unimpacted by adaptive processes. Yet, neither of these assumptions is likely to be universally true. While adaptive variation is necessary for populations to adapt to new environmental conditions, there is no one-to-one relationship between adaptive genetic diversity and responses. Both neutral and demographic factors, such as population size and gene flow, can actually alter this relationshipCarex gayana as a model. A key challenge in these types of studies is how to quantify adaptive responses. Ideally, these should be assessed directly from genes under selection27, but such genes are in most cases unknown28. While this issue has been partially resolved thanks to large-scale next-generation sequencing, which allows candidate-selected loci29 with generally strong effects to be identified, tracking changes in allele frequencies driven by environmental factors is often impossible, particularly in natural populations. Here, we used multivariate analyses  of single nucleotide polymorphism (SNP) loci datasets to quantify divergence between candidate climate-selected loci and genome-wide non-candidate loci datasets. By applying this approach, we demonstrate that genome-wide genetic diversity of non-candidate loci is a more powerful predictor of evolutionary responses of climate-linked loci in C. gayana than genetic diversity of the loci used to measure these responses.We investigated this by analyzing the extent to which genomic responses of climate-linked loci relate to the genetic diversity of candidate climate-selected loci as well as genome-wide genetic diversity of non-candidate selected loci over a strong latitudinal gradient, using the high Andean wetland plant 30, we identified candidate climate-selected loci from the WorldClim database based on redundancy analysis (RDA), a genotype-environment association method31 linking genotypes at each locus to variables likely to exert selective pressures. The final RDA model used for this purpose (Supplementary Note on variable selection and detection of candidate climate-selected loci) included annual precipitation (BIO12), annual mean temperature (BIO01) and mean diurnal range (BIO02) as predictor variables. The model explained 14% of the total genetic variance  and identified three significant RDA axes  comprising 90 putatively adaptive loci notable for their outlier scores  of these loci did not correlate with those of the non-candidate loci .Using an original database of 1709 SNPs genotyped for 158 specimens by Pfeiffer et al.32 (CoIA), we projected the SNP profiles of the genotyped individuals for the candidate and non-candidate selected loci onto a common multivariate space. For these analyses, we used a dataset composed of 90 loci randomly selected from the full non-candidate loci dataset  to ensure a matching number of loci between the non-candidate and candidate selected loci datasets. The CoIA, which captured 80.2% of the total inertia of the non-candidate and candidate selected SNP matrices on its first three axes, characterized spatial genetic structure, showing that individuals from the same wetland, and sites from the same basin, tended to be grouped . This indicates that a large fraction of the genetic variation of the candidate and non-candidate selected loci did not co-vary. To ensure that this pattern was truly specific to these two datasets, we compared the observed RV correlation to a null distribution simulated by a bootstrap procedure from two datasets of 90 loci randomly selected from the full non-candidate loci dataset. None of the 10,000 bootstrap iterations produced an RV coefficient as low as that of the candidate and non-candidate selected SNP datasets . Candidate climate-selected loci data diverged less strongly from non-candidate ones in the Huasco, Elqui and Limarí basins relative to the northernmost (Copiapó) and southernmost (Choapa) basins . Effects were entirely independent, with mean annual precipitation explaining slightly more variation in population divergence than non-candidate loci genetic diversity  and genetic diversity of the candidate and non-candidate selected loci. The regression model revealed a significant effect of mean annual precipitation and of genetic diversity of the non-candidate loci. In contrast, no such effects were found with respect to genetic diversity of the candidate climate-selected loci Fig. c–e. Altoity Fig. .Figure 4C. gayana genomic responses of climate-linked loci than genetic variation at the loci used to measure this response. This suggests that genome-wide genetic variation may be an important indicator of population adaptive potential, which is also supported by a recent laboratory study demonstrating that it favored evolutionary responses of fitness-related traits in the vinegar fly22.Our results demonstrate that genome-wide genetic diversity was more informative in predicting 26. Indeed, a growing body of evidence indicates that natural selection can impact genetic diversity through its effects on linked neutral diversity34. In addition, it has recently been suggested that genetic variants with small individual effects along the genome, originally considered to be