In situ and ex situ conservation are the two main approaches for preserving genetic diversity. The advantages and disadvantages of the two approaches have been discussed but their genetic effects have not been fully evaluated.In this study we investigate the effects of the two conservation approaches on genetic diversity in red clover. Seed samples collected from wild populations in Sweden and Norway in 1980, their subsequent generations created during seed regeneration at the gene bank and samples recollected from the same location as the original samples, were analyzed with microsatellite markers, alongside reference samples from cultivars.Overall, there was a differentiation between cultivars and the wild material and between wild material from Sweden and Norway. In general, the original collections clustered together with the later generations of the same accession in the gene bank, and with the recollected samples from the same location, and the level of diversity remained the same among samples of the same accession. Limited gene flow from cultivated varieties to the wild populations was detected; however, some wild individuals are likely to be escapees or affected by gene flow.in situ and ex situ, as is also to be expected in any living population. However, we observed only limited genetic changes in both in situ and ex situ conservation over the generations included in this study and with the relatively large populations used in the ex situ conservation in the gene bank at NordGen.In conclusion, there were examples of genetic changes within individual accessions both  It is typically grown in mixtures with grasses such as timothy (Phleum pratense L.) and meadow fescue (Festuca pratensis Huds.) (Rhizobium bacteria reduces the need for supplementary nitrogen fertilizers (Bombus sp) (Red clover (s Huds.) . The abitilizers . In additilizers  and it imbus sp)  and it imbus sp) . In cultmbus sp) . Studiesmbus sp)  and planmbus sp) .ex situ collection, efforts have been made to sample across the whole geographic distribution in the Nordic countries. Today the Nordic collection includes over 500 red clover accessions  and in the gene bank (ex situ) (The main aim of this study is to investigate and compare the genetic effects of ex situ) Figure 122.1ex situ and in situ conservation of the same plant population  and their offspring generations at the gene bank, a selection of cultivars and landraces were included Table 1.In total, six cultivars, one landrace and ten accessions of wild red clover stored at NordGen were genotyped. For each wild accession, individuals from three or four conservation stages were included. These were the original collection  and regeneration cycle (generation) 1 (henceforth “gen1”) and sometimes 2 (henceforth “gen2”) at the gene bank, and for all accessions individuals collected in 2013 at, or close to, the original sampling location  .2.2Dried leaf samples were powdered in a mixer mill (Merck Retsch mm 300) with a steel ball and DNA extraction carried out using the protocol described by The material was genotyped for a total of 23 microsatellite markers developed by 2.3In total, 655 individuals from 41 samples, stemming from 18 accessions, were analyzed for the 23 loci. Initially, individuals for which more than 30% of the loci failed  were removed. Following this, only three individuals remained of the tetraploid cultivar ‘Betty’ (NGB 13205), which were removed. Finally, one locus (RCS3620) with more than 30% failed individuals was also removed. This resulted in a data set consisting of 590 individuals from 40 samples (17 accessions) analyzed for 22 loci which was subsequently used for data analyses . The amova.result and amova.test functions in the package poppr were used for carrying out AMOVA and for testing for significance, respectively.Genetic diversity was estimated as Nei’s h  using a ST values, through PCA and with the software STRUCTURE (v 2.3.4)  . Wright’3.4) , with K ranging from 1 to 15. The software CLUMPP (v 1.1.2)  (v 1.1.2)  was used (v 1.1) .33.1The analysis described in this paper was not designed to identify polyploids. However, there are indications that a few of the analyzed plants may be polyploid. In total, there were seven individuals where four or more loci amplified more than two alleles. This could be due to technical problems or suggest that these individuals are tetraploids. Of the seven, two were individuals from the original collections in 1980 the other five were from recollections in 2013. The tetraploid reference ‘Betty’ had an average of 7.4 loci per individual producing more than two alleles .3.2Per locus statistics are reported in No significant differences in genetic diversity were detected between the original collections and the recollections of the same accession  nor between the original collections and the first generation , respectively. Later generations were not compared due to the limited number of populations with two or more generations. Within individual accessions there were examples both of regenerated samples having more and less genetic diversity than the corresponding original collections. Likewise, examples of both recollection samples with higher and lower genetic diversity than the original collections occurred.3.3ST values  and 0.102 between ‘Lea’ (cultivar from Norway) and W-1574-orig . Most, but not all, FST values were non-significant.To quantify the genetic differentiation among samples, FT values  were cal-9). FST values were higher for comparisons between pairs of original collections than for pairs of cultivars or pairs of recollections. Within-sample comparisons of accessions showed that FST values between the original collect and generation 1 were not significantly different from those when the original collect was compared with the recollections . Removing accession W-1571, where the FST value between the original collect and generation 1 was unusually high (mean 0.062) did not affect these conclusions .ANOVA showed that comparisons between different conservation stages Figure 4ST values calculated between the original collections and generation 2, were in two of the three available cases higher than the corresponding FST values between the original collections and generation 1 . Comparisons of the original collections of accessions with generation 1 showed a higher proportion of shared alleles (0.483) than comparisons with the recollection (0.430), though not significantly so .On average , 0.35 of the alleles detected in one sample were shared when compared with another sample. Samples of the same accession at different conservation stages shared an average 0.44 alleles while samples from different accessions shared significantly fewer alleles, on average 0.363 , four clusters best described the data Figure 6STRUCTURE analysis of the full data set at K = 2 .PCA of the full dataset further elucidated the distribution of genetic diversity. The first and second principal component (PC) explained 7.75 and 5.65% of the variation, respectively and together separated the cultivars, the landrace and the accession W-1577, thereby replicating the results of the at K = 2 7. NotabFor some accessions, for example W-1410 pink in Figure 74Red clover is, and has been, an important fodder crop in both Sweden and Norway. Already around 1850, a considerable number of landraces of red clover existed in the Nordic countries, most of them in Sweden but also in Norway and Finland. When modern plant breeding started in the Nordic countries these landraces were the main gene pool from which the new cultivars were developed. In Sweden, modern breeding of red clover was initiated during the first half of the 20th century . The bre4.1Many studies support the existence of gene flow or introgression between crops and their close wild relatives  and it iST values are also overall higher when comparing cultivated and wild material than when comparing samples within the two groups. This pattern of differentiation between the two groups can also be seen for morphological traits (With the exception of NGB 7786 (‘Pradi’), the cultivars included in this study have been frequently cultivated in the investigated regions from the early 1950s and forward Table 1,l traits  and addsl traits  and Russl traits . Taken tSTRUCTURE analysis, W-1577 clusters with the cultivars and landrace , but the genetic diversity of the regeneration samples  is not markedly lower than that of the original collect. ex situ than in the plants remaining in the wild.For the accession W-1572 the original collect and the recollect are located relatively close to each other in the PCA but separate along PC2 Figure 8ST values are higher when involving the recollect sample than when not and in the PCA the recollect sample is separated from the other two samples along PC2 (ex situ conservation in the gene bank has conserved the variation in the original sample, even though the original population is most likely is the result of gene flow from a cultivar as discussed above.In contrast, in W-1577 the data suggest higher differentiation between the original sample and the recollect than between the original and the regenerated sample. Pairwise Flong PC2 Figure 7ST values. In both cases these FST values involved the sample from generation 1, W-1571-gen1. The differentiation of W-1571-gen1 from the original collect can also be seen in the STRUCTURE analysis (The accession W-1571 is a particular case. Although the levels of genetic diversity of the different samples are comparable, it is the only accession with significant within-accession Fanalysis Figure 6analysis . The reain and ex situ conservation in this study we conclude that neither conservation method can be considered consistently superior to the other within the studied time frame and with neutral genetic markers. Chance events and the relative balance of evolutionary forces in any given population will determine the degree to which the genetic integrity of an accession is conserved. To some extent these forces can, and should, be controlled and minimized in ex situ conservation (Based on the genetic changes detected during ervation , but theex situ conservation of genetic resources and stresses the need for active in situ conservation to assure long-term survival and continued evolution.Red clover is not a protected species in Norway or Sweden and none of the wild populations are located within protected areas. For some of the populations from which collections were made in 1980, the original location where the sample was collected no longer contain red clover. Among the eight Norwegian accessions three were originally sampled in areas that are today replaced by urban development or cultivated fields. This highlights the importance of The original contributions presented in the study are included in the article/JH, PM, LO, SS, AA and AP contributed to conception and design of the study. PM, LO and AP carried out field collections. KA carried out the lab work. JH carried out the genetic analysis. JH and AP wrote the first draft of the manuscript with help from KA. All authors contributed to the article and approved the submitted version.


