The sweet cherry is an important fruit species that is widespread globally. In addition to the well-known traditional and modern varieties, a myriad of landraces is present in Europe, as well as in southern Italy. This study aims to evaluate the population structure, genetic relationships, and cases of duplicate samples in a collection of 143 accessions using GBS-derived SNP markers. The genetic material under investigation includes modern commercial varieties, ancient European and American varieties, landraces, and individuals retrieved from small orchards. Some of the known varieties were genetically analyzed here for the first time. In addition, several genotypes were collected from the Basilicata region , an area largely unexplored for sweet cherry genetic resources. The relationships among genotypes were assessed using four different methods: allele frequency and ancestry estimation, principal component analysis, Neighbor-Joining tree, and identity-by-state estimation. The analyses returned quite congruent results and highlighted the presence of four main genetic groups, namely: (i) American varieties, (ii) the ‘Germersdorfer-Ferrovia’ cluster, (iii) the ‘Burlat’ group, and (iv) the group of Italian landraces. The main drivers of clustering were ancestry, geographical distribution, and some important traits such as self-compatibility. The sweet cherries from Basilicata, herewith examined for the first time, were mostly distributed within the group of Italian landraces, being particularly linked to the autochthonous varieties of the Campania region. However, some genotypes were outside this group, thus suggesting the introduction of genetic material from other Italian regions or from European countries. The considerable amount of American and European modern varieties analyzed are genetically very closely related, suggesting a reduced genetic basis. In addition, we highlighted the discriminating ability of SNP markers to distinguish between an original variety and its mutant. Overall, our results may be useful in defining conservation strategies for sweet cherry germplasm and developing future breeding programs to enlarge the genetic basis of commercial varieties. Prunus avium L.) is a diploid species (2n = 2x = 16) belonging to the Rosaceae family, whose fruits are highly appreciated for their organoleptic quality. In addition, cherries are rich in health-promoting components such as anthocyanins, carotenoids, quercetin, potassium, hydroxycinnamates, fiber, melatonin, and vitamin C. Thanks to these compounds, the intake of sweet cherries can prevent diabetes, cancer, Alzheimer’s disease, cardiovascular, and inflammatory diseases [The sweet cherry  . AlthougInformation on genetic diversity is essential for developing more productive, nutritious, and resilient crop varieties as part of a breeding program with the aim of safeguarding food security to better cope with a changing environment. The use of molecular markers allows for the estimation of genetic variation among genotypes. In the last decades, several studies have been carried out to describe the diversity of sweet cherry using molecular techniques. Many of these investigations were based on simple sequence repeat (SSR) markers, for instance, to assess genetic variation in the local sweet cherry germplasm of different countries ,14,15,16Fernandez i Marti et al.  analyzedOver the past decade, the development of next-generation technologies has allowed the efficient and cost-effective sequencing of plant genomes and the scoring of genome-wide SNPs. In sweet cherry, the first reference genome sequence was released for the Japanese variety ‘Satonishiki’ . SubsequGenome resequencing was performed to characterize genetic diversity, population structure, and allelic variation in a panel of 20 Greek genotypes and a wild accession, allowing for the identification of high-impact SNPs in genes involved in flowering time, dormancy, and response to biotic stress, thus providing useful resources for breeding programs .On the other hand, a reduced representation of the genome can be achieved through Genotyping-by-Sequencing (GBS), which allows the simultaneous discovery and genotyping of thousands of SNPs to analyze germplasm diversity in a series of multiplexed samples ,27,28,29In the present study, we compared newly collected germplasms of sweet cherry with known varieties. We used many GBS-derived SNP markers to analyze a wide collection of 143 genotypes. These included more than 100 old and modern commercial cultivars of various geographical origins, along with several Italian landraces, with the aim to assess (i) population structure, (ii) genetic relationships, (iii) the presence of duplicate samples, and (iv) the possibility to discriminate between an original variety and its mutant clones. Many of the varieties analyzed in our study had never been genotyped before, at least not using SNPs. Particular attention was devoted to local germplasm, especially from the Basilicata region , which has been a neglected area for the analysis of sweet cherry variation so far, to evaluate the diversity preserved even in family gardens and small orchards, and to compare this new material with known varieties.The genetic material analyzed in this study by means of SNP markers originates from a wide range of European and American countries and Asia  . SeveralThe sequencing of a starting set of 149 sweet cherry samples resulted in 486 million paired-end reads, with an average of 3.2 million reads per sample. The SNP calling returned 104,982 polymorphic sites.The mean depth of coverage, the number of SNPs per individual, is reported in Transitions (Ts) were more abundant (59.21%) than transversions (Tv) (40.79%), with a Ts/Tv ratio of 1.45 . SNP calThe cross-validation test  indicateThe 5B  form group III, together with some modern Italian varieties, which have Canadian germplasm in their genealogy  and North American varieties  genetically related to ‘Burlat’ .ST values were calculated between the four groups observed in the PCA plot. The highest genetic differentiation was observed between group II and group IV (FST = 0.21). The genetic distance between group I vs. group III and group I vs. group IV was the lowest (FST = 0.11). The genetic variation in all other pairwise comparisons ranged from 0.13–0.17 , the first large clade  includes mainly American  varieties and other varieties obtained using American genotypes as parents. Cluster 2 extends from ‘Techlovan’ to ‘Germersdorfers’ and includes only European varieties, with the exception of one from Iran (‘Noire de Meched’). Cluster 3, from ‘Cashmere’ to ‘Durone locale’, comprises modern varieties (from the USA and Europe), the old ‘Moreau’ and ‘Burlat’, as well as some varieties from Basilicata . Excluding the external varieties ‘Primulat’ (France) and ‘Hartland’ (USA), the fourth group (cluster 4) is the largest and contains mainly local Italian germplasm.The main branches of the tree can be divided into subclades, in which interesting groupings can be observed. Within the North American clade, the subclade from ‘Sam’ to ‘Summit’ includes only Canadian varieties. ‘Sylvia’ and ‘Summit’ derive from ‘Van’ x ‘Sam’. ‘Canada Giant’, although of unknown pedigree, is phenotypically similar to ‘Summit’. The following subclade  contains five modern Italian varieties of unknown parents, which were developed in Italy (Bologna). All these varieties are self-compatible, except ‘Sweet Valina’.‘Garnet’ (from the USA) and ‘Isabella’  share the female parent , which have Canadian germplasm as a donor. All the varieties in this clade are related to the traditional variety ‘Van’.‘Black Giant’ from the USA and the Hungarian ‘Early Magyar’, both of unknown origin, are early-ripening varieties ,31.The small subclade from ‘Adriana’ to ‘Lucrezia’ consists of Italian varieties from the Veneto region, which were developed by the same breeder, G. Bargioni . In partThe following subclade groups three Czech varieties and one from Basilicata. In particular, ‘Techlovan’ and ‘Vanda’ share the same parents, ‘Van’ x ‘Kordia’. ‘Katalin’ and ‘Linda’ were developed in the same research institute in Hungary in 1990 and share ‘Germersdorfer’ as a common parent, while the German ‘Regina’ derives from ‘Schneiders’.The subclade from ‘Krupnoplodnaja’ to ‘Germersdorfer’ includes varieties of different geographical origins but with similar phenotypes ,31. ThesThe varieties from ‘Cashmere’ to ‘Vigred’, from distinct geographical areas, share ‘Burlat’ as a parent, with the exception of ‘Chelan’ (USA), which has a French male parent, ‘Beaulieu’ . ‘Black The following subclade groups three Italian varieties from the Emilia Romagna region  with a genotype from Basilicata (‘Durone tenero’), in addition to two more distantly related varieties, i.e., ‘Hartland’ from the USA and ‘Primulat’ from France. The varieties from ‘Elisa’ to ‘Magda’ do not share a common geographical origin, and their genealogy is not well known.The subclade from ‘Mora di Cazzano sel. 40′ to ‘Sandra Precoce’ assembles Italian varieties from the Veneto region, except for ‘Duroncino di Alberobello’ (from Puglia).From ‘Della Recca’ to ‘Zucchero e Cannella n. 4′, all the Italian autochthonous varieties from Campania, together with three genotypes from Basilicata, and one from Puglia (‘Molfetta’) are included. Finally, the next subclade clade consists of all the remaining (N = 18) genotypes from Basilicata.Relationships among the 143 sweet cherry genotypes were also assessed in pairs by estimating identity-by-state (IBS) allele-sharing values using 13,117 unlinked SNPs. The frequency distribution of IBS estimates  showed tThe highest values (>0.990) were scored for two pairs of genotypes from the Basilicata region, for ‘Del Monte’ and ‘Del Monte Falsa’, and for the pair ‘Ferrovia Spur’ and ‘Gégé’. Several pairs of genotypes with values > 0.980 are from Basilicata. Most of the pairs belonging to the ‘Germersdorfer’-’Ferrovia’ group had values between 0.980 and 0.989. Within the ‘Burlat’ group, ‘Burlat’, ‘Burlat C1′, and ‘Burlat Fr.’, as well as two genotypes from Basilicata shared values between 0.980 and 0.989. ‘Durone compatto Vignola’ and ‘Nero II CL 90′ had an IBS value of 0.985. The pairs ‘Van’-’Early Van Compact’ and ‘Van’-’Lala Star’, as well as ‘Napoleon’ and ‘Limone’, showed values > 0.980.In this study, we used SNP markers to assess population structure, genetic diversity, and relationships in a wide germplasm collection of 143, including old and modern commercial cultivars of different geographical origins (more than 100) and landraces mostly from southern Italy, especially newly collected material from the Basilicata region. Moreover, we investigated the possibility of ascertaining duplicate samples and discriminating between an original variety and its mutant clones. Several known varieties were genotyped here for the first time using SNPs. Others, especially the genotypes from Basilicata, are cultivated in restricted