The main goals of this investigation were to i) assess the population structure and genetic diversity and ii) determine the efficiency of the ongoing breeding program in a closed flock of Angora rabbits through pedigree analysis.The pedigree records of 6,145 animals, born between 1996 to 2020 at NTRS, ICAR-CSWRI, Garsa were analyzed using ENDOG version 4.8 software package. The genealogical information, genetic conservation index and parameters based on gene origin probabilities were estimated.e), ancestors (fa), founder genomes (fg) and non-founder genomes (fng) were 18, 16, 6.22, and 9.50, respectively. The fe/fa ratio was 1.12, indicating occasional bottlenecks had occurred in the population. The six most influential ancestors explained 50% of genes contributed to the gene pool. The average generation interval was 1.51 years and was longer for the sire-offspring pathway. The population lost 8% genetic diversity over time, however, considerable genetic variability still existed in the closed Angora population.Analysis revealed that, 99.09% of the kits had both parents recorded in the whole dataset. The completeness levels for the whole pedigree were 99.12%, 97.12%, 90.66%, 82.49%, and 74.11% for the 1st, 2nd, 3rd, 4th, and 5th generations, respectively, reflecting well-maintained pedigree records. The maximum inbreeding, average inbreeding and relatedness were 36.96%, 8.07%, and 15.82%, respectively. The mean maximum, mean equivalent and mean completed generations were 10.28, 7.91, and 5.51 with 0.85%, 1.19%, and 1.85% increase in inbreeding, respectively. The effective population size estimated from maximum, equivalent and complete generations were 58.50, 27.05, and 42.08, respectively. Only 1.51% of total mating was highly inbred. The effective population size computed via the individual increase in inbreeding was 42.83. The effective numbers of founders (fThis study provides important and practical insights to manage and maintain the genetic variability within the individual flock and the entire population. Rabbit farming is a profitable business with tremendous scope for improving the living standard of small and marginal rural farmers . AccordiKnowledge of genetic variability is very essential to evaluate the population under selection in every generation and is a prerequisite for deciding and formulating effective breeding strategies . GeneticGene origin statistics provide a historical perspective of changes occurring in a population. The probability of gene origin is used to identify the extent to which individuals influence the genetic history of a population. Inbreeding and effective population size is useful for long-term management of genetic variability and monitoring of genetic trends. Also, the knowledge of population structure and its genetic changes could assist in future management decisions, allowing for policies for genetic improvement and adaptation of a breed to a specific region .German Angora flock was established in 1986 at North Temperate Regional Station (NTRS), ICAR-Central Sheep and Wool Research Institute (CSWRI), Garsa, India. In 1997, superior germplasm from Germany was introduced and maintained as a closed flock. The population was well acclimatized to the Himalayan terrain of Himachal Pradesh. The flock was genetically improved over the last 24 years covering 22 generations of selection. The germplasm was subjected to genetic selection for improvement of production performance. Nationally, this station has been recognized as a germplasm centre for pedigreed German Angora rabbits. The superior germplasm was made available to state Animal Husbandry department, farmers, different developmental agencies, and private entrepreneurs. Genetic variation and relatedness among the breeds are prerequisite information because genetic variability is considered the primary biological resource that can be utilized in future breeding programme. There are no studies available on population structure and GD in German Angora rabbits. Therefore, the present investigation was carried out to assess the population structure and GD of a nucleus flock of the German Angora rabbits through pedigree analysis.ad libitum feeding in the morning and evening. Animals were fed seasonal grasses ad libitum and concentrate (15% to 20% crude protein) in graded quantities ranging from 90 to 220 g according to their age and physiological status. The lactating doe and kits were kept together in the kindling cage until weaning at 42 days of age. The weaned kits were transferred to individual wire mesh cage under similar housing and management practices. Each cage was equipped with the steel or earthen bowls for offering the concentrate feed and water. Sexing and ear tagging were done at the time of weaning. The rabbits were mated as and when they attain sexual maturity at 6 to 7 months of age. The female rabbit was brought to the cage of her assigned buck for breeding and returned to her cage after mating. In case of a failed conception, the doe was remated to the same breeding buck after pregnancy diagnosis. Mating of closely related individuals was avoided to keep the inbreeding levels to as minimum as possible. Symptomatic treatment was adopted for disease management under the direction of a veterinarian.The pedigree information of 6,145 German Angora rabbits born between 1996 to 2020 were collected from the Angora rabbit unit maintained at NTRS ICAR-CSWRI, Garsa, Kullu  India. The farm is located at 31.28°N latitude and 77.20°E longitude with an altitude of 1,400 to 2,100 meters above average sea level in the north temperate Himalayan valley of Himachal Pradesh. The climate is sub-temperate where the temperature ranges from −4°C to 35°C with an average annual rainfall of about 840 mm, mainly during the monsoon season. The superior germplasm was introduced in 1997 by importing 40 bucks and 60 does of German Angora rabbit from Germany. The flock was subsequently closed to outside breeding, where approximately 40 to 60 breeding does were maintained yearly with a male to female ratio of 1:5. The management system was fully intensive with the provision of clean drinking water and Data from 6,145 German Angora rabbits born between 1996 and 2020 were used for pedigree analysis and population structure characterization. The pedigree of these animals was traced as far back as possible in the pedigree register maintained at the farm. The analysis included all the ancestors and relatives of each individual. Animals with both known parents were used as the reference population for estimation of gene origin statistics using ENDOG v4.8 program [The pedigree completeness index (PCI) was calculated to provide information about the quality of the pedigree. The PCI was computed based on the completeness of the pedigree in the previous generations as described by MacCluer et al .Csire and Cdam indicate the contribution from the sire and dam lines, respectively. The contributions were computed from the following formula [where  formula .ai indicates the percentage of ancestors known in the ith generation and d is the total number of generations taken into consideration for the calculation of pedigree completeness. In this study, when calculating the average PCI for each individual according to the birth year, complete pedigrees were considered up to fifth generations of ancestors. EVA software was used to compute this index [where is index .n of all known ancestors [For each individual, following parameters were estimated to avoid introduction of individuals with incomplete pedigree records. The maximum number of generations indicates the number of generations between the individual and its most distant ancestor. The equivalent generation indicates the number of generations between the individual’s second-generation ancestors and the offspring of the furthest generation. Ancestors with unknown parents were considering as founders (generation 0). The complete generation was estimated by averaging over the sum of (1/2)ncestors . Where nsb), sire to doe (Lsd), dam to buck (Ldb), and dam to doe (Ldd). The mean GI of the population was calculated as the average of the four pathways according to Falconer and Mackay [The generation interval (GI) is the mean age of the parents at the time of birth of their progeny. The GI was computed by considering the four-selection pathway model, sire to buck (Ld Mackay .Fi) was computed using the basic formula described and modified by Gonzalez-Recio et al [The inbreeding coefficient is the probability that two alleles at a locus in an individual are identical by descent . The F wio et al –18.Fi is the coefficient of inbreeding for the individual i and ti is the corresponding equivalent complete generations. For each individual, the number of equivalent complete (EqG) generations was determined aswhere where n is the number of generations between each known ancestor and the sum is computed across all known ancestors of ith individual .The average relatedness (AR) is used as a complement or alternative to the coefficient of inbreeding to predict the long-term inbreeding of a population. The AR is the probability that an allele selected at random from the whole population belongs to a given animal . Hence, e) is the number of breeding individuals that would give rise to calculated sampling variance or rate of inbreeding (ΔF) in the form of an idealized population. Ne was estimated using the individual increase in inbreeding coefficients [The effective population size  was calculated as the number of founders that would be expected to contribute equally with genetic material to produce the same GD as the population under consideration [fe was calculated from the following formula.The genetic background in terms of the probability of gene origin was determined by computing the various parameters as explained by Boichard . The effderation . The fe f is the total number of founders and qk is the estimated proportional genetic contribution of founder k as determined by the founder’s average relationship to each animal in the current population.where, fa) is the minimum number of individuals (founders or non-founders) required to explain the complete GD of the current population. fa was computed to assess the population bottlenecks [The effective number of ancestors  accounts for genetic variation that may be lost due to random drift in small populations despite an equal contribution of all the founders in the population [fg.The marginal contribution is the genetic contribution produced by an ancestor that could not be explained by another ancestor previously selected . The foupulation . The invf indicates the average co-ancestry between participants in the reference population. The fg should be smaller than both fa and fe, which would account for all factors that influence gene loss during segregation. The non-founder genome equivalent (fng) accounts for GD loss due to genetic drift accumulated over non-founder generations. The fng was computed according to Caballero and Toro [where, and Toro .fa50) and the ratio of fe/fa. The fa is expected to be smaller than the fe in the presence of a bottleneck, which can be indicated by fe/fa ratio.The genetic bottleneck was determined by calculating the number of ancestors in the population that contributed to 50 percent of the genes  was estimated from the genetic contributions of all the identified founders of the reference population as described by Alderson .pj is the percentage of genes of jth founder contributed to the pedigree of ith animal.where The analysis of data generated on German Angora rabbits was carried out using the software package ENDOG 4.8 to estimate the genetic diversity and population structure in a well-organized farm .German Angora is a fine fibre Angora rabbit breed maintained in a close flock at NTRS, ICAR-CSWRI, Garsa, Kullu . Due to the closed nature of the flock and the inability to introduce fresh germplasm from outside it is expected that the flock may have high