From genes to ecosystems: Analyzing Ormosia microphylla endangerment driven by multiple dimensions

From genes to ecosystems: Analyzing Ormosia microphylla endangerment driven by multiple dimensions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article From genes to ecosystems: Analyzing Ormosia microphylla endangerment driven by multiple dimensions Xiao-ming Tian, Bin Yuan, Cun Mou, Guang-feng Xiang, Lu Zhu, Gao-fei Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7231308/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Background The ecologically and economically important species Ormosia microphylla faces a high extinction risk. However, systematic studies on the multiple threats faced by these populations and mechanisms underlying the interactions among threats remain limited. To formulate effective conservation strategies, we conducted population surveys across seven natural populations within its main distribution range. Results Overall, 71% of the studied populations exhibited unstable age structures. Population genomic analyses classified the populations into three genetic clusters that were all characterized by low genetic diversity. The slowest decline rate was predicted for the HN (individuals from Tongdao and Cengbu; lacking young individuals) cluster than for the GX (individuals from Nandan) and HG (individuals from Jianhe, Liping, Jingzhou, and Tangbaocun; lacking mature trees or dominated by senescent individuals) clusters. However, the GX cluster possessed a more stable age structure. Selective sweep analysis further revealed enhanced fatty acid biosynthesis and metabolism pathways in the GX cluster. Further, saplings predominated within the HG cluster. Although all populations produced viable seeds, seed production declined annually and germination was restricted by the seed coat. Illegal logging (tree stumps) evidence was observed in all populations, with populations exhibiting more stumps having fewer mature adult trees. Finally, soil nutrients were not significantly correlation with seedling number, whereas bird diversity was positively correlated with seedling numbers. Conclusions These results help reconcile the apparent contradictions between field observations and genetic predictions and highlight the critical importance of curbing illegal logging and monitoring bird diversity for the recovery and persistence of O. microphylla populations. anthropogenic logging Ormosia microphylla plant conservation population genetics seed spreader Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Biodiversity is the cornerstone of Earth’s life-support systems and establishes ecosystem functions through mechanisms such as functional complementarity and resilience stabilization [1]. Driven by habitat loss, overexploitation, and climate change, the escalating extinction crisis threatens this foundational framework [2]. According to the International Union for Conservation of Nature (IUCN) Red List [3], 43% of plant taxa face the risk of extinction. This trend is particularly prominent in tropical and subtropical regions. The decrease in plant diversity disrupts carbon sequestration and nutrient cycling [4] and destabilizes mutualistic networks, such as plant–pollinator interactions. These changes in turn trigger cascading impacts on ecosystem services that are critical to human well-being, including food security and climate regulation [5]. Therefore, protecting plant diversity is important for global ecological protection and sustainable development. Ormosia plants have attracted considerable attention as an important group within Fabaceae because of their beautiful red seeds and high-quality wood [6]. This genus is widely distributed in tropical and subtropical regions and plays an important role in forest ecosystems [7]. Its seeds are often spread by birds and form a complex interaction network with various animals and plants, which is important for maintaining biodiversity [8]. However, factors such as habitat loss and climate change are increasingly impacting the habitats of Ormosia [9, 10]. For example, Ormosia microphylla , a representative Ormosia taxon, has shown a sharp population decrease and reduced distribution range [11]; thus, it has been listed as an Endangered species by the IUCN. The Endangered status of O. microphylla reflects the conservation challenges affecting Ormosia and highlights the urgency of protecting plant diversity. Current research on the decline of O. microphylla populations has focused on analyzing single factors. Meanwhile, systematic studies on the multiple threats faced by these populations and mechanisms underlying the interactions among threats are still limited [12]. This hinders the design of holistic conservation strategies that align with the 2030 targets of the Global Biodiversity Framework [13, 14]. Therefore, we aimed to explore protection strategies for O. microphylla , improve the conservation status of this Endangered species, and provide a scientific reference for protecting plant diversity and sustainable ecosystem development [15]. Results Population surveys of O. microphylla We conducted population surveys of O. microphylla at four sites in Hunan, two sites in Guizhou, and one site in Guangxi; we found a total of 1,329 individuals of O. microphylla (Fig. 1 A). Among them, Jianhe (JH) in Guizhou had the largest number of individuals, with 1,020 individuals found. Liping (LP) in Guizhou had the smallest number of individuals, with only one individual at a height of 25 m and diameter at breast height of 155 cm (Fig. 1 B, Table S1 ). In the other populations, the number of O. microphylla individuals did not exceed 100. By combining the cluster analysis and survey results, the seven O. microphylla populations were classified into four categories (Figs. 1 B and S1): Category 1 only included the populations from Cengbu (CB) and LP, wherein the individuals had heights greater than 5 m; Category 2 included the population from Tongdao (TD), wherein more than 60% of the individuals were young trees with heights ranging from 1.3 to 2.5 m; Category 3 included the populations from Jingzhou (JZ) and Tangbaocun (TBC), wherein more than 60% of the saplings had heights ranging from 0 to 1.3 m; and Category 4 included populations from Nandan (ND) and JH, wherein the age structure of the trees was suitable for sustainable development. Population structure analyses To clarify the genetic relationships among the 7 populations, 62 individual leaves were collected for genome sequencing (Fig. 1 C). A principal component analysis (PCA) ellipse was drawn based on the sequencing results, and the 62 individuals were categorized into 3 genetic clusters (Fig. 1 C). All individuals from ND were individually defined as the GX genetic cluster, which separated from the other two groups along the PC1 axis. Individuals from JH, LP, JZ, and TBC were defined as the historical analysis of the HG genetic cluster. Individuals from TD and CB were defined as the HN genetic cluster (Fig. 1 C and D). Each population occupied a different geographic distribution area. These sequencing results may support the potential for past genetic exchange between populations within each genetic cluster. However, genetic exchange was not detected between each genetic cluster (Fig. S2). A comparison of the nucleotide diversity of the three genetic clusters showed that all three had a nucleotide diversity below 0.005, with the HN genetic cluster showing the highest diversity, followed by the HG genetic cluster (Fig. 1 E). This was also illustrated by the linkage disequilibrium results within the populations (Fig. 1 F). Analysis of the population history showed a persistent decline in the population number of O. microphylla over the last 10 years. The predicted population of the GX genetic cluster had fewer individuals than the HG and HN populations (Fig. 1 G). However, the number of individuals in the GX genetic cluster of O. microphylla was higher than that in the HN genetic cluster. Selective sweep analysis Integrated analysis of the population age structure and genomic population structure revealed that the GX population exhibited the most stable age structure compared to the HN (lacking young individuals) and HG (lacking mature trees or dominated by senescent individuals) populations. To investigate the contribution of genetic adaptation to these age structure differences, we performed selective sweep analysis using GX as the reference population against HG and HN. Employing a combined threshold approach (top 5% of π , F ST , and XPCLR values), we identified 282 genes under selection (Fig. 2 A). Compared to those in GX, selected genes in HG exhibited F ST >0.19, 0.29 < θ π 0.21, 0.43 < θ π < 1.40, and XPCLR ≥ 8.03 (Fig. S3). Among the 165 selected genes in HG, significant enrichment was observed in the cutin, suberine, and wax biosynthesis (ko00073) pathways (p-adjust < 0.05) relative to those in GX. Additional pathways, including RNA polymerase (ko03020) and Phenylpropanoid biosynthesis (ko00940), were also enriched (Fig. 2 B a). For the 117 selected genes identified in HN, KEGG annotation revealed enrichment in pathways such as other glycan degradation (ko00511), phenylpropanoid biosynthesis (ko00940), and cutin, suberine, and wax biosynthesis (ko00073, Fig. 2 B b) relative to those in GX. Similarly, to elucidate the mechanisms underlying the stable age structure of GX, we used HG and HN as reference populations and identified 176 and 150 selected genes in GX, respectively (Fig. 2 C). Genes selected in GX were predominantly enriched in linoleic acid metabolism (ko00591), fatty acid biosynthesis (ko00061), and fatty acid metabolism (ko01212) pathways relative to those in HG. In contrast, genes selected in GX showed primary enrichment in galactose metabolism (ko00052), other types of O-glycan biosynthesis (ko00514), and protein export (ko03060) pathways (Fig. 2 D) relative to those in HN. Seed number and seed vigor of representative populations Based on the above findings, we subsequently focused on the CB, TBC, and TD populations because they belonged to different age groups. The TD and CB populations belonged to the HN genetic cluster, and the TBC population belonged to the HG genetic cluster. In this study, we determined the plant seed number and seed vigor in three representative populations—TD, TBC, and CB. The results showed that populations of different genetic clusters could naturally produce fruit. However, the lack of adult trees within the populations or the decline in the reproductive capacity of old trees could cause a reduction in seed numbers. Seeds were collected from all three populations selected in this study. However, the number of seeds collected varied, and the total seed number declined annually (Fig. 3 ). The TBC population had the highest seed number each year from 2019 to 2024. The TD population had a significantly higher seed number than the CB population from 2019 to 2023, and the seed number in 2024 was not significantly different from that of the CB population. Meanwhile, the CB population annually produced only a small number of seeds (p < 0.05, Fig. 3 A). The 2023 count showed higher numbers of adult and old trees in TD (9) and CB (10) than in TBC (5). The TD and CB populations had significantly lower individual fruiting rates in 2024 than did the TBC population (p < 0.05, Fig. S4). Seed vigor was similar in different populations, although the seed numbers of the different populations varied. The sowing of seeds obtained from the population showed that seed viability in each region ranged from 20–37% and was not significantly different from each other (p > 0.05, Fig. 3 B). Breaking the seed coat (T2) promoted the germination of O. microphylla seeds. Three treatments were used to treat the O. microphylla seeds. All three treatments significantly increased the germination rate of O. microphylla seeds (p < 0.05; Fig. 3 C). Among them, the 45 ℃ water immersion treatment (T1) and concentrated sulfuric acid immersion treatment (T2) were the most effective and increased seed germination by 40%. This result also highlights the importance of the soil environment and seed dispersal in the germination of O. microphylla seeds. External risks affecting O. microphylla populations Illegal logging All three representative populations selected were subjected to varying degrees of logging, with the TD population being the most severely logged. Stumps of O. microphylla were observed within all three populations examined. The number of stumps in the TD population was significantly higher than that of the TBC and CB populations (p < 0.05, Fig. 4 A). This also implies that the TD population may have experienced the greatest degree of anthropogenic disturbance among the populations studied. This result also indicated that all three groups had larger population sizes in the past. Soil conditions Twelve soil variables were measured, including pH. PCA showed that the soil conditions of the TD and TBC populations were similar and significantly different from those of the CB population (Fig. 4 B). The ANOVA significance analysis of the 12 indicators did not reveal significant differences in pH and total phosphorus among the three regions (p > 0.05, Fig. S5). Except for pH, NO 3 -N, and total phosphorus, the soil composition of CB was significantly higher than that of the TBC and TD populations (p < 0.05). This implies that the soils in the CB area were of higher quality than those in the other two sites. Meanwhile, the soil conditions in the TBC area were more suitable than those in the TD area because the contents of seven components, including nitrate nitrogen, humus, fulvic acid, and humin, were significantly higher in the TBC soil than in the TD soil (p 0.05, Fig. S6). PCA was performed to analyze the species composition of birds at the three sites (Fig. 4 C, a). The PC1 loading was 50.11%, and the PC2 loading was 26.49%. Ellipse plotting with PC1 and PC2 as the axes showed that the bird species compositions of the TD and TBC areas were similar, although both differed significantly from that of the CB area. Further analysis of bird diversity at the three sites showed that the Shannon and Simpson indices of birds in the CB and TBC were significantly higher than those in the TD (p < 0.05, Fig. 4 C b, c). In contrast, the Shannon and Simpson indices did not significantly differ for the birds in the CB and TBC. The bird diversity in the TD group was substantially lower than that in the CB and TBC groups. Discussion O. microphylla currently faces a high extinction risk due to synergistic anthropogenic and genetic threats. Given its ecological significance in biodiversity maintenance and economic significance as timber, we conducted comprehensive population surveys across seven naturally occurring populations in three Chinese provinces where O. microphylla is almost exclusively distributed. All populations exhibited critical vulnerability, necessitating urgent identification of extinction drivers to formulate science-based conservation strategies. Unstable age structure in most populations Usually, plant populations in the field present a certain capacity for natural regeneration and a suitable age structure [16, 17]. However, the field survey results showed that the age structure of O. microphylla at more than 70% of the surveyed sites was not conducive to population expansion and stable reproduction. These populations were characterized by an over-representation (> 60%) of young trees and saplings with a tree height between 0 and 2.5 m or old trees with a tree height > 5 m. Given the overabundance of young trees and saplings within the population, Ormosia trees may be unable to obtain sufficient nutrients for interspecies competition. Ormosia spp. are sun-loving plants, and their slow growth relative to other companion species may prevent them from obtaining sufficient sunlight to develop into large trees [18]. Moreover, when old trees are overrepresented in the population, plant productivity is degraded and the plant cannot produce a sufficient number of seeds to sustain the population [19, 20]. The results of the 6 consecutive years of seed number counts supported this hypothesis and suggested that sustained population decline is almost unavoidable. The results of many surveys of threatened plants show that populations of these plants usually have a poor age structure and are usually characterized by an overabundance of old trees and a low number of young trees [21, 22]. This differs from the dynamics of rare Ormosia plants and thus implies that O. microphylla populations with a sufficient number of young trees were still naturally regenerating at some point in the past [23]. Inconsistencies between the population genetic analyses and field survey data Population genetics techniques have been widely employed to assess genetic risks in endangered plants, including genetic diversity and adaptive potential. Our analyses revealed uniformly low genetic diversity across all O. microphylla populations, which represented a significant threat to species persistence [24]. Despite the well-established age structure and natural regeneration of the ND (belonging to the GX cluster) and JH (belonging to the HG cluster) O. microphylla populations, their low nucleotide diversity raises concerns regarding their long-term survival [25]. The results of the population genetics analyses also suggest that populations from different genetic clusters will all experience sustained population decline in the future. Population history analysis suggests that this decline occurred 10 years ago. However, the population decline of many threatened plants occurred 1000 years ago, suggesting that O. microphylla populations have been strongly disturbed in recent years [26, 27]. In addition, gene flow is lacking between the different genetic clusters, which is not conducive to the genetic diversification of populations [28]. Habitat fragmentation also reduces the gene flow in populations within genetic clusters [29]. This low nucleotide diversity and limited gene flow within a population may cause the accumulation of deleterious alleles and reduce its adaptability to environmental stress [30]. Selective sweep analyses indicated positive selection in the GX cluster for genes enriched in linoleic acid metabolism, fatty acid biosynthesis and metabolism, and other types of O-glycan biosynthesis pathways, potentially reflecting local adaptation [31, 32]. Enhanced fatty acid and linoleic acid metabolism may contribute to the membrane structure, energy storage, and signaling processes, while linoleic acid may be involved in membrane stabilization and support stress tolerance under challenging conditions [33]. Elevated seed lipid biosynthesis may also enhance seed disperser attractiveness [34]. However, both the HN and HG clusters were enriched in the cutin, suberin, and wax biosynthesis pathways, suggesting enhanced seed coat constraints relative to the GX cluster [35] despite the