Pomegranates were one of the first domesticated fruit crops, and their long history resulted in the development of local cultivars all over the world. Spain is one of the main producers and exporters of this crop in the Mediterranean Basin, but in order to maintain the competitiveness of this crop, new varieties should be developed. For this purpose, the pomegranate germplasm collection hold at the Agricultural Experiment Station of Elche, a public institution dependent on the Valencian regional government, is an interesting tool. However, the detailed characterization of any germplasm collection is a fundamental requirement to be able to make the most of these resources, allowing to identify putative promising accessions and to optimize the design of the future crosses. In this work, the genetic diversity of 94 accessions of this collection was analyzed using 19 microsatellite markers. As a result, 85 different genotypes were identified. These genetic profiles could be useful for varietal identification. Despite this genetic diversity, no clear substructure was observed, except for the ornamental accessions, that could be related to the vegetative propagation of the species. Additionally, the morphological characterization of this collection has made it possible to identify some materials that may be of interest as a source of traits for breeding. Results presented here pave the way for further genetic analyses, allowing the selection of parents to obtain segregating populations, as well as their descendants by the use of molecular assisted selection. Punica granatum L., commonly known as pomegranate, belongs to the Punica genus, included in the order Myrtales, recently placed under the family Lythraceae [thraceae . Pomegrathraceae . Althougthraceae . From ththraceae . PomegraP. granatum cultivars found today reflect local priorities [The long history of pomegranate domestication resulted in development of local cultivars in the different areas where it spread . Due to iorities . More thiorities . Pomegraiorities ,7,8,9,10iorities ,12.Several international efforts have been made in order to preserve pomegranate diversity (as revised by ). RemarkAs breeding objectives, pomegranates have been traditionally selected on the basis of fruit size, rind and aril colors, taste and juiciness, and yield level, although some desirable characteristics differed by region . For insThe detailed characterization of any germplasm collection is a fundamental requirement to be able to make the most of these resources, allowing to identify putative promising accessions and to optimize the design of the future crosses. Different kinds of molecular markers have been used for pomegranate genetic diversity studies. However, simple-sequence repeats (SSRs) and single nucleotide polymorphisms (SNPs) displaced other types of markers and are preferentially used nowadays. In our group we developed a collection of 117 microsatellite markers from the cultivar Mollar because they were scarce a decade ago . Recent In this work, we analyze the genetic diversity present in 94 pomegranate accessions from the EEA-Elx/IVIA collection using 19 microsatellite markers. Moreover, some morphological traits have been analyzed in these accessions. This work is a new step to improve the characterization of our pomegranate germplasm collection, allowing to generate new tools to optimize its use, as well as to open a new line of work aimed at identifying genes of interest and to apply assisted selection in the breeding program.Ninety-four accessions from the pomegranate germplasm collection maintained at the EEA-Elx  were analyzed in this work , as an eAlternaria alternata, a major pomegranate disease that impacts production worldwide, 15 accessions showed more sensitivity to fungal infection in stored fruits, due to the black heart detection within their fruits. On the other hand, higher variability was observed for other fruit traits  were conducted to explore the patterns of variation in the collection . For claFinally, a third FCA was performed without the 8 ornamental accessions in order to observe in more detail the relationships between the rest of the accessions b. In thiAs a second approach to explore the relationships between the accessions, a Neighbor-Joining tree was built using Bruvo’s distance  Figure . BootstrFinally, a Bayesian-based population assignment allowing admixture was carried out using the Structure software  Figure . DiffereTaking into account that the classification in geographical regions used in this work reflects the putative origin of the samples, several diversity indexes were calculated in order to characterize these geographic groups . OverallPomegranate was among the first fruit crops to be domesticated and has been cultivated for more than 5000 years . This loIn recent years, this crop has gained attention primarily due to its potential medicinal properties and its nutritional benefit in the human diet . HoweverMain breeding goals to develop a new pomegranate cultivar are related with its destination for fresh consumption, industrialization or export. In general, an early ripening, attractive dark rind and arils color, presence of soft seeds and high antioxidant content are highly appreciated traits . In thisGenotyping could also be useful to manage the germplasm collection as it could detect putative synonymies  and homonymies (different accessions with the same name). This is a quite common problem in this species since the interchange of planting material must have been intense. Moreover, passport data in genebanks may not be as complete as desired, especially on the origin of the sample. For instance, in this work six small groups (with 2 to 6 accessions) with identical SSR profiles were identified. However, some morphological and pomological differences were observed within each group, so a more detailed description should be performed to confirm them as duplicates. These efforts will allow us to eliminate or at least reduce the redundancy within the collection.Regarding the diversity observed, the microsatellites used in this work showed a great genotypic richness in terms of multilocus genotypes, so they are very informative in these collections. In this sense, 85 of the 94 accessions analyzed in this work showed a different genetic profile. The normalized multilocus genotypes eMLGs, that eliminates the influence of population size, showed similar values for Central Asia, North America and Southern Europe groups. Moreover, the accession from South Asia has shown a greater differentiation with respect to the rest of the materials under study. This is the seedling from India named ‘Elx-13’ (No. 27), that appeared separated from the rest of the analyzed accessions and showed 5 exclusive alleles, and has red rind color, soft seeds and low acidity in our conditions. Interestingly, all these traits are similar to those shown by ‘Mridula’ or ‘Bhagwa’ [Despite the genetic diversity observed in the EEA-Elx/IVIA collection, the materials do not show clear and differentiated groupings, except perhaps the varieties of ornamental use. This could be related to the vegetative propagation of the species, which in practice means avoiding frequent recombinations to obtain new generations through sexual reproduction. Similar results showing lack of significant genetic divergence by geographical origin were already observed by other authors. Genetic diversity of pomegranate germplasm from different origins has been previously analyzed using different morphological or pomological traits ,12,28,29In summary, results presented here pave the way for further genetic analyses, allowing the selection of parents to obtain segregating populations, as well as their descendants by the use of molecular assisted selection. Additionally, the phenotyping of new traits is in progress in order to increase the potential utility of the EEA-Elx/IVIA collection.Ninety-four pomegranate accessions from different origins were PCR screened in this work . This coAlternaria in stored fruits were also screened. TA of fruit juice was determined by titrating 1 mL of juice sample with 0.1 mol/L sodium hydroxide to an end point of pH 8.1 and expressed as percentage of citric acid. TA values were classified as very low (<0.35 g/100 g), low (0.35–1.00 g/100 g), medium (1–1.50 g/100 g), high (1.50–2.0 g/100 g) and very high (>2.0 g/100 g). In order to detect the incidence of Alternaria alternata in stored fruits of these accessions, presence of symptoms consisting of internal black rot of arils and membranes was screened opening 100 fruits of each one [Eight interesting traits for pomegranate breeding were evaluated in this germplasm collection . The traeach one . FinallyDNA was extracted following the method described by Doyle and Doyle . DNA quaNineteen microsatellite (SSR) markers  were selHistograms, obtained using R programming language, were used to visualize the distribution of the morphological and pomological traits in the collection.2, where pij is the frequency of the jth allele for the ith marker locus and summation extends over n alleles. Observed (Ho) and expected  were calculated. PIC was calculated based on allele frequencies of all cultivars analyzed as: PICi = 1 − Σpijcted (He ) heterozcted  were carried out using the Genetix program . Genetichttps://web.stanford.edu/group/pritchardlab/structure.html (accessed on 13 April 2022)). We used the basic admixture model with unlinked loci, correlated allele frequencies among groups and no prior population information. Twenty runs were performed for each number of populations (K) set from 1 to 13, with a burning period of 100,000, and a post-burning simulation length of 1,500,000 for each run. The most probable K-value was determined by Structure Harvester [http://omicsspeaks.com/strplot2/ (accessed on 13 April 2022)) was used to draw the STRUCTURE bar plots [The accessions were classified into genetic clusters using the Bayesian model-based clustering proposed by Pritchard and collaborators  implemenarvester , using tarvester  to generar plots .The present study provides a perspective of the genetic variation of the Spanish pomegranate germplasm collection maintained at the Agricultural Experiment Station of Elche/Instituto Valenciano de Investigaciones Agrarias (EEA-Elx/IVIA). Some promising materials with interesting morphological and pomological characteristics for breeding have been identified in the collection. Moreover, the SSR genotyping data provided valuable information for an effective management of the collection and the identification of the materials, as an interesting tool to increase the protection of breeder’s intellectual rights. Results presented pave the way for further genetic analyses that could increase the efficiency of the pomegranate breeding programs.