areas and lack references for both phenotypic traits and genetic characterization. The relationships among varieties/genotypes were assessed using four different methods: allele frequency and ancestry estimation, PCA, NJ tree, and IBS analysis. If we compare the results obtained by these methods, we can observe a similar trend in the grouping of varieties, determined mainly by the known genealogy of the material and/or by the geographical origin and patterns of dissemination.Almost all the North American varieties were grouped together . It has The group including the varieties ‘Germersdorfer’ and ‘Ferrovia’  according to sampling and classification protocols reported by UPOV guidelines .Leaves were collected at the end of the summer season, and healthy tissues were used for genomic DNA isolation and purification using the DNeasy plant mini kit .Prunus [The quality and concentration of each DNA sample were assessed before shipping the samples to the Elshire Group Ltd.  for GBS assay. Since the ApeKI restriction enzyme had already been used successfully to generate GBS libraries in Prunus , this enPrunus . Brieflyhttps://github.com/kdmurray91/axe; accessed on 20 December 2021), and technical Illumina sequences were removed. High-quality reads were then aligned to the P. avium reference genome  with Bowtie2 [http://catchenlab.life.illinois.edu/stacks/; accessed on 20 December 2021 [Raw reads were demultiplexed with axe-demux ( Bowtie2  in end-tber 2021 ) was useber 2021 ) with thber 2021 . The PLIThe relationships among genotypes were assessed using four different methods: identity-by-state estimation, allele frequency and ancestry estimation, principal component analysis, and Neighbor-Joining tree.ST values between populations were computed using the fst.hudson function as part of the KRIS v1.1.6 software package .PLINK was used to populate the matrix of genome-wide IBS (identity-by-state) pairwise distances. Pairwise Fary 2022 ). The fsary 2022  and Hudsary 2022 . A heatmAn ADMIXTURE version 1.3.0  was perfPrincipal component analysis (PCA) was used on the pruned SNP dataset to describe the overall population structure and derive patterns of relatedness between individuals.https://www.megasoftware.net/; accessed on 10 March 2022 [https://itol.embl.de/; accessed on 10 March 2022 [The same SNP dataset was used to generate the Neighbor-Joining (NJ) tree based on the Tamura-Nei genetic distance model  using MErch 2022 ) with 50rch 2022 ).SNP markers obtained via GBS proved to be an excellent tool for assessing the genetic diversity and relationships within sweet cherry, consistent with pedigree and/or geographical origin when applied to a large and well-assorted germplasm panel. The present study highlights that the significant number of American and European modern varieties analyzed here are genetically closely related, suggesting a reduced genetic basis. Moreover, SNPs were able to discriminate between varieties previously considered synonyms  and between varieties and their selected clones . Additionally, we were able to confirm some relationships previously observed using different types of molecular markers, e.g., the genetic similarity between ‘Napoleon’ and ‘Limone’, as well as the high similarity among the varieties included in the ‘Germersdorfer’-’Ferrovia’ group, although these varieties could be distinguished, confirming the discriminant power of the SNP markers.Furthermore, for the first time, we have explored the germplasm from Basilicata , highlighting that most of this material has genetic affinities with landraces from other Italian regions, especially Campania, from which genetic material could have been transferred. In addition, some of the genotypes from Basilicata appear more closely related to foreign varieties, suggesting that this genetic material could derive from the local adaptation of ancient European varieties, which probably arrived in southern Italy several decades ago.The conservation and availability of variable germplasm preserved over time can warrant the retention of a potentially useful gene sink for future breeding programs, broadening the genetic basis of commercial cultivars. Furthermore, local varieties should also be preserved to ensure production and marketing in the communities where they were developed.Finally, the availability of SNP markers for a wide panel of individuals could guarantee the reuse of data for identifying SNP loci associated with key agronomic traits.


The authors have no conflict of interest.Dear Editor,® (Sekisui Diagnostics), ZYMUTEST™ (Hyphen BioMed), Human TF ELISA (Abcam), and Quantikine® ELISA (R&D Systems). These ELISAs were evaluated carefully for their ability to detect TF in cell lysates from multiple human cancer cell lines that express different levels of TF using western blotting with the anti‐human TF monoclonal antibody 10H10.® kit for measurement of TF antigen in plasma from healthy controls and patients with cancer. They found that the median plasma levels of TF antigen signal were 39.1 pg/mL (range 16.8‐87.3) for healthy individuals, 47.2 pg/mL (range 0.0‐224.8) for patients with cancer who did not develop thrombosis, and 56.0 pg/m/L (range 37.6‐318.7) for patients who developed thrombosis.Kobayashi et al.® ELISA for TF antigen signals in plasma from healthy individuals.® ELISA with their own calibrator to measure TF‐like antigen in the plasma of 91 healthy individuals and found that the majority  had levels below the quantitative limit of the assay (65.4 pg/mL), 6 had levels between 65.4 and 163.5, and in 13 individuals the non‐specific signal was higher than the specific signal.Measurement of TF antigen levels has been found to be problematic when using commercial ELISAs due to the high background signal in plasma that is not observed for cell lysates or culture supernatant samples containing extracellular vesicles (EVs) from cell lines.® ELISA with the standard from the kit.™ ELISA gave a low signal (8.8 ± 1.9 pg/mL) in the negative controls, but there was no increase in the positive controls.Ourselves and others use plasma prepared from whole blood with or without LPS stimulation of monocytes as positive (containing TF + EVs) and negative controls, respectively, for TF antigen and activity assays. We used an in‐house extracellular vesicle tissue factor (EVTF) activity assay to measure the levels of TF + EVs in samples and observed a large increase in signal in the positive controls compared with the matched negative controls.® ELISA. Interestingly, the Quantikine® ELISA was used to measure plasma TF antigen levels in 805 patients in the Comparison of Acute Treatments in Cancer Hemostasis (CATCH) trial for patients with cancer.® ELISA to measure levels of TF antigen in plasma.™ ELISA, there was no increase in the signal in positive controls compared with negative controls with this ELISA. We wrote an “understanding the pathway” article on the study by Khorana et al.® ELISA does not measure TF antigen in plasma.Kobayashi et al.® ELISA to detect TF antigen in cell supernatant samples containing EVs from TF‐positive and TF‐negative human pancreatic cancer cell lines, and in plasma samples from whole blood with or without LPS stimulation.® ELISA gave a strong signal with the TF‐positive cell line, HPAF‐II, and no signal with the TF‐negative cell line, MIA PaCa‐2 (Figure ® ELISA detects TF in cell lysates and culture supernatants containing EVs from TF‐expressing cell lines, but does not detect low levels of TF antigen in plasma.Here, we assessed the ability of the Quantikine2 Figure . However2 Figure . We beli


Several sites were sampled within Okinawa Trough to examine intra-population diversity while two other locations 1400–2400 km away were chosen for inter-population comparisons. For synaphobranchid eels Simenchelys parasitica and Synaphobranchus kaupii, our results showed significant intra-population diversity but no inter-population genetic differentiation, suggesting strong genetic connectivity and/or large population sizes. In addition, single nucleotide polymorphism analysis also confirmed strong genetic connectivity for Simenchelys parasitica. Among scavenging amphipods, we detected seven putative species using molecular phylogenetic analysis. We evaluated population structures of the most abundant species of amphipods and a decapod species (Nematocarcinus lanceopes). Our results provide basic information on the genetic population structures of benthic megafaunal species near hydrothermal vent fields, which can be used to select candidate species for future connectivity analysis with high-resolution genetic markers and aid understanding of the potential population impacts of environmental disturbances.Deep-sea mining of hydrothermal deposits off the coast of Japan is currently under consideration, and environmental baseline studies of the area are required to understand possible impacts. The aim of this study is to clarify population structures of dominant benthic megafaunal species near hydrothermal vent fields in the Okinawa Trough, using a population genetics approach. We examined dominant deep-sea scavenging species including eels, several amphipods, and a decapod and performed population genetic analyses based on the mitochondrial cytochrome  One such resource, seafloor massive sulfides (SMS), is associated with deep-sea hydrothermal vents4. However, the chemosynthetic environments of hydrothermal ecosystems have fostered endemic fauna that are also a potential target for conservation. While vent communities themselves have been studied extensively, the marine fauna near hydrothermal vent fields have received little attention in comparison5. The exploitation of SMS is likely to release suspended particles and heavy metals that will not only affect vent communities but also fauna near hydrothermal vent fields that are not directly associated with them, hereafter referred to as “near-vent organisms”. The potential effects of such a scenario are still not well understood.Resource developers and those involved in biodiversity conservation have raised numerous concerns about the extraction of seafloor minerals7], among other impacts. To understand environmental impacts from exploitation of SMS, JOGMEC started a technical survey project in 2008 to explore massive sulfide deposits in a seafloor depression near Okinawa in southwest Japan8. JOGMEC performed multiple biological, physical, and geochemical surveys in accordance with guidance provided by the International Seabed Authority9. This included a baseline survey to collect near-vent organisms from the Okinawa Trough.To achieve a stable supply of mineral resources for the country, the Japan Oil, Gas and Metals National Corporation (JOGMEC) is exploring the extraction of SMS resources, including environmental impact assessments from mining activities. Previous studies have suggested that deep-sea mining of SMS may result in decreases in biodiversity through removal of habitat, release of