Malus domestica, to its three closest wild relatives, M. sylvestris in Europe, M. orientalis in the Caucasus, and M. sieversii in Central Asia. We found footprints of introgression from M. domestica to M. orientalis (3.2% of hybrids), M. sieversii (14.8%), and M. sylvestris (36.7%). Malus sieversii and M. orientalis presented weak, but significant genetic structures across their geographic range. Malus orientalis displayed genetic differentiation with three differentiated populations in Turkey, Armenia, and Russia. Malus sieversii consisted of a main population spread over Central Asia and a smaller population in the Tian Shan Mountains. The low Sp values suggest high dispersal capacities for the wild apple relatives. High potential for crop‐to‐wild gene flow in apples needs to be considered in the implementation of in situ and ex situ actions for the conservation of wild apple genetic resources potentially useful to plant breeding.Crop‐to‐wild gene flow have important evolutionary and ecological consequences and require careful consideration in conservation programs for wild genetic resources of potential use in breeding programs and in assessments of the risk of transgene escape into natural ecosystems. Using 26 microsatellites and a set of 1181 trees, we investigated the extent of introgression from the cultivated apple,  The anthropogenization of landscapes and ecosystems, with landscape fragmentation and the introduction of crops over extended areas, has greatly increased the likelihood of contact between domesticated and related wild taxa that were previously isolated either geographically or ecologically  or ex situ . In breeding programs, genetically differentiated populations are often used to improve the local adaptation of cultivars or to enhance resistance to pathogens of cultivars, by selecting for sources of disease resistance from wild populations  is one of the most widely grown fruit crops of temperate regions, with an annual worldwide production of about 70 million tons (http://faostat.fao.org/). Apple trees are self‐incompatible, favoring intra‐ and interspecific gene flow. The cultivated apple was domesticated in Central Asia from Malus sieversii  at 56 sites across Europe; (ii) M. orientalis (N = 217) at 27 sites across the Caucasus: mainly in Armenia (25 sites) and two additional sites at the extreme geographical range of M. orientalis, in Russia (1 site) and Turkey (1 site); (iii) M. sieversii at 28 sites across Central Asia  (N = 168). Details of the sampling sites are provided in Table S1. We chose 40 M. domestica individuals previously identified as displaying no introgression from M. sylvestris, M. sieversii, or M. orientalis, that is, with membership coefficients exceeding 0.9 for the M. domestica gene pool in a STRUCTURE analysis  ® plant DNA extraction kit II . Multiplex microsatellite PCR amplifications were performed using a Multiplex PCR Kit . We used 26 microsatellites distributed across the 17 chromosomes (one to three microsatellites per chromosome) using 10 different multiplex reactions, as previously described  simulations to infer the proportion of ancestry of genotypes from K distinct clusters. The underlying algorithms attempt to minimize deviations from Hardy–Weinberg and linkage disequilibria. Preliminary Bayesian analyses with STRUCTURE showed that the most relevant numbers of populations (Kw) for the wild species were Kw = 3 for M. sylvestris . We then used STRUCTURE on three data sets, each including the 40 reference M. domestica genotypes and one wild species, setting the number of clusters (K) to values corresponding to the number of clusters (Kw) determined in preliminary analyses on the focal wild species, plus one for M. domestica, that is, K = Kw + 1, with K = 3 for M. sieversii and K = 4 for both M. orientalis and M. sylvestris. We considered that individuals assigned to a wild gene pool with a membership coefficient <0.9 were introgressed by M. domestica. The admixture proportion of a wild genotype assigned to the M. domestica gene pool is denoted Pdomestica.We used the individual‐based Bayesian clustering methods implemented in STRUCTURE 2.3.3  Gaussian model of admixture with linear trend surface, setting the spatial interaction parameter (ρ) to 0.6. These parameters (ρ and trend) affect the weight given to spatial distance when clustering genotypes. Ten independent analyses were carried out for each number of clusters K (2 ≤ K ≤ 6), using 500 000 MCMC iterations after a burn‐in of 50 000 steps. Outputs were processed using CLUMPP v1.1.2 .For the analysis of within‐species genetic variation, we excluded hybrids previously identified as introgressed by P‐values obtained in Ewens–Watterson tests, and no pair of markers was in significant linkage disequilibrium  for M. orientalis and N = 6  for M. sieversii, corresponding to the smallest number of observations for sites. Heterozygosity, Weir and Cockerham F‐statistics, and Hardy–Weinberg genotypic linkage equilibrium were assessed using GENEPOP 4.0 .We tested for the occurrence of null alleles using MICRO‐CHECKER 2.2.3  patterns, as previously described  , 25 hybrids were identified in M. sieversii (14.8% of the sample), and M. sylvestris included many hybrids, with 292 admixed individuals (36.7% of the sample). Misidentified individuals  were detected within M. sylvestris (N = 37) and M. sieversii (N = 4). The distribution of Pdomestica, averaged across sites for each Pdomestica class  and for each wild species is presented in Fig. P < 0.01). All M. orientalis trees displaying introgression from M. domestica had low proportions of admixture, M. sieversii included some individuals with higher admixture proportions, whereas M. sylvestris presented the whole range of admixture values , and even included trees that appeared to be hybrids backcrossed with M. domestica or feral M. domestica displaying introgression from M. sylvestris (0.55 < Pdomestica < 0.9) across its distribution range.Each wild species included genotypes showing signs of ol) Fig. . HoweverM. sieversii and M. orientalis in Table M. sieversii and M. orientalis (average sample sizes: 10.9 ± 6.1 and 12.9 ± 3.5 genotypes respectively). On average, across sites and markers, allelic richness (AR) and gene diversity (HE) were AR = 3.5 ± 0.3 [3.1–3.9] and HE = 0.75 ± 0.03 [0.69–0.76] in M. sieversii, and AR = 6.2 ± 0.4 [5.4–7.1] and HE = 0.81 ± 0.02 [0.69–0.76] in M. orientalis. FIS values were low, with a mean of 0.02 ± 0.04 per site and marker for M. sieversii and 0.04 ± 0.06 for M. orientalis. The species‐wide heterozygote deficit was highly significant (P < 0.001), but low . Between‐site differences in allelic frequencies, estimated from the mean FST across loci, were low , but significant for respectively 15 and 58 pairs of sites for M. sieversii and M. orientalis .Summary statistics for population structure and diversity are shown for M. sieversii and M. orientalis, even when setting K > 2  and K > 3 , respectively. We thus concluded that K = 2 and K = 3 were the biologically most relevant numbers of populations, for M. sieversii and M. orientalis, respectively, as observed in preliminary analyses with full data sets. For M. sieversii, clustering patterns at K = 2 revealed a large cluster spreading across Central Asia (green) and a small cluster (red) consisting mostly of individuals from the Kaz Kuz population in the Tian Shan Mountains  and ‘Main’  populations.Spatially explicit clustering analyses with TESS revealed two and three well‐defined clusters for M. orientalis, clustering patterns at K = 2 indicated a north/south genetic differentiation between a cluster encompassing the Turkish and Armenian Shikahogh populations (green) and another cluster comprising most of the Armenian populations (red) . At K = 3, a third cluster (blue) appeared, including the Russian population and the Armenian Jermouck population , the ‘Central’ , and the ‘Southern’  populations.For ion Fig. . Six genN > 10 , genetic differentiation and geographic distance were significantly correlated, consistent with an IBD model. The Sp statistic can be used to quantify spatial structure and is useful for comparisons between populations and/or species. Lower Sp values are associated with greater dispersal capacities and/or effective population sizes. Sp values were very low, close to 0, but were significant in the M. sieversii population  and in M. orientalis populations . These results suggest that the wild Malus species have high dispersal capacities and/or a large effective population size.Within the three populations for which M. domestica to wild apple relatives from Europe and the Caucasus  and to the main Asian progenitor of cultivated apple (M. sieversii). The results obtained in this study, combined with our previous results for the European crabapple (M. sylvestris)  . Unfortunately, the available information concerning landscape features for our sample was insufficiently detailed for assessments of the influence of specific factors. We also note that our estimates of crop–wild gene flow might be conservative. Admixed individuals were identified using ‘pure’ domesticated apple samples for the crop samples, and we cannot exclude that those chosen trees actually have a lower probability for having donated alleles to the wild.Introgression from the cultivated apple to wild relatives has been investigated in M. sylvestris) had suggested that the low reproductive fitness of interspecific hybrids might account for this finding   . In wheat, crop‐to‐wild gene flow levels may differ between species due to differences in mating systems, with Aegilops species, which are the most allogamous, showing the highest levels of hybridization , an allele from the crop, delaying flowering, might confer a selective advantage on hybrids in natural conditions  and the other in more northerly latitudes (in Russia).M. orientalis and M. sieversii, as previously reported for M. sylvestris  were of a similar order of magnitude to those estimated for wind‐dispersed trees, such as Larix laricina, Quercus robur, and Fraxinus excelsior .Isolation‐by‐distance patterns were detected for both Sp values and weak spatial genetic structure. Apples are allogamous, with self‐incompatibility systems favoring gene flow and low levels of population genetic differentiation, as typically observed in trees , whereas the three wild species had similar Sp values. Density thus does not appear to be an important factor underlying the weak spatial genetic structure observed in the three wild apple relatives.The combined dispersal capacities of the seed and pollen dispersers may account for the low M. orientalis, M. sieversii and M. sylvestris , resistance to apple scab , fruit quality, and drought tolerance that may serve as gene source for breeding apple programs  to red rice (Oryza sativa f. spontanea), a weed species  among Malus sieversii sites (N > 6)Table S3. Pairwise genetic differentiation (FST) among Malus orientalis sites (N > 6)Figure S1. Bayesian clustering results for Malus sieversii in Central Asia (N = 101) using the program TESS from K = 2 to K = 6.Figure S2. Bayesian clustering results for Malus orientalis in the Caucasus (N = 217) using the program TESS from K = 2 to K = 6.Figure S3. Maps representing the mean membership proportions for K clusters, for samples of Malus sylvestris collected from the same site.Click here for additional data file.


