Streptococcus thermophilus in yogurts and plant-based yoghurts; Lactococcus lactis in milk kefirs and one water kefir; and Lactiplantibacillus plantarum in kimchi, sauerkraut, and fermented cucumber. The broad range of fermented foods included in this study and their diverse bacterial communities warrant further investigation into the implications of microbial compositions for product traits and potential impact on human health.Fermentation has long been utilized to preserve and enhance the flavor and nutritional value of foods. Recently, fermented foods have gained popularity, reaching new consumer groups due to perceived health benefits. However, the microbial composition of many fermented foods re-mains unknown. Here, we characterized the bacterial composition, diversity, and richness of 47 fermented foods available in Sweden, including kombucha, water kefir, milk kefir, yogurt, plant-based yogurt alternatives, kimchi, sauerkraut, and fermented vegetables. Via 16S rRNA gene sequencing, we identified 2497 bacteria (amplicon sequence variants). The bacterial composition was strongly associated with the type of fermented food, and lactic acid bacteria and/or acetic acid bacteria dominated most samples. However, each fermented food had a unique composition, with kombucha and water kefir having the highest diversity across and within samples. Few bacteria were abundant in multiple foods and food groups. These were  Fermented foods represent a diverse group of foods defined as “foods made through desired microbial growth and enzymatic conversion of food components” . During Recent development and application of sequencing technologies has complemented traditional microbiology approaches and led to the identification of both culturable and unculturable microbes in fermented foods, and the characterization of large and complex microbial communities that vary in taxonomy, function, and abundance ,6. LactiIn the present study, we aimed to investigate the bacterial composition, diversity, and richness of a broad range of fermented foods that were available on the Swedish market or prepared in home environments in Sweden. Both home-made and commercial products were selected to represent different types of fermented products available to the population and to allow for comparisons between home-made and commercially produced foods. The foods belong to eight different fermented food groups, namely, kombucha, water kefir , milk kefir, yogurt, plant-based alternatives to yogurt, sauerkraut, kimchi, and various fermented vegetables, including fermented beetroot and cabbage, cucumber, and carrot.In this study, we collected 49 fermented foods, but only 47 were included in the statis-tical analysis due to the exclusion of two samples during microbiota data preparation, as described below . FermentSauerkrauts were prepared by two individuals using white cabbage from different grocery stores and variations of the same recipe . In brieFermented foods were stored in refrigerators in home environments after preparation or purchase (unopened containers) prior to sampling. One representative sample was taken from each of the 49 foods; these were placed in separate 15 mL falcon centrifuge tubes  and immediately placed in the freezer. Hands were washed with water and soap prior to sampling. Solid foods were sampled using sterile disposable spoons and by sampling different spatial locations of the food. Liquids were stirred with sterile disposable spoons, then poured. Each sterile spoon was only used for one sample. Within 30 days after freezing, all samples were transported on ice for about 1.5 h on one occasion. Samples remained frozen and were stored at −80 °C prior to microbial analysis.® High-Fidelity PCR Master Mix kits  were used, and the PCR products were purified with Qiagen Gel Extraction Kits  and quantified with a Qubit®3.0 Fluorometer. The final libraries were generated with NEBNext® UltraTM DNA Library Prep Kits (New England Biolabs) that incorporated barcodes and adaptors. The amplicons were then sequenced using the Illumina Novaseq 6000 (2 × 250 bp) platform provided by Novogene, Beijing, China. One sample that did not pass the initial quality control was not included in the sequence analysis.Total DNA was extracted from 4 mL of sample aliquots of each fermented food. Prior to collection of the aliquots, falcon tubes containing the fermented samples were thawed and thoroughly homogenized via vortex mixing. Subsequently, two 2 mL aliquots were transferred to two 2 mL sterile Eppendorf tubes. The aliquots were centrifuged for 5 min at 13,000 rpm to pellet particles. The supernatant was discarded and the remaining pellet in each tube was resuspended in 0.5 mL InhibitEX buffer. Replicate resuspensions from the same falcon tubes were then pooled, and DNA was isolated from the samples using a QIAamp Fast DNA Stool mini kit  according to the protocol from the manufacturer, except that the bacterial cell walls were mechanically disrupted with 0.1 mm Zirconium/Silica beads  2 × 60 s, using a Precellys Evolution . Amplicons from the V3 and V4 regions of the 16S ribosomal RNA gene were generated from the extracted DNA using the primers 341F and 805R. For the polymerase chain reactions (PCRs), Phusionhttps://benjjneb.github.io/dada2/training.html, accessed on 5 January 2021). This included assignments applying the former Lactobacillus classification, which since has been re-classified into 25 genera [The raw demultiplexed reads from the sequencing were processed using the DADA2 pipeline to denoise , truncQ = 2, rm.phix = TRUE, compress = TRUE), dereplicate reads, merge pair end reads, and remove chimeras . The tabhttps://blast.ncbi.nlm.nih.gov/, accessed on 27 May 2022) for ASVs present at ≥1% in at least one sample (N = 143) (Table S1). Notably, for species level assignments, the accuracy cannot be assured; rather, the best match is reported, whilst the genus level is generally considered reliable. In the manuscript, taxa were reported using the name recognized by NCBI [Taxonomic assignments were performed using the ASV sequence and the default pa-rameters in NCBI Nucleotide blast suite  were first converted and saved as a phyloseq object using the readxl, tibble, and phyloseq packages. Alpha diversity (Shannon and Inverted Simpson diversity) and richness (observed richness and Chao1) were calculated using Phyloseq after normalizing the ASV data using the scaling with ranked subsampling method (SRS package) . Resultsphyloseq  and visup-value, or a p-value corrected for multiple comparisons when applicable, of <0.05 was considered statistically significant. Figures were generated using the ggplot2 package.The relative abundance of genera commonly classified as LAB (N = 8)  or AAB , respectThe microbiota of 47 out of the 49 fermented foods were included in the analysis and are discussed below . ExcludeAlpha diversity and richness were significantly different across fermented food groups, indicating differences in the community structure (number and proportions) of bacteria responsible for the fermentation and bacteria otherwise present in the foods . Kombuchp < 0.001)  . Yogurts< 0.001) a. Some sBacteria belonging to Firmicutes, a phylum containing most LAB, were present in all fermented food groups, whilst variation within the Proteobacteria phylum contributed to differences across food groups and were absent in yoghurts and plant-based alternatives to yoghurts b. BacterLactobacillus, Lactococcus, Leuconostoc, Pediococcus, Schleiferilactobacillus, and Streptococcus and genera formerly classified as Lactobacillus [Lactiplantibacillus, Latilactobacillus, Lentilactobacillus, Levilactobacillus, and Loigolactobacillus), AAB  or Bifidobacteria and in rare cases ethanol-producing bacteria (Zymomonas), Bacillaceae, or Enterobacter (contaminant)  .The estimated total proportion of LAB and AAB showed LAB to predominate in kimchi, various vegetables, yoghurt, and most sauerkrauts and to be highly abundant in milk kefirs and plant-based alternatives to yoghurt . AAB werGluconobacter spp. and Acetobacteraceae spp., either dominating in a commercial kombucha (Sample 20) or in two home-made kombuchas (Samples 1 and 2)  . Interescrobiota ,30,31,32Gluconobacter sp. and an unknown Acetobacteraceae species decreased gradually and considerably, from 30.8% and 42.1% to 0.4 and 0.2%, respectively.We found no clear distinction between commercial and home-made kombucha, or resemblance among home-made kombucha, although the home-made kombuchas were made consecutively using the same SCOBY and recipe. Instead, several ASVs with lower abundance, including several LAB, increased gradually from the first to the third and final batch, whilst Home-made water kefirs and commercial Sample 30 were characterized by higher AAB, whereas remaining commercial water kefirs had higher proportions of LAB . Each waAcetobacter sp., Enterobacter cloacae complex, and Gluconobacter sp. Proportions of the E. cloacae complex, a likely contaminant, were considerably lower in the water kefir that had fermented for 4 days (Sample 27) compared to that which had fermented for 3 days (Sample 26), while the estimated proportion of LAB and AAB increased twofold. Water kefirs from the same company (Samples 30 and 31) showed limited similarity and differed in terms of flavour ingredient (rhubarb juice vs. rosehip). Sample 30 had high proportions of an unknown Enterobacterales bacteria and several Gluconobacter. In contrast, Sample 31 had the highest diversity amongst water kefirs and was dominated by L. lactis, the latter similar to milk kefir . Home-made milk kefir Samples 8–10 had high proportions of both LAB and AAB taxa, contributing at the most to 39.8–52.4% of the total abundance whilst the second most abundant ASVs contributed with 18.5–28.2% to each sample , and two home-made yoghurts also contained high proportions of a bacterium classified as Lactobacillus delbrueckii subsp. bulgaricus (11.6–34.3%). This is in accordance with the known composition of the starter culture of the home-made yoghurt Sample 36 and common yoghurt production processes [Lactobacillus delbrueckii subsp. Bulgaricus in commercial yoghurts was unexpected and lacks explanation. The composition of the starter cultures for commercial yogurts were unspecified or partially provided. Remaining ASVs had low abundance (≤5%) and included Lentilactobacillus buchneri, Schleiferilactobacillus harbinensis, Lactiplantibacillus sp., Gluconobacter, L. lactis, and unknown bacteria.All yoghurts were dominated by one h sample . Additiorocesses . In thisS. thermophilus and/or B. animalis, as well as L. delbrueckii subsp. bulgaricus in commercial Sample 43 and to lesser extent L. plantarum and Bacillus spp. in home-made Sample 42  and B. animalis (9.2–40.6%), likely due to the composition of the starter cultures. The bacterial composition of Sample 40 corresponded well to the listed starter culture, and there was partial resemblance  for Sample 39 in this regard. For unknown reasons, we did not detect L. acidophilus in Sample 39 or in other foods where this bacterium was listed among the starter cultures . Two home-made samples (Samples 41 and 42) were also dominated by B. animalis (61.9–81.1%). Although yoghurt alternatives can be produced from many plant-based sources, as represented by the samples in this study , many seem to be made using similar starter cultures [Plant-based alternatives to yoghurt were dominated by ample 42 . The exccultures .L. sakei, and Sample 7, made with red cabbage instead of white cabbage, also contained Latilactobacillus curvatus, Leuconostoc mesenteroides, and Lactiplantibacillus spp. A fourth home-made sauerkraut (Sample 4) showed little resemblance to other sauerkrauts and had high proportions of Enterobacter spp. and Klebsiella taxa but limited LAB, possibly resulting from contamination and a shorter fermentation time [Lactiplantibacillus taxa