Perna perna and the invasive Mytilus galloprovincialis. Genetic structure was found within P. perna, which, together with evidence from Lagrangian particle simulations, points to significant reductions in gene flow between sites. Despite this, the expected diversity pattern between centre and edge populations was not found for either species. We conclude that the genetic predictions of the Abundant-Centre Hypothesis are unlikely to be met by high-dispersal species with large population sizes, and may only become evident in species with much lower levels of connectivity.The ‘Abundant-Centre Hypothesis’ is a well-established but controversial hypothesis stating that the abundance of a species is highest at the centre of its range and decreases towards the edges, where conditions are unfavourable. As genetic diversity depends on population size, edge populations are expected to show lower intra-population genetic diversity than core populations, while showing high inter-population genetic divergence. Here, the genetic implications of the Abundant-Centre Hypothesis were tested on two coastal mussels from South Africa that disperse by means of planktonic larvae, the native  Both biotic and abiotic factors determine the success of a species within its realised niche2, and it is expected that population abundance, reproductive output and recruitment will decline towards the periphery of its distribution, due to the less favourable conditions prevailing there3. Patterns of population genetic structure across a species’ range are, in turn, expected to be influenced by these ecological effects. Because genetic diversity depends on population size, it is predicted that populations at the periphery of the species’ range will show lower intra-population diversity and higher inter-population divergence when compared to centre populations4. These differences are attributed to reduced gene flow, bottleneck effects, habitat fragmentation and random genetic drift in smaller populations5. The reduced diversity and increased genetic differentiation of edge populations may translate into lower adaptive potential particularly at the periphery of a species’ range7, which suggests that maladaptation may cause these populations to be demographic sinks.The Abundant-Centre Hypothesis (ACH) states that species perform better at the centre of their range, where high densities are observed, and that densities decline towards the range edges8. Similarly, another review assessing population genetic structure across the geographical ranges of 115 species of plants and animals showed that, on average, only 64% of studies detected a decline in genetic diversity towards the range margins, although there was a geographic bias as the majority of studies concerned the northern range limits of northern hemisphere species9. Taken together, these studies show that species abundance, demography and genetic diversity appear to be more variable than predicted by the ACH, as they are determined by the interaction of multiple fluctuating contemporary environmental drivers plus historical effects10.Several recent studies have raised doubts about the generality of these demographic and genetic expectations. A review found that there is limited empirical evidence for the ACH, with support from only 39% of the reviewed studies, which covered a wide range of genetic and demographic predictions derived from the hypothesis12, making common rocky shore fauna particularly suitable for studying the ACH. We tested the predictions of the ACH for the dominant rocky shore mussels in this region11, the invasive Mytilus galloprovincialis and the native Perna perna. The latter is represented by two distinct regional genetic lineages12, effectively providing edge and centre populations for three model organisms with similar biology. We hypothesised that centre populations of the three mussels would have greater genetic diversity than edge populations and tested this by comparing genetic diversity for the two population types.Along the South African coastline, rocky shores are a very common habitat type. They constitute nearly 60% of the coastline and span four major biogeographic regionsPerna perna has a wide range from the warm-temperate south coast of South Africa to the tropical coastline of southern Mozambique on the east coast11. Due to the cold upwelled water of the Benguela Current on the west coast, P. perna does not occur west of the Cape of Good Hope, but it re-emerges in Namibia, and its range stretches along the west coast of Africa all the way to the Mediterranean Sea13. Previous genetic work using mitochondrial DNA (mtDNA) sequences and microsatellite loci of P. perna identified a phylogeographic break on the south-east coast of South Africa, where the ranges of a western and an eastern lineage overlap along 200 km of coastline, from approximately Port Alfred to Haga Haga15  onto mussel beds. Site M3  and a suite of microsatellites loci. Only microsatellites were assessed for the invasive M. galloprovincialis since its COI diversity is very low14. Moreover, mitochondrial DNA evolves comparatively slowly and would only reveal historical demographic information from the species’ native European habitat14.Genetic diversity in P. perna and M. galloprovincialis were collected from edge and centre populations as shown in Fig. Fifty adult mussels (shell length > 4 cm) were collected from each site in November 2014, and mantle tissue weighing 20–30 mg was dissected from each mussel, preserved in 100% ethanol and stored at –20 °C. Specimens of 20. From each specimen, a piece of tissue the size of a match head was excised and placed into a labelled 1.5 ml Eppendorf tube and left to dry. One ml of CTAB buffer  and 10 µl of Proteinase K were then added. The tubes were kept at 55 °C for three hours (or at 37 °C overnight) and occasionally shaken until tissues had dissolved completely, whereupon 500 µl of a 1:24 isoamylalcohol-chloroform mixture was added, and tubes shaken vigorously for 30 seconds. The tubes were then centrifuged at 13 000 rpm for 5 min, whereafter 500 µl of the supernatant was transferred to newly labelled 1.5 ml Eppendorf tubes. Next, 500 µl of isopropanol was added to the tubes, shaken, and the mixture was centrifuged for 10 min at 13 000 rpm. Tubes were then decanted, and DNA pellets washed twice with 700 µl of 70% ethanol, and centrifuged at 13 000 rpm for 10 minutes. All liquid was removed from the tubes, and DNA pellets were left to dry completely at room temperature for 30 min. 20 µl of dilute TE (Tris-EDTA) buffer  was added to each Eppendorf tube, vortexed and stored in the freezer at −20 °C.Whole genomic DNA was extracted using the CTAB method21. PCR reactions were performed in reaction volumes of 20 µl containing the following reagents: 2 µl of DNA template, 1.2 µl of 25 mM MgCl2 and 2 µl of 10x PCR buffer (Promega), 0.64 µl of 1 mM dNTP mixture (Sigma-Aldrich), 0.5 µl of forward and reverse primers (10 mM), 0.24 µl of BSA, 0.16 µl of Super-Therm Taq polymerase , and 12.7 µl of distilled water. PCR amplifications were performed on a Multigene Optimax Thermal Cycler (Labnet) using the following profile: an initial denaturation step (94 °C for 2 min), 40 cycles of denaturation (94 °C for 30 sec), annealing (50 °C for 40 sec), and extension (72 °C for 1 min), and a final extension step (72 °C for 10 min). Gel electrophoresis was performed to confirm DNA amplification. PCR products were then purified with the Wizard SV Gel and PCR Clean-Up System (Promega), cycle sequenced both in the forward and reverse directions using Big Dye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems), and visualized on an ABI 3730 Genetic Analyser.The mtDNA was amplified with the universal primers LCO1490 (5′-GGT CAA CAA ATC ATA AAG ATA TTGG -3′) and HCO2198 (5′-TAA ACT TCA GGG TGA CCA AAA AAT CA-3′)P. perna and M. galloprovincialis was carried out using a set of 10 and