Falco cherrug) and the gyrfalcon (F. rusticolus) triggered debates about its contentious taxonomy. The weak phylogenetic signal associated with traditional genetic methods could not resolve this uncertainty. Here, we address the controversial evolutionary origin of Altai falcons by means of a genome-wide approach, Restriction-site Associated DNA sequencing, using sympatric eastern sakers falcons, allopatric western saker falcons and gyrfalcons as outgroup. This approach provided an unprecedented insight into the phylogenetic relationships of the studied populations by delivering 17,095 unlinked SNPs shedding light on the polyphyletic nature of Altai falcons within eastern sakers. Thus we concluded that the former must correspond to a low taxonomic rank, probably an ecotype or form of the latter. Also, we found that eastern sakers are paraphyletic without gyrfalcons, thus, these latter birds are best regarded as the direct sister lineage of the eastern sakers. This evolutionary relationship, corroborated also by re-analyzing the dataset with the inclusion of outgroup samples (F. biarmicus and F. peregrinus), put eastern sakers into a new light as the potential ancestral genetic source of high latitude and altitude adaptation in descendent populations. Finally, conservation genomic values hint at the stable genetic background of the studied saker populations.The Altai falcon from Central Asia always attracted the attention of humans. Long considered a totemic bird in its native area, modern falconers still much appreciated this large-bodied and mighty bird of prey due to its rarity and unique look. The peculiar body characteristics halfway between the saker falcon ( Falco Linnaeus  are the most important birds used for falconry1. Amongst them, two species from the subgenus Hierofalco Kleinschmidt, 1901, the Holarctic gyrfalcon  and, especially, the Palearctic saker falcon —the two largest falcon species of the world—have been the most preferred ones2. These species represent the crown-clade of the monophyletic group of Hierofalcons4, which include the species F. rusticolus, F. cherrug, F. biarmicus , F. jugger  and F. subniger , while saker falcons and gyrfalcons are currently considered as sister species5. The typical habitats of these two species are remarkably similar, as both of them have adapted to short-grass open environments: tundra and steppe, respectively. Nevertheless, there is also a stark contrast in distribution and ecological preferences between the two species: sakers live in arid grasslands across the Eurasian Steppe Zone6, from the Pannonian Basin through Asia Minor and Middle Asia to Mongolia and the Qinghai–Tibetan Plateau7, while gyrfalcons have a circumpolar distribution8.Large falcon species of the genus 1. By the twentieth century, however, habitat loss, direct persecution, pesticides, electrocutions, and unsustainable harvest have resulted in rapid global decline of sakers7, which was therefore listed as “Endangered” by International Union for Conservation of Nature (IUCN) Red List9. In contrast, the gyrfalcon is listed as “Least concern” by IUCN10, since its breeding population is quite stable in terms of occupancy and productivity11.For many centuries, harvest for falconry did not affect significantly populations of these species due to relatively low pressure and sustainable traditional practices12, but saker falcon also displays significant phenotypic diversity13. Falconers have long valued a large-bodied and dark morph , who identified this bird with their mythological ancestor and called it ‘Turul’16. The Altai falcon has the highest frequency of occurrences in the Altai-Sayan Region of the Tuva Republic in the Russian Federation17. The rarity of this bird and the remoteness of the area it is found contributed to sparking debates about its systematic position.The variability of gyrfalcons is clearly recognizable through the different morphs defined by the melanocortin-1 receptor geneHierofalcon group: Falco [Hierofalcon] altaicus Menzbier, 1891 and Falco [Hierofalcon] lorenzi Sushkin, 1901. Later, Sushkin18 recognized them as the same and phenotypically variable taxon with plumage ranging from normal saker coloration (see Karyakin13) to dark forms even within the same nest. Dementiev19 originally associated the dark form with gyrfalcon, but later revised his views20 and considered it to be a distinct subspecies. Additionally, other taxonomic accounts postulated the hybrid origin of these birds resulting from introgression from gyrfalcons24. To sum up, the taxonomic placement of the Altai falcon is highly contentious (reviewed by Karyakin13), being regarded either as (1) a morph or subspecies of the saker falcon, (2) a subspecies of the gyrfalcon, and (3) a hybrid between these two species. Nota bene, these views are not mutually exclusive and some authors changed their opinion through time.Originally, the Altai falcon was described and named as two distinct species of the 25. Lack of phylogenetic resolution provided by traditional markers  is an issue in molecular systematics of Hierofalcon27, although genome-wide methods have delivered an unforeseen insight into saker phylogeography29. Currently, there are two main clinal population groups, connected only by occasional gene flow along a genetic gradient, recognized within sakers: a western and an eastern group26 resulting from an eastward diversification process28. As for the exact timing of this split, two radically different estimates exist. Pan et al., for which the species originated 34 kyr (1000 years) ago, set the split at around 2 kyr ago29. In more recent work based on whole-genome resequencing, Hu et al.30 reinterpreted previous results and assessed the separation of gyrfalcons and sakers to much earlier, ca 300 kyr ago, and the subsequent expansion of sakers from Europe to Asia at 41 (38–78) kyr ago. Morover, a recently published study31 set the divergence between the above-mentioned species at ca 109 kyr ago.However, the question of Altai falcon has not been addressed by using molecular genetic methods that provide enough resolution power32 mostly due to illegal harvest for falconry1. Consequently, there is a need for investigating the genetic status and elucidate the phylogenetic position of the Altai falcon using a genomic approach in order to set up conservation priorities and strategies33. Here, we apply such a technique, the reduced-representation genomic method, Restriction-Site Associated DNA-sequencing (RADseq)35, to gain insight into the phylogenomic placement of the Altai falcon by comparing birds with typical ‘Altai appearance’ to sakers from the eastern and western part of the range, and to their sister species, the gyrfalcon8. Here, we use the power of this genomic approach to test the possible hybridization pattern between the study groups, thus evaluating the likelihood of the hybrid origin invoked for the eastern saker.In spite of our basic understanding of the evolutionary history of the saker falcon, the origin Altai falcon has not been addressed in any genomic investigations even though it is on the brink of extinction in the wildFalco samples , the final single nucleotide polymorphism (SNP) dataset consisted of 63,566 variable sites (dataset: “ref”), whereas the randomly exported sites from each locus were 17,095 (dataset: “rand”). The quality check of samples in the ref SNP dataset showed that all samples had mean and median genotype quality (GQ) score of 40  and a mean read depth of 30.2 × , which corresponded to mean median value of 28.4 ×  (Supporting Information).F. biarmicus and F. peregrinus genomes (see Methods for details) as a secondary outgroup36. Our bioinformatic mining of homologous regions identified 423 genomic parts that were unambiguously aligned to our RADseq loci in total length of 165,640 bp (mean ± s.d. = 391 bp ± 111 bp) with a minimum length of 157 bp and maximum length of 842 bp. In terms of phylogenetic information content, the dataset contained 1550 variable positions out of which 782 were parsimony-informative.As our phylogenetic results questioned the usage of gyrfalcon as an adequate outgroup, we decided to use the whole 37.In the first place, we built a phylogenetic network using the Neighbour-Net algorithm based on Kimura-2p distance Fig. . Three g29 yielded an unexpected topology, where—with full statistical branch robustness separating the populations as defined above—the Pannonian population is sister to the Tuvan population (including samples from Tuva and Mongolia), which leads to the gyrfalcons . Our analysis based on minimal ancestor deviation38 placed the root at the same place   approach with an automated model selection taking ascertainment bias (ASC) into account yielded a well-resolved tree Fig. . RootingF. biarmicus as a close relative from the Hierofalco group and F. peregrinus as a distant relative from the sister group of hierofalcons36. The maximum likelihood and maximum parsimony analyses yielded identical topology, where the root was placed between the Pannonian and the Tuvan populations , ons Fig.  were alsThe phylogenetic placement of our ingroup samples is not fully resolved in our ML tree based on the “ref” dataset Fig. . AlthougWe explored the “rand” dataset, which only contains unlinked SNPs, using ordination and k-means clustering approaches. The first three axes of the Principal Component Analysis (PCA) of SNP frequencies Fig.  accounteA multivariate method, Discriminant Analysis of Principal Components (DAPC), was used to infer groups within the samples. DAPC analysis found K = 3 as the optimal number of clusters for our genomic data, and separated the samples into three, highly distinct groups Fig. : (1) PanWe also accomplished a co-ancestry-based sparse non-negative matrix factorization (snmf) analysis of SNP data to explore potential admixture between the samples Fig. . K = 2 wst estimate showed that gyrfalcons are the most genetically differentiated from the two saker populations , and the lowest level of differentiation (Fst = 0.06) can be seen between the two studied saker populations. The largest genetic differentiation—as measured by mean Fst of the populations—occurred in gyrfalcons (mean Fst = 0.1), followed by Pannonian (mean Fst = 0.09) and Tuvan sakers (mean Fst = 0.05).Genetic differentiation between the studied populations using Weir and Cockerham weighted FFinally, we were interested in the basic conservation genomic characteristics of the endangered Hungarian and Tuvan populations, so we calculated genome-wide heterozygosity and inbreeding values. In all cases the Hungarian population displayed slightly higher mean and median values for the analyzed genetic diversity values Fig. .Figure 726 in their comprehensive work. We focused only on the eastern sakers as it is already known that their western counterparts only yield haplotypes from the western (‘B’) haplogroup26. We successfully sequenced a 415 bp control region fragment and aligned it with the homologue sequences published by Nittinger et al26 (GenBank accession numbers: OM937743–OM937753). Haplotype building based on statistical parsimony could connect the network with 95% “parsimony probability” at eight steps and resulted in a haplotype network identical to that one published before26  control region  used by Nittinger et al.e26 Fig. . Our eas30 was published during the review process of this work that prompted us to conduct exact statistical tests to assess the proportion of derived alleles between the clades identified  with 100 jackknife resampling of block size of 850 bp  proved to be significantly positive: D  = 0.232 (SD = 9.8 × 10–3) (Z-score = 23.57) (p = 0.000), thus indicating introgression and gene flow between the studied populations. To further explore this relationship, we ran two additional analyses: (2) sliding windows analysis of FdM statistics39 . As the pseudo-haploid genome we used as outgroup can be suboptimal due to the lack of complete allelic variation information, we decided to focus on the latter two statistics which describes the relationship between the three ingroup populations and does not require a fourth (outgroup) sample.A recent paper by Hu et al.see Fig.  in an AB232 SD = .8 × 10–3FdM-statistics, it is positive if there is gene flow between population 2 (P2) and population 3 (P3), and negative if the gene flow exists between population 1 (P1) and P3. Our results clearly indicated gene flow between eastern and western sakers with 28 and 35 RAD regions showing introgression from western to eastern sakers and contrariwise, respectively. At the same time, 35 and 10 genomic regions indicated gene flow from the eastern sakers to gyrfalcons, and vice versa, respectively. The potentially introgressed number of loci between gyrfalcons