Obolodiplosis robiniae, a highly invasive species that originated in North America, spread widely throughout Asia and Europe in the past decade. Here, we used 11 microsatellite DNA markers to analyze the genetic variation of 22 O. robiniae populations in China (the introduced region) and two additional US populations (the native region). A relatively high level of genetic diversity was detected among the introduced populations, even though they exhibited lower diversity than the native US populations. Evidence for genetic differentiation among the introduced Chinese populations was also found based on the high Fst value compared to the relatively low among the native US populations. Phylogenetic trees, structure graphical output, and principal coordinate analysis plots suggested that the Chinese O. robiniae populations  cluster into two main groups independent of geographical distance. Genetic variation has been observed to increase rapidly during adaptation to a new environment, possibly contributing to population establishment and spread. Our results provide insights into the genetic mechanisms underlying successful invasion, and identify factors that have contributed to colonization by an economically important pest species in China. In addition, the findings improve our understanding of the role that genetic structure plays during invasion by O. robiniae.Invasive species often cause serious economic and ecological damage. Despite decades of extensive impacts of invasives on bio-diversity and agroforestry, the mechanisms underlying the genetic adaptation and rapid evolution of invading populations remain poorly understood. The black locust gall midge,  Obolodiplosis robiniae (Robinia (Fabaceae)  . Its mai‘Frisia’ . The gal‘Frisia’ .Obolodiplosis robiniae was first recorded in China  in 2004 (R. pseudoacacia) has been planted extensively across China, and O. robiniae is now found in most of these areas  are suitable for routine genetic diversity analyses , as theyO. robiniae populations using a partial mitochondrial DNA cytochrome c oxidase subunit I (COI) sequence marker. However, only 10 individuals exhibiting haplotypic variation and a mere four haplotypes were detected in 560 O. robiniae samples. Thus, the genetic mechanisms behind successful invasion, the genetic structure in the process of colonization, and the phylogenetic relationships among the Chinese O. robiniae populations remain poorly understood.Previously, O. robiniae populations and ascertain how the species has spread widely in new regions, we used 11 microsatellite markers to analyze the genetic structure of 22 Chinese O. robiniae populations. Two native populations from the United States (US) were also assessed based on the same loci for comparison with the Chinese populations, in order to explain how genetic diversity is altered during the invasion process.Accordingly, to gain further insight into the genetic structure of the Chinese O. robiniae adults from two regions of the United States were obtained from the Quarantine Lab at the Institute of Forest Ecology, Environment, and Protection in the Chinese Academy of Forestry. Details of the sample collection and population codes are listed in We collected the gall midge larvae and pupae contained within rolled leaves of host trees growing in 22 cities across China . GeneralO. robiniae body following the instructions described by O. robiniae individuals.Genomic DNA was extracted from the entire 2O. The forward primer of each primer pair was labeled with a fluorescent dye . The microsatellite cycling protocol was: 5 min at 95°C ; followed by 30 cycles of 94°C for 30 s, 53°C  or 56°C (the remaining loci) for 45 s, 72°C for 45 s, extension at 72°C for 10 min, and finally maintained at 16°C. PCR products were examined using a DNA analyzer  and the results were analyzed using Genotyping was carried out using a 3730xl automated DNA sequencer . Alleles were scored using GeneMarker software version 2.2. .Microsatellite amplifications were performed in a 15 μL reaction volume containing 1 μL genomic DNA (10 ng), 1 μL of each primer (5 μmol/L), 7.5 μL 2X Taq PCR Master Mix , and 4.5 μL ddHNa), effective number of alleles (Ne), Shannon’s information index (I), observed heterozygosity (Ho), expected heterozygosity (He), and Nei), which were calculated using GenePop software version 4.3 (GN), gene diversity (GD), and polymorphism information content (PIC) were calculated using PowerMarker software version V3.25  and gene flow (Nm) through AMOVA  approach which were performed using the Arlequin program version 3.5 (K) using STRUCTURE software version 2.3.4 (K) was assumed to be from 1 to 22, without admixture and with correlated allele frequencies. To determine the most likely number of subpopulations, the optimum K-value was obtained by calculating the ΔK value  observed per locus varied from 2 (W83 and W107) to 14 (W3 and W5), with a mean of 6.5 per locus. The effective numbers of alleles (Ne) varied from 1.4087 (W83) to 7.9971 (W3), with an average of 3.7255 per locus. The gene diversity index (GD) per locus ranged from 0.2901 (W83) to 0.8755 (W3), with an average of 0.6511, indicating that a high level of information was provided by the 11 microsatellite markers. Shannon’s information index (I) ranged from 0.4767 (W83) to 2.2573 (W3), with a mean of 1.3188. The polymorphism information content (PIC) for the microsatellite loci ranged from 0.2494 (W83) to 0.8627 (W3), with an average of 0.6031. The observed heterozygosity (Ho) ranged from 0.2271 (W107) to 0.7477 (W3) and expected heterozygosity (He) ranged from 0.2905 (W83) to 0.8760 (W3). For each locus, both Na and Ho values markedly changed among populations, whereas, He value minorly altered (p < 0.05) in 68 of 242 tests (28.10%) (p < 0.05) in 251 of 1210 tests (20.74%) from 11 loci in the 22 Chinese populations.In this study, locus polymorphism and diversity were determined based on 22 Chinese populations using 11 microsatellite markers, with each population consisting of 20 individual samples  . Amplify altered . In addi(28.10%) . ExaminaNa, Ne, I, Ho, He, and Nei) were evaluated. As shown in Na were moderately or considerably lower than those of the native US populations, although the sample size of the Chinese populations (40) was markedly higher than that of the US populations (8). Regarding the three most important indices, I, He, and Nei, the lowest Chinese values  occurred in the DY population, and the highest  in the TS population. Increased values of these three indices occurred in the SY , YT , and DD  populations. The inbreeding coefficient (Fis) ranged from −0.0295 (TY) to 0.2011 (CD), with significant various observed for each locus among populations. Both US populations exhibited high He values of 0.6544 (US_f) and 0.6606 (US_g).The genetic diversity of 22 Chinese populations and two US populations was assessed. Six indices of genetic diversity  to 0.3364 (W82) with an average of 0.0873 alleles per locus across populations (Nm) ranged from 0.3093 at W31 to 1.5921 at W33 and averaged 1.0036. Meanwhile, the pairwise Fst (p < 0.001) values  were >0.15, while 48 (22.54%) were >0.25, which suggests that significant genetic differentiation exists among the sampling sites and there is some restriction in gene flow between them (Fst = 0.377), followed by GY and QH (Fst = 0.372), then DY and QH, DY and GY (Fst = 0.361 for both). According to the coefficient of genetic differentiation , genetic variation within populations (81.66%) was substantially higher than that among populations (18.34%) (p < 0.001) (Nm) ranged from 0.414 (BJ and GY) to 11.05 (HF and WH) . Regarding the native US populations, they had a relatively low Fst value , a high Nm value (2.685), and variation within populations of 91%, while variation among populations was 9% (p < 0.02). This further indicates the existence of extensive genetic differentiation among the introduced populations.The ulations . Gene fl) values  between een them . The mos< 0.001) . Gene fl and WH) , with an and WH) . All invA dendrogram depicting the genetic relationships among the 22 Chinese populations was constructed based on the microsatellite data . The popO. robiniae samples were further assessed for population stratification using STRUCTURE software. Microsatellite data were analyzed with possible cluster numbers  ranging from 1 to 22. ΔK was clearly maximized when K = 2 (ΔK = 4298.3743), indicating the occurrence of two distinct groups among the 22 populations (K = 3) revealed that QD, GY, and TY in group I had certain structural similarities to YT, SY, NJ, YC, and XA in group II.The 436 Chinese ulations , which vr = −0.02, P = 0.456; r = −0.026, P = 0.419; In addition, PCoA based on the marker genotypes also revealed two distinct clusters of the Chinese populations , which wO. robiniae populations across all loci, with the average expected heterozygosity (He) and Nei) being 0.5346 and 0.5192, respectively. The highest He was 0.6606, which occurred in the US_g population despite the fact that it consisted of only three O. robiniae individuals. Nonetheless, He, Nei (GD), and polymorphism information content (PIC)   are miningiferae .O. robiniae across China in the past decade to the extent that this species is now established in most regions where its host exists  among populations from sites separated by distances of up to 2,540 km. Hence, the relatively high genetic diversity likely caused by significant differentiation among populations has been conducive to the rapid colonization and establishment of O. robiniae in China, and it is an important factor contributing to the successful invasion of O. robiniae.The relatively high level of genetic diversity has likely contributed to the spread of t exists . O. robi in 2004 , whereas in 2004 . MoreoveO. robiniae.The observed population differentiation likely resulted from rapid evolution during adaptation to the new environment. Invasive species may evolve rapidly in response to selection pressures driven by novel habitats , and sucO. robiniae, in light of the short history in China, its high genetic diversity and colonization success might be also related to multiple independent invasive events.Furthermore, high levels of genetic diversity in an invasive species might be caused by multiple introductions or large founding populations. It is thought that multiple introductions are associated with increased diversity because they supply increased variation and new genetic communities . MultiplK = 2) analyses indicated that the Chinese O. robiniae populations are divided into two independent clusters, although this division appears to be unrelated to geographical distribution. Meanwhile, gene flow was found among some Chinese populations, with QD, GY, and TY in group I having highly similar structures to YT, SY, NJ, YC, and XA in group II. In addition, some subgroups  were clustered according to their geographical distribution, which likely represents different routes of spread in the new environment. Furthermore, our results revealed that each group of the Chinese O. robiniae populations exhibited differences in geographical distribution and genetic distance, suggesting that the two groups do in fact represent two different sources. This implies the introduced populations likely experienced two independent invasive events, which initially shaped the genetic structure of the Chinese O. robiniae populations.Many studies have shown that the genetic structures of invasive species are well developed in their new ranges . Indeed,O. robiniae populations.On the other hand, the fact that the division of the two groups was unrelated to geographical distribution  suggests that human activity is likely another important contributing factor. Transport, business trips, and long-distance vacations have recently increased not only in frequency but also in distance. High levels of human-mediated dispersion can increase the genetic diversity of an invasive population, thereby substantially modifying the genetic structure and potential management units ; these fAll datasets generated for this study are included in the article/Y-XY and W-XZ: conceptualization, funding acquisition, project administration, and supervision. R-ZL, W-XH, and JY: data curation. Y-XY, X-PS, and JY: formal analysis, investigation, and resources. Y-XY and X-PS: methodology. Y-XY and R-ZL: software. Y-XY, R-ZL, and W-XH: validation. Y-XY, X-PS, R-ZL, and W-XH: visualization. Y-XY, X-PS, R-ZL, W-XH, JY, and W-XZ: writing – original draft and review and editing.