Aedes albopictus, is a vector of multiple viral diseases. Therefore, it is crucial to understand its distribution and the genetic diversity of Ecuadorian populations. This study used a genetic marker to understand aspects of the dynamics of the Ecuadorian populations of Aedes albopictus from five sites along the coastal, Amazon basin, and Andean lowland regions. Our results evidence two haplotypes within the Ecuadorian populations. Haplotype 1 was found in the coastal regions and Amazon basin, while haplotype 2 was found in the northeastern lowlands. Worldwide, haplotype 1 has been found in 21 countries in temperate and tropical habitats, and haplotype 2 has been found in five countries in tropical habitats. Hence, a difference in adaptation traits could be speculated between both haplotypes. Our study presents a prediction model that shows the suitable habitats for Aedes albopictus in Ecuador. Our results showed that the vector could spread through all country regions, including the Galapagos Islands. Thus, understanding the different aspects of the local populations can help establish better vector surveillance and control strategies.The tiger mosquito, Aedes albopictus, also known as the tiger mosquito, is widespread worldwide across tropical, subtropical, and temperate regions. This insect is associated with the transmission of several vector-borne diseases, and, as such, monitoring its distribution is highly important for public health. In Ecuador, Ae. albopictus was first reported in 2017 in Guayaquil. Since then, the vector has been identified in the Northeastern lowlands and the Amazon basin. This study aims to determine the genetic diversity of Ecuadorian populations of Ae. albopictus through the analysis of the mitochondrial gene COI and to describe the potential distribution areas of this species within the country. The genetic diversity was determined by combining phylogenetic and population genetics analyses of five localities in Ecuador. Results showed two haplotypes in the Ecuadorian populations of Ae. albopictus. Haplotype 1 (H1) was found in the coastal and Amazon individuals, while haplotype 2 (H2) was only found in the three northeastern lowlands sites. In a worldwide context, H1 is the most widespread in 21 countries with temperate and tropical habitats. In contrast, H2 distribution is limited to five countries in tropical regions, suggesting fewer adaptation traits. Our prediction model showed a suitable habitat for Ae. albopictus in all regions  of Ecuador. Hence, understanding different aspects of the vector can help us implement better control strategies for surveillance and vectorial control in Ecuador. Ecuador is located on the northwest coast of South America, and its territory includes the continental mainland and the Galápagos Islands in the Pacific Ocean. The mainland of Ecuador is divided geographically into coastal lowlands in the west of the Andes Mountains, the Andes or highlands, with valleys and mountains with snowed peaks of up to 6300 m, and lowlands on the east of the Andes that are part of the Amazon basin.Aedes albopictus , commonly known as the tiger mosquito, is native to Southeast Asia and is considered one of the most dangerous exotic species [Ae. albopictus is a widespread mosquito species across tropical, subtropical, and temperate regions of the world [Ae. aegypti, Ae. albopictus is classified as highly important in transmitting vector-borne diseases [Dirofilaria immitis, a parasite of veterinary importance [s Skuse, 894, commhe world . Along ws Skuse, 894, commdiseases  and is as Skuse, 894, commdiseases . It is aportance . The widportance .Ae. albopictus was first reported in 2017 in Guayaquil [In Ecuador, Generally, molecular markers such as allozymes, nuclear, or mitochondrial genes are used to study diversity or inter and intraspecies genetic differences . A recenMitochondrial genes, such as COI, are widely used to understand vector insect populations and their molecular, genetic evolution, dispersal patterns, phylogeny, and population dynamics ,12. ThesAe. albopictus in the Ecuadorian territory through the analysis of the mitochondrial gene cytochrome c oxidase subunit 1 (COI), using a combination of phylogenetic and population genetic analyses. Furthermore, we aim to present the potential distribution areas for this species using maximum entropy models to predict suitable areas for its dispersion.This study aims to determine the genetic diversity of Ae. albopictus were collected between 2017 and 2021 in five localities of Ecuador , following the manufacturer’s instructions.Depending on the number of specimens per site, samples were obtained from either legs or the entire body from fresh material or dry specimens. Genomic DNA was extracted using the DNeasy Blood & Tissue Kitc oxidase subunit I (COI) was done with the primers LCO1490 (5′-GGTCAACAAATCATAAAGATATTGG-3′) and HC02198 (5′-TAAACTTCAGGGTGACCAAAAAATCA-3′) [® Safe 10000X. PCR products were sequenced using the Sanger sequencing method at Macrogen, Seoul, South Korea.Amplification of mitochondrial cytochrome ATCA-3′) . Each reWe determined the number of circulating haplotypes in Ecuadorian populations using PopART  and DnaSAe. Albopictus, available in the GenBank-NCBI database. Sequences in this portion of the analyses included individuals from America, Europe, Africa, and Asia. We constructed a preliminary haplotype network using the entire data set (PopArt) to establish the sequences close to the Ecuadorian haplotypes  and Ae. aegypti  were used for family-level outgroup representation. Voucher specimens of this study are deposited in the “National Collection of Arthropod Vectors” at the National Institute of Research in Public Health—Dr. Leopoldo Izquieta Perez (INSPI-Quito). Phylogenetic analyses were performed in MrBayes on XSEDE [Phylogenetic analyses were performed to compare the Ecuadorian haplotypes with previously recorded COI haplotypes across the globe. For this analysis, we chose 57 sequences of up to 1525 bps of different haplotypes distributed around the world. These sequences have been used in different prior studies . Sequencon XSEDE  and RAxMon XSEDE  through on XSEDE . The anas Say, 18 and Ae. Ae. albopictus were analyzed using default settings in DnaSP [Ae. albopictus populations (Tests 1 and 3) and compare populations with both haplotypes (Test 2). Furthermore, major geographic barriers appear to be shaping the genetic diversity of other insect lineages [The genetic diversity and population structure of the five Ecuadorian populations of in DnaSP  and Arlein DnaSP . To detelineages . To testlineages . We calclineages  using a Ae. Albopictus presence were downloaded from the Global Biodiversity Information Facility  [In order to apply the maximum entropy model Maxent , the worF, 2021) . The poiThe explicative variables consisted of the following rasters obtained by satellite images processing: elevation layer and 2005–2019 monthly climatic variables (EpiClim database), including average humidity, total precipitation, average daily temperature, average nightly temperature, and average vegetation index.The Maxent version 3.4.4 was used with the default option of the cloglog unit. This unit gives an estimate between 0 and 1 of the probability of presence.Ae. albopictus spanned 684 bps, with two parsimony informative sites. Haplotype designation revealed two haplotypes in the Ecuadorian populations of Ae. albopictus Ae. albopictus clade through bootstrap and posterior probability . These results were supported by the haplotype diversity of the sampled sites. Only one population was detected in the structure analyses . No migrants were detected among sites, and most of the variation in the AMOVA was found within groups when testing for regional representation and haplotype distribution  in 2017 [Ae. albopictus in the Amazon basin (Francisco de Orellana) and localities in the northwestern Andean lowlands  at the Imbabura province. The dispersion of this species in Ecuador is ongoing since we have not recorded the mosquito in several lowland locations that are constantly monitored .The first reported record of Ae. albopictus was initially found in Cachaco, and it expanded to Lita and Guadual. These three sites are located on a road that connects Ecuador and Colombia. This geographical connection may help understand the similarities found in this work between Ecuadorian Ae. albopictus H2 and specimens from Colombia. Therefore, our results showed the possibility of more than one introduction and colonization event in the country. The global haplotype network  and that most genetic variation was within populations (Ae. albopictus as an invasive species is based on its dispersal capacity in human transport networks, specifically on roads and navigation [Ecuadorian populations of ulations . Even thulations . High leulations . We alsoulations , which avigation ,41. ContAe. albopictus depends on weather abnormalities, urbanization, and socio-economic factors [Ae. albopictus in the Ecuadorian territory using present bioclimatic variables [Ae. albopictus in Ecuador.Studies predict that climate change and human mobility will promote the spread of the tiger mosquito, increasing the risk of pathogen transmission affecting human health ,42. Furt factors . Our stuariables . Suitablariables . This di