Strigops habroptilus). We detected a signal of population expansion coinciding with the end of the Pleistocene last glacial maximum (LGM). Also, we found some evidence for northern and southern lineages, supporting the hypothesis that the species may have been restricted to isolated northern and southern refugia during the LGM. We observed an important loss of neutral genetic diversity associated with European settlement in New Zealand but we could not exclude a population decline associated with Polynesian settlement in New Zealand. However, we did not find evidence for fixation of deleterious mutations. We argue that despite high pre-decline genetic diversity, a rapid and range-wide decline combined with the lek mating system, and life-history traits of kākāpō contributed to a rapid loss of genetic diversity following severe population declines.Understanding how species respond to population declines is a central question in conservation and evolutionary biology. Population declines are often associated with loss of genetic diversity, inbreeding and accumulation of deleterious mutations, which can lead to a reduction in fitness and subsequently contribute to extinction. Using temporal approaches can help us understand the effects of population declines on genetic diversity in real time. Sequencing pre-decline as well as post-decline mitogenomes representing all the remaining mitochondrial diversity, we estimated the loss of genetic diversity in the critically endangered kākāpō ( Significant attention has been devoted to examining the effects of extrinsic processes such as habitat modification, over-hunting or persecution on species persistence. However, there is still a gap in our understanding on how intrinsic genetic processes contribute to extinctions ,2,3. TheStrigops habroptilus), an endemic flightless parrot of New Zealand, has gone through a severe population decline since human arrival and in particular in the last 200 years [In the current 6th mass extinction , it is c00 years . Despite00 years , kākāpō 00 years , all des00 years ,16,17,1800 years , fossil 00 years  or low f00 years ,10.The species’ range contracted following Polynesian arrival c. 1280 CE , throughThe current 150 extant kākāpō are actively managed on offshore island sanctuaries . This poHere we generated high-quality mitogenomes for 39 historical (1800s–1900s) and 79 modern kākāpō representing all the extant mitochondrial diversity to (1) quantify the loss of genetic variation associated with human settlement in New Zealand and (2) to examine population fluctuations in relation to post-glacial expansion and anthropogenic effects.n = 42) and some of their offspring  were provided by the New Zealand Department of Conservation (DOC).39 kākāpō historical skin specimens collected between 1847 and 1985 from the South Island of New Zealand and representing the pre-European population were sourced from various museum collections . These 3Genomic DNA was extracted from historical skins using a DNeasy Blood & Tissue Kit . Appropriate precautions were taken to minimize the risk of contamination in historical samples . For conFor historical specimens, double stranded Illumina libraries were prepared according to Meyer & Kircher  using urBcl to Fastq conversion was performed using bcl2Fastq 1.8.3 from the CASAVA software suite. SeqPrep 1.1  was usedSequencing reads were processed with BWA 0.7.13  and SAMtFor modern data, forward and reverse reads were mapped against the reference genome using the BWA mem command and then BAM files were generated using the SAMtools view command. Extraction, coordinate sorting, indexing, and duplicates removal for reads mapping to the mitochondrial genome was performed using SAMtools. For both historical and modern data, reads with mapping qualities below 20 were filtered out.® 7.0.336 [BAM files generated using SAMtools were then uploaded to Geneious 7.0.336  and consSF [SF values < 0 suggest an excess of recent mutations , characteristic of a recent population expansion, while positive Fu’s SF values indicate a deficiency of rare alleles, suggesting a population bottleneck or overdominant selection.Indices of genetic diversity including haplotypic diversity (Hd), nucleotide diversity (π) Tajima’s D  and Fu’sFS  were calFS  for histWe then used a temporal approach to determine the loss of mitochondrial genetic diversity in kākāpō through time between historical (1847–1985) and modern samples  using the R script TempNet v1.4. . A media−8 site−1 year−1 [Nef).A phylogeny and demographic reconstruction were inferred in BEAST 1.8.044  using th1 year−1 . Three d1 year−1 . To sele1 year−1 . Bayes F1 year−1  and the 1 year−1 . Tracer 1 year−1  was usedD within each time period sample as summary statistics for the whole mtDNA. We calculated normalized euclidian distances between the observed dataset and each of the simulated datasets using the local linear regression method of Beaumont et al. [Finally, we tested for competing bottleneck models and estimated values of demographic parameters using an approximate Bayesian computation (ABC) approach  implement et al. . We estit et al. ,48. We fBAM files for complete mitogenomes of 39 historical and 79 modern kākāpō are deposited in the European Nucleotide Archive (ENA), under the accession number (PRJEB25924).D and Fu’s FS were both significantly negative in historical samples suggesting a recent population expansion : 15,000–65,000) and 11 kya , respectively  to c. 4000 (95% HPD: 1200–8000) around 10 kya and dropped to c. 500 (95% HPD: 80–1200) some 500–1000 years ago.The Bayesian Skyline model had the highest log marginal likelihood among the compared models . The BayNe-pre-human of 497,000  to a Ne-modern of 17.3 (95% HPD: 12.2–45.9). Bottleneck timing was estimated at ~120 ya (95% HPD: 57–145)  . Populat 57–145) . When teectively . Type I Using full mitogenomes we find high pre-European genetic diversity in kākāpō and strong support for a severe bottleneck coinciding with European arrival in New Zealand, which is consistent with Bergner et al. . HoweverD and Fu’s FS in historical samples, the increase in Nef dating back 10–15 kya, and the divergence of distinct clades between 30 and 10 kya, are consistent with a population expansion at the end of the last glacial maximum (LGM), some ~14–18 kya [Xenicus gilviventris) [Apteryx sp.) [The star-shape haplotype network, the significantly negative Tajima’s 4–18 kya ,51,52,534–18 kya  that ide4–18 kya , the mos4–18 kya . Interes4–18 kya . Kākāpō 4–18 kya ,56, suchventris) ; Kiwi (Aryx sp.) ). This rNestor notabilis) [Apteryx sp.) [It is also possible that the observed lineage divergence may be due to the underrepresentation of samples from the central and northern South Island. Obtaining historical samples from these regions is required in future studies to better investigate the existence of glacial refugia, confirm the ‘beech-gap’ hypothesis and to determine whether this split is the result of sampling bias. The potential of sampling bias raises the possibility that kākāpō either were restricted to a northern and southern refugium from where they rapidly expanded at the end of the LGM, or that they were continuously distributed on the eastern side of the South Island through the LGM. The latter scenario is possible given that kākāpō are considered successful habitat generalists, inhabiting forests, shrubland, grassland as well as transition zones between communities  , which wtabilis)  and in bryx sp.) , which mThere has been speculation that the presence of kākāpō on Stewart Island may be the result of a recent introduction either by pre-European Polynesian settlers or by Europeans in the 1880s . No fossContrary to previous results based on microsatellite and mtDNA control region data , our BayOne reason for the lack of signal associated with Polynesian settlement may be due to the technical difficulty of detecting sequential bottlenecks so close in time , as well as the estimation of the bottlenecks’ magnitudes. Obtaining historical samples predating both Polynesians and Europeans settlement would thus be the only way to reliably estimate the timing and magnitude of such bottlenecks. In particular, the magnitude of the bottleneck is important to consider, because if the bottleneck associated with Polynesians was only moderate, its genetic signature may have been erased by the subsequent more severe bottleneck. While it is known that Polynesians hunted kākāpō ,64, it sNestor notabilis) which is apparently more resilient to population decline and more resistant to loss of genetic diversity [Despite the pre-European abundance of kākāpō, the species’ low reproductive rates, intermittent breeding  linked to podocarp tree masting, and lek-breeding behaviour ,23,68 maiversity  owing toOur results highlight the use of temporal approaches to examine the genomic consequences of population declines. Recent studies on woolly mammoths showed an increase in deleterious mutation in both mitogenomes  and nucl


Mussismilia hispida, one of the main reef builders in the Southwestern Atlantic Ocean. For this, 15 sites were sampled along its entire distributional range employing 10 microsatellite loci. M. hispida was divided into five genetically differentiated populations by Structure analysis. Population structure and migration estimates are consistent with present-day oceanographic current patterns, zones of upwelling and historical sea-level changes. The Central Region and Oceanic Islands populations had the highest genetic diversity, were possibly the main sources of migrants for other populations and presented mutual migrant exchange. This mutual exchange and the high diversity of Oceanic Islands, a peripherical population, is highly interesting and unexpected, but can be explained if these sites acted as refugia in past low sea-level stance. This is the first connectivity study in the region using hyper-variable markers and a fine sampling scale along 3,500 km. These results enlighten the population dynamics of an important reef building species and shows how oceanographic processes may act as barriers to dispersal for marine species, providing valuable information for management strategies.Understanding connectivity patterns has implications for evolutionary and ecological processes, as well as for proper conservation strategies. This study examined population genetic structure and migration patterns of the coral  It also leads to better conservation strategies, since natural gene flow patterns can be maintained, ensuring long term conservation given that evolutionary processes are preserved2.Genetic connectivity studies can provide fundamental information to better understand the ecology and evolution of a species. Estimated patterns usually indicate the population dynamics at contemporary and historical time scales, including the paths, directions and frequency of migration, colonization history and source and sink dynamics4. To evaluate gene flow in marine habitats, genetic markers based on frequency data, such as microsatellites, have shown to be useful for estimating connectivity at ecological timescales4 and have increasingly been employed for scleractinian corals’ studies in recent years7. However, some regions of the world’s oceans receive less attention and their connectivity patterns are still poorly known. For instance, in the Atlantic Ocean the majority of connectivity studies for scleractinian corals have focused on Caribbean populations9, while less is known for corals on the Southwestern Atlantic (SWA), a biogeographic region also known as the Brazilian Province10.Understanding connectivity patterns in marine habitats is not as evident as in land. This is because marine species can have high