and western sakers (3) is the lowest . The other population arrangements were all positive  and a close (F. biarmicus) outgroup sample  with microsatellite panel including the same loci—does not confirm their species-specificity on a larger sample . Similarly, an extensive sharing of mt control region haplotypes for different species is reported, which was interpreted as hybridization and/or incomplete lineage sorting42. In contrast, the phylogeographic analysis of gyrfalcons42 based on mitochondrial and nuclear DNA variation found a 1.5 k-long fragment of their mitogenome to be a private haplogroup originating from eastern sakers  and characterized by a star-like topology and lower haplotypic diversity compared to sakers. These elements point to gyrfalcons as sister group of eastern sakers and not as the result of introgression that would lead to shared haplotypes between them. These mitochondrial data are consistent with our interpretation and results, although the unidirectional introgression put forth previously can also explain this pattern30. Having said that we still consider the direct ancestry of gyrfalcons from eastern sakers a biologically meaningful hypothesis based on the present results.The prevailing hypothesis on the origin of the Hierofalco group was based on results of mt DNA analysesca 34 kyr) for the saker falcon and an eastward expansion29. In contrast, WGS data suggests an older split (ca 300 kyr) of ancestral sakers from gyrfalcons, yet, this is not supported by our findings. A third stem age of sakers was estimated by Al-Ajli et al.31 who report the separation at 109 kyr ago. Given this uncertainty in saker stem age, we cannot set the exact timing of evolutionary events, but hypothesize below the possible evolutionary history based on our results.The reconstruction of ancestral population sizes based on transcriptome data suggests a recent Holocene diversification 47. Most importantly, ancestral gyrfalcons may have originated from eastern sakers by forming of pre-zygotic, spatial and/or behavioral isolation. Part of ancestral eastern sakers better adapted to hunting large prey might have followed the retreat of the ice shield and have become isolated from that source area as a consequence of the northward shift of the tundra zone and partly the formation of the taiga zone, as already put forth by Potapova et al.25. This could have happened recently on an evolutionary timescale. Genetic differentiation (Fst) values (see Results) between our studied populations further underpin this hypothesis as gyrfalcons show the highest level of differentiation (as measured by meanFst). This can be the result of a single, recent colonization event of the tundra habitat, which resulted in significant genetic differentiation due to founder effect, as already pointed out by Johnson et al42 based on the analysis of SSRs and mtDNA. The unbalanced genetic admixture between the gyrfalcons and eastern sakers  in our FdM analysis  and—along with other genomic regions—are now observed as shared between eastern sakers and gyrfalcons. This would also explain the significant admixture found in our study  that settled and bred in Crimea53. However, both locations are within the western saker population’s range. Some of the eastern saker falcons tagged in the Altai Mountains roamed west as far as western Kazakhstan54, but no evidence for further dispersal on that direction were found. The normal migration route of eastern sakers is on the north–south axis55. Finally, Pan et al.29 reported a 2–4 orders of magnitude higher eastwards migration rate than the opposite direction on an evolutionary timescale. This observation can also explain our finding of gene flow between saker populations, which is probably the evidence of historical event that occurred at a minimum extent. The now extinct7 populations of European Russia and those from Kazakhstan now on the brink of extinction likely mediated this gene flow. In conclusion, our current understanding of the large-scale movements of sakers does not support the hypothesis of contemporary genetic exchange between eastern and western sakers.Indeed, contemporary long-distance dispersal between eastern and western sakers has not been confirmed by any other techniques so far. Even if roughly 5000 individuals were ringed across the entire range since 1950 (Prommer pers. comm.), and about 200 were satellite tracked since 200456 (ILS) as the source of eastern mtDNA haplotypes in the western area and vice versa. Although it is notoriously difficult to separate ILS from hybridization57, we are inclined to make ILS liable, at least partly, for the incongruent distribution of haplotypes between the two main saker groups. As the isolation of the eastern and western populations took place in the Holocene, at least according to one estimate29 , we may correctly assume that even fast-mutating mtDNA could not undergo complete lineage sorting during this time , thus, casting doubts about the existence of at least occasional contemporary hybridization between the two saker groups. This result clearly demonstrates why fast-evolving mitochondrial haplotypes alone cannot provide an ultimate insight into the evolutionary history of Hierofalcons.As an alternative to ongoing hybridization, we can refer to incomplete lineage sortingF. cherrug rusticolus) could be an apparently correct taxonomic treatment from genetic perspective as paraphyly alone cannot argue against the species-level separation of a young42 taxon according to the unified species concept58. Having said this, we ought to emphasize that further studies are needed to establish the firm taxonomy of the clade formed by F. cherrug and F. rusticolus, especially because such newly formed species may have other traits  arguing for the species-level separation. Clearly, the limited admixture we detected between gyrfalcons and sakers  and explicit outgroups to provide a comprehensive picture. We need to acknowledge that the lack of any samples from the intermediate geographic regions  between the Altai and the Pannonian Basin , could confound the phylogeographic picture if there is a clinal variation in genetic structure. Keeping this in mind, we need to echo that a more thorough understanding of the evolutionary relationships can only be obtained with a more complete taxonomic samplingHierofalco subgenus using a genome-wide approach, RADseq, that effectively and representatively samples the whole-genome60. Disregarding an early attempt by Sushkin in 193817 to study the heritability of the morphological characteristics of an Altai male falcon, the origin and taxonomic status of these birds were not investigated by genetic methods. Here, we showed that Altai falcons are deeply nested within the genetic variability of eastern sakers  or between the eastern saker and gyrfalcons . This question was addressed by our estimate of admixture proportions via the snmf analysis as implemented in LEA  saker, and thus used the former as outgroup in their analyses. Consequently, their test for introgression using the f3-statistics  and to field-collected gyrfalcons. Based on this sampling, the authors found western Mongolian sakers to be admixed between gyrfalcons and captive sakers. Moreover, they report the “gyrfalcon-like” Mongolian sakers more admixed with gyrfalcons than those they classified as “saker-like” Mongolian sakers. We must note that the exact geographic origin of the captive sakers, which served as the basis of comparison between the different populations, is unknown and that makes any conclusion questionable. Also, if those birds represent the European population , then the finding of the preprint is exactly mirroring what we reported here: the eastern sakers can be interpreted as the result of admixture with appropriate intermediate genetic position between western sakers and gyrfalcons.The other studyWe need to further reflect to a very important taxonomic conclusion: the authors argue for the species-level separation of the  western Mongolian sakers. We need to note the failure of clarifying the geographic origin of the “pure” sakers in that work. However, if they are representative of the western sakers, their results corroborate ours: gyrfalcons are the sister group of eastern sakers, and the intermediate genetic position of the latter can be the reason for finding a significant admixture between (western) sakers and gyrfalcons (see the preceding chapter for further discussion). Although in their phylogenomic analyses two mitogenomes of gyrfalcon-like sakers form a monophyletic unit, this does not hold for the W-chromosome haplotype of the three gyrfalcon-like saker specimens they examined. We regard the former result as non-conclusive , and the latter as a further support for our results: eastern sakers displaying an ‘Altai’ phenotype do not form a monophyletic unit, thus, they are with different phylogenetic origin. These results as now described in the above preprint do not support elevating eastern sakers with an ‘Altai’ phenotype to be at the species level.15, who went further on to speculate that this form represents an atavistic, ‘ancestral’ form of sakers. Although we cannot exclude this possibility, in our view it is more likely that the Altai phenotype appears sporadically in the populations probably as a result of a point-like change  in the genome. As mentioned earlier, the dark coloration of gyrfalcons is clearly associated to a single gene61, and this can also be behind the origin of the dark form in eastern sakers. However, several other genetic or epigenetic mechanisms can also stand behind this phenomenon. For instance, the insertion of a single transposable element can greatly alter the phenotype of birds as was recently demonstrated by Weissensteiner et al62 in the case of a songbird species. Whether this change in genotype is a result of an external stimulus  or fully accidental remains a question. The geographically confined occurrence of this form , which shows an increasing gradient towards the highest part of the mountains17, hint at the existence of such environmental influence (or influences) that triggers the appearance of the Altai form. As such alterations to phenotype during early embryonic life can be predictively or immediately adaptive63, it can explain the growing density of dark plumage phenotype at high altitudes where more harsh environmental conditions exist. If gyrfalcons are descendent of ancient eastern sakers as our results suggest  and gyrfalcons.In light of our genomic results, we consider the Altai birds to be phenotypic morph of eastern sakers, thus, echoing the opinion of Pfeffer7, and their basic conservation genetic characteristics  can influence their long-term existence64 as well as targets of nature conservation management65. Here, we reported  was relatively high in both populations as it can theoretically only take up values between 0 and 0.569, which is reassuring for conservation work: both populations harbor a substantial genetic diversity which is the basis for their long-term adaptability64. In this respect, it is remarkable also that the Pannonian population possesses an apparently higher genetic diversity, although it has undergone a documented bottleneck in the past 80 yr70. On the other hand, the studied Tuvan population has also undergone a dramatic population size reduction and has just recently started to recover as a result of conservation actions72. Reduction in population size of sakers on the western part of the area has been strongly connected to habitat loss, electrocution and direct persecution, and partly to the steep decline of prey populations, particularly ground-squirrels (genus Spermophilus)73. Fortunately, the decline of souslik population has recently halted in Hungary70 and the Czech Republic74 thanks to legal protection, habitat management practices75 and re-introductions. Both saker populations studied by us are recovering now and reporting considerable genetic diversity in both populations is promising for the future. Similarly, the inbreeding coefficient is also close to zero , are highly relevant conservation issues. Clearly, a range-wide phylogeographic work is needed to better understand the possible adaptive role of population isolation within sakers, because this can be the key to plan genetic rescue65 of this globally endangered species. The phylogenetic resolution provided here by RADseq together with its capability to sample the adaptive variance78 makes this marker system appealing for this work.In contrast to some previous findings, our phylogeographic results Figs. , 5 and 670, representing the western group, was sampled for seven birds all across Hungary , and the Tuva population71, representing the eastern group, was sampled