Of those tested for genetic health risk, 66.5% shared with HCP/GCs, 38.7% with FDRs, 66.6% with a spouse/partner, 12.8% with a friend, and 14.1% did not share results with anyone. Of those who underwent ancestry testing, very few shared results with HCP/GCs (2.6%), whereas modest sharing was reported with FDRs, spouses/partners, and friends. Discussion: These data add empirical evidence about the population prevalence of genetic information sharing and serve as a metric for public engagement with genetic testing.Introduction: Sharing genetic test results with different stakeholders such as family members, healthcare providers and genetic counselors (HCP/GCs), spouses/partners, and friends is a health behavior of clinical importance in genomic medicine. Methods: Using nationally representative population-based data collected from the Health Information National Trends Survey , we identified the prevalence and factors associated with genetic test result-sharing behavior for high-risk cancer tests, genetic health risk tests, and ancestry tests within four groups: HCP/GCs, first-degree relatives (FDRs), spouse/partner, and friend/other. Results: Overall, 68.4% of those who underwent high-risk cancer genetic testing shared their results with FDRs, whereas 89.9% shared with HCP/GCs. In adjusted multivariable analyses, women were nine times more likely than men to share ( Sharing genetic test results is a health behavior of much interest in genomic medicine. Genetic tests currently available to most individuals include multigene panels that can identify inherited susceptibility to cancer, cardiovascular disease, and a range of other disorders ,2. OutsiSharing behavior is perhaps most salient for results of high-risk cancer testing, which should be shared with family members because the information can inform relatives’ decisions regarding health risks, cascade genetic testing , and subBRCA1/2 variants), most tend to be peripherally or not related to healthcare, such as the propensity for dry or wet earwax. Still, the public appetite for these tests is worth investigating as these tests have been suggested as a way to address access-to-care gaps ?” The role of genetics in cancer prevention and early detection was measured using two items, “How important is knowing a person’s genetic information for: a) preventing cancer; and, b) detecting cancer early?” For all risk perception questions, response options ranged from 1 (a lot) to 4 . Responses were dichotomized as not at all/a little vs. very/somewhat for analysis.Cancer worry was measured using the item, “How worried are you about getting cancer?” Response options ranged from 1  to 5 (extremely) and were trichotomized as high cancer worry (extremely/moderately), moderate cancer worry (somewhat), and low cancer worry . Additional potentially relevant beliefs were assessed using participants’ agreement with two items, “There’s not much you can do to lower your chances of getting cancer” and “There are so many different recommendations about preventing cancer, it’s hard to know which ones to follow,” on a scale of 1 (strongly agree) to 4 (strongly disagree). Cancer fatalism was assessed using the item, “It seems like everything causes cancer,” with response options ranging from 1 (strongly agree) to 4 (strongly disagree). Responses to the last three items were dichotomized as strongly agree/somewhat agree vs. somewhat disagree/strongly disagree for analysis. Self-efficacy regarding the ability to engage in behavior change based on genetic information related to cancer was assessed using the item “If I found out from a genetic test that I was at high risk of cancer, I would change my behaviors such as diet, exercise and getting routine medical tests,” with response options ranging from 1 (strongly agree) to 4 (strongly disagree). Desire to know about mutation status was measured using one item, “How much would you want to know if you have a genetic change that increases your chances of getting cancer?” with response options ranging from 1 (a lot) to 4 . Both responses were dichotomized for analysis. We incorporated survey sampling weights specified for HINTS 5, cycle 4, into our analyses to account for the complex sampling framework used in the HINTS survey and to provide estimates representative of the U.S. population. Units of survey weights were inversely proportional to the probability of selection and response. The computation of full-sample weights included calculating the houshold-level base weights for each household in the sample, adjustments for non-response, initial person-level weight for the adult in each responding household, and calibration of survey weights to the American Community Survey 2018. Additional details about the specific weighting methodology can be found in the Methodology Report . p-value ≤ 0.05. Statistical analyses were conducted using survey analysis procedures SAS/STAT (version 9.4).Survey-weighted prevalence of cancer genetic testing was calculated for the overall study sample and for groups after stratification by risk perceptions, cancer-related beliefs, and sociodemographic factors. We used the survey-weighted chi-squared test and t-test for bivariate analyses for sharing behavior by categorical variables and sharing behavior by age, respectively. We used survey-weighted multivariable logistic regression using the PROC SURVEYLOGISTIC procedure in the SAS/STAT software incorporating complex survey sample design accounting for strata, cluster, weights, and replicate weights with the jackknife variance estimation method to identify factors associated with genetic test result sharing. Pre-selected covariates, based on factors associated with genetic test result sharing in the literature ,20,21, wOf the 3859 respondents to HINTS, 142 (3.3%) reported undergoing high-risk cancer genetic testing. Seventy-seven of these respondents were excluded as they did not respond to the question on sharing test results, leaving 65 respondents in the analysis of high-risk cancer genetic test result-sharing behavior. Most test takers were female (88.3%), non-Hispanic White (91.7%), insured (100%), had an annual household income of $75,000 or higher (41.3%), and resided in an urban zip code (81.8%). Of those who underwent high-risk cancer genetic testing, 94% had a first-degree family history of cancer, and 80% had a personal history of cancer. It is possible that individuals with a personal and family history of cancer met the referral criteria and, thus, were more likely to undergo testing. p = 0.018), have ≤ high school education , have a personal history of cancer , and reside in a rural area . Overall, most test takers rated the importance of genetics for cancer prevention as little or not at all, yet these respondents who believed that were more likely to share results with an HCP/GC (p = 0.019). Other sociodemographic and clinical characteristics were comparable between sharers and non-sharers , and those with a personal history of cancer were less likely to share than those without   .p ≤ 0.001) than those without, but all other demographic, clinical, and psychosocial characteristics were similar between sharers and non-sharers  of high-risk cancer testers shared results with an FDR. Those with a family history of cancer were more likely to share their results with FDRs  underwent genetic health risk testing, and 1.9% (n = 71) responded to the question on sharing. Of the test takers, 66.5% shared with an HCP/GC, 38.7% with an FDR, 66.6% with a spouse/partner, 12.8% with a friend, and 14.1% did not share results with anyone . The disOf all respondents, 12.4% (n = 538) underwent genetic ancestry testing, and 7.1% (n = 303) responded to the question on sharing. Very few of these people shared their results with an HCP/GC (2.6%), whereas modest sharing was reported with an FDR, spouse/partner, or friend . Additionally, 5.2% of ancestry testers did not share results with anyone . The disBRCA1/2 and Lynch syndrome) shared results with HCP/GCs, whereas only 68% shared results with their FDRs. Our findings may help contextualize the feasibility of proband-mediated cascade genetic testing that traditionally relies on sharing genetic test results with relatives. The findings also provide empirical data on public engagement with different types of genetic tests and offer indications of how these results are used by test takers. Sharing genetic test results with various stakeholders is a health behavior of clinical relevance, as well as one that is key to improving public awareness of genetic testing. In this analysis of population-representative data on sharing clinical genetic test results, 90% of those who underwent high-risk cancer genetic testing (e.g., Our finding that 90% of those who underwent high-risk cancer genetic testing shared results with HCP/GCs is an increase from the 73% sharing reported in 2015 . Female In our limited sample of people who underwent high-risk cancer genetic testing, 68.4% shared their results with an FDR. This is lower than the 75% sharing reported in the 2015 HINTS survey  but wellOutside of high-risk cancer genetic testing, we found that 66% of those who underwent DTC genetic health risk testing shared the results with an HCP/GC, whereas only 2.6% of those who underwent ancestry testing shared the results with an HCP/GC. However, the rates of sharing these results with FDRs were similar—39% among genetic health risk testing and 41% for ancestry testing. To our knowledge, this is the first report of genetic test result-sharing behavior using population-representative data that extends beyond cancer genetic testing. A continuing concern in the field of genomic medicine is the integration of DTC genetic testing with medical practice and its effects on the healthcare system and healthcare utilization. It is estimated that nearly half of DTC consumers seeking clinical confirmation of their results might be wasting healthcare resources and healthcare providers’ time . While tWhile our study offers important empirical data on the population prevalence of genetic information sharing, a limitation of this study is the small sample sizes associated with various types of genetic testing, which prevented us from including additional variables in the logistic regression models. However, ours is the first study to provide sharing data related to two types of DTC genetic testing as well as sharing with spouses and friends, which are key for public engagement with genetics. Although respondents included in this analysis were largely female and non-Hispanic white, they are representative of consumers of genetic testing today, where structural and institutional barriers prevent other groups from accessing tests. With our use of survey weights, the results on sharing are still generalizable to the U.S. population. Due to the cross-sectional study design and lack of survey items on when genetic testing was performed, we cannot evaluate the timeframe over which sharing occurred. Although most high-risk cancer results are shared with relatives within weeks to months of testing, these patterns could be different for genetic health risk or ancestry testing. Since HINTS did not provide “not applicable” as an option on the survey, we do not know if respondents who did not report sharing with a stakeholder, e.g., a parent, did not so because they did not share genetic test results with a parent or they did not have a living parent to share it with. Future surveys on this topic should provide nuanced answer choices and probe answers with follow-up questions to better understand how genetic information is shared.In conclusion, these population-level data regarding sharing different types of genetic test results add important empirical evidence on the population prevalence of genetic information sharing and serves as a metric for public engagement with genetic testing in the United States.


