Cerastoderma edule and compare it to environmental variables and current‐mediated larval dispersal within a modelling framework. Hydrodynamic modelling employing the NEMO Atlantic Margin Model (AMM15) was used to simulate larval transport and estimate connectivity between populations during spawning months (April–September), factoring in larval duration and interannual variability of ocean currents. Results at neutral loci reveal the existence of three separate genetic clusters (mean FST = 0.021) within a relatively fine spatial scale in the north‐west Atlantic. Environmental association analysis indicates that oceanographic currents and geographic proximity explain over 20% of the variance observed at neutral loci, while genetic variance (71%) at outlier loci was explained by sea surface temperature extremes. These results fill an important knowledge gap in the management of a commercially important and overexploited species, bringing us closer to understanding the role of larval dispersal in connecting populations at a fine geographic scale.Population dynamics of marine species that are sessile as adults are driven by oceanographic dispersal of larvae from spawning to nursery grounds. This is mediated by life‐history traits such as the timing and frequency of spawning, larval behaviour and duration, and settlement success. Here, we use 1725 single nucleotide polymorphisms (SNPs) to study the fine‐scale spatial genetic structure in the commercially important cockle species  For this purpose, we first tested for neutral population structure and then assessed the role of current‐mediated larval dispersal in shaping it. We then investigated the relationship between environmental factors and adaptive divergence, after identifying outlier markers, with a particular focus on abiotic factors such as water temperatures (surface) and ocean stratification during the spawning season. To do this, larval transport between sites was estimated and connectivity matrices derived from oceanographic modelling, accounting for interannual variability due to biophysical parameters, like spawning time and larval duration.22.1Cockles were collected during 2010 and 2011 from seven locations off the coasts of Ireland (Bannow Bay [BAN] and Flaxfort Strand [FLX]) and Britain   for restriction site‐associated DNA sequencing (RADseq). RAD libraries  were produced according to Etter, Preston, Bassham, Cresko, and Johnson , using Samtools 0.2  filters of 0.05, 0.025 and 0.01 were then applied, generating three data sets. No significant effect of MAF filter upon heterozygosity, global FST and population structure was detected, and so a MAF filter of 0.01 across all sites was applied . Finally, SNPs that were found to deviate from Hardy–Weinberg equilibrium  in four out of seven populations were removed  of individual genotypes and is known to perform particularly well in the presence of weak structure, admixture or range expansions . For pcadapt, we chose the most appropriate number of clusters K in the scree plot, which displays in decreasing order the percentage of variance explained by each principal component (PC). Q‐values were used to account for false discovery rate, and SNPs were considered as significant outliers at alpha values ≤.05. Genetic diversity was estimated on three data sets: overall, neutral and outliers, in order to disentangle the role of demographic processes versus selection. Heterozygosity (expected and observed) was estimated with the R package hierfstat  as implemented in Jombart,  and the genotype_curve of the poppr package.The power of the neutral and outlier data sets to discriminate and assign individuals was determined with a genotype accumulation curve Figure , calcula2.4C. edule  developed to simulate potential larval transport and connectivity between the seven sampling locations. The modelling considered natural variability in larval dispersal caused by the timing of spawning relative to the lunar tidal cycle and the variability of ocean currents over seasonal and interannual timescales. Each PTM simulation time was varied from 20 to 40 days to reflect the pelagic larval duration of 2.5C. edule larval behaviour, the effects of vertical swimming were not incorporated into the PTM. The PTM was programmed in Python.The particle tracking model simulates the lagrangian movement of individual particles in space and time, based on oceanographic dispersion and individual particle behaviour. In our case, particle behaviour was represented by varying the pelagic larval duration to reflect the observations in the field   in turn estimated from Cartesian‐transformed coordinates using the geoXY function in the SoDA package  between nodes (the 7 sampling locations). Each edge has an associated “weight,” based on the simulated probability of connectivity. AEMs were calculated with the aem function in R.For the environmental association analysis (EA), simulated connectivity was represented as asymmetric eigenvector maps or AEM   with 1,000 permutations, to finally establish which factors were most correlated with genetic variation. Redundancy analysis (RDA) and partial RDA (corrected for geographic distance between populations) were performed on the putatively neutral (1711) and outlier (14) SNP data sets, and parsimonious RDAs were carried out using the variables selected  and surface–bottom temperature difference (SBTD—the difference between the temperature at the surface and the bottom of the water column) representing ocean stratification, were extracted from the ocean model Figure . The rel33.1FIS, minimum allele frequency, Hardy–Weinberg equilibrium and linkage disequilibrium, 138 individuals and 1725 SNPs were retained  and outliers (14 SNPs).After filtering for missing data, ed Table . BayeScaHE and HO) were similar across locations, ranging between 0.148–0.157 and 0.103–0.134, respectively  and 0.38 (Burry–Flaxfort Strand), but DAPC’s find.clust indicated that one cluster explained the structure in the data. All population‐level neutral FIS values were significant  and positive, ranging between 0.08 and 0.27  for the majority of sites. The largest differences were found for Red Wharf Bay, where particles dispersed similar distances but north‐eastwards in the hourly runs and north‐westwards in the daily simulations; and Flaxfort Strand, where differences in the degree of retention close to the Irish coast can be seen. However, for all sites, correlations between connectivity networks for the 2014 hourly versus daily current data were high (r2 > .95). For the daily current data spanning 2008–2014, dispersal maps for all sites and years   Figure , suggestThe AMM15 simulation showed two distinct circulation patterns. Firstly, in April, the region is vertically well mixed and mostly governed by tides  influenced the population's ability to connect with other sites Figure . This isOur simulations suggest a high degree of isolation at Dyfi  July SBTD explained 10% of the genetic variance ; (b) geographic distance (dbMEM‐1) explained 7% ; and (c) simulated connectivity (AEM2) explained 4% . Partial RDAs on neutral data using these three variables were not significant.The p = .025, adjR2 = .71) and September min SST . These SST values were the lowest daily mean SST in each month , although it was significant when including two at a time: geographic distance (dbMEM‐1) + July mean SBTD (p = .008), and simulated connectivity (AEM2) + geographic distance (dbMEM‐1) (p = .022). Partial RDAs were not significant. The parsimonious RDA run on outliers was globally highly significant  between sites. This axis separates the northern sites of Red Wharf Bay, Dee Estuary and Dyfi Estuary from the southern sites of Flaxfort Strand (FLX), Bannow Bay (BAN), Gann Estuary (GAN) and Burry Inlet (BUR), describing a north–south divide that is not connected by oceanic currents. The second axis (RDA2) was influenced predominantly by an asymmetrical eigenvector representing simulated connectivity (AEM2) and July mean SBTD, a proxy for maximum seasonal ocean stratification. On this axis, the southernmost population Flaxfort Strand (light green) is strongly related to simulated larval connectivity (AEM2)  were identified as potential drivers of adaptive divergence.4.1FIS in all sampling locations; Table FIS could be the Wahlund effect, caused by the presence of substructure within populations. This was not considered to be the case in this data set, given that no subpopulation pattern was found in the clustering analysis , a lack of or weak population structure was expected. Previous studies in the same geographic area identified a lack of genetic structure in shellfish species using microsatellite markers . The relatively high level of genetic differentiation between the neighbouring Burry Inlet and Gann Estuary populations indicates that gene flow between these populations is low, seemingly contradicting the larval transport model's prediction of high connectivity. This result suggests that the Burry Inlet population may able to persist through self‐recruitment, rather than forming a sink population depending upon immigration from healthier populations elsewhere, and could explain the low genetic diversity and high levels of inbreeding detected there. However, the model showed that larval dispersal is possible from the Gann to Burry estuaries, although we assumed a relatively large settlement zone that extended beyond the mouth of the Burry Inlet , indicating that isolation by distance plays an important role in shaping the observed genetic structure in this species, despite its long pelagic larval duration. Nevertheless, it is the interplay between isolation by distance, extremes in temperature‐driven currents (July SBTD) and simulated connectivity (AEM2) that shapes neutral population structure. The summer stratification which strengthens the Celtic Sea front current, directed from south Wales to Ireland . However, a degree of interannual variability was detected along exposed coasts and in regions of fronts (e.g. Red Wharf Bay and Flaxfort Strand). Although this variability does not affect our results because the populations were coastal and generally, a high level of coastal retention can be seen for most sites, other species that spawn offshore or have longer PLDs  would likely show higher interannual variability in dispersal and connectivity and hence population structure. This would be expected especially of populations close to oceanographic fronts that are affected by interannual variations in climate are likely show differences in dispersal patterns.Considering the 14 outlier loci, the minimum sea surface temperatures recorded in April and in September 2014 explained most of the genetic variance observed within this data set. In particular, the Burry Inlet was strongly associated with the SSTs in September, which was warmer compared with SSTs recorded at other locations for the same time Figure . If cock4.2The results from our modelling are likely to be relevant to fishery management in terms of the potential seasonal and interannual variability in larval supply to cockle grounds. This study demonstrates the existence of three distinct units of cockles using both genomic tools and larval dispersal modelling. As with other recent studies (Lal, Southgate, Jerry, Bosserelle, & Zenger, Given the incidence of recurrent mass mortality events at the Burry Inlet (Callaway et al., None declared. Click here for additional data file.