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Acanthus ilicifolius, Aegiceras corniculatum, Avicennia marina, Bruguiera gymnorrhiza, Kandelia obovata, Lumnitzera racemosa, and Rhizophora stylosa), which are predominant along the coastlines of South China, were genotyped at nuclear (nSSR) and chloroplast (cpSSR) microsatellite markers. We estimated historical and contemporary gene flow, the genetic diversity and population structure of seven mangrove species in China. All of these seven species exhibited few haplotypes, low levels of genetic diversity  and high levels of inbreeding (FIS = 0.104–0.637), which may be due to their marginal geographical distribution, human-driven and natural stressors on habitat loss and fragmentation. The distribution patterns of haplotypes and population genetic structures of seven mangrove species in China suggest historical connectivity between populations over a large geographic area. In contrast, significant genetic differentiation  indicates that populations of mangroves are isolated from one another with low levels of contemporary gene flow among populations. Our results suggest that populations of mangroves were historically more widely inter-connected and have recently been isolated, likely through a combination of ocean currents and human activities. In addition, genetic admixture in Beibu Gulf populations and populations surrounding Hainan Island and southern mainland China were attributed to asymmetric gene flow along prevailing oceanic currents in China in historical times. Even ocean currents promote genetic exchanges among mangrove populations, which are still unable to offset the effects of natural and anthropogenic fragmentation. The recent isolation and lack of gene flow among populations of mangroves may affect their long-term survival along the coastlines of South China. Our study enhances the understanding of oceanic currents contributing to population connectivity, and the effects of anthropogenic and natural habitat fragmentation on mangroves, thereby informing future conservation efforts and seascape genetics toward mangroves.Mangrove forest ecosystems, which provide important ecological services for marine environments and human activities, are being destroyed worldwide at an alarming rate. The objective of our study was to use molecular data and analytical techniques to separate the effects of historical and contemporary processes on the distribution of mangroves and patterns of population genetic differentiation. Seven mangrove species ( Rhizophora mucronata in Southeast Asia, Rhizophora racemosa in East Atlantic, Rhizophora mangle in Florida, Sonneratia alba across the Indo-West-Pacific region, showed that historical events  and contemporary oceanographic conditions  contributed to demographic histories and population genetic structure , Aegiceras corniculatum (Myrsinaceae), Avicennia marina (Verbenaceae), Bruguiera gymnorrhiza (Rhizophoraceae), Kandelia obovata (Rhizophoraceae), Lumnitzera racemosa (Combretaceae), and Rhizophora stylosa (Rhizophoraceae)] across their entire distributional range in China. These seven species are dominantly distributed in the South China Sea (SCS). Owing to differences in pollination vectors and propagule types, these species are expected to have different potentials for gene flow. For this purpose, our main goals were to (i) investigate the patterns of genetic diversity and population genetic structure of seven dominant mangrove species with varying reproductive traits along the coastlines of southern China; (ii) examine whether and how oceanic currents along the South China Sea contributed to the biogeographic processes and population genetic structure of mangrove species. We then discuss the effects of major evolutionary processes, as well as the oceanic currents in the South China Sea that might have played dominant roles in shaping the current distribution and genetic patterns of mangrove populations. We further discuss the relative influence of historical versus contemporary processes on shaping the spatial distribution of genetic variation. This will allow us to assess the effects of anthropogenic habitat fragmentation on mangroves, thereby informing conservation efforts toward them.Ocean currents create a complex physical environment that influences dispersal, isolation, population connectivity and genetic structure of marine systems . OceanicA. ilicifolius, A. corniculatum, A. marina, B. gymnorrhiza, K. obovata, L. racemosa, and R. stylosa). Kandelia spp. is mainly distributed along the coastlines of Asia and has a wide distribution from western and eastern India, through the South China Sea region to southern China and southern Japan , and the other is named as K. obovata (distributed from northern South China Sea to southern Japan)  . AlthougB. gymnorrhiza, K. obovata, and R. stylosa) are viviparous, two species (A. corniculatum and A. marina) are crypto-viviparous, and the remaining species (A. ilicifolius and L. racemosa) are non-viviparous  markers, respectively, and 7, 5, 13, 11, 8, 16, and 8 chloroplast microsatellite (cpSSR) markers, respectively. Details of these nuclear SSR (nSSR) and chloroplast SSR (cpSSR) markers are presented in For microsatellite analysis, samples of nh), number of polymorphic loci (np), and Nei’s unbiased haplotype diversity estimates  and geographical distances was assessed by Mantel test  were performed using 1,000,000 Markov Chain Monte Carlo (MCMC) repetitions and 100,000 burn-in periods  of runs.Bayesian methods were also used to recover the optimal number of clusters representing these populations. This analysis was performed using STRUCTURE 2.3 software . Twenty  periods , no prioNe generations in the past (We used BAYESASS 3.0.4  and Migrthe past .m) between populations without assuming genetic equilibrium, which reflects gene flow over the most recent several generations , allele frequencies (a), and inbreeding coefficients (f) so that posterior acceptance rates for each parameter were between 20 and 60% as suggested by A. corniculatum, A. ilicifolius, A. marina, L. racemosa, and R. stylosa, we used m = 0.15, a = 0.4, and f = 0.5; for B. gymnorrhiza and K. obovata, we used m = 0.15, a = 0.4, and f = 0.7, respectively. Convergence of the MCMCs was checked by comparing the traces of each run using TRACER v. 1.7 (BAYESASS uses a Bayesian approach and a Markov chain Monte Carlo (MCMC) algorithm to estimate migration rates (erations . We ran R v. 1.7 . Among tmh) among populations  and the mutation-scaled migration rate (M) (M = mh/μ) between population pairs. Migrate-N assumes that populations are in migration-drift equilibrium, that population sizes and migration rates are constant through time, and that populations are randomly sampled. To estimate migration, we ran five replicates for each population in Migrate-N using Brownian motion mutation model with constant mutation rates and starting parameters based on FST calculations. The program estimates the parameters θ and M using a Bayesian method (Nm) into each population using the equation 4 Nm = θ∗M. We estimated θ and M with slice sampling and uniform prior distribution . Each of the five replicate runs was performed with 10,000-recorded genealogies at a sampling interval of 5 increments after discarding the first 100,000 genealogies (burn-in). Four-chain adaptive heating at temperatures  was used to increase the efficiency of the MCMC. The confidence interval for θ and the migration parameter M was calculated using a percentile approach  to 3.2 (A. marina), and from 0.3 (A. ilicifolius) to 2.1 (K. obovata), respectively (HE), ranging from 0.050 (R. stylosa) to 0.263  and allelic richness (R) per locus and per population, based on nSSR marker data, ranged from 1.8 (R. stylosa) to 7.2 (K. obovata), and 1.6 (R. stylosa) to 6.3 (K. obovata), respectively (HO) and expected (HE) heterozygosities ranged from 0.088 (R. stylosa) to 0.544 (K. obovata), and 0.160 (R. stylosa) to 0.587 (K. obovata), respectively  ranged from 3.6 (A. ilicifolius) to 206.0 (A. corniculatum)  .R) and expected heterozygosity (HE) data are shown in R, HE, and PA values (K. obovata) to 0.637 (L. racemosa), indicating that most of these mangrove populations experienced at least moderate levels of inbreeding  among the populations was for R. stylosa (0.923), followed by A. ilicifolius (0.834), L. racemosa (0.787), A. marina (0.530), K. obovata (0.438), B. gymnorrhiza (0.274), and A. corniculatum (0.173). Significant correlations, between geographical distance and genetic distance (GST) estimates, were observed for A. marina and K. obovata (P < 0.01), but not among the other five species , L. racemosa (50.7%), and R. stylosa (58.0%), while, for the other four species, most of the genetic variation was detected within populations  to 0.629 (A. ilicifolius), which indicated a high level of genetic differentiation among populations for these species , 0.025 to 0.373 (A. corniculatum), 0.040 to 0.314 (A. marina), 0.004 to 0.254 (B. gymnorrhiza), 0.015 to 0.351 (K. candle), 0.165 to 0.579 (L. racemosa), and 0.007 to 0.747 , with the exception of A. corniculatum . For all of the species, only a few haplotypes were detected in most of their populations . The remaining haplotypes were found in a few locations. Among seven mangrove species, significant phylogeographic structure was only observed in R. stylosa  (The polymorphic cpSSR markers yielded 3 to 11 haplotypes among the seven mangrove species. The highest number of haplotypes was found in  < 0.05] .m < 0.1) for most population pairs of all seven mangrove species (Nm) for historical times as calculated by Migrate-N were high (Nm > 1) in seven mangrove species   , 5. For stylosa) , 5. Furt obovata , 5. Stro obovata , 5. In a species , 5.HE from 0.160–0.361), with the exception of K. obovata (HE = 0.587). High levels of inbreeding (FIS = 0.104–0.637) were also detected in most populations. Small recent effective population sizes for all mangrove species (3.6–206.0) were also detected. The cpSSR markers identified only a few haplotypes that were found in most populations, while the remaining haplotypes only occurred in a few populations  is one of the world’s most diverse marine ecosystems , and is R. mucronata is maintained by ocean features. The patterns of cpSSR haplotype distribution and population genetic structures of the seven mangrove species showed that although different species aggregated in different clusters, populations within a similar geographical distance had similar genetic structures , 3, whicNm > 1) between the populations in the northeast Beibu Gulf around Hainan Island (11–16) and the northern Beibu Gulf (5–8) for five mangrove species were found , over 450 km across the Qiongzhou strait  were higher than for B. gymnorrhiza (0.165).Furthermore, the extent of gene flow varied among each mangrove species. The quantification of the numbers of propagules available for dispersal, the timing of propagule release in combination with the tidal framework and seasonal changes in ocean circulation, propagule buoyancy, viability period of propagules in seawater are the potential factors for the mangrove propagule dispersal . Among t species , 5. The cifolius , which wMangroves are being fragmented and destroyed, which were attributed primarily to human activities . As a reNE) ranged from 3.6 (A. ilicifolius) to 206.0 (A. corniculatum)  . This isculatum) . During culatum) , and as FST = 0.165-0.629 (nSSR); GST = 0.173-0.923 (cpSSR)] according to the AMOVA, especially the A. ilicifolius, L. racemosa and R. stylosa populations   (K. obovata (GST = 0.5548)  , C. taga= 0.529) , K. obov 0.5548) . Furtherm < 0.1) was lower than historical gene flow (Nm > 1)  , 5. Thisiculatum , C. tagaC. tagal , H. littttoralis , K. cand. candel , L. raceracemosa , and Nypruticans . ReducedK. obovata. Local population extinction could result in a decreased potential to adapt to environmental changes, through the loss of alleles with adaptive value. In addition, a reduction in the number of populations and their sizes could enhance genetic drift and inbreeding, leading to reduced fitness. The conservation of mangrove genetic resources involves not only preventing extinction, but also ensuring the preservation of alleles that could provide adaptive values in changing environments  could be identified based on genetic structure results, and any activities resulting in gene mixing with each other ought to be prevented. We suggest that future ecological restoration initiatives should establish these bioregions as conservation units. Within the different bioregions of each species, plants could be transplanted to diminish the risk of fitness decline and loss of genetic diversity of the native plant populations.