for ten birds in the Republic of Tuva, Russian Federation   which is an offspring of a mother displaying an Altai phenotype that nested in natural conditions with an eastern saker male in Tuva. Four samples of gyrfalcon, collected from birds of Far Eastern arctic Russia natural population origin confiscated by the All-Russian Research Institute of Environmental Protection, were included from natural populations of Far Eastern arctic Russia (abbreviated as ‘Gyr’) as outgroup samples (Table https://arriveguidelines.org). In addition, all methods were performed in accordance with the relevant guidelines and regulations. We also note anesthesia was not used and no animals were sacrificed during the study.To represent the two main groups of sakers , samples of growing feather from chicks were collected from two populations: the Pannonian population79. This tissue part was used in isolation of total genomic DNA using a standard mammalian nucleic acid extraction protocol. In short, we incubated the dissected material from samples individually in the extraction buffer  with Proteinase K  at 55 °C overnight for complete lysis. Lysed samples were incubated with RNase A (Thermo Scientific) for 30 min at 37 °C. RNase treated samples were incubated in the freezer for 10–15 min with 0.5 V 7.5 M ammonium acetate, then centrifuged with 1 V of chloroform and isoamyl-alcohol (24:1) added. DNA precipitation was performed by adding 1 V ice-cold isopropanol, incubating for 1 h at − 20 °C and centrifugation at maximum speed. After washing with 70% ethanol, the pellets were dissolved and preserved in 10 mM Tris–HCl (pH 8.5). Double-stranded genomic DNA quantity was checked by a Qubit v.3.0 fluorometer (Thermo Scientific), with dsDNA High Sensitivity (HS) Assay Kit, and the integrity of DNA was checked using 1% agarose gel electrophoresis.Hungarian samples were processed at the laboratory of the Evolutionary Genomics Research Group, Debrecen, whereas the Russian samples were processed at the Core Centrum of the Institute of Developmental Biology Russian Academy of Sciences, Moscow, using exactly the same protocol. DNA was extracted from ethanol (in case of Russian samples) or silica-gel (in case of Hungarian samples) fixed growing feathers by removing the mesenchymal pulp with the blood clots and tips from the calamus34 deployed in the Hungarian lab. To multiplex our samples, we used a system of double index barcoding combined with inline barcodes in the adaptor sequences ligated to the sticky ends: seven different, custom-modified P1 adaptors and three different custom-modified P2 adaptors were used. We used 210 ng of high-quality  genomic DNA (gDNA) from each sample. In presence of the draft genome of Falco cherrug28, we predicted the number of expected RAD-loci if using the enzyme PstI and SbfI by in silico digestion as implemented in fragmatic80. Based on these results, we selected SbfI-HF  to restriction digest our samples according to manufacturer’s protocol. Modified P1 adaptors were then ligated onto each digested sample, which were subsequently sonicated using a Bioraptor Pico sonicator  for five cycles of ultrasonic shearing of 30 s ‘on’ and 30 s ‘off’. Than DNA fragments came through size selection on both sides using the SPRI Select Beads Kit . After blunting and poly-A ligation P2 adaptors were ligated onto the randomly sheared end of our sample DNA using T4 DNA ligase (New England Biolabs). The sub-libraries were pooled equimolarly and went through two-side size selection using the SPRI beads. Compared to the original protocol, a modest PCR amplification was carried out with High-Fidelity PCR Master Mix with HF Buffer (New England Biolabs) in eight separate 50 μl volumes each with 14 amplification cycles. PCR products aliquots were combined before a final size selection on both sides using SPRI beads. After size selection, the library was checked on a Bioanalyzer device . The final library was sequenced on an Illumina HiSeq platform providing paired-end, 150 bp long reads at Novogene Co. Ltd. .In order to overcome the limited phylogenetic resolution, commonplace with conventional genetic markers within this species, we used RADseq using one type-II restriction enzyme81. Further quality filtering and adapter removal were accomplished by using trimmomatic v.0.3982 using default values and specifying the custom adapter and primer sequences used during the library preparation as a collection of potential adapter sequences. The filtered reads were aligned to the available draft reference genome assembly (GCF_000337975.1) of saker falcon28 using BWA v.0.7.12 with default parameters. The mapped reads were processed with the ref_map module of Stacks, and SNPs were exported from the mapped RAD-loci using the populations module of the software package Stacks. This module was run two-times: (1) with default settings to export all SNPs from the whole dataset (dataset: “ref”); (2) again to export a single random SNP from each RAD-locus (dataset: “rand”) to minimize linkage between the filtered SNPs. The first two datasets were further filtered using vcftools v.0.1.1683: “ref” dataset was filtered for maximum 20% individual missingness and minor alleles present in more than two individuals; the dataset “rand” was filtered for maximum 10% individual missingness, minor alleles present in more than two individuals, and sites that are in Hardy–Weinberg-equilibrium at p = 0.01 level of significance. The latter dataset was also used to estimate the genetic differentiation between the studied populations by vcftools.The Illumina reads were de-multiplexed by the process_radtags module of the software package Stacks v.2.2F. peregrinus (assembly: GCA_001887755) and F. biarmicus (assembly: GCA_023638135). The RAD loci sequences with an observed heterozigosity of < 0.05 and zero missingness  were recovered with Stacks, then checked for polymorphic sites. Loci containing at least one polymorphic site (n = 422) were subject to consensus sequence generation using the EMBOSS tool consambig84. Loci sequences were matched against the reference genome of F. peregrinus and F. biarmicus using blastn v. 2.5.085 with an expected e-value of 1e-25. The genomic region with the highest score was retained and aligned with RAD loci sequences using MUSCLE v. 3.886. Alignments of loci were concatenated with AMAS.py87. The resulting fasta file were used in downstream analyses of this dataset.The explicitly root our phylogenetic tree, we mined our RAD loci’s homologous genomic bits from the publicly available reference genome of 37 that helped us to locate the samples in the phylogenetic space. Then, we employed a phylogenetic tree reconstruction method to gain insight into the phylogenetic relationship of our samples: (1) we used concatenation to build a maximum likelihood (ML) tree using IQ-tree v.1.6.1288 with automatic model selection using ASC and assessing branch robustness via Approximate Likelihood-Ratio Test (aLRT)89 and non-parametric bootstrap (bs)90. Branches were considered to be statistically supported if aLRT ≥ 80% and bs ≥ 75%.The whole SNP set (dataset: “ref”) derived from 21 birds was subject to different phylogenetic analyses. First, we used Kimura-2p genetic distance to build a Neighbour-Net phylogenetic network in SplitsTree v.4.14.4F. peregrinus and F. biarmicus, utilized to approaches: a maximum parsimony (MP) analysis with 1000 non-parametric bootstrap to test for statistical robustness as implemented in Paup v.4.0a16991. We run the heuristic search using 1000 random starting replicates and keeping 10 trees at each branch-swapping step. The resulting trees were saved as phylogram rooted on the sample of F. peregrinus. After assuring the same most parsimonious trees were found in 1000 replicates and single search mode, 1000 non-parametric bootstrap pseudo-replicates were run in non-replicated tree search. One tree of the resulting most-parsimonious trees were chosen that reflected the topology of the tree resulting from the maximum likelihood (ML) tree search (see below), and statistical robustness was indicated by superimposing the bs values at the corresponding branch. An additional phylogenetic tree reconstruction using the ML search criterion was conducted in IQ-tree using automatic model selection using the partitioned dataset (without merging data partitions sharing the same evolutionary model) generated from the outgroup dataset. Branch robustness in this analysis was assessed by 1000 aLRT and 1000 ultra-fast bootstrap (UFboot) analyses supplemented with 100 non-parametric bs pseudo-replicates. These values were then superimposed onto the phylogenetic tree at the corresponding branch presented on Fig. The phylogenetic analyses using two explicit outgroups, 92 using always default settings. The filtered vcf file was imported into R by vcfR93 and checked the quality of SNPs in each sample by calculating distribution characteristics of read depth and genotype quality as implemented in the package adegenet v.1.394. The latter software was used to carry out PCA and DAPC, whereas we used the package LEA v.2.4.095 for the co-ancestry based snmf calculations. In order to assess the optimal number of PCA axes for DAPC, we used the alpha-score metric to tradeoff between power of discrimination and over-fitting. Finally, we used hierfstat v. 0.04-2296 to calculate the basic conservation genetic measures, which were visualized using ggplot2 v.3.3.297. The same software was used when calculating population genetic differentiation.The unlinked SNP set (dataset: “rand”) derived from 21 birds was used in these analyses all accomplished in RIn order to place our results into a larger context, we sequenced the mitochondrial control region used by Nittinger et al. in their phylogeographic work. We focused only on the ‘eastern’ birds as it is already known that the western sakers possess mitochondrial haplotype only from the western (‘B’) haplogroup. We amplified fragments of the control region sequences using three primer pairs Table . PCR was98 using the on-line version of blastn at the National Center for Biotechnology Information (NCBI). Finally, our sequences were aligned into the dataset published by Nittinger et al.26 available in GenBank by eye using BioEdit v.7.2.599. We adapted the nomenclature of haplotypes and haplotype groups of the above work. Our sequences were assigned to the published haplotypes by including them in parsimony-based haplotype network building using TCS v.1.21100 with default parameters. The resulting network was visually improved using the web-based version of tcsBU101.The PCR products were examined by gel electrophoresis on 2% agarose gel and were directly sequenced in both directions with ABI 3500 sequencer (Thermo Scientific) using the BigDye Termination Cycle Sequencing Kit version 3.1 (Thermo Scientific) following the manufacture’s instruction. Sequences were manually folded by SeqMan . The sequences were checked for their possible “nuclear copies of mitochondrial loci” (NUMT) originFalco biarmicus, was subject to classical ABBA/BABA-test (Patterson’s D-statistics) as implemented in the function CalcPopD of the evobiR v 1.3102 R package. Furthermore, the f3-statistics was also assessed for the three focal populations using the 3-Population Test as implemented in AdmixTools v. 7.0.240. In order to assess the direction of gene flow, we conducted another analysis in sliding windows as implemented in the script ABBABABAwindows.py from the collection ‘genomics general’  with a focus on the FdM statistics39 that assesses introgression in lack of an outgroup. The sliding windows size was set to use around 100 sites per window in two arrangements: (1) P1: gyrfalcons, P2: Tuvan sakers, P3: Pannonian sakers; (2) P1: gyrfalcons, P2: Pannonian sakers, P3: Tuvan sakers; (3) P1: Tuvan sakers, P2: Pannonian sakers, P3: gyrfalcons. As the FdM statistics is positive when there is gene flow between P2 and P3 and negative when there is gene flow between P1 and P3, the visualization of FdM values along the scaffolds inform us about the direction of gene flow.Two datasets were used to test for significant deviation from an allelic distribution resulting from random allele sorting. The dataset generated with the inclusion of an explicit outgroup, https://birdnet.org/), which was strictly kept during sampling.Handling of wild birds for sampling and actual sampling was conducted under the permissions of the respective authorities  using protocols and methods approved by the respective authorities . The issuances of the above permissions includes the pre-requisite to handle wild birds for sampling using methods and protocols in accordance with international ethical standards of use of wilds birds in research (Supplementary Information.