selectively neutral, may in fact be functional as a result of their combined effects26. Traits important in plant responses to climate change are often controlled by genetic regulatory networks involving complex signaling pathways and the effects of several genes 36, which suggests that many genetic variants with small effects on adaptive responses to climatic variables could be spread along the genome of C. gayana. Since genetic-environmental methods only detect loci with major effects on adaptation37, there is a high likelihood that a large number of variants with small individual effects on climate-selected traits were included in the non-candidate climate-loci dataset, and could have contributed to genomic responses of climate-linked loci. Nevertheless, this does not explain why these responses were better predicted by genome-wide genetic diversity than genetic variation at the loci used to measure them, given that an equal number of loci between the two datasets was used.The effects of genome-wide genetic diversity detected in this study may have resulted from the persistence of loci relevant to climate adaptation in the non-candidate loci datasets. Recent theoretical and empirical advances clearly question the definition of neutral genetic diversity, and point towards a larger role of natural selection in genome-wide genetic diversity levelsC. gayana could have limited genomic responses of climate-linked loci and could therefore also have contributed to the apparent association of the latter with genome-wide genetic diversity. Since they fuel all genetic variation  and can cause its loss4, neutral and demographic processes determine the availability of and population capacity to produce new genetic material for selection to act upon. In addition, they can also limit evolutionary adaptive responses even in the presence of substantial adaptive genetic variation38, which may occur in the event of demographic constraints38 linked, for instance, to small population size, or where gene flow is extensive and outweighs selection effects40. In discrete ecosystems such as high Andean wetlands, neutral processes can strongly impact species and genetic diversity due to greater potential for drift, demographic stochasticity and limited opportunity for dispersal41. The importance of neutral processes on genome-wide diversity of C. gayana is supported by previous evidence demonstrating strong effects of wetland isolation on this genetic component42. Here, we found that high and low precipitation levels were driving genomic responses of climate-linked loci of relatively similar amplitude in the north and south of the study region. Yet, genomic responses of candidate climate-selected loci were more restricted in northernmost wetlands where C. gayana populations displayed lower levels of genome-wide genetic diversity. This suggests that demographic constraints could be limiting adaptive potential of this species in the northern sites, which tend to be both smaller and more isolated than the southernmost wetlands42.Constraints imposed by neutral processes on the evolutionary adaptive potential of C. gayana populations at climate-selected loci are not solely conditioned by the amount of adaptive genetic variation at loci used to measure this response. As mentioned earlier, there is a high probability that a large number of variants contributing to genomic responses of climate-linked loci  were absent from the candidate climate-loci dataset. In addition, adaptive genetic diversity may not be very informative when de novo mutations make up a substantial part of the evolutionary response, as may be the case in later phases of the adaptive process16. Finally, even when adaptation from standing genetic variation dominates, high genetic variation at adaptive loci does not guarantee that variation does exist in the direction of selection and thus may not necessarily facilitate evolutionary adaptive responses. In sum, adaptive genetic diversity may also fail to capture the adaptive potential of populations. Of course, our results do not rule out the utility of adaptive genetic diversity to predict the ability for populations to adapt in situ, nor did they demonstrate the superiority of genome-wide genetic diversity to do so. However, they indicate that this diversity component could be relevant and should not to be discarded. Far more research is needed to understand the circumstances under which genome-wide genetic diversity can be expected to be a reliable predictor of a populations’ adaptive potential.The poor performance of genetic diversity at candidate climate-selected loci in predicting genomic responses may be attributable to several factors. Firstly, genetic diversity estimates from contemporary samples may inadequately represent levels of genetic variation available at the climate-selected loci prior to selection. Alternatively, this result may indicate that adaptive responses of 30, produced by genotyping-by-sequencing43 and consisting of 1709 SNPs filtered and genotyped for 158 specimens of the plant C. gayana (Cyperaceae) distributed among 17 high Andean wetlands in Chile´s Norte Chico region . This region