Y-chromosome short tandem repeats (Y-STRs) have become important supplementary evidence in forensic science. Nowadays, the Y-chromosome STR haplotype reference database (YHRD) contains abundant Y-STR haplotype data from all over the world, while haplotype data of Guizhou Miao and Tujia are scarce. Hence, genetic polymorphisms of 37 Y-STRs were investigated in 446 unrelated males  residing in Guizhou Province. A total of 206 and 242 unique haplotypes with the highest diversity value of 0.9665 and 0.9470 were obtained. The heatmap, multidimensional scaling (MDS), the unweighted pair-group method with arithmetic means (UPGMA) tree and principal component analysis (PCA) based on the genetic distance (Rst) value within our studied populations and other 26 populations indicated that population structures follow the boundary of the continent. Guizhou Miao and Guizhou Tujia populations have intimate relationships with East Asian populations, especially the geographically close, similar history and the same language family populations. The YHRD website contains abundant Y-STR haplotype data from diverse populations and ethnic groups all over the world. However, there are fewer population data reports of Y-STRs in Guizhou Miao and Guizhou Tujia populations. The AGCU Y37 PCR amplification kit contains six-colour fluorescence, which can greatly improve the polymorphic information content and individual discrimination (http://www.agcu.cn/page8?article_id=25&brd=1).Y-chromosome short tandem repeats (Y-STRs), with the characteristics of male-specific, patroclinous and haplotype genetic, have been widely used in human evolution , genealohttps://en.jinzhao.wiki/wiki/China#cite_note-23). All ethnic groups comprise the Zhonghua Minzu. Miao and Tujia are the fourth and eighth largest minorities in China, which constitute approximately 0.7% and 0.6% of the total population (http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexch.htm). Miao, also named Hmong, is an international ethnic group originated from China. At present, the Miao populations mainly live in southern China  and some of the Miao populations have migrated out of China into Southeast Asia  (https://en.jinzhao.wiki/wiki/Miao_people). According to the sixth population census in 2010 (http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexch.htm), the Miao populations are the largest ethnic group in Guizhou Province, with a population of 3.13 million. The Miao people have their own language (Hmong), which belongs to the Hmong-Mien language group of the Sino-Tibetan language family. In the early twentieth century, Miao people created their own writing system. Tujia is an ethnic group with a long history in China. Nowadays, the Tujia populations mainly reside in Guizhou Province, Hunan Province, Hubei Province and Chongqing Municipality bordering the Wuling mountains. Based on the sixth population census in 2010 (http://www.stats.gov.cn/tjsj/pcsj/rkpc/6rp/indexch.htm), about 1.02 million Tujia populations settled in Guizhou Province. The Tujia populations speak the Tujia language but have no script. The Tujia language is one of the Tibeto-Burman languages of the Sino-Tibetan language family (https://en.wikipedia.org/wiki/Tujia_people).China, located in East Asia, is the world's most populous country. Additionally, China legally recognizes 56 distinct ethnic groups with a population of more than 1.4 billion in 2020 . Among them, 206 Miao populations live in Kaili City, Eastern Guizhou, while the other 246 Tujia populations dwell at Tongren City, Northeast Guizhou. The inclusion criteria were as follows: (i) the volunteers were males; (ii) ancestors were Miao or Tujia populations; (iii) the people have been in Guizhou province for more than three generations. All volunteers provided informed consent under the approval of the Ethics Committee of the Zunyi Medical University (KLLY-2019-080).. 2.2−1. The AGCU Y37 PCR amplification kit  was used to co-amplify 37 Y-STR loci  on ProFlex 3 × 32-well PCR System  following the manufacturer's protocol. A total of 10 µl PCR reaction volume were employed, including 4 µl of reaction mix, 2 µl of 37 Y-STR primers, 0.4 µl of Taq DNA polymerase, 1 µl of DNA template and 2.6 µl of sdH2O sterile deionized H2O (sdH2O). PCR condition consisted of predegeneration of 2 min at 95°C; 30 cycles of 30 s at 94°C, 1 min at 60°C, 1 min at 72°C; final extension of 20 min at 60°C and hold at 4°C. The amplification products were detected and separated by ABI 3500XL Genetic Analyzer . Finally, 37 Y-STR loci profiles were analysed using GeneMapper ID v. 1.4 . A negative (ddH2O) and a positive (DNA 9948) control run through each genotyping batch. Raw haplotype data of Guizhou Miao and Guizhou Tujia individuals were submitted to the YHRD and received the accession numbers of YA004671 and YA004672.Genomic DNA was separated by the salting-out method . NanoDro. 2.3n(1 − ∑pi2)/(n − 1), where n means the sample size, and pi denotes the frequency of the ith allele or haplotype. Match probability (MP) was determined as the sum of allele frequencies squared. Discrimination capacity (DC) was the ratio between the number of different haplotypes and the sample size. Twenty-eight populations from home and abroad were performed using analysis of molecular variance (AMOVA) [Haplotype and allele frequencies were carried out by direct counting. Three multi-copy loci  were regarded as allelic combinations. Genetic diversity (GD) and haplotype diversity (HD) were computed by Nei's formula [ (AMOVA)  and mult (AMOVA)  tool on  (AMOVA)  and SPSS (AMOVA) .. 3. 3.1Haplotypes containing 37 Y-STRs from 206 Miao male individuals and 246 Tujia male individuals residing in Guizhou Province were obtained . A total of 206 and 242 unique haplotypes were found, respectively. There were four different haplotypes with two repetitions in Tujia males. Allele frequencies and gene diversity values are presented in electronic supplementary material, tables S3 and S4. The allele numbers at single-copy loci ranged from three (DYS645) to 13 (DYS518) in Guizhou Miao individuals and varied from four  to 15 (DYS518) in Guizhou Tujia samples with the allele frequencies spanned from 0.0049 to 0.9709 and 0.0041 to 0.9675. For multi-copy loci, the number of alleles were 29 (DYS527a/b) to 50 (DYS385a/b) and 31 (DYS527a/b) to 44 (DYS385a/b) with the allele frequencies 0.0049–0.1214 and 0.0041–0.1423, respectively. GD values ranged from 0.0571 (DYS645) to 0.9665 (DYS385a/b) for Miao, and from 0.0638 (DYS645) to 0.9470 (DYF387S1) for Tujia. The HD and MP values of Miao were 1 and 0.00485, which of Guizhou Tujia were 0.999 and 0.00433. DC values of Miao and Tujia were computed as 100% and 98.37%. Moreover, the forensic parameters of the Yfiler and Yfiler Plus amplification systems (17 and 27 Y-STR loci) in the Guizhou Miao and Guizhou Tujia were separately calculated . In the . 3.2To better understand the paternal genetic relationships between our studied populations and others, 26 different populations from home and abroad were obtained from YHRD. Pairwise genetic distances (Rst) calculated using AMOVA are shown in electronic supplementary material, table S5. The minimum genetic distance was between Yanbian Korean and South Korea Korean (0.0000), while the maximal was between Chamdo Tibetan and Laos Laotian (0.4223). For our studied populations, Guizhou Miao (0.0049) and Tujia (0.0041) had the closest genetic distance with Hunan Miao, while they both had the farthest genetic distance with Chamdo Tibetan (0.2432 of Miao and 0.2266 of Tujia).The heatmap of pairwise Rst is shown in As presented in the MDS plot , Hubei Tujia, Hunan Dong, Yanbian Korean and South Korea Korean were in the first quadrant; Chamdo Tibetan, Sichuan Tibetan, Kazakhstan Kazakh, Hulun Buir Mongolian and Inner Mongolia Daur were in the second quadrant; Xinjiang Kazakh, Italy Italian, Xinjiang Uighur, Bahrain Bahraini, Saudi Arabia Central Arab and Madhya Pradesh India Indian were in the third quadrant; Guizhou Miao, Hunan Miao, Hunan Yao, Qinghai Hui, Ningxia Hui, Guizhou Bouyei, Guangxi Han and two Southeast Asian countries (Laos Laotian and Thailand Thai) were in the fourth quadrant; Guizhou Tujia, Heilongjiang Han and Japan Japanese straddled the first and fourth quadrants. The Tibetans (Sichuan and Chamdo) located alone in the upper left corner, Xinjiang Uighur was close to West Asia (Saudi Arabia Arab and Bahrain Bahraini) and the European populations got together far from the Asian populations.In the UPGMA tree , there wTo further distinguish the genetic relationship among 28 populations, a PCA plot was employed . The PCA results were presented as the plots of the first three principal components (PCs). PC1 (63.34%) can differentiate the Asian populations from the European populations. Among them, the East Asian populations and the Southeast Asian populations cluster tightly. PC2 (22.27%) can separate the European related populations . PC3 (6.49%) can distinguish the Tibeto-Burman-speaking populations  from the other populations.. 4Presently, a novel 37 Y-STR loci system was tested in the Miao and Tujia populations residing in Guizhou Province. GD values of 37 Y-STRs were all higher than 0.5 with the exception of DYS391, DYS437, DYS438 and DYS645. Three multi-copy loci  showed higher gene diversity than single-copy loci (GD > 0.9). Additionally, the AGCU Y37 PCR amplification kit includes all of Y-STRs in the previously developed forensic commercial kit (such as Yfiler and Yfiler Plus) and adds another seven low-medium mutation loci . More low-medium mutation loci increase the individual discrimination and play a vital role in forensic family research. And the additional new Y-STRs included in the AGCU Y37 significantly enhance the HD and DC, but decrease the MP. The results showed that 37 Y-STRs are highly polymorphic and informative. Thus, the AGCU Y37 amplification kit can be used in forensic for supplementary of autosomal chromosome STRs.Furthermore, 27 Y-STRs included in the AGCU Y37 PCR amplification kit were used to explain the population genetic relationships. In the present study, we used multifarious bioinformatics methods  to reconstruct the population relationship of Guizhou Tujia and diverse ethnic groups from nine major language families coming from 11 countries . These bioinformatics methods generally employed the population comparisons in the forensic medicine –17. The . 5We firstly reported the forensic parameters of 37 Y-STR loci from Miao and Tujia male individuals residing in Guizhou Province. This study followed the use of Y-STRs in forensic analysis under the recommendations of the International Society for Forensic Genetics (ISFG) . All hapClick here for additional data file.