Globodera pallida populations revealed a clear south to north phylogeographic pattern, with five well‐supported clades and maximum diversity observed in the south of Peru. In order to investigate this phylogeographic pattern more closely, we genotyped a larger collection of Peruvian populations using both cathepsin L gene sequence data and a new set of 13 microsatellite loci. Using different genetic analyses , we consistently obtained the same results that led to similar conclusions: the presence of a larger genetic diversity than previously known suggesting the presence of cryptic species in the south of Peru. These investigations also allowed us to clarify the geographic borders of the previously described G. pallida genetic clades and to update our knowledge of the genetic structure of this species in its native area, with the presence of additional clades. A distance‐based redundancy analysis (dbRDA) was also carried to understand whether there was a correlation between the population genetic differentiation and environmental conditions. This analysis showed that genetic distances observed between G. pallida populations are explained firstly by geographic distances, but also by climatic and soil conditions. This work could lead to a revision of the taxonomy that may have strong implications for risk assessment and management, especially on a quarantine species.Our knowledge of the diversity of potato cyst nematodes in their native areas still remains patchy and should be improved. A previous study based on 42 Peruvian  Isolated populations accumulate genetic differences across their genomes as they diverge, whereas gene flow between populations counteracts this divergence. Speciation occurs by the accumulation at specific loci of mutations that reduce the fitness of hybrids, thereby preventing gene flow  are a major pest of potato native to South America. An extensive sampling campaign was carried out in Peruvian potato fields in 2002 in order to improve our understanding of the evolutionary history of G. pallida should be considered a species complex rather than a single species has been raised by several authors   to reinvestigate more in‐depth the genetic diversity of G. pallida in its native area.Thanks to the increase in the number of populations from South Peru investigated and to the use of two different genotyping tools with different tempos of evolution, our objectives were (a) to explore the geographic distribution of 22.12O, 5 µl of 5 × buffer, 3 µl of MgCl2 at 25 mM, 1 µl of each primer at 10 µM, 0.25 µl of Taq Golfex 5 U/µl, 0.5 µl of dNTPs at 10 µl each and 2 µl of DNA). The PCR conditions were a denaturation step at 95°C for 1 min, followed by 30 cycles  and a final elongation step at 72°C for 5 min. Amplification products were observed after migration on an agarose gel (1.5%). The amplification products obtained for European G. rostochiensis populations are about 705 bp, amplification products for European G. pallida populations are about 690 bp, and those for G. ellingtonae are about 880 bp  and was named “10locix84pop.” In the second data set used for further analysis, we chose to retain all the genetic information  and then removed populations where no or too few individual amplifications were obtained . This second data set was named “13locix78pop.”2.3FIS , and th Wright,  in R and Wright, .FST  ranking from 2 to 50. This analysis assumes an admixture ancestry model based on allele frequency. Results were analysed with pophelper  )  hypothesis following instruction of Rousset  and usinSlatkin,  and the Slatkin, .FST values with abiotic and climatic variables. We chose six variables which, from our knowledge, could impact the distribution of PCN. These variables could be geographic (latitude and longitude), climatic  and pedologic , or they could reflect plant diversity (Shannon index). These variables were extracted from WorldClim v2  based on population GPS coordinates. We performed dbRDA using the Capscale function implemented in the Vegan package  from a start saturated model including all variables cited above, and latitude and longitude, to take into account the geographic position:We also conducted a distance‐based redundancy analysis  were observed. The 800 bp product was observed in 10 populations from the northern part of the studied area, and always in association with the fragment corresponding to G. pallida, except in population 196. The 845 bp product was observed in 22 populations often in association with the fragment corresponding to G. pallida, but in four cases  as a unique amplification product. In all, 25 populations were uniquely or mostly constituted by G. rostochiensis (705 bp amplification product), and we removed them from further investigations because of the difficulty in obtaining sufficient G. pallida individuals in the mixed populations. All G. rostochiensis individuals found were located in the south of the study area.Amplification of the cathepsin marker on 119 populations revealed either one or two bands of different sizes. Overall, four amplification products of different sizes were observed with a length of approximately 690, 705, 800 and 845 bp Table . AmplifiG. pallida. Only one population (196) was found with solely the 800 bp amplification product. This 800 bp product size was subsequently further observed in other Peruvian populations from clades IV and V  and found that they also did not amplify these three loci. These Chilean populations were therefore included in some of the subsequent analyses, and the group formed by populations TDF, CAS, 309, 284, 264 and 224 will now be referred to as the “pallida Chilean type.”FST values are distributed from 0 to 0.5697 . This correlation is even higher when we exclude from the analysis populations from group 4 .We found a highly significant IBD pattern when considering either, all populations or all populations without the ones from group 4  that may be cryptic species within the G. pallida species complex. Furthermore, we recorded the presence of G. rostochiensis in the studied area and the absence of G. ellingtonae. All populations of G. rostochiensis were found in the south of Peru, which is consistent with previous observations , which are geographically distributed from south to north Peru. In parallel, the cathepsin L marker identified three groups, the first corresponding to groups 1a, 1b, 2 and 3, the second to group 4, and the third to the “pallida Chilean type.” Compared with the conclusions reached by Picard et al. , groups n et al.  seems toG. pallida populations studied. However, we should be cautious when interpreting the results of our phylogenetic analysis because of the low confidence associated with nodes lower than 0.5.Overall, our results are still consistent with those reported by Picard et al.  and Subb4.2With the exception of the ambiguous delimitation between groups 1a and 1b where gene flow still occurs, the other groups were well separated by all the analyses conducted here. A very important point is the consistency of results from the two different genetic markers used that follow different evolution models. A second point is the consistency between outputs from the STRUCTURE and DAPC analyses which have different assumptions. This distinction between markers and analyses could even provide us with insights into the range of differentiation between the observed groups.G. pallida and G. rostochiensis, species for which hybrids are observed but are not fertile  affect the diversity and distribution of Solanaceae and thereby the differentiation of G. pallida. Moreover, the available climatic and soil data used here are from the last 30 years, while the differentiation is probably older. Therefore, we have to hypothesize that different climatic conditions were at least conserved or even higher at the time the genetic differentiation of these groups occurred.Beside the impact of geographic distance and relief, our results also showed the importance of climatic conditions in the genetic differentiation observed. The dbRDA analysis separated mostly group 4 from group 2 by precipitation conditions, and group 1 from the others by temperature. Group 4 evolved in drier conditions, while group 1 is located in a more temperate zone around Lake Titicaca. We could imagine that these particular conditions lead to some genomic adaptations, and thereby to genetic differentiation. We note that the variable plant diversity seems to have no effect on the differentiation between groups. This finding should be considered with caution, because in parasite species, hosts are known to be a driver of speciation  or between G. pallida and G. mexicana . Second, it should be noted that three microsatellite loci failed to amplify most of, if not all, individuals belonging to this group. This type of failure of amplification is also observed for G. rostochiensis individuals using the Gp111, Gp112 and Gp145 loci (data not shown). Third, the phylogenetic analysis suggests that population 224, which belongs to the “pallida Chilean type,” is not phylogenetically related to the G. pallida species history, and rather clusters outside G. pallida, like the out‐group species G. mexicana, used to root the tree. Nonetheless, the question remains open, as other results are conflicting. The recent molecular tools developed for quarantine PCN diagnosis  than G. rostochiensis is found in sympatry with G. pallida (9 out of 26 co‐occurrences). It could therefore be hypothesized that this group was in fact also introduced into Europe, but failed to establish itself and survive in European conditions. This once again suggests strong differences with G. pallida also in terms of fitness and life‐history traits that would rather support the view of different species. Clearly, further investigations are required before being able to conclude on a novel PCN species. In a perspective of integrative taxonomy  both  Dayrat, , the morG. pallida in Peru. The best explanation is therefore that—in contrast to G. pallida phylogeographic history mostly due to Andes orogenesis—the “pallida Chilean type” phylogeographic history is rather the result of anthropic dispersion between these two countries. Considering the low levels of heterozygosity and allelic richness observed in the CAS and TDF populations, compared with the 224, 264, 284 and 309 populations, it can even be assumed that these populations were introduced to Chile from south Peru. These two countries were at war until 1884 (end of the War of the Pacific), with battles that were fought in the Pacific Ocean, but also mostly in the Atacama Desert and mountainous regions of the Peruvian and Bolivian Andes. Similarly to what occurred for the PCN species G. rostochiensis—detected in the United States for the first time in 1942 in Long Island and probably introduced with contaminated military equipment returning from Europe after the First World War  and south Peru. The genetic proximity observed between these populations that are distant by more than 6,000 km is quite surprising, considering the divergence observed for G. pallida which is listed as a quarantine species because of the significant damage that it could produce in potato crops. A description of the potential new species would imply revision of its taxonomy and expand G. pallida sensu stricto to G. pallida sensu lato. The groups identified as potential new species appear to be absent from Europe, but they could be introduced. The current regulations and controls on G. pallida, that is, the ban on introduction into Europe of soils originating from non‐EU countries, are able to prevent this risk of new introduction. Nonetheless, reinforced controls applied to at‐risk regions and maintaining G. pallida on the list of quarantine nematode species appear important, if not essential.The possible presence of a species complex may be important in terms of economic impact on crop harvests and must be taken into account if a quarantine species is involved. This is particularly true for The authors have no competing interests to declare. Click here for additional data file. Click here for additional data file.


