Zingiber belonging to the pan-tropic family Zingiberaceae represents a good system to study contributions of different breeding behavior on genetic diversity and population fitness, as this genus comprises species with contrasting breeding systems. In this study, we analyzed breeding behavior, amplified fragment length polymorphism diversity and response to the soft-rot pathogen Pythium aphanidermatum in 18 natural populations of three wild Zingiber spp.: Z. neesanum, Z. nimmonii, and Z. zerumbet, together with the obligately asexual cultivated congener, ginger . Ginger showed an exceptionally narrow genetic base, and adding to this, all the tested cultivars were uniformly susceptible to soft-rot. Concordant with the postulates of Muller’s ratchet, the background selection may be continuously pushing ginger into the ancestral state, rendering it inefficient in host-pathogen coevolution. Z. neesanum and Z. nimmonii populations were sexual and genetically diverse; however, contrary to Red Queen expectations, the populations were highly susceptible to soft-rot. Z. zerumbet showed a hemiclonal breeding behavior. The populations inhabiting forest understory were large and continuous, sexual and genetically diverse, but were susceptible, whereas populations inhabiting the revenue land were fragmented and monoclonal, but were resistant. It may be possible that, when genetic recombination becomes at a premium due to the genetic constraints imparted by habitat fragmentation or pathogen pressure, Z. zerumbetMode of reproduction is generally considered to have long-range evolutionary implications on population survival. Because sexual reproduction produces genetically diverse genotypes, this mode of reproduction is predicted to positively influence the success potential of offspring in evolutionary arms race with parasites (Red queen) whereas, without segregation and recombination, the obligate asexual multiplication may push a species into extinction due to the steady accumulation of deleterious mutations (Muller’s ratchet). However, the extent of linearity between reproductive strategies, genetic diversity and population fitness, and the contributions of different breeding strategies to population fitness are yet to be understood clearly. Genus  In principle, assumptions of Red queen hypothesis and the Muller’s ratchet, the two prominent theories on sex, essentially represent two sides of the same coin. While Red queen postulates that the genetic recombination associated with sexual reproduction is essential for both host and pathogen to develop new variants in order to lock in an evolutionary arms race, the Muller’s ratchet hypothesize the extinction of asexuals by the accumulation of deleterious mutation due to their inability to purge out harmful mutations by sexual selection .Oenothera spp.  Ramamoorthy, Z. nimmonii (J. Graham) Dalzell, and Z. zerumbet (L.) Smith in conjunction with the cultivated congener and world renowned spice crop ginger , which is notoriously asexual  pathogen responsiveness vis-à-vis reproductive strategy and genetic diversity in Zingiber spp.The genus is genus . Similar species . With th species  and a na species , respect species . In this asexual  and high asexual . We analZ. nimmonii and Z. neesanum are endemic to South India while Z. zerumbet is distributed abundantly in South and Southeast Asian countries , Nadia and Bhaisey , Himgiri , Suprabha, Suravi, and Suruchi, V3S1-8 , Athira and Karthika , and Maran, a local cultivar in Kerala, were used in the study  shower in late May or early June, tillers (pseudostem) emerge and flowering occurs in September–October. The tillers dry by January with the onset of summer. The Z. neesanum populations were collected from forest in the cooler mountain ranges of eastern highlands of Kerala. The Z. nimmonii populations except ZnTRL and the Z. zerumbet populations, except the three populations from southern Kerala: ZzKLZ, ZzAPR, and ZzKLR, were from unmanaged revenue land . The three south Kerala populations of Z. zerumbet occupied the understory of the evergreen forest at the slope or foot hills of the Western Ghats mountain ranges. No Z. zerumbet populations were found at the hill-tops (∼1000 m above mean sea level) of Western Ghats. The three south Kerala populations of Z. zerumbet occurred in continuous stretches in forest ranges, whereas the other seven populations of Z. zerumbet and all the populations of the other two species were occurred in isolated patches.Altogether, 18 natural populations comprised of three, five, and 10 populations of Zingiber spp. were carefully examined for seed set under natural habitats for three consecutive years since 2008, between September and December. Approximately 30–40 randomly selected plants that were minimum 3 m apart were chosen from seed-setting populations in one of the seasons (2009) for seed sampling and the seeds collected from the selected plants were pooled and brought to the laboratory. Damaged, infected or undersized seeds were removed from seed lots and 500 good seeds were used for germination test from each seed-setting population. As many seed as possible were collected if the number of plants in a population was not adequate to yield 500 seeds. Seed germination tests were performed in Petri dishes (9 cm) lined with two sterile filter paper disks, with 20 seeds per Petri dishes. The seeds were stored in room temperature (22°C) for 45 days and the seeds were moistened with deionized sterile water every alternate day. Germinated seeds were tabulated and were transplanted into 10 in. earthen pots in a sand-soil-compost mixture. One-way ANOVA was performed to test the significance of number of seeds germinated between Zingiber spp. For seed setting populations, the plants emerged from seeds were used for further studies whereas for non-seed-setting populations, the plants raised from rhizomes were used. The plants were maintained in a wire-mesh net house at Rajiv Gandhi Centre for Biotechnology.Ginger never set seeds, although flowers profusely . PopulatPythium aphanidermatum (Edson) Fitzp. (RGCB P117) obtained from the Indian Institute of Spices Research, Kozhikode, Kerala, as described before  (Supplementary Tables Amplified fragment length polymorphism (AFLP) genotyping was performed on altogether 249 individuals, comprising of three individuals each from the 13 ginger cultivars, 10 individuals each from three populations of Zingiber spp.  following the manufacturer’s instructions. Initially, we evaluated 26 primer combinations in a set of altogether 44 individuals selected randomly from the four Zingiber spp.Amplified fragment length polymorphism profiles were independently scrutinized by two persons and the bands that were consistently occurred in both the readings were chosen for further analyses. Bands were scored as either present (1) or absent (0), and the resulting 1/0 matrix was used to compute the genetic diversity parameters and the population genetic characteristics of h) and Shannon information index (I), and the gene flow between populations (Nm) were computed using the software POPGENE (h between populations was tested by performing one-way ANOVA. Allele richness (Ar) and private alleles (Pa) were evaluated by using the soft ware ADZE  version 1.0 (The genetic diversity parameters: percentage of polymorphic bands (PPB), Nei’s genetic diversity  between K= 1–10 and identified the optimum number of clusters in the dataset by using the second order statistics (ΔK) developed by ad hoc procedure described by Z. zerumbet individuals was constructed using the SAHN  module of the software NTSYSpc2.02 I based on pair-wise DICE genetic distance. Reliability of the topology of the resulting dendrogram was tested by determining the cophenetic correlation using the COPH and MXCOMP procedures of NTSYSpc.Principle coordinate analysis (PCoA) was performed based on pair-wise genetic distance matrix, in order to ordinate relationships among individuals and populations within a species, as implemented in the software program GenAlEx ver. 6.5 . In order. 2.3.4 . We perfMolecular differentiation between the populations within a species was tested following analysis of molecular variance (AMOVA) (significance tested with 1023 permutations) using the software ARLEQUIN ver. 3.0 .Z. neesanum and Z. nimmonii populations and the three populations of Z. zerumbet from forest ranges  were flowered profusely in natural habitats and produced seeds. The percentage germination of seeds collected from these populations is given Table 1. The percentage ranged from 7.5% (ZnTRL) to 27.4% (ZnWGN) in Z. neesanum populations, 13.0% (ZcPVD) to 28.8% (ZcCU) in Z. nimmonii populations and 19.6% (ZzAPR) to 46.8% (ZzKLZ) in the three Z. zerumbet populations. ANOVA showed no statistical significance (p= 0.16889) in seed germination between the Zingiber spp. All of the seven Z. zerumbet populations sampled from revenue lands  were also flowered excessively under natural habitats, but produced no seeds. A few plants in two of the Z. zerumbet populations (ZzKTK and ZzPNR) produced a few seeds under experimental conditions, but the seeds were not viable.All of the P. aphanidermatum and yielded a disease score 9 , ranging from 46 plants in ZcPVD population to all the plants in ZcVRR population. In the remaining 37 plants, score 7 was recorded in 20 plants, followed by score 5 in nine plants and score 3 in six plants. Two of the plants did not show any symptoms  and the other consisting of seven populations sampled from revenue lands . Of the 163 plants screened in total from the three south Kerala populations, 85 plants (52%) were wilted within 25 dpi (score 9), whereas in the remaining 78 plants, score 7, 5 and 3 were recorded in 32, 18 and 15 plants respectively. Altogether 13 plants were immune to the disease , Z. nimmonii (0.78%), and Z. zerumbet (0.26%) was negligible as compared to the AFLP error rate reported earlier in other species, such as Juniperus communis  in h between populations.The AFLP error rate obtained in ginger (0.11%), s 12.6%,  and Campa 1.56%, . PopulatTable 2; Supplementary Table Table 2). The primer combinations E-ACT × M-CTA and E-AGC × M-CTC produced three and two polymorphic fragments, respectively, whereas the remaining three