ST value for the four populations was 0.080 (95% CI 0.056–0.109) and differed significantly from zero (P < 0.001). The highest FST value was observed between the populations North Macedonia and Slovenia and the lowest between Slovenia and Croatia. We also found a signal for the existence of isolation by distance between populations. Our results showed that wildcats are divided in two genetic clusters largely consistent with a geographic division into a genetically diverse northern group  and genetically eroded south-eastern group . Hybridisation rate between wildcats and domestic cats varied between 13% and 52% across the regions.Habitat fragmentation and loss have contributed significantly to the demographic decline of European wildcat populations and hybridization with domestic cats poses a threat to the loss of genetic purity of the species. In this study we used microsatellite markers to analyse genetic variation and structure of the wildcat populations from the area between the Dinaric Alps and the Scardo-Pindic mountains in Slovenia, Croatia, Serbia and North Macedonia. We also investigated hybridisation between populations of wildcats and domestic cats in the area. One hundred and thirteen samples from free-leaving European wildcats and thirty-two samples from domestic cats were analysed. Allelic richness across populations ranged from 3.61 to 3.98. The observed Ho values ranged between 0.57 and 0.71. The global F Felis silvestris silvestris Schreber, 1777 and Felis silvestris caucasica Satunin, 1905, distributed in European forest habitats including islands of Britain, Sicily and Crete1. The species is legally protected by both Bern Convention2 and European Union’s Habitats Directive, which consider it "strictly protected"3. International Union for Conservation of Nature (IUCN), categorized wildcat as “Least Concern”, since it has been evaluated together with Felis lybica species distributed over vast regions of Asia and Africa, and without considering the demographic decline and fragmentation of European wildcat populations4.According to a revised taxonomy the European wildcat is classified into two subspecies 5. In addition, transport networks, urban areas as well as agricultural landscapes divide natural habitats into small isolated patches and create barriers that restrict gene flow and ultimately leads to a hidden genetic structure within the European wildcat populations8. Many recent studies12 showed that wildcat populations are geographically structured and conservation strategies should improve gene flow by restoring ecological corridors within biogeographical units14. Human-induced mortality and disease transmission17 are also important threats to wildcats in Europe18, but the loss of genetic purity due to hybridisation with domestic cats (Felis catus) 21 is a threat that has attracted the most attention from the scientific community and the public.Hundreds of years of combined negative factors, including habitat loss, have resulted in the extinction of the European wildcat from most of its former range in many parts of Europe22. Introgression of artificially selected traits of domestic cats into species of conservation concern may affects their fitness and leads to outbreeding depression in wild populations23. The extent of gene flow from domestic cats to wildcats varies in intensity across Europe and may exhibit significant local differences, most likely based on historical or ecological traits27. For example, a high level of hybridisation has been observed in Scottish24, and Hungarian26 wildcats, while a low hybridisation rate was found in sampled wildcats from Italy, Bulgaria, Portugal, and Germany29. Identifying areas with different levels of the domestic cat gene introgression in European wildcat populations could help recognizing factors that have facilitated introgression rates in the past and/or that currently hinder or accelerate hybridisation. Since the level of hybridisation appears to be low in some regions and high in others, it is likely that other factors, such as differences in habitat structure and behaviour, have played a role in reducing hybridisation9.Hybridisation between wildcats and domestic cats can lead to (i) disruption of local genetic adaptations, (ii) loss of genetic integrity of the European wildcats and even extinction of the subspecies28. These geographically distinct groups represent the living remains of the Pleistocene refugial population30. More detailed analyses within each of the phylogeographic clusters could clarify the current patterns of structuring within population, since a possible influence of the "refugia within the refugia" existed throughout the Pleistocene31. On the other hand, recent habitat loss and fragmentation have led to population bottlenecks and a reduction in genetic diversity26. But despite the awareness of the importance of gaining more insight into the underlying patterns of genetic variability and genetic integrity of local populations, data on population structure are lacking in several European regions. The work of Mattucci et al.11, which included 39 samples from the area of Slovenian Dinaric Alps, but did not include any samples of wildcat populations of Balkan Peninsula, underlines the importance of clarifying evolutionary history of the wildcats in this area.According to the genetic analysis, the European wildcats are subdivided into five main phylogeographic clusters, each corresponding to five biogeographic groups, distributed in the Iberian Peninsula, Italian Peninsula and the region of Sicily, Central Europe, Central Germany and Northern Balkans (Dinaric Alps)In this study we used microsatellite markers to analyse genetic variation and structure of the wildcat populations from north-western Dinaric Alps to the Scardo-Pindic mountain system. Regardless they probably originate from the same Pleistocene refugium, we investigated whether geographical isolation is reflected in the genetic structure of wildcat populations. Finally, we investigated hybridisation between populations of wildcats and domestic cats.P < 0.05). In the 18 microsatellite loci  the average null alleles frequencies per locus ranged from zero (FCA096) to 0.091 (FCA088) with an average of 0.033. No identical genotypes were observed, the low values for probability of identity (PID) suggest that individuals in the study were not highly related: PID = 7.8 × 10–19, PIDsibs = 1.2 ×  × 10–7 in wildcats; PID = 3.7 × 10–21, PIDsibs = 3.2 × 10–8 in domestic cats.A total of 20 loci  on 145 individuals (113 wildcats and 32 domestic cats) from four countries were examined , additionally the population from MK showed deviation also based on FIS . It can be expected that the deviation from HWE in MK population is a consequence of the small sample size included in the analysis. The HR population showed no significant deviation from HWE after the Bonferroni correction  and differed significantly from zero (P < 0.001). The pairwise FST values between populations ranged from 0.004 to 0.148 with the mean of the pairwise FST = 0.070 ± 0.060 (± SD) and also differed significantly from zero (P < 0.05)  Table . The higAll controls were correctly identified by the NEWHYBRIDS software, with posterior probabilities of 0.99 for wildcats and 0.95 for domestic cats. The results of the identification of genotypes of the simulated hybrids are presented in the Supplementary Fig. n = 28) of hybrids across all wildcat samples using the exclusion criterion given by a z value of less than 0.85 (NEWHYBRIDS) and a q value of less than 0.80 (STRUCTURE). Numbers of hybrids varied across countries: SI 13% (n = 3), HR 16% (n = 9), SR 52% (n = 15), MK 14% (n = 1). Nineteen of the 28 hybrids were classified as F2 hybrid , four were classified as back-crosses of F1 to wildcat , four as back-crosses of F1 to domestic cat  and two as domestic cats P2 population  . Domestic cats belong to an independent cluster. The first principal component distinguished between domestic cats and wildcat clusters, and the second principal component showed a distinction between two wildcat clusters   Table .Table 3Ht-value = 3.012, P = 0.003, R2 = 0.0013), supporting the hypothesis of isolation by distance  than reported in the study by Matucci et al.ST values in our study were observed between the northern SI population and the southern MK population, and lowest between the closest SI and HR population, which is in congruent with geographical distances. We found a signal for the existence of isolation by distance  and Mediterranean  refuges which increase genetic divergence between European wildcats in the Eastern Alps and Apennines. They also showed that the European wildcat populations are subdivided into at least five main biogeographic groups with divergence times from the Late Pleistocene, but sampling in Dinaric Alps, Pannonian Plain and Scardo—Pindic mountains was scarce (only samples from Slovenia and Bosnia and Herzegovina were included in the study)11. So Mattucci et al.11 already noted that STRUCTURE results suggest that local populations could be genetically subdivided at smaller geographical scale but additional genetic markers analysis is needed to point us into this direction. Studies on Eurasian lynx, the only other wild Felidae present in this area, showed that the mitochondrial lineage restricted to the Balkan lynx population was the first one to diverge from the other Eurasian lynx haplogroups. Balkan lynx haplogroup was the most divergent and its split was dated around 96.6 kya45, while the three Mediterranean wildcat lineages probably originated 21,000–125,000 years ago11. Past population isolation in Balkan refuges with genetically diverse groups of populations, leading to a more recent ‘refugia within refugia’ concept as also seen in other species (e.g. wild boar)46. Lack of information on the demographic history of the researched populations and gaps in our sampling prevent us from drawing firm conclusions, so additional studies based on fine-grained grid sampling are needed to reveal fine-scale genetic structure in this area.Furthermore, our northern and south-eastern populations might display the recolonisation from different refugia. A model of late Pleistocene isolation and genetic diversification of European wildcat populations into three main Mediterranean glacial refuges in the southern Iberian, Italian, and Balkan peninsulas was proposed by Hewitt27, however in some areas introgression can be substantial26 and could be caused by environmental circumstances that enable long-lasting hybridisation47. In our