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.QG, CL, and TH conceived the study. QG performed the experiments, analyzed the data, and wrote the manuscript. JT helped to collect the samples. ZW, MK, and HL provided useful suggestions on the manuscript. All authors contributed to the manuscript revision, read and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Increased genetic diversity in plants is probably associated with greater ambient temperatures. To test this hypothesis, we studied genetic diversity and differentiation of weedy rice populations occurring in the early- and late-season rice cultivation fields in Leizhou of southern China. Data collected from 10-year climatic records showed a higher average temperature in the late rice-cultivation seasons than in the early rice-cultivation seasons. Results obtained based on 27 SSR (simple sequence repeat) loci indicated greater genetic diversity in the late-season weedy rice populations, in addition to the considerable genetic differentiation between the early- and late-season weedy rice populations. We therefore conclude that a higher ambient temperature might be an important factor to promote the formation of genetic diversity in the late-season weedy rice populations.Oryza sativa f. spontanea) populations occurring naturally in early- and late-season rice fields that share nearly the same ecological conditions but with slightly different temperatures. Data collected from 10-year historical climatic records indicated a ~2 °C higher average air temperature in the late rice-cultivation seasons than in the early seasons. Results based on molecular fingerprints of 27 SSR (simple sequence repeat) loci showed a higher level of genetic diversity in the late-season weedy rice populations than in the early-season populations. In addition, a positive correlation was detected between the increased proportion of genetic diversity (ΔeH) and genetic differentiation among the weedy rice populations, suggesting limited gene flow. Therefore, we conclude from this study that increased genetic diversity in the late-season weedy rice populations is probably caused by the higher ambient temperatures. This finding provides evidence for the possible association between genetic diversity and ambient temperatures. Hypotheses regarding the association of increased species or genetic diversity with gradually warmer regions as a global pattern have been proposed, but no direct and solid experimental data are available to approve the association between plant genetic diversity and ambient temperatures. To test the diversity-temperature hypothesis, we studied genetic diversity and genetic differentiation of weedy rice ( Biodiversity plays an important role in maintaining the balance of the biosphere and sustaining human livelihood ,2. To adGenetic diversity is the fundamental sources of biodiversity at the genic level ,17. The Oryza sativa f. spontanea, also referred to as red rice, O. sativa), but with strong competition, seed shattering, and prolonged seed dormancy. Consequently, weedy rice is extremely difficult to control, causing great yield and quality losses of cultivated rice [Weedy rice  Is the abundance of genetic diversity in the two-season weedy rice populations affected by temperature differences between the two rice-cultivation seasons? (2) Does genetic differentiation occur between the corresponding early- and late-season weedy rice populations? (3) Is there any correlation between genetic diversity and genetic differentiation in weedy rice populations? The answers to these questions will facilitate our understanding of the relationships between genetic diversity of plant populations and the ambient temperatures, in addition to explaining the possible reasons regarding how genetic diversity can be maintained in plant populations, particularly between the early- and late-season weedy rice populations.A total of 18 weedy rice populations were collected from nine different rice fields in Leizhou, the Guangdong Province of China in 2018 . The 18 2, and 6.7 μL ddH2O in the 2720 Thermal Cycler . Total genomic DNA was extracted from weedy rice seedlings at about the four-leaf stage, following the CTAB protocol . All ricThe PCR products were separated and analyzed on a capillary electrophoresis analyzer . Amplified DNA fragments were scored as genotype data (size of the SSR fragments) for each weedy rice sample, owning to the co-dominant feature of the SSR markers ,32,38,39t-test [The data of air temperatures, including the minimum and maximum daily values in Leizhou, were collected from the Tianqi Weather Database  from thet-test .eN); (ii) the percentage of polymorphic loci (P); (iii) Shannon’s information index ; and (iv) Nei’s expected heterozygosity (eH). The analysis of molecular variance (AMOVA) was carried out to estimate the partition of genetic diversity within and among populations, at a level of p < 0.001 and 9999 permutations. The statistical analyses were conducted in the software GenAlEx version 6.5 [The genotypic data matrix based on the 27 SSR loci of 720 weedy rice individuals representing nine population pairs were analyzed to examine the genetic diversity. The following parameters were calculated: (i) the number of effective alleles per locus  and number of private alleles (PAS) were computed to estimate genetic differentiation between the early- and late-season weedy rice populations. The statistical analyses were carried out using the software GenAlEx version 6.5 [eH) with the stF values and difference in number of private alleles (ΔPAS) between the early- and late-season weedy rice populations were analyzed, using the software Prism 8 [eH was estimated based on the calculation of absolute values obtained from eH of the late-season weedy rice populations subtracting eH of the corresponding early-season weedy rice populations; whereas the ΔPAS was estimated based on the calculation of absolute values obtained from PAS of the late-season weedy rice populations subtracting PAS of the corresponding early-season weedy rice populations.The F-statistics  was about 1.4–1.8 °C higher in the late rice-cultivation seasons than in the early rice-cultivation seasons in the collected region . The aveThe 10-year average values of the maximum and minimum air temperatures in the late rice-cultivation seasons varied between ~30.9 and ~24.0 °C, respectively. However, the 10-year average values of the maximum and minimum air temperatures in the early rice-cultivation seasons varied between ~29.1 and ~22.6 °C, respectively. Noticeably, the 10-year average air temperature at the initial stage of rice growth (seedling) was substantially higher (7.7–8.7 °C) in the late rice-cultivation seasons than in the early seasons . This obeN), the percentage of polymorphic loci (P), Shannon’s information index (I), and expected heterozygosity (eH), was obviously higher in the late-season weedy rice populations than that in the early-season weedy rice populations  between the corresponding early- and late-season weedy rice populations varied from 0.032–0.103  and genetic differentiation (stF), as well as between ΔeH and differences in PAS (ΔPAS) in the early- and late-season weedy rice populations  was found between the increased proportion of genetic diversity (ΔeH) and genetic differentiation (stF) in the early- and late-season weedy rice populations  was also detected between ΔeH and the differences in the number of private alleles (ΔPAS) in the corresponding early- and late-season population pairs  in association with temperature, such as the latitudinal biodiversity gradient model were proposed ,22,52,53Results from the above studies may have not provided sufficient evidence to approve the diversity-temperature hypothesis at a small scale. In contrast, findings provided by this study are based on the experimental assessment of genetic diversity of early- and late-season weedy rice populations occurring in exactly the same rice fields. At the same sites, many ecological factors are the same, but temperatures are different between the early and late rice-cultivation seasons. Therefore, the results obtained from this study provided direct and relatively solid evidence for the hypothesis that the higher ambient temperature can promote the formation of genetic diversity, although other factors also play important roles.Elymus species [Weedy rice is a strictly self-pollinating taxon ,54. Usua species  were alsstF) and differences in the number of private alleles (ΔPAS). This finding clearly demonstrates limited gene flow between the early- and late-season weedy rice populations. Limited gene flow between weedy rice populations is commonly reported, as indicated by stF values or the mixed mating models generated form molecular fingerprints [Our results further indicated considerable genetic differentiation between most of the corresponding early- and late-season weedy rice populations, based on both the fixation index  in the studied weedy rice populations. As expected, significant positive correlations were detected between genetic differentiation, as represented by the fixation index (stF) and differences in the number of private alleles (ΔPAS), and the increased proportion of genetic diversity (ΔeH). In other words, the observed high proportion of increased genetic diversity might be maintained in the late-season weedy rice populations by limited gene flow between the weedy rice populations occurring in different seasons at the same field sites. Generally, limited gene flow can effectively reduce the exchange of genetic materials, causing genetic heterogeneity of plant populations. In contrast, excessed gene flow can increase the exchange of genetic materials, causing genetic homogeneity of plant populations. Consequently, the observed phenomenon of genetic heterogeneity between the early- and late-season weedy rice populations may cause the reduced exchange of genetic materials, which may play an important roles in maintaining the increased proportion of genetic diversity formed in the late-season weedy rice populations. It is therefore interesting to understand the relationships between genetic differentiation and the increased