nine loci, respectively, with markers previously reported to conform to the expectations of Linkage Disequilibrium and Hardy-Weinberg equilibrium , 2 µl 1 µM dNTP mixture (Sigma-Aldrich), 0.4 µl of forward and reverse primers (10 mM), as well as 0.6 µl of universal fluorescently labelled M13 primer, 0.24 µl of BSA, 8.7 µl of deionised water, and 0.16 µl of Super-Therm Taq polymerase . The PCR amplification of P. perna was performed on a GeneAmp 9700 thermocycler (PE Applied Biosystems) under the following conditions: an initial denaturation step (95 °C for 5 min), 35 cycles of denaturation (95 °C for 30 sec), annealing at a primer-specific annealing temperature , 30 cycles of denaturation (92 °C for 1 min), annealing at a primer-specific annealing temperature , with GeneScan Liz 500 (Applied Biosystems) as size standard.A post-PCR multiplexing technique was employed for both species, where loci were amplified separately and then pooled for genotyping, as previously described for P. perna were aligned and edited in MEGA723. For the microsatellite data, GENEIOUS microsatellite plug-in 1.424 was used to analyse, score and bin alleles. Two researchers confirmed the scoring of alleles independently. The quality of the microsatellite data was assessed by exploring the occurrence of null alleles and by calculating departures from Linkage Disequilibrium (LD) and Hardy-Weinberg equilibrium (HWE). The proportion of null alleles per locus was calculated in FreeNA25, while LD and HWE were calculated in Arlequin v3.5.2.226. Likelihood ratio tests for LD between all pairs of loci were calculated for each sampling location, with 1000 permutations to test for significance, two initial conditions for the expectation-maximisation algorithm, and Bonferroni correction27 for multiple tests. Exact tests for HWE were conducted by specifying 106 steps in the Markov chain and 105 dememorisation steps.Mitochondrial DNA sequences of P. perna were visualized by constructing a median-joining network27 in PopART 1.728. Samples that were not part of the species’ dominant lineage in the region where they were found were excluded from subsequent analyses ; in both cases these were eastern haplotypes that were present at sites that were dominated by western haplotypes and thus reflect long-distance migration. To assess genetic differentiation within the ranges of individual lineages that may indicate reduced gene flow between centre and edge sites, population pairwise ΦPT was calculated using GenAlEx 6.50129, and Bonferroni correction was applied to account for multiple comparisons.The evolutionary relationships between the COI sequences of 30 was used to assess genetic structuring in the microsatellite data of both P. perna and M. galloprovincialis, with both the admixture and ‘locprior’ models invoked to infer the ancestry of individuals. The program was run on the Lengau supercomputer at the CSIR Centre for High Performance Computing in Rondebosch, South Africa, by specifying up to five genetic clusters (K = 1–5) with a burn-in of 1,000,000 iterations followed by 10,000,000 MCMC iterations. Each run was repeated ten times with different starting seeds to confirm that the program was run sufficiently long for the results to be consistent. Evanno’s ΔK31 was calculated with STRUCTURE HARVESTER32 to identify the best-supported number of distinct genetic clusters comprising each species. After confirming with CLUMPAK33 that all ten independent runs per K converged on the same solution, a barplot was created from one of the Q-matrices for P. perna with STRUCTURE PLOT 2.034. Further regional genetic subdivision in P. perna was explored by analysing western and eastern sites separately, using the same settings as above. GenAlEx was used to calculate pairwise FST values, as well as G”ST, a genetic structure statistic similar to FST that is particularly suitable for microsatellites35. Bonferroni correction was applied as described above. The program FreeNA was used to determine whether null alleles affected pairwise FST, with significant differences between corrected and uncorrected estimates determined by calculating 95% confidence intervals that were based on 1000 bootstrap replications.STRUCTURE 2.3.4Poppr 2.3.036. Average Bruvo’s distance37 over all loci in a population was calculated, the model for missing data was set to average addition/loss and the repeat lengths of each locus were entered.Evolutionary relationships between the lineages were also visualized using a minimum spanning network that was obtained from the R package h38 and nucleotide diversity π39. When h is high, this implies that a population has a large number of haplotypes, whereas a high π indicates that the haplotypes present in a population are very different from each other. Both are typical of large populations, and it was expected that h and π would be greater for centre sites than for edge sites. To determine whether these diversity indices differed between pairs of sites, we used the R package genetic_diversity_diffs 1.0.540 and specified 1000 iterations to calculate Bonferroni-corrected P-values.For the COI data, we used Arlequin to calculate site-specific haplotype diversity r) in the R package diveRsity41. Compared to the more commonly used expected heterozygosity (HE), this statistic is more likely to be affected by stochastic processes in small populations at the range edge, and for that reason is more sensitive in detecting differences between centre and edge sites9. Significant differences in the magnitude of Ar between sites were identified by calculating 95% confidence intervals based on 1000 bootstrap replications, with non-overlapping confidence intervals between two sites implying significant differences. As it is not possible to calculate confidence intervals for Ar in diveRsity when rarefaction (correction for differences in sample size) is applied, we reduced the number of individuals at all sites to the number at the site with the smallest sample size by excluding individuals with the most missing data, which was 41 individuals for P. perna. No reduction was necessary for M. galloprovincialis because all sites had 48 individuals.To assess genetic diversity for the microsatellite data, we calculated allelic richness , a product delivering ocean current fields on a daily basis . This is a model forced by heat flux, precipitation, wind stress and wind speed that is able to resolve meandering currents, eddies, filaments and fronts44, i.e., important mesoscale oceanographic processes required to accurately simulate passively dispersing larvae45.To provide an independent estimate of the dispersal potential of 46 was used to define source/sink locations 1 km apart. From these points, individual particles simulating pelagic mussel larvae were released on a daily basis, and allowed to drift at the surface of the virtual environment for up to 30 days18 until they eventually ended up on the shore, or were lost in the open ocean. The position of each particle was determined every hour using the bilinear interpolation of HYCOM’s ocean velocity fields. Individual trajectories were aggregated to matrices representing the probability of connectivity between every pair of coastal locations, by dividing the number of times a particle released from location i reached location j, by the total number of particles released from location i.The region of simulation comprised ~2750 km of coastline that slightly exceeded the range from which genetic samples were obtained, from St. Helena Bay  in the west to Ponta Dobela  in the east. A high-resolution polygon representing global landmassesTo account for inter-annual variability in ocean flows, the simulations were run individually for a period of 10 years (2003 to 2012), and a final asymmetrical connectivity matrix between all pairs of coastal locations was produced by averaging the annual matrices. Overall, a total of 2,697,350 