toxic metals, and burial of organisms from sedimentation . Because hydrothermal vent distributions are variable and distances between vents can be far apart, it has been suggested that megabenthos inhabiting hydrothermal vents have considerable dispersal ability14. On the other hand, studies on population genetics of near-vent organisms are still limited. Therefore, understanding the population structure of near-vent organisms, in combination with studies on vent organisms provides important insights into the formation and maintenance mechanisms of hydrothermal ecosystems and their surrounding environments as well as for assessing the impacts of future resource exploitation.Information on genetic diversity and connectivity patterns  is useful for estimating how populations of near-vent organism communities could recover from the impact of mineral extractionc oxidase subunit I (COI) region. The mitochondrial COI region is used for relatively high-resolution analysis of interspecies- and intraspecies-level structure15, and it is often used for comparing genetic population structures among deep-sea species16. Such comparisons are informative to infer whether a species is sensitive to environmental disturbances as small population/species ranges are generally associated with higher sensitivity. We also conducted population analyses using larger sets of single nucleotide polymorphisms (SNPs) for a single eel species to provide greater resolution and a more comprehensive understanding of genetic structure. Finally, in conjunction with our findings we discuss possible future research efforts targeting near-vent organisms needed for SMS mining to occur.Given the above context, we performed population genetic analyses of several dominant scavenging near-vent megabenthos in the Okinawa Trough based on the mitochondrial cytochrome Simenchelys parasitica Gill, 187917 (n = 102) and Synaphobranchus kaupii Johnson, 186218 (n = 6), one decapod species, Nematocarcinus lanceopes Spence Bate, 188819 (n = 23), and multiple amphipod species (n = 43) using baited traps  with Pacific saury during cruises conducted to carry out a technical survey of seven locations in the Okinawa Trough (OT) between 2013 and 2015  and Suruga Bay  near Shizuoka Prefecture, Japan . The distances are ~ 1400 km between the Okinawa Trough and Suruga Bay and ~ 2400 km between the Okinawa Trough and Hokkaido.For the two synaphobranchid eel species, we also examined specimens from two regions outside the Okinawa Trough using organisms from markets to estimate connectivity across large geographic distances; offshore of Hokkaido  from tissues of preserved specimens according to the manufacturer’s protocol. Extracted DNA was checked by NanoDrop  and quantified by Qubit dsDNA HS assay kit .20. PCR extensions failed for eels, so we designed new primer sets (Table Si. parasitica (accession no. NC_013605). The PCR conditions are as follows: 1 min at 94 °C, 35 cycles of 0.5 min at 94 °C (denaturation), 0.5 min at 60 °C , and 1 min at 72 °C (extension), followed by an additional extension for 10 min. For amphipods, in addition to universal primers above, we used GrajapCOIF and GrajapCOIR21 and a new primer set , 0.5 μL reverse primer (20 μM), 1.6 μL dNTP, 2 μL 10X ExTaq buffer, 0.1 μL ExTaq HS , and 14.8 or 14.3 μL distilled water. We first used universal primers LCO1490 and HCO2198 and PCR condition: 30 cycles of 0.5 min at 94 °C (denaturation), 1 min at 50 °C , and 1.5 min at 72 °C (extension), followed by an additional extension for 5 mints Table  based onet Table  designedEach PCR product was cleaned with ExoSAP-IT  following the manufacturer’s protocol for direct sequencing. The primers used for the sequencing were the same as those for the PCR amplification (both forward and reverse primers). The purified PCR products were sequenced using the ABI 3730xl DNA Analyzer . Subsequently, partial sequences of COI were obtained through checking the DNA chromatograms by eye and used for the analyses below. Sequence identity was confirmed by NCBI BLASTN. Nucleotide sequences were translated into amino acid sequences to check for the presence of stop codons. Sequence data obtained in this study were deposited in the DNA Data Bank of Japan with the accession nos. LC532844–LC533073.Simenchelys parasitica, SNPs were obtained using the protocol of multiplexed ISSR genotyping by sequencing (MIG-seq)22 from 12 to 16 specimens from each site  by using universal primer pairs (MIG-seq primer set 1) for the 1st PCR. Then, we pooled DNA libraries with different indexes added by the 2nd PCR and sequenced by DNBSEQ-G400 (MGI Tech.) as paired-end reads (2 × 100 bp). All fastq files have been deposited in the DDBJ database (accession no. DRA014289).For te Table . MIG-seq23, running packages including ape [functions: read.dna, dist.dna, haploNet;24 and others maintained by the Bioconductor project (https://www.bioconductor.org/), after alignment with MAFFT v7.40225 with the default settings in each species. We also used SeqKit Version: 0.8.126 to obtain basic information  on the FASTA files used in this study. We constructed haplotype networks and calculated genetic diversities by using the R package pegas [functions: haplotype, hap.div, nuc.div;27 under default settings. We also performed analysis of molecular variance (AMOVA) with 1,000 permutations based on R package pegas [function: amova;27 using two (Okinawa Trough and Suruga Bay) populations of Si. parasitica  analysis was performed using RAxML-NG version 1.0.330 with 1,000 bootstrap replicates under a GTR  + G + I model. Bayesian inference (BI) of phylogenetic analysis was performed using MrBayes v3.2.731 under a GTR + G + I model and 1 MCMC chain . To identify molecular operational taxonomic units (MOTUs) of deep-sea amphipods, we used Assemble Species by Automatic Partitioning (ASAP)32 based on K80 distance model.For deep-sea amphipods, Neighbor-Joining (NJ) trees were constructed on genetic distances of Kimura 2-parameter models by using MEGA 7Si. parasitica, we filtered reads obtained by MIG-seq with FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/index.html) using a fastq-quality-filter v0.0.13 (–Q 33 –q 30 –p 40). Adapter sequences were removed by Cutadapt v2.533. Then we removed shorter reads (< 40 bp) using Seqkit v0.9.126. SNPs were called using the denovo_map.pl pipeline implemented in Stacks v2.034. The parameters used in stacks were as follows: ustacks (-m 3 -M 4) and cstacks (-n 4). Then, we made genepop files using populations  of Stacks. Biallelic loci were filtered with Plink version 1.935 and loci showing deviation from Hardy–Weinberg equilibrium (p < 0.001) and minor allele frequencies (–maf 0.03) were filtered. We obtained basic parameters of genepop files using the package “adegenet” v2.1.336 in R v4.0.323. Principal component analysis (PCA) was also performed with a matrix of individual genotype frequencies using R package “hierfstat”37. In addition, Permutational Multivariate Analysis of Variance (PERMANOVA) with 1st–4th PCs based on Euclidean distances and 999 permutations was performed using the adonis function in the R package vegan38.For Simenchelys parasitica ; a 591-bp sequence from Synaphobranchus kaupii ; and a 669-bp sequence from Nematocarcinus lanceopes .We obtained a 767-bp COI sequence of Si. parasitica populations .Our data indicated high genetic diversity in ons Fig.  based onN. lanceopes included 11 haplotypes . The level of genetic diversity was relatively moderate 40  near-vent fields attracted by baited traps. While the species attracted by baited traps may make up a small percentage of the diversity in an area, they can represent a large majority of the abundance as measured with trawl catches43. Synaphobranchid eels are dominant components of the scavenging community in the deep sea46. We performed genetic analyses on populations of far-flung synaphobranchid eel populations > 2000 km apart, but we could not detect significant differentiation in either eel species among the various sites. This indicates that both species may disperse over large distances  and maintain large populations. Some Synaphobranchus species possess polycyclic ovaries47, suggesting frequent opportunities for sexual reproduction among their congeners, which would increase the opportunity for random mating by many individuals. In addition, it is reported that Synaphobranchus kaupii shows high swimming and metabolic activity48, as this species has even been found in abundance in different ocean basins49. Thus, these strategies on life history may serve to increase genetic exchange among populations.Much attention has been given to active deep-sea hydrothermal vents and the unique communities present in these habitatsNematocarcinus lanceopes. The haplotype network of N. lanceopes showed a typical star-like topology . Therefore, these two amphipod clades can be considered as good targets for future connectivity analysis focused on organisms near hydrothermal vent fields, at least those in Okinawa Trough. The availability of a relatively large number of amphipod samples makes them suitable for conducting reliable population genetic analyses. Our population analysis of individuals in Clade I of amphipod . Future studies of movement patterns, either by swimming or by passive larval dispersal, would be useful to explain the population structures we identified in this study.Roughly 80% of the amphipods examined in this study belonged to the superfamily Lysianassoidea, which is ubiquitous in the deep seara Table , showed 16, the genetic marker we chose was not as variable as those commonly used for coastal marine organisms54. In future studies, analyses using highly variable genetic markers such as microsatellites55 and single nucleotide polymorphisms (e.g. MIG-seq used in this study) should be used to detect more detailed population structures. Highly polymorphic markers have already been used in deep-sea amphipods56, and it is hoped that similar analyses will be applied to near-vent amphipods from this study in the future. Such information on genetic connectivity would be essential in assessing the environmental impacts of mineral mining around hydrothermal vents, as well as to determine potential locations of deep-sea marine protected areas58. Although a trial has been conducted to infer community resilience in hydrothermal vents considering a dispersal network10, the connectivity data supporting this dispersal network is still limited, thus it is necessary to accumulate connectivity data for hydrothermal vents and their surrounding ecosystems in the future.In this study, we mainly used COI sequences to clarify basic information on the genetic population structures of organisms near hydrothermal vent fields. It is important, however, to be cautious when interpreting our results. While there have been numerous studies that have based their results and conclusions on COI sequences including for deep-sea speciesSupplementary Information.