Camellia taliensis) populations at different altitudes  in Qianjiazhai Nature Reserve, Zhenyuan country, Yunnan province, were investigated using EST-SSR molecular markers to compare their genetic variation against altitude. In total, 182 alleles were detected across all loci, ranging from 6 to 25. The top one informative SSR was CsEMS4 with polymorphism information content (PIC) of 0.96. The genetic diversity of this species was high, with 100% of loci being polymorphic, an average Nei’s gene diversity (H) of 0.82, and Shannon’s information index (I) of 1.99. By contrast, at the population level, the genetic diversity of wild ancient tea tree was relatively low, with values of H and I being 0.79 and 1.84, respectively. Analysis of molecular variance (AMOVA) revealed a minor genetic differentiation (12.84%) among populations, and most of the genetic variation (87.16%) was detected within populations. Using population structure analysis, we found that the germplasm of wild ancient tea tree was divided into three groups, and there was a substantial gene exchange among these three groups at different altitudes. Divergent habitats caused by altitudes and high gene flow played important roles in genetic diversity of wild ancient tea tree populations, which will provide new opportunities for promoting their protection and potential utilization.In this study, the genetic diversity and population structure of 4 wild ancient tea tree ( As the basis of life evolution and adaptation, the level of genetic diversity can reflect the adaptability of species with environmental changes. The genetic diversity of plant populations is not influenced only by genotypes, but also by external factors such as geographic areas, altitude, climate, and soil types. The complex environmental aspects  at different altitudes have an important influence on the phenological population size; hence, altitude as one of the most critical ecological factors has a strong impact on plant distribution . PreviouThe wild ancient tea tree is an irreplaceable germplasm resource mainly distributed in the natural forest, and plays an important role in scientific research on the origin, breeding and varietal improvement of tea tree . The wilC. taliensis) community is the dominant plant population in Qianjiazhai Nature Reserve, and is the largest and best-preserved wild ancient tea tree community in the world  germplasm belonging to 8 species (PPB = 98.90%) [C. sinensis, C. sinensis var. assamica and C. sinensis var. pubilimba (PPB = 99.7%) [C. sinensis L. (cultivated tea) and its wild relatives (21.4–50.0%) [H was 0.82, I was 1.99, and the values of Ho (0.79) and He (0.82) of 86 tea germplasm resources were similar (He (0.82) of wild ancient tea tree detected in this research was higher than the SSR-based estimates for other tea species in the same family, such as C. reticulata (He = 0.46), C. saluenensis (He = 0.34), C. pitardii (He = 0.24), and C. yunnanensis (He = 0.17) [The EST-SSR marker technology was used in this study to reveal the genetic relationships among the wild ancient tea tree populations at four altitudes in Qianjiazhai Nature Reserve of China. The ymorphic . These 1bove 0.5 , suggest 98.90%) , tea cul= 99.7%) , and C. 4–50.0%) . The H w similar , indicat = 0.17) . These r = 0.17) . Secondl = 0.17) . The proH) and Shannon’s information index (I) for the four populations of wild ancient tea tree were 0.79 and 1.84, respectively . This indicated an absence of a clear effect of the altitude in this region on the distribution of genetic diversity of wild ancient tea tree populations, which is similar to the research results on Abies lasiocarpa [Nothofagus pumilio [At the population level, the values of Nei’s gene diversity  was 0.03, which also confirmed that the genetic variation existed mainly within, rather than among, the populations of wild ancient tea tree [Cymbidium tortisepalum (89.25%) [Lilium cernuum (84%) [Genetic diversity can indicate the level and distribution pattern of genetic variation, with the genetic differentiation of plant populations affected mainly by their own genetic characteristics and changes in the external environment ,47. The tea tree . A simil(89.25%)  and Lilium (84%) . This caum (84%) . In geneum (84%) .Nm is considered to indicate genetic differentiation and gene flow among and within populations. If Nm is <1, the dominant factor affecting the genetic structure of population is genetic drift; if Nm is >1, there is sufficient genetic exchanges to prevent the genetic differentiation made by genetic drift among populations [Nm values of ancient Chengbudong tea trees population in Xishuangbanna were 1.62 and 7.01, which indicated there was a strong gene flow among the tea plants. So, high gene flow in this study (Nm = 8.52) is a plausible reason for the low genetic differentiation among the wild ancient tea tree populations. This is probably because wild ancient tea tree is one of typical wind-pollinated plants whose pollen can be spread over a long distance. Compared with other tree species, the wild ancient tea tree has some unique biological characteristics, including monoecious, high outcrossing proportion, high tree ages, and over-lapping generations, which contribute to gene migration among the wild ancient tea tree populations and increase heterogeneity among them at different altitudes, thereby reducing the genetic differentiation among populations.The ulations . Previouulations ,55 reporGenetic distance is one of the common indicators of genetic differentiation among plant populations . In the We found that 86 wild ancient tea tree specimens in Qianjiazhai Nature Reserve can be divided into three clusters (k = 3) based on the genetic structure analysis , but theWe studied the genetic diversity of wild ancient tea tree collected from different altitudes in Qianjiazhai Nature Reserve by using EST-SSR molecular markers. The results indicated the high total genetic diversity of wild ancient tea tree populations in Qianjiazhai Nature Reserve because of the area’s unique biological characteristics and living habits. The strong gene flow may be a dominant factor resulting in the high genetic diversity among the wild ancient tea tree populations. Furthermore, altitude affected the genetic diversity of wild ancient tea tree populations in this region. The population of wild ancient tea tree at low altitude  showed a higher level of genetic diversity than the populations at middle and high altitude  probably because environmental disturbance posed a negative effect on the diversity along the altitude gradient, whereas natural reserve played a significant positive role in protecting species diversity. These findings enhance our understanding of the interactions between genetic diversity of species and environmental factors and provide new insights into the factors influencing genetic characteristics of wild ancient tea tree at different altitudes.S1 FigA: CsEMS72, B: CsEMS78, C: CsEMS189, D: CsEMS201. Pop1: 1–26, Pop2: 27–46, Pop3: 47–66, Pop4: 67–86.(TIF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.