dispersal potential, due to planktonic life stages, the non-obvious nature of barriers to dispersal in the sea and complex biological and physical factors that shape survival and settlement11. These southern coral communities have low diversity of zooxanthellate scleractinian corals (at least 16 species), but its level of endemicity is higher than 20%12. Despite its uniqueness, only a few studies have addressed gene flow and genetic diversity of scleractinian corals in this region. While connectivity has been assessed for five species thus far , these studies have either employed relatively conserved markers or have sampled at low spatial resolution such that current knowledge about coral connectivity in the SWA is limited to restricted locations and to more ancient timescales.In the SWA, coral reef communities are distributed along more than 3,500 km of coastline, including the largest biogenic coral reef formation in the South Atlantic Ocean, the Abrolhos Reef Complex in Bahia State, BrazilMussismilia hispida (Verrill 1902), an endemic species that has a broad distributional range, occurring from Maranhão (0°S) to São Paulo States (24°S)11. This coral is a broadcast spawner with an annual reproductive cycle and lecithotrophic larvae16 and, although no studies have evaluated its pelagical larval duration (PLD), preliminary studies indicate that it lasts approximately 10 days17. The time of gamete release for M. hispida has been estimated in four locations in Brazil and an asynchrony in spawning time was observed among them. Gamete release was estimated to take place between April and June in the Abrolhos Coral Reef Complex (17°S)16, while less than 200 km to the north, in Porto Seguro, Bahia (16°S), spawning was reported to occur between August and November17. Further south, the time of spawning was estimated to be between February and March at Armação dos Búzios, Rio de Janeiro (22°S)18 and it was observed in situ to occur in April at Laje de Santos, São Paulo (24°S)19. Although population connectivity does not rely solely on gamete dispersal, this asynchrony raises questions about reproductive isolation and gene flow patterns in M. hispida. Such facts make this species a great model to understand connectivity patterns in the SWA, besides providing means to test the effects of reproduction asynchrony in a wide geographical range . Therefore, in this study we aimed to address various questions concerning population connectivity of M. hispida populations along its distributional range using microsatellite data. We tested the hypotheses that (1) the reproductive asynchrony in this species influences its patterns of population structure; (2) M. hispida is structured across its distributional range; (3) the main barriers to gene flow coincide with previously reported biogeographical breaks in the SWA; and (4) that the gene flow directions agree with the major ocean currents in the region.One of the main reef-building corals in the SWA is Mussismilia hispida were genotyped for 13 microsatellite loci , except Mhi16 and Mhi17 (<0.2)  and all the other sites, while between these two localities, although significant, the FST value was relatively low . When the Oceanic Islands were excluded from the analysis, since they had high geographic distances compared to all other localities, the correlation remained weak and significant .The isolation by distance analysis showed significant but weak correlation between genetic distance and geographic distance (RK) was five. For LnPD and Delta K values, see Supplementary Fig. K showed which sites were the most differentiated  of the Northern Region was 0.008, the Oceanic Islands was 1.088, the Central Region was 1.988, the Southeastern Region was 0.188 and finally the Southern Limit was 0.008  tropical region. Results show that M. hispida’s genetic structure can be explained by oceanographic processes, such as currents direction, upwelling events and past sea-level changes. Since gene flow intensity and direction and what barriers to dispersal may shape them are still unknown for most species in the region, we believe this study could serve as a baseline for hypothesis testing regarding the biogeographical barriers of sessile invertebrates in the SWA.The present study is the first to evaluate the genetic connectivity and migration patterns of M. hispida individuals are structured into five populations according to Structure and FST analyses , and the genetic diversity observed is similar to what has been found for other corals in the Atlantic with microsatellite data9. However, the two southernmost sites of M. hispida, had lower levels of genetic diversity. Reduced genetic diversity and higher genetic differentiation at peripheral populations appear to be common in animals and plants20 and has been observed for hydrocorals21 and scleractinian corals in the Southwestern Atlantic15, South Africa6 and Australia22. Interestingly, for M. hispida this pattern occurs in the southern range (Southeastern Region and Southern Limit), but it is not observed in the northern distributional edge (Northern Region). This asymmetry may be explained by the proximity of the Northern Region with the largest and more genetically diverse populations or to the stressful lower water temperatures24 in southernmost sites, that could limit dispersal or lead to selection of certain genotypes. All populations presented heterozygous deficits when compared with the expected under the Hardy-Weinberg Equilibrium (HWE), which appears to be common in microsatellite estimates of coral populations26. This deviation is likely due to assumptions of HWE not being met in natural populations. For instance, random mating is likely not met in coral populations, considering that adults are sessile and have high longevity, which can lead to generation overlap. Thus, inbreeding rates may be high for M. hispida and can be further reinforced by larval retention, but additional investigation is needed to confirm this.M. hispida does not appear to influence the observed patterns of gene flow. For example, in this study AB and PS individuals belong to the same population  even though individuals in these two areas presented different spawning periods17. In contrast, individuals from Armação dos Búzios (BZ) and Santos have similar reproduction periods19, while genetic analyses placed individuals from BZ and IB (90 km north of Santos) in different populations (Southeastern Region vs. Southern Limit). Asynchrony in spawning times has been observed for other corals, like Pocillopora damicornis, and it was explained as either the result of local colonization by different populations with different spawning times or the existence of cryptic species27. For M. hispida, the presence of another species is unlikely given the high genetic similarity found among individuals in AB and PS using hyper-variable markers. However, it is possible that differences in spawning time of AB and PS are due to different local abiotic conditions or cues to spawning.Contradicting one of our initial hypotheses, the reproductive asynchrony reported for M. hispida. Oceanic Islands and Central Region populations can be considered sources of migrants to the other ones. Such pattern is expected for the Central Region considering the central-margin hypothesis20, given that this population covers approximately 1,600 km of coastline, includes the largest coral reef system on the SWA (the Abrolhos Reef Complex) and has the highest mutation-scaled effective population size. Conversely, the high genetic diversity and migration patterns for the Oceanic Islands is highly unexpected, given that islands usually are considered peripheral and a low genetic diversity was expected20. Finally, the Northern Region and Southern Limit can be considered sink populations, as expected for populations at the limit of a species’ distribution20.The estimated migration rates and observed genetic diversity demonstrate how is the source-sink dynamics for 29. The northwestern flow of the NBC could explain why there is genetic structure between the Northern Region and the remaining populations and the migration from Oceanic Islands to the Northern Region. A genetic break in this area also appears to be shared by the corals Favia gravida and Siderastrea radians13, but this is not general for all invertebrates30. Besides, the separation of the Northern Region population found here does not match previous biogeographic studies, such as the subdivisions of coastal marine provinces in the tropical Atlantic31 and reef fish biogeography32. The NBC current may not limit gene flow from most species, but its role as a barrier to dispersal appears to be important for corals.Connectivity and migration patterns of the Northern Region can be explained by the main ocean currents in the area. The South Equatorial Current (SEC) that crosses the Atlantic Ocean at 5–10°S splits into two major oceanic currents in the SWA that follow the coast of Brazil: the North Brazil Current (NBC), that flows in a northwestern direction, and the Brazil Current, a western boundary current that flows southwards 33, the entrance to the Todos os Santos Bay and the adjacent Salvador canyon34. Spalding et al.31 considered this whole area as a single marine province , including the oceanic islands, and recognize a possible subdivision of ecoregions nearby Salvador (13° S). For the scleractinians F. gravida and S. radians, a genetic discontinuity has been observed between JP and AB locations13, which agrees with Spalding et al.31. This could be explained by a more limited dispersal and differences in reproductive modes, since M. hispida is a broadcast spawner and F. gravida and S. radians are brooders35, although reproductive mode not always explains differences in connectivity patterns25. Besides, this area has also been shown to limit the distribution of two Millepora species21 and the diversity of Symbiodinium associated with M. hispida36. However, it is important to note that there is few information on PLD and coral coverage for SWA corals and the influence of these factors shaping the apparent distinct connectivity of these species can only be speculated. Nevertheless, these potential barriers do not appear to be effective for M. hispida and the occurrence of barriers to dispersal between 10–13°S in the SWA may not be generalized for all marine organisms.The Central Region population had high levels of connectivity, possibly because this region is under the homogenizing influence of the Brazil Current. Although no structure was found for M. hispida is also shared with the coral S. stellata14 and reef fish, including genetic38 and biogeographic studies32.This particular area (18–23°S) has been considered a transitional zone almost since the beginning of zoogeographical studies39 and is characterized for having upwellings and higher prevalence of colder water masses29. However, since the migration pattern estimated here shows gene flow from the Central to the Southeastern Region, such oceanographic processes may act more as a filter than as a barrier in this specific area. Furthermore, there is another genetic break for M. hispida near this colder water region, separating the Southeastern Region and the Southern Limit. This could be explained by the strongest upwelling events along the Brazilian coast, located in capes São Tomé and Frio (21–23°S)40. In fact, zooxanthellate coral diversity is greatly reduced south of this region, with only two species occurring down to São Paulo State (M. hispida and Madracis decactis) and only one species occurring as far south as Santa Catarina State (M. decactis)11. Upwelling events have been shown to limit the settlement of invertebrate larvae42 and this may be the case