Pheidole megacephala (Fabricius), has achieved worldwide distribution through globalization. Since the late 19th century, Taiwan has served as a major seaport because of its strategic location. The population genetic structure of P. megacephala in Taiwan is likely to be shaped by international trade and migration between neighboring islands. In this study, we investigated the population genetics of P. megacephala colonies sampled from four geographical regions in Taiwan and elucidated the population genetic structures of P. megacephala sampled from Taiwan, Okinawa, and Hawaii. We observed a low genetic diversity of P. megacephala across regions in Taiwan. Moreover, we noted low regional genetic differentiation and did not observe isolation by distance, implying that long‐distance jump dispersal might have played a crucial role in the spread of P. megacephala. We sequenced the partial cytochrome oxidase I gene and observed three mitochondrial haplotypes (TW1–TW3). TW1 and TW3 most likely originated from populations within the species' known invasive range, suggesting that secondary introduction is the predominant mode of introduction for this invasive ant. TW2 represents a novel haplotype that was previously unreported in other regions. P. megacephala populations from Taiwan, Okinawa, and Hawaii exhibited remarkable genetic similarity, which may reflect their relative geographic proximity and the historical connectedness of the Asia‐Pacific region.Global commerce and transportation facilitate the spread of invasive species. The African big‐headed ant,  Pheidole megacephala exhibits low genetic diversity across regions in Taiwan. Significant yet low regional genetic differentiation and the absence of isolation‐by‐distance implied that long‐distance jump dispersal may be relatively prevalent. Secondary introduction is the prevailing mode of introduction for P. megacephala and populations from Taiwan, Okinawa, and Hawaii exhibit remarkable genetic similarity. We inferred the ant population dynamics and postinvasion dispersal pattern in Taiwan. Moreover, we determined whether P. megacephala populations in Taiwan share a common origin with one or more native/introduced populations that share the same patterns as populations in the United States and Australia. Conversely, P. megacephala populations may have arisen multiple times from an unidentified origin, considering that research on the phylogenetics of the test species is limited. Accordingly, we investigated the genetic relationships among the P. megacephala populations from Taiwan, those from two Pacific islands, and those with genetic data on GenBank .To fill the aforementioned research gap, the present study used mitochondrial DNA (mtDNA) and microsatellite DNA to examine the population genetic structure of 22.1P. megacephala colonies were sampled from urban parks in four Taiwanese regions  sequences, commonly used in DNA barcoding, were amplified using the primers LCO1490 (5′‐GTCAACAAATCATAAA GATATTGG‐3′) and HCO2198 (5′‐TAAACTTCAGGGTGACCAAAAAATCA‐3′) to target a 708 bp fragment , and were subjected to Sanger sequencing in both the forward and reverse directions.Genomic DNA was extracted from eight workers from each colony by using the Gentra Puregene Tissue kit (Qiagen) and stored at 4°C for subsequent genetic analyses. The DNA of these eight workers from each colony was used for sequencing and genotyping. The partial cytochrome oxidase I gene  was subjected to the following thermocycling conditions: initial denaturation at 95°C for 15 min, followed by 35 cycles, each consisting of 94°C for 30 s, 60°C for 90 s, and 72°C for 60 s, and a final step at 60°C for 30 min. The second group  was subjected to the following thermocycling conditions: initial denaturation at 95°C for 15 min, followed by 35 cycles, each consisting of 94°C for 30 s, 56°C for 90 s, and 72°C for 60 s, and a final step at 60°C for 30 min. All PCR products were analyzed using the ABI 3730XL DNA Analyzer (Applied Biosystems) by Genomics BioSci and Tech (Taipei); GeneMarker  was used to visualize and score alleles.Eight workers from each colony were genotyped, resulting in a total of 384 individuals, at seven dinucleotide‐repeat microsatellite loci: 2.3NA, expected heterozygosity HE, and observed heterozygosity HO for every locus and region. The allelic richness Ar was calculated using FSTAT ; NA was compared using the Kruskal–Wallis test.GenAlEx ] coefficients and the logarithm of the geographical distance. Isolation by distance was tested at among‐colony and among‐region levels. The significance of the correlation was tested using the Mantel test in GENEPOP . Each K was tested 10 times. Structure Harvester , with each run sampling every 500 generations. A rapid bootstrap analysis and a search for the best‐scoring maximum‐likelihood tree were conducted using the extended majority rule‐based bootstrapping criterion .Two to six alleles were identified across seven polymorphic microsatellite loci Table . A totalNo significant heterozygosity excess was observed in all regions under the TPM model Appendix . The all3.2FRT = 0.085), 43% among colonies (FSR = 0.474), and 48% within colonies (FST = 0.519). The pairwise FST values were moderate but significant across all combinations  mainly distinguished colonies in KH (southern Taiwan) from those in TP (northern Taiwan). The population structures of the colonies sampled from TC and HT, located in the central region of Taiwan, considerably overlapped with each other. Although the colonies sampled from KH and TP partially overlapped with those sampled from TC and HT, the second principal component  indicated that the colonies sampled from KH possessed unique genetic structures distinct from those of the colonies sampled from the central region . However, the differentiation between Taiwan and Okinawa and that between Taiwan and Hawaii were moderate , Australia, Cameroon, Mauritius, and Caribbean countries, whereas TW1 resembles haplotypes recovered from specimens collected from Hawaii and Okinawa Figure . By cont4P. megacephala in Taiwan has experienced a substantial reduction in genetic diversity. Comparisons of the genetic diversity of P. megacephala in Taiwan, Australia, and South Africa  is lower than that in South Africa (6.129 ± 0.631) but similar to that in Australia (2.545 ± 0.226). Similar patterns of reduced genetic diversity have been commonly reported in the introduced ranges of most invasive ants  generations ago . We also observed no significant positive relationship between geographical and genetic distances (isolation by distance). Our finding corroborates those of most previous studies that have reported limited isolation by distance in the ant's introduced ranges. For example, a study observed no correlation between genetic and geographical distances of up to 3000 km in Australia, which was engendered by a high level of gene flow between P. megacephala populations in human‐dominating habitats  and the other in KH (southern Taiwan), both of which were most likely introduced from the ant's nonnative regions. Moreover, this speculation is supported by our phylogenetic analysis of P. megacephala collections from Taiwan compared with other populations from native and exotic ranges, suggesting two or three possible lineages.The bridgehead effect is a major pathway facilitating global ant invasion. Bertelsmeier et al.  demonstrly Yuju, . To dateP. megacephala populations may have arisen multiple times from unknown origins, considering that research on the phylogenetics of the test species is limited. Additionally, the PCA and STRUCTURE results indicate an apparent nuclear genetic similarity among the three P. megacephala populations. All of these results suggest a high level of connectivity among the three islands and that the interisland spread of P. megacephala might have been expected. This suggestion is supported by the extensive human immigration events occurring in Japan, Taiwan , and Hawaii during the late 19th and early 20th centuries , in southern Japan, Taiwan, and Hawaii . In 2018, 100 colonies of imported red fire ants were discovered in the container yard at the Port of Kaohsiung (received cargo from China (60%) and the United States (40%); Wylie et al., Kuan‐Ling Liu: Conceptualization (supporting); formal analysis (lead); investigation (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Shu‐Ping Tseng: Methodology ; writing – review and editing . Haruki Tatsuta: Resources (supporting); writing – review and editing . Kazuki Tsuji: Resources (supporting); writing – review and editing . Jia‐Wei Tay: Resources (supporting); writing – review and editing (supporting). G. Veera Singham: Validation ; writing – review and editing . Chin‐Cheng Scotty Yang: Conceptualization (supporting); methodology (supporting); validation ; writing – review and editing . Kok Boon Neoh: Conceptualization (supporting); funding acquisition (lead); supervision (lead); writing – original draft (lead); writing – review and editing .The authors declare no competing financial interests.Appendix S1.Click here for additional data file.Appendix S2.Click here for additional data file.Appendix S3.Click here for additional data file.Appendix S4.Click here for additional data file.Appendix S5.Click here for additional data file.


