DATES  that leverages ancestry covariance patterns across the genome of a single individual to infer the timing of admixture. DATES provides reliable estimates under various demographic scenarios and outperforms available methods for ancient DNA applications. Using DATES on~1100 ancient genomes from sixteen regions in Europe and west Asia, we reconstruct the chronology of the formation of the ancestral populations and the fine-scale details of the spread of Neolithic farming and Steppe pastoralist-related ancestry across Europe. By studying the genetic formation of Anatolian farmers, we infer that gene flow related to Iranian Neolithic farmers occurred before 9600 BCE, predating the advent of agriculture in Anatolia. Contrary to the archaeological evidence, we estimate that early Steppe pastoralist groups (Yamnaya and Afanasievo) were genetically formed more than a millennium before the start of Steppe pastoralism. Our analyses provide new insights on the origins and spread of farming and Indo-European languages, highlighting the power of genomic dating methods to elucidate the legacy of human migrations.Recent studies have shown that admixture has been pervasive throughout human history. While several methods exist for dating admixture in contemporary populations, they are not suitable for sparse, low coverage ancient genomic data. Thus, we developed  Recent studies have shown that population mixture (or ‘admixture’) is pervasive throughout human history, including mixture between the ancestors of modern humans and archaic hominins , as well as in the history of many contemporary human groups such as African Americans, South Asians, and Europeans . UnderstTo characterize patterns of admixture, genetic methods use the insight that the genome of an admixed individual is a mosaic of chromosomal segments inherited from distinct ancestral populations . Due to While highly accurate for dating admixture events using data from present-day samples, current methods do not work reliably for dating admixture events using ancient genomes. Ancient DNA samples often have high rates of DNA degradation, contamination (from human and other sources), and low sequencing depth, leading to a large proportion of missing variants and uneven coverage across the genome . AdditioAn extension of admixture LD-based methods, recently introduced by DATES  that measures the ancestry covariance across the genome in a single admixed individual, weighted by the allele frequency difference between two ancestral populations. This method was first introduced in DATES by carrying out extensive simulations for a range of demographic scenarios and comparing the approach to other published genomic dating methods. We then apply DATES to infer the chronology of the genetic formation of the ancestral populations of Europeans and the spatiotemporal patterns of admixture during the European Holocene using data from ~1100 ancient DNA specimens spanning ~8000–350 BCE.Motivated by the single sample statistic in DATES estimates the time of admixture by measuring the weighted ancestry covariance across the genome using data from a single diploid genome and two reference populations . DATES works like haplotype-based methods as it is applicable to a single genome and not like admixture LD-based methods, which by definition require multiple genomes to be co-analyzed; but unlike haplotype-based methods, it is more flexible as it does not require local ancestry inference. There are three main steps in DATES: we start by first learning the genome-wide ancestry proportions by performing a simple regression analysis to model the observed genotypes in an admixed individual as a linear mix of allele frequencies from two reference populations. For each marker, we then compute the likelihood of the observed genotype in the admixed individual using the estimated ancestry proportions and allele frequencies in each reference population . This information is, in turn, used to compute the joint likelihood of shared ancestry at two neighboring markers, accounting for the probability of recombination between the two markers. Finally, we compute the covariance across pairs of markers located at a particular genetic distance, weighted by the allele frequency differences in the reference populations .DATES computationally tractable, we implement the fast Fourier transform (FFT) for calculating ancestry covariance as described in ALDER . Briefly, we simulated individual genomes with 20% European and 80% African ancestry by using phased haplotypes of northern Europeans  and west Africans  from the 1000 Genomes Project, respectively   . With large samples, allele frequencies of the reference populations are more reliably computed, which in turn, can improve the precision of inferred dates (To assess the reliability of ectively . As refel (HGDP) . We firsed ALDER  to theseed ALDER , though rations) . Further samples  and even samples . Howevered dates .DATES for features such as varying admixture proportions and use of surrogate populations as reference groups. By varying of European ancestry proportion between ~1% and 50% (the rest derived from west Africans), we observed DATES accurately estimated the timing in all cases (DATES for estimating ancestry proportions. DATES works reliably for dating admixtures between related groups such as Europeans and Mexicans (STF~ 0.03), though it was unable to distinguish mixtures of Southern and Northern Europeans (STF< 0.005)  .DATES is robust to the use of highly divergent surrogates as reference populations. For example, the use of Khomani San as the reference population instead of the true ancestral population of Yoruba (STF ~ 0.1) provides unbiased dates of admixture (We found dmixture . In thisDATES) can be biased under some scenarios. To verify DATES provides reliable results under this setup, we applied DATES with a single reference population and used the admixed population as the other reference. Like ALDER, our inferred dates of admixture were accurate and comparable to using two reference populations. , large proportions of missing genotypes (between 10% and 60%), and pseudo-haploid genotype calls  in reference and/or target samples. DATES showed reliable results under various setups, even when only a single admixed individual was available (DATES yielded reliable results with large amounts (~40–60%) of missing data, either in the target or references, even with highly divergent reference populations  where the gene flow occurred at two distinct time points  or the population had maintained a low population size (eN < 100) until the present  . In the case of continuous admixture, etrotter . In the tleneck) . In humatleneck) . We note history .DATES in real data by comparing our results with published methods: Globetrotter, ALDER, and ROLLOFF. These methods are designed for the analysis of present-day samples that typically have high-quality data with limited missing variants. In addition, Globetrotter uses phased data which is challenging for ancient DNA samples. Thus, instead of rerunning other methods, we took advantage of the published results for contemporary samples presented in DATES and ALDER to 29 target groups using the reference populations reported in Table S12 in DATES, we inferred significant dates of admixture in 20 groups, and 14 of those were consistent with estimates based on Globetrotter. In the case of the six populations that disagreed across the two methods, most of the populations appear to have a history of multiple pulses of gene flow either involving more than two populations  were found to be highly concordant  HG-related ancestry that is closely related to Mesolithic HGs from Europe; (b) Anatolian farmer-related ancestry related to Neolithic farmers from the Near East and associated to the spread of farming to Europe; and (c) Steppe pastoralist-related ancestry that is related to the Yamnaya pastoralists from Russia and Ukraine . Many opEl Mirón individual from Spain, suggesting that the GoyetQ2-related gene flow occurred well before the Holocene. However, the WHG-EHG cline was formed more recently during the Mesolithic period, though the precise timing remains less well understood.Pre-Neolithic Europe was inhabited by HGs until the arrival of the first farmers from the Near East . There wn=101) with estimated ages of ~7500–3600 BCE. We first verified the ancestry of each HG group using qpAdm that compares the allele frequency correlations between the target and a set of source populations to formally test the model of admixture and then infer the ancestry proportions for the best-fitted model  where target = Mesolithic HGs. We observed that none of the target groups had a stronger affinity to Anatolian farmers than WHG  as an additional source population, the AHG ancestry proportion was not significant . Our samples of the Iron Gates HGs include a wide range of C14 dates between 8800 and 5700 BCE. We confirmed our dates were robust to the sampling age of the individuals as we obtained statistically consistent dates when all samples were combined as one group or when subsets of samples were grouped in bins of 500 years  sampled from 16 regions in Europe, ranging from ~6000 to 1900 BCE and encompassing individuals from the early Neolithic to Chalcolithic periods  where target = Neolithic European groups. We observed that four groups had a stronger affinity to Steppe pastoralists compared to Anatolian farmers, and hence we excluded these from further analysis  spanning from middle Neolithic to late Chalcolithic, we inferred the admixture dates ranged from ~6100 to 4500 BCE. Under a model of a single shared gene flow event in the common ancestors of all individuals, we would expect to obtain similar dates of admixture (before present) after accounting for the age of the ancient specimens. Similar to Earlier analysis has suggested that farming spread along two main routes in Europe, from southeast to central Europe  and along the Mediterranean coastline to Iberia  . ConsistTrichterbecher) from Poland, we dated the Anatolian farmer-related gene flow occurred on average ~4700 BCE (5300–4200 BCE). Following the TRB decline, the Baden culture and the Globular Amphora culture appeared in many areas of Poland and Ukraine , consistent with the occupation of both HGs and farmers in this region until the late Neolithic . In east Ukraine . These c Ukraine . Applyinn Europe .Along the Mediterranean route, we characterized Anatolian farmer-related gene flow in Italy, Iberia, France, and the British Isles. Using samples from five groups in Italy, we inferred the earliest dates of gene flow of ~6100 BCE, and within the millennium, the Anatolian farmer-related ancestry spread from Sardinia to Sicily . In IberFinally, we inferred recent dates of admixture in Neolithic samples from the British Isles  with the median timing of ~5000 BCE across the three regions. Interestingly, unlike in western and southern Europe, we obtained overlapping dates across eight groups including early to late Neolithic samples from British Isles. This is consistent with previous studies that suggest there was no resurgence in HG ancestry during the Neolithic in Britain . Thus ouqpAdm, we first tested how well this model fits the data from eight early Steppe pastoralist groups, including seven groups associated with Yamnaya culture and one group related to the Afanasievo culture . For all but two Yamnaya groups , we found this model provides a good fit to the data  and Afanasievo (n=19), we inferred the admixture occurred between 40 and 45 generations before the individuals lived, translating to an admixture timing of ~4100 BCE (STF ~ 0.000–0.006) (The beginning of the Bronze Age (BA) was a period of major cultural and demographic change in Eurasia, accompanied by the spread of Yamnaya Steppe pastoralist-related ancestry from Pontic-Caspian steppes across Europe and South Asia . The arcthe data . We note4100 BCE . We obta0–0.006) . Thus, w0–0.006) . These d0–0.006)  and haveqpAdm. We excluded 20 groups that could not be parsimoniously modeled as a three-way mixture even after removing individual outliers. After filtering, we retained 79 groups for dating Steppe pastoralist-related gene flow across Europe  and the other group that is a the proxy for the ancestral Neolithic Europe population using pooled samples of WHG-related and Anatolian farmer-related individuals.Over the following millennium, the Yamnaya-derived ancestry spread across Europe through CWC and Bell Beaker complex (BBC) cultures. Present-day Europeans derive between ~10% and 60% Steppe pastoralist-related ancestry, which was not seen in Neolithic samples. To obtain a precise chronology of the spread of Steppe pastoralist-related ancestry across Europe, we analyzed 109 late Neolithic, Chalcolithic, and BA samples