Falco peregrinus), a cosmopolitan circumpolar species with 19 named subspecies. We used whole‐genome resequencing to generate over two million single nucleotide polymorphisms (SNPs) from multiple individuals of all peregrine falcon subspecies. Our analyses revealed extensive variation among subspecies, with many island‐restricted and nonmigratory populations possessing lower overall genomic diversity, elevated inbreeding coefficients (FROH)—among the highest reported, and extensive runs of homozygosity (ROH) compared to mainland and migratory populations. Similarly, the majority of subspecies that are either nonmigratory or restricted to islands show a much longer history of low effective population size (Ne). While mutational load analyses indicated an increased proportion of homozygous‐derived deleterious variants  among nonmigrant and island populations compared to those that are migrant or reside on the mainland, no significant differences in the proportion of heterozygous deleterious variants  was observed. Our results provide evidence that high levels of inbreeding may not be an existential threat for some populations or taxa. Additional factors such as the timing and severity of population declines are important to consider in management decisions about extinction potential.In efforts to prevent extinction, resource managers are often tasked with increasing genetic diversity in a population of concern to prevent inbreeding depression or improve adaptive potential in a changing environment. The assumption that all small populations require measures to increase their genetic diversity may be unwarranted, and limited resources for conservation may be better utilized elsewhere. We test this assumption in a case study focused on the peregrine falcon ( By exploring genomic diversity among all named peregrine falcon subspecies, we identified a pattern showing reduced diversity and higher inbreeding levels and runs of homozygosity among island and nonmigratory compared to mainland and migratory subspecies. These patterns are consistent with the effects of long‐term small population size among island and nonmigratory populations, which was further supported by PSMC analyses. Additional analyses focused on mutational load supported these conclusions, and further highlights that high inbreeding levels may not necessarily be of immediate concern and that other factors such as habitat loss and other human‐related activities may be of greater importance in influencing population persistence depending on its demographic history. Further, to what degree low genome‐wide diversity and high inbreeding levels corresponds with increased mutational load  is an important consideration because a generalized measure of genetic diversity may not provide the necessary information to infer a populations' level of fitness or likelihood of experiencing inbreeding depression , and others include long‐distance migrants with large geographic distributions throughout the Arctic during their breeding season . In addition, multiple subspecies possess both island and mainland populations  or varying levels of migratory behavior depending on local environmental conditions and resource availability  were generated from libraries prepared using the Illumina Nextera DNA Flex Library Preparation Kit and sequenced at ~10× average depth of coverage on two S4 150PE Flow Cell lanes on an Illumina NovaSeq 6000 platform at North Texas Genome Center at the University of Texas in Arlington. Sequence reads for F. p. japonensis (n = 2) were prepared using New England BioLabs Next Ultra DNA Library Prep Kit and generated (~4.5× depth) on an Illumina MiSeq platform using the MiSeq Reagent Kit v3 at Nagoya City University, Japan, while libraries for seven museum samples  were prepared following Meyer and Kircher (The majority of raw sequence reads from the contemporary samples ( Kircher  and thenF. p. peregrinus (n = 14) and F. p. calidus (n = 9) were obtained from GenBank (accession number PRJNA686418), including a subset of samples identified as “F. p. [harterti]” (n = 8) from northeast Russia, which has been grouped with F. p. calidus or F. p. japonensis depending on the authority  or generated as described above .To obtain whole‐genome sequence data from all 19 peregrine falcon subspecies, additional raw sequence reads for https://github.com/jstjohn/SeqPrep) and Cutadapt  had low average depth of coverage (<3×) and were excluded from further analysis.All raw sequence reads were processed using standard bioinformatic tools and filtering criteria when working with contemporary and museum sample whole‐genome resequencing data. Specifically, adapters were removed using SeqPrep , remapped to the reference genome using BWA‐ALN, and base quality scores were rescaled using mapDamage. The rescaled BAM files generated for the museum samples were used in all subsequent analyses.All generated BAM files from the merged museum sample files were processed using mapDamage v.2.1.1 , mapped reads were realigned to minimize mismatched bases using RealignerTargetCreator and IndelRealigner with GATK v.3.8  partitioned as intergenic dataset .Prior to further analysis, we partitioned the resulting filtered SNP dataset into coding (genic) and noncoding (intergenic) VCF files using the functionally annotated assembly and then separated into autosomal (chromosomes 1–18) and Z chromosome datasets. SNPs were then pruned for linkage disequilibrium for each dataset using BCFtools v.1.6 , F. p. pelegrinoides (Canary Islands and Israel), F. p. anatum (Alaska/Canada and western United States), F. p. pealei (Aleutian Islands and SE Alaska), F. p. cassini , and F. p. peregrinator . All remaining individual samples were grouped according to subspecies.A species tree was also reconstructed under the coalescent module using SVDThe SVDQ analyses were run as implemented in PAUP* v.4.0a  were calculated based on the correlation between uniting gametes using Genome‐wide Complex Trait Analysis v.1.93.0  and spatial distributions (island vs. mainland), we calculated the inbreeding coefficient and the proportion of SNPs that were heterozygous for each sample. The proportion of total SNPs identified as heterozygous for each sample was calculated using BCFtools v1.6, and individual inbreeding coefficients  were calculated for each sample using BCFtools with option ‐G 30 and the filtered autosomal dataset including chromosomes 1–18 and both genic and intergenic regions. We then estimated F2.5To determine the effects of the SNP variants found across falcon population samples, we performed effect prediction using SnpEff v.4  because of the increased likelihood of inbreeding and the elimination of recessive variants by purifying selection when exposed as homozygotes  between each grouping category under the assumption that a higher homozygous value equates to a comparably higher drift load, whereas a lower heterozygous value represents a correspondingly lower inbreeding load, or a reduced risk of future inbreeding depression, as expected for small long‐term Ne.We normalized each measure by dividing by the total number of called genotypes observed for each individual. This helped control for differences in depth of coverage and missing genotypes among samples. Similar to the diversity measure comparisons, we used the two‐tailed Mann–Whitney 2.6Ne) among sampled subspecies. Consensus sequences were generated for autosomal chromosomes 1–18 using the previously filtered VCF datafiles including both genic and intergenic biallelic sites using GATK v.3.8 FastaAlternateReferenceMaker. The number of iterations (N) was set to 25, t (Tmax) was 15 and p  was 64 (4 + 25*2 + 4 + 6) with 100 bootstrap replicates for each run. We used the mutation rate of 1.65 × 10−9 per year and a generation time of 6 years . Because the accuracy of PSMC for estimating Ne for the most recent time points is low, often resulting in a noisy signal due to a lower number of coalescent events, we excluded the first four most recent time points from our calculations of mean Ne for each sampled individual  to 0.181 ± 0.003 (n = 9) in F. p. nesiotes and F. p. calidus, respectively  and nonmigratory  populations significantly lower than mainland  and migratory  populations . A similar pattern was observed with individual inbreeding coefficients (FUNI), which ranged from 0.044 ± 0.012 (n = 9) to 0.647 (n = 1) in F. p. calidus and F. p. radama, respectively .Genome‐diversity measures were consistently lower among both island and nonmigratory groupings of subspecies compared to mainland and migratory groups, respectively Figures , 3s. They Figure , with avy Figure , with isF. p. pealei and F. p. nesiotes) and mainland  populations , the island populations generally had lower heterozygosity and higher inbreeding coefficients  and ≥2 Mb  or island populations  and among island and mainland populations .Variation in both homozygous‐ and heterozygous‐derived variants identified as either HIGH Impact or LOF was observed among subspecies Figure . In gene3.4Ne over the last one million years compared to mainland  and migratory  subspecies, respectively  and negatively correlated with both FUNI  and proportion of ROH segments ≥100 kb  and ≥2 Mb  for individuals belonging to either migrant or nonmigrant  and mainland or island  populations  and mainland or island  populations  or even between island populations of the same subspecies , far exceeding those obtained from multiple endangered avian and mammalian species reported elsewhere based on the same metric , F. p. cassini from South America (Chile), and F. p. macropus from Australia  tend to have higher mutational load in the form of inbreeding load compared to those with long‐term small Ne. Because deleterious alleles are relatively rare within populations with large Ne, they are less frequently expressed and remain as heterozygotes allowing the population to maintain its mutational load assuming random mating. In contrast, those populations that have existed at small Ne where inbreeding has been more common for long periods of time have had the opportunity to purge their inbreeding load by exposing deleterious alleles to selection and thereby reduce their frequency within the population. However, this pattern is based on only those populations that have survived and therefore currently observable, with an unknown number of other small undocumented populations having gone extinct  possesses will depend on its demographic history , and undoubtedly long‐term persistence will depend on their ability to meet those challenges  and almost 4× the level of inbreeding (FUNI = 0.525 ± 0.063) compared to the average estimate from mainland subspecies , with the latter population possessing a much higher proportion of segments ≥2 Mb  on Steward Island in New Zealand compared to its mainland populations following recent decline due to introduced predators  and Iwo Islands (F. p. furuitii), as both subspecies possess a relatively high proportion of ROH segments ≥2 Mb in size , individuals sampled from islands generally had reduced heterozygosity  and lowest heterozygosity (h = 0.057 ± 0.007) compared to all other mainland populations . In general, avian natal dispersal distances tend to be greater in migratory species than in closely‐related resident species  from Israel had levels similar to other sampled migratory mainland subspecies .Considerable overlap was observed among subspecies classified as either island or mainland and migratory or nonmigratory subspecies, rendering it difficult to discern which life‐history trait may influence genomic diversity specifically. Of the nine mainland subspecies that we were able to classify as possessing either migratory or nonmigratory behavior, only three were non‐migratory. Of those three nonmigratory mainland subspecies, both Australian subspecies, Ne being similar between its island and mainland populations and likely a reflection of their recent shared ancestry. While Australian peregrines  are thought to be fairly common ranging from 3000 to 5000 pairs  genes, which in most vertebrates is highly polymorphic due to balancing selection and its important role in mounting an adaptive immune response to exposed pathogens ; data curation (lead); formal analysis (lead); funding acquisition ; investigation (lead); methodology (lead); project administration (lead); resources (lead); software (lead); supervision (lead); validation (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead). Giridhar Athrey: Data curation (supporting); formal analysis ; methodology ; software (supporting); validation ; writing – review and editing (supporting). Clifford M. Anderson: Conceptualization (supporting); funding acquisition ; investigation (supporting); resources ; writing – review and editing (supporting). Douglas A. Bell: Resources ; writing – review and editing (supporting). Andrew Dixon: Resources ; writing – review and editing (supporting). Yoshinori Kumazawa: Resources (supporting); writing – review and editing (supporting). Tom Maechtle: Resources . Garrett W. Meeks: Investigation (supporting); methodology (supporting); writing – review and editing (supporting). David Mindell: Resources (supporting); writing – review and editing (supporting). Keiya Nakajima: Funding acquisition (supporting); resources ; writing – review and editing (supporting). Ben Novak: Conceptualization ; funding acquisition ; investigation (supporting); project administration (supporting); resources (supporting); writing – review and editing (supporting). Sandra Talbot: Resources ; writing – review and editing (supporting). Clayton White: Resources ; writing – review and editing (supporting). Xiangjiang Zhan: Resources (supporting); writing – review and editing (supporting).The authors declare no conflict of interest.A research collaboration was created with scientists from countries providing samples for this study and all collaborators are included as co‐authors. This research addresses a priority concern by being the first study to include genomic samples from all named peregrine falcon subspecies with results on their evolutionary relationships.Data S1.Click here for additional data file.