Genetic diversity sustains species adaptation. However, it may also support key ecosystems functions and services, for example biomass production, that can be altered by the worldwide loss of genetic diversity. Despite extensive experimental evidence, there have been few attempts to empirically test whether genetic diversity actually promotes biomass and biomass stability in wild populations. Here, using long-term demographic wild fish data from two large river basins in southwestern France, we demonstrate through causal modeling analyses that populations with high genetic diversity do not reach higher biomasses than populations with low genetic diversity. Nonetheless, populations with high genetic diversity have much more stable biomasses over recent decades than populations having suffered from genetic erosion, which has implications for the provision of ecosystem services and the risk of population extinction. Our results strengthen the importance of adopting prominent environmental policies to conserve this important biodiversity facet. Experimental evidence indicates that genetic diversity can promote biomass stability, but does this process occur in wild populations? Focusing on three freshwater fish species from two river basins in southwestern France, this study shows that the biomass of genetically diversified populations has been more stable in recent decades than populations that have suffered from genetic erosion. By promoting trait complementarity or redundancy among species, interspecific diversity allows ecological communities to optimally capture essential resources, to transform those resources into biomass and to recycle them4. In species-rich communities, these ecological processes are maintained even in the face of environmental fluctuations, thus promoting ecosystem productivity and stability over time:6 this is the insurance effect of species richness7.Biodiversity sustains critical ecosystem services, such as water filtering, pollination or biomass production8. Intraspecific genetic diversity can play a role similar to species diversity in driving ecological processes at the basis of ecosystem services, such as biomass production10. Beyond its positive influence on individual fitness and thus on per capita biomass production, intraspecific genetic diversity may favor functional complementarity or redundancy among individuals, thereby fostering a more efficient exploitation of available resources9. Genetically diversified populations are therefore predicted to harbor both larger individuals (higher per capita biomass) and higher and more stable levels of total biomass than genetically impoverished populations10. This direct relationship between intraspecific genetic diversity and biomass is expected to be particularly strong in ecosystems where species diversity is naturally low, which is actually the norm in many temperate ecosystems11. In such cases, the functioning of ecosystems probably depends more on the complementarity among genotypes than on the complementarity among species13, emphasizing the importance of maintaining genetic diversity to preserve ecosystem functions and services9.Although biodiversity erosion is often associated to species loss, another form of erosion is silently underway: the loss of intraspecific genetic diversity14. However, observational studies conducted in natural settings are still scarce and mostly concern plants15. Although these studies offer a number of advantages, experiments do not make it possible to cover large spatial and temporal scales or to investigate the influence of historical contingencies. Local levels of intraspecific genetic diversity indeed result from the interplay between long-term evolutionary trajectories  and more recent –if not ongoing– ecological processes affecting individual life history traits or population demography . This natural complexity cannot be fully grasped by experimental studies. Observational field surveys are on the contrary more realistic and may provide important insights into the contribution of intraspecific genetic diversity, and the loss of it, to biomass and biomass stability in natural settings15. They yet raise several difficulties. First, assessing the influence of genetic diversity on biomass and biomass stability over several generations or seasonal cycles implies long-term monitoring programs of both population density and biomass, but such data are usually difficult to collect and are still scarce. Furthermore, the relationships between genetic diversity and biomass in across-population studies may be masked by the interplay with other factors also involved in biomass production, such as population density and environmental conditions, making it difficult to disentangle their respective contributions15. This last issue may, however, be partly alleviated through the use of causal modeling procedures , making it possible to thoroughly confront theoretical expectations and experimental findings with the real world20.Most studies investigating the relationship between intraspecific genetic diversity and key-ecological parameters such as biomass are based on experimental or semi-experimental settings, where population densities and levels of intraspecific genetic diversity are manipulated, while environmental conditions are controlled and maintained constant over timePhoxinus dragarum, Gobio occitaniae and Squalius cephalus) from two large river basins in southwestern France , while controlling for the effect of environment (upstream-downstream gradient and eutrophication levels), of per capita biomass  and of past demographic events, using path analyses22. Total fish biomass stood for the total weight of all individuals from the three focal species collected at a given site (in g.m−2), standardized and then averaged across species and over years. Local levels of intraspecific genetic diversity were computed for each species using both microsatellite and SNPs data and similarly averaged across species. We addressed the following questions: Do the positive relationships found experimentally between intraspecific genetic diversity and total biomass and total biomass stability hold true in natural settings? If any, is the contribution of intraspecific genetic diversity to these ecosystem functions comparable in magnitude to that of other environmental determinants? Finally, is it possible to detect the impact of contemporary genetic erosion -i.e., the loss of intraspecific genetic diversity in response to a recent reduction in population size- on biomass and biomass stability of fish populations? This latter point is of high concern: with conservative estimates of 6-15% loss of intraspecific genetic diversity in wild organisms in the Anthropocene24, the impact of human-induced genetic erosion on natural ecosystems’ capacity to provide critical provisioning and regulating services to humanity may actually be much more important than anticipated. We show that populations with high genetic diversity do not reach higher biomasses than populations with low genetic diversity, but that they have much more stable biomasses over recent decades than populations having suffered from genetic erosion.Here, we capitalized on long-term field surveys of three parapatric non-commercial freshwater fish species , reinforcing the hypothesis that biomass stability was rather indirectly fostered by mechanisms such as functional complementarity or redundancy among phenotypes27, acting as a biological insurance against natural environmental fluctuations7.Contrastingly, we found that fish populations with higher levels of intraspecific genetic diversity displayed higher biomass stability over time than genetically-impoverished populations, whatever the temporal stability in per capita biomass  was much higher than that of considered environmental determinants R² = 4%; Fig. %; Fig. 41.1% was 7. By favoring higher functional complementarity among phenotypes, higher genetic diversity likely allows populations to maintain an efficient exploitation of available resources in the face of natural environmental fluctuations, ensuring a stable production of biomass7. Interestingly, this buffering effect of intraspecific genetic diversity did not come with a performance-enhancing effect on biomass production7 , and we focused on the three most common species: the minnow Phoxinus dragarum, the gudgeon Gobio occitaniae and the chub Squalius cephalus. These generalist cyprinids vary in their mean body length 40. They mainly feed on invertebrates  but occupy different habitats: chubs are primarily found in downstream sections at relatively low densities (~0.01 ind.m−2), minnows are primarily found in upstream sections at relatively high densities (~0.10 ind.m−2), whereas gudgeons are found all along the river basin in various habitats and at relatively high densities (~0.08 ind.m−2)40. All stations are monitored yearly by the French Office for Biodiversity (OFB) with a constant sampling effort since 199041. Demographic and biomass data from the three focal species were extracted from the ASPE database42. We only retained stations monitored from 1993 to 2020 with at least eight sampling sessions, resulting in 42 stations  to reflect the environmental variability existing along the upstream–downstream gradients. Fish communities in these basins are generally poorly diverse (3 to 15 species−2) and computed per capita biomass  as total fish biomass divided by local fish density. For each species, both total fish biomass and per capita biomass were standardized to make data comparable across species. For each station, we computed (i) Mean Total Biomass  as the mean of total fish biomass  across species and over years, and to capture temporal fluctuations in biomass measures, (ii) Mean Total Biomass Stability  as the inverse of the squared coefficient of variation of Mean Total Biomass  over years44. Per capita Biomass (stability) was here considered to determine whether Total Biomass (stability) was directly driven by the average individual biomass (stability) alone, or by other mechanisms such as functional complementarity among phenotypes. Each station was also assigned a sampling weight (ranging from 0.29 to 0.98) computed as the average of the relative local survey duration  and of the relative local number of sampling sessions .For each species, station and year of survey, we collected local fish density , total fish biomass . We checked for multi-locus deviation from Hardy-Weinberg Equilibrium and for gametic disequilibrium