particles were released.47. Floyd–Warshall’s algorithm, which minimises the overall sum of log‐transformed probabilities, was used to estimate the shortest path between sampled locations, from which probabilities of connectivity were determined by a product function. Finally, clusters of locations with higher connectedness were determined by finding the optimal partitioning for maximizing the modularity index  over all possible partitions. Dispersal simulations and network analyses were performed using the R packages igraph48, dismo v1.1-449, parallel v3.6.1 (R-core), raster v0.4650, and vegan v2.5-651.The potential connectivity between the locations sampled for genetics was determined with direct probability estimates from the final asymmetrical matrix, and by considering stepping-stone probability estimates. For the latter approach, network analysis was implemented, with vertices being locations and edges the probabilities of asymmetrical connectivityA research permit (RES2014/12) issued jointly by the Department of Environmental Affairs and the Department of Agriculture, Forestry and Fisheries was used to sample mussels from the South African coastline.14, the two lineages were equally represented at site P5, while at site P6, 30% of the mussels were assigned to the western and 70% to the eastern lineage. Given mussel cover of 62.2% and 52.1% at sites P5 and P6 respectively, the resulting cover for each lineage was estimated to be 33.1% (P5) and 15.6% (P6) for the western lineage, and 33.1% (P5) and 36.4% (P6) for the eastern lineage. Centre locations of Mytilus galloprovincialis also had higher cover than edge locations .Mussel cover was on average 0.13% at edge sites of the western lineage, increasing to 58.47% at centre sites Table . Centre Perna perna were recovered as distinct genetic clusters , we decided not to exclude any loci from subsequent analyses.FreeNA analyses indicated that null alleles were quite common for some microsatellite loci Tables  and S5, es Table . In contnt Table . Departuna Table  than foris Table . Such reFST and G”ST values  but 3  were found in three cases  based on the microsatellite data is reported together for the two lineages of P. perna because they were not as clearly differentiated as was the case for the mtDNA data  a clear division into centre (M1 and M2) and edge (M3 and M4) sites in M. galloprovincialis.The particle simulations over the 10-year period showed a sharp decline of potential connectivity with distance Fig. , with hi56, the Abundant-Centre Hypothesis (ACH) has subsequently been extended to include ecological aspects of species, such as physiological performance57. Empirical studies do not always support this hypothesis, and it is useful to consider the definition of an edge population. Two definitions have been suggested56: populations whose size fluctuates dramatically and which have a high likelihood of becoming extinct, and populations whose members are sparse and physiologically stressed. Such stress could be due to environmental conditions or competitive interactions58. This makes the important point that marginal populations are not necessarily found at the periphery of a species’ distribution59, and this in turn is compatible with the observation that species’ ranges need not contract from the periphery to the centre60. In terms of testing the ACH, coastlines have the advantage of being essentially one-dimensional, making it relatively easy to identify central and peripheral populations. The South African coastline, apart from being much more linear than many other coastlines, has the additional advantage that it encompasses a wide range of climatic and oceanographic conditions that define at least four marine biogeographic provinces12. These are separated by biogeographical transition zones that represent the range edge in numerous species61 and that are so distinct environmentally that they can drive the evolution of genetic lineages endemic to these transition zones63. On this basis, we have made the assumption that populations near the species’ distributional limits are indeed marginal56. The clear spatial arrangement of centre and edge regions theoretically offers an ideal setting that facilitates multiple tests of the ACH. The rejection of the hypothesis in two evolutionary lineages of the native mussel Perna perna and in the invasive Mytilus galloprovincialis provides insights into the conditions that prevent species from developing the genetic patterns expected to result from the ACH.Originally developed in the context of population geneticsP. perna were similar in that both markers identified the western and eastern lineages. However, smaller-scale genetic structure did not clearly conform to the pre-defined divisions into center and edge populations, and also differed between markers. The western COI lineage could be further subdivided into a western and an eastern group (with a boundary between sites P3 and P4) whose ranges did not match the biogeography-based division into centre and edge sites, but which may nonetheless reflect a thermal gradient in this area65. A similar result was found for the microsatellite data, although the location of the boundary differed (it was located between sites P1 and P2) and may reflect marker-specific differences in lineage sorting66 or selection67. Similarly, the distinctness of the COI haplotypes from the northernmost site (P9), which was not found with microsatellites, matches the location of previously reported phylogeographic breaks that separate subtropical from tropical fauna70, and may thus also reflect differential adaptation across a temperature gradient.Genetic structure results for mtDNA and microsatellite data of P. perna than in eastern P. perna (mtDNA data), and higher in the microsatellite data of P. perna than in those of M. galloprovincialis. As mentioned earlier, no mtDNA data were generated for the latter species because its genetic diversity is very low14. In addition, given the slow mutation rate of this marker, its genetic diversity likely still reflects the species’ demographic history in the northern hemisphere prior to its introduction to southern Africa. However, the microsatellite data were also considerably less variable than in P. perna, possibly because of a reduction in diversity during the founder event(s) that occurred at the beginning of the species’ invasion of southern Africa in the 1970s.In terms of genetic diversity, there were clear trends between species or lineages, but none were related to differences between centre and edge sites. Genetic diversity indices were higher in western 71. Hence, even when the abundance of mussels is lower in edge populations, reduced genetic diversity is only expected when migration from the centre is so low that some alleles are so rare in the periphery that they are not represented in a reasonably large genetic sample.A reduction in genetic diversity towards the range edge expected under the ACH is predicted on the basis of a combination of smaller population size and increased spatial isolation from the centre of the rangeCrepidula with very different dispersal potential identified support for the ACH for both species72. Although the absolute reduction in genetic diversity that was found for marginal populations was relatively small, this implies that dispersal capability is irrelevant. In our case, all three taxa have relatively long-lived planktonic larvae and are expected to have a high capacity for dispersal, although empirical evidence suggests that the numbers of larvae undergoing long-range dispersal on this coast may not be high in terms of ecological connectivity73. Accordingly, previous research using mtDNA sequence data of P. perna identified surprisingly well-developed genetic structure within the western lineage that was driven by coastal topography55 and even resulted in sex-biased genetic structure between bays and the