Perna perna. Additionally, by performing environmental niche modelling we assessed the potential and realized niche of P. perna along its entire native distributional range and the environmental factors that best explain its realized distribution. Perna perna showed evidence of panmixia across >4,000 km despite several oceanographic breaking points detected by LPS. This is probably the result of a combination of life history traits, continuous habitat availability and stepping-stone dynamics. Moreover, the niche modelling framework depicted minimum sea surface temperatures (SST) as the major factor shaping P. perna distributional range limits along its native areas. Forthcoming warming SST is expected to further change these limits and allow the species to expand its range polewards though this may be accompanied by retreat from warmer areas.Oceanographic features shape the distributional and genetic patterns of marine species by interrupting or promoting connections among populations. Although general patterns commonly arise, distributional ranges and genetic structure are species-specific and do not always comply with the expected trends. By applying a multimarker genetic approach combined with Lagrangian particle simulations (LPS) we tested the hypothesis that oceanographic features along northeastern Atlantic and Mediterranean shores influence dispersal potential and genetic structure of the intertidal mussel  In the marine realm, species’ distributional arrangements and genetic discontinuities are often caused by dispersal barriers  and environmental gradients  that interrupt demographic connectivity among populations6. Importantly, there is increasing modelling and experimental evidence that pronounced alterations to oceanographic features due to climatic change are re-arranging species’ genetic patterns and distributions globally8.The physical environment influences species distribution patterns and shapes the genetic structure of their populations11. The absence of genetic structure has been related, for instance, to species life history traits, such as the presence of a pelagic phase and larval behaviour11. Historical events are also key drivers of genetic patterns12. For example, there is ample evidence that, during the Last Glacial Maximum (LGM), species retreated to restricted glacial refugia areas, persisting throughout unsuitable conditions to reveal contemporary genetic signatures that are the result of accumulated genetic diversity14.Species inhabiting a specific bioregion do not all necessarily show the same genetic breaks as some are able to sustain high levels of gene flow among populations regardless of the presence of oceanographic barriers15, represents a geographical break for several species17 and is a driver of genetic differentiation18. The Almeria-Oran Front  separates the Western Mediterranean region from the Alboran Sea (Atlantic-Mediterranean) waters19, affecting the genetic structure of several species inhabiting both sides of the front20. Towards the Atlantic, the Strait of Gibraltar is the meeting point where Atlantic water enters the Mediterranean Sea at the surface, overriding the denser Mediterranean water mass19 and forming the focus of several studies of the effect of regional oceanographic barriers on genetic structure (reviewed in ref. Sardina pilchardus21) and shapes the genetic structure of intertidal organisms . Likewise, a genetic break has been detected close to Cape Boujdour in two fish ecotypes (Engraulis encrasicolus23). Other upwelling areas or capes along this stretch of coast such as upwelling off Cape Juby and upwelling off Cape Blanc24 may potentially affect species’ population dynamics. Additionally, the Mediterranean and northeastern Atlantic coasts have also seen contractions and expansions of warm- and cold-water species, particularly along Portuguese shores26, as a response to recent increases in SST (up to 0.4 °C/decade27). For example, two species of the brown algal genus Fucus (F. vesiculosus and F. guiryi) have exhibited major distributional contractions along Atlantic and Mediterranean Iberian and northern African shores linked to rates of SST warming over the last three decades28.The Mediterranean Sea and the northeastern Atlantic are ideal regions to study the effects of dispersal barriers and environmental gradients on species distribution and genetic patterns. In the Mediterranean basin, the Strait of Sicily connects the Western and the Eastern Mediterranean regionsPerna perna was described from north Africa to southern Iberia29. This dominant habitat-forming species occurs naturally along the northern, eastern and western coast of Africa and in the Arabian Peninsula. This intertidal subtropical species has also become invasive in the Gulf of Mexico and eastern South America  use mitochondrial and nuclear markers coupled with Lagrangian particle simulations to test the hypothesis that potential dispersal and genetic structure are strongly influenced by oceanographic features (e.g. dominant currents and upwelling systems), (II) perform environmental niche modelling along the entire species’ native range to assess its potential and realized niche and predict the climatic variables of interest across the northeastern Atlantic and Mediterranean shores. Finally, the distribution of P. perna along South African shores was used as an ideal case study to highlight the relative significance of strongly correlated environmental variables. The South African coastline covers a wide range of very distinct climatic and oceanic conditions that can be divided into three major biogeographic regions31. These are the subtropical East Coast, the warm-temperate South Coast and the cool-temperate West Coast. Interestingly, P. perna dominates intertidal shores in the sub-tropical and warm-temperate bioregions but it is absent from the cold waters of the Benguela system33. This distributional, oceanographic and climatic setting provides unique conditions to disentangle the relative roles played by correlated variables.Here, we combine extensive field surveys, multimarker genetic analyses, dispersal simulations and environmental niche modelling to investigate the factors dictating the distribution and the drivers of genetic structure on Perna perna was detected at 14 locations out of the 49 surveyed  from 730 specimens revealed 127 haplotypes and 112 polymorphic sites .Pairwise ɸSpatial analysis of shared alleles (SAShA) indicated that the observed distribution of geographical distance between pairs of haplotypes (OM) was not statistically different from the expectation (EM) under panmixia .The Akaike Information Criterion corrected for small sample sizes (AICc) selected TrN + G as the best-fit model to be implemented in Arlequin software (gama shape = 0.932). AMOVA analyses attributed most of the variation to within populations  and observed (HO) heterozygosities varied between 0.536 (CB) and 0.796 (LP) and 0.643 (SG) and 0.773 (AN), respectively, resulting in a minor heterozygosity deficit (FIS ranged from 0 to 0.128). Population genetic diversity standardized to the smallest sample sizes, Â(2) and Â(22), varied between 2.714 (LP) and 3.123 (CB) and between 11.966 (IM) and 14.714 (PM), respectively. A total of 105 unique alleles were described with CB reporting the highest number ) returned from the Bayesian admixture analyses implemented in STRUCTURE suggested K = 1 as the best fitting K Fig. . AlthougAMOVA indicated that most of the genetic variation arose within populations  suggested K = 9 as the best fitting number of clusters. All clusters were distributed across the entire study area, revealing no spatial differentiation among groups . On average, the particles drifted for 110.4 km ± 123.9 (maximum 1019 km) and most connectivity events were produced in the first days of ocean drifting (mean transport time of 2.1 days ± 1.9).The Lagrangian particle simulations (LPS) using HYCOM ocean velocity fields over the 11-year period released ~3300 particles per cell . The algorithm was allowed to identify 10 distinct regions Fig. , with th35, the most ecologically representative variables for intertidal species were prioritized in each correlation  and surface air temperature (SAT) and between nitrate and phosphate concentrations. Although the discarding of correlated variables may be an arbitrary procedureP. perna native distribution, including its expanding front towards southern Iberia  and the Arabian Peninsula (Yemen and Oman) under the effect of upwelling cells. RandomForest (RF) performed better than other techniques  resulted in an accurate overall description of ria Fig. . Along nria Fig. . In addiP. perna is absent compared to where the species is present  in shaping P. perna distributional range limits along its native areas including the southern Iberian expanding front.Lagragian particle simulations (LPS) performed along northeastern Atlantic and Mediterranean shores found evidence of the existence of several oceanic barriers with the potential to reduce connectivity between populations to a high degree, as previously described for several marine species. However, our results indicate a lack of genetic differentiation of 37, though barriers for dispersal do not affect all species equally11.In marine organisms with sessile or highly sedentary adults, genetic structuring is often directly linked to dispersal barriers influencing the transport of planktonic propagules39. However, in recent years, several studies have challenged this view, showing significant genetic structure in organisms with long pelagic larval stages41. For example, South African P. perna populations are characterised by marked genetic heterogeneity at both meso- and macro- spatial scales. At large scales, strong genetic division between two geographically defined groups of populations  in southern Africa are highlighted by mitochondrial (Cox1)32 and nuclear  markers, though this appears to reflect the evolutionary history of the species. At smaller scales, mitochondrial data show that populations within