The international community has paid extensive attention to the numerous engineering problems faced by karst areas caused by the increasingly frequent human activities. China has a wide variety of karst forms. Among them, carbonate karst is the most widely distributed, and the development of carbonate karst is relatively strong in many areas. Countless property losses are caused by karst disasters every year. This article aims to study the real-time monitoring and timely early warning of karst collapse through the use of multisensor Internet of Things technology. To this end, this article proposes an improved method for multisensor data fusion. It optimizes and improves the transmission and delivery efficiency of its data. This makes the improved multisensor more in line with the research content of this article in terms of monitoring efficiency. At the same time, related experiments and analyses are designed to compare and analyze the karst collapse and the monitoring efficiency of the sensor. The experimental results of this article show that after the improvement, the anti-interference ability of the monitoring system is increased by 34%. The frequency of early warning has also been improved by 24%, which has high practical application value. Karst areas are mainly composed of carbonate rock series. In Guizhou Province, Guangxi Zhuang Autonomous Region, and Yunnan Province, the outcropping area of carbonate rocks accounts for about half of the total area of the three provinces. Due to the humid climate and abundant rainfall in these provinces, karst is extremely developed. There are so many types; it can be called the crown of the world. In the areas along the railway in Southwest China, there are mountains, hills, plains, and other karst areas widely distributed. However, the stage of development and the degree of development are different.Karst is the general term for the chemical dissolution and mechanical destruction of soluble rocks caused by water and various phenomena and forms caused by these effects. The distribution area of soluble rocks in China is 340 kmDuring the construction of karst tunnels, the shape, size, development degree, scale, and exact location of exposure of the karst in front can be accurately predicted. It recognizes the influence laws of environmental stability under different working conditions. In this way, the construction plan can be adjusted in time, and safety measures can be set to avoid the occurrence of karst collapse. Karst collapse may occur at any time if the advance geological forecast fails to be proved or the treatment plan is unreasonable during construction. Not only will the construction safety be seriously threatened, but the construction progress will be slowed down. And it will leave a safety hazard after completion. Even deteriorating the environment causes irreversible environmental impacts and economic losses. It can be seen that the mechanism of karst collapse and the influence of karst on structural stability are analyzed and studied. Therefore, it is of great theoretical and practical significance to propose the corresponding forecasting technology and prevention technology.The innovation of this article lies in how to improve the effective karst monitoring and early warning. It is used for multisensor Internet of Things technology and focuses on the analysis and research of data fusion between multisensors. This makes it possible to achieve better results in terms of data transmission and transmission frequency. It is effectively applied to the actual karst collapse monitoring and early warning evaluation.Xue et al. proposed a vision-centric multisensor fusion framework for traffic environment perception methods for autonomous driving. The framework consistently fuses camera, lidar, and GIS information through geometric and semantic constraints for efficient self-localization and obstacle perception . A commeAccording to the requirements of the monitoring and early warning system studied in this article, it is necessary to send the data of multiple wireless sensor nodes to the remote monitoring center. It is a multi-point-to-point communication method. The network topology can be divided into three types: star network, tree network, and mesh network. The wireless transmission of this system adopts a tree-type network structure, and The whole system can be divided into three parts: wireless sensor network node, coordinator node, and monitoring center. Wireless sensor nodes integrate multiple sensors and are responsible for measuring multiple parameters. After the data are processed, they are sent out through the wireless module. The monitoring center is the main console of the system. The received data can be saved and displayed in real time through the monitoring software, and the data exceeding the threshold can be alarmed in time. The user can monitor the work of each node through the monitoring center . The cooμC/OS-II embedded real-time operating system, application program interface, and application program. The STM32-embedded microprocessor is the core of the node hardware layer. By connecting temperature and humidity sensors, air pressure sensors, accelerometers, and wireless communication modules, the hardware foundation of data acquisition, processing, and wireless transmission is formed. The software layer is built on the basis of the hardware layer, and μC/OS-II embedded real-time operating system is the core of the software layer. It provides a software platform and application programming interface. Based on this, applications can be easily written to achieve multitask parallelism , it can be obtained:It assumes that multiple sensors estimate the same target value. Let fference . Throughn sensors measuring the same parameter, the confidence distance measure dij is as follows:Assuming that there are Dn, soIt can form a multisensor confidence distance matrix φij will be obtained, and for dij, there areWhen multiple sensors measure the same target from multiple orientations, a fusion upper limit value i and j is good and the mutual support is high. Otherwise, the compatibility is poor. If the sampling data of a sensor are supported by all other sensors, the sampling data are valid. If the sampled data of a certain sensor are only supported by a very small number of sensors, it is considered that the sampling data of the sensor is invalid and should be eliminated in the fusion stage to improve the fusion efficiency , then all intervals are considered to be supportive evidence, and at this time, A has the maximum support. Then the following relationship can be derived:U be a recognition frame and 2U be the power set of U. If function m : 2U⟶, and the following conditions are met:Let The classic D-S evidence combination rule is as follows:K is described as follows:In the formula, K is often called the conflict factor, and its value range is . The K value reflects the degree of the irrelevance of the pairwise evidence. The larger the value of K, the higher the degree of conflict between the evidence, and the value of K cannot be 1.The karst (KARST) collapse mechanism includes many aspects. There are mainly subsurface erosion theory, vacuum absorption theory, vibration theory, liquefaction theory, and gas explosion theory. Hydrodynamic conditions are generally considered to be the external cause of the ground collapse. The change of groundwater level is the inducing factor of karst collapse, and the structure of the geological body is the internal cause of the ground collapse. The upper overburden and the lower karst development are necessary conditions for karst collapse. The main mechanisms of ground collapse are as follows.The generally covered karst road sections have unobstructed groundwater flow conditions. At the same time, as the groundwater level dropped sharply, unstable soil and rocks were also washed away. This, in turn, produces erosion and hollowing out, and the hollowed-out area gradually expands. That is, soil caves are generated in the quaternary overburden and the underlying karst bedrock. At this time, the top of the soil cave will collapse due to the gradual change of the original stress to form a surface collapse. Hydrodynamic action can be transverse or longitudinal. Underground erosion of water is generally the action of groundwater, but it is also mostly caused by the infiltration of surface water. Underground erosion can be formed one or more times, and it has become the main inducement for the formation of soil subsidence. Many experts believe that this form of subsidence is due to the increase of hydraulic gradient, which causes the action of subsurface erosion to transport the cave filling. Over time, the rate of latent corrosion is accelerated, and when the normal stress of the upper covering layer is greater than the original resistance, it will lead to collapse. It is easier to determine that karst collapse is caused by seepage and undercurrent, and soil particles are lost to form soil caves, which are unstable and then collapse.When the groundwater level in the confined karst stratum drops below the bottom of the quaternary overburden due to pumping and drainage, that is, vacuum suction can be formed in the voids of the karst fracture zone some scholars have used model experiments to calculate the magnitude of vacuum suction. They believed that the pressure difference effect caused by the change of groundwater level is the main reason for the formation of karst collapse. In “On the Formation Mechanism of Karst Collapse,” the viewpoint that the pressure difference caused by the water level change further causes the vacuum absorption effect is expressed. It has also been successfully used to explain the karst collapse phenomenon in the Liuzhi mining area in Guizhou and the coal mining area in Hubei.The change of groundwater will definitely cause the underground erosion of the soil layer to occur. This is a long-term process, and the vacuum suction effect caused by the pressure difference will collapse only when the critical condition is reached. From the viewpoint of latent erosion and vacuum absorption, it is believed that karst collapse is caused by the above two effects. Some scholars have also successfully analyzed and explained the causes of ground collapse in Lujia Street, Wuchang, using the viewpoint of “subsurface erosion-vacuum absorption.”Vibration will cause rock and soil mass to produce fracture displacement, soil mass liquefaction and other effects. It reduces the strength of rock and soil mass and leads to the formation of collapse, which is called vibration collapse. This collapse is often caused by external vibration.The formation of overburdened soil cavities (cavities) will directly lead to the occurrence of karst collapse. People have been focusing on the study of karst cavities but neglected the research on the formation mechanism and formation conditions of soil cavities. This led to remedial measures after the collapse. However, in the construction process, they only focus on efficiency and interests and forget the damage to the underground environment caused by construction activities. For example, excessive and rapid groundwater extraction damages the mechanical stability of the overburden soil, resulting in ground collapse.The formation and development of soil caverns without human interference and the collapse after reaching the critical soil cavern height have to go through a long process. However, the development of human society destroys the underground homeostasis, which greatly reduces this time period. Some activities can create and rapidly expand cavities to the surface hours after groundwater is pumped. The causes of karst collapse are shown in First, there should be karst voids, fissures, or faults under the quaternary overburden. That is, there is a place for the storage of land particles carried away by groundwater activity.The formation of soil caves must have a certain thickness of overburden rock and soil mass. The shear strength and compressive strength of the overburden covering limestone, dolomite, and other rock mass karsts are low, especially near the bedrock surface. Due to the long-term immersion of karst void water in the aquifer, the soil strength is even lower. The soil layer is basically soft or fluid-plastic with extremely low tensile and shear strengths. The high-frequency fluctuation of groundwater under the intervention of human activities increases its hydraulic gradient and velocity. As a result, the stress on the soil above the karst and the karst cave is greater than the critical shear stress of the soil itself, resulting in undercutting, scouring, and hollowing, and the fine soil particles are transported away. Larger soil particles are unstable