Fortunella hindsii) using genomic, geographical, environmental, and phenotypic data, and forward simulations. Genome resequencing data from 73 accessions from the Fortunella genus were combined to investigate population structure, demography, inbreeding, introgression, and genetic load. Population structure was correlated with reproductive type  and with a significant differentiation within the sexually reproducing population. The effective population size for one of the sexually reproducing subpopulations has recently declined to ~1,000, resulting in high levels of inbreeding. In particular, we found that 58% of the ecological niche overlapped between wild and cultivated populations and that there was extensive introgression into wild samples from cultivated populations. Interestingly, the introgression pattern and accumulation of genetic load may be influenced by the type of reproduction. In wild apomictic samples, the introgressed regions were primarily heterozygous, and genome-wide deleterious variants were hidden in the heterozygous state. In contrast, wild sexually reproducing samples carried a higher recessive deleterious burden. Furthermore, we also found that sexually reproducing samples were self-incompatible, which prevented the reduction of genetic diversity by selfing. Our population genomic analyses provide specific recommendations for distinct reproductive types and monitoring during conservation. This study highlights the genomic landscape of a wild relative of citrus and provides recommendations for the conservation of crop wild relatives.Conservation of crop wild relatives is critical for plant breeding and food security. The lack of clarity on the genetic factors that lead to endangered status or extinction create difficulties when attempting to develop concrete recommendations for conserving a citrus wild relative: the wild relatives of crops. Here, we evaluate the conservation of wild kumquat ( Fortunella hindsii, using a combination of genomic, geographical, environmental, and phenotypic data, as well as forward simulations. Fortunella hindsii, listed on the registry of National Key Protected Wild Plants in China, has two types of reproduction, sexual and apomictic. Conservation genomics provided insights into the genetic diversity and structure, which are critical for developing effective conservation strategies. Our analysis also helped to assess the risks of hybridization and introgression from cultivated to wild populations. We found that different patterns of introgression and genetic load may be influenced by reproductive type; for example, deleterious variants may hide in the heterozygous state in apomictic populations. Sexually reproducing samples with the self-incompatibility mechanism can prevent rapid loss of genetic diversity caused by selfing. This study serves as an example of conservation genomics and the importance of utilizing important wild relatives of crops to inform broader conservation efforts.Conservation genomics offers a comprehensive approach to understand the underlying genetic and environmental factors affecting the conservation of species. Despite its importance, the conservation genomics of most crop wild relatives remains poorly understood. In this study, we investigated the population fragmentation, inbreeding, gene flow, and genetic load of a citrus wild relative,  Crop wild relatives play a crucial role in crop breeding and food security because they frequently contain beneficial traits absent in cultivars [de novo domestication of rice from a wild allotetraploid rice [Ostrya rehderiana [The intensive farming of domesticated crops has led to the dramatic decline and fragmentation of populations of crop wild progenitors  and, somultivars . For exaultivars . Evidencoid rice . The conoid rice . Howeveroid rice , wolves oid rice , and thehderiana . ConservNe)) leads to the reduction of genetic diversity [First, genomic data can be used to gain insight into stochastic demographic and environmental processes and to elucidate genetic components, such as the effects of inbreeding and genetic load in a small population . The deciversity  and in tiversity . At the iversity . Therefoiversity . Althougiversity .Ne is small in an endangered population. Therefore, genetic drift is expected to increase the frequency of disadvantageous alleles as introgression continues [Second, genomic analysis can track uncontrolled gene flow from cultivated to wild populations, a major concern for conservation . Introgrontinues . Furtherontinues ,22, espeontinues . MoreoveCitrus and Fortunella. Changes in the environment led to extreme declines in Citrus populations. Indeed, some wild relatives now exist only as individual trees [Fortunella still exist [Fortunella includes cultivated kumquat (F. crassifolia and F. japonica) and wild kumquat  [F. hindsii is distributed across Guangdong, Fujian, Zhejiang, and Jiangxi provinces in China, south of the Nanling Mountains [F. hindsii was listed as endangered on the List of National Key Protected Wild Plants in China (https://www.forestry.gov.cn/). The two types of reproduction found in various citrus varieties  are found in both the wild and cultivated populations of Fortunella [Fortunella (commonly known as kumquat) as a genetic system to study the conservation of citrus wild relatives. We collected short reads from 73 accessions from the Fortunella genus to evaluate population structure, demography and inbreeding. Furthermore, these data were combined with biogeographical data to investigate the ecological niche overlap, introgression, and genetic load. We considered the different reproductive types in our genomic conservation analyses and practices of wild Fortunella. Our study aims to answer five questions: 1) Is there a clear divergence in the ecological niche occupied by wild and cultivated populations? 2) How much genetic diversity is present in wild populations, and how do levels of inbreeding and population demography impact genetic diversity? 3) Does genetic evidence support the ongoing gene flow between wild and cultivated populations? If gene flow has continued, what are the genetic effects at the genomic level? 4) Two different reproductive types, apomixis and sexual reproduction, coexist in wild populations. How do the different reproductive types impact the deleterious load in the endangered wild species? 5) Can insight from population genomics help provide concrete recommendations for wild citrus species conservation practices?Citrus is an important perennial fruit crop. Multiple closely-related but distinct cultigens of citrus are grown in more than 140 countries around the world . The tenal trees . In contll exist . This sikumquat) . F. hindountains . Phylogeountains ,28,29. Rrtunella . Facultartunella . This lertunella . AlthougFortunella conservation, we constructed the phylogeny of Citrinae and highlighted the reproductive types and wild populations and individuals  and fruorldClim ). To out methods . The hea methods  and the  methods . AlthougFortunella, short-read sequencing from 73 accessions with approximately 30-fold coverage were collected, including 14 newly sequenced wild F. hindsii individuals  were used as an outgroup in our genomic analysis. Fortunella samples were identified that perform two different types of reproduction (see Fortunella population was divided into five groups: cultivated apomicts (CULAPO), cultivated sexual (CULSEX), wild apomicts (WILDAPO), wild sexual subpopulation1 (WILDSEX1) and wild sexual subpopulation2 (WILDSEX2). We found a high level of differentiation (Fst) between WILDSEX1 and WILDSEX2 groups (Fst = 0.1332) and that was striking higher than the differentiation compared with the WILDAPO group   Figs  and S12.Ne declined to ~1,000 at 1,000 generations before present (Ne are prone to inbreeding because mating among close relatives becomes unavoidable in populations with finite sizes [π) and Tajima’s D value in wild and cultivated kumquats  based on three groups (samples) comparisons and assuming that the outgroup was the primitive citrus Atalantia buxfoliata  trees and the species ML tree see . We foungression . We foungression . Some vagression . Furthergression . As a poTo elucidate the demographic histories and selection patterns that drive the apomictic and sexually reproducing groups toward the most well adapted state, we estimated the genome heterozygosity of five groups. The results provide evidence that the apomictic group had higher rates of heterozygosity relative to the sexually reproducing wild groups . AlthougDeleterious alleles were defined as variants that potentially determined fitness . TherefoAlthough the apomictic wild group accumulated the most heterozygosity and heterozygous deleterious mutations, the question remained whether apomicts prevent the loss of fitness caused by deleterious homozygous alleles in leaky sexual reproduction . The happ < 0.01) than the standard neutral model (SNM) and was protected against loss of genetic diversity relative to the selfing population  is common in citrus . SI promsexually . Furtherrtunella . The resrtunella . In contOur goal was to explore the ecological niche competition, high level of inbreeding and uncontrolled gene flow in the endangered wild kumquat. We found that decreased effective population size and high levels of inbreeding in wild kumquats that reproduce sexually because of population fragmentation . At the Access to genomic data has recently opened up new avenues for conservation research . Our stuNatural selection, genetic drift, gene flow and reproductive patterns can shape the genetic load patterns in a finite population . We demoGenomic flatlining refers to the reduced genetic diversity of endangered species and obviously depends on population size and selfing rates . GenomicS-locus polymorphisms  based on geographical information and classification based on the reproductive systems. Based on our conclusions from genomic data, we propose to set up conservation management units based on the reproductive patterns . To prevFortunella. This included 19 accessions of cultivated kumquats and 54 accessions of wild kumquats. There are two different reproductive types in wild Fortunella samples. The cultivars  were collected from five major production areas and related provinces including Guangxi, Hunan, Jiangxi, Fujian and Zhejiang.Apomixis contributed to the offspring with identical genotypes as the maternal lineage. Therefore, the wild apomictic samples derived from the nuclear adventitious embryonic pathway were excluded from our conservation genomic analyses. Only one sample was maintained from each somatic lineage. Therefore, a total of 73 diploid samples were collected to represent the diversity of Apomictic samples of wild kumquats were distributed over a broad geographic range. In contrast, the sexually reproducing wild samples occurred as small and isolated populations. To ensure representative genetic diversity in local areas, we collected sexually reproducing samples with a radius of at least 500m during the field investigation. For apomicts, collected 1–2 samples in each local region to avoid somatic samples. The 24 apomictic wild kumquats were identified and collected from Guangdong, Jiangxi, Hunan, Fujian and Zhejiang Provinces ,28. The Atalantia buxifolia is a kind of primitive citrus. A total of 15 previously published short read sequences were downloaded from the NCBI database. Those reads were used as an outgroup in this study. The outgroup genotypes were used in phylogenies, introgression statistics and genetic load analyses. A phylogeny of 13 citrus species was constructed based on the variation map generated by Wang et al. [https://zenodo.org/record/574866 2#.Y3YUqcdBxaY).In addition, g et al.  . The earliest collection was recorded in 1935. The original and complete records were maintained including records for 244 wild and 152 cultivated samples (https://worldclim.org/data/bioclim.html). All 19 variables were used for a principal component analysis (PCA) of the 396 sites of cultivated and wild Fortunella. In addition, we used least absolute shrinkage and selection operator (LASSO) regression to select variables that are the most informative with respect to the distribution of wild and cultivated populations using the Glmnet v4.1.4 package in R [We collected 396 distribution records for  samples . The geoage in R . The genage in R . Finallyage in R  with thrage in R  with 100Fortunella and 15 accessions from Atalantia buxifolia were mapped to the Hongkong kumquat chromosome-level reference genome using BWA-MEM v0.7.17 (https://github.com/lh3/bwa). Subsequently, we removed the adapter sequences using the Fastp program [https://github.com/broadinstitute/picard). Finally, genotype information was obtained using binary alignment and map (BAM) files with Deepvariant (rc1.0.0) and the default settings [Fortunella samples for downstream analyses.Whole genomic DNA from 14 newly collected wild kumquats was extracted from fresh young leaves. The quality of the DNA was checked using pulsed-field gel electrophoresis. Approximately 12 Gb (30-fold coverage) of paired-end short reads were generated for each sample using the Illumina NovaSeq 6000 platform. The short reads of 73 accessions from  program . The seq program  and Picasettings . The 88 settings . To obtahttp://tree.bio.ed.ac.uk/software/figtree). The PCA analysis was performed using the PLINK program [K, from 2 to 5 with fivefold cross-validation (—cv  =  5). Notably, genotype information from the outgroup was used only as a root in the phylogeny. In contrast, the PCA analysis and ancestry composition estimation excluded outgroup samples. The consensus results from those analyses were used to infer the population structure in the Fortunella genus. Our analysis identified two accessions that recently changed their mode of reproduction, BLS07 and LT01. These two accessions were highlighted and excluded from the downstream analyses. Finally, we defined five groups  that include 71 accessions of Fortunella based on population structure. Finally, the linkage disequilibrium (LD) decay of each group was determined using the variation map.The variation map was first used to investigate population structure based on a maximum-likelihood (ML) phylogenetic tree, PCA analysis and ancestry composition estimation using SNPs from the entire genome. A ML phylogeny was constructed using IQ-TREE version 2.0  with 100 program . Then, t program , evaluatFst) and divergence (Dxy), were also investigated using this Python script [Tajima’s D value was circulated using 100-kb windows in each group using the VCFtools program [Furthermore, we calculated the nucleotide diversity (π) based on a 25-kb window (examined by LD decay) in each group using the Python script popgenWindows.py described by Martin et al. . The pain script . Meanwhi program .The genome-wide runs of homozygosity (ROHs) were detected using PLINK  with the−8 per site [Fortunella. Genome regions were masked when the coverage depth was < 15 after reads with mapping quality < 20 removed to improve reliability. We phased the variations in the diploid genomes to obtain haplotype information using Beagle v5.0 [SMC++  was usedper site  per genegle v5.0 . Furthergle v5.0 . The resThe recombination rate was estimated using the demography generated from SMC++ and Pyrho . Our anaWe first estimated heterozygosity using the k-mers frequency method and the deep sequencing data (average 30-fold coverage) from 71 accessions of Fortunella. We counted the occurrences of k-mers based on the paired reads with k-mer sizes of 17, 19, 21, 23, 25 and 27 using Jellyfish v2.3.0 . The k-mFortunella were constructed using a variation map created using Treemix v1.11 [The graph and the migration of the five groups from ix v1.11 . We prepD statistic  to examine gene flow between the test groups [Atalantia buxifolia samples were used as the outgroup to test the topology . The genotypes shared between P2 and P3 were detected, and the statistics were analyzed using a jackknife. To obtain reliable results, different populations were considered as the P1 group. The extensive introgression between the wild (WILDAPO and WILDSEX2) and cultivated populations (CULAPO and CULSEX) were analyzed in six combinations. A topology analysis  was performed to find the most shared homozygous genotype between the WILDAPO and WILDSEX2 groups. In addition, we estimated the D statistic based on BAM files at the individual level using the ANGSD program [D statistics at the population-level. Although the D statistic contributes to the potential migration events between the two test groups, the combination of sister clades appeared to discriminate between single events and independent migration events in related clusters. Therefore, we calculated the fb statistic using the Dsuite program and the topology generated from allele frequencies based on whole genome SNPs [fb value indicates the proportion of shared genotypes between the two test groups. The potentially independent migration events were detected in the internal clade of wild populations. The fb value calculated using WILDAPO and WILDSEX2 indicated that they were the most similar genotypes and was used as a positive control.In addition to estimating introgression among the five groups based on allele frequencies, we incorporated Patterson’s t groups . We dete program . The topome SNPs . The fb https://github.com/simonhmartin/twisst). Genome-wide subtrees were generated using a 25-kb window and IQ-TREE version 2.0 with 1000 ultrafast bootstrap replicates and the ‘GTR + I+G’ model. To obtain reliable results, we kept the subtrees with more than 500 variations in each window. Our analyses are consistent with sexually reproducing and apomictic cultivars contributing to the WILDAPO and WILDSEX2 groups. Therefore, we created a CUL group by merging the CULAPO and CULSEX samples to detect uncoordinated topologies from comparisons of the wild and cultivated populations. In addition, there were no introgressed signals in the sexually reproducing WILDSEX1 group. The two combinations  and  were also analyzed to investigate genome-wide fd statistics using a 25-kb window [The monophyletic nature of the five groups results from extensive introgression and hybridization and creates difficulties when trying to represent the evolutionary history of the five groups. To resolve this problem, we used the topology weightings method to examine the incoordination between the genome-wide phylogenetic tree and the local tree using Twisst  to distinguish cultivated and wild populations as described by Wu et al. . We idenAtalantia buxifolia samples. We created genomic databases using the SIFT 4G Annotator (https://github.com/paulineng/SIFT4G_Create_Genomic_DB). Based on the SIFT annotation database, an amino acid substitution was predicted to be deleterious if the score was ≤0.05 and tolerated if the score was >0.05. We counted the number of deleterious, heterozygous and homozygous deleterious alleles in each individual in the five groups as recommended by Zhou et al. [We estimated the genome-wide genetic load as the number of deleterious mutations in the genome . The genu et al. . Howeveru et al. . In addiu et al. . Becauseu et al. .https://github.com/isaacovercast/easySFS).The site frequency spectrum (SFS) was calculated based on the deleterious mutation dataset. Our primary aim was to study the conservation of wild Fortunella and to determine whether domestication might influence the SFS pattern of cultivated populations. Therefore, we compared the SFS patterns of deleterious mutations in the three groups  to study the effects of reproductive patterns in wild kumquats. The frequencies of each group were classified into ten clusters, excluding the reference , using the easySFS program  haplotype phylogeny of local ML trees (apomictic determining region) constructed with 71 accessions from the five groups and 2) the haplotype divergence analysis of the two haplotypes in each individual in the WILDAPO group.https://github.com/edgardomortiz/vcf2phylip). Then, we constructed the haplotype phylogeny of the apomictic region using IQ-TREE version 2.0 with 1000 ultrafast bootstrap replicates and the ‘GTR + I+G’ model. The haplotypes that did not cluster with the WILDAPO group were highlighted and named hap2. Neither haplotype from the wild apomictic variety DB clustered with WILDAPO. These haplotypes were highlighted and named DB hap1 and DB hap2.The genomic region that determines apomixis was identified by Wang et al. . Given tBesides, we calculated the haplotype divergence of each apomictic wild sample using a 25-kb window using two steps. 1) We prepared the phased haplotypic VCF file for each window. 2) We calculated the genetic distance of each haplotype using the VCF2Dis program. Therefore, the genetic distance represents the divergence of each haplotype in the window. To compare the distances of the windows, we normalized the genetic distance by introducing the mean value of each window as a background. For example, the value of NQ03 Hap1-Hap2 represents the normalized distance between the two haplotypes of the apomictic variety NQ03 in each window. On the other hand, the value of Hap2-WILDSEX1 represents the average distance between Hap2  from NQ03 and the haplotypes from the samples in WILDSEX1, which was calculated to evaluate the possibility of outcrossing during leaky sexual reproduction in the apomictic variety NQ03.S-RNases using the BWA-MEM program. Using this alignment, the eight S-RNase genotypes reported for citrus were identified in the WILDSEX1 kumquat population.To characterize the outcrossing mechanism in the wild sexually reproducing kumquat, we tested for self-incompatibility in the 13 wild sexually reproducing varieties including nine samples from the WILDSEX1 group and four samples from the WILDSEX2 group . Additionally, a cross-pollination experiment was performed with the apomictic variety DB and the sexually reproducing varieties. The cross-pollination and self-pollination experiments were performed 1 d before anthesis. Five d after pollination, pistils were excised and fixed in a mixture of alcohol and acetic acid (4:1). The growth of the pollen tubes within the style was observed using the aniline blue fluorescence staining method. These samples were maintained in a garden by grafting and thus, the self-pollination experiments could be strictly