and had relatively high proportions of L. brevis and either Leuconostoc sp. (Sample 11) or Pediococcus sp. and L. curvatus (Sample 13). The third commercial sauerkraut (Sample 12) had high proportions of L. mesenteroides and L. curvatus spp. The bacterial composition and the dominance of LAB show high resemblance to prior studies, except that high proportions of L. sakei and contaminants have not been as evident previously [For sauerkrauts, the bacterial composition was predominated by a few highly abundant LAB . Three hion time  , L. brevis (1.8–51.6%), and to some extent also L. sakei (0.5–10.5%)  . The res process , where t process . Howevery others .Lentilactobacillus taxa in Samples 44, 45 and 47, which was unique to these fermented foods . This fermented cucumber product also contained Loigolactobacillus coryniformis and Schleiferilactobacillus harbinensis whereas another fermented cucumber product (Sample 46) had high proportions of Lactiplantibacillus sp., including L. plantarum and L. sakei, showing higher resemblance to the literature on other fermented cucumber products, also reporting high proportions of e.g., Pediococcus [Lactobacillaceae family and to lesser extent Lentilactobacillus spp. and Pediococcus spp. In reference to a study comparing close to 40 samples of fermented carrot juice, there was little resemblance other than a high proportion of LAB [The group of various fermented vegetables had relatively high proportions of different LAB, including several ed foods . In factn of LAB .The samples included in this study were selected using a practical approach, namely purchasing fermented foods from local grocery stores and health stores and gathering samples from individuals making fermented foods at their own leisure in their homes. Systematic investigations addressing narrower research questions can complement our study, e.g., in terms of specific product traits or factors affecting microbial composition. Due to the lower resolution of 16S rRNA sequencing in comparison to, e.g., metagenomic sequencing, strain level detection was not possible and some ASVs were matched at the genus and family level. Furthermore, we did not investigate microbial functions, for which metagenomics sequencing is better suited than 16S rRNA sequencing. These limitations may have masked additional differences and similarities across samples. Sequencing methods are also limited in the regard that it is not possible to distinguish living from dead microbes, which could be a reason for why we detected some unexpected or uncommon bacteria in the fermented foods, e.g., common intestinal bacteria. While it is not possible (and sometimes not preferable as in the case of spontaneous fermentation) to completely avoid contamination from the environment , properS. thermophiles, which was highly prevalent in yoghurts and plant-based alternatives to yoghurts; L. lactis highly abundant in milk kefirs and one water kefir; and L. plantarum, characteristic of kimchi and with high abundance in one sauerkraut and a fermented cucumber product. The broad range of fermented foods included in this work and their diverse bacterial communities warrant further investigations into the implications of microbial compositions for product traits and potential impact on human health.This study characterized and compared the bacterial composition, diversity, and richness of a broad selection of fermented foods available on the Swedish market and prepared in home environments. We found the bacterial composition to vary depending on the type of fermented food, i.e., kombucha, water kefir, milk kefir, yogurt, plant-based alternatives to yogurts, kimchi, various fermented vegetables, or sauerkraut. However, the fermented foods had unique compositions, and the kombucha and the water kefir were particularly diverse across and within samples. There were also differences between home-made fermented foods and commercial products, as was most evident for milk kefirs but also plant-based alternatives to yoghurts and sauerkraut, and to some extent differences linked to the use of starter cultures in comparison to living symbiotic colonies . Only a few bacteria were highly abundant in multiple foods and fermented food groups, including


Sclerotium rolfsii Sacc. is a globally dispersed pathogenic fungus that causes southern blight disease in many crops and Chinese herbal medicine. The high degree of variation and diversity in the fungi altered population genetic structure. Therefore, the important factors of variation within the pathogen population should be considered during the development of management strategies for the disease.S. rolfsii isolates from 13 hosts in 7 provinces of China were collected and analyzed to identify their morphological features and perform molecular characterization. To develop EST-SSR primers, transcriptome sequencing was performed on isolated CB1, and its SSR loci were comprehensively analyzed. In addition, we analyzed the polymorphisms among different populations based on screened EST-SSR primers.In this study, The results showed that all of these clean reads with total 36,165,475 assembled bases were clustered into 28,158 unigenes, ranged from 201 bp to 16,402 bp on the length, of which the average length was 1,284 bp. Of these, the SSR sequence appeared at an average interval of 15.43 kB, and the frequency of SSR was 0.0648 SSR/kB. Polymorphism of 9 primers was observed among 22 populations, and was verified by the Shannon’s index (average = 1.414) and polymorphic information index (> 0.50). The genetic diversity analysis revealed diversity in all host populations and geographical populations. Further, molecular variance analysis (AMOVA) showed that the differences between groups were mainly related to geographical location. Based on cluster analysis, the 7 populations were roughly divided into 3 groups, and the results were highly consistent with those based on the geographical location, ultimately aligning with the results of STRUCTURE analysis.S. rolfsii in the southwest area of China, adding value to current knowledge base on the population structure and genetic diversity of S. rolfsii, specifically in the context of Chinese herbal medicine cultivation in China. Overall, our findings may provide valuable information for breeding of crops with enhanced resistance toward S. rolfsii.The findings build on current knowledge of the distribution of  The DNA was quantitatively and qualitatively determined using NanoDrop (Themo Scientific), diluted to a working concentration of 20 ng/μl, and stored in aliquots at −20°C until further use.2.3.LSU primers with NL1 as forward and NL4 as reverse primer were used for PCR amplification of the 120 isolates . The cycling program was as follows: initial denaturation at 95°C for 4 min followed by 35 cycles of denaturation at 94°C for 45 s, optimized annealing temperature for 45 s and extension at 72°C for 1 min, and a final extension at 72°C for 10 min. The PCR amplicons were electrophoresed on 1% agarose gel in 1× TBE buffer  and visualized in a UV transilluminator.To verify the species identity of some of the newly collected isolates, universal ITS primers with ITS1 as forward and ITS4 as reverse primer and isolates ; Table 12.4.Macleaya cordata in Hunan Province. The collected samples were immediately frozen in liquid nitrogen and stored at −80°C for further use. Total RNA was extracted according to the manufacturer’s instructions for the Trizol reagent kit . The mRNA of each sample that met the quality requirements was enriched by Oligo (dT) beads. Then the enriched mRNA was fragmented into short fragments using fragmentation buffer and reverse transcript into cDNA with random primers. Second-strand cDNA were synthesized by DNA polymerase I, RNase H, dNTP and buffer. Then the cDNA fragments were purified with 1.8X Agencourt AMPure XP Beads, end repaired, poly(A) added, and ligated to Illumina sequencing adapters. The ligation products were size selected by agarose gel electrophoresis, PCR amplified, and sequenced using Illumina HiSeq™ 4,000 by Gene Denovo Biotechnology Co . Transcriptome de novo assembly was carried out with short reads assembling program – Trinity , sclerotia of early development stage (S2) and mature sclerotia (S3) stage, were collected from strain CB1 of  Trinity .All raw sequencing data were submitted to the Genome Sequence Archive (GSA) database under the BioProject accession number CRA009668 at the BIG Sub website.2.5.S. rolfsii transcriptome. Primer 6.0 software was used to design the primers for the SSR locus. The main selection parameters for screening the primers were as follows: I. sites with repetition units of 2, 3, 4, and 5 bases with only one repetition type; II. fragment length greater than 150 bp and less than 300 bp; III. location of genes not markedly concentrated, preferential selection of the loci with polymorphism, and even selection of the duplicate units of different combinations; IV. primers with length between 20 and 23 bp and TM value of approximately 60°C; V. primers with base repeats less than or equal to 4; VI. 5′ and 3′ ends do not contain two consecutive A/T bases; and VII. no repeat sequence in the primer.The detection and localization of potential SSRs were searched and implemented from 25,185 unigenes using the MISA tool.2.6.2O. The PCR amplification conditions were as follows: 94°C for 5 min, followed by 35 cycles at 94°C for 30 s, 60°C for 30 s, 72°C for 30 s, and then a final extension at 72°C for 5 min, using a Thermal Cycler . All primer pairs were used for amplification of 8 samples with obvious morphological differences to select the primers that resulted in clear amplification bands. Then 16 samples from different hosts and different geographical sources were used as templates, and primers with good polymorphism were screened by capillary electrophoresis.To validate the quality of the primer, 200 primer pair sequences were randomly selected and synthesized by Tsingke Biotechnology Co., Ltd.  and used for PCR amplification. The reaction volume of 20 μL comprised 10 μL 2 × TSINGKE Master mix , 1 μL forward primer , 1 μL reverse primer (10 mM), 1 μL genomic DAN (20 ng/μL), and 7 μL ddHThe obtained PCR products were detected by capillary electrophoresis with 3,730 XL sequencer . Primers with high specificity, good polymorphism, and good repeatability were screened by analyzing the locus peak and data information. To clarify the polymorphism of EST-SSR primers and populations, the number of alleles (Na), effective number of alleles (Ne), Shannon index (I), polymorphism information content (PIC), and observed (Ho) and expected (He) heterozygosity were calculated using Popgene 1.32 and GenAlEx 6.501 .2.7.S. rolfsii, and a dendrogram was generated from this clustering.The obtained EST-SSR primers were used to analyze the population genetic diversity of 120 strains from 22 populations. To determine the genetic variation between geographic populations and host populations, including within populations, among populations, within groups, and among groups, the analysis of molecular variance (AMOVA) function in Gene AlEx 6.501 was applied. The genetic structure of 7 geographic populations was analyzed using Structure 2.3.4 based on Bayesian analysis. Twenty independent runs were performed for each K value . The bur3.3.1.S. rolfsii mycelium stage (S1), sclerotia of early development stage (S2) and mature sclerotia (S3) stage, and then sequenced by Illumina technology. After filtering, 27,173,090, 26,896,318 and 22,913,500 clean reads were obtained from S1 stage, 24,936,756, 23,481,220 and 27,038,696 clean reads were obtained from S2 stage, 27,164,438, 28,988,848 and 25,025,724 clean reads were obtained from S3 stage, respectively. All of these clean reads with total 36,165,475 assembled bases were clustered into 28,158 unigenes, ranged from 201 bp to 16,402 bp on the length, of which the