for other species and for M. hispida. Interestingly, the rocky shores immediately south of Cape Frio, which are under the strongest influence of the upwelling, have a distinct fauna with subtropical characteristics, where no scleractinian corals are found44. On the other hand, the more protected area north of this headland has a tropical fauna that includes zooxanthellate scleractinians11 and corresponds to the sites AC and BZ from the present study. However, this protected area also has cold water temperatures, and temperature may not be the only factor that prevents coral recruitment in the area, which could also be influenced by local hydrodynamism.The break separating the Central Region from the Southeastern Region in M. hispida in two ways: 1) larvae may rarely cross into or out of the cold-water region because currents or temperature may impede dispersal or 2) local selection limits recruitment of certain genotypes in the region. Upwelling in the Cape Frio typically occurs during the austral spring and summer45, coinciding with M. hispida spawning in the region18. Since sea water temperatures during these events can drop to 11 °C45, larvae formation could be inhibited, as shown for the coral Oculina varricosa with temperatures ranging from 10–17 °C46. Upwellings can also create retention zones due to water stratification47, which would hinder larval dispersal. Selection may also play a role, as local environmental conditions could favour the survival of certain genotypes adapted to lower temperatures or other characteristics of the area. Furthermore, the processes causing reduced dispersal and selective pressure may act synergistically to restrict connectivity. While the exact way these upwellings act as a barrier could not be discerned, the present data indicate that they restrict gene flow for M. hispida and could possibly do so for other marine organisms. Indeed, the Cape Frio upwelling has been suggested as a biogeographic barrier based on levels of endemicity48 and many marine organisms in the SWA appear to have their southern distribution limit near this area, including several coral species11. However, to our knowledge, these are the first genetic data to support its role as a barrier for a marine invertebrate.The upwellings in São Tomé and Frio’s capes could act as a barrier to 22. Since the direction of the SEC (that originates NBC) varies seasonally49 and larvae could potentially reach the edge of the continental shelf depending on the period, this current may not prevent gene flow among these sites. Nevertheless, a high dispersal capability and/or intermediate habitats would be needed to maintain this gene flow. Long distance connectivity has been observed in other corals, but this is usually observed in species with a long PLD, such as Acropora millepora (200 days)7, which is not the case of M. hispida (PLD≈10 days)17. Ocean currents patterns could also explain the high dispersal capability, but these are difficult to estimate given that they vary with latitude, season and proximity of the coast, and an oceanographic modelling approach would be needed to understand the influence of these processes. Other fact that could also account for this pattern is the occurrence of M. hispida in mesophotic reefs, especially in seamounts and/or wave-cut erosion steps and beachrocks in the continental shelf margin50. Such mesophotic populations could provide a more continuous patch of suitable habitats, favouring the maintenance of gene flow between the Oceanic Islands. Complex currents coupled with populations in intermediary mesophotic reefs may account for this unexpected observed connectivity.The patterns of genetic diversity, connectivity and migration concerning the Oceanic Islands is one exception that has no obvious explanation in the currents flow or prevailing water masses. Four unexpected patterns were observed: the high gene flow between Trindade Island (TR) and the other two oceanic islands; the restricted gene flow of these sites with their geographically closer mainland; the high genetic diversity of the Oceanic Islands; and the apparent exchange of propagules between Oceanic Islands and Central Region populations. The high gene flow among the three Oceanic Islands sites was not expected due to: i) the presence of a large oceanic current (NBC) that runs westwards crossing Fernando de Noronha archipelago (FN) and Rocas Atoll (AR), separating these two from TR, which lies far to the south; ii) the large distance that separates TR from both FN and AR ; and iii) the lack of nearby shallow reef habitats between TR and FN-AR, which would increase this species dispersal capabilitiesGinglymostoma cirratum51 and the queen angelfish Holacanthus ciliaris38. This region is also considered a biogeographic break for reef fish32 and distinct ecoregions in the tropical Atlantic31. This could be due to differences in coastal vs. open ocean environments and currents pattern. The Brazil Current may maintain offshore M. hispida localities well mixed, while closer to the coast different factors limit offshore dispersal, such as river outflows, high turbidity, and complex coastal circulation patterns. This would explain the separation of the islands and the geographically closer mainland sites. Still, that was not expected for TR, since it is connected to the mainland via the Vitoria-Trindade seamount chain52, which acts as stepping-stone for reef fish species53. However, genetic connectivity between TR and the mainland is not observed, even though M. hispida colonies have been recorded in mesophotic reefs along this seamount chain and on the continental shelf north of TR54. One explanation is that TR may be isolated from the mainland due to deep water channels that exist between the island itself, the Columbia Seamount, and the Dogaressa Bank52. Currents along these channels could restrict coral larvae dispersal, which is more limited in comparison to fish55. In general, few genetic studies have been conducted in the SWA that include both the mainland and oceanic islands and what causes this probable barrier to dispersal is an interesting feature that warrants further investigation.The second unexpected pattern is the genetic break between the Oceanic Islands’ sites and the mainland. Such differentiation has also been observed for the shark 56. This is because all three islands have relatively small areas, two of them (AR and FN) are separated from the mainland with no intermediary suitable habitats and M. hispida appears to have low densities in all of them . With this, one would expect that the extinction risk in these areas is high and their population is mainly dependent on subsequent colonization. Nonetheless, this scenario would unlikely result on the formation of a distinct genotypic cluster with high genetic diversity, like the one observed here. One possible explanation is that the oceanic islands have been isolated from the mainland sites long enough to accumulate the observed levels of genetic diversity, however this goes against the fourth unexpected pattern found here, the mutual exchange of migrants between the Oceanic Islands and Central Region. Therefore, an alternative explanation may account for both patterns.The third unexpected pattern is the high genetic diversity of Oceanic Islands population. Under the perspective of island biogeography, the oceanic islands are not expected to harbour large genetic diversity58 , Maranhão State; Fortaleza (FZ), Ceará State; João Pessoa (JP), Paraíba State; Tamandaré (TE), Pernambuco State; Salvador (SA), Porto Seguro (PS), and Abrolhos Reef Complex (AB), Bahia State; Guarapari (GP), Espírito Santo State; Armação dos Búzios (BZ), Arraial do Cabo (AC), and Ilha Grande (IG), Rio de Janeiro State; Ilhabela (IB), São Paulo State. Additionally, three oceanic sites were sampled: Rocas Atoll (AR), Rio Grande do Norte State; Fernando de Noronha (FN), Pernambuco State; and Trindade Island (TR), Espírito Santo State  until DNA extraction.Fifteen sites were sampled along the Brazilian coast Fig. , coverinate Fig. . Fragmenet al.65. DNA quality and concentration were assessed with the pattern Lambda DNA (125 ng/µL) on a 0.8% agarose gel stained with GelRed (Biotium) and visualized under UV light.Genomic DNA was extracted using phenol:chlorophorm according to Fukami loci 66 were amplified by PCR, following Schuelke’s protocol of tailed primers67. Each PCR contained 1U GoTaq (Promega), 5 × PCR Buffer (Promega), 200 µM dNTP mix (Invitrogen), 1.5–2.5 mM MgCl2, 1 mg/ml BSA (Invitrogen), 0.2 μM of forward tailed primer, 0.4 μM of fluorochrome labeled primer, and 0.8 μM of reverse primer in 10 μL reactions with approximately 5–10 ng of DNA template. Cycling conditions had an initial cycle at 95 °C for 3 min; followed by 5 cycles at 95 °C for 30 s, 52–62 °C for 30 s, 72 °C for 45 s; with an additional 35 cycles at 92 °C for 30 s, 52–62 °C for 30 s, 72 °C for 55 s; and a final cycle at 72 °C for 30 min. Concentration of MgCl2 and annealing temperatures varied according to the marker and followed Zilberberg et al.66. Loci amplification success, overall size and concentration were assessed using a 100 bp DNA ladder (Fermentas) on a 2% agarose gel stained with GelRed (Biotium) and visualized under UV light.Thirteen species-specific microsatellite locus were re-amplified and re-genotyped once. If poor resolution was observed twice, that locus was left blank for the particular sample. Only samples with more than nine genotyped loci were used in the analyses.Up to four PCR products with different fluorescent dyes were pooled together and were genotyped in an ABI3500 sequencer using a GS600-LIZ size standard (Applied Biosystems). Allele sizes were scored manually using the software GeneMarker (Soft Genetics). At each run, two to three samples that had previously been genotyped were re-genotyped as a positive control and to ensure that allele scores were consistent. Samples with poor genotype resolution in a loci were tested for null alleles using Micro-Checker68 and linkage disequilibrium using FSTAT69. Loci that presented probability of null alleles for the majority of locations or linkage disequilibrium were removed from the subsequent analyses. The number of alleles per locus, the number of exclusive alleles per locality and the observed and expected heterozygosity for each site were calculated using the Microsatellite toolkit70. Loci statistics were calculated using FSTAT. FIS indexes were calculated to evaluate deviations from Hardy-Weinberg equilibrium using Genetix71, assuming sampling sites  as populations.All ST fixation indexes, assuming localities as populations, with 1,000 permutations, in Genetix. Principal coordinates analysis (PCoA) of Fst values among localities was also calculated using GenAlEx72. The existence of isolation by distance was verified with a Mantel Test performed in IBDWS73. FST values were used for genetic distances and pairwise geographic distances in kilometres were measured in Google Earth (http://maps.google.com/) as straight paths between localities that did not cross the continent. Additionally, a Bayesian clustering algorithm was implemented using the software Structure 2.3.474 without a priori information of sampling location. The analysis was made with an admixture ancestry model and correlated allelic frequencies. Each analysis had 1,000,000 Markov-Monte Carlo chains from which 200,000 were discarded (burnin chains). The number of probable populations (K) tested was from 1 to 15, with ten iterations each. The best value of K was defined using the higher likelihood mean (LnPD)74 as suggested by Waples and Gaggiotti75 and the results were visualized using Structure Harvester76. A second analysis was made with the same