Sino-Tibetan is the most prominent language family in East Asia. Previous genetic studies mainly focused on the Tibetan and Han Chinese populations. However, due to the sparse sampling, the genetic structure and admixture history of Tibeto-Burman-speaking populations in the low-altitude region of Southwest China still need to be clarified. We collected DNA from 157 individuals from four Tibeto-Burman-speaking groups from the Guizhou province in Southwest China. We genotyped the samples at about 700,000 genome-wide single nucleotide polymorphisms. Our results indicate that the genetic variation of the four Tibeto-Burman-speaking groups in Guizhou is at the intermediate position in the modern Tibetan-Tai-Kadai/Austronesian genetic cline. This suggests that the formation of Tibetan-Burman groups involved a large-scale gene flow from lowland southern Chinese. The southern ancestry could be further modelled as deriving from Vietnam’s Late Neolithic-related inland Southeast Asia agricultural populations and Taiwan’s Iron Age-related coastal rice-farming populations. Compared to the Tibeto-Burman speakers in the Tibetan-Yi Corridor reported previously, the Tibeto-Burman groups in the Guizhou region received additional gene flow from the southeast coastal area of China. We show a difference between the genetic profiles of the Tibeto-Burman speakers of the Tibetan-Yi Corridor and the Guizhou province. Vast mountain ranges and rivers in Southwest China may have decelerated the westward expansion of the southeast coastal East Asians. Our results demonstrate the complex genetic profile in the Guizhou region in Southwest China and support the multiple waves of human migration in the southern area of East Asia.The online version contains supplementary material available at 10.1186/s12864-023-09767-7. East Asia (EA) has great cultural, genetic, linguistic, and ethnic diversity. Many language families exist in EA, such as Sino-Tibetan, Austroasiatic, Hmong-Mien, Tai-Kadai, Austronesian, Indo-European, Turkic, Mongolic, Tungusic, Japonic, Koreanic, Yukaghiric, Chukotko-Kamchatkan –5. The mModern Tibeto-Burman and Sinitic populations have common ancestral components from the Upper and Middle Yellow River Basin populations. The origin and expansion of Sino-Tibetan populations were suggested to be related to the development of the Yangshao and Majiayao cultures in the Neolithic Age with the rapid development of advanced millet agriculture. Studies of language genealogy have recently suggested that the Sino-Tibetan language family originated from the millet-farming groups in the Yellow River basin in Northern China , 15. The2 and three provinces of China, including Gansu, Sichuan, and Yunnan (from north to south). The most famous ethnicities within this region were the Han, Tibetan, and Yi; hence, it gained the renowned name “Tibetan-Yi Corridor”. The TYC was believed to play a major role in the dispersal of Tibeto-Burman-speaking populations. Many studies on whole genomes from the TYC region have been focused on articulating the contribution of Tibetan and Han ancestries. Yao et al. analyzed genome-wide SNPs from 10 Tibetans and 10 Han Chinese from the northern Tibetan-Yi Corridor region, concluding that TYC populations are a mixture of ancestries related to Tibetans on the Tibetan Plateau and surrounding EA groups in the low-altitude area [Along the eastern edge of the Tibetan Plateau was the Tibetan-Yi Corridor (TYC), harboring several ethnically and linguistically diverse human populations. This region has numerous rivers and colossal mountain ranges, mostly from north to south and with descending altitude. It was an important region for West China's north-to-south interactions of various human populations. It covers 0.88 million kmude area . A recenude area . Populatude area . It was th to 20th centuries, more Han people migrated to this region due to the developing interactions and increased defences on the western border of the Sinitic kingdom. Therefore, populations in this region also have a strong genetic affinity with Han Chinese populations. Of the 56 officially recognized ethnic human groups of China, 20 could be found in this region, including some Tibeto-Burman speakers. Bai, Qiang, and Yi groups are typical of them. Bai, Qiang, Yi, and Han groups were on the same branch of the clustering tree [Since the Ming Dynasty, the TYC began to be a bridge between the Tibetan Plateau and the more eastern populations of China. During the Qing Dynasty, from the 18ing tree . Bai groing tree . These tYunnan was in the southern TYC region. A multi-layered process with population migrations and linguistic and cultural interactions reshaped the genetic structure and ethnolinguistic landscape at the crossroads of EA and SEA since the Neolithic. A recent study in Yunnan suggested that the genomic legacy of populations associated with Neolithic millet farming was primarily preserved in North Yunnan, especially in the Tibeto-Burman-speaking populations . The higIn the southern TYC, the genetic structure and cultural landscape of the southern EA and SEA had been deeply influenced by rice-farming expansion from east to west since the Middle Neolithic. The influence of the rice-farming groups’ expansion might have followed both inland and coastal routes. The current Tibeto-Burman-speaking populations in Yunnan were mixes of three genetic components, including Yellow River millet-farming groups in the north, mainland SEA rice-farming groups, and the Southeast coastal groups. Therefore, multiple large-scale migrations from Northern China, southeast EA, and mainland SEA have significantly reshaped the genetic profile of the southwest region of China.Guizhou province was in the northeast of the Yunnan-Guizhou Plateau, and it was in the east of TYC. Many huge mountain ranges and rivers existed between Guizhou and the TYC region. In the southward expansion of the Tibeto-Burman populations, some branches migrated eastward to the Guizhou region. Many Tibeto-Burman-speaking populations live in Guizhou , 33. ThiThe lack of dense sampling and high-density genome-wide data limited our understanding of the genetic profile of the TBG. Therefore, we collected DNA from 157 saliva samples from (TBG), including Bai, Qiang, Yi, and Tujia groups. Then, we genotyped these samples with 700,000 genome-wide single nucleotide polymorphisms (SNPs) arrays. We explored the genetic structure and uncovered the admixture history of the TBG groups.Our study and sample collection were reviewed and approved by the Medical Ethics Committee of Guizhou Medical University and Xiamen University and were in accordance with the recommendations provided by the revised Helsinki Declaration of 2000. The participants provided their written informed consent to participate in this study.We collected a total of 157 saliva samples from the TBG, including 50 samples from the Bai ethnic group in Qixingguan in Bijie, 11 samples from the Qiang ethnic group in Qishuping in Jiangkou, 45 samples from the Yi ethnic group in Weining in Bijie, as well as 51 samples from Tujia ethnic group in Bapanzhen in Jiangkou. The ethnicities of all participants were used as their self-declaration based on their family migration history and corresponding family records. The sample collection region is presented in Fig. −6 –maf 0.01 –mind 0.05. Then, we removed the samples with inbreeding coefficients F > 0.0625. We removed individuals whose third-degree or closer relatives with PI_HAT > 0.125. After the quality control process, 440,036 SNPs and 127 TBG individuals remained. Of the remaining 127 TBG individuals, 44 samples were from the Bai group in Qixingguan in Bijie, 9 samples were from the Qiang group in Qishuping in Jiangkou, 27 samples were from the Yi group in Weining in Bijie, 47 samples were from the Tujia group in Bapanzhen in Jiangkou. Then, we merged our TBG samples with the previously published data of the ancient and modern populations mainly in EA and SEA [We used PureLink Genomic DNA Mini Kit (Thermo Fisher Scientific) to extract DNA and measure the concentration via the Nanodrop-2000, following the manufacturer’s instructions. All these qualified samples were genotyped using the Illumina WeGene Arrays, covering about 700,000 SNPs at the WeGene genotyping centre in Shenzhen. We used Plink v1.9  software and SEA , 44–46. smartpca package v16000 [http://www.r-project.org).Principal Component Analysis (PCA) was performed using the e v16000 . The PCAFor pruning SNPs with strong linkage disequilibrium in Plink , we carrf statistics using ADMIXTOOLS with the default parameters and calculated standard errors  using a block jackknife resampling across the genome. We carried out f3-statistics by the qp3Pop v 651 and f4-statistics by the qpDstat v 980 programs [f3-statistics  of the form f3  to measure the shared genetic drift between population X and Y since their separation from an outgroup Mbuti from Africa. We conducted the heatmap visualization of outgroup f3 values using the pheatmap package v 1.0.12 in the R software. In the f4-statistics , a significantly non-zero value of f4  suggests that populations A and B (or C and D) are not consistent with being descended from a homogeneous ancestral population that split may earlier from the ancestral populations of the other two populations. A significantly positive value of an f4-statistics of the form f4  indicates an excess allele sharing between population A and C or population B and D, but a significantly negative value of it suggests an excess allele sharing between population B and C, or population A and D. The Test in our f4 statistics mean the TBG groups.We computed programs . We compqpWave v 810 as implemented in ADMIXTOOLS [p-value > 0.05 for rank = 0 suggests that the two populations are genetically homogeneous relative to a set of outgroups. A p-value < 0.05 for rank = 0 indicates that a minimum of two streams of ancestry were needed to relate pairwise groups to the outgroups. We presented p-values of the results at rank = 0 with a heatmap by R software.We used pairwise MIXTOOLS  to test qpAdm v 810 in ADMIXTOOLS [f4-based admixture modelling to investigate whether a batch of target populations was consistent with being related via N streams of source populations from a basic set of some outgroups. We calculated the admixture proportions of the given source populations quantitatively.We investigated the number of streams of ancestry, plausible admixture sources, and corresponding proportion using the MIXTOOLS . We usedsmartpca [smartpca with the parameters inbreed: YES and fstonly: YES, then output the results by phylipoutname parameter . Then, we plotted a phylogenetic Neighbor-Joining (NJ) tree (NJ tree) using the Fst values of the populations in EA by applying the NJ algorithm in the MEGA software v7 [The Fst values were calculated by the smartpca . We ran tware v7 .We first carried out a PCA to explore the genetic substructure of EA and TBG Fig. . From thIn the PCA pattern in Fig. We next performed the model-based ADMIXTURE clustering analysis. We observed the lowest CV error at K = 6 Fig. . At K = We also computed the Fst genetic distance for our newly reported TBG and other populations in EA. In the phylogenetic tree Fig. , our stuf3 statistics in the form of f3  to quantify the population differentiation across EA discovered by PCA and ADMIXTURE. We showed the results of Outgroup-f3 statistics in Fig. Firstly, we calculated the Outgroup-f4 statistics in the form f4  and f4   and f4 , suggesting that TBG received genetic influence from the Yellow River farming groups in the north  to test whether the TBG and TYC groups had a different genetic affinity to the Southeast coastal groups in EA. The results  showed that the east Guizhou Tibeto-Burman populations had a stronger genetic affinity to the southeast coastal populations than the west Guizhou Tibeto-Burman groups. As a result, it suggested a genetic difference between the TBG mainly caused by the different proportions of the genetic components from the southeast coastal region of China. It indicated that the expansion of the genetic influence of the southeast coastal groups had a genetic gradient in Southwest China.Our studied TBG comprised four groups. Figure Since the Neolithic, multiple waves of population migrations and cultural interactions have shaped the genetic structure in the southwest region of China . The TibIn this study, we generated