contribution of these pathways to seed protection. The HG cluster exhibited more significant enrichment (p-adjust < 0.05) than HN (p-adjust = 0.46), which was supported by the seed germination experiments; however, the differences were not significant. Dormancy imposed by hard seed coats in Ormosia typically requires scarification or animal digestion for germination, which was experimentally validated [36]. Thus, dormancy is recognized as a reproductive barrier in endangered plants, and intensified wax biosynthesis in HN and HG population may exacerbate physical dormancy, potentially limiting sapling recruitment. Although population genetic insights partially explain the population declines and unstable age structures, discrepancies with empirical field data emerged. (1) Endangered plants usually undergo a process of population decline [37, 38]. The population history analysis showed that the number of individuals in the HN genetic cluster was greater than that in the GX genetic cluster. This result contradicts the field survey data. (2) The selected genes of the HG population were significantly enriched in the cutin, suberine, and wax biosynthesis pathways, suggesting thatthe number of saplings may be too low. In reality, the HG population was dominated by saplings at three out of four sites. These findings imply that external factors may be significantly accelerating the decline in the O. microphylla population. External risks may explain the contradiction result External risks, including human logging and declining bird diversity, may directly explain these contradictions [39]. Given the economic value of O. microphylla , overharvesting may also be a key factor contributing to its population decline [40, 41]. Traces of logging were found in all three populations (TBC, TD, and CB), and individuals with tree heights of 2.5–5.0 m were lacking in all three populations. The TD area with the highest number of stumps only had one adult tree. The number of stumps may not accurately reflect the extent of logging because stumps may erode over time due to rain, microorganisms, and wind [42, 43]. However, the available results are sufficient to demonstrate deforestation of O. microphylla , and they explain the presence of a certain number of young trees but a lack of adult trees in some populations. These substantial economic gains are driving illegal harvesting and logging and thus pose a serious threat to global biodiversity. Therefore, a sound regulatory system for the efficient management and conservation of biodiversity is urgently required [44]. Such systems will benefit not only O. microphylla but also fish, mammals, tropical timber species, and other taxa. Additionally, in the TD population, although a certain number of seeds was observed annually, the number of saplings was extremely limited. Seed germination rates were similar in different populations of O. microphylla , and germination rates were significantly higher after treatment of the seed coat with acid treatment or hot water soaking. This is similar to the findings in other Ormosia plants such as O. microphylla , which exhibits seed coat limitation [45]. Additionally, this implies that the TD population lacks suitable conditions for the germination of O. microphylla seeds. However, the soil conditions in TD were similar to those in TBC, suggesting that soil was not responsible for the lack of saplings. Considering the bird-dependent seed dispersal of O. microphylla , we suggest that the significantly lower bird diversity in TD than in the other areas may be a key factor limiting population development, although the population can normally produce seeds [46, 47]. Similarly, the higher bird diversity in the TBC area (HG population) may explain why the strong seed coat restrictions did not result in a lack of saplings. These results support the hypothesis that bird diversity may influence the sustainability of the O. microphylla populations by influencing the number of saplings. The decline in the number of O. microphylla individuals was accompanied by a decline in seed production, implying that birds feeding on O. microphylla seeds have difficulty obtaining sufficient food, which further exacerbates the decline in bird diversity. These results emphasize the importance of biological interactions in biological conservation, and the maintenance of these interactions may help achieve better biological conservation benefits. Future measures to protect O. microphylla O. microphylla faces serious internal and external threats, and the interaction between these two risks exacerbates the decline in population size. However, regardless of the reasons that led to the initial population decline, we can conduct conservation studies on the existing populations of O. microphylla . Based on our findings, we propose the following conservation recommendations: (1) human intervention, such as transplanting O. microphylla or felling companion trees, should be considered in the future to help increase its population number [48]; (2) artificial pollination should be performed to improve genetic diversity; (3) regulations on illegal logging activities should be strengthened [49, 50]; and (4) seed dispersers should be protected to indirectly promote population recovery [51–53]. Implementing these measures can help to alleviate the survival pressures faced by O. microphylla and guarantee the long-term survival of its population. Many threatened taxa that are as economically valuable as O. microphylla , including plant and animal taxa, may present similar population declines. Therefore, to determine plant threat status and develop effective conservation programs with predictable benefits, we call for a more detailed assessment of future risk factors that includes more varied parameters than just the impacts on the plants themselves. Materials and methods Study area overview The study area is located in south-central and southwestern China (107°14′E-110°19′E, 25°7′N-26°28′N), including four sites in Hunan Province, that is, Chengbu (CB, 110°19′E, 26°22′N), Tongdao (TD, 109°56′E, 26°10′N), Jingzhou (JZ, 109°42′E, 26°28′N), and Tangbaocun (TBC, 110°02′E, 25°49′N); two sites in Guizhou Province: Liping (LP, 109°08′E, 26°12′N), Jianhe (JH, 108°36′E, 26°38′N); and one site in Guangxi: Nandan (ND, 107°14′E, 25°07′N). The altitudes of the study sites vary from 564 m to 970 m. The vegetation type is subtropical evergreen broad-leaved forest. The region has a typical warm and humid subtropical monsoon climate with abundant light. The average annual temperature is 16–22 ℃, the average temperature of the warmest month (July) is 25–30 ℃, and the average temperature of the coldest month (January) is 5–12 ℃. The average annual precipitation is 1,200–1,800 mm, with approximately 85% concentrated between April and October. The average annual relative humidity is above 75%. In this study, a field survey was conducted from April to July each year from 2019 to 2024. For each sample site, we randomly selected 62 robustly growing mother trees of O. microphylla from each sample site. Plant materials were collected from natural populations in the above regions. The species was formally identified by Dr. Lihong Yan ( [email protected] ), and voucher specimens (voucher No. OMi-2024-001 to OMi-2024-062) have been deposited at Hunan Botanical Garden (Hunan Province, China). Permission for field sampling was obtained from the local forestry authorities. All collections and experimental research on plants complied with institutional, national, and international guidelines, as well as local legislation on biodiversity conservation. Quadrat and population surveys Field surveys were conducted from April to July (2019–2024) to investigate the population structure of seven natural populations of O. microphylla in ND, JH, LP, TD, JZ, TBC, and CB. The distribution ranges of the seven natural populations were determined through field surveys combined with records from local foresters. To cover all O. microphylla individuals at the distribution sites, we referred to the method of Ohsawa (1984)M Ohsawa [54] for the population structure survey and made some adjustments in light of the actual situation. We set 20 × 20 m to 30 × 30 m sample plots based on the distribution range of the natural population, and 13 quadrats were established. The number and height of O. microphylla in each quadrat were recorded. Individuals were classified into four height categories: (1) 0 m < tree height ≤ 1.3 m; (2) 1.3 m < tree height ≤ 2.5 m; (3) 2.5 m < tree height ≤ 5 m; and (4) 5 m < tree height. These height categories were grouped into four age classes: sapling (tree height ≤ 1.3 m), young tree (1.3 m < tree height ≤ 2.5 m), adult tree (2.5 m 5 m). Population genomic analyses At the seven sampling sites, we randomly selected healthy growing trees of O. microphylla to collect leaves for genome sequencing. A total of 62 trees were sampled. An Illumina Bipartite Library was constructed for each sample. Based on these data, we performed variation, population structure, chain imbalance, gene flow, and population history analyses. The reference genome ( Ormosia henryi ) was 2.64 G. The comparison rates of the 62 samples ranged from 98.53–98.98%, the reference genome coverage ranged from 42.75–47.34%, and the average sequencing depth ranged from 6.316× to 13.56×. Based on the comparison results, the samples were tested to detect mutations using GATK (Version: 4.6.0.0; Parameters: HaplotypeCaller, CombineGVCFs, and