parameters but using sampling locations as priors. One bar plot summarizing each value of K was generated using CLUMPP77 and Distruct78. Furthermore, each previously defined population was analysed individually in Structure with the same parameters as the first analysis to verify the existence of substructure.Population structure was assessed using pairwise F79. Genetic clusters determined by Structure were used as populations, given the limited sample size in some localities, the low genetic differentiation of localities from the same cluster and the substantial increase in model parameters if more populations were tested. The Brownian model for microsatellite data was used with constant migration rates. All analyses were made by Bayesian inference with constant distribution for Θ and M priors from 0.0 to 20.0 and 0.0 to 200.0, respectively. Four long chains were run with static heating between them, where there were 40,000 recorded steps and 10,000 steps of burnin for each chain. The mutation-scaled effective population size Θ was estimated for each population, where μ is the mutation rate per site per generation, calculated based on the microsatellite data by the program. The number of migrants per generation (Nem) between localities was calculated using the values of Θ and M estimated in each run of Migrate, where 80. The tested scenarios were chosen based on known biogeographic breaks32, on the results of the population structure analyses and on the main oceanic currents flow in the Southwestern Atlantic (SWA). The first scenario simulated panmixia, where all possible gene flow directions were accounted for (Fig. Migration rates and different migrations scenarios were estimated using Migrate-Nios Fig.  were tesfor Fig. . The secfor Fig. , while ifor Fig. . The foufor Fig. . The fiffor Fig. . Finallyfor Fig. . The bes81.Input files for all analyses except the one made with the Microsatellite toolkit were generated using CREATE softwareThe datasets analysed during the current study are available from the corresponding author on reasonable request.Supplementary material


Cynodon dactylon (L.) Pers.], an important turfgrass used in public parks, home lawns, golf courses and sports fields, is widely distributed in China. In the present study, sequence-related amplified polymorphism (SRAP) markers were used to assess genetic diversity and population structure among 157 indigenous bermudagrass genotypes from 20 provinces in China. The application of 26 SRAP primer pairs produced 340 bands, of which 328 (96.58%) were polymorphic. The polymorphic information content (PIC) ranged from 0.36 to 0.49 with a mean of 0.44. Genetic distance coefficients among accessions ranged from 0.04 to 0.61, with an average of 0.32. The results of STRUCTURE analysis suggested that 157 bermudagrass accessions can be grouped into three subpopulations. Moreover, according to clustering based on the unweighted pair-group method of arithmetic averages (UPGMA), accessions were divided into three major clusters. The UPGMA dendrogram revealed that accessions from identical or adjacent areas were generally, but not entirely, clustered into the same cluster. Comparison of the UPGMA dendrogram and the Bayesian STRUCTURE analysis showed general agreement between the population subdivisions and the genetic relationships among accessions. Principal coordinate analysis (PCoA) with SRAP markers revealed a similar grouping of accessions to the UPGMA dendrogram and STRUCTUE analysis. Analysis of molecular variance (AMOVA) indicated that 18% of total molecular variance was attributed to diversity among subpopulations, while 82% of variance was associated with differences within subpopulations. Our study represents the most comprehensive investigation of the genetic diversity and population structure of bermudagrass in China to date, and provides valuable information for the germplasm collection, genetic improvement, and systematic utilization of bermudagrass.Bermudagrass [ Cynodon (family Poaceae) contains 9 species and 10 varieties, with Cynodon dactylon (L.) Pers. (common bermudagrass) being the most widespread. Bermudagrass is found on all continents and islands between approximate latitudes of 45°N to 45°S [The genus  to 45°S , 3.Evaluation of genetic diversity and genetic relationships within germplasm can provide useful information for breeding programs . The genBermudagrass is very abundant in China and widely distributed in tropical, subtropical and warm-temperate regions . SeveralSince the 90’s, the turfgrass research group at the Institute of Botany in Jiangsu Province & the Chinese Academy of Sciences have collected abundant wild bermudagrass germplasm mostly from China. But the genetic diversity and population structure of these germplasm have not been systematically studied by using molecular markers. Therefore, a comprehensive research on genetic diversity is still needed to evaluate these bermudagrass germplasm for its effective utilization in breeding. The present study was undertaken to systematically analyze genetic diversity and population structure in a set of 157 bermudagrass accessions using SRAP markers. The objectives of the study were: a) to assess levels of diversity present among accessions collected from different regions in China, b) to evaluate the population structure of these accessions.No specific permissions were required for these locations. These materials were collected from roadside, river side, sea side, grassland, or open field, and were grown in 30-cm diameter pots in the greenhouse. Necessary fertilization and irrigation were made to ensure healthy and homogeneous materials. 157 natural bermudagrass accessions were analyzed collected from 20 provinces of China. Each accession listed in 2, 0.26 mM dNTPs, 1 U Taq DNA polymerase, 0.2 μM primer and 50 ng DNA template. Amplifications were performed on a TC-412 thermal cycler . PCR cycling conditions were according to Wang et al. [3 solution.Twenty-six SRAP markers that produced high level of polymorphism and clear banding pattern were selected from the primers reported by Wang et al.  Tables  and 3. Sg et al. : an initf is the frequency of the marker in the data set. PIC for dominant markers is a maximum of 0.5 for f = 0.5 [The distinct and reproducible bands of each SRAP marker were scored as either 1 (present) or 0 (absent). Genetic diversity parameters were calculated with the PIC (polymorphism information content). PIC for dominant markers was calculated as: f = 0.5 .K) was set from 1 to 15. For each run, the initial burn-in period was set to 100,000 with 100,000 Monte Carlo Markov Chain interactions. The number of subpopulations was determined using the DeltaK method proposed by Evanno et al. [STRUCTURE software version 2.3.3  which iso et al. . Accessio et al. . If membThe NTSYS-pc version 2.1 software package  was usedFST [Hierarchical analysis of molecular variance (AMOVA) was analyzed in GenAlEx 6.2  to eluciFST  to estimTwenty-six SRAP markers yield clear, high-stability polymorphic bands. The total number of bands, the number of polymorphic bands, the percentage of polymorphic bands (PPB) and PIC were showed in ΔK is 3, showed the most suitable number of subgroups to be three  based on the Mantel test.The genetic distance matrix ranged from a low of 0.04 between C596 and C597 (two accessions from Yunnan province) to a high of 0.61 between C003 collected from Jiangxi province and C794 collected from Xinjiang, with an average of 0.32. A dendrogram based on the genetic distance matrix of the SRAP data was generated using the UPGMA algorithm . In thisGenetic relationships among bermudagrass accessions were further studied using Principal coordinate analysis. A two- dimensional scatter plot has shown that the first two PCoA axes accounted for 26.00% and 23.29% of the genetic variation, respectively . The PCoP<0.001). The results of AMOVA indicated that majority of variance occurring within subpopulations accounted for 82% (P<0.001) of the total variation, and 18% (P<0.001) of variation was attributed to differences among subpopulations (P<0.001) was observed between subpopulation C1 and C3 and the lowest  was observed between Mixed and C2. Therefore, it could be inferred that C1and C3 subpopulations have diverged to a greater extent as compared to the mixed and C2 subpopulations  analysis was used to evaluate within and among subpopulation diversity components. Genetic differentiation among subpopulations was detected by AMOVA, the overall PhiPT values among subpopulations was 0.175  agreed with the existence of three clusters or subpopulations. Despite minor differences, the results were largely consistent. Bayesian cluster analysis was used to infer the genetic structure and presence of possible populations and to estimate the ancestry of the sampled individuals . In the The dendrogram constructed using the UPGMA clustering algorithm grouped the accessions into three clusters which was largely in accord with the result of the model-based method. All accessions of subpopulations C1, C2 and C3 were found in cluster I, II and III respectively, as identified in the distance-based method. These clusters, in most instances, revealed the majority of accessions that were geographically close were generally clustered into the same cluster except several accessions. For example, C019 collected from Fujian province was not clustered into the same cluster as other accessions from Fujian. The Mantel test also revealed little correlation between genetic and geographic distances. Similar results were obtained earlier in bermudagrass using AFLP markers  and SRAPThe accessions of mixed subpopulation were clustered into two clusters (cluster I and cluster II) in UPGMA. Furthermore, most of the accessions in mixed subpopulation were located between clusters in the UPGMA tree as observed in the studies by Tyagi et al.  on the UAMOVA results obtained in this study indicate that there is a higher amount of genetic diversity within subpopulations than among subpopulation, indicating the existence of low genetic differentiation among subpopulations. A similar result was shown by Ling et al.  that 29.A principal coordinates analysis was conducted to further assess the population subdivisions identified using Structure. The PCoA analysis clearly separated the accessions into three gene pools which consistent with the results based on Structure, UPGMA, and AMOVA analysis. PCoA of collections from Northwest China showed that C3 subpopulation was very distinct, forming a separate group. In PCoA, accessions from Mixed subpopulation showed close association with other subpopulations demonstrated admixture in Structure analysis confirming their relatedness within the diverse gene pool.In this study, we used simultaneously four methods including Bayesian clustering, UPGMA clustering, PCoA and AMOVA analysis to clarify the genetic relationship and genetic structure in the collection of 157 bermudagrass accessions. Despite minor differences, the results were largely consistent. By comprehensive analysis of the genetic diversity and population structure of Chinese wild bermudagrass germplasm, three ancestral gene pool were determined for the first time based on the different statistical methods. The UPGMA dendrogram revealed that accessions from identical or adjacent areas were generally, but not entirely, clustered into the same cluster. The results also provide evidence of abundant genetic diversity in these accessions and greater genetic variation within than among subpopulations. In summary, the results from the present study should lay foundation for further research, such as construction genetic linkage map, association studies, and molecular breeding studies.