the temperate group that occupy different bays are genetically distinct from each other and from populations on the open coast43.The general assumption is that an extended dispersal phase coupled with very large population sizes will result in high levels of connectivity over large geographic scales and across biogeographic provincesP. perna. Lagrangian particle simulations (LPS) detected 10 oceanographic regions with several breaking points. This result supports previous studies reporting oceanographic barriers and genetic discontinuities at (I) the Strait of Gibraltar, meeting point between Atlantic and Mediterranean (Alboran Sea) waters44; (II) the Almeria-Oran front, separating the Alboran Sea from Western (European and African) Mediterranean20; (III) the Strait of Sicily, connecting Western and Eastern Mediterranean18, (IV) Cape Ghir upwelling22 and around (V) Cape Boujdour. However, these known oceanographic barriers did not cause genetic discontinuities in P. perna. Additionally, the LPS detected two possible new breaking points: (VI) Cape Roca, central Portugal and (VII) Cape Barbas, Western Sahara. The Atlantic Iberian coast has been the focus of several studies investigating the genetic structure of marine species46. Despite clear genetic discontinuities along the west coast of Portugal47, the potential of Cape Roca to act as an oceanographic barrier to species distribution and genetic structure warrants further investigation. Interestingly, this break point matches the expected northernmost limit for P. perna distribution in Iberia, detected through the niche modelling approach  and for having the potential to delimit the distribution of marine species (Sardina pilchardus)50. Although LPS predicted an oceanographic barrier for P. perna larval dispersal at Cape Barbas, future studies are needed to understand if this translates into a genetic discontinuity. Studies along this region are scarce, presumably due to continuous conflict; the few genetic studies performed rarely target sites south of the Dakhla Peninsula , increasing the potential of P. perna to colonize suitable habitats on a year-to-year stepping stone scenario. Additionally, hydrological features such as oceanic currents may enhance or limit large-scale genetic homogenization of a species’ gene pool57. The slope currents contouring northern Africa and southern Iberia59 may play a role in enhancing P. perna dispersal by promoting larval transportation and homogenizing the gene pool across the study area, although the general patterns of the dispersal simulation suggest otherwise. Nonetheless, these were the regions of our study where the detected oceanographic break points allow higher potential for connectivity (reductions down to 81%), potentially contributing to the lack of genetic structure described in this study.Habitat continuity and stepping-stone dynamics influence species genetic structureDiplodus sargus, Pagellus bogaraveo, Pagrus pagrus and Scomber japonicus20; mitochondrial markers (Cox1 and 16S): abalone Haliotis tuberculata, chiton Chiton olivaceus60). While we explain the genetic pattern of P. perna in terms of life history traits and ecological variables, other panmitic genetic signatures have been explained as unlikely to be due to dispersal, but rather the result of fluctuations in populations size (i.e. extinction/colonization and migration) and historical demography60.Evidence of panmixia has been previously provided for other species across some of the regions considered in this study : fish species 60, rangewide panmixia is extremely rare61. The panmitic genetic pattern described from northern Africa and southern Iberia strongly contrasts with the genetic pattern observed from South African populations of P. perna. In South Africa, the Brown mussel shows a pronounced genetic break which corresponds to a marked biogeographic disjunction between the temperate and subtropical provinces, separating two distinct genetic lineages32. Although P. perna shows similar life history traits, habitat continuity and stepping stone dynamics in both northern and southern Africa, the genetic structure in South Africa has a historical origin and is presently maintained by the nearshore influence of the powerful Agulhas current, which impedes larval dispersal and thus promoting local adaptation42. Along northern Africa and southern Iberia, neither historical nor contemporaneous genetic discontinuities were detected, as evidenced by the complete agreement between markers  with different rates of evolution.Despite general evidence of panmixia across several taxa26, with several new distributional patterns resulting from range expansions and contractions being attributed to warming SST , the Medieval Warm Period (XII century), the late XIX (1888–1899) and the early XX (1938) centuries64. These occurrences coincided with warmer periods of SST . Similarly, new northern limits for the limpet Patella rustica68 and macroalgae such as Codium adhaerens and Padina pavonica on Atlantic Iberian shores have been related to warming of SST26.The Iberian Peninsula is an interface region where several cold- and warm-water species reach their southern or northern distributional limitssee refs  and mirrLittorina saxatilis, the crab Carcinus maenas and the tunicate species Styela clava in ref. 72. For example, out of 24 variables, SST is among the top range distribution predictors of two intertidal gastropod cryptic species of the genus Melampus70. Moreover, bottom water temperature and depth had the greatest impact on the distribution of 14 benthic species out of 10 environmental variables modelled in71. The results of the present study further support the determinant role of SST in explaining the distribution of nearshore organisms. Minimum SST was the predictor that best explained P. perna native distribution. The sharp drop in SST at Cape Gata73 sets the northeastern limit of P. perna Iberian distribution, highlights the lack of suitable conditions farther east and is consistent with a preference for subtropical conditions. As SST and surface air temperature (SAT) are commonly linked to latitudinal gradients, difficulties arise when trying to tease apart their separate effects on biogeographic patterns74. Although SST and SAT were positively correlated across the entire native distribution, additional analyses of thermal regimes in South Africa provide an ideal scenario to disentangle the relative significance of SAT and SST. South African shores are characterised by sharp environmental clines associated with abrupt species distributional changes31. Perna perna is absent from the cool west temperate province  but distributed across the southern warm temperate one . The west coast of South Africa is permanently affected by the cold waters of the Benguela upwelling system, whereas the south flowing Agulhas current transports warm water along the east and south coasts of South Africa76, offering suitable conditions for P. perna. Our results show that minimum SST are significantly lower along the west than the south coast, while SAT shows no such pattern. Our findings support previous studies indicating SST as a major predictor of intertidal species distribution, increase our understanding on the role of minimum SST and SAT in setting species range limits at a regional level and emphasise the importance of low SST in limiting P. perna distribution in the Iberian Peninsula. The inability of P. perna to persist under cold water conditions can be the result of sub lethal effects on its metabolism. Extremely low densities of rare adult P. perna individuals along the west coast of South Africa indicate that the species can survive after settlement but it is unable to reproduce under low SST, even where food availability is particularly high33.Seawater temperature, whether minimum, mean, maximum SST or water bottom temperature, is a key variable in explaining the modelled distribution of nearshore species. In some case studies, SST is found to be the main environmental contributor in the projection of species distribution , although slightly lower than the previous example. The slight disagreement between observed and predicted niche ranges implies that some suitable habitats still remain unoccupied and that additional abiotic or biotic factors may affect the distribution of P. perna. The northwesternmost observed Iberian distributional limit of P. perna, Castelejo , is approximately 180 km south of the predicted limit (Cape Roca), depicting a stretch of coast that has not yet been colonised, despite the existence of suitable environmental conditions. Plausible explanations for this discrepancy include insufficient time for colonisation or biological interaction. Perna perna specimens could be absent from most southwest Portuguese shores due to a slow occupation of the potential niche. However, the species is characterized by highly invasive behaviour, spreading for hundreds of km under suitable environmental conditions79. Perna perna, could, however, be ecologically excluded from suitable habitat. Interaction with recipient communities is a major determinant of the potential establishment of a species80. As Mytilus galloprovincialis exhibits competitive dominance over P. perna on South African shores 81, the Brown mussel might be similarly outcompeted by M. galloprovincialis from southwest Portugal by means of higher recruitment rate82, faster growth83 and/or greater colonisation ability84.Total agreement between modelled and observed distributions reflects a complete occupation of the species potential nicheWe report the most extensive genetic continuity so far observed for an intertidal organism distributed across northeastern Atlantic and Mediterranean shores, despite the existence of several oceanographic barriers to dispersal that are predicted by Lagrangian particle simulations and previously described in the literature. The results highlight the importance of adopting a multidisciplinary approach based on species distribution, larval dispersal simulations, genetic