Quercus aquifolioides) populations throughout the species' range and pool‐sequenced 587 individuals at drought‐stress candidate genes. We analyzed patterns of genetic diversity and differentiation for 381 single nucleotide polymorphisms (SNPs) from 65 candidate genes and eight microsatellites. Outlier loci were identified by genetic differentiation analysis and genome–environment associations. The response pattern of genetic variation to environmental gradient was assessed by linear isolation‐by‐distance/environment tests, redundancy analysis, and nonlinear methods. SNPs and microsatellites revealed two genetic lineages: Tibet and Hengduan Mountains–Western Sichuan Plateau (HDM‐WSP), with reduced genetic diversity in Tibet lineage. More outlier loci were detected in HDM‐WSP lineage than Tibet lineage. Among these, three SNPs in two genes responded to dry season precipitation in the HDM‐WSP lineage but not in Tibet. By contrast, genetic variation in the Tibet lineage was related to geographic distance instead of the environment. Furthermore, risk of nonadaptedness (RONA) analyses suggested HDM‐WSP lineage will have a better capacity to adapt in the predicted future climate compared with the Tibet lineage. We detected genetic imprints consistent with natural selection and molecular adaptation to drought on the Qinghai–Tibet Plateau (QTP) over a range of long‐lived and widely distributed oak species in a changing environment. Our results suggest that different within‐species adaptation processes occur in species occurring in heterogeneous environments.Long‐lived tree species are genetically differentiated and locally adapted with respect to fitness‐related traits, but the genetic basis of local adaptation remains largely unresolved. Recent advances in population genetics and landscape genomic analyses enable identification of putative adaptive loci and specific selective pressures acting on local adaptation. Here, we sampled 60 evergreen oak ( We hypothesized that the combination of FST outlier detection methods, SNP annotations, and various procedures testing associations between genetic and environmental variables could identify molecular signatures consistent with local adaptation to drought, a major climatic driver limiting plant growth in the Himalaya–Hengduan Mountains biodiversity hotspot. Furthermore, in order to determine how adaptation would contribute to Q. aquifolioides response to ongoing climate change, we predicted the “adaptedness”  of resolution . These included the full suite of 19 mean annual bioclimatic variables along with average monthly climate data for precipitation, solar radiation, wind speed, water vapor pressure, and minimum, mean, and maximum temperature. To avoid biased estimates of model coefficients and spurious significance levels resulting from multicollinearity, we excluded highly correlated climate variables with the threshold values of 0.7 using a variance inflation factor (VIF) test in “usdm” R package  and future predictions (2050 and 2070) were extracted from WorldClim Version2 raster layers at 30 s (~1 km2.3Quercus petraea and Quercus robur were downloaded from the EvolTree database (Table http://www.evoltree.eu/index.php/candidate‐genes‐db). We used Primer3web  to design PCR primer pairs for each candidate gene. We tested each pair of primers on eight individuals under various PCR conditions. The PCR products were visually checked on agarose gel and validated by the Sanger sequencing . We retained 65 polymorphic gene fragments for further genotyping. Sequences were submitted to GenBank and used as a reference in further analyses.A set of 180 drought‐related stress candidate genes in We designed pair‐end barcode sequences to distinguish each individual after pool sequencing Figure . Specifihttp://broadinstitute.github.io/picard), and the reads around any insertion/deletion (indel) were realigned by the Genome Analysis Toolkit , and performed a blastx search of the candidate genomic sequences in Q. aquifolioides against Q. robur amino acid sequences. If the high scoring segment pairs (HSPs) of the blast top hit contained no stop codons, we assumed that the segments were likely to be coding sequences (CDSs). We used SnpEff , and SSRs. Based on results of the PCA, we assigned the genetic variation of Q. aquifolioides to two known geographic clusters , mean observed heterozygosity (HO), mean expected heterozygosity (HE), and mean nucleotide diversity (π) for each lineage based on all SNPs and non‐FST outlier SNPs using STACKS 1.47 , mean observed heterozygosity, mean expected heterozygosity, and mean Shannon index (I) for each lineage were calculated by GenAlEx 6.5  or lower (negative outliers) genetic differentiation (FST) compared with the neutral background level, respectively  neutral envelope  is calculated for each SNP, representing the ratio of probabilities of a correlation between the allele frequency variation in the SNP and an environmental factor versus the null model given by the covariance matrix alone. Ten independent runs of 105 MCMC cycles were produced for each SNP, and the means of the results were used to estimate correlations between allele frequencies and environmental variables. SNPs with log10 (BF) >0.5 have strong support for associations between allele frequencies and environmental gradients  can improve detection accuracy using latent variables and taking the population structure into account  using R package “ecodist”  and isolation by environment (IBE) were performed to assess linear relationships between geographic distance  or environmental distance  and genetic distance (pairwise pRDAs) were used to detect linear relationships between genetic variations (from SNPs and SSRs datasets) and multivariate climatic gradients, using “vegan” R package  and partial redundancy analyses  alone  that could not be distinguished on the basis of neutral markers  and precipitation during the dry season (prec01) contributed the most to SNP variation  explained most genetic variance in Tibet. In HDM‐WSP and all populations, prec06 and prec01 were the two environmental variables most explanatory, respectively .The most represented environmental factor identified by RONA analyses was the precipitation during the dry season (prec01) in Tibet and HDM‐WSP lineages. The RONA value of Tibet lineage was on average higher than that of HDM‐WSP lineage under both RCP26 and RCP85 predictions in 2050 and 2070 Figure , indicat4Q. aquifolioides populations throughout the species' range in the Himalaya–Hengduan Mountains. We first relied on FST outlier and GEA methods to reveal SNP markers consistent with locus‐specific imprints of natural selection in each lineage individually and throughout the species range. Based on annotations of these potentially adaptive SNP variants and their predicted coding effects, we can compare the functional differences between genes identified in the distinct lineages and further analyze the effects of these differences on the ecological adaptation of Q. aquifolioides in different lineages. We also performed linear and nonlinear multivariate analyses by integrating genetic and environmental factors to identify complex relationships between SNP frequency gradients with climate variables and determine key climate variables most likely to trigger adaptive response in Q. aquifolioides. We finally predicted the capacity of Q. aquifolioides to adapt to future local climate, based on two modeled scenarios of climate warming.Our study integrated numerous population genetics and landscape genomic methods to detect putative adaptive genetic variation in Q. aquifolioides showed contrasted patterns of local adaptation in the two lineages. In Tibet lineage, the genetic variation was mainly driven by geographical distance, whereas in HDM‐WSP lineage, the climatic variables, especially precipitation, were instrumental in shaping a genetic imprint consistent with adaptive genetic variation.We found that genetic variation in 4.1FST outlier SNPs or SSRs. Diversifying selection can reduce effective gene flow and increase divergence across populations . Higher FST values reported for analyses including all SNPs suggested that at least some of the positive FST outlier loci could have been affected by diversifying selection among homogenous gene pools (or be in linkage disequilibrium with such loci). Another explanation for the higher FST values is that random genetic drift eventually caused fixation of different alleles in each lineage due to limited gene flow. However, this seems unlikely because GEAs suggest some gene SNPs are actually related to environmental variables, a genomic imprint also consistent with local adaptation.Results from AMOVA and PCA revealed higher level of genetic differentiation when all SNPs were considered compared with reduced marker datasets including only non‐The higher level of genetic diversity along with a greater proportion of private alleles in the HDM‐WSP lineage than in the Tibet lineage reflects the demographic history of the species inferred from chloroplast DNA and microsatellite markers  methods was used, as this should increase the robustness of outlier detection. Another challenge is to find the most likely causes of outlier syndrome. In this respect, the recent release of the oak genome  that plays an important role in the anabolism and catabolism of fatty acids. Fatty acids are the basic building blocks of most lipids, including waxes and cutin important for protecting plants from biotic and abiotic stresses  in both Tibet and HDM‐WSP lineages, suggesting some level of parallel adaptation to drought in the species. Moreover, GEAs identified two genes displaying lineage‐specific association with climatic variables in HDM‐WSP (CL6004CT6724_02 and CL9715CT14526_03). Three SNPs within these two genes were associated with temperature during the driest quarter (bio09) and precipitation during the dry season (prec01). These three markers were also flagged as positive 4.3Mantel and partial Mantel tests as well as MRM analysis all revealed significant IBD patterns in the Tibet lineage and in all populations combined. These results were corroborated by GDM and RDA: In Tibet, geographic distance, not the environment, explained high variable importance and a high percentage of explained variance (PVE), respectively. Together, these results indicate that the genetic signature of the Tibet lineage mainly reflects demographic evolution, but not strong selective pressure from the environment.FST and GEA) in HDM‐WSP compared with Tibet, indicating HDM‐WSP lineage might include more potentially adaptive polymorphisms than Tibet. Hence, the Tibet lineage might be more vulnerable under future climate and will have to shift higher allele frequencies than HDM‐WSP lineage in order to persist locally. Relatively warm and humid climatic conditions experienced by populations of the HDM‐WSP lineage might have been instrumental for local adaptation, as reported in other organisms, including fishes  was the most significant environmental factor driving genetic variation in the HDM‐WSP lineage. Furthermore, RONA analyses predicted that HDM‐WSP lineage should have a higher capacity to adapt compared with Tibet lineage in the face of projected climate warming. This result is also supported by the higher number of lineage‐specific outliers  are key drivers of the genetic variation in the Tibet lineage, whereas adaptive processes  prevail for shaping genetic diversity of the HDM‐WSP lineage. More specifically, we identified two genes that were correlated with climate gradients only in the HDM‐WSP lineage, supporting lineage‐specific signals of local adaptation. Our study thus provides valuable insights on local adaptation in trees and their main environmental drivers. In the changing climatic environment associated with global warming, the HDM‐WSP lineage may provide valuable genetic resources for the species. In the near future, more studies integrating genomic, phenotypic, and environmental data are required to gain further insights into the mechanisms of oak adaptation.Combining the results from population genetics and landscape genomic methods, we detected contrasted patterns of genetic variation in response to environmental gradients in None declared.FD designed the research; TRW and YYW performed the experiments; FD, TRW, YYW, and SU performed the analysis; FD, SU, and GdL wrote the manuscript; and all authors revised the manuscript.Figure S1‐S12Click here for additional data file.Table S1‐S7Click here for additional data file.Table S8‐S16Click here for additional data file.