using GENEPOP 4.2.146 after sequential Bonferroni correction to account for multiple related tests47. In each species, the presence of null alleles was assessed by analyzing homozygote excess at each locus in five populations previously identified as panmictic, using MICROCHECKER 2.2.348. We discarded from further analyses any locus showing significant gametic disequilibrium and/or evidence of null alleles, resulting in the withdrawal of one locus (CtoG-075) in P. dragarum, two loci (Lsou5 and Gob12) in G. occitaniae and three loci  in S. cephalus, for a total number of 17, 13 and 16 loci in each species, respectively. Although the three focal species are of limited interest for anglers49, discriminant analyses of principal components50 performed on microsatellite data allowed identifying outlier populations, possibly resulting from past stocking events51. All outlier populations , adaptor ligation, RAD tag amplification and sequencing on two Hiseq lanes . The procedure resulted in demultiplexed paired-end short reads that were subsequently processed for SNP identification and allelic frequencies estimation. All paired raw fastq files were filtered using the process_radtags and the clone_filter functions from Stacks52, in order to remove reads with uncalled bases or low quality scores and discard PCR duplicates. For each species, a single fastq file, corresponding to an outlier population as identified from microsatellites data  were then indexed using both the index function from bwa54 and the faidx function from SamTools55. All filtered paired-end fastq files were aligned on their draft genome using the aln and sampe functions from bwa. Aligned SAM files were converted to BAM format with the view and sort functions from SamTools, and filtered for unpaired, unmapped or badly mapped reads  using the filter function from BamTools56. For each species, all indexed and filtered BAM files were then assembled in a single mpileup file using the mpileup function from SamTools. These mpileup files were synchronized in Popoolation257 with the mpileup2sync.jar java script. SNP allelic frequencies were finally determined using the snp-frequency-diff.pl perl script in Popoolation2 with a minimum allele count of 4 and a coverage ranging from 30 to 400. The whole procedure led to the identification of 10137, 13671 and 5897 SNPs in P. dragarum, G. occitaniae and S. cephalus, respectively. In P. dragarum and G. occitaniae, allelic frequency reliability was assessed for each SNP and each station by comparing allelic frequencies between pairwise replicates. When allelic frequencies were available for the two replicates and when ΔAF, the difference in allelic frequencies between pairwise replicates, was lower than 0.25, the final allelic frequency was computed as the average of allelic frequencies across replicates. Otherwise, the final allelic frequency was set as missing data. In each species, we finally followed a two-step filtering procedure: (i) we first discarded any SNP with available allelic frequencies for less than 15 stations; (ii) we then discarded any station with available allelic frequencies for less than 150 SNPs. This final filtering procedure generated a total of 1244, 1892 and 1847 SNPs in 27, 30 and 17 populations in P. dragarum, G. occitaniae and S. cephalus, respectively . In 58 the expected level of heterozygosity across SNPs loci (sHe) and the observed level of SNP polymorphism (sPo), computed as the number of non-fixed loci  divided by the total number of loci with non-missing data in a given population. We then used microsatellite data to compute two additional metrics, the expected (µHe) and observed (µHo) levels of heterozygosity across microsatellite loci, using the software GENETIX 4.359. These four metrics of genetic diversity naturally range between 0 and 1 and are thus directly comparable across species: for each station, we thus averaged each metric over species and then used a principal component analysis  to get a synthetic predictor of the overall level of genetic diversity at the station level. Only the first principal component (PC) was retained, accounting for 75.9 % of variance in genetic data, with genetically impoverished sites on the one hand (negative coordinates) and genetically diversified sites on the other hand  and the N-ratio computed with VarEff63 . Note that the M-ratio can only detect signals of population decline (bottlenecks), but that both the MIratio and the VEratio can also detect signals of population expansion.We used microsatellite data to compute three different quantitative measures of the degree of genetic erosion that populations underwent in recent generations: the M-ratio61, and has been shown to be particularly relevant in river systems35. For each station and each species, the M-ratio was computed for each microsatellite locus and then averaged over loci.The M-ratio is the ratio between the number of observed alleles at a microsatellite locus and the allelic range of that locus, the latter being supposed to decrease slower than the number of alleles during a demographic collapse. This index, ranging from 0 to 1, is inversely proportional to the degree of genetic erosion62. The MIratio was estimated using the OnePopVarSize model, considering a single past change in population size, and a generalized stepwise mutation model (GSM). For each station and each species, PAC-likelihood computations were based on four iterations, 500 points and iteratively 2000 (first computation) or 20,000 runs per point (second computation), to check consistency and improve convergence. In each case, we kept the estimate associated with the lowest RMS residual error, except when the algorithm failed to converge with the first computation, in which case we kept the second estimate.The MIratio was computed as 63. For each station and each species, and following authors’ recommendations, we first ran preliminary tests with short Markov chain Monte Carlo (MCMC) batches (1000 batches of length 1) to identify the best values for the number of past changes in effective population size (Jmax), for the effective size prior value (Nbar) and for the number of generations since the assumed origin of the population (Gbar). We then use these best values to run long MCMC batches , get effective population sizes at generation 1  of the overall level of genetic erosion at the station level, with negative coordinates corresponding to stations with low genetic erosion . Only the PC was retained, accounting for 61.3 % of variance : Temperature (in °C), oxygen concentration (in mg.L−1) and saturation (in %), Biochemical oxygen demand (in mg.L−1), as well as concentrations (in mg.L−1) in nitrogen compounds , in phosphorus compounds  and in dissolved organic carbon. Following criteria used by French managers to assess the ecological status of rivers from various physicochemical parameters according to the French implementation of the European Water Framework Directive 2000/60/EC  and the distance to the tributary source (in m) with the riverdist R-package65, we used path analyses67. We designed two full causal models describing the expected direct and indirect links among variables and their first-order interactions  and Total Biomass on the one hand (model A) and Biomass Stability on the other hand (model B). To consider the influence of per capita biomass on total biomass variables, we hypothesized that intraspecific genetic diversity would also indirectly promote Total Biomass , through a pathway involving per capita Biomass . We further hypothesized that the environmental characteristics of stations  would affect per capita and total biomass variables both directly (for instance through higher intraspecific competition in harsh conditions) and indirectly, through pathways involving intraspecific genetic diversity  as well as bottleneck (triggered for instance by pollutants). All variables were standardized to z-scores before using weighted path analyses22 with MLM maximum likelihood estimation, Satorra-Bentler scaled test statistic and station sampling weights. We simplified each model by removing non-significant paths one at a time, provided that cross-products were always associated with their additive terms69 and that removal led to an increase in the relative fit of the model . The validity of final models was assessed according to their absolute fit  < 0.09 and Robust Comparative Fit Index (CFI) > 0.96)71. Additionally, we verified that there was no significant discrepancy between the sample and the fitted covariance matrices71 . All path analyses were run using the R-package lavaan72. Predicted values of endogenous variables  the amount of variance (R²) explained by each model. To assess the relative contribution of per capita biomass, intraspecific genetic diversity and environment to the variance in total biomass variables, we computing R² from further simplified models with (b) all variables related to per capita biomass being discarded (amount of variance explained by both environment and intraspecific genetic diversity), then (c) with all variables related to intraspecific genetic diversity (intraspecific genetic diversity and associated cross-products) being discarded (amount of variance explained by environment only). The relative contributions of per capita biomass and intraspecific genetic diversity to the variance in total biomass variables were respectively obtained by subtracting R² of (b) from R² of (a) and by subtracting R² of (c) from R² of (b).24, we used the following approach. Predictions were realized over k = 10,000 iterations. For each iteration k, we first computed an eroded IGD predictor (eIGD) as:k, we then computed the predicted Biomass Stability BSTA from IGD and the predicted eroded Biomass Stability eBSTA from eIGD, using linear models and Eutrophication gradient EG as a covariate , as estimated by several authors in wild organismsThe predicted overall expected change in biomass stability given a 6 to 15% decline in IGD was finally computed as Further information on research design is available in the Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Reporting Summary