average length was 1,284 bp. The GC percentage was 48.29% and the N50 was 2,379. Of these, 3,299 SSR sequences were detected, including 579 unigenes containing multiple SSRs n in tri-repeats, (AAAG/CTTT)n in tetra-repeats, (AAAAG/CTTTT)n in penta-repeats, and (AAGGAG/CCTTCT)n and (ACCAGC/CTGGTG)n in hexa-repeats, with frequencies of 11.91, 3.27, 0.61, and 0.15%, respectively ectively .The SSR length of strain CB1 ranged from 12 to 212 bp, with an average length of 72.78 bp. Most microsatellite sequences were 12–36 bp in length, accounting for 39.46% of the total microsatellite length distribution. There were 175 microsatellite sequences longer than 36 bp. The number of microsatellite sequences of 15 bp was the largest, accounting for 15.96% of the total number, followed by 14 bp and 20 bp microsatellite sequences, accounting for 12.96 and 13.58%, respectively .3.3.Primer 6.0 software was used to design primer pairs that meet the required criteria. Briefly, we randomly selected and synthesized 200 primer pairs, and initially tested the polymorphism of SSR primers . Thirty-3.4.Na) were detected in the 22 populations at an average of 7.333 alleles/locus. BJ184 was the locus with the largest number of alleles (11). The values for Ne, He, and PIC were 1.963, 0.493, and 0.477, respectively. The value of Ne was in the range of 1.963 and 5.993, with an average of 3.370. BJ112 had the highest value of I (1.981) and BJ071 had the lowest value of I (1.044), with an average value of 1.414. The value of He ranged from 0.493 to 0.837, with a mean of 0.671. The value of PIC varied from 0.477 to 0.814, with an average of 0.630. For the overall PIC, one locus had values between 0.25 and 0.50 (moderately informative), while BJ015, BJ071, BJ082, BJ112, BJ130, BJ152, BJ156, and BJ157 had values >0.50 . The highest and lowest levels of diversity were found in D. nobile and A. rivieri, respectively. The fixation index (F) of the geographical population and host population were significantly greater than or less than 0, indicating the absence of heterozygotes or homozygotes.The genetic diversity of the the host . The est3.6.The seven populations were analyzed to determine the genetic variation among and within populations using AMOVA . Based oS. rolfsii clusters were further analyzed using hierarchical AMOVA and pairwise genetic differentiation. In the AMOVA test, total genetic variation was partitioned at two levels: among clusters and among isolates within clusters. This partition revealed that 39% of the total genetic variation was due to genetic differences among clusters, while the remnant and larger source of diversity (61%) was located among isolates within clusters  to 17.531 (S. ningpoensis/P. ternata), and the genetic differentiation values ranged from 0.014 (S. ningpoensis/P. ternata) to 0.990 , except for the A. rivieri/A. hypogaea and P. ternata/C. glaucescens groups . Comprehensive analysis of the genetic diversity index of the host populations revealed that A. rivieri had the lowest genetic diversity index and D. nobile had the highest genetic diversity index. The results of the present study align with those of A total of 66 allelic loci were detected by the 9 primers in the 120 strains. The mean value of the Shannon index (I) was 1.4139, while that of the polymorphic information index (PIC) was 0.6303, indicating good polymorphism with S. rolfsii based on mycelial compatibility, rDNA, RAPD, and AFLP is commonly used to understand the distribution pattern of populations within and between different geographical regions , suggesting a correlation between the genetic and geographical location of the population. The sampling point of Anhui is located at the junction of Anhui and Hubei, and Hubei and Hunan are adjacent to each other with frequent exchanges. As a result, Anhui and Hunan belonged to one branch in the clustering results. The limited number of specimens may also be the reason for the results. The sample size should be sufficiently large to ensure the results reflect the true ecological patterns that could otherwise lead to contingencies.5.S. rolfsii isolates from 13 hosts in 7 provinces of China were isolated, purified, and identified. Based on the transcriptional sequencing of strain CB1, the SSR loci were analyzed and nine EST-SSR primers with good polymorphism were developed. To our knowledge, this is the first use of the EST-SSR method to analyze the genetic diversity of S. rolfsii, especially from areas that grow Chinese medicinal materials in China, which revealed the existence of broader genetic variations among the pathogen. Such finding will facilitate the development of effective disease management strategies based on molecular breeding and other advanced approaches. The results of population STRUCTURE analysis were consistent with those of geographical location, which preliminarily proved that geographical location had a certain correlation with the genetic diversity of pathogen. In future studies, we will collect more S. rolfsii specimens from more regions and hosts. Using larger datasets should better reflect the distribution and genetic status of S. rolfsii.In this study, 120 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/FW designed and performed the research, analyzed transcriptomic data, developed EST-SSR primers, and wrote the manuscript. TT and TM collected the experimental samples. YD and XG participated in the genetic diversity analysis, participated in the revision of the manuscript. JY conceived this work, revised, and approved the final version of the manuscript. All authors contributed to the article and approved the submitted version.This work was supported by the Science and Technology Planning Project of Enshi Tujia and Miao Autonomous Prefecture (XYJ2022000027), the Science Funds for Young Scholar of Hubei Academy of Agricultural Sciences (2023NKYJJ30), Central government guides local science and technology development fund projects (2022BGE256), China Agriculture Research System (CARS-21), and the Forestry Science and Technology Project of Central Finance (2021TG16).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.


We aim to study the temporal patterns of activity in points of interest of cities around the world. In order to do so, we use the data provided by the online location-based social network Foursquare, where users make check-ins that indicate points of interest in the city. The data set comprises more than 90 million check-ins in 632 cities of 87 countries in 5 continents. We analyzed more than 11 million points of interest including all sorts of places: airports, restaurants, parks, hospitals, and many others. With this information, we obtained spatial and temporal patterns of activities for each city. We quantify similarities and differences of these patterns for all the cities involved and construct a network connecting pairs of cities. The links of this network indicate the similarity of temporal visitation patterns of points of interest between cities and is quantified with the Kullback-Leibler divergence between two distributions. Then, we obtained the community structure of this network and the geographic distribution of these communities worldwide. For comparison, we also use a Machine Learning algorithm—unsupervised agglomerative clustering—to obtain clusters or communities of cities with similar patterns. The main result is that both approaches give the same classification of five communities belonging to five different continents worldwide. This suggests that temporal patterns of activity can be universal, with some geographical, historical, and cultural variations, on a planetary scale. In the last decade, the development of technologies applied to inform the activities of humans has made available an unprecedented amount of data associated with the digital trace of humans in cities5. For instance: the use of credit card transactions6, the use of digital devices to access transportation services, like taxis7, buses and subway8, bicycle9, and telecommunications networks like cell phones16 and GPS devices18.In recent years, cities have become a topic of considerable scientific interest19. In these, people share information about the places they visit that may be of interest to other people in this social network. These venues, also known as Points of Interest (POIs), correspond to places within the city that can be characterized by features, such as restaurants, bars, gyms, hospitals, museums, parks and so on. Besides the type of feature, LBSN identify, for each POI, the spatial location (coordinates or addresses) and the time of visit (date and hour); these visits are recorded as check-ins using, typically, a mobile-phone application. Therefore, we end up with a data set with detailed information of the type of place and the exact location, together with the day of the week, and time of the visit. LBSN provide valuable information on the interaction of people among themselves, as well as the physical places where these interactions occur. This interplay has been explored in studies of urban mobility20, human behavior22, social interactions23 and encounter networks24.A very important source of information for the study of human behavior in cities are Location-Based Social Networks (LBSN)25. This LBSN has been used previously in several studies34, of human mobility and social relationships, encounter networks due to human mobility, spatial and temporal activity patterns, among others. A more detailed description of Foursquare will be given in the next sections. In this paper, we will focus mainly on the temporal patterns of human activities in cities throughout the world. This temporal signature of the vastness of human dynamics can be captured through time series, using different sources such as email records35, mobile phone calls36, data sets from the public sector37 and the analysis of the frequency of check-ins during the visitation of POIs in cities38. In this work, we will study the temporal patterns that emerge due to the frequent visitation of points of interest in different cities around the world. The data set we analyzed contains more than 11 million POIs of 632 cities of 87 countries located in 5 continents. We will focus mainly on the temporal patterns of activities on a weekly basis, that distinguish weekdays and weekend. Using network science39, we generate an undirected weighted network connecting pairs of cities. The links between each pair of cities have a weight that quantifies the similarity of the temporal patterns of visitation of POIs. To measure the similarity between the two distributions of temporal patterns, we used the relative entropy (Kullback-Leibler divergence)40 between the corresponding distributions. Once we have our network of cities, we obtained the community structure of this network using a well-known method of community detection: the Louvain algorithm. Then we locate these communities of cities geographically and notice a strong correlation between geography and temporal patterns of activities. On the other hand, besides the network science approach, we use a Machine Learning approach to classify the clusters in the data set of temporal distributions of activity. In particular, we used the unsupervised agglomerative hierarchical clustering algorithm41 to obtain clusters or communities of cities that have similar time distributions of check-ins in Foursquare. Both approaches, network science and machine learning, give a very similar classification of five communities that distribute geographically in five different continents throughout the world.In this study, we use data from one of the more relevant location-based social networks: FoursquareThe dataset used comprises over 90 million records made in more than 11 million POIs. Each record contains information about the place and time at which a person visited a POI. Additionally, using another dataset