Marsupenaeus japonicus) includes two cryptic species, which are distributed mostly allopatrically but co-occur in the northern South China Sea (from Huilai to Beihai). To obtain a better understanding of the fine-scale genetic structure and parapatric diversification of these two varieties in the northwestern Pacific region, we used a genotyping-by-sequencing (GBS) and comparative transcriptomics approach to establish their phylogenetic relationships. Using the GBS technique, we genotyped 28891 SNPs in 160 individuals in the Northwest Pacific. The results supported two highly diverged evolutionary lineages of kuruma shrimp (var. I and II). The ND and XM populations showed complex genetic patterns, which might be affected by the complex environment of the Taiwan Strait. In addition, the migration rates and inbreeding coefficients of XM and BH were much lower than those of the other populations, which might be related to the land-sea changes and complex ocean currents in the Taiwan Strait and Qiongzhou Strait. Based on the synonymous substitution rates (ds) of 2,491 candidate orthologs, we estimated that the divergence time between the two varieties was 0.26~0.69 Mya. Choice and no-choice interbreeding experiments provided support for the biological species concept, by showing the existence of reproductive isolation or incompatibility. In view of these differences between the two Marsupenaeus species, we believe that it is essential and urgent to establish a genetic database for each and reevaluate their ecological suitable conditions in order to improve species-specific culturing techniques. Moreover, this research can serve as a case study for future research on speciation and hybridization.The kuruma shrimp ( Oratosquilla oratoria cryptic species using the mtDNA COI and nrDNA ITS genes  Pacific marginal seas comprise approximately 75% of the world’s marginal seas . Due to TS genes . Two phyhina Sea . Shen eteciation .Marsupenaeus japonicus), a commercially important crustacean, is widely distributed in the East and South China Sea, the region off Australia and the western Indian Ocean  markers. Mitochondrial genes are maternally inherited, which makes them inappropriate for the detection of hybridization . These pMarsupenaeus species, which will facilitate further studies on the molecular mechanisms underlying genetic differentiation.In this study, we investigated the areas of sympatry and whether there is hybridization among the wild populations by sampling from a narrow sympatric zone. Furthermore, we combined orthologous genes with genotyping-by sequencing (GBS) to establish the fine-scale population structure and phylogenetic relationships of kuruma shrimp in the NW Pacific region. To verify the existence of reproductive incompatibility, we implemented choice and no-choice interbreeding experiments in purse seines. This study provides comprehensive insight into the two ®  and Qubit fluorometer , respectively. Only nondegraded samples with an OD260/280 from 1.7 to 1.9 were subjected to genotyping-by-sequencing. The hepatopancreas tissues of ten HL1 individuals  and ten HL2 individuals  individuals from the Huilai population were rinsed separately with RNase-free water for RNA extraction.Kuruma shrimp samples were collected from eight locations along the coast of China from September 2016 to May 2018 Figure 1dS) were calculated using PAML . In this study, we screened a single copy nuclear genes (SCNGs), which was annotated as caspase (EF079670.1), and the caspase gene has high sequence diversity . Orthologous genes were screened by OrthoMCL . Subsequiversity . DnaSP viversity  and Netwiversity  were useLibraries were constructed following the GBS protocol described by The original data obtained by high-throughput sequencing were transformed to raw reads by base calling. Clean data were obtained by removing reads containing adapters, ploy-N sequences and low-quality bases from the raw data. Then, the enzyme catch ratio was calculated to evaluate the efficiency of enzyme digestion. The high-quality clean reads were mapped to the reference sample (XM08) using the Burrows-Wheeler Aligner (BWA) with the command ‘mem-t4-k32-M’ . The SAMoH) analysis, expected heterozygosity (EH) analysis and Analysis of Molecular Variance (AMOVA) were performed in the Arlequin v3.5 (Fst) and nucleotide diversity (π). Isolation-by-distance mantel tests were performed to examine correlations between genetic distance and geographic distance matrices by IBD (Isolation By Distance) software (http://www.bioinformatics.org/project/?group_id=84) was used to produce the pairwise population matrix of Nei’s standard genetic distances (Dst). To estimate contemporary migration rates, we used the Bayesian algorithm implemented in BayesAss software (v 3.0.4)  .http://cnsgenomics.com/software/gcta/#PCA) and visualized by R language. The population genetic structure and ancestry of each sample were analyzed by the program FRAPPE v1.1  mitochondrial gene and sodium-potassium ATPase alpha-subunit (NaK) gene to detect hybridization. Mitochondrial genes are maternally inherited, and nuclear genes contain genetic information from both parents. It was a rapid and accurate method for interspecific hybrids identification by combining Cytb, NaK and available sequences of NCBI . About 600 wild individuals from sympatric areas Huilai, Zhuhai and Zhanjiang were detected by amplifying the Cytb and NaK gene sequences. Meanwhile, we performed the choice and no-choice interbreeding experiments for detecting the reproductive isolation between two varieties. Two hundred healthy individuals, approximately 100 each of var. I  and var. II , were transported from Huilai to an aquaculture farm in Dongshan (Fujian) and acclimated for four weeks  until the primary molt was complete. The rest of the healthy individuals (59% for var. I and 79% for var. II) were used for interbreeding experiments. The seawater was renewed every 2 d, and the shrimps were fed twice daily with oysters or squid. We assessed the mating condition of female individuals by checking spermatophores every 3 d. Once mating was completed, the female individuals were moved to a prepared 5 m3 circular barrel. On the second day, the eyestalk on one side was cut off disinfected by 0.5% povidone iodine solution. Thereafter, the females were fed nereids to stimulate ovarian development.The two varieties have only subtle morphological differences in carapace color banding patterns. We screened the cytochrome b (dN/dS (ω) ratios of 2491 pairs of orthologous genes  was amplified from 160 genomic DNA samples. There were 57 unique haplotypes, with high diversity (0.891). Network analysis identified two well-supported clades consisting of the expected lineages of variety I  and variety II  (We sequenced and compared the hepatopancreas transcriptomes of HL1 (var. I) and HL2 (var. II) Table S1osophila . Accordiosophila , we estiGBS sequencing of 160 samples produced 112.45 Gb of raw reads, with an average of 0.7Gb per individual. After quality filtering, a total of 112.44 Gb of clean reads was retained, which represented an average effective rate of 99% Table S3EH, 0.2447~0.28842; OH, 0.30286~0.38663; π, 0.0018~0.0032) (OH of 0.357 was significantly higher than the mean EH of 0.269 (p < 0.001), which suggests heterozygote excess among samples. The AMOVA results showed that 92.4% of the genetic variation was within populations and only 7.6% occurred among populations  to 0.3378 (BH vs ND)  was statistically significant for all eight populations (R2 = 0.534) (2 = 0.002) and var. II (R2 = 0.229) was insignificant. The calculation results of contemporary migration rates showed that recent migration rates from var. I to var. II and vice versa were very low  Table S5Principle component analysis demonstrated that the ZS, ND, XM, HL samples were clustered with var. I on the left and ZH, ZJ, BH, QH samples were clustered with var. II on the right Figure 3Cytb and NaK sequences of about one hundred ZS individuals and one hundred QH individuals, and found stable heterozygous loci  of all individuals were consistent with genotypes  and Qionghai  populations have pure genetic backgrounds. Firstly, we amplified the enotypes S5. AmplI higher than those of var. II. In the beginning, there were 20 individuals per variety in the purse seine one. However, we captured only four individuals of var. II and were unsure of the conditions they had experienced. By comparing the different treatments, we found that the attraction within varieties was significantly higher than that between varieties. Our primary focus was cross-combinations, most of which failed. However, fortunately, we obtained two hybrid individuals from purse seines 1 and 2, both of which were var. I females. The two individuals were numbered N0101 and N0201 and then moved to a five m3 circular barrel. It was strange that the two individuals did not complete spawning and this phase lasted 6 d, ending up shedding the spermatophore.A total of 138 individuals were divided into eight breeding-pair treatments in purse seines (M. japonicus (var. I) was found to be confined to the East China Sea (including Japan) and the northern South China Sea. Var. II  was widely distributed in the South China Sea, Australia, the Red Sea, the Mediterranean and the western Indian Ocean. However, the authors did not explicitly identify the sympatric areas. M. japonicus were hyper-osmoconformers and has a weak salinity tolerance  to Beihai (Guangxi) with a tendency change to some extent. In previous studies by olerance . Thus, tFst values, including in sympatric areas, was 0.263 with weak gene flow, which indicated strong genetic differentiation (m) exchanged rather than the number of migrants (Nm) that matters , which was consistent with the findings of previous studies based on limited SSR markers and mitochondrial genes . Both wiCytb and NaK genes of about 600 sympatric individuals and the carapace coloring patterns were in accordance with genotypes. There was no natural hybridization in wild populations based on the current sample size. In the eight breeding treatments, only two var. I females mated with var. II males. The successful selfing rate was significant higher than that of hybridization, with var. I exhibiting higher values than var. II. Unfortunately, the two individuals did not complete spawning, ending up shedding the spermatophore. Only a few examples of interspecies hybridization between penaeids were using spermatophore transplantation, and spawn rate, hatch rate and the survival of hybrids were lower than that of intraspecific matings  of seminal vesicles by comparing dozens of var. I and II females at different developmental stages . Landry et al. revealed the rapid evolution of gamete recognition and sperm morphology of 00 years . Sexual  species . In addi species .Marsupenaeus japonicus (Form I) and Marsupenaeus pulchricaudatus (Form II)  . As TsoiThe datasets generated for this study can be found in NCBI BioProject PRJNA489160 and PRJNA596055.All experimental procedures were conducted in conformity with institutional guidelines for the care and use of laboratory animals in Xiamen University, Xiamen, China.PW, YM, and PX conceived and designed the experiments. YM, YS, and JW obtained funds for the study. PW, WC, and HZ completed sample collection. PW, JZ, WC, and HZ designed and implemented the choice and no-choice interbreeding experiments. PW and BC conducted computational and statistical analyses. PW, BC, and YM drafted the manuscript. PX, YM, YS, and JW participated in the manuscript revision. All authors read and approved the final manuscript.This project was funded by the Project of China Agriculture Research System (Grant No. CARS-48), the Marine Economy Innovation and Development Project of Xiamen (Grant No. 16CZY009SF05), Special funds for Ocean and Fishery Structure Adjustment of Fujian Province in 2019 , Science and Technology Plan Project of Ningbo (2019B10011), the Industry-University Collaboration Project of Fujian Province (2019N5001), the State Key Laboratory of Large Yellow Croaker Breeding  , the Fundamental Research Funds for the Central Universities (Nos. 20720180123 & 20720160110).