Mussaenda kwangtungensis using eleven nuclear microsatellite and three chloroplast markers, evaluated effects of the island area and distance to mainland on genetic diversity of island populations, and simulated the potential distribution over the past by ecological niche modeling, together with the genetic data to detect the role of islands during the glacial periods. The island populations displayed comparable levels of genetic diversity and differentiation with mainland populations, overall high levels of unique polymorphisms, and the greatest values of specific within‐population genetic diversity. No significant correlation was detected between genetic diversity of island populations and distance to mainland, as well as area of islands, except that allelic richness was significantly positively correlated with the area of islands. Nuclear microsatellites revealed two main clusters, largely corresponding to islands and inland populations, which divergence dated to a time of island formation by ABC analysis. Ecological niche modeling predicted a highly climatic suitability on islands during the last glacial maximum (LGM). Our results suggest that the islands have acted as refugia during the LGM and highlight the role of in situ glacial survival in maintaining high levels of genetic diversity of M. kwangtungensis in continental islands of subtropical China.Generally, island populations are predicted to have less genetic variation than their mainland relatives. However, a growing number of studies have nevertheless reported exceptions, indicating that the relationships were impacted by several factors, for example, historical processes. In the present study, we chose a group of subtropical islands located in South China as the study system, which are quite younger and much closer to the mainland than most of the previous studied island systems, to test the hypothesis that in situ glacial survival contributes to high levels of genetic diversity in island populations. We conducted a comparison of genetic variation between 12 island and 11 nearby mainland populations of  Mussaenda kwangtungensis in a young and near mainland–island system. Our results suggest that the islands have acted as refugia during the LGM and highlight the role of in situ glacial survival in maintaining high levels of genetic diversity of M. kwangtungensis in continental islands of South China.We conducted a comparison of genetic variation between island and mainland populations of  This plant occurs only in the southern part of Guangdong Province, commonly on islands and the adjacent mainland. This species is placed within a very young genus which derived in the Miocene (7.82–14.44 Ma) . We report here a comparison of genetic diversity between 12 island populations and 11 adjacent mainland populations of M. kwangtungensis carried out using 11 nuclear microsatellite and three chloroplast markers. Data enabled us to detect the impacts of isolation and demographic history on the genetic structure of M. kwangtungensis. Thus, by combining approaches from molecular phylogeography and ecological niche modeling, we tested the hypothesis that in situ glacial survival contributes to high levels of genetic diversity in island populations. We addressed the following questions: (a) Do island populations have lower genetic diversity compared to mainland populations following general expectations? (b) Is genetic diversity significantly correlated with island characteristics such as area and distance to mainland? (c) Did island regions likely act as refugia for M. kwangtungensis during the LGM? (d) What is the demographic history of the existing M. kwangtungensis populations?The climbing shrub 22.1Mussaenda kwangtungensis H. L. Li (Rubiaceae) is a perennial, climbing shrub, between 1 m and 2.5 m in height. It has papery leaves with 3–9 cm × 1–3 cm in size and lanceolate‐elliptic to elliptic‐oblong in shape. Its inflorescences are compact cymose‐to‐subcapitate, and each possesses white, blade oblong‐ovate, elliptic, or elliptic‐ovate petaloid calycophylls with 3.5–5 cm long and 1.5–2.5 cm wide  method  in each population were successfully sequenced for three chloroplast intergenic spacer regions: ndhf‐rpl32, psbE‐petL, and rpl32‐trnL , allelic richness , and observed (HO) and unbiased expected heterozygosity (HE). Analyses were performed using FSTAT. Further, in order to investigate differences in genetic diversity between mainland (inland + peninsula) and island populations, we compared AR, HO, and HE between the two groups by permuting populations 1,000 times using FSTAT. In order to check the correlation of genetic diversity with island characteristics, we conducted linear regressions between genetic diversity of island populations and island characteristics  using R 3.3.1  using FSTAT. Standard error was estimated by Jackknifing for overall FST, and 1,000 bootstraps were used to estimate the 95% confidence interval. As FST is likely to underestimate genetic differentiation between populations for markers which show high levels of allelic variability, we also calculated F'ST, a standardized parameter of genetic differentiation as F'ST = FST/FSTmax  was carried out using ARLEQUIN 3.5.2.2 ) against corresponding logarithmic (log10) geographical distances for all populations and two groups. These analyses were performed with the R package “ecodist”  and delta K (ΔK) were calculated to determine the most likely number of clusters, as suggested by Evanno, Regnaut, and Goudet  reduction in effective population size was detected using BOTTLENECK based on microsatellite data  or in island (scenario 2); the presence of two refugia located in inland and island, respectively (scenario 3–6); and the presence of three refugia, one located in inland and two in islands (scenario 7). Prior values used for all parameters are listed in Table S3 (Appendix Approximate Bayesian computations (ABC) are powerful tools for inference on population divergence and demographic history and thus have been frequently used in population genetics and phylogeographic studies . Four‐chain heating at temperatures of 1.0, 1.5, 3.0 and 100,000 was implemented to increase the efficiency of the MCMC. We repeated the runs three times until posterior probabilities stabilized using prior estimates of θ  and M as starting parameters. Maximum‐likelihood estimates and confidence intervals were reported from the final run. To compare historical and contemporary migration rates generated by BAYESASS and MIGRATE, we used the values of m directly generated by the former and converted M values generated by the latter to m based on the formula M = m/μ, where μ is the mean mutation rate of microsatellites. The μ was set to 10–4–10–3, the same as that used in ABC analysis.In terms of assessing historical migration rates, MIGRATE was run with subsets of the data  using default settings (10 short chains of 102.42.4.1h) and nucleotide diversity (π) per population using ARLEQUIN. The significance of differences in h and π between mainland and island populations was investigated by the t test at the 5% level  and island characteristics  were tested using linear regression analyses performed with R 3.3.1. To access the genetic differentiation, we computed FST values for all populations and both groups (mainland and island) identified by cpDNA sequencing using ARLEQUIN. An analysis of molecular variation (AMOVA) was also carried out to determine the level of genetic differentiation between mainland and island populations in ARLEQUIN. We also calculated pairwise FST values and tested for isolation by distance by evaluating the significance of the correlation between genetic differentiation and log geographical distances.Haplotypes were identified using DNASP 5.10  between observed and expected mismatch distribution, and Harpending's raggedness index (HRag) using 1,000 parametric bootstrap replicates. The expansion time (T) for expanding groups was calculated based on the formula T = τ/2μkg , k is the average sequence length used for analysis , and g is the generation time in years. A value for μ was set as 1.52 × 10–9 s/s/y, as proposed by Yamane, Yano, and Kawahara  method as implemented in BEAST v.2.6.2 . Bioclimatic variables were downloaded from the WorldClim database (http://worldclim.org) for three different periods: the last interglacial , the LGM (~21 kya), and current. Palaeoclimatic layers were drawn both from the Community Climate System Model (CCSM)  and the maximum number of iterations (500), using repeatedly randomized samples of 25% of localities for cross‐validation model training and testing, respectively. The regularization multiplier was set to 15 to fit smoother curves as these are thought to perform better when projected to different time periods, and because they do not overfit to training data. Model performance was evaluated using two different indices, the area under the curve (AUC) of the receiver operating characteristic and the true skill statistic (TSS). The predicted distribution at different periods was visualized on the map by ArcGIS v.10.2. Suitable areas for simulated models from MAXENT during different periods were calculated using ArcGIS by eligible considering distribution threshold greater than 0.5.To identify the potential species distribution during the LGM, we calibrated climatic envelope models using georeferenced native occurrence records for the species and the Maxent algorithm. Current presence records of 33.1p > .05) indicating that all 11 loci are independently inherited in M. kwangtungensis. Tests of deviation from the HWE, apart from CT12, revealed significant departures in four to 15 populations of other loci. Similarly, except for locus CT12, the other ten showed signals of null alleles in two to 15 populations with an average frequency between 3.15% and 13.83%. Analysis of genetic variation of M. kwangtungensis was therefore based on these 11 loci.Comparing with an adjusted P value, no linkage disequilibrium was found in any population among loci (HE) was 0.705. At the population level, the mean number of alleles (NA) across all 11 loci ranged from 5.7 (BL) to 10.7 (SC). When correcting for the sample size, allelic richness (AR) varied from 5.01 (WL) to 6.62 (SC). Observed heterozygosity (HO) ranged from 0.494 (DX) to 0.680 (ZM) and expected heterozygosity (HE) from 0.602 (DG) to 0.757 (BL) , while correlating with the area of islands, only allelic richness (AR) was significantly positively correlated (p = .016) , while standardized F'ST was 0.247, indicating moderate differentiation. Genetic differentiation of mainland populations (FST = 0.073) was not significantly different compared to island populations (FST = 0.067) (p = .698) (Table F'ST/ (1 − F'ST) with geographical distance (Mantel test) among all populations, as well as in both mainland and island populations  Table . There w3.2K), increased slightly from K = 2, and the ΔK statistic was greatest for K = 2, with a second peak at K = 3 . At K = 2, all populations were divided into two clusters, geographically corresponding to inland and island plus peninsular regions, respectively. In terms of K = 3, inland cluster remained consistent, while DX, DG, XC, and SC were further clearly separated from island cluster, and WS, XW, and BL showed a large amount of mixture among gene pools  from island cluster (gene pool 2), designated by t1, appears to have started 3,930 BP  compatible with the time of island formation during the Holocene. The median value of t2  was 61,200 BP , indicating an increase of effective population size during the LGM , with a probability of 0.457 (95% CI: 0.365–0.549), associated with the hypothesis that there was one refugium in island and that mainland populations were derived from this area. Scenario 1 had the second highest support with a probability of 0.337 (95% CI: 0.229–0.447). Posterior probabilities for scenarios 3, 4, 5, 6, and 7 were 0.060, 0.053 0.023, 0.061, and 0.007, respectively. Posterior distribution of parameters for scenario 2 is shown in Table 3.4m) from the mainland to islands was 0.005 (−0.002 to 0.013) while that from islands to the mainland was 0.016 (0.004–0.028). The overlapping 95% confidence intervals indicated no asymmetrical gene flow between island and mainland clusters. Similarly, MIGRATE did not reveal a pattern of asymmetrical gene flow between island and mainland. The relative migration rates (M) computed by MIGRATE were 4.450 (3.767–5.067) from the mainland to islands and 3.317 (2.533–4.033) in the other direction. When converting M to m for comparison, the median value of historical migration rate from the mainland to islands was 0.000445–0.00445 with μ ranging from 10–4 to 10–3, nonsignificantly different from contemporary migration rare, and vice versa.Based on BAYESASS analysis, the contemporary migration rate  was 0.471, and nucleotide diversity (π) was 0.370 × 10–3. On the mainland, each population had 1 (BZ) to 4 (ZM) haplotypes with a mean = 2.5. The value of h varied from 0 to 0.867 with a mean of 0.558, while π ranged from 0 to 1.327 × 10–3 (YM) (Table h ranged from 0 to 1.000 (XC and WS) with a mean of 0.391 and π ranged from 0 to 1.137 × 10–3 (XC). None of diversity parameters (h and π) showed significant difference between mainland and island populations (p > .269). Correlation analysis between diversity at cpDNA (h and π) and island characteristics (area and distance to mainland) revealed no significant relationship (p > .160).For FST = 0.402. Island populations showed a slightly stronger genetic differentiation (FST = 0.459) than their mainland counterparts (FST = 0.315). The results of AMOVA showed that just 0.34% of genetic variation existed among mainland and island groups (Table S4). No pattern of isolation by distance was revealed by Mantel tests in either all populations or within each group (p > .201).Population differentiation in cpDNA was revealed by 3.6D  and Fu's Fs  were negative. The mismatch distribution for haplotypes was unimodal with nonsignificant SSD and HRag , unimodality of mismatch distribution  and Fs  were positive. The mismatch distribution was bimodal with significant SSD  and MIROC  models, compared with that at present . The area with climatic suitability >0.5 from the LIG  was a little larger than that during the LGM based on the CCSM model, but far smaller than that from LGM based on MIROC model. Data show that when climatic suitability was greater than 0.8, CCSM model revealed greater area in inland than on islands, while in MIROC model, more suitable habitats were located on islands.The prediction of the bioclimatically suitable areas for 44.1hT = 0.855) was higher than the mean value of angiosperms (hT = 0.68)  was higher than values reported for narrow‐distributed/endemic species (HE = 0.56/0.42)  Nybom, . Species) Nybom, . It is cM. kwangtungensis is pollinated predominantly by butterflies , a distance that ensures pollen can be transferred between most islands and adjacent populations (Table S1). In addition, the color of berry fruits turns to purple black when they mature and attracts foraging birds , which may provide opportunities for the long‐distance dispersal of seeds. At the same time, however, the high levels of private haplotypes on islands  based on microsatellites in M. kwangtungensis .As proposed by the equilibrium theory of island biogeography   Table . Both sc2) Table .5M. kwangtungensis, an endemic species to South China with a narrow distribution, revealed a similar level of genetic diversity in islands compared with the mainland, contradictory with the general expectation that insular populations often harbor lower genetic variation due to founder effects and geographical isolation ; Data curation (lead); Formal analysis (lead); Funding acquisition ; Methodology (lead); Software ; Writing‐original draft (lead); Writing‐review & editing . Yuyuan Wang: Formal analysis ; Investigation . Tingting Duan: Methodology ; Software . Xin Qian: Formal analysis ; Visualization . Tong Zeng: Methodology ; Software ; Visualization . Dian‐Xiang Zhang: Conceptualization ; Funding acquisition ; Supervision ; Writing‐review & editing .Appendix S1Click here for additional data file.Appendix S2Click here for additional data file.