Pseudois nayaur) across the distribution range in the Western trans Himalayas of India and found about 18,775 km2 area suitable for blue sheep. The explicit Bayesian based spatial and non-spatial population structure analysis assigned blue sheep into two genetic populations, i.e., Ladakh and Lahaul-Spiti. We found relatively high genetic divergence in blue sheep which is also supported by the low current flow in Circuitscape model. With the multiple evidences, we explain landscape resistance facilitated by the landscape heterogeneity, and large patches of unsuitable habitats forced population divergence and poor functional connectivity. We found that blue sheep population has been demographically stable in the past, but showed a slight decline within the last few decades. This study is the first range-wide attempt to exhibit landscape features in shaping the spatial distribution, genetic structure and demography patterns of blue sheep in Western Himalayas, and will be of use in the conservation and management planning of blue sheep.The trans-Himalayan region of India, although have xeric features, still supports a unique assemblage of biodiversity, including some of the charismatic and endemic species. In the present study, we studied blue sheep ( Despite the profound impact of such changes on global biodiversity, there is a scarcity of direct evidence on how species respond to such changes, while landscape changes  are more evident and visible than the species adaptive response. Therefore, to facilitate long term persistence of a species, functional connectivity need to ensured so as to enable gene flow and sharing of alleles throughout the landscape4. In free ranging terrestrial wildlife, gene flow is often limited by the degree of landscape permeability and connectivity between different habitats patches5. Assessing the factors impacting habitat connectivity and dispersal are essential measure to interpret evolutionary consequences and to forbid local and regional extirpations. Landscape genetics is a trans-disciplinary approach, particularly useful in elucidating the cumulative influence of ecological, landscape and other anthropogenic factors on gene flow and natural selection8. In this context, maternally inherit mtDNA and nuclear microsatellite markers are used in addressing phylogeography, evolutionary history and current population genetics parameters with respect to the landscape features and habitat connectivity9. With the ensemble approach of coupling environmental, ecological and other anthropogenic variables with the genetic information of the natural populations may depict the current spatial genetic structure and provide empirical information of management interventions e.g., assigning large geographical barriers10, finding unique taxonomic units11 and identifying habitats for population8.Heterogeneity in large landscape often delineate species boundary and primarily forms the foundation of genetic differentiation in free ranging wildlife. In this context, human-induced habitat loss and climate change are known to be the key threats to biodiversity conservation worldwidePseudois nayaur) is one of the widely distributed medium-sized alpine ungulate distributed from the Qilian Mountains in the north to The Himalayas in the south. Globally, it is distributed in China, Bhutan, India, Myanmar, Nepal, Pakistan and Tajikistan13. In India, it is distributed in Ladakh, Himachal Pradesh, Uttarakhand, Sikkim and Arunachal Pradesh between the elevation ranges from 2500 to 5500 m asl16. They typically prefer plateau, alpine barren and inter-valley grassland16 and play a key ecological role in the maintaining the mountain ecosystems by imparting a crucial prey base to the large carnivores and determine their density and distribution in that region18. Blue sheep are listed as least concern (LC) under the IUCN red list and listed as Schedule I under the Wildlife (Protection) Act, 1972 of India19. However, no firm information is available of the population size of blue sheep19. Previous studies reported climate change and urbanization are the major threats to blue sheep across distribution range and interestingly, blue sheep show relatively less seasonal altitudinal migration with respect to changing seasons 21. Moreover, blue sheep usually found in large herd and avoid steep slope. While, the mountain ranges of the Western Trans Himalayas of India i.e. the Ladakh and Zanskar range appear to separate the mountainous landscape by challenging topography such as steep cliffs and canyons that could act as barriers to blue sheep movement. In addition, blue sheep also compete with other mountain ungulates and livestock for limited resources and often impacted with their high densities24. In the present study, we adopted a landscape genetics approach to testify the functionality of biological corridor to support the movement and gene flow of blue sheep in Western trans-Himalaya.Blue sheep  was found suitable for the blue sheep that lies primarily in Leh region of Ladakh (LA) and Spiti region of Lahaul-spiti (LS)  Fig. , Fig. S1On analysing 137 sequences of cytochrome b gene of blue sheep, we obtained seven haplotypes Fig. a with 0.We generated 137 consensus genotypes with nine loci with considerably good amplification success (≥ 80%) Table . The ADOID sibs) was 2.1 × 10–3 (2.1 match in 1000 genotypes). In total, 118 unique individuals were identified out of 137 consensus genotypes generated. Genetic diversity indices of blue sheep population showed a mean Na and Ne of 9.89 ± 1.15 (LA) to 10 ± 0.71 (LS) and 4.37 ± 0.58 (LA) to 3.80 ± 0.50 (LS), respectively. The mean Ho was 0.44 ± 0.06 in LA and 0.35 ± 0.06 in Spiti, while the mean He was 0.72 ± 0.05 in LA and 0.70 ± 0.03 in Spiti . The genetic partitioning showed the presence of geo-spatial structure in the population i.e. 56 individuals from LA assigned to Cluster I while 47 individuals from LS were assigned to Cluster II . Blue sheep prefers low precipitation and rely on the mean Temperature of Driest Quarter. Blue sheep are usually found in large herds and have relatively less dispersal which is also supported by our study showing low migration rate between the two regions i.e., Ladakh and Lahaul-Spiti . The present study suggested that low connectivity between LA and LS in addition to complex topography and high expanses among habitat patches.Previous studies on blue sheep in India mainly focused on the ecological aspect and no study was conducted on the population genetics of blue sheep29 and other caprinae species e.g., wild goats30; Hangul31; and Iberian ibex32. The neutrality tests were non-significant, indicating a stable population size. Mismatch distribution curve also revealed a stable population of blue sheep with no bottleneck in the past. The BSP analysis showed a stable population in the past with experiencing a slight demographic decline in the recent past. Thus, all the demographic data corroborate with each other. Mitochondrial data suggested gene flow in the past as one haplotype (hap_1) was distributed in all the region and Zanskar haplotype (hap_7)  and Karakoram (KWS) sanctuaries of LA where blue sheep majorly reported have no definite boundary till now. Moreover, these regions had encountered drastic changes in the last few years including increased presence of defense forces and large influx of tourists leading to unplanned construction that have disturbed the harmonious association and affected the native wildlife. A few earlier studies have identified that anthropogenic changes often restrict gene flow among populations and exhibited the utility of landscape genetics in conservation and management planning in ungulates2   districts of Himachal Pradesh and Leh and Kargil districts of Ladakh union territory (LA-UT), falling under the Western Trans Himalayan region covering a total area of 1,06,014 kmwww.gbif.org). For distribution modelling, combination of primary as well as secondary data were used. To reduce over fitting of the model spatially independent locations were used after rectifying the location points based on the home range of blue sheep28.Field surveys were performed from 2018 to 2021 in the studied landscape. We opted well established contour transect in the studied landscape and in total, 350 occurrence points of blue sheep were recorded . In addition to the primary occurrence records, secondary data was extracted from the Global Biodiversity Information Facility (GBIF) database  samples, i.e., 118 pellet samples from LS region and 157 samples from LA region possibly belonging to blue sheep . The sanger sequencing was performed following Big-Dye Terminator Cycle Sequencing Kit version 3.1  after exo-sap clean up on an ABI 3730 Genetic analyzer .For species verification, we used partial fragments of the mitochondrial cytochrome b30 and 7 loci of cervidae;37 . We started with a set of 30 habitat variables including present bioclimatic variable, topographic variables, physiological variables and anthropogenic variables. The 19 bioclimatic variables were obtained from the WorldClim data base at 30 arc second scale (https://www.worldclim.org/)39 (Table https://earthexplorer.usgs.gov) and ArcGIS 10.6  was used for calculating the Euclidian distance. Human influence index was downloaded from Socioeconomic Data and Application (SEDAC). The variables were resampled at 1 km resolution and converted to ASCII format in ArcGIS 10.6 . Multi- collinearity among the variables were tested in the R platform using ENM tool40 and the variable with Person Correlation Coefficient (r) more than 0.8 were dropped from the analysis. Finally, eighteen spatially independent predictors (Table 41 on the basis of lowest Akaike information criterion (AICc) value42. Further, for the model evaluation, we used 70% of species presence points as training data and 30% as testing data. The modelling was assessed based on Receiver Operating Characteristics (ROC) Area under the curve (AUC) with the threshold value higher than 0.8 < AUC < 1 which provide good prediction model.To identify suitable habitat for blue sheep, we used a Maximum Entropy (MaxEnt) species distribution model  and validated through the BLAST tool of NCBI/GenBank. The identified sequences were aligned by multiple sequence alignment using CLUSTAL W in MEGA version X44, Neutrality tests  and genetic diversity indices i.e. no. of haplotypes (H), nucleotide diversity (π), haplotype diversity (Hd), average number of nucleotide differences (K) and mismatch distribution test for demographic expansion, equilibrium or bottleneck were computed. The NETWORK45 software was used to build a median-joining network. Subsequently, the obtained haplotype was then plotted on the map to visualize the haplotype sharing across the studied areas, using ArcMap 10.6 . Bayesian Skyline Plot (BSP) was constructed to determine the history of population over times using BEAST software