combinations produced only monomorphic fragments , out of the 203 fragments produced in total by five AFLP primer combinations  and 157 out of 321 fragments (PPB = 48.91) were polymorphic, respectively  and 32.09 (ZcTRL) (Table 2). Corresponding with the PPB data, the h, I and Ar values were also relatively high at the species and population levels (Table 2) in both Z. neesanum and Z. nimmonii. The distribution of Pa was low in Z. neesanum and Z. nimmonii populations except in the Z. neesanum population ZnTRL.The diversity analysis yielded relatively high values for all the diversity parameters in the two seed setting species, Zingiber zerumbet yielded the highest level of polymorphism among the four Zingiber spp. analyzed with 312 fragments being polymorphic (PPB = 94.26) out of the 331 fragments produced by six primer combinations  in ZzPZR, 0.3 (one polymorphic fragment) each in ZzKNR, ZzCLT and ZzKLD and 0.9 (three polymorphic fragments) in ZzPVD . In opposite to this, the remaining five populations, including the two non-seed-setting populations from revenue land (ZzKTK and ZzPNR) and all of the three seed-setting populations from forest ranges , were highly diverse . ADZE analysis produced low Pa for these populations, except in ZzKTK (Table 2). Besides, the inter-population comparison of AFLP profiles depicted the occurrence of two distinct multi-locus genotypes within Z. zerumbet populations; one shared between ZzPVD and ZzKLD and the other between ZzPZR, ZzCLT, and ZzKNR  was generated in each species  and examined the pattern of population sub-division within a species together with the results produced by Bayesian algorithm STRUCTURE . Population sub-division produced by STRUCTURE analysis . In Z. nimmonii, the PCoA depicted four distinct groups . The ZcPVD individuals were separated into two distinct groups: group I and group II. A few individuals each from ZcTRL and ZcVRR entered into group III while the remaining individuals from these populations were nested with individuals from ZcKNR and ZcCU populations and formed the group IV. The four subpopulations identified by Evanno’s ΔK statistics at K= 4 . Bayesian algorithm detected inter-populational genetic admixing within Z. nimmonii . The two non-seed-setting populations, ZzPVD and ZzKLD, which shared one of the two distinct multi-locus genotypes, together produced group I while the other three non-seed-setting populations ZzPZR, ZzCLT, and ZzKNR, which shared the other multi-locus genotype, together with a few individuals from ZzKTK produced group IV. Most of the individuals belonging to the seed-setting population ZzKLR produced a distinct group (group II). Group III was heterogeneous and consisted of individuals belonging to the two seed-setting populations ZzKLZ and ZzAPR, all individuals of non-seed setting population ZzPNR and most of the individuals from non-seed setting population ZzKTK. The pattern of population sub-division revealed by STRUCTURE mostly corresponded with the results of PCoA  at K = 4  between population partitioning of total genetic variability in Z. neesanum (Fst = 0.78), Z. nimmonii (Fst = 0.37), and Z. zerumbet (Fst = 0.5)  (Supplementary Table Z. neesanum (Nm = 0.1964) whereas it was relatively moderate and high in Z. zerumbet (Nm = 0.32) and Z. nimmonii (Nm = 0.6921), respectively.Analysis of molecular variance revealed significant (Table 1) depicted sexual reproduction in Z. neesanum and Z. nimmonii, as reported in other plant species . The level of genetic diversity in two clonal populations of Z. zerumbet, ZzKTK and ZzPNR, represented an exception, which, as discussed later, can be addressed in the light of available literature.Consistent with the results of other studies reported earlier , high geZingiber spp. that have a potential bearing on population fitness. Despite analyzing cultivars sampled from different parts of India, the genetic diversity in ginger was practically nil .The population genetic parameters obtained in the study permit a discussion about certain characteristics of the population genetic structure of Z. neesanum and Z. nimmonii (Table 2), as reported before in certain other endemic species such as C. sabatia (p< 0.05) population differentiation observed in Z. neesanum (Fst = 0.70) may be a corollary of very low gene flow (Nm = 0.1964) observed in this species, because the populations of a species with low gene flow are tended to differentiate locally , AMOVA revealed significant (p< 0.05) local differentiation (Fst = 0.37). Population differentiation amid gene flow has been reported earlier in Eperua falcata (Fabaceae) and is hypothesized due to molecular divergence associated with local adaptation under heterogeneous environmental conditions  were genetically diverse while clonal populations , except the ZzKTK and ZzPNR populations, were genetically narrow (Table 2). The ZzKTK and ZzPNR populations that sparingly produced seeds in experimental conditions may be rarely producing seeds in natural habitats also. The relatively low genetic differentiation   (Supplementary Table Z. zerumbet is distributed abundantly in south and Southeast Asian countries (On the other hand, clonality must be an outcome of ecological adaptation in ulations . Despiteountries . Adaptivountries .Z. zerumbet populations. In fragmented populations, such as Z. zerumbet populations in revenue land, clonality may persist due to limited reproductive success (Pythium spp. on roots exposed to sunlight (Z. zerumbet from southern Kerala, the pathogen may persists by their frequent dispersal between host patches and exerts continuous stress on hosts (Pythium disease (Yet, the ecological factors prevailing in forest understory and revenue land may have had complemented the trade-offs between sexual and clonal recruits in  success , local e success  and poorsunlight . On the on hosts , promption hosts . In addi disease . Thus, d disease .Z. neesanum and Z. nimmonii populations were highly susceptible to P. aphanidermatum. The data suggest that genetic variability can be generated by virtue of sexual reproduction, but resistance specificities do not necessarily emerge concomitantly. Further, the data suggest that, by default the genetic diversity do not buffer the host against biotic stress (Z. neesanum and Z. nimmonii populations and the complete destruction of the host populations by P. aphanidermatum raise a possibility that the Z. neesanum and Z. nimmonii populations and P. aphanidermatum were not cohabited in the past for sufficiently longer period for the co-evolutionary trajectories to shape resistance specificities in the host and virulence characteristics in the pathogen (Contrary to the expectations of Red Queen hypothesis , the genc stress  and we sc stress . The lacpathogen . Anotherpathogen  and as oThe study deciphered a key role for breeding behavior in deciding the survival fitness and population expansion. The obligate asexuality drives the population into genetic shallowness, at the same time; genetic diversity, generated consequential to sexuality, alone does not guarantee population fitness. The hemiclonality should be viewed as an evolutionary destination with a positive effect on species continuum. Species with clonal traits are increasingly being identified in different kinds of living organisms  and clonGET, KG and LA performed plant collection and maintenance, inoculation experiments, genotyping using AFLP and analysis of data. SM conducted field survey and identified populations. GT designed the work, evaluated the data and prepared the 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.


Carassius carassius (L.) is a threatened freshwater fish thought to be native to much of Europe. However, its native status in England is based only on anecdotal evidence. Here, we devise an approach that can be used to empirically test the native status of English fauna. We use this approach, along with 13 microsatellite loci, population structure analyses, and Approximate Bayesian Computation (ABC), to test hypotheses for the origins of C. carassius in England. Contrary to the current consensus, we find strong support for the human‐mediated introduction of C. carassius into England during the 15th century. This result stimulates an interesting and timely debate surrounding motivations for the conservation of species. We discuss this topic, and the potential for continued conservation of C. carassius in England, despite its non‐native origins.A fundamental consideration for the conservation of a species is the extent of its native range, that is, regions naturally colonized. However, both natural processes and human‐mediated introductions can drive species distribution shifts. Ruling out the human‐mediated introduction of a species into a given region is vital for its conservation, but remains a significant challenge in most cases. The crucian carp  The earliest known record of live fish translocations into the UK was the movement of common carp, y Lever, . HoweverCarassius carassius (Linneaus 1758), a freshwater fish native to much of central and eastern Europe. Currently, C. carassius is thought to be native in to southeast England, a consensus which is largely based on its distribution in southeast England , ruffe Gymnocephalus cernuus (L.), burbot Lota lota (L.), and spined loach Corbitis taenia (L.)  to have occurred within a certain amount of time  all English populations originate from natural colonizations from Continental Europe more than 7,800 YBP; (2) all English populations were introduced by humans from Continental Europe sometime in the last 2,000 years; or (3) some English populations are native and some have been more recently introduced. Our ultimate aim is to increase the knowledge available for the assessment of status and conservation of C. carassius in England and Continental Europe.In this study, we devise an approach that can be used to empirically test the native status of fauna in England . This method capitalizes on the time constraints imposed on natural colonization of the UK by the existence or absence of the Doggerland land bridge. We use this method, along with ABC and highly polymorphic microsatellite markers, to test the status of 22.1C. carassius, from 11 populations from southeast England, three Belgian populations