and analyzed genome-wide SNP data from modern human groups encompassing 4 TBG. We comprehensively compared and analyzed our newly reported TBG populations with the previously published ancient and modern worldwide populations to infer Guizhou's detailed genetic profile and demographic admixture history.f4 statistics, the TBG received genetic contributions from many human groups, including the northern farmer and hunter-gatherers and southern EA and SEA mainland populations. The TBG were like the Tibeto-Burman speakers in Yunnan. Moreover, we explored the gene flow from proper ancestral populations of our reported groups and reconstructed these populations' deep genetic history in the Guizhou region.We found the genetic affinity of TBG with the surrounding Tibetans, southern Tai-Kadai and Austronesian groups, and northern farming groups in the Yellow River Basin in the Neolithic Age from the PCA pattern. The Yi group was closer to the Tibetans in the high-altitude region. Other ethnic groups in our research were related closely to Han groups. The results of the ADMIXTURE analysis also proved it. From the result, the Yi group contained more Tibetan-related ancestral components than others, suggesting its closer genetic history with the TYC region. From the qpAdm-base admixture models  and the southeast coastal EA region (Taiwan_Hanben). The els Fig.  providedBai, Qiang, and Yi groups were the typical Tibeto-Burman-speaking populations in the TYC region. However, the Bai, Qiang, and Yi groups in Guizhou were not genetically homogeneous with them. The vast mountains and rivers between Guizhou and the TYC limited the population interactions. Our results indicated that compared to the Tibeto-Burman populations in the TYC, TBG composed additional gene flow from the southeast coastal EA populations. Due to complex geographical factors, the westward expansion of the rice-farming groups of the southeast coastal region was decelerated in the southwest region of China. The precipitous mountain ranges and rivers were the natural barriers to human gene flow.f3 results, the Bai and Yi groups were clustered together, while Qiang and Tujia groups were clustered together. The pairwise qpWave testing . Supplementary table 2.f4 . Supplementary table 3.f4 . Supplementary table 4.f4 . Supplementary table 5. Two-way and three-way qpAdm models of studied Tibeto Burman populations. Supplementary table 6.f4 . Supplementary table 7. Admixture models of Tibeto-Burman groups for southeast coastal gene flow. Supplementary table 8.f4 . Supplementary table 9.f4 . Supplementary table 10. Information for the reference populations. Supplementary table 11. Other three-way qpAdm models with different ancient groups in MSEA and the southeast coastal EA.


Owing to the development of sequencing technology, paleogenomics has become an important source of information on human migration and admixture, complementing findings from archaeology and linguistics. In this study, we retrieved the whole genome and Y chromosome lineage from late Neolithic Honghe individuals in the Middle Amur region in order to provide a bioarchaeological perspective on the origin and expansion of Transeurasian languages in the Amur River basin. Our genetic analysis reveals that the population of the Amur River basin has a stable and continuous genetic structure from the Mesolithic Age up to date. Integrating linguistic and archaeological evidence, we support the hypothesis that the expansion of the Transeurasian language system in the Amur River basin is related to the agricultural development and expansion of the southern Hongshan culture. The spread of agricultural technology resulted in the addition of millet cultivation to the original subsistence mode of fishing and hunting. It played a vital role in the expansion of the population of the region, which in its turn has contributed to the spread of language. Media summary: Agriculture emerged in the Amur River basin in the late Neolithic. It remains unclear what caused the transition from fishing and hunting to agriculture and whether it can be associated with population migration and language spread. We retrieved genomic data from late Neolithic Honghe individuals in the Amur River basin and provided a bioarchaeological perspective on the origin and expansion of Transeurasian languages in the Amur River basin. Our genetic analysis supports the hypothesis that the expansion of the Transeurasian languages in the Amur River basin is related to agricultural development and expansion of the southern Hongshan culture. The spread of agricultural technology resulted in the addition of millet cultivation to the original subsistence mode of fishing and hunting.Humans have been on the move throughout their long history, spreading their language and culture with them. Archaeologists study material remains of past human life to understand minor and major innovations in culture across time. Historical comparative linguists compare words between related languages to recover certain aspects of the unattested ancestral language and how it changed over time. In addition to material remains and languages, the human genome also retains traces of the past. With the development of sequencing technology, beginning around 2010, genomic data can be retrieved directly from ancient human remains. These data provide an independent source of information to understand the biological relationships among populations across time. Comparing ancient genomes with those of current populations can reveal something about the origins of the ancient populations, previous population movements and admixture events  originates in the eastern foothills of Kent, Mongolia, and rises at the junction of the Selenga River and the Argun River. Passing through the northeastern part of China, it receives many tributaries, such as the Zeya, the Songhua, the Nen and the Ussuri Rivers. In Khabarovsk it ceases to define the Russian–Chinese border until it finally enters the Pacific Ocean through the Tartar Strait. The AR basin is rich in aquatic resources and includes diverse ecosystems such as forest-steppe and dry steppe landscapes in the upper reaches in the west, forest-meadows in the middle reaches and grassland in the eastern lowlands. These geographical and natural features have made the AR basin a corridor connecting the northern Asian inland with the northern Pacific coastal area.Archaeological studies have shown that, from the Mesolithic Age, the subsistence pattern of AR populations mainly relied on fishing and hunting. The emergence of pottery in the Neolithic period was not associated with agricultural activities as the evidence of farming is quite late in comparison with pottery making Kuzmin, . The potThe AR region is home to a variety of ethnic groups speaking languages belonging to different families, such as Tungusic, Mongolic, Amuric (Nivkh), Sino-Tibetan (Mandarin) and Indo-European (Russian) languages. The general presence of Russian and Mandarin is due to a relatively recent expansion of these languages in the region. Nivkh goes back to an ancient local lineage that became isolated when the Tungusic and Mongolic languages expanded. The latter languages are thought to derive from an ancestral Transeurasian language, the descendants of which are widespread across Eurasia. A partial explanation for this wide distribution may be the adoption of agriculture by the early speakers.In spite of controversy in the linguistic literature  light (254 nm) for 30 min. This allowed us to remove most contamination from soil and researchers. The archaeological and anthropological data of the ancient individuals is shown in https://github.com/stschiff/sequenceTools). Finally, we restricted our analysis to individuals with at least 15,000 target SNPs, and the HQHM3 individuals were excluded from the main analysis because of low coverage (6843 covered SNPs).All four genomic DNAs were extracted from teeth and the double-stranded libraries of Honghe individuals (HQH_LN) were prepared following published protocols  to determine mtDNA haplogroups using self-made script. The variants of terminal haplogroup were double-checked using IGV software  together with our own most updated Y-tree to determine Y haplogroups using a self-made script. The variants of terminal haplogroup or those in doubt were double-checked using IGV software  the negative extraction and amplification controls were free of contamination; (b) the nucleotide misincorporation patterns characteristic of ancient DNA at the 3′- and 5′- ends of the DNA sequences were observed ; (c) the sequences show very low contamination estimates for mtDNA 1–2%; ; and (d)We have classified the Y chromosome of two male samples in Honghe Village, Ang'angxi Culture, as shown in To understand the genetic profile of the late Neolithic Honghe individuals, we provided a PCA of present-day Eurasian populations and projected Honghe individuals and other published ancient genomes against the background of the modern populations. Whereas PC1 separates eastern and western Eurasian populations, PC2 differentiates eastern Eurasian populations along a North–South cline with northernmost Siberian Nganasans occupying one end and the Ami and Atayal from Taiwan the other. The late Neolithic Honghe population approximately occupies the intermediate position of the North–South cline and forms a tight cluster with modern Tungusic-speaking populations from the AR basin . Similarf3-statistics analyses, we concluded that the HQH_LN has less genetic affinity with WLR_BA and LS_LN. In addition, we observed that HQH_LN, as other populations in the AR basin, had a close genetic relationship with an outlier individual (WLR-BA_o) in the Upper Xiajiadian culture populations from WLR region  and late Neolithic Yellow River populations (LS_LN) from the Central Plain of China . To further clarify the relationship among these ancient groups and other present-day Asians, we restricted the dataset and just carried out a PCA of all modern Asians . We founR region .Figure f3 analysis. Integrating observations from archaeology, our genetic results suggest that the Amur genetic profile is reflected in the early fishers and hunters in the region and continues in the ensuing millet farmers. As the AR basin is rich in aquatic systems and wildlife resources, the people here developed a subsistence pattern that heavily relies on fishing and hunting. The emergence of agriculture in this area came relatively late, notably with the cultivation of millet in the late Neolithic. In Northeast China, broomcorn and foxtail millets were first cultivated by 7600 years ago south of the AR basin, within the context of the early Neolithic Xinglongwa culture (8200─7500 BP) in the West Liao River basin  and Xianbei (130–180 AD) tribes of Western Manchuria, the presumably Japanic Puyo (Fuyu) in the Sungari basin and the presumably Tungusic Sushen (ca. 300 BC), later named ‘Yilou’, of Eastern Manchuria (Robbeets, From our study it appears that there is a long-term genetic stability in the Amur region, whereby the contemporary speakers of the Tungusic, Mongolic and Nivkh in the region are genetically continuous with the ancient Amur populations. This indicates that the contemporary populations may have a common ancestral population with an Amur genetic profile. Thus, when the Transeurasian languages spread and local people of non-Transeurasian languages abandoned their own language and shifted to Transeurasian, this resulted in admixture between different populations with Amur genome. C2a is the core Y-chromosome haplogroup in the extant populations from the AR basin. It is represented in speakers of Tungusic languages such as Oroqen, Manchu, Xibe and Hezhe as well as in speakers of Mongolic languages such as Khalkha Mongolian and Buryat (Wei et al., To sum up, we hypothesize that the expansion of the Transeurasian language system in the AR basin is related to the agricultural development and expansion of the southern Hongshan culture. Although there is no visible sign of immersion with genetic components, population admixture of different Amur-like profiles was probably involved. The spread of agricultural technology has enabled the cultivation of millet to be added to the original subsistence mode of fishing and hunting. It has played a vital role in the expansion of the population of the region, which in its turn has contributed to the spread of language.