and fall into the cracks in the lower karst cave. The result is a cavity at the karst opening at the bottom of the layer. This is the prototype of the cave.Groundwater activity is an important condition for the formation of soil caves. There are two effects of groundwater flow on soil particles. One is the positive pressure acting on the soil by vacuum water flow and static pore water pressure. The second is the shear force acting on the side of the soil, which is consistent with the direction of the water flow and is caused by the hydrodynamic pressure. The change in water level directly destroys the stability of the soil layer. The change of water level not only causes high-intensity scouring of soil. And as the water level continued to drop, the volume of the soil cavity increased. The air pressure in the cavity decreases continuously, making the cavity lower than the standard atmospheric pressure outside and forming a low-pressure state. Finally, the stress of the soil around the cavity is greater than its compressive and shear strength, resulting in collapse.There are three necessary conditions for the formation of soil caves:It is based on statistics of karst collapse overburden conditions in the work area. The degree of susceptibility was graded by two factors, the thickness of the capping layer, and the structure of the capping layer. Overburden is the main body of overburden karst collapse. Its thickness and structure determine the danger of karst collapse.The work area is hilly and mountainous, with frequent terrain fluctuations and large slopes. The cover layer is very uneven in both plane distribution and thickness variation. Generally, it is distributed on the gentle slopes below the mountainside. The terrain is relatively steep, and the upper part of the mountainside is generally rarely distributed or the bedrock is exposed.For the overburden structure, the one-dimensional structure area accounts for 86% of the soil distribution area, and most of the karst collapses develop in the one-dimensional structure area. The binary structure is rarely distributed, the ternary structure is almost absent, and the distribution of karst collapse is relatively rare. Areas without soil cover are basically less likely to collapse. The single-factor hierarchical properties for the overlay are shown in Hydrogeological conditions are one of the main factors influencing the occurrence of karst collapse. It is based on the statistics of the hydrogeological conditions of karst collapse in the work area. The susceptibility is graded by two factors: karst groundwater type and water-rich. The classification is shown in There are various types of karst groundwater in the work area. The corresponding karst collapse development numbers are high, medium, and low, respectively. The bedrock fissure water in the nonsoluble rock area is regarded as a less prone area.The greater the frequency of groundwater level fluctuations, the greater the intensity of rock-soil deformation. The more frequent the fluctuation, the higher the rate, and the higher the rate of rock-soil deformation. Because these data require long-term detection statistics, due to this limitation, no assignment is made in the susceptibility partitioning.Human engineering activities are the main inducing factors of karst ground collapse. An important activity type for karst collapse in the working area is underground mining and drainage of groundwater. The formed karst water drop funnels result in the decline of the groundwater level and the strong groundwater runoff zone. Therefore, the manifestations of human engineering activities are assigned according to the intensity of groundwater pumping and the distance from the pumping and drainage. Its classification is shown in The distribution range, density, scale, and degree of damage are the most direct manifestations of the risk of karst collapse and indirect reflections of triggering factors. Generally speaking, the wider the distribution range and the greater the development density of the existing collapse, the higher the damage degree and the higher the corresponding risk level. A single collapse, and a collapse that does not appear as a group of pits, lasts for a long time and is dangerous. Its susceptibility is also low. According to the existing data level, evaluation scope, and precision requirements, this study selects karst collapse density as one of the indicators to evaluate the susceptibility of karst collapse. Karst collapse density refers to the number of karst collapses per unit area. The greater the density, the greater the susceptibility. It is divided into high-density area, medium-density area, and low-density area. The corresponding risk levels are high, medium, and low susceptibility. For nonsoluble rock, it is regarded as a less prone area; at the same time, according to the development scale of karst collapse and the distribution of group pits and single pits, different classifications and assignments are also given. The details are shown in Geological structure controls the development of karst. Generally speaking, due to the fault structure, the soluble rock mass is broken, and the fractures are highly developed, which is easy to conduct water and causes the karst phenomenon. The tensile fault has better water conductivity and strong groundwater activity. Compressive faults have poor water conductivity and tend to accumulate water at one end of the fault disk, and groundwater is relatively abundant. The more dense and developed the fault structure, the stronger the karst, and the stronger the groundwater activity, which is more conducive to the karst collapse. The greater the fault density, the greater the susceptibility. In addition, the closer the karst collapse is to the structure, the greater the probability of collapse is, and the greater the susceptibility is.In this study, the karst collapse risk degree is classified and assigned according to the nature of the fault and the distance from the structure. The details are shown in When the horizontal section of the karst cave roof is rectangular and the width is close to the pile diameter, the karst cave roof can be considered as the force of the beam. The rock formations at both ends of the beam are complete and, in good contact, can be regarded as fixed support. In this section, the force model of the beam supported at both ends will be taken as the analysis object. Using the designed multisensor network, the relationship curve between the ultimate bearing capacity and displacement and span of the cave roof with different thicknesses collected is drawn as shown in It can be seen from the figure that for a fixed beam with the same span, the greater the thickness of the roof, the greater the ultimate bearing capacity. For the karst roof with the same thickness, the ultimate bearing capacity decreases gradually with the increase of the span. And the decreasing trend will slow down. When the thickness of the roof is 1 m, the change of the ultimate bearing capacity with the span is more significant than when the thickness of the roof is 3 m. And under the same load conditions, the greater the thickness of the roof, the smaller the central displacement of the roof, and the less obvious the change. It can be inferred that when the karst roof reaches a certain thickness, the influence of the span on the ultimate bearing capacity can be ignored. The roof failure at this time tends to be without karst.When the horizontal section of the roof of the karst cave is circular and the surrounding rock formation is complete, the contact is good. It can consider the roof of the karst cave according to the force of the fixed circular plate. This section will take the force model of the clamped circular plate as the analysis object. It uses the designed multisensor network to draw the relationship curve between the ultimate bearing capacity and displacement and span of the roof of karst caves with different thicknesses, as shown in It can be seen from the figure that the ultimate bearing capacity of the clamped circular plate under the same thickness condition decreases with the increase of the span. The decreasing range also gradually becomes smaller, indicating that the influence of span on the ultimate bearing capacity of the karst roof is decreasing. Under the same span condition, the greater the thickness, the greater the ultimate bearing capacity. And under the same load conditions, the smaller the thickness of the roof, the more obvious the change of the center displacement of the roof, and there is a slowing trend.m sequence, and the amplitude of the signal is 1. The information code rate is 10 Kbit/s, the carrier frequency is 100 MHz, the sampling frequency is 20 GHz, and the spreading code rate is 10 Mbit/s. In the multisensor network communication protocol, the time-domain waveform diagram before the spread spectrum and the time-domain waveform diagram after the spread spectrum is shown in In the MATLAB7.1 simulation software, the algorithm of the physical layer in the cooperative communication protocol of the multisensor network is simulated and analyzed. The simulation parameters of the physical layer in the multisensor network cooperative communication protocol are as follows. The modulation method is BPSK, the pseudorandom code is As can be seen from the figure, the spectrum of the physical layer signal after pseudorandom code spread and the physical layer signal without spectrum spread is broadened. Error-free transmission of such signals at low signal-to-noise ratios is possible. Thereby, the antijamming performance of the physical layer in the multisensor network communication system is improved.Based on the above analysis, it can be seen that the improved multisensor-based karst collapse monitoring and early warning evaluation improves the anti-interference performance by 34%. The frequency of early warning can be increased by 24%, which greatly prevents the loss of personnel and property caused by karst.This article mainly studies how to improve the current karst collapse monitoring and early warning system through the use of multisensor Internet of Things technology. To this end, this article has made great efforts in the key multisensor data fusion. Through the data fusion problem faced by multisensor, this article has carried out a separate study to improve it. And in the analysis part, the verification and comparison analysis of its effect is carried out. At the same time, a detailed understanding of the causes of karst collapse was carried out. This facilitates better timely early warning in the actual monitoring and early warning work.


Cynopterus brachyotis (Müller 1838) is a generalist and widespread fruit bat species which inhabits different types of habitats in Southeast Asia. This species plays an essential role as a seed disperser and pollinator. Morphological study and phylogenetic analysis using mtDNA markers (cyt-b and D-loop) revealed that this species had two different forms in peninsular Malaysia and Borneo and six lineages in Southeast Asia that lead to new species formation. In addition, this species is also reported to have high genetic diversity in Malaysia and Thailand based on the D-loop sequence. However, a phylogenetic and genetic variation study of C. brachyotis in Indonesia has not been conducted yet. These two studies are important as additional information for taxonomic and population genetic studies of this species. Thus, we performed the phylogenetic and genetic diversity analysis of the C. brachyotis population collected from seven habitats on Java island, including open-fragmented habitats  and closed habitats  using the mtDNA D-loop marker.C. brachyotis from seven habitats on Java island overlapped between habitats and could not be distinguished according to their habitats and lineage. Intrapopulation and intraspecies analysis revealed high haplotype diversity of this species on Java island (Hd = 0.933–1.000). The haplotype network was split into two haplogroups, showing haplotype sharing between habitats. These phylogenetic and genetic variations analysis of C. brachyotis bats on Java island indicated that this species is widespread and adapt to different habitats.The phylogenetic tree using the Bayesian inference (BI) and genetic distance using the Kimura-2 parameter (K-2P) demonstrated that 33 individuals of C. brachyotis on Java island collected from seven different habitats has overlapped and genetically close and has high genetic variation. Our results provide the first reported study of C. brachyotis on Java island and provide data to understand the phylogenetic and genetic diversity of this species in Indonesia.This study of  Cynopterus brachyotis (Müller 1838) belongs to the