characterization and ecological niche modelling if one wishes to understand how species distributions and range limits are likely to respond to climate warming. We also emphasise that this approach identifies potential distributions; realised ranges will reflect the additional effects of biological interactions.P. perna along the Atlantic and Mediterranean Iberian Peninsula  amplification of Cox1 region. PCR amplification was performed in a 25 μl reaction volume containing 10 to 100 ng of total DNA, 0.2 μM of each primer, 0.08 mM of each dNTP, 2 mM of MgCl2, 1x GoTaq Flexi Buffer  and 1 U GoTaq DNA Polymerase . Amplification used an initial denaturation during 2 min at 94 °C followed by 35 cycles of denaturation at 94 °C for 60 s, annealing at 55 °C for 60 s, extension at 72 °C for 90 s and a final extension at 72 °C for 5 min. PCR products were then purified for sequencing using ExoSap  and sequenced directly with PCR primers using the BigDye Terminators version 3.1 Cycle Sequencing Kit  in an ABI PRISM 3130 genetic analyzer (Applied Biosystems). Eight microsatellite loci P01, P02, P05, P08, P16, P20, P26 and P27 were amplified and genotyped as in ref. 89. Additionally, by combining mitochondrial (slower mutation rate) and nuclear (higher mutation rate) markers, we aimed to understand whether historical and/or contemporary oceanographic barriers are responsible for restricting the gene flow in P. perna  and nucleotide (π)92 diversities for individuals collected at the same location (hereafter referred to as a population). Total numbers of haplotypes (H) and unique haplotypes (UH) were estimated for each population in DNAcollapser from FaBox93.Mitochondrial DNA: DNA sequences were edited and aligned using Geneious 4.8.2 (Biomatters Ltd.). DnaSP 5.094 to select the best fitting model of sequence evolution to analyse the dataset in Arlequin 3.1195. Genetic differentiation between pairs of populations was calculated by estimating ɸST based on haplotype frequency. Statistical significance was assessed by performing 10,100 permutations under the null hypothesis of no differentiation, and adjusted with q-value correction96 (implemented in R97). Spatial analysis of shared alleles (SAShA)98 was used to complement ɸST differentiation values and statistical significance was determined by running 1000 permutations of the haplotype matrix. The minimum geographic distance between pairs of locations was measured in kilometres using the path ruler tool in Google Earth, from a height of 20 km.The Akaike Information Criterion corrected for small sample sizes (AICc) was used in jModelTest 0.1.1Populations .To evaluate population genetic structure, a hierarchical analysis of molecular variance (AMOVA) was conducted in Arlequin 3.11 using 10,100 permutations. To understand how genetic variation is partitioned between distinct groups, among locations within groups and within locations, the seven groups described above were designated 99.Intra-specific genealogical relationships between groups and the relative frequency of haplotypes were determined by a median-joining haplotype network built on Network 4.5.0.2http://www.vgl.ucdavis.edu/informatics/STRand), binned with the StandArich package in R 2.10.1 software and manually reviewed for ambiguities. MICRO-CHECKER100 was used to test for stuttering, null alleles and large allele dropout at each locus and population. All loci had less than 5% of missing data with the exception of P16 (22%), which was excluded from the following analyses.Microsatellite markers: Allele sizes were scored using STRAND software (O) and expected (HE) heterozygosity, inbreeding coefficient (FIS101) and allelic richness (Â) were estimated for each population. Allelic richness (Â) was additionally standardized to two of the smallest sample sizes using GENETIX 4.05102 and FSTAT103. Allelic frequencies for each marker and population were plotted using StandArich. Deviations from Hardy-Weinberg equilibrium were tested running 10,000 permutations using GENETIX. Pairwise genetic differentiation was estimated as FST101 and as Jost’s D104 using diveRsity105 in R, with significance tested 1,000 bootstrap. Pairwise genetic distance between populations was estimated based on the proportion of shared alleles106 according to107 using Populations 1.2.30108 testing significance with 999 bootstrap. A neighbour-joining (NJ) phylogenetic tree was constructed using MEGA5109.Observed  assessed ranged from 1 to 28 (maximum number of populations plus one) and five independent runs with 100,000 Markov Chain Monte Carlo (MCMC) iterations and 50,000 burn-in were performed for each K. STRUCTURE HARVESTER111 and CLUMPP112 estimated the most probable number of Ks (clusters) and found the consensus of the five replicated runs for the selected K respectively. The replicate consensus was plotted with Ruby package Bar Plotter . A discriminant analysis of principal components (DAPC) was performed in R with adegenet113 based on the matrix of individual genotypes with seven microsatellite loci, to characterize the genetic variation of the study area. Each individual was assigned to its sampling population. The function find.cluster returned the best fitting number of clusters (K) in the dataset, correspondent to the minimum K after which the Bayesian Information Criterion changes by a negligible amount114.STRUCTURE 2.3.4 softwarea priori based on potential oceanographic barriers as described above.A hierarchical analysis of molecular variance (AMOVA) was conducted in Arlequin 3.11 running 10,100 permutations, to understand hierarchical population structure with groups designated P. perna throughout its northeastern Atlantic and Mediterranean range distribution. The simulations used high-resolution data of ocean currents assembled from the Hybrid Coordinate Ocean Model (HYCOM). The coastlines of the northeastern Atlantic and Mediterranean region (comprising ~6500 km) were gridded to cells with a common spatial resolution of 0.01° , from which individual passive particles simulating pelagic states of P. perna were released every 12 hours, from February to June (spawning period of P. perna115). The particles were allowed to drift in the simulated environment for a maximum period of 30 days6 until ending up on shore. The position of each individual particle was computed every hour using the bilinear interpolation of HYCOM’s daily velocity fields. The final particle trajectories were used to estimate the asymmetrical connectivity between all pairs of coastline cells, by averaging the time taken (days) of all particles released from cell i that ended up on cell j. Inter-annual variability in ocean currents was taken into account by averaging simulations running independently per year, for a period of 11 years (2002 to 2012).Lagrangian particle simulations  against the minimum transport time between sites, on the stepping-stone scenario. A null model was performed with genetic distances against shortest marine distances. Adjusted R-squared and Akaike Information Criteria (AIC) were adopted to infer the efficiency of each model in explaining genetic differentiation.A network analysis  was performed in R using the igraph package to consider a year to year stepping-stone scenario116, that maximized modularity, an index that quantifies the goodness of fit of a given network117. This approach removed surplus connections with irrelevant information. The leading eigenvector algorithm117, commonly used to detect communities in networks118, was then used to assign a unique membership to the nodes  of the percolated network. In practice this allowed the delineation of unique oceanographic regions structured by ocean currents, where the likelihood of cells being connected within the same region (i.e. membership) is higher than the likelihood of connection among regions. The statistical significance of this step was inferred by testing the proportion of 1e104 membership assignments performed randomly that retrieved a modularity higher than observed. Finally, the strength of barriers separating each oceanographic region was quantified by dividing the absolute number of coastal cells connecting the regions i with j on at least one dispersal event, by the absolute number of connected cells within region i, and multiplying this by 100 so that the strength of barriers would be reflected as % reduction in connectivity.The stepping-stone connectivity matrix was further used to identify major oceanographic regions. To this end, network percolation was performed to remove the weak connections between cells until a threshold was reached that allowed all cells to be connected into a unique network120. The selection method to generate pseudo-absence data conditions the predictions of the model120. By performing a random selection of pseudo-absences the modelled range is not overpredicted, allowing a better assessment of the variables affecting the realized distribution of the species119.A total of 118 presences were compiled from extensive field surveys and from records in the existing literature where the species is native . As the number of true absences detected in the field was relatively low (52) and biased towards the areas where most of the field surveys are available , pseudo-absences from non-surveyed areas were randomly added and included in the models (Supplementary Table http://www.gebco.net/).The intertidal area was delimited by extracting the coastal cells covering a range from −2 to 1 m from the General Bathymetric Chart of the Oceans (GEBCO) gridded bathymetric data set with a spatial resolution of 30 arc-seconds . These included minimum and maximum surface air temperature (SAT)122, minimum and maximum sea surface temperature (SST), nutrients (nitrate and phosphate concentrations), salinity and mean cloud cover fraction , generalized boosting model (GBM), generalized linear model (GLM), flexible discriminant analysis (FDA), randomForest (RF), and multiple adaptive regression splines (MARS) using biomod2 package123 in R. Pseudo-absences were selected at random to complement