open coast53. In the present study, we identified numerous examples of genetic structure that imply some degree of spatial isolation, but the data from neither genetic marker supported division into groups of centre or edge sites.A recent study on two species of the gastropod genus P. perna. Interestingly, the modelling results supported the distinctness of the originally-defined edge populations of both species, even though their distinctness was not strongly supported by the present genetic data. While a 30-day dispersal period may potentially generate connectivity over a distance of up to 1000 km, connectivity events on average were in the order of only ~60 km in ~5 days43. Nevertheless, the possibility of year-to-year stepping-stone connectivity47, together with the mussels’ high reproductive output and long pelagic larval phases, may facilitate the spread of genes across each species’ entire range.The Lagrangian modelling allowed the assignment of locations to three distinct clusters with high within-group and lower between-group connectedness, the clusters largely coinciding with the biogeography-linked regional patterns of genetic structure found for P. perna, and (c) reduced larval connectivity between some of the originally-defined centre and edge populations identified using Lagrangian modelling, implies that the genetic consequences of the ACH may only become evident when levels of connectivity are so low that they manifest at the evolutionary  timescale. In addition, population sizes even of edge populations may be sufficiently large to prevent diversity-reducing factors that affect small populations (e.g. genetic bottlenecks). Hence, occasional larval exchange between large central and peripheral populations of South African marine mussels may prevent geographic genetic divergence, and be sufficient to nullify the conditions expected to underpin an ACH effect. We suggest that the genetic predictions of the ACH are unlikely to be supported by species with high dispersal capabilities such as those with planktonic, wind- or vector-driven dispersal of propagules.The rejection of the genetic expectations of the ACH, despite: (a) lower population densities of edge populations, (b) clear departures from the expectations of panmixia in Supplementary information.


Pleuragramma antarctica) is a critically important forage species with a circumpolar distribution and is unique among other notothenioid species for its wholly pelagic life cycle. Previous studies have provided mixed evidence of population structure over regional and circumpolar scales. The aim of the present study was to test the recent population hypothesis for Antarctic silverfish, which emphasizes the interplay between life history and hydrography in shaping connectivity. A total of 1067 individuals were collected over 25 years from different locations on a circumpolar scale. Samples were genotyped at fifteen microsatellites to assess population differentiation and genetic structuring using clustering methods, F-statistics, and hierarchical analysis of variance. A lack of differentiation was found between locations connected by the Antarctic Slope Front Current (ASF), indicative of high levels of gene flow. However, gene flow was significantly reduced at the South Orkney Islands and the western Antarctic Peninsula where the ASF is absent. This pattern of gene flow emphasized the relevance of large-scale circulation as a mechanism for circumpolar connectivity. Chaotic genetic patchiness characterized population structure over time, with varying patterns of differentiation observed between years, accompanied by heterogeneous standard length distributions. The present study supports a more nuanced version of the genetic panmixia hypothesis that reflects physical-biological interactions over the life history.The Antarctic silverfish ( Pleuragramma antarctica) is a critically important forage species in the Southern Ocean that connects higher and lower trophic levels in the continental shelf ecosystem2. Having a circumpolar distribution, silverfish dominate the high-Antarctic neritic assemblage in terms of both biomass and abundance3. Atypical for a notothenioid, silverfish have a wholly pelagic life cycle, including a cryopelagic egg and larval phase4. Silverfish have adopted a relatively inactive, energy-conserving life strategy similar to related benthic species, in which their neutral buoyancy allows them to remain suspended in the water column without active swimming5. Remaining in the water column throughout their life history exposes silverfish to current and front systems over the slope and shelf that have been hypothesized by Ashford et al.6 to play an integral role in shaping their circumpolar distribution6.The Antarctic silverfish  drives westward transport in the Antarctic Slope Front Current. In this paper, ASF refers to this system, including the Antarctic Slope Front Current. It is located over the continental slope and thought to be continuous from the Amundsen Sea along the Ross Sea continental shelf and East Antarctica, forming the southern limb of the Weddell Gyre and reaching waters north of the Antarctic Peninsula9  is typical of coastal buoyant plumes12. Eggs and larvae are found from 0–100 m among the platelet ice layer beneath coastal fast ice13. Descent to deeper layers begins at the post-larval phase, and post-larvae and juveniles up to 2 + years are found at depths up to 400 m3. Adult silverfish, ranging in length between approximately 8–25 cm, are typically found at depths from 400–700 m, exhibiting a diel migration in which a greater abundance of fish are present in the shallower portion of their distribution by night, and in deeper waters by day14. These changes in habitat occupancy correlate with ontogenetic shifts in diet composition, such that silverfish occupy several trophic levels throughout their 12–14 year lifespan15. While feeding almost exclusively on zooplankton, silverfish have been shown to exhibit dietary plasticity, including cannibalism18. Thus, early-life dependence on coastal sea-ice zones and later movement into deeper waters in pursuit of prey and avoidance of predators expose fish to hydrographic features along the continental shelf. This results in a complex life history which generates the conditions for a dynamic population structure and underlying patterns of connectivity6.Entrainment of silverfish in transport pathways is facilitated by a life history characterized by vertical migrations associated with different life stages20. Schools of adult silverfish have been observed moving inshore along the Antarctic Peninsula21. Assemblages of silverfish eggs and larvae have been found in the summer polynya of the eastern Weddell Sea22, in waters along the Antarctic Peninsula23, under sea ice in the western Ross Sea24, and in the Dumont d’Urville Sea25 .Estimates of genetic differentiation are a principal tool for defining population structure, by assessing gene flow between populations, as well as the extent to which inbreeding and genetic drift contribute to population differentiation28. This was supported by genetic homogeneity found among multiple species of the families Nototheniidae and Channichthyidae inhabiting the Scotia Arc30. At the same time however, this panmictic hypothesis was challenged by evidence of population structuring based on genetic heterogeneity in multiple other species, including Dissostichus mawsoni32, Chionodraco hamatus33, Chaenocephalus aceratus35, and Champsocephalus gunnari35.Highly connected populations resulting in genetic panmixia have previously been the null hypothesis to describe genetic structuring in notothenioids. This hypothesis is based on the expected influence of circumpolar currents on the transport of pelagic larvae, which would sustain high levels of gene flowet al.36 found little indication of population structuring in silverfish on a circumpolar scale, although