incidence of inbreeding. The present study revealed that the pedigree data was well maintained and provided meaningful information about the genetic architecture of the German Angora rabbit. The statistical analysis of pedigree data of the German Angora rabbit is presented in The average number of maximum generations, equivalent generation and complete generations for the studied population were 10.28±0.07, 7.91±0.05, and 5.51±0.03, respectively . The maxThe average inbreeding coefficients and AR for the whole analyzed pedigree were 8.07% and 15.82% respectively. The inbred animals had an average inbreeding coefficient of 9.30%. However, females 9.33%) had a slightly higher inbreeding coefficient than males 9.27%). The lower estimate of the inbreeding coefficient has also been reported in three different populations of Pannon , Botucatu (7%) and Sika (6.5%) rabbits ,30,31, w%. The lo% had a se computed via the individual increase in inbreeding was 42.83 in the present population. The Ne estimates measured from the pedigree information are presented in e computed from regression and log regression analyses on equivalent generations was 38.85 and 38.43 and those on the birth date were 39.29 and 38.49, respectively. The Ne estimated from equivalent generations, maximum generations and complete generations was 27.05, 58.50, and 42.08, respectively. However, these Ne values are quite higher than the estimates reported by Sakthivel et al [e from 37.19 to 91.08 in a closed population of Pannon white rabbits. The trends of Ne over the years estimated by the variances of family sizes are depicted in e estimates showed a fluctuating trend over the year’s up to 2012 and dropped suddenly in 2014 due to the stoppage of breeding and again showed an increasing trend from 2017 onwards after the resumption of breeding from the same flock. However, the values of Ne were not constant and changed with the passage of time according to the level of inbreeding. This situation can get amplified with the increasing trends of inbreeding. Ne in the present study is far below the critical value recommended by Food and Agriculture Organization [e indicate that the present flock is at risk for the instant effects of inbreeding depression. Thus, the population is under threat from reduction of adaptive genetic variation and difficult to improve through selection.The realized Nel et al . Similarel et al  reportednization . Normallnization ,36. The e and preserving the genetic structure of the population. In our study, GI for sire-offspring pathways was longer relative to the corresponding value for dam-offspring pathways, in agreement with the findings of Nagy et al [In the present study, the GI estimates for the four-selection pathways were 1.75 (Sire-buck), 1.65 (Sire-doe), 1.37 (Dam-buck) and 1.32 years (Dam-doe), giving an average value of 1.51 years , which igy et al  in Pannofe) and ancestors (fa) was 18 and 16, respectively, indicating that the reference population had somewhat similar genetic diversity as the founders. A lower estimate than the current study was reported in New Zealand White rabbits as 10 and 11, respectively [fe and fa are generally higher in larger populations, particularly when the size of the founder populations in the beginning was also high [fe/fa ratio was 1.12, indicating that founder contributions were unequal and only modest bottlenecks occurred in the population. A comparable ratio of 1.10 was also reported in New Zealand white rabbit [fe/fa ratio would be one and any deviations indicate unbalanced use of sires posing a significant risk for the loss of original genetic diversity. However, in the present study, fe/fa was greater than one indicating occasional genetic bottlenecks occurred in the flock. Bottlenecks are likely to have occurred, when fa was larger than fe, and the fe/fa ratio, resulting in a more intense bottleneck [fe/fa ratio is used to determine how much genetic diversity lost in the founders is a result of bottlenecks [The parameters describing the probability of gene origin in the German Angora rabbit were calculated using animals with both parents known, which we defined as the reference population . The totectively . Howeverectively  for Pannlso high . The fe/e rabbit . Ideallyttleneck . The fe/tlenecks .fg) and non-founder genome equivalents (fng) values in the present population were 6.22 and 9.50, respectively. The fg is linked to genetic variability loss caused by genetic drift in subsequent generations, whereas the fng calculates the amount of genetic drift that has occurred across the population since its inception. The lower fg estimate indicates a small portion of founder’s genes are present in the population. The loss of genetic variability was also reported to be higher when fe estimates became higher than fng due to accumulation of the genetic drift in the non-founder generation [Numbers of ancestors explaining 100, 75, and 50 percent genetic diversity of the gene pool were 61, 13, and 6 respectively. The six most influential ancestors explained half the genetic diversity of the population, whereas the most influential ancestor solely contributed to 7.75% of the variations. Similarly, four ancestors accounted for 50% of the genetic variability in a New Zealand white rabbit population . Differeneration .fg is observed to be less than the values of Ne, fe, and fa and it compensates for unequal founder contributions as well as fraction of founder genomes lost from the pedigree by genetic drift during bottlenecks. Unequal contributions of the founder gene are confirmed by the low estimates of fg in the present reference population. Further, the difference between fg and fa also revealed the random loss of alleles from founder animals and small percentage of founder genes in the reference population. According to the fg/fa ratio, the reference population still retained 39.05% of its ancestral genetic diversity. In the present Angora population, fe was observed to be lower than fng, implying that the loss in genetic variability was mostly due to genetic drift accumulated in non-founder generations.The estimated value for GD in the reference population relative to the base population or Nei expected heterozygosity was 0.920, indicating that approximately 8% of the genetic diversity in the base population was lost during the study period. However, the genetic diversity estimated for the base population (GD*) was 0.985. In the founder generation, losses in heterozygosity due to genetic drift and bottleneck effects (1-GD) and uneven contributions of founder alleles (1-GD*) were calculated as 0.080 and 0.015, respectively. The present findings are in accordance with the earlier reports in Adani goats  and MuzaThe average GCI and their corresponding frequency distribution are presented in The present German Angora population was established from a small number of individuals. The flock is closed genetically and thus subsequent loss of genetic diversity is expected. The present population showed an upward trend in the inbreeding coefficient as well as AR while effective population size declined over time and generations. Declining effective population size and increasing inbreeding coefficient is expected to have a negative impact on population and genetic variability. These findings threaten the long-term utilization of this important germplasm. Therefore, it is recommended that there is a need to introduce superior germplasm from outside for optimal selection response to maintain genetic diversity and inbreeding at desirable levels.


Elephas maximus maximus Linnaeus, the Sri Lankan subspecies is the largest and the darkest among Asian elephants. Patches of depigmented areas with no skin color on the ears, face, trunk, and belly morphologically differentiate it from the others. The elephant population in Sri Lanka is now limited to smaller areas and protected under Sri Lankan law. Despite its ecological and evolutionary importance, the relationship between Sri Lankan elephants and their phylogenetic position among Asian elephants remains controversial. While identifying genetic diversity is the key to any conservation and management strategies, limited data is currently available. To address such issues, we analyzed 24 elephants with known parental lineages with high throughput ddRAD-seq. The mitogenome suggested the coalescence time of the Sri Lankan elephant at ~0.2 million years, and sister to Myanmar elephants supporting the hypothesis of the movement of elephants in Eurasia. The ddRAD-seq approach identified 50,490 genome-wide SNPs among Sri Lankan elephants. The genetic diversity within Sri Lankan elephants assessed with identified SNPs suggests a geographical differentiation resulting in three main clusters; north-eastern, mid-latitude, and southern regions. Interestingly, though it was believed that elephants from the Sinharaja rainforest are of an isolated population, the ddRAD-based genetic analysis clustered it with the north-eastern elephants. The effect of habitat fragmentation on genetic diversity could be further assessed with more samples with specific SNPs identified in the current study. Elephas maximus Linnaeus, 1758) is an endangered species [Elephas maximus indicus Cuvier from Asian mainland/India (1798) [Elephas maximus sumatranus Temminck (1847), Sri Lankan elephant, Elephas maximus maximus Linnaeus (1758), and later the Borneo pygmy elephant, Elephas maximus borneensis (1970) was identified [Elephas maximus maximus has the largest body size and the darkest skin color compared with other subspecies, while the skin color of E. m. indicus, with smaller patches of depigmentation, is darker than that of E. m. sumatranus [The Asian elephant  sequence variation is currently the most common genetic approach for speciation and population genetics , 15. It Asian elephants are forest animals that are considered endangered due to widespread and intense elephant-human conflict , 18. AppE. m. maximus [E. m. maximus specifically and elephants in general. To lay the foundation, here we describe the first de novo assembly and annotation of the mitogenome of the Sri Lankan elephant. We generated the first genome-wide SNP dataset for Asian elephants with ddRAD sequencing data from 24 diverse individuals of E. m. maximus. We also assessed the evolutionary relationships of Asian elephants using available sequencing data.The majority of work on Sri Lankan elephants in the public domain focused on behaviour, ecology, and conservation , 20–25.  