is characterized by a large precipitation gradient increasing with latitude, with climatic conditions varying from Mediterranean to hyperarid. Sample size per wetland ranged from 4 to 10 C. gayana individuals. Detailed information regarding the study area, study sites, species biology and sampling, DNA extraction and SNP data production can be found elsewhere44. A map of the study area is provided in Supplementary Fig. We used the same SNP dataset used by Pfeiffer et al.https://cran.r-project.org). We performed redundancy analyses (RDA) to detect putatively selected loci based on the correlation between individual genotypes and climatic variables potentially acting as selective factors. Prior to this analysis, we used a clustering of variables around latent variables (CLV)45 to reduce the initial set of 21 predictor variables, composed of longitude, latitude and 19 standard WorldClim bioclimatic variables (BIO1-19) at 30 s spatial resolution, downloaded from WorldClim version 246 (available at https://worldclim.org/version2). We followed the recommendations of Vigneau et al.47 in determining the number of clusters, then selected the variable most representative of each  to check for multicollinearity problems considering a maximum value of 10. Significance of the RDA model and each individual RDA axis was tested using ANOVA-like permutation tests with 9999 randomizations. We identified outlier loci on each significant axis with a cut-off of 2.5 SD around the mean. Based on these results, we separated the SNPs into candidate climate-selected and non-candidate loci datasets, composed of outlier loci associated with climate variables (i.e. 90) and loci not identified as outliers according to multiple detection methods (i.e. 1421), respectively. In order to remove as many loci with potentially large effects as possible, the non-candidate loci dataset was constructed by eliminating the 229 outliers detected by Pfeiffer et al.30, 17 monomorphic loci, and 42 of the 90 climate-selected loci that were unique to the present study, from the 1709 original loci. However, to ensure adequate comparison of variation patterns of candidate climate-selected and non-candidate loci using a matching number of loci, we created a non-candidate dataset by randomly selecting 90 loci out of the 1421 non-candidate loci. This dataset was used in all subsequent analyses. Note that all analyses were also performed using the full non-candidate loci dataset. Patterns detected with the 90 and 1421 non-candidate selected loci were always consistent  from two data matrices, in this instance the non-candidate and candidate climate-selected loci datasets, along successive canonical axes. The CoIA axes are calculated such that congruence between the two tables is maximized, approximated by their squared covariance (i.e. inertia). CoIA first requires separate ordinations of the two matrices, which we performed by subjecting the non-candidate and candidate climate-selected datasets to principal component analyses. The number of principal components that we retained accounted for 90% of the total variation of each dataset. To test the significance of the correlation between the genetic variation of the non-candidate and candidate climate-selected loci datasets, we calculated the RV coefficient, a multivariate generalization of the Pearson correlation coefficient33, and tested its significance based on 9999 permutations. Both the principal component and CoIA analyses were performed using the ade4 package49. The projection of the genetic profiles of the individuals onto the co-inertia space allows common genetic patterns between the non-candidate and the candidate climate-selected data to be visualized. The distance between the non-candidate and candidate climate-selected dataset in the CoIA space reflects the effects of processes causing the candidate and non-candidate selected loci datasets to diverge. Thus, to quantify the genome-wide signature of these processes, we computed the Euclidean distances between their CoIA scores on the first seven canonical axes, which together represented 94.7% of the co-inertia. We analyzed whether the divergence levels varied geographically by applying a linear model to test for the effects of basins and sites nested within basins.Genetic differentiation between wetlands was assessed through various traditional approaches (Supplementary Note on population genetic structure). In addition, we investigated the genetic structure common to both the non-candidate and candidate climate-selected SNP data by carrying out co-inertia analyses (CoIA)50 as the expected heterozygosity (He) calculated for each SNP dataset (non-candidate and candidate selected loci, Supplementary Table 30. We used the leaps package51 to identify the optimal subset of bioclimatic variables for use in detecting candidate climate-selected loci. We considered that these variables can have both linear as well as quadratic effects. The best model was selected based on a performance analysis returned by leaps using the AICcmodavg package52. To estimate the contribution of each of the predictors found to be significant, we performed a variation partition using vegan’s varpart function48. Before running these analyses, we ensured that there was no effect of the number of genotyped individuals, as this may act as a possible confounding factor.We searched for factors linked to population divergence between the non-candidate and candidate climate-selected loci datasets using linear models. To assess divergence between the two datasets at the population level, we averaged divergence estimates of all individuals belonging to the same wetland. The set of explanatory variables included climatic variables potentially acting as selective factors and genetic diversity of candidate and non-candidate selected loci, estimated using gstudioSupplementary information.