A total of 15 populations of the genus were collected. Nine chloroplast haplotypes and two nuclear genotypes were detected. Both the nuclear and chloroplast data showed two lineages in Helianthemum songaricum, respectively, distributed in Yili Valley and western Ordos Plateau. A total of 66.81% (p < 0.001) of the genetic variation was supported by this lineage split. A Mantel test showed a significant correlation between genetic distance and geographical distance . Based on genetic analyses, cpSSRs data support strong genetic divergence between regions. We speculate that the climate change during the late Tertiary and early Quaternary isolated H. songaricum into their current distribution, resulting in interruption of gene flow, leading to isolation and genetic divergence between the two regions. Meanwhile, possible selfing would increase genetic drift in small fragmented populations, that might account for the observed genetic divergence in both regions. Given the loss of genetic diversity and genetic divergence in small populations of Helianthemum in northwestern China immediate conservation management steps should be taken on the species.Population genetic studies provide a foundation for conservation planning, especially for endangered species. Three chloroplast SSRs ( Helianthemum is a shrub or subshrub mostly distributed in the Mediterranean, extending to Central Asia  is essential to make effective conservation strategies for endangered species  are a highly polymorphic molecular tool in the population genetic analysis  and need to be further improved with nuclear genome data. In addition, population structure in the two regions are still unclear because of the limited polymorphism in the two chloroplast spacers  Whether populations of Helianthemum in western Ordos Plateau represent a distinct taxa, H. ordosicum? (2) if so, what is the genetic structure within the two species? (3) What are the conservation implications from the genetic structure analysis?The primary conclusion in our previous study, that was significant genetic divergence existed between Yili Valley and western Ordos Plateau, was only based on two chloroplast spacers , implemented in a Biorad T100 thermocycler (Biorad). The cpSSRs were amplified using three Helianthemum-specific polymorphic loci detected in regions trnL-trnF, trnL5-trnL3, and trnS-trnG: mtrnSf-trnGr, mtrnL2-trnF, and mtrnL5-trnL3 , total gene diversity (hT), genetic differentiation index  were calculated in the program HAPLONST, using U-test to determine whether NST is significantly larger than GST.The number of different alleles (Na), the effective number of alleles (Ne), and Nei's genetic distence Nei, , were caST) as the variance components  was 0.805 (SE 0.0632), and within-population gene diversity (hS) was 0.209 (SE 0.0679). Genetic differentiation index GST was 0.740 (SE 0.0784), and NST was 0.803 (SE 0.0617). As shown by the results of a U-test , NST was significantly higher than GST, suggesting a significant phylogeographical structure within Helianthemum. In Yili Valley, hT was 0.583 (SE 0.1501), hS was 0.04 (SE 0.0395), and genetic differentiation index was ; in western Ordos Plateau, hT was 0.634 (SE 0.0748), hS was 0.463 (SE 0.0836), and genetic differentiation index was . AMOVA analysis showed that 79.32% (p < 0.001) of the total variation occurred among populations. When populations were grouped by geographical region, 66.81% (p < 0.001) of the total variation occurred among the regions (Table p < 0.001) of the total variation occurred among populations; in western Ordos Plateau, 22.36% (p < 0.001) of the total variation occurred among populations. Mantel's test showed a significant correlation between genetic distance and geographical distance  was 1.267, and effective alleles number (Ne) was 1.150. Across the entire study area, total gene diversity , compared with other desert plants, such as Ammopiptanthus mongolicus (hT = 0.434), A. nunas (hT = 0.041)  , using two chloroplast intergenic spacers  For in situ conservation, all the natural habitat of Helianthemum populaton should be preserved by local governments. Nature reserves for H. ordosicum have been set up in western Ordos Plateau. However, in Yili Valley, conservation of H. songaricum has not been given enough attention, and we propose nature reserves for H. songaricum should be set up at once. In addition, for serious fragmentation in both species, extinct populations should be reestablished to connect remnant populations in each species, using progenies from populations with nearest geographic distance. Also, population sizes should be augmented by transplanting progenies propagated from original populations. (iii) For both species, ex situ conservation site should be established first. Seeds collection should capture all detected genetic variations to represent the genetic diversity of each species in maximum, avoiding artificially induced bottlenecks  and the rest of the populations should be tested ex situ to prevent potential outcrossing depression.Habitat fragmentation is a significant threat to the survival of plant species in many terrestrial ecosystems (Young et al., Conceived and designed the experiments: ZS, BR. Performed the experiments: ZS. Analyzed the data: ZS, BR, LZ, and XJ. Contributed reagents/materials/analysis tools: ZS. Wrote the paper: ZS.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.


Bunias orientalis L. (Brassicaceae) is a perennial herb known for its 250 years of invasion history into Europe and worldwide temperate regions. Putative centers of origin were debated to be located in Turkey, the Caucasus or Eastern Europe. Based on the genetic variation from the nuclear and plastid genomes, we identified two major gene pools in the Caucasian-Irano-Turanian region and close to the Northern Caucasus, respectively. These gene pools are old and started to diverge and expand approximately 930 kya in the Caucasus. Pleistocene glaciation and deglaciation cycles favoured later expansion of a European gene pool 230 kya, which was effectively separated from the Caucasian-Irano-Turanian gene pool. Although the European gene pool is genetically less diverse, it has largely served as source for colonization of Western and Northern Europe in modern times with rare observations of genetic contributions from the Caucasian-Irano-Turanian gene pool such as in North-East America. This study largely utilized herbarium material to take advantage of a biodiversity treasure trove providing biological material and also giving access to detailed collection information.Turkish Warty cabbage,  Species and genus level phylogeographic analyses provided very surprising and unexpected results indicating, for example, multiple intercontinental dispersal5, unexpectedly fast colonization of large areas6, survival in glaciated landscapes7, or mosaic structure of species assemblages of different age of presumably old vegetation types8. Moreover, phylogeographic studies open an avenue to ask the question to what extent phylogenetic-phylogeographic history might have a major impact on later diversification and functional trait evolution. This phenomenon of constrained trait evolution by phylogenetic history has been introduced as phylogenetic inertia. Although the concept of phylogenetic inertia has changed during the history of evolutionary biology9 from ‘pattern’ definitions11 to ‘process’ definitions13, the concept generally remains important, and it is also reflected with a similar concept of phylogenetic niche shifts and conservatism14. A special case of rapidly changing patterns in space and time is given with invasive species. These species often colonize different continents within a few decades, thereby raising the question if phylogenetic constraints or rapid adaptation play a dominant role during such short time frames. There is little empirical evidence, and one large scale meta study focusing on the entire British flora came to the conclusion that the transition from non-invasive to invasive is not related to phylogenetic distinctiveness, but instead to environmental preferences15. Species invasiveness was correlated with higher nitrogen and moisture preference as abiotic traits highly associated with human-mediated disturbances. However, the situation might be different, when species become invasive in natural habitats and here, phylogenetic inertia, niche shifts and adaptation do play a major role.The evolutionary history of a species is a sum of complex spatio-temporal processes on very different scales often spanning several hundred thousand of years and whole continents. During the past 20 years, detailed phylogeographic studies accumulated aiming to resolve biogeographic patterns and evolutionary processes. These studies serve as important background information to understand and explore contemporary processes such as biological interactionsBunias orientalis L. (Brassicaceae) is one of these invading species. The species has a primarily Eurasian distribution range, but its original distribution is not known. It was believed that the native range of B. orientalis is reaching from Eastern Europe to Central Asia including the Caucasus, the Irano-Turanian region and Central Asia16 alpine regions of the Southern Caucasus19. Interestingly, B. orientalis has only one sister species, B. erucago L., and there are no other members within the entire tribe Buniadeae, which is assumed to be older than 12 million years. This is a rare phenomenon in the entire family21 and might indicate only little evolutionary potential to diverge and adapt to new environmental conditions. However, during the last 250 years the species rapidly spread all over Central Europe and Scandinavia, throughout Asia towards China, and also reached the US and Canada several decades ago24. In most of these colonized regions, the long-lived perennial and self-compatible species turned out to be invasive, but no obvious shifts in its ecology could be detected25. Habitats in which the species is becoming invasive are often linear landscape elements such as grassland along rivers, rural sides along railways and field margins; but often the species is also able to colonize disturbed grassland.The Turkish Wartycabbage or Turkish Rockcress 26, and a 9-year monitoring experiment in one population using also RAPDs showed that the population was able to maintain its genetic variation over time27. Thus, more detailed studies on its genetic variation are necessary.Inconsistent results from few populations and RAPD and ISSR markers have been reported from LithuaniaB. orientalis over the last few millions of years. Furthermore, plastid DNA variation was screened on a large geographic scale to identify ancestral genepools and follow genetic footprints of this maternally inherited molecular marker, which is consequently indicating potential dispersal routes. Nuclear genome-wide polymorphisms were analyzed using amplified fragment length polymorphisms (AFLPs) to further confirm gene pools and detect potential admixture.Herein, we used