GenotypeGVCFs, https://github.com/broadinstitute/gatk ) to detect variants in the samples. The identified single nucleotide polymorphisms (SNPs) and indels were filtered separately using GATK (version 4.6.0.0; parameters: VariantFiltration) according to the hard-filtering criteria officially recommended by GATK. To measure the specific variations in each sample, the filtered results were assessed for variations. Information on base transitions, inversions, inversion transition ratios, pure genotypes, heterozygous genotypes, and heterozygous ratios was obtained for each sample. Population genetic structure was determined using ADMIXTURE (version: 1.3.0; parameter: -cv inputFile K, https://dalexander.github.io/admixture/index.html ), with K values ranging from two to nine. Genome-wide linkage disequilibrium (LD) analysis was performed using PopLDdecay (version: v3.41; parameters: default parameters, https://github.com/BGI-shenzhen/PopLDdecay ). TreeMix ( https://bitbucket.org/nygcresearch/treemix/wiki/Home ) was used to infer population differentiation and gene flow from a set of population histories. Moreover, TreeMix 1.3.1 was used to return a maximum likelihood (ML) tree for a population by obtaining allele frequencies from multiple populations and inferring possible hybridization events. In this study, we used smc++ (version: latest; base mutation rate: 4e-9; years per generation: 50) to infer the effective population size of the population to which the individual belonged at various times using individual weight sequencing data. Selective sweep analyses To identify key genomic regions subject to selection, this study compared three populations divided according to sequencing results and combined three indices (π-ratio, F st , and XPCLR) for selective sweep analysis. The genomes were divided into equal-sized intervals by sliding windows. VCFtools (version: 0.1.16; parameters: -weir-fst-pop, -window-pi, https://vcftools.sourceforge.net/man_latest.html ) was used to calculate the F st values and π -ratio between each window of the two compared populations, and the Python version of XPCLR (version: 3.42, https://github.com/hardingnj/xpclr ) was used to calculate the XPCLR values of the two compared populations between each window on each chromosome. Then, the intersection region of the three indices (all top 5%) was identified. Subsequently, for the selected genomic regions, KEGG enrichment analysis was performed to identify sets of functional genes and metabolic regulatory pathways with associations. Seed number determination We collected the fruits of adult and old O. microphylla trees from October to November between 2019 and 2024 to determine the seed number and viability of different O. microphylla populations. The plants for fruit collection were selected as described in Section 2.2. Five branches were randomly selected from each individual, and the fruit from each branch was collected. After fruit picking, the seeds were stripped in the laboratory and the total number of seeds was recorded. The average seed number was expressed as the average number of seeds per adult and old O. microphylla tree. Seed vitality We conducted a sowing test from November 2019 to February 2021 in the Conservation Nursery of Hunan Provincial Botanical Garden to determine the viability of O. microphylla seeds in terms of germination rate. Owing to the limited number of seeds collected each year, a sowing trial was conducted over two years. The collected seeds were sown immediately in November 2019, and 30 O. microphylla seeds from TBC, TD, and CB were randomly selected. The selected seeds were treated for 3 days by soaking in cold water soaking. Then, the seeds were evenly sown on the planting bed, covered with 1 cm of soil, and then covered with film to maintain warmth. During the experiment, seeds were watered with tap water [55]. Seed germination was measured in April 2020. To compare the germination of O. microphylla seeds under different pretreatment methods, we conducted a sowing test in February 2021. The following four methods were applied to pretreat the seeds before sowing: (1) no soaking (CK); (2) soaking in hot water at 45 ℃ for 6 h (T1); (3) scratching the seed coat (T3); and (4) soaking in concentrated sulfuric acid for 3 h (T4). The collected seeds of O. microphylla were mixed well. For pretreatment, randomly selected seeds were evenly sown on the planting bed, covered with 1 cm of soil, and then covered with a film to maintain warmth. Thirty seeds were sown for each treatment. Seed germination was determined in April 2021. Germination rate (%) = (number of normal germinated seeds/total number of treated seeds) × 100 Detection of traces of human logging We recorded the number of O. microphylla logged within each quadrat as an indicator of the degree of anthropogenic disturbance to the O. microphylla population. As Chinese government regulations do not allow logging in natural forests, the discovery of these stumps also indicates that illegal logging is taking place. Soil composition testing We conducted field sampling in June 2024 at TBC, TD, and CB using the standardized protocol for soil sampling. Three standardized sample plots measuring 20 × 20 m were randomly deployed at the three sample sites, and nine sample plots were established. Within each quadrat, we used the five-point mixing method to collect samples, randomly selected five soil cores (diameter 5 cm, soil depth 30–60 cm), and mixed the soil samples from the three quadrats to form a composite sample. Nine soil samples were collected (three sampling sites × three replicates). The soil samples were placed in self-sealing bags and refrigerated for immediate transportation to the laboratory after being sieved through a 2-mm sieve to remove stone particles and plant roots. The soil pH, dry matter, organic matter, NO 3 -N, NH 4 -N, available phosphorous, total nitrogen, total carbon, total phosphorus, and humus, including humic acid, fulvic acid, and humin were measured to assess the soil conditions. Soil pH was determined using the methods of Hu et al. (2021a) [56] with some adjustments. Briefly, soil sample solutions (1:2.5, ratio of soil sample to deionized water) were assessed using a potentiometric method with an acidimeter (Sartorius PB-10). The dry matter content of the soil was determined based on soil dried to a constant weight using the drying and weighing method with an electric blast-drying oven (Boante BAT240-LGF). The organic matter content in the soil was determined by the potassium dichromate oxidation-external heating method (the ratio of the soil sample to the solution was 1:40), which was heated by an oil bath at 170–180 ℃, cooled, washed with water, and titrated with ferrous sulfate solution [57]. The NO 3 -N and NH 4 -N contents of the soil samples were determined using an ultraviolet spectrophotometer (Shanghai Mepuda UV-1800PC). Fresh soil samples were extracted with a 1 mol/L potassium chloride solution for NO 3 -N and 2 mol/L potassium chloride solution for NH 4 -N [58]. Soil samples were air-dried and sieved to extract the available phosphorus content using ammonium fluoride–hydrochloric acid leachate (1:10 soil sample-to-leachate ratio). After shaking, centrifugation, and filtration, the available phosphorous content was determined using the molybdenum antimony colorimetric method [59]. The total carbon and nitrogen contents of the soil were determined by dry combustion using an elemental analyzer (Thermo Fisher Scientific) [60]. The soil total phosphorus content was determined according to the methods of Pan et al. (2023) [61] with some adjustments. The soil samples were decomposed using sodium hydroxide fusion (1:8 ratio of soil sample to sodium hydroxide solids). Molybdenum antimony anticolorant was added under acidic conditions. The total phosphorus content of the soil samples was determined using an ultraviolet spectrophotometer (Shanghai Mepuda UV-1800PC). The soil humus content, including humic acid, fulvic acid, and humin, was determined as described by Li et al. (2018) [62] with some adjustments according to the actual situation [63]. Humic acid and fulvic acid in the soil samples were extracted using a 0.1 mol/L sodium pyrophosphate–sodium hydroxide mixture and the ratio of soil sample to mixture was 1:20. The humic and fulvic acid contents were determined by oxidation, precipitation, and filtration with potassium dichromate, and the humin content was determined using difference calculations. Bird diversity statistics Between April 2022 and May 2023, a bird diversity survey was conducted at three sites: TD, TBC, and CB. The sample line method was used for bird observations, with 3–5 km long sample lines set up at each site, totaling 312 sample lines. Observations were performed under clear and windless weather at a walking speed of 1–2 km/h along the set sample lines, and each observation lasted from 07:00 to 09:00 or from 16:00 to 18:00. The names and numbers of birds that appeared and called within 30 m of the left and right sides of the sample line were recorded. The Shannon–Wiener (H) and Simpson diversity indices (D) were calculated for each of the three sites [64]. H = \(\:-{\sum\:}_{i=1}^{S}\left({P}_{i}\text{ln}{P}_{i}\right)\) D = 1 \(\:-\sum\:_{i=1}^{S}{{P}_{i}}^{2}\) where S is the species number and Pi is the relative abundance of species i. Data analysis The seven sample plots were clustered according to the community structure of O. microphylla . Tukey’s test was used to test the