differences were found between sampling years in the eastern Weddell Sea, and between one sample in the eastern Weddell Sea and one of two taken in the western Ross Sea36. A more recent study on silverfish connectivity around the Antarctic Peninsula employing microsatellite markers revealed that fish taken from Marguerite Bay and Charcot Island along the western Antarctic Peninsula (WAP) were significantly different from those along the northern Antarctic Peninsula (NAP) at Joinville Island and on the eastern side of the peninsula at Larsen Bay (LB)19  data from over half of the samples collected indicated differences in SL distributions between sampling locations, and we used these to test for chaotic genetic patchiness. Where more than one SL mode existed, sampling locations were further divided into groups of smaller and larger fish to examine for genetic differentiation between and among different cohorts. Although temporal replicates differed between sampling locations, and more locations were sampled in some geographic regions than others, the dataset allowed us to test for genetic panmixia on a circumpolar scale for the first time in silverfish.The present study builds upon the Matschiner ca Table . The folC. hamatus19 can be used in P. antarctica. While amplifying successfully in all individuals, one locus (Ch11230) was problematic in terms of accurate genotyping, due to stuttering and interference from the other fluorophores used to detect fragments in sequencing. Subsequent analyses were carried out on both datasets, including and excluding this locus, and no major differences were obtained in the results. Thus, results are presented based on 15 microsatellite loci, excluding locus Ch11230, for verisimilitude. No significant linkage disequilibrium was observed between loci. Significant departures from Hardy Weinberg Equilibrium (HWE) were found at 22 of 304 tests after SGoF + correction for multiple tests (threshold for significance with 304 comparisons P = 0.008) with no locus exhibiting Hardy Weinberg Disequilibrium (HWD) in more than 3 sampling locations and no sampling location containing more than 5 loci in HWD (Table HE > HO) was not found by population or by locus, though greater numbers of loci within populations displayed extreme excesses of HE than such excesses of HO . However, only 2 of the 27 instances of extreme HE excess coincided with a significant instance of HWD. Thus, given that the effect size is quite small for the majority (20 of 22) of the instances of HWD, it is unlikely that these departures from HWE are of biological importance41. Neither ARLEQUIN nor LOSITAN identified any locus as a putative outlier, with all simulated FST values falling within neutral expectations. High levels of genetic variation were observed in comparable values of NA, AR, HO, and HE (Table P > 0.05) across all sampling locations.Genotypes from 16 microsatellite loci were successfully obtained for all 505 newly sequenced individuals, further supporting that microsatellites developed for WD Table . Of the ocus Ch1130 was prHE Table , showingFST values above 0.0025. The analysis was performed considering various combinations of observed allelic frequencies and sample sizes at multiple iterations of effective population size (Ne)  and generations (2–201)  ranged from 0.00168 to 0.00572. This confirms that all non-significant comparisons can be presumed to be such for the present dataset, and are not non-significant due to the inability of the markers used to detect differences among samples.A power analysis showed that the 15 microsatellites employed in the analysis of genetic differentiation had a 100% chance to detect 1) Table . For FSTk = 6 groups based on geographic region (Ross Sea (RS), Weddell Sea (WS), Larsen Bay (LB), northern Antarctic Peninsula (NAP), South Orkney Islands (SOI), and western Antarctic Peninsula (WAP), Table FCT = 0.00159; P = 0.00218 ± 0.00050) while minimizing within group heterogeneity . Genetic differentiation was present  but very low, with <1% of the total variation due to sampling location. Only 0.16% of variation was attributed to variation among groups. Locus-by-locus AMOVA confirmed that no particular locus had a significantly outsized impact on the calculation of global F-statistics.Hierarchical AMOVA Table  indicatea priori parameters suggested the existence of nine clusters  randomly assigned individuals to groups, with no correlation to sampling location or geographic region Fig. , S1. DAPers Fig. , with k ria Fig. . The samria Fig. . PCoA onria Fig. . PCoA onria Fig. . HoweverFST provided support for geographic population clusters. Within sampling locations, there were no significant differences between samples collected over multiple years . Such discrepancies are due to the use of 15 loci instead of 16 loci as in Agostini et al.19 as well as to changes in significance thresholds resulting from the increased number of comparisons in the present study (nineteen sampling locations versus nine). Indeed, when the dataset of nine sampling locations from Agostini et al.19 was reanalyzed with the removal of the problematic locus Ch11230, three differences were found with respect to the original analysis , as well as between the South Orkney Islands and all groups except for the Weddell Sea, where the comparison was not significant after SGoF + correction for multiple tests  data were available for 671 (63%) samples. SL ranged from 3.7–21.5 cm. Post-larvae and juveniles (SL < 9 cm)ts Table . No evidts Table , nor wasts Table .et al.19 observed a pronounced bimodal distribution in the sample taken from Joinville Island 2010 (JI10), with modes at SL = 5 cm and SL = 10 cm. After dividing the JI10 group into small and large fish representative of more and less recent cohorts, pairwise FST analysis revealed that the small fish (n = 68) and large fish (n = 72) were significantly different. Seven of eight comparisons were significant between all groups and JI10 small, whereas three of eight comparisons were significant between all groups and JI10 large after SGoF + correction for multiple tests19 (threshold for significance with 190 comparisons P = 0.0453). When this analysis was extended to the sampling locations considered in the current study, this result was largely replicated: six of eight comparisons were significant between the original AP groups and JI10 small, and two of eight comparisons were significant between the original AP groups and JI10 large after SGoF + correction for multiple tests,  and JI10 large , in contrast to the original analysis. Consistent with results seen in the initial and replicated AP analysis between JI10 small and Larsen Bay 2007 (n = 46), JI10 small significantly differentiated after SGoF + correction for multiple tests from Larsen Bay 2011 , a comparison that was not significant when analyzing the undivided JI10 group. Similarly, the comparison between JI10 small and Ross Sea 1997 (n = 84) was significant after SGoF + correction for multiple tests , despite no indication of differentiation with the undivided JI10 group. Finally, seven additional pairwise comparisons were significant after SGoF + correction for multiple tests in the JI10 cluster analysis, despite not involving the newly introduced JI10 small or large clusters (see Table Standard length (SL) distributions varied considerably between sampling locations Fig. . Agostins, Table . HoweverFST analysis was performed including only sampling locations collected in 2014 within the eastern Weddell Sea. No significant differences were seen between any of the sampling areas, which included west Filchner Trough (n = 23), east Filchner Trough (n = 27), Coats Land (n = 29), Halley Bay (n = 52), and Atka Bay (n = 25) , with group n ranging from 14–43 individuals. When no significant differences were found between length-based groups after SGoF + correction for multiple tests (FST ranged from −0.0081 to 0.01019), an exploratory analysis of various pools of the six original length-based groups was conducted. The smallest length-based pool was defined as SL ≤ 12 cm and then subsequently as SL ≤ 14 cm. The largest length-based pool was defined as SL < 16 cm and various combinations of the intermediate groups were created. Testing these length-based pools allowed for the control of the influence of group size, and none of the pairwise comparisons were significant after SGoF + correction for multiple tests (FST ranged from −0.0081 to 0.0103).Spatially recurring length modes among sampling locations collected in the eastern Weddell Sea in 2014 provided evidence for episodic connectivity  and associated Antarctic Slope Front Current on a circumpolar scale was accompanied by indications of reduced gene flow in regions not reached by the ASF. The Discriminant Analysis of Principle Components (DAPC), revealed evidence of nine clusters, but these were independent of geographic region. Instead, they may have arisen due to historical extinction and recolonization events predicted by Ashford et al.6, related to glacial periods that recurrently structured past populations of silverfish. Overall levels of genetic differentiation were low (maximum FST = 0.0153), and no significant differences were found from the Ross Sea westward to the northern Antarctic Peninsula. However, limited evidence of genetic structuring was found. We corroborated the findings from Agostini et al.19 that Charcot Island and Marguerite Bay represent a homogeneous population with significantly reduced gene flow from populations around the AP and beyond, while also discovering significant reductions in gene flow to the population at the South Orkney Islands. These results were broadly consistent with the Matschiner et al.28 prediction of gene flow facilitated by the large-scale circulation. However, the genetic structuring we found corresponded to discontinuities between major current systems along the shelf. Accordingly, we argue for a more nuanced version of the genetic panmixia hypothesis that incorporates physical-biological interactions over silverfish life history.The present study tests the hypothesis of genetic panmixiaFST analysis provided evidence for six possible silverfish populations among the geographic regions analyzed. Moreover, the Ross Sea did not differ from the Weddell Sea, Larsen Bay or Joinville Island. Joinville Island and the South Shetland Islands formed a cluster, which did not differ from the others, suggesting gene flow along the ASF into the Bransfield Strait. Notably, comparisons made by Agostini et al.19 showed evidence of differentiation between Larsen Bay and Joinville Island, whereas in the present analysis, not a single comparison among sampling locations from Joinville  and Larsen Bay (LB07 and LB11) revealed significant differentiation. This is consistent with the predictions made in La Mesa et al.26 of connectivity between Larsen Bay and the Bransfield Strait. Thus, the inclusion of the additional samples from Larsen Bay (LB11) clarified connectivity along the ASF between the western Weddell Sea and Bransfield Strait.Similarity between samples from the Ross Sea, eastern Weddell Sea, Larsen Bay and Joinville Island indicated gene flow along the shelf corresponding to the ASF. Pairwise et al.19 based in part on the decrease of silverfish in Adélie penguin diets at Palmer Station42. The loss of intermediate populations that may have previously facilitated connectivity between the northern and southern extents of the western Antarctic Peninsula was further supported by the total absence of silverfish in sampling surveys at Anvers and Renaud Islands and the Croker Passage midway up the western Antarctic Peninsula in 2010  and Marguerite Bay (MB) represented one cluster, which differentiated genetically from all other geographic regions. Discontinuity in silverfish distribution between the northern and southern regions of the western Antarctic Peninsula was first discussed in Agostini 19. Furthermore, the position of MB11 along the trough inflow in Marguerite Trough . Specimens from MB01 and MB02 exhibited a bimodal distribution of SL representative of more and less recent cohorts. In contrast, specimens from MB10 and CI10 exhibited a much tighter unimodal distribution around SL = 15 cm, as well as MB11, which had a similarly tight unimodal distribution around SL = 10 cm Fig. . This di19 and the current results are likely attributable to changes in significance thresholds resulting from the increased number of comparisons in the present study, as well as the use of 15 instead of 16 loci due to the removal of one problematic locus. In the original Antarctic Peninsula analysis, Agostini et al.19 found 9 out of 36 significant comparisons around the Antarctic Peninsula, while in the current analysis, only four out of the thirty-six Antarctic Peninsula comparisons were significant. This was mainly due to weaker differentiation between WAP locations and JI10 and JI12. Discrepancies in patterns of differentiation between the current study and the original investigation of population connectivity in Zane et al.36 can be attributed to their use of mtDNA as opposed to microsatellite markers to resolve patterns of genetic heterogeneity40, in addition to differences in sample size  the ASF, which could transport enough individuals from the eastern Weddell Sea and beyond to render the Joinville population genetically indistinguishable from upstream populations48. Although the path of the AACC south of Joinville Island is undefined, buoyancy forcing is likely to generate connectivity that is shallow and close to the coast7. This could result in larval transport north from Larsen Bay which would explain the differentiation between size modes in the Antarctic Sound  the AACC, which could transport fish from Larsen Bay the AACC38, consistent with a local breeding population. Furthermore, a recent bathymetric analysis of the South Orkney Islands microcontinent found seven trough systems on the northern and southern sides of the islands49. Features supportive of a locally breeding silverfish population were found in Signy Trough between Coronation and Signy Island49, including active glaciers at the trough head, thought to be necessary for early-life stages6 , a keystone species in waters beyond the continental shelf60, exhibit a similar lack of population structure, as shown in studies using mtDNA and microsatellites61, as well as restriction-site-associated DNA sequencing62. Such panmixia observed in Antarctic krill was explained by very large population sizes and ongoing gene flow62. However, it was stressed that genetically indistinguishable populations do not imply ecologically independent populations63. Evidence for a recent population expansion observed in analysis of mtDNA in Bortolotto et al.61 emphasized the differential impact of glaciations on the population structure of pelagic versus benthic Antarctic species. Thus, impacts of climate change may result in range restrictions that will have the opposite effect of glacial expansions on pelagic species, cutting off intermediate populations which can result in cascading effects on the connectivity of ecologically interdependent populations65.Nevertheless our results show no basis for rejecting along-shelf connectivity predicted by Ashford 2. Understanding their population structure and connectivity in the context of their life history and the impact of hydrography is critical to assessments of Southern Ocean ecosystem health.Antarctic silverfish are a pillar of the Southern Ocean ecosystem36. A further 562 individuals collected between 2001 and 2012 from Larsen Bay (LB07), Charcot Island (CI10), Marguerite Bay , and Joinville Island  were included from a previous connectivity study focused on the Antarctic Peninsula19. The remaining 256 individuals collected in 2011 and 2014 