Tritia neritea, using Bayesian skyline plots (BSP), multivariate analyses and Bayesian clustering. The BSP framework revealed population expansions, dated after the last glacial maximum. The haplotype network revealed a strong geographic clustering. Multivariate analyses and Bayesian clustering highlighted the strong genetic structure at all scales, between the Black Sea and the Adriatic Sea, but also within basins. Within basins, a random pattern of genetic patchiness was observed, suggesting a superimposition of processes involving natural biological effects  and putative anthropogenic transport of specimens. Contrary to the introduced area, no isolation-by-distance patterns were recovered in the Mediterranean or the Black Seas, highlighting different mechanisms at play on both native and introduced areas, triggering unknown consequences for species’ evolutionary trajectories. These results of Tritia neritea populations on its native range highlight a mixture of ancient and recent processes, with the effects of paleoclimates and life history traits likely tangled with the effects of human-mediated dispersal.To better predict population evolution of invasive species in introduced areas it is critical to identify and understand the mechanisms driving genetic diversity and structure in their native range. Here, we combined analyses of the mitochondrial COI gene and 11 microsatellite markers to investigate both past demographic history and contemporaneous genetic structure in the native area of the gastropod  However, it is always difficult to disentangle the natural effects of life history traits and historical events (such as past climate changes) over species geographic ranges from recent human-related range expansion5. This is even more true for species whose introduced area is close to the native range, and only genetic tools can discriminate between a natural spread or a human-mediated introduction9. Life history traits are considered to be major drivers of population genetic diversity and structure10. In particular, species with direct development (no planktonic stage) have reduced potential for dispersal and usually show strong genetic structure13. The effects of past climate changes on population genetic structure and distribution have also been extensively reported15. For instance, sea-level changes in the Pleistocene often led to population fragmentations thus creating a dynamic of expansion/contraction of populations over time16. In the last decades, cases of human-related range expansion have increasingly been reported and the natural patterns of biodiversity have been altered by artificial translocation patterns19. The superimposition of these various evolutionary processes makes it difficult to evaluate the potential outcome of the future of introduced species.The description of mechanisms driving genetic diversity and structure of invasive species in their native range has proven to be of considerable use to predict further evolution in their introduced areasTritia neritea , a scavenging nassariid gastropod, is a direct developer, with juveniles hatching as crawl-aways from single-embryo capsules attached to hard substrates21. This species was long known in the literature as Cyclope neritea, but the former genus Cyclope was recently moved to Tritia based on genetic results22. Two living species are currently recognized in the study region25, i.e. T. neritea  and T. pellucida . Tritia neritea is euryhaline, found in lagoonal and river-influenced sandy and muddy bottoms26, which results in discontinuities of populations, with high densities in lagoons, bays, estuaries30. Its native range spreads across the Mediterranean, Black and Azov Seas to the adjacent eastern Atlantic coasts of Morocco and south Iberian Peninsula35. Although presenting a patchy distribution as well36, the case of the Black Sea is somewhat different given that its average salinity ranges between 18 and 22 psu, suitable to euryhaline species such as T. neritea.37, Tritia neritea originates from an early Pliocene ancestor identified as Cyclope migliorinii . Because of the basin-wide geographic range of T. migliorinii, it is impossible to formulate a robust hypothesis about the center of origin of T. neritea. Tritia migliorinii was characterized by a planktotrophic development and went extinct in the Pleistocene whereas T. neritea, lacking a larval phase, survived37. Such