Gentianella austriaca was performed by HPLC identification and quantification of a number of secondary metabolites, and evaluation of time series of peak areas by chemometric analysis. Phytochemical analysis of G. austriaca revealed the presence of iridoids, flavone-C-glucosides and xanthones. Twelve secondary metabolites were identified in the aerial parts, roots and seeds, including swertiamarin (SWM), gentiopicrin (GP), sweroside (SWZ), isoorientin (ISOOR), swertisin (SWE), demethylbellidifolin-8-O-glucoside (DMB-8-O-glc), bellidifolin-8-O-glucoside (BDF-8-O-glc), mangiferin (MGF), corymbiferin (CBF), corymbiferin-1-O-glucoside (CBF-1-O-glc), bellidifolin (BDF) and campestroside. Multivariate statistical analyses showed relatively low variability among populations according to secondary metabolite content. However, some pharmacologically important compounds were found in higher amounts in a few populations, which could be useful for conservation and future biotechnological procedures.Phytochemical profiling of six natural populations of  Gentianella austriaca (A. & J. Kern.) Holub is an endemic species that inhabits mountainous semi-dry grasslands, pastures and wet meadows. The species usually forms large populations and metapopulations in the mountains, while its populations in the lowlands are smaller and more isolated [The biennial gentian isolated . The losisolated . It is fisolated .Gentianella nitida whole plant is used in traditional Peruvian medicine as a remedy for hepatitis, as a cholagogue, and in the treatment of obesity [G. austriaca displays radioprotective effect on human lymphocytes in vitro [Gentianella resulted in the isolation of xanthone aglycones and glycosides, C-glucoflavones, iridoids [Gentiana, Gentianella, Swertia, and Centaurium, stimulate gastric juice and bile secretion, improving appetite and digestion. Furthermore, they have beneficial pharmacological effects on the central nervous system and smooth muscle relaxation, whereas flavonoids have antidepressant, antiglioma, immunostimulant, antiplatelet aggregation, hypoglycemic, and antihyperlipidemic properties [C-glucoflavones are widely distributed in monocots, but only in a few dicot families, such as Leguminosae, Gentianaceae, and Asteraceae [C-glucoflavones in Gentiana species are isovitexin and isoorientin, both of which are biosynthetically primary compounds. The Swertia genus produces mostly isovitexin and swertisin, whereas Gentianella species contain isoorientin and swertisin [Gentianellas, have long been used in traditional medicine of South America as substitutes of related Gentiana species, to treat digestive problems . The aqu obesity . Recentlin vitro . All geniridoids ,8,9,10, iridoids ,12,13, uoperties . C-glucoteraceae . The moswertisin .O-glycosides. Along with them, some Gentianella species contained xanthones with additional oxygenation at C-4. Hence, the chemosystematically oriented study of four Gentianella species from Balkan Peninsula revealed existence of 1,3,4,7,8-oxygenated xanthone for the first time in European gentianellas [G. austriaca and G. bulgarica were reported to contain campestroside [G. campestris [G. germanica, and G. ramose [Gentianella species  [Naturally occurring xanthones with 1,3,5,8-, and less frequently 1,3,7,8-oxidation pattern are typical for gentianellas. These xanthones belong to the bellidifolin type and mostly appear in the form of ianellas . This ruianellas  who havestroside , the commpestris , G. germlbanica) .G. austraica shoot cultures for the production of xanthones has been successfully developed [G. austriaca extracts [G. austriaca and G. kochiana disclosed that dihydroxylation at positions 7, 8 of the xanthone nucleus is the key structural feature responsible for the ability to induce microtubule-associated G2/Mcell block and apoptotic cell death in glioma cells [G. austriaca [G. austraica and its xanthones have been shown to have neuropharmacological in vitro activity, further research on its antidepressant potential based on in vivo studies is needed [In vitro propagation of eveloped , and in extracts . Studiesextracts . Evaluatma cells . Two tetustriaca . Althougs needed .Plant secondary metabolites are well-established as important components in both biochemical and ecological processes, given their role in plant survival and successful reproduction in natural habitats . ConsideGentianella austriaca.Intraspecific variability of secondary metabolites has become a hot topic in a new research field called “eco-metabolomics”, which links metabolomics with ecology to understand the biochemical mechanisms governing species interactions with the environment and with other mechanisms . More emG. austriaca natural populations indicated that all tested samples have a similar profile of secondary metabolites  and swertisin (8), whose permanent coexistence is specific for Genianella species. The xanthone composition consists of seven identified compounds. Among them, three xanthones occure in the form of glucosides-bellidifolin-8-O-glucoside (9), demethylbellidifolin-8-O-glucoside (6) and corymbiferin-1-O-glucoside (10). In addition to glucosides, corresponding aglycones bellidifolin (11) and corymbiferin (12) have also been identified. A very specific compound of gentianellas, campestroside (5), which is a partially saturated analogue of the demethylbellidifolin-8-O-glucoside, has been detected in all the samples. This xanthone was tentatively identified based on comparison of UV spectral data with reference data obtained from our previous studies on tetra-substituted xanthones [C-glucoside, was detected in a smallest quantity among the xanthones.Three secoiridoids were identified including swertiamarin (1), gentiopicrin (2) and swerodside (3), as well as two anthones . MangifeG. austriaca plants were shown in Chemical structures of the secondary metabolites identified in O-glc, SWE, BDF-8-O-glc, CBF, and BDF) differed significantly between populations, whereas the contents of four secondary metabolites  did not. Population IV had the highest levels of SWM, GP, DMB-8-O-glc, CBF, and BF, as well as very high levels of SWM and BDF-8-O-glc. Furthermore, populations III and V had significantly high levels of certain secondary metabolites , followed by the belowground parts (B), and the lowest contents were present in the seeds (S), except for GP (B > A > S) and SWZ (A > B = S) .O-glc and BDF-8-O-glc  was performed on the set of six groups of individuals belonging to six populations. The first discriminant function explained 50%, the second 29%, and the third 12% of the discrimination . The fir-8-O-glc . The fir-8-O-glc . DA show-8-O-glc a,b.Medicinal plants from the gentian family (Gentianaceae) are highly valued as sources of pharmacologically important compounds. However, many of them are facing declining populations due to climate change and habitat loss . ConsideG. austriaca populations on Maljen Mt. had less than 40 reproductive (flowering) individuals spread out in small patches, making it difficult to attract enough pollinators. Multivariate analyses of the G. austriaca dataset revealed inconspicuous separation and partial overlap of individuals, indicating relatively low interpopulation variability based on the secondary metabolites investigated. Interpopulation variability of secondary metabolites was found to be similarly low in small populations of Gentiana pneumonanthe [Gentiana asclepiadea [Studies on chemodiversity among plant populations have been commonly conducted in the context of natural genetic variation and are based on distance-based measurements ,35,36,37monanthe  and Gentlepiadea .G. austrica in late flowering/early fruiting stage reveals a similar set of secondary metabolites in all plant parts.The greatest differences in secondary metabolite content were found between plant parts. The majority of the compounds tested were found in both below- and aboveground plant parts and seeds. Only GP was found in comparable amounts in both below- and aboveground parts, whereas the amounts of other compounds were much lower in belowground parts. SWZ  and MFG and ISOOR  displayed the most prominent differences between the content of secondary metabolites in plant parts. Secondary metabolite biosynthesis and accumulation demonstrate organ and/or tissue specificity, primarily in four plant morphological compartments: roots and stems, leaves, flowers, fruits, and seeds . These pO-glc, and MGF) was similar among all populations studied. However, the amounts of seven compounds differed significantly between populations, contributing to the separation of populations III, IV and V based on the high contents of BDF, DMB-8-O-glc, BDF-8-O-glc CBF, and GP. This result suggests the importance of using qualitative/quantitative analyses in phytochemical profiling of species. Quantification of compounds of pharamacological interest can provide information about intraspecies chemodiversity, which is the fundamental for bioprospecting, and can lead to multidisciplinary approaches encompassing ecological, biological, chemical, pharmaceutical, and biomedical disciplines [O-glycosides and are considered to be carriers of a variety of pharmaceutical properties. The two dominant chromatogram peaks belong to demethylbellidifolin-8-O-glucoside and bellidifolin-8-O-glucoside, xanthones with 1,3,5,8-oxidation pattern characteristic for Gentianella species. Bellidifolin and demethylbellidifolin are both antioxidants with antimicrobial, anti-diabetic, and cardioprotective properties, with inhibitory effects on acetylcholinesterase and monoamino oxidase [The findings show that the content of four compounds , which is located in the central part of Mt. Maljen in northwest Serbia. The six  km area , distribn = 42). Belowground parts (B) and seeds (S) were analyzed as a mixed sample per population due to the small amount of material .The field research was carried out in August 2022. Seven individuals from each population were harvested immediately following flowering (late flowering/early fruiting phase). The collected plants were determined by Dr. Z. Popović at the Institute for Biological Research, Department of Ecology, using standard keys for the determination of the plant species ,68. PlanSamples including belowground and aboveground plant parts, and seeds, were ground to fine powder using a mortar and a pestle. Fifty milligrams of powdered samples were extracted with 1 mL of methanol  in an ultrasonic bath at room temperature for 20 min. After sonication extracts were centrifuged at 10,000 rpm for 10 min. The supernatants were filtered through 0.45 μm nylon syringe filters  before injection into the HPLC system for analysis.v/v solution of orthophosphoric acid in water) and solvent B  using a gradient elution of 98–90% A 0–3 min, 90% A 3–6 min, 90–85% A 6–8 min, 85% A 8–13 min, 85–70% A 13–18 min, 70–30% A 18–25 min, and 30–0% A 25–30 min. Column elution was monitored at 260 and 320 nm, with a flow rate of 1 mL min−1.Identification and quantification of secondary metabolites in tested extracts were carried out on an Agilent Series 1100 HPLC system equipped with diode array detector (DAD). A reverse phase ZorbaxSB-C18 (Agilent) analytical column  was used for the component separation. The mobile phase consisted of solvent A .The quantification of secondary metabolites was done using the external standard method by preparing calibration standards and recording the calibration curves at 260 nm for secoiridoids, G. austriaca were compared on the basis of the contents of eleven secondary metabolites . The assumption of normality was tested with the Shapiro-Wilk test . ANOVA was applied for comparing the populations when the variables appeared to follow normal distribution, whereas a non-parametric Kruskal-Wallis rank sum test was used when the variables did not appear to follow a normal distribution. The False Discovery Rate (FDR) p-adjustment method was used to control the type I error rate in hypothesis testing. Linear discriminant analysis (LDA) was carried out on log-transformed data (y′ = log10(y + 1), y = original data value) in order to visualize the data structure. Statistical analyses were performed using R statistical software, version 4.2.2 [Six populations of on 4.2.2 .G. austriaca is a rich source of pharmacologically important compounds. The overall intraspecies variability in the total dataset was relatively low, which is characteristic of small and isolated populations. However, the amounts of some compounds  with high pharmacological potential  were significantly higher in a few populations, which could be useful for conservation and future biotechnological procedures. These findings highlight the significance of qualitative/quantitative analyses of natural populations and evaluating their quality-related characteristics.The findings suggest that