whole plastid genome sequence data to provide a robust temporal framework for the phylogenetic history of 28 with their availability of material from otherwise hardly accessible areas and species’ distribution ranges spanning entire continents, and potentially important material from the recent past (decades) possibly highlighting introduction history. However, a drawback is that these collections most often provide single individuals per geographically defined population, and, therefore, an initial genetic screening on population level was conducted confirming that within population diversity can be ignored for the proposed questions to be unraveled.Unravelling a comprehensive evolutionary history of any species requires a representative sampling. Therefore, the present study was designed to take advantage of herbaria collectionsB. orientalis, to (2) identify the gene pool giving rise to invasive and spreading populations, and to (3) test whether range expansion is explained by bioclimatic niche shifts or genetic differentiation. This study should provide the spatio-temporal framework for future detailed experiments focusing on population-level processes30.In particular, we aimed to (1) unravel the putative center of origin of 20 and not discussed in detail herein. The final BEAST chronogram : T417 = H9, OS09 = H16, RO12 = H2, BO26 and BO118 = H6 . Within B. orientalis the split between European and Irano-Turanian-Caucasus haplotypes was dated with 1.23 Mya . The split between the two selected Irano-Turanian-Caucasus haplotypes H9 and H16 was set to 0.99 Mya . The split between the European (H2 from Romania) and the Northern Caucasus (H6a) was set to 0.30 Mya . Both H6 haplotypes are similar to each other and are separated by 0.50 My of divergence, indicating the minimum age of this respective Caucasian subgroup. Using the divergence time between B. erucago and B. orientalis, the mean plastid genome-wide mutational rate is about μ = 7.798 × 10−10 mutations/site/year20. However, the mean rate obtained for the concatenated alignment of the three plastid sequences is faster and was calculated with μ = 2.004 × 10−9 mutations/site/year, which was used for further analyses.The fully assembled and annotated plastid genomes are databased with GenBank accession codes ENA LN877374-LN8773777. Phylogenetic analysis and divergence time analysis using BEAST were fully in agreement with our previous resultsB. orientalis only. The finally trimmed alignment was 1512 bp in length. In total 18 plastid haplotypes (H1-H17) were detected in B. orientalis and B. erucago as the outgroup. The haplotypes for individual accessions are indicated with Suppl. Material Table 16, the first two regions carry low amounts of genetic variation compared to the proposed native range  was estimated with 2  and separates the same gene pools as demonstrated by plastid DNA analysis. This is also documented with congruent results from STRUCTURE and network analysis  are shown with Suppl. Material Fig.  2 Fig. S and sepaK = 2 to K = 3 compared to the population-based dataset with much fewer accessions  = 17.69, p < 0.001, Suppl. Material Fig. 2y, y, x and y2) were retained. Thus, the final model formula was ~X2y + y + x + y2 + condition (gene pool: Caucasian-Irano-Turanian versus European). Variance inflation factors then ranged in 1.259 and 2.57531. In the final model the proportion of conditional variance (gene pool) was 22.83% of total variance, proportion of constrained variance  was 27.03% of total variance, and residual variance (not accounted for by the model) was 50.13%. The observed value of F for the whole model was significant in 1000 permutations, F = 10.649, p(F’> F) ≤ 0.001 (Table Correlations among BIOCLIM variables 10 (mean temperature of the warmest quarter) and 11 (mean precipitation of the warmest quarter), 10 and 19 (mean precipitation of the coldest quarter), and 18 (mean temperature of the coldest quarter) and 19 were significant (p < 0.05), but not for variables 11 and 18. All correlations were weak (| r | < 0.5) and therefore all four variables were maintained at this stage. After forward model selection, only four model terms  climatic parameters, (b) spatial origin, (c) ancestral gene pools . Our results did not show a significant signal that bioclimatic variables can sufficiently describe patterns of genetic structure of gene pools , which is in particular true for the Central European region; however, a clear signal of genetic isolation by distance can be found, e.g., when comparing Central European and Scandinavian accessions  approximately 20 Mya with differing ages of crown group radiation often leading to species-rich and more rarely species-poor clades20. The only two species from tribe Buniadeae, Bunias erucago and B. orientalis, have a primarily (Eastern) Mediterranean-Asian distribution. There is no explanation for why this lineage did not radiate, and maybe this is related to the observation that genome size is very large compared to the average genome size in Brassiaceae thereby hindering genetic differentiation. Chromosome number is 2n = 14 and genome size is of about 2 GBp, which is 15X times larger than Arabidopsis thaliana and 5X larger than assumed ancestral crucifer genome size)32. In contrast, B. orientalis is one of the various perennial successful world-wide colonizers among Brassicaceae, and B. orientalis is among the very few Brassicaceae species categorized as invasive not only in Europe but also the United States. Therefore, B. orientalis is an excellent system to study sudden and explosive range expansion under severe environmental change (last 200 years). However, this requires elucidating its evolutionary history in order to formulate and test hypothetical causes of recent range expansions and respective adaptive processes, and to infer phylogenetic-evolutionary constraints explaining present-days processes34. Herein, we show that the tribe Buniadeae originated approximately 12 Mya, and the split between the only members of the tribe, B. orientalis and B. erucago, dates back 5 Mya, resulting in 7 My of net diversification rate of zero. The species split correlates well with the end of the Messian desiccation crisis (7.3 to 5.3 Mya) in the Mediterranean at the Miocene-Pliocene climatic transition and further confirms the observation that diversification events in Brassicaceae are often correlated with major environmental transitions21.This study shed light on the unknown evolutionary history of the biologically very interesting enigmatic B. orientalis16 reaching from Eastern Europe to Central Asia including the Caucasus, the Irano-Turanian region and Central Asia  global cold period climates intensified, which coincides with the 100 ka Milankovitch cycles causing severe cold periods allowing the development of continental-scale ice sheets35. There are only very few estimations of divergence and expansion times for plants from this region. In case of the genus Aubrieta Adans (Brassicaceae) which has a distribution and diversity center in Anatolia and the Irano-Turanian region, the onset of expansion-radiation of the entire genus was estimated to be 1.17 Mya. The P-S-M group (taxa from Eastern Turkey and distributed further towards Iran and Iraq) started to expand 1.11 Mya36. This phylogeographic expansion pattern was correlated with niche shifts towards higher temperature and larger temperature ranges36. A similar pattern has been described for Arabis alpina37, which grows sympatrically at many places with different Aubrieta species. The diversification of Anatolian genetic diversity of the A. alpina complex was calculated at 0.7 Mya37.Previous studies provided a hypothesis about the extent of the native range of 16 and is redrawn here . The region connects Europe and Asia both biologically and culturally. Caucasian ecosystems encompass moist forests at the coasts of the Black and Caspian Seas (Arcto-Tertiary forests), open and xerophytic woodlands, mountain habitat types including meadows as well as steppes that become more dominant towards the East. It is also important to note that many aspects of the Caucasian biodiversity patterns can only be understood in a wider geographical context because the Caucasus is a “melting pot” of elements of different floristic regions with different histories and origins. The Caucasus is particularly interesting because it lies at the border of the Euro-Siberian and Irano-Turanian floristic regions and harbors elements from both floras. Understanding the evolutionary history and origin of the Caucasian flora will therefore also allow for a better understanding of floristic connections in Eurasian mountains, which have been hypothesized based on floristic studies but not yet shown in biogeographic analyses39. Our data demonstrate that Bunias originated south of the Caucasus, but it is likely that B. orientalis is an indigenous floristic element of open grassland in mountainous and highland-(sub)alpine regions of the Southern Caucasus. This idea has been mentioned earlier19, but others proposed a much wider original distribution range including South and Central Russia, West Siberia and southeastern Europe up to the southern borders of contemporary Slovakia and east Hungary41. However, mismatch distribution analysis of the European gene pool demonstrates an expansion time of about 230 kya, which is predating the maximum ice-sheet extension during the Saale-Riss glaciation approximately 150 kya, which severely affected also the Northern Caucasus. The northernmost population analyzed herein from the Caucasus region (North Georgia) carries a rare European haplotype (H6). This might indicate that the mountain ranges in Northern Georgia served as strong barrier after maximum glaciation 150 kya, which gave rise to a secondary distribution area north of the Caucasus that reached south and central Russia, west Siberia and southeast Europe. However, with our data we cannot define precisely a geographic region of this secondary center of distribution and genetic diversity. There is very little macrofossil evidence of Bunias to test for spatial distribution pattern independently, but there are plant remains of B. orientalis from Northwest Altai from the 4th and 3rd century BC, which were also confirmed by DNA sequence data42. Published DNA sequence data from this material are from ITS , but unfortunately we found no variation among all of our accessions (data not shown) and cannot assign this old material to any gene pool. This Scythian burials are interpreted in a way that Altai people at around the foothills used this plant in common and ritual fields and it must be assumed that the species has been widely distributed in Siberia and Altai and went extinct afterwards, long before the 19th and 20th century and the start of present-day expansion history.The Caucasus is of particular interest, because it is one of the global biodiversity hotspotsversus Scandinavia) nor differentiate among the gene pools and it remains open what the causal factors are that drive populations in Central