constructed from public information on Foursquare, it is possible to know the precise coordinates of venues and the type of place based on different categories used by this social network to classify sites. Thus, this is a database that provides spatial and temporal information on 2,733,324 individuals around the world, as well as information on the type of activities they perform. In this manner, we obtained spatial and temporal patterns of activity in many cities. In this work, we will focus mainly on the temporal patterns of activities on a weekly basis.24 by two of the authors, we explore precisely this emergence by analyzing data from Foursquare in two cities: New York City and Tokyo. We had the spatial and temporal data corresponding to the check-ins of many users visiting POIs in both cities. Thus, we explore the co-occurrences of two users in the same place at the same time in order to obtain the encounter or contact network. The motivation was, among other things, to study the propagation of viruses during an epidemic. By the way, this study was done and published previously to the current COVID-19 pandemic, where these contact networks were important to track the onset of the pandemic at the beginning of 2020. Years later, we keep studying the rich data set of Foursquare, but, instead of two cities, we explore more than 600 cities around the globe. In particular, the points of interest in cities are precisely the places where many people congregate and therefore can lead to an explosion of ideas and innovation, but at the same time can lead to the origin of epidemics of infectious diseases. That is part of our motivation to study in more detail the mobility and the temporal patterns of activity of POIs and to make a quantitative comparison of these patterns using metrics such as the Kullback-Leibler divergence. With this metric, we constructed a network of cities on a planetary scale and obtained the community structure of this network.Finally, it is worth mentioning part of the motivation that led to this research. The origins of the present paper can be traced back to our interest in the interplay between human mobility in cities and the encounter (or contact) networks that emerge. In a paper published in 2017The paper is structured as follows: After the Introduction, we show first the Results with all the details in separate sections, then a Discussion and finally the Methods we employ.In this work, we use Foursquare check-ins as a proxy of human activity in cities. Check-ins are spatio-temporal interactions between users and points of interest (POIs). They provide vast information about people’s interests, site characteristics, and behavioral patterns in cities, among many others. This is why Foursquare metadata and location-based social networks in general, can provide useful information for studies of mobility, infrastructure, human behavior, and public policy, just to mention a few examples.28 that is publicly available42. The data set contains 90,048,627 check-ins made by 2,733,324 users from April 2012 to January 2014. The authors collected active check-ins from Twitter by searching a hashtag generated automatically for users that linked their Foursquare activity with that social network. Then, with the set of places where check-ins occurred, they collected the POIs description; this information is available to the public from the Foursquare platform. The records include detailed information about the POIs, their coordinates, and the exact time of each user check-in28. In this manner, the database provides the space-temporal activity obtained from the sequence of successive check-ins for each user. Furthermore, the registers include a short description that labels each POI with specific keywords  and a social network of users defined via the mutual following between Twitter accounts. We did not incorporate this information in our study  and The variety of results observed for the probabilities i, j). With these values, we obtained the histogram shown. We show, as an inset, the In Fig. , in Eq. , to compH. If two cities have values H then these cities are considered similar. All this information defines a similarity network for each value H. The respective i, j given byIn this section, we apply methods of network science to analyze the similarities between cities. To this end, we define a network in which nodes represent cities and links the similarity relationship between cities. In this way, two nodes are connected if the respective cities have similar temporal activity. To generate this structure, it is necessary to define what is considered sufficiently similar. We use a threshold value S in Eq.  lower orH. In Fig. H. In this manner, for each H all the information of H explored in Fig. In Fig. H corresponding with a network that captures the nature of the similarity between the cities that we are analyzing. The results for H that is analogous to a percolation threshold39, separating two regimes: one with In the results in Fig. H and the geographical distance between cities, we definei and j and H. The results show that for small H, cities having very similar temporal activity histograms are also geographically closer. These average distances remain relatively low up to the network with On the other hand, in order to explore the relationship between the edges in each network generated with the value 47. In our similarity network, these communities represent groups of cities with comparable activity Once we established a criteria to build similarity networks using the temporal activity of users of Foursquare in Fig. 48 implemented in the library NetworkX in Python49, see “In Fig. The average curves in Fig. i, which in turn is a city with given coordinates, in Fig. In addition, considering that each probability curve Once we defined the communities of the network, we can compare the fraction of intra-community and inter-community links. Defining Finally, it is worth mentioning that, unlike other cluster identification methods, e.g., agglomerative clustering (discussed in the next section), information on similarity between specific regions within the same communities is preserved in the individual links. In Fig. 50. In particular, we used the unsupervised hierarchical agglomerative clustering to classify our temporal patterns of activity in cities. When we apply this algorithm to our data set, we obtained five clusters, as shown in Fig. Machine learning can be used as an alternative approach, with many algorithms involved, to classify objects or patterns in a very efficient wayIn this work, we use Foursquare check-ins as a proxy of human activity in cities. The data set explored provides vast information about people’s interests, site characteristics, and behavioral patterns in cities, among many others. The information analyzed contains 90,048,627 check-ins made by 2,733,324 users from April 2012 to January 2014 with active check-ins obtained from registers on Twitter by searching a hashtag generated automatically for users that linked their Foursquare activity with that social network. Information from 632 cities from 87 countries, with more than 10,000 POIs, was used. We explore the spatial properties of POIs and check-ins and the temporal features of the check-ins which, as self-reported spatio-temporal interactions between people and places, provide valuable information about activities carried out in cities. In particular, we analyze statistically the check-ins per hour on weeks for each city.H to decide if two cities have a similar activity or not. We explore the size of the largest connected component as a function of H; in particular, we found that around From the probabilities describing the temporal activity of each city, we apply a symmetrized Kullback-Leibler divergence to compare these probabilities across all 632 cities. From this information, we define similarity networks in terms of a threshold value 50. Both approaches, although very different, lead to the same classification of five different communities worldwide. Even more surprising, these five communities clearly correspond to five different regions on a planetary scale: 1) North America, (2) South America, (3) West Europe, (4) East Europe and Middle East, and (5) East Asia.We apply Louvain’s algorithm for community detection to the similarity network generated with It can be clearly noticed that practically in all cities there are these patterns of maxima and minima of human activity at the same hour during the week. At the same time, we notice a different pattern during the weekdays and another one slightly different during weekends. These patterns are very robust in the sense that the maxima and minima are clearly noticeable, even in the average curves involving the 632 cities in the analysis. Since these 632 cities are distributed in many parts of the world, spanning 5 continents, we think that the patterns are related to some universal aspects of human behavior. For instance, the fact that we humans are universally a diurnal species. This is one of the so-called human universals in the literature of human evolution. This fact can be seen in the minima of activity for all cities at deep night (around of 2 or 3 am). This universal periodicity is also related to circadian rhythms in humans and other internal clocks studied in chronobiology. However, the 3 peaks during the day, which appear early in the morning (between 6 and 7 am), at noon (around 12 or 13 hours), and in the afternoon (around 19 and 20 hours), can be related more with modern routines associated with working hours.In summary, using a vast data set from location-based social networks, we analyze 632 cities of 87 countries around the world to obtain temporal patterns of human activity that characterize points of interest within the cities. Using network science and machine learning algorithms we unravel communities with different patterns of human behavior. This suggests that human activity patterns can be universal with some geographical, historical and cultural variations on a planetary scale.51:urban communities: 64%, towns and rural communities: 36%.medium-sized town: 28%, large city: 26%This means that we have users of Foursquare from both urban, town and rural communities. Even though the urban percentage is larger, as expected, the difference is not that high; the rural or town percentage is roughly 1 in 3. This means that the data set used in our study reflects not only an urban feature but reflects small town or rural features as well.In this section, we recall some recent demographics of Foursquare. The user demographics is the following52:male: 52%, female: 48%.ages: 18-24 years old: 19%, 25-34 years old: 32%, 35-44 years old: 20%, 45-54 years old: 14%, 55-64 years old: 10%, 65+ years old: 6%.Here we notice that the percentage of males and females is basically the same: half and half. Therefore, our data set and analysis can be applied to both females and males. Regarding age distribution, we notice that about half (50%) of users of Foursquare have ages between 25 and 44 years old. This is expected since people at that age: young adults, are the ones that show more mobility and activity. It is clear that the mobility, activity and interest in reporting through check-ins to the social network tends to diminish with age. However, the distribution is somewhat broad.On the other hand, the gender and age distribution is as follows53: Arts and Entertainment, Business and Professional Services, Community and Government, Dining and Drinking, Event, Health and Medicine, Landmarks and Outdoors, Retail, Sports and Recreation, Travel and Transportation. This points to a large diversity of interests that are captured in the data set that we used in our analysis.Regarding the type of places visited and checked as POIs, it is worth mentioning that the categories are very broad. There are in Foursquare