Pliocene-planktotrophic/Recent-non-planktotrophic pairs are common among gastropods of the Mediterranean while the reason for such trend is not yet clearly understood38. Climate changes strongly influenced the evolutionary and biogeographic patterns of marine organisms, with sea-level fluctuations triggering isolation of populations. In particular, for the Mediterranean Sea, Plio-Pleistocene sea-level changes often led to population fragmentations shaping species distributions and population genetic structures of many fish or marine invertebrates42. However, in the specific case of euryhaline gastropods, such as T. neritea, this process is not so obvious. On one hand, the populations restricted to lagoons are, in theory, exposed to habitat loss during sea-level changes, thus compromising population survival. In contrast, populations that inhabit coastal nearshore bottoms may prove to be more resilient to disconnection of their habitat during sea-level fluctuations.The evolutionary history of the species and genus is complex. According to Gili & MartinellTritia neritea clearly depicts a rather complex situation with respect to population connectivity through space and time. Furthermore, starting from the 1970s, T. neritea was recorded further North, in Galicia43, in the Bay of Biscay45 and up to the English Channel46. Mitochondrial sequence data demonstrated the introduction in these areas47 . However, the history of introduction seems to be complex, with multiple independent introduction events from several sources. For instance, the North Adriatic region was proposed as a source for the North Western Iberian Peninsula invasion with the Manila clam, Ruditapes philippinarum, trade as the likely vector47, whereas the North Western Mediterranean and Portuguese areas were pointed as sources for the Bay of Biscay’s invasive population with the oyster trade as vector45. These and other studies on T. neritea focused on the introduced areas and included only one to few localities within the native range. Only Simon-Bouhet et al.46 included multiple localities in the native range, however, they only analyzed mitochondrial markers. While microsatellite markers were developed48, they have never been used to further our collective understanding of the population genetic structure of T. neritea. To add further complexity to such an intricate distribution, some populations are thought to represent recent introduction events within the ‘native’ range, such is likely the case for the Gulf of Gabès49. Consequently, it may prove very challenging to track unequivocally clear dispersal pathways  for this species, and a better understanding of the population structure in the native area is critical.The natural history background of Tritia neritea within its native range throughout the Mediterranean and the Black Seas . Noticeably, like most of the sequences from the west Mediterranean Sea already published (263 specimens), our 23 sequences from Fusaro lake and Cape Bon (Tunis) correspond to haplotype H1.A 524 bp portion of the COI gene was sequenced for 128 specimens, resulting in 20 haplotypes; 12 of which are new  ranged from 0.356 to 0.567 and mean allele numbers ranged from 5.09 to 11.55 . Using the sampling localities as prior (ocean basin) did not change the results (data not shown). Noticeably, the Western Mediterranean and the Aegean Sea samples clustered with samples from the Adriatic Sea and samples from the Black Sea form another cluster  with samples from the Aegean Sea  and samples from the Adriatic Sea Fig. . Samplester Fig. . The samT. neritea in the Adriatic Sea, all the Fst values were significant, except for the Venice-Ancona pairwise comparison. The Structure plot revealed the most likely partitioning to be of K = 2 groups  Costinesti2, Kaliakra1, Kaliakra2, CostinestiB; (ii) CostinestiA, Sile and Karadag, Tarkhankut; such as also separated on the PCoA  and the Black Sea  basins.For ups Fig. B correspups Fig. . For T. CoA Fig. . FurtherA total of 82 fullsib and 206 halfsib relationships were recovered among the 786 multilocus genotypes Table . Most ofAs expected for species characterized by a direct cycle without a pelagic larval stage, we observed genetically divergent populations even at local scales within small seas such as the Adriatic Sea or the Black Sea. The close investigation of this species within its native range demonstrates complex diversity patterns, genetic structure and historical changes that improve our understanding of patterns in the current and future introduced areas.55. Five mitochondrial clades correspond to marine ecoregions as defined in56: (i) South European Atlantic shelf and Western Mediterranean, (ii) Adriatic Sea, (iii) Aegean Sea and (iv) the Black Sea and (v) the 5th one is composed of specimens from both the Adriatic Sea and the Western Mediterranean Sea (Gulf of Lion). Noticeably, a group of haplotypes lies in the middle of the network, but was only recorded in the invaded area (North-Eastern Atlantic) while their source in the Mediterranean Sea remains unknown54. This group likely comes from an unsampled area of the native range (such as the South Eastern Mediterranean and