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Gnathia marleyi. We sequenced a portion of the cytochrome oxidase I gene for 432 gnathiids, collected from six localities, ranging from western Puerto Rico to St John, US Virgin Islands. Importantly, multiple years of pre-hurricane sample collection allowed us to characterize temporal genetic patterns under undisturbed conditions and detect the changes subsequent to the 2017 hurricanes. Our results revealed no change to genetic diversity or structure for the years prior to the 2017 hurricanes, with genetic structure occurring at the local and regional levels, with three main clusters corresponding to Southwest Puerto Rico, East Puerto Rico, and the US Virgin Islands. However, directly following the 2017 hurricanes, genetic diversity increased at five of the six sampled localities. Additionally, we found a clear homogenizing effect prompted by increased shared genetic diversity among geographically distant regions and sites that resulted in substantially decreased among-region and among-site differentiation. Our work shows that severe disturbances caused by major tropical hurricanes facilitate gene-flow and increase overall genetic diversity and population admixture of dispersal limited coral reef species, potentially impacting the ecology and evolution of a key regional endemic.Severe disturbances can substantially alter eco-evolutionary processes and dynamics. While the impacts of catastrophic events on the biophysical attributes of communities are sometimes assessed, their effects on the genetic patterns of species remain poorly understood. To characterize how severe disturbances impact species at the molecular level, we examined the effects of the most energetic North Atlantic hurricane season in 50 years on the genetic diversity and structure of a dispersal-limited isopod,  Disturbances are integral processes that can alter community assemblages, re-structure spatial distribution of species, and drive biodiversity patterns5. Severe disturbances often modify the physical landscape, potentially affecting habitat suitability and/or availability6. As a result, they can induce mortality and affect population connectivity, which are key biological processes that determine genetic diversity and structure of populations7. The effects of these perturbations can be observed at the molecular level by measuring changes in the spatial distribution of alleles and patterns of gene-flow; over long time scales these processes can ultimately affect population adaptation and viability9. For instance, wildfires and large volcanic eruptions can cause mortality and habitat fragmentation, leading to population bottlenecks and loss of genetic diversity11. However, if after a disturbance survival and gene-flow are high, and sites are re-colonized from multiple sources, the genetic diversity can be maintained or even increased13. Notably, much of disturbance ecology theory has been informed largely by terrestrial case studies while few empirical investigations have assessed the genetic signatures of severe disturbances in marine systems14.A major focus in ecology has been to understand how environmental disturbances impact species and communities16. Cyclones can damage the physical structure of coral reefs18, displace massive amounts of water19, diminish water quality20, and alter fish community assemblage23. The fact that major cyclones are difficult to anticipate, even at relatively short timescales, makes it difficult to conduct proper Before-After-Control-Impact (BACI) -type sampling needed to rigorously test their effects24. Nevertheless, a few studies have attempted to measure the effects of similar disturbances on the genetic structure of marine taxa. For example, Brante et al.25 found that a species’ capacity to disperse can greatly influence how it responds to major disturbance, however, they only measured changes to genetic structure following a major disturbance without sampling beforehand. Other studies that incorporated pre- and post- disturbance sampling have focused on species with high dispersal potential for which disturbance only impacted a small portion of a much wider distributional range . In another, study, pre and post-disturbance genetic structure was compared using different molecular markers with large time gaps before and after the purported disturbance (an estuarine fish impacted by a 2005 hurricane28). Thus, additional research is still required to understand the acute effects of severe environmental disturbances on the genetic diversity and structure of coastal marine species, especially for regional endemics with limited dispersal.Major tropical cyclones are amongst the most severe disturbances in marine ecosystems, particularly for shallow, coastal habitats, such as coral reefs29. While they occur from the intertidal to the abyss, they are particularly abundant in coral reef systems where they play a key role in cleaning symbiosis and other trophic interactions30. After feeding on a single host, first-stage juvenile gnathiids detach and shelter in the substrate where they molt into the next instar following a period of 5–7 days , upon which the feeding-molting cycle is repeated two additional times . After the third feeding, juveniles refuge in the benthos, where they metamorphose into adults. Unusual among parasites, adult gnathiids do not feed, and spend the entirety of their lives in the benthos. Males may live for a month or more after metamorphosis, however, females die after giving birth to about 30 live young31. Thus, tropical gnathiids can complete their life cycle in as little as 30 days33. While gnathiids can swim in short bursts, they cannot swim long distances and lack a pelagic dispersal phase (direct brooders). Although they attach to swimming hosts, their association is temporary (1–4 hours) and many of their known hosts have high site fidelity38, limiting the distance over which gnathiids can be dispersed through host-attachment39. The spatial genetic structure of gnathiid species has so far been considered only for geographically distant populations, at scales of more than 2000 km40; yet, the scale of genetic structure in dispersal-limited marine species is often much smaller41 and fine-scale population genetic structure of gnathiids remains unexplored.Gnathiid isopods (Peracarida) are a key functional group within the community of small, mobile, benthic marine invertebrates. Analogous to ticks and mosquitos, the larval stages feed on blood and body fluids of a wide range of benthic and demersal fishesGnathia marleyi42, a species of fish-parasitic gnathiid isopod endemic to the northeastern Caribbean, a region that is vulnerable to tropical cyclones. The 2017 North Atlantic hurricane season, which included two category five hurricanes, was one of the most energetically intensive of the past 50–100 years43 and caused a 25% decline of key benthic coral reef species44. We hypothesized that due to their limited innate dispersal ability and stable environmental conditions, pre-hurricane gnathiid populations would show a strong pattern of isolation by distance, with individuals collected at each locality being genetically differentiated. Following the hurricanes, we expected to find an increase in genetic diversity due to putative hurricane-induced migration among populations. Further, we expected increased gene flow to reduce population differentiation particularly among geographically close populations (e.g. at the intra-island level). Additionally, since the hurricane trajectories generally moved from east to west, we predicted the induced gene flow to be directional, with western populations receiving haplotypes from “upstream” eastern counterparts.Herein, we characterize the impacts of the 2017 hurricane season on the genetic diversity and structure of 45 that were deployed before sunset and retrieved the following morning after sunrise. Upon retrieval, contents of light traps were filtered and visually scanned under 10x magnification to separate gnathiids from other organisms. When sampling with light traps was not possible, gnathiids were collected using live fish-baited cages placed on the reef29. Caged fish were set 30 minutes before sunset and retrieved 30 minutes before sunrise the following morning and gnathiids were then gently dislodged from the fish. All samples were preserved in 96% ethanol and stored at −20 °C.Gnathiids were collected between late spring and summer (May to August) of 2013–2018 from six sites in the Northeast Caribbean Sea, spanning from southwestern Puerto Rico (17°57′18.0″N 67°03′03.7″W) to southeastern St John, US Virgin Islands (18°18′58.6″N 64°43′18.8″W) , 0.5 µL of each primer (10 µM), and 0.5 µL gDNA. The PCR temperature profile included a step of initial denaturation at 95 °C for 15 minutes, followed by 35 cycles of 94 °C for one minute, 55 °C for one minute, 72 °C for one minute, and a final extension step at 72 °C for 10 minutes. Due to the low primer specificity, species-specific primers were newly designed (gmarCOI5for: GGGATTTTTAGAGAATGAGCA and gmarCOI5rev: TCCAAACCCTGGAAGGATA) using PrimerBLAST47 and targeting a 700 bp portion of COI. Final PCR reactions consisted of 3.5 µl of water, 5.0 µL QIAGEN master mix, 0.5 µL each of forward and reverse primer (10 µM), and 0.5 µL gDNA and the following temperature profile: initial denaturation at 95 °C for 15 minutes, 35 cycles of 94 °C for 45 s, 55 °C for 45 s, and 72 °C for 45 s, and final extension at 72° for 10 minutes. PCR products were sent for sequencing at GENEWIZ . Amplicon sequences were edited using CodonCode Aligner V.8.0.1  and aligned using MAFFT48 with the default parameters. The multiple sequence alignment was then translated to amino acid sequences using MEGA49 to check for stop codons. A BLAST search was performed to validate that we had amplified correct DNA amplicon. Gnathia marleyi sequences diverged from Pacific congenerics by 31.5 to 36.2% similar to values observed among Pacific gnathiids50. The final alignment was truncated to 517 homologous base pairs.In the laboratory, individual gnathiids were placed into 2.0 ml Eppendorf tubes, rinsed with distilled water, and incubated at 37 °C until completely dry. Once dry, gnathiids were pulverized inside the Eppendorf tubes using a steel rod and used for genomic DNA (gDNA) extraction with the Invitrogen PureLink genomic DNA mini extraction kit, following the manufacturer´s protocol . The gDNA was eluted into 55 µL working stocks with a final concentration of 5–10 ng/µL. Polymerase chain reactions (PCR) were performed to amplify a ~1200 base pair fragment of the cytochrome oxidase subunit I mitochondrial DNA (COI) using the primers crust-coix1f: ACTAATCACAARGAYATTGG and crust-coix1r: TAGTCTGAGTANCGTCGWGG51. Haplotype evenness and the expected number of haplotypes (i.e. number of haplotypes standardized for sample size) were calculated using the poppr package in R52. Haplotype networks were constructed to visualize the spatial distribution of haplotypes in the samples collected before and after the hurricanes using TCS v1.2153 with a connection limit set to 95% and imported to tcsBU for graphical representation54. To assess the temporal variation of genetic diversity under undisturbed conditions, we performed permutational multivariate analysis of variance (PERMANOVA)55 considering only data collected before the storms. The effect of Year (year of sample collection) was tested for each molecular diversity index, with permutations constrained within Site (sample location). To test whether the 2017 hurricanes significantly altered genetic diversity, PERMANOVAs were performed for each molecular index with Disturbance (data from pre/post-hurricanes) and Site as factors (Disturbance x Site), and by constraining permutations within Site. For all analyses, Manhattan distances were used, and 10,000 permutations were performed. PERMANOVAs were performed in R using the function adonis implemented in the vegan package56.Genetic diversity indices were estimated for each sample collection, including average number of pairwise differences (Pi), haplotype diversity, and nucleotide diversity, using Arlequin 3.557. Finally, an analysis of molecular variance (AMOVA) was used to test two population structure scenarios: a) complete panmixia, and b) population structure at the regional level, according the haplogroups recovered from the haplotype network