dated between 3000 and 750 CE from 18 regions, including samples associated with the CWC and BBC cultures. We first confirmed that most target samples had Steppe pastoralist-related ancestry, in addition to European HG-related and Anatolian farmer-related ancestry using s Europe . As BA EDATES, we inferred that the oldest date of Steppe pastoralists gene flow in Europe was ~3200 BCE in Scandinavia in samples associated with Battle Axe Culture in Sweden and Single Grave Culture in Denmark that were both contemporary to CWC. The samples from Scandinavia showed large heterogeneity in ancestry, including some individuals with majority Steppe pastoralist-related ancestry (and negligible amounts of Anatolian farmer-related ancestry), consistent with patterns expected from recent gene flow  led to the final integration of Steppe pastoralist-related ancestry in Europe. In southern Europe, EBA samples had limited Steppe pastoralist-related ancestry, though present-day individuals harbor between ~5% and 30% of this ancestry . Using pDATES to eight MLBA Steppe pastoralist groups, we inferred the precise timing for the formation of these groups beginning in the third millennium BCE. These groups were formed chronologically, with the date of genetic formation of ~3200 BCE for Sintashta culture, followed by ~2900 BCE for Srubnaya and Andronovo cultures. In the central Steppe region (present-day Kazakhstan), we obtained median dates of ~2800 BCE for the expansion of Steppe pastoralist-related ancestry in four Kazakh cultures of Maitan Alakul, Aktogai, and Kairan. By ~2700 BCE, most of these cultures had almost 60–70% Yamnaya Steppe pastoralist-related ancestry  and Neolithic individuals from central Europe . Applyinancestry . These gDATES that measures ancestry covariance patterns in a single diploid individual genome to estimate the time of admixture. Using extensive simulations, we show that DATES provides accurate estimates of the timing of admixture across a range of demographic scenarios. Application of DATES to present-day samples shows that the results are concordant with published methods – ROLLOFF, ALDER, and Globetrotter. For sparse datasets, DATES outperforms published methods as it does not require phased data and works reliably with limited samples, large proportions of missing variants, as well as pseudo-haploid genotypes. This makes DATES ideally suited for the analysis of ancient DNA samples. We illustrate the application of DATES by reconstructing population movements and admixtures during the European Holocene. We confirm and extend signals that were previously identified such as the resurgence of HG ancestry during the Neolithic and provide new details about the genetic formation of the ancestral populations of Europeans and the spread of CWC and BBC cultures across Europe. Together, our analysis provides a detailed timeline and insights into the dynamics of the Neolithization of Europe and the spread of Steppe pastoralist-related ancestry across Europe.We developed DATES (assuming a single instantaneous admixture), we inferred that the Iran Neolithic gene flow occurred around 10,900 BCE . An alternate possibility is that there was a long period of gradual gene flow between the two groups and our dates reflect intermediate dates between the start and end of the gene flow. An upper bound for such a mixture comes from the lack of Iran Neolithic ancestry in AHGs at 13,000 BCE, and a lower bound comes from the C14 dates of early Anatolian farmers, one of which is directly dated at 8269–8210 BCE . We obtained statistically consistent results for all overlapping samples (within two standard errors) (DATES to infer the timing of Neolithic gene flow in 32 groups (vs. 86 groups in our study). We find the performance of both versions of DATES is similar, though some implementation details have improved  . An advaimproved .The second major migration occurred when populations associated with the Yamnaya culture in the Pontic-Caspian steppes expanded across Europe. Our analysis reveals the precise timing of the genetic formation of the early Steppe pastoralist groups – Yamnaya and Afanasievo – occurred ~4400–4000 BCE. This estimate predates the archaeological evidence by more than a millennium  and suggRecent analysis has shown remarkable parallels in the history of Europe and South Asia; with both groups deriving ancestry from local indigenous HGs, Near Eastern farmers, and Steppe pastoralist-related groups . InteresDATES provide an independent and complementary approach for reconstructing population history. By focusing on the genetic clock based on recombination rate, we provide an independent estimate of the timing of evolutionary events up to several thousands of years. Our analysis also has advantages over the temporal sampling of ancient DNA, in that we can obtain direct estimates of when a population was formed, rather than inferring putative bounds for the timing based on the absence/presence of a particular ancestry signature (which may be sensitive to sampling choice or density). Genetic approaches provide complementary evidence to archaeology and linguistics as they date the time of admixture and not migration. Both dates are similar in many contemporary populations like African Americans and Latinos, though this may not be generally true . We filtered this dataset to remove samples that were marked as contaminated, low coverage, outliers, duplicates, or first- or second-degree relatives. We grouped individuals together from a particular culture or region. Details of sample affiliation and grouping used is described in We analyzed 1096 ancient European samples from 152 groups restricting to data from 1,233,013 autosomal SNP positions that were genotyped using the Affymetrix Human Origins array , across pairs of markers S1, S2 separated by distance d asWe can compute the ancestry covariance, S(d) is a set of markers S1, S2 located d Morgans apart.where d with the rate of decay depending on the time since admixture (The ancestry covariance t+1) comes from the insight that in the first generation following admixture, the admixed population derives one chromosome from each ancestral group. The mixing of chromosomes only begins in the following generations as the chromosomes recombine. This means that if we fit t generations, we are likely to underestimate the time of admixture. We note that previous methods like ALDER and ROLLOFF, however, incorrectly fit t generations to infer the time of mixture. In practice, however, this has little effect on the inference except maybe in case of very recent admixture dates. We infer the time of the mixture by fitting an exponential distribution with affine term using least squares. DATES is applicable for dating admixture in a single individual. When multiple individuals from an admixed population are available, DATES computes the log-likelihood by summing over all individuals.The factor of  is the number of generations since admixture (t). We computed standard errors using weighted block jackknife, where one chromosome was removed in each run  The estimated dates of admixture were considered significant if the (a) 28 years . For anc28 years . When muDATES (version v753) was released in An earlier version of Use of regression model vs. likelihood approach: In v753, we used a regression model to infer the residuals at each site in the genotype by conditioning on the allele frequency in the reference population and the genome-wide estimate of the admixture proportion . In contt generations. However, this assumes that mosaic chromosomes are formed in the generation when the gene flow occurs. However, in reality, the mixing of ancestry only begins in the following generations as the chromosomes of distinct ancestry recombine. To correctly account for this effect, we fit an exponential with the rate of (t+1) in DATES v4010. In practice, this has a minor effect on the dates reported earlier, as in most cases the uncertainty is much larger than one generation.Rate of decay of exponential fit: In v753, like ALDER and ROLLOFF, we fit an exponential decay with the rate of Goodness of fit test: In v4010, we implemented the NRMSD to assess the fit of the exponential curve. NRMSD computes the deviation between the empirical estimate and fitted data in order to provide a statistical way to characterize the noisiness of the fitted curve. Lower values of NRMSD suggest a better fit, however, there is no clear interpretation of the absolute value of NRMSD. Based on the empirical distribution of NRMSD values in our study samples , we infeDATES can be used in any species.Support for arbitrary number of chromosomes: Unlike v753 that was optimized for parameters in humans, the new version supports an arbitrary number of chromosomes (inputted by the user) so DATES using simulated data  the time of mixture  Once the ancestry is chosen, we randomly pick a haplotype from the ancestral pool (without replacement) and copy its sequence to the genome of the admixed individual. This process is continued until we reach the end of the chromosome. Using this approach, we generate the genomes of We constructed admixed genomes following the approach described in ividuals . For mor9) where the proportion of missing genotypes ranged between 5% and 60% in our simulations. We also evaluated the impact of the choice of reference populations used in DATES in case of simple and multiple pulses of admixture.We used 111 CEU and 112 YRI phased 1000 genomes phase 3 dataset  for genehat site . To gene–8 and recombination rate 1 × 10–8 per base pair per generation, respectively  individuals. In the study, DATES is applied to >1000 ancient human genomes to characterize major admixture events during the European Holocene. This work will be of interest to scholars in the fields of population genetics, paleogenomics, archeology, biological anthropology, and history. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by George Perry as the Reviewing and Senior Editor. The reviewers have opted to remain anonymous.Thank you for submitting your article "The spatiotemporal patterns of major human admixture events during the European Holocene" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions: I am in consensus with the reviewers that new simulations better reflecting the properties of the data will be essential for the revision. All other comments/requests for clarification also seem reasonable to me; thus, please see and respond to all of the reviewer comments provided below.Reviewer #1 (Recommendations for the authors):– My main concern with this manuscript is that the main text lacks details about the differences between this version of DATES and the one introduced in Narasimhan et al. (2019). I would strongly suggest authors incorporate some information from Supplementary File 10 into the main text.– Would it be possible to explain a bit how the simulations are done? In particular, it is unclear to me how the different admixture times are simulated. I see that Moorjani et al. 2011 is cited as a reference for that, but it would be great to have some details about it in the manuscript.– On page 4, you assess the use of Khomani San instead of Yoruba as a reference population (FST=~0.1). That is using a population that is divergent from the true admixture source. In this case, both populations are present-day samples. In aDNA studies, sometimes we see present-day populations being used as surrogates for ancestral populations in admixture studies. If possible, could you please provide some insight about how much this could affect DATES estimates and whether your method is robust to that?