study none of the hybrids found were classified as hybrids of the first generation which shows low level of recent hybridisation in all studied populations. In SI and SR mainly second-generation hybrids were found, while in HR a half of hybrids were classified as back-crosses and a half as second-generation hybrids. Overall, 25% of the sampled wildcats were admixed, which is comparable to the observation in a wildcat population in the Swiss Jura (21%)21. In the case of SR, due to high hybridisation rate, this result could reflect a long-lasting hybridisation that persists in the area and might be the consequence of high density of stray domestic cats, especially on the periphery of remote village areas. By using genomic approaches Mattucci et al.28 found that some of the hybrids with possible F2 origins actually showed admixture traces dating back from 9 to 11 generations in the past. Since F2 hybrids are expected to occur in rare cases, a high proportion of F2 hybrids in the SI and SR populations could be the result of method bias and brings misassignments of individuals that can represent the product of repeated crosses among F1 or F2 individuals, rather than true second-generation hybrids. We used STRUCTURE and NEWHYBRIDS for classifying the hybrids. But most likely, the highly polymorphic, non-diagnostic genetic markers we have used are not accurate enough to classify the hybrids with repeated crossbreeding between different hybrid generations. Results of Q scores generated by STRUCTURE allow for a relatively simplistic tracking of estimated proportion of ancestry, so that backcrosses cannot be distinguished from more complex hybrids. Even if NEWHYBRIDS classifies the hybrids into generational categories, some misassignments of individuals could occur due to strict hybrid categories used in this approach.Our analyses are in line with previous studies showing that the genetic integrity of the European wildcat is not compromised in some regions of Europe47, and the need for practical measures that could be relevant to conservation has been highlighted48. The simulation project in the Swiss Jura shows that reducing the number of domestic cats and hybrids and improving the quality and quantity of habitats by supporting the highest possible wildcat densities are priorities for wildcat conservation48.In the area from the Dinaric Alps to the Scardo-Pindic Mts. wildcats are divided in two genetic clusters largely consistent with a geographic division into a genetically diverse northern group  and genetically eroded south-eastern group . But wider sampling, especially in MK and neighbouring countries, like Bosnia and Herzegovina, would help to clarify the evolutionary history in this part of Europe. The apparent loss of genetic integrity due to hybridisation with domestic cats found in Serbia urges specific conservation measures to maintain evolutionary potential of this species. Hybridization with domestic cats is considered the greatest threat to wildcats in several countriesThe study area, across four countries  is located at the intersection of three major European geographical units, namely Dinaric Alps, Pannonian Basin, and the Scardo-Pindic Mts. system, which is a continuation of Dinarides in the southern part of Balkan Peninsula.Fagus sylvatica), fir  and spruce (Picea abies) associations49. The climate varies, but roughly with a continental climate in the north-east Pannonian region, a severe alpine climate in the mountain regions and a sub-Mediterranean climate in the coastal region along the Adriatic Sea50. Topographically, the area is highly heterogeneous, interrupted by ditches, bays and rocks developed on limestone and dolomite rocks. Besides wildcat, several other carnivorous species are present in the region; brown bear (Ursus arctos), grey wolf (Canis lupus), Eurasian lynx (Lynx lynx), golden jackal (Canis aureus), red fox (Vulpes vulpes), and several species of mustelids (Mustelidae). Unlike the mountainous regions, there are no large carnivorous species in the Pannonian region, only mesocarnivores, but there are areas with increasing populations of the golden jackal.The two-mountain systems, Dinaric Alps (extending from Slovenia to Albania) and Scardo-Pindic Mts. (from Kosovo to Greece) are characterized by small plateaus and meadows at high altitude up to 2,764 m a.s.l. The forest cover consists mainly of beech , Croatia (HR), Serbia (SR) and North Macedonia (MK) . According to the manufacturer's instructions we used 2 µL template DNA and 0.3 mM final concentration for each primer used in the set. The amplification was performed under the following conditions: initial PCR activation for 3 min at 95 °C, followed by 35 cycles of denaturation for 15 s at 95 °C, annealing for 30 s at 58 °C, extension for 30 s at 72 °C and final extension for 10 min at 72 °C. The fragment analysis was performed on a SeqStudio sequencer (Thermo Fisher Scientific) using the GeneScan LIZ500 (−250) standard (Applied Biosystems). The results were validated with the software GENEMAPPER v.5.0 (Applied Biosystems). Negative controls were included in all extraction and PCR steps. About 10% of the randomly selected samples were replicated independently to check for false alleles.Genomic DNA was extracted using the peqGOLD Blood & Tissue DNA Mini Kit  according to manufacturer’s instructions. The concentration and purity of the extracted DNA in the final elution volume was measured with Qubit® dsDNA BR Assay Kit (Invitrogen) on 3.0 Qubit Fluorometer (Life Technologies). Nineteen autosomal dinucleotide and one tetranucleotide (FCA 441) microsatellites  at each locus in each population with respect to the fact that the presence of null alleles can cause a significant heterozygote deficit and deviation from Hardy–Weinberg equilibrium (HWE). We used the software GENEPOP 4.254 to test for deviations from HWE. The exact test to assess the heterozygosity deficiency was performed for each population (country). The baseline significance level was set at 0.05 and a Bonferroni procedure was applied in multiple comparisons to compensate for the risk of a bloated type 1 deficiency.Using the FREENA programO) and expected (HE) heterozygosity55, and inbreeding coefficients (FIS) were calculated for each population with GENETIX 4.05.256 separately for the domestic, and wildcat populations. The probability of identity and sibling-identity were calculated with the Excel macro GenAlEx v6.557. Allelic richness for each population (AR) was estimated following a rarefaction method in the program FSTAT 2.9.458. The genetic differentiation between wildcat populations and between domestic cats and wildcats was estimated using pairwise FST in GENEPOP 4.2 according to Weir & Cockerham59, and significant differences from zero FST estimates were tested with 1,000 permutations. All subsequent analyses were performed after excluding individuals with admixed genotypes (see paragraph “Control population for hybrid simulation”).The mean number of alleles (A), observed (Hz designation) by NEWHYBRIDS v1.160. These z values were used to determine the ten animals from each population that were the “most pure”, with the z value above 0.99. These individuals were used as control animals for each population in subsequent analyses.Based on the initial STRUCTURE analysis, there was a statistically supported split between wildcat and domestic cat clusters Fig. a. Ten in61 for: parental wildcats (P1) and domestic cat populations (P2), F1, F2, back-crosses of F1 to wildcat (P1Bx) and domestic cat (P2Bx) and a second back-cross of P1Bx to wildcat controls (P1Bx2) and P2Bx to domestic cat controls (P2Bx2).One hundred simulated genotypes (for each population independently) were generated from the control populations P1 and P2 in HYBRIDLAB v1.062.The simulated genotypes were analysed using NEWHYBRIDS. A burn- in period of 5 000 was followed by 10 000 sweeps based on the graphical version of NEWHYBRIDS  to compare the results of hybrids identification by NEWHYBRIDS; the admixed genotypes were identified at a threshold qi < 0.80. All individuals with assigned admixed genotype using NEWHYBRIDS and STRUCTURE methods were removed from the data sets.In addition, we used program STRUCTURE  wildcat populations and domestic cats, and (ii) wildcat populations only. In STRUCTURE, ten independent runs were performed for each K-value in the range of one to ten using a model assuming admixture with correlated allele frequencies. Each run included a burn-in period of 100,000 replications followed by 100,000 Markov Chain Monte Carlo (MCMC) iterations. The results of the replicated runs for each K value from two to ten were combined using STRUCTURE HARVESTER v 0.6.9464 and the optimal K value was selected using the ΔK method developed by Evanno et al.,65. The results of the replicated runs for the optimal K value were combined using the Greedy algorithm of CLUMPP 1.1.166 and the summary results were plotted with DISTRUCT 1.167.Population genetic clusters were revealed using STRUCTURE 2.3.468, using RStudio69 and R version 3.6.262. We used the Discriminant Analysis of Principal Components (DAPC)70, multivariate method in this package to identify the most likely number of clusters (K).The genetic relationships between all genotyped individuals were represented by Factorial Correspondence Analysis (FCA) using GENETIX. The distribution of the individuals in a 2D space was compared by eye with the geographical location of the localities. We also investigated the genetic structure with the R-package, adegenet 2.0.071 was performed in ARLEQUIN 3.5.72 to test the genetic differences between individuals and populations and between the optimal number of clusters identified by STRUCTURE (K = 2). The statistical significance of the variance components was investigated using 999 permutations in the R-package ade4 v1.7–13. 73. Finally, we tested isolation by distance (IBD) patterns within all populations using the Mantel function in the R-package adegenet.The analysis of molecular variance (AMOVA)Samples of European wildcats were collected from dead  or from live-trapped individuals for telemetry studies.All methods were carried out in accordance with the Ethical and Welfare Standards presented in the , Regulation on the Protection of Animals Used for Scientific Purposes , with the approval of the Bioethical Committee for the Protection and Welfare of Animals of the University of Zagreb Faculty of Agriculture (UR.BR. 251-71-29-02/19-21-2).74.The study is reported in accordance with ARRIVE guidelinesSupplementary Information.