and to the geographical proximity among sites . AMOVAs were performed separately for pre- and post- hurricane datasets to assess whether the amount of genetic variance explained by among-site and/or among-group differences changed after the 2017 hurricane season. PhiST estimates and AMOVAs were calculated in Arlequin 3.5 based on pairwise differences among haplotypes and 10,000 permutations of the data.Pairwise PhiST comparisons were used to assess temporal genetic differentiation within-site under undisturbed conditions, using only pre-hurricane samples for sites with multiple years of sampling . To assess whether the 2017 hurricanes altered differentiation within-site and among-sites, pairwise PhiST tests were also performed using pre- and post-hurricane datasets. Since PhiST values within sites were not significantly different between years sampled prior to the 2017 hurricane season, all sample collections prior to 2017 hurricanes were pooled per site prior to estimation of pairwise PhiST values between pre- and post-hurricane datasets. Statistical significance of pairwise PhiST values were corrected for multiple tests with a Holm-Bonferroni correctionTo test whether gnathiids are structured following a pattern of isolation by distance (IBD), a linear regression of the geographic distance between sampling locations onto pairwise PhiST values was performed. Geographic distances were square root transformed to normalize distributions, and analyses were run using R. The IBD test was performed for both pre-hurricane samples and post- hurricane samples.58. We selected the log_home/log_max assignment criteria to identify first-generation migrants and their likely population of origin with populations assigned according to sampling locality. Analyses were performed using the Bayesian method described in Rannala and Mountain59 and implemented the Paetkau et al.60 simulation algorithm, with the number of individuals set to 100,000 and the alpha value set to 0.005.Finally, to infer migration events and directionality of geneflow before and after the storms, we performed Monte Carlo resampling under a Bayesian framework using Geneclass2In total, 432 gnathiids were sequenced across sites and years Table . OverallThe post-hurricane haplotype network revealed the same three haplogroups but the overall topology of the network changed, with haplogroups now differing only by 4 to 5 mutations. The reticulation in the post-hurricane haplotype network was the result of a new haplotype (hap_03) recovered in Lindquist Beach, which connected haplogroups A and C by five mutation steps. The most notable changes in haplotype distribution were the presence of haplogroup A sequences (hap_43) in Maho Bay (previously found only in Puerto Rico), and of haplogroup C sequences (hap_15 and hap_24) in La Parguera (previously absent from this location). Haplogroup B was still predominantly represented by individuals from Punta Soldado, however an increase in individuals collected at this locality and assigned to haplogroups A and C was observed. After the 2017 hurricane season, the overall proportion of haplotypes belonging to group C increased substantially , mostly driven by an increase in singleton haplotypes. Moreover, after the 2017 hurricanes, a greater proportion of individuals collected from the U.S. Virgin Islands had haplotypes that clustered into groups A and B, and the most abundant haplotype overall (hap_15) had representatives from every site sampled , indicating that the impact of hurricanes on evenness varied among sites  of global panmixia among 2 = 0.82, 0.94, respectively), suggesting a clear and consistent pattern of IBD in G. marleyi , with geographically distant locations showing the largest genetic distances (i.e. La Parguera vs. sites in the U.S. Virgin Islands; Fig. In accordance with our expectations, the results from the present work confirm that 61. Similarly, genetic diversity of intertidal gobies did not change after the record earthquake and subsequent tsunami that struck eastern Japan in 201126. Interestingly, although within-population abundances of a widespread intertidal mud snail, Batillaria attramentaria, showed strong declines after the 2011 tsunami, the direct impact of this disturbance on genetic diversity is difficult to assess given the large temporal variability observed in both pre- and post-tsunami conditions27. Only one previous study attempted to examine the effects of a tropical cyclone on the genetic structure of an aquatic organism28. In that study, the authors compared genetic diversity of a single population of the estuarine poeciliid fish Poecilia latipinna obtained with microsatellite data after hurricane Dennis in 2005, with those obtained with alloenzyme data 20 years prior62. In their study, they observed an increase in genetic diversity and reduced population differentiation which they attributed to the storm surge driven by the cyclone. However, the 20-year time lag between two sampling points and the differences in molecular markers made it difficult to link this change to the hurricane itself.Previous studies on the impacts of other types of environmental disturbance on other coastal species have also found that genetic diversity can be maintained or even increased after major disturbances. For instance, genetic diversity of marine iguanas was maintained despite high mortalities following the El Niño Southern Oscillation in 1998et al.27 and the present study included pre-disturbance temporal replication and estimates of differentiation for multiple populations. In the present work, annual sample collections allowed the analysis of non-overlapping generations of G. marleyi, consequently, we were able to unequivocally assess the effects that major hurricanes can have on the genetic diversity patterns of regional endemics with limited dispersal.While the above-mentioned studies provided important insights regarding the potential impacts of disturbances on gene flow of marine taxa, they focused on species with innate potential for long distance dispersal, and thus theoretically more resilient to disturbance. Importantly, only Miura 64. Ultimately, such events can contribute to changes in geographic distribution and the evolutionary potential of these species. While increased gene-flow and genetic diversity may buffer species from the loss of alleles due to genetic drift following stochastic demographic events, such as population bottlenecks65, these same processes may also dilute or even displace locally adapted alleles67. Aside from potentially impacting evolutionary outcomes of species, major disturbances may more immediately influence ecological interactions. In the case of parasitic gnathiid isopods, in addition to the direct effects on hosts via tissue damage and blood loss33, they can also function as vectors of diseases70. Understanding the spatial scales at which these potential vectors may spread and how major disturbances might alter such patterns is therefore critical.Although our study focused on a single species, inferences gained herein may extend to other species with similar ecologies and geographic distribution. For instance, otherwise dispersal-limited marine coastal species may benefit from increased and more frequent long distance dispersal events in regions periodically affected by tropical cyclones, especially since storm intensity and frequency is expected to increase due to ongoing climate change73 and these are also known to impact the ecology of gnathiids74. Clearly, more molecular studies are needed to address how these disturbances can affect other members of the coral reef community, including cryptic species, to better understand their potential resilience to future disturbances.The work presented here characterizes the acute effects of a severe climatic disturbance on the genetic diversity and structure of a coastal marine species. We demonstrate that major hurricanes can increase genetic diversity and reduce genetic differentiation locally and regionally, in a low-dispersal coral reef species. Besides tropical cyclones, coral reef ecosystems face numerous other threats prompted by global climate change, such as bleaching events, rising sea levels, and increased ocean acidificationSupplementary information


Leptinotarsa decemlineata), to environmental and landscape factors along an invasion front in Northwestern China. We first used microsatellite markers and spatial‐temporal samples to assess broad patterns of genetic diversity as well as fine‐scale changes in patterns of genetic divergence. We then distinguished the relative contributions of five factors to genetic divergence among front populations: geographic distance (isolation by distance), climate dissimilarity (isolation by environment), and least‐cost distances (isolation by resistance) modeled with three factors: climate suitability, cropland cover, and road networks. Genetic diversity broadly decreased from West to East, with the exception being Eastern China. Low levels of genetic diversity and varying degrees of divergence were observed in Northwestern China, reflecting the potential effect of landscape heterogeneity. Least‐cost distance across cropland cover was most positively correlated with genetic divergence, suggesting a role of croplands in facilitating gene flow. The contribution of climate to genetic divergence was secondary, whether modeled in terms of local adaptability or connectivity of the climatic landscape, suggesting that constraints to CPB gene flow imposed by a harsh climate may be ameliorated in agricultural landscapes. No evidence was found for an obvious effect of road networks on genetic divergence and population structuring. Our study provides an example of how agricultural landscape connectivity can facilitate the spread of invasive pests, even across a broad climatic gradient. More broadly, our findings can guide decisions about future land management for mitigating further spread.The population genetic structure of invasive species can be strongly affected by environmental and landscape barriers to dispersal. Disentangling the relative contributions of these factors to genetic divergence among invading populations is a fundamental goal of landscape genetics with important implications for invasion management. Here, we relate patterns of genetic divergence in a global invasive agricultural pest, Colorado potato beetle (CPB;  IBD, or the phenomenon in which stepwise patterns of gene flow result in greater genetic divergence as the geographic distance between populations increases, can serve as a null model when testing for environmental and landscape effects on genetic divergence , is a quarantine agricultural pest native to North America that invaded Europe in the early 1900s , Rapid evolution can accompany invasions and population expansions, but is often missed by landscape genetics studies that focus sampling effort on a single point in time  to the genetic divergence observed among CPB populations in the invasion front, which we did using a variety of landscape genetics approaches. In the complex terrain and harsh environment of the invasion front, we hypothesized that geographic distance, habitat (cropland) connectivity, climate (temperature and precipitation), and transportation infrastructure (roads) may affect the genetic divergence between populations, though the magnitude of their impact might differ. Having a greater understanding of how these factors contribute to genetic divergence among CPB populations can guide decisions about future land management practices for mitigating further spread.22.1We opportunistically collected 1,068 CPB adults by hand from commercial potato fields at 15 locations near Xinjiang between 2003 and 2018  with a DNeasy Blood & Tissue Kit (Qiagen) following the manufacturer's protocol. Nine microsatellite loci for CPB developed and characterized by Grapputo  with adjHs), observed heterozygosity (HO), inbreeding coefficient (FIS), allelic richness (AR), and population‐specific FST, βs  in which values of pairwise FST between populations were calculated using GENEPOP  on the extracted climate variables among sample sites using the ade4 R package . The first two axes of PCA were also used as environmental predictors in following generalized dissimilarity modeling (GDM) analysis.Pairwise geographic distances among sampling localities along the invasive front were calculated using the Hijmans, . IBE wasHijmans, . BrieflyTo quantify landscape resistance, we computed least‐cost distances (LCDs) based on three factors hypothesized to effect CPB dispersal and gene flow: (a) climate suitability (IBR‐climate); (b) cropland cover (IBR‐landcover); and (c) road networks (IBR‐road).costDistance function in the gdistance R package  were calculated among all pairwise population combinations using the All inputs for the subsequent tests  of isolation models  are summarized in Table 2.5.1A climate suitability surface that encompassed the invasion front region  was constructed using an ecological niche model (ENM) implemented with MaxEnt 3.4.1  is highly restricted , and created a raster layer where roads were designated as 1 and nonroads as 0 in ArcGIS 10.2.2. We matched CPB populations to the road resistance surface by adjusting population coordinates to the nearest road.We included a resistance surface based on the configuration of major roads because we expected that CPB might be dispersed by movement of host material along roadways  GDM  partial Mantel test using the Partial Mantel test improve upon Mantel test by allowing the examination of the correlation between genetic distance matrix and some environmental distance matrix , after accounting for any correlations with another variable (often geographic distance matrix). GDM is a matrix regression technique, that calculates the relative  and combined  contributions of predictor variables to genetic divergence. dbRDA is a constrained linear ordination method that combines multiple linear regression and principle component analysis. We used dbRDA to detect whether there are some environmental factors promoting genetic divergence, after having removed the distance‐based effect by constraining the distance variable with the highest degree interpretation to genetic divergence . The environment variables that we considered were the 19 bioclimatic factors extracted from individual coordinates. To avoid high collinearity of all variables, we performed loop pre‐dbRDAs, removing the variable with the highest variance inflation factor (VIF) each run, until VIF values of the remaining variables were <10. The following variables were retained: Bio1 , Bio2 , Bio6 (minimum temperature of the coldest month), Bio8 (mean temperature of the wettest quarter), Bio12 , and Bio15 . Pairwise LCD‐landcover distance at the population level was allocated to the individual pairs and then transformed into principal coordinates of neighborhood matrices (PCNMs) as rectangular data. We tested the respective and relative effect of environment variables on genetic divergence according to two models: a model with only climate variables, and a model with climate variables controlled by PCNM variables. For comparability, we also tested two other models: a model with only PCNM variables, and a model with PCNM variables controlled by climate variables.33.1p < .01). Linkage disequilibrium was observed for 33 of the 1,692 locus‐locus pairs within populations and in two of 36 pairs across all populations (p < .01). Importantly, no locus exhibited significant deviations from Hardy–Weinberg or evidence of linkage disequilibrium across all populations.A heterozygote deficit was detected in 25 of the 423 locus‐population pairs, while 24 pairs showed heterozygote excess after Bonferroni correction (HS) ranged from 0.643 to 0.655 in the USA, from 0.601 to 0.628 in Europe, from 0.422 to 0.539 in Central Asia, and from 0.433 to 0.608 in Xinjiang, China , except for populations in Altay and Eastern China , followed by Europe then Asia. Gene diversity  was much greater than year (R2 = .02), although several sites were not completely symmetrical in temporal collection.Our analysis of change in population structure over time revealed substantial changes in TTC in Tacheng, and AAL and AHB in Altay Figure . Accordi3.3The two ENMs used to create landscape resistance surfaces showed credible performance (AUC = 0.99). The jackknife tests indicated that two variables: Bio1  and Bio18 (precipitation of warmest quarter) contributed the most to isolation in the first ENM Figure a, and la3.4Visualization with EEMS highlighted regions with lower and higher effective migration rates than IBD expectations Figure . High mip values ranging from .0004 to .0009, R2 from .131 to .237, and with the IBR‐landcover model showing the highest correlation. The MMRR test result for IBE was also significant . In GDM models, all predictor variables  together explained 38.82% of genetic divergence among CPB populations. IBR‐landcover was the most important explanatory variable, explaining 29.66%, and IBE independently explained 26.00% of the observed genetic divergence. IBR‐landcover and IBE together explained 38.81%. Similar results were obtained when using Nei's D as genetic distance  for local residents or diffusion across climatic resistance surfaces (IBR‐climate), to that of cropland connectivity in shaping genetic divergence among CPB populations. Though we acknowledge that the statistical power of data from nine microsatellite markers to quantify genetic divergence may be limited, we still consider our data informative, as they were able to distinguish interrelated effects of geographic distance, climate dissimilarity, and landscape resistance on genetic divergence. The climate‐related patterns of IBE should be treated as indicators of how environmental variation might affect population process such as the life cycle and genetic drift. We therefore tentatively suggest that CPB may be able to adapt to constraints imposed by the harsh climate in the invasion front in China, though a detailed analysis of genome‐wide genetic markers among geographic populations is needed.Despite explaining a relatively small amount of genetic divergence among CPB individuals, our dbRDA analyses still identified several potentially influential climate variables related to temperature (e.g., Bio1, Figure 4.4Understanding the sources and main pathways of invasive species is essential for accurate risk assessment and management (Sakai et al., Specifically, this study and previous studies (Zhang et al., Fig S1‐S4Click here for additional data file.Table S1‐S4Click here for additional data file.


Various databases on genetically modified organisms (GMOs) exist, all with their specific focus to facilitate access to information needed for, e. g., the assistance in risk assessment, the development of detection and identification strategies or inspection and control activities. Each database has its unique approach towards the subject. Often these databases use different terminology to describe the GMOs. For adequate GMO addressing and identification and exchange of GMO-related information it is necessary to use commonly agreed upon concepts and terminology.A hierarchically structured controlled vocabulary describing the genetic elements inserted into conventional GMOs, and GMOs developed by the use of gen(om)e-editing is presented: the GMO genetic element thesaurus (GMO-GET). GMO-GET can be used for GMO-related documentation, including GMO-related databases. It has initially been developed on the basis of two GMO databases, i.e. the Biosafety Clearing-House and the EUginius database.The use of GMO-GET will enable consistent and compatible information (harmonisation), also allowing an accurate exchange of information between the different data systems and thereby facilitating their interoperability. GMO-GET can also be used to describe genetic elements that are altered in organisms obtained through current targeted genome-editing techniques. Since the initial advent of conventional genetically modified organisms (GMOs)  or—as deNew genome editing techniques may result in relevant alterations in an organism´s genome but may also lead to minor modifications that are indistinguishable from untargeted or spontaneous mutations in the organism’s genome. According to the European Union´s (EU) legislation, such gen(om)e-edited organisms are covered by the EU GMO legislation , 3. TheyOn the molecular level, a conventional GMO is determined by the genetic elements inserted into the endogenous DNA of the recipient organism. In case of GE-GMOs, an identifiable genetic element has been altered. The term genetic element describes a string of nucleotides (continuous DNA sequence) with a distinct function originating from one or multiple donor organism(s) or of synthetic origin, i.e. a molecular unit. Genetic elements are combined in a construct that comprises one or more coding DNA sequences (translated into proteins) or silencing elements together with appropriate regulatory DNA elements  acting during transcription or translation on DNA or RNA level to ensure correct expression of the integrated construct Fig. .Fig. 1SiProcedures for the enforcement of GMO legislation need a clear overview of the characteristics of, on the one hand, authorised GMOs that have been assessed for their safety for human and animal consumption and the environment, and of unauthorised GMOs, on the other hand, that have not (yet) been assessed by regulators of the respective countries. For an effective overview of the known GMOs, authorised and unauthorised, and also for inspection services and enforcement laboratories, it is important to have user-friendly databases providing easy access to all required information on each known GMO, being conventional or GE-GMO.https://euginius.eu/euginius/pages/intrestingLinks.jsf). Each database has its unique approach towards the subject. While each database contains unique information, there is a considerable overlap of information among them as exemplarily shown for some selected databases in Table Various GMO databases have been compiled in the recent years and decades, all with their specific focus to facilitate access to information needed for, e. g., the assistance in risk assessment, the development of detection and identification strategies or inspection and control activities. A list of GMO-related websites is given in the EUginius database , are specifically assigned with a unique identifier (UID) for unambiguous designation (not including the more recent GE-GMOs) , 5. Howewww.bch.cbd.int) [www.euginius.eu) [To this end, this paper presents a hierarchically structured controlled vocabulary describing the genetic elements introduced or altered in conventional GMOs or GE-GMOs. We propose the term GMO genetic element thesaurus (GMO-GET). GMO-GET can apply to any GMO-related documentation, including any GMO-related database. It has initially been developed on the basis of three GMO databases, i.e. the database of the Biosafety Clearing-House  , the Eurcbd.int) , 7 and tnius.eu) . The lathttp://obo.sourceforge.net and are accepted by the scientific community as authoritative. All bio-ontologies assign an identifier (ID) for each term and these allow the archiving, storing and accessing of data in databases. Ontology IDs provide a means of exchanging data with unambiguous, shared meaning between databases, an ability known as 'semantic interoperability'.Many bio-ontologies are stored at http://oboedit.org/) [www.euginius.eu/euginius/pages/gmo_genetic_elements.jsf) [Several bio-ontologies relate to plant genetic elements such as Gene Ontology , the Plait.org/) , 16. GMOnts.jsf) .www.euginius.eu/euginius/pages/gmo_genetic_elements.jsf) [Ontologies provide a means of formalizing knowledge in complex hierarchies that are composed of terms and rules , 19. Thents.jsf) .Table 2Acry delta endotoxin genes) and/or with comparable/analogous features (e.g. variants of the epsps gene) are grouped. Those level_3 terms serve as a label for the actual genetic elements and are described with an abbreviation following specific syntax rules. Therefore, each genetic element listed in GMO-GET has an unambiguous designation (e.g. P-nos-RHIRD).Level_0 is the root representing the general idea of a genetic element. Attached to the root are the level_1 terms. Level_1 describes the general functional types of genetic elements: coding sequence, enhancer, gene silencing elements, genomic sequence, intron, leader, promoter, other regulatory elements, terminator, transit peptide, unknown origin, vector