– Can you please clarify why using the admixed samples themselves as one of the reference populations works reliably (beginning of page 5)?Reviewer #2 (Recommendations for the authors):This paper would be substantially strengthened by the inclusion of simulation results which mirror the properties of the empirical data; especially with respect to Fst between reference populations, and average sample sizes, non-contemporaneous sampling, data missingness and pseudo-haploid genotypes in both the target and reference populations.It would also be very useful for potential users of DATES for the authors to specify what they think are the lower bounds for the use of their method with respect to sample size and data quality.There are some issues of consistency in the main text in the way the age estimates are presented, with many results lacking supporting information such as sample sizes and +/- standard errorsIn the extended data figures, some of the population names are inconsistent with the labels present in the supplementary tables, making it difficult to follow exactly which samples were used. Please check that all the population names are consistent with the labels in Supplementary_File_5.eLife. Your revised article has been evaluated by George Perry (Senior and Reviewing Editor).Thank you for resubmitting your work entitled "The spatiotemporal patterns of major human admixture events during the European Holocene" for further consideration by I am satisfied with your major revision efforts, but there are a few points from the reviewers that you addressed in the response to reviewers document, but not in the manuscript itself, which was the intention.Specifically, these three points:1. On page 4, you assess the use of Khomani San instead of Yoruba as a reference population (FST=~0.1). That is using a population that is divergent from the true admixture source. In this case, both populations are present-day samples. In aDNA studies, sometimes we see present-day populations being used as surrogates for ancestral populations in admixture studies. If possible, could you please provide some insight about how much this could affect DATES estimates and whether your method is robust to that?2. Can you please clarify why using the admixed samples themselves as one of the reference populations works reliably (beginning of page 5)?3. It would also be very useful for potential users of DATES for the authors to specify what they think are the lower bounds for the use of their method with respect to sample size and data quality. Reviewer #1 (Recommendations for the authors):– My main concern with this manuscript is that the main text lacks details about the differences between this version of DATES and the one introduced in Narasimhan et al. (2019). I would strongly suggest authors incorporate some information from Supplementary File 10 into the main text.DATES.We agree with the reviewer. We have now added a section in the Methods which describes the differences in the model and implementation of the old and new versions of – Would it be possible to explain a bit how the simulations are done? In particular, it is unclear to me how the different admixture times are simulated. I see that Moorjani et al. 2011 is cited as a reference for that, but it would be great to have some details about it in the manuscript.We have now added detailed description of how the simulator works in the Methods.– On page 4, you assess the use of Khomani San instead of Yoruba as a reference population (FST=~0.1). That is using a population that is divergent from the true admixture source. In this case, both populations are present-day samples. In aDNA studies, sometimes we see present-day populations being used as surrogates for ancestral populations in admixture studies. If possible, could you please provide some insight about how much this could affect DATES estimates and whether your method is robust to that?DATES, reference samples are mainly used for inferring the allele frequencies in the ancestral populations of the target individuals. As long as the allele frequencies in the presentday samples are correlated to the ancestral allele frequencies, the use of present-day samples should not bias the inference. However, recent demographic events  could have occurred in the history of the present-day samples that can lead to significant changes in allele frequencies, making present-day samples a poor surrogate of the allele frequencies in the ancestral populations.In our analysis, we do not use present-day reference samples for dating events in ancient DNA samples. In – Can you please clarify why using the admixed samples themselves as one of the reference populations works reliably (beginning of page 5)?Admixed individuals have mosaic ancestry from the relevant admixing source populations and in turn they have allele frequencies that are intermediate to the frequencies in the two source populations. Thus, they can provide a useful source of ancestral allele frequencies, especially in cases where genetic data from all ancestral mixing groups is not available. Moreover, Loh et al. 2013 showed that the use of admixed populations as one of the references does not bias the rate of decay of the weighted ancestry covariance , though the amplitude of the decay curve can be impacted . Admixed samples can thus be used for the inference .Reviewer #2 (Recommendations for the authors):This paper would be substantially strengthened by the inclusion of simulation results which mirror the properties of the empirical data; especially with respect to Fst between reference populations, and average sample sizes, non-contemporaneous sampling, data missingness and pseudo-haploid genotypes in both the target and reference populations.STF values between the admixing populations is fairly large. Specifically, the STF=0.123, STF=0.109 and, STF=0.055. We also have large sample sizes from all reference populations  and moderate sizes for target populations  (Supplementary file 1A).We thank the reviewer for this suggestion. We note that the parameters of the simulations were chosen to match the estimates in empirical data. In empirical analysis, the To address the reviewers concern, we have now added a new simulation  which includes combinations of features highlighted by the reviewer. We also note that two previous simulations also investigated combined effects of data limitations in ancient DNA .n admixed individuals with 20% European (CEU) and 80% African (YRI) ancestry with pseudo-haploid genotypes. The references used for inference are also pseudo-haploid genotyped. We further varied three key features of the data, missing genotype rate in reference populations (between 0-40%), missing genotype rate in target populations (between 20-60%) and divergence between true source populations and reference population used in DATES. We evaluated the performance of DATES for this setup for (supplement 8) target population size of n = 10 and (supplement 9) target population size of n = 1.Figure 1 – supplement 8 and 9 : We simulated data for We have added the following to the main text:DATES yielded reliable results even with large amounts of missing data (~40-60%) in the target or references individuals and use of highly divergent reference populations . This holds true even when using only a single target sample for dating but as expected with increasing missing data or larger divergence between the reference and true source populations, the inference becomes noisier .”“We also generated data for combinations of features including small sample sizes, pseudohaploid genotypes with large proportions of missing genotypes in both target and reference samples and use of highly divergent reference samples. We found It would also be very useful for potential users of DATES for the authors to specify what they think are the lower bounds for the use of their method with respect to sample size and data quality.DATES gives reliable results using a single sample, with diploid or pseudo-haploid genotypes for least 50,000 genome-wide SNPs and with at least 5 samples from each reference population (with STF ≤ 0.1). In empirical data, we find there is substantial robustness for dating recent admixture events, compared to older mixture events even with limited data. Thus, rather than suggesting lower bounds, we provide good of fit metrics  to evaluate the reliability of the inferred parameters.While knowing the boundary conditions of an approach is useful for study design, we find it is hard to define lower bounds for inference, especially with the use of ancient DNA where the quality of data varies widely across individuals depending on sample preservation or sequencing coverage of the specimen. Moreover, the power of the inference depends on the demographic history, availability of data from reference populations related to the true source populations and age of the target samples . Based on simulations, we find There are some issues of consistency in the main text in the way the age estimates are presented, with many results lacking supporting information such as sample sizes and +/- standard errorsIn the main text, we report average (or median where specified) dates of admixture, though details of all dates are provided in Figure 3 and Supplementary file 1B including sample size, average and standard errors in generations and years.In the extended data figures, some of the population names are inconsistent with the labels present in the supplementary tables, making it difficult to follow exactly which samples were used. Please check that all the population names are consistent with the labels in Supplementary_File_5.We thank the reviewer for noting these inconsistencies. We have fixed the sample/ population names in all tables and figures.I am satisfied with your major revision efforts, but there are a few points from the reviewers that you addressed in the response to reviewers document, but not in the manuscript itself, which was the intention.Specifically, these three points:1. On page 4, you assess the use of Khomani San instead of Yoruba as a reference population (FST=~0.1). That is using a population that is divergent from the true admixture source. In this case, both populations are present-day samples. In aDNA studies, sometimes we see present-day populations being used as surrogates for ancestral populations in admixture studies. If possible, could you please provide some insight about how much this could affect DATES estimates and whether your method is robust to that?We have added the following to the main text:We found DATES is robust to the use of highly divergent surrogates as reference populations. For example, the use of Khomani San as the reference population instead of the true ancestral population of Yoruba (FST ~ 0.1) provides unbiased dates of admixture . In this regard, for ancient DNA where sometimes only sparse data is available, one can also use present-day samples as reference populations to increase the quality and sample size of the ancestral groups. In principle, as long as the allele frequencies in the reference samples are correlated to the ancestral allele frequencies, the inference of admixture dates should remain unbiased (Methods). In practice, however, recent demographic events  in the history of the present-day samples could lead to significant deviation from the ancestral allele frequencies. Thus, the reference populations should be carefully chosen.2. Can you please clarify why using the admixed samples themselves as one of the reference populations works reliably (beginning of page 5)?We have added the following to the main text:“Another idea is to use the admixed populations themselves as one of the reference populations as demonstrated by the single reference setup in ALDER . Admixed individuals have intermediate allele frequencies to the ancestral populations and thus weighted LD or ancestry covariance can be computed with only one reference population . Loh et al., 2013 showed that the use of admixed populations as one of the references does not bias the rate of decay of the weighted LD , though the amplitude of the decay curve (not used in DATES) can be biased under some scenarios. To verify DATES provides reliable results under this setup, we performed simulations and applied DATES with a single reference population and used the admixed population as the other reference. Like ALDER, our inferred dates of admixture were unbiased .”3. It would also be very useful for potential users of DATES for the authors to specify what they think are the lower bounds for the use of their method with respect to sample size and data quality.Lower bounds for statistical methods typically derive from theoretical limits of the method. Simulations can guide about the failure modes and serve as recommendations for parameter values that are likely to work, though these should not be considered as hard limits. Moreover, all our simulations are based on demographic and evolutionary parameters in humans, though the method is applicable (and being applied) to data from other species that differ in diversity, recombination map, data quality and demographic history compared to humans. Thus, rather than suggesting lower bounds, we have provided users with detailed investigation of the performance of DATES for varying sample size and data quality of reference and target individuals. Specifically, we provide recommendations for the following parameters in the main text under the “Overview of DATES: Model and simulations” section. Also where useful, we have added additional clarifications and caveats.a) Dates of admixture – lines 137-139b) Sample size of the target population – lines 140-141c) Sample size of the reference populations – line 142d) The genetic divergence of the source populations from the true ancestral population – lines 163-166e) The quality of the data, including the proportion of missing genotypes in reference and target groups- lines 186-189.