family Pteropodidae and is widely distributed in Southeast Asia, including Indonesia. It has an important role in the ecosystem as seed dispersal and pollinator [C. brachyotis is generalis and eurytopic species where it can be found in many habitats (most frequently in the disturbed and fragmented area), such as the urban area, secondary forest, dipterocarp forest, gardens, mangroves, and strand vegetation [Lesser short-nosed fruit bat s Müller 838 belonllinator . C. bracgetation , 6.C. brachyotis  were revealed based on morphological study and inhabited two contrasting habitats in Peninsular Malaysia and Borneo. Moreover, the larger form (Sunda) was found to inhabit open areas (urban and plantation), whereas the smaller form (Forest) was confined to natural forests [This species in Southeast Asia has been reported to have morphological variations  and kary forests .D-loop demonstrated that C. brachyotis Sunda and Forest have different clades and separated population. Moreover, this analysis also revealed that C. brachyotis is a species complex with six distinct lineages: Sunda, Forest, India, Philippines, Myanmar, and Sulawesi [C. brachyotis species may occur [Phylogenetic analysis using mitochondrial DNA markers including cyt-b and the control region Sulawesi . Accordiay occur , 9. HoweC. brachyotis Sunda and Forest lineage can be found both in Peninsular Malaysia and Borneo and has contrasting habitats. A C. brachyotis sample was collected from Java island (Jakarta) and is included in the Sunda lineage [D-loop markers in Borneo, Thailand, and Peninsular Malaysia [Furthermore, according to that phylogenetic analysis,  lineage . UnfortuMalaysia , 10.D-loop is a hypervariable control region of mitochondrial DNA having high nucleotide variations and mutational hotspots [D-loop region was essential for understanding the genetic diversity and species population, including bats in particular habitats [D-loop can be used to estimate the impact of the anthropogenic process on an animal population. Genetic diversity can affect a species’ ability to adapt to environmental changes [C. brachyotis is fruit bat species closely related to habitat fragmentation [hotspots . Haplotyhabitats , 13. Gen changes . Besidesentation , 3.2 with a population projection is 56.1% of the Indonesian population makes this island the most densely populated island in Indonesia [D-loop in C. brachyotis have not been explored in Indonesia, especially on Java island Only karyotype and hematological studies of C. brachyotis were conducted on this island [C. brachyotis collected from different types of habitats on Java island inferred from the mitochondrial D-loop gene sequence. The results of this study are expected to be the basis for ecological management of C. brachyotis species and genetic research on other bat populations in Indonesia.Java island is an Indonesian island that has a very high and rapid population growth rate. This island belongs to the Greater Sunda islands alongside Peninsular Malaysia, Sumatra, and Borneo. A total area of about 128,297 kmndonesia . Thus, ts island , 17. TheThe ethical clearance for conducting this research was taken from the Research Ethics Committee of the Faculty of Veterinary Medicine, Gadjah Mada University, Indonesia . Animal handling and sampling (including tissue sampling) were carried out following their guidelines and supervision. This ethical clearance also covered other analyses, such as morphological measurements and hematological analysis of this species.C. brachyotis were collected from seven habitats including urban area, coffee plantation, rubber plantation, pine forest, secondary forest, natural forest, and mangrove forest in East Java, a special region of Yogyakarta, Central Java, and West Java  and H16498 (5′-CCTGAAGTAAGAACCAGATG-3′) [Genomic DNA extraction of membrane tissue was conducted using GS100 gSYNC™ DNA Extraction Kit  following manufacture instructions. Fragment of GATG-3′) . AmplifiGATG-3′) . PCR proD-loop. Alignment of D-loop sequence was performed using Opal in Mesquite v.3.51 [The ambiguous bases of chromatograms were corrected manually in GeneStudio software  to get a consensus sequence of e v.3.51  and Cluse v.3.51 .D-loop sequence nucleotides between habitats (intrapopulation) and all samples (intraspecies) were analyzed using the MEGA X software [Nucleotide composition analysis was used to determine the nucleotide differences between samples which indicated genetic variation. Differences in software .7 generations and a sampling frequency of 1000 generations.Reconstruction of the phylogenetic tree was performed using the Bayesian inference (BI) tree in BEAST 1.10 software . The besD-loop sequences of C. brachyotis from four distinct lineages, including Sunda , Forest , Myanmar (AY629073), and India (two accessions: AY629069 and AY629070) collected from GenBank were included in this analysis. Moreover, the D-loop sequence from two Rousettus bats, Rousettus amplexicaudatus (NC045044), and Rousettus leschenaultii (our data) were used as an outgroup. The analysis of genetic distance was conducted using the Kimura-2 parameter (K-2P) method in the MEGA-X software [A total of 48 software .Genetic variation analysis among habitat types including haplotype number (h), haplotype diversity (Hd), and nucleotide diversity (π) was performed using DnaSP 6.0 software . The levT = 27.29%, C = 29.17%, A = 30.16%, and G = 13.38%. The nucleotide composition analysis of intraspecies also revealed an AT-rich (57.45%) compared to GC (42.55%).The nucleotide composition analysis showed that the percentage of nucleotides T, C, A, and G of samples between habitats (intrapopulation) was varied. The average percentage range of T, C, A, and G nucleotides in seven habitats was 26.67–27.57%, 28.44–29.87%, 29.97–31.14%, and 12.57–13.87%, respectively. This analysis also showed AT-rich in all habitats with an average range of 57.39–58.35%. Meanwhile, the GC was 41.65–42.61%. The average nucleotide percentage of all sample (intraspecies) was D-loop sequence of 33 samples in this study and GenBank data yielded a fragment length of 412 bp. The phylogenetic tree demonstrated the D-loop sequence of C. brachyotis from seven habitats in this study, and GenBank data formed a single clade  had haplotypes ranging from 3 to 5 haplotypes with the number of samples of 3–6 individuals per habitat. The haplotype diversity and nucleotide diversity of seven habitats were 0.933–1.000 and 0.00980–0.01115, respectively . The first haplogroup consisted of H1-H22 (excluding H16 and H18) haplotypes originating from seven habitats . The second haplogroup consisted of H16 and H18 haplotypes from the secondary forest and natural forest habitats. Haplotype sharing in the first haplogroup, namely H1, H2, H4, and H11 haplotypes, was overlapping between habitats in different sampling locations. Meanwhile, other haplotypes were unique haplotypes . No previous studies explained the genetic distance threshold based on D-loop sequences in bats (Chiroptera). However, the genetic distance between samples in cattle  was less than 0.5 considered to have a close relationship [The phylogenetic tree reconstruction and genetic distance analysis using tionship .Miniopterus pallidus bat in Iran also showed a higher genetic distance within a population [The higher genetic distance within a habitat population than between populations in this study may indicate that genetic variation is more related to differences between samples in a habitat . In addipulation . Thus, hC. brachyotis bats on Java island could not be grouped into their habitat type and lineage based on the D-loop sequence marker. This finding is supported by the external morphology measurement data (data are not shown). Samples from seven habitats had no significant difference in external morphology measurement. The forearm length ranged from 58.69 to 62.55 mm, tibia length 20.79–24.18 mm, and body weight 30.17–36.08 g. In addition, the sample from open habitats (such as urban) had relatively the same average value of forearm length, tibia length, and body weight compared to secondary and natural forests. Further analysis using the cytochrome b (cyt-b) marker is needed to support species identification.The results showed that C. brachyotis D-loop sequence from seven habitats on Java island may describe the rapid evolutionary adaptation of this species. The higher nucleotide pair AT of the D-loop may cause the structure of C. brachyotis mitogenome to become less stable (due to the AT pair have two hydrogen bonds) and accelerate its evolutionary adaptation [C. brachyotis would be able to survive and adapt to different types of habitats on Java island.The average percentage differences in nucleotide composition between habitats in this study also indicated nucleotide variations of intrapopulation and may reflect the genetic variation. Nucleotide composition is a modest way to characterize the genome. Furthermore, the AT-rich of aptation . Evolutiaptation . Thus, iC. brachyotis on Java island has high haplotype diversity (Hd = 0.8–1.00). A previous study showed C. brachyotis population from Peninsular Malaysia and southern Thailand also has high haplotype diversity [C. brachyotis from the natural forest habitat in this study also have a higher haplotype diversity and nucleotide diversity than in other habitats. Those results are in line with this study. C. brachyotis Forest that inhabits closed habitat  has higher haplotype diversity (0.995 ± 0.0023) than C. brachyotis Sunda that inhabit the fragmented area (0.982 ± 0.0003). In addition, the nucleotide diversity of C. brachyotis Forest is about three times higher compared to C. brachyotis Sunda [According to Nei’s (1987) category, iversity . C. bracis Sunda .C. brachyotis population that inhabits different types of habitat, especially the natural forest on Java island, may represent a high genetic diversity and is considered capable of surviving environmental changes in any habitat types.High haplotype diversity could describe high genetic diversity. The genetic diversity of a population is essential for the adaptation process and long-term survival to environmental changes. Moreover, high genetic diversity in a population will degrade the risk of species extinction because they will be able to survive or adapt to environmental changes . TherefoC. brachyotis on Java island overlapped between habitats. This result was congruent with the phylogenetic tree. The samples of C. brachyotis in the haplotype sharing H1, H2, and H4 originated from different types of habitats in the East Java, Yogyakarta, Central Java, and West Java regions. The distance between these locations is more than 500 km, and geographical barriers such as mountains have existed. Therefore, sample connection or gene flow between the samples should not occur and lead to unique haplotype formation [A haplotype network analysis is used to analyze and describe the relationship between haplotypes or DNA sequences in populations (intrapopulation) or species (intraspecies) , 32. Anaormation , 34. HowPenthetor lucasi between the Miri and Kuching populations in Sarawak, Malaysia (the distance between populations of 348 km), based on the D-loop indicates that these two populations became the source of the P. lucasi population in Sarawak during the Pleistocene refugium [The haplotype sharing formation in this study is still unknown. However, according to previous studies, the haplogroups and haplotype sharing formations between fruit bat populations within long population area distances are related to demographic history during Pleistocene refugium. Furthermore, the formation of haplogroup and haplotype sharing in fruit bat species refugium , 36.Rattus rattus and Mus musculus. Several haplotypes of R. rattus and M. musculus in the Indochina region (including Java) were also shared based on the D-loop and cyt-b markers. It is due to the long-distance distribution that began in the mid-Pleistocene and is related to human activities in the modern era [C. brachyotis on Java island, but it is necessary to be studied further.The Pleistocene refugium theory explained that habitat fragmentation due to declining sea levels caused the isolation and diversification of taxons during the Pleistocene era, including bats , 37. It dern era , 39. TheC. brachyotis bats on Java island may indicate that this species is a generalist and widespread species and suspected can adapt to different types of habitats. Nevertheless, C. brachyotis species rely on their foraging activities and roosting habitat in the trees or forest stands [Java island is one of the islands in Indonesia that have the highest human population and massive habitat fragmentation . The fort stands , 45. TheC. brachyotis have been categorized as the least concern on the IUCN Red List, its existence is essential for the ecosystem’s sustainability and plant regeneration, especially for several trees in the forest and fragmented habitats. Previous studies showed that these species have a role in the pollination and seed dispersal of more than 16 plant species [C. brachyotis on Java island is needed.Although  species , 3. HencD-loop sequence, C. brachyotis collected from seven habitats on Java island formed a single clade, overlapped between habitats, and was genetically close. This species on Java island also had high haplotype diversity (Hd = 0.933–1.000). A total of 22 haplotypes from 33 samples were found and split into two haplogroups. Furthermore, the haplotypes shared among habitats were also found in this study. This study result provides D-loop sequence data of C. brachyotis from Indonesia inferred from the mitochondrial DNA D-loop.According to phylogenetic and genetic variation analysis using mtDNA