significance of the variability in O. microphylla fruiting and seed vitality among the different treatment groups, external risk variables, and bird diversity. A p value of 0.05 was selected as the threshold for significant differences. PCA was performed to assess the population genetics of O. microphylla within the seven sample plots and the bird species composition in the three representative sample plots. All data processing and plotting were performed using SPSS version 26 (SPSS Inc., Chicago, IL, USA) and Origin (Origin Pro 2025, OriginLab, Northampton, Massachusetts, USA). Declarations Ethics approval and Consent to participate This study did not require ethical approval and did not involve human research. Consent to Publish declaration Not applicable. Competing Interest The authors declare no competing interest. Funding This study was funded by the 2023 Central Finance Second Batch of Subsidized Projects for Conservation of National Key Wildlife and Animals. Author Contributions X.M.T., B.Y., F.L.H., and H.L. designed the research; C.M., G.F.X., L.Z., and G.F.L. collected the sample; C.L. and X.P.L. performed the research; X.M.T. and B.Y. analyzed the data; and X.M.T., B.Y., F.L.H. and H.L. wrote the paper. All authors read and approved its content. Availability of data and material The whole-genome sequencing data for O. henryi (No. GWHFICR00000000.1) and DNA sequencing data for the O. microphylla population (No. PRJCA037388) reported in this paper have been deposited in the Genome Warehouse at the National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation. The accession number is publicly accessible at https://ngdc.cncb.ac.cn/gwh. Clinical Trail Number Not applicable. Acknowledgements Not applicable. References Hamilton J, Humphries MM, Bennett EM: A mixed method evaluation of ecosystem services and services-to-ecosystems illuminates culturally important trees in a settled landscape. Ecosystems and People 2025, 21(1). Šmejkal M, Kalous L, Auwerx J, Gorule PA, Jarić I, Dočkal O, Fedorčák J, Muška M, Thomas K, Takács P et al : Wetland fish in peril: A synergy between habitat loss and biological invasions drives the extinction of neglected native fauna. 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T-test for all variables, * * *p\u0026lt;0.001. \u003cstrong\u003e(G)\u003c/strong\u003ePopulation history analysis of GX, HG, and HN genetic clusters. JH, Jianhe population; LP, Liping population; ND, Nandan population; TBC, Tangbaocun population; JZ, Jingzhou population; TD, Tongdao population; and CB, Chengbu population.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7231308/v1/9e79e2f3aa42447696763bcf.png"},{"id":90419166,"identity":"739c8790-fa80-498b-b1d6-24e19058957b","added_by":"auto","created_at":"2025-09-02 13:44:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":154331,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of gene selective swapping. (A) Screening of selected genes using HG (a) and HN (b) populations, respectively, with the GX population as the background population; (B) KEGG annotations of selected genes from the HG (a) and HN (b) populations screened with the GX population as the background population; (C) Screening of selected genes using the GX population, respectively, with HG (a) and HN (b) populations; (D) Screening of selected genes with the HG (a) and HN (b) populations, respectively, with the KEGG annotations of selected genes from the GX population screened with the HG (a) and HN (b) populations as background population.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7231308/v1/b4da4b1482abaeddc72e90f9.png"},{"id":90419165,"identity":"a2504ffd-e280-4614-bd94-e407d94c50b1","added_by":"auto","created_at":"2025-09-02 13:44:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":67771,"visible":true,"origin":"","legend":"\u003cp\u003eFirmness and seed vitality of \u003cem\u003eO. microphylla\u003c/em\u003e. \u003cstrong\u003e(A)\u003c/strong\u003eStatistics on the number of \u003cem\u003eO. microphylla\u003c/em\u003e seeds for six consecutive years. TD, Tongdao population; CB, Chengbu population; TBC, Tangbaocun population. Different letters represent significant differences at \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05. \u003cstrong\u003e(B)\u003c/strong\u003e Germination rate of \u003cem\u003eO. microphylla\u003c/em\u003e seeds. TD, Tongdao population; CB, Chengbu population; TBC, Tangbaocun population. T-test for all variables. \u003cstrong\u003e(C)\u003c/strong\u003e Germination rate of \u003cem\u003eO. microphylla\u003c/em\u003e seeds under different treatments. CK, natural treatment group; T1, water immersion treatment at 45 ℃; T2, skin coat laceration treatment; T3, concentrated sulfuric acid immersion treatment. Different letters represent significant differences at \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7231308/v1/d70653c84e199c50bfc15409.png"},{"id":90419167,"identity":"a209e0d8-febd-47b4-b272-a4b65bc2767c","added_by":"auto","created_at":"2025-09-02 13:44:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":92453,"visible":true,"origin":"","legend":"\u003cp\u003eExternal risks of \u003cem\u003eO. microphylla\u003c/em\u003e. (A) Number of stumps within the population; (B) PCA of soil conditions; (C) bird species diversity. TD, Tongdao population, CB, Chengbu population, and TBC, Tangbaocun population. Different letters represent significant differences at \u003cem\u003ep\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7231308/v1/8c1475abdd5d22115c613adb.png"},{"id":90421746,"identity":"dc65a426-b80e-4c62-8041-02cdca7044f4","added_by":"auto","created_at":"2025-09-02 14:08:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1319429,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7231308/v1/80f0d197-6235-4980-91cb-54f25c8838bc.pdf"},{"id":90420030,"identity":"ab4b4cdc-c815-47ac-8d7e-80bb10ad09b3","added_by":"auto","created_at":"2025-09-02 13:52:04","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":761478,"visible":true,"origin":"","legend":"","description":"","filename":"SOmicrophylla20250712.docx","url":"https://assets-eu.researchsquare.com/files/rs-7231308/v1/b88bc9c98732a43c56889305.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"From genes to ecosystems: Analyzing Ormosia microphylla endangerment driven by multiple dimensions","fulltext":[{"header":"Background","content":"\u003cp\u003eBiodiversity is the cornerstone of Earth\u0026rsquo;s life-support systems and establishes ecosystem functions through mechanisms such as functional complementarity and resilience stabilization [1]. Driven by habitat loss, overexploitation, and climate change, the escalating extinction crisis threatens this foundational framework [2]. According to the International Union for Conservation of Nature (IUCN) Red List [3], 43% of plant taxa face the risk of extinction. This trend is particularly prominent in tropical and subtropical regions. The decrease in plant diversity disrupts carbon sequestration and nutrient cycling [4] and destabilizes mutualistic networks, such as plant\u0026ndash;pollinator interactions. These changes in turn trigger cascading impacts on ecosystem services that are critical to human well-being, including food security and climate regulation [5]. Therefore, protecting plant diversity is important for global ecological protection and sustainable development.\u003c/p\u003e\u003cp\u003e\u003cem\u003eOrmosia\u003c/em\u003e plants have attracted considerable attention as an important group within Fabaceae because of their beautiful red seeds and high-quality wood [6]. This genus is widely distributed in tropical and subtropical regions and plays an important role in forest ecosystems [7]. Its seeds are often spread by birds and form a complex interaction network with various animals and plants, which is important for maintaining biodiversity [8]. However, factors such as habitat loss and climate change are increasingly impacting the habitats of \u003cem\u003eOrmosia\u003c/em\u003e [9, 10]. For example, \u003cem\u003eOrmosia microphylla\u003c/em\u003e, a representative \u003cem\u003eOrmosia\u003c/em\u003e taxon, has shown a sharp population decrease and reduced distribution range [11]; thus, it has been listed as an Endangered species by the IUCN. The Endangered status of \u003cem\u003eO. microphylla\u003c/em\u003e reflects the conservation challenges affecting \u003cem\u003eOrmosia\u003c/em\u003e and highlights the urgency of protecting plant diversity.\u003c/p\u003e\u003cp\u003eCurrent research on the decline of \u003cem\u003eO. microphylla\u003c/em\u003e populations has focused on analyzing single factors. Meanwhile, systematic studies on the multiple threats faced by these populations and mechanisms underlying the interactions among threats are still limited [12]. This hinders the design of holistic conservation strategies that align with the 2030 targets of the Global Biodiversity Framework [13, 14].\u003c/p\u003e\u003cp\u003eTherefore, we aimed to explore protection strategies for \u003cem\u003eO. microphylla\u003c/em\u003e, improve the conservation status of this Endangered species, and provide a scientific reference for protecting plant diversity and sustainable ecosystem development [15].