maximus . TherefoThe study was conducted under the research permit number: WL/3/2/2017/1 issued by the Research Committee of the Department of Wildlife Conservation, Sri Lanka (DWC). We followed relevant guidelines and regulations for animal research approved by the committee. The DNA samples for sequencing were exported under CITES  permit (Security Stamp 1888968) obtained from the DWC.Twenty-four fresh whole blood samples were collected directly from both domestic and wild elephants with known origin and morphology , S1 Fig.Elephant blood DNA was extracted using the Wizard Genomic DNA Purification kit  following the manufacturers’ guidelines. DNA samples were quantified using the Nanodrop 2000 Spectrophotometer (Thermo Scientific) and assessed the quality with Nanodrop readings and running on a 1% agarose gel. The DNA samples with 230/280 and 260/280 values of about 1.80–2.00 and a clear single band on agarose gel were processed. The total DNA amount was adjusted to about 1–1.5 ug. For ddRAD sequencing, all 24 samples were sent to Admera Health Biopharma Services, New Jersey, USA.Isolated genomic DNA was quantified with Qubit 2.0 DNA HS Assay  and quality assessed by Tapestation genomic DNA Assay . The total DNA concentration was adjusted to about 1.00 ug and the quality values of DNA Integrity Number (DIN) above 9 were considered satisfactory. The sequencing libraries were prepared using the KAPA Hyper Prep kit  per the manufacturer’s recommendations with Illumina® 8-nt dual-indices. The quantity of the final libraries was assessed by Qubit 2.0 , and quality was assessed by TapeStation D1000 ScreenTape  where electopherograms and DIN values were taken as parameters. Libraries were sequenced on an Illumina HiSeq  with a read length configuration of 150 PE for 600 M PE reads per sample (300 M in each direction).de novo reconstruction, and annotation of E. m. maximus mitochondrial genome. According to the instruction given in Mitoz, 3-5GB of data is sufficient to completely assemble a mitochondrial genome, and we used BBMap reformat.sh [de novo assembler wasn’t successful in reconstructing the D-loop region of the circular genome. This region was still not resolved after using the multi-kmer mode recommended in Mitoz. The reason could be the inability of short reads to resolve and assemble such complex and repetitive regions [Elephas maximus mitochondrial genome (NC_005129) [http://www.geneious.com/) software. After the manual curation of annotations, OrganellarGenomeDRAW v1.3.1 [We used the Mitoz v2.3  program ormat.sh  tool to  regions . Therefo_005129)  using BW_005129) , and theW v1.3.1  was usedElephas maximus mitochondrial genome (NC_005129) [Mammuthus primigenius  [Elephas maximus  [E. m. maximus mitochondrial genome (this study) for the sequence alignment. The mtDNA D-loop region was excluded from the alignment.We downloaded seven Asian elephant whole-genome datasets and two African elephant whole-genome datasets from NCBI SRA . All whole-genome sequencing datasets  were map_005129)  using BW_005129) . Geneiou_005129)  with itsYakutia) , Elephasn–Burma)  from NCBE. m. maximus mitochondrial genome (this study) using the MAFFT aligner (v.7.450) [For the nucleotide diversity analysis, we used the multiple sequence alignment of all the Asian elephant consensus mitogenomes and the v.7.450)  with itsv.7.450)  We calcuE. maximus and M. primigenius as a normal distribution with a mean of 6.01 Mya [The divergence time of the elephant specimen was estimated using the BEAST v2.6.5 software package with the fossil calibration date of 6.01 Mya , 45. The6.01 Mya , and the6.01 Mya .Isolated genomic DNA was quantified with Qubit 2.0 DNA HS Assay  and quality assessed by Tapestation genomic DNA Assay  and quality was assessed by TapeStation D1000 ScreenTape . Based on QC values, Illumina 8-nt dual-indices were used and pooled equimolar libraries. Illumina HiSeq sequencing was performed , with a read length configuration of 150 PE for 6.6 M PE reads per sample (3.3M in each direction).in silico to digest the genome using the tool FRAGMATIC [E. maximus [Loxodonta africana LoxAfr3 (Ensembl 101 release), Loxodonta africana LoxAfr4 [E. maximus preprint genome by the DNA Zoo team [SphI-EcoRI enzyme combination, which yielded relatively even and high fragment recovery across the genus was selected. We chose 300–450 bp fragment size, which has been widely used in previous studies [We tested 16 different enzyme combinations  in silicRAGMATIC . This to maximus , Loxodon LoxAfr4  and E. mZoo team . The Sph studies .We used Stacks , a well-population program for the PCA. ADEGENET [The Principal Component Analysis (PCA) is used to discern the underlying genetic diversity among the sampled individuals , 56. HerADEGENET  v2.1.4 RADEGENET , and facADEGENET  package ADEGENET . To speeADEGENET , a progrADEGENET  implemenADEGENET  was usedADEGENET  was deriADEGENET  to permuADEGENET , 70, incADEGENET , which aADEGENET , a nonpaE. maximus (NC_005129) [The assembled mitochondrial genome of Sri Lankan elephant is 16,896 bp long with 38.7% of GC content. It encodes typical metazoan mtDNA genes, including 13 protein-coding genes (PCGs), 22 tRNA genes, two rRNA genes, and one non-coding control region (D-loop) . The gen_005129)  mitogenoThe mitochondrial genome of the Sri Lankan elephant was analyzed in conjunction with previously published Asian elephants, African elephants, and fossil mitogenomes . The fosElephas-Mammuthus clade has diverged approximately ~5.9 million years (95% CI: 5–6.9 million years) ago. The estimated coalescence time of α and β clades in Asian elephants is ~1.2 million years (95% CI: 0.75–1.5 million years). The coalescence time of the β clade is estimated at ~0.4 million years, while the Sri Lanka elephant is estimated at ~0.1 million years. Kadol the Sri Lankan elephant and Icky, the Myanmar-born elephant nested closely with elephants found in South India rather than the other India-born elephants. Moreover, Uno from North India has deeply diverged from the other Asian elephants.ND1 mitochondrial gene of the Asian elephant species, and its nucleotide variability (pi) is higher than 0.015  were retained across 24 animals. Of those, each sample had an alignment rate of >93% to the Asian elephant reference genome . DAPC is a supervised multivariate statistical method that can provide an efficient description of genetic clusters with few synthetic variables. It produces synthetic variables maximizing differences between predefined sample groups while minimizing variation within groups. Assuming three clusters exist among the samples, individuals were assigned to clusters using the K-means algorithm . These cStack’s population program identified 67,432 assembled RAD loci with their calculated haplotype diversity and included 50,490 Genome-wide SNPs to analyze the population structure of Sri Lankan elephants. Higher haplotype diversity means its polymorphism is also higher . The numLoxodonta africana, Elephas maximus and Mammuthus primigenius provided insights into the historical biogeography of the elephants [de novo, and available references assisted in filling the gaps in the D-loop region. The phylogenetic analysis conducted to understand the evolution of Asian elephants included twelve mitochondrial genomes: two Loxodonta, one Mammathus, and nine Asian elephants, including the newly assembled Sri Lankan one. So far, the mt-D loop region and CYTB have been used to clarify the phylogeographic relationship and divergence of Asian elephants [Elephas-Mammuthus clade diverged approximately ~5.9 million years (95% CI: 5–6.9 million years) ago.Modern, historical, and ancient mitogenomic data from all three elephant species lephants , 68. Thelephants , 16, 69.lephants , 68, 70.lephants , 71, 72 Kadol was nested with a Myanmar elephant named Icky. It was with an estimated coalescence time of ~0.1 million years, possibly due to the historical trade of elephants as gifts between Sri Lanka and Myanmar. Moreover, other South Indian elephants namely Asha, Parvathy and Jayaprakash has placed sister to Kadol and Icky. This further suggests the existence of β1 subclade in Sri Lanka and the Southern part of India and even Myanmar. Further, Moola and NC_005129 also clustered distantly with Icky, though all those samples are from Myanmar (β1 sub-clade). During warm periods, the Asian elephants of the β clade may have moved from Indian peninsular and Sri Lanka to the eastern and southeastern parts of Asia [et al., in 2005 [Kadol from mid-latitude points out the prevalence of β1 sub-clade in the central part of Sri Lanka. However, further studies are needed with a larger number of samples to explore the coexistence of α and β clades in Sri Lanka. This coexistence of both the α and β clades in Sri Lanka and the predominance of the α clade on the mainland explained by the direction of gene flow from the larger mainland population to the smaller Sri Lankan population which occurred during more recent events of reconnection and was influenced by climate change and reflected genetic admixture of haplogroups in the Sri Lankan population [Chendra distantly clustered with the other β1 main sub-clade. This genetic difference of the Bornean elephant from other mainland Asian elephant subspecies makes it one of the highest priority populations for Asian elephant conservation [Uno which clustered distantly from the other Asian elephants is probably from the α clade. The male elephants disperse from herds, mediating nuclear gene flow [The α and β clades divergence from the Elephas is approximately 1.5 million years ago, which supports with the previous , 74. The of Asia , 75. Vidpulation , 16. Altpulation , in our ervation . The α cervation . Accordiene flow –80, and ene flow , 82. As ene flow . Therefoene flow . IncludiCYTB and ND1 mitochondrial genes exhibit the highest nucleotide polymorphism with respect to other mitochondrial genes in our study. These two genes CYTB [ND1 [Since previously utilized SSR regions have specificity issues, Asian elephant-specific makers are of critical need . Interesnes CYTB  and ND1 YTB [ND1  have beeYTB [ND1 , 14, 16.E m. maximus identified would be utilized for future ecological and evolutionary studies. These SNPs were evenly distributed throughout the genome, showing that ddRAD is an efficient next-generation sequencing method for genetic diversity studies. The SNPs identified can be further validated with more samples and used as an efficient base for the design of markers for the identification of Sri Lankan elephants.The above findings prompted us to investigate whether there is genetic diversity among elephants on the small island of Sri Lanka. Genetic diversity is the basis for evolutionary changes within a natural population. Maintaining or preserving high levels of genetic diversity is fundamental in conservation genetics . Small iOur results provide insights into elephant habitat fragmentation in Sri Lanka which may have led to different sub-populations. The genetic diversity within Sri Lankan elephants is higher, suggesting a significant differentiation at a geographical level in Sri Lanka resulting in three significant clusters; Northwest-Mahaweli, Northeast, and Southern. Habitat fragmentation may have overall positive effects on the genetic diversity of the Sri Lankan elephants. Although Fernando and colleagues  found thHowever, our recent work showed the challenges associated with utilizing existing SSR markers in elephant dung DNA work. As a result, using available genomic data with long reads followed by validation of a polymorphic set of SNPs would reduce the cost of such studies while producing high-quality data. The current analysis suggests possible genomic regions with a reasonable number of polymorphic SNPs. The size of the fragments is within the range of easy PCR amplification and Sanger sequencing and capillary electrophoresis techniques. Therefore, validation with more samples from Sri Lanka and other regions may lead to the identification of a unique set of SNPs for Sri Lanka elephants. The availability of such sensitive analytical methods will assist in overall conservation and management strategies when the Sri Lankan elephant population has fallen almost 65% since the 19th century.de novo assembled mitogenome presented here may further improve with long-read sequencing approaches. Our data strongly suggests a genetically closer relationship between Sri Lankan elephants to Southern Indian elephants and Myanmar elephants being in β1 clades A total of 50,490 genome-wide SNPs were identified among 24 Sri Lankan elephants, and further analysis clustered them into three clusters suggesting considerable difference. A set of polymorphic SNPs were identified for further validation. The results suggest the importance of social organization and biogeographic barriers in shaping the distribution of genetic variation among Asian elephant populations in Sri Lanka. These variables could be further assessed by including more individuals in the analysis.The first S1 Fig(A) Kamani (B) Sandali (C) Uthpala (D) Anuradha (E) Kadol (F) Wanamali (G) Gangana (H) Abaya (I) Pillu (J) Kumari (K) Anusha (L) Menika II (M) Mathalee (N) Meena (O) Sapumalee (P) Sukumalee (Q) Malee (R) Nilgala (S) Migara (T) Kadira (U) Parami (V) Sinaharaja _066 (W) Bandula (X) Madawee.(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S1 Table*No significant phenotype found.(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.