absence data surveyed in the field. A proportion presence/absence of 1:10 , using the threshold which optimized ROC and TSS scores123, were used to evaluate the models. Each of the six algorithms ran 50 iterations and a “committee averaging” ensemble model was performed averaging the binary predictions of models with TSS > 0.7 to predict the probability of occurrence of the species.The environmental niche of 126, running three permutations. The subtraction of 1 minus the correlation was calculated and each predictor was scored 0–1 (lowest to highest importance)123. Subsequently, a mean of the scores of the three permutations was calculated for each variable.The relative contribution of each variable was calculated by estimating the correlation between each model without a variable and the full modelP. perna distribution, two subsets of nine locations equitably distributed along approximately 2,200 km of the cool temperate southwest (CT) and the warm temperate (WT) southern African provinces31 were selected. Perna perna has been extensively investigated along these shores where a wide distributional gap has been described (P. perna is absent from CT but present in WT)32. The contrasting distributional pattern of P. perna along the CT and the WT regions coupled to SST and SAT data allows a better understanding of the environmental factors setting the distributional limits of this species. While all nine absence records from the CT were included, three different subsets of nine locations along WT were selected. The extents of coastlines and the distances between locations were estimated on Google Earth at an altitude of 100 m.To disentangle the individual significance of minimum SST and SAT on P. perna is absent or present. The design consisted of one factor: Record  and the analyses were performed three times. All tests and respective significance values were performed with STATISTICA (StatSoft). When data did not fulfil the pre-requisites for parametric analysis (Cochran’s Test or Shapiro-Wilk’s W), analyses were performed using PERMANOVA128 running 999 permutations.One-way ANOVA was used to test the null hypothesis that minimum SST and minimum SAT did not differ significantly between the two regions in South Africa where The mitochondrial and nuclear genetic data generated and analysed during the current study have been deposited in GenBank (accession numbers KY514494 - KY515223) and can be found in Supplementary Table Supplementary Information


Opecarcinus hypostegus specimens collected off Curaçao. In this study, an additional 176 specimens were sequenced and used to characterize the population structure along the leeward side of Curaçao. Extremely high COI genetic variation was observed, with 146 polymorphic sites and 187 unique haplotypes. To determine the cause of this high genetic diversity, various gene flow scenarios  were examined. Adaptive genetic divergence across Agariciidae host species is suggested to be the main cause for the observed high intra-specific variance, hypothesised as early signs of speciation in O. hypostegus.The effectiveness of migration in marine species exhibiting a pelagic larval stage is determined by various factors, such as ocean currents, pelagic larval stage duration and active habitat selection. Direct measurement of larval movements is difficult and, consequently, factors determining the gene flow patterns remain poorly understood for many species. Patterns of gene flow play a key role in maintaining genetic homogeneity in a species by dampening the effects of local adaptation. Coral-dwelling gall crabs (Cryptochiridae) are obligate symbionts of stony corals (Scleractinia). Preliminary data showed high genetic diversity on the COI gene for 19  A central challenge in evolutionary biology is to establish the influence of spatial and ecological processes on the evolutionary patterns of species, including local adaptation, colonization and speciation145289101112The population genetic structure in marine species can be affected by several mechanisms. Gene flow patterns may be proportional to geographic distance, whereby genetic differentiation increases with distancePocillopora damicornis Many marine invertebrates exhibit a pelagic larval stage. The effectiveness of migration is determined by the duration of the pelagic larval phase and the strength of oceanic currents, together affecting the realized larval dispersal distance, as well as factors such as the survival and reproduction rate of the successfully dispersed larvae in a novel habitat16Opecarcinus hypostegus  were collected off CuraçaoOpecarcinus hypostegus is associated with five Agaricia species and Helioseris cucullata  of the family Agariciidae2931O. hypostegus includes very shallow as well as deeper reefs down to at least 60 mO. hypostegus for the deeper reefs. Prevalence rates at 6 m were highest in Agaricia agaricites  and at 12 m and 18 m highest in Agaricia lamarcki Milne Edwards and Haime, 1851Agaricia coral hosts. Seventy-six polymorphic sites, resulting in a nucleotide diversity (π) of 0.02617 and a haplotype diversity (h) of 1.00 were retrieved . These results were surprising, because most Indo-Pacific members of the Cryptochiridae show very low haplotype diversity at the COI gene across large distances and COI is most commonly used to infer phylogenetic relationships at species level2435The importance of environmental factors on the genetic structuring of populations has been shown in marine species2223252627O. hypostegus in more detail. COI sequence data was used to characterize O. hypostegus population structure and infer patterns of O. hypostegus gene flow along the leeward side of Curaçao. Factors that are expected to limit gene flow and increase genetic differentiation at this small geographical scale include: (I) geographical distance along the leeward side of Curaçao, (II) genetic partitioning over depth, or (III) genetic differentiation between individuals inhabiting different Agaricia species.The purpose of this study is to examine the possible barriers that affect the genetic structure of h = 0.9994). Of these, 123 were third codon position changes, along with 23 first codon position changes and zero second codon position changes. Overall nucleotide diversity (π) = 0.02558  (Agaricia lamarcki (n = 117), Agaricia agaricites (n = 66), Agaricia humilis Verrill, 1901 (n = 7), Agaricia grahamae Wells, 1973 (n = 4) or Agaricia fragilis Dana, 1846 (n = 1), revealed a significant relationship between genetic differentiation (Φst) and geographic distance (km) for individuals sampled from A. agaricites     and the on (Φst)  (r = 0.1on (Φst) . Partiti 0.0022) . No sign 0.3085) . For theanalysis .A. agaricites    and the difference in depth of collection  between  0.8599) , nor for 0.3372) . For theanalysis .A. lamarcki, A. agaricites and A. grahamae, but not statistically significant , followed by A. humilis and A. lamarcki , A. agaricites and A. lamarcki  and A. agaricites and A. grahamae . Negligible differentiation was measured between A. grahamae and A. lamarcki  (A. grahamae (n = 4) and A. humilis (n = 7), possibly due to the small samples sizes. These results are in concordance with the median joining network  . No sign network  that revaricites . The groaricites  are in atic tree  and 4.Opecarcinus hypostegus specimens collected from Curaçao, 146 COI polymorphic sites were found and 187 unique haplotypes 40First and second COI codon positions are highly conserved, whereas third codon positions can evolve rapidly, making this locus a common choice for population genetics and phylogeography38h = 0.9994, mean π = 0.02558), from a very small geographic area, can be classified as an extreme level of intra-specific variance compared to the reported mean and median values for haplotype (0.63388 and 0.70130) and nucleotide diversity (0.00388 and 0.00356) for 23 animal species in a meta-analysish and π. The values obtained in our study strongly deviate from their values, with π being much higher. A combination of high nucleotide and haplotype diversities has been linked to large stable populations with a long evolutionary history and possible secondary contact between differentiated lineagesThe genetic diversity obtained in this study , where an O. hypostegus individual was observed inhabiting an A. lamarcki coralO. hypostegus distribution more completely. This could increase the likelihood of revealing genetic differentiation over depth, because sampled individuals living over a larger depth distribution face more variable environmental conditions.Due to the technical limitations of scientific diving, our sampling was restricted to a maximum of 38 m depth. The depth distribution of O. hypostegus, which is expected to be an important alternative strategy to direct competition for the same host in CryptochiridaeUnder an ecology-driven gene flow scenario, gene flow may be strongest among similar environments645464748O. hypostegus mates with females on the same coral colony, a genetic preference for that coral host might end up getting fixed in a population. The study on gall crab occurrence rates on the leeward side of Curaçao revealed significant higher O. hypostegus prevalence in A. lamarcki compared to A. agaricites and A. humilis, suggesting a preference for inhabiting A. lamarckiCryptochirid males “visit” females inhabiting separate galls or pits, dubbed the “visiting” mating system29O. hypostegus, with distinct clustering of individuals inhabiting the hosts A. lamarcki, A. agaricites and A. humilis. In the haplotype network, the observed groupings show different patterns. A star-shaped burst pattern (interlinked haplotypes with few mutation steps between them) can be observed for the individuals inhabiting A. agaricites  and no evidence for genetic differentiation over depth. The observed clustering in the haplotype network and phylogenetic tree, however, suggests that adaptive divergence over the coral hosts is present. We hypothesise that this divergence is an early sign of (sympatric) speciation. This divergence