Cytochrome C Oxidase (COI) sequences among four major population groupings for extant and putatively extinct populations only known from herbarium specimens of the dominant Laminarian kelp Ecklonia radiata in the south-western Pacific, a region warming at 2–4 times the global average. Six haplotypes occurred across the region with one being widespread across most populations. Three unique haplotypes were found in a deep-water range-edge population off Moreton Island, Queensland, which likely represents both a contemporary and historic refuge during periods of climatic change. Hindcasting E. radiata cover estimates using extant data, we reveal that this region likely supported the highest kelp cover in eastern Australia during the last glacial maximum. The equatorward range edge, deep-water kelp populations off Moreton Island represent a genetically diverse evolutionary refuge that is currently threatened by warming and requires prompt ex-situ conservation measures.Climate change has driven contemporary decline and loss of kelp forests globally with an accompanying loss of their ecological and economic values. Kelp populations at equatorward-range edges are particularly vulnerable to climate change as these locations are undergoing warming at or beyond thermal tolerance thresholds. Concerningly, these range-edge populations may contain unique adaptive or evolutionary genetic diversity that is vulnerable to warming. We explore haplotype diversity by generating a Templeton–Crandall–Sing (TCS) network analysis of 119  Equatorward-range edge kelp populations are particularly vulnerable to climate change as these locations are undergoing warming at or beyond kelp thermal tolerance thresholds8. Concerningly, these rear edge populations may contain unique adaptive10 or evolutionary6 genetic diversity that is also under threat. Kelp can sometimes persist at lower latitudes, aided by cool water upwelling or in deep-water refugia where they are protected by thermoclines14. Identification of these refuge areas is vital to ensure important genetic diversity is protected and to understand how extant and past climates shape species distribution and evolutionary diversity.Climate change has driven contemporary decline and loss of kelp forests globally (e.g.15), evolutionary diversity may be high due to historical processes16. During the Pleistocene glaciation, many marine organisms were only able to persist within ice-free refugia at lower latitudes19. As temperature and sea level rose and glaciers retreated, species rapidly dispersed poleward and recolonised new areas. The signatures (‘founder effects’) of such distributional changes can be seen in patterns of evolutionary genetic diversity with past low latitude refugia often harbouring high genetic diversity. In contrast, areas that were newly recolonised following extinction or range contractions exhibit lower diversity. Examples of low latitude glacial refugia with high genetic diversity can be found in temperate seaweeds including Durvillaea antarctica (Chamisso) Hariot, 189220, Saccorhiza polyschides (Lightfoot) Batters, 190211 and Laminaria ochroleuca Bachelot de la Pylaie, 182421.While contemporary genetic diversity can be constrained in low latitude, rear edge populations of temperate species  as sea levels fell and the availability of rocky reef dramatically declined (or became non-existent) in many regions24.Marine environments in Australia have a stable climatic history and have not directly experienced glaciation eventsEcklonia radiata (C. Agardh) J. Agardh, 1848 is the dominant and most widely distributed Laminarian kelp in the Southern Hemisphere2 and the only kelp across much of its range, providing the dominant biogenic habitat structure and supporting immense ecological and economic values3. Previous research on evolutionary diversity in E. radiata in south-eastern Australia26 and Western Australia6 revealed genetic diversity to be low and dominated by a single cosmopolitan haplotype, which was also found in South Africa and Oman (where it is now extinct)6, in addition to a few endemic regional haplotypes. Durrant et al.26 suggested that this low diversity in south-eastern Australia was due to rapid colonisation from the northern hemisphere rather than recolonisation following extinction because no refugial areas with higher genetic diversity were found. However, latitudes lower than 35° S on Australia’s east coast have the densest contemporary E. radiata forests29 and represents the area where Pleistocene temperature would have been optimal for climate refugia to occur30 but have never been sampled.E. radiata on Australia’s east coast is at Moreton Island  where this species only occurs on cooler, deep water reefs (30-80 m) below thermoclines31. This region is also interesting from an historical climate change perspective. Pleistocene glaciation caused large drops in sea level and temperature that would likely have caused significant shifts in both the latitudinal and vertical distribution, including colonisation of what are currently deeper rocky reefs by E. radiata. At this time the Moreton Island region would have experienced optimal temperatures (21–24 °C) to support E. radiata populations30. Moreover, widespread local extinctions were also likely as the already narrow continental shelf (and availability of rocky reef) became markedly reduced south of Moreton Island with a fall in sea level33. As such, the extant deep water Moreton Island populations may represent both an historical and contemporary refuge from changing climates and harbour high genetic diversity. Ecklonia radiata also historically occurred on the isolated Lord Howe Island (31° S) with two herbarium specimens confirming its existence there. Further north, a remote deep-water population of E. radiata is present east of Norfolk Island (29° S)34 where a single herbarium sample exists (Auckland Museum—AK146440) from 1930, and a recent specimen was dredged up during fishing . There have been no contemporary confirmations of extant populations on Lord Howe Island despite widespread habitat surveys. Consequently, we presume that this population is extinct. This low latitude island may also represent a lost refugia that once harboured unique or high genetic diversity.The low latitudinal limit of distribution of 37 which has driven contemporary declines in E. radiata7, it is pertinent to identify evolutionary refugia to better understand how climate drives the distribution of this critical species and to protect unique genetic diversity. While current understanding of E. radiata population genetics indicates that these low latitude populations have low neutral genetic diversity due to limited extant connectivity38 but have been selected for higher thermal tolerance10, it remains unknown how historical processes have shaped this foundation species. We use the mitochondrial Cytochrome C Oxidase (COI) marker to examine evolutionary genetic (haplotype) diversity among extant and putatively extinct E. radiata populations from across its full latitudinal range in eastern Australia and across the south-west Pacific. We predicted that haplotype diversity would be higher at lower latitudes due to their potential role as historical refugia.Given that eastern Australia is warming at a rate that is approximately four times greater than the global averageEcklonia radiata across the south-west Pacific, we use a mix of freshly collected E. radiata (collected as in Ref.15) and accessed herbarium specimens of rare  and the putatively extinct population at Lord Howe Island , , accession AK146440), and Lord Howe Island, collected in June 1933 , Sydney, accession NSW817202). We also obtained a Lord Howe Island sample collected in 1966  that was held at the QLD herbarium (AQ1019163) fixed in 10% formalin, and two deep water, dried herbarium specimens from the Kermadec Islands .To examine patterns of evolutionary diversity of Ecklonia radiata were collected from several locations in south-eastern Australia  and New Zealand  (see 10) or dried in silica gel (as in Ref.15). One specimen collected at Mooloolaba, Queensland, 60 km north of the known extant range of E. radiata, was collected as dried beach wrack. As the sampling methods for herbarium specimens are largely unknown, we treat their geographic origin as that originally stated by the collector. All methods were performed in accordance with relevant guidelines and regulations; samples were collected under scientific permits issued by state or federal governments permits (P01/0059(A)-2.0 and QS2018/MAN471); and herbarium specimens were obtained under agreements with each herbarium/institution.Fresh samples of 39 or (b) the Qiagen DNeasy Plant Pro kit, following the manufacturer’s instructions. For both protocols the tissue lysis step was extended to 10 days incubation at 65 °C with samples vortexed twice daily. Extracted DNA from fresh material was purified using a Qiagen DNeasy PowerClean Pro clean-up kit following the manufacturer’s protocol. Purified DNA that was deemed of sufficient quality and quantity (at least ~ 3 ng/µL with > 600 bp molecular weight) was PCR amplified for a portion of the mitochondrial cytochrome c oxidase 1–5′ barcoding region (COI) with the previously published primers, Gaz F2 (5′ CCAACCAYAAAGATATWGGTAC 3′) and Gaz R2 (5′ GGATGACCAAARAACCAAAA 3′)40 in 50 µL volumes using the Invitrogen Platinum II Hot-start PCR Master-Mix (2x). To reduce the risk of contamination, DNA extractions and PCRs were done in separate areas with their own pipettes and equipment, and PCR set-up was carried out using freshly opened packages of consumables with all work carried out in a UV-sterilised laminar flow cabinet42. A PCR cocktail was prepared as per the manufacturer’s instructions (7.5 µL template) and cycled in an Eppendorf Mastercycler Nexus Gradient thermocycler. Visualisation of PCR products was carried out using a 2% agarose plate to determine the reaction success for each marker. Many samples failed to amplify using the Invitrogen Platinum II Hot-start kit, so we also amplified 1.0 µL of DNA extract in a repeat PCR in a Thermo Scientific Phire Plant Direct PCR Master Mix kit following the manufacturer’s ‘dilute and store’ protocol. This kit is designed to amplify DNA directly from plant tissues without the need for extraction and purification. The polymerase is optimised to tolerate PCR inhibitors such as polyphenols and polysaccharides, and requires very small amounts of DNA template. We have found it to work well for poorer-quality, low-concentration, herbarium derived DNA. In accordance with the Thermo Scientific Phire Plant Direct PCR Master Mix kit manufacturer instructions, a gradient series PCR was performed, using purified DNA, to determine the optimal annealing temperature for the primer/template pair (53.5 °C). PCR cycling consisted of an initial denaturation for 5 min at 98 °C, followed by 45 cycles of: 5 s at 98 °C, 5 s at 53.5 °C and 45 s at 72 °C. A final extension was carried out for 1 min at 72 °C. Successful amplicons that produced a clear band on a gel with a clear negative control were outsourced for purification and sequencing at the Australian Genomic Research Facility (AGRF), Sydney. We note that for the herbarium samples it often took two to three repeat extractions and/or three to four PCR reactions tweaking master mixes, template volumes and thermocycler conditions to obtain suitable