Eucalyptus melliodora) trees in endangered Box-Gum Grassy Woodland in SE Australia, some 30 years after planting with seeds of reportedly local provenance. Using DArT sequencing for 1406 SNPs, we compared genetic diversity and population structure of planted E. melliodora trees with remnant bushland trees, paddock trees and natural recruits. Genetic patterns imply that natural stands and paddock trees had historically high gene flow (among group pairwise FST = 0.04–0.10). Genetic diversity was highest among relictual paddock trees (He = 0.17), while diversity of revegetated trees was identical to natural bushland trees (He = 0.14). Bayesian clustering placed the revegetated trees into six genetic groups with four corresponding to genotypes from paddock trees, indicating that revegetated stands are mainly of genetically diverse, local provenance. Natural recruits were largely derived from paddock trees with some contribution from planted trees. A few trees have likely hybridised with other local eucalypt species which are unlikely to compromise stand integrity. We show that paddock trees have high genetic diversity and capture historic genetic variety and provide important foci for natural recruitment of genetically diverse and outcrossed seedlings.Assessment of woodland restoration often focusses on stand demographics, but genetic factors likely influence long-term stand viability. We examined the genetic composition of Yellow Box ( The goal of restoration is to create a self-supporting ecosystem that is resilient to adversity4, but the effectiveness of ecological restoration programs is dependent upon a suite of population genetic issues that influence the success of ecological restoration and revegetation planting programs7. First, the level of population subdivision among remnant or revegetated populations has implications for the level and distribution of genetic diversity, and the likelihood that restoration will exacerbate inbreeding or outbreeding and may produce inbreeding or outbreeding depression11. In the long term, natural genetic subdivision for any species will reflect the dynamics of pollen and seed dispersal and patterns of extinction and colonisation. However, in restoration programs, fragmentation will initially be determined by patterns of planting and consequently by potentially atypical gene flow (e.g.11). If there are high levels of subdivision and low gene flow among natural stands, restoration planning should consider the likelihood of site-specific adaptation in sourcing material for transplantation12. Second, remnant or revegetated populations may lack sufficient or appropriate genetic diversity as a result of genetic drift, founder effects, inbreeding and inappropriate sourcing of material for transplants. Fitness can be reduced in inbred offspring either because of loss of beneficial heterozygous traits, or by the expression of recessive deleterious traits, and can include reduced seed crops, smaller seeds, poor seedling survival and lower capacity to respond to environmental change (reviewed in13). Restoration programs should typically aim to avoid elevated inbreeding due to small stand sizes and/or sourcing seed or seedlings from small numbers of closely related individuals. Such programs should also ensure that revegetation programs have at least natural levels of genetic diversity because it is positively associated with population fitness in the short-term, and adaptive capacity and resilience to environmental change in the long-term15. Third, many species that are targets for woodland restoration have been shown to hybridize with native and exotic species20 which can be a problem for restoration programs since first or later generation hybrids may have lower viability than pure-bred individuals16, and hybridization will also limit the value of restoration programs if conserving pure gene pools is a priority21. It is important to note however, that a range of studies have argued the benefits of utilizing first generation hybrids in restoration programs if they possess beneficial traits such as drought tolerance or elevated resistance to pests or climate change24.In many parts of the world, woodland ecosystems have been extensively cleared for agriculture and urban development, often leaving only small remnants. As a result, trillions of dollars are spent on ecological restoration and revegetation planting programs each year to restore biodiversity to degraded landscapes25. Eucalypts have a mixed mating system, but are predominantly outcrossed via insect pollination with outcrossing rates typically between 0.44 and 0.9626. Interestingly, the degree of outcrossing within a single tree can vary from the top down due to pollinator foraging patterns as they move through the canopy27. While eucalypt pollen can travel up to 2 km28, more typical dispersal distances are around 200 m30. These characters imply that even moderate spatial isolation will increase the likelihood of selfing or inbreeding for paddock trees and remnant stands and that populations will display evidence of subdivision. Planning or evaluating the success of restoration programs is further complicated by the fact that eucalypts can sometimes hybridize17, though the likelihood of hybridization can vary dramatically with a range of factors including the genetic distance between potentially hybridizing taxa31, their relative and absolute density, and pollen dispersal distances32.Eucalypt woodlands are a key component of the Australian landscape and were once widespread in southern Australia. However, large areas have been extensively cleared since European settlement, and in many landscapes where clearing has reduced the extent of remnant woodlands, remnant paddock trees are a key resource for woodland restoration34. Broadhurst’s study13 using genetic (microsatellite) data to assess the diversity and mating systems of small numbers of scattered and revegetated Eucalyptus melliodora (Yellow Box) trees is an important exception. Broadhurst13 collected samples from five restoration sites each within 200 km of Canberra in the Australian Capital Territory. Despite considerable among-site variation, both in the design of the restoration plantings and proximity to paddock trees, that study highlighted the importance of paddock trees as sources of genetic diversity and as pollen sources for seed on revegetated trees13. Broadhurst13 also found considerable differentiation among paddock and planted trees and concluded that many were not of local provenance. We set out to build on Broadhurst’s work by evaluating, from a genetic perspective, the success of the restoration program in Warrumbungle National Park (WNP) where there is a set of E. melliodora plantings that were carried out over approximately 10 years to supplement a small number of remnant paddock trees that are potentially isolated from surrounding natural stands and that have formed foci for some natural regeneration. The restoration program is located within a 500 ha valley that had been extensively cleared for pastoral activity and grazed from the 1900s until the 1960s. Although we have observed in situ seed set and recruitment in the valley area, it is not known whether there is sufficient genetic diversity to maintain populations in the long term, or whether inbreeding is likely to constrain their persistence. Moreover, plantings are thought to have been locally sourced, but this has not been confirmed genetically, nor is anything known of the genetic make-up of either natural or revegetated stands, their mating systems or extent of genetic connectivity throughout the system.In eastern Australia, the White Box-Yellow Box-Blakely’s Red Gum Grassy Woodland ecosystem (known as Box-Gum Grassy Woodland) is critically endangered and has been the target of restoration programs that have been largely unassessed (but seeOur specific objectives were to: (a) assess and compare the genetic differentiation and mating systems of natural stands surrounding the restoration area with that of surviving paddock trees, recruits and planted stands to estimate levels of connectedness; (b) determine whether paddock trees, recruits and planted trees capture similar levels of genetic diversity to the natural stands; and (c) use these data to infer the provenance of planted trees and to test for evidence of hybridization.Eucalyptus melliodora) in Box-Gum Grassy Woodlands in WNP. Box-Gum Grassy Woodlands are critically endangered and are protected by Australian Commonwealth35 and NSW state legislation36. Due to the occurrence of these woodlands on fertile soils, they were extensively cleared for agriculture, and intact remnants are now extremely rare. Current estimates indicate that only 405,000 ha of the woodland ecosystem remain (~ 10%), with few high-quality remnants anywhere across its former range37. These woodlands are characterised by a dominance or prior dominance of White Box  and/or Yellow Box (E. melliodora) and/or Blakely’s Red Gum (E. blakelyi) trees. Box-Gum Grassy Woodland was previously abundant within the central valley of WNP , but extensive land clearing led to the widespread removal of trees prior to the establishment of the reserve. Scattered relictual E. melliodora paddock trees remain in the central valley. These trees were often solitary and were distributed broadly across the valley.This study focused on Yellow Box .We selected samples from remnant populations on the valley sides at a distance > 1 km from plantings on the uncleared slopes of the valley or separate to the central valley, with a preference for larger (likely older) trees, to increase the probability that these trees pre-date the germination of planted trees and hence provide a better reference for remnant 39. Extraction of genomic DNA was conducted in accordance with a modified CTAB protocol produced by DArT.We used DArTseq services  for both DNA extraction from leaf material, and sequencing of SNPs (Single Nucleotide Polymorphisms) using a high-density microarray developed for eucalyptsPstI/TaqI based method developed by38, using enzymatic breakdown to select for more active genomic regions and remove repeat sequences. Next Generation Sequencing (NGS) was used to detect SNP polymorphisms across the genome of each sample against a library developed for eucalypts38 detecting dominant, biallelic SNP loci. Polymorphic SNP loci were sequenced across the genome all leaf samples, using the DArTseq microarray at a ‘high intensity’ run. Sequencing was carried out on an Illumina HiSeq 2,500 using 75-cycle single end reads. Raw reads were processed using DArT's proprietary variant calling pipeline, DArTsoft-14.Complexity reduction was conducted through a 40 using the following filters: secondaries removed, read depth ≥ 10, call rate ≥ 0.9, minor allele frequency ≥ 0.01, reproducibility rate ≥ 0.99, monomorphic loci removed and individuals with 20% missing data were removed. In addition to the excluding loci with a reproducibility rate of < 0.99, we also included 10 duplicate samples in the sequencing run  to check the reliability of the sequencing results. For each duplicate we compared the sequencing result at each locus  and counted the number of times this result differed between the replicates, then divided this by the total number of loci.We filtered the primary dataset at a stringent level to ensure only high-quality markers were retained, and therefore genotypes were accurate. Loci were filtered in DARTRFST in DARTR40, and conducted a Principal Components Analysis (PCA) in Adegenet41 together with a Bayesian cluster analysis in the software ParallelStructure 2.3.4 on the CIPRES portal42 to identify the number of genetic clusters in the dataset. We used the admixture model of ancestry with correlated allele frequencies, a burn-in length of 80,000 followed by 120,000 MCMC replicates after burn-in, and we conducted 5 iterations each of K = 1 to K = 14. To estimate the number of genetic clusters (K), we used StructureSelector43, and considered DeltaK44 and MedMeaK, MaxMeaK, MedMedK and MaxMedK45. A subpopulation is considered to belong to a cluster if its arithmetic mean (for MedMeaK and MaxMeaK) or its median (for MedMedK and MaxMedK) membership coefficient to that cluster is greater than a threshold value (set to 0.545).To analyse population structure, we calculated pairwise 46. To detect hybrids, we compared the genotypes of a subset of E. melliodora trees with other trees in the study site. Included in this analysis were 61 samples of E. melliodora selected randomly from all populations in the main dataset (except recruits) including natural stands, paddock trees, and planted trees, together with 42 samples of E. albens, and 11 samples from other species growing in close proximity including E. crebra and E. blakelyi (labelled “other spp.”). Eucalyptus melliodora, E. albens and E. crebra all belong in Eucalyptus Section Adnataria and are relatively likely to hybridise with each other47. Eucalyptus blakelyi