Then, we summarize and categorize the drugs that target Stat3, providing potential therapeutic strategies for their use in bone-related diseases. In conclusion, Stat3 could be a future target for bone-related diseases.Signal transducer and activator of transcription 3 (Stat3) is activated by phosphorylation and translocated to the nucleus to participate in the transcriptional regulation of DNA. Increasing evidences point that aberrant activation or deletion of the Stat3 plays a critical role in a broad range of pathological processes including immune escape, tumorigenesis, and inflammation. In the bone microenvironment, Stat3 acts as a common downstream response protein for multiple cytokines and is engaged in the modulation of cellular proliferation and intercellular interactions. Stat3 has direct impacts on disease progression by regulating mesenchymal stem cells differentiation, osteoclast activation, macrophage polarization, angiogenesis, and cartilage degradation. Here, we describe the theoretical basis and key roles of Stat3 in different bone-related diseases in combination with  Bone-related diseases are a group of chronic diseases that are most prevalent in the elderly and obese population, including osteoarthritis (OA), osteoporosis (OP), bone dysplasia and bone defects. As the global population ages, bone-related diseases are often characterized by multiple co-morbidities, are a predominant contributor to the disability of the elderly, and constitute a primary factor in the global health care burden and rising costs to society . Bone foSignal transducer and activator of transcription 3 (Stat3) is a cytoplasmic transcription factor which activates by inflammatory cytokines or growth factors and then translocates to the nucleus, where it is involved in the regulation of DNA transcription . Stat3 hHere, we review the structure and biological function of the Stat3, the regulatory role and therapeutic prospects of targeting Stat3 signaling pathway in bone-related diseases. The purpose of this study is to provide a further reference for novel insights into the treatment of bone-related diseases and the clinical translational application of Stat3 inhibitors.α, Statβ, Statγ, and Statδ  . Phosphond Statδ . During iability .Stat3 was first identified in 1996 by researchers at Rockefeller University in the intracellular transduction of epidermal growth factor (EGF) and interleukin 6 (IL-6), and is commonly believed to be an acute response factor mediating growth factors and inflammatory factors . Stat3, In vivo and in vitro experiments demonstrated that it is the activation of Stat3 in chondrocytes that induces cartilage destruction and osteophyte formation in OA, but not ERK1/2 (α (RORα) was positively correlated with the severity of OA and RORα restored chondrocyte type II collagen (Col-2) and aggrecan expression by reversing the IL-6-induced increase in p-Stat3 levels , IL-1β and IL-6 (γ (IFN-γ), macrophages are stuck in M1 type and secrete massive levels of IL-1β and TNFα into the cell supernatant  being the primary risk of disability and death in the elderly . In the steoblast. Thus, tOn the other side of the disorder of osteohomeostasis, the engagement of Stat3 in osteoblast bone formation is likewise crucial. First, Stat3 upregulates Runx2 transcriptional activity, induces alkaline phosphatase (ALP) activation and calcium nodule mineralization by binding to the osteogenesis-associated transcription factor Runx2 . The subStat3 is generally recognized as an intracellular effector activated by inflammatory disorders in most diseases, but the latest evidences show that Stat3 is revealed to be an integral part of chondrogenesis and skeletal development . The delBone repair and regeneration are regulated by cytokines expressed locally in the skeletal microenvironment as well as those elevated locally and systemically under inflammatory conditions . MultiplCurrent therapeutic regimens targeting Stat3 are mainly achieved through direct regulation phosphorylation, dimerization, DNA binding activity and nuclear translocation of Stat3 . As showα axis to arrest cartilage hypertrophy in human chondrocyte lines  degradation , etc.  and Icariin increase the level of Stat3 phosphorylation in MSC, while facilitating the expression of osteogenic differentiation markers at the transcriptional and translational levels, such as ALP, Runx2, OCN, osteopontin (OPN), collagen 1-1), etc. . In addi1), etc. . Micro-C1), etc. . HoweverRNA and proteins are critical regulators in mammals, with multiple reports suggesting that they mediate bone-related diseases by targeting Stat3. EVs-derived miR-216a-5p promotes chondrocyte proliferation, migration and anti-apoptosis by restraining JAK2 expression and Stat3 phosphorylation . LuciferLeptin is the most secreted adipokine in white adipose tissue and it also seems to trigger obesity-related OA via Stat3 . ElevateStat3 is a transcriptional regulator and is activated by a broad range of cytokines in the bone microenvironment. Increasing reports indicate that Stat3 exerts regulatory effects via multiple pathways affecting bone-related diseases, including inflammatory stimulation, cartilage degradation, osteoclast activation, osteoblast differentiation, and macrophage polarization. Activation of Stat3 in MSC or vascular endothelial cells could promote their proliferation and differentiation in favor of bone defect repair, while on the other hand inhibition of Stat3 in chondrocytes and osteoblasts would alleviate OA and OP. However, the role of Stat3 in the regulation of bone-related diseases has not been clearly described, patients with bone-related diseases continue to cause great disturbance in their daily life. Current research supports the use of Stat3 as an emerging target for the management of bone-related diseases with exciting results.Nonetheless, targeting Stat3 to achieve the treatment of orthopedic diseases still has a long way to go from bench to bedside. Targeting Stat3 as a therapeutic agent is like a double-edged sword, with unpredictable potential toxicity and side effects while being highly effective. As a very dense tissue in the human body, the effective and targeted delivery of bone still faces great challenges, and how to achieve precise drug delivery will be the direction of future research. Picking the suitable period of treatment is also extremely valuable, as the choice of timing of drug interventions often correlates with preferable efficacy. After overcoming these challenges, targeting Stat3 has the potential to be a promising therapeutic option for bone-related diseases.


Elymus sibiricus) due to its excellent ecological utilization value and forage production potential in China, particularly in the Qinghai–Tibet Plateau (QTP) regions. In this study, we applied two chloroplast (cp) genes (matK and rbcL), three cp spacer regions , and six cpSSR markers to the genetic and phylogenetic analysis of 137 wild E. sibiricus accessions from 23 natural populations that represent the main distribution regions in China. The results show the highest genetic diversity (h = 0.913) and haplotype richness (10 haplotypes) for the QTP population, which indicates QTP as the probable diversity center and geographic origin of E. sibiricus in China. Population divergence was high, indicating a significant phylogeographic structure together with a significantly higher Nst value  at the species level, QTP+XJ (combined populations from QTP and Xinjiang), QTP+NC (combined populations from QTP and North China), and XJ+NC (combined populations from Xinjiang and North China) group levels, respectively. An expansion was revealed in the distributional range of E. sibiricus in China from paleo times up to the recent past, while a dramatic range of contraction was predicted for the near future. The predicted main limiting factor for the further spread of E. sibiricus is an increasing global mean temperature. We recommend that the combination of Es-cpDNA1 and Es-cpDNA3+4+5 can be used as effective markers for phylogenetic analysis and phylogeographical history analysis of E. sibiricus. These findings shed new light on the historical population dynamics of cold-season herbs in the QTP region and the north of China and are of great significance for the future establishment of protection and collection strategies for wild E. sibiricus germplasm.A detailed understanding of the distribution and degree of genetic variation within a species is important for determining their evolutionary potential, which in return facilitates the development of efficient conservation strategies aimed at preserving adaptive genetic variation. As an important perennial, cool-season grass in temperate Eurasia, increasing attention has been paid to Siberian wildrye ( Geographical events and global climate oscillations have led to repeated and drastic changes in the environment, something that has had a great impact on the distribution and genetic structure of plants in different ecological distribution areas . PhylogematK and rbcL genes have contributed significantly to the study of phylogeography  . Such geeography , more theography . Howeveriability . Anotheriability . The comElymus genus (Triticeae and Poaceae) and an ancient wild grass species, Elymus sibiricus is widely distributed in the Eurasian continent. Its distribution areas in China include Northwest and North China, particularly in the QTP regions  and three cp spacer regions , by blasting the chloroplast genomes of E. sibiricus and its related species Elymus nutans. These hotspot regions, combined with six polymorphic cpSSRs, were applied in the present study to explore the genetic diversity and phylogeographic structure of 23 natural populations composed of 137 wild accessions. Further, the ENM prediction based on the records of the occurrence of E. sibiricus germplasms was used to detect the environmental factors involved in shaping current and future distribution patterns.The Qinghai–Tibet Plateau (QTP) once acted as a refuge for plants in the north temperate region during the Quaternary glacial period and has been recognized as one of the important places of origin and radiation of modern north temperate plants after the glacial period . As a mo regions . The gerE. sibiricus. We detected a high genetic diversity of E. sibiricus at the species level, in which the QTP group had the highest diversity, which was reflected by the highest Na, Ne, I, and h values (The polymorphism information content (PIC) values of six cpSSR markers varied from 0.8945 (Es-cpSSR6) to 0.9555 (Es-cpSSR2) with an average of 0.9288 . The deth values .matK, rbcL, and “Es-cpDNA3+4+5,” respectively  for E. sibiricus was 2, based on ΔK estimation , we divided all the individuals into three obvious clusters . Clustertimation . IndividThree cpDNA regions were applied to connect a haplotype network based on their nucleotide substitutions. The phylogenetic tree was also constructed to reveal the inter-haplotype relationships. As shown in st value further revealed the largest divergence between the NC and XJ groups based on non-functional cp regions (cpSSRs and intergenic cpDNA regions) and the maximum differentiation between QTP and NC or XJ based on functional cp genes  when assuming species, QTP+XJ, QTP+NC, and XJ+NC, which indicates a clear phylogeographical structure between pairwise populations  indicated a significant genetic differentiation among the populations based on cpSSRs and cpDNA . The paicp genes . Gst andulations . Bidireculations .st) and geographical or environmental distance. The longitude, latitude, bio-climates, and geographical distance all predicted a significant relationship with Fst based on cpDNA and cpSSR  approach further confirmed a significant correlation between Fst and bio-climate distance  and geographical distance   best explained the genetic difference (DIC = −51.576). However, for cpSSR, there was no obvious difference among these models.The Mantel test was applied to model the correlation between pairwise genetic differentiation (Fnd cpSSR . A signir cpDNA) . Among tFs values at the species, QTP, XJ, and NC levels , which assumed QTP as the common ancestor and then the separation of NC and XJ, was adopted based on its maximal posterior probability  represents the allele frequency differences between the populations. On the one hand, isolation by distance (IBD), which is caused by remote geographical distances or genetic geographic barriers, such as rivers, mountains, and deserts, is known to weaken intrapopulation migration, mating, and gene flow (Nm), thus resulting in a significant phylogenetic structure. On the other hand, isolation by environment (IBE), which relates to habitat heterogeneity, is also believed to be responsible for population divergence. In previous studies, IBD or IBE patterns were found in several Elymus species, such as E. tangutorum      , E. nuta= 0.010) , E. exce < 0.01) , and E.  = 0.04) . Considet values , obvioust values , and sigt values .Genetic diversity is the basis of species survival and evolution . AdequatElymus sibiricus, a cool-season perennial grass species with excellent cold adaptability, is believed to be able to endure −40°C temperatures in winter  to the recent past (1970–2000). However, the ecological niche distribution in the near future (2021–2040) is predicted to undergo a dramatic shrinkage , strongly concentrated ecological niche distribution, and ancient phylogenetic pattern. In spite of the confused genetic structure and haplotype network, significant population divergence was found in this study, which could be attributed to IBD and IBE patterns. The predicted ecological niche shrinkage of E. sibiricus in the near future indicates that measures must be undertaken to deal with its intolerance to high temperatures, particularly continued exposure to warmer temperatures.The QTP was identified as the diversity center of E. sibiricus in China  and its related species Pseudoroegneria libanoticus (NCBI accession number: KX822019.1) were adopted for population amplification. Polymerase chain reaction (PCR) was carried out in a 15-μl reaction mixture that consisted of 3 μl of template DNA, 7.5 μl of dye-free Taq Mix, 0.4 μl each of forward and reverse primers, 0.2 μl of Taq DNA polymerase, and 3.5 μl of ddH2O. The PCR reaction conditions followed those of Six polymorphic cpSSR primer pairs , the number of effective alleles per locus (Ne), Shannon’s information index (I), and Nei’s genetic diversity (h) of both the populations and primers. At the same time, the PIC index was calculated using PICcalc , and Harpending’s raggedness index (HRag). Typically, the significantly negative Tajima’s D or Fu’s Fs, as well as the non-significant mismatch parameters SSD and HRag, indicate potentially historical demographic expansions , Xinjiang (XJ), and North China (NC), according to the ecological niche prediction results and corresponding geographic features . All fur PICcalc  to refle PICcalc  was used PICcalc  was usedpansions . We combvia ΔK estimation. Principal component analysis (PCA) was conducted in GenAlEx 6.5  is another determinant of population genetic structure  approach was also used to reconfirm whether the population’s genetic structure was driven by environmental variables. The analysis was implemented using the R package MCMCGLMM  with the software DIYABC v. 2.1 . In our The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/XM, SB, DL, and JY conceived the ideas. XL, YLX, and QY contributed to the sampling procedure. YX, XL, and XS conducted the data analyses. YX and YLX wrote the manuscript. All authors have read and approved the final manuscript.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.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Understanding the sustainable development goal (SDG) 11.3.1-ratio of land consumption rate (LCR) to population growth rate (PGR) is an important prerequisite for planning a guide for sustainable urbanization. However, little is known regarding the degree of accuracy of the estimated LCR due to the inconsistency of data on built-up areas. We extracted four built-up areas, based on inverse S-shaped law and area proportion method, and produced more precise built-up area data (LS_BUA) for the period 2000–2015. Chinese population density data in 2000–2015 was generated based on 26 million points of interest, 19 million roads, other multi-source data, and random forest (RF). Finally, the coupling between LCR and PGR for 340 Chinese cities was calculated during the same period. The results showed that (1) the accuracy of LS_BUA was higher than that of the other built-up area data production methods; (2) the accuracy of test sets in RF exceeded 0.86; (3) the LCR value of mainland China was 0.024 and the PGR value was 0.019 during 2000–2015. The LCR consistently exceeded the PGR and the coordination relationship between LCR and PGR continued to deteriorate. Our research eliminated the difference of SDG 11.3.1 from different data sources and could therefore help decision makers balance land consumption and population growth. The proportion of the global urban population increased from 30% in 1950 to 55% in 2018 [With the acceleration of global urbanization, urban area increased from 362,700 kmSDG 11 is defined as Sustainable Cities and Communities, and aims to build inclusive, safe, resilient and sustainable cities with a focus on providing housing security and public transport, sustainable urban development—with an additional focus on preserving world cultures, reducing natural disasters, and improving air quality and provide public spaces, etc. Ratio of land consumption rate (LCR) to population growth rate (PGR) sustainable development goals (SDG 11.3.1) are meant to quantify whether the relationship between the urban built-up area growth and the population growth in a given spatial extent  and time period is coordinated and orderly. The United Nations estimated the LCRPGR of 194 regions and cities around the world and found that the LCR was higher than the PGR in most cities . A globaAlthough the metrics and methods of the SDG 11.3.1 are relatively simple , the monThere are a series of global and regional ISA and LULC data. (1) Impervious surface: 30m global ISA data from 1985 to 2018 were developed by Gong et al. , with a All of the above products could be used to extract built-up areas. However, due to the difference between definitions and methods , the difIn order to address data inconsistency in the built-up area, the objectives were as follows: we aimed to quantify the spatial differences in land use efficiency in mainland China from 2000 to 2015. Then, the spatial heterogeneity and dynamic trends of LCR, PGR, and LCRPGR in 340 mainland cities were quantitatively analyzed in China from 2000 to 2010 and 2010 to 2015.The article used the following data: CLUD data included 6 first-level classifications such as construction land, woodland, etc., with accuracy higher than 75% ,23. GAIAt+n and Urbt represent the built-up area of the current year and the past, respectively. Popt+n and Popt represent the current and past population within the built-up area (urban population) respectively. Y represents the interval year. SDG 11.3.1 monitors the relationship between the growth rate of the built-up area and the population within a given time and space. The formula  is as foStep 1: extracting urban built-up areas. We identified the percentage of urban built-up areas at the level of 1 × 1 km for year 2000, 2010, and 2015 through Step 2: calculating urban population density data. Through Step 3: calculating SDG 11.3.1. The LCR, PGR, and LCRPGR indicators were calculated according to the data obtained in Step 1 and Step 2 and Equations (1)–(3).The SDG 11.3.1 calculation process includes the following three steps : Step 1:The LCRPGR value was divided into 6 classifications provided by UN-HABITAT . ConsideThis paper used two methods to extract the built-up area: (1) using GAIA as the data source, we used the inverse S-shaped law (detailed in 2) was greater than 0.88, and 95% of the cities have R2 greater than 0.95, suggesting that the method is suitable for extracting urban built-up areas in large-scale research areas. The formula is as follows:Jiao  took the(1)2 around each pixel.Calculate the percentage of ISA within a circle of 1 km(2)Divide 340 cities into two categories according to the urban population. In the first category, the total urban population must be greater than 50,000; all other cities are placed in the second category . For theThe method was proposed by UN-HABITAT for extracting the built-up area , and reqIn this paper, we constructed the nonlinear relationship between the modeling factors and censuses at the county level and transferred the relationship to grid level to achieve census downscaling . Conside2). If R² is closer to 1, the fitting result is better and vice versa. RE, RMSE and MAE can measure the deviation between the observed value and the true value. The smaller the value is, the smaller the deviation. The equation is as follows:i and Ci are the observed value and the true value, respectively, and n is the number of samples.The accuracy of the data was verified by the relative error (RE), root mean square error (RMSE), mean absolute error (MAE), and goodness of fit  as independent variables to calculate the correlation coefficient and Rectively . Taking 2, the L_BUA data were often overestimated—while the S_BUA data were slightly underestimated. The S_BUA data were closer to 0 and had fewer discrete points than the L_BUA data. When the built-up area of the city was greater than 150 km2, the median RE value of the L_BUA data was closer to 0, with fewer discrete points. Therefore, this paper combined cities with built-up areas less than 150 km2 in the S_BUA data and cities with built-up areas greater than 150 km2 in the current year to obtain the LS_BUA built-up areas in mainland cities from 2000 to 2015  > western (0.103) > eastern (0.057), and the growth areas were: 3732.5 km2, 2596.7 km2, and 4360.9 km2, respectively. The average values of LCR were: central (0.012) > eastern (0.011) > western (0.010), and the newly increased built-up areas were: 3255.8 kmThe average PGR of large megacities during 2000–2010 was 0.045, slightly higher than the 2010–2015 period . The aveAs shown in In addition, a total of 14 small and medium-sized cities, including Yibin, Dazhou, and Luliang, etc., experienced a decline in urban population in 2000–2010. From 2010 to 2015, 18 small and medium-sized cities, including