proportion of genetic diversity  of Sorghum (Sorghum bicolor) in the cultivated gene pool and wild gene pool among different climatic zones. As a result, no obvious differences in genetic diversity  were detected within cultivated gene pool and wild gene pool. However, the authors detected a high level of genetic differentiation (stF=0.40) among different racial sorghum types, and consequently, considerable differences in genetic diversity . These findings clearly suggested that the limited gene flow, as indicated by genetic differentiation, played a certain role in maintaining genetic diversity within Sorghum populations. In addition, Jacquemyn et al. (2010) [Cirsium acaule) in the calcareous grassland area, which has played an active role in maintaining a low level of genetic diversity within populations. Likewise, evidence was also found in many other plant species, such as the tropical tree Copaifera langsdorffii Desf [Juglans regia) [Based on our study, we propose a hypothesis—the maintenance of genetic diversity, no matter high or low, formed in the early- and late-season weedy rice populations depends on limited gene flow between these populations. This is probably true to other plant populations/species with limited gene flow. For example, Sagnard et al. (2011)  provided. (2010)  also repfii Desf  and walns regia) . TherefoIn this study, we found a relatively high level of genetic diversity in the late-season weedy rice populations exposed to slightly higher ambient temperatures compared with the early-season populations from the same field sites with lower temperatures. This finding provided direct experimental evidence to support the diversity-temperature hypothesis in which the higher ambient temperature can promote greater genetic diversity. We also found considerable genetic differentiation between the corresponding early- and late-season weedy rice populations collected from the same fields, probably caused by limited gene flow. The positive correlation between the increased proportion of genetic diversity and genetic differentiation suggests a low level of the exchange of genetic materials, which probably acts as a factor to maintain genetic diversity within the early- or late-season weedy rice populations during the evolutionary processes. Whether or not the richness and maintenance of genetic diversity in plant populations/species follow such underlying mechanisms still needs further investigations.


Populations are fundamental units of ecology and evolution, but can we define them for bacteria and archaea in a biologically meaningful way? Here, we review why population structure is difficult to recognize in microbes and how recent advances in measuring contemporary gene flow allow us to identify clearly delineated populations among collections of closely related genomes. Such structure can arise from preferential gene flow caused by coexistence and genetic similarity, defining populations based on biological mechanisms. We show that such gene flow units are sufficiently genetically isolated for specific adaptations to spread, making them also ecological units that are differentially adapted compared to their closest relatives. We discuss the implications of these observations for measuring bacterial and archaeal diversity in the environment. We show that operational taxonomic units defined by 16S rRNA gene sequencing have woefully poor resolution for ecologically defined populations and propose monophyletic clusters of nearly identical ribosomal protein genes as an alternative measure for population mapping in community ecological studies employing metagenomics. These population-based approaches have the potential to provide much-needed clarity in interpreting the vast microbial diversity in human and environmental microbiomes.This article is part of the theme issue ‘Conceptual challenges in microbial community ecology’. Being defined as the locally coexisting representatives of species, populations are, in practice, also the units of diversity that are used when we wish to measure species diversity to assess ecological interactions as well as ecosystem stability and resilience . For micDefining bacterial and archaeal populations, and by extension species, is, therefore, an important endeavour for community ecology, but can we do it? Is microbial diversity organized into natural units to which we can ascribe biologically meaningful properties? Specifically, do fundamental evolutionary processes organize coexisting genotypes into units through which adaptations can specifically spread, giving rise to ecological units with clearly different dynamics? If we can define microbial populations in such a way, then we may be able to apply the rich evolutionary and ecological theory developed for animals and plant populations ,8; if noHere, we explore the question of whether bacteria are organized into genetically clearly delineated, ecologically differentiated populations. We argue that although bacterial and archaeal recombination, both homologous and non-homologous, is unidirectional and promiscuous, environmental structure and selection have the potential to structure gene flow sufficiently for ecologically differentiated units to arise. We next discuss why recognizing such units has remained so difficult and show that, by estimating only very recent gene flow, congruent units of gene flow and ecology are indeed recovered. Although many more examples are still needed, these units may be the bacterial and archaeal equivalent of populations and their identification may ultimately contribute to solving the microbial species problem. We conclude by drawing implications for measuring biologically meaningful diversity in the environment.2.Although gene flow is potentially promiscuous in the sense that any microbe can, in principle, share genes with any other ,10, it nThis increased encounter and recombination of actively growing genotypes has important consequences for creating and maintaining ecological cohesion . If an aVibrio cyclitrophicus that were differentially distributed in ocean samples, one being associated with organic particles and the other occurring free-living. Both populations contained genome regions that differentiated them, including regions that contained much reduced nucleotide diversity, indicating a recent sweep of a specific allele, as well as regions that showed differential gene presence as expected from recent population-specific additions or losses. Some of these differentiating alleles and genes were clearly associated with biofilm formation and attachment, leading to the hypothesis that the ability to associate with particles was either lost or gained in one of the populations [The trade-offs discussed above are often difficult to identify because they require the examination of very recently speciated populations. Among more divergent species, too many genetic changes have typically accumulated as well as been lost to identify the trait associated with the trade-off. One clear example comes from recently speciated bacterial populations in the ocean . A compaulations .This hypothesis of differential adaptation based on observed genetic differences was subsequently confirmed by behavioural observations of representatives of the two populations that suggested a competition–dispersal trade-off . Microfla priori and instead such inference requires an approach where some measure of diversity is mapped onto environmental samples. We next outline reasons for this difficulty of recognizing population or species boundaries among bacteria and archaea based on genetic information alone.Although the above example demonstrates the power of population genomics combined with fine-scale environmental sampling, the discovery of such recently speciated populations was nonetheless fortuitous. It was aided by the fact that a protein-coding gene used as a marker to differentiate isolates initially was linked to a sweep region and thus clearly differentiated these two populations . In most3.Escherichia coli isolates that there is no majority tree, even though, paradoxically, a similar tree is always produced when averaging over different larger genome regions [In a recent opinion piece, Rocha  outlined regions . This isV. cyclitrophicus populations, where essentially every genomic region they shared had its own unique evolutionary history and both populations appeared completely intermixed [The issue with phylogenetic methods may be that they integrate over too long evolutionary timeframes to be useful for population differentiation. In particular, among recently speciated populations, only a very small fraction of the genome supports a distinction between them . This istermixed . This istermixed .Even many methods designed to measure recombination may suffer from a similar problem of integrating over evolutionary timeframes that are too long to capture speciation events. We recently carried out a simple experiment where we simulated a burst of recombination among a group of otherwise clonally evolving genomes and observed how the signal of recombination decayed as mutations accumulated . When re4.If current methods cannot recover species or population boundaries, is there an alternative that can correctly identify such boundaries? We have recently proposed such a method that relies on measuring the homogenizing force of recombination between two genomes and is able to identify much more recent gene transfer than other methods . This meImportantly, the measure of gene transfer provided by PopCOGenT can be used to construct a network to examine how recombination structures genetic diversity . In the When applied to several bacterial and archaeal model systems for which population structure has been estimated , PopCOGenT was able to recapitulate the original predictions . These mV. cyclitrophicus and Sulfulobus icelandicus [Prochlorococcus. Such single-cell genomes are usually difficult to compare by traditional methods because they are incomplete in random areas. However, PopCOGenT can handle incomplete information because it relies on pairwise comparisons as long as sufficient overlap between pairs is available. What constitutes sufficient information remains poorly explored and datasets can also easily be confounded by contaminating DNA that may be scored as gene transfer connections among unrelated genomes. Nonetheless, the potential to carry out population genomics with single-cell genomes and thus sidestep cultivation represents a potential advantage of PopCOGenT. Overall, the observation of clusters and subclusters among closely related genomes suggests that estimates of gene flow alone can be used to hypothesize genetic and ecological units. However, how can we be sure that the correct boundaries among these units have been identified?These initial clusters in the raw gene flow network had no connection to other such clusters, indicating that recent gene flow between many ecological populations is essentially undetectable . When a landicus ,33, indilandicus . One of 5.V. cyclitrophicus and S. islandicus suggest that such properties can be identified [Ruminococcus gnavus genomes isolated from healthy individuals as well as patients with Crohn's disease and ulcerative colitis [To answer this question, we return to the argument that for genetic and ecological units to be congruent, adaptations must be able to spread in a species- or population-specific manner. A critical test is, therefore, whether there are properties that differentiate the most closely related sister populations. Both examples of the speciation models of entified ,33. We, entified  , which has become the basis of a popular species definition ,44. WhenA further important property of populations defined by gene flow is that the pan-genome remains of considerable size . That is9.The approach