version 2 6.346 by Bayesian Skyline method47. The HKY model and the Markov Chains Monte Carlo (MCMC) were run for 108 steps that yielded effective sample sizes (ESS) under a strict molecular clock and a stepwise skyline model. The molecular evolution was calibrated assuming a substitution rate of 2.0E−7 substitutions/ site/year following48 and the results were visualized using Tracer version 1.749.Using the program DnaSP version 6.12.03ID) and siblings (PID sibs) were calculated in GenAlEx version 6.5150 and the number of unique individuals were estimated in GeneCap version 1.2.251 and the PIC value for each locus was estimated using CERVUS version 3.052. While the nine markers were used to calculate the genetic diversity indices that included numbers of observed (Na) and effective alleles (Ne), observed heterozygosity (HO), expected heterozygosity (HE), and Wright's inbreeding coefficient at each locus utilizing GENAIEX version 6.550. We tested the deviation of any loci from Hardy–Weinberg equilibrium (HWE) using the program FSTAT version 2.9.453 and null allele frequencies were calculated using CERVUS version 3.052. Linkage disequilibrium (LD) was tested using GENEPOP version 4.7 following 10,000 dememorizations, 100 batches and 10,000 iterations per batch54.For individual identification, we selected a panel of six loci on the basis of their least or no genotyping error, relatively short amplicon size, high amplification success and a high discriminating power to avoid the overestimation of individuals. The locus wise and cumulative probability of identity for unrelated individuals  repetitions and 20 independent replicates at each ‘K’. The appropriate K value was determined by calculating ad hoc quantity (ΔK)56. Individuals were assigned to the inferred clusters using a threshold proportion of membership (q) i.e., q ≥ 0.80, otherwise an individual was determined as admixed if the q value was less than 0.8059. Secondly to spatially explicit the genetic discontinuities between the populations, we performed Bayesian clustering analysis in GENELAND version 4.0.360 through an extension of R package. In the analysis, along with multi-locus genotype, spatial coordinates were also taken into account. We conducted 20 independent runs for each K ranging from 1 to 10, with 1,000,000 iterations and 1000 thinning with other parameters were kept as default values . The runs showing the highest average logarithm of the posterior probability were selected and post-processing was conducted using 100 × 100 pixels in the spatial domain with a burn in period of 200. Finally non-Bayesian clustering methods i.e., DAPC were run to assign the possible clusters in Adegenet version 1.3.4 package of R61. To evaluate the migration rate between the LA and LS population, we run BAYESASS version 1.362 at 9 × 106 MCMC iterations, 106 were discarded as burn-in and sampling frequency was done at every 2000 intervals. Runs were carried out at migration rate prior of 0.05, while other parameters were kept at default.To determine whether discrete population structure was present in the blue sheep population, we used two Bayesian clustering (STRUCTURE and GENELAND) and one Non-Bayesian clustering (DAPC) method. First Bayesian clustering approach was performed in STRUCTURE version 2.3.450. Then, we mapped the pattern of genetic distances (independent of any a priori model of landscape resistance) using the Genetic Landscape GIS Toolbox63 in ArcGIS 10.6 . We used the Single Species module, to calculate surface of the pair-wise population genetic differentiation by generating a network. Midpoint of each edge of the network becomes associated with genetic diversity value. In the package, both spatial location points as well as relative genetic divergence values were used. In addition, presence of Isolation by distance (IBD) across the study landscape was also generated with Mantel test. The Mental test and sPCA was performed in R.4.0 software with Adegenet version 1.3.4 package61. Lastly, we built connectivity model based on circuit theory in the Circuitscape software version 4.064 to test the connectivity between the habitat patches to understand the dispersal and gene flow following Dalui et al.6. For conductance modelling resistance raster was constructed with ensemble of genetic divergence and habitat suitable model using the ArcGIS 10.6 software  extension Gnarly landscape utilities package Resistance and Habitat Calculator65.To explore the potential landscape barriers and corridors effecting the gene flow and population divergence of blue sheep, we first estimated genetic divergence in GenAlEx version 6.51Supplementary Information.


Rhus plant species. Horned galls with high tannin content are the most widely cultivated gall type, and Wufeng county of Hubei province in China is the center of cultivation. However, long-term artificial cultivation and domestication of horned galls to meet the increasing production demand have led to quality degradation. Understanding the reasons underlying quality degradation is urgent for horned gall production and application. The present study used a combination of metabolic, genetic, and ecological analyses to investigate the quality and genetic differentiation of the horned galls under long-term domestication as well as the potential relationships between them.Galla chinensis is a traditional Chinese medicine (TCM) produced due to the interaction between the Fordinae aphids and the Rhus chinensis trees in WJP, and between WJP and the other populations was detected based on SSR molecular markers, however, no significant difference in genetic structure was seen for the aphid populations. Among the various ecological factors examined, temperature was identified as the primary one affecting the quality of horned galls.Analysis of gallic acid content and other three phenotypic traits  revealed quality differentiation of horned galls collected from five locations in Wufeng, in which the cultivated samples from Wang Jiaping (WJP) showed the highest degradation. Genetic differentiation between the cultivated and wild Both genetic and ecological factors caused quality differentiation of horned galls. The collection of diverse germplasm of host trees and aphids will help reduce the quality degradation of horned galls in Wufeng.The online version contains supplementary material available at 10.1186/s12870-023-04442-1. Rhus plants. According to the shape, Galla chinensis is divided into three types: horned galls, gallnuts, and flower-like galls , more than half of the villages in Wufeng, such as Bai Nianguan (BNG), Bai Luzhuang (BLZ), Huang Liangping (HLP), and Huo Shan (HS), produce horned galls. In WJP, the cultivation of the host plant iversity –13, and iversity –16. TherAstragalus compactus [chrysanthemum [Genetic and environmental factors influence the synthesis and accumulation of chemical ingredients in diverse medicinal plants –23. Studompactus , whereasanthemum . Meanwhianthemum –28. HoweR. chinensis host trees and the S. chinensis aphids, and the environmental factors influencing the production of horned galls were investigated to elucidate the potential mechanism underlying the degradation of cultivated horned galls. Our results will assist in improving the cultivation and applications of Galla chinensis.The previous studies on Galla chinensis primarily focused on illustrating the formation of galls and explored the host plant–parasitic aphid interaction mechanism –9, 29. HP < 0.05)  to 68.66% (BLZ), and the content in WJP samples was significantly lower than that in BLZ (05) Fig. . The com05) Fig. , especia05) Fig. B. Thus, 05) Fig. .Fig. 1AnThe analysis of three other phenotypic traits also revealed significant differences between wild and cultivated horned galls. The gall fresh weight and size in the wild populations were significantly lower than in the cultivated populations, especially in WJP, BLZ, and HLP  and the genomic SSRs (Rc-gSSR), respectively. The clean reads obtained from RNA-seq were de novo assembled into 34,976 unigenes, which were further functionally annotated , followed by dinucleotides  and trinucleotides   were detected from the genome  compared with the other populations  and extremely low genetic differentiation (FST < 0.05) between populations   Fig. A and B. 01) Fig. A and B. R. chinensis, the transcriptome and reduced-representation genomes were used to develop SSRs in this study. The SSRs derived via these two approaches consistently revealed high polymorphism but no significant difference  lead to the quality degradation of horned galls, particularly the cultivated ones, among which the former was the primary reason.Numerous studies have revealed that the content of active ingredients in plants were comprehensively adjusted by a combination of genetic and environmental factors, with the genetic factors having a major impact. The accumulation of pharmacologically active ingredients of g factor . The geng factor . The prer = 0.86, P < 0.01) was observed between the fresh weight and gall size of horned galls . The compound leaves bearing horned galls of 102 cultivated and wild samples were collected in October 2021 from WJP, HLP, BNG, BLZ, and HS locations of Wufeng County, Hubei Province, China  method . The aphThree galls were randomly selected from a single host tree to evaluate the phenotypic traits, such as the gall fresh weight, gall size, and wall thickness. Due to the irregular shape of horned galls, the size of galls was converted to the volume of water using the displacement method. The wall thickness was measured at three different regions of a gall and represented as a mean of these values.