controlled. These newly collected samples have yet to bloom. In addition, 17 known alleles of S-RNase genes in citrus were identified previously . To idenS-locus. The S-locus has been undergone balancing selection [The pollination experiments have been performed only in four samples from the WILDSEX2 group. Therefore, to obtain a big picture for the pattern of self-incompatibility in Fortunella, we constructed a local phylogeny to investigate the genetic features of the S-locus using IQ-TREE version 2.0 with 1000 ultrafast bootstrap replicates and the ‘GTR + I+G’ model. A total of 71 accessions from five groups were used to construct the phylogenetic tree for the election . Our refN = 1,000 individuals that were run for 10*N generations to reach an equilibrium. We then introduced a 0.25*N size bottleneck at generation 10,000 until generation 2,000. For the subpopulations, we assumed two types of mating systems: outcrossing and selfing. We introduced mutations into the simulation that could have one of three different effects on fitness based on the simulated genomic region. (1) ‘Neutral’: all mutations within the coding region are neutral (s = 0). (2) ‘Deleterious’: recessive mutations that are deleterious are present in the populations. The effect on fitness is drawn from a gamma distribution (DFE) [h = 0). All simulated genomic regions had a length of 1 Mb. We used the length of the exons from chromosome 1 in the kumquat reference genome. The per base pair mutation rate was fixed at 2.2e-8 [We used the SLiM  softwareon (DFE)  Click here for additional data file.S2 FigThe cultivated kumquat is a kind of intensive farming with uniform genotype by grafting. There are a series of agricultural technologies in this process.(TIF)Click here for additional data file.S3 FigBioclimatic variables were derived from the monthly temperature and rainfall values to generate more biologically meaningful variables. The bioclimatic variables represented annual trends, seasonality and extreme or limiting environmental factors. The bioclimatic matrix was generated based on the 396 geographical data of Fortunella genus.(TIF)Click here for additional data file.S4 FigThe PCA analysis was constructed based on bioclimatic matrix of the 396 geographical data and used to investigate the most informative variables related to the distribution of wild and cultivated samples. PC1 represented the first principal component with interpretation of 47.5% variations. PC2 represented the second principal component with interpretation of 20.6% variations. The wild and cultivated populations were distinguished with different color. Each characteristic influences a principal component is highlighted.(TIF)Click here for additional data file.S5 FigFortunella were calculated using 19 environmental variables. (A) The distribution of overlap probability that an individual from cultivated population was found in the niche region of the wild population. (B) The distribution of overlap probability that an individual from wild population was found in the niche region of domesticated population. The solid line showed the average of niche overlap probability and the dashed lines showed 95% probability of niche overlap probability.Niche overlap was calculated as the probability that an individual from species A is found in the niche region of species B. The niche regions and pairwise niche overlap of wild and cultivated populations in (TIF)Click here for additional data file.S6 FigFortunella genus was used to predict using the Glmnet program. (A) The cross-validated fit for selected values of lambda . (B) The coefficients curve corresponds to variables were plotted. The best fitting was highlighted. The y-axis indicated the number of coefficients at the current lambda.The bioclimatic matrix of (TIF)Click here for additional data file.S7 Fighttps://www.tianditu.gov.cn/. The base layer is under CC BY 4.0 license.The 19 bioclimatic variables were used for distribution modeling. The heatmap showed the predicted distribution of wild and domesticated populations using Generally Linear Model (GLM) and Generalized Boosting Model (GBM) models, respectively. The color presented the probability of prediction, and the resolution is 5 min. The base layer of the map from Tianditu, the National Platform for Common Geospatial Information Services (NPCGIS) (TIF)Click here for additional data file.S8 Fighttps://www.tianditu.gov.cn/. The base layer is under CC BY 4.0 license.The heatmap showed the predicted distribution with default parameters and generated with 100 iterations. (A) The predicted distribution of wild kumquats. (B) The predicted distribution of cultivated kumquats. The base layer of the map from Tianditu, the National Platform for Common Geospatial Information Services (NPCGIS) (TIF)Click here for additional data file.S9 FigFortunella using PLINK. The samples from different groups were distinguished with different color.The pairwise IBD values were calculated based on genome-wide variations of the 54 sequenced samples in (TIF)Click here for additional data file.S10 Figπ) of the WILDAPO, WILDSEX1 and WILDSEX2 groups were calculated based on the variation map. Those statistics were calculated based on 25 kb non-overlapping windows.The genome-wide genetic diversity ((TIF)Click here for additional data file.S11 FigThe genome-wide differentiation (Fst) was performed between three combinations: between WILDAPO and WILDSEX1 groups, between WILDAPO and WILDSEX2 groups, and between WILDSEX1 and WILDSEX2 groups. Those statistics were calculated based on 25 kb non-overlapping windows.(TIF)Click here for additional data file.S12 FigThe genome-wide divergence (Dxy) was performed between three combinations: between WILDAPO and WILDSEX1 groups, between WILDAPO and WILDSEX2 groups, and between WILDSEX1 and WILDSEX2 groups. Those statistics were calculated based on 25 kb non-overlapping windows.(TIF)Click here for additional data file.S13 FigThe x-axis indicated the number of ROHs, while the y-axis indicates the length of ROHs in the genome. The samples from different groups were distinguished with different color.(TIF)Click here for additional data file.S14 FigTajima’s D of the CULAPO, CULSEX, WILDAPO, WILDSEX1 and WILDSEX2 groups are calculated based on the variation map. Those statistics were calculated based on 25 kb non-overlapping windows.The genome-wide (TIF)Click here for additional data file.S15 Fig(A) The allele frequencies of five groups were used to estimate the graph structure and the potential introgression events with the parameter m = 3. (B) The pair wise residuals of the graph modeling were calculated based on the genome-wide allele frequencies in five groups.(TIF)Click here for additional data file.S16 FigAtalantia buxifolia samples were used as the outgroup, while the 11 samples from the WILDSEX1 group were used as the sister clade. The samples from WILDAPO and WILDSEX2 were distinguished with different color. The two groups of CULAPO and CULSEX were combined as the CUL group.The D statistic was performed based on combinations of  or  at individual-level using the ANGSD program. The 15 samples (TIF)Click here for additional data file.S17 FigThe sequences were generated from the species-specific variations, and the network phylogeny was constructed using the SplitTree program with the default parameters.(TIF)Click here for additional data file.S18 FigThe statistics were performed using the population-level variation map based on two combinations  and . The two groups of CULAPO and CULSEX were combined as the CUL group.(TIF)Click here for additional data file.S19 FigThe heatmap shows heterozygous or homozygous introgressed fragments in the three samples from WILDAPO  and three samples from WILDSEX2 . The x-axis indicated the genome reference of nine chromosomes.(TIF)Click here for additional data file.S20 FigThe sSNPs and dSNPs are calculated based on genome-wide variation data set. (A) The uSFS was performed using the variations in 24 wild apomictic samples from the WILDAPO group. (B) The uSFS was performed using the variations in 11 wild sexually reproducing samples from the WILDSEX1 group. (C) The uSFS was performed using the variations in 17 wild sexually reproducing samples from the WILDSEX2 group.(TIF)Click here for additional data file.S21 FigThe samples from different groups are distinguished with different color. The x-axis indicated the individuals ordered based on the numbers.(TIF)Click here for additional data file.S22 FigThere are three potential reproductive patterns in the apomictic wild kumquat. The leaky sexual reproductions were highlighted with dotted lines (Selfing and Outcrossing). Apomixis could keep the heterozygous deleterious alleles in heterozygous state. The outcrossing might keep the heterozygous deleterious alleles in heterozygous state when hybrid with unrelated individuals, whereas the selfing will lead to the deleterious alleles in homozygous state in the next generation. This image is made by the author and under CC BY 4.0 license.(TIF)Click here for additional data file.S23 FigP < 0.01 (Student t-test).The distance of two haplotypes within each sample was calculated based on a 25 kb non-overlap window. The standardized haplotype distance was similar with the pattern of heterozygosity in five groups, supporting the reliability of our analysis. **, (TIF)Click here for additional data file.S24 FigThe distance of two haplotypes within the samples from the WILDAPO group and the distance to haplotypes from the WILDSEX1 group were calculated based on a 25 kb non-overlap window. The Hap1 and Hap2 indicated the two haplotypes from the sample in the WILDAPO group, while the Hap2 represented the haplotype that did not clustered with the WILDAPO group. The Hap1-Hap2 indicated the standardized haplotype distance of two haplotypes. The Hap2-WILDSEX1 indicated the standardized haplotype distance of Hap2 and the haplotypes from the WILDSEX1 group.(TIF)Click here for additional data file.S25 FigThe demographic model used in forward simulation was inferred from the WILDSEX1 group using the SMC++program. Going forward in time, after a burn-in period of 10*N generations (100k generations), the ancestral sexual population splits into two subpopulations with 0.1*N population size, one capable of selfing (selfing rate = 1), one capable of outcrossing (selfing rate = 0) The dashed lines represent the time of demographic shift reproductive types. The parameters were simulated independently with 100 repeats.(TIF)Click here for additional data file.S26 FigThere were two S-alleles in the sexually reproducing sample ‘QLS’ from the WILDSEX2 group. (A) The IGV plot indicated the coverage of short-paired reads in the S9 sequences. The directions of reads were indicated by different colors. (B) The IGV plot indicated the coverage of short-paired reads in the S23 sequences. The directions of reads were indicated by different colors.(TIF)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.S3 Table(XLSX)Click here for additional data file.S4 Table(XLSX)Click here for additional data file.S5 Table(XLSX)Click here for additional data file.S6 Table(XLSX)Click here for additional data file.S7 Table(XLSX)Click here for additional data file.