for hypothesizing population structure based on gene flow followed by testing of the hypothesis by the identification of population-specific sweeps allows for a reverse ecology approach that predicts ecological units from genomic information alone ,51. In tin situ hybridization probes to directly visualize how closely related populations are differently distributed in the environment. We envision that this will allow for more targeted testing of fine-scale environmental associations that far exceeds the efficiency of traditional forward ecological approaches, which often rely on mapping microbial groups onto coarse environmental variables and then using genomics to find potential differences [Loci under selection are particularly useful for assessing the abundance of populations in environmental samples because their within-population diversity is exceptionally low, while the diversity between populations is much higher because evidence so far indicates that most loci arose by horizontal gene transfer from divergent sources . These pferences .Vibrionaceae bacteria into coexisting populations in ocean water. We typically find around 20 or so coexisting populations that were originally defined by the fine-scale environmental sampling of isolates, sequencing of protein marker genes and application of mathematical modelling to link genetic diversity to environmental structure [hsp60) with different 16S rRNA gene fragments used to define operational taxonomic units (OTUs) for their potential to differentiate ecological units in samples.How do populations defined by gene flow compare to the traditional measurement of microbial diversity by 16S rRNA gene sequencing often used to map microbial populations onto environmental samples? To answer this question, we use an example from our own work where we have delineated tructure –55. Thestructure  enablingThis comparison shows disturbingly low resolution of the 16S rRNA genes when compared with populations defined by gene flow . EspeciaConsidering that the prediction of population structure by gene flow requires isolates or single-cell genomes, is there a proxy that can be developed for species and population identification in metagenomes? Potentially yes. One interesting feature of the populations we have identified is that they are quite well approximated by nearly identical ribosomal protein sequences ,45. Alth10.The identification of populations as gene flow clusters that are also ecological units has major implications for microbiology, which has long suffered from the fuzzy definition of populations . We suggFinally, considering that many of the populations defined here display a very high degree of genetic isolation, it is tempting to invoke the biological species concept, which posits that species are reproductively isolated groups of organisms . However


Population structure across a species distribution primarily reflects historical, ecological, and evolutionary processes. However, large‐scale contemporaneous changes in land use have the potential to create changes in habitat quality and thereby cause changes in gene flow, population structure, and distributions. As such, land‐use changes in one portion of a species range may explain declines in other portions of their range. For example, many burrowing owl populations have declined or become extirpated near the northern edge of the species' breeding distribution during the second half of the 20th century. In the same period, large extensions of thornscrub were converted to irrigated agriculture in northwestern Mexico. These irrigated areas may now support the highest densities of burrowing owls in North America. We tested the hypothesis that burrowing owls that colonized this recently created owl habitat in northwestern Mexico originated from declining migratory populations from the northern portion of the species' range . We used 10 novel microsatellite markers to genotype 1,560 owls from 36 study locations in Canada, Mexico, and the United States. We found that burrowing owl populations are practically panmictic throughout the entire North American breeding range. However, an analysis of molecular variance provided some evidence that burrowing owl populations in northwestern Mexico and Canada together are more genetically differentiated from the rest of the populations in the breeding range, lending some support to our migration‐driven breeding dispersal hypothesis. We found evidence of subtle genetic differentiation associated with irrigated agricultural areas in southern Sonora and Sinaloa in northwestern Mexico. Our results suggest that land use can produce location‐specific population dynamics leading to subtle genetic structure even in the absence of dispersal barriers. Migration‐driven breeding dispersal in the burrowing owl, whereby long‐distance migrants from Canada and the United States became year‐round residents in the newly created irrigated agriculture areas in northwestern Mexico, may explain the species' population decline in northern latitudes. In support of this hypothesis, (a) owl populations in northwestern Mexico and Canada together are more genetically differentiated from the rest of the populations, and (b) there is a subtle genetic differentiation associated with irrigated agricultural areas in northwestern Mexico. We performed PCR reactions in a 15 μl volume containing 10–50 ng genomic DNA, 1X PCR buffer , 0.2 mM each dNTP, 0.02 μM unlabeled M13‐tailed forward primer, 0.2 μM reverse primer pig‐tailed with GTGTCTT, 0.2 μM fluorescently labeled M13 primer, 2 mM MgCl2, 0.4 U Taq DNA polymerase (Invitrogen®), and 0.02% BSA. We used 1 touchdown protocol for all loci consisting of an initial denaturation at 94°C for 4 min followed by 10 cycles at 94°C for 30 s, annealing at 60–52°C for 90 s (2°C decrease every 2 cycles), extension at 72°C for 30 s, followed by 30 cycles at 94°C for 30 s, annealing at 50°C for 30 s and 72°C for 30 s, and a final extension of 7 min at 72°C. We analyzed PCR products on an Applied Biosystems 3730 Genetic Analyzer and used an Applied Biosystems Genotyper 3.7 to score alleles. We used program Tandem   Jost,  to test gh Jost, . D range Mantel,  to test AMOVA; Weir & Cockerham, ARLEQUIN 3.1.1 to test our prediction that declining migratory populations in the north and populations in agricultural areas in the south, pooled together, would be genetically differentiated from the remainder of the breeding populations within the species' range (pooled together). AMOVA is analogous to a nested Analysis of Variance and uses a permutational approach to test the statistical significance of any given classification of study locations in explaining the overall genotypic variation. We performed 2 AMOVAs, one based on allele sizes (STR) and the other based on the number of different alleles (STF)  together with northern locations , and Group 2 including all other locations. Our large sample size  may confer enough statistical power to reject the null hypothesis for any grouping of study locations. To explore this possibility, we conducted 7 additional AMOVAs using 2‐group classifications by replacing northern study locations  from Group 1 with other study locations and moving them to Group 2.We performed an Analysis of Molecular Variance  = Prob[Data |K]) given existence of K burrowing owl populations under Hardy–Weinberg equilibrium and estimates the posterior probability of membership of each individual owl to each of the K populations. We used study locations as prior information to assist the inference of population structure  for each K from correlated allele frequencies and an admixture model. This approach is superior at detecting subtle genetic structure when population differentiation is low compared to the use of uncorrelated allele frequencies and a nonadmixture model  to assess the number of inferred populations. STRUCTURE HARVESTER estimates the statistic ΔK at each value of K; we used ΔK because it performs better in detecting population genetic structure than L(K)  and low pairwise STF statistics . Low levels of genetic differentiation were also evident in our estimates of actual differentiation D, ranging from 0.00 to 0.11. In this regard, we found a subtle, positive relationship between genetic distance and geographic distance among our study locations  but the slope was positive . Moreover, all but one of the pairwise comparisons of genetic and geographic distances among northern study locations and southern agricultural locations fall below the Mantel regression line  and southern agricultural study locations  together are genetically differentiated from the rest of the study locations  although this result did not hold true for the 2 AMOVAs based on STR . In addition, only 1 of the 7 additional AMOVAs based on STF was significant for both the standard AMOVA and the weight‐averaged AMOVA over all loci  within Group 1.Low levels of genetic differentiation among populations were also highlighted by our STRUCTURE revealed a genetic structure consisting of 3 populations in the burrowing owl in spite of the low levels of genetic differentiation among study locations shown by STF and D statistics. Mean log‐likelihood of the observed genotypic data and ΔK was highest at K = 3  which differentiates this Sinaloan population  from the rest of the study locations (p = .005). This Sinaloan fingerprint is relatively common within nearby populations in Sonora, southern Arizona, and as far as Chihuahua (CHI), northern Texas (TXP), and the Central Valley of California (DIX)  and concluded that burrowing owl populations were practically panmictic. Our estimate of STF = 0.008 is slightly lower but still within the 95% confidence interval of their STF estimate. Our study was more comprehensive than Korfanta et al. . Korfanta et al.  estimatea et al.  because a et al.  versus 8a et al. . TherefoSTF index. Hence, the use of our genetic markers to detect past and current patterns of breeding dispersal is imperfect. We used numerous analytical methods and algorithms that made use of the individuals' genotypic data  to unveil subtle patterns of genetic differentiation. Estimates of ΔK revealed a subtle genetic structure and identified 3 populations. However, ΔK cannot be computed for K = 1   did not provide support of the hypothesis. However, measures of allele size have been criticized for having large sampling errors and low efficiency in reconstructing simulated phylogenies , Janos (JAN), and Ahumada and Coyame (CHI) locations in Group 1, which makes sense because of regional gene flow.The 10 DNA microsatellite loci we used may have been too few to detect subtle genetic structure using the The validity of the genetic structure found in this study is corroborated by the known current patterns of migratory connectivity in the burrowing owl. Recent satellite telemetry data  revealed that burrowing owls breeding in Great Plains winter through the Mexican Highlands and the eastern coastal plains of the Gulf of California, whereas burrowing owls breeding west of the Rocky Mountains winter in California and the Baja California Peninsula. Accordingly, the distinctive Mojave population centered in southern Nevada , from 0.54 to 0.62 in the ferruginous pygmy‐owl None declared.Alberto Macias‐Duarte: Conceptualization (supporting); data curation (lead); formal analysis (lead); investigation ; methodology ; writing–original draft (lead); writing–review and editing . Courtney J. Conway: Conceptualization (lead); funding acquisition (lead); investigation (supporting); resources ; writing–original draft ; writing–review and editing . Melanie Culver: Formal analysis ; investigation ; methodology ; resources (lead); supervision ; writing–original draft ; writing–review and editing .Fig S1Click here for additional data file.Fig S2Click here for additional data file.