Agave kerchovei, a representative species of the Tehuacán Valley, generating 2,188 bp from 128 individuals sampled from eight populations throughout the species range. We used this data set to (i) characterize the levels of genetic diversity and genetic structure in A. kerchovei; (ii) predict the distribution of A. kerchovei for the present day, and to reconstruct the past geographical history of the species by constructing ecological niche models (ENM); and (iii) compare the levels of diversity in this species with those estimated for the widely distributed Agave lechuguilla. Agave kerchovei has high levels of total chloroplast genetic variation (Hd = 0.718), especially considering that it is a species with a very restricted distribution. However, intrapopulation diversity is low (zero in some populations), and genetic structure is high , which can be expected for endemic species with isolated populations. Our data suggest that Pleistocene glacial cycles have played an important role in the distribution of A. kerchovei, where the climatic variability of the region – likely associated with its topographic complexity – had a significant effect on the levels of genetic diversity and population dynamics, while the potential distribution of the species seems to be stable since the middle Holocene (6 kya). We conclude that in A. kerchovei there is a core group of populations in the Tehuacán Valley, and peripheric populations that appear to be evolving independently and thus the species is fundamentally an endemic species from the Tehuacán Valley while the populations outside the Valley appear to be in the process of incipient speciation.The Tehuacán-Cuicatlán Valley, located at the southeast of the state of Puebla and the northeast of the state of Oaxaca in Central Mexico, south of the Trans-Mexican Volcanic Belt (TMVB), is of particular interest for understanding the evolutionary dynamics of arid and semi-arid environments, being one of the main reservoirs of biological diversity for the arid zones of North America, including the highest diversity of Agavaceae worldwide and high levels of endemism. Studying in detail the phylogeography, environmental history and population genetics of representative species will hopefully shed light on the evolutionary and ecological dynamics that generated the tremendous biodiversity and endemism of this important region in Mexico. We sequenced three non-coding regions of chloroplast genome of  The Pleistocene glacial periods have been major factors influencing the geographical distribution, demographic dynamics and patterns of genetic diversity of many species . During Of particular interest for understanding the evolutionary dynamics of arid and semiarid environments in North America is the Valley of Tehuacán-Cuicatlán Biosphere Reserve  . The Teh2) and that the current vegetation of the area is of recent origin .Considering that the total area of the Tehuacán Valley is relatively small  predict the distribution of A. kerchovei for the present day and reconstruct the past geographical history of the species, by constructing ecological niche models (ENM); and (iii) compare the levels of diversity in this species with those estimated for the widely distributed A. lechuguilla, a related species from the Chihuahuan desert. We expected to detect low levels of genetic diversity given the small size and restricted distribution of A. kerchovei populations, as well as a high genetic structure, given population isolation. Due to the high topographic complexity of the Tehuacán Valley  mentions varieties of A. kerchovei, including A. kerchovei var. beaucarnei, also included in the Tropicos Database2, these varieties are now regarded as synonyms of A. kerchovei ].In this study, we will follow Gentry’s classification (1982), who does not recognize subspecies or varieties of Agave kerchovei is a species with a narrow distribution. Little is known about its natural history or ecology  did not detect a single plant of the species  extraction protocol  and resupsbJ-petA, rpl32-trnL (trnL-trnF (® PCR system 2700 (Applied Biosystems) in total reaction volumes of 30 μl, containing 25–40 ng of total DNA, 1X of PCR buffer , 1.5 mM MgCl2 (for primers rpl32-trnL and trnL-trnF) and 2 mM MgCl2 (for primer psbJ-petA), 0.2 mM of each dNTP, 0.3 μM of each primer, and 1 unit of Taq DNA polymerase. The cycling conditions consisted of an initial denaturation at 94°C for 5 min, 35 cycles of 94°C for 30 s, 55°C for 30 s (for primers rpl32-trnL and trnL-trnF) or 55°C for 50 s (for primer psbJ-petA), and 72°C for 1 min followed by a final extension at 72°C for 8 min. PCR products were purified and sequenced in the High Throughput Genomic Unit, University of Washington, USA.Three non-coding chloroplast (cpDNA) regions [l32-trnL  and trnLrnL-trnF ] were amThe quality of the sequences and the forward and reverse assembly was assessed by direct inspection using the Phrap-Phred, Consed V 19.0 software . SequencAgave stricta and A. lechuguilla, from To assess genetic relationships among haplotypes, we constructed a haplotype network, as implemented in the program TCS v1.21  using 95h) and without indels (h∗), haplotype diversity (Hd), nucleotide diversity (π), and the Watterson estimator of theta (θ) for each population were obtained using the program DnaSP v5.10.1 , without a priori assignment of individuals to population groupings (K) that are geographically homogeneous and genetically differentiated from each other while maximizing the proportion of total genetic variance due to differences among groups of locations (FCT). We explored K-values with 100 permutations for each group of populations. The most likely number of groups (K) was determined by running the program with different groups of populations which ranged from 2 to 14 groups, choosing those partitions with a maximum FCT value, as suggested by FST and 10,000 permutations were used to calculate the corresponding probabilities in Arlequin version 3.5  using Samova version 1.0 to explore the population groupings that maximized the proportion of genetic variance at the total population level (roupings . SAMOVA sion 3.5 .D with DnaSP v5.10.1 (D (D (P < 0.05) statistic can indicate no neutrality, or population expansion.We calculated Tajima’s  v5.10.1  to infer.10.1 (D  statistiA. kerchovei for the present day (PRE). MaxEnt is a presence-background algorithm that estimates a species’ potential ecological niche by finding a probability distribution of environmental variables that best describes the occurrence localities, while being able to differentiate between occurrence and background sites .We approximated the accessible area (M) for -regions  inhabite-regions  to identDistribution models were built with 20 replicates using 10,000 random background points, a RM of 0.7, with hinge features only, without extrapolation and no clamping. Models were validated using 25% of the occurrence data using the receiver operating curve (ROC) statistic. We evaluated the replicate models using the area under the receiver operating curve (AUC), where models with values below 0.8 were dismissed. The remaining replicates models were combined to construct the present-day model and then projected into the past climate layers.A. lechuguilla using 97 unique occurrence localities at a 30 arc-second resolution. These models were projected into the LIG, LGM and MH. In this case, the models were built after selecting eight layers corresponding to Annual Mean Temperature (bio 1), Mean Diurnal Range (bio 2), Mean Temperature of the Driest Quarter (bio 9), Mean Temperature of Warmest Quarter (bio 10), Mean Temperature of Coldest Quarter (bio 11), Annual Precipitation (bio 12), Precipitation of the Driest Month (bio 14), and Precipitation Seasonality (bio 15).We followed the same modeling procedure to generate present-day distribution models for A. kerchovei and A. lechuguilla to visualize the changes in suitable climatic conditions (climate suitability) to which populations may have been subjected since the Last Interglacial period. For this, we extracted the climatic suitability values through time  for those grid-cells associated with the occurrence localities of sampled populations, being 0 no suitability and 1 the maximum suitability. To account for the possible bias of using a single grid cell to characterize the climate suitability of populations’ localities, we generated 100 replicates of sample localities by adding random noise to the populations’ geographic coordinates within a buffer of ∼10 km2 (0.08°) centered on the sampling locality. For each population, we estimated the half sample mode (HSM) of the suitability values across replicates to visualize the changes in climatic conditions through time.We used the resulting distribution models for A. kerchovei and A. lechuguilla  for sampled populations of huguilla . We usedhuguilla  and the A. lechuguilla were assigned into four different groups according to their geographic location and genetic composition . Total haplotype diversity (Hd) was 0.718, but the average per population diversity was far lower (Hd = 0.107), indicating strong genetic differentiation among populations (The combination of the three non-coding chloroplast (cpDNA) regions [l32-trnL ] and trnrnL-trnF  resultedulations . Total nA. kerchovei . According to the PERMUT analysis, there are no significant differences comparing NST (0.943) vs. GST (0.880), suggesting lack of phylogeographic structure. Therefore, a Mantel test was not significant (data not shown), which suggests that there is no evidence of isolation by distance in A. kerchovei. Tajima’s D, although positive, 0.37320, was not significant (p = 0.704).The prevalence of private haplotypes within populations resulted in a high genetic differentiation , in the North, while the rest of the groups are conformed only by one population each  was explained by differences among populations, whereas only 7.20% was found within populations . Total gion each .A. kerchovei during the LIG shows the most restricted distribution across all the time periods analyzed, where ideal climate conditions for the species were geographically restricted to an area equivalent to 75% of the present-day distribution , with this geographical extent remaining stable ever since. Our models revealed two main areas that have remained more or less stable and isolated from each other: (1) the Tehuacán Valley in the North, and (2) the Central Valleys of Oaxaca in the South.The predictive performance of the bioclimatic models was adequate with an AUC > 0.82 across replicates. The projected distribution for ribution . SubsequAccordingly, we characterized the changes in suitable climatic conditions (climate suitability) to which the geographic regions corresponding to present-day sampled populations may have been subjected since the LIG. We found that the climatic suitability varies significantly through time  among the occurrence localities of each sampled population . InteresAgave species with wider distribution we