Hegang, Yichun, Qitaihe, Shuangyashan, and Ordos, also experienced a decline in urban population, and the number of cities increased slightly from the previous period. Among them, the urban population of Bijie continued to decrease from 2000 to 2015. The phenomenon of population decline mostly occurred in small and medium-sized cities. The population outflow from small and medium-sized cities to other cities is not conducive to urban sustainable development.During the 2000–2015 period, the average LCR and PGR values of large megacities decreased at the same time, while the LCR value decreased faster . For exaFrom 2010 to 2015, there were 107 (31.4%) cities in the category of moving toward sufficient land per person, a decrease from the previous period. Some cities actually changed to show insufficient land per person. For example, the LCR and PGR values of Chengdu both declined, and the LCR value decreased more rapidly. A total of 19 (5.5%) cities were moving toward the efficiency type, less than in the previous period. There were 45 (13.2%) cities with efficient land use types. For example, the LCR and PGR increased in the Qamdo area of Tibet during both periods, but the LCR was faster than the PGR. There ere 67 (19.7%) cities with insufficient land per person, mainly distributed in Qingdao, Dongying, and other cities with higher population density. A total of 45 (13.2%) cities were moving away from efficiency in land use, such as Ganzhou. There were 57 (16.7%) inefficient land-use cities distributed in low population density areas, such as Xinjiang and Tibet in the west. In summary, the number of cities with efficient land use, moving toward efficiency, and moving toward sufficient land per person type decreased from 203 (59%) to 171 (51%), and the number of cities with inefficient land use, moving away from efficiency, and insufficient land per person type cities increased from 137 (41%) to 169 (49%) between 2000–2010 and 2010–2015. The LCR value was 0.024, the PGR value was 0.019, and the LCRPGR value was 1.27 in 2000–2015.2, and the urban population increased by 110 million from 2000 to 2015. China’s built-up area increased by 2.6 times, and the urban population increased by 1.9 times. Wang et al. [This article made a comparative analysis with previous research results due to lack of truth-value. We found that China’s newly built-up area was 25,929 kmg et al.  indicateg et al.  showed tOur study revealed that the LCR value was 0.024, the PGR value was 0.019, and the LCRPGR value was 1.27 from 2000–2015, values which were found to be similar to those in other contexts . During 2. We also calculated the MAE and RMSE between the statistics and the aforementioned data. From 2000 to 2015, the correlation coefficients between built-up area statistics and LS_BUA data were 0.9, 0.91, and 0.88, while the R2 values were 0.81, 0.83, and 0.79, respectively . We found that both Popi data and Xu [To verify the accuracy of the population density data, this paper separately summarized the total population values in the administrative units of Popi and Xu  populatia and Xu .There were differences in LCR in different regions. The eastern region was in an advantageous position in terms of location, economy, and policies. In 1984, 14 cities in the east were designated as coastal cities. Since then, the Yangtze River Delta, the Pearl River Delta, and the Xia-Zhang-Quan Delta have been regarded as coastal economic open areas. Coastal open cities have good conditions, e.g., geographic location, natural resources, economic foundation, and technical management. For example, in 2000–2010, the LCR value of Dongguan reached 0.23. In general, the land use efficiency values in eastern cities were higher, promoting urban intensification and slowing down urban expansion. In the central region, the LCR values of cities such as Zhengzhou, Changsha, Hefei, and Luoyang gradually increased. The central region had a larger area and more available land. Therefore, the LCR values of the central region were slightly larger than those of the eastern region. Except for Chengdu, Chongqing, Lhasa, Kunming, and Xi’an, most cities in the western region were slowly expanding. Since the Western Development Policy in 2000, the process of industrialization in many cities has accelerated. As capital cities, Chengdu and Xi’an have played a radiating and leading role in southwest and northwest China. Overall, urban expansion concentrated in the eastern coastal region, while the growth in the central and western regions was mainly reflected in capitals.Due to differences in factors such as social economy, regional location, and policy orientation, there were differences in LCR indicators between the southeast coastal cities. For example, Guangzhou and Shenzhen were the two first-tier cities in the Pearl River Delta located in the southeast coastal region, one of the most economically developed regions in China. In the two periods of 2000–2010 and 2010–2015, Guangzhou’s LCR increased, and Shenzhen’s LCR decreased. Likely explanations for these dynamics could be as follows: firstly, it has. been restricted with administrative division. The administrative division area in Guangzhou was approximately 3.7 times than that of Shenzhen, and the available land resources were relatively abundant. Secondly, it was restricted in terms of the topography and geographical location. The hilly area of Shenzhen was larger than that of Guangzhou. The terrain of Guangzhou and surrounding was flat, and thus conducive to the formation of an economic belt in Guangfo City with Foshan in the west. Thirdly, it was restricted with supply chain industries. Shenzhen’s unique position and preferential policies have given it competitive advantages in innovation and technology and accelerated the city’s supply chain access to the Chinese mainland market. However, infrastructure provided by Guangzhou was more abundant than that of Shenzhen, resulting in more urban expansion in Guangzhou. In addition, Dongguan’s was significantly faster than other cities in the Pearl River Delta, such as Zhaoqing, Huizhou, and Jiangmen. Likely explanations for these differences could be as follows: first, Dongguan was one of the fastest-expanding cities in China during the periods of study. Industrial development is one of the most important factors driving development of China’s land. Rapid industrial development requires a large amount of land, which drove the observed rapid expansion of Dongguan. Second, driven by the radiation of first-tier cities, adjacent cities expanded rapidly. The proportion of forest land in Zhaoqing (69%), Huizhou (60%), and Jiangmen (46%) were relatively high, going against the expansion of built-up areas. The above three cities were also far from economically developed cities such as Guangzhou, and thus, their urban expansion rates were slow. The Constitution of the People’s Republic of China and the Land Administration Law of the People’s Republic of China led to the formation of two markets for agricultural land and nonagricultural land in China’s land market (https://baike.baidu.com/item/%E5%9F%8E%E4%B9%A1%E4%BA%8C%E5%85%83%E4%BD%93%E5%88%B6%E6%94%B9%E9%9D%A9/7970059 (accessed on 2 March 2021)). Low land acquisition costs and rapid industrial development transformed a large number of agricultural land markets into nonagricultural land markets, driving the growth of the LCR [Regulations of the People’s Republic of China on Household Registration had a depressive effect on the PGR. People with rural household registration used to live in rural areas. Urban expansion needs to absorb more rural people, but it cannot provide them with necessary living conditions, such as employment, medical care, education, and housing, slowing down urban population growth. The LCR values of many cities in China were higher than the PGR in the same period due to rapid urban expansion and an aggravated imbalance between built-up areas and population. For example, the built-up area of Dongguan city on the southeast coast more than tripled from 2000–2015. There are several possible reasons for this. First, in terms of external systems. The dual land system  proposed in Second, we considered internal mechanisms. To attract investment, the price of industrial land is usually lowered, leading to an increase in urban LCR. However, the price of residential land has increased, increasing the cost of population migration and inhibiting the PGR. This leads to a phenomenon in which farmers’ land has been urbanized, but farmers have difficulty integrating into the city. Third, we considered urban planning. Attracting investment and generating income based on land sales is a common policy tool in China . As the Other factors that affect LCRPCR included national or local development plans, priorities and policies, and local socioeconomic issues, which will require more concentrated research and analysis. For example, the high PGR in developed cities in the Pearl River Delta resulted mostly from the population flow after the reform and opening policy since 1978. Population growth, per capita income, and informal settlements were the main driving forces behind rapid urban expansion . The incFinally, the living space needed to accommodate the population may be obtained through urban space expansion or be solved through urban vertical development, as seen in Hong Kong and Lanzhou City, China. Hong Kong has been limited by administrative division areas, while Lanzhou was developed by an extension of the river valley. The area of land available for development in both cities has been and remains restricted. The LCRPGR indicator cannot quantify the relationship between urban three-dimensional spatial expansion and population growth, as it ignores the third dimension of urban geography, that is, the impact of height information on urban expansion. Frantz et al.  and Li e(1)The accuracy of the LS_BUA as higher than the other four built-up area data types. The RMSE values of the LS_BUA in 2000, 2010, and 2015 were 80.9, 74.9, and 89.0, respectively, and the MAE values were 47.2, 45.1, and 51.8 respectively;(2)In 2000–2015, the test set accuracy exceeded 0.86, and the proportion of cities with an absolute value of RE of Popi data < 0.25 exceeded 0.96;(3)The number of inefficient land use, moving away from efficiency, and insufficient land per person type cities increased from 137 (41%) to 169 (49%) between 2000–2010 and 2010–2015. The PGR value was 0.019, and the LCRPGR value was 1.27. The LCR consistently exceeded the PGR and the coordination relationship between LCR and PGR continued to deteriorate.Our research used remote sensing image data, statistics, and geographic Big Data to calculate the percentage of built-up area and population density data at 1 × 1 km grid level in mainland China in the period 2000–2015. It also analyzed the temporal and spatial relationships, evolutionary laws, and formation reasons of LCRPGR value in 340 cities from 2000 to 2015. The conclusions were as follows: Our research could be extended to other countries for accurate estimated SDG indicators, realizing the long-term quantitative analysis of urbanization processes. As such, our research could also be essential for guiding decision-makers to balance land consumption and population growth.