Epipogium aphyllum, addressing the joint issues of genetic structure of its populations over Europe and possible consequences for mycorrhizal specificity within the associated fungal taxa. Out of 27 sampled E. aphyllum populations, nine were included for genetic diversity assessment using nine nuclear microsatellites and plastid DNA. Population genetic structure was inferred based on the total number of populations. Individuals from 17 locations were included into analysis of genetic identity of mycorrhizal fungi of E. aphyllum based on barcoding by nuclear ribosomal DNA. Epipogium aphyllum populations revealed high genetic diversity (uHe = 0.562) and low genetic differentiation over vast distances (FST = 0.106 for nuclear microsatellites and FST = 0.156 for plastid DNA). Bayesian clustering analyses identified only two genetic clusters, with a high degree of admixture. Epipogium aphyllum genets arise from panmixia and display locally variable, but relatively high production of ramets, as shown by a low value of rarefied genotypic richness (Rr = 0.265). Epipogium aphyllum genotype control over partner selection was negligible as (1) we found ramets from a single genetic individual associated with up to 68% of the known Inocybe spp. associating with the plant species, (2) and partner identity did not show any geographic structure. The absence of mosaicism in the mycorrhizal specificity over Europe may be linked to preferential allogamous habit of E. aphyllum and significant gene flow, which tend to promote host generalism.Some plants abandoned photosynthesis and developed full dependency on fungi for nutrition. Most of the so-called mycoheterotrophic plants exhibit high specificity towards their fungal partners. We tested whether natural rarity of mycoheterotrophic plants and usual small and fluctuating population size make their populations more prone to genetic differentiation caused by restricted gene flow and/or genetic drift. We also tested whether these genetic characteristics might in turn shape divergent fungal preferences. We studied the mycoheterotrophic orchid The online version contains supplementary material available at 10.1007/s10265-021-01364-7. Mycoheterotrophy, the ability to obtain organic carbon from mycorrhizal fungi, supports the nutrition of some achlorophyllous plants Leake . Most myi.e. more stringent MHP selection for beneficial fungi  Does genetic structure imply isolation of sub-populations, and potential cryptic speciation in E. aphyllum? (3) Does the mycorrhizal interaction display finer specificity for Inocybe subgenera or rare mycorrhizal partners in local populations?Our study addresses the joint issues of genetic diversity, spatial genetic structure and inferred gene flow in allogamous Epipogium aphyllum  population. Root samples were also obtained in Ojcowski National Park , Tatra Mountains  and Kozi Rynek . In WEP, KRP and 2 plants from TMP one rhizome fragment (0.5 cm long) was harvested per plant (in compliance with obtained sampling authorisation) by digging soil y et al. b in the E. aphyllum samples from Wejherowo (WEP). Amplifications were performed in 10 μL of reaction mix containing: 2 × Type-it Mix , 0.4 mM of fluorescently labelled forward primer, 0.4 mM of reverse primer and 50 ng of DNA template. The following PCR conditions were used: 5 min of initial denaturation at 96 °C followed by 35 amplification cycles  for 30 s, then 72 °C for 45 s), and a final extension step of 72 °C for 10 min. Genotyping was performed on an ABI 3130 sequencer (Applied Biosystems) with LIZ-500 size standard (Applied Biosystems). Alleles were manually scored, and genotypes were determined for each individual, using GeneMapper™ (Applied Biosystems).Total genomic DNA from flowers and rhizome samples was extracted with a CTAB-based method , rps4 (478 bp) and rps12-trnL intron . In total, 158 samples were sequenced for those three regions. PCR amplification was carried out in 10 μL of reaction mix containing: 1 × MyTaq HS Mix , 0.35 mM of both primers using parameters described in Table S4. Amplicons were purified with Exo-BAP  and sequenced as in Selosse et al.  region of fungal rDNA using the primers ITS1F and ITS4B designed for Basidiomycetes  with primers 17SE and 26SE /(N − 1) and defined as the estimated proportion of unique genets (G) in the total number of sampled ramets (N). In clonal organisms the value of R, as a function of sample numbers, follows a power law distribution, sample sizes should be as large as possible to ensure the stability of R values  was estimated as the expected number of MLGs after rarefaction (eMLG). We averaged values of Rr and R (for comparison with other studies) for populations with ≥ 10 samples.Prior to analyses, we checked for the occurrence of genotyping errors and null alleles using For subsequent analyses, clone-corrected data-sets were produced, where samples representing replicated MLGs were removed based on nSSR and consistently on plastid markers. Although in some instances non clone-corrected data may be applied in Hardy–Weinberg based calculations  and unbiased expected (uHe) heterozygosity, as well as the number of private alleles specific to a population (pA), was calculated in GenAlEx v. 6.5  and private allelic richness (pAR) were calculated in hp-rare  for analysis of genetic structure and genetic diversity at a regional level and “clone-corrected, population sample set” consisting of 9 populations with > 4 individuals each (54 MLGs for nSSRs and 50 plastid haplotypes) to study genetic structure and genetic diversity at a population level (Table S1), based on which inbreeding coefficient 3 Goudet ; nuclearwww.fluxus-engineering.com). To avoid possible homoplasy, microsatellite regions (A(n) and T(n) patterns) in the rps12-trnL intron were excluded. The shortest possible tree was calculated by using the minimum spanning network pre-processing and the maximum parsimony heuristic search post-processing.Plastid haplotypes (Table S5) were assigned based on the combination of alleles at polymorphic sites (substitutions and insertion/deletion) using GenAlEx v.6.5  to partition genetic variation within and among populations in two sets: (1) the clone-corrected, population sample set and (2) the clone-corrected, complete sample set pooled into 6 regional groups according to their geographic provenance  among 9 populations was tested for plastid and nuclear markers with a Mantel test Mantel  on the c Slatkin .structure 2.3.3  at a 3% sequence divergence threshold used as a proxy for species in Inocybe , formed on joint 5.8S and LSU alignment of the fully identified g et al. : based og et al.  using Seg et al. . The alig et al.  under thy et al.  and by Ly et al. .versus their hosts’ geographic distance. These calculations were carried out separately on the (1) total datasets and (2) including only taxa belonging to Inocybe.Second, we aligned selected sequences from Roy et al. , Liebel E. aphyllum ITS sequences differed among populations by less than 0.4% , and no excess of failed amplification occurred for nSRR loci in any population (Online resources 3), supporting conspecificity of E. aphyllum Eurasian samples. Multilocus genotypes of 248 samples from 27 E. aphyllum populations were successfully amplified for 9 nSSRs (Table S1), yielding 60 alleles in total. We discarded four other loci due to missing data, or evidence for null alleles in all populations (Table S2). Permutation test in R package Poppr showed that the combination of the nine remaining loci was powerful enough to discriminate all distinct MLGs . Within all sampling sites, all individual shoots with similar MLGs were unlikely to result from sexual recombination (Psex < 0.05) and were thus considered as ramets of the same genet, which was further supported by the sharing of identical plastid haplotypes (see Online resources 3). Clonal diversity varied between populations, ranging from Rr = 0 in WEP , to Rr = 0.778 in ZDR; Table S1, Online resources 3) with an average of Rr = 0.265 (respectively R = 0.328). Repeated sampling of a ramet from the same genet was omitted from subsequent analyses performed on clone-corrected data, i.e. 82 distinct MLGs (Table S1), none of which were shared between populations.E. aphyllum from the clone-corrected, population sample set (i.e. populations with ≥ 4 MLGs) exhibited similar, moderate to high levels of nuclear genetic diversity  and regional level  pooling populations into six regions; see their delineation in Fig. All nine populations of ty Table . All locFIS = 0.094 ± 0.048, which is not significantly different from 0 . There was no linkage disequilibrium between loci after Bonferroni correction, neither within the clone-corrected, population sample set, nor after pooling all samples (except for the loci Epi-8 and Epi-17 in OPP only).For all local populations, inbreeding was low, with an average  0 Table . AccordiR = 0 in WEP  was also variable, ranging from 0 to 1, but in the clone-corrected, population sample set, 6 out of 9 populations displayed more than one haplotype and a higher HD (0.90 on average). The most diverse populations were from the Alps , Carpathians  and Asia (ZDR), while the less diverse were Pyrenean (TUV) and from the Baltic region  and ZDR . When pooling samples from the clone-corrected, complete sample set into 6 regions . Pairwise comparisons consistently revealed a significant differentiation of Pyrenean populations (Western) from Eastern (FST = 0.434) as well as Southern populations , while other comparisons revealed low or non-significant differentiation (Table S7).In the haplotype network based on plastid markers Fig. b, core nns Table , differeFST = 0.106), and very low, although significant, when calculated from the clone-corrected, complete sample set pooled into six regions (Table FST = 0.044). Pairwise comparisons between populations or regions (Tables S6 and S7) showed the highest and significant differentiation between the populations from the Western edge of the range, i.e. Pyrenees and Massif Central  and the Eastern one (ZDR).Nuclear markers showed similar patterns, with moderate level of genetic differentiation when calculated from the clone-corrected, population sample set  showed the best clustering for K = 2 clusters . No rare E. aphyllum mycorhizal partner i.e. Hebeloma, Lactarius or Thelephora, were detected. Among the 13 different OTUs identified, the most abundant ones were related to Inocybe terrigena (32% of sequences), I. leucoblema (20%) and I. mixtilis (15%) (Table S8). Inocybe OTUs in WEP fell into six out of seven alignment groups found for E. aphyllum  thus representing closely related species . The phylogenetic breadth of fungal partner, in the unique WEP genetic individual with multiple ramets was