This may fundamentally change our thinking about how microbes and hosts interact in influencing fitness and adaptation to changing environments. Here we explore how reductions in population size commonly experienced by threatened species influence microbiome diversity. Consequences of such reductions are normally interpreted in terms of a loss of genetic variation, increased inbreeding and associated inbreeding depression. However, fitness effects of population bottlenecks might also be mediated through microbiome diversity, such as through loss of functionally important microbes. Here we utilise 50 Drosophila melanogaster lines with different histories of population bottlenecks to explore these questions. The lines were phenotyped for egg-to-adult viability and their genomes sequenced to estimate genetic variation. The bacterial 16S rRNA gene was amplified in these lines to investigate microbial diversity. We found that 1) host population bottlenecks constrained microbiome richness and diversity, 2) core microbiomes of hosts with low genetic variation were constituted from subsets of microbiomes found in flies with higher genetic variation, 3) both microbiome diversity and host genetic variation contributed to host population fitness, 4) connectivity and robustness of bacterial networks was low in the inbred lines regardless of host genetic variation, 5) reduced microbial diversity was associated with weaker evolutionary responses of hosts in stressful environments, and 6) these effects were unrelated to Wolbachia density. These findings suggest that population bottlenecks reduce hologenomic variation . Thus, while the current biodiversity crisis focuses on population sizes and genetic variation of eukaryotes, an additional focal point should be the microbial diversity carried by the eukaryotes, which in turn may influence host fitness and adaptability with consequences for the persistence of populations.It is becoming increasingly clear that microbial symbionts influence key aspects of their host’s fitness, and  It is becoming increasingly clear that organisms and the microbes that live on or in them–their microbiome–affect each other in profound ways that we are just beginning to understand. For instance, a diverse microbiome can help maintain metabolic functions or fight pathogens causing diseases. A disrupted microbiome may be especially critical for animals and plants that occur in low numbers because of threats from e.g. human exploitation or climate change, as they may already suffer from genetic challenges such as inbreeding and reduced evolutionary potential. The importance of such a reduction in population size, called a bottleneck, on the microbial diversity and the potential interactive effects on host health remains unexplored. Here we experimentally test these associations by investigating the microbiomes of 50 inbred or non-inbred populations of vinegar flies. We found that restricting the population size constrain the host’s genetic variation and simultaneously decreases the diversity of the microbiome that they harbor, and that both effects were detrimental to host fitness. The microbial communities in inbred host populations were less robust than in their non-inbred counterparts, suggesting that we should increasingly consider the microbiome diversity, which may ultimately influence the health and persistence of threatened species. It is becoming increasingly clear that most eukaryotes live in intimate and complex relationships with microbial communities both in their external environment and on or within their body including their gut . NumerouBecause interactions between microbes and their hosts shape so many aspects of life, the impact on core biological processes including evolutionary adaptation may need to be reconsidered –22. The These conjectures raise the issue of how microbes respond to changes in the host’s population size. Populations that undergo repeated or persistent reductions in size frequently suffer from inbreeding depression and genetic drift resulting in lowered fitness and reduced genetic variations, ultimately impeding evolutionary capacity and increasing the risk of extinction –34. ThesDrosophila melanogaster that have experienced a variable number of generations of population bottlenecks. These populations were subsequently phenotyped for fitness components and sequenced to obtain a genome-wide molecular measure of genetic variation [Fig 1). We consider two main questions. 1) How is microbiome richness and diversity associated with host genetic variation imposed by population bottlenecks? 2) Are both host microbiome diversity and genetic variation associated with host fitness, and if so, do they contribute independently? Answers to these questions increase our understanding of the importance of microbiomes in conservation genetics and evolutionary biology. We hypothesise that reduced host genetic variation provides a less diverse environment for microbes leading to reduced microbial diversity through fewer host genotype-microbial taxon-specific interactions . Evidence suggests that increased microbiome diversity is adaptive for the host and that increased hologenome genetic variation facilitates faster evolutionary responses to selection [Here we investigate microbial composition and abundance in 50 populations of ariation . This seelection ,25,36. Oet al. 2019 [et al. 2019 [s = 0.45; p < 0.001; Fig A inS1 Text) indicating inbreeding depression for viability which is used here as a proxy for Darwinian fitness. For the microbiome characterization, two groups each consisting of 25 lines were selected based on measures of standardized viability and π . The inbreeding procedure resulted in varying degrees of nucleotide diversity in the resulting lines, and as such the genetic variation of the outbred lines and the inbred lines in the ‘high’ group did not significantly differ . However, to distinguish between effects of genetic variation within inbred lines from the effects of bottlenecks itself (inbreeding/outbreeding), we separated lines into three categories: ‘low genetic variation’ and ‘high genetic variation’ inbred lines and ‘outbred’ (OB) control lines . Six lines evenly distributed among the genetic variation groups were removed due to a high relative abundance of the microbial endosymbiont Wolbachia, which might affect host fitness and/or abundance of other microbial taxa, resulting in 44 lines being analysed . These al. 2019  for detaFig 2). Generally, we observed an increase in microbiome diversity with increasing fly host genetic variation and viability, regardless of which measure we used to quantify the microbiome diversity. Richness indices, namely observed amplified sequence variant (ASV) richness and chao1 estimated ASV richness , showed a stronger trend than the diversity indices accounting for relative abundances . Even at the class level, the microbiomes from the low genetic variation group were less taxonomically heterogeneous than those from the high genetic variation and outbred groups . Most bacteria in the D. melanogaster microbiomes belonged to the Alphaprotoebacteria, Bacilli, and to a lesser extent Bacteroidia and Actinobacteria. The increasing level of taxonomic diversity with increasing host genetic variation was further evident at the genus level, with the low genetic variation group harbouring the fewest number of genera, while the outbred flies had the highest number . Non-metric multidimensional scaling (NMDS) analysis based on the weighted and unweighted UniFrac distances  showed that the microbiomes differed depending on the fly host’s level of genetic variation. Generally, there was better segregation of the fly groups when only the microbiome membership (unweighted UniFrac) was considered , while the abundance patterns showed greater similarity among the low genetic variation flies than the higher variation and outbred flies, due to the high relative abundance of a few ASVs in common in the low variation flies . PERMANOVA analysis showed that the fly groupings explained 22.8% and 43.0% of the microbial community membership and structure (i.e. membership + relative abundance using weighted UniFrac), respectively (p < 0.001). The host traits, nucleotide diversity and viability were significantly associated with the microbiome community patterns . Viability explained a greater proportion of the microbiome composition than did nucleotide diversity, where nucleotide diversity and viability explained 15.9% and 17.8% of the community membership, while explaining 27.7% and 35.3% of the community structure.Loss of host genetic variation was associated with decreased microbiome richness and diversity. The microbiomes from the outbred flies and the high genetic variation lines had a higher level of community diversity than the microbiomes of flies from the low genetic variation group (S1 Text). For Alpha richness, we observed a significant positive interaction between host genetic variation and microbial diversity on host fitness, meaning that they did not act independently. In fact, the positive interaction coefficients suggested synergism; i.e. in lines with high genetic variation, the effect of increasing microbiome richness was greater than in lines with low genetic variation and vice versa , while for chao1 ASV richness, Shannon-Wiener and Simpson’s indices, host genetic variation and microbial diversity contributed independently to host fitness (Table A inS1 Text). In all cases, the full models had higher explanatory power than each individual model , based on χ2 tests (S1 Text). Using data from Ørsted et al. (2019) [Fig F inS1 Text). For details on assessment of these phenotypes, see Ørsted et al. (2019) [To identify drivers of host fitness, we fitted generalized linear mixed effects models (GLMMs) with viability as a function of host nucleotide diversity (π) and microbiome diversity and their interaction   for the . (2019) .S2 Table). In comparison, only one Acetobacter ASV was significantly more abundant in the bottlenecked flies, i.e. the low and high genetic variation groups. More generally, at both the ASV and genus levels, Acetobacter relative abundances were particularly high in the low genetic variation lines compared to the lines with higher genetic variation , with Acetobacter ASVs making up the majority of the communities in the low genetic variation lines (median 94.0% for ASVs 1+2 and 96.9% for the genus). In contrast, five ASVs belonging to Lactobacillus and Enterococcus were significantly more abundant in the high genetic variation and outbred flies, while at the genus level, Enterococcus but not Lactobacillus showed this same trend . In addition, certain ASVs displayed incremental increases in relative abundances with increasing level of genetic variation and outbreeding, despite DESeq results being non-significant . Since relative abundances are used throughout the present study, these differential abundances do not correspond to simple changes in diversity, but actual changes in certain ASVs and genera shifting between genetic variation groups.The subset of the bacterial taxa that were differentially more abundant in the outbred flies were more diverse than in the inbred lines (i.e. the low and high genetic variation groups), with as many as 54 ASVs belonging to five bacterial classes and 12 orders (Table 2). Eleven of these ASVs persisted as part of the core microbiome across all of the fly groups, and belonged to the Acetobacter, Enterococcus, Lactobacillus, Leuconostoc, and Bacilli. Meanwhile, nine ASVs belonging to the Corynebacterium, Empedobacter, Nocardiaceae, Enterococcus, Mesorhizobium, and Lactobacillus, were only found in the core microbiomes of the high and outbred flies. The core microbiome of the lower genetic variation