characterized the temporal variation in climatic suitability in the widespread A. lechuguilla from the Chihuahuan Desert, finding that the temporal changes in climate suitability are different from those observed for A. kerchovei . The first PCA was most strongly positively correlated with annual precipitation, summer precipitation and altitude, whereas it showed the most negative correlation with summer temperature. On the other hand, the second PCA was most positively correlated with winter precipitation, and negatively with winter temperature and precipitation seasonality (data not shown).The first two components of the PCA on environmental variables for A. kerchovei and A. lechuguilla (ENV) conditions, and estimating the variance for the first two principal components , whereas the PC2ENV was most strongly associated with temperature and precipitation during the winter months  (ENV and PC2ENV), we obtained each population’s scores and estimated the variance within geographically defined groups of populations  . For theulations . The ranhuguilla .b = 0.11, p = 0.7) between levels of haplotype diversity within groups of populations and their variance along PC1ENV  between the levels haplotype diversity within groups of populations and their variance along PC2ENV  nor with estimates of environmental variance along PC2ENV , but it was correlated with environmental variance along PC1ENV . The high environmental variance along PC2ENV observed among populations of A. kerchovei – which is associated with variance in winter temperatures and precipitation  than those estimated for A. stricta (Hd = 0.524), which is an endemic of the Tehuacán Valley  , the sister species of A. stricta  , while A stricta .A. kerchovei . High genetic structure using similar chloroplast sequences was also found in A. striata   , usually P7, P8) , 2009. I P7, P8)  and beloA. kerchovei is further supported by the absence of a clear phylogeographic signal, the absence of isolation by distance among populations and the fact that some populations consist of very few individuals. Another important biological factor that may be relevant to explain the genetic patterns observed in A. kerchovei is clonal reproduction, possibly leading to patches of genetically identical individuals. Accordingly, A. kerchovei. Clonal propagation would reduce the number of genetic individuals in a population (Strong genetic differentiation among populations of pulation  and woulAgave (A. kerchovei has been dated to the Pliocene-Pleistocene transition (∼2.6 Mya)  . The mai2.6 Mya) . Accordir period , A. kerctraction , possibltraction . It has traction . Afterwatraction , with a A. kerchovei: (1) the Tehuacán Valley in the North and (2) the Central Valleys of Oaxaca in the South. This is in accordance with our genetic analysis of the distribution of variation in A. kerchovei, as the northern populations share a common haplotype, while the rest of the populations have unique haplotypes, resulting in high genetic structure, as shown by the analysis of AMOVA and SAMOVA. In the latter, almost every location is designated as a separate group. This could be attributed to genetic drift and local differentiation and adaptation, as for instance different microenvironmental conditions are found within each area in this region of Mexico .EA-P contributed to laboratory work, genetic analysis, and drafting the manuscript. JP-L contributed to laboratory work, genetic analysis and the design of some figures. SR-B contributed to fieldwork, data analysis, and helped in drafting and correcting sections of the manuscript, and designing some figures. ES contributed with genetic analysis. RL-S contributed with logistics and ideas for the design of the project. LE project leader, designed and coordinated the project, logistics, drafted and corrected the manuscript. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Nyssorhynchus darlingi  is the primary malaria vector in the Amazon River Basin. In Brazil, analysis of single nucleotide polymorphisms (SNPs) previously detected three major population clusters, and a common garden experiment in a laboratory setting revealed significant population variation in life history traits. Increasing temperatures and local level variation can affect life history traits, i.e. adult longevity, that alter vectorial capacity with implications for malaria transmission in Ny. darlingi.Ny. darlingi from 7 localities across Brazil utilizing SNPs and compared them to a comprehensive Ny. darlingi catalog. To test the effects of local level variation on life history traits, we reared F1 progeny from the 7 localities at three constant temperatures , measuring key life history traits .We investigated the population structure of Ny. darlingi were genotyped at 33,759 loci. Results revealed three populations (K = 3), congruent with major biomes , with greater FST values between biomes than within. In the life history experiments, increasing temperature reduced larval development time, adult lifespan, and wing length in all localities. The variation of family responses for all traits within four localities of the Amazonia biome was significant . Individual families within localities revealed a range of responses as temperature increased, for larval development, adult lifespan, wing length and survival time.Using nextRAD genotyping-by-sequencing, 93 of the field-collected Ny. darlingi were clustered by three major Brazilian biomes. Our laboratory results of temperature effects demonstrated that population variation in life history traits of Ny. darlingi exists at the local level, supporting previous research demonstrating the high plasticity of this species. Understanding this plasticity and inherent variation between families of Ny. darlingi at the local level should be considered when deploying intervention strategies and may improve the likelihood of successful malaria elimination in South America.SNP analysis of several Brazilian localities provided results in support of a previous study wherein populations of  Nysorrhynchus darlingi , that edarlingi  and in pdarlingi –8. This p to 20% , 9, thoup to 20% –12. Adulp to 20% , can varp to 20% , 7 and ap to 20% . Globallp to 20% . Whereasp to 20% , the effNyssorhynchus darlingi includes diverse South American biomes [Ny. darlingi presence in Peruvian breeding habitats [Plasmodium of Ny. darlingi in the Brazilian Amazon led to a high basic reproductive rate (R0) of malaria (mainly caused by Plasmodium vivax) [Ny. darlingi presents a major challenge to malaria elimination.The geographical distribution of n biomes  and is an biomes , as welln biomes  and damsn biomes . Habitathabitats  and has habitats . A mathem vivax) . The het1 progeny from field collected An. coluzzii  and P. falciparum [Average global temperatures are projected to increase 1–4 °C over the next 100 years because of climate change  and tropoduction , 31. A mlciparum . ModeledAnopheles has shed light on population structure [Ny. darlingi in South America have been mixed. Analysis of Ny. darlingi collected from 12 states across Brazil detected three genetic clusters [vs old settlement) to test for local differentiation in biting behavior found no significant genetic variation [The analysis of single nucleotide polymorphisms (SNPs) in tructure , 34 and tructure , 36. Howclusters  associatclusters , althougNy. darlingi using a common garden experiment approach to address the following questions: (i) What is the scale of genetic differentiation among populations of Ny darlingi? and (ii) Is there evidence of small-scale variation in life history traits and in plastic responses to temperature variation? Our research combined broad-scale population genetic assays with empirical data from a common garden experiment. We investigated the effects of variation in rearing temperature on a major Neotropical vector, allowing us to assess the extent to which population differences in life history traits were due to environment (temperature), genetics, or both.The aim of this study was to investigate local level variation in population structure and in life history traits of Ny. darlingi. This variation could help tailor current intervention efforts, such as long-lasting insecticide nets (LLINs), indoor residual spraying (IRS) and larval source management (LSM), to regional and local scales for maximum efficiency and malaria elimination.Here, we identified molecular genetic variation across biomes, significant phenotypic and genetic variation in life history traits, as well as within-population genetic variation for plasticity of Ny. darlingi with mosquitoes collected within the same year, over a range of seven locality sites. In order to increase our chances of identifying fine-scale population genetic structure, we created a catalog incorporating sequences of Ny. darlingi from Peru and Brazil and used this to reexamine the population structure of Brazilian Ny. darlingi and to investigate the possibility of fine-scale differentiation within three Brazilian biomes. We then investigated the among- and within-locality variation in life history traits such as larval development time, adult lifespan and body size, among these seven localities across Brazil. A common garden experiment of the mosquito populations from the seven localities was conducted in three constant temperature environmental chambers. Mosquitoes were observed from egg hatch to adult death and life history traits recorded.In this study, we first analyzed the population structure of Brazilian Ny. darlingi mosquitoes were collected from 7 localities across Brazil .Adult female il Table , Fig. 1,darlingi  and mainn = 15) into each of three constant temperature environmental chambers   of the field-collected Ny. darlingi (used to create the families in the life history experiment) was genotyped to evaluate population structure  based on (i) successful egg laying (with priority given to the dams of the families used in the life history research); (ii) complete wing data; and (iii) a DNA concentration between 2.87 and 16.2 mg/ml. Genomic DNA was extracted from all specimens using Qiagen DNeasy Blood and Tissue kit  and concentrations were quantified with a Qubit Fluorometer . Individuals were sequenced using nextRAD genotyping-by-sequencing methods as described in Emerson et al. [A subset  and PerNy. darlingi from the seven collection localities  chain of 500,000 generations. CLUMPAK [ade4 package v.1.7.13 [via the dudi.pca function and visualized with factoextra package v.1.0.5 [adegenet v.2.1.1 [poppr v.2.8.3 [STRUCTURE analysis was run using StrAuto v1.0 , allowin CLUMPAK  was usedv.1.7.13  via the  v.1.0.5  fviz_pca v.1.0.5  with the v.2.1.1 . A hiera v.2.8.3 .survival v.2.44.1.1 [survminer v.0.4.3 [All statistical analyses were conducted in R v. 3.6.0) , which we expect to be comparable to calculated FST. In order to compare population genetic structure results from the sequenced P generation  and their laboratory-reared F1 progeny, FST and PST values were calculated, respectively. Pairwise FST