from the South Orkney Islands (SOI11), Larsen Bay (LB11), Filchner Trough (FT14), Atka Bay (AB14), and Halley Bay (HB14) have not previously been examined. Table 66. No manipulations were carried out on live samples.A total of 1067 individuals from 19 sampling locations collected by multiple institutions between 1989 and 2014 were included in the analysis  data were available from all individuals except from those collected from RS96, RS97, and HB91. SL data from HB89 and SSI96 represent the length distribution of fish collected at the sampling stations, but lack identification numbers to correspond them to the tissue samples collected for genetic analysis. The distribution of SL was used to identify modes for consideration in the eventual genetics analysis.67. Genomic DNA was extracted from all previously unexamined samples, as well as from tissue samples remaining from individuals on which the mitochondrial DNA analysis was performed in Zane et al.36. Concentration and quality of the extracted DNA (260/280 nm and 260/230 nm) was checked using a NanoDrop UV–Vis spectrophotometer (Thermo Scientific) prior to PCR amplification. All extracted DNA was of high enough quality to use in subsequent PCR reactions.Muscle tissue or fin clips were preserved in 95–100% ethanol at the time of sampling. Genomic DNA was extracted from specimens using a standard salting-out procedureChionodroco hamatus69, that had been previously shown to cross-amplify successfully in P. antarctica19. The 16 loci were amplified in two multiplex PCR reactions , where they were sequenced using an ABI 3730xl automated sequencer . Microsatellites were analyzed using GeneMarker ver. 2.6 (SoftGenetics). Genotypes of individuals included in the microsatellite analysis published by Agostini et al.19 were integrated into this analysis. To ensure that the datasets were comparable, two individual samples from Agostini et al.19 were processed alongside samples from the present study as positive controls during each amplification and sequencing run. This allowed the comparison of the raw microsatellite data between the previous and current study and confirmed that there was no change in microsatellite sizing. Binning was automated with FlexiBin 270 and refined by eye to assure accuracy with the corresponding binnings established in Agostini et al.19.PCR products were prepared for microsatellite genotyping and sent to an external service  probability was tested with GENEPOP online72. Deviations from Hardy-Weinberg equilibrium were calculated with the R package diveRsity ver. 1.9.8973. The software Micro-Checker 2.2.3 was used to detect the presence of null alleles and scoring errors due to large allele drop-out and stuttering74. Significance levels for multiple comparisons were adjusted using the program SGoF+ 75.A suite of programs was used to assess population differentiation and genetic structuring based on the microsatellite genotypes. Input files were created in the appropriate software-specific formats using CREATE ver 1.3776 and ARLEQUIN 3.5.1.377. Both software generate coalescent simulations based on observed data and a chosen demographic model of migration to create the expected distribution of FST versus HE with neutral markers. Outlier loci are detected based on the presence of significantly higher or lower FST values compared to neutral expectations. In LOSITAN, 1,000,000 simulations were run under neutral mean FST, simulating 19 groups (for each sampling location), confidence interval of 0.95% and a false discovery rate of 0.01 assuming a Stepwise Mutation Model (SMM)19. In ARLEQUIN, genetic structure was assessed by running 50,000 simulations, simulating 19 groups (for each sampling location) and 6 groups (corresponding to the number of geographic regions), and assuming 100 demes per group under the hierarchical island model78.Detection of loci under selection was carried out using the methods implemented in LOSITAN ver. 1.0.0HO) and expected (He) heterozygosity, number of alleles (NA), and allelic richness (AR) using the R package diveRsity ver. 1.9.8973. Genetic variability was compared between sampling locations using a one-way ANOVA performed in R79.Within-sampling location genetic variability was assessed by calculating observed  algorithm that uses an iterative method to divide genotypes into k clusters. This method has been shown to allocate clusters more accurately than common Bayesian methods when population structure is weak84.The extent to which sampling locations could be clustered into the same group was assessed using various methods. Discriminant analysis of principal components (DAPC), a multivariate method in which fish are assigned to known groups, and Principle Coordinate Analysis (PCoA), a multidimensional scaling method in which the matrix of dissimilarities was built from 85. Observed sample sizes and allele frequencies taken from the 19 sampling locations were used to gauge the statistical power of the 15 microsatellites to detect FST values ≤ 0.01 using 1000 replicates. In accordance with the POWSIM manual, several combinations of Ne  and generations (t = 2–201) were tested to account for variability in the estimation of Ne.The power of microsatellite loci to detect differences in the sample sizes analyzed was assessed applying Fisher’s and Chi-square tests in POWSIM ver 4.1FST86 between sampling locations as well as between SL-defined groups. Hierarchical analysis of molecular variance (AMOVA) was also used to assess population differentiation. Pairwise genetic differences and AMOVA were calculated in ARLEQUIN 3.5.1.377. Significant differentiation was determined with 10,000 permutation tests. The significance threshold was adjusted according to SGoF + correction for multiple tests75.Population differentiation was determined by calculating pairwise genetic distances represented by Supplementary Information


Adaptation to pollution has been studied since the first observations of heavy metal tolerance in plants decades ago. To document micro‐evolutionary responses to pollution, researchers have used phenotypic, molecular genetics, and demographic approaches. We reviewed 258 articles and evaluated the evidence for adaptive responses following exposure to a wide range of pollutants, across multiple taxonomic groups. We also conducted a meta‐analysis to calculate the magnitude of phenotypic change in invertebrates in response to metal pollution. The majority of studies that reported differences in responses to pollution were focused on phenotypic responses at the individual level. Most of the studies that used demographic assays in their investigations found that negative effects induced by pollution often worsened over multiple generations. Our meta‐analysis did not reveal a significant relationship between metal pollution intensity and changes in the traits studied, and this was probably due to differences in coping responses among different species, the broad array of abiotic and biotic factors, and the weak statistical power of the analysis. We found it difficult to make broad statements about how likely or how common adaptation is in the presence of environmental contamination. Ecological and evolutionary responses to contamination are complex, and difficult to interpret in the context of taxonomic, and methodological biases, and the inconsistent set of approaches that have been used to study adaptation to pollution in the laboratory and in the field. This review emphasizes the need for: (a) long‐term monitoring programs on exposed populations that link demography to phenotypic, genetic, and selection assays; (b) the use of standardized protocols across studies especially for similar taxa. Approaches that combine field and laboratory studies offer the greatest opportunity to reveal the complex eco‐evolutionary feedback that can occur under selection imposed by pollution. HoweverWabs) results in population decline  and