Europe to become invasive.Our bioclimatic analysis supports these ideas, because if we remove the biogeographic component (explains 40% of genetic differentiation) from PCoA, bioclimatic variables explain only 2.6% of the structure of genetic variation, and RDA indicates that a large proportion of structure in genetic variation is best explained by isolation-by-distance. If we consider that the phylogenetic diversity of a species mainly follows a biogeographic signal, then it is noteworthy that bioclimatic parameters do not explain Europe-wide genetic differentiation patterns  potentially important material from the recent past, often highlighting introduction history, is available44. There is, of course, a quality effect of herbarium vouchers, but this does not exclude “old” samples a priori as we have shown in this study45. We have successfully analyzed samples collected from within the last 81 years, which makes herbaria a valuable treasure trove for quantification of past  genetic diversity. In our data set this is best exemplified by North American samples. Bunias orientalis has been introduced into Canada around 194446 and into the US in the 1950s46. Our samples span the Northern US and Southern Canada and indicate a diverse genetic set-up. We identified a voucher from the US , which has been collected in 1936 and notes “Abundant and well established in low meadows”. This indicates that published documentations miss 20 years of potential introduction history and that herbarium vouchers are important long-term archives for such kind of knowledge with its physical documentations. Accessions from the northwestern US carry plastid and AFLP genotypes from the Caucasian-Irano-Turanian gene pool, whereas Canadian accessions exclusively show European plastid haplotypes H1, H2 and H3 and a respective genomic background. These findings correspond well with independent, multiple introductions into the North American continent, and it remains important for the next future to follow potential amalgamation of these two gene pools with additional adaptive potential for further and increased invasiveness (Reynoutria in Europe as an example47).This study also highlights the ongoing value of herbarium vouchers for two reasons: (1) hardly accessible areas have been sampled, for which herbaria very often offer the unique opportunity to collect reliably material with detailed collection dataB. orientalis expanded into Central Europe from the adjacent regions of the Caucasus not earlier than the 18th century48 and having in mind that the species has already successfully invaded into most parts of Central Europe since the second half of the nineteenth century, genetic pattern suggest multiple and also long distance dispersals all over the European distribution range thereby blurring any regional spatial pattern of genetic diversity. The example from Asian Altai demonstrate that we cannot exclude that B. orientalis entered Eastern-Central Europe earlier than the 18th century, but it is obvious that human corridors massively promoted the species’ spreading 41.The rapid range expansion in Western Europe and Central Europe is not accompanied with a decrease of genetic diversity in this regions. Considering that eventually B. orientalis, but we provide a comprehensive evolutionary framework any future study can rely on. The species does not show a pattern of leading edge colonization, but a mixture of continuous long and mid-range dispersal all over Central Europe. Since we did not observe a climatic signature explaining neither distribution nor genetic variation, but also having climatic gradients in the species’ distribution range , we might have to assume a greater potential to adapt locally. This fits well with missing evidence for significant shifts in ecological traits comparing plants of non-invasive (exotic) and invasive populations/regions25 .A first comprehensive screening of genetic variation on population level was done with 16 populations  of Bunias and to obtain values for molecular mutation rates for further genetic data analysis , six Bunias samples  were selected for sequencing and annotating their entire plastid genomes . Plastome sequence data from Hesperis matronalis, Clausia aprica  were selected for further analysis, because this evolutionary lineage was under-represented in prior studies20. The newly sequenced genome data were incorporated into a Brassicaceae family and angiosperm wide plastome data set20 in order to obtain stem and crown group ages. The lack of reliable fossil evidence for time calibration in Brassicaceae makes it necessary to choose outgroups outside the family. Details of library preparation, assembly and annotation have been documented earlier20. Further details on plastome assembly, alignment preparation using MAFFT v.7.01753,54, phylogentic analysis and divergence time analysis using BEAST55 are provided with Supplementary material text.In order to provide a temporal framework of the evolutionary history of the genus trnLUAA-intron, (2) trnLUAA-trnFGAA intergenic spacer, and (3) trnGUCC intron57. Details on primer sequences and PCR conditions used are provided with the Supplement. Parsimony Network analysis of concatenated alignemnts of these three plastid sequences, defined as discrete haplotypes, was performed using the programs TCS v.1.2158 and SplitsTree4 v. 4.1459. Length variation in polyA, T, or G regions were not considered and not coded as characters.Based on plastid genome sequence information, we selected three informative regions with a sufficient number of SNPs (single nucleotide polymorphisms) to reconstruct haplotype networks based on the entire set of accessions and individuals. (1) 60 of plastid sequence data was performed using a sudden (stepwise) expansion model61 of the main regions. Goodness-of-fit was tested using the sum of squared deviations (SSD) between observed and expected mismatch distributions and Harpending’s raggedness index (HRag)62. For groups with expanding populations, the expansion parameter (τ) was converted to an estimate of time  since the start of expansion began using T = τ/2 u63. The neutral mutation rate for the concatenated sequence (haplotype) per generation of u is calculated as u = μkg, where μ is the substitution rate, k is the average sequence length of the DNA region under study, and g is the generation time in years. Generation time g was set to two years. Total alignment length was 1512 base pairs. The substitution rate was set to 2.004 × 10−10 mutations/site/year using the crown group age of the genus  and referring to the three-marker-set. A parametric bootstrap approach61 with 1000 replicates was used to assess the goodness-of-fit of the observed mismatch distribution to the sudden expansion model, to test the significance of HRag, and to obtain 95% confidence intervals (CIs) around τ. Tajima’s D64 tests of selective neutrality were conducted. All the above analyses were carried out with Arlequin 3.5.2.2.65 and DNAsp66.In order to test a demographic expansion scenario, mismatch-distribution analysis67. The large-scale analysis included 134 accessions of which 25 individuals representing 16 populations from the population dataset were serving as internal controls to compare results among experiments   using three combinations of selective primers . To assess technical genotyping error, we included replicate samples from duplicated DNA extractions in our setup. In total, we generated 100 replicate genotypes for 55 individuals. Samples were assigned randomly to an experimental block (96 well reaction plate). Rate of genotyping error was determined over all pairs of replicates70 implemented in the R package car71. Alpha was adjusted for multiple comparisons using Bonferroni’s correction72. For final ordination analysis, we excluded samples with a number of fragments falling outside the range of the µ + 2σ interval estimated from specimens collected after year 2002. Associations of all pairs of samples were expressed by a dissimilarity coefficient with Euclidean properties73). Principle Coordinate Analysis (PCoA)74 and eigendecomposition of the resulting association matrix was performed with the eigen function in R75.The calculated error rate yielded high reliability and reproducibility of the fingerprints . Since herbarium vouchers often provide varying qualities of DNAs that may affect AFLP banding patterns, the large-scale herbarium voucher dataset was analyzed further to improve data quality. We tested for homogeneity of variance (HOV) of the number of AFLP fragments generated for every individual specimen using the Brown-Forsythe test76. using the correlated allele frequencies and the recessive alleles options77. Burnin period comprised 150,000 MCMC steps and data collection was carried out over another 250,000 steps. Ten replicate simulations were run for each value of K  ranging from 1 to 10. Individual ancestry was averaged across all replicate simulations for each value of K using CLUMPP Version 1.1.278. A formal determination of the optimal value of K was carried out using Evanno’s Mean Delta K79 and Symmetric Similarity Coefficients 81Genetic assignment of accessions was inferred under the admixture model implemented in STRUCTURE 2.3.465. SplitsTree graphs were generated with SplitsTree4 v. 4.1459.Analysis of Molecular Variance (AMOVA) and estimation of molecular diversity indices were performed with Arlequin v. 3.583 on a data matrix comprising significant eigenvectors obtained by PCoA of the association matrix of genetic dissimilarities (AFLP dataset). The set of constraining variables comprised (in this order) nine 3rd degree orthogonal polynomials of geographic coordinates84 and BIOCLIM variables 10, 11, 18 and 19 at a spatial resolution of 10 arc minutes . Latitude and longitude were Z standardized prior to calculating the polynomials. A binary factor coding whether a specimen’s plastid DNA type belonged to either the Caucasus-Irano-Turanian or the European gene pool was used as a conditional term. Since the corresponding split in the plastid gene pools clearly pre-dated the time frame of the historic range expansion of European B. orientalis, we treated phylogenetic relatedness among individuals as a (undesired) covariate.We performed  redundancy analysis (RDA)75 using the corresponding functions from the R package vegan86 to perform RDA, forward model selection, permutation based significance tests and variance inflation factors. We performed forward model selection on a full model comprising all explanatory variables  and all pairwise interactions terms between all pairs of spatial and environmental terms (N = 4 × 9 = 36). A model term was included in the final model, if the proportion of pseudo F-ratios F’ that were larger than the observed pseudo F did not exceed α = 0.01 in 1000 random permutations87.All statistical analyses were performed in RThe datasets generated and analysed during the current study are available from GeneBank and from the corresponding author on reasonable request.Supplementary Information