product for sequencing.For fresh, dried and preserved herbarium specimens approximately 50 mg of tissue was used for extraction. For the single formalin fixed sample, 150 mg of internal stipe tissue was used for extraction as thallus tissue failed to produce DNA or amplify. DNA was isolated using (a) a combined CTAB and SDS protocol42 and edited by eye. A further 76 COI sequences derived from E. radiata from the south-western Pacific26 were retrieved from GenBank 45 where samples were aggregated into regional populations for visualisation and analysis based on shared haplotype identity: South Australia, Victoria and Tasmania ; New South Wales (NSW); Queensland (QLD); and Lord Howe Island, Norfolk Island, Kermadec Islands and New Zealand . A Templeton-Crandall-Sing (TCS) haplotype network46 was reconstructed using PopART software47. The resultant network graphic was improved and overlaid on a vector map using InkScape 1.148 Remote or deep-water locations made sample collection difficult, and for this reason sample numbers for some locations are low. As a result, conclusions based on sampling from these locations need to be regarded with caution because there may be unsampled haplotypes.The COI nucleotide alignment of 29 given established links between population size, genetic diversity, and the a priori assumption that regions with high kelp cover would likely have supported high genetic diversity50. Here, the relationship between kelp cover and mean annual sea surface temperature throughout eastern Australia (18–38° S) was derived from field surveys undertaken over a ~ 1000 km latitudinal gradient and remotely-sensed ocean surface temperature . This relationship was used to predict and compare the distributions of mean kelp cover estimated for the present-day and the LGM. Present-day cover was estimated using a 25-year average of mean annual sea surface temperature encompassing the period 1996–2020 sourced from the Copernicus Marine Environment Monitoring Service . Kelp cover at the LGM was estimated using mean annual sea surface temperature data reconstructed for the oceans around Australia using planktonic foraminifera assemblages by Barrows and Juggins30. Estimated kelp cover for the present-day and LGM were then averaged latitudinally within the maximum range of depths (i.e. 0–80 m14) that E. radiata is known to occur. Given that sea level was approximately 120 m lower at the LGM, the analysis for this period encompassed continental shelf that is 120–200 m deep at the present-day sea level (representing 0–80 m depth at the LGM). However, this analysis does not take into account any cross-shelf variability in habitat availability (rocky reef) that may have occurred as sea level fell, due to uncertainty regarding local geomorphological processes and the subsequent distribution of rocky substrate during the LGM. We also acknowledge that factors other than ocean temperature  contribute to the realised distribution of kelp cover29 and these were not incorporated here.To illustrate the likely temperature-driven latitudinal shift in kelp distributional patterns between the LGM and present, we used current day estimates of kelp coverE. radiata from northern NSW, southern Queensland, Lord Howe Island, Norfolk Island and New Zealand (E. radiata (> 98%).A total of 43 COI sequences (658 bp) were newly-generated for this study, representing the first COI barcode dataset for  Zealand . MegaBLAE. radiata samples from the study area (H1 and H8 to12) . Although nucleotide differences among haplotypes were small (Table n = 74), was found across the entire study area from south-eastern South Australia, south to western Tasmania, north along the NSW coast into southern QLD east to Norfolk Island (NI), the Kermadec Islands (KI), south to the north island of New Zealand (NZ) and at Lord Howe Island (LHI)  found in Bass Strait and south-eastern Tasmania . Conversely, four haplotypes were restricted to subtropical waters north of 30°S. One haplotype (H8 (n = 1)) was found in northern NSW (Solitary Islands), two were from Moreton Island, QLD (H10 (n = 7) and H11 (n = 1)) and a single haplotype that was three base-pair substitutions different from H1, H9 (n = 1), was found at Mooloolaba, QLD  and Moreton Island (~ 27°S)51 and, consistent with contemporary biomass distributions29, this region would likely have supported dense populations of E. radiata  off Moreton Island corroborating the likely role of this reef as a refuge under past climates. Moreover, unique diversity was found in a single specimen of beach wrack ~ 60 km north of the known extant range of E. radiata in Mooloolaba, suggesting that undiscovered deep-water populations may exist along this coast. This is supported by spatial modelling based on ecophysiological and oceanographic data that indicated that during the LGM, kelp forests were likely to have been extensive and dense in south-eastern Queensland.Warm, rear edge populations of temperate species can often harbour high and unique evolutionary genetic diversity as they represent historic refugia from past climatesE. radiata [~ 23 °C]29 was estimated to be located between Rockhampton (~ 21° S) and Moreton Island (~ 27°S)52 and, consistent with contemporary biomass distributions29, this region would likely have supported large and dense populations of E. radiata  and areas with unconsolidated rocky substrates31. Further, most of the rocky reef south of Moreton Island where the continental shelf is very narrow (only ~ 25 km wide)53, would have emersed, limiting habitat availability and restricting the distributions of rocky reef marine organisms along much of the NSW coast.The relatively high and unique kelp genetic diversity off south-eastern Queensland (Moreton Island and Mooloolaba) suggests that these rocky reefs were likely an historic refuge for temperate marine organisms under past climates when waters were cooler and sea levels lower. During the last glacial maximum  the optimal mean annual temperature isotherm for E. radiata suggests rapid recolonisation from refugia following glacial extinction or range contraction. Post-glacial warming would likely have permitted rapid poleward dispersal and expansion including across the Tasman Sea past Lord Howe and Norfolk Islands to New Zealand into the present-day latitudinal range for E. radiata (some 10° of latitude south of LGM isotherms). The concomitant rise in sea level of approximately 60–120 m flooded large areas of what is now shallow continental shelf waters off NSW where the newly available rocky benthos provided space for the primary succession of this pioneer genotype. As discussed by Hewitt54 pioneer genotypes (H1 haplotype) would dominate this new range and any subsequent genotypic dispersal is less likely to establish due to maximal carrying capacity and competition (i.e. high-density blocking) within the established populations55.Indeed, the dominance of haplotype H1 throughout much of the present-day range of E. radiata populations in southern Queensland would have become increasingly thermally stressed over timescales of weeks to months as temperatures exceeded the thermal threshold for E. radiata (~ 26 °C2) except at depth. Indeed, observations suggest that E. radiata occasionally recruit into shallower areas around Moreton Island in cooler winter months (~ 20 °C) but rapidly succumb to warmer temperatures in spring and summer when temperatures exceed ~ 27 °C (pers. obs. T. Stevens). Similarly, Lord Howe , where E. radiata is putatively extinct, sits within the flow of the warm east Australian current where temperatures now mostly exceed thermal tolerances in summer56. Norfolk Island (~ 29° S) is slightly cooler (SST 18–25 °C) which allows E. radiata to exist in isolated deep-water habitats. Surveys for E. radiata in deep water refugia are warranted on Lord Howe Island (to assess presence/absence) and Norfolk Island (to map geographic extent), in order to further elucidate how past and present climate shapes the distribution of this key kelp and to prioritise areas for conservation.As glaciers further retreated, and marine isotherms shifted further south facilitating present day species distributions, the refugial Ecklonia radiata populations in the Indian Ocean exhibit similar haplotype diversity  to the south-Western Pacific . The H1 haplotype extends westward into the Indian Ocean from southern Western Australia, west to southern Africa, and, historically at Oman in the north6. In several range edge populations at the Cape Province of South Africa and at the Abrolhos Islands in Western Australia, the widespread H1 is absent, replaced by regionally-unique, private haplotypes6 suggesting these areas have been isolated historically or physically . These patterns in evolutionary diversity contrast with contemporary patterns in neutral diversity in this species38 and other macroalgal taxa57 which decline with increasing latitude along the same coastline. This is due to the strong poleward flow of the East Australian Current that isolates temperate macroalgal populations at low latitudes. However, low diversity at low latitudes can also be adaptive10 and these populations have loci under selection linked to higher and more variable temperatures, including those off Moreton Island . Understanding the relationships between evolutionary and contemporary patterns of diversity would benefit from genotyping samples using both COI and SNP markers as well as thermal stress experiments on selected haplotypes.On a broader scale, 58), this study is one of the few to successfully amplify and sequence DNA from old algal herbarium samples. Coleman et al.6 successfully amplified DNA from 47 year old samples from Oman stored in three different herbaria. Similarly, Goff and Moon59 amplified DNA from 29 year old samples and Nahor et al.60 from a 117 year old sample. We also successfully extracted DNA from a formalin preserved sample. We note that this remains a challenging task. Successful DNA extraction and PCR amplification often required multiple attempts, tweaking both extraction and PCR protocols. We recommend the Themo Fisher Phire Plant Direct PCR kit which is designed to amplify small amounts of DNA in the presence of plant-derived PCR inhibitors. Moreover, extracting DNA from formalin preserved specimens may be easier using the internal portions of the thicker thallus or stipe in which formalin does not penetrate as deeply. Herbariums are an underutilised resource for examining historical genomic patterns yet hold great promise for uncovering the drivers of seaweed distribution and climate impacts61.While utilising herbarium collections to examine patterns of genetic diversity is beginning to gain traction for terrestrial plantsE. radiata along this coastline7. Moreover, projections suggest further declines and range contractions of kelp under future climate scenarios29 as has occurred in range edge E. radiata populations globally8. While it is unlikely that in situ protection could halt declines of rear edge kelp populations under scenarios of warming, their unique genetic diversity could be protected and studied ex situ in culture banks for use in restoration, hybridisation62 or assisted adaptation strategies.Contemporary climate change is threatening the high and unique genetic diversity found among eastern Australian low-latitude range-edge populations with warming causing declines of Supplementary Information.