Procyon lotor invasion in Europe to investigate the effect of gastrointestinal pathogen pressure on non-MHC immune genetic diversity of newly established invasive populations. Despite distinct differences in parasite prevalence between analysed populations, we detected only marginal associations between two analysed SNPs and infection intensity. We argue that the differences in parasite prevalence are better explained by detected earlier associations with specific MHC-DRB alleles. While the escape from native parasites seems to allow decreased investment in overall immunity, which relaxes selective pressure imposed on immune genes, a wide range of MHC variants maintained in the invasive range may protect from newly encountered parasites.Understanding the evolutionary mechanisms behind invasion success enables predicting which alien species and populations are the most predisposed to become invasive. Parasites may mediate the success of biological invasions through their effect on host fitness. The evolution of increased competitive ability (EICA) hypothesis assumes that escape from parasites during the invasion process allows introduced species to decrease investment in immunity and allocate resources to dispersal and reproduction. Consequently, the selective pressure of parasites on host species in the invasive range should be relaxed. We used the case of the raccoon  According to the enemy release hypothesis, newly established populations of non-native species harbour fewer enemies (pathogens and parasites) in the introduced range compared to the native range. As a result population regulation is reduced and spatial expansion accelerates5. According to the EICA hypothesis, i.e. evolution of increased competitive ability such an escape from parasites should favour introduced species that can decrease investment in immunity and allocate resources to dispersal and reproduction, thereby enhancing their invasive potential6. Along with leaving pathogens behind, the selective pressure on host species should be lower in invasive than in native ranges. However, non-native populations may come into contact with native species and accumulate novel pathogens, gaining high infection intensities over a relatively short time (e.g.7). If this is the case, ability to fight those infections is crucial to a successful invasion.Invasive species disrupt ecological communities, drive population declines and species extinctions. Parasites may affect the invasion success of their hosts through their effect on fitness and thus on host population growth and stability8. Since pathogens exert strong selection pressure on their hosts, immunity-related genes are presumed to be under selection due to host–pathogen coevolution10. However, such relationships have been rarely studied in invasive populations. The importance of immune gene diversity is supported by significant associations between specific alleles and susceptibility to infections. This has been reported in many species; however, most of these examples are based on the components of the adaptive immunity with a wide range of studies focussing on MHC genes15. The role of other immunity genes in coping with parasite infection is often studied using a small number of Toll-like receptor (TLR) or cytokine genes22 although several hundred genes are involved in innate and acquired immune response. Genes evolving under balancing selection maintain high levels of diversity. MHC genes, which are usually characterised by dozens to hundreds of allelic variants segregating in natural populations15 or TLRs23, may play an important role in the immune defence of invasive species24. This is because the high level of the invasive populations' immune standing genetic variation, is crucial for their pathogen resistance (reviewed by25). The innate immune response is responsible for recognizing the pathogen and then initiating the acquired immune response27. The PRRs (pattern recognition receptors) are the first line of pathogen detection28). Cytokines are signalling molecules capable of triggering and modulating the immune response, are the crucial link between innate and adaptive immunity. They mediate in removing the larval stages of gastrointestinal parasites and indirectly regulate the functioning of the mechanisms of the acquired immune response and enhancing the response from Th2 lymphocytes28. Nevertheless, the type of selection shaping the diversity of innate immune genes usually leads to maintaining limited number of genetic variants. In case of positive or purifying selection that is found to shape diversity of many classes of innate immunity genes29 low frequency variants may be lost due to bottleneck occurring when the invasive population is established24. Therefore, we may expect different roles of different groups of immune related genes in the response to the pathogen pressure in an invasive environment.A recent meta-analysis indicated that parasite success is limited by the host's genetic diversityProcyon lotor invasion in Europe to investigate the associations between a large set of immune genes and gastrointestinal parasites in newly-established invasive populations. The raccoon is a medium-sized carnivore whose native distribution in North America extends from southern Canada to Panama30. The first successful introduction in Europe occurred in Germany in the 1930s with a limited number of individuals31. Recently, approximately 1,000,000 raccoons were estimated to be living in Germany, and the range of the species in Europe has extended to the west, east and south of the invasion core33. Native populations of raccoon are a reservoir of numerous viral , bacterial  and parasitic  pathogens36, but invasive populations are relatively mildly infected with parasites39 in comparison with native raccoon populations and other carnivore species in the invasive range44. Multiple genetic clusters detected throughout the European range of the raccoon provide the evidence for multiple independent introductions46. Previously reported associations between specific MHC-DRB resistance and susceptibility alleles and digenean parasite Isthmiophora melis infection suggest an important role of this gene region in providing local adaptation to intestine parasites37. Specific infection-associated alleles were partly fixed in populations established from different introductions, causing extreme infection-level differences. This finding underlines the role of standing genetic variation, at least in MHC loci, in shaping host-parasite relationships in invasive populations and provides empirical support that functional genetic variation may be responsible for differences in invasion success37. In the present work, we analysed four previously studied invasive raccoon populations, varying considerably in the prevalence of intestinal parasites. The first population was located at the invasion core in central Germany and possessed relatively high levels of genetic variation, possibly due to mixing with divergent raccoon populations46. Two others, one from eastern Germany and another from western Poland, were located towards the invasion front and form the eastern edge of the raccoon invasion. The fourth population, located in northern Czech Republic, was established putatively from individuals which escaped from captivity was separated from the remaining ones and was characterised by a significantly lower level of genetic diversity47.In the present study, we used the case of the raccoon The objective of this study was to test if a wide range of single nucleotide polymorphisms (SNPs) located in non-MHC immune related genes, in addition to MHC-DRB locus diversity, shape the levels of gastrointestinal infections in invasive raccoon populations. We aimed to reveal the main genetic determinants of infection in invasive raccoon populations. We expected that the associations between infection levels and genetic diversity may differ for the MHC gene, where high levels of genetic variation were retained in invasive populations and non-MHC immunity genes were characterised by relatively lower numbers of genetic variants. Our second objective was to discuss our findings in light of the EICA hypothesis. Invasive raccoon populations have lower parasitic infection levels in comparison to both the native raccoon populations and other carnivores that share a habitat with the raccoons in the invasive range. The putative release from enemies could allow invaders to outcompete native species and invest less into immune responses. The lowered parasite pressure might have resulted in relaxed selection pressure. Then visible as lack of associations between immune genetic variants and intestinal parasites is expected. The relaxation in selection pressure should be visible mainly in terms of costly innate immunity.https://isap.sejm.gov.pl/isap.nsf/download.xsp/WDU19951470713/U/D19950713Lj.pdf, Germany: https://ljv-sachsen-anhalt.de/?wpdmpro=jagdzeiten-in-sachsen-anhalt, https://www.ljv-mecklenburg-vorpommern.de/Jagd-in-M-V/Jagd-und-Schonzeiten/ and the Czech Republic: The Hunting Act of 27th November 2001 https://www.zakonyprolidi.cz/cs/2001-449). Additional carcasses were obtained from road accidents. For these reasons, there were no experimental protocols to be approved by a named institutional and/or licensing committee. All methods were carried out in accordance with relevant guidelines and regulations and are reported in accordance with ARRIVE guidelines.Our study involved the raccoon as a study species but no animal was captured or killed specifically for the purpose of the project. No experiments involving live animals were performed for the purpose of this particular study. Most of the carcasses used in the study were obtained courtesy of hunters controlling this species in accordance to national hunting laws in all three countries . A detailed protocol describing MIP design and subsequent resequencing in studied individuals is described in47. The list of genes where analysed SNPs are