fragment. The lower level (level_2) is composed of an abbreviation of the element type plus a generally comprehensive long name referring to the detailed biological function, e.g. CS-nopaline synthase. Below level_2, genetic elements of common/homologous origin , a name part (YYYY) and a donor part (ZZZZZ). For gene silencing elements and elements modified using genome editing techniques, the name part also includes information about the particularity of the elements as a suffix after YYYY. For example, YYYY_genome_edited indicates an element modified by the use of genome-editing techniques, YYYY_siRNAs indicates a sense orientation of elements leading to gene silencing through siRNA, YYYY_siRNAas an antisense orientation and YYYY_siRNAu the undefined orientation of this element.The prefix  indicates the element type, i.e. whether the genetic element is a coding sequence (CS-), an intron (I-), a promotor (P-) or something else. All currently used prefixes are listed in Table Agrobacterium tumefaciens (update scientific name Rhizobium radiobacter) strain CP4. As there is no specific abbreviation for this strain “CP4” will be part of -YYYY-, e.g. CS-CP4epsps-RHIRD.The abbreviation of the element name is written in lowercase letters unless the commonly used abbreviation uses uppercase letters. If the common abbreviation includes a species abbreviation, this should be deleted from the name since this will be indicated in the suffix of the element name ; an exception from this rule is the naming of the Special characters are avoided as much as possible and are exchanged by an underline “_”. E.g. “.”, “/” and “´” are interpreted as special characters.Greek symbols are written out instead of using the one letter code .The 3′ UTR and 5′ UTR (untranslated region) should be removed from element names since this seems obvious from the fact that the element is a terminator (T-) or promoter (P-).The middle part of the syntax  accounts for an abbreviation of the element. The abbreviation should reflect the most common abbreviation of the element. If an abbreviation does not exist or a common abbreviation is difficult to pinpoint, an easy-to-understand abbreviation of the element name should be used upon agreement by the constructors of the thesaurus. The abbreviation should be the same for all types of an element . Some rules were established to design the middle part of the syntax:http://www.uniprot.org/docs/speclist) [http://bio-mirror.im.ac.cn/mirrors/pvo/vide/acrindex.htm) or, if not found there, from the Ninth Report of the International Committee on Taxonomy of Viruses (2012) [Finally, for conventional GMOs, the syntax includes an organism code as suffix or donor part , which denominates the donor species or source of the genetic element by giving an abbreviation for the species in capital letters; hybrid elements have the donor SYNTH. The organism code follows the recommendations suggested by UniProt (peclist)  except fs (2012) .Genetic elements are used to identify a conventional GMO, i.e. by describing the genetic elements that are present, either as part of the insert or as endogenous (flanking of species-specific) sequence, a conventional GMO can be identified unambiguously. Therefore, GMO-GET enables the precise description of a conventional GMO by its genetic elements and the thesaurus makes it possible to integrate data from databases which use these terms even if from different hierarchical levels. For example, the BCH annotates GMOs in its databases with genetic elements that are defined with two terms, name and abbreviation, but without hierarchy. EUginius adopted those terms and incorporated them into GMO-GET: The BCH name became level_2 and the BCH abbreviation is level_3. EUginius uses level_3 terms of GMO-GET to describe GMOs and uses the hierarchical structure of GMO-GET to allow identification of related genetic elements and subsequently corresponding GMOs and/or methods. When the hierarchy of the thesaurus is used, a query with a level_2 term will find the same GMOs (if present) in other databases using GMO-GET, because the query will also find GMOs annotated with children (level_3 and level_4) of this particular level_2 term.The EUginius database uses GMO-GET in its approach to provide major and relevant information on GMOs. This database is based on four interconnected modules. (1) The GMO module lists existing conventional GMOs and genome edited organisms and enables sorting and filtering by specific criteria like trait, company or genetic elements. It also provides detailed information on the molecular characterisation including annotated sequences. (2) The detection module contains information on detection methods including reference materials, tools supporting the development of screening strategies and relevant literature. (3) The analysis module provides a tool for the interpretation of screening test results. (4) The authorisation module offers detailed authorisation status and EU application details on food and feed.GMO-GET is used (1) in the GMO module to describe the inserts in GMOs, and (2) especially for identifying non authorized GMOs: It is the structural basis for the GMO method matrix of the detection module and the analysis module.By using GMO-GET, EUginius offers a way to include all information available on GE-GMOs—including trait, genetic element affected, developer, etc.—in a structured and standardized way. The standardisation makes the information on GE-GMOs available to systems that support inspection and control activities.Based on the dual entity relation of many genetic elements it is also possible to use a computer to predict which GMO can be detected with a particular element-specific method. No manual intervention is required. EUginius uses this information, for example, for its Detection module and Analysis module, which facilitate the selection of appropriate detection methods and the interpretation of corresponding results of GMO analysis experiments.The genetic element thesaurus GMO-GET presented here is the first ontology that provides a controlled vocabulary for GMO-derived genetic elements using an unambiguous and harmonised vocabulary with a predictable but flexible set of syntax rules. The thesaurus can easily be expanded, if needed. GMO-GET thereby allows exchange of information between databases with overlapping information by increasing the possibilities for automatic data exchange between databases. Only the element ID (or element name) needs to be transferred as the properties of the element are already defined in GMO-GET for the linked databases. Thus, linkage of databases using GMO-GET will enable supplementing the information from one database with additional information from the other database(s). Thereby the user of the databases does not have to perform a new, modified, query for each database, but can rely on asking for information on the same genetic element in all linked databases. The GMO-GET thesaurus with syntax rules and clear hierarchy enables the extrapolation of new terms from different parties without lengthy discussions on individual terms.GMO-GET is the only known system that addresses organisms derived through gen(om)e-editing techniques, which are considered GMO in the EU but not necessarily everywhere else in the world.Furthermore, GMO-GET allows explicit assignment of PCR methods for detection of genetic elements and of corresponding GMOs that contain the DNA sequence of the construct used for transformation. Since GMO-GET also offers information on the relationship of the genetic elements, using the hierarchical properties of the thesaurus, this also enables linking properties of one genetic element to another, related, genetic element. This increases the investigative potential of existing PCR detection methods by linking them not only to GMOs containing the confirmed target elements but also to GMOs containing ‘children’ of those elements. It names the modified gene of GE-GMO and supports the development of specific detection methods.GMO-GET will thus facilitate harmonised enforcement strategies based on a clear overview of the characteristics of known authorised as well as unauthorised GMOs.


ST = 0.129 ± 0.012), and most likely clusters existing in the dataset are seven. Phylogenetic clustering was in line with the geographical locations of sheep populations. Mitochondrial sequences revealed high haplotype diversity with the existence of maternal haplogroups A, B, and C, and signals of population expansion. Decreased genetic diversity and unique maternal lineage (C) in endangered Tibetan and Bonpala sheep breed, warrant their immediate scientific management. Overall, the quantitative data reported here on the extant variability, and genetic relationships among the Indian sheep breeds, provide critically important inputs that will be valuable for the decision-making process on their management, both for the conservation of endangered breeds, and formulation of breeding programs to check genetic erosion.Sheep farming has been fundamental to many civilizations in the world and is practiced in India since antiquity. Several thousand years of adaptation to local environmental conditions and selective breeding have evolved 44 sheep breeds in India. They are paramount in terms of economic, scientific, and cultural heritage. Genetic characterization information is imperative for sustainable utilization and conservation of ovine heritage. In this study, the genetic diversity, differentiation, and structure of 11 indigenous sheep breeds from three different agro-ecological zones of India were explored with genomic microsatellite loci and mitochondrial DNA (D loop). The estimated diversity parameters indicated that populations retained high levels of genetic diversity , which provides an optimistic viewpoint for their survival. However, significant inbreeding was also observed in the nine populations. Moderate genetic differentiation existed among the groups (F Ovis aries, are a highly versatile and adaptable species. From their domestication in the Fertile Crescent, approximately 11,000 years ago, sheep now span the sundry terrains of all inhabited continents and are utilized as a source of food  and clothing 1. India has the second-largest sheep inventory in the world (6.4%) after China, with 74.26 million heads, and sheep number has increased (14.13%) in the recent past (2012 to 2019)2. Sheep are an important species of livestock for India. Sheep contribute immensely to the agrarian economy and to the livelihoods of small and marginal farmers in the harshest areas, where agriculture and dairying are not cost-effective. Mountains from Afganistan to Armenia have various wild varieties of sheep , who were possibly the ancestors of Indian sheep3. Urial and Argali stock are considered to be the progenitor of Indian sheep3. Thousands of years of natural selection for adaptation to varied agro-ecological conditions4, and planned field level breeding for environmental tolerance, behavioral, and commercial traits are reflected in the form of 44 sheep breeds in India (www.nbagr.res.in).Sheep, 5. It plays a vital role in providing traits responsible for improvement, survival, and adaptation of a species6. Future challenges, such as climate change, emerging diseases, pressure on land and water, and shifting market demands, make it even more important to ensure that livestock genetic resources are conserved and used sustainably7. The vast Indian sheep biodiversity is being eroded rapidly, with more than 50% of sheep breeds currently under threat8. Thus, a better understanding of the indigenous sheep genetic diversity can pave way for devising conservation strategies; facilitate their effective use for future breeding programs and, formulating management policies. Genetic diversity is often assessed by molecular markers, including the nuclear microsatellites, also referred to as the simple sequence repeat markers (nSSR) and mitochondrial DNA (mtDNA)9. Nuclear SSR has been widely accepted as a useful tool for measuring genetic diversity and divergence within and among livestock populations10. Microsatellite markers are consistent with Mendel’s laws of inheritance, are highly polymorphic, and generally have the co-dominant inheritance. Moreover, these are highly reproducible and