and one German population  and Carassius auratus (L.)  for all loci and populations. Genetic diversity within loci and populations was estimated using Nei's estimator of gene diversity (He) , which was standardized to the smallest sample size (n = 7) using the rarefaction method  at four hierarchical levels of population isolation, the population‐level (separate ponds within countries), the country‐level (between Belgium and Germany), the landmass‐level  and also at the level of the DIYABC pools used (described below). In the latter case, hierarchical FSTs were used to validate the population poolings used for the DIYABC as in Pedreschi et al. (2014).First, the data were tested for allele dropout and the presence of null alleles using Microchecker  Nei,  and alle2.3FST). The number of samples per population in our study ranged from 4 to 37 and the number of samples per DIYABC pool (see below) ranged from 13 to 88; we therefore tested the power and error rates at sample sizes of 4, 12, 20, 28, 36, 60, and 80. For the magnitude of divergence, we tested cases where FST was 0.001, 0.005, 0.01, 0.05, 0.1, and 0.3. For each combination of sample size and FST, 1000 data simulation runs were performed. Power was then measured as 1‐β, where β is the type II error rate. β is calculated from the proportion of chi‐squared or Fisher's exact tests across all simulation runs that identified a significant (p < .05) difference in allele frequencies between the simulated subpopulations  error rate in the data, which would represent cases where the null hypothesis was incorrectly rejected, that is, finding population divergence where none exists. These were tested for by running POWSIM at an FST of 0, and quantifying the proportion of runs where significant allele frequencies were found between simulated subpopulations.In the context of population structure analyses, a type II error refers to false acceptance of the null hypothesis that allele frequencies between two populations are not different. Thus 1‐β is the probability of correctly rejecting the null hypothesis and therefore identifying population divergence where it exists. For the purposes of this study, a high type II error rate would result in an under estimation of divergence between 2.4C. carassius in England, an Approximate Bayesian Computation (ABC) approach was taken, implemented in the program DIYABC . To inform these poolings, it was first necessary to perform a fine‐scale population structure analysis of the 15 populations used. This was carried out using Discriminant Analyses of Principal Components (DAPC), implemented in the Adegenet R package  were pooled together (see Section C. carassius introduction under our three hypotheses. To test hypothesis (1)—the natural colonization of C. carassius more than 7,800 YBP, the time prior for the oldest split between English and Continental European populations was set to 4,000–10,000 generations , which very conservatively encompasses all dates of possible live fish translocations to the UK by humans. Finally, to test hypothesis (3) that some populations were native and some introduced, we used multiple combinations of both native and introduced prior dates (as used in hypotheses (1) and (2) scenarios, respectively) for different population splitting events (scenarios 45–56). In the interests of completeness, we also tested an intermediate time window of 10–2,500 generations . Analyses were performed in a sequential manner, whereby a summary statistic datasets million datasets per scenario were first simulated in DIYABC. For all analyses, the single‐sample summary statistics used were the mean and variance of gene diversity across all polymorphic loci and the mean gene diversity across all loci. The two‐sample summary statistics used were mean and variance of FST and Nei's distance for loci with FST greater than zero between two samples and the mean FST and Nei's distance for all loci. For scenarios including admixture events, the maximum‐likelihood estimates of admixture proportions were also used. See Cornuet et al. .In total, 56 scenarios were tested: six, 39 and 11 representing hypotheses (1), (2) and (3), respectively Fig. . The numt et al.  for the To reduce computation time, simulated datasets were grouped according to the hypothesis they represented , (2), or (3)) and these groups were separately compared to the observed data using both approaches offered in DIYABC, logistic regression and “direct estimate.” The latter of which is a count of the number of times that a given scenario simulates one of the closest datasets to the real dataset  to 2.37 (GER2) and correlated with Ho . FIS showed mild inbreeding in several British populations  a Belgian population (BEL3) and the German population (GER2) but randomization tests showed that this was only significant in BEL3. Signs of significant outbreeding were observed in three British populations  and were significant in GBR1 and GBR2, although sample sizes in these two populations are small and may not represent the true level of heterozygosity in the population  ranged from 0.03 (GBR2) to 0.4 (GER2). A3.2FST ≥ 0.05, sample sizes as low as 4 still resulted in more than 80% probability of correctly identifying true population divergence  was below 80%. However, when populations of similar genetic composition were pooled for DIYABC analyses, the pool sizes ranged from 13 to 88 (mean = 42.8). The smallest of these pools  still had over 80% chance of finding population structure as low as FST = 0.01. Thus, although several individual populations in this dataset suffer from low sample number, population pools used for DIYABC analyses contained high levels of power for accurately detecting even subtle structure between them.POWSIM analyses showed that the power in the dataset was generally high. At nce Fig. . At sampFor all sample sizes, the false‐positive rate was less than 5%, meaning that the chance of overestimating divergence times between populations is extremely low.3.3FST among populations showed that structure was weakest (FST = 0.0) between the two Belgian populations BEL2 and BEL3, strongest (FST = 0.736) between GBR2 and GBR4  with the remaining within‐pool variation being low, though still significant . Population and country assignments also explained significant amounts of the genetic variation ; interestingly however, the landmass to which an individual was assigned  explained none of the variation between individuals . Taken together, these results suggest considerable shared history between populations in England and Continental Europe and support the use of these poolings as a summary of the relationships between populations.Pairwise BR4 Fig.  and follnt Table  and wellIn the DAPC analysis of population structure, ten genetic clusters were indicated by BIC scores Fig. . The res3.4For the DIYABC analyses, populations were grouped into six pools on the basis of the above DAPC results , 42 and 34 for hypothesis (2) and 52 and 56 for hypothesis (3). These final six scenarios were then tested against each other, again using logistic regression to find the single most likely scenario of all 56 tested. Scenario 42, representing hypothesis (2), produced datasets that were, by far, the closest to the real data, with a posterior probability of 0.91  was the split between German and Belgian populations approximately 547 generations ago . However, the most important demographic event for the purposes of testing our hypotheses is the split between English populations  and Continental populations (pools GER2 and BELG), at time “t11” in Scenario 42  = 9.83 × 10−2  and SNI is the single nucleotide insertion rate , although slow, is still within the realms of that observed in the closely related C. carpio  of scenario 42 using the scaled parameter estimate, t11 (μ + SNI). This gave an estimate of 885 generations, or 1,770 years (with a two‐year generation time), which, although older than the un‐scaled estimate, is still over 6,000 years later than the possible natural colonization window. In fact, in order for the scaled estimate to fit the hypothesis of natural colonization , assuming a two‐year generation time, the mutation rate would have to be approximately 1.0 × 10−5 mutations/locus/generation, at least one order of magnitude lower than reported for microsatellite loci.To validate the choice of this scenario as the most likely, we first tested the “goodness‐of‐fit” of Scenario 42 simulated datasets to the real data using statistical model checking (as implemented in DIYABC). This showed that the observed data fell well within the predictive posterior parameter distribution of the simulated data Figure . DespiteC. carassius into the UK (t9) approximately 250 (95% CI = 59–540) generations or 500  years ago (UK pool 3), from a source population closely related to the Belgian populations sampled here.Further population splits have occurred more recently from this initial introduction, and there is also support for a second independent introduction of 44.1C. carassius arrived in southeast England naturally. Of the 56 colonization scenarios tested, one was clearly identified as being the most likely, which inferred that the oldest possible date for the arrival of C. carassius in England was circa 1126 YBP, almost 7000 years after the loss of the Doggerland land bridge. No scenario with an introduction time consistent with the natural colonization of England received support. This result implies that the only evidence that previously suggested that C. carassius were native to England, the pharyngeal bone found at a Roman excavation site . This predates the first mention of the species in the literature in the mid‐1700s , this does not seem to be the case in UK ponds, with C. carassius often co‐existing with recruiting T. cristatus populations , C. gibelio is not yet present and therefore England, with its benign climate that favors C. carassius growth and reproduction  species in the UK would stand up to the same tests as performed here if the data were available to perform it? And what do we do about it if they don't? The approach used in this study allows us to address the first of these questions, but the second remains the subject of hot debate.None declared. Click here for additional data file.