more than 1100 venue categories, distributed in ten major Foursquare categorization groups of points of interest POIs55:Foursquare Places has over 100 million POIs across 247 countries and territories, as of January 4, 2023.100M+ global POI200+ countries and territories14 billion user verified check-ins1100+ venue categories1 billion + photos, tips, reviewsRegarding the numbers of the particular data set used in this research, let us point out that we used data of Foursquare from April 2012 to January 2014. All the details of the data set and the processing, data mining, and data analytics, can be found in the following sections: Discussion, Methods—Dataset description, and Methods—Grouping POIs by urban area. Many of the results can be found in Tables User: 2,733,324POIs: 11,180,160Check-ins: 90,048,627Countries: 253 (87 of which include 99% of the data)Cities: 6,463 Continents: 5Categories of POIs: 519Given all these demographics together with the numbers, diversity, and vastness of the data set analyzed, and the use of aggregate data, the results can be a good description of the temporal activity in cities worldwide.Other relevant data about Foursquare are the following29 and it is publicly available42. The datasets are divided into two sets. The first one is a list of check-ins obtained through an automated search on Twitter with the help of its API streaming service29. Foursquare allows linking users’ accounts with other social media such as Twitter or Facebook, to share the check-ins on these platforms too. If this is the case, when one user registers their presence in a place, automatically, a tweet or a post is generated with the information of the check-in and some elements like hashtags and the URL of the venue’s page at Foursquare. Although the users that link their accounts in this way are only a subset of all the Foursquare users, this dataset contains information on about 2,733,324 users made in almost 22 months . With the information of tweets, a check-in dataset Check-ins information was collected by Yang, et al.From the information in the tweets, it is possible to obtain the POI profile from Foursquare’s site and with it the information contained in the second dataset, mentclass2pt{minimWith the information of each set mentclass2pt{minim57. In this work, we opt for the definition of Functional Urban Area used by the European Commission, which integrates factors such as infrastructure, population, and economy, and with which the Joint Research Centre generated the Global Human Settlement—Urban Centre Database (GHS), a dataset with the borders of 13,135 urban areas worldwide. This information is contained in a shapefile publicly available58 that includes the name of the city, its population, coordinates, area, the country, and region in which it is located, if it extends beyond the borders of a single region within the same country  or even in more than one country ; among much more information. We use this dataset to group POIs by functional urban areas using Geopandas, a Python package for data analysis with geographic information59. Of all the urban areas contained in the GHS, 6,463 cities have, at least, one Foursquare POI. The POIs that are located within these cities represent 74 percent of the total; 82% of the total check-ins were carried out in them. We focused on the 632 urban areas that have more than 10,000 check-ins, which represent 63% of the total POIs , and 76% of the total check-ins . That is, more than three-quarters of the total check-ins in the database were made in urban areas with more than 10,000 check-ins. The 31 cities with more records are shown in Table Our topic of interest is the behavior of people in cities, so classifying POIs and check-ins by country is not enough. The definition of what a city is and what its borders are is a complex topic and has been dealt with by different authors60. This formula calculates the physical distance between cities based on the great-circle distance between two points on a sphere, specifically the Earth’s surface, as followsi and r the radius of the sphere. To perform this calculation, we utilized the Haversine 2.8.0 library for Python61.Each city, as well as each POI, has associated coordinates. To measure the distance between cities, the Haversine formula is requiredP(z) and Q(z) describing a stochastic variable z. For discrete distributions, this divergence is given by44Q acts as a reference distribution. Also, it is important to emphasize that P and Q is not necessarily the same as between Q and P. Also, from the definition in Eq. .In the implementation of the method, the main goal is to generate a partition of the set of nodes in communities with labels 62. ML algorithms build models based on training data to make predictions or decisions. There are two main categories of Machine Learning algorithms: supervised and unsupervised. In supervised learning, the algorithm is provided with labeled data, which consists of a set of input-output pairs. The goal of the algorithm is to learn a general rule or function that maps inputs into outputs. This is done by building a mathematical model of the data. The algorithm iteratively optimizes an objective function to learn a function that can accurately predict the output associated with new inputs.Machine learning (ML) is a branch of artificial intelligence that focuses on creating methods that can learn and improve their performance on tasks by leveraging data62. Unsupervised learning algorithms discover patterns in the data and adapt their behavior based on the presence or absence of these patterns in new data. As a part of unsupervised learning, cluster analysis is the process of dividing a group of observations into subsets or clusters, where each one contains similar observations50. The objective of clustering is to categorize untagged data into clusters based on a specific metric of similarity or distance. Essentially, a cluster is regarded as a collection of data points that exhibit some common pattern or structure. Clustering techniques vary in their assumptions on the data’s structure and use different similarity metrics to evaluate the internal compactness and separation of clusters.In unsupervised learning, the algorithm is provided with unlabeled data and must identify patterns or structure in the data on its own63 python library was used.Hierarchical clustering is one of the most commonly used methods for grouping unlabeled data into clusters based on some similarity measure. This approach involves using either agglomerative or divisive algorithms to create nested clusters by either combining or separating previous clusters. Agglomerative clustering initially considers each data point as a separate cluster and merges them pairwise until all data is contained in a single cluster, or until a specific condition is met, such as grouping data into a specific number of clusters. To accomplish this, a distance metric is used to determine the distance between each pair of data points, and a criterion for determining which clusters to merge at each stage is employed. In this study, the Euclidean distance was used to measure the distance between check-in distributions, and the criterion for merging clusters was to combine the two whose union minimized the variance of distances within all clusters. To do this, the Scikit-learn


F-statistic (Fst) for the identified subpopulations were 0.253, 0.330 and 0.244, respectively. In addition, the expected values of heterozygosity (He) for these sub-populations were recorded as 0.45, 0.46 and 0.44, respectively. Therefore, SSR markers can be useful not only in genetic diversity and association analysis of wheat but also in its germplasm for various agronomic traits or mechanisms of tolerance to environmental stresses.Wheat genotypes should be improved through available germplasm genetic diversity to ensure food security. This study investigated the molecular diversity and population structure of a set of Türkiye bread wheat genotypes using 120 microsatellite markers. Based on the results, 651 polymorphic alleles were evaluated to determine genetic diversity and population structure. The number of alleles ranged from 2 to 19, with an average of 5.44 alleles per locus. Polymorphic information content (PIC) ranged from 0.031 to 0.915 with a mean of 0.43. In addition, the gene diversity index ranged from 0.03 to 0.92 with an average of 0.46. The expected heterozygosity ranged from 0.00 to 0.359 with a mean of 0.124. The unbiased expected heterozygosity ranged from 0.00 to 0.319 with an average of 0.112. The mean values of the number of effective alleles (Ne), genetic diversity of Nei (H) and Shannon’s information index (I) were estimated at 1.190, 1.049 and 0.168, respectively. The highest genetic diversity (GD) was estimated between genotypes G1 and G27. In the UPGMA dendrogram, the 63 genotypes were grouped into three clusters. The three main coordinates were able to explain 12.64, 6.38 and 4.90% of genetic diversity, respectively. AMOVA revealed diversity within populations at 78% and between populations at 22%. The current populations were found to be highly structured. Model-based cluster analyses classified the 63 genotypes studied into three subpopulations. The values of  Triticum aestivum L.) is one of the most important species belonging to the genus Triticum in the Poaceae family [Triticum urartu Thuman ex Gandil (A genome), Aegilops speltoides Tausch (B genome) and Aegilops tauschii Coss (DD genome) [Bread wheat (e family . The gen genome) . The rat genome) . TherefoThe green revolution has resulted in increased yield and quality in wheat production and emergence of high-yield varieties. As the world’s population continues to grow, climate change and the resulting global warming are having a serious impact on food supplies. World wheat production is expected to increase by about 50% by 2050 to meet the food needs of a growing population ,5. HowevPlant breeding programs mainly focus on genetic diversity, inheritance, conservation and evolution . HomogenTürkiye encompasses a high level of bread wheat genetic diversity as it is a major center of wheat domestication and diversity. However, there is little information on the population structure and germplasm diversity of wheat. Therefore, the main objective of this study was to investigate genetic diversity and population structure in a set of Türkiye wheat genotypes using SSR markers.Triticum aestivum L.) were used as plant material. Variety names and locations are given in In this study, 63 genotypes of bread wheat  was used for PCR amplification. PCR reactions were carried out in a volume of 10 µL and consisted of 25 ng template DNA, 0.5 U Taq polymerase, 0.25 mM dNTP, 1 µM (20 pmol) primer, 10× buffer and 2 mM MgClTotalLab TL120 software  was used to generate matrices . SeveralInformation on the descriptive parameters of SSR markers is shown in Exp-He ranged from 0.00  to 0.359 (GWM 350) with a mean of 0.124. uHe values ranged from 0.00  to 0.319 (GWM 350) with a mean of 0.112 . GD valuPrincipal coordinate analysis (PCoA) was conducted using Nei’s neutral genetic distance. The three principal coordinates explained 12.64, 6.38 and 4.90% of genetic diversity, respectively . The presence of genetic diversity was confirmed by the distribution of genotypes in the diagram . The resPhylogenetic relationships were investigated for 63 bread genotypes using 120 SSR markers. Dice similarity coefficients were calculated for the 120 SSR markers, and a UPGMA tree was generated . GeneticF-statistic (Fst) for the first, second and third sub-populations were estimated to be 0.253, 0.330 and 0.244, respectively. The expected heterozygosity (He) was determined as 0.452 for the first, 0.463 for the second and 0.444 for the third sub-population  with a membership probability as <0.8 . Accordipulation . Accordipulation .Detection of genetic variation