−1.The sampled horned galls were first boiled for two minutes, dried for seven hours under 100 ℃, and cracked to remove the aphids and other impurities inside for analysing the gallic acid content . The dri−1. Then, 20 μL of the samples was loaded into the system, and the gallic acid was detected with a UV–VIS detector at 273 nm  and the peak area as the ordinate (y) showed a good linear relationship (R2 = 0.9993)  using an Ultimate XB-C18 column . The binary elution system used methanol and 0.1% phosphoric acid as solvents A and B. The elution was carried out using 5% A and 95% B for 15 min. The column temperature was set to 40 ℃ at a flow rate of 1.0 mL minR. chinensis using TRIzol reagent  according to the manufacturer’s instructions. Then, cDNA libraries were prepared using the extracted RNA and sequenced on the Illumina NovaSeq 6000 sequencing platform . The raw sequencing reads were filtered to obtain the high-quality RNA-seq data, and transcriptome assembly was accomplished using Trinity software [http://www.ncbi.nlm.nih.gov), Kyoto Encyclopedia of Genes and Genomes Ortholog  [http://www.ncbi.nlm.nih.gov/COG), Gene Ontology , Unified Protein , and the protein families  databases.Total RNA was extracted from the compound leaves of software . The funp/kegg/) –56, ClusSac I and Mse I (New England Biolabs (Beijing) Ltd., Beijing, China) in a 25 µL reaction containing 1 × restriction buffer. The enzyme product was ligated with the restriction fragment utilizing the SacAD and MseAD adaptors. Then the quantitative ligated products were pooled and purified, and the DNA fragments were further enriched by PCR with the KOD-Plus-Neo polymerase (TOYOBO (Shanghai) Biotech Co. Ltd., Shanghai, China). Fragments varied from 500 to 550 bp were selected for final library construction with the Agilent DNA 12,000 kit using the 2100 Bioanalyzer system (Agilent Technologies (China) Co., Ltd., Beijing, China). The libraries were sequenced on the Illumina NovaSeq 6000 platform following the paired-end 150 (PE150) strategy.For ddRAD-seq, 100 ng of the genomic DNA was double digested with 5 U of R. chinensis and the genome of S. chinensis (Accession: GCA_019022885.1) [Simple sequence repeats (SSRs) were searched on the sequences generated by RNA sequencing and dd-RAD sequencing of 22885.1)  using a 22885.1) . The SSR22885.1) . The forThe genomic DNA was then amplified in a 10 μL reaction system containing 2 μL genomic DNA, 5 μL 2 × Taq PCR MasterMix, 0.04 μL forward primer, 0.25 μL reverse primer, 0.15 μL M13-FAM/M13-HEX/M13-ROX, and 2.56 μL ultrapure water. The mixed PCR amplifications were performed on a BiometraTone 96G  and the PCR products were detected by automatic fluorescence using an ABI 3730XL Sequence Analyzer for primer screening. The SSR primers with high amplification efficiency, good reproducibility, and high polymorphism were retained for the overall genetic analysis. GeneMarker® software was used to analyze the amplified fragment size of the different samples at each SSR locus.The latitude and longitude of the five locations were imported into Wheat A (version 1.4.9) to extract the monthly data on ecological factors for the past decade (2012 to 2021) with an accuracy of 10 kms. The key ecological factors, including mean annual temperature (MAT), mean growing-season (May to October) temperature (MGST), mean annual sunshine hours (MASH), mean growing-season sunshine hours (MGSSH), mean annual precipitation (MAP), mean growing-season precipitation (MGSP), mean annual relative humidity (MARH), and mean growing-season relative humidity (MGSRH), were fetched for the latter statistic. Data on all ecological factors used in this study were derived from The Famine Early Warning Systems Network Land Data Assimilation System (FLDAS), provided by the National Aeronautics and Space Administration (NASA) and the Famine Early Warning Systems Network (FEWS NET) with 0.1° × 0.1° spatial resolution and monthly temporal resolution .www.graphpad.com.). The Principal Component Analysis (PCA) analysis was performed using SIMCA® 17. The genetic diversity parameters such as allele frequency, number of alleles (NA), effective number of alleles (NE), observed heterozygosity (HO), expected heterozygosity (HE), Shannon’s information index (I), Nei’s gene diversity index (H), fixation index (FST), and gene flow (Nm) were calculated using the POPGENE software (version 1.3.2) [PIC). Neighbour-Joining (NJ) cluster analysis was carried out using the Analysis of Phylogenetics and Evolution (APE) package [The ordinary one-way ANOVA analyses of the gallic acid content and ecological factors among populations were conducted using GraphPad Prism 8.3.0 for windows  . PIC Caln 1.3.2)  was used package  in R  Horned gall. (B) Gallnut. (C) Flower-like gall. Supplementary Figure 2. Analysis of the key phenotypic traits. Comparative analysis of gallic acid (A), fresh weight (B), gall size (C), and wall thickness (D) between wild and cultivated horned galls. The fresh weight (E), gall size (G), wall thickness (I) of the horned galls from five locations. And the fresh weight (F), gall size (H), wall thickness (J) of wild and cultivated horned galls in each location. . Supplementary Figure 3. Principal Component Analysis (PCA) analysis of Bai Nianguan (BNG) (A), Bai Luzhuang (BLZ) (B), Huang Liangping (HLP) (C), and Huo Shan (HS) (D) populations based on phenotypic traits. Supplementary Figure 4. Function annotation of Rhus chinensis unigenes on the basis of public database. (A) Summary of annotations of unigenes in six databases. (B) GO classification of annotated unigenes. (C) Functional classification of unigenes based on the KEGG pathway. (D) The COG functional distribution of annotated unigenes. Supplementary Figure 5. The genetic paraments of each R. chinensis and Schlechtendalia chinensis population . (A). The number of alleles (N). (B) The average number of alleles (NA). (C) The effective number of alleles (NE). (D) observed heterozygosity (HO). (E) expected heterozygosity (HE). (F) Shannon’s information index (I). (G) Nei’s gene diversity index (H). (H) Polymorphism information content (PIC). Supplementary Figure 6. STRUCTURE and PCA analysis of host trees and aphids based on SSR (Simple Sequence Repeat) loci. (A) Population structure of 102 R. chinensis accessions. (B) Population structure of 102 S. chinensis accessions. (C) Population structure of R. chinensis accessions in WJP and BLZ. (D) PCA analysis of R. chinensis accessions in WJP and BLZ. (E) Population structure of R. chinensis accessions in WJP. (F) PCA analysis of R. chinensis accessions in WJP. Supplementary Figure 7. Neighbour-Joining (NJ) analysis based on the SSR loci information of R. chinensis accessions. NJ tree of individuals from BNG (A), BLZ (B), HLP (C), and HS (D). The red stars represent the wild accessions and the blue stars represent the cultivated accessions. Supplementary Figure 8. Chromatograms of negative sample (A), gallic acid standard (B), and horned gall sample (C). Supplementary Table 1. Phenotypic data among 102 horned galls. Supplementary Table 2. Correlation analysis of phenotypic traits. Supplementary Table 3. Statistic of RNA-seq, ddRAD-seq data to R. chinensis and the reference genome data of S. chinensis. Supplementary Table 4. Genetic characterization of 23 pairs of R. chinensis SSR primers and 9 pairs of S. chinensis SSR primers. Supplementary Table 5. Analysis of molecular variance (AMOVA) for R. chinensis and S. chinensis populations. Supplementary Table 6. Sampling information. Supplementary Table 7. Method validation of the high-performance liquid chromatography (HPLC) analysis in horned galls.


Hydrophis are important components of animal diversity in Iranian waters of the Persian Gulf and Gulf of Oman. Ten species of Hydrophis have been identified from the these waters and, in this study, genetic structure of seven species was compared with other populations in the eastern Indian Ocean and the West Pacific. We found that six species  show high genetic similarity with conspecific populations in the Indian Ocean and Australia. However, H.curtus from southern Iran shows a high level of genetic differentiation from conspecific populations in Sri Lanka and Indonesia . Variation between Iranian and Southeast Asian populations may reflect new genetic lineages and suggest the need of further morphological evaluations to re-evaluate their taxonomic position.Sea snakes of the genus  Hydrophiini share a common ancestor approximately six million years ago, but the major speciation events and radiation within the group and the genus Hydrophis have taken place over the last 3.5 million years  of species of sea snakes of the genus Hydrophis from the Persian Gulf and the Gulf of Oman were compared to populations in other areas of the Indian Ocean, Southeast Asia, and Indonesia. Examining the genetic structure of the species in the Persian Gulf and comparing them with other populations can illustrate the degree of genetic connection between these populations.In this study, available genetic markers  during fieldwork in 2013  and were16S rRNA (16S) (COI), and one anonymous nuclear marker (G1888)  were calculated (for 16S and COI) and number of variable sites (V) and parsimony informative sites (Pi) were obtained using MEGA v. 6.0 for all markers. The sequences of the COI gene fragment were few and we only calculate genetic distance among some clades that revealed relatively high values.Two mitochondrial sequences, NA 16S)  and cyto (G1888)  were use (G1888) , but COICTTGA-3' . All DNA6S  and cCTTGA-3' , and theCTTGA-3'  to check16S: 523 bp; G1888: 492 bp) and COI gene fragment (708 bp) separately were considered to reconstruction phylogenetic trees using Bayesian inference (BI) and maximum-likelihood (ML) methods. The best substitution models for each alignment were: TrNef+I, F81+I, TIM+G, GTR+I+G and TVM+I+G for the first, second, and third codon positions of COI and 16S and G1888, respectively. RaxML v. 7.4.2  and COI  and one nuclear anonymous fragment G1888  totaling 1531 bp. Both the ML and BI trees show similar topology and therefore we present only the BI tree  shows clear divergence from Indian Ocean (Sri Lanka) and Indonesian samples  among the lineages  in Hydrophis. Iranian lineages of H.curtus are differentiated from those in Southeast Asia by 0.6% and 6% in 16S and COI gene fragments, respectively , but the other conspecific samples (Pacific Ocean) had a different distinct haplotype (haplotype number 16).Tree topology was used to group the sequences and calculate the uncorrected genetic distance  indicate that the former may representative a new genetic lineage in Southwest Asia  dispersal from their source populations  and reveals a new distinct genetic lineage. The genetic difference between DNA sequences of H.curtus populations in the Persian Gulf and Pacific Ocean is very likely due to genetic drift. In general, biodiversity conservation requires information (demographic and microhabitat information) from local populations. Only then we can encourage conservationists to reassess the status of the species in the Persian Gulf and the Gulf of Oman, because the latest International Union for the Conservation of Nature (IUCN) status of most species is Least Concern and one is Data Deficient (The total diversity of the true sea snakes of the genus  species . Accordi species , H.schiseficient .