Microtus longicaudus. We focused on populations along the North Pacific Coast to refine our understanding of diversification by exploring the potentially compounding roles of multiple glacial refugia and more recent fragmentation of an extensive coastal archipelago. Through a combination of genetic data and species distribution models (SDMs), we found that historical climate variability influenced contemporary genetic structure, with multiple isolated locations of persistence (refugia) producing multiple divergent lineages  during glacial advances. These vole lineages all occur along the North Pacific Coast where the confluence of numerous independent lineages in other species has produced overlapping zones of secondary contact, collectively a suture zone. Finally, we detected high levels of neoendemism due to complex island geography that developed in the last 10,000 years with the rising sea levels of the Holocene.Quaternary climate fluctuations restructured biodiversity across North American high latitudes through repeated episodes of range contraction, population isolation and divergence, and subsequent expansion. Identifying how species responded to changing environmental conditions not only allows us to explore the mode and tempo of evolution in northern taxa, but also provides a basis for forecasting future biotic response across the highly variable topography of western North America. Using a multilocus approach under a Bayesian coalescent framework, we investigated the phylogeography of a wide‐ranging mammal, the long‐tailed vole,  Pleistocene glacial–interglacial cycles repeatedly affected divergence and speciation processes at high latitudes in both the Northern , a species that spans more than 35 degrees latitude across western North America in montane and mesic herbaceous habitats , we test previous phylogenetic hypotheses of lineage diversification across the broad range of n = 52) and the University of Alaska Museum of the North (n = 91). Sampling now represents 46 localities spanning the geographic range of M. longicaudus. We focused most intensively on northwestern sampling to explore population structure across the Alexander Archipelago (AA) of southeast Alaska and identify the extent of contact between island and northern clades in southeast Alaska, with a higher sampling density near Haines  sequences were obtained from GenBank for 67 individuals of M. longicaudus, one for each outgroup (M. pennsylvanicus and M. montanus), as well as a single Rag1 sequence for M. pennsylvanicus (Appendix S1).Museum specimens were obtained via fieldwork conducted annually since 1991. Frozen heart or liver tissues are archived at the Museum of Southwestern Biology  cyt b  and nuclear (nuDNA) gene sequences, including Protein C‐est‐2 , β‐fibrinogen , and recombination‐activating protein 1 . Polymerase chain reaction, sequencing, visualization, editing, and phasing procedures for alleles were detailed in Sawyer  E.Z.N.A. kits or salt extraction . Independent, unlinked loci (mtDNA and each nuDNA locus) were partitioned by gene and set to appropriate substitution models (Table S2) calculated in modeltest. An a priori uncorrelated lognormal relaxed clock was set for cyt b with a mutation rate of 4% Myr−1 based on previous estimates ranging from 3 to 5% Myr−1  or species tree analyses.We used a multilocus approach to reconstruct species relationships Maddison . The spe Rambaut . Becausebayesass v3.0.3  across the islands of southeast Alaska, as well as among northern populations with secondary contact or in close geographic proximity. bayesass uses a nonequilibrium, multilocus Bayesian approach to estimate migration rates under a MCMC algorithm. We used phased multilocus data (mtDNA and nuDNA) and ran 200 million iterations with a 20 thousand burn‐in and sampling every 2 thousand. Mixing parameters of allele frequencies, inbreeding coefficient, and migration rates were adjusted following program guidelines.We used b clade, we reconstructed extended Bayesian skyline plots  and cyt b Bayesian skyline plots (Heled and Drummond beast. Colorado Plateau (COP) and North Pacific Coast (NPC) were omitted due to low sample sizes. Strict molecular clocks for all phased loci and appropriate models of evolution (Table S2) assigned for each of three independent runs per data set included a MCMC chain of 2 billion steps, sampled every 2 million steps. tracer was used to assess convergence. Significant population size change occurred in EBSPs if zero was excluded from the 95% confidence interval (CI) of the estimate of the number of size‐change steps , haplotype diversity (Hd), and nucleotide diversity (π)] for both mtDNA and nuDNA in dnaSP 5.10.1  using the raster calculator. Minimum threshold values for climate suitability were the low median threshold values over all replicates  southern, comprised of two lineages ; (2) central ; (3) COP (Arizona into Idaho); and 4) northwest, consisting of NPC , northern , and island . Two mainland southeast Alaska locations, Haines and Juneau, have representatives of both northern and island clades.We found stronger geographic structure within the faster evolving cyt Both northern and island clades showed substantial internal structure, with many geographically restricted lineages identified , and both cyt b skyline plots and EBSPs , although generated independently, were assessed for consistency across loci. The genetic signature of the island clade is consistent with a founder event that then experienced rapid growth, with high cyt b Hd a result of genetic drift in the small, fragmented populations of the archipelago. The northern clade signature is also consistent with reduced ancestral population size followed by rapid expansion, while the central clade was historically stable. The southern clade represents a glacially stable population that may have experienced a bottleneck (this signature only in the cyt b data). Small sample sizes for the NPC and COP clades preclude inference.Nuclear loci had varying levels of allelic diversity (Table S2), with ETS2 the most geographically structured. No selection was detected in HKA tests for all loci. Ramos‐Onsin and Rozas SPs Fig. . Populatb clades within northern regions of secondary contact .The multilocus estimation places the north/island clade TMRCA before the LIG  and relatively few northern continental localities were included, the full northern range currently occupied was not predicted in the models  may impact species and ultimately ecosystems. In the case of coastal Alaska, our understanding of diversity, island biogeography, and community assembly across the vast Alexander Archipelago was predicated on the hypothesis that the region was entirely glaciated until about 12,000 years ago, and this suggested a narrow timeframe for colonization and diversification of the contemporary biota Klein . SubsequM. longicaudus. Instead, we uncovered substantial structure in coastal Alaska, with admixture in some mainland populations that reflected complex histories of origin. Contemporary coastal mainland populations are derived from historically persistent  populations in Southeast Alaska  on Prince of Wales Island  during the LGM, followed by expansion onto the islands and mainland and then recent isolation on multiple islands as sea levels rose Fig. . Heaton and Fig.  that rad Carlson .M. longicaudus is the only vole that occurs on most of the islands (Baranof island is a curious exception). On the mainland of southeast Alaska, this vole is syntopic with two congeners, M. oeconomus (root vole) and M. pennsylvanicus (meadow vole); however, the phylogeographic histories of those two species remain poorly understood in the region. Both species are thought to be Holocene colonizers of southeast that originated outside of the coastal region , a vole specialist predator  of ermine. Hence, although the broader regional patterns of lineage divergence due to persistence in multiple glacial refugia are shared among this predator and its prey, idiosyncratic differences due to distinct colonization abilities, lineage persistence, or random events are evident on individual islands and on the mainland in southeast Alaska. These limited examples point to the need for more detailed comparative assessments with other taxa, such as plants and associated arthropods, across this complex landscape.Lack of connectivity between insular populations produced extensive interisland structure, with populations on 13 of the 19 islands displaying island‐specific haplotypes and significant mtDNA divergence  and the multiple sites of geographic proximity southward along the mainland coast of southeast Alaska indicate the possibility of additional locations of contact between distinctive lineages  during glacial advances. The central and southern clades were effectively isolated and relatively demographically stable since the mid‐ to late‐Pleistocene. Lastly, the COP clade, previously identified from a limited region in northern Arizona, was found to occur northward into Utah and potentially contacts both the S2 subclade and central clade.Species distribution models are consistent with persistence of Deciphering signatures of incomplete lineage sorting versus secondary admixture is critical for interpreting how historical climate shaped contemporary distributions and genetic structure. Shallow divergence between northern and island populations near Haines may reflect recent (Holocene) secondary contact or incomplete lineage sorting. Future studies should incorporate more intensive sampling of individuals, especially in suspected contact zones, and expanded genomic coverage to explore potential admixture  ETS2, (B) FGB, and (C) Rag1 nuclear loci with posterior probabilities of ≥0.95 represented with open circles on branches of the solid consensus tree.Figure S4. Phased nuclear haplotype distribution in Northern and Island clades of M. longicaudus.Table S1. Primer list and PCR annealing temperatures.Table S2. Diversity indices, expansion statistics and models of evolution.Table S3. Between group net genetic distance.Table S4. Locality abbreviations.Table S5. Island and Northern cytb clade populations near the geographic regions of contact .Table S6. Bayesian migration estimates for Southeast Alaska populations determined in bayesass for M. longicaudus.Table S7. Cyt b and phased multilocus divergence date estimates.Data S1. Cyt b data ‐ Methods and Results.Click here for additional data file.


Dodonaea viscosa ssp. angustissima, and examine its genetic diversity and population structure. We used a hybrid-capture target enrichment technique to selectively sequence over 700 genes from 89 individuals across 17 sampling locations. We compared 815 single nucleotide polymorphisms among individuals and populations to investigate population genetic structure. Three distinct genetic clusters were identified; a Flinders/Gammon ranges cluster, an Eastern cluster, and a Kangaroo Island cluster. Higher genetic diversity was identified in the Flinders/Gammon Ranges cluster, indicating that this area is likely to have acted as a refugium during past climate oscillations. We discuss these findings and consider the historical range dynamics of these populations. We also provide methodological considerations for population genomics studies that aim to use novel genomic approaches (such as target capture methods) on non-model systems. The application of our findings to restoration of this species across the region are also considered.The Adelaide geosyncline, a mountainous region in central southern Australia, is purported to be an important continental refugium for Mediterranean and semi-arid Australian biota, yet few population genetic studies have been conducted to test this theory. Here, we focus on a plant species distributed widely throughout the region, the narrow-leaf hopbush,  Ecological and evolutionary responses to contemporary climate change are evident in many species around the world1315Even if populations are able to maintain high genetic diversity, connectivity and dispersal, rapid and/or extreme climate change can push species beyond their adaptive limits in at least parts of their range. During past climate oscillations, particularly those experienced during the Pleistocene, refugia are thought to have played a major role in the persistence of a vast number of species787Under contemporary climate change, the Adelaide geosyncline has the potential to offer refuge from climate extremes, providing altitudinal and latitudinal gradients for species to migrate across. However, the capacity of this area to be an effective future refugium may be compromised by its highly fragmented state, where habitat modification over the last 200 years has led to little of the historical woodlands and forest remaining1213Contractions to and expansions from refugia leave genetic signatures across the genome, which contribute to the structuring of genetic diversity in contemporary populations. Populations persisting in past refugia generally maintain higher genetic diversity than the populations that have expanded from them16Dodonaea viscosa ssp. angustissima (D. v. angustissima hereafter), a widely distributed endemic woody shrub of Australia with a range extending throughout the southern and central regions of the continent. Its hardy nature is reflected in its wide distribution across diverse habitats such as open woodlands, sand plains, and on margins of sand dunesD. v. angustissima’s distribution across the Adelaide geosyncline region  present across our samples. We genotyped 89 Heliconius butterfies using a targeted sequencing approachSequencing of hybrid-capture libraries from all 89 individuals resulted in a total of ~332 million reads, with the number of reads sequenced per individual ranging from 2.3 million to 5 million . The percentage of reads that mapped back to the transcriptome reference was 15.7%, which is low but not to be unexpected with the approach taken. Targeted sequencing using hybrid-capture baits is a relatively new approach, particularly for organisms without reference genomes. A similar approach was used in a study of grey wolf genomic variation and they achieved mapping success of ~86% of raw reads mapping to the dog reference genomeFST outlier SNPs as they were deemed to be non-neutral. Of the remaining 2,458 SNPs, a further 1,643 SNPs were removed for having