using molecular markers is highly dependent on the mode of reproduction, domestication history and size of the samples analyzed. Collection, conservation and management of genetic resources are key issues in sustainable agriculture development . AssessiUsing 120 SSR markers, 651 alleles were identified in 63 wheat genotypes. The number of polymorphic alleles ranged from 2.00 to 19.0 with an average of 5.442. Polymorphism can result from SSR expansion, contraction or interruption . The curExp-He values ranged from 0.00 to 0.359 with an average of 0.124. The differences in Obs-He values can be attributed to several factors, including the molecular markers used, the number of selections and the geographic location of the wild–type origin and location of the samples. Our result is higher than that of Teshome et al.  with 0–0Genetic diversity (GD) values ranged from 0.031 to 0.920, with an average value of 0.460. Arystanbekkyzy et al.  indicateIn this study, the highest value of h, ne and I with 1.667, 1.578 and 0.459, respectively, were observed, while the lowest values of h, ne and I were 0.556, 1.00 and 0.00, respectively. A higher number of effective alleles indicates greater genetic diversity and is therefore generally desirable in breeding programs. The Shannon information index is also an indicator of genetic variation in a population. Teshome et al.  reportedPIC and MAF values indicate significant genetic variability among all wheat species used. They are also reliable indicators of genetic diversity in the plant. The current MAF values ranged from 0.143 to 0.984 with an average of 0.631. Our result was higher than Mohi-Ud-Din et al.  with an Principal coordinate analysis (PCoA) is commonly used to spatially represent relative genetic distances between populations . It is aGenetic differences between populations play a huge role in the conservation of genetic resources . Our resThe current SSR markers were able to group all genotypes well based on phylogenetic relationships. The UPGMA method divided the present genotypes into three main clusters. Cluster I included 56 (88.88%); cluster II included 6 (9.52%); and cluster III included 1 (1.58%) genotype. UPGMA analysis showed a mix of frequencies as submissions from different geographic regions were grouped into the same subgroups. The current results showed that the clustering models were not able to clearly distinguish between wheat genotypes based on geographic origin. Clustering of genotypes showed no significant relationship between geographic origin and genetic similarity. Such a case indicated gene flow between genotypes. Differences between genotypes were attributed to the greater genetic distance between them. Grouping based on geographic origin was not clear. Such findings were also supported by analysis of population structure. The present results are consistent with those of Mohammadi et al.  and PourNatural diversity is used to analyze population structure to detect genes/qTLs of agronomic traits . Such anF-statistic (Fst) value was determined to be 0.253 for the first, 0.330 for the second and 0.244 for the third sub-population. Th expected value of heterozygosity (He) was determined as 0.452 for the first, 0.463 for the second and 0.444 for the third sub-population. Mohi-Ud-Din et al. [p < 0.01) in paired population Fst values.The smallest number of genotypes included in the mixed groups indicated that the genotypes present had a wide range of genetic pools. This study analyzed the population structure of wheat genotypes representing the diversity of Türkiye wheat genotypes. The Bayesian model yielded similar clustering results to UPGMA and PCoA. STRUCTURE analysis revealed three groups  at K = 3. Group B contained the highest number of genotypes. The present results on population structure are consistent with the findings of Mohi-Ud-Din et al. , dividinFst values for the assumed sub-populations were 0.253, 0.330 and 0.244, respectively. In conclusion, our findings again showed that there is a high level of genetic diversity among Türkiye bread wheat genotypes, which in turn can be taken into account in future wheat breeding programs.To facilitate the conservation, classification and maintenance, as well as the use of these valuable genes available in genetic resources, genetic diversity analysis is needed. In Türkiye, many efforts have been made to identify the best wheat genotypes in terms of yield and agromorphological traits. Although wheat genotypes collected from some regions have been previously characterized using other marker systems, here the SSR marker set was used to assess genetic diversity and population structure in set of bread wheat genotypes. Our results showed acceptable values for average allele number, PIC, GD, Ex-He, u-He parameters. In addition, the mean values of Ne, H and I for all genotypes tested were estimated at 1.190, 1.049 and 0.168, respectively. AMOVA showed that variability within populations was higher than between them (78% vs. 22%). In addition, the


Stenella coeruleoalba, Delphinus delphis and Tursiops truncatus in the Azores, Canary Islands, the Alboran Sea and the Western Mediterranean basin between 1996 and 2012 were analyzed. The acoustic structure of communication signals was evaluated by analyzing parameters of whistles in relation to the known genetic and habitat-driven population structure.Prioritizing groupings of organisms or ‘units’ below the species level is a critical issue for conservation purposes. Several techniques encompassing different time-frames, from genetics to ecological markers, have been considered to evaluate existing biological diversity at a sufficient temporal resolution to define conservation units. Given that acoustic signals are expressions of phenotypic diversity, their analysis may provide crucial information on current differentiation patterns within species. Here, we tested whether differences previously delineated within dolphin species based on i) geographic isolation, ii) genetics regardless isolation, and iii) habitat, regardless isolation and genetics, can be detected through acoustic monitoring. Recordings collected from 104 acoustic encounters of Recordings from the Atlantic and Mediterranean were accurately assigned to their respective basins of origin through Discriminant Function Analysis, with a minimum 83.8% and a maximum 93.8% classification rate. A parallel pattern between divergence in acoustic features and in the genetic and ecological traits within the basins was highlighted through Random Forest analysis. Although it is not yet possible to establish a causal link between each driver and acoustic differences between basins, we showed that signal variation reflects fine-scale diversity and may be used as a proxy for recognizing discrete units.We recommend that acoustic analysis be included in assessments of delphinid population structure, together with genetics and ecological tracer analysis. This cost-efficient non-invasive method can be applied to uncover distinctiveness and local adaptation in other wide-ranging marine species.The online version contains supplementary material available at 10.1186/s40850-021-00085-7. Distinguishing unambiguous groupings of organisms, or ‘units’, that are relevant to the proper implementation of management actions is still a matter of debate , 2. In oA Unit of Conservation (UC) can be defined as a segment within a species to be considered distinct for conservation purposes . The mosRecent developments include investigations into how variation in acoustic signals may be a line of evidence supporting the significance for conservation and management of different populations . VariatiEven though vocal diversity is high both among and within some animal species, vocal patterns have been used to reconstruct hypotheses of evolutionary histories: geographic distances and genetic variation among gibbon populations are strongly correlated with variation in song structure , 28, as In dolphins, intra-specific variation in the characteristics of tonal whistles has been described at macro- and micro-geographic scales –43 amongTursiops truncatus), short-beaked common dolphins (Delphinus delphis) and striped dolphins . In detail, we examine if whistles can be used to verify the possible existence of UCs among a poorly studied area by comparing groups defined according to three different criteria. Specifically, we tested:the effect of geographic isolation;the effect of genetics, regardless the isolation;the effect of habitat, regardless isolation and geneticsDetermining discrete units of conservation for dolphins can be an arduous task , given tPrincipal features for each sampling area/species are summarized in Table Bottlenose dolphins – Genetic studies have shown a limited amount of gene flow between Mediterranean and Atlantic populations of bottlenose dolphins , 61. AccShort-beaked common dolphins – Even if, mtDNA data suggested gene flow mediated by females from across the Gibraltar Strait , the AlbStriped dolphins – Genetic data based both on nuclear and mtDNA analyses report no sharing of haplotypes between the two ocean basins , 58, 65.The Azores Archipelago is a Mid-Atlantic island chain located in between two current systems: the Gulf Stream that generates meanders and filaments from the western side, and the Azores Current propagating eastward and generating westward eddies . FurtherThe Western Mediterranean can be subdivided into main regions: the Alboran Sea, the Algero-Balearic Basin, the Corso-Ligure-Provencal Basin and the Tyrrhenian Sea. The ecology and biogeography of these areas are shaped and characterized by drivers such as bottom morphology, water temperature, salinity, wind regimes, temporal thermoclines and currents, among others . The AlbThe Atlantic waters, coming from the Alboran sea, flows southward in the Algerian Basin, the largest of the Western Mediterranean, between the Balearic Chain and the Algerian margin. Here, an energetic mesoscale circulation pattern generates an intense inflow and outflow regime that has repercussions on biochemical parameters . TherefoHigh levels of primary production are known to characterize the Corso-Ligure-Provencal Basin, where the spring phytoplankton bloom is mainly driven by the cyclonic circulation system. This system generates a frontal zone among the coastal and offshore waters and an upwelling of cold waters nutrient-rich with spatio-temporal interannual changes .The Tyrrhenian Sea is located along the western coast of Italy, eastern of the islands of Corsica and Sardinia. Surface circulation of the water masses is dominated by the entrance of the Atlantic waters from southwest that splits into a vein directed north/northeast and another that proceeds farther eastward along the northern Sicilian coast . The Seathe effect of geographic isolation by comparing whistles characteristics of dolphins inhabiting the Atlantic Ocean versus the Mediterranean Sea;the effect of genetics, regardless the isolation by comparing whistles characteristics of dolphins inhabiting the Atlantic Ocean versus the Alboran Sea versus the Mediterranean Sea;the effect of habitat, regardless isolation and genetics by comparing whistles characteristics of dolphins inhabiting the different localities sampled: Azores and Canary Islands (in the Atlantic Ocean), Alboran Sea, Ligurian Sea, Tyrrhenian Sea, Sardinian waters, Provençal Sea, Spanish waters and Balearic Sea (in the Mediterranean Sea).Basing on the environmental and genetic features previously described, that can generate units isolated enough to be considered separately for management purposes, we tested:Dolphin groups were sampled at four locations, selected to maximize coverage of the East Atlantic and Mediterranean basins. In the Atlantic, we sampled at the Azores (between 36° and 40° latitude North and 24° and 32° longitude West), and the Canary Islands (between 27° and 30° latitude