Stock enhancement aggressively replenishes depleted wild finfish populations. However, stock enhancement of black sea bream in Taiwan with complex genetic sources, especially when successful, maintains genetic diversity but dramatically changes the genetic structure within and among wild populations.Acanthopagrus schlegelii), a major commercial species. During 2004–2015, even management agencies conducted stock enhancement projects, leading to numerous private releases that have not been recorded. Stock enhancement by a private hatchery without accurate genetic records may lead to a genetic structure change in wild populations. Using allele frequencies at nine microsatellite loci, we studied the genetic effects of stock enhancement in 19 samples collected from populations in the hatcheries and the wild. In 458 individuals from nine hatchery samples, most populations showed weak but significant genetic differences and complex clusters in structure analysis, indicating dramatic stock change within and among hatcheries. The 10 wild populations (n = 773) also had a complex genetic composition and were genetically different among sampling sites and times. However, a simple and clear cluster in structure analysis was found for only one sampling site, which had no release history. Thus, stock enhancement with complex genetic sources helps maintain genetic diversity but dramatically changes the genetic structure within and among wild populations, especially when stock enhancement is successful.Stock enhancement, used for replenishing depleted wild finfish populations, is an aggressive approach. Stock enhancement projects in Taiwan involve black sea bream ( Advances in fishing technology have led to fish stocks, which are renewable fishery resources, being exhausted. Approximately half of all fish stocks have been deemed “fully exploited” or “overexploited” ,2. PolluAiming to improve fishery resources, Taiwan’s government promotes a massive stock enhancement program in its coastal waters every year ,9. UnderAcanthopagrus schlegelii), an economically vital species for both fisheries and aquaculture, is widely distributed along West Pacific coasts from Japan and Korea to the East China Sea and Taiwan. In southern China, black sea bream males become sexually mature within 1 year, and 50% of them change sex by two years old. In this fish, reproduction occurs at 1–2 years of age in coastal waters and at river mouths ; these groups exhibited the highest intergroup variance ; ; (MT_C); (KS_C2); (XM_C); (KS_C3); and (KM_C)] was determined to have the highest CTΦ and variance  with the highest e 1.20%; . In the In the STRUCTURE analysis, the best estimation of the K value (number of groups) was eight, and this corresponded to a stable representation  and wild populations . Black sea bream has been cultured for more than 30 years in Taiwan. This is the first study analyzing the genetic diversity of cultured and wild black sea bream populations in Taiwan coastal waters. Nine hatchery populations were collected, including one in northern China (as an outgroup population), QD_C (Qingdao City); one in southern China, XM_C (Xiamen city): two from Taiwan’s offshore islands, KM_C (Kinmen) and MT_C (Matsu); and five from southern Taiwan, PR_C1, PR_C2, KS_C1, KS_C2, and KS_C3 (Kaohsiung City). According to the allele number (Na), observed heterozygosity (Ho), and expected heterozygosity (He) of nine microsatellite loci, the genetic diversity of the cultured populations was slightly lower than that of the wild populations, except for some such as KS_C1 (Pagrus major) in Kagoshima Bay, Japan, the released hatchery fish clearly reduced the genetic diversity of the wild population [Black sea bream, as KS_C1 . The allas KS_C1 . Geneticas KS_C1 . This inpulation . Due to Rhabdosargus sarba) presented two distinct clusters (hatchery and wild population clusters) [Nm = 10.895). Second, black sea bream is more abundant and widely distributed in Taiwan’s coastal waters than silver sea bream. The stock population was initially established independently and from different areas and may have helped to maintain genetic diversity.When hatchery fish are cultured, high gene flow among hatchery populations and between wild populations cannot usually be maintained. As broodstocks are not changed each year, no random mating occurs. After several generations, hatchery populations tend to show different genetic structures to wild populations . Our stulusters) . As silvSiganus fuscescens) [STF. Notably, the hatchery populations KS_C1, PR_C1, and PR_C2 exhibited a lower pairwise STF with other wild populations than with ML_W2, PH_W, and CY_W. This indicated that the hatchery populations KS_C1, PR_C1, and PR_C2 had high gene flow with the wild populations, and changes in their genetic structure were mainly caused by fish release.We collected 10 wild populations from the following: Japan (as an outgroup population)—JP_W (Nagasaki City); Taiwan’s offshore islands—KM_W (Kinmen) and PH_W (Penghu); northern Taiwan—ML_W1, ML_W2, ML_W3 (Miaoli City), and TP_W (Taipei City); and southern Taiwan—YL_W (Yunlin City), CY_W (Chiayi City), and TN_W (Tainan City). The fish from Taiwan’s offshore islands, northern Taiwan, and southern Taiwan were expected to have a clear genetic structure such as one population   or two pcescens) . HoweverFST) between KS_C1 and ML_W1 is 0, the effect on the genetic structure should be minor. However, according to the present STRUCTURE analysis, KS_C1 was different from ML_W1, and stock enhancement led to evident genetic changes over three consecutive years , and their relatedness value (r) of >0.25 accurately excluded unrelated individuals. In each population, we could not find obvious inbreeding groups, and further guaranteed stock enhancement should not reduce the diversity of the wild population (Through a literature review, Araki and Schmid  summarizpulation . Howeverpulation . PairwisStock enhancement of black sea bream in Hiroshima Bay, Japan, is a successful example . GonzaleIn Taiwan, official stock enhancement and private religious release of black sea bream are conducted frequently and on a large scale. Such diverse and unpredictable fish larvae prevent the decline of overall diversity. Although determining the short-term effect of stock enhancement in Taiwan is difficult, the contribution of stock enhancement to wild populations is evidenced by changes in the genetic structure and the inconsistency of such structure.


Digitaria insularis is a weed species that has gained considerable importance in Brazil's soybean production areas that rely on glyphosate‐resistant cultivars. Herbicide‐resistant weed populations of this species have been reported in many regions in Brazil, first in the south, followed by later reports in the north. We hypothesized that the spread of herbicide‐resistant D. insularis is facilitated by movement of agricultural machinery from the southern regions of Brazil.FST = ), moderate expected heterozygosity  and low inbreeding  in D. insularis populations. Our data supported the hypothesis that herbicide resistance gene flow predominantly occurred in a south‐to‐north direction based on a migration analysis. We also found evidence of local adaptation of resistant populations in the northern soybean‐growing regions of Brazil.Population genomics revealed a weak or no genetic structure (D. insularis is associated with movement of agricultural machinery, although local selection pressure seems to play an important role in the evolution of herbicide resistance throughout the country. Our results suggest preventive practices such as equipment sanitation should be implemented to limit the spread of herbicide resistant D. insularis. © 2021 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.Evidence in our work suggests that gene flow of glyphosate‐resistant  Digitaria insularisacross the country. This represents our results that glyphosate‐resistant D. insularis was likely dispersed via movement of agricultural machinery from the south to the north.An agricultural machine transporting  For instance, more environmentally friendly compoundsDigitaria insularis is a diploid (2n = 36), outcrossing,4 perennial weed species native to South America that propagates by seed and rhizomes and is commonly found throughout the tropical regions of South, Central, and North America. The seeds have silky hairs that aid in long‐distance wind dispersal and attach to heavy machinery, whereas the rhizomes may aid dispersal following their fragmentation during agricultural practices . Once plants become established, control is challenging due to the dual reproduction system this species exhibits. D. insularis is predominant in South America's crop‐growing regions, where the primary cropping system is a double‐crop year of soybean–corn followed by soybean–corn. Typically, corn and soybean varieties with glyphosate resistance traits are widely adopted during both crop seasons. Overreliance on glyphosate as the primary weed management tool selected for glyphosate‐resistant populations of D. insularis in Paraguay in 2005,D. insularis throughout Brazil follows routes used in the movement of machinery (mainly combines and sprayers) from southern to northern regions of the country.In 2012, most of the glyphosate‐resistant populations were present in the southern states of Brazil . However, years later, many other glyphosate‐resistant populations were identified in the central and northern regions of Brazil.D. insularis sampled in the most relevant soybean‐growing regions in Brazil. We hypothesize that alleles associated with glyphosate resistance have a single origin in southern Brazil and have dispersed northward aided by agricultural machinery and seasonal migration to the north. Understanding the sources of initial dispersal of herbicide‐resistant weed populations may help develop management practices and policies to contain herbicide resistance gene flow.This research aimed to investigate population genetic structure and gene flow among glyphosate‐resistant populations of 22.1Digitaria insularis specimens were sampled from 12 soybean farms in four different states in Brazil  using Teejet XR11002 nozzles calibrated to deliver 200 L ha−1 of herbicide mixture. The response to glyphosate was assessed visually using a 0–100% scale, where 0% represents the absence of any visual symptoms and 100% represents complete plant death. Each population was classified as susceptible , segregating , and resistant . Only three individuals per population were screened because this step was part of a much larger project to map glyphosate resistance in D. insularis, and space constraints prevented a greater number of replications.zil Fig. . For eac2.2−1 of glyphosate to confirm the response to glyphosate for each individual in the populations. Glyphosate resistance status in this expanded screening agreed with the preliminary evaluation in Section After the conclusion of the initial glyphosate resistance screening, seeds from the original field populations were germinated (eight plants per population). Leaf tissue was collected from the