\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePopulation surveys of O. microphylla\u003c/h2\u003e\u003cp\u003eWe conducted population surveys of \u003cem\u003eO. microphylla\u003c/em\u003e at four sites in Hunan, two sites in Guizhou, and one site in Guangxi; we found a total of 1,329 individuals of \u003cem\u003eO. microphylla\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Among them, Jianhe (JH) in Guizhou had the largest number of individuals, with 1,020 individuals found. Liping (LP) in Guizhou had the smallest number of individuals, with only one individual at a height of 25 m and diameter at breast height of 155 cm (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). In the other populations, the number of \u003cem\u003eO. microphylla\u003c/em\u003e individuals did not exceed 100. By combining the cluster analysis and survey results, the seven \u003cem\u003eO. microphylla\u003c/em\u003e populations were classified into four categories (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eB and S1): Category 1 only included the populations from Cengbu (CB) and LP, wherein the individuals had heights greater than 5 m; Category 2 included the population from Tongdao (TD), wherein more than 60% of the individuals were young trees with heights ranging from 1.3 to 2.5 m; Category 3 included the populations from Jingzhou (JZ) and Tangbaocun (TBC), wherein more than 60% of the saplings had heights ranging from 0 to 1.3 m; and Category 4 included populations from Nandan (ND) and JH, wherein the age structure of the trees was suitable for sustainable development.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePopulation structure analyses\u003c/h3\u003e\n\u003cp\u003eTo clarify the genetic relationships among the 7 populations, 62 individual leaves were collected for genome sequencing (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). A principal component analysis (PCA) ellipse was drawn based on the sequencing results, and the 62 individuals were categorized into 3 genetic clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). All individuals from ND were individually defined as the GX genetic cluster, which separated from the other two groups along the PC1 axis. Individuals from JH, LP, JZ, and TBC were defined as the historical analysis of the HG genetic cluster. Individuals from TD and CB were defined as the HN genetic cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eC and D). Each population occupied a different geographic distribution area. These sequencing results may support the potential for past genetic exchange between populations within each genetic cluster. However, genetic exchange was not detected between each genetic cluster (Fig. S2). A comparison of the nucleotide diversity of the three genetic clusters showed that all three had a nucleotide diversity below 0.005, with the HN genetic cluster showing the highest diversity, followed by the HG genetic cluster (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). This was also illustrated by the linkage disequilibrium results within the populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). Analysis of the population history showed a persistent decline in the population number of \u003cem\u003eO. microphylla\u003c/em\u003e over the last 10 years. The predicted population of the GX genetic cluster had fewer individuals than the HG and HN populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). However, the number of individuals in the GX genetic cluster of \u003cem\u003eO. microphylla\u003c/em\u003e was higher than that in the HN genetic cluster.\u003c/p\u003e\n\u003ch3\u003eSelective sweep analysis\u003c/h3\u003e\n\u003cp\u003eIntegrated analysis of the population age structure and genomic population structure revealed that the GX population exhibited the most stable age structure compared to the HN (lacking young individuals) and HG (lacking mature trees or dominated by senescent individuals) populations. To investigate the contribution of genetic adaptation to these age structure differences, we performed selective sweep analysis using GX as the reference population against HG and HN. Employing a combined threshold approach (top 5% of \u003cem\u003eπ\u003c/em\u003e, \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e, and XPCLR values), we identified 282 genes under selection (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Compared to those in GX, selected genes in HG exhibited \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u0026gt;0.19, 0.29\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eθ\u003c/em\u003e\u003csub\u003eπ\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.49, and XPCLR\u0026thinsp;\u0026ge;\u0026thinsp;6.71, while selected genes in HN showed \u003cem\u003eF\u003c/em\u003e\u003csub\u003eST\u003c/sub\u003e\u0026gt;0.21, 0.43\u0026thinsp;\u0026lt;\u0026thinsp;\u003cem\u003eθ\u003c/em\u003e\u003csub\u003eπ\u003c/sub\u003e\u0026thinsp;\u0026lt;\u0026thinsp;1.40, and XPCLR\u0026thinsp;\u0026ge;\u0026thinsp;8.03 (Fig. S3). Among the 165 selected genes in HG, significant enrichment was observed in the cutin, suberine, and wax biosynthesis (ko00073) pathways (p-adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) relative to those in GX. Additional pathways, including RNA polymerase (ko03020) and Phenylpropanoid biosynthesis (ko00940), were also enriched (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB a). For the 117 selected genes identified in HN, KEGG annotation revealed enrichment in pathways such as other glycan degradation (ko00511), phenylpropanoid biosynthesis (ko00940), and cutin, suberine, and wax biosynthesis (ko00073, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eB b) relative to those in GX.\u003c/p\u003e\u003cp\u003eSimilarly, to elucidate the mechanisms underlying the stable age structure of GX, we used HG and HN as reference populations and identified 176 and 150 selected genes in GX, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). Genes selected in GX were predominantly enriched in linoleic acid metabolism (ko00591), fatty acid biosynthesis (ko00061), and fatty acid metabolism (ko01212) pathways relative to those in HG. In contrast, genes selected in GX showed primary enrichment in galactose metabolism (ko00052), other types of O-glycan biosynthesis (ko00514), and protein export (ko03060) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003eD) relative to those in HN.\u003c/p\u003e\n\u003ch3\u003eSeed number and seed vigor of representative populations\u003c/h3\u003e\n\u003cp\u003eBased on the above findings, we subsequently focused on the CB, TBC, and TD populations because they belonged to different age groups. The TD and CB populations belonged to the HN genetic cluster, and the TBC population belonged to the HG genetic cluster.\u003c/p\u003e\u003cp\u003eIn this study, we determined the plant seed number and seed vigor in three representative populations\u0026mdash;TD, TBC, and CB. The results showed that populations of different genetic clusters could naturally produce fruit. However, the lack of adult trees within the populations or the decline in the reproductive capacity of old trees could cause a reduction in seed numbers. Seeds were collected from all three populations selected in this study. However, the number of seeds collected varied, and the total seed number declined annually (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The TBC population had the highest seed number each year from 2019 to 2024. The TD population had a significantly higher seed number than the CB population from 2019 to 2023, and the seed number in 2024 was not significantly different from that of the CB population. Meanwhile, the CB population annually produced only a small number of seeds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The 2023 count showed higher numbers of adult and old trees in TD (9) and CB (10) than in TBC (5). The TD and CB populations had significantly lower individual fruiting rates in 2024 than did the TBC population (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig. S4).\u003c/p\u003e\u003cp\u003eSeed vigor was similar in different populations, although the seed numbers of the different populations varied. The sowing of seeds obtained from the population showed that seed viability in each region ranged from 20\u0026ndash;37% and was not significantly different from each other (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Breaking the seed coat (T2) promoted the germination of \u003cem\u003eO. microphylla\u003c/em\u003e seeds. Three treatments were used to treat the \u003cem\u003eO. microphylla\u003c/em\u003e seeds. All three treatments significantly increased the germination rate of \u003cem\u003eO. microphylla\u003c/em\u003e seeds (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Among them, the 45 ℃ water immersion treatment (T1) and concentrated sulfuric acid immersion treatment (T2) were the most effective and increased seed germination by 40%. This result also highlights the importance of the soil environment and seed dispersal in the germination of \u003cem\u003eO. microphylla\u003c/em\u003e seeds.\u003c/p\u003e\n\u003ch3\u003eExternal risks affecting O. microphylla populations\u003c/h3\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eIllegal logging\u003c/h2\u003e\u003cp\u003eAll three representative populations selected were subjected to varying degrees of logging, with the TD population being the most severely logged. Stumps of \u003cem\u003eO. microphylla\u003c/em\u003e were observed within all three populations examined. The number of stumps in the TD population was significantly higher than that of the TBC and CB populations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). This also implies that the TD population may have experienced the greatest degree of anthropogenic disturbance among the populations studied. This result also indicated that all three groups had larger population sizes in the past.