North and 13° and 19° longitude West). In the Mediterranean, we sampled groups in the Alboran Sea (between 35° and 36° latitude North and 2°and 6° longitude West), and in the Western Mediterranean  were present within one kilometer. The sampling effort for the three species is summarized in Table Data were collected using a variety of equipment: a mono or stereo towed Benthos hydrophone AQ4 , an HTI-94-SSQ hydrophone , an array of two Benthos AQ4, or an array of two Benthos AQ4 and two spherical ceramic hydrophone elements . Sounds were collected either on a digital tape recorder TascamVR DA-P1  , or directly digitalized on a laptop at a sampling rate of 32 kHz, 44.1 kHz, 48 kHz or 192 kHz. Because of the differences in sampling rates, all signals collected at a sampling rate above 48 kHz were down-sampled to that value, because the maximum fundamental frequency of most whistles was found to be below 24 kHz. In recordings collected with higher sampling rates, sounds (0.81%) that went beyond the Nyquist frequency  were not included in the analyses. Signals sampled at 32 kHz or 44.1 kHz did not contain contour sections above their Nyquist frequency.N = 855, − 1.09 < Z < − 1.50, 0.13 < P < 0.27; for maximum freq: Z = − 8.11, P < 0.001). This discrepancy in the maximum frequency  is due to the low TRIA sensitivity in detecting and measuring low intensity sounds occurring at the highest frequencies. From this comparison, it emerges that human analysts can measure low intensity signals better than the automated program. To avoid pseudo-replication due to the presence of stereotyped whistles that could result from the influence of behaviour and/or social interactions, and address the independence of each whistle, sounds with similar time–frequency contours, visually matched by expert observers, were included in the analysis only once.We used only whistles with sound pressure level (SPL) at least 20 dB higher than the background noise (see ). Nine pTo test the effect of geographic isolation, we performed stepwise discriminant function analyses (DFA) with cross-validation. To meet normality and homoscedasticity criteria and to reduce the weight of each whistle within a recording, we used the mean  values of whistle parameters recorded during each sighting (acoustic encounters) for the DFA. By using as sampling unit the acoustic encounter, and as a variable the mean of each parameter for each encounter, we also met the independence criterion. Even if social structure varies considerably among populations, dolphins living in fission–fusion societies associate in small groups that change in composition on a daily or hourly basis . When inTo test the effect of genetics, regardless the isolation and the effect of habitat, regardless isolation and genetics, we considered the single signals as units in order to maintain all the intra-sighting variability. Due to the visual acoustic preliminary analysis previously described, potential issues of pseudo-replication were avoided. Furthermore, only sightings with at least three good quality whistles were included [see 43 for details].In order to deal with a dataset that violates the a priori assumption of normality and homoscedasticity, we used the Random Forest (RF) machine learning method . This meWe implemented six RF models with three species  and two response variables .We used whistle frequency  and duration [s] parameters in the RF models because these variables had lower Coefficients of Variation (CV) than those that describe modulation patterns . We constructed classification trees using a bootstrap aggregating algorithm  that allRandom Forest models and SMOTE technique has been implemented using respectively the “randomForest”  and “UBLWe collected 39.72 h of recordings during 104 acoustic encounters and analyzed 2209 whistles. For the Mediterranean, we analyzed 22.47 h of recordings from 38 sightings and we extracted 1293 whistles  signals were analyzed. Data collection is summarized in Table All whistle parameters exhibited intra-specific variation higher than 10%. By contrasting variability of frequency parameters with those of duration and modulation , we found that CVs of signal duration were intermediate (ranging from 27.19 to 66.97%) between those of frequency (ranging from 10.19 to 46.21%) and modulation parameters (ranging from 65.12 to 230.44%).P < 0.001, coefficients of the function: number of inflection points = − 1.40, number of minima = 1.46; Fisher’s F = 28.02, Wilks’ Lambda = 0.54, P < 0.001, coefficients of the function: end frequency = 0.90, number of inflection points = 0.82 Fisher’s F = 10.73, Wilks’ Lambda = 0.38, P < 0.001, coefficients of the function: number of inflection points = 1.42, number of maxima = − 0.94) , showing a high level of discrimination among the genetic groups considered. Variable importance analysis allowed us to identify the variables that contributed the most to classification accuracy  and eight (striped dolphin)  can be identified acoustically at a correct classification score to greater than 83%. Differences in the spectral and temporal features of whistles matched the geographic isolation suggesting that it may translate into differences in acoustic parameters. Samarra et al.  found a The discriminant power of acoustic signals is prominent between ocean basins, where the gene flow is presumably lower than it is within ocean basins. However, acoustic analysis reveals to be once again a good tool to potentially delineate the range of different genetic groups even over geographically close areas.A relation among genetic features and acoustic pattern has been already demonstrated also for North Atlantic fin whales  and sperTursiops truncatus both in the Atlantic and in the Mediterranean [However, our results show also that whistles can be predictive of the finer-scale habitat-driven population structure, both in the Atlantic and in the Mediterranean, where dolphin populations are known to be structured based on local habitat dependencies , 61, 68.erranean . HoweverThe preliminary map of acoustic patterns drawn from this study suggests that comparison of the acoustic characteristics of whistles can be a tool to complement genetic methods usually applied to identify distinct UCs for at least some delphinid species.We recommend that acoustic analysis be embedded in assessments of delphinid population structure, together with genetics and ecological tracer analysis. Passive acoustic monitoring systems represent a cost-efficient non-invasive method to collect signals for identifying potential units of conservation, based on its correlation with other lines of evidence (e.g. genetic data). Acoustics studies provide a framework to guide population viability analyses that can be applied over larger spatial and temporal scale, improving efforts in the management of units in need of conservation.Additional file 1. Supplementary 1 Results of the Random Forest Analysis. Mediterranean and Atlantic sites are indicated by the letters M and A respectively. Acoustic parameters are reported in order of variable importance as estimated by the model and the most important variables (i.e. Mean Decrease Accuracy > 0.2) are indicated in bold. Names of acoustic parameters are abbreviated as follows: dur = duration; beg.f = beginnig frequency; end.f = end frequency; min.f = minimum frequency; max.f = maximum frequency.


Ochotona princeps) populations at historically occupied sites started being documented in the literature during the early 2000s. Commensurate with global climate change, many of these losses at peripheral and lower elevation sites have been associated with changes in ambient air temperature and precipitation regimes. Here, we report on a decline in available genetic resources for an iconic American pika metapopulation, located at the southwestern edge of the species distribution in the Bodie Hills of eastern California, USA. Composed of highly fragmented habitat created by hard rock mining, the ore dumps at this site were likely colonized by pikas around the end of the 19th century from nearby natural talus outcrops. Genetic data extracted from both contemporary samples and archived natural history collections allowed us to track population and patch-level genetic diversity for Bodie pikas across three distinct sampling points during the last half- century . Reductions in within-population allelic diversity and expected heterozygosity were observed across the full time period. More extensive sampling of extant patches during the 1988–1991 and 2013–2015 periods revealed an increase in population structure and a reduction in effective population size. Furthermore, census records from the last 51 years as well as archived museum samples collected in 1947 from a nearby pika population in the Wassuk range  provide further support of the increasing isolation and genetic coalescence occurring in this region. This study highlights the importance of museum samples and long-term monitoring in contextualizing our understanding of population viability.Declines and extirpations of American pika ( The spatial distribution of an organism’s habitat can vary at local, regional, and global scales . How an th to early 21st century for a unique metapopulation of the American pika (Ochotona princeps), a small, thermally sensitive alpine lagomorph  as well as population genetic data collected at BSHP in 1988–1991 and again in 2013–2015 on the majority of extant patches. To provide a historical context of the population genetic diversity once present at this site, we also compare these two sets of more contemporary genetic samples (1988–1991 and 2013–2015) with specimens collected at BSHP in 1948 and 1949 by J.H. Severaid and curated at the Museum of Vertebrate Zoology (MVZ), University of California at Berkeley. We provide a comparison of population-level genetic diversity for each of the three time periods  at BSHP with mid-20th century historical samples collected at another site in the region about 35 km northeast of BSHP (Big Indian Mountain (BIH), Wassuk Range, Mineral County, Nevada, collected by S.B. Benson and O.P. Pearson in 1947 and curated at MVZ).For the present study, the main goal was to investigate further how these patterns of genetic diversity and structure have fluctuated in the BSHP population over time, and to determine whether the previously documented metapopulation dynamic  has beenth and 21st centuries would include: (1) a reduction in the proportion of patches occupied over time, (2) evidence of genetic coalescence, where standing genetic variation is reduced to the amount that can be maintained in consistently occupied habitat  a decrease in the effective population size (Ne), (4) increasing isolation among occupied ore dump habitat patches within the BSHP site and (5) increased genetic differentiation between the BSHP population over time with mid-century BIH.This study is unique in being able to provide a long-term and fine-scale perspective on patterns of patch occupancy as well as levels of population genetic diversity and structure. Recent reductions in population size and losses of genetic diversity  may indi habitat , and genThis research was approved by the Institutional Animal Care and Use Committees (IACUC) at the University of Nevada, Reno (protocol No. 00557). Scientific collecting permits were obtained from the California Department of Parks and Recreation (No. 2014-03); California Department of Fish and Wildlife (SC-012184); US Department of Agriculture (USDA) Forest Service and USDA Forest Service, Inyo National Forest (No. LVD14021 and No. RMT143).Pikas are small alpine lagomorphs (~150 g), which inhabit rocky substrates  in the mountain ranges of the American west. Adult pikas are individually territorial and defend their territories from conspecific intrusions . As lagoTo examine trends in summer temperature at the study site from 1940 to present day, we