is from Section Exsertaria and is less likely to hybridise with these species31. Loci were filtered as above , and a genetic cluster analysis was conducted in ParallelStructure. We used the admixture model of ancestry with correlated allele frequencies, a burn-in length of 80,000 followed by 120,000 MCMC replicates after burn-in, and we conducted 5 iterations each of K = 1 to K = 6. To estimate the number of genetic clusters (K), we used the Puechmaille method45 on StructureSelector43, which calculates MedMeaK, MaxMeaK, MedMedK and MaxMedK.To estimate genetic diversity and mating system, we calculated observed heterozygosity and expected heterozygosity under Hardy–Weinberg equilibria, and F (inbreeding coefficient) in GenAlEx 6.51bThe experimental research and field studies on plants (either cultivated or wild), including the collection of plant material, complied with relevant institutional, national, and international guidelines and legislation. Samples were collected under NSW Scientific Licence SL102262.The DArTseq high-throughput microarray yielded 64,416 polymorphic SNP loci across the 221 samples. This data was of a high quality with 35.2% of loci at a call rate ≥ 85%, and an average call rate of 63.5%. A total of 219 individuals and 1406 loci were retained under our high stringency filters. Out of the 10 duplicate samples, the average similarity was 99.4% (9 duplicates had an average of 99.97% and one had an average of 94.3%). In all cases, the difference between the duplicates was due to a difference in the level of missing data (i.e. the number of loci that were scored), with no instances of loci being genotyped differently between the duplicates.FST analysis, PCA and Bayesian clustering analysis in Structure indicated that there was moderate genetic subdivision across the entire set of sampled E. melliodora trees. Global FST across all sites was 0.11, while pairwise FST values ranged from 0.01 to 0.32 and all were significantly different from zero  that was well represented within the paddock trees, while the remaining two natural stands (locations 2 and 4), were distinct and not represented among paddock trees, planted trees or recruits. The widely scattered paddock trees showed some differentiation from the natural stands  have the lowest pairwise FST of all other comparisons with population 14 .The relictual paddock trees had the highest genetic diversity of any of the groups (mean FIS) for the valley side stands averaged 0.203, while the relatively isolated paddock trees and surrounding recruits had mean inbreeding coefficients of 0.194 ± 0.004 and 0.172 ± 0.004 respectively , E. albens (n = 42) and individuals of other species (n = 11) representative of the seven common eucalypt species within the valley. The MedMeaK, MaxMeaK, MedMedK and MaxMedK values indicated that the most likely number of clusters was K = 3. Our analysis of these data using ParallelStructure 2.3.4 showed that both the E. melliodora and E. albens trees formed clear clusters differentiated from the other species , one E. melliodora X other species hybrid and one E. albens X other species hybrid. Both of these were sampled from natural stands.After loci were filtered, genotypes were available for 1137 loci which comprised bens n = 2 and indEucalyptus melliodora populations in WNP is one of the few studies that assess the genetics of woodland tree restoration by comparing the genetic structure of revegetated stands to both paddock trees and intact natural stands (e.g.7). We found that at a broad level, paddock trees and surrounding natural stands form comparably diverse and well-connected populations; moreover, the 30-year old Warrumbungle NP restoration program has been largely successful in adding genetically diverse stands derived from locally sourced seeds. In addition, our study further supports earlier work with both E. melliodora and other eucalypts in highlighting the value of paddock trees as both sources of existing genetic diversity and foci for recruitment29. This study builds on the work of Broadhurst13 who emphasised both the long and short-term value of paddock trees as reservoirs of genetic diversity and as potentially important pollen sources that minimise the effects of inbreeding within seed crops on planted trees. Our data show that paddock trees include genetic diversity not conserved elsewhere in the nearby stands, and that they were clearly parents of the majority of our sampled recruits. However, our data also show that similar to Broadhurst’s study13, a proportion of planted trees were not locally sourced and at least one paddock tree was a recent generation hybrid. Overall, our data suggest that the WNP Box-Gum Grassy Woodland restoration program has been particularly successful in capturing the genetic diversity of the broader population of E. melliodora within WNP, and the combination of this restoration and recruitment centred on paddock trees should ensure that the population retains diversity for future generations.Our study of the population genetics of natural and revegetated 13 that the revegetated  individuals of E. melliodora would have lower genetic diversity than natural populations due to a low number of source individuals, we found that genetic diversity in the planted population was similar to the surrounding natural populations and the paddock trees, with the planted trees showing no reduction in genetic diversity. Other studies have shown varying results in restoration programs with some restoration populations having higher genetic diversity than reference sites, while others have lower diversity than reference sites. For example, a recent meta-analysis of 26 studies showed that levels of genetic diversity were strongly associated with the number of source sites used, and most revegetation projects that used multiple source sites showed higher genetic diversity than those that used only one site48. The comparable level of genetic diversity between the planted trees and the natural reference population in the surrounding bushland suggests that sourcing seed from six populations for a replanting program of this size would be sufficient to maintain genetic diversity in the revegetated population. From a conservation perspective, the genetic diversity of naturally occurring seedlings and the contribution of genetic material from remnant paddock trees provides stronger evidence of successful restoration. By extending the work of Broadhurst13 from looking at potential recruits (seeds), to established naturally occurring seedlings, we can have more confidence in our findings, since these plants have already survived local selective pressures and any genetic load inherited from interactions within and among planted and remnant trees.Contrary to our expectation based on Broadhurst25 and13). Here, we demonstrate that they are a significant source of genetic variation which is important to capture in revegetation programs to increase adaptive potential, retain locally adapted genotypes, enable efficient selection, mitigate inbreeding, and minimise genetic drift50. The planted trees that were likely sourced from the paddock trees showed similar levels of genetic diversity to natural stands, suggesting that seed collected from paddock trees is of sufficient quality to contribute to population regeneration, as also identified in other studies30.As a group, the paddock trees were the most genetically complex and diverse trees of those sampled. The importance of paddock trees in maintaining ecosystem health and biodiversity within agricultural landscapes has been widely reported . Our study is one of the few studies to use DArT-seq markers in a population genetic study of eucalypts (but see54). These studies also found relatively high levels of structure within species . The use of DArT-seq markers, primarily due to the generation of large numbers of SNPs across the genome, appears to improve population differentiation55, but caution is advised in comparisons with F statistics derived from other markers55.Within WNP, we found that the natural populations of E. obliqua found that local adaptation in response to different elevations can occur over as little as a few hundred meters56. This in turn might imply that it is important to maintain strict local provenance in attempting restoration, to maintain site-specific adaptations. However, given that at least some trees have recruited since the clearing associated with pastoral activity, it is also possible that recruits derived from the isolated relictual paddock trees are simply the result of more erratic patterns of long-distance pollination than would occur in valley side stands. Most pollen dispersal in E. melliodora and other eucalypts is thought to occur over tens of metres, although intraspecific and interspecific pollination over several kms has been inferred for a range of eucalypt species57 including E. melliodora13.Bayesian clustering analysis detected a number of unique genetic clusters within the paddock trees which suggests that some genotypes may be favoured within the valley floor rather than valley side populations. Planted population 14 and the relictual paddock tree from which it appears to have been seeded, showed the greatest level of genetic differentiation from other populations, which could indicate that selection is driving micro-site adaptation on the valley floor. A previous study of population genetic differentiation in 53 in order to improve ecological outcomes and increase adaptive potential to environmental change58. However, the issue of micro-site genetic variation and adaptation (identified above), also presents a challenge for mixed provenance strategies.Our genetic cluster analyses indicated that the planted trees were sourced from at least six different populations, and confirmed that a mixed provenancing strategy was used in this revegetation program. Planted stands 10, 11 and 14 were sourced from local seed from paddock trees and possibly surrounding natural stands, while populations 12 and 13 were probably not sourced locally. There is strong evidence that some of the revegetated trees planted in close proximity to paddock trees have interbred with these paddock trees to contribute to natural recruitment. There is increasing support for adopting strategies of mixed provenancing  to broaden the genetic base of revegetated areas59. However, in this study neither the (often isolated) paddock trees, planted trees or the two sampled stands of recruits showed evidence that inbreeding levels were higher than those seen in the six natural stands. It is noteworthy, however, that Broadhurst13, while acknowledging that E. melliodora has a mixed mating system, reported an outcrossing rate (t) estimated through maternal adult/seed comparisons that was consistently close to 1.0. In contrast, the genotypic make-up of both adult and recruit stands in this study suggests that all sampled WNP populations were more inbred. Estimation of t assuming a stable equilibrium within our sampled populations ranged from ~ 0.7 to 0.8 (where t at equilibrium = (1 − F)/(I + F)60).Elevated inbreeding with the consequent risk of inbreeding depression are threats to the long-term survival of both isolated plants and stands restored from small numbers of individualsE. melliodora and other members of Eucalyptus section Adnataria are common [47 and references therein]. Section Adnataria also includes E. albens, E. crebra and E. sideroxylon, all of which are common within study area and have long and overlapping flowering seasons. The restoration plantings have increased connectivity and relative abundance of multi-species pollen in the restoration area and may increase the likelihood of hybridisation32. An assessment of naturally occurring offspring would provide insight into the significance of this phenomenon for restoration practice.Perhaps surprisingly, given the relatively low outcrossing rates of the WNP stands and the apparent isolation of the paddock trees, we detected little evidence of hybridisation across the trees sampled in our study. The four recent inter-specific hybrids that we detected were within the scattered paddock trees or natural stands and likely represent a cohort of trees that pre-date agricultural clearing, suggesting historic or even pre-colonial hybridisation. Hybrids among E. melliodora in WNP are largely comprised of genetically diverse, mostly local provenance trees. The high genetic diversity and multiple seed-sources evident in the planted populations are testament to the success of this component of the ecological restoration project. Relictual paddock trees have been an important source of historic genetic diversity for this restoration program, and these trees can continue to be an important source of new recruits in the revegetated area and a valuable source of genetic diversity for restoration programs.In conclusion, the results of this study indicate that revegetated populations of Supplementary Information.