easy to perform11. Therefore, genetic diversity data obtained by nSSR is used for surveying the genetic variability of livestock species that provides theory references to prevent the loss of genetic diversity, over the time12. These are being used to estimate the diversity of autochthonous sheep breeds all over the world14, including India15.Genetic diversity  reflects population size, history, ecology, and adaptability17. Multiphyletic origins involving 5 phylogenetically divergent mtDNA haplogroups (A–E) have been delineated in sheep, with haplogroups A and B being recorded in many countries and continents across the globe, while for haplogroups C, D, and E, the geographic distribution is relatively less widespread18. Studies on Indian domestic sheep have unveiled that haplogroup A is mainly represented in indigenous breeds, followed by haplogroup B19. Haplogroup C, which is considered a less frequent haplogroup worldwide, has also been seen in < 1% of the sampled animals, in one study on Indian sheep breeds20.Mitochondrial DNA has contributed immensely to elucidating the molecular phylogeny of many livestock species due to a higher mutation rate than the nuclear DNA, maternal inheritance, lack of recombination, and existence as multiple copies. The control region or the displacement region (D-loop) has particularly attracted the attention of researchers, to analyze phylogenetic relationships, and explore domestication events across species3, that is (1) North temperate region (NT), (2) North-Western arid and semi-arid region (NWASA), (3) Southern peninsular region (SP), and (4) Eastern region (ET)4. Himalaya mountains constitute the NT region. The NWASA consists of scattered hills in vast alluvial plains and an undulating landscape of the sandy desert. This is the primary region in the country for carpet-wool production and has the second largest population of sheep among the four regions. The SP region has the largest sheep population in the country, and these are maintained primarily for meat. The zone has a hot and humid coastline and semi-arid central peninsular region. Its topography includes sandy tracts, mountain ranges, plateaus, deltas, planes and, even highlands. The ET region is primarily hot and humid, apart from a few sub-temperate and humid areas of eastern states. The landscape is highly variable with alluvial plains along with undulations, plateaus and table-land, and Himalaya hill ranges as well as valleys in the north-eastern area. Here, sheep are primarily maintained for meat and produce extremely coarse and hairy fleece (https://www.dahd.nic.in).The sheep breeds in India have been classified into four groups, based on agro-ecological regions5. So far, the characterization of sheep diversity in India has largely been explored using microsatellites, with limited studies utilizing mitochondrial DNA. These are mainly restricted to a single population21, with very few studies targeting more number of breeds23. Genetic variation and relationships among the majority of recognized sheep breeds of NWASA region have been established using microsatellite markers24. Comprehensive knowledge about the sheep breeds of the remaining three agro-ecological regions of India is still sought after, for designing suitable and sustainable sheep breeding programs and conservation strategies.The adaptation of different Indian breeds to a wide range of agroecology offers the necessary variability, that provides the opportunity to meet the future demands for food, and endows with the flexibility to respond to altered markets and needsThe present study was undertaken to assess the genetic diversity, population structure, and phylogeography of 11 unexplored indigenous sheep breeds from three agro-ecological regions  of the country , representing 5 well-defined sheep haplogroups. Sequence analysis yielded a total of 83 novel haplotypes in Indian sheep, with overall haplotype diversity (Hd) value of 0.8502 ± 0.024, and nucleotide diversity (π) equal to 0.00373 ± 0.00029. The sequences were defined by 26 singleton variable sites and 44 parsimony informative sites. The maximum number of haplotypes was observed in Bonpala and Poonchi sheep, and minimum in Tibetan sheep. The Hd values ranged from 0.7526 ± 0.099 in Changthangi to 0.9004 ± 0.056 in Poonchi. The estimates of nucleotide diversity (π) were maximum for Tibetan sheep and minimum for Bandur sheep. Detailed statistics summarizing various genetic diversity parameters are given in Table In this study, we amplified a 1246 bp fragment of ovine mitochondrial control region encompassing part of 12S rRNA coding region and tRNAA median joining (MJ) network constructed using the haplotypes from Indian sheep breeds along with 10 reference sequences for the 5 known sheep haplogroups, revealed that Indian sheep were undoubtedly separated into three distinct clusters that corresponded to haplogroups A, B, and C. Out of a total of 154 Indian sheep specific haplotypes, 134 grouped with haplogroup A, 18 with haplogroup B and 2 with haplogroup C Fig. . The proP = 0.97), 0.043 (P = 0.85), 0.013 (P = 0.76) and 0.010 (P = 1.00), respectively, in sheep breeds from NT, NWASA, SP and ET regions, hinting at demographic expansion. Additionally, the sum of squared deviations (SSD) between the observed and expected mismatch values also presented non-significant low values, again suggesting population expansion. This observation was also substantiated by the results of Fu’s Fs and Tajima’s D neutrality tests. Significant negative Fu’s Fs values, which are inferred as signs of either purifying selection or demographic expansion were evident in all groups. Similarly, Tajima’s D value was negative, but significant only in the case of sheep from ET and SP zones of India. Details of population demographic parameters based on analysis of the mtDNA D loop region in Indian sheep are presented in Table Assessment of mismatch distribution in Indian sheep breeds revealed Raggedness index of 0.012  heterozygote deficiency in all the populations, except for the Poonchi and Bonpala, which on the contrary depicted heterozygote excess  amounted to 20%. An overall significant (P < 0.05) deficit of heterozygotes (FIS) of 8.5% occurred in the analyzed loci because of inbreeding within the populations. There was a moderate (0.05 < Fst < 0.15) breed differentiation between the 11 populations. The multi-locus FST value of breed differentiation indicated that 12.9% of the total genetic variation was due to the unique allelic differences between the breeds. The remaining 87.1% corresponded to the differences among the individuals of a breed, across the 25 markers. Pair-wise FST coefficients between the two populations are shown in Table ST values were significant (P < 0.05). In summary, it revealed the least differentiation between the Bellary-Hassan populations of the SP region. The highest divergence was observed between Changthangi (NT) and Bandur (SP) and was closely followed by Tibetan (ET) and Bandur (SP). The average estimated effective number of migrants per generation, between pairs of the populations (Supplementary Table ST results (12.9%). The pair-wise fixation index (FST) value provided by AMOVA through 99 permutations differed significantly from zero at P < 0.05. The largest genetic distance (DA) was recorded between Changthangi (NT) and Bandur (SP) sheep breeds, while Bellary and Hassan of the SP region were closest.To describe the level of heterogeneity within and between the Indian sheep populations, F-statistics values were determined. Results for each of the 25 loci, across all the populations, are presented in Table Genetic relationship among populations was further confirmed by the construction of phylogenetic tree (NJ), using Nei’s genetic distance Fig. . TibetanThe overall accuracy of population self-assignment was very high  including 84.68%, 13.96%, and 1.35% samples, respectively, were evident in the investigated sheep populations. Haplogroups D and E, which are considered rare globally, were not observed in this study. Our results are in agreement with the previous studies in Indian sheep, involving breeds other than those considered for the present investigation, wherein haplogroup A was observed in 89.6% and haplogroup B in 10.38% of the total samples19. Five breeds of NWASA region namely Marwari, Nali, Kheri, Muzaffarnagri, and Sonadi were components of clade B in the phylogenetic analysis19. Subsequently, haplogroup B was identified in four more breeds , representing 15% of total samples. Additionally, Haplogroup C was observed at low frequency (< 1%) in Nali and Jaisalmeri sheep, which are again the breeds of NWASA region20. Interestingly, haplogroup B and C were observed for the first time in the other three major agro-ecological zones of India, in the present study. Our observations of identification of three haplogroups in Indian sheep are in concordance with many international studies on mtDNA that have registered the presence of haplogroups A and B in Asia and predominance of haplogroup B in Europe26. Although haplogroup C is less frequent, however, its presence has been recorded in Turkey, Portugal, the Caucasus, and China25. In Tibetan sheep from China, major haplogroups that have been identified are A, B, and C. However, one animal out of a total of 636 sheep investigated also represented haplogroup D26. Despite the predominance of haplogroup A in India, the presence of 2 more haplogroups (B and C) in all the major agro-ecological regions of India suggests introgression due to gene flow between breeds of Asia and Europe.Molecular diversity studies of farm animal genetic resources using mitochondrial DNA have extensively traced domestication events and proposed multiphyletic origins across different species, including sheep. This study explored the membership of 11 breeds of Indian sheep to the 5 well-defined sheep haplogroups identified across continents20. The number of haplotypes ranged from 6–14 across different breeds, and each breed also had its unique haplotypes. All these statistics point towards high maternal genetic diversity in the populations investigated and also substantiates that the correct sampling strategy of including unrelated animals was followed. Signature of population expansion was evident based on Fu’s Fs, Tajima’s D, goodness-of-fit indices, and mismatch distribution assessed under sudden expansion model in sheep breeds from all the four agro-ecological zones. Mismatch distribution analysis revealed a unimodal distribution which suggests that the populations have undergone a rapid expansion in size. Fu’s Fs test which is based on haplotype frequencies as well as Tajima’s D test that relies on the difference between the number of polymorphic sites, and the mean number of nucleotide pairwise differences, also suggested expanding populations in all the major zones and whole of India. Despite vast sheep genetic diversity and huge population, lack of political will and financial support have resulted in poor coverage of indigenous sheep under genetic improvement programs. Therefore, it has not been possible to curtail mating between breeds in neighboring areas because of shared pastures. This inference was also supported by the presence of a large number of haplotypes that differed from the most common haplotype by few variations only.The average haplotype diversity value observed in the present investigation was 0.850, which was intermediate of the previous two attempts that have explored the mitochondrial DNA diversity in Indian sheep (0.603 and 0.987), but the nucleotide value was higher than either of these studies22. Estimates were also comparable with that of the Kajali sheep, that belong to the NWASA region21 and four sheep populations of the SP region; Madras Red, Mecheri, and Pattanam27 and Tiruchi Black28. The genetic variability observed in this study was higher than those reported in Jordan sheep29, and sheep breeds of the neighboring country, Pakistan30. However, it was comparable to the values reported across the world, for sheep32. Much higher diversity indices were recorded in Turkish sheep breeds that can be attributed to Turkey being considered as a major sheep domestication center33.Based on the microsatellite diversity and differentiation, Indian sheep breeds had sufficient