used monthly historical PRISM data  extractei.e., after 2008), earlier years have some idiosyncratically distributed missing data . To ameliorate the influence of missing data, we linearly imputed missing occupancy and count data over time  to evaluate the strength and significance of trends over time for patch occupancy, population count, and number of pikas per occupied patch. We used quasibinomial logistic and negative binomial regressions, respectively for occupancy and population count data, to visualize trends over time. Population count trends were evaluated for the entire patch network as well as the southern , middle (patches on Silver Hill south of Mono Shaft) and northern  patch networks  were surveyed in 1972, 1977, 1989, 1991–2001, 2003–2006, and 2008–2020 . While aver time . We usedorks see . Patch oorks see .Mid-20thcentury museum samples (1947–1949). Approximately 200 pika museum specimens collected during the mid-20th century at BSHP by Late 20thcentury samples (1988–1991). Pika ear tissue was collected from live-trapped individuals in BSHP as part of a metapopulation dynamics study of 24 occupied patches in the northern portion of the study area . All adult samples (n = 75) from the 1988–1991 sampling period were used to estimate population level genetic metrics for this study.Early 21stcentury samples (2013–2015). Fecal DNA samples were collected from 26 occupied habitat patches in the northern half of the BSHP site during June-September 2013–2015 to be used for comparison to the earlier time periods. Adult pikas defecate in well-defined latrine sites within their territories  from single latrine sites identified using methods of ritories . Differeth century museum samples (1947–1949) were conducted in a separate laboratory in order to avoid DNA contamination from contemporary pika samples that have been isolated in our laboratory.Total genomic DNA was isolated according to the manufacturer’s protocol  with modifications specific to each sample type. Details regarding modifications to DNA extraction protocol for fecal and museum genetic samples can be found in GQ461705.1–GQ461722.1, direct submission Genbank). We chose microsatellite loci that were smaller (in terms of fragment length) and that produced a consistent and scorable PCR product from both the fecal and museum DNA samples. PCRs were carried out in 12 μl reaction volumes on a MBS Satellite 0.2 G thermal cycler , and included a 15 min hot start at 94 °C, followed by 41 cycles at 94 °C, 30 s each for denaturing, a touch down annealing temperature for 90 s  and an elongation step at 72 °C for 30 s (modified from https://www.unr.edu/genomics/services). All alleles were scored, binned, and genotyped using ABI Prism GeneMapper software (version 5.0).We used eight di-, tri-, and tetranucleotide repeat, polymorphic microsatellite loci of the 28 that have been developed for the American pika , false alleles (ε2), and genotyping success rate were estimated using replicate genotypes for fecal DNA and museum specimens through a maximum-likelihood based approach   . In addiion 7.0)  and Freeion 7.0)  were useIS) per locus per time period and to calculate a global θ for each time period . We also used FSTAT to calculate pairwise FST estimates for individual patches sampled in both 1988–1991 and 2013–2015 that had sufficient data for comparisons . We also calculated a pairwise FST at the BSHP population scale for comparison between the two time periods (1988–1991 and 2013–2015) that had data from extensive sampling.We used FSTAT  was estimated for the 1988–1991 and 2013–2015 time periods only using the linkage disequilibrium module based on a single point sample in NeEstimator  across the three time periods using a 50,000-iteration burn-in followed by ten 500,000 Markov Chain Monte Carlo (MCMC) replications per k, for k = 1–10 and the Δk method to determine the optimal number of genotype clusters  in GenAlEX, which uses a pairwise genetic distance matrix calculated from multi-allelic, multi-locus microsatellite data, was used to characterize any changes in the spatial partitioning of genetic diversity within BSHP across the three time periods and between BSHP and BIH. We then used a Bayesian genotype clustering approach  for each time period using the r per patch for all patches with n > 3 individuals. We also calculated a genetic similarity metric among all individuals across the entire population, among individuals per patch and made pairwise comparisons of genetic similarity between patches sampled in 1988–1991 and again in 2013–2015 for which we had sufficient data  using the allele sharing matrix module in Microsatellite toolkit in Excel  (P = 9.1 × 10−9).From 1940 to 2022, mean summer temperature increased by 0.38 °C/decade  . Mean sus= −0.47, P = 0.0062; 1989–2022: rs = −0.36, P = 0.049; s = −0.15, P = 0.42; s = 0.45, P = 0.0092; 1989–2022: rs = 0.39, P = 0.032; s = −0.67, P = 0.000017; southern: rs = −0.52, P = 0.0021). The southern and middle portions of the study area are dominated by small (1–4 territories) and medium (5–9 territories) patches  and 2013–2015 (OCP10 and 17), but there were no consistent patterns across loci, patch network, or time. The fecal DNA samples had an amplification success rate of 95.5% with average rates of allelic dropout and false alleles at 2.9% and 1.6%, respectively . The musn = 22; 1988–1990, n = 75; fecal samples 2013–2015, n = 191). The 1947–1949 museum samples failed to amplify at OCP7, which was subsequently removed from all analyses that included museum samples. The 1948–1949 dataset was too small to calculate heterozygous excess and deficiency on a per patch basis. We did not observe heterozygous excess or deficiency for any locus on any per patch for the 1988–1991 dataset. However, we observed both heterozygous excess and deficiency in the 2013–2015 fecal DNA dataset, but primarily heterozygous excess, for subpopulations on five of the largest 26 patches sampled consistent with a signature of genetic bottleneck . Because fecal samples were collected fresh and individual collections were made from distinct latrine sites on individual territories, we conclude that we sampled single individuals per fecal sample collected. The probability of identity calculated for the 2013–2015 fecal DNA dataset after duplicate samples were removed using the Multilocus Matches DS module in GenAlEx was 1.9E-05.A total of 298 unique adult individuals were successfully genotyped from the contemporary and historic BSHP mining district as well as the historical samples from the Big Indian Mountain (BIH) site  in the 1988–1991 dataset for the High Peak subpopulation only. For the 2013–2015 dataset, three locus pairs were in linkage disequilibrium, but only for two patches, High Peak (OCP8 × OCP9) and Hobart  . On a BSHP population wide basis, a significantly larger FIS was observed for OCP10 in the 1948–1949 and 1988–1991 datasets  and significantly smaller FIS than random for OCP4 (FIS= −0.286), OCP9 (FIS = −0.236) and OCP 21 (FIS = −0.232)  in the 2013–2015 dataset (IS) are summarized per locus per time period in We observed linkage disequilibrium at one locus pair , and Pa , and He , but not Ho  over the three time periods at the BSHP site , whereas the 1988–1991 and 2013–2015 values did not differ . The 1948–1949 He estimate was significantly higher than 2013–1015, but not 1988–1991 , whereas the 1988–1991 and 2013–2015 He values did not differ (P = 0.296).We observed significant declines in Rs . Effective population size also declined between the two later time periods 1988–1991  and 2013–2015  and 1988–1991  but an r value consistent with an average relatedness among first cousins  in 2013–2015 .We did not observe significant pairwise F network . Global k = 3 was the best fit of the data (Bayesian genotype clustering (STRUCTURE) results for samples from all three time periods including the historical BIH samples indicate that the data  and 7B. the data . These rthe data . Whereask = 2 and 4  = −2492.92, SD ± 1.540, Δk = 33.59457; k = 4, average LnP(D) = −2,466.82, SD ± 9.583, Δk = 31.943; Although an assignment test approach, such as Bayesian genotype clustering analysis, is not well suited to capture rapidly shifting population dynamics, STRUCTURE results did reveal an increase in population genetic structure among BSHP subpopulations in 2013–2015 compared to 1988–1991. There was no statistical support for population subdivision in the 1988–1991 dataset. However, in the 2013–2015 dataset, there was statistical support for both Range-wide levels of genetic diversity and its dispersion across the landscape set the stage for species’ adaptive potential under changing climatic conditions. Because maintenance of genetic diversity is often dependent upon movement of individuals within and among local populations , it is ivia a decline in allelic richness and expected heterozygosity although not observed heterozygosity. Together with a decrease in the effective population size (Ne), these data suggest an increasing isolation among occupied ore dump habitat patches within the BSHP site as well as at the regional scale.Despite past evidence of an active metapopulation dynamic  and signMetapopulation theory predicts accelerated population decline as the probability of re-colonization of extirpated patches and dispersal among extant patches decreases . In addir) and genetic similarity at both the population and individual patch level show a shift to higher mean values in 2013–2015 when compared with patch dynamics from 1988–1991. Losses of genetic diversity  and a decline in overall effective population size (Ne) from ~76 individuals during the 1988–1990 time period to ~47 individuals in 2013–2015 are also consistent with a pattern of genetic coalescence as per Consistent with a change in the extinction-colonization dynamic the occupied ore dump habitat patches within the BSHP site are becoming more isolated from each other. Genetic variation within the habitat patch network has a larger among-patch partition now than it did approximately 30 years ago  and therth century (1988–1991).Movement dynamics among patches in the earlier via random genetic drift–a result of demographic and genetic stochasticity inherent to small populations  in the Wasskuk range, another site in the region about 35 km northeast of BSHP. These mid-century (1947–1949) samples from BSHP and BIH overlap completely in PCA space which suggests that genetic connectivity on the landscape once occurred between the BSHP and BIH populations although likely across generations and via intervening habitat patches given that these sites are over 30 km apart from one another and way beyond the dispersal capacity of an individual animal. Loss of landscape level connectivity is further supported by recent survey data showing that most of the remaining natural rock outcroppings in the Bodie Hills are currently unoccupied by pikas despite evidence of recent 20th and 21st century occupation  Distribution of missing patch occupancy data over time. (B) Illustration of the linear imputation technique used to ameliorate the impact of missing data. See approxfun in [R], method = ‘linear’, rule = 2.Click here for additional data file.10.7717/peerj.15962/supp-2Supplemental Information 2Upper (U) and lower (L) confidence limits bound the 95% confidence interval about the null hypothesis of ‘No Difference’ across the populations as determined by permutation .Click here for additional data file.10.7717/peerj.15962/supp-3Supplemental Information 3(A) The natural log of the probability of the data ((LnP(D)). (B) Delta k values showing support for k = 2 and k = 4. (C) Proportional membership per genotype cluster per individual for k = 2. (D) Proportional membership per genotype cluster per individual for k = 4.Click here for additional data file.10.7717/peerj.15962/supp-4Supplemental Information 4Click here for additional data file.