Plasmodium falciparum, in a more spatially comprehensive manner than any previous work. With 1804 samples from 44 sites collected in western Kenya in 2012 and 2013, we performed resistance surface analysis to show that Lake Victoria acts as a barrier to transmission between areas north and south of the Winam Gulf. In addition, Mantel correlograms clearly showed significant correlations between genetic and geographic distance over short distances (less than 70 km). In both cases, we used an identity-by-state measure of relatedness tailored to find highly related individual parasites in order to focus on recent gene flow that is more relevant to disease transmission. To supplement these results, we performed conventional population genetics analyses, including Bayesian clustering methods and spatial ordination techniques. These analyses revealed some differentiation on the basis of geography and elevation and a cluster of genetic similarity in the lowlands north of the Winam Gulf of Lake Victoria. Taken as a whole, these results indicate low overall genetic differentiation in the Lake Victoria region, but with some separation of parasite populations north and south of the Winam Gulf that is explained by the presence of the lake as a geographic barrier to gene flow. We recommend similar landscape genetics analyses in future molecular epidemiology studies of vector-borne diseases to extend and contextualize the results of traditional population genetics.This study employs landscape genetics to investigate the environmental drivers of a deadly vector-borne disease, malaria caused by  This means the environment plays a critical role in understanding and combating malaria transmission , and each individual who was willing to participate in the study.2.2 |Malaria transmission in Western Kenya is moderate to high with contemporaneous prevalence estimates ranging from 40% to 60% in the lowlands near Lake Victoria  to aroun2.3 |P. falciparum DNA samples collected in 2012 and 2013 across 44 sites in Western Kenya  and 10 μl of 2×DreamTaq Green PCR Master Mix (Thermo Scientific). PCR cycling conditions were as follows: 2 min, 94 C;  for 40 cycles; and 5 min, 72 C. After amplification, the products were combined into three groups based on size and separated on an ABI 3730 sequencer. The allele sizes were recorded using two methods, depending on the sample: manual visualization using Peak Scanner and automated extraction in the Thermo Scientific Cloud Microsatellite Analysis Software. In both cases, a threshold of 300 relative fluorescent units was used for peak detection to filter out background noise. For each microsatellite, the dominant allele and any other alleles with at least 33% of the dominant allele’s height were scored. Five hundred and seventy-four samples were processed with both methods, and 79% of the overlapping alleles were scored identically. The automated method was considered more accurate and was used for alleles scored with both methods.Eight single-copy microsatellite loci  were genP. falciparum samples were grouped into populations according to the clinic where they were collected . Of these populations, only those with at least five samples were used in further analyses. 81.3% of samples passed both filters. The preprocessed microsatellite data are provided in Samples were filtered by both the number of successfully scored loci per sample and the number of samples per study site. Only samples with at least six successfully scored loci were included in further analyses. This ensured that every pair of samples would have at least four overlapping loci. The 2.4 |Linkage disequilibrium (LD) was estimated by computing the  of loci . This wa of loci , both wiGenetic clustering of samples was first assessed with principal component analysis (PCA), a method which transforms the input matrix into a set of orthogonal components ranked in descending order of the variance they explain. Visualizing combinations of the highly ranked components allows one to identify the number of clusters present in the data. In this case, the microsatellite genotypes were converted into binary format, meaning one column per locus-allele combination, one row per sample, and a value of 0 or 1 based on whether that allele was present. This format was chosen because it allows flexible representation of samples with multiple clones. Missing data values were replaced with the mean frequency of the allele in question, as this allows all samples to be used in PCA without biasing the results. PCA was then performed on this binary matrix using the R programming language  and visurmaverick, was used to assign individuals to clusters and estimate admixture coefficients for each individual , the study site coordinates were reprojected into planar space and geographic and genetic distances were compared using both a Mantel correlogram and the test for congruence among distance matrices (CADM). Coordinate reprojection converts the elliptical coordinate space that describes the Earth’s curved surface into a two-dimensional space that is amenable to analysis. The map projection selected for this study was the WGS 84-based coordinate system for UTM zone 36 N. Once reprojected, Euclidean distance was calculated between all pairs of study sites. Mantel correlograms  were com2.6 |P. falciparum transmission or that influence host and/or vector movement were selected for inclusion in resistance surface analysis . This is consistent with weak population structure in a high transmission environment.The PCA results suggested that the potential number of distinct genetic clusters is between three and six. The first four components of the PCA explain 5.9%, 3.8%, 3.6% and 3.2% of the overall variance, respectively. Visualization of these components shows two to three distinct clusters delineated by the first component and two weakly separated clusters in the third component . The othrmaverick was run for K values from one to six. The admixture bar plots show considerable mixing overall but some structuring according to latitude . However, the correlogram shows this relationship disappears as geographic distance increases, until eventually significant negative correlation was found at higher geographic distances. This surprising result can be understood by studying the MEMGENE map discussed above. While the majority of the sites in the second cluster, corresponding to negative MEMGENE values, are south and east of the Winam Gulf, several of the sites near the Ugandan border north of the Gulf also belong to this cluster  was used with all output surfaces generated by both replicates. This is displayed for the 10 best-fitting surfaces in ys small , suggestys small .In the resistance surfaces themselves , pixels 4 |rmaverick analysis gave the first indication of this conclusion, in that samples collected from north and south of the Winam Gulf of Lake Victoria tended to have membership in different genetic clusters. The pattern of spatial clustering in the first MEMGENE variable showed the same result, with one geographic cluster of genetic similarity in the lowlands north of Lake Victoria and the other encompassing the areas east and south of the lake. Finally, the resistance surface analysis suggested both that Lake Victoria was an important variable in dictating landscape resistance, as seen through the ranking of best-fitting surfaces, and that the lake is associated with a high resistance to gene flow, as evidenced by the surfaces themselves.The results presented in this study support an isolation-by-barrier (IBB) hypothesis, where Lake Victoria acts as an obstacle to gene flow between the northern and southern parts of our study area. The Other studies using data from a similar time period and region have by- and-large shown high gene flow  leading Our results are also consistent with previous investigations into the clustering of genetic relatedness in this area. P. falciparum populations in this region, but it is only noticeable at relatively short distances  and between certain locations.Previous studies investigating isolation-by-distance in western Kenya have yielded inconsistent results. Qualitative assessments of IBD have found none , but morIn terms of IBB and IBR, few studies have been performed on malaria, but those that do exist for our study region did not identify Lake Victoria as a barrier to gene flow. Previous work on human movement in this area suggests relatively frequent travel within the Lake Victoria region . These sAnopheles gambiae s. l. in the Lake Victoria region and, while other landscape factors were found to be more important, a landscape genetics analysis did show Lake Victoria to be an area of relatively low gene flow Supplemental Text 2: STRUCTURESupplemental Table S1upplemental Table S2upplemental Table S3upplemental Table S4upplemental Table S5upplemental Table S6


Coffea arabica and Coffea canephora, to extreme weather events, with notable uneven increases in market prices. Taking into account the narrow genetic plasticity of the commercial coffee cultivars, wild-relatives and underutilized Coffea species are valuable genetic resources. In this work, we have assessed the occurrence of Coffea species in to understand the degree of genetic relationships between Coffea species in the country, as well as the patterns of genetic diversity, differentiation, and genetic structure. Only one wild species was found, C. racemosa, which showed a high level of genetic separation with C. arabica, based on plastid, as well as SSR and SNP analysis. C. arabica presented low levels of diversity likely related to their autogamous nature, while the allogamous C. racemosa presented higher levels of diversity and heterozygosity. The analysis of the functional pathways based on SNPs suggests that the stress signaling pathways are more robust in this species. This novel approach shows that it is vital to introduce more resilient species and increase genomic diversity in climate-smart practices.Climate change is negatively affecting the coffee value chain, with a direct effect on approximately 100 million people from 80 countries. This has been attributed to the high vulnerability of the two-mainstream species,  Coffea L.) plays a prominent agricultural, social, and commercial role, standing as one of the world’s largest agricultural supply chains. The livelihoods of almost 25 million people are directly dependent on coffee cultivation, and more than 100 million people in over 80 countries are involved across the entire value chain [Coffea arabica L. (Arabica coffee), which dominates the world market, and one of its ancestors, Coffea canephora Pierre ex A Froehner (Robusta coffee) [Coffee ( coffee) . However coffee) ,5, with  coffee) . Traditi coffee) . Seasona coffee) . The eff coffee) ,12,13.Coffea racemosa Lour., Coffea zanguebariae Lour., or Coffea liberica Hiern in the value chain, has been pointed out as one of the most promising approaches [C. racemosa  