youngest fully expanded leaves on 10‐day‐old plants and immediately frozen in liquid nitrogen and stored in a −80°C freezer until further manipulation. After tissue collection, samples were treated with 960 g a.e. ha−1 and digested with the restriction enzymes PstI and MseI (New England Biolabs).Genomic DNA extraction was performed using the CTAB method,et al.The method proposed by Elshire 2.3de novo pipeline in Stacks was adopted to first build loci de novo in ustacks using the parameters M = 2, m = 3 and n = 2, following the parameter optimization guidelines.cstacks module, followed by alignment of the reads to the catalog built using sstacks, and finally gstacks for genotype calling. The populations module filtered the dataset to keep loci that are common in at least 40% of the individuals in each population (r = 0.4), minor allele frequency of 5% (–min‐maf = 0.05), and maximum observed heterozygosity of 75% (–max‐obs‐het = 0.75). Outputs were saved in genepop, vcf, and structure (.str) formats for downstream analyses.Samples were demultiplexed and processed with the process_radtags module of Stacks v.2.437,2.4‐nbp 20) with a length of 5000 (‐pilot 5000) were performed. We then performed a burn‐in of 50 000 (‐burn 50 000) interactions with 10 thinning intervals (‐thin 10). Prior odds for the neutral model was the default value (‐pr_odds 10) and posterior distribution of 0.95 as candidates for positive selection.We used BayeScan2.5HE) and inbreeding coefficient (FIS) based on the single nucleotide polymorphism (SNP) data set obtained. FIS was calculated as 1 − (HO/HE), where HO was the observed heterozygosity. Values near zero indicate random mating, whereas positive and negative values indicate inbreeding and outbreeding, respectively. We determined the indices of associations, r¯d,We used the R package adegenet2.6GST statistics using 10 000 permutations to infer significance.K were obtained from 1 to 15. The optimum K values were obtained using cross‐validation to infer the most probable number of ancestral populations.Relative migration between pairs of populations was calculated based on the allele frequency using the R package diveRsity,3HE was observed among the sampled populations , whereas six locations had positive FIS values. Two populations had FIS values equal to zero.In total, 4245 SNPs were generated using Stacks and were subsequently used for the population genomics analysis in this study. Considerable variation in ely Fig. . Four poFST values varied from 0 to 0.16, indicating low to moderate differentiation  compared with population MTSOS. Low genetic structure was observed for most pairwise comparisons indicating high gene flow. Unrooted phylogenetic analysis  in 7 of the 12 sampled locations, indicating disequilibrium between markers in those locations , as well as farm ownership will play an important role in the direction of gene flow.An asymmetric direction of dispersal was observed in DIR Fig.  . Popularks Fig. . If our K = 3 was the most appropriate number of clusters given the populations under study . Using ADMIXTURE results on markers putatively under positive selection, we proposed grouping labels to aid visualization: G1, MTLRR and PRPGR (blue); G2, MABAI (purple); G3, MTLVR (pink); G4, MTNMS, MTSOS and MTSRS (green); and G5, MTDIR, MTSPR, PRDVR and TOPAR (orange). Locations sharing the same resistance status within the same broad geographical location tended to share more ancestry and be more closely grouped. A similar pattern was observed in the susceptible samples.When we used only SNPs under positive selection, a clearer clustering pattern could be observed. Structure analyses using markers putatively under selection revealed clustering patterns compatible with geographical location and resistance status , but both locations in the south seem to be well connected to Mato Grosso's central locations. These results may support the hypothesis that movement of agricultural machinery could have assisted the spread of D. insularis was reported. Unfortunately, we did not include the Paraguayan population in our analysis, but the high gene flow observed in this study might suggest that resistance genes have spread across the borders. Movement of D. insularis propagules between countries is not unexpected because many farmers own land on both sides of the border.Considering the timeline over which glyphosate resistance has developed in South America, we can relate our results to historical facts. Population PRDVR was collected from south Paraná state, near the border with Paraguay, where the first glyphosate‐resistant HE (genetic diversity) and glyphosate resistance status or geographic region can be drawn. For instance, MTDIR exhibited the lowest HE, whereas MTLRR exhibited one of the largest values. These results indicated that local D. insularis populations might be involved in the evolutionary rescue of glyphosate‐resistant populations, increasing HE after the initial bottleneck caused by the herbicide.D. insularis, genetic diversity may be restored by gene flow from other localities. This may also enable populations to adapt more rapidly to the management practices other crop production systems.HE results indicate that the magnitude of D. insularis dispersion is greater than observed in the outcrossing weed species Alopecurus myosuroides, where it was found that HE varied from 0.09 to 0.14, approximately, in populations exhibiting different patterns of herbicide resistance.HE value of D. insularis is larger than that of the predominantly self‐pollinated weed species Bromus tectorum which exhibited a mean HE of 0.2.HE levels in D. insularis further support the idea that admixture plays an important role in maintaining genetic diversity in populations.Overall, no clear relation between the observed FIS) indicated that populations exhibited limited inbreeding because these values ranged between −0.1 and 0.1, suggesting the D. insularis populations studied maintained their outcrossing behavior. The observed low inbreeding coefficients could indicate the limited ability of D. insularis to self‐pollinate under local environmental conditions. For example, in Lolium multiflorum, an outcrossing weed species, FIS ranged from 0.374 to 0.475 for 14 glyphosate‐resistant populations from California.D. insularis was particularly important for 7 of the 12 populations studied, according to the index of association analysis ? Answering these questions will help weed scientists develop better predictive models and understand how selection pressure by herbicides shapes weed populations and the best management practices to slow the evolution of herbicide resistance.Here, we uncover important aspects of FIGURE S1 Unrooted phylogenetic tree. Circles represent individual populations, colored by population. Red: MABAI. Gray: MTLVR, MTLRR, MTSPR, MTSRS, MTDIS, MTSOS, MTNMS, MTDIR. Blue: PRDVR. Purple: PRPGR. Green: TOPAR.Click here for additional data file.TABLE S1Digitaria insularis population origins and glyphosate resistance status.Click here for additional data file.TABLE S2 Genetic differentiation (GST) values representing amount of gene flow from populations in the first column to populations in the first row. For instance, population PRDVR to TOPAR exhibits the greatest gene flow that is statistically significant. Bold numbers indicate statistical significance based on 95% bootstrapping confidence intervals.Click here for additional data file.


Phalaenopsis pulcherrima, a specialist lithophyte of tropical Asian inselbergs, both at the scale of individual outcrops and across much of its range in Indochina and on Hainan Island, to infer patterns of gene flow in the context of an exploration of island syndrome traits.Plants confined to island-like habitats are hypothesised to possess a suite of functional traits that promote on-spot persistence and recruitment, but this may come at the cost of broad-based colonising potential. Ecological functions that define this island syndrome are expected to generate a characteristic genetic signature. Here we examine genetic structuring in the orchid We sampled 323 individuals occurring in 20 populations on 15 widely scattered inselbergs, and quantified genetic diversity, isolation-by-distance and genetic structuring using 14 microsatellite markers. To incorporate a temporal dimension, we inferred historical demography and estimated direction of gene flow using Bayesian approaches.We uncovered high genotypic diversity, high heterozygosity and low rates of inbreeding, as well as strong evidence for the occurrence of two genetic clusters, one comprising the populations of Hainan Island and the other those of mainland Indochina. Connectivity was greater within, rather than between the two clusters, with the former unequivocally supported as ancestral.P. pulcherrima also possesses traits that promote landscape-scale gene flow, including deceptive pollination and wind-borne seed dispersal, generating an ecological profile that neither fully conforms to, nor fully contradicts, a putative island syndrome. A terrestrial matrix is shown to be significantly more permeable than open water, with the direction of historic gene flow indicating that island populations can serve as refugia for postglacial colonisation of continental landmasses by effective dispersers.Despite a strong capacity for on-spot persistence conferred by clonality, incomplete self-sterility and an ability to utilize multiple magnet species for pollination, our data reveal that  HoweverTraits central to defining a hypothetical island syndrome relate primarily to the ability of individuals to persist in confined environments . This isMany of the ecological functions that define island trait syndromes are expected to generate characteristic signatures in terms of genetic structuring and gene flow . Thus, tEven so, more research is needed before an understanding of the significance of different ecological strategies can be synthesised from a representative cross-section of insular systems . One sysPhalaenopsis pulcherrima (Lindl.) J.J.Sm. was consistently associated with shallow depressions and rock platform fringes  how genetically diverse is P. pulcherrima at a range of spatial scales, (ii) how ecologically inter-connected are its geographically isolated populations, and (iii) to what extent do underlying functional traits determining its dispersal, persistence and reproductive strategy conform to a putative island syndrome?In contrast to all other members of ustralia , P. pulcast Asia , the speast Asia . As a ch22.1Phalaenopsis pulcherrima is a lithophytic herb that is distributed primarily in the seasonal Asian tropics, from northeast India and Myanmar, through Thailand to Indochina and Hainan Island in South China, with a few outlying localities in Peninsular Malaysia, Sumatra and Borneo , number of alleles per locus (Na), number of effective alleles (Ne), total heterozygosity (Ht), observed heterozygosity (Ho), expected heterozygosity (He), Shannon’s information index (I), Wright’s F statistics , number of private alleles (Np), gene flow (Nm) and proportion of polymorphic