negative FIS values as a conservative method of excluding potential paralogous SNPs. This resulted in a final SNP set of 815 SNPs for population genetic diversity and structure analysis.Of the reads that mapped, 67.7% mapped in pairs. Following the calling of variants by identifying SNP differences between the reference and mapped sequences, rigorous and stringent filtering steps were taken to provide a reliable set of neutral SNP calls with high coverage across all individuals. Filtering of raw SNPs on depth of coverage, minimum minor allele frequency, and percentage of missing data per SNP resulted in a set of 25,329 SNPs. These SNPs were then pruned of SNPs in LD, reducing the SNP set to 8,462. The requirement of at least 100 bp between each SNP reduced the SNP set further to 2,800 SNPs. We excluded an additional 342 K = 3 had the lowest Bayesian information criterion (BIC) value, with a clear ‘elbow’ in the graph at this K value  with K = 3 the second most likely . When K = 2, the same FGR cluster and Eastern cluster as in the DAPC analysis were identified, with the Kangaroo Island samples having ~50% assignment to each of these clusters ,  K value . Two dis K value  and 2. Tclusters . When K  cluster , matchinFST estimates indicated the greatest differentiation was between the Eastern and KI clusters (FST = 0.280), with the least differentiation between the FGR and KI clusters (FST = 0.138). Genetic differentiation between the FGR and Eastern clusters was intermediate to these values (FST = 0.189).Nested AMOVA analysis revealed that the majority of the genetic variance was within individuals 69%, . There wO) and expected (HE) heterozygosity were 0.123 (95% CI = ±0.007) and 0.141 (95% CI = ±0.007) respectively, with lowest HO and HE in the Peterborough subpopulation, greatest HO in the Telowie Gorge population and greatest HE in the Brachina Gorge population  we ascertained that the proportion of the total genetic variation that is explained by the spatial variables is equivalent to an FST of 0.089. For the Flinders/Gammon Ranges cluster, 15.8% of the total genetic variation was constrained by latitude . Overall FST in this cluster was 0.044, and so the proportion of the total genetic variation explained by the spatial variables is equivalent to an FST of 0.007. Spatial variables did not explain significant levels of total genetic variation in the Eastern cluster.Redundancy analysis (RDA) performed on all samples demonstrated that 58% of the total genetic variation was constrained by spatial variables  during Mid-Pleistocene climate oscillationsD. v. angustissima. Evidence for the presence of D. v. angustissima in the Central Flinders Ranges  during the Early to Mid-Holocene has been found in an analysis of stick-nest rat middensThe Flinders Ranges, with its varied topography and high elevation, provides ideal refugial conditions enabling species to remain within their preferred climatic envelopes with only short migration distancesA steep rainfall gradient exists across the Flinders Ranges, with rainfall rapidly decreasing to the east of the ranges. This means that populations in the FGR and Eastern clusters are inhabiting contrasting environments, in terms of rainfall at least. For example, Gammon Ranges population four was less than 35 km from population five, yet the two populations fall into distinct genetic clusters. Considering the high genetic similarity between all FGR populations, which extend over a much larger distance, gene flow would be expected between these two populations. The two sampling sites differ greatly in their elevation (27 m at GR4 versus 700 m at GR5), their annual mean precipitation (13 mm at GR4 and 24 mm at GR5), and their annual mean aridity index values (0.07 at GR4 and 0.14 at GR5), so gene flow may be unsuccessful despite the short distance, resulting in isolation by environment .An alternative, or perhaps complementary explanation for the observed genetic differentiation between the FGR and Eastern clusters is that the region may represent a contact zone between distinct range expansions, with the eastern samples representing the edge of a range expansion from the southeast. A lack of admixture between these two genetic sources would result in the patterns we observe here. This is also supported by the lower differentiation between the FGR and KI clusters compared to that between the FGR and Eastern clusters. Further sampling of more eastern populations would help to ascertain the origin of the Eastern cluster.D. v. angustissima seed can remain viable in sea water for extended periods of timeThe STRUCTURE analysis provided most support for two distinct clusters, with the KI samples being an admixture of the FGR and Eastern clusters. Admixing of the FGR and Eastern clusters suggests that the KI populations may have experienced gene flow from the mainland. KI is only 13.5 km offshore; D. v. angustissima do not extend to coastal regions of the Fleurieu peninsula, the closest part of the mainland to KI. Also, KI has been separated from the mainland since the retreating ice sheets led to sea level rise at the end of the Pleistocene, around 10,000 years agoIn the DAPC analysis, the KI populations were identified as genetically distinct from the mainland populations, despite the possibility for long distance gene flow. Differentiation between the mainland and KI populations may be explained by prolonged separation of these populations. The contemporary ranges of mainland populations of Redundancy analysis showed that 58% of the genetic variation across all samples could be explained by spatial location of populations, suggesting isolation by distance. However, as most of the genetic variation was distributed among genetic clusters as well as the fact that the three identified clusters are (mostly) spatially separated, the constrained variation cannot be attributed solely to isolation by distance. Testing for the influence of space on within-cluster variation found that spatial location explained only a small percentage of genetic variation in the FGR cluster and did not significantly explain any in the Eastern Cluster. This adds to the evidence that most of the genetic variation is distributed among the identified clusters, rather than within.The target capture method used in the current study35FST can be biased if sample sizes are too small4243FST estimates can more readily be increased by sampling more individuals per population rather than sampling more markers per individual, particularly when FST is lowet al.FST = 0.2. As few as four samples per population have been shown to be sufficient for FST estimates when using a large number of markers 25In our study, 5–8 individuals were sampled per population. These relatively small numbers were constrained by the fact that, as in most population genetic studies, compromises must be made between the number of populations sampled and the number of samples per population due to budget restrictions. This trade-off between per-population sample size and number of populations when using NGS genotyping methods has led to several published studies having fewer than ten individuals per population40FST among our sampled populations was 0.16, which is quite low, and so our use of only 5–8 individuals per population may have resulted in low power for our FST estimates. However, the assignment of individuals to genetic clusters through the genetic structure analyses meant that we were actually working with sample sizes of 51, 28, and 10 for the FGR, Eastern and KI clusters respectively. This, along with our use of a large number of SNPs (815) should have provided sufficient power to reliably detect differentiation among the clusters without having to compromise on the number of sampling sites.Average pairwise D. v. angustissima, a species commonly used in revegetation projects, the distinct genetic clustering and clear assignment of individuals to these clusters demonstrates that the three populations are genetically isolated from one another, and adaptive differences are likely to be present. As such, movement of seed between these regions may result in maladapted plants and outbreeding depression. Further investigation into the phenotypic differences among plants across these genetic clusters through reciprocal transplant experiments are required to fully assess the risks of mixing seed from across the identified genetic clusters.The Adelaide Geosyncline has a number of National and Conservation Parks where natural stands of native vegetation are protected. Between these protected areas much land has been cleared, leaving protected areas fragmented across the landscape. Large-scale restoration is carried out across the region to increase the cover of native vegetation, re-connect these fragments, and return functional, native ecosystems. Recent work has focussed on improving success rates of plantings under climate change, due to the questionable success rates of locally sourced material192019D. v. angustissima throughout the Adelaide geosyncline, with sampling effort stretching from Kangaroo Island in the south, through the Mount Lofty and Flinders Ranges to the Gammon Ranges in the north  was used to mask interspersed repeats and low complexity DNA sequences based on the Arabidopsis thaliana genome during the bait design.The previously published transcriptome for this speciesa priori expectations that they may be under selection and so informative for a separate study focussing on signatures of selectionAlthough the targeted gene sequences were mainly selected based on www.mycroarray.com/pdf/MYbaits-manual-v2.pdf) using the high stringency wash buffer and 12 cycles of post-capture PCR. Following capture 100 bp paired-end sequencing with dual indexing of 89 samples was performed on one lane of an Illumina HiSeq 2000 at AGRF . Sequence data was subsequently processed using the Illumina CASAVA pipeline (version 1.8.2).DNA was extracted using the Machery-Nagel Nucleospin Plant II Kit at the Australian Genome Research Facility . The extracted DNA was then sonicated for random sheering and Illumina’s TruSeq Nano DNA protocol was used for size selection and sequencing adapter and barcode ligation. The hybrid-capture enrichment reactions were carried out following the MyBaits protocol v.2 (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Raw sequence quality was very high, negating the need for any trimming. Mapping of raw sequence reads to the reference transcriptome fromhttp://broadinstitute.github.io/picard/) were used to compress the resulting SAM files, sort the sequences by reference contig and mark duplicated sequence reads. Mapping characteristics were assessed using SAMtoolsSequence quality was assessed using FastQC (http://pngu.mgh.harvard.edu/~purcell/plink/). This ran independent pairwise regressions between all SNPs. A cut-off r2 > 0.5 was used, whereby one of a pair of SNPs was removed from the dataset if the coefficient of determination between the pair was greater than 0.5, thus removing SNPs showing strong signals of LD. A further requirement of at least 100 bp between each SNP was also implemented.SNPs were subsequently filtered using VCFToolsFST-based outlier analysis implemented in BayeScan ver. 2.0We then removed outlier SNPs using an D. v. angustissima genome was unknown. If present, paralogous sequences may map together during the mapping stage. This could skew allele frequency estimates and bias results. Paired-end sequencing was employed in this study, and the requirement of both members of a pair to be present when mapping to a reference can help to reduce the chance of mapping paralogous regions together. As an extra control, FIS values of the generated SNP set were calculated in GENODIVE and SNPs displaying significantly negative FIS values  were removed using VCFTools.The hybrid capture baits were designed based on transcriptome sequences and, as a reference genome was lacking, the presence of duplicated or paralogous sequences of the bait targets within the K = 1–10 and the K value with the lowest BIC was selected as the optimal number of clusters. A discriminant analysis was then performed on the first 40 principal components using the function dapc, implemented in R, in order to efficiently describe the genetic clusters and assign samples to each cluster.Population genetic clustering analyses were performed in order to group genetically-similar individuals together. We used a non-model based method called a discriminant analysis of principle components DAPC, and theK values 1–10 were assessed, with 10 replicates per K value. ΔKK value in Structure Harvester ver.0.6.94K. Results from replicate runs of the most likely K were combined using CLUMPPSecondly, the most likely number of clusters and individual assignment to those clusters was assessed using STRUCTURE ver. 2.3.4. An admixture model was used to determine the number of population clusters (K) with a burn-in of 200,000 followed by 1,000,000 iterations. FIT), among individuals nested within populations (FIS), among populations nested within genetic clusters (FSC), and among genetic clusters (FCT) were calculated. Significance of each fixation index was evaluated using permutation tests with 10,000 permutations in order to assess the partitioning of genetic variation among subpopulations as well as among the genetic clusters. Pairwise FSTA nested analysis of molecular variance (AMOVA)FSTIn order to measure the spatial component of the among-population variation a redundancy analysis (RDA) was performed on the population allele frequencies using a modified R script from46In order to account for the fact that identified genetic clusters were geographically disparate, the RDA analysis was performed separately on only populations from the FGR cluster, only populations from the Eastern cluster, as well as all samples together. The Kangaroo Island populations were not analysed separately due to the low number of samples and limited geographic variation.Accession codes: Sequence reads are archived at the NCBI SRA with accession number SRP077342. The variants file is available as a How to cite this article: Christmas, M. J. et al. Targeted capture to assess neutral genomic variation in the narrow-leaf hopbush across a continental biodiversity refugium. Sci. Rep.7, 41367; doi: 10.1038/srep41367 (2017).Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.