values by locality  were calculated with the populations program from STACKS [Ny. darlingi. Life history data of the reared progeny were used to estimate PST, a phenotype-based analog for FST that measures the amount of genetic variation among populations relative to the total genetic variation [PST values by locality for each life history trait were calculated with the Pstat R package v.1.2 [PST to FST values is a useful proxy for estimating the strength of selection [FST is typically estimated from neutral loci, deviations of PST from FST can lead to inferences of selection: if PST > FST, directional selection can be inferred, conversely, if PST < FST, stabilizing selection is indicated.Trait-based data can be used to estimate the amount of genetic variance among populations  clusters were examined from STRUCTURE and STRUCTURE Harvester analyses  sequences, or reads, per individual (n = 93) after quality filtering. The average number of reads per stack (or unique groups of matched reads) was 3,002,165  with an average number of 100,369  stacks per individual. The final SNP dataset included one biallelic SNP from each locus genotyped in at least 40% of the 93 individuals, for a total of 33,759 loci. The average coverage depth was 49X at each locus. Multiple values for adegenet  in prepaF = 52.0, P < 0.0001), Rondônia  = 15.3, P < 0.0001) and Tocantins  = 6.7, P = 0.01) states for larval development time  = 4.57, P = 0.03)  = 32.9, P < 0.0001)  = 3.77, P < 0.0001), whereas the two localities in Rondônia had significantly different larval development time at 20 °C (t(1049) = 5.23, P < 0.0001) and 28 °C (t(1049) = − 3.41, P < 0.0001). Only the localities in Amazonas State had significantly different adult lifespan (t(968) = −  2.05, P = 0.04) and wing length (t(940) = 2.44, P < 0.0001) at 24 °C. In contrast, localities in Tocantins had significantly different wing lengths at 20 °C (t(320) = 2.44, P = 0.02)  = 6.71, P < 0.0001; APR: F = 5.48, P < 0.0001; RPV: F = 4.61, P < 0.0001; RMO: F = 1.77, P = 0.04), adult lifespan  = 3.74, P < 0.0001; APR: F = 4.88, P < 0.0001; RPV: F = 3.94, P < 0.0001; RMO: F = 3.96, P < 0.0001) and wing length  = 2.07, P = 0.03; APR: F = 2.66, P < 0.0001; RPV: F = 9.03, P < 0.0001; RMO: F = 6.72, P < 0.0001) for both localities of Amazonas and Rondônia states  = 2.30, P = 0.01) and wing length  = 3.75, P < 0.0001) in the southern high latitude population. Populations from Tocantins showed little genetic variation among families, with the exception of wing length for TLC  = 6.82, P < 0.0001).There was significant genetic variation among families within populations for larval development time , and the genetic variation was mainly accounted for within individuals (71.8%), followed by among individuals (19.1%) and between states (9.1%). Pairwise FST between localities of the field collected females (P generation) ranged from 0.045 to 0.183, with the lowest FST values between paired sites within the same state, as expected  by locality were generally greater than FST for larval development (range: 0.660–0.995)  lowest within (range: 0.046–0.070) compared to between biomes (range: 0.081–0.183) ; one study detected significant differentiation when comparing an older, highly deforested agricultural settlement 60 km from a newly settled one that retained high forest cover levels [In partial support of previous findings of differentiation according to biome and physical barriers , populatr levels , and anor levels .An. gambiae (s.s.) and An. coluzzii, collected from 15 sites across Africa and tested with over 50 million SNPs, revealed clustering by geographical region rather than species, and as predicted, lower FST values within rather than between biomes [Anopheles species that have been analyzed is strongly affected by geographical division; such demarcations may break down in the future as the integrity of biomes is eroded by deforestation and climate change. Specifically, in South America, under a high CO2 emission model, there could be substantial reduction (3%) in tropical forest area in South America in the next 10 years and up to 18% by 2100 [Ny. darlingi is primarily associated with forested areas, its range [A study of the closely related species n biomes . Populat by 2100 . As Ny. ts range  and popuNy. darlingi [PST values [PST values are nearly uniformly substantially greater than the parental FST values, we infer that there is directional selection driving genetic differentiation of life history traits among regions. Combined with our evidence that there is standing genetic variation for performance at different temperatures within populations, future selection could favor phenotypes that tolerate increased temperatures.Our study extends previous findings of regional variation in life history traits of darlingi  to the dT values  of localAn. gambiae (s.s.) development: increased larval rearing temperature (23–31 °C) resulted in smaller larvae and adults whereas increased adult temperatures reduced the proportion of egg hatch [An. gambiae (s.s.) in Kenya revealing greater fecundity and vectorial capacity of mosquitoes placed in homes that were 0.7–1.2 °C warmer compared with control homes [The environment substantially influences mosquito vector traits. In our study, increased temperature reduced larval development time in all populations with the magnitude of reduction in adult lifespan and body size dependent on population. The dramatic differences in larval development time however, were not a clear predictor of adult longevity due to differences between populations. Our data show that the relationship between larval conditions and adult traits is not linear but rather complex. Temperature of larval and adult environments had significant effects on gg hatch . The effol homes .Anopheles were found to be larger and more likely to survive exposure to a LC50 dose of permethrin [Ny. darlingi. Interventions such as long-lasting insecticidal nets (LLINs) are highly effective and target adult mosquitoes that are mainly endophagic and endophilic. The biting behavior of Ny. darlingi is variable [Ny. darlingi reveal endophagy and exophagy at different times throughout the night [An. arabiensis fecundity by nearly 60% after deployment of LLINs compared to LLINs alone, supporting the use of combination interventions to help achieve population elimination [Vector control interventions need to consider variation in life history traits , behaviormethrin . Data frvariable , 61, comhe night . Ivermecmination .Ny. darlingi, including biting behavior [Ny. darlingi, and significantly, presents evidence for genetic variation in plasticity within populations.The plasticity of behavior , 41, hosbehavior , 63 and behavior –21 prefeNy. darlingi from a second locality in Rio de Janeiro although we had previous evidence of its presence [FST values were calculated from field collected mosquitoes and PST values from laboratory-reared progeny, it is unlikely that there would accrue significant genetic variation between parent and offspring in one generation. To date, Ny. darlingi has not been tested for polyandry, although An. gambiae exhibits low polyandry (12%) [An. arabiensis females had been multiply inseminated [A potential study limitation was that we were unable to collect presence . This liry (12%)  whereas eminated . We treaThe study design of our laboratory experiment to observe life history traits at a constant temperature throughout the mosquito life cycle was somewhat limited by space and resources. The temperatures in this experiment were chosen to avoid extremes that can lead to excessive mortality that would limit comparisons , and refPlasmodium that can be affected by temperature and Ny. darlingi population specificity. The establishment and maintenance of a laboratory colony in Peru [Plasmodium infection of colony mosquitoes [Our treatment of adult mosquitoes  deviates from the natural adult environment which involves sugar feeding and potential blood meals for females as well as sugar-feeding for males. On the other hand, the average adult longevity in our study was 3.09 days compared to field data of daily survival rates that detected between 3.73 and 23.9 days for adult females from two Peruvian sites . Our res in Peru  and Braz in Peru  as well squitoes  would faNy. darlingi in Brazil. The genomic signatures indicate that genetic divergence occurs at the level of biomes, with phenotypic traits varying more than molecular markers indicating a role for natural selection via climate or vegetation structure in driving differentiation. A key result is our finding that there is genetic variation for both life history traits and their plastic responses within populations to temperature, indicating future adaptive capacity to changes in temperature. Future research that further quantifies the effects of environment and population on life history traits relevant to transmission will be vital for predicting variation in transmission potential and informing modeling efforts.This study identified the population structure and degree of genetic variation and phenotypic plasticity of Additional file 1: Table S1. Number of larvae and families from each locality at each temperature. Table S2. Generalized linear model results for larval development, adult lifespan, and wing length. Table S3. Supplemental collection site data.Additional file 2: Dataset S1 STRUCTURE file used for STRUCTURE, PCA, DAPC and FST analyses, with genotypes for 93 individuals at 33,759 loci.Additional file 3: Dataset S2. Life history data used for ANOVA, PST, GLM and Kaplan–Meier analyses.Additional file 4: Figure S1. Estimation of the number of clusters in SNP dataset using STRUCTURE and Discriminant Analysis of Principal Components (DAPC). a STRUCTURE ΔK using Evanno method (left) and probability by K (right) [K = 1–7. b Bayesian information criterion (BIC) for 1 to 20 clusters using K means clustering preparation for DAPC. (right)  for K = Additional file 5: Figure S2. STRUCTURE plots for K = 1–7.Additional file 6: Figure S3. Average larval development time of localities within each state. Standard error bars and ANOVA results in each panel: G, genetic variation ; E, phenotypic variation (temperature); GEI, genotype-by-environment interaction ; *P < 0.05, **P < 0.01, ***P < 0.001.Additional file 7: Figure S4. Average adult lifespan of localities within each state. Standard error bars and ANOVA results in each panel: G, genetic variation ; E, phenotypic variation (temperature); GEI, genotype-by-environment interaction ; *P < 0.05, **P < 0.01, ***P < 0.001.Additional file 8: Figure S5. Average wing length of localities within each state. Standard error bars and ANOVA results in each panel: G, genetic variation ; E, phenotypic variation (temperature); GEI, genotype-by-environment interaction ; *P < 0.05, **P < 0.01, ***P < 0.001.Additional file 9: Figure S6. Kaplan-Meier survival curve of families within locality. Each family is uniquely color coded and consistent with all other figures’ color scheme for family.