Crassostrea gigas, was voluntarily introduced from Japan and British Columbia into Europe in the early 1970s, mainly to replace the Portuguese oyster, Crassostrea angulata, in the French shellfish industry, following a severe disease outbreak. Since then, the two species have been in contact in southern Europe and, therefore, have the potential to exchange genes. Recent evolutionary genomic works have provided empirical evidence that C. gigas and C. angulata exhibit partial reproductive isolation. Although hybridization occurs in nature, the rate of interspecific gene flow varies across the genome, resulting in highly heterogeneous genome divergence. Taking this biological property into account is important to characterize genetic ancestry and population structure in oysters. Here, we identified a subset of ancestry-informative makers from the most differentiated regions of the genome using existing genomic resources. We developed two different panels in order to (i) easily differentiate C. gigas and C. angulata, and (ii) describe the genetic diversity and structure of the cupped oyster with a particular focus on French Atlantic populations. Our results confirm high genetic homogeneity among Pacific cupped oyster populations in France and reveal several cases of introgressions between Portuguese and Japanese oysters in France and Portugal.The Pacific oyster,  Hybridization between species or evolutionary lineages is a common phenomenon in plant and animal species, including numerous animal groups, such as mammals, fish, birds, insects, and molluscs ,2. Such Carcinus maenas), two closely related evolutionary lineages have been co-introduced and exchange genes though hybridization in the new invaded area [Semi-isolated species pairs continue to evolve non-independently because some regions of their genome can still be exchanged through gene flow until speciation is complete. These exchanges of genetic material between divergent populations often occur during secondary contact episodes, as recently revealed by several population genomic studies ,14,15,16Crassostrea gigas and C. angulata. The two cryptic species are presumed to be parapatrically distributed in their native range in the Northwestern Pacific. C. angulata is mainly found in Southern China, Taiwan, and Vietnam [C. gigas has a more northern distribution in northern China, Korea, Japan and Russia. The location of putative contact zones in the natural distribution range remains largely unknown. C. angulata, also called the Portuguese oyster, is presumed to have been non-voluntarily introduced in Portugal by merchant ships during the 16th century, probably from Taiwan [Ostrea edulis), for shellfish farming, when this latter encountered overexploitation and disease problems during the 20th century. Recent studies have shown that C. angulata is currently widely distributed in Asian seas where it shows a high genetic diversity [Another original case comes from the Pacific cupped oyster, which has been taxonomically subdivided into two sister species,  Vietnam , whereasm Taiwan ,21, althm Taiwan . It was m Taiwan ,24,25,26iversity ,27,28,29C. gigas, was voluntarily introduced from Japan and British Columbia into Europe in the early 1970s, mainly to replace the Portuguese oyster in the French shellfish industry, following a severe disease outbreak [C. angulata has almost disappeared from Europe, being only found in Portugal in two rias, but also in the Cadiz region in Spain and Morocco [C. gigas is now the species on which the European shellfish farming mostly relies on [The Pacific oyster, outbreak . Its intoutbreak . Since toutbreak ,32. At p Morocco . The Pacelies on , but theC. gigas and C. angulata truly represent biological species, semi-isolated species, or populations of the same species, has largely been discussed. The two taxa can be cross-fertilized in the laboratory to form viable and fertile offspring [C. angulata and C. gigas supported their belonging to different but genetically closely related species [Whether ffspring ,36,37. Bffspring ,39. Howe species ,20,40,41 species . This fiC. angulata still can be found in Southern Europe [C. gigas has been largely introduced for shellfish farming , recent genetic admixture is expected to happen. Natural hybridization has already been suggested in Portugal where the two species are in contact [C. angulata spat have been sold by a French hatchery since 2017, potentially increasing admixture opportunities in France or even in Europe. Furthermore, the two species display differences in resistance to diseases, growth, oxygen consumption, reproductive effort, time of settlement [European shellfish farming is traditionally based on numerous transfers of animals of different stages within and among different bays from different countries. Hence, whatever the origin of the spats , local populations of oysters likely result from complex histories of transport between different seashores. The growing of oysters from spat to adults in a single place is, rather, an exception. Therefore, as farmed oysters spawn in the sea, human-mediated transports of oysters for farming, may have resulted in complex spatiotemporal patterns of gene flow. As n Europe  and in p contact ,32. Morettlement ,44,45,46Carcinus maenas, which were independently introduced to eastern North America [C. gigas and C. angulata, and (2) describe the genetic diversity and spatial structure of the cupped oyster with a particular focus on French Atlantic coasts. The newly developed panels revealed to be useful to characterize the cupped oyster resources present in Europe. We discuss our results in the context of a potential interest for conservation and management of cupped oyster resources.Thanks to the use of high throughput sequencing (HTS) approaches, thousands of polymorphic markers have been discovered ,47, prov America . Here, wC. gigas and C. angulata from both their native Asian and introduced Atlantic Ocean areas  using two panels of SNPs. The first panel contained 384 SNPs developed in [STF value. We chose unlinked SNPs with the highest STF values to develop a panel of ancestry informative markers differentiating C. gigas and C. angulata. The characteristics of the 15 selected markers are given in We genotyped four wild populations of loped in  and the loped in . From eaA second panel was developed in order to further characterize within-species genetic structure among the cupped oyster populations. Therefore, we gathered 11 of the 15 ancestry informative SNPs from the previous panel with 69 other SNPs, chosen mainly from , but alsC. gigas and C. angulata from both their native Asian and introduced Atlantic Ocean areas. They are presented in We sampled four wild populations of C. gigas and C. angulata populations, we added four samples to the four previous populations . Between 11 and 12 animals of each family were sampled for this study for a total number of 104 HYB animals. Reproduction, larval, and post-larval rearing, as well as growing, took place at the Ifremer experimental hatchery in La Tremblade . All of the 289 samples were genotyped thanks to one or two methods described below, mainly Sanger sequencing for the four added samples in this Dataset 2.In order to confirm the power of the 15 SNPs panel to differentiate ulations . We obtaC. gigas and C. angulata were added in this third study in order to characterize each species genetic background (C. gigas and C. angulata), as well as reference the North European population of C. gigas from Denmark. The 80 SNPs panel was used to genotype these 985 individuals. All of these populations are described in A large spatial scale sampling was performed in 2017 along the French Atlantic coast from Normandy to Arcachon Bay and along the Mediterranean Sea coast. It was completed by samples collected earlier (2010) in Brittany, Marennes-Oléron, and Arcachon Bay. The objective here was to characterize the genetic diversity of French cupped oyster populations across space and time, a decade after the beginning of important mortalities on spat, and more recently, adult oysters. Reference populations of ® DNA clean-up system  was used when the samples were collected in 70% or 90% alcohol, and a QiAamp DNA mini Kit  when the samples where fresh or frozen. DNA quantification was performed with a Nanodrop spectrophotometer .DNA was extracted from gill tissues. A wizardAccording to the scale of the study and the number of SNPs used or expected, several genotyping methods were performed and combined: (1) genotyping by sequencing methods, such as Sanger sequencing and RADseq, and (2) genotyping methods with Illumina GoldenGate technology or MassARRAY system.