Cycas segmentifida D. Y. Wang and C. Y. Deng, a threatened cycad species endemic to Southwest China. High levels of genetic diversity and genetic differentiation were revealed in C. segmentifida. Haplotypes of networks showed two evolutionary units in C. segmentifida, with the exception of the nuclear gene GTP network. Meanwhile, the UPGMA tree, structure and PCoA analyses suggested that 14 populations of C. segmentifida were divided into two clades. There was significant effect of isolation by distance (IBD) in this species. However, this species did not display a significant phylogeographic structure. The divergence time estimation suggested that its haplotypes diverged during the Middle Pleistocene. Additionally, the population dynamics inferred from different DNA sequences analyses were discordant. Bottleneck analysis showed that populations of C. segmentifida did not experience any recent bottleneck effect, but rather pointed to a contraction of its effective population size over time. Furthermore, our results suggested that the population BM which held an intact population structure and occupied undisturbed habitat was at the Hardy–Weinberg equilibrium, implying that this population is a free-mating system. These genetic features provide important information for the sustainable management of C. segmentifida.Climate change, species dispersal ability and habitat fragmentation are major factors influencing species distribution and genetic diversity, especially for the range-restricted and threatened taxa. Here, using four sequences of chloroplast DNAs (cpDNAs), three nuclear genes (nDNAs) and 12 nuclear microsatellites (SSRs), we investigated the genetic diversity, genetic structure, divergence time and population dynamics of  Southwest China is seen as one of the world’s biodiversity hotspots because of its complicated terrain, diversified climates and habitats . The regCycas (Cycadaceae), in China ], and 12 microsatellite markers (C. segmentifida. We aimed to address the following questions: (1) What is the genetic diversity level of C. segmentifida? (2) Are the 14 populations of this species divided into two groups according to the sand and karst habitats? (3) When did the haplotpes of C. segmentifida began to diverge, and how did the population dynamics respond to climate fluctuations during historical period?Here, we performed a comprehensive study using four cpDNAs , three TP genes ; PHYP, p markers  to invesC. segmentifida. Young and healthy leaves were dried in silica gel immediately after collection. Within the 238 samples, 7 to 10 individuals from each population were chosen for chloroplast and nuclear DNA sequencing, while all of the 238 individuals were used for the microsatellite study. Geographical information of the 14 populations and numbers of individuals used in the DNA sequencing and microsatellite analyses are presented in Supplementary Table Figure 1, respectively.A total of 238 individuals were obtained from 14 populations, representing the entire natural distribution area of psbA-trnH, trnS-trnG, psbM-trnD, and trnL-trnT, and three nuclear genes, GTP, PHYP, and PPRC, for complete analysis after preliminary screening from universal chloroplast and nuclear primers. PCR amplification procedures were the same as those used in C. simplicipinna  and haplotype diversity (Hd). Gene diversity in total populations (HT), within-population gene diversity (HS),  using Arlequin, version 3.11 (r) was calculated using a modified equation: r = mp/ms = [(1/FST (n) - 1) - 2(1/FST (c) - 1)]/(1/FST (c) - 1)  taken as estimators of population differentiation are derived from AMOVA, mp is the pollen migration rate, ms is the seed migration rate, FST (n) is the nuclear (nDNA) FST and FST (c) is the chloroplast (cpDNA) FST. Here, rG = mp (GTP)/ms; rP = mp (PHYP)/ms; rR = mp (PPRC)/ms.First, we used DnaSP, version 5.0, to detect recombination in nuclear genes. We calculated indices to measure the level of genetic variation, haplotypes, ty (HS),  and two rmut 1.0 . We compion 3.11 . The pol(c) - 1) , where FCycas edentata and Cycas rumphii as outgroups. We also used Network, version 4.2.0.1  analysis. The ESS parameter of each process was checked by TRACER, version 1.5 , effective number of alleles (AE), private alleles (AP), expected heterozygosity (HE), observed heterozygosity (HO), information index (I), fixation index (F) and percentage of polymorphic loci (PPB), were calculated using GenAlEx, version 6.3, and POPGENE, version 1.32 (AR) was calculated in the software FSTAT, version 1.2 ] with geographic distance for all pairs of populations. Genepop, version 4.1.4 (FST/(1 -FST). The pollen/seed migration ratio (r) was also calculated. Tests for departure from Hardy–Weinberg equilibrium (HWE) were performed in each locus and each population as well as a globally unified population using Genepop, version 4.1.4  was performed using TEPGA, version 1.3 (K) was set from 1 to 20, and each K run 20 times under 1 × 105 subsequent MCMC samplings and a subsequent burn-in of 1 × 105 iterations. The combination of admixture and correlated-allele frequencies model was used in this analysis. We evaluated the most likely number of groupings using ΔK and the log-likelihood value in the program STRUCTURE HARVESTER, version 0.6.8   with a 95% confidence interval . The botle size)  which wale size) . When se history .P = 1, >0.5) based on the congruency test, suggesting that there was a high degree of homogeneity among the four cpDNA regions. They contained 22 polymorphic sites and seven haplotypes (segH1–segH7) across the 137 individuals . NST was not significantly greater than GST (P > 0.05), indicating that C. segmentifida shows no correspondence between haplotype similarities and their geographic distribution.Genetic diversity indices GTP, PHYP, and PPRC, respectively. Except for the gene GTP, FST values for cpDNA and the other two nuclear genes ranged from 0.286 to 0.998 (Table 1), indicating highly significant genetic differentiation among C. segmentifida populations. The pollen/seed migration ratios were calculated as rG = 49399, rP = 1243.755 and rR = 581.086, suggesting that pollen flow was significantly higher than seed flow in C. segmentifida.The AMOVA revealed that almost all variation (99.80%) was partitioned among populations based on cpDNA data, whereas higher variations  were revealed within populations than among populations based on nuclear genes C. edentata and C. rumphii as outgroups. The four phylogenetic trees were strongly supported with high Bayesian probabilities, revealing the monophyly for C. segmentifida haplotypes . The remaining haplotypes were linked to these central haplotypes by one to nine steps in a star-like network. Absence of some haplotypes caused reticulate evolutionary relationships in networks of cpDNA and PPRC. In the network of GTP , haplotype segG1 occurred at the highest frequency and was in an internal node location, indicating it may be an ancestral haplotype. The remaining four haplotypes were linked to the central haplotype by one or four steps in a star-like network.The haplotype network of cpDNA was concordant with the haplotype network of nuclear genes Figure 3). The seven cpDNA haplotypes clustered into two lineages and diverged at approximately 1.701 million years ago (MYA). Of the two lineages, one included five haplotypes with segH1 as the ancestral haplotype and the other included two haplotypes with segH5 as the ancestral haplotype . For the three nDNA haplotype trees, haplotypes were also divided into two lineages, coalescing at 0.524 MYA (GTP), 0.480 MYA (PHYP), and 0.684 MYA (PPRC), respectively . In total, most tip haplotypes in the four BEAST-derived trees diverged recently from their common ancestors. The divergence times estimated from the four DNA sequences entirely implied that haplotypes of C. segmentifida diverged in the Middle Pleistocene.The BEAST-derived trees for the four DNA sequences revealed similar topologies . During the recent 0.01 million years, rapid population growth and subsequently stabilization were detected in C. segmentifida based on the gene GTP , while the Bayesian Skyline Plot of the gene PHYP  showed this species has had a population contraction since approximately 0.02 MYA, with subsequent slight expansion in demography. In contrast, the Bayesian Skyline Plot of the gene PPRC showed that C. segmentifida experienced a constant population size over 0.27–0.07 MYA, before undergoing a decline from approximately 0.07 MYA, and then experienced a population expansion from 0.01 MYA to present .Values for Tajima’s C. segmentifida, and the number of alleles per locus was between 3 (Cy-TaiEST-SSR11) and 44 (cha-estssr01). The locus cha-estssr01 had the highest genetic diversity while Cy-TaiEST-SSR11 had the lowest by comparison of genetic parameters AR, NA, AE, and I (Supplementary Table Table 2), with the highest and lowest measures of diversity consistently found in populations BB and LK, respectively. Fixation indices (F)  were greater than 50. The AMOVA revealed that more variation (77.11%) was partitioned within populations than between populations, with the significant genetic differentiation coefficient FST = 0.229 (Table 1). The pollen/seed migration ratio (r) was evaluated as 1678.039, suggesting more pollen flow than seed flow. The correlation between genetic and geographic distances was significant , suggesting that C. segmentifida has significant effect of IBD .A total of 117 alleles were identified by the 12 microsatellites across the 238 individuals of ices (F)  were negFigure 6A). Structure  and PcoA  revealed the same results as UPGMA, indicating that 14 populations were grouped into two clusters.The UPGMA clustering dendrogram showed that individuals belonging to populations BY, JZ, LK, LKA, BM, NZ, and BD clustered into one group (Clade I), while the remaining individuals clustered into a second group (Clade II) (Table 3). Meanwhile, Mode shift model tests revealed that all of the populations were under a normal L-shaped distribution, indicating that C. segmentifida had not experienced a severe bottleneck recently. However, Garza-Williamson indices of 14 populations were lower than the critical Mc value of 0.68 (Table 3), indicating that C. segmentifida had apparently experienced a population size decline (bottleneck) in history.No population had a significant excess of heterozygosity in the two methods under two models in the bottleneck analysis, indicating that 14 populations did not deviate from mutation-drift equilibrium     (Table 2), illustrating that C. segmentifida populations are notably deficient in heterozygosity and have experienced severe levels of inbreeding. The fact is that its large and heavy seeds germinate and grow near the mother plant, increasing