comparable with the ones observed in the 29 plants sampled from 16 populations throughout the Eurasian range of the species , although not as intensively studied, showed the same pattern as WEP. In one of the large clones, 5 different OTUs were found among Inocybe and Hebeloma. Individuals forming mycorrhizae with both these genera were also found in SAN. At the opposite, genetically diverse individuals from OPP associated with the same genotype of Hebeloma sinapizans.In the monoclonal Wejherowo (WEP) population, fungal ITS was successfully identified from 40 plants (after cloning PCR products for 8 samples), leading to one ixtilis 1%  or based on Inocybe data only . It thus appears that the diversity of fungal associates is not distributed in a geographical pattern over the E. aphyllum range.Finally, there was no correlation between fungal OTU phylogenetic distance and their hosts’ geographic distance either calculated in the whole data set , and continues until autumn. These observations are in line with overall low clonal diversity (Rr = 0.265). Large variation in clonal diversity between populations is most likely due to small and unequal sample size, which affect this metric. More samples, preferably in successive flowering years, are needed to properly assess the clonal diversity. At the extreme, the in-depth sampled WEP population harboured a single MLG over more than 3500 m2. This indicates that bulbils may travel faster than stolon growth, most probably due to water, animal disturbance or gravity after stolon decay, as reported by Robin  and the possibility of agamospermic  seed formation is negligible  maFIS = 0.094), despite frequently small population sizes. Thus, E. aphyllum appears to be mainly outcrossing, confirming the experimental pollinations by Krawczyk et al. , and homogeneity for these parameters among populations may seem surprising, since in often small E. aphyllum populations  and low inter-population differentiation . However, Pyrenean and Massif Central populations showed the lowest genetic diversity, and the highest divergence supported by unique haplotypes and private alleles. This can be caused by distance to the main range, which entails higher genetic differentiation due to smaller effective population size and greater geographic isolation  variation in ITS sequence and the sharing of certain network-core nSSR haplotypes  among European populations.Our study documented a low genetic structure among Eurasian E. aphyllum does not differ from autotrophic, allogamous orchids, which tend to show lower values of FST in comparison with other plant groups, pointing to a low overall differentiation and weak genetic structure  of this genus sensu Ryberg et al.  still warrant analysis. On the one hand, targeting multiple species from one fungal lineage is a frequent feature of MH plants especially from temperate forests, e.g. in Neottia nidus-avis  Hebeloma is more distantly related to Inocybe than was previously thought  two subgenera of Inocybe, namely Inosperma (AG 3) and Mallocybe (AG 16), were elevated to generic rank based on their genetic divergence , where 40 ramets sampled for mycorrhizal assessment represented the same genetic individual, y et al.  and Lieby et al.  involve g et al. . However Matheny , (2) as E. aphyllum associations revealed no clear pattern of geographical specialisation that would fit a geographic mosaic of coevolution with fungi (Thompson Inocybe and Hebeloma (e.g. at WEP and TMP). In WEP, a single clone harboured 68% of Inocybe OTUs known to be associated with E. aphyllum, distributed in six AGs sensu Ryberg. The small portion of the population as well as small rhizome part we were authorised to sample at WEP likely makes this value underestimated. Second, a single rhizome can be mycorrhizal with several Inocybe OTUs . Finally, partner identity showed no geographic structuring over the Eurasian populations observed, strongly arguing against a geographical mosaic of association, despite our large sampling range Supplementary file2 (PDF 502 KB)Supplementary file3 (XLSX 143 KB)Below is the link to the electronic supplementary material.


Platanthera leucophaea, provides a rich dataset to address this issue, allowing us to examine range‐wide genetic changes. Midwestern and Northeastern United States. We sampled 35 populations of P. leucophaea that spanned the species’ range and varied in patch composition, degree of patch isolation, and population size. From these populations we measured genetic parameters associated with increased extinction risk. Using this combined dataset, we modeled landscape variables and population metrics against genetic parameters to determine the best predictors of increased extinction risk. All genetic parameters were strongly associated with population size, while development and patch isolation showed an association with genetic diversity and genetic structure. Genetic diversity was lowest in populations with small census sizes, greater urbanization pressures (habitat loss), and small patch area. All populations showed moderate levels of inbreeding, regardless of size. Contrary to expectation, we found that critically small populations had negative inbreeding values, indicating non‐random mating not typically observed in wild populations, which we attribute to selection for less inbred individuals. The once widespread orchid, Platanthera leucophaea, has suffered drastic declines and extant populations show changes in the genetic parameters associated with increased extinction risk, especially smaller populations. Due to the important correlation with risk and habitat loss, we advocate continued monitoring of population sizes by resource managers, while the critically small populations may need additional management to reverse genetic declines.Protecting biodiversity requires an understanding of how anthropogenic changes impact the genetic processes associated with extinction risk. Studies of the genetic changes due to anthropogenic fragmentation have revealed conflicting results. This is likely due to the difficulty in isolating habitat loss and fragmentation, which can have opposing impacts on genetic parameters. The well‐studied orchid,  Platanthera leucophaea, provides a rich dataset to evaluate the potentially opposing impact of habitat loss and fragmentation on genetic parameters, allowing us to examine range‐wide genetic changes. The once widespread orchid has suffered drastic declines and extant populations show changes in the genetic parameters associated with increased extinction risk, especially in smaller populations.Protecting biodiversity requires an understanding of how anthropogenic changes impact the genetic processes associated with extinction risk. The well‐studied orchid,  Over tiThe local extinction of a plant species is typically driven my multiple interacting factors. Under anthropogenic changes to landscapes such factors can include changes in habitat suitability  Lindley (eastern prairie fringed orchid). We sampled populations spanning from critically small (n < 5) to large, robust population sizes (n > 1000). Our objectives were to (i) investigate range‐wide genetic patterns in P. leucophaea to determine if we can detect the underlying genetic structure of the species before fragmentation, (ii) determine the relationship between population size and genetic variation, and (iii) determine if changes in genetic parameters might indicate if populations impacted by fragmentation are heading toward extinction.Successfully predicting the effects of anthropogenic habitat loss and fragmentation on genetic diversity and species resilience requires an integrated approach that considers genetics, demographics, isolation, and habitat quality across a species’ range  is a long‐lived, perennial, terrestrial orchid  and 21 n USFWS, .Platanthera leucophaea can self‐pollinate but relies on pollinators to produce seed. The main pollinators are nocturnal flying hawkmoths in the family Sphingidae . PCR was run at 94°C for 5 min, then 35 cycles of 94°C for 40s, 60°C for 40s, 72°C for 3 min, and then a final extension at 72°C for 10 min for six primers , while for the remaining primers , the extension step was extended to 1 min. Genotypes were scored using a CEQ 8000 Genetic Analysis System and CEQ FRAGMENT ANALYSIS software (Beckman Coulter).To characterize the genetic structure of each population, genomic DNA was extracted using a CTAB extraction protocol  the USFWS ranked categorical habitat sizes , National Landcover Data. As the 1, 10, and 20km proportions were all equivalent, we ultimately focused on the 1 km buffered area. To identify the spectrum of preferred habitat types of this species, we generated a list of the natural landcover types which intersected each population centroid from across the sampling area. We assumed, given that the species was found within these landcover types, that they represent potentially suitable habitat Table . Therefoes Table . NaturalThe degree to which each population was isolated was determined using the average pairwise distance between all populations, average distance to the 5 and 10 nearest extant populations, and landscape resistance. The average Euclidian pairwise distance was calculated in SPAGeDi , and expected heterozygosity (He), which were calculated in GENALEX  and Queller and Goodnight’s Relatedness (R) in GENALEX  genetic diversity  for attaining accurate population genetic metrics . We randomly sampled individuals from each population to generate populations that ranged in sample sizes from 2 individuals to 25 individuals and repeated 10 times for each sample size. We then calculated the expected heterozygosity using the R package StrataG.R  as detailed in Evanno et al. ; Rousset, ST , FIS and relatedness (inbreeding), and Neff (effective population size) against latitude and longitude using the correlation coefficient panel for pairs function in R Statistical Software  summarizing all variables into a single metric that captures the spectrum of variation for that trait. All variables were first tested for normality and were log‐transformed when appropriate ). For population size, we used five variables: census in 2015 (Census), median census size (MedCensus), minimum and maximum size (Min & MaxCensus), and the categorical census size (CatPopSize). For patch composition, we used four variables: percent suitable habitat, percent natural area, percent agriculture, and percent development. Finally, for patch isolation, we used four measurements: average pairwise distance (Distance), average distance to 5 and 10 nearest extant populations , and landscape resistance as calculated in Circuitscape (resistance). For each PCA, we used the get_pca_var function in the R package factoextra 1.0.6 . In addition to genetic parameters, we were also interested in how population sizes vary with anthropogenic changes. Therefore, we modeled both census size and effective population size, Ne, against the PCA axes calculated to estimate the impacts of anthropogenic change, patch isolation, and patch composition. Since effective population size is not always correlated with census size, we modeled this term with and without the size PCA axes. After running the models, we found that the two disjunct populations in Missouri and Maine were two‐ to fourfold further from any other populations, making them large geographic outliers that had a disproportionate impact on the model analyses. Since these were effectively isolated, and to get a more accurate representation of processes in the center of its range, we excluded these populations from the model analyses.To investigate whether population and landscape variables explain genetic parameters, we used linear models that included all of the extracted axes that cumulatively explained at least 80% of the variation for the population size, patch isolation, and patch composition PCAs (see Table 2.5.5e), expected heterozygosity (He), and Weir and Cockerham’s (1984) estimates of inbreeding in GENALEX  and inbreeding (Fis) to the natural log of the average census size. This allowed us to look for trends that were less impacted by sampling bias.To determine if the trends in genetic parameters associated with including the critically small populations is a product of sampling bias or decreasing population sizes, we randomly selected seven individuals  from all populations sampled using the stratified.R package. From this subset of data, we recalculated the effective number of alleles per locus (N33.1e) and inbreeding (Fis) for our dataset was n = 18. For those populations , the average number of alleles by population (Na) ranged from 6.3 to 9.1 (ave 7.9), the number of effective alleles (Ne) ranged from 3.4 to 6.1 (ave 4.8), and expected heterozygosity (He) ranged from 0.66 to 0.79 (ave of 0.66). For the 10 populations where sample sizes were below 18, the range of the number of alleles (Na) was from 2.6 to 6.8 (ave 4.5), the number of effective alleles (Ne) ranged from 1.8 to 4.5 (ave 3.3), and expected heterozygosity (He) ranged from 0.39 to 0.72 (ave of 0.61), all of which were at the lower end of the ranges seen. The range for Weir and Cockerham’s (1984) estimates of Wright’s Fis varied from 0.01 to 0.20 (Fis = 0.10), suggesting a moderate level of inbreeding across populations; however, populations less than 18 spanned a large extreme from highly outcrossed (Fis = −0.47) and highly inbred (Fis = 0.44). Although, the highest measured inbred population was located in Maine (Fis = 0.44), which is a geographically disjunct population and likely an outlier, the next highest value was in Michigan (Fis = 0.14).Four of the 12 microsatellite primers were excluded  from our analyses after testing for null alleles, departure from Hardy–Weinberg equilibrium (HWE), and mis‐scoring. The sample effort curve revealed that the minimum sample size needed for an accurate measurement of genetic diversity (H3.2st) for population pairs greater than 18 individuals ranged from low (0.01) to relatively high (0.16) with a low average of 0.06  between our populations with a slight geographic gradient from west to east  contributed equally to the variation along the first axis Table . PCA axiTo quantify population size, we used four variables . Differences between population sizes were 98% explained by the first four PCA axes using four measurements of population sizes , with 47% of the variation explained by census 2015, median census size, and categorical population size along axis 1 (pcaSize1). The second axis explained an additional 30% of the variation and was explained by the minimum census size. The remaining two axes explained 12% and 10% of the variation  or only those with larger sample sizes . And, patch isolation (pcaIso1) was also significantly negatively correlated with patch area (pcaArea2) regardless of whether we used the dataset that include all populations  or only those with larger sample sizes . This suggests that for our dataset we were unable to parse out completely the impacts of patch size (pcaArea2) and development (pcaArea1) from the degree of patch isolation. Interestingly, in this data, increasing isolation was associated with decreased development and larger population sizes. To investigate if there was a geographic pattern to the fragmentation parameters, we tested for correlations with longitude and latitude. We found no correlations between longitude and size (pcaSize1 or pcaSize2), but there was a significant positive relationship with patch size (pcaArea2) for both the complete dataset with all populations  and the dataset restricted to only larger populations , suggesting that patch areas to the east are larger. Similarly, the significant negative correlations between longitude and patch isolation  suggest most populations to the eastern edge of the range are also less isolated. Additionally, there was a significant negative relationship between latitude and patch isolation , suggesting southern populations are less isolated. There were no correlations between latitude and patch size (pcaArea2), development (pcaArea1) or size (pcaSize1), but there was a significant positive correlation with the second PCA axis of size (pcaSize2) and latitude for both datasets . Although this axis only explained 6–12% of the variation for size, it is strongly negatively influenced by minimum census size. This might suggest that northern populations have recorded some of the smallest census sizes.Before we ran the models, we tested all PCA axes for evidence of correlation between axes, as well as between latitude and longitude. Most pairs of axes showed no significant correlations with each other. An exception was patch isolation (pcaIso1), which was significantly negatively correlated with development (pcaArea1), and this was regardless of whether we used the dataset that includes all populations  included population size  and longitude  also retained size  and longitude (p = .01), but also included patch isolation , amount of development , and latitude (p = .04). When we used the more restricted dataset the best model for effective number of alleles (Ne) was less supported  but still included both population size  and longitude (p = .17). While with expected heterozygosity, the best model  also retained size  and longitude (p = .005), but it also included latitude (p = .01). In all scenarios, the genetic diversity metrics decreased with population size  in the full dataset  and the large population‐only dataset  both retained population size a predictor , and the large populations dataset also retained development (pcaArea1 p = .04) and longitude (Longitude p = .02) as strongest predictors. By contrast, the models that tested for Relatedness (R) using only large populations did not retain any variables, but when we included all populations, the best model  was negatively correlated with population size  and patch area . The models for degree of genetic differentiation  when using only large populations did not retain any variables; however, when we included all populations, the models for both Fst  and Gst  retained population size , patch isolation , and patch area . In all cases, the degree of differentiation increased with decreasing population size  retained only patch area , while the model using all population  retained both patch area  and longitude (p = .07). For effective population, only the models using large population  retained patch area , while models using all populations  retained development  and longitude (p = .01). This suggests that patch area is the best predictor of population size, with larger patches having larger populations. The association of size and longitude likely reflects the higher number of small populations to northeast of the range . By contrast, the average standard deviation between the complete and subsampled data for inbreeding (FIS) was much greater (SD = 0.15). The large range of variation in the inbreeding coefficient likely reflected that populations comprised of only seven individuals will vary more in the ratio of related and unrelated individuals. Although we found negative FIS values within subsampled datasets for most populations, all replicates of the critically small populations’ subsampled datasets had low‐to‐negative inbreeding values suggesting that for these populations this result is not a product of chance or small sample size alone.For critically small populations, sample size can make it difficult to draw conclusions when comparing genetic data from populations with larger samples sizes (>30). To correct for differences in sample sizes, we subsampled all populations to generate populations with seven randomly selected individuals . We found that the adjusted average values of NE, F1,368 = 113.6, p < .001; and HE, F1,368 = 98.36, p < .001). Interestingly, FIS of the subsampled populations was also negatively influenced by population size based on the 2015 census size , like the results from the complete population samples . Southern populations had slightly more genetic diversity than the northern populations. The low genetic differentiation and weak isolation by distance suggest historically high levels of gene flow between P. leucophaea populations. Over the last 100 years, human development and agricultural encroachment have fragmented P. leucophaea habitat, and as a consequence many populations have gone extinct and over 70% of the remaining populations have been reduced in size  most of the populations with critically low numbers had negative inbreeding coefficients. Previous studies have reported similarly high levels of inbreeding for larger populations of P. leucophaea . Although heterosis is beneficial in some systems  were those populations to the north. Together these analyses show that populations in the Chicago Metropolitan area, at the northwestern edge of the range, have some of the smallest population sizes. The association between urbanization and population size suggests these populations are at high risk of going extinct.P. leucophaea populations in modern fragmented landscapes ; Formal analysis ; Investigation ; Writing – original draft ; Writing – review & editing . Laura Steger: Investigation ; Writing – review & editing . Cathy Pollack: Conceptualization ; Writing – review & editing . Rachel Wells: Investigation ; Writing – review & editing . Jeremie Fant: Conceptualization ; Formal analysis ; Writing – original draft ; Writing – review & editing .Supplementary MaterialClick here for additional data file.