Modelling herd as a random effect produced EBVs with the highest accuracies. Breeding has increased genetic gain for dairy cattle in advanced economies but has had limited success in improving dairy cattle in low- to middle-income countries (LMIC). Genetic evaluations are a central component of delivering genetic gain, because they separate the genetic and environmental effects of animals' phenotypes. Genetic evaluations have been successful in advanced economies because of large data sets and strong genetic connectedness, provided by the widespread use of artificial insemination (AI) and accurate recording of pedigree information. In smallholder dairy production systems of many LMICs, the limited use of AI and small herd sizes results in a data structure with insufficient genetic connectedness between herds to facilitate genetic evaluations based on pedigree. Genomic information keeps track of shared haplotypes rather than shared relatives captured by pedigree records. Therefore, genomic information could capture “hidden” genetic relationships, that are not captured by pedigree information, to strengthen genetic connectedness in LMIC smallholder dairy data sets. This study's objective was to use simulation to quantify the power of genomic information to enable genetic evaluation using LMIC smallholder dairy data sets. The results from this study show that (1) genetic evaluations using genomic information were more accurate than those using pedigree information in populations with a high effective population size and weak genetic connectedness; and (2) genetic evaluations modeling herd as a random effect had higher or equal accuracy than those modeling herd as a fixed effect. This demonstrates the potential of genomic information to be an enabling technology in LMIC smallholder dairy production systems by facilitating genetic evaluations with in situ records collected from herds of ≤4 cows. The establishment of routine genomic evaluations could allow the development of LMIC breeding programs comprising an informal set of nucleus animals distributed across many small herds within the target environment. These nucleus animals could be used for genetic evaluation, and the best animals could be disseminated to participating smallholder dairy farms. Together, this could increase the productivity, profitability, and sustainability of LMIC smallholder dairy production systems. LMIC), despite the potential benefits. Recent estimates from Kenyan smallholder farms suggest that average milk production per cow is approximately 5 L/d, and there is little evidence of significant genetic improvement in recent decades  genetic connectedness between herds or management groups ; (2) larPBLUP) to 0.85 for genomic-based BLUP  of 1,035 in the final generation, to reflect the high genetic diversity found in cattle populations in Africa (TBV) were calculated by summing the average effects of the animal's genotype at each QTL. The QTL additive effects were sampled from a standard normal distribution,A genome consisting of 10 chromosome pairs was simulated. The Markovian Coalescent Simulator MaCS;  and Alphn Africa . The 2,0terature . The TBVNe) of 1,035.Recent (burn-in) breeding for milk yield was simulated over 5 discrete generations of selective breeding on phenotype. The features of this breeding stage were 225 sires per generation, 1,000 dams per generation, and 2,000 offspring per generation. These numbers were chosen to match the base population . Therefore, a second step randomly sampled herds to correct the difference between the sampled herd sizes and the required herd sizes to leave us with exactly 8,000 “slots” across all of the herds. This resulted in training populations with final average herd sizes of 1.58, 2.32, 4.06, 8, and 16. At the end of the simulation's evaluation phase, 8,000 phenotyped cows were randomly selected and randomly assigned to herds to serve as the training populations for the genetic evaluations. This number reflected the number of genotyped animals in the Africa Dairy Genetic Gains project at the time.y is a vector of phenotype records measured on cows; b is a vector of fixed effects; u is a vector of breeding values for which we assumed thatA is the pedigree numerator relationship matrix  we excluded it, which gave us the basic model with intercept as the only fixed effect; (2) we modeled herd as a fixed effect; and (3) we modeled herd as a random effect, for which we assumedBreeding values were estimated using the following basic model:p matrix  based onThe PBLUP and GBLUP models were run using the Wombat software . The dif2 = 0.1, 0.3, and 0.5), but this superiority declined as heritability increased (data not shown). The superiority of modeling herd as a random effect was consistent across trait heritabilities (data not shown).The results from this study showed that genomic information enabled accurate genetic evaluation of phenotyped cows using data sets that comprised small herds with weak genetic connections. The main trends observed were that (1) genetic evaluations using genomic information had higher accuracy than those using pedigree information across all breeding designs; and (2) genetic evaluations with genomic information and modeling herd as a random effect had higher or equal accuracy compared with modeling herd as a fixed effect. The superiority of genetic evaluations using genomic information over pedigree information was consistent across trait heritabilities , and electronic marketplaces for agricultural products in LMICs  show that many components of such a model are already in place.However, the infrastructure required for such breeding programs and the associated technologies is expensive, potentially creating a new cost barrier to animal breeding success in LMIC smallholder dairy production systems. New business models are needed to overcome this barrier in a self-sustaining way. These business models could bundle technology, data recording, extension services, and a marketplace for LMIC smallholder farmers. This type of self-sustaining platform would maximize the benefits and cost-efficiency of any component . The Africa Dairy Genetic Gains and Public Private Partnership for AI Dissemination  projects


Dipodomys elator, (Texas kangaroo rat) is a kangaroo rat that is classified as threatened in Texas and field surveys from the past 50 years indicate that the distribution of this species has decreased. This suggests geographic range reductions that could have caused population fluctuations, potentially impacting effective population size. Conversely, the more common and widespread D. ordii (Ord’s kangaroo rat) is thought to exhibit relative geographic and demographic stability. We assessed the genetic variation of D. elator and D. ordii samples using 3RAD, a modified restriction site associated sequencing approach. We hypothesized that D. elator would show lower levels of nucleotide diversity, observed heterozygosity, and effective population size when compared to D. ordii. We were also interested in identifying population structure within contemporary samples of D. elator and detecting genetic variation between temporal samples to understand demographic dynamics. We analyzed up to 61,000 single nucleotide polymorphisms. We found that genetic variability and effective population size in contemporary D. elator populations is lower than that of D. ordii. There is slight, if any, population structure within contemporary D. elator samples, and we found low genetic differentiation between spatial or temporal historical samples. This indicates little change in nuclear genetic diversity over 30 years. Results suggest that genetic diversity of D. elator has remained stable despite reduced population size and/or abundance, which may indicate a metapopulation-like system, whose fluctuations might counteract species extinction.Species with low effective population sizes are at greater risk of extinction because of reduced genetic diversity. Such species are more vulnerable to chance events that decrease population sizes (e.g. demographic stochasticity).  One such context to help understand the potential impacts of fluctuations of Ne is comparison between more restricted, possibly threatened species and a widespread congener, which are presumed to harbor more genetic variation.Measuring genetic variation in rare, threatened, endemic, or endangered species has important implications for management and is integral to conservation efforts . Populate , the nume –9, and tDipodomys elator) is a monotypic, heteromyid rodent that has a limited distribution in north-central Texas [D. elator has a small geographic range and a presumably low dispersal capability [D. elator is listed as “vulnerable” by the International Union for Conservation of Nature and Natural Resources [The Texas kangaroo rat , and for recent samples, investigate differences in genetic diversity across the distribution. We make several predictions: 1) D. elator will exhibit a lower effective population size than D. ordii, and concomitantly, lower nucleotide diversity, lower observed heterozygosity, and higher inbreeding coefficients; 2) there will be greater genetic diversity among contemporary samples of D. elator than in historical samples, as contemporary samples were taken from across the distribution, compared to historical samples collected from three counties in the middle of its distribution; and 3) historical Ne from a coalescent approach for D. ordii and D. elator will demonstrate that D. elator will show a historical decline in Ne, while D. ordii will show stable or increasing Ne.Here, we compare population genetic parameters of D. elator  taken as a voucher specimen for deposition at the Natural Science Research Laboratory (NSRL) at the Museum of Texas Tech University, in which DNA from the liver was used or, 2) had between two to four whiskers extracted from either side of the rostrum [D. elator individual as described in detail in Halsey et al. 2021 [Animal handling protocols were approved by the Institutional Animal Care and Use Committee at Texas Tech University (#T14083) and the Texas Parks and Wildlife Department (TPWD #SPR-1013-154). Kangaroo rats were captured using Sherman live traps  during surveys within the historical range of . elator  from 201 rostrum . In the  rostrum . A buccaal. 2021 .D. elator tail lengths average about 196 mm [in situ from rats from 1986–1995 as part of a population survey of the species by REM and KGM. These samples were not suspended in any buffer and were kept at around -20°C to prevent degradation of DNA.Other methods of collecting DNA from rats included tail salvages and from toe clips from museum specimens . D. elatt 196 mm  and at tt 196 mm . Toe cliD. elator samples were analyzed from one of five tissue types  and two time periods . For liver, toe clips, and tail salvages, the manufacturer’s recommendations were followed. Using a protocol similar to Moraes-Barros et al. 2007 , instrumRADseq libraries were prepared following the 3RAD protocol found in Bayona-Vásquez et al. 2019 . DetailsD. ordii genome assembly  using the Burrows-Wheeler aligner [Stacks v1.48 and v2.01  was empl aligner . AlignedData were grouped into putative loci, and polymorphisms were identified with the ‘gstacks’ module in Stacks. Common population genetic statistics such as observed and expected heterozygosity, nucleotide diversity, and inbreeding coefficients were calculated using the ‘populations’ module. This step was conducted with four different missingness values (-r) to identify the most appropriate parameter setting. The -r value  has beenIS and FST values were calculated using hierfstat [For genetic diversity, observed and expected heterozygosity were calculated using the ‘summary’ function in the R package adegenet version 2.1.1 . To asseierfstat  and an aierfstat . Nei’s gierfstat  were detierfstat . Relatedierfstat .e using three methods: linkage disequilibrium, molecular co-ancestry, and a temporal method. The first two methods were used to determine contemporary effective population sizes per species.To determine effective population size using NeEstimator v2.1 , the Gene of D. elator, the Extended Bayesian Skyline Plot (EBSP) coalescent test as implemented in BEAST 2.0 [D. ordii samples. However, only one individual from Dickens County . For D. elator, there were 33 samples for the historical dataset and 37 samples for contemporary dataset. Before filtering within the ‘populations’ module, there were 330,326 SNPs suitable for analysis. Approximately 1.5% of reads per sample were removed from the dataset following AfterQC filtering. After all filtering steps, 3,935 SNPs remained from 55 D. elator individuals .In all, 96 kangaroo rats were sequenced and analyzed from two species in eight counties in north-central Texas. Because these data were aligned using the D. ordii samples produced over 10 million reads . Prior to ‘populations’ filtering, approximately 382,514 SNPs were eligible for further analysis. Fewer than 2% of reads per sample were removed from the dataset. Read depth and average number of reads per individual can be found in Similar analysis for the 26 Across the four -r values evaluated , there were as few as 7 single nucleotide polymorphisms (SNPs) to as many as 61,000 SNPs to analyze . The genD. elator samples showed lower levels of observed heterozygosity  than D. ordii  which suggests lower genetic diversity in D. elator. Inbreeding coefficient FIS was positive in all D. elator groups (0.015–0.031) except for in historical samples (-0.007). With an AMOVA, we found that FST values demonstrated low but significant genetic differentiation  between pairwise east and west samples. Based on climate, vegetation, edaphic, and land use characteristics across the study area [When compared to each other using the -r 0.75 dataset contemporary udy area , our a pD. elator. The estimated Ne of the east group was 171.3, with a 95% confidence interval (CI) of 158–186.9 using the lowest allele frequency (>0). For the west group, both the linkage disequilibrium and molecular co-ancestry methods returned ‘Infinite’ for Ne at all allele frequencies. In the historical Hardeman County dataset, in which all samples were from a single location, we estimate a Ne of 54.8 (CI = 53.7–56.0) using the lowest minor allele frequency (>0). No method with NeEstimator was able to provide an estimate of population size for D. ordii, other than ‘Infinite.’Only the linkage disequilibrium method in NeEstimator v.2.1 produced a value other than ‘Infinite’ for effective population size for D. elator was constructed with 24 individuals and 47 loci. The plot for D. elator showed a decline in effective population size over the last 10,000 years, to an approximate current Ne of 500. For D. ordii, we used 15 individuals and 49 loci. In this case, Ne has increased or remained stable in the last 5,000 years. The current value is estimated to stand at about 20,000 individuals  cluster near the origin and a difference in data quality could result in the three outlying samples . This is seemingly incongruent with our results in which we observed little genetic differentiation (FST = 0.041), but the difference could simply be the result of the markers used . Furthermore, lower heterozygosity in examined nuclear SNPs could reflect the low relative age or variation of the species as most SNPs are likely to be neutral. Similarly, bottleneck event(s) that may have occurred, could have potentially wiped arising genetic variation and homogenized standing populations. Such event was hypothesized in Pfau et al. 2019, but not supported (or detected) by either the estimations of Ne or the Extended Bayesian Skyline Plot method   observedtructure .ST within this species. In Hamilton et al. 1987 [ST was found to be 0.102, Pfau et al. 2019 [ST of 0.096 from their late 1960s samples, and our study, at the greatest resolution of all previous studies, reveals a mean FST value of 0.041. Our lower mean value includes individuals sampled from localities not present in the previous two studies (i.e. Cottle and Childress counties). These results suggest low population differentiation corresponding with geography.Together, these three studies, using allozyme, mtDNA, microsatellite, and RAD-Seq markers, offer numerous mean geographic estimates of Fal. 1987 , the meaal. 2019  estimateDipodomys elator, similar to what Statham et al. [D. ingens, the giant kangaroo rat. Like D. elator, the giant kangaroo rat is a species of conservation concern because habitat loss or change has reduced its historical range. So, despite a decline in distribution and census size, which is typically greater than Ne, the genetic diversity of the species is sufficiently high to offset any short-term effects of inbreeding depression. This is supported by our NeEstimator and EBSP estimate of Ne of 170–500, though the NeEstimator value must also be taken under caution because many of the values were ‘Infinite’. This estimate exceeds the recommended minimum value to curtail inbreeding depression, as outlined by the 100/1000 rule [e of this species was between 65 and 490 individuals.From an overall genetic diversity perspective, our data suggest that there has not been a substantial loss in genetic diversity over the last 30 years, despite what seems to be a decrease in the distribution (and possibly abundance) of m et al.  found in000 rule . Within ST < 0.05). The STRUCTURE algorithm determined the best value of k to be 3, a finding also described by Pfau et al. 2019, who suggests weak genetic structure overall between east and west samples. The STRUCTURE plot indicates gene flow between hypothesized east and west demes, though it seems unclear exactly how many clusters the contemporary samples may represent. More clusters (i.e. subpopulations), while possible, does not seem parsimonious. Second, newly colonized subpopulations on the fringes of ranges can exhibit lower levels of genetic diversity than expected [ST (< 0.05) does not seem to support cluster sizes of k = 4 and k = 5.However, STRUCTURE, the PCA, and Nei’s genetic distances do not support two distinct subpopulations, suggesting there is a fair amount of gene flow in the region.Results from our contemporary samples confirm that subpopulation differentiation is not substantial  would lD. ordii samples exhibited higher genetic diversity estimates in nearly all categories despite our samples being collected from only five counties in north-central Texas. This emphasizes the substantial genetic diversity and evolutionary potential displayed by the more common D. ordii, compared with its much rarer congener. However, we were surprised to find that D. ordii had a greater inbreeding coefficient than D. elator across some analyses. This pattern can be attributed to sampling bias, given that we sampled from a small portion of the D. ordii range, and half of the D. ordii samples were collected from a single ranch in Dickens County, Texas. However, a relatedness test was performed, and no relatives were identified in the D. ordii set. This is possibly due to the poor data quality of the Dickens County samples  as glaciers receded after the Last Glacial Maximum 20,000 years ago [D. elator populations [e of D. elator declined over time possibly due to land use changes, but this may be an oversimplification.With NeEstimator, we were unable to generate a value of N species . Insteadears ago , 69. Theulations . The Ne e estimates, and population surveys that recover or fail to locate D. elator in different localities, one possibility is that this population exhibits characteristics of a metapopulation [D. elator population may benefit from management consideration stemming from metapopulation theory.Coupled with low Npulation , 71. Metpulation , but emppulation , 74). Onpulation  is the spulation , the autpulation  there isD. elator to regions where the species appears extirpated. Should managers elect for more extreme measures to manage D. elator populations, such as captive breeding or population supplementation with the end goal of increasing genetic diversity, knowledge that the population is a metapopulation is critical if managers do not want to inadvertently remove local adaption. Lastly, it is important to note that the metapopulation in a conservation context has several assumptions. One assumption is the “equilibrium” between colonization and extinction across long time scales . This seems unlikely in many natural populations [D. elator despite its presumed low dispersal capabilities. This type of assumption can be used to appropriately model changes in demography and genetics of D. elator.However, because this connection to metapopulation theory is still tenuous, the overall population should still be monitored  and a loulations  but may D. elator. These studies have greatly improved our understanding of this elusive rodent [D. elator seems to track favorable habitat, albeit in a more restricted range than previously recorded [There is no lack of research on habitat associations, mainly those evaluating soil and vegetation changes, as they influence e rodent , 79, 80,recorded .D. elator exhibits genetic variation lower than that of a species with a predictably greater effective population size. However, contemporary samples show no substantial decrease in genetic diversity from historical samples, suggesting that the D. elator population, though small and constantly shifting, has managed to maintain its genetic diversity.Overall, the population of D. elator is estimated to be around 171 to 500 individuals, perhaps small population sizes are the status quo for this species. Increasing population size may be unsustainable for this species , but could come at the cost of reduced adaptive potential.This study demonstrates the effectiveness of using samples from gradations across the range, rather than at two extremes. Sampling from the extremes of a population range could lead researchers to inappropriate conclusions that could wrongly influence management decisions. Though the current effective population size of D. elator population could be a metapopulation that must be continuously monitored so that managers can detect immediately any significant losses in genetic diversity and evolutionary potential. Genomic data generated from this study can be used in future investigations. For example, data here can be used to represent another snapshot of the genetic diversity of the species, as the from historical samples served in this study. The data here can be used as a starting point to investigate adaptive loci within the species. Furthermore, given the current advances in molecular techniques and analyses, it is no longer necessary to limit samples in the temporal dimension. Doing so, especially for species that remain understudied, will prove detrimental to any long-term plan for management. We advise, whenever possible, the inclusion of historic and geographically represented samples to fully encapsulate temporal and spatial genetic variability within a possibly imperiled species.Researchers interested in natural genetic variation and population structure of mammals should consider the possibility that the population of their organisms of study could be exhibiting the characteristics of a metapopulation. This is especially important for species that are rarely seen or captured. Our findings suggest that the S1 FigThe patchy arrangement of individuals between contemporary spatial demes suggests gene flow between the hypothesized east and west populations, possibly indicative of a metapopulation.(TIF)Click here for additional data file.S2 FigThe three samples not clustering with all other is likely a result of different data quality.(TIF)Click here for additional data file.S1 FileSome samples were found on private land, so those coordinates have been withheld.(DOCX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.S3 File(DOCX)Click here for additional data file.S4 FileD. elator samples explained 9.9% of the variation, whereas PC2 accounted for an additional 5.7% of the variation. For historic D. elator samples, PC1 comprised of 6.9% of the variation, and PC2 explained 6.3% of the total variation. PC1 explained about 10% of the variation for contemporary D. ordii samples, and PC2 accounted for 9.8% of the total variation.PC1 for the contemporary (XLSX)Click here for additional data file.S1 TableMuseum codes are MSB (Museum of Southwestern Biology), MSU (Midwestern State University), and TTU (Texas Tech University).(DOCX)Click here for additional data file.S2 TableSample indicated with asterisk were removed from downstream analyses due to missing data. Samples KR_01, KR_02, KR_04, KR_05, and KR_06 were not part of the initial sequencing but were added later. Raw read number has been included for these 5 samples.(DOCX)Click here for additional data file.S3 TablePrivate alleles are those alleles not shared with any other subpopulation.(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.S5 TableD. elator are 0.042 and 0.038, respectively. π is a measure of nucleotide diversity. FIS indicates the inbreeding coefficient. The heterozygosity found in the Wichita samples is very large compared to what was found in the species as a whole. This is likely an artefact of which SNPs were used in this comparison. The SNPs evaluated are shared among both D. elator and D. ordii, and D. ordii is a common species, so the SNPs evaluated would be expected to have greater levels of diversity than SNPs evaluated in just D. elator.Private alleles are those alleles not shared with any other subpopulation. Observed and expected heterozygosity are the proportion of loci that are heterozygous based on Hardy-Weinberg frequencies. Historical observed and expected heterozygosity for (DOCX)Click here for additional data file.