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eSoil conditions\u003c/h3\u003e\n\u003cp\u003eTwelve soil variables were measured, including pH. PCA showed that the soil conditions of the TD and TBC populations were similar and significantly different from those of the CB population (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). The ANOVA significance analysis of the 12 indicators did not reveal significant differences in pH and total phosphorus among the three regions (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig. S5). Except for pH, NO\u003csub\u003e3\u003c/sub\u003e-N, and total phosphorus, the soil composition of CB was significantly higher than that of the TBC and TD populations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This implies that the soils in the CB area were of higher quality than those in the other two sites. Meanwhile, the soil conditions in the TBC area were more suitable than those in the TD area because the contents of seven components, including nitrate nitrogen, humus, fulvic acid, and humin, were significantly higher in the TBC soil than in the TD soil (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\n\u003ch3\u003eBird diversity\u003c/h3\u003e\n\u003cp\u003eIn the bird observation experiment, 56 bird species were identified (Table S2). The total number of birds did not significantly differ among the three sites (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig. S6). PCA was performed to analyze the species composition of birds at the three sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, a). The PC1 loading was 50.11%, and the PC2 loading was 26.49%. Ellipse plotting with PC1 and PC2 as the axes showed that the bird species compositions of the TD and TBC areas were similar, although both differed significantly from that of the CB area. Further analysis of bird diversity at the three sites showed that the Shannon and Simpson indices of birds in the CB and TBC were significantly higher than those in the TD (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e4\u003c/span\u003eC b, c). In contrast, the Shannon and Simpson indices did not significantly differ for the birds in the CB and TBC. The bird diversity in the TD group was substantially lower than that in the CB and TBC groups.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cem\u003eO. microphylla\u003c/em\u003e currently faces a high extinction risk due to synergistic anthropogenic and genetic threats. Given its ecological significance in biodiversity maintenance and economic significance as timber, we conducted comprehensive population surveys across seven naturally occurring populations in three Chinese provinces where \u003cem\u003eO. microphylla\u003c/em\u003e is almost exclusively distributed. All populations exhibited critical vulnerability, necessitating urgent identification of extinction drivers to formulate science-based conservation strategies.\u003c/p\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eUnstable age structure in most populations\u003c/h2\u003e\u003cp\u003eUsually, plant populations in the field present a certain capacity for natural regeneration and a suitable age structure [16, 17]. However, the field survey results showed that the age structure of \u003cem\u003eO. microphylla\u003c/em\u003e at more than 70% of the surveyed sites was not conducive to population expansion and stable reproduction. These populations were characterized by an over-representation (\u0026gt;\u0026thinsp;60%) of young trees and saplings with a tree height between 0 and 2.5 m or old trees with a tree height\u0026thinsp;\u0026gt;\u0026thinsp;5 m. Given the overabundance of young trees and saplings within the population, \u003cem\u003eOrmosia\u003c/em\u003e trees may be unable to obtain sufficient nutrients for interspecies competition. \u003cem\u003eOrmosia\u003c/em\u003e spp. are sun-loving plants, and their slow growth relative to other companion species may prevent them from obtaining sufficient sunlight to develop into large trees [18]. Moreover, when old trees are overrepresented in the population, plant productivity is degraded and the plant cannot produce a sufficient number of seeds to sustain the population [19, 20]. The results of the 6 consecutive years of seed number counts supported this hypothesis and suggested that sustained population decline is almost unavoidable. The results of many surveys of threatened plants show that populations of these plants usually have a poor age structure and are usually characterized by an overabundance of old trees and a low number of young trees [21, 22]. This differs from the dynamics of rare \u003cem\u003eOrmosia\u003c/em\u003e plants and thus implies that \u003cem\u003eO. microphylla\u003c/em\u003e populations with a sufficient number of young trees were still naturally regenerating at some point in the past [23].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eInconsistencies between the population genetic analyses and field survey data\u003c/h2\u003e\u003cp\u003ePopulation genetics techniques have been widely employed to assess genetic risks in endangered plants, including genetic diversity and adaptive potential. Our analyses revealed uniformly low genetic diversity across all \u003cem\u003eO. microphylla\u003c/em\u003e populations, which represented a significant threat to species persistence [24]. Despite the well-established age structure and natural regeneration of the ND (belonging to the GX cluster) and JH (belonging to the HG cluster) \u003cem\u003eO. microphylla\u003c/em\u003e populations, their low nucleotide diversity raises concerns regarding their long-term survival [25]. The results of the population genetics analyses also suggest that populations from different genetic clusters will all experience sustained population decline in the future. Population history analysis suggests that this decline occurred 10 years ago. However, the population decline of many threatened plants occurred 1000 years ago, suggesting that \u003cem\u003eO. microphylla\u003c/em\u003e populations have been strongly disturbed in recent years [26, 27]. In addition, gene flow is lacking between the different genetic clusters, which is not conducive to the genetic diversification of populations [28]. Habitat fragmentation also reduces the gene flow in populations within genetic clusters [29]. This low nucleotide diversity and limited gene flow within a population may cause the accumulation of deleterious alleles and reduce its adaptability to environmental stress [30].\u003c/p\u003e\u003cp\u003eSelective sweep analyses indicated positive selection in the GX cluster for genes enriched in linoleic acid metabolism, fatty acid biosynthesis and metabolism, and other types of O-glycan biosynthesis pathways, potentially reflecting local adaptation [31, 32]. Enhanced fatty acid and linoleic acid metabolism may contribute to the membrane structure, energy storage, and signaling processes, while linoleic acid may be involved in membrane stabilization and support stress tolerance under challenging conditions [33]. Elevated seed lipid biosynthesis may also enhance seed disperser attractiveness [34]. However, both the HN and HG clusters were enriched in the cutin, suberin, and wax biosynthesis pathways, suggesting enhanced seed coat constraints relative to the GX cluster [35] despite the contribution of these pathways to seed protection. The HG cluster exhibited more significant enrichment (p-adjust\u0026thinsp;\u0026lt;\u0026thinsp;0.05) than HN (p-adjust\u0026thinsp;=\u0026thinsp;0.46), which was supported by the seed germination experiments; however, the differences were not significant. Dormancy imposed by hard seed coats in \u003cem\u003eOrmosia\u003c/em\u003e typically requires scarification or animal digestion for germination, which was experimentally validated [36]. Thus, dormancy is recognized as a reproductive barrier in endangered plants, and intensified wax biosynthesis in HN and HG population may exacerbate physical dormancy, potentially limiting sapling recruitment.\u003c/p\u003e\u003cp\u003eAlthough population genetic insights partially explain the population declines and unstable age structures, discrepancies with empirical field data emerged. (1) Endangered plants usually undergo a process of population decline [37, 38]. The population history analysis showed that the number of individuals in the HN genetic cluster was greater than that in the GX genetic cluster. This result contradicts the field survey data. (2) The selected genes of the HG population were significantly enriched in the cutin, suberine, and wax biosynthesis pathways, suggesting thatthe number of saplings may be too low. In reality, the HG population was dominated by saplings at three out of four sites. These findings imply that external factors may be significantly accelerating the decline in the \u003cem\u003eO. microphylla\u003c/em\u003e population.