Far Eastern Mediterranean basins) and its central position points toward an ancestral nature. As previously mentioned, if T. neritea originates from the widespread T. migliorinii, it is difficult to formulate a robust hypothesis about its center of origin. However, Gili & Martinell37 postulated a derivation of ‘Cyclope’ from a Nassarius (Plicarcularia) group containing two extant Mediterranean species (Tritia gibbosula and T. circumcincta), with a Levantine/southern Mediterranean distribution. Additionally, based on an east–west genetic diversity gradient, Simon-Bouhet46 hypothesized an eastern origin for T. neritea. Accordingly, our sample from the Aegean Sea demonstrates a high number of alleles and a high number of private alleles at nuclear loci. Further sampling on the South Eastern and Far Eastern Mediterranean coasts is warranted to locate and clarify the origin of these so far unsampled native haplotypes.The mitochondrial dataset confirms a strong geographic clustering of haplotypes Fig. , as alre46 hypothesized the persistence of T. neritea populations in several distinct refugia during glacial periods. Several refugia have been well documented in the Atlanto-Mediterranean basin57 corresponding to the Iberian Peninsula, the Western Mediterranean, the Eastern Mediterranean, etc. Based upon its current geographic distribution, T. neritea appears to be a temperate marine taxon. It might have responded to the lowered sea-surface temperature at glacial times, which favored the basinal spread of boreo-celtic species58, by shrinking its geographic range in the Mediterranean Sea to milder sectors of the basin46, e.g. the Levantine region. It is thus conceivable that we had geographically-distant stocks at the peak of the last glacial period in a disconnected mosaic-like situation. The post-glacial warming may have allowed for the progressive recolonization of sectors of the basin, resulting in the present pan-Mediterranean distribution. The high private allele number in Amvrakikos and Karaburun supports this hypothesis. Specimens from a refugium located in the Ionian Sea might have recolonized the Adriatic Sea, as seen in the decapod Carcinus aestuarii59. Likewise, the high private allele number at the Aegean location suggests a refugial area.Whatever the origin of the species, the five mitochondrial lineages corresponding to distinct marine basins were likely differentiated during paleoclimate sea-level fluctuations. Simon-BouhetT. neritea. Furthermore, the Bayesian skyline plots revealed population expansions in both seas when enough sequences are available  completely secluded from the Mediterranean Sea64. This fact rules out any chance that any marine refuges existed  within the Black Sea during the last glaciation, a theoretical claim put forth for other organisms but not applicable to T. neritea. In agreement with this age, fossil evidence from the Kerch Strait supports the occurrence of T. neritea in the Late Holocene65, thus setting a tempo for the Black Sea population presence in the basin. The settling of the modern Black Sea by T. neritea is thus a geologically-young phenomenon  that took place from the Aegean Sea via the Marmara Sea. Interestingly, the expansion of population for the Black Sea is dated at 5000–10,000 ya, which would agree with the timeframe of the recolonization of this sea proposed in the literature.Therefore, our dataset seems consistent with the hypothesis that past climate changes impacted the genetic structure of ble Fig. . InteresTritia neritea in its native range  or the broadcast spawner C. fornicata69 (0.806 < He < 0.838). Additionally, all the Fis values are high and significant, demonstrating a heterozygote deficiency in every populations. Such high values are not surprising for a species with a lack of planktonic larval phase, thus prone to inbreeding50 but were also detected for the mussel Mytilus galloprovincialis, a species with long-lived pelagic larvae70. Heterozygote deficiency is a common phenomenon in marine molluscs71. Some of the proposed explanations to this phenomenon are null alleles, local inbreeding, selection against heterozygotes and life history traits that produce spatial or temporal population substructure leading to a Wahlund effects73. Parentage analysis , as previously noticed for several species of fish77. Only few studies have investigated population genetics for species distributed both in the Black Sea and the rest of the Mediterranean Sea, however a genetic differentiation appears to be the trend for the fish populations studied so far81. Furthermore, the mussel Mytilus galloprovincialis also showed strong genetic differentiation between its populations of the Black Sea and the rest of the Mediterranean Sea70. Some explanations for these differentiations can be found in the oceanographic currents, the presence of narrow straits (e.g. Bosphorus) and specific environmental conditions  of the Black Sea81. Additionally, the Otranto Strait and the Aegean Front have been demonstrated to influence population genetic structures, however to a lesser extent for species without long pelagic larval phases compared to species with long