lines were subsets of those present in the higher genetic variation lines and outbreds, with 11/11 low genetic variation line ASVs represented in the high genetic variation lines, and 20/22 high genetic variation line ASVs represented in the outbred lines (Table 2).The number of ASVs that were present in at least 80% of lines was defined as the core microbiome. The number of ASVs belonging to this core decreased with decreasing host genetic diversity in the fly groups, with 48, 22, and 11 ASVs in outbred, high, and low genetic variation flies respectively . Fly viability and nucleotide diversity co-varied with a large cluster of bacterial ASVs within the same modular cluster . Generally, viability co-varied with a larger group of bacterial ASVs than nucleotide diversity, giving 40 and 13 associations, respectively, while 12 of these ASVs co-varied with both viability and nucleotide diversity. Interestingly, most of the ASVs that co-varied with viability were either differentially enriched in the outbred flies or in both the outbred and high genetic variation flies . The ASVs that were differentially enriched in both outbred and high genetic variation flies belonged to 24 different taxonomic groups (S1 Text). In contrast, the two Acetobacter ASVs that were significantly enriched in the low genetic variation flies belonged to separate modular clusters as highly prevalent negatively correlated ASVs . The smaller modules were often exclusively made up of related ASVs, notably modules with Lactobacillus and Enterococcus species. The networks of each individual fly group  revealed that inbreeding the flies resulted in a lower number of co-varying ASVs, and decreased the degree of connectivity (i.e. the number of significant correlations) and overall complexity of the network, with the latter consisting of only a few ASVs in a modular structure. Having a higher host genetic variation did not result in a higher degree of covariation among ASVs among inbred lines. Host genetic variation was both positively and negatively correlated with the abundance of bacteria in the outbred network, while host genetic variation did not significantly co-vary with bacterial abundance in the high and low genetic variation networks (at correlation adjusted p < 0.05). Moreover, the ASVs belonging to the Acetobacter, that were significantly enriched in the low genetic variation flies, displayed covariation only with Lactobacillus among the inbred flies, . Only a few previous studies have investigated the association between components of the microbiota and host genetics in invertebrates, and those that did have mainly focused on genetic variability in the host’s ability to control the presence and/or abundance of specific endosymbionts, e.g. in [In this study, we investigated the effects of host genetic variation on the microbiome diversity in terature –8, we sh e.g. in –40. Conv e.g. in , potenti e.g. in ,43 or by e.g. in ,45.Fig F inS1 Text). Thus, we show that populations with high host genomic variation harbor the most diverse microbial community, and vice versa for populations with low genetic variation, and that these lines are the least resilient to evolutionary pressure. According to the hologenome hypothesis, the holobiont constitutes a single unit of selection, thus our results suggest that small and fragmented populations have a reduced potential for responding to selection pressures not only due to reduced genetic variation and high rates of inbreeding but also due to reduced microbiome variation, effectively reducing hologenomic variation. Maintaining a diverse microbiome community may therefore be crucial for a host’s responsiveness to environmental change [Despite recent calls for an integration of microbiome research in evolutionary and conservation biology ,36,46,47l change –24,42.D. melanogaster, especially the gut bacteria, are mainly horizontally acquired [Fig 3). Similar to drift, these effects could be exacerbated in small populations, but unlike genetic drift, which is a stochastic process, the effects of bottlenecks yield seemingly directional and predictable effects on microbiome diversity.Our study sheds light on the ‘inheritance’ of microbiomes and on hosts as ‘environments’ for the microbiota. While many animals, including a range of insects, have transovarial vertical transmission of bacterial symbionts, i.e. via the egg to the embryo ,48, the acquired , and conacquired . Interesacquired  for an eDrosophila have highlighted the impact of fly and larval density on microbial communities and how this can impact fitness, producing a type of Allee effect [The trend of decreasing microbiome diversity with increasing host inbreeding has also been found in inbred populations of Diannan small-ear pigs and tortoises ,52. Thise effect . Simultae effect . We are Drosophila sp. can increase when there is high genetic heterogeneity in cultures, presumably because multiple genotypes can better utilize different aspects of the environment [Despite this, we show that while host genetic variation has the strongest association with fitness, the microbiome diversity also contributes synergistically to host effects. We envision that increased homozygosity in hosts in a population provides a less optimal habitat for maintaining stable and diverse microbial communities. In this respect, a number of studies have previously reported that hosts which are unhealthy and/or ill, harbor different and less diverse microbiomes compared to their healthier counterparts ,54–57. Mironment ,65. The ironment . It remaFig 6). This suggests that outbred lines harbour healthier and more robust microbial ‘ecosystems’ than bottlenecked lines, where functional redundancy within the diverse microbial community of outbred flies promotes community stability and subsequent host health. This means that removal of individual microbial species is expected to have less impact compared to in low and high genetic variation lines, where the removal of one key species may cause more dramatic effects. The importance of functional redundancy in host-associated microbiome stability and host health has been previously demonstrated in various hosts as well as in many other ecosystems [Wolbachia was included or not, as Wolbachia can affect both host fitness and the presence/abundances of other microbial taxa [Fig H inS1 Text).The outbred lines investigated in our study have a much more complex network of co-varying, and interacting microbiomes compared to the low and high genetic variation groups . From the mass bred population, we created 40 lines of each of three different expected levels of inbreeding for a total of 120 lines, from which we could obtain nucleotide diversity measures for 109 lines. This is described in detail in Ørsted et al. (2019) [The . (2019) . In summet al. (2019) [Fig A inS1 Text). Because we selected lines regardless of their inbred/outbred status, nine of the ten outbred control lines were included in the ‘high genetic variation’ lines due to their high Z-scores sum. In all analysis, we differentiate between three groups of flies: ‘low’ and ‘high’ genetic variation lines within the inbred lines and ‘outbred’ (OB) lines that have not been through population bottlenecks. Following establishing the lines, population sizes were flushed to ca. 200 individuals per line, and in the F1 generation egg-to-adult viability was assessed by distributing 15 eggs into each of five vials per line and calculating the proportion of eggs developing successfully to the adult stage [We obtained a measure of the realised genetic variation in all inbred and outbred lines using genotyping-by-sequencing (GBS) to calculate nucleotide diversity (π) from genomic SNPs (described in detail in Ørsted . (2019) ; averagelt stage . Flies uet al. (2019) [et al. (2015) [The whole fly genomic DNA was extracted following the same protocol as has been described in Ørsted . (2019) . In brie. (2015) , and thehttps://github.com/eyashiro/AmpProc), which relies on USEARCH version 11.0.66_i86linux64 [Wolbachia can affect host fitness and/or the presence/abundance of other microbial taxa, complicating interpretation of results [Wolbachia abundance >85%. These six lines were evenly distributed among the genetic variation groups . This threshold was chosen to maintain a relatively high number of reads per line/sample, i.e., at least 14,000 reads per sample, after Wolbachia reads were removed. Prior to further analysis, we also tested whether removing Wolbachia from the frequency table would skew our results and interpretations. There was no difference in relative abundance of Wolbachia between genetic variation groups . Further, we found no significant correlations between Wolbachia abundance and nucleotide diversity  or fitness , and there was no significant difference between the six removed lines and the 44 remaining lines in mean nucleotide diversity  or mean fitness . Thus, our main results namely that lines with low genetic variation and fitness harbor a less diverse microbiome were not affected by removing lines high in Wolbachia abundance. However, the prevalence of Wolbachia reads above 10% per sample distorted the relative abundance of other bacterial taxonomic groups to a small extent , thereby leading to slightly different interpretations of the co-abundance networks .The demultiplexed paired-end reads were quality filtered, assembled to make consensus amplicon reads, clustered into ASVs, and taxonomically assigned using a custom workflow AmpProc version 5.1.0.beta2.11.1  by randomly keeping three, two, and three lines from the high, medium, and low inbreeding levels, respectively, from the high and low genetic variation groups [Next, QIIME version 1.9.1  was usedn groups , and allp < 0.05) of the fly host traits were displayed as arrows and fly host trait gradients across the ordination space as isolines using the ordisurf function. PERMANOVA analysis using vegan’s adonis function was undertaken for the UniFrac matrices as a function of fly lines, nucleotide diversity and viability. Differentially enriched ASVs were identified with DESeq2 . The efn 1.22.2 ) using t2 difference tests. We detected no over-dispersion in any of the models . Conditional coefficients of determination of the GLMMs interpreted as the variance explained by the entire model, including both fixed and random effects, were calculated as et al. (2017) [To assess drivers of fitness measured as egg-to-adult viability, we fitted generalised linear mixed effect models (GLMMs) in the R-package ‘lme4’  assuming. (2017) ).p < 0.05 and correlation coefficients > 0.5 (or < -0.5) were kept. In Gephi version 0.9.2, the network was generated with the Fruchterman Reingold algorithm and minor crossing of edges were corrected manually to improve the visualization of the clusters. From among Gephi’s built-in features, the Leiden algorithm was used to calculate the modularity structure using edge weights and default settings, and the degree of connectivity was calculated as the number of nodes with which each node is connected by an edge.For co-occurrence network analysis, the ASV table of all of the fly lines was used for the global network, and the subset of ASV tables generated for the core microbiomes analysis for outbred, high genetic variation, and low genetic variation groups were used as the starting frequency tables. In each of these ASV frequency tables, the fly nucleotide diversity and egg-to-adult viability measurements were included. The ASVs present in fewer than three lines and ASVs with less than 30 reads in the respective frequency tables were removed. The ‘Hmisc’ R-package  was usedS1 Text(DOCX)Click here for additional data file.S1 TableWolbachia was not prefiltered, and which includes the six lines that were removed from the original 50 lines due to high relative Wolbachia abundance (in a separate tab). Headers of the metadata sheets are described in the last tab.S1 Table found online as a Microsoft Excel spreadsheet. Nucleotide diversity, fitness and microbiome diversity , and ASV table rarefied to 14,488 reads per sample of the 44 fly lines used in the main results, as well as ASV table rarefied to 29,000 reads per sample where (XLSX)Click here for additional data file.S2 Tablep < 0.1), the fly group in which the ASVs or genera are enriched is indicated.DESeq2 table is found online as a Microsoft Excel spreadsheet. The log fold change calculations are done on the ASV (L7 tabs in the spreadsheet) and genus level frequency tables (L6 tabs in the spreadsheet). The DESeq contrasts were done among outbred, high and low genetic variation fly groups, and the pairwise contrasts are displayed in separate sheets. For the most significant pairwise comparisons ((XLSX)Click here for additional data file.