loci (PPL). Additionally, standardised Fst (F′st = Fst/Fstmax)   was calcon 2.9.3  and RECOon 2.9.3 .2.4Fst and analysis of molecular variance (AMOVA) with 1,000 permutations in GenAlEx 6.5.A neighbour-joining (NJ) tree was calculated in POPTREEW  and consPopulation genetic structure was evaluated across all alleles using STRUCTURE 2.3.3 , with pr2.5Based on the results of the STRUCTURE, PCoA and NJ analyses, the 20 populations were divided into a Hainan Island group  and a mainland Indochina group , within which we checked for evidence of genetic bottlenecks at both the population and group level. This was done using BOTTLENECK ver. 1.3.2 . Of the To estimate the direction of gene flow, we assessed the data against six possible models in MIGRATE 4 : (M1) ICP. pulcherrima, based on an approximate Bayesian computation (ABC) algorithm  for three possible divergence scenarios: (S1) IC group originated from HN group and diverged at t1; (S2) HN group originated from IC group and diverged at t1; and (S3) both HN and IC groups originated from a single combined HN+IC ancestral population and diverged at t1  within each population  estimates were calculated for intervals defined as 0–20 m (d = 5 m), 21–50 m (d = 10 m), 51–100 m (d = 50 m or end-point) and 101–600 m (d = 100 m or end-point), and 95% confidence intervals (CI) associated with the null hypothesis of no genetic structure [ijF(d) = 0] were constructed using 1,000 random permutations. Significant positive or negative structure was inferred if the CIs did not overlap.We ran spatial auto-correlation analysis in SPAGEDI  to test pulation , mean FibLF(d) [linear regression of ijF (d) on ln (d)] to test whether there was significant deviation from the null hypothesis of no genetic structure [bLF(d) = 0]. To compare overall intensity of fine-scale genetic structure among populations, we also calculated the Sp statistic (Sp = -bLF(d)/[1-F (1d)], where F(1d) is the average kinship coefficient between individuals of the first distance class , ijF.We then regressed the slope tatistic , given bp) and seed (σs) dispersal to total gene flow, σ (ijF(d) for all samples from each population, we regressed the residuals [f(d): ijF(d) - ijF(d)exp] on ln(d) by a polynomial regression of the third power: f(d) = a + b ln(d) + c [ln(d)]2 + d [ln(d)]3, where ijF(d)exp is the dependent variable of the linear regression equation at independent variable ln(d). The curvature of f(d) is given by the second derivative, k = 2c + 6d*ln (d1), where d1 is the average distance of the first distance class. A concave curve at short distances or k >0 suggests more restricted seed dispersal than pollen dispersal (σs ≪ σp), whereas a convex shape or k <0 suggests more restricted pollen dispersal or no particular restriction in seed dispersal (σs ≥ σp)  . Statistσs ≥ σp)  and SPSS33.1Nt ranged from 4 (L53) to 27 (L33) with a mean of 14.786; Na ranged from 1.95 (L53) to 5.95 (L64) with a mean of 4.214; and Ne ranged from 1.612 (L53) to 4.131 (L33) with a mean of 2.918. Shannon’s Information Index ranged from 0.494 (L53) to 1.481 (L33) with a mean of 1.076; Ho and He ranged from 0.392 (L22) to 0.840 (L54) and from 0.328 (L53) to 0.732 (L33), with means of 0.644 and 0.568, respectively. He was lower than Ho at 12 loci, with the two exceptions being L22 and L46   to 116 (NPA2), whereas Na and Ne per population ranged from 2.286 (YD) to 7.786 (PXH1) and from 1.759 (DL) to 4.880 (PXH1), respectively (Ho and He ranged from 0.465 (LD2) to 0.857 (DPV), and from 0.353 (DL) to 0.739 (PXH1), respectively. Private alleles were detected at 13 of the 20 populations, with the exceptions being WX2, DL, DLH, JF and LD2 on Hainan Island, and DPV and PXH2 in Laos. The inbreeding coefficient, Fis, was positive for LD1, LD2 and PXH1, indicating a deficit of heterozygosity at these populations, possibly due to inbreeding. The 11 mainland Indochinese populations had higher mean genetic diversity and more private alleles than did the nine Hainan Island populations, with He = 0.599 vs. 0.531, I = 1.137 vs. 1.002 and Np = 3.636 vs. 0.889, respectively  ranged from -0.650 (L53) to 0.054 (L22), with a mean of -0.146, indicating an overall excess of heterozygosity (Fit) ranged from -0.340 (L53) to 0.491 (L51), with a mean value of 0.170. Genetic differentiation per locus across all populations (Fst) was positive at all loci with a mean value of 0.281, whereas standardised Fst (F′st) ranged from 0.230 (L53) to 0.650 (L29), with a mean value of 0.457. Gene flow (Nm) ranged from 0.229 (L51) to 1.080 (L53), with a mean value of 0.719  and the IC group , WX3 (p = 0.030), PKK (p = 0.005), XP (p = 0.048) and PHR (p = 0.008).The best-fit TPM model detected evidence of a significant bottleneck in both the HN group  to be the most probable  was found to best explain the population history of both HN group  and IC group  over the first 5 m at each of the five populations with more than 20 individuals, with ijF= 0.019 at WX1, ijF= 0.037 at WX2, ijF= 0.025 at WX3, ijF= 0.092 at NPA2 and ijF= 0.162 at PXH1  was detected at the 100 m (ijF = -0.006) and final distance classes  at WX1 (ijF = -0.043), 30 m (ijF = -0.059), 40 m (ijF= -0.034) and 50 m (ijF= -0.055) distance classes at NPA2 (ijF = -0.069) at PXH1  and logarithm of geographical distance was significantly negative (p <0.05) for all five of these populations, with bLF(d) = -0.0088 at WX1, -0.0047 at WX2, -0.0075 at WX3, -0.0498 at NPA2 and -0.0339 at PXH1  > PXH1 (0.0404) > WX1 (0.0088) > WX3 (0.0077) > WX2 (0.0050).The slope of the linear regression between ijF(d) - ijF(d)exp] on ln(d) for these five populations are shown in k = 0.0416 at WX1, 0.0792 at WX2, 0.0073 at WX3, 0.2065 at NPA2 and 0.2402 at PXH1 (s ≪ σp) at distances of <10 m.Polynomial regression curves of third-power residuals [4Phalaenopsis pulcherrima reveals high genotypic diversity  and high heterozygosity  at the species level, and low rates of inbreeding in all but three populations (Fis = -0.146). Across a huge swathe of the species’ range in tropical Asia, we uncovered strong evidence for the occurrence of two genetic clusters, one comprising the populations of mainland Indochina and the other those of Hainan Island, with values for a number of genetic diversity metrics  being higher in the former. However, whilst the presence of significantly more variation within populations than between them indicates clear genetic structuring, gene flow was not negligible, with greater variation between the clusters of mainland Indochina and Hainan Island than within them, as well as moderate values for the fixation index (Fst = 0.281), standardised Fst (F′st = 0.457) and proportion of migrants (Nm = 0.719), suggesting a degree of interbreeding and migration at the landscape level. Neither fully conforming to, nor fully contradicting, a putative island syndrome, this genetic profile differs from that of many terrestrial habitat island plants studied to date. As such, an explanation for how P. pulcherrima balances habitat specificity with ecological connectivity at different geographic scales – and across different matrix types – necessitates a synthesis of the species’ biogeographic and demographic history, as much as an integrated view of its functional ecology.Our analysis of spatial genetic structuring and gene flow in 20 widespread but ecologically confined populations of 4.1via pollen, seed, or both – is key , which are attracted by any of a number of co-flowering, rewarding, magnet plants whose flowers have overlapping reflectance spectra in bee colour space  indicate greater consistency with those reported for several other outcrossing orchids  suggests that seed may well travel farther at the site level, with significant negative kinship in general being detected only over much larger distances. In P. pulcherrima, a combination of self-sterility, pollination by deceit and high seed motility therefore seem important in promoting panmixia and expanding adaptive possibilities within the constraints of an ecologically confined environment.Fine-scale structure was detected in  by wind . Althougtatistic , Sp valu orchids , as well orchids . At the 4.2The Mantel test revealed significant correlation between genetic distance and geographic distance across all 20 populations. With >1,100 km separating the two most distant populations (KRS in Cambodia and DL in China), however, such a relationship is not surprising: Nm) between populations throughout the region ranged from 0.229–1.080 and the mean was less than 1, suggesting that genetic divergence is marked and could become progressively entrenched  than the mean for the species as a whole, suggesting greater gene flow at the scale of either landmass. Similarly, whilst our Fst estimate for P. pulcherrima is somewhat higher than the mean calculated for the orchid family as a whole . Thus, a demographic scenario involving expansion and then abrupt contraction was found to explain the population history of both groups, with broad synchronicity as well as a consistent lag of a few thousand years in the IC group relative to the HN group in the timing of these events, being suggestive of common forcing factors, potentially climatic. Not only does logistic regression corroborate evidence of corresponding, regional declines provided by the TPM model, but these analyses also confirm the marked isolation of populations either side of today’s Gulf of Tonkin as revealed by STRUCTURE and other clustering approaches. The divergence of island and mainland lineages in 4.4P. pulcherrima exhibits several features typical of terrestrial habitat island plants but not the full suite of functional traits predicted by theoretical framing of a putative island syndrome. Within the constraints of eco-evolutionary trade-offs for on-spot persistence and landscape-scale connectivity, it therefore appears that life in terrestrial habitat fragments drives multitudinous adaptive strategies, not fixed responses. Mirroring the course of debate on the existence of syndromes in other areas of ecological theory, such as pollination (By combining strong clonality with self-sterility, and generalised food-deception of specific pollinators with wind-borne seed dispersal, lination  and planlination , identiflination , as willTo our knowledge, this is the first study to focus on the landscape genetics of a specialist of tropical Asia’s granitic inselbergs. Beyond an examination of the link between adaptive characters and gene flow that is relevant to understanding island syndromes, our results highlight the need for more work on this threatened habitat type, both for the conservation of the vulnerable and often endemic species they host as well as the temporo-spatial landscape features they embody .The original contributions presented in the study are included in the article/SG conceived and designed the study. SG, JL, SS, SB and LA conducted field work and collected the samples. SG and JL processed the samples. ZZ analyzed the data and generated all statistical outputs. SG wrote the manuscript with input from ZZ and JL. All authors contributed to the article and approved the submitted version.