located is given in Supplementary Table DNA was extracted using the NucleoSpin Tissue Kit . We selected a set of 246 non-MHC genes, proved to be associated with helminth infection, most of which are involved in innate immunityadegenet50. Multivariate analyses are the method of choice when the assumptions of Hardy–Weinberg and linkage equilibrium within populations are violated, which might be the case for genes under selection. First, using the function find.clusters, we identified the number of clusters that best reflects the genetic structuring in the data without a prior assignment of samples to given populations, using BIC scores (Bayesian Information Criterion50). A two-step procedure was used to establish an optimal number of principal components used in the DAPC. First, maximum number of principal components was established with a cross-validation function Xval.Dapc. The cross validation was performed as follows: 14,000 replicates were used to establish the optimal value from group mean, the maximum number of tested PCs was 80 and the training set comprised of 80% of observations, The resulting number of PCs was used to perform the DAPC. The number of PCs was further refined using α-score (a-score function). The resulting number of the optimal PCs was used in a second round of the DAPC. The results of the DAPC were plotted using the scatter function, with a population as a grouping factor.To visualise the population structure displayed at the SNP loci and identify discrete population clusters for inter-population description of parasite infections and defining the cofactor for models used in associations analysis, we used discriminant analysis of principal components (DAPC) implemented in the R package 2 > 0.65). Selection can affect HWE, but there can also be also other reasons for disequilibrium. We decided to remove loci not in HWE, following recommendations for association analysis . This step is advised as such variants may result from genotyping errors. Furthermore, using R we checked for correlations between SNPs situated in different chromosomes, as it was not possible to test for associations between them using LD. From each group of loci correlated with r > 0.7 we randomly selected one locus for further analyses. These rather conservative filtering criteria resulted in 197 SNPs located in exon fragments of 115 immune related genes  rather than species. The exception was the most frequent fluke I. melis, Digenea, Cestoda and Nematoda) were tested in R package SNPassoc51 under five genetic models  with genetic cluster membership, identified by the DAPC, included as a cofactor. Effect sizes were estimated using odds ratio implemented in the package SNPassoc.The effects of SNPs on the presence/absence of parasites (I. melis were tested in R using zero inflated models implemented in R package glmmTMB52 with population (genetic cluster) as random effect. We used negative binomial rather than Poisson distribution to control for the overdispersion. Effect size was estimated using the coefficient of determination R2 calculated separately for fixed effects and the whole model using r.squared GLMM function from the MuMIn package53. In several models we encountered a problem of complete separation what resulted in glmmTMB function failing to estimate model parameters. The separation occurred when the rarest genotype  was observed in just one of four studied populations. Thus, were removed such genotypes before fitting the models what solved the separation problem. In the case of six SNPs we could not apply this procedure, as these SNPs had only two genotypes so after removing the one causing separation, only one genotype would have remained. In 10 models, the glmmTMB function returned convergence problem due to non-positive-definite Hessian matrix. Following the package tutorial we removed the zero-inflated part resulting in flat mixed-effect models with negative binomial distributions. In all models, we applied the most conservative correction for multiple comparisons which is the Bonferroni correction.The SNPs' effects on infection intensity with The prevalence differed between populations Table . Parasit54. Such variants usually require large sample sizes which is hard to achieve in a study of free-living species. Thus, to check a detectability threshold of our study, we performed power analysis using epi.sscc function from the package epiR55 following the example 8.18 from Woodward56.A common problem in association analysis is the difficulty to detect variants of low effects (low odds ratio) or in low frequencies in a populationThe DAPC analysis performed on 197 SNPs with seven PCs retained  were screened for parasite infection. The analyses revealed a total of 15 parasite species taxa representing four main groups: 2 = 26.295, padj = 0.004, . We also found a significant effect of the SNP 194809_624, located in IL6ST; interleukin 6 signal transduction gene . These same effect was observed for both SNPs when two populations with the lowest parasite load were removed from the models.When we considered all four populations, we found a significant effect of SNP 26438_387 on infection intensity with the fluke I. melis  under five analysed genetic models. The exact p-values for each SNP and each model are given in (Supplementary Table We found no significant associations between risk of infection (presence/absence of a given parasite) and genotype for any parasite group tested (When we rejected two populations with the lowest parasite load D1 and CZ), we detected one significant association after correcting for multiple analyses. The SNP located in NRCAM gene was associated with the prevalence of Nematoda infection under recessive model: in the infected group, individuals with the genotype TT constituted 57% compared to the other genotypes combined (CC and CT). In the non-infected group, they represented just 11% of individuals. Still, taking into account that the number of infected individuals was only 4 and 3 in D3 and PL, respectively, we consider this result as an artefact. The exact p-values are given in Supplementary Table  and CZ, The power analysis Fig.  online sI. melis). The presence/absence of MHC-DRB alleles in different populations reflected demographic process related to population establishment during invasion but, in turn, allowed for creating strong differences in population susceptibility to infection. Here we studied the importance of a wide range of non-MHC immunity genes in controlling gastrointestinal infections in invasive raccoon populations.In this study, we used a targeted sequencing approach to investigate how the associations between the non-MHC immunity gene diversity and gastrointestinal parasite infections are shaped in invasive populations of the raccoon. We studied four populations of invasive raccoons with different levels of parasite infection. In our previous study, we identified MHC-DRB alleles altering the probability of Digenean . Its prevalence in D3 and PL was 0.412 and 0.69, respectively  located in the continuous range of invasive raccoon populations are characterised by different frequencies of mitochondrial haplotypes and were probably established by separate introductions and D3 lI. melis. It could suggest that adaptive immune response, linked mainly to MHC loci variation, could play a primary role in fighting I. melis infection. MHC genes that exhibit extremely high polymorphism maintained by balancing selection may be more important in coping with infections in recently established invasive populations than other immune genes. A wide range of genetic variants maintained by balancing selection creates relatively high standing genetic variation that enables invasive populations to respond quickly to novel environmental conditions61. The release from enemies believed to occur during invasion helps invaders outcompete native species. According to the increased competitive ability (EICA) hypothesis, invaders are predicted to invest less in immune responses5, but this lowered response is expected mainly for costly innate immunity. Innate defences are mobilised quickly and are effective against novel pathogens but impose higher metabolic and inflammatory costs62 and may be downregulated in highly active and dispersing organisms63. On the other hand, the adaptive immune response is specific to various pathogens but acts effectively only after primary contact with the pathogen. In the case of invading species that can get rid of pathogens during the invasion and do not encounter many novel enemies in the new range, adaptive immune response, that is associated with lower cost, may act more effectively. This process was observed in invasive edge populations of cane toads64 or in expansive populations of neotropical thrushes that exhibited lower infection levels and lowered inflammation compared to native populations65. The level of gastrointestinal infections in invasive raccoon populations is relatively low compared to native species. It suggests the "escape" from higher pathogenic load and potential decrease in the innate immune response. The studied 197 SNPs are located in non-MHC, mostly innate immunity genes. In the case of the suppressed innate immune response, the associations between studied SNPs and parasite load would rather not be established, especially in relatively recently formed populations. The lack of associations suggests that analysed SNPs of non-MHC genes do not show signs of pathogen-induced selection. At least they are not under a divergent selection in invasive raccoon populations, as we did not find associations explaining between-populations differentiation in infection level. Although it is established that specific groups of innate immunity genes follow divergent evolution pattern67, a short time since the establishment of the invasive population may be not enough to observe any clear selection pattern. On the contrary, a wide range of specific functional MHC variants transferred to the new range16 may constitute sufficient basis of genetic variation on which pathogen mediated selection could act.Although it is hard to define ecological factors explaining why two genetic clusters (D1 and CZ) present very low infection levels, genetic factors may play a major role in shaping these differences. As we previously detected, the MHC-DRB allele found in CZ cluster, but not in others, was associated with resistance to Our study shows that non-MHC innate immunity gene diversity does not play a crucial role in fighting gastrointestinal infections in invasive raccoon populations. This observation is in line with the increased competitive ability hypothesis that relates the downregulation of innate immunity with higher dispersal and survival in novel environmental conditions. At the same time, we suggest that invasive populations may acquire immune response via adaptive immunity as specific MHC-DRB variants regulate gastrointestinal infection levels in studied invasive populations.Supplementary Figure S1.Supplementary Figure S2.Supplementary Table S1.Supplementary Table S2a.Supplementary Table S2b.Supplementary Legends.Supplementary Information 1.Supplementary Information 2.Supplementary Information 3.Supplementary Information 4.Supplementary Information 5.Supplementary Information 6.Supplementary Information 7.Supplementary Information 8.Supplementary Information 9.Supplementary Information 10.Supplementary Information 11.