Cardamine pratensis, composed of diploid endemics in the European Mediterranean and diploid-polyploid lineages more widely distributed across Europe, focusing on the poorly understood variation in Central Europe. To elucidate the evolution of Central European populations we analyzed ploidy level and genome size variation, genetic patterns inferred from microsatellite markers and target enrichment of low-copy nuclear genes (Hyb-Seq), and environmental niche differentiation. We observed almost continuous variation in chromosome numbers and genome size in C. pratensis s.str., which is caused by the co-occurrence of euploid and dysploid cytotypes, along with aneuploids, and is likely accompanied by inter-cytotype mating. We inferred that the polyploid cytotypes of C. pratensis s.str. are both of single and multiple, spatially and temporally recurrent origins. The tetraploid Cardamine majovskyi evolved at least twice in different regions by autopolyploidy from diploid Cardamine matthioli. The extensive genome size and genetic variation of Cardamine rivularis reflects differentiation induced by the geographic isolation of disjunct populations, establishment of triploids of different origins, and hybridization with sympatric C. matthioli. Geographically structured genetic lineages identified in the species under study, which are also ecologically divergent, are interpreted as descendants from different source populations in multiple glacial refugia. The postglacial range expansion was accompanied by substantial genetic admixture between the lineages of C. pratensis s.str., which is reflected by diffuse borders in their contact zones. In conclusion, we identified an interplay of diverse processes that have driven the evolution of the species studied, including allopatric and ecological divergence, hybridization, multiple polyploid origins, and genetic reshuffling caused by Pleistocene climate-induced range dynamics.Recurrent polyploid formation and weak reproductive barriers between independent polyploid lineages generate intricate species complexes with high diversity and reticulate evolutionary history. Uncovering the evolutionary processes that formed their present-day cytotypic and genetic structure is a challenging task. We studied the species complex of  Polyploidy is a widespread evolutionary phenomenon and a major mechanism of sympatric speciation in plants . DiploidCardamine pratensis species complex, which is widespread in Europe and exhibits extensive but poorly resolved variation in Central Europe. It is one of the most complicated polyploid complexes of the family Brassicaceae, comprising sexually reproducing, allogamous perennials capable of vegetative propagation  and in Bulgarian mountains , and representatively covered their geographic, elevational and cytotypic variation  distributed in Central Europe and extending to Southeastern Europe  so far reported from two disjunct areas in Central Europe, growing in lowlands and the montane belt up to 600 m above sea level (C. matthioli was inferred (n = 34 have been rarely observed (inferred . Aneuploobserved . In totaCardamine pratensis s.str. (further referred to without ‘s.str.’) is widely distributed throughout most of Europe, except its southernmost areas  was described from the Rhodope Mountains in Bulgaria  and microsatellite analyses . Chromosome numbers were counted from 58 populations (219 plants). A selection of 48 accessions, representing the observed overall taxonomic, geographic, ecological, and cytotypic variation, was subjected to Hyb-Seq analyses .Mitotic chromosomes were counted from young anthers or root tips fixed in an ethanol:acetic acid (3:1) mixture and stored in 70% ethanol at −20°C. Chromosome spreads were prepared following Solanum lycopersicum ‘Stupické polní rané’  using the AT-selective DAPI fluorochrome , b. Each buffers , b and tTotal genomic DNA was extracted from silica gel-dried young leaves using the DNeasy Plant Kit .Cardamine amara and C. pratensis  following The microsatellite markers used in this study were developed from genomic sequence data of two individuals of x), we did not estimate the allele copy number in partial heterozygotes from peak heights, as described in Allele sizes were recorded using Geneious R7.1 and R10 software  with the1 (Cardamine parviflora (NCBI accession number: SRR11977919), a mitochondrial sequence of Arabidopsis thaliana (NCBI accession number: NC_001284.2), a plastome sequence of C. impatiens (NCBI accession number: KJ136821.1), and transcriptome sequences of C. amara (NCBI accession number: SRR11977918) were used. These sequences of C. parviflora and C. amara were generated as part of this study on the HiSeq2500 Illumina platform. The transcriptome of C. amara was assembled using Trinity 2.0.6 . After cleanup of adaptor-ligated DNA with the QIAquick PCR Purification Kit (Qiagen), size selection was performed by SPRIselect beads  to produce 500–600 bp long fragments. Amplification was performed with 8 cycles of PCR, using index primers from NEBNext® Multiplex Oligos for Illumina®. The amplified libraries were cleaned up with AMPure XP beads (Beckman Coulter) and pooled equimolarly (24 accessions/pool). The pooled libraries were enriched by hybridization with synthesized RNA baits (at 65°C for 26 h) using the MYbaits kit, following the protocol v. 3.02 (Arbor Biosciences). The target-enriched libraries were PCR amplified for nine cycles with the KAPA HiFi HotStart mix , purified with the QIAquick PCR Purification Kit and submitted for sequencing with 150 bp paired end reads on an Illumina MiSeq system at BIOCEV or CEITEC, Czechia.Genomic DNA (400 ng per accession) was fragmented with Covaris S220 or M220 sonicators  to a target fragment size of 500 bp. Sequencing libraries were prepared using the NEBNextEnvironmental data were acquired for each of the sampling sites of the studied species, complemented by published records  that couDifferences in relative DNA content were tested in R version 3.3.2  using thC. matthioli, C. majovskyi, and C. rivularis. It was employed to elucidate the deeper structure within these species, and to identify potential gene flow and the occurrence of hybrids in the contact zone of C. matthioli and C. rivularis in Bulgaria. (3) Prat Matrix: This matrix comprised samples of C. pratensis, and was used to uncover the deeper structure within this species. The latter two data matrices corresponded to the clusters identified by STRUCTURE analyses (see below) of the Complete Matrix. Hybrid populations (see below for details on their identification) were generally omitted from the partial datasets but retained in cases when both parental clusters were present in the partial dataset.Genotypic data from SSR loci were exported from Geneious in the GeneMapper format, which was loaded in the POLYSAT 1.5-0 package . The POLST and expected heterozygosity, HE) could not be calculated (T), all three calculated in GenoDive, and Shannon diversity index (ShDI) calculated in POLYSAT. In addition, frequency down-weighted marker indices for individuals and populations  as described in A neighbor-joining (NJ) tree was calculated using the package ape  in R 3.33.Demultiplexed reads were trimmed of adapters and quality-filtered using Trimmomatic-0.36 . Bases aTwo approaches were employed for further data processing: the first was based on the assembled genes , while the second one utilized single-nucleotide polymorphism (SNP) calling and Bayesian clustering of the SNP datasets.Consensus sequences were assembled using HybPiper version 1.3 . Reads wMaximum-likelihood gene trees were constructed for 499 genes containing at least 32 phylogenetically informative sites . This pipeline uses BWA (C. pratensis (Prat dataset) and the remaining samples (MajMatRiv dataset) were analyzed separately.Bayesian clustering of the SNP datasets was obtained using STRUCTURE 2.3.4 . Variantuses BWA  to alignuses BWA  for sortuses BWA  for variuses BWA  and vcfRuses BWA  for alleuses BWA , which wuses BWA  and drawuses BWA . To uncoThe environmental niches of the species under study and their genetic lineages were defined using 72 GIS-derived habitat characteristics , rescaleWe used redundancy analysis RDA;  to test Fm were calculated from 10,000 unrestricted permutations of the least-absolute-deviation residuals.A matrix of standardized Euclidean distances was used for principal coordinate analysis (PCoA) and niche breadth was quantified using the dispersion of sites from their spatial median in ordination space. A distance-based test of homogeneity of multivariate dispersions  was emplFinally, multinomial generalized linear models GLMs;  were useAnalyses were performed in R  using thn = 16, 17, 18, 19, 20, 22, 28, ca. 28–30, 30, 31, 32, ca. 33, 34, 36, 37, 38, ca. 34–40, ca. 42–44, 44, 48, 52, which represent diploid, tetraploid and hexaploid cytotypes (with the base number x = 8) along with frequent occurrence of probable aneuploids and dysploids. Although the chromosomes did not differ significantly in size, the presence of B chromosomes cannot be ruled out either. In addition, triploids and heptaploids were inferred from relative DNA content (not confirmed by chromosome counting) and based on the number of alleles recorded per microsatellite locus.Chromosome numbers were counted for 219 plants from 58 populations . We founFlow cytometry analyses mostly yielded high-resolution histograms, with an average CV for the samples of 2.09% (range 1.1–4.9%) and 1.89% (range 0.94–4.62%) for the standards. In several populations, some individuals showed minor differences in their relative 2C values, which was most likely caused by aneuploidy and dysploidy .All samples of eded 40% . At the C. majovskyi were tetraploid. Intraspecific variation in nuclear DNA content reached 22.65% , which is, as in the case of C. matthioli, attributable to aneuploidy . In the population Maj_LOG, where plants with 2n = 31 and 2n = mosaic of 36 and 40 were recorded  exhibited large variation in relative DNA content (C. matthioli (see below).Samples of  content . Althoug content . Thus, tC. rivularis analyzed were cytologically uniform, one consisting of diploids and the other of triploids. The variation in relative DNA content in both populations was less than 5%, and their monoploid relative genome sizes were almost identical  or C. rivularis (expected mean 2C ∼ 0.759). The co-occurrence of diploids with triploids was observed in one population, whereas in four populations, only triploids were recorded . Their monoploid genome size, however, was larger than that of diploids from Romania  whereas diploids and hexaploids made up 20 and 12% of the samples, respectively . In addition, two rare odd-ploidy level cytotypes were inferred