® TM reader were analyzed with GenomeStudio for automated genotype clustering and calling. Clusters were then visually inspected to ensure high data quality. The RAD-derived SNP genotyping data were taken from [For the development of the 15 SNPs panel, the Illumina GoldenGate technology  was used for part of the genotyping reactions, which were performed in accordance with the manufacturer’s protocol, as described in . Brieflyken from .A Sanger-based amplicon sequencing method was used for LAFG0, SADG0, HYB, and TAV1 samples. PCR primers were designed using the online Primer3 software package  based onSNP genotyping was performed for the 27 populations included in Dataset 3 using the iPLEX Gold chemistry following  on a Mashttps://doi.org/10.17882/71545 [As described above, the samples were genotyped by several methods, with often some common samples between the methods in order to confirm the identity of the genotypes. In those cases, no discrepancies were observed between the results obtained by the different methods. The genotypes of the three datasets are available at 82/71545 .STF [Genetic differentiation between pairs of populations was estimated SNP by SNP using FST  in GenetFST . Within-FST .Bayesian clustering of the genetic data was performed using Structure version 2.1 ,58. We rSTF between pairs of populations at each SNP. Although we did not find any fixed difference in allele frequency between the two species using the four populations sampled, we sorted all the SNPs according to the STF between pairs of interspecific populations and chose the seven and eight SNPs with the higher values respectively in the BeadXpress (384) and RAD sequencing  panels. The frequency of the most frequent allele in C. angulata  at each SNPs in each of the four populations is presented in C. angulata , it was always present in at least one of the two C. gigas populations . Reciprocally, “gigas” alleles that were fixed in one population of C. gigas  were found segregating in C. angulata. Therefore, none of these 15 markers, selected among the most highly differentiated regions of the genome, was fully diagnostic. We also found differences in the extent of allele frequency differences between C. angulata and C. gigas populations according to the fact that the SNP was extracted from the BeadXpress panel (ANGI1 to 7) or the RAD sequencing panel (ANGI8 to 15). This reflected a stronger effort to detect the most strongly differentiated markers in the RAD dataset compared to the BeadXpress SNP dataset.Using four populations from both species (two native and two introduced), we calculated allele frequencies within each population and estimated C. gigas and C. angulata samples. Although not a single SNP was differentially fixed between species, the combination of 15 SNPs with high STF values in interspecific population pairs was able to clearly distinguish the populations from the two species and their hybrids. The second axis principally separated C. angulata samples with 1.71% of the total variance explained. Moreover, the PCA indicated potential events of introduction/introgressions in natural populations. For example, one TAV1 sample was found in the C. gigas part of the DAPC and four TAV1 samples appeared close to the hybrid group. More generally, the TAV1 population was slightly shifted toward the center of the first axis, potentially indicating increased introgression from C. gigas in this population closely located to the contact zone.The between-population genetic structure revealed by the dAPC  separateSTF estimates .The genetic diversity within populations of the large-scale study was highly homogenous in French and the Japanese populations, whereas it was lower in Norway and Portugal . Regardistimates  were notC. gigas and C. angulata genetic background . The two C. angulata populations SAD1 and TAV2, however, showed a different pattern. SAD1 was considered as a non-admixed reference population for C. angulata in Europe. By contrast, TAV2 showed a high number of admixed individuals. This result is confirmed by the position of TAV1  in the dAPC analysis .This global pattern was confirmed by the assignment of individuals to two genetic clusters , which canalysis . FurtherC. gigas and C. angulata. On the basis of two previously published sets of SNP markers [STF values between pairs of interspecific populations. Although our final panel consisted of 15 SNPs that were evenly chosen from the two initial datasets, the most informative markers were mainly obtained from the RADseq SNPs panel . This simply reflects the fact that this high-dimensional SNP dataset offered a far higher number of SNPs, among which the most significant outlier loci could be searched. This was also illustrated in C. gigas and C. angulata populations. The overall same amount of variance was explained by axis 1 (19.52%) on the discriminant analysis of Dataset 2 than in [Although HTS has allowed the development of medium-density SNP panelsand whole genome resequencing of hundreds of individuals in oysters ,60,61,62 markers ,49, we s than in . Since wC. gigas and C. angulata does not contain any region with fixed differences, as previously observed using more than 10 thousand SNPs [This new panel of 15 SNPs is clearly more powerful than the few markers already available ,32 even and SNPs . In thisA more challenging aspect of using small SNP panels to characterize individual ancestry in oysters is the detection of introgression. Shared variation between the two species across their genome has been attributed to postglacial gene flow in addition to incomplete lineage sorting (ILS) . AlthougC. angulata. Our samples from the Sado estuary (samples SADG0 and SAD1), which represents one of the last sites where the Portuguese oyster is known to be present in Europe [C. gigas is presumed to be absent or rare [C. angulata ancestries. By contrast, the Ria Formosa lagoon (samples TAV1 and TAV2) displayed a completely different situation with a high rate of introgression from C. gigas, which is known to have been introduced for more than 15 years for aquaculture purpose [In Portugal, such a comparison allowed us to evidence introgression within n Europe ,33, and  or rare , were in purpose . In this purpose , it doesC. gigas also displayed varying rates of C. angulata ancestry. Sampling locations such as VSM1 and 2, or SCB1 or 2, contained a high number of slightly introgressed animals compared to all other locations. This suggests that recent gene flow has been relatively more pronounced in these locations. This situation might be a remainder of the ancient presence of C. angulata 50 years ago. Hence before C. angulata almost disappeared and C. gigas invaded France, both species could have been in contact during several years [C. angulata hatchery spat. This alternative explanation is, however, less likely to involve a fully inverted picture as the one evidenced in Portugal, due to the very high number of C. gigas bred in France.In France, the studied populations of al years . This maal years . This coC. angulata and C. gigas and its evolutionary consequence is clearly needed, at least in Portugal and France. Therefore, samples collected regularly in successive years should be analyzed, possibly with a higher number of markers to increase statistical power, and beginning with the populations already identified as introgressed, whatever the reasons underlying this situation.Quantifying gene flow between The detection of hybridization also leads to conservation issues and even policy implications as it is related to the integrity of the genetic structure of populations . Being aC. angulata. This could be seen as a paradox as both species are introduced species, with C. gigas being even considered in some places as invasive and noxious [C. gigas is well documented [C. gigas, but for example, from Australia [C. gigas is considered a noxious species in areas where it can outcompete native oysters. The Netherlands is the only European country where significant mass reduction trials have been conducted [C. gigas. Furthermore, transnational management might be hard to achieve especially when there are variations between countries, regions, or even between stakeholders on the perception of a species to be a menace or a resource. In this context managing the spread of an invasive species even at early stages of expansion [Conservation issues are especially relevant to the preservation of the least introgressed populations of  noxious ,67,68. Hcumented ,69, showcumented  and summcumented , the specumented . Aquaculcumented , and thexpansion  might beC. angulata conservation and/or restoration actions could be inspired by some programs launched for real indigenous oysters. Interesting examples can be found in Northern Europe concerning the European native flat oyster, Ostrea edulis. After having suffered from overexploitation and diseases during the twentieth century, those oyster reefs are now seen, far beyond a valuable food resources, as a characteristic benthic community, which offers the possibility of biodiversity enhancement and ecosystem services in the marine environment [Crassostrea virginica, took place during the last 10 years [Therefore, conservation and/or restoration actions in specific Portuguese rias could be considered for the Portuguese oyster. As being considered a naturalized species in Europe, and almost an indigenous one in Portugal, ironment ,76. Currironment . An enco10 years  and showC. gigas and C. angulata species, and besides ecosystem services, conserving one endangered resource (C. angulata) is of particular interest as they also differ by several phenotypic traits. Such a different resource, if less economically interesting at present, might become more when considering the rapid evolution of the marine environment. However more studies are needed in Europe to be able to exclude phenotypic plasticity as a source of quantitative differences, which requires careful common-garden studies [Concerning the  studies , such as studies ,80,81.C. gigas, for which several repeated introductions from Canada and Japan of adults, but also high quantities of spat, happened by the end of the 1960s in Europe, especially in France and the Netherlands [C. gigas as several other studies did even with a far higher number of markers [C. gigas populations and the northern group [C. gigas and C. angulata and the differentiation between northern and southern European populations. Concerning the French populations sampling, we also confirm a high genetic homogeneity as already observed with several microsatellite markers [C. gigas/C. angulata Portuguese and French samples could be of interest, as a recent study on Chinese C. gigas populations, performed with the whole-genome re-sequencing of 371 oysters, provided evidence of a weak but significant differentiation among oyster populations at fine spatial scales [In non-native species, we may expect founder effects  on the bherlands ,84. Henc markers ,61,85,86 markers  because rn group ,61,86,87rn group  had been markers ,88 with  markers , obtaine markers ,61. Howel scales .