might result in several closely related species of Opecarcinus inhabiting Agaricia corals in the Caribbean, in a similar way to its congeners inhabiting various closely related Agariciidae corals in the Indo-Pacific53The main objective of the present study was to examine which spatial and ecological factors influence Opecarcinus hypostegus were collected by chiselling off a small piece of their agariciid coral host from depths between 5 and 38 m at 29 localities on the leeward side of Curaçao . Four samples were collected at ca. 20 m depth from the island Klein Curaçao, located approximately 10 kilometres southeast of Curaçao  and the Coral IDC tool (http://www.researchstationcarmabi.org)O. hypostegus gall crab samples were collected from five Agaricia host coral species. Crabs were preserved in ethanol . All collected specimens are deposited in the Crustacea collection of the Naturalis Biodiversity Center in Leiden .During field surveys in 2013 (16 Oct–9 Nov) and 2014 (12 Mar–28 Apr), specimens of  Curaçao , S1. TheAgaricia lamarcki (n = 117), A. agaricites (n = 66), A. humilis (n = 7), A. grahamae (n = 4) and A. fragilis (n = 1). Polymerase chain reaction was carried out with the following conditions; PCR CoralLoad Buffer (containing 15 mM MgCl2), 0.5 μL dNTPs (2.5 mM), 1.0 μL of each primer, LCO-1490 and HCO-2198μL Taq polymerase (15 units per μL), 18.7 μL of extra pure PCR water and 1.0 μL DNA template. Thermal cycling was performed by initial denaturation at 95 °C for 3 min, followed by 39 cycles of 95 °C for 10 s, 48 °C for 1 min and 72 °C for 1 min, and a final elongation step of 5 min at 72 °C. PCR products were sequenced by BaseClear BV . Sequences were assembled and edited in Sequencher® v. 5.3  and aligned with ClustalW in BioEdit 7.2.5For 195 specimens, the DNA was isolated from muscle tissue of the fifth pereiopod using the NucleoMag 96 Kit (Machery-Nagel) according to the manufacturer’s protocol for animal tissue. The coral hosts of these 195 individuals were: π), haplotype diversity (h), Fu and Li’s F and Tajima’s D values were calculated using DnaSP 5.10.0161The number of polymorphisms (P), nucleotide diversity  calculated with MEGA 6.06http://popart.otago.ac.nz). For continuously distributed populations a Mantel’s test between the genetic differentiation and geographical distance can detect an isolation-by-distance pattern. A Mantel’s test was performed in the program IBDWS v. 3.23A median joining haplotype network, to display the COI sequence variationO. hypostegus. Runs were performed using the default range of priors  using the JC69 Jukes-Cantor measure of distance. The analysis involved 195 sequences with a total of 675 positions in the final dataset.The web version of ABGDHow to cite this article: van Tienderen, K. M. and van der Meij, S. E. T. Extreme mitochondrial variation in the Atlantic gall crab Opecarcinus hypostegus (Decapoda: Cryptochiridae) reveals adaptive genetic divergence over Agaricia coral hosts. Sci. Rep.7, 39461; doi: 10.1038/srep39461 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Meleagris gallopavo) extends from Mexico to southeastern Canada and to the eastern and southern regions of the USA. Six subspecies have been described based on morphological characteristics and/or geographical variations in wild and domesticated populations. In this paper, based on DNA sequence data from the mitochondrial D-loop, we investigated the genetic diversity and structure, genealogical relationships, divergence time and demographic history of M. gallopavo populations including domesticated individuals.The distribution of the wild turkey , followed by a stable period 100 Kya and finally a decline ~ 10 Kya (Pleistocene–Holocene boundary). In Mexico, the Trans-Mexican Volcanic Belt may be responsible for the range expansion of the C3 group. Two haplotypes with different divergence times, MGMDgoB/MICH1 and MICH2, are dominant in domesticated and commercial turkeys.Analyses of 612 wild and domesticated turkey mitochondrial D-loop sequences, including 187 that were collected for this study and 425 from databases, revealed 64 haplotypes with few mutations, some of which are shared between domesticated and wild turkeys. We found a high level of haplotype and nucleotide diversity, which suggests that the total population of this species is large and stable with an old evolutionary history. The results of genetic differentiation, haplotype network, and genealogical relationships analyses revealed three main genetic groups within the species: mexicana as a population relict (C1), merriami (C2), and mexicana/intermedia/silvestris/osceola (C3). Haplotypes detected in domesticated turkeys belong to group C3. Estimates of divergence times agree with range expansion and diversification events of the relict population of M. gallopavo covered a wide geographic distribution from the north to the center of America (USA and Mexico). The mexicana, merriami, and mexicana/intermedia/silvestris/osceola genetic groups originated after divergence and range expansion from northwestern Mexico during the Pliocene–Pleistocene and Pleistocene–Holocene boundaries. Old and new maternal lines of the mexicana/intermedia/silvestris/osceola genetic group were distributed within the Trans-Mexican Volcanic Belt where individuals were captured for domestication. Two haplotypes are the main founder maternal lines of domesticated turkeys.During the Pleistocene, a large and stable population of The online version of this article (10.1186/s12711-018-0388-8) contains supplementary material, which is available to authorized users. Meleagris gallopavo is an original neartic species with a distribution that extends from Mexico to southeastern Canada and to the eastern and southern regions of the USA . Within M. gallopavo, the differentiation of the basal group from northwestern Mexico (mexicana population) began during the Pliocene (3.39 Mya)  of the genera P = 0.35) and Hri  indicate that the analyzed data fit the sudden expansion model [Finally, to describe the changes in effective population size through time, we investigated the demographic history of the species. The mismatch distribution graph shows that the highest frequency of pairwise differences is around 2, which indicates that the population analyzed in this study has few mutations, and represents closely-related individuals. The values recovered from SSD   Fig. b. BetweeBased on the analysis of sequences generated for this study and from the NCBI GenBank database, our results show a high level of haplotype and nucleotide diversity which suggests that the turkey population has remained stable with an old evolutionary history . AnalysiM. gallopavo were identified: (1) mexicana, (2) merriami, and (3) intermedia/silvestris/osceola/domesticated/commercial. This result indicates that although these groups share haplotypes, the proportion of unshared haplotypes exclusive to each group is significant and variable [In agreement with the levels of pairwise genetic differentiation and distribution of genetic variation by AMOVA analysis, approximately three groups of variable .M. gallopavo there is no sub-speciation; this is in agreement with previous reports for the species [We found 64 haplotypes that differ from each other by a small number of mutations, with some of these haplotypes being shared by domesticated and wild populations, which indicates that in  species , 14, 15. species . This co species .Fig. 5MaOn the one hand, our results indicate that the dominant haplotypes MGMDgoB/MICH1 and MICH2 are the main founding maternal lines of domesticated turkeys. On the other hand, since these two maternal lines were detected in individuals of the commercial line Bronze and in domesticated individuals from Canada and the USA, they probably constituted the basis of the current highly selected commercial lines. Our results agree with those of a study that evaluated the genetic diversity of different turkey populations by using single nucleotide polymorphisms (SNPs), i.e. that the heritage turkey varieties Royal Palm and Narragansett and commercial populations derive from the wild mexicana population, that the commercial lines share the same origin, and that possibly specific haplotypes (nuclear DNA) were selected in the modern domesticated turkey . In addiMeleagris and Gallus genera i.e. 33.66 Mya agrees with the estimates reported by Claramunt and Cracraft [M. gallopavo and M. ocellata 5.35 Mya coincides with the Miocene–Pliocene boundary, which indicates a deep divergence between lineages. We identified different diversification events in turkey particularly during the Pleistocene, which coincides with reports on fossils dated between 0.3 and 2.6 Mya [M. gallopavo. Similar to the results reported by Mock et al. [Our estimate of the date of divergence between the common ancestor of Cracraft . The sep 2.6 Mya . At the k et al. , we founM. gallopavo there is no genetic discontinuity in the region  to Michoacan led us to propose that the Trans-Mexican Volcanic Belt (TMVB) may be responsible for the expansion of the range of the C3 group Figs. , 5. We aM. g. gallopavo.Finally, we found that the haplotypes present in domesticated turkeys originate from the genetic group C3, which includes the MGMDgoB/MICH1 haplotype and its peripheral haplotypes, and by expansion from the SCI, which includes the MICH2 haplotype and its peripheral haplotypes. Thus, domesticated turkeys do not originate from an extinct subspecies M. gallopavo makes it difficult to determine the center of origin with more precision.The presence of the MGMDgoB/MICH1 and MGMDgoD haplotypes in the mexicana relict population, indicate that the mexicana population and the C3 group have been in contact, probably in central-northwestern of Mexico Figs. , 3. HoweThe rate of substitutions per site obtained in this study coincides with the range of substitutions per site per million years of mitochondrial genes for birds reported in the literature . The mulThe slight decline of the population about 10 Kya coincides with the cooling during the