from FCM analyses: one heptaploid population and a single triploid individual in an otherwise diploid population. Almost 90% of the populations sampled consisted exclusively of one ploidy level. Sympatric occurrences of two cytotypes were encountered in only nine populations . Diploid populations occupied three geographically distinct areas: in the Eastern Carpathians, the Alps, and central and southern Germany. The distribution of tetraploids overlapped with that of diploids and in general covered the whole distributional range of C. pratensis. Hexaploid populations were predominantly found in the Western Carpathians and adjacent areas. Several spatially isolated hexaploid samples were found in four mixed-ploidy populations in Switzerland, Slovenia, and Lower Austria. The single heptaploid population was recorded in Styria, Austria , tetraploid C. pratensis possessed two cytotypes: regular tetraploid (2n = 4x = 32) and hypotetraploid caused by chromosome fusion . Both cytotypes are widely distributed across the sampled range. At the hexaploid level, 2n = ca. 42–44, 44, 48, 52 chromosomes were confirmed (n = 6x = 44) is more common than the regular hexaploid one  had markedly larger genome size than C. matthioli and diploid C. pratensis.Relative genome size values (2C) of diploid ratensis . By contThe 18 microsatellite loci used yielded 394 alleles. The total number of alleles and phenotypes detected per locus ranged significantly, from five alleles (eight ‘marker phenotypes’) in the locus Card12 to 58 alleles (528 ‘marker phenotypes’) in the locus Card19 . The errC. matthioli and C. majovskyi, (2) C. rivularis, and (3) C. pratensis. The neighbor-joining clustering was consistent with this structure  and that C. rivularis is genetically closer to C. matthioli – C. majovskyi than to C. pratensis .The STRUCTURE analysis of the Complete Matrix comprising all 1,104 accessions and 394 alleles showed the following optimal genetic partitioning into three clusters : (1) poptructure . AMOVA structure . The paiC. majovskyi and seven populations of C. pratensis from the contact zone between these two species , hybrid origins were indicated for 11 populations: four populations of C. rivularis and C. matthioli with C. majovskyi (see above) were merged into a single dataset (MajMatRiv Matrix) comprising 389 samples from 54 populations and 254 alleles. STRUCTURE analysis of this matrix at K = 2 resulted in the same clustering pattern (results not shown) as the analysis of the complete matrix above. With increasing K to its optimal value (K = 3), individuals of C. matthioli and C. majovskyi were separated into two clusters irrespective of their ploidy level , which are clearly differentiated also in the NJ tree. These two lineages  showed a non-random spatial structure. The Widespread lineage is widely distributed across the species’ distribution range, including its western areas , northern areas  and southeastern areas (Bulgaria). By contrast, the Central lineage is localized in a relatively small area of Slovenia, northern Croatia, extending also to southeastern Austria (Styria and Burgenland) and western Hungary  comprised 635 samples from 80 populations and 368 alleles. STRUCTURE analyses indicated that a division into three clusters best explained the genetic structure of this dataset  whereas peripheral Bulgarian populations appeared genetically depauperate (C. matthioli and C. majovskyi), they displayed very similar genetic diversity values; the Central lineage appeared more diverse at the level of populations, but the overall estimated allelic richness was slightly higher in the Widespread lineage  exons were recovered from all 48 samples. After inspection, 1,487 supercontig sequences were kept and concatenated to 963 genes of the overall length of 1,186,798 bases. The gene length ranged from 110 to 10,705 bp, with a mean of 1,232 bases. The proportion of parsimony informative sites ranged from 0.5 to 22.7%, with a mean of 3.8%. The variant calling approach (the snipStrup pipeline) utilized 936 genes (out of 963), which contained at least one variant for each sample. The genes harbored between one and 231 SNPs (omitting SNPs that occurred only in one sample), with an average of 41.21 and 47.86 variants per gene for the MajMatRiv and the Prat datasets, respectively.C. rivularis, C. pratensis, and C. matthioli intermingled with C. majovskyi, were identified as genetically clearly distinct. The extensive intraspecific variation observed within the latter three species is geographically structured, which is largely concordant with the patterns inferred from microsatellites. The same two genetic lineages, Central and Widespread, were identified within the clade of C. matthioli–C. majovskyi. Within C. pratensis, the same three lineages, denoted as Yellow, Orange, and Red, were resolved by STRUCTURE at K = 3  . The multinomial LASSO model identified 42 habitat characteristics (C. rivularis occurrence (0% misclassification). The rate of misclassification was greater in the case of C. majovskyi (28%), C. matthioli (29%), and C. pratensis (22%). The environmental niches of those species were of significantly different breadths . C. pratensis and C. rivularis have broader niches than C. majovskyi (p = 0.0033 and p < 0.0001) and C. matthioli (p = 0.0004 and p < 0.0001). On the other hand, we found comparable niche breadths between C. pratensis and C. rivularis (p = 0.2541), and between C. majovskyi and C. matthioli (p = 0.9630).Redundancy analysis revealed significant differences in environmental niches of the four  0.0001) . The nicevations . The enveristics  that disC. majovskyi and C. matthioli , were significantly different  and each was statistically distinguishable from the others in pair-wise comparisons , but the other groups reached acceptable rates of misclassification . We found the niche breadths of the four defined groups to be unequal , but the only significant difference was between the two species within the Widespread lineage .The environmental niches of genetic lineages within parisons . The maiariables  which prC. pratensis exhibited significantly different environmental niches . In the pairwise-comparison, habitat characteristics of the Yellow (Western) lineage differed significantly from both the Red and Orange lineages, while the latter two lineages were statistically indistinguishable ; the Orange lineage has a broader niche than the Red lineage (p = 0.0335), while the other pairs are statistically comparable.Genetic lineages within uishable . In contuishable  relativeC. pratensis complex in Central Europe, present both among and within the species. Apart from the euploid numbers, based on the primary base chromosome number x = 8, numerous aneuploids and dysploids were found. Aneuploidy was recorded in the diploid C. matthioli, tetraploid C. majovskyi, and across all ploidy levels of C. pratensis. In addition, descending dysploidy occurs at the tetraploid and higher levels in C. pratensis. The hypotetraploids with 2n = 30 possess one pair of markedly longer chromosomes, which were noticed already by n = 32 to 30 . Based on their distribution patterns we assume that they have arisen both by secondary contacts between the two cytotypes (in the case of diploids and tetraploids) and by multiple in situ polyploidization events (in the case of tetraploids and hexaploids). Populations with the co-occurrence of diploids and tetraploids were found only in the western part of the study area, where these ploidy levels grow largely intermingled, enabling and furthering their secondary contacts. By contrast, tetraploid-hexaploid populations were found scattered in areas occupied by tetraploid populations, suggesting that the hexaploids originated multiple times within those populations. A similar scenario of recurrent hexaploid formation within tetraploid populations has been inferred in ferns based on their Cx value variation alpine species, which was probably facilitated by their long-term disjunction  and evolution in allopatry. As is known from other studies, differential evolution of repetitive DNA and/or selection can generate genome size differences across populations and species, which can be fixed in the absence of gene flow  than between the two species. Previous studies on other species groups have indicated that autopolyploids may exhibit lower rates of niche evolution than allopolyploids and may consequently persist in geographically close areas and occupy similar niches as their ancestors , also indicates that the polyploidization event predated at least the last glacial period. Unlike C. majovskyi, these hypotetraploids have spread widely within as well as beyond Central Europe . Tetraploids with 2n = 32 were recorded in both the Yellow and Orange lineages, and it is reasonable to assume that they have originated by polyploidization independently in each of them. In contrast to populations with 2n = 30, those with 2n = 32 grow at higher (sub)alpine sites in the Alps and were recorded also in northern Spain  as well as in later  periods, as has been documented also in other polyploid complexes  indicates that gene flow occurs between them. In some of these populations, hybridization was manifested also by increased genome size variation.The present results have also revealed that, under favorable conditions, the species studied may hybridize. C. matthioli and C. rivularis in Bulgarian mountains. Even though C. matthioli occurs predominantly at lower elevations and only rarely reaches the upper montane belt, and C. rivularis is generally a (sub)alpine species  as well as lowland to subalpine populations, which complicates the reconstruction of their phylogeographic history. Genetic diversity patterns do not indicate any clear geographic trends that are expected for simple scenarios of out-of-refugia colonization routes alpine taxon zed here , 2014. IC. pratensis has been extensively investigated by many authors since the 1950s, who attempted to split it into more homogeneous entities alpine populations commonly attributed to C. rivularis auct. are not genetically closely related and have probably originated multiple times from adjacent lower-elevation populations. Similar scenarios of independent colonization of alpine habitats by several distinct genetic lineages from foothill areas were recently inferred also in Arabidopsis arenosa  can be found below: KM and JZ-L conceived and designed the study. BŠ, KS, TK, MŠ, and KM sampled plant material. AM, TK, MŠ, BŠ, KS, MS, GŠ, JZ-L, MC, TM, ML, and LM generated data and performed data analyses. MŠ, JZ-L, KM, and MS wrote the manuscript. MK designed the Hyb-Seq probes. All authors have read, revised, and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.