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eExternal risks may explain the contradiction result\u003c/h2\u003e\u003cp\u003eExternal risks, including human logging and declining bird diversity, may directly explain these contradictions [39]. Given the economic value of \u003cem\u003eO. microphylla\u003c/em\u003e, overharvesting may also be a key factor contributing to its population decline [40, 41]. Traces of logging were found in all three populations (TBC, TD, and CB), and individuals with tree heights of 2.5\u0026ndash;5.0 m were lacking in all three populations. The TD area with the highest number of stumps only had one adult tree. The number of stumps may not accurately reflect the extent of logging because stumps may erode over time due to rain, microorganisms, and wind [42, 43]. However, the available results are sufficient to demonstrate deforestation of \u003cem\u003eO. microphylla\u003c/em\u003e, and they explain the presence of a certain number of young trees but a lack of adult trees in some populations. These substantial economic gains are driving illegal harvesting and logging and thus pose a serious threat to global biodiversity. Therefore, a sound regulatory system for the efficient management and conservation of biodiversity is urgently required [44]. Such systems will benefit not only \u003cem\u003eO. microphylla\u003c/em\u003e but also fish, mammals, tropical timber species, and other taxa.\u003c/p\u003e\u003cp\u003eAdditionally, in the TD population, although a certain number of seeds was observed annually, the number of saplings was extremely limited. Seed germination rates were similar in different populations of \u003cem\u003eO. microphylla\u003c/em\u003e, and germination rates were significantly higher after treatment of the seed coat with acid treatment or hot water soaking. This is similar to the findings in other \u003cem\u003eOrmosia\u003c/em\u003e plants such as \u003cem\u003eO. microphylla\u003c/em\u003e, which exhibits seed coat limitation [45]. Additionally, this implies that the TD population lacks suitable conditions for the germination of \u003cem\u003eO. microphylla\u003c/em\u003e seeds. However, the soil conditions in TD were similar to those in TBC, suggesting that soil was not responsible for the lack of saplings. Considering the bird-dependent seed dispersal of \u003cem\u003eO. microphylla\u003c/em\u003e, we suggest that the significantly lower bird diversity in TD than in the other areas may be a key factor limiting population development, although the population can normally produce seeds [46, 47]. Similarly, the higher bird diversity in the TBC area (HG population) may explain why the strong seed coat restrictions did not result in a lack of saplings. These results support the hypothesis that bird diversity may influence the sustainability of the \u003cem\u003eO. microphylla\u003c/em\u003e populations by influencing the number of saplings. The decline in the number of \u003cem\u003eO. microphylla\u003c/em\u003e individuals was accompanied by a decline in seed production, implying that birds feeding on \u003cem\u003eO. microphylla\u003c/em\u003e seeds have difficulty obtaining sufficient food, which further exacerbates the decline in bird diversity. These results emphasize the importance of biological interactions in biological conservation, and the maintenance of these interactions may help achieve better biological conservation benefits.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eFuture measures to protect O. microphylla\u003c/h2\u003e\u003cp\u003e\u003cem\u003eO. microphylla\u003c/em\u003e faces serious internal and external threats, and the interaction between these two risks exacerbates the decline in population size. However, regardless of the reasons that led to the initial population decline, we can conduct conservation studies on the existing populations of \u003cem\u003eO. microphylla\u003c/em\u003e. Based on our findings, we propose the following conservation recommendations: (1) human intervention, such as transplanting \u003cem\u003eO. microphylla\u003c/em\u003e or felling companion trees, should be considered in the future to help increase its population number [48]; (2) artificial pollination should be performed to improve genetic diversity; (3) regulations on illegal logging activities should be strengthened [49, 50]; and (4) seed dispersers should be protected to indirectly promote population recovery [51\u0026ndash;53]. Implementing these measures can help to alleviate the survival pressures faced by \u003cem\u003eO. microphylla\u003c/em\u003e and guarantee the long-term survival of its population. Many threatened taxa that are as economically valuable as \u003cem\u003eO. microphylla\u003c/em\u003e, including plant and animal taxa, may present similar population declines. Therefore, to determine plant threat status and develop effective conservation programs with predictable benefits, we call for a more detailed assessment of future risk factors that includes more varied parameters than just the impacts on the plants themselves.\u003c/p\u003e\u003c/div\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStudy area overview\u003c/h2\u003e\u003cp\u003eThe study area is located in south-central and southwestern China (107\u0026deg;14\u0026prime;E-110\u0026deg;19\u0026prime;E, 25\u0026deg;7\u0026prime;N-26\u0026deg;28\u0026prime;N), including four sites in Hunan Province, that is, Chengbu (CB, 110\u0026deg;19\u0026prime;E, 26\u0026deg;22\u0026prime;N), Tongdao (TD, 109\u0026deg;56\u0026prime;E, 26\u0026deg;10\u0026prime;N), Jingzhou (JZ, 109\u0026deg;42\u0026prime;E, 26\u0026deg;28\u0026prime;N), and Tangbaocun (TBC, 110\u0026deg;02\u0026prime;E, 25\u0026deg;49\u0026prime;N); two sites in Guizhou Province: Liping (LP, 109\u0026deg;08\u0026prime;E, 26\u0026deg;12\u0026prime;N), Jianhe (JH, 108\u0026deg;36\u0026prime;E, 26\u0026deg;38\u0026prime;N); and one site in Guangxi: Nandan (ND, 107\u0026deg;14\u0026prime;E, 25\u0026deg;07\u0026prime;N). The altitudes of the study sites vary from 564 m to 970 m. The vegetation type is subtropical evergreen broad-leaved forest. The region has a typical warm and humid subtropical monsoon climate with abundant light. The average annual temperature is 16\u0026ndash;22 ℃, the average temperature of the warmest month (July) is 25\u0026ndash;30 ℃, and the average temperature of the coldest month (January) is 5\u0026ndash;12 ℃. The average annual precipitation is 1,200\u0026ndash;1,800 mm, with approximately 85% concentrated between April and October. The average annual relative humidity is above 75%.\u003c/p\u003e\u003cp\u003eIn this study, a field survey was conducted from April to July each year from 2019 to 2024. For each sample site, we randomly selected 62 robustly growing mother trees of \u003cem\u003eO. microphylla\u003c/em\u003e from each sample site. Plant materials were collected from natural populations in the above regions. The species was formally identified by Dr. Lihong Yan ([email protected]), and voucher specimens (voucher No. OMi-2024-001 to OMi-2024-062) have been deposited at Hunan Botanical Garden (Hunan Province, China). Permission for field sampling was obtained from the local forestry authorities. All collections and experimental research on plants complied with institutional, national, and international guidelines, as well as local legislation on biodiversity conservation.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eQuadrat and population surveys\u003c/h2\u003e\u003cp\u003eField surveys were conducted from April to July (2019\u0026ndash;2024) to investigate the population structure of seven natural populations of \u003cem\u003eO. microphylla\u003c/em\u003e in ND, JH, LP, TD, JZ, TBC, and CB. The distribution ranges of the seven natural populations were determined through field surveys combined with records from local foresters. To cover all \u003cem\u003eO. microphylla\u003c/em\u003e individuals at the distribution sites, we referred to the method of Ohsawa (1984)M Ohsawa [54] for the population structure survey and made some adjustments in light of the actual situation. We set 20 \u0026times; 20 m to 30 \u0026times; 30 m sample plots based on the distribution range of the natural population, and 13 quadrats were established. The number and height of \u003cem\u003eO. microphylla\u003c/em\u003e in each quadrat were recorded. Individuals were classified into four height categories: (1) 0 m\u0026thinsp;\u0026lt;\u0026thinsp;tree height\u0026thinsp;\u0026le;\u0026thinsp;1.3 m; (2) 1.3 m\u0026thinsp;\u0026lt;\u0026thinsp;tree height\u0026thinsp;\u0026le;\u0026thinsp;2.5 m; (3) 2.5 m\u0026thinsp;\u0026lt;\u0026thinsp;tree height\u0026thinsp;\u0026le;\u0026thinsp;5 m; and (4) 5 m\u0026thinsp;\u0026lt;\u0026thinsp;tree height. These height categories were grouped into four age classes: sapling (tree height\u0026thinsp;\u0026le;\u0026thinsp;1.3 m), young tree (1.3 m\u0026thinsp;\u0026lt;\u0026thinsp;tree height\u0026thinsp;\u0026le;\u0026thinsp;2.5 m), adult tree (2.5 m\u0026thinsp;\u0026lt;\u0026thinsp;tree height\u0026thinsp;\u0026le;\u0026thinsp;5 m), and old tree (tree height\u0026thinsp;\u0026gt;\u0026thinsp;5 m).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003ePopulation genomic analyses\u003c/h2\u003e\u003cp\u003eAt the seven sampling sites, we randomly selected healthy growing trees of \u003cem\u003eO. microphylla\u003c/em\u003e to collect leaves for genome sequencing. A total of 62 trees were sampled. An Illumina Bipartite Library was constructed for each sample. Based on these data, we performed variation, population structure, chain imbalance, gene flow, and population history analyses.\u003c/p\u003e\u003cp\u003eThe reference genome (\u003cem\u003eOrmosia henryi\u003c/em\u003e) was 2.64 G. The comparison rates of the 62 samples ranged from 98.53\u0026ndash;98.98%, the reference genome coverage ranged from 