shows relevant resistance to several pests and diseases [The quite low levels of genetic variation found in most commercial coffee cultivars  constituproaches . These sproaches ,20,21,22diseases ,21,23. Sdiseases . Another recommended approach to mitigate climate change impacts on the coffee crop is the shift from intensive production under the full sun (and monocrop systems) to agroforestry systems (AFS) using native or other economic-important trees for shade, constituting refuges for biodiversity and providing multiple ecosystem services  ,24,25,26C. racemosa and C. zanguebariae and elucidated their distribution in northeastern Mozambique (Cabo Delgado Province) [C. racemosa in central Mozambique (Inhambane Province) was first reported in 1876 [C. arabica plantations across the country [Located in southern Africa, Mozambique might contribute significantly to the coffee value chain, although it is currently not included in the list of coffee-producing countries: (i) it is a promising source of coffee wild-relatives ,20,23, arovince) . The sam in 1876 , and the country . Four reCoffea species in southern and central Mozambique  polymorphisms to understand the patterns of genetic diversity, differentiation, and genetic structure. For that, we compared the cultivated C. arabica plants  (0.268). The fixation index was negative in all C. racemosa accessions, as well as in the C. arabica cultivars from Niassa, while the cultivars from the CaAFS and CIFC collection showed positive values of fixation  when compared with C. racemosa samples . Based on nuclear microsatellites, a total of 101 alleles were found among all samples: 56 in the set of racemosa . The meafixation . EstimatC. racemosa samples from Sofala and Maputo vs. Inhambane  provinces , and the three cultivars from CIFC included in this study (CV)  and ΔK values obtained in STRUCTURE HARVESTER . The difrovinces A. In C. udy (CV) A. Howeve samples B.C. racemosa but significantly higher in C. arabica (p > 0.05) with the increasing physical distance of SNPs.No genetic admixture was detected between samples, either using SSRs or GBS data. Linkage disequilibrium (LD) was overall low in  arabica . In bothC. racemosa from C. arabica samples . The variance was equally attributed among the K = 5 groups found by STRUCTURE (50.45% and 67.32%), and within sites  based on SSR and GBS data, respectively. Overall, genetic differentiation was significantly high , supporting the previously reported results of genetic structure.A large genetic divergence was found between from GBS . It is wPlant–pathogen interaction’, ‘Protein processing in endoplasmic reticulum’, and ‘Phenylpropanoid biosynthesis’  and the ‘Plant hormone signal transduction’  had more SNPs involved in C. racemosa than in C. arabica while ‘Amino sugar and nucleotide sugar metabolism’ was higher in C. arabica than in C. racemosa .Sequencing yielded a total of 170,720,052 raw reads, which were reduced to 51,115,669 after quality filtering . Overallnthesis’ . Only thabolism’  showed nC. zanguebariae, for which herbarium data suggested a sparse distribution in these regions [Coffea species, C. racemosa . Molecular analysis based on plastid markers congruently found two main maternal lineages, splitting this species from C. arabica . This was not unexpected and might be interconnected with the fact that (i) Mozambique is among the most disaster-prone countries on a global scale [Coffea racemosa has been reported as endemic to southern and central Mozambique, distributed across coastal and riverine forests as well as deciduous woodlands and bushlands (0 to 500 m above sea level—a.s.l.), while C. zanguebariae was considered endemic to northern Mozambique, distributed across dry deciduous forests and riverine and coastal thickets (0 to 350 m a.s.l.) [C. racemosa and C. zanguebariae are in fact so similar that they have often been believed to be the same, and only recently, DNA markers allowed accurate species discrimination [One explanation for the absence of other wild relatives in these areas could be the genetic drift of al scale , and hasal scale ,40; (ii)al scale . The sec a.s.l.) ,35. On tmination . It is, Coffea arabica presented very low levels of genetic diversity in comparison with C. racemosa , supporting the genetic bottleneck hypothesis in commercial Arabica varieties [C. racemosa are likely due to the allogamy of the species [Warburgia salutaris from southern Mozambique [Ocotea rotundata from the northern Andean forests [C. racemosa retains high levels of genetic diversity, especially when compared with C. arabica. The low levels of heterozygosity are likely due to the autogamous nature of  arabica ,47,48,49 arabica . Indeed,arieties . In cont species . These gzambique  or Ocote forests , suggestC. racemosa from the C. arabica sample, either when considering STRUCTURE results  that segregated all  results , the top results . The larfrom GBS . It is aiversity . The aut(but see ). C. racemosa might have to implement sustainable changes in the coffee sector, fundamental basic data, such as the type of breeding system, and the type of pollinators and dispersers involved in this species are unknown. This is particularly significant as we found differences in the functional pathways of these two species that could reflect differences in the tolerance to environmental stresses  [Caffeine metabolism’ showed no significant differences between the two cultivars  ,56. Inteultivars , even thcaffeine ,57. Basecaffeine . This hiC. arabica plants used in the AFS, which could affect the long-term sustainability of this system. In this context, the introduction of new Arabica cultivars in the Gorongosa AFS would be an asset, given the low genetic diversity of the implanted cultivar. More efforts involving coffee producers should be developed to create awareness of the importance of conserving C. racemosa. Additionally, attempts to introduce C. racemosa and other crop-wild relatives into the value chain should be placed on the agenda. Wild coffee species are already being farmed in Kwa Zulu Natal in South Africa (Hluhluwe) [C. arabica and C. canephora, their specific attributes regarding environmental stress tolerance and flavor could be useful for new blends [The use of AFS in Gorongosa Nacional Park is seen as a promising and compatible approach to help adapt to climate change while reconciling biodiversity conservation and local development ,26,59. Tluhluwe) , and altw blends . RecogniC. arabica  and 48 wild relatives collected in three provinces from central and southern Mozambique  , totaling 86 samples. The main variety of C. arabica cultivated in the Gorongosa Mountain is a commercial variety imported from Zimbabwe due to similar agroecological conditions in both countries. The cultivar is labeled Costa Rica (CR) and it is claimed to be tolerant to coffee leaf rust and coffee berry disease. The CIFC cultivars are certified hybrids of the Catimor group (CR-95). Coffea arabica is a tetraploid species with 2n = 44 chromosomes that usually behave genetically as diploid [C. racemosa is a diploid species with 2n = 22 chromosomes [Thirty-five samples of  Sofala)  were inc diploid  while C.omosomes . In eachFresh leaves were collected for each sample, dried on silica gel, and stored at −80 °C until DNA was extracted. Total genomic DNA was extracted from 100 mg of ground leaves using the InnuPrep Plant DNA kit  according to the manufacturer’s protocol. Mean yield and purity were evaluated spectrophotometrically by readings of OD230, OD260, and OD280  and visualized by 1% agarose gel electrophoresis under UV light.rbcL and matK) previously used in Coffea , 0.2 U Taq Meridian Bioscience , 0.28 mg/mL BSA, and 40 ng μL−1 of genomic DNA. Cycle sequencing reactions were carried out using the Bio-Rad PCR System MyCycler™ thermocycler. The PCR program for rbcL consisted of 4 min at 94 °C followed by 35 cycles of 30 s at 94 °C, 1 min at 55 °C, and 1 min at 72 °C, with a final extension of 10 min at 72 °C. For matK, amplifications consisted of 5 min at 95 °C followed by 40 cycles of 30 sec at 95 °C, 30 s at 52 °C, and 1 min at 72 °C, with a final extension of 5 min at 72 °C. Amplified products were purified using QIAquick purification columns , as described in the manufacturer’s protocol, and sent for sequencing . Consensus alignments for each gene were created in Geneious v.11.1.5  using the MAFFT alignment algorithm v.7.450 [Two barcode organelle regions (n Coffea  were fir v.7.450  and manu v.7.450  (GTR) an v.7.450 . In addi v.7.450 .Coffea (The 86 samples were genotyped at 14 nuclear single-sequence polymorphic repeats (SSRs) previously developed for Coffea . Based oCoffea samples  for 4 h at 16 °C, then 20 min at 65 °C, and final incubation at 12 °C. Individually barcoded samples were cleaned and size-selected (350–500 bp) using agarose gel. After dilution to 1 ng µL−1, the Agilent®2100 bioanalyzer was used to assess insert size. Each library was then PCR-amplified to the desired concentration and paired-end sequenced on an Illumina®HiSeq PE150.Genomic DNA (0.3~0.6 µg) of a subset of C. arabica downloaded from the NCBI  using BWA version 0.7.16 [Coffea samples using GATK 4.2.6.1 [C. arabica as mentioned above. Associated genes were mapped to the KEGG [p < 0.05) with Coffea genes.FastQC  was usedn 0.7.16  with then 0.7.16 , removin 4.2.6.1  with bas 4.2.6.1 . FilteriK = 1 to K = 10 genetic clusters (K), with 10 repetitions per K. Models were run assuming ancestral admixture and correlated allele frequencies using run lengths of 200,000 interactions for each K after 50,000 burn-in steps. The optimum K value was determined using STRUCTURE HARVESTER [K based on both the posterior probability of the data for a given K and the ∆K [K were then post-processed using CLUMPAK [http://www.R-project.org/, accessed on 22 June 2021). Functional annotation of the SNPs was defined using the Blast2GO V5.0 tool [−5) implemented in the KEGG database [Since genetic data of the two species were diploidized , we used the Bayesian program STRUCTURE v.2.3.4  to test ARVESTER , which id the ∆K . The res CLUMPAK . To visu CLUMPAK . Differe CLUMPAK . This ap CLUMPAK . Pairwis CLUMPAK  and PopP CLUMPAK . We also CLUMPAK  with a w5.0 tool  (E-valuedatabase .Here, we show for the first time how genetic diversity is needed to assure sustainable agriculture practices. Even though the implementation of AFSs is of interest since they can offset deforestation in tropical environments, while increasing biodiversity, productivity, social profitability, and environmental stewardship, guaranteeing the genetic diversity of the species/cultivars is an essential condition to ensure the long-term sustainability of AFSs. In this sense, the introduction of crop wild relatives in coffee AFSs provides an opportunity to increase the productivity and resilience of agricultural systems as they contain useful genetic diversity, which as reported here is not present in cultivated Arabica coffee.