FTO in Finnish people). Further studies are needed to fully elucidate the evolutionary dynamics  that shaped these nucDNA–mtDNA interactions and their functional role.Cold climates represent one of the major environmental challenges that anatomically modern humans faced during their dispersal out of Africa. The related adaptive traits have been achieved by modulation of thermogenesis and thermoregulation processes where nuclear (nuc) and mitochondrial (mt) genes play a major role. In human populations, mitonuclear genetic interactions are the result of both the peculiar genetic history of each human group and the different environments they have long occupied. This study aims to investigate mitonuclear genetic interactions by considering all the mitochondrial genes and 28 nuclear genes involved in brown adipose tissue metabolism, which have been previously hypothesized to be crucial for cold adaptation. For this purpose, we focused on three human populations  of European ancestry from different biogeographical and climatic areas, and we used a machine learning approach to identify relevant nucDNA–mtDNA interactions that characterized each population. The obtained results are twofold: (i) at the methodological level, we demonstrated that a machine learning approach is able to detect patterns of genetic structure among human groups from different latitudes both at single genes and by considering combinations of mtDNA and nucDNA loci; (ii) at the biological level, the analysis identified population-specific nuclear genes and variants that likely play a relevant biological role in association with a mitochondrial gene (such as the “obesity gene”  The mitonuclear interaction can be considered the most successful mutualism in the history of life according to the complex dynamics of conflicts and cooperation that has been established between the mitochondrial and nuclear genomic backgrounds . In fact+, ROS, oxygen, and other metabolites) act as mitochondrial messengers, and they signal mitochondrial activity to other cellular components. Mitochondria communicate also with distant tissues through circulating molecules, called mitokines , which are nuclear-encoded signaling molecules secreted by cells that experienced mitochondrial stress.Mitochondria actively influence and physically interact with other cellular components, such as the lysosomes, the endoplasmic reticulum, and cytosolic pathways, creating a mitocellular communication network based on a variety of signals. Then, small molecules  imposed a selection on mitochondrial DNA (mtDNA) in human populations . These mHowever, the identification of nucDNA–mtDNA interactions meets the statistical and computational challenge to test a huge number of hypotheses: the routine methods often fail to find informative nucDNA–mtDNA combinations , even ifDuring human evolution, the main selective pressures that anatomically modern humans had to face were linked to pathogen variability and load, dietary changes, and climatic conditions. In this study, we investigate mitonuclear genetic interactions in relation to the latter condition.Most likely, metabolic adaptations to low temperatures occurred in several human populations when modern humans spread across Europe . The relHere, we aim to identify statistically significant nucDNA–mtDNA interactions in human populations of European ancestry from different biogeographical and climatic areas by an ML approach. We analyzed a subset of 28 nuclear genes  involved1).We selected representative populations of European ancestry from different latitudes from the 1000 Genomes Project dataset . GeneticWe estimated relevant mtDNA–nucDNA interactions by identifying those combinations of mtDNA–nucDNA variants that manifest statistically significant association to the considered populations. In detail, they were formalized as features that received high importance scores for ML discrimination of the populations and made up classifiers with higher accuracy than those based on exclusively mitochondrial or nuclear genomes.In the first step, we filtered out those single nucleotide variants (SNVs) in the considered genes that did not vary between all subjects. By considering mitochondrial DNA only and by fixing a certain population as the reference one, we calculated the mean frequency We followed the same approach for nuclear DNA variants, taking into account three variants , and for combinations of mtDNA–nucDNA variants, taking into account six combinations variants . In result, the dimension of data for an individual was reduced to the number of mtDNA or nucDNA genes or their product.The frequency scores were treated as features for the random forest (RF) algorithm to build and investigate binary classification models between each pair of populations. The implementation was taken from the Python package “scikit-learn” version 0.23; Python version 3.7. The number of decision trees was set to 500. Tenfold cross-validation was applied to test the effectiveness of the produced model, yielding the average accuracy over cross-validated models.We thus performed a two-step classification experiment. The first one employed all constructed features assigning them with classification importance scores from the interval . Using the ranked feature lists, we performed serial RF experiments, increasing the number of features each time by one, until it made the whole list. The resulting accuracy plots are given in We also defined genes and gene pairs with population-specific SNVs as those present in all resulting classification lists that were obtained for a given population (we termed them “population-specific genes”). For example, for the GBR population, it would be the intersection of the feature lists for the following classifiers: GBR (reference) vs. FIN (target), GBR (reference) vs. TSI (target), FIN (reference) vs. GBR (target), TSI (reference) vs. GBR (target). The main steps of the algorithm are presented in FTO) and all mitochondrial DNA genes, we also performed a random forest approach for SNVs, without choosing a reference population and by averaging over genes (we termed them “population-specific SNVs”). We treated SNV combinations  as features for the random forest, and classification accuracy was calculated. A total of 100 SNV combinations with the highest classification importance scores for all experiments  were obtained. The population-specific SNV combinations were also defined as those present in all resulting features lists that were obtained for a given population.For a single nuclear DNA gene (r2) of a given couple was higher than 0.1, keep only one of the two SNVs, move the window forward of 10 SNVs and repeat the procedure.To explore patterns of population genetic structure among the three selected populations and to remove possible bias due to correlation between SNVs, the PLINK software v.1.9  was usedThe obtained pruned data-sets were used to perform population structure analyses commonly used in population genetics studies: discriminant analysis of principal components (DAPC)  and the DAPC describes genetic relationships among predefined groups of individuals minimizing the variance within the groups and optimizing the variance between the groups. To visualize the genetic distance among the three populations, we plotted DAPC results for the individuals in the coordinates defined by the first two principal components (PCs). The Euclidean distance between the centroids of the three populations was computed to quantify their genetic divergence.p-value for the Fst value observed on the original data. A gene was considered discriminant for a given couple of populations if the empirical p-value was lower than 0.05. A gene was considered specific for a single population (for example FIN) if the empirical p-value was significant in the two Fst analyses involving the population itself (example FIN vs TSI and FIN vs GBR but not TSI vs GBR). A gene was considered specific for all the considered populations if the empirical p-value was significant in all the three Fst analyses.Fst is a measure of population substructure and is most useful for examining the overall genetic divergence among subpopulations. Here, we computed the pairwise Fst test between all population pairs, so three Fst analyses were carried out for each gene. Then, to evaluate the significance of the observed Fst values, we performed 200 simulations, where individuals were randomly assigned to one of the three studied populations, and for each simulation, the Fst statistic was computed. At the end of this procedure, we were able to fit a probability density function for the Fst statistic and to compute an empirical adegenet package  are listed in PDRM16, LEPR, and DIO2) were common as well; meanwhile, the other two genes (PPPARG and NRF1) resulted specific for the Finnish population, and three genes  turned out to be specific for the Italian population only.All 10 mitochondrial population-specific genes were found to be common for the considered populations. Three nuclear population-specific genes  only mitochondrial DNA, (ii) only nuclear DNA, and (iii) combinations of mitochondrial and nuclear DNA genes.The two most interesting cases are reported in Last, FTO nuclear gene in combination with all mitochondrial genes. We applied the RF model, using SNV combinations variants as an input and performed binary classification experiments for all population pairs. As for the previous method, the classification accuracy was higher for the combinations in comparison to only mitochondrial or nuclear DNA  with rs12933928 (FTO), 12618 (MT-ND5) with rs12933928 (FTO), and 7124 (MT-CO1) with rs12933928 (FTO). SNV rs12933928 resulted specific for the FIN population in terms of nuclear DNA experiment, and SNV rs2388451 was specific for TSI.There were no common SNV combinations, important for all populations. We also intersected lists for certain populations and found three specific combinations for FIN population SNV pairs: 9066 , we showed a perfect match regarding the results of DAPC and Fst analyses and those pointed out by the ML method for nuclear genes. Conversely, for mtDNA genes, RF effectively increased the predictive power when compared to Fst, whereas single genes may be unable to discriminate populations.MT-ND5 and MT-ND6 as population specific, while ML identified 10 genes  the unwanted sensitivity to associations between variables (that is expected to be present under epistasis hypothesis) and (ii) the ambiguity in determining the importance of the found multivariate principal components for the discrimination between the groups . In partOn the contrary, RF is free from these flaws: building several decision trees and combining them together increase the accuracy of the result. Moreover, the features are assigned with importance score that allows for the straightforward interpretation and comparison of different gene contributions to the performance of a multivariate classifier. Besides, the use of several trees reduces the risk of overfitting. The main limitation of an RF approach is that many trees can make the algorithm slow and computationally inefficient, which made us introduce aggregate gene SNV frequency scores instead of using all SNVs.In this study, we thus identified population-specific combinations of nuclear and mitochondrial genes.UCP2 was the only gene that is not population specific alone but in combination with MT-CYB and MT-ATP6 genes. For GBR, no combinations of nuclear and mitochondrial genes have been identified likely because GBR represents an intermediate population in terms of latitude. For FIN, the nuclear genes HOXA1 and FTO turned out to be population specific only in combination with mitochondrial genes .For TSI, the nuclear gene UCP2 is expressed in many cell types, such as white adipose tissue and pancreatic beta cells, but its functions are still controversial  that confer an advantage to cope with cold climates at the vascular level.The cardiovascular system is a crucial player in the cold adaptation as much data suggested. Exposure to cold temperatures causes rises in vasomotor tone, hemodynamic parameters, platelet aggregability, and other hematological and endothelial parameters . It is tFTO gene emerged as the only gene that enables one to distinguish populations only when considered in combinations with certain mtDNA genes  located in the FTO locus far from the body mass index (BMI)-associated FTO intron 1 region. This variant has been shown to increase the risk of the degenerative disk disease and melanoma independent of obesity in vivo or in vitro);The fact that we identified statistical interactions at the genetic level, which need a functional validation using biological models ((2)The absence of phenotypic data to test direct associations ;(3)We cannot distinguish if the interactions identified may be due to migration/admixture patterns and the peculiar genetic history of the populations analyzed or to a potential selective advantage of having some combinations of nuclear and mitochondrial variants. Further studies are needed in this direction.However, recent data support the fact that mitonuclear interactions are consistent with our understanding of the demographic history of human populations, stressing the absence of selective pressures to maintain mitonuclear combinations . DespiteThe main strengths and novelty of this study are twofold :•The method seems to be useful to detect patterns of genetic structure among human groups and to grasp different dimensions of the genetic variability that characterize human populations. It may be applied to single genes and combinations of genes. It is always concordant with the results of Fst and DAPC analyses, and in certain cases, it reported more informative genes with good accuracy . The use of ML was applied to describe genetic variation among human populations from different latitudes, but can be potentially applied to many other case studies of biological interest.•FTO). The rationale used to select the analyzed genes supports the probability that these interactions may play a role in cold adaptation and related traits and gives suggestions for new target genes to be further investigated by in vivo and in vitro studies.The analysis identified nuclear genes that likely play a relevant biological role in association with a mitochondrial gene and that are population specific (such as https://www.internationalgenome.org/category/phase-3/.Publicly available datasets were analyzed in this study. This data can be found here: The studies involving human participants were reviewed and approved by the 1000 Genomes Project Steering Committee. The patients/participants provided their written informed consent to participate in this study.CG, MI, CF, and PG involved in the study design. AK, MI, VI, and SJ set up the mathematical models and performed analysis. CG, MS, and CF performed literature searching and biological interpretation. AK and CG wrote the first draft and all authors were involved in reviewing, and editing. All authors are responsible of the final contents and have read and approved the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.