Xanthocyparis vietnamensis is an endangered species that is currently restricted to karst mountains in southwestern China and Vietnam. This rare conifer was first recorded in 2002 from northern Vietnam and then in 2013 from Guangxi, China, yet nothing is known about its genetic diversity nor ploidy level variation, although previous cytological study suggest that Vietnamese populations are tetraploids. There have been about 45 individuals found to date in Guangxi, China. Here, we genotyped 33 X. vietnamensis individuals using 20 newly developed, polymorphic microsatellite loci, to assess the genetic variability of its extremely small populations. The genetic diversity of X. vietnamensis (HE = 0.511) was lower than that of two other heliophile species, Calocedrus macrolepis and Fokienia hodginsii, which have similar distribution ranges. This is consistent with the signature of a genetic bottleneck detected in X. vietnamensis. Although the population genetic differentiation coefficient across loci is moderate (FST = 0.125), STRUCTURE analysis revealed two distinct genetic clusters, namely the northern and southern population groups; DAPC analysis grouped the southern populations together in one cluster separate from the northern populations; AMOVA analysis detected a significant genetic differentiation between the two population groups , and BARRIER analysis detected a genetic barrier between them. Moreover, we detected differentiation in ploidy level between northern and southern populations, sampled individuals from the former and the later are all diploid and tetraploid cytotypes with mean genome sizes of 26.08 and 48.02 pg/2C, respectively. We deduced that heterogeneous geomorphology and historical events  may have contributed to population fragmentation and small population size in X. vietnamensis. Considering both genetic and ploidy level differentiation, we propose that two different management units (northern and southern) should be considered and a combination of in situ and ex situ conservation measures should be employed to preserve populations of this endangered species in southwestern China in the light of our findings.Population genetic assessment is crucial for the conservation and management of threatened species.  Understanding the level and distribution of genetic variation has implications for the conservation of species and ecosystems . Measurein situ long-term survival, differentiation, and speciation  and hence were placed into a genus that comprised of only these two species  and the Old World Cypresses (Cupressus sensu strico) into a single genus (Cupressus sensu lato)  (the HCX clade) (Cupressus sensu strico) is sister to junipers (Juniperus) (X. vietnamensis diverged from the other species around late Eocene (Xanthocyparis (sensu stricto), it is high likely that it has survived a long history of environmental changes since late Eocene, and high likely that the species had a considerably wider distribution in warmer geological periods. Hence, this recently discovered, endangered conifer merits further investigations to aid its conservation.Although previous studies suggested that  species , anothersu lato) . HoweverX clade) ; the HCXniperus) . Within e Eocene . AlthougX. vietnamensis is tetraploid with a genome size of 44.60 pg/2C, which is unexpected considering the generally low frequency of polyploidy in extant gymnosperms  reveal the current level and distribution of genetic variation across sampled sites  in Guangxi, China, 2) identify ploidy level variation of these population and test the connection between patterns of genetic and ploidy level variations, 3) detect the effects of habitat fragmentation and isolation on genetic characteristics, and 4) provide useful insights for optimal conservation strategies.Southwestern China and northern Vietnam, with their warm, humid climates and diverse topographies, have been identified as long-term climatically stable refugia likely to preserve ancient lineages, meaning these refugia may be prioritized for the conservation of relict plants . These rodginsii , yet no X. vietnamensis in Guangxi, China from 2013 to 2020, there were eight karst mountains in Guangxi where this species was found. Five and three mountains were located in the northern and southern border of Guangxi respectively  > 0.4), it was deemed to contain excessive null alleles. Loci were tested for natural selection using BayeScan . The amplification program was performed as follows: initial denaturation at 98°C for 3°min; followed by 40 cycles at 98°C for 10 s, annealing temperatures see  of the pBayeScan , and theA), number of effective alleles (AE), Shannon’s diversity index (I), observed heterozygosity (HO), expected heterozygosity (HE), inbreeding coefficient (FIS), and genetic differentiation coefficients between groups (FST) per locus. The statistical significance of the deviation of loci from Hardy-Weinberg equilibrium (HWE) and the linkage disequilibrium test between two loci were assessed by the Markov chain method in Genepop v.4.7  using GenAlEx v.6.5 : number op v.4.7 . Sequentp < 0.05 . We alsop < 0.05  in GenAlatistics , using d−4 and pre-bottleneck effective population size of 400 (θ = 4Neμ = 0.16) and 4,000 (θ = 1.6) to estimate the M-ratio. The mean size of non-single-step mutations and the percentage of mutations larger than a single step were set to 3.5 and 0.1, respectively (M0) estimated using the program M_P_Val was compared to the 95% critical M-ratio (Mc) estimated using the Critical_M program. We assumed that the population experienced a significant bottleneck if M0 < Mc when Ne = 4,000, and a moderate bottleneck if M0 < Mc when Ne = 400.The bottleneck test for population was estimated using the M-ratio method , which iectively . The M-rX. vietnamensis populations. This analysis was run for 10 independent runs per K value (1–10) with a burn-in period of 50,000 iterations and 100,000 Markov chain Monte Carlo (MCMC) iterations, using the admixture  model. Structure Harvester   was used to infer the optimal number of clusters  . Discrimood L(K) . The numclusters . The funΘ , mutation-scaled migration rate M , and the number of migrants Nm (Nem) between the two geographic groups (Θ (rang 0–80), M (range 0–300), and reported the mean and 97.5% confidence intervals for Θ and M. The starting values for Θ and M were estimated using the FST. Monmonier’s maximum difference algorithm implemented in BARRIER v.2.2 (MIGRATE-n (version 3.3.1) was used to estimate the mutation-scaled effective population size c groups . We usedER v.2.2  was usedER v.2.2 .X.vietnamensis (Cupressus funebris (2n = 22 and 2C = 21.52 pg DNA) (Pseudolarix amabilis (2n = 44 and 2C = 52.2 pg DNA)   and Pseu pg DNA)  were selhttp://data.kew.org/cvalues) and Index to Plant Chromosome Number (IPCN) (http://legacy.tropicos.org/Project/IPCN).BD FACSCalibur™ Flow Cytometer  equipped with a solid-state laser for PI excitation was used to detect DNA content . Three replicates were run per individual, recording at least 10,000 particles (including nuclei and fragments) per replicate. The data analysis was performed in BD CellQuest™ Pro (version 6.1). The resulting histograms were analyzed with ModFit LT 4.1.7 software. When CV values of G0/G1 peaks were below 5%, the analyses saved. The determination of ploidy level was based on the relative nuclear DNA contents and published chromosome counts . The datX. vietnamensis populations analyzed in this study (HO) and expected heterozygosity (HE) per locus ranged from 0.156 to 0.940 and from 0.219 to 0.839, respectively. The inbreeding coefficient (FIS) per locus ranged from −0.379 to 0.575. Five loci displayed significant deviations from Hardy-Weinberg equilibrium and 10% locus pairs showed evidence of linkage disequilibrium after sequential Bonferroni correction. All loci were retained for further analysis. The genetic differentiation between the geographic groups (FST) varied among loci, ranging from 0.008 to 0.450 (Twenty microsatellite markers were polymorphic in the is study . No lociis study . The obsto 0.450 .HE (0.439), and N4 had the lowest HE (0.373) at the population level. At the group level, the north had a slightly higher HE (0.469) than the south (0.438). The M-ratio test for the X. vietnamensis populations detected a significant bottleneck .The genetic diversity estimates at the population and group levels are summarized in K) continued to increase up to K = 4, and the K = 2 model received the highest delta K value  suggested a moderate level of differentiation between the northern and southern population groups (FRT = 0.089) was statistically significant (p < 0.05), while the genetic differentiation among populations and among individuals were not statistically significant  from south to north (0.770) being less than from north to south (2.008) (The mean  (2.008) . The BAR (2.008) .n = 2x = 22 for the individuals belonging to the northern populations, class 2 possessed higher values  corresponded to tetraploid with 2n = 4x = 44 for the individuals belonging to the southern populations  corresponded to diploid with 2ulations . Flow cyX.vietnamensis revealed that its populations from southwestern China exhibit lower diversity  than the long-lived perennial , widespread , regional  and narrow range plant populations , but exhibit higher diversity than endemic plant populations  according to the mean values provided by X. vietnamensis  was lower than that of C. macrolepis        and F. hviduals) , yet higviduals) , Cathayaviduals)  and Abieviduals) . HoweverC. argyrophylla and A. ziyuanensis were restricted to a few isolated alpine habitats with cold microclimates. These species tend to have lower levels of genetic variation and are faced with a greater threat of losing their last refuge in South China in the face of global warming  among X. vietnamensis populations in southwestern China. This is similar to the mean values of anemochores (0.13) (F.hodginsii (0.157) (C. macrolepis (0.163) (A. ziyuanensis (0.209–0.250)   , lower t (0.157) , C. macr (0.163) , A. ziyu9–0.250) , and G. tations)  in a simtations) . The kartations) . The hiltations) . The hetX. vietnamensis, as both STRUCTURE and DAPC analyses clearly revealed a distinct division between the northern and southern population. In addition, a relatively strong barrier to gene flow between the northern and southern populations was identified by BARRIER analysis, and the estimated Nm from south to north or vice versa  using another internal standard and nuclei isolation buffers   and southern (tetraploid) populations, microsatellite diversity varied little between them. These differences may reflect the recent origin of tetraploid from the diploid . HoweverFIS) . We testS) (FIS , and foutibility . Thus, ttibility . Our dattibility . It is lX. vietnamensis are isolated as their habitat is confined to the mountain peaks. To reduce inbreeding and minimize bottleneck effect, artificial pollination or transplanting among fragmented populations in each unit should be implemented. Considering the rarity of wild individuals at present, we suggest in situ artificial regeneration programmes immediately for the populations, and urgent trials of ex situ plantations in Guangxi, China, through the collection, germination, cultivation of seeds and twig cuttage, similar to the strategy that has proven successful in Vietnam for this species.There is clear genetic and ploidy level distinction between the north and south populations in southwestern China according to the STRUCTURE analysis and genome size analysis. The disjunct geography and topography of the karst mountains and over 300 km of geographical distance between the northern and southern populations may have acted as barriers to gene flow. Hence the northern and southern populations should be treated as two different management units. Populations of X. vietnamensis. The northern populations are mostly located in the Guangxi Mulun National Nature Reserve, and the southern populations are located in the Guangxi Laohutiao Provincial Nature Reserve. In general, management effectiveness of nature reserves in China are relatively good, especially those with higher management level; but there are still some problems of nature reserves in China, such as the lacking information of boundaries and tenures, the poorly organized institution, the lacking of staff, fund and so on (X. vietnamensis was accessible for agricultural exploitation, as we saw during the field survey. We recommend more attention and tighter management for the conservation of forest trees in this reserve.In our study, sampled populations were restricted to provincial or national borders, remote from the human residential belt. The regions between the northern and southern populations are substantially impacted by humans . These ind so on . The Laond so on . Unfortu