Zanthoxylum nitidum (Roxb.) is a commonly used traditional Chinese medicine. However, the collection and protection of wild germplasm resources of Z. nitidum are still insufficient, and there is limited research on its genetic diversity and fingerprint. In the present study, 15 simple sequence repeat (SSR) markers were developed by genotyping based on multiplexed shotgun sequencing. The genetic diversity of 51 populations  of Z. nitidum was evaluated using these 15 SSRs. A total of 245 alleles (Na) were detected, with an average value of 16.333, and the average polymorphism information content was 0.756. The genetic distance among 51 populations was 0.164~1.000, with an average of 0.659. Analysis of molecular variance showed low genetic differentiation (40%) and high genetic differentiation (60%) between populations and individuals, respectively. The genetic differentiation coefficient (Fst) of the population was 0.338, indicating that 66.2% of the genetic variation occurred within the population, and the gene flow (Nm) was 0.636, demonstrating that the gene exchange between populations was low. Clustering analysis revealed that the genetic similarity coefficient was 0.30, dividing the 51 populations into 4 groups of 2, 17, 3, and 29 populations. There was no specific relationship between geographical location differences and genetic distance. The genetic diversity level of Z. nitidum is relatively high, and our results provide a theoretical basis for the rapid identification of Z. nitidum germplasm resources and variety selection. Zanthoxylum nitidum (Roxb.) DC., a notable medicinal plant, belongs to the genus Zanthoxylum in the Rutaceae family, the root used as traditional medicine is named Liang-Mian-Zhen1. Z. nitidum was first recorded in "Shennong Materia Medica Classic" during the Qin and Han dynasties under the name "Manjiao"2. It mainly grows in Guangxi, Guangdong, and other locations in China3. The roots mainly contain alkaloids5, sesquiterpenoids6, coumarins7, lignans8, and other components that have various pharmacological activities, such as antiinflammatory10, antibacterial12, anticancer13 and analgesic14 activities. Z. nitidum is not only the main raw material of more than 60 famous traditional Chinese patent medicines and simple preparations, such as Sanjiu Weitai Granules, LiangMianZhen Analgesic Tablets, and Dieda Wanhua Oil, but its extracts are widely used in toothpaste, soap, shampoo and other daily personal products16.Zanthoxylum nitidum can be subdivided into Z. nitidum var. nitidum and Z. nitidum var. tomentosum according to whether each section of the plant has short, rough hairs, particularly on both sides of the leaves. Furthermore, Z. nitidum var. nitidum is separated into three types based on the number of thorns on branches and leaf axes, as well as the size and thickness of leaflets17. Due to the high demand for Z. nitidum in the market, wild resources have been plundered recklessly for a long time, and have become increasingly scarce18. The fundamental measure to solve the resource crisis is to transform the source from wild to artificially planted. However, in the process of introducing and domesticating wild Z. nitidum germplasm, it is difficult to accurately distinguish resources with such similar traits using traditional morphological identification methods. Hence, there is a serious shortage of excellent germplasm basic materials for artificial cultivation, and the unclear phenotypic characteristics and genetic background of cultivated germplasm are unclear, which seriously limits the development of related industries for Z. nitidum.19. SSR molecular markers have been widely used in genetic diversity analysis, population structure analysis, and fingerprint construction of medicinal plants, such as for Astragali Radix20, Akebia trifoliata21, and Paris polyphylla22, providing new ideas for the protection of core germplasms, functional gene identification, variety identification, and molecular marker assisted breeding of medicinal plants. However, studies on the genetic diversity of Z. nitidum are limited, and one of the most important reasons is the lack of effective molecular markers for this species.Some of the advantages of simple sequence repeat (SSR) molecular markers are rich polymorphism, wide distribution, easy operation, and high sensitivityZ. nitidum from Guangxi and Guangdong. We detected an abundance of SSRs based on the multiplexed shotgun genotyping (MSG) data, and we developed SSR molecular markers to assess the level of genetic diversity for the collected germplasm resources. Our data will provide a scientific basis for species identification and selective breeding of Z. nitidum.In this study, great efforts were made to collect wild germplasm resources of Z. nitidum var. nitidum and Z. nitidum var. tomentosum individuals from 51 populations in Guangdong and Guangxi were collected, including 16 germplasms from 7 artificially cultivated populations and 126 germplasms from 44 wild populations. The germplasm numbers and source information are shown in Table From September to October 2022, a total of 142 23. We purified the libraries and selected DNA fragments in the 400 bp size range using AMPure XP beads , and then amplified them using PCR for 14 cycles. The Illumina NovaSeq platform  was used to obtain raw sequence data. The original data were filtered using the sliding window analysis method of fastp (v0.20.0)24, and the sequences were integrated using FLASH (v1.2.11)25 to obtain high-quality data. The sequencing data have been deposited in China National GeneBank (CNGB) with project accession number CNP0004226 (https://db.cngb.org/search/project/CNP0004226/).Three germplasms with significant phenotypic differences  were selected for constructing the library. We prepared the sequencing library using the MSG method proposed by Andolfatto et al.http://pgrc.ipk-gatersleben.de/misa/) was used to search for SSR loci in all high-quality sequences. Cd-hit software26 was used to cluster the sequences. The Perl program was used to analyze the clustering results and evaluate polymorphisms27. Finally, PCR primer pairs flanking the SSR repeats were designed using primer 3 (v2.3.6)28.The Microsatellite Identification Tool (MISA) . The reaction conditions were as follows: predenaturation at 95 °C for 5 min, 25 cycles of denaturation at 95 °C for 30 s, gradient annealing at 62~52 °C for 30 s, extension at 72 °C for 30 s, followed by extension at 72 °C for 20 min. Polymorphism of each successfully amplified marker was evaluated by GeneMarker software.SSRs were classified based on the length of the SSR motifs in their sequences. Different repeats of the SSR motifs were used to analyze the characteristics of the SSRs. We randomly selected 192 pairs of primers, and preliminarily screened 10 germplasms with significant phenotypic differences . PCR reaction was performed using a Veriti 384 PCR instrument. The PCR reaction system was as follows: template DNA 1 μL, forward and reverse primers are both 10 pmol/μL , 0.5 μL each, 5 μL of MIX enzyme  supplemented with 3 μL ddHFluorescent primers were obtained from Wuhan Tianyi Huiyuan Biotechnology Co., Ltd. , and the fluorescent dyes used were FAM, HEX, and TAMRA. One microliter of fluorescent PCR product, 0.5 μL of GeneScan™500 LIZ, and 8.5 μL of Hi-Di™ formamide were added to the upper plate, centrifuged, denatured (95 °C for 5 min), and cooled. Finally, the samples were analyzed using an ABI3731XL sequence analyzer .29, including the number of observed alleles (Na), effective alleles (Ne), observed heterozygosity (Ho), expected heterozygosity (He), fixation index (F), Shannon’s information index (I), genetic differentiation coefficient (Fst), gene flow (Nm) and analysis of molecular variance (AMOVA). Linkage Disequilibrium between the SSR loci was carried out using the SHEsis plus30. The genetic distance among populations was calculated using Powermarker (V3.25)31. The genetic structure of the germplasm was analyzed using STRUCTURE software (v2.3.4)32, and cluster analysis between populations was based on unweighted pair group with arithmetic average (UPGMA).GenAlEx (v6.501) software was used to calculate genetic diversity indicatorsZ. nitidum var. nitidum and Z. nitidum var. tomentosum) were collected in compliance with institutional, national, and international guidelines and legislation. The plant material in our collection is preserved in the Germplasm Repository of Guangxi Botanical Garden of Medicinal Plants, Guangxi Province, China. The voucher ID for each sample is shown in Table The plant materials used in this article did not involve disputes. We hereby declare that all of the plant materials  were 8.63 Gb, 6.66 Gb, and 9.35 Gb, respectively. After filtering, high-quality data were generated, which were 1.30 Gb, 0.93 Gb, and 1.20 Gb, respectively. Furthermore, 2,903,693 pairs of read pairs were merged from a total of 12,014,757 pairs. Using MISA software, 261,267 SSR loci were found, distributed among 227,023 unigenes. The frequency of SSR occurrence  was 7.82%. The average distance  was 2.73 kb. Among them, there were 30,512 unigenes containing more than one SSR locus, and 29,314 SSRs were present in composite form  was the most abundant repeat type, followed by dinucleotide loci , trinucleotide loci , tetranucleotide loci , hexanucleotide loci , and pentanucleotide loci . Moreover, the three types of repetitive motifs with the highest number were A/T , AT/AT , and AAT/ATT   from 15 developed SSR markers were amplified from the 51 populations . The total effective alleles (Ne) were 85.828, with an average of 5.722, and the proportion of effective alleles was 35.03%. Shannon’s information index (I) ranged from 1.313 to 2.994, with an average value of 1.984. The polymorphism information content (PIC) ranged from 0.531 to 0.922, with an average of 0.756. All loci were not conforming to the Hardy–Weinberg equilibrium  of the population was 0.338, and the gene flow (Nm) was 0.636  sequencing36 and whole-genome resequencing (WGD)37, and it is more effective and flexible for detecting SSRs. In this study, 261,267 SSR loci by MSG were discovered.SSRs, as important molecular markers, have been widely used in genetic diversity evaluation, genetic map, construction, and finger mapping in medicinal plants, such as for the medicinal plants Na, Ne, Ho, He, F, I, and PIC of primers are important indicators for measuring polymorphism of SSR markers. When PIC > 0.5, it indicates that the primer has a high degree of polymorphism38. In this study, we designed 768 SSRs based on MSG data, and screened out specific markers of Z. nitidum form them. To evaluate the effectiveness of the designed SSR primers, we randomly selected 192 SSRs and conducted two rounds of screening based on 10 germplasms with significant phenotypic differences. Twenty-six SSRs were found to be effective. Fifteen SSRs were selected from 26 SSRs for genetic diversity analysis on 142 individuals. The PIC of these 15 SSRs ranged from 0.531 to 0.922, with an average of 0.756. These 15 SSRs have high polymorphism and can effectively reveal the genetic diversity of Z. nitidum. This is the first time SSR markers have been used for genetic diversity analysis of Z. nitidum populations; at the same time, our results confirm that SSR markers have the advantages of a large number of loci and high identification efficiency.The number of He)39 and Shannon’s information index (I)40 are important. The larger their values are, the richer the genetic diversity of the population is. This study found that the He of these 51 populations ranged from 0.133 to 0.600, with an average of 0.380; the I ranged from 0.185 to 1.043, with an average of 0.619. These data indicate that the 51 populations have rich genetic diversity. In general, Fst > 0.25 indicates a high level of genetic differentiation among populations41; Nm < 1 indicates that the genetic differentiation among populations is caused by migration or genetic drift42. In this study, the Fst of 51 populations was 0.338, and the Nm was 0.636, which indicated that the genetic differentiation among populations of Z. nitidum is at a high level. Additionally, the genetic differentiation between populations was caused by migration or genetic drift, which is consistent with the conclusions reached by previous researchers using ISSR molecular markers43, and similar to the results of genetic differentiation studies of other Zanthoxylum plants45.Among the indicators of population genetic diversity, expected heterozygosity , indicating a distant genetic relationship between type 1 and the other two types. The third cluster was identified as Z. nitidum var. nitidum (type 3), all of which originated from Guangdong, showing obvious regional characteristics. The cluster was identified as Z. nitidum var. nitidum (type 2), with the closest genetic relationship to type 3, and included 7 artificially cultivated populations, indicating a decrease in genetic diversity after transitioning from wild to domestic. The conclusion of the genetic relationship between the four types of Z. nitidum through cluster analysis is consistent with traditional morphological identification methods and proves the effectiveness of the SSR molecular markers selected in this study for identifying Z. nitidum germplasm.Conducting variety identification by combining molecular marker technology with phenotypic analysis is reliable and convenient Z. nitidum. Our data will be useful for germplasm identification, genetic improvement, and variety selection.In this study, MSG data were used to develop SSRs, and 261,267 SSRs were detected. Fifteen SSRs were powerful molecular markers for genetic diversity analysis and variety identification inSupplementary Table S1.Supplementary Table S2.Supplementary Table S3.Supplementary Table S4.Supplementary Table S5.Supplementary Table S6.