Phytophthora colocasiae is an important pathogen that causes great economic losses in taro production in tropical and subtropical regions, especially in Japan. Understanding the genetic variations in P. colocasiae populations and their transmission patterns in Japan is essential for effective disease control. Here, the genetic diversity of 358 P. colocasiae isolates, including 348 from Japan, 7 from China, and 3 from Indonesia, was assessed using 11 simple sequence repeat (SSR) primer pairs with high polymorphism. The phylogenetic tree of the SSR locus showed that the isolates from Japan could be divided into 14 groups, with group A being the dominant group. Among foreign isolates, only six from mainland China were similar to those from Japan and clustered in groups B and E. Analysis of molecular variance (AMOVA), principal components analysis (PCA), and cluster analysis (K = 3) results revealed a moderate level of genetic diversity, mainly within individuals. Populations showed high heterozygosity, a lack of regional differentiation, and frequent gene flow. Analysis of mating types and ploidy levels revealed that A2 and self-fertile (SF) A2 types and tetraploids were dominant across populations. Explanations and hypotheses for the results can provide more effective strategies for disease management of taro leaf blight. Phytophthora colocasiae Raciborski is a major oomycete pathogen that threatens the sustainable production of taro (Colocasia esculenta (L.) Schott). Since its discovery in Java in 1900, this pathogen has spread rapidly throughout tropical and subtropical regions, including southeast Asia, many Pacific islands, and portions of Africa and Oceania [ Oceania . The pat Oceania . In Japa Oceania . RecentlPhytophthora colocasiae produces asexual structures, such as zoospores, which can swim and spread in water and infect other host plants [Phytophthora species are thought to be heterothallic [Phytophthora has been increasingly detected, which can reportedly self-fertilize to produce oospores [P. colocasiae has also been confirmed in our previous studies, and results suggest that five mating types are plainly identified in Japan: heterothallic A1 and A2, as well as self-fertile (SF) A1, A2, and A1/A2 [t plants . Additioothallic . Furtheroospores ,8. P. cond A1/A2 . Such reToxicity identification is a traditional method used to study the genetic diversity of pathogen populations , and theP. colocasiae, particularly in Japan, has not been reported. Therefore, this molecular marker technique was used as the main source of genotyping data for this study.SSR markers  are highly mutable and repetitive sequences, commonly used as genetic markers . MicrosaP. colocasiae population genetic analysis, (ii) determining the predominant genotype of P. colocasiae, (iii) calculating the genetic diversity within P. colocasiae populations, and (iv) comprehending the modes of P. colocasiae transmission throughout Japan.Specifically, the purposes of this study included (i) developing trustworthy SSR markers for P. colocasiae were obtained from Japan, China, and Indonesia ). All 35 primer pairs were then tested using a variety of isolates from various geographic locations to choose the primers that consistently amplified the repeat patterns, had the predicted size after electrophoresis, and revealed numerous repetitions by sequencing.The entire genome sequence of t Finder . SSR mar2 and 500 mM KCl), 2.0 µL of 2.5 mM dNTPs, 0.2 µL of 25 µM of each forward and reverse primer, 0.01 mg bovine serum albumin , 0.125 µL rTaq DNA polymerase , 1 µL of template DNA (1 ng/µL), and 15.875 µL ddH2O. The amplification was carried out using a Gene Amp PCR system 2700 thermal cycler  under the following conditions: pre-denaturation at 94 °C for 2 min; and then 35 cycles of denaturation at 94 °C for 30 s, annealing at optimum temperature  and M13Rv (CAGGAAACAGCTATGAC). Approximately 12 effective PCR products were purified utilizing the ExoSAP-IT kit as directed by the manufacturer’s instructions . The purified solutions were cycled once again using a Big Dye Terminator ver 3.1 Cycle Sequencing Kit  under the single primer M13M4, and then ethanol-precipitated in the dark to make the final sequence using an ABI 3100 or ABI 3130 Genetic Analyzer (Applied Biosystems). The frequency and diversity of the repetitive sequences were analyzed to select feasible primers for subsequent genotyping. The process is shown in The remaining PCR products were recovered and used for sequence analysis according to a process outlined by Masanto et al. . The TOPP. colocasiae, listed in In all, 358 isolates of The effective number of alleles, observed heterozygosity (Ho), expected heterozygosity (He), polymorphism information content (PIC), and the analysis of molecular variance (AMOVA) were calculated using PolyGene V1.4 software ,33. The n = 4) and allele frequency. Population structure analysis was performed using the Bayesian clustering method in PolyGene. Each population was divided into subpopulations according to the estimated ploidy level, and K = 1–5 was calculated independently. Then, one of the results for K = 3 was selected manually. Hierarchical clustering of the population of the estimated tetraploid isolates was performed using PolyGene. Finally, a dendrogram was generated using the UPGMA clustering method based on Nei’s standard genetic distances.In the phylogenetic analysis, a neighbor-joining tree with bootstrap values calculated from 1000 replicates was constructed using PAUP* version 4.0a (build 169) previously described by Hattori et al. . The genP. colocasiae was analyzed using Tandem Repeat Finder, and 35 primers were selected to amplify the microsatellite loci. Three isolates from Japan and India with different mating types  were tested, and 11 loci with multiple alleles were selected for subsequent SSR marker studies . A 7 bp tail (GTGTCTT) was added to the reverse primer of the 11 SSR primers to promote 3′ non-templated nucleotide addition, and 358 P. colocasiae isolates . The mean Ho was 0.52, whereas the maximum (1.00) and the minimum (0.004) were found at the GCT_5986 and AGAC_2040 loci, respectively. The GCT_5986 locus had the highest He detection rate (0.75), whereas the AGAC_2040 and GA_106 loci had the lowest . Several loci were polymorphic, with a mean PIC of 0.37, ranging from 0.01 (AGAC_2040 and GA_106) to 0.70 (GCT_5986). The PIC value indicated that the locus was reasonably informative (0.5 < PIC > 0.25) and could provide reliable genetic information for this study  [stepwise mutation model (SMM) genetic distance [To evaluate the differences and contributions of genetic diversity between or among populations, some statistical methods were fully utilized to calculate AMOVA, such as homoploidy, anisoploidy, the maximum likelihood method, and weighted genotypes . Here, wel (IAM)  or the sdistance .ITF = −0.162 and −0.174), a negative level of inbreeding between individuals within populations (ISF = −0.19 and −0.196), and low differentiation among populations (STF = 0.024 and 0.018). The value (0.05 < STF > 0) indicated that there was essentially no genetic variation among populations because as STF values decline, genetic variation between populations becomes less significant [ITF and ISF, and indicated heterozygosity within the population.Our AMOVA results  showed tnificant , while mThe ploidy levels of the individuals were determined based on the maximum and mean number of alleles per locus for all samples and across all loci using the R package “polysat” ,40. An iThe neighbor-joining dendrogram was used to analyze all 358 isolates and grouped the isolates from 7 populations in Japan (except for the isolate from Okinawa) into 14 groups from A–N . Three iIn addition, nine populations containing tetraploid strains were analyzed using the UPGMA clustering method. The results showed that populations from central and northern Japan were consistent, and their genetic relationships were similar to those from southern Japan. However, the populations from Japan are quite different from those from China, especially the population from Taiwan, probably due to the small number and long history of population surveys in Taiwan .P. colocasiae isolates were divided into two major groups. The first and second axis explained 89.11% and 9.69% of the overall variation, respectively, for a total explanation of 98.80%. One group encompassed all isolates from Japan, except the one from Okinawa and the isolates from mainland China. Meanwhile, the other group contained isolates from Indonesia, Okinawa, and Taiwan. The PCA outputs were consistent with those obtained from the phylogenetic tree. Moreover, the other two genetic distances of Bruvo and Lynch were also used for PCA analysis for all isolates, and only the Bruvo distance was calculated to consider the ploidy and allele frequency for tetraploid isolates ). Nevertheless, PIC and gene diversity analyses showed that the P. colocasiae isolates had abundant alleles and gene diversity.With the increasing transmission capacity of ce genes . Therefoophthora ,42. In tch locus ,44. We h50 hosts  and P. hAll isolates were analyzed using the topology of a neighbor-joining tree. The results showed that the isolates from Japan differed from those from Indonesia and Taiwan. It is possible that the isolates obtained from Indonesia and Taiwan were quite old; nevertheless, the isolates collected from Indonesia in 2017 were quite different. These results indicate that gene exchange between Indonesia and Japan has seemingly been limited. In addition, the genotypes of the pathogen changed greatly with time when comparing the isolates from the last and this century. Isolates from Japan were subdivided into 14 groups. Group A (65%) was the dominant genotype and was distributed across all populations under study, showing no sign of decrease from 2014 to 2020 and remaining the predominant group . The genSTF value was lower than 0.05, the largest variation was within populations. Third, genetic distance analysis of all tetraploid strains showed that populations in northern and central Japan were slightly, but not significantly, different from those in southern Japan. This is consistent with the SSR genotyping results. The former genotype was relatively simple, whereas the latter was relatively mixed. Fourth, PCA results revealed that no clusters were developed in Japan, indicating no correlation between genetic characteristics and geographical location. Finally, this conclusion was supported by cluster analysis of all populations with K = 3.Our results indicate that all populations in Japan are genetically similar among them. First, the results of SSR genotyping showed that group A constituted the majority in each population. Second, AMOVA results indicated that the variance within populations was more than 100% and that among populations was negative, and as the Phytophthora pathogens [ITF and ISF, indicating an obvious heterozygote surplus. Thus, although sexual reproduction is possible in Japan, genetic variation within populations is mainly caused by random mutations in asexual reproduction rather than by sexual recombination.Oospores can reportedly contribute to the genetic diversity of athogens . Consistathogens , here, wPhytophthora species have been traditionally considered diploid. To date, the cause of polyploidy has not been clearly explained. Polyploid production is considered long-established rather than random and is thought to be related to environmental conditions, as harsh or disturbed environments are more conducive to adaptation [In addition, our analysis of chromosome ploidy level showed that most of the isolates were tetraploid and only a few were triploid, although aptation . In thisP. colocasiae isolates. Population genetic information showed that the isolates from Japan could be divided into 14 groups, with group A being dominant. A relatively high level of genetic diversity was revealed, mainly within individuals. In addition, there is a lack of regional differentiation in Japanese populations, with frequent gene flow, most likely from the southern to the central and northern regions of the country. The A2 and SF A2 mating types and tetraploids were common in all populations studied. Sexual reproduction and its polyploid characteristics make P. colocasiae more likely to survive in the environment and promote the development of genetic diversity. While the original source of P. colocasiae in Japan was not identified in this study, this article improves the understanding of P. colocasiae gene flow, and provides valuable information for better control of taro leaf blight in Japan.Eleven SSR loci were analyzed in