pelagic larval phases82. Noticeably, the relatedness of individuals within basins could also trigger some proportion of the differentiation detected, as family groups are known to influence the inferences made with the program Structure84. However, recent literature advises against purging related individuals in population genetics85, since the removal of individuals also reduces precision of genetic estimates. This holds particularly true when errors in pedigree reconstructions are expected, as in our case due to the limited number of loci. Finally, discrepancies between the Atlantic and the Mediterranean population structures also exist. In the Mediterranean, we did not find a signal of isolation-by-distance, contrary to what was shown in the Atlantic population of T. neritea using the COI mitochondrial gene55 . This discrepancy might be linked to the very distinct mechanisms at play during introductions on new areas and natural evolutionary history on the native areas. The introduced area was likely home to a linear spread along the Atlantic shore over a short period of time, with founding events typical of colonization of new areas47, whereas in the native area, the strong genetic structure and chaotic pattern are likely due to rare long-distance dispersal events in the Mediterranean Sea over evolutionary periods and relicts of glacial disconnected refuges.Regarding genetic differentiation between basins, the pairwise F83, suggesting that these results should be taken with caution. The Adriatic Sea did not show an east/west genetic differentiation, contrary to a recent study on the scorpionfish, Scorpaena porcus, sampled at the same localities53. Here, a north/south differentiation seems to arise, however, not perfect, as the Otranto sample clusters with the northern localities and the Amvrakikos sample comprises individuals with genetic make-up of the northern cluster. Within the Black Sea, spatially close samples such as Costinesti A and B or Kaliakra 1 and 2 also show genetic differentiation. Life history traits, such as local vs. broadcast dispersal, are known to have strong effects on genetic structure11 and the strong genetic differentiation at local scales is thus not surprising for T. neritea, a species without a dispersal stage. The Polyplacophora Lepidopleurus cajetanus, with a low potential for dispersal (lecithotrophic larvae) also exhibited a ‘chaotic patchiness’ pattern in the Mediterranean basin defined by a high genetic variability with locality-exclusive haplotypes, high genetic divergence, and a lack of geographic structure86. The term “chaotic genetic patchiness” was defined for species with a potential for dispersal and that show strong genetic structure at scales smaller than their dispersal potential and where “instantaneous drift” between cohorts is common87. Tritia neritea has no larval dispersal phase but seems to be especially prone to demonstrate such “random” genetic patchiness. The mechanisms underlying this pattern remain unknown but are likely a mixture of ‘natural’ and ‘artificial’ processes. For direct developers, rafting on floating objects has been proposed as a surrogate89, and would likely be sporadic, not recurrent, and not linked to geography, thus responsible for creating this kind of genetic patchiness. Oyster culture is also a vector for the introduction of Tritia neritea on the Atlantic coast45 and populations could be moved similarly within the native range (see below).Within basins, strong genetic structures were also detectable, however they were “unpatterned” or “random” with no clear geographic clustering. Importantly, the half-sib and full-sib relationships detected within and among localities might increase the number of detected clusters54, based on mito-nuclear discordance raised the possibility of the introduced nature of a population at Mar Menor (Western Mediterranean Sea) and Aissaoui et al.49 documented a recent introduction event in the Gulf of Gabès (Tunisia), both populations are located in the native area. Likewise, the mixed genetic make-up of our sample from Sile, might be due to recent introductions of Mediterranean specimens into the Black Sea. Sile has a marina for yachts and small to medium sized fishing boats, which make seasonal trips to the Marmara and Aegean Seas. The discrepancy between nuclear and mitochondrial markers could be explained by mitochondrial capture (i.e. complete introgression), a phenomenon known in many organisms92 and mitochondrial introgression has been reported for mollusks94. Alternatively, if only male specimens were introduced, the nuclear dataset would be of foreign origin while the mitochondrial marker would remain from the geographic location sampled. However, no data on sex ratio is available for T. neritea to support this second hypothesis. Further study is warranted to verify if this population shows any effects of admixture (i.e. mixing of specimens from different genetic clusters), such as hybrid vigor. Rius & Darling95 recently highlighted the unknown evolutionary trajectory for such admixed populations. Human activities are responsible for the translocation of vast amounts of