Alces alces) over its whole range in Sweden by sequencing 87 modern and historical genomes. We found limited impact of the 1900s near-extinction event but local variation in inbreeding and load in modern populations, as well as suggestion of a risk of future reduction in genetic diversity and gene flow. Furthermore, we found candidate genes for local adaptation, and rapid temporal allele frequency shifts involving coding genes since the 1980s, possibly due to selective harvesting. Our results highlight that genomic changes potentially impacting fitness can occur over short time scales and underline the need to track both deleterious and selectively advantageous genomic variation.Ungulate species have experienced severe declines over the past centuries through overharvesting and habitat loss. Even if many game species have recovered thanks to strict hunting regulation, the genome-wide impacts of overharvesting are still unclear. Here, we examine the temporal and geographical differences in genome-wide diversity in moose ( Collection and analysis of 200 years of Swedish moose genomes reveals regional differences in genetic load, candidate regions for positive selection and an overall limited impact on human-driven bottlenecks on today’s moose population. Over the past few centuries, many ungulate species have experienced severe population declines through habitat modification and overharvesting and some of them were nearly extirpated from their native range3. Management programmes combining habitat restoration, sustainable harvesting and reintroductions aided by the development of wildlife management institutions have allowed several game species to recover4, even in regions with high human-wildlife interactions such as Europe6. Most management plans recognise the role of ungulates in maintaining ecosystem functions as well as their impact on their ecosystem8. Consequently, ungulate management plans include consideration of population dynamics, migration, multi-ungulate competition, interactions with carnivores and humans, genetics, cascading effects on vegetation, and disease management7. In practice, these plans often incorporate seasonal hunting limits, quotas and selective harvesting 6.Wild species are under increasing pressure from anthropogenic activities with up to 16–33% of all vertebrate species being threatened with extinction9, their integration in ungulate management7 and in conservation as a whole is still considered weak11. This is rather surprising because it is well recognised that genetic processes play a crucial role for the long-term survival of species14. Furthermore, declining populations are exposed to a number of genetic threats, referred to as genomic erosion16. These threats include loss of adaptive potential limiting the ability to adapt to long-term changes in the environment13, but also increase in genetic load  through drift and the reduced effect of purifying selection, maladaptation , and genetic introgression after hybridisation19. Estimation of parameters associated with genomic erosion is thus increasingly considered as a major component of species conservation programmes20.However, while the use of genetic tools in conservation programmes has increased rapidly over the past decades21 and from a growing number of genomic resources being generated for rare and threatened species22. These resources allow to reconstruct species’ demographic fluctuations and to estimate temporal and geographical changes in selection regimes, genome-wide diversity, and the amount of genetic load20. Several initiatives have advocated for a better use of genetics in conservation23. For instance, the new Global Biodiversity Framework of the Convention on Biological Diversity  includes goals to maintain genetic diversity of populations of all species to secure their adaptive potential. To secure this goal, the use of DNA-based techniques and the integration of quantitative goals to safeguard the retention of genetic variation in species management programmes are needed26. The new CBD framework also includes a monitoring framework with a Headline indicator for genetic diversity focusing on effective population size (Ne) and Essential Biodiversity Variables (EBVs) that include genome-wide diversity, inbreeding and gene flow, which are becoming increasingly important to monitor trends of genetic diversity 27. Estimating those indicators and other genomic parameters in intensively hunted game species would thus allow to assess the genomic impacts associated with natural disturbances or intensive hunting and greatly improve their long-term monitoring and management27.Conservation and management of wild game species have greatly benefited from decreasing costs in sequencing30). For instance, the magnitude and speed of a decline and recovery, as well as life-history traits, will have a strong impact on genetic load and of its components32. Theoretical predictions and empirical data indicate that purifying selection is most efficient at purging highly deleterious mutations31 while mildly deleterious mutations, which can have a non-negligible impact on fitness33, tend to accumulate. To date, only a few studies have examined the post-bottleneck genomic variation in game species. For instance, the Alpine ibex (Capra ibex), which had declined to ~100 individuals by the early 1900s and now numbers more than 50,000 individuals spread over most of its former range35, has lost ~80% of its pre-bottleneck mitochondrial genetic variation36. Moreover, a comparison of whole nuclear genomes of Alpine ibex and other caprinae indicates that after this historical bottleneck, the strong founder effects associated with reintroductions facilitated the purging of its highly deleterious genetic variation whereas mildly deleterious mutations accumulated37.A growing number of empirical studies have investigated the genomic consequences of near-extinction in the wild and have illustrated the complex dynamics of genetic load in small populations  populations were severely reduced in Fennoscandia from the 15th century onwards43. Intensification of hunting during the 18th and 19th centuries led to near-extinction of moose in Fennoscandia44. Moose were rare outside South-central Scandinavia at the time and harvesting data from the 1890s onwards suggest that the population numbered in the low thousands46. Since the 1930s, increased commercial forestry has benefitted the moose43 and hunting legislation allowed the species to recover and its harvest is now intensively regulated47. The Swedish population currently numbers ~350,000 individuals and ~1/3 of its population is harvested annually48. In spite of this near-extinction event, the Swedish moose population has relatively low inbreeding levels relative to North American populations, which could be explained by rapid recolonisation of the range from isolated and less impacted populations in Norway or Finland after the near-extinction event49. However, Scandinavian moose also has one of the lowest genetic diversity in Europe and shows a high degree of isolation and genetic differentiation relative to other Fennoscandian populations52. Furthermore, such a severe decline to near -extinction suggests that the pre-decline diversity could have been much higher, and that the moose population may be exposed to genome erosion. Furthermore, there is evidence for geographical variation in body size and antler morphology at the extremes of moose range possibly associated with local adaptation to varying habitat types54. The Swedish moose population is thus an ideal model species to develop and test genomic tools for game management and monitoring55.Moose  from a sample of the same female individual and by adding long-read Hi-C scaffolding. The new moose assembly has a scaffold N50 of 76.6 Mb with 99% of the assembly composed of 34 scaffolds, suggesting that our assembly is at or close to chromosome-level. A total of 8660 out of 8771 single-copy mammalian BUSCOs were found within these 34 scaffolds. A synteny analysis against red deer (Cervus elaphus) allowed us to identify 32 autosomes as well as the X and Y chromosomes  from across the whole range of moose in Sweden  during this period.Past demographic reconstruction using SMC++ indicated a gradual population decline starting ~100,000 years Before Present (BP) from a NROH) and reduction in heterozygosity (N het. sites/1000 bp) in a southward direction , the data supported a significant increase in inbreeding  was significantly different among modern populations, with the highest diversity found in the North and Transition clusters  and Moderate  impact variants among modern populations, with a general reduction in total load towards the south the range . The Ne indicator was classified as Acceptable. The South cluster showed an Acceptable reduction in heterozygosity whereas changes in π and FROH and Ne indicators were classified as Warning  values identified 206 candidate genes within outlier windows when comparing the North and South clusters  as well as other genes  known to be associated with the cardiovascular system. Finally, we identified outlier genes associated with retinal development (SEMA6D) as well as nervous system  South cluster is below the CBD threshold value of 500, which is cause for concern and calls for an assessment of the present Ne of this region. Finally, genome scans of selection identified regions putatively under positive selection due to local adaptation and temporal changes involving genes associated with antler development and other biological functions potentially impacting fitness. These results suggest that the moose population in Sweden is not at immediate risk of exposure to genomic erosion. However, future reduction in gene flow and increases in inbreeding could result in the expression of this genetic load16 and harvesting practices could impact functional variation and population fitness21.We analysed 87 modern and historical genomes over the whole range of moose in Sweden to examine the genome-wide impacts of near-extinction in the 1848. While this cline could have resulted from colonisation from both the North and South, sharing of rare European mitochondrial haplotypes distinct from the Finnish lineage in the North and South of its range57 supports a colonisation from the South after the Last Glacial Maximum. Moreover, our data indicates that the island population of Öland belongs to the South cluster and suggests ongoing gene flow. However, even though moose can swim the short distance (~10 km) between the mainland and Öland, non-invasive DNA data suggests that gene flow is probably limited58.The population structure described here is consistent with a previous study based on microsatellite data which identified a North-South genetic cline49. This decline, which is congruent with a lack of moose fossil remains from Europe during the Last Glacial Maximum ~25,000 years BP59, could indicate shifts in the preferred moose habitat characterised by boreal forests and taiga during the cold periods49. However, the SMC++ did not detect any reduction in Ne associated with hunting intensification of the 18th and 19th centuries. In contrast, the linkage disequilibrium approach in GONE, which outperforms other methods for recent time frames60, showed strong support for a demographic decline at that time, as previously shown by Wennerström et al.48. We note that the signature of decline on Öland could also correspond to a founder effect.Demographic reconstructions supports gradual decline starting ca. 100,000 BP consistent with a previous demographic reconstruction using the PSMC62 but is in stark contrast with previous studies on Norwegian and Finnish moose that did not find genetic evidence for reduction in Ne64 associated with intensive hunting. This is most likely due to local variation in hunting intensity or alternatively to the lower resolution of markers used in these studies . Interestingly, Öland as well as the Transition and the North clusters show a recent  increase in Ne, coinciding with the reduction in hunting pressure and the implementation of new hunting regulation in the 1930s47.The overall decline observed here is consistent with a reduction in Major Histocompatibility Complex (MHC) diversity in modern moose populationsROH in modern moose and maximum ROH length in the South cluster is consistent with a more intense bottleneck in that part of the range resulting from either a founder effect after recolonisation of this area at the beginning of the 20th century or a strong hunting pressure over the past decades65. In contrast, while the Öland population is small  and the most inbred population in Sweden, the management strategy and reduced number of individuals harvested ranging from 0 to 43 individuals per year, suggests that the main causes of the high inbreeding are the founder effect and reduced gene flow58.Estimates of inbreeding were relatively low and ROH distributions were in majority of a length <10 Mb, indicating only a moderate impact of the recent decline. However, the southward increase in FROH or heterozygosity