Younger Dryas. Moreover, it has been reported that 12,900 years ago an extraterrestrial impact occurred in northern North America that contributed to the late Pleistocene megafaunal extinctions and adaptive shifts among Paleoamericans in North America . EnvironM. gallopavo occupied a wide geographical distribution from the north to the center of America (USA and Mexico) during the Pleistocene. Due to the expansion of their geographical range and to divergence events during the Pliocene–Pleistocene and Pleistocene–Holocene boundaries, three genetic groups originated within the species: mexicana, merriami and mexicana/intermedia/silvestris/osceola. The MGMDgoB/MICH1 and MICH2 haplotypes and their peripheral haplotypes that belong to the mexicana/intermedia/silvestris/osceola group, are the main maternal lines that were captured for domestication in the center of Mexico (Trans-Mexican Volcanic Belt), the only region of turkey domestication. Domesticated turkeys populations from backyards in Michoacan come from the founding domesticated population. To confirm these results further, sampling of turkeys should be extended to key regions of Mexico . Finally, we provide new data on the haplotype diversity that prevails among domesticated turkeys from backyards in Mexican rural communities, with six haplotypes, which, to date, had not been reported in M. gallopavo.A large and stable population of Additional file 1: Table S1. Haplotypes identified, polymorphic sites and absolute frequencies for each population sampled, including sequences obtained from databases.


Ichthyosaura alpestris, within the Italian peninsula. Nuclear and mitochondrial markers consistently identified three distinct genetic lineages, whose divergence dates to the Early Pleistocene (1.9 and 0.8 million years ago). Our results show that the Italian peninsula provided multiple Pleistocene refugia to this cold-adapted species, and suggest that allopatric fragmentation followed by secondary admixture have been key events in the formation of its current pattern of genetic diversity. Indeed, estimates of population genetic diversity clearly identified contact populations as those achieving the highest levels of diversity. Such concordance among cold-adapted and temperate species in terms of processes triggering the formation of regional patterns of genetic diversity provides strong support for the hypothesis that gene exchange between divergent lineages, rather than long-term stability of refugial populations, has been the main step toward the formation of hotspots of intraspecific biodiversity.Mediterranean peninsulas are major biodiversity hotspots, and cold-adapted species are an important component of this biodiversity. However, cold-adapted species contributed surprisingly little to our knowledge of the intimate links between Quaternary environmental changes, species’ responses to these changes, and current patterns of intraspecific biodiversity. Here, we investigated the genetic structure and evolutionary history of a cold-adapted amphibian, the Alpine newt  After over three decades of investigating phylogeographic patterns in temperate species7, it is now well established that Pleistocene climatic oscillations have played a prominent role in this regard. By promoting cycles of contraction and expansion in species’ ranges, they triggered the formation of range-wide patterns of genetic diversity, such as the so-called “southern richness and northern purity” pattern11. Moreover, through their interactions with the topographical complexity of the Mediterranean peninsulas and their influence on sea-level changes, Pleistocene climatic oscillations also contributed to the formation of complex patterns of population genetic structure and divergence within these regions, such as the so-called “refugia-within-refugia”12. Most of the microevolutionary processes that prompted the formation of current hotspots, cold spots, and melting pots of genetic diversity were triggered by interactions between climatic oscillations and topographical complexity18. Accordingly, despite differences among species that are related to their ecological characteristics, genetic imprints of Pleistocene climatic oscillations have been observed in virtually all of the temperate species studied to date . Biological diversity within Mediterranean peninsulas, however, is not restricted to temperate species.Understanding how past climatic oscillations, topographic features and species-specific ecological characteristics have contributed to shape spatial and temporal patterns of intraspecific genetic variation has been a major goal of the phylogeographic exploration since its inception20. Although their role in the evolutionary history of local biota has not been overlooked, they have mainly been characterized as barriers to dispersal for temperate species. However, mountain areas also contain a wealth of cold-adapted species21, whose contribution to peninsular biodiversity is not negligible23 and that are expected to be particularly sensitive to climatic oscillations24. Several observations suggest that the responses of cold-adapted and temperate species to Pleistocene climate changes differed considerably. In southern Europe, climatic optima of cold-adapted species are mainly arranged along steep altitudinal clines within strongly discontinuous mountain ranges, which per se form the basis for population fragmentation. However, interglacial phases were shorter than glacial phases, suggesting that there were shorter time periods for cold-adapted species to accumulate substantial differentiation among populations28. Therefore, the large amount of data available for temperate species appears to provide only a limited foundation for predictions about cold-adapted species’ responses to climatic oscillations. At the same time, the evolutionary histories of cold-adapted species in response to Pleistocene climate changes within Mediterranean peninsulas are still largely understudied. This paucity of phylogeographical studies is particularly true of the Italian peninsula. This peninsula is crossed throughout its length by the Apennine, a mid-to-high-altitude mountain chain that runs for over 1200 km along a NW-SE direction. The Apennines contain a wealth of cold-adapted taxa that often exhibit disjointed and relict-like distributions. Despite this, a literature search using combinations of the terms Phylogeography, Italian/Italy, and Apennines  retrieved 43 papers dealing with the phylogeographical structure of populations within the Italian peninsula, but only six papers concerned cold-adapted species34 (see Discussion). Furthermore, the few studies that have been conducted were based on a limited sampling of the Apennine populations and/or on the use of only mitochondrial DNA as a molecular marker, as was customary until recently.Mountain areas are widespread within these peninsulas, and disproportionately contribute to habitat heterogeneityIchthyosaura alpestris , within the Italian peninsula. This species is widely distributed in central and eastern Europe, while in the Apennines its distribution is fragmented and closely linked to mid- and high-altitude ponds35  to the Late Miocene41.In this study, we investigated the genetic structure and evolutionary history of a cold-adapted amphibian, the Alpine newt Here, using information from both mitochondrial and nuclear markers in combination with population genetic, phylogenetic and Bayesian phylogeographical tools, we aimed to assess (1) the current patterns of genetic diversity of the Alpine newt within the Italian peninsula, (2) the geographical structure of genetic variation among its populations, and (3) its Pleistocene evolutionary history. To the best of our knowledge, this is among the few studies that have combined exhaustive sampling with a multi-locus dataset to investigate the Pleistocene evolutionary history and genetic diversity of a cold-adapted animal species within Mediterranean peninsulas, and the first one for the Italian peninsula.CytB) and an 878-bp fragment of mitochondrial NADH dehydrogenase subunit 2 gene (ND2). No indels, stop codons or non-sense codons were observed on either gene. In the combined mtDNA dataset (1277 bp), we found 13 haplotypes defined by 35 variable positions, 31 of which were parsimony informative. The mean haplotype diversity (h) and nucleotide diversity (π) values for this dataset were 0.803 (±0.019 SD) and 0.0077 (±0.0002 SD), respectively.We sequenced two mitochondrial DNA (mtDNA) fragments from 149 alpine newt individuals: a 399-bp fragment of the Cytochrome B gene (β-FIB) from 104 individuals, a 565-bp fragment of the platelet-derived growth-factor receptor gene (PDGFR) from 111 individuals, and a 670-bp fragment of the growth hormone gene (GH) from 103 individuals. In the β-FIB alignments, we found six haplotypes that were identified by eight variable sites, seven of which were parsimony informative; h = 0.365 (±0.041 SD) and π = 0.0041 (±0.001 SD). In the PDGFR alignments, we found five haplotypes that were identified by four variable sites, all parsimony informative; h = 0.413 (±0.038 SD) and π = 0.0018 (±0.0002 SD). In the GH alignments, we found eight haplotypes that were identified by 17 variable sites, 15 of which were parsimony informative; h = 0.376 (±0.039 SD) and π = 0.0017 (±0.0004 SD). No recombination events were indicated by the PHI tests conducted on the nuclear gene fragments . Using phased haplotypes as alleles, a multilocus genotype matrix was built that included 121 individuals for the three nuclear loci, with 8% of missing data. A full list of all of the haplotypes found for each gene in each sampled population is presented as Supplementary Table The nuclear DNA (nDNA) dataset included a 328-bp fragment of the β-fibrinogen gene  to 19 (Copta 9). Allele frequency data for each locus in each population are given as Supplementary Table A total of 185 individuals were genotyped at the nine microsatellite loci. 43).The Bayesian clustering analyses conducted with TESS clearly and consistently