organisms, altering natural patterns of dispersal and gene flow. Anthropogenic effects on the native area have been demonstrated in the ascidian Ciona intestinalis that displayed genetic homogeneity among both close and distant sites and dissimilar genetic composition between close sites5. Similarly, a recent study on hydroids revealed contrasted patterns of strong genetic structure on local and regional scales contrasting with some haplotypes shared among ocean basins, revealing the likely effect of human-mediated transport96.Samples from Sile demonstrated a mixed genetic make-up at the nuclear loci compared to the remaining of the Black Sea populations. The mitochondrial sequences, however, belong to the Black Sea haplogroup. Similarly, Simon-BouhetTritia neritea on its native range seem to be a mixture of recent and ancient processes, with the effects of paleoclimates and life history traits likely tangled with the effects of human-mediated dispersal. As anthropogenic pressure grows, it is going to be more and more difficult to disentangle the natural and artificial patterns of biodiversity.The patterns seen from our samples of 97, a total of 786 specimens from 17 localities were analyzed  and 6 localities from the Black Sea, see Fig. In this study, through the application of the sampling scheme of the CoCoNet project45. PCR mixture and cycling parameters were as given in this publication. The 42 haplotypes available from GenBank55 together with their frequencies were added to this newly generated dataset in order to place our samples in a broader context. Notably, a group of these haplotypes lies in the middle of the haplotype network, but have been only recorded in the invaded area and their source remains unknown54.A portion of the Cytochrome oxidase subunit 1 (COI) was amplified for a subset of 128 specimens including all locations, using the specific primers Cy2 5′-GTTAAAATTTCGATCTGTTA-3′ and CyR 5′-GGATTAGTTGGTACAGC-3′48 and 7 come from a new microsatellite library  generated specifically for this study  using the Median Joining algorithm100. Mr AIC101 was used to determine the best fit model of nucleotide substitutions. Changes in population sizes were investigated using the Bayesian skyline plot (BSP) framework in Beast 2102. As the assumptions of this analysis framework are that the sequences represent a small sample from a haploid population evolving under Wright–Fisher dynamics, the BSPs were computed for each basin separately (Adriatic Sea / Black Sea) in order to try and work on samples that are closer to a single panmictic population. A total of 10,000,000 generations sampled every 100th generation were run. As no mutation rate is calibrated for Tritia neritea, a mean mutation rate ranging from 0.012 to 0.016 (substitutions per site per My), used for the COI gene in Protostomia103 and in limpets104 was applied.COI sequences were aligned using Mmicro-checker105. As null alleles are common in mollusks106, we decided to discard the three microsatellite markers that presented an estimated frequency of null alleles above 0.10. Further analyses were run on the dataset with 11 loci. Summary statistics were computed in Genetix v.4.05.02107: the mean number of alleles (A), the expected (He) and observed (Ho) heterozygosities, the inbreeding coefficient (Fis) and the fixation index (Fst). The correction for multiple comparisons of Benjamini & Hochberg108 was applied to the Fstp values. A Principal Coordinate Analysis (PCoA) was computed in Genalex v6.5109. The most likely number of clusters present in our dataset was estimated using Structure v2.3110. This analysis was run both with and without prior on the location. After several first runs, the parameters were set as follows: a burn-in period of 100,000 iterations followed by 500,000 recorded iterations for K = 1 to K = 8 clusters and 15 iterations per K values. The most probable number of clusters present in this dataset was determined using the Evanno’s ΔK approach111 using Structure Harvester online112. Finally, a pattern of Isolation-by-distance was tested by a Mantel test in Genetix using the genetic distance [Fst/(1 − Fst)]113 and the log coastal geographical distance (in km) between each pair of localities within basins. Significance was obtained using a random permutation procedure implemented in Genetix . Finally, a parentage analysis was run using the software Colony114. Even if the number of loci is low for this kind of analyses84, and given that heterozygote deficiency is often detected in mollusk population genetic analyses71, we wanted to verify any sign of family structure in our dataset. This relatedness analysis was performed on the whole dataset including the 786 multilocus genotypes and with the following input parameters: a mating system with female and male polygamy, with possible inbreeding and without clones. The method was composed of 3 runs set as ‘long’, with a full likelihood method and a likelihood precision that was set to ‘high’.The presence of null alleles, scoring errors or large allele drop-out were verified using Supplementary Table 1.Supplementary Table 2.Supplementary Figure 1.Supplementary Figure 2.