since the 1800s, except in the South and North clusters. We caution that more genomes would be required to confirm the trend observed between the 1800s and 1980s. Yet, inbreeding estimates based on single genomes can be highly informative in inferring the timing and magnitude of past bottlenecks . Since recombination breaks ROH into smaller fragments, large fragments reflect recent inbreeding events, whereas smaller ones reflect older inbreeding events67. Here, the distribution of ROH indicates that most inbreeding events occurred >168 years prior sampling of historical and modern genomes, and thus date back to ca. 1670–1850, a period coinciding with intensive hunting. However, inbreeding among closely-related individuals may have occurred as recently as 10–20 years ago in the South cluster and on Öland. Consequently the low proportion of large ROH suggests that overharvesting did not impact inbreeding levels substantially or that the population recovered rapidly after the introduction of the new hunting regulation. Alternatively, gene flow and immigration from populations less impacted by hunting in eastern Europe or other parts of Scandinavia may have compensated for the strong bottleneck effect associated with overhunting49. While uncertainty around the recombination rate for moose can bias inference of the timing of inbreeding events, our estimate is consistent with the period of intense hunting of the 18th to 19th centuries and also reveals that contemporary moose populations may be potentially exposed to the negative effects of high inbreeding.We found little evidence for temporal changes in FAdditional genomes are also required to further examine the temporal dynamics of load in Swedish moose. Nevertheless, two competing hypotheses can explain the pattern observed here. In contrast with heterozygosity and inbreeding, modern genomes had a higher load than historical ones, suggesting that drift may have facilitated the increase in frequency of deleterious alleles during the near-extinction event. Alternatively, if an increase really occurred, drift alone may not have been sufficient to increase genetic load. We thus cannot exclude the possibility that new deleterious variation was introduced via gene flow from genetically distinct subpopulations during the recovery stage of the population.33. The moose population on Öland, which showed the highest inbreeding and a long-term small Ne over the past 2,000 years, did not have a significantly different total genetic load compared to the South cluster. However, we found a reduction in the frequency of High impact variants and an accumulation of Moderate impact ones. This result is consistent with theory and recent genomic studies  showing that inbreeding facilitates the removal of highly deleterious variation through purifying selection, whereas mildly deleterious variation, with lower impact on fitness, tends to accumulate32. Temporal data would be required to formally examine the dynamics of purging on Öland.When considering modern populations only, we observed an overall reduction in High and Moderate impact variants towards the South of the range. Higher inbreeding may have facilitated the exposure of deleterious variation in homozygous state  and their removal through purifying selection69. It has been hypothesised that poor body condition in females associated with suboptimal foraging habitat as well as a temporal mismatch between resource availability and parturition could explain this difference. However, our data suggest that higher exposure of deleterious alleles  and an excess in Moderate impact variants relative to the South cluster may also contribute to lower female conditions and lower calf survival. Furthermore, while Öland and the mainland may be connected by occasional gene flow58, which could lead to a genetic rescue effect, there is also a risk of the introduction of new deleterious variation from less inbred and more genetically diverse individuals from the mainland70. For instance, the migration of a single male into the highly inbred Isle Royal wolf (Canis lupus) population induced a fitness reduction associated with the introduction of new deleterious alleles71.Recent data supports a lower fitness on Öland, with calf survival rate being lower (~0.22) compared to other populations (~0.7)16. While the realised load affects individual fitness in the current generation, recessive deleterious mutations in heterozygote  state do not16. However, deleterious variants in heterozygous state are likely to be expressed in future generations if inbreeding increases, whereas outcrossing could mask homozygous variants into heterozygous state. The interpretation of these metrics is further complicated by the lack of information on selection (s) and dominance (h) coefficients, epistasis as well as variation in non-coding  regions31.It is worth noting that various metrics can be used as proxies for genetic load and that these need to be interpreted carefullye in the North and Transition clusters remain above acceptable levels, the South cluster exhibits NeLD < 500, interpreted as a Warning due to the risk of losing adaptive and evolutionary potential. Additionally, inbreeding has increased and nucleotide diversity decreased over the past 150 years. However, additional historical and modern samples in this part of the range are required to confirm the observed temporal diversity patterns. Furthermore, population divergence has increased over time between the North and South clusters and between the North cluster and the Transition cluster, in both cases to such a degree that an Alarm is warranted for the FST-indicator. This suggests that migration has decreased between genetic clusters, possibly due to higher hunting pressure and conflicts with humans , presenting a risk of genetic erosion if populations become more isolated, especially in the South cluster.Genomic indicators raise concerns for the genetic health of Swedish moose. While N38. Our genome scan analyses identified a number of outlier windows containing 206 genes under putative selection due to local adaptation, with 71 of those showing a significant association with latitude. The 206 candidate genes included functions associated with skin pigmentation, keratin synthesis, cardiovascular and nervous system. We also found 14 genes previously that are expressed in skin and horn of ungulates, including roe and sika deer antlers56. There is evidence for regional and habitat differences in moose antlers in Eurasia72 and North America73. For instance, in North America, moose from open tundra habitat have larger antlers than those from boreal forest 73. Yet, the relative genetic or diet type basis for such association is unclear73. Moreover, moose from mountain habitat in North America (A. a. shirasi) and Asia (A. a. cameloides) both have small body and antler size74. In Europe, moose from Sweden and Finland, distributed from open tundra habitat in the North to boreal forest in the South also show latitudinal variation in growth pattern and body size53 as well as an association between habitat and antler morphology, with the ‘palmated’  type most prevalent in the North and the ‘cervina’  type most prevalent in the South54. Such association between habitat and morphology may thus indicate local adaptation and genes within the outlier windows identified here could thus be candidates for positive selection75.Assessing the functional variation in wild populations is highly relevant to species monitoring78. For instance, antler size in Alaskan moose has been reduced with increased harvest intensity79. Moreover, the Finnish moose population, which has been subjected to similar hunting pressures as the Swedish moose81, has experienced a rapid reduction in the ‘palmated’ type as a result of intensive hunting and/or fitness differences between antler types in managed forests between 1976 and 199954. However, selective hunting alone may not be the only cause for horn size reduction and a combination of environmental factors, inbreeding depression and selective hunting may induce changes in male ornamentation . When comparing 1980 and 2019/2020 genomes for the North cluster, our genome scan analysis identified a number of outlier windows containing genes associated with antler development56 as well as other functions . Thus, over a short period of time , hunting pressure may not only have induced allelic changes in the vicinity of genes associated with male ornamentation but may have also impacted other types of functional variation with potential important fitness effects. Yet, strong drift during the bottlenecks can also induce changes in the frequency of certain alleles and thus result in genome-wide differentiation which could be interpreted as selection83. For instance, it has been suggested that the high number of outlier regions among populations of three-spine stickleback (Gasterosteus aculeatus) from different environments may have arisen due to repeated bottlenecks and strong drift from small lake populations84.Selective or trophy hunting may also induce allelic shifts involving genes associated with antler morphology as well as a reduction in antler size or even body size over time27. We thus advocate for the inclusion of metrics estimating both deleterious and selectively advantageous variation in genetic monitoring programmes. Doing so will also allow the incorporation of genetic metrics with more relevance to species survival within the IUCN Red List85 and Green Status of Species86.’ Our study thus illustrates the need for continued genomic monitoring using genomic indicators and to incorporate estimates of deleterious and adaptive variation for the effective management of wild species.In conclusion, our study suggests that while the near-extinction of moose in Sweden did not severely impact its genome-wide diversity, at least in terms of inbreeding, our modern data raises concerns that reduction in gene flow, sudden declines and hunting practices may negatively impact the functional variation of moose populations. At present, the indicators proposed by the CBD do not include estimates of adaptive or deleterious variationThe moose samples used for the assembly generation were obtained from frozen tissue banks collection from 1980 maintained by L.L. and N.R. at Stockholm University and from 2019/2020 maintained by G.E. and G.S. at Umeå University. No Ethics approval was required.87.We extracted DNA from a total of 87 genomes  and five independent PCRs were run to increase complexity. Libraries were sequenced on a NovaSeq S4 flowcell using a 2x150bp and 2x100bp setup for modern and historical libraries, respectively.Genomic library preparation from modern DNA extracts was performed using a PCR-free protocol at the Science for Life Laboratories (SciLifeLab), Stockholm. Genomic libraries for historical DNA were prepared as described in Dussex et al.49 (GenBank accession: GCA_015832495.1) by using Hi-C scaffolding. In brief, 36 mg of fresh frozen muscle tissue from the same female individual from central Sweden (Province of Gävleborg) was ground to a fine powder and used as input for Omni-C™ Proximity Ligation Assay, Mammalian Samples Protocol version 1.2 from Cantata Bio according to the specifications from the manufacturer. The resulting library was pooled and sequenced to approximately 25% lane capacity on an Illumina NovaSeq 6000 instrument using S4 Reagent Kit v1.5 (300 cycles). The scaffolding was performed following Dudchenko et al.91, which includes Juicer (v1.6) for read-mapping and filtering, 3D-DNA (v180922) for draft scaffolding and Juicebox (v1.11.08) for manual curation of Hi-C scaffolds. For assembly evaluation we used QUAST (v5.0.2)92 and BUSCO (v. 5.3.1)93 with the “mammalia_odb10” lineage dataset.We improved the assembly from Dussex et al.http://www.repeatmasker.org/] and CpG sites using the GenErode bioinformatics pipeline94. Finally, we used Minimap295 to perform a synteny analysis against red deer  and identify sex-linked scaffolds. Since we also mapped the data to O. hemionus for the SNPeff and genome scan analyses, we used the same approach to identify the X chromosomes (HiC_scaffold_35) in O. hemionus.We identified repeats using repeatmodeller v1.0.11 and repeatmasker v4.0.7 [Smit, A.F.A. and Hubley, R. (2008–2015) RepeatModeler Open-1.0, 94. Briefly, adapter trimming was done with fastp v0.22.096 for modern data while we used Seqprep v1.1 (https://github.com/jstjohn/SeqPrep) to trim and merge forward and reverse reads for historical data. Reads for modern and historical data were then mapped using BWA v0.7.17 mem and aln algorithms, respectively, while sorting and removing PCR duplicates were done using SAMtools v1.12. Finally, reads were realigned around indels using GATK IndelRealigner v3.4.097.Mapping and variant calling of modern and historical genomes was done using the GenErode bioinformatics pipeline99. We used a minimum depth of coverage (DP4) of ~1/3  of the average depth of coverage, and filtered SNPs by base quality QV ≥ 30 and those within 5 bp of indels. Sites with SNPs in heterozygous