the chances of inbreeding within the species. Consequently, this species presents a high level of genetic differentiation.Generally, as an ancient and woody gymnosperm species, cycads are considered to possess high genetic diversity within population and low level of genetic differentiation among populations . However= 0.998)  within C= 0.670) . In contentifida . The polfida (r)  illustraices (F)  basicallC. debaoensis were divided into two groups according to two types of habitats ; however, it is not clear whether its populations can be differentiated into two different clusters. NST was not significantly greater than GST following U tests, which indicated that there was no distinct phylogeographic structure in C. segmentifida. However, a significant correlation between nuclear genetic and geographic distance in this species was observed, indicating this species was in accord with IBD. In addition, a clear genetic structure of two genetic groups was detected in C. segmentifida, but the two genetic groups did not completely comply with the division of sand and karst habitats. The two central populations  suggested that there are two star-like evolutionary units separately dominating two lineages . The fewer mutational steps indicated a recent rapid divergence. Additionally, the UPGMA, Structure analysis and PCoA clustering of microsatellite data showed the 14 populations comprise two clades (I and II) , and with similar patterns in haplotype distributions of the gene PPRC , but had a little conflict with cpDNA haplotype distributions . The population BA situated at the border of the two clades geographically clustered into Clade II based on microsatellites, while clustered into another group with the seven populations belonging to Clade I based on cpDNA haplotype distributions. This little conflictual relationship may reflect ongoing pollen-dispersed gene flow among nearby populations, insufficient lineage sorting at nuclear loci or shared ancestry due to recent species divergence. Meanwhile, the BEAST-derived trees also revealed that haplotypes were separated into two groups with an ancestral haplotype at the inner node of each group, further indicating a recent divergence.Parsimony network analysis   , namely,C. debaoensis . According to the wild field investigation, we found that the habitat of population BM is not destroyed, the regeneration ability of the population is very strong, it has a high population density, and different ages of individuals exist in the population. The HWE test showed that the population BM was in HWE, indicating that it is a free-mating group. It was habitat destruction and massive illegal digging of C. segmentifida that jeopardized the existing populations of this species and its effective population size. Therefore, we suggest that protection zones or plots in the distribution areas of C. segmentifida be established to protect the habitat for this species. In this study, two genetic clusters were detected in C. segmentifida. We proposed that these two genetic clusters could be managed as two evolutionary units and should be given the highest priority protection. The divergence of C. segmentifida and network of its haplotypes indicated that this lineage recently evolved rapidly. We suggest protecting the species in its natural habitat (in situ). Alternatively, ex situ conservation such as seeds or seedling collection from distinct populations that possess pivotal genetic components and reintroduction are needed. Meanwhile, wild introgression from other Cycas species should be avoided. In addition, more efforts should be made to raise local farmers’ conservation awareness and a prohibition on deforestation in Cycas distribution areas should be implemented.The objective of conservation of threatened species is to maintain their contribution to overall genetic diversity . This stXF was in charge of finishing the molecular genetic studies, performing the data analysis and writing the manuscript. JL contributed to materials collection, DNA extraction, and manuscript revision. XG and Y-CC designed the study, collected research materials and drafted the manuscript. All authors read 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.


RAD sequencing to estimate the genetic relationships among a subset of these populations. We found a large amount of phenotypic variation among populations, most prominently in armour and spine traits. Despite large variation in the abiotic environment, namely in ion composition, depth and dissolved organic Carbon, more phenotypic variation was explained by the biotic variables (presence of predators and density of predator and competitors), than by associated abiotic variables. Genetic structure among populations was partly geographic, with closer populations being more similar. Altogether, our results suggest that differences in body shape among stickleback populations are the result of both canalized genetic and plastic responses to environmental factors, which shape fish morphology in a predictable direction regardless of their genetic starting point.There has been a large focus on the genetics of traits involved in adaptation, but knowledge of the environmental variables leading to adaptive changes is surprisingly poor. Combined use of environmental data with morphological and genomic data should allow us to understand the extent to which patterns of phenotypic and genetic diversity within a species can be explained by the structure of the environment. Here, we analyse the variation of populations of three‐spined stickleback from 27 freshwater lakes on North Uist, Scotland, that vary greatly in their environment, to understand how environmental and genetic constraints contribute to phenotypic divergence. We collected 35 individuals per population and 30 abiotic and biotic environmental parameters to characterize variation across lakes and analyse phenotype–environment associations. Additionally, we used  Anolis lizards and Darwin's finches that have captivated evolutionary biologists for decades  and the evolution of one or a small number of traits . Three‐spined sticklebacks are a classic example of recent adaptive radiations where a lot of focus has been on the divergence of a few adaptive traits, such as armour and spines and the genetic changes underlying them . Atlantic salmon  and charr  are present in some lochs, but the former are only present seasonally and do not usually eat in freshwater, while the latter are rare. Avian predators including terns (Sterna spp.) and divers (Gavia spp.) occur on most lochs but at very low density . Insect predators of stickleback  and dragonfly (Odonata) larvae) also occur, but are rare except in lochs with no trout . Stickleback on North Uist are infected by numerous species of parasites  that are relatively common and widespread, with the potential to cause selection .We obtained the relative densities of planimeter software   and maximum depth (‘Max. depth’) of each lake were either estimated using a Plastimo Echotest II hand‐held depth sounder to record the depth at several points (20–180) along each lake, or taken from existing literature (Murray & Pullar 2 area) was estimated by visual inspection and probing with a pole every 50 m around the margin of each lake, or for 500 m either side of where the traps were set.Littoral substrate at 0.5 m depth  with the maximum transmission rate. We also estimated the relative transmission of long wavelengths, which can be quantified by the ‘orange ratio’, or the integral of 400–550 nm transmission divided by the integral of 550–700 nm transmission , length of 1st dorsal spine (‘DS1’) and 2nd dorsal spine (‘DS2’), length of biggest armour plate (‘BAP’), pelvic spine length (‘PS’), length of the horizontal process of the pelvis (‘LP’) and height of the ascending process of the pelvis (‘HP’) . All variables were scaled and centred prior to the PCA.r v3.0.0 (http://www.R-project.org.).Finally, we tested for collinearity of the traits analysed by performing Pearson's correlations between individual measurements for each population separately and with all individuals from all populations pooled. We adjusted the statistical significance in the above tests for multiple comparisons using a false discovery rate (FDR) of 0.05  of 0.05  from the analyses. The same program was used to calculate within‐population fixation indices  using the default value of conversion criterion of 10e‐6 and the simple prior. We then run the algorithm installed in the same program to find the appropriate number of model components that explain structure in the data set.We analysed population genetic structure and assigned individuals to populations using a variational Bayesian framework for posterior inference from large SNP genotype data as implemented in FST/(1−FST)) and the matrix of geographic distances, and tested the significance of the correlation using a Mantel test with 10 000 permutations as implemented in arlequin  by estimating the correlation between the matrix of genetic distance between pairs of populations .To analyse associations between the phenotype, the genome and environment, the phenotypic data were related to environmental and genomic variables using canonical redundancy analyses (RDA) Rao . This co Ezekiel . RDAs anGyrodactylus arcuatus and Schistochephalus solidus, presence and percentage of P. pungitius and trout presence). We then combined the best explanatory abiotic and biotic variables in one explanatory matrix to assess the percentage of the phenotypic variation explained by the two sets of explanatory variables combined. Additionally, we repeated the analyses using a reduced data set of 23 populations so we could include quantitative information on trout catch rate (TCR) (rather than just presence) in the biotic explanatory variables.The first part of the analyses focused on the relationship between the whole phenotype and the environment and involved relating the response matrix to two environmental explanatory matrices in two separate RDAs: the first matrix consisted of 10 PCs of abiotic environmental variation and the second of 4 biotic variables  distinguished pronounced shape changes associated with the position of the beginning of the dorsal and anal fins and length of the caudal peduncle, and PC2 (‘bodyPC2’) with the position of the end of the dorsal fin, position of the anal fin and lower jaw length  traits revealed that the first four PCs of phenotypic variation explained 83% of the variance among individuals . In this analysis, all armour and spine measurements and counts had heavy positive loadings on PC1 (‘armour PC’), which explained 55% of the variation. Standard length had a heavy positive loading on PC2 (‘SL PC’), which accounted for 10% of the variation. We found a gradient