showed that the population genetic structure of the Alpine newt within the Italian peninsula is best represented by three distinct clusters. Indeed, K = 3 was the best clustering option for the nDNA dataset, because only a minor decrease in the deviance information criterion (DIC) values was observed at higher K values Fig. . DIC valThe spatial distribution of the three clusters had a clear geographical structure: one cluster was mainly restricted to the north-western populations (9–15), one was by far the most represented among populations in the north-central Apennines 2–10), and the third was geographically restricted to the southernmost population (1). Bar plots showing the individual admixture proportions and pie-charts showing the average proportion of each cluster within each sampled population are presented in Fig. –10, and CT: 0.25), 14.5% to the among-population within groups level (FSC: 0.19), 60.9% to the within population level (FST: 0.39); all variance components and fixation indices were statistically significant (P < 0.001).The analysis of molecular variance (AMOVA) analysis was performed with the following groupings: [N-W clade: samples 11–15], [S-E clade: samples 2–10], [Calabrian: sample 1]. With this grouping, 24.6% of variation was attributed to the among-group level of variation  southern populations  interval between 1.766 × 10−9 and 8.08 × 10−9]. A chronogram based on the maximum clade credibility (MCC) tree is presented in Fig. The divergence time estimation among mtDNA haplogroups yielded by BEAST runs fully converged to a stationary distribution, with high effective sample size (ESS) values (>200) for all the parameters of interest. The substitution rate was estimated as 4.586 × 10I. a. apuana range  ESS values. The ancestral areas of these lineages (at their respective TMRCAs) were estimated to have occurred in close geographical contiguity, along the north-western part of the see Fig. . HoweverI. alpestris on the Apennine peninsula41 one restricted to the southernmost and isolated populations in Calabria, one ranging from the northern to the central Apennines, and one ranging from the western to the eastern Ligurian Apennines.Mitochondrial and nuclear markers consistently supported the existence of three strongly differentiated lineages of 44). Despite a substantial divergence, the other two lineages exhibited a contiguous distribution with a contact zone occurring in the eastern Ligurian Apennines, where the populations are strongly admixed. The contact zone was wider for nuclear than mitochondrial genes, as only one population shared the mitochondrial haplotypes of both lineages. This type of mito-nuclear discordance can be accounted for by male-biased dispersal45, which can result in a more rapid dispersal of nuclear genes  than mitochondrial genes . Male-biased dispersal has been reported in juvenile I. alpestris46, and has previously been invoked to explain mito-nuclear discordance in this species41. Male-biased dispersal could have increased due to the high prevalence of paedomorphic females in the Apennine47, which do not disperse from natal populations48.The Calabrian populations are over 500 km from the nearest population in the central Apennines, and exhibit genetic divergence of a likely mid-Pleistocene origin. The low level of genetic diversity found in this area was not unexpected, because population size was estimated to be extremely low , and it marks the range boundary of several other taxa, including amphibians . Such a clustering of contact zones, recolonization routes, and biogeographical breaks suggests that this area might be a suture zone (sensu Remington51)52. Whether common patterns among species and intraspecific lineages occur by chance or are the outcome of shared historical processes is an open question that requires further investigation.The contact zone observed among the Alpine newt lineages within the Ligurian Apennines falls within a ‘crossroad’ area of recolonization routes for species that altered their ranges to cope with Pleistocene climate changes53.In principle, the peculiar climatic and/or environmental features of this area might have promoted its role as a barrier to dispersal along an east-west axis. However, obvious geographical discontinuities are not apparent in this area. On the other hand, the Ligurian Apennines are particularly close to the sea in this area, which has major consequences for the local climate, and climatic discontinuities between the western and eastern Ligurian Apennines have been identified both for temperature and rainfallI. alpestris, and among them and other European lineages, suggests that the occurrence of the Alpine newt on the Apennine peninsula is of ancient origin. Recuero et al.41 estimated the divergence among Apennine and other European lineages to about 9.2 ± 2.1 mya, suggesting that I. alpestris might have colonized the Apennine peninsula during the Late Miocene . Subsequently, the evolutionary history of I. alpestris on the Apennine peninsula seems to have been strongly affected by the climatic revolutions of the Pleistocene.The strong genetic differentiation found among the Apennine lineages of 54. The general cooling of climatic conditions induced by early glaciations promoted the spread of suitable habitats for frigophilous species, such as I. alpestris, at lower altitudes and latitudes55, likely triggering range expansions along the peninsula. A general diffusion of other cold-adapted taxa during the Early Pleistocene has also been reported by refs56. On the other hand, warm interglacials might have fragmented the distributions of these species after their early expansions, trapping populations within small areas, and promoting their differentiation.The divergence between north-western Apennine and north-central Apennine lineages was estimated to have occurred during the Early Pleistocene (1.86 mya ± 1.0), which coincided with the onset of the major glacial-interglacial cycles57. The amplitude and duration of the glacial cycles substantially increased during this epoch, with major biogeographical effects5. Along the Italian peninsula, major changes in community assemblages occurred59, and several species’ ranges underwent severe fragmentation followed by genetic differentiation61. Noteworthy, two particularly humid glacial cycles  caused the expansion of Alpine forests in Calabria58, which probably promoted a southward expansion of I. alpestris and the consequent establishment of the Calabrian population. Such a large expansion may have been a unique event, as evidenced by the strong differentiation between lineages, and by the lack of evidence in our data for successive contacts and admixture between them.The mean values of the TMRCA for both the Calabrian and the central Apennine lineages were estimated at about 800 000 years ago, i.e. close to the so-called Mid-Pleistocene revolution62), and was completed during the last glacial phase. This scenario supports the hypothesis that I. alpestris populations underwent major expansions during cold, glacial phases. Noteworthy, the penultimate glacial phase was particularly cold and humid, and was interspersed by several interstadials with intermediate climatic conditions64. Such unusual climatic conditions promoted the expansion of montane forests in southern Europe62, and could also have favoured a greater expansion of I. alpestris from its refugia.In contrast, the secondary contact between the north-western and north-central Apennine lineages was estimated to have occurred during the last glacial phase, and was driven by an eastward expansion of the former. However, our spatio-temporal phylogeographical reconstruction revealed that most of the expansion from the north-central Apennine refugia had already occurred at the end of the penultimate glacial period , but was similar to that observed within other cold-adapted amphibians, such as Rana temporaria32. In addition, the population genetic diversity as estimated by the microsatellite markers was lower than that of other European populations of the same species68. These low levels of genetic diversity could have been caused by repeated bottlenecks of the Apennine populations during the Pleistocene, which were caused by the fragmentation and isolation of the peninsular populations. The lowest values of genetic diversity were observed within both the southernmost and the northernmost populations (samples 1 and 15). Low levels of genetic diversity are commonly observed in peripheral populations69, as a result of both historical and recent processes70. Our phylogeographic reconstruction suggests that the observed paucity of genetic diversity in the northernmost population is linked to its recent foundation, whereas in the southernmost population it could have been caused by ancient isolation. On the contrary, genetic diversity increased towards the centre of the species’ range in the Apennines. Interestingly, the highest levels of genetic diversity were found within the populations closest to the contact zone, where all of the individuals were highly admixed. This pattern closely parallels similar observations in temperate taxa, in which the highest levels of genetic diversity have been found within zones of admixture among previously allopatric lineages, rather than within populations from the inferred refugia71. In turn, this does not support the long-invoked hypothesis that the formation of hotspots of genetic diversity has been promoted by the prolonged stability of populations within refugia73. Instead, it highlights the importance of sub-refugia in shaping genetic variation, and of melting-pot areas in reshuffling such variation and promoting the formation of hotspots13.Haplotype diversity within each group and population was low compared to that of other peninsular amphibians (i.e. 79), Pleistocene refugia for cold-adapted species within the Italian peninsula do not match those identified for most temperate species. Indeed, while refugia for temperate species have been mainly found in the southern part of the