state were filtered out if the allele frequency fell outside an allelic balance  of <0.2 and >0.8 in order to avoid biases caused by contamination, mapping or sequencing error. After merging all individual vcf files, we masked CpG sites using BEDtools v2.27.1100 to limit possible biases from DNA damage in historical genomes. We also masked repeat sites with BEDtools, and excluded scaffolds linked to X and Y chromosomes. Finally, we examined DNA damage patterns for the seven historical genomes using MapDamage101. The USER treatment removed the majority of typical post-mortem damage . After filtering for missing data and excluding the sex chromosome, we obtained a total of 5,305,454 SNPs.For analyses that required the calling of variants relative to an ancestral allele , we mapped the data as described above against mule deer 103 analysis to estimate individual-based ancestry and identify genetic clusters (K = 1–5). This approach assumes that individuals are unrelated and uses a cross-validation procedure to determine the best number of possible genetic groups present in the dataset.We performed an ADMIXTURE v1.3.0ST104 between the North and South clusters as well as between the North and Transition clusters for each time period  with VCFTools v0.1.16105 using non-overlapping windows of size 50 kb .We estimated pairwise F106. Like PSMC107, this approach relies on Sequential Markov Coalescent (SMC) simulations from unphased genome data from multiple genomes instead of one single diploid genome, thereby increasing the number of recent coalescent events to estimate the effective population size (Ne). Using the same substitution rate and generation time as for the PSMC from Dussex et al.49 , past demography was estimated using the ‘cross validation’ approach with --em-iterations 5000, and --thinning 1300 and --regularisation-penalty 6.To estimate the recent past demography , we used the SMC++ v.1.15.260 which estimates changes in NeLD calculated as the geometric mean over 40 independent estimates from the observed spectrum of linkage disequilibrium . We only retained the 32 largest autosomal chromosomes and used the following parameters: PHASE = 2; cMMb=1; DIST = 1; NGEN = 2000; NBIN = 400; MAF = 0.0; ZERO = 1; maxNCHROM=85; maxNSP = 50000; hc = 0.05; REPS = 40; threads = −99. For both of these analyses, historical genomes were excluded. We performed this analysis separately for the three genetic clusters identified by Wennerström et al.48 and the island population of Öland to avoid any underestimation caused by population substructure. We also used a generation time of seven years49.We also examined the past demography of moose over the past 100–200 generations using GONE108 which estimates the individual mutation rate (θ). Under the infinite sites model, θ approximates the genome-wide heterozygosity measured as the number of heterozygous sites per 1,000 bp. We downsampled each genome to the average coverage of the genome with lowest coverage , filtered out bases with quality (-Q) < 30, mapped sequencing reads with mapping quality (-q) <30 and positions with root-mean-square mapping quality (MQ) < 30 from the historical and modern bam files. We then filtered out sites with depth <1/3  of the average depth of coverage. We also estimated nucleotide diversity (π) per population and time point using VCFTools v0.1.16 and with 50 kb non-overlapping windows.We estimated heterozygosity using mlRho v2.7ROH) were estimated based on Runs of Homozygosity (ROH) that were identified using the sliding-window approach implemented in PLINK v1.9. We used a set of strict parameters to avoid overestimation and overrepresentation of long ROH: a sliding window size of 100 (homozyg-window-snp 100); no more than 1 site per window to assume a window as homozygous (homozyg-window-het 1); at least 5% of all windows including a given SNP to define the SNP as being in a homozygous segment (homozyg-window-threshold 0.05); a homozygous segment was defined as a ROH if the segment included ≥25 SNPs (homozyg-snp 25) and covered ≥100 kb (homozyg-kb 100); the minimum SNP density was one SNP per 50 kb (homozyg-density 50); and the maximum distance between two neighbouring SNPs was ≤1,000 kb . Finally, we set the value at 750 heterozygous sites within ROH (homozyg-het 750) in order to prevent sequencing errors to cut ROH. The inbreeding coefficient FROH was then estimated as the overall proportion of the genome (autosomes only) comprising ROH. We statistically tested for differences in heterozygosity and FROH among modern populations using ANOVAs and used Tukey’s HSD tests to test for pairwise differences among time periods for each population and among modern populations in R109.Inbreeding coefficients , 24 (168 years), 9.6 (67 years), 4.8 (33 years) and 1.6 generations (11 years) before present (BP), respectively.Using the length of ROH, we also estimated the timing of inbreeding events using:111 to annotate synonymous and non-synonymous nucleotide substitutions in coding regions. All genomes were mapped against mule deer  to reduce reference mapping and annotation bias. After removing gene models with in-frame STOP codons from the annotation , missing START and terminal STOP codons (-J option) and genes labelled as pseudogenes (--no-pseudo option) with Cufflinks v2.2.1112, we obtained a total of 22,732 genes.We used SnpEff v4.3O. hemionus using the protein sequences extracted from the annotation. We first identified putative deleterious variants by allocating variants to three different impact categories as defined in the SnpEff manual: a) Low/Synonymous: mostly harmless or unlikely to change protein behaviour; b) Moderate: non-disruptive variants that might change protein effectiveness; c) High: variants assumed to have high (disruptive) impact on protein, probably causing protein truncation, loss of function or triggering nonsense-mediated decay and including stop gained codons, splice donor variant and splice acceptor, start codon lost111. We also excluded intergenic (-no-intergenic) and intron (-no-intron) variants. We estimated individual genetic load in two ways. First, after removing variants fixed in all individuals, we estimated the total individual load by summing the number of variants of each category i. We also corrected for potential mapping biases dividing the number of each category by the total number of synonymous SNPs, following Xue et al.113. Secondly, we estimated the individual realised load . By taking into account the mode of dominance, this estimate allows us to estimate the proportion of potential load that is realised or expressed in each individual. We statistically tested for differences in genetic load among modern populations using ANOVAs and used Tukey’s HSD tests to test for pairwise differences among time periods for each population and among modern populations in R109.Next, we generated a database for xy ratio of Moderate and High impact variants for modern population pairs and for temporal comparisons following von Seth et al.115. An Rxy equal to 1 corresponds to no change in frequency between two populations, whereas Rxy < 1 or >1 corresponds to a decrease/deficiency or an increase/excess in frequency in population x relative to population y, respectively. We used a jack-knife procedure in R109 to estimate the variance in the ratio.To take into account the frequency of variants in each population, we also calculated the R24. The indicators are based on standard population genetic metrics for within and between population variation and agree with Essential Biodiversity Variables (EBVs) for genetic diversity26 and are also relevant for the indicator assessments required by the new monitoring framework of the Convention on Biological Diversity (CBD)27. Two indicators assess diversity within populations (ΔH and Ne)24. The third indicator (ΔFST) reflects temporal changes in genetic diversity among populations as well as the number of populations maintained over time.Three indicators to monitor trends in genetic diversity have recently been introduced in Swedish monitoring work following science-management elaborationsROH based on our estimates of heterozygosity, nucleotide diversity, and inbreeding (see above). For the North cluster, three temporal stratifications were possible; 1800s, 1980, and 2019/2020. Two stratifications were made for each of the remaining two clusters; 1980 and 2019/2020 for the Transition cluster and for the South cluster 1800s and 1980. For the Ne indicator, we used estimates from the GONE analysis from the most recent generation . Three indicator signals  were given. For the ΔH indicator, and in cases of significant temporal change, the difference in diversity between two time points over the whole sampling period (between 40 and 170 years for our different comparisons) was translated into an annual change. The threshold values for annual reduction  were applied to the three measures of genomic diversity . For the Ne indicator, NeLD ≥ 500 is classified as Acceptable116, 50< NeLD < 500 Warning, and NeLD < 50 Alarm based on117.Next, for each indicator, threshold values reflecting specific rates of change over time were used to assess trends in genetic diversity of populations . A ΔFST reflecting an increase or decrease in migration was classified as follows: reduced migration by 25% and 50% was considered Warning and Alarm, respectively and increased migration of 50% and 100% classified as Warning and Alarm, respectively. This indicator was also classified as Alarm if any population went extinct over the monitoring period.The ΔF24 and statistical testing of indicators was performed in R109 using t-tests and Wilcoxon signed-rank tests. Only results from the t-test are reported in cases where these two tests are concordant with each other.Only statistically significant changes in genetic metrics were consideredST between the North (n = 30) and South (n = 30) clusters in ANGSD118. We first calculated the sample allele frequency (saf) for the data mapped to O. hemionus with the following arguments: -doSaf 1; -gl 1; -minMapQ 30; -minQ 30. We used O. hemionus as reference for the ancestral state to estimate the unfolded Site Frequency Spectrum (SFS). Next, we calculated the SFS using the realSFS command to calculate 2d sfs for the North vs South populations comparison using the following options: -P 10; -r. We then used the realSFS fst index command to calculate Fst binary files. We used the realSFS command to extract Fst values and a sliding with the following options: -win 50,000; -step 10000. We performed a Z-transformation of FST values and retained FST-outlier windows with a Z-score >5 . Z(FST) was calculated as Z(FST) = (FST – μ FST)/σ FST, where FST is the FST value in a window, μ FST the average FST over all windows, and σ FST the standard deviation of FST values over all windows. Finally, we extracted those Z(FST) outlier windows as a bed file and cross-referenced them with the annotation for O. hemionus  using BEDtools intersect100.To identify candidate regions under putative positive selection and potentially due to local adaptation, we estimated pairwise F120. We used BEDtools intersect to extract the variants that were overlapping with Z(FST) outlier windows from our 80 modern genomes vcf file (data mapped to O. hemionus). Latitude was used as a proxy for temperature and as the explanatory variable in the model. We ran the model with K = 3 and for 20 repetitions. Significance was applied using an alpha value of 0.001 and a Bonferroni correction for multiple testing. We then extracted candidate SNPs and cross-referenced them with the annotation for O. hemionus using BEDtools intersect.In order to test for association between the identified outlier windows and climate, we used a Latent Factor Mixed Model (LFMM) implemented in the LEA R packageST between the 1980 (n = 17) and 2019/2020 (n = 17) periods for the North cluster to identify recent temporal genomic differentiation as described above. This analysis was only performed for this cluster since it was the only one for which sample size was large enough for a temporal comparison and since substructure over the moose range could bias our results.Finally, we estimated pairwise Fwww.informatics.jax.org) to manually retrieve gene ontologies and mammalian phenotype information for each candidate gene. We also compared our list of candidate genes with that of Wang et al.56.For all genes identified with the genome scans analyses, we used the Mouse Genome Informatics database (https://github.com/ndussex/Moose_genomics) contains details on the code and analyses used in this study.The research sample included 80 modern and 7 historical moose samples collected across the whole range of the species in Sweden. All statistical tests were conducted using publicly available programs and packages as described in the methodological sections above. Reproducibility can be accomplished by following the laboratory and analytical protocols described above and by following. The author’s GitHub (Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary DataReporting Summary