In this review, we aim to summarize the existing literature in autism  to chart a putative pathway from (i) imaging-derived neuroanatomical cortical phenotypes to (ii) underlying  biological processes, and (iii) associated genomic variation.Autism has been associated with differences in the developmental trajectories of multiple neuroanatomical features, including cortical thickness, surface area, cortical volume, measures of gyrification, and the gray-white matter tissue contrast. These neuroimaging features have been proposed as intermediate phenotypes on the gradient from genomic variation to behavioral symptoms. Hence, examining what these proxy markers represent, i.e., disentangling their associated molecular and genomic underpinnings, could provide crucial insights into the etiology and pathophysiology of autism. In line with this, an increasing number of studies are exploring the association between neuroanatomical, cellular/molecular, and (epi)genetic variation in autism, both indirectly and directly  Autism spectrum disorder  is a lifelong, complex, multifactorial, neuropsychiatric condition reported to occur in 1 out of 54 individuals . Core fein vivo, across age, and for different neuroanatomical features.Previous studies suggest that inter-individual neuroanatomical variability at the macroscopic level may be influenced by microscopic variability. For example, postmortem studies have linked neuroanatomical differences to minicolumnar pathology, abnormal neuronal numbers and sizes etc ; and  within each ontogenetic column . FollowiSurface area, in contrast, is traditionally thought to be driven by the early amplification of radial glia, i.e., the amount of proliferative units and therefore the number of ontogenetic columns , which lCortical volume is regulated through the interplay of the neurodevelopmental mechanisms that modulate its constituent components, namely cortical thickness and surface area. Hence, the neurobiological mechanisms affecting cortical volume development are complex, and inherently difficult to describe. Moreover, as mentioned above, the relative contributions of cortical thickness and surface area to cortical volume may vary across developmental stages; and may exert region-dependent effects . We therThe lGI captures the degree of regional folding (gyrification) of a certain brain area . Gyrification, i.e., the formation of gyri and sulci, commences at 10–15 weeks of life, generating an adult-like pattern by the third trimester . There aThe GWC is initially determined by the finely tuned patterning (acquisition of distinct cell identities) and spatial organization of cells and their components during development. In neurotypicals, fully migrated neurons in the cortical plate begin to elaborate dendritic and axonal elements from mid gestation through infancy . These eSurface-based neuroanatomical measures and their underlying  neurobiological processes are regulated through genetic and environmental factors, as well as gene-gene and gene-environment (epigenetics) interactions. Here, we will focus on genetic factors, which are thought to explain the majority of brain morphology [estimated heritability: 60–80% ]. For reMultiple commonly used statistical approaches exist to assess the genetic correlates of neuroanatomy and its associated biological processes. For example, single variant approaches examine genetic underpinnings  within a candidate gene framework focusing on functionally characterized polymorphisms  that areIn combining these approaches, previous studies have explored the link between genetic variation and inter-individual variability in neuroanatomy indirectly . Taken together, these studies have identified genetic and transcriptomic variation that converges in several key biological pathways, which may jointly influence cortical thickness, surface area, cortical volume, the local gyrification index, and the GWC. Further, emerging evidence, facilitated by progress in neuroimaging and statistical approaches, is examining the specific genetic factors contributing to neuroanatomical variability directly. In the following, we will provide an overview of the current state of research in both the neurotypical and autistic brain.In the neurotypical brain, neuroanatomy and its development [beginning 2 weeks post-conception ] are larNotably, the influence of these genes may vary across brain regions. For instance, a study in neurotypicals highlighted regional heterogeneity in the magnitude of genetic influence on cortical thickness . FurtherIn autism, differences in neuroanatomy have also been linked to genetic factors, although heritability estimates may differ from those in neurotypicals . EvidencChromatin modification can influence early neuroanatomical development, e.g., by altering the expression of genes regulating the fate of neuronal progenitor cells . ConseqNotably, these processes are heavily interlinked (epistasis). For example, chromatin-related genes, such as CHD8, are thought to help shape regulatory expression networks in the developing brain; and to influence other autism risk/liability genes , such asIn sum, neuroanatomy has been associated with a wealth of genomic factors that may further interact; and whose disruptions  may give rise to atypical neuroanatomy and neuroanatomical development. In the following, we will briefly review the existing literature on how genomic factors may influence individual cortical features in the neurotypical and atypical (autistic) brain.In neurotypicals, estimates of how much variance in cortical thickness is determined by genetic factors vary across studies . Among In autism, surface area is also thought to be largely heritable , but litIn neurotypicals, cortical volume  is thought to be determined largely (up to ∼90%) by genetic factors . For insIn autism, cortical volume is thought to be determined largely by genetic factors ; but fewIn neurotypicals, gyrification  has been reported to be determined largely by non-genetic factors ; and theIn autism, similar to neurotypicals, heritability studies suggest that gyrification is only minorly influenced by genetic factors ; and resIn neurotypical adults, the GWC is thought to be determined (in part) by regionally specific genetic influences [heritability 0.24–0.5 ]. As eviIn autism, the genetic variation influencing atypical GWC remains largely unstudied. Studies in autistic children and adolescents have linked atypical white matter (volume) to mutations in PTEN, which has been associated with neural connectivity, plasticity, and apoptosis . Howeverp = 0.01 (in an independent sample t-test), not yet accounting for multiple comparison corrections ; and novel statistical and technological approaches . Combinrections . Studiesrections . These swww.r2d2-mh.eu the autism genetic resource exchange  and are therefore often limited to within-sample cross validation etc. Fortunately, several research collaborations and consortia  are curexchange .Combined, this research is crucial not only to elucidate biological mechanisms in autism, but also to help inform clinical practice. For example, the stratification of autistic individuals into neurobiologically homogeneous subgroups may aid the development of interventions tailored to these subgroups (“precision medicine”); for a recent “white paper” regarding the use of biomarkers in precision medicine, see . FurtherIn this review, we have discussed commonly studied neuroanatomical features and their potential biological mechanistic and genetic underpinnings in the neurotypical and autistic brain. Most of the reviewed studies focused on cortical thickness and surface area; while research into other features  and their neurobiological correlates was scarce. Combined, the reviewed literature suggests that neuroanatomical features possess both distinct and shared neurobiological contributors whose variability between regions and across age is regulated dynamically by a complex interplay of epigenetic factors. Examples of distinct contributors include the number of proliferative units (which determines surface area) compared to the number of cells in each unit  [see radial unit hypothesis ]. HoweveBoth authors were involved in the conceptualization, writing, draft preparation, review and editing of the manuscript and read and agreed to the published version of the manuscript.