Italian peninsula81, refugia for cold-adapted species have been identified in the northwest (as in the case of the Alpine newt) and elsewhere in the northern half of the Apennine chain34. Moreover, glacial phases had the opposite effect on cold-adapted taxa than on temperate taxa, because they promoted range expansions rather than contractions. Nevertheless, despite such differences, common patterns are emerging, that are shared by temperate and cold-adapted species. Refugial ranges were often fragmented, promoting genetic differentiation within the peninsular region. The areas hosting populations with the highest levels of genetic diversity do not strictly overlap with areas of long-term demographic stability (i.e. refugia). Instead, hotspots of genetic diversity are often found in areas of admixture between intraspecific genetic lineages, which have survived periods of unfavourable climatic conditions within separate sub-refugia.As previously observed in different Mediterranean regions  primers used, their sequences and their annealing conditions are presented in Table 2 (2 mM), four dNTPs (0.2 mM each), two primers (0.2 μM each), the enzyme Taq polymerase , its reaction buffer  and 40–200 ng of DNA template. The PCRs were conducted with an initial step at 95 °C for 5 min, 32 cycles at 94 °C for 1 min, 40″ of annealing . The purification and sequencing of the PCR products were conducted on both strands by Macrogen Inc. (http://www.macrogen.com), using an ABI PRISM® 3730 sequencing system (Applied Biosystems). All of the sequences were deposited in GenBank (accession numbers: KY911407–KY911451).DNA extractions were performed following the standard cetyltrimethylammonium-bromide (CTAB) protocolCopta1, Copta3, Copta8, Copta13, Copta9, TaCa1, Ta3Ca8, Ta3Caga2, and Ta2Caga3), following previously published protocols85. We chose a subset of available loci after excluding those that exhibited reaction inconsistency in over 30% of samples. Forward primers were fluorescently labelled and PCR products were electrophoresed by Macrogen Inc. on an ABI 3730xl genetic analyser (Applied Biosystems) with a 400-HD-size standard.We also analysed genetic variation at the level of nine microsatellite loci  in SplitsTree 4.13.1. All of the subsequent analyses were conducted using phased nuclear data, and indels were treated as missing data. Nucleotide variation was assessed using MEGA 6.0; haplotype and nucleotide diversity were estimated using DnaSP 5.10.Electropherograms of sequence data were visually checked using FinchTV 1.4.0 (Geospiza Inc.), and sequences were aligned using Clustal X 2.0. Heterozygous nuclear sequences were phased using the PHASE method® 4.1. Micro-Checker 2.2.3 was used to test for null alleles and large-allele dropout influences. Because the tetranucleotide locus Copta9 exhibits a dinucleotide variation in some populations, each allele was sequenced as above; sequence alignment revealed a two-base-pair insertion in the flanking region of the simple sequence repeat (SSR) of some alleles. To use only true SSR polymorphisms in our analyses, the fragment-length values of these alleles were corrected by subtracting 2. Allelic frequencies and estimates of genetic diversity were based on the mean allelic richness and the mean observed and expected heterozygosity, and we tested for deviations from the expected Hardy-Weinberg and linkage equilibria using the programs GENETIX 4.05 and FSTAT 2.9.3.The microsatellite data were analysed using GeneMapper89 with the actual geographical distributions of individuals as prior information, because it performs better than other Bayesian methods with shallow genetic structures and a limited number of loci90. For the nuclear sequences dataset, we compiled a multilocus genotype data matrix using phased haplotypes as alleles. For both datasets, analyses were conducted by modelling admixture using a conditional autoregressive model (CAR). Preliminary analyses were conducted to assess model performance, with 20 000 steps (the first 5000 were discarded as burn-in) and 10 replicates for each K value (i.e. the number of clusters) between 2 and 10. The final analysis contained 100 replicates for each K value, with K = 2–10; each run consisted of 50 000 steps, with the first 20 000 discarded as burn-in. The spatial interaction parameter was initially kept at the default value (0.6), and the option to update it was activated. For both datasets, the model that best fitted the data was selected using the deviance information criterion (DIC). DIC values were averaged over the 100 replicates for each K value, and the most probable K value was selected as the one at which the average DIC reached a plateau. For the selected K value, the estimated admixture proportions of the 10 runs with the lowest DIC were averaged using CLUMPP 1.1.2.The population genetic structure across the study area was evaluated using the microsatellite and nuclear sequence datasets separately, in order to check for consistency among marker types and to keep the amount of incomplete multilocus genotypes to a minimum. We used the Bayesian clustering algorithm implemented in TESS 2.3.191 on the microsatellite dataset using ARLEQUIN 3.5.1.3. Groups of populations were defined according to the results of the spatial clustering analysis with TESS. Admixed populations were attributed to the most represented cluster. The significance of variance components and fixation indices were tested using 1092 permutations.To estimate the amount of variation attributable to differences among population groups, among populations within groups, and within populations, we performed a locus by locus analysis of molecular variance (AMOVA)92 implemented in PAUP* 4.0B10, the two mtDNA fragments were combined into a unique haplotype using Concatenator 1.1.0. All of the subsequent analyses were conducted on the combined dataset.Because no differences were detected by a partition-homogeneity test93]. The robustness of the inferred tree topology was assessed using the non-parametric bootstrap method with 1000 pseudo-replicates. The estimated tree topology was then converted into a haplotype genealogy using Haplotype Viewer94. Genealogical relationships between the mtDNA haplotypes were also investigated using the statistical parsimony approach in TCS 1.2195, with a 95% cut-off criterion for a parsimonious connection. The statistical parsimony approach was also used to investigate genealogical relationships between haplotypes for each nDNA gene fragment. MEGA 6.0 was used to compute the net sequence divergence among the main haplogroups found.Phylogenetic relationships between the mtDNA haplotypes were inferred using the maximum likelihood (ML) algorithm in PhyML 3.10. We used the default settings in PhyML for all of the parameters, except for the type of tree improvement (SPR and NNI) and the substitution model [HKY; selected using the Bayesian information criterion (BIC) in jModelTest 2.1.396 was used to estimate the divergence time among the main haplogroups that were identified by the previous phylogenetic analyses. For these analyses we only used the mtDNA dataset, because the nuclear gene fragments exhibited limited sequence variation (see Results). PartitionFinder 1.1 was used to select the optimal partitioning strategy and substitution models for each partition, using linked branch length options and forcing the software to choose only among the models implemented in BEAST. According to the BIC, the best scheme was the same for both genes: HKY for the first and second positions and TrN93 for the third position. This partition scheme was used for all subsequent analyses. A Bayesian skyline was chosen as a coalescent tree prior97. The molecular clock was calibrated by setting a normal prior for the treeModel.rootHeight parameter with a mean of 2.1 million years (standard deviation of 0.45), following the fossil-calibrated estimate of the TMRCA of I. a. apuana in Recuero et al.41. This calibration was preferred over a previous alternative40, which was based on hypothesized biogeographic scenaria and their influence on distantly related organisms . Preliminary analyses were run with an uncorrelated relaxed lognormal molecular clock model98; however, because the standard deviation (ucdl.stdev) was close to zero in the relaxed clock model, subsequent analyses were conducted with a strict clock model. Two final runs were performed, each with a Markov chain Monte Carlo (MCMC) length of 10 million generations, with sampling every 1000 generations. Traces were inspected using Tracer 1.6 to evaluate the ESS of the estimated parameters and the convergence between runs, after removing the first 10% of samples as burn-in. The two runs were combined using LogCombiner 1.8.1, and an annotated maximum clade credibility (MCC) tree was computed with TreeAnnotator 1.8.1 (in the same package).The Bayesian procedure implemented in BEAST 1.8.1I. a. apuana throughout its range were jointly estimated using a Bayesian Phylogeographic (BP) analysis in continuous space as implemented in BEAST99. To avoid any potential bias caused by the population structure100, we performed separate analyses with the same settings for each main haplogroup identified by the previous phylogenetic analyses. Geographical coordinates were provided for each individual after a slight perturbation of ±0.001 was applied to duplicate coordinates. BP analyses were run using a strict molecular clock model, the Bayesian skyline as a coalescent tree prior, MCMCs of length 90 million generations that were sampled every 9000 generations, and Cauchy as a model of spatial diffusion .The spatial and temporal diffusion patterns of We visualized the spatial and temporal diffusion patterns by generating an annotated MCC tree with TreeAnnotator, which was then projected onto a geographical map using SPREAD 1.0.7. Finally, the full posterior of trees was analysed using the timeSlicer option in SPREAD, in order to estimate the geographical location of the MRCA of each lineage.Supplementary informationSupplementary information