diversity, as evidenced by the number of alleles and heterozygosity per breed. Except for Tibetan and Bonpala, the mean observed number of alleles (Na) across other breeds was more than 8. Similarly, excluding Tibetan, mean observed heterozygosity (Ho) was more than 0.6 in all the populations Table . Genetic8. Genetic variation of similar magnitude (0.46–0.55) has also been reported in the endangered Namaqua Afrikaner sheep, indigenous to South Africa34, whereas, higher heterozygosity has been reported in the two endangered Spanish breeds, Churratensina (0.66) and Churralebrijana (0.67), despite their small population size35. It should be mentioned here that the comparison of diversity estimates should be considered as a suggestive indication of diversity since microsatellite loci used across multiple studies are not uniform. A significant positive FIS value was observed for all the breeds, except very small negative values in Poonchi and Bonpala  and Bonpala (− 0.021) can be interpreted as possible signs of outbreeding, most likely due to a recent admixture of two (or more) populations.The lowest diversity parameters  were observed in the Tibetan sheep which may be resulting from its extremely small population size. It is an endangered sheep breed with only 215 animal leftnitude 0.6–0.55 haST = 0.129 ± 0.012). The Tibetan breed appeared as a distinct breed separated from the others. These results reflected that the within-breed genetic variation was high (87.1%) and this variation could be a valuable tool for genetic improvement and conservation of Indian sheep. Genetic differentiation of similar magnitude (11.1%) has been documented in some other indigenous sheep breeds22. FST values of the examined Indian sheep breeds were comparable to those reported for Baltic, Chiapas, and Akkarman sheep33. However, it was much higher than the FST values reported for several others like Spanish, European, Middle Eastern, Colombian, Pakistani, and Romanian sheep breeds37. Much lower differentiation (5.6–5.9%) has been reported in Indian sheep breeds of SP region27. In the present study also, the breeds of SP region were less differentiated. Bellary and Hassan were least differentiated among them which may be attributed to the considerable exchange of germplasm among the two populations (Nm = 22.55). Differentiation can be an outcome of geography and reproductive isolation, genetic drift, and founder effect, different production goals, and preferences for the specific traits. In the present study, overall moderate genetic differentiation could be due to the fact that breeds were spread across three agro-ecological zones and larger geographical distances, in India. Similarly, visualization of breed relationship through NJ tree showed the clustering of the breeds, mainly in congruence with their geographic location , for increasing apparel wool production with the concomitant dilution of these breeds. Geographic continuity, ecological similarity, and cultural connections amongst the major sheep rearing communities of the SP region could be contributing towards the gene flow between different sheep populations.4. Tibetan and Bonpala should be prioritized for conservation. These two unique gene pools have low genetic diversity. It is worth mentioning that maternal lineage B dominates in Tibetan sheep, unlike other Indian sheep breeds. Lineage C in Tibetan and Bonpala pointed towards their distinct evolutionary history from other Indian sheep breeds. Similarly, Changthangi represents a unique gene pool with good adaptation to the extremely cold and low oxygen conditions. These animals thrive in the high-altitude cold desert, where, the average altitude of the area is around 14,600 m above sea level. Habitat of Tibetan, Bonpala, and Changthangi is common across the Tibetan plateau38. Tibetan and Bonpala sheep migrated to the Eastern agro-ecological region of India with the Tibetan traders, who used them as beasts of burden for transporting various merchandise39. They might share ancestral variation and historical gene flow with the sheep breeds of the high-altitude Himalayan region of China. All the three temperate breeds might carry unique adaptation alleles, therefore, these should be considered as a priority group for conservation. Moreover, sustainable breeding programs and policies are required to prevent indiscriminate gene flow among populations and regions, as well as, crossbreeding with the exotic sheep breeds.Following the results, conservation programs need to be undertaken for endangered sheep breeds that are threatened with extinction. These include Changthangi, Rampur Bushair, Poonchi, Bonpala, and TibetanAnimal breeding farms and research institutes can play a pivotal role in the planning and execution of conservation programs. The establishment of breed associations and nucleus breeding farms is the first step towards conservation and sustainable utilization of indigenous sheep resources. Frequent exchange of the rams among farms and flocks of the same breed needs to be in place to increase the genetic diversity. Incentives to livestock keepers, extension, and health services to migratory flocks, availability of quality breeding rams for each breed, and community involvement can contribute enormously to this endeavor.The findings of this study contribute to the knowledge of the genetic diversity and the structure of native Indian sheep breeds. Data generated could be a useful source for breeding management to decrease heterozygote deficiency and breed dilution, and to ensure sustainable management and conservation activities to preserve sheep's genetic heritage. Overall, Tibetan sheep emerged out to be the most interesting gene pool, from both microsatellite and mitochondrial analysis. Genetic diversity indices warrant immediate scientific management and conservation of this invaluable germplasm, that is considered endangered at the moment.10 were followed in selecting animals. For each breed, blood samples were collected from unrelated sheep belonging to multiple flocks, and across different villages. Farmers were interviewed in detail to ascertain the unrelatedness of collected samples. Blood sampling was carried out between 2015 and 2019. All the 477 samples were utilized for the microsatellite analysis, whereas the first 20–22 samples of each breed were used for the mitochondrial study. Genomic DNA was extracted from blood using standard phenol–chloroform protocol40. The quantity of DNA was analyzed using a NanoDrop 1000 spectrophotometer (Thermo Scientific).In total, 477 animals of 11 breeds , Hassan and Bellary) were sampled after field survey in their respective distribution areas, spread across Northern, Eastern and Southern India  was aseptically collected by the trained veterinarians from the jugular vein with the consent of the farmers. The study has the approval of the Institute Animal Ethics Committee of Indian Council of Agricultural Research, National Bureau of Animal Genetic Resources, Karnal (PME Ref. 11/ 2017-18), and their relevant guidelines were followed during blood sample collection.41. Primers, F-AACTGCTTGACCGTACATAGTA, and R-AGAAGGGTATAAAGCACCGCC amplified a 1,246 bp fragment that spans part of the control region, the tRNA-Phe and 12S rRNA coding RNA genes. The amplified fragments were enzymatically purified using Exonuclease I and Antarctic Phosphatase , followed by sequencing in ABI 3100 Automated DNA Sequencer  using Big Dye Terminator Cycle Sequencing Kit .DNA samples from all 11 breeds were amplified using previously reported primers and cycling conditions42. The genetic diversity indices such as the number of haplotypes, haplotype diversity, nucleotide diversity, and the number of polymorphic sites for each breed and across all breeds were estimated using DnaSP v543. The analysis of molecular variance within and among agro-ecological zones was done using the AMOVA program implemented in the Arlequin version 3.544. To get a comprehensive insight into the phylogenetic relationship of Indian sheep in the context of global ovine diversity, sequences from previous studies in Indian sheep19 and 10 reference sequences for the 5 known sheep haplogroups identified across the globe were retrieved from NCBI, and a meta-analysis was carried out with the sequences of the present study. The purpose of this exhaustive analysis was to include breeds that represent all the four major agro-ecological regions of India. Departures from neutrality using Fu’s Fs45 and Tajima’s D statistics46, demographic changes based on Harpending's raggedness index, and the sum of squared deviations (SSD) between the observed and expected mismatch, as well as, population pairwise FST were examined using Arlequin version 3.544. Reference sequences of the 5 known sheep mitochondrial haplogroups were retrieved from NCBI, as per the assignment given by Meadows et al.18: Haplogroup A: DQ852105 and DQ852113; Haplogroup B: DQ852157 and DQ852189; Haplogroup C: DQ852252 and DQ852260; Haplogroup D: DQ852274 and DQ852275; and Haplogroup E: DQ852276 and DQ852278. These sequences were included along with the sequences obtained in this study for constructing a Median-joining (MJ) network using NETWORK 10.0.0.047, and Neighbour-joining (NJ) tree using MEGA6, for exploring the association between the haplotypes.The amplified sequences were aligned using the Clustal W algorithm embedded in MEGA version 6.010. These simple sequence repeat markers (nSSR) were highly polymorphic, spread all over the genome, and with the ability to co-amplify in PCR reactions (Supplementary Table 8 (2) multiplex genotyping using an ABI 3100 Automated DNA Sequencer  along with the internal line control (3) electropherogram analysis using GeneMapper software  for allele size estimation, and (4) statistical analysis using different software packages.Twenty-five International Society of Animal Genetics and FAO-recommended sheep nuclear microsatellites were analyzed in all the individual animals48 was used to examine large allele dropout, stuttering, and null alleles as potential sources of error. The genotype data were analyzed using GenAlEx 6.5 software49 to calculate allele frequencies at each locus for each population, the average number of allele per population; observed (Na) and effective numbers of alleles (Ne) and heterozygosity values  and heterozygote deficit (FIS) per locus across breeds and markers. Average values were expressed as Mean ± SE from values at each locus. Chi-square tests of deviations from Hardy–Weinberg equilibrium (HWE) were derived.Allelic polymorphisms at each nSSR locus were evaluated. The program MICRO-CHECKER50, analysis of molecular variance (AMOVA), and genetic distances were applied. Nei’s genetic distance51 and Cavalli-Sforza Chord distance were estimated by GenAlEx 6.549. Statistical significance of F estimates was tested using FSTAT, version 2.9.3 software52. The dendrograms of phylogenetic trees were built from different distance matrices and were visualized by MEGA642 using the NJ approach. Genotypic disequilibrium was tested by using FSTAT 2.9.352 for all pairs of loci with 1,000 permutations. Population assignment was performed using multilocus genotypes of individuals. STRUCTURE ver 2.3.4 software53 was used to further assess the degree of population differentiation within and between the 11 populations. Individuals were assigned into clusters using the Bayesian method under an admixture model. The algorithm estimates allele frequencies for each gene pool (cluster) and population memberships for every individual. The Simulation was run ten times for each value of K (2–13), where K is the number of tested clusters. All runs were performed with a 300,000 burnin period and 500,000 MCMC repeats after burnin. Software CLUMPP54 was used to align multiple replicates for each K to facilitate the interpretation of clustering results. The DISTRUCT application55 was used to graphically display the results. To determine the optimal number of groups (K), we utilized both the log-likelihood method of Prichard et al.56 as well as ΔK value of Evanno et al.57 and was calculated and plotted using Structure Harvester application58.To study the relationships and the genetic differentiation among tested populations, different F statistics estimatesSupplementary Information.