resulting in population recovery through the process of evolutionary rescue  by means of plastic responses without changes in genetic composition  identify a trait(s) that can provide a fitness advantage in dealing with the stressor, (b) assess the presence of suitable genetic variation for the particular trait(s); (c) show that selection (as opposed to genetic drift) has taken place; (d) assess the contribution of the advantageous trait(s) to the population fitness by estimating population growth rate Bell, . AlthougSeveral literature reviews have summarized studies focused on the ability of organisms to tolerate pollutants , taxonomic groups , methods (field and laboratory studies), and pollutants . We performed a quantitative meta‐analysis with a subset of the data to evaluate the effect of metal pollution on the magnitude of phenotypic response  in invertebrates. We also evaluated how shifts in methodological approaches have changed our understanding of micro‐evolutionary responses to pollution. In particular, we assessed molecular evidence for adaptation and the candidate genes potentially involved in the pollution‐induced evolutionary processes .2Google Scholar. We used the keywords “genetic adaptation,” “adaptation,” “micro‐evolution” in combination with “pollution” or “pollutants” or “contaminants.” These search terms reduced bias to a particular approach or method.In this study, we reviewed articles published since 1992 found by searching on 2 increase), polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), and various other chemical pollutants . In these cases, random genetic drift was ruled out. Since it is common in evolution studies to substitute time for space and use geographic variation in resistance as an alternative for temporal changes /3.92. When the sample size was <60, we replaced 3.92 with values obtained from tables of the t distribution with degree of freedom equal to the group sample size minus 1.We identified a set of response variables that are measured in laboratory and field experiments to study the evolution of resistance through molecular markers and/or quantitative traits. Studies were then assessed and classified according to the approach(es) used: (a) analysis of phenotypic responses to pollution leading to resistance (“phenotypic assays”); (b) characterization of the genetic basis/underlying genetic variation of the advantageous phenotypic traits (“genetic assays”); (c) tests for evidence of selection against random genetic drift and gene flow (“selection assays”); (d) assessment of population growth rate  to estimate the displayed values. When sample size was given as a range, we calculated the average. The dataset included a large variety of experimental designs that were mainly classified as either “field” or “experiment”  and created a numerical metric for the analyses: the natural logarithm of the metal concentration divided by the threshold concentration specific for each metal and habitat type determined by the Canadian Council of Ministers of the Environment (Supporting Information Table ρ) to calculate the variance components for the between‐study and within‐study heterogeneity. We also recorded the number of generations studied in the subset of papers used for the meta‐analysis (n = 108). Finally, we mapped all study sites to show where pollutants have been studied around the world. For the meta‐analysis, we used the R packages metaphor  focused on metal pollution  were on invertebrates, followed by vertebrates (n = 74), plants (n = 60) and algae (n = 17). Specifically, metals were the most studied pollutants for terrestrial arthropods and to a lesser extent for terrestrial annelids, aquatic arthropods, and mollusks. Plants were also studied primarily in relation to metals. The effect of acidification on algae, invertebrates (Echinodermata), and vertebrates was investigated in 8% of the studies. Polychlorinated biphenyls (PCBs) and polycyclic aromatic hydrocarbons (PAHs) were the least represented (9%) with a dozen studies almost exclusively conducted on fish. The rest of the studies (~20%) focused on pollutants like pesticides, dioxin‐like compounds, radiation, waste heap, tributyltin, and other chemical compounds and were grouped together as “other” , Chironomus (invertebrate: Insect), Daphnia (invertebrate: Branchiopoda), Arabidopsis (plant: Eudicots), Orchesella (invertebrate: Entognatha) was common. These species were so extensively studied that they have been the subject of several important literature reviews. For example, Weis  followed by studies on trait heritability (n = 16). Selection assays were the second most abundant assays (n = 121) after phenotypic assays. Studies on invertebrates were the most common followed by vertebrates and plants. FST‐based outlier tests were the dominant way to find evidence for selection. Together with survival, these tests were the most commonly used to study adaptive responses of organisms in polluted conditions. Among the studies that found evidence of selection, the majority (86.5%) tested also for a link between responses to selection and the environment: 15.7% found evidence for genotype–environment correlations and another 15.7% for phenotype–environment correlations (Supporting Information Table n = 31) on invertebrates and microalgae included population growth rate estimates.Phenotypic assays included the study of survival in different concentrations of pollutants , changes in development , growth , morphology , physiology , and reproductive traits . Survival and reproductive traits were the most studied in invertebrates, while in plants and vertebrates all traits were more or less equally studied Figure . Genetic3.2After grouping the results from different articles, we obtained 198 studies Figure , 63% of n = 47) followed by physiological traits (n = 26) and morphological traits . They found that populations from contaminated soil and some populations sampled downstream of the mine had 100% tolerance while populations sampled upstream showed variable tolerance that was related more to geographic location than copper concentration in the soil. Through the study of life‐history traits during experimental selection, they were able to demonstrate that tolerance was heritable and widespread in populations from contaminated soil due to beneficial genetic variation. Shirley and Sibly  was also affected by pollution. Moreover, Messiaen et al. , mathematical models can be crucial for strong inferences about the long‐term effects of pollution on fitness.Kelly et al.’s  study ony Figure . The eff4We tested the relationship between metal concentration and effect sizes of weight, number of neonates, and body metal content. We accounted for the phylum and subclass of the studied organism, the habitat, the type of metal, and other factors such as the presence of other metals in the original habitat and the study type (laboratory experiment or field monitoring). We expected that body weight and number of neonates would decrease with increasing metal concentration and that body metal content would increase.SE = 0.16; Supporting Information Figure ρ) were quite large for body weight  demonstration that the changes in the trait studied are genetically determined and are subject to natural selection; (b) assessment of potential confounding environmental variables; (c) the demonstration that the increase in adaptive trait value can sustain a positive population growth rate and thus the long‐term persistence of the population  long‐term monitoring programs of populations in polluted habitats that integrate demographic studies with phenotypic, genetic, and selection assays; (b) use of standardized protocols among studies of similar species to make evolutionary toxicology studies more comparable (Oziolor et al., Data for this study will be available at Fig Share: 10.6084/m9.figshare.7730255.None declared. Click here for additional data file.