of populations along PC1, ranging from populations of individuals with no armour plates or dorsal and ventral spines to populations of individuals that have all spines and some armour plates. The second axis showed a gradient starting with populations with the shortest individuals and ending with populations with the longest individuals  >0.5, P‐value ≤0.05 after adjustment for multiple testing) . Other variables such as SL and bodyPC1 and bodyPC2 were also significantly correlated with some armour traits, although not as strongly. Within populations, the number of traits correlated varied greatly: on average, 10 correlations were significant within populations, ranging between 1 (DAIM) and 24 (EISI). Of 405 correlations among all armour traits (except number of plates), 175 (43%) were significant.Phenotypic variation had a modular structure, with certain groups of traits being consistently positively correlated with each other, but not with some other traits. When individuals from all populations were pooled together all armour traits were highly positively correlated  to an average of 5 parasites per fish (BHAR), and infections with Gyrodactylus arcuatus ranged from 0  to 6 (GILL) parasites per fish.The lakes sampled varied in presence and abundance of brown trout and nine‐spined stickleback. In total, 3 lakes had no trout and no nine‐spined sticklebacks in them, 13 had both species present, 9 had only trout, and 1 (REIV) had only nine‐spined stickleback in them, in addition to three‐spined stickleback. Density of brown trout (as measured by TCRs) ranged between 0.053 ± 0.01 (SE) (DUBH) and 2.29 ± 0.42 (MORA) fish per angler per hour. In lakes where 9‐spined stickleback was presented, they made up between 6% and 64% of the fish caught in the minnow traps. The prevalence of parasites found on fish was also quite variable: lakes ranged from no three‐spined stickleback infected with r) >0.5, P‐value < 0.05 after adjustment for multiple testing, Table S9, Supporting information): pH, salinity, conductivity, Zn, Ca, Na, Mg, K and orange ratio formed one group, and average and maximum depth and DOC formed a second group of highly correlated variables with high loadings on the first (PC1) and second (PC2) major axes of environmental variation, respectively.The first PC of abiotic environment (‘envPC1’) explained 38.1% of the variance among lakes and discriminated between alkaline lakes with high levels of alkali metals and clear water, such as REIV, SANN, GROG, FEIT and HOST at one end and acidic lakes with low conductivity such as MAIG, SCAD and BHAR at the other Fig. b see Tab. The lopopulations retained 9135 SNPs across all data , but we found high variability among comparisons . The lowest FST values were found between populations from the same catchment that have streams connecting them: SCAD and MORA (FST = 0.023), and TROS and GILL (FST = 0.024). Populations that have connections to the sea, such as HOST and GEIR, also tended to be less differentiated. The highest genetic divergence was found between the two least genetically diverse populations, BUAI and CHRU (FST = 0.575). Comparisons involving these two populations generally had high FST values .Genetic divergence among populations as represented by faststructure showed that statistically K = 16 was the best supported number of clusters. Each step from k = 10 to k = 15 gradually differentiated among geographically close populations, and at K = 16, 14 clusters were formed by individual populations and two clusters were formed by pairs of populations from the same catchment  .The Bayesian clustering analysis with AD) Fig. . The PCAAD) Fig. c and theThe RDA showed that when all phenotypic traits were analysed together the abiotic environmental variables envPC1 and envPC2 explained 36% of the variation in the phenotype across populations when all 27 populations were included and 37% of the phenotypic variation when 23 populations with more detailed ecological data were included. The relationship was highly significant Table .P. punigitius and trout presence explained 25% of the phenotypic variance, while in the reduced data set P. pungitius presence and trout presence and TCR explained 51% of the phenotypic variance (Table The inclusion of TCR in the matrix of biotic explanatory variables greatly increased the explanatory power of the biotic model. In the complete data set (27 populations), proportion of ce Table . Therefoce Table , but onlce Table .As expected, the models combining the biotic and abiotic variables in one explanatory matrix explained a higher percentage of the phenotypic variation than the biotic or abiotic variables separately Table , Fig. 4.The RDA relating phenotypes and genetic axes of variation revealed that only the first axis of genetic variation (‘genPC1’) explained some of the phenotypic variation (8%) and this model was not significant Table .G. arcuatus, proportion of P. pungitius and genPC9 explained 50% of the variation in fish length across populations. Contrary to the ‘SL PC’, the ‘1st body shape PC’ was only significantly associated with abiotic environmental variables and genetic variables, but not with the biotic environment. Combined, several axes of abiotic and genetic variables explained 79% of the variation in body shape across populations. The ‘2nd body shape PC’ was significantly associated with biotic, abiotic and genetic variables. However, when all explanatory variables were combined the biotic variables were excluded from the best fit model, which included 4 axes of abiotic variation and 4 axes of genetic variation and explained 87% of the variation in body shape.The analyses of the association between individual phenotypic axes of variation and environmental and genetic variables revealed that the ‘Armour PC’ was similarly significantly associated with abiotic and biotic environmental variables and genetic variables, and that combined envPC1, TCR and several genetic PCs explained 79% of the variation in armour traits found across lakes Table a. The ‘SThe analyses of the association between individual genetic axes of variation and the environment revealed that genetic variation was largely associated with the abiotic environment. Seven of 10 genomic PCs were significantly associated with abiotic environmental PCs, 1 PC was significantly associated with the biotic environment (genPC8) and 2 PCs genPC4, genPC5) were not associated with the environment  explained a high percentage of the phenotypic variation. Consistent with MacColl et al.  for an oet al. . Perhapset al.  and the et al. . The relet al. The fact that we found phenotypic variation to be strongly associated with environmental variation across lakes is consistent with central tenets of the ecological theory of adaptive radiation Schluter . Also, iet al. et al. et al. et al. faststructure analyses clearly indicated that most genetic clusters were formed by populations that are either in the same catchment or geographically near each other, which suggests a role for geography in the genetic divergence of populations.We found a strong genetic divergence among the populations analysed, comparable to that found among freshwater populations that are geographically further apart in other stickleback systems  and therefore not associated with the phenotypes. This would be especially true if much of the overall divergence between populations is the stochastic result of drift, which should be expected when population sizes are small, which seems to be the case for a few populations, but not most of them.We hypothesized the observed phenotypic variation among populations to be the result of either adaptation, neutral genetic drift within relatively isolated populations, or common ancestry. For all cases, we would predict a correlation between genomewide genetic variation and multivariate phenotypic variation. However, when all phenotypic traits were analysed together, we found that genetic variation explained very little of the variation in the phenotype across populations. To the extent that the phenotypes we examined are heritable, an explanation for the lack of association between the two is that phenotypes are genetically controlled by a relatively small number of loci that are not in linkage disequilibrium with the markers we analysed. If this is the case then the divergence represented by the genomewide FThe strongest evidence supporting the fact that we need to look at factors other than just genetic ones to explain phenotypic variation across North Uist is that we find similar morphologies both in genetically very divergent populations, such as BUAI and CHRU, as well as in genetically similar populations, such as MORA and SCAD Fig. . This reet al. et al. et al. et al. Despite the lack of association between genetic variation and phenotype when all phenotypic traits were analysed together, the multiple linear regressions revealed strong associations between individual traits and environmental and genetic variation, supporting the idea of a combined action of these two factors shaping variation in those traits. The environment is probably to influence trait variation among populations both within the lifespan of an individual (phenotypic plasticity) and over multiple generations through selection on different genotypes  collected for processing, individuals respectively.Table S2a Collection date of abiotic environmental variables and measured physical and chemical characteristics of 27 lochs, from left to right: mean depth (Mean depth), maximum depth (Max depth), surface area (area), pH, conductivity, salinity, composition of the margin substrate .Table S2b Water light spectra (orange ratio), chlorophyll A and chemical characteristics of 27 lochs.Table S3 Trait means (M) and standard deviations (SD) per population in mm (from left to right).Table S4 Loadings of landmarks on the first two PC axes and the percent variance explained by each axis.Table S5 Population‐level summary statistics for RAD sequences.Table S6 Loadings of morphological variables on the 10 PC axes and the percent variance explained by each axis.Table S7 Pearson correlation coefficients (r) among traits for all populations pooled together and each population separately.Table S8 Loadings of abiotic environmental variables on the axes of environmental variation and the percent variance explained by each axis.Table S9 Pearson correlation coefficients (r), P‐values and FDR corrected P‐values of correlations among abiotic environmental variables.Table S10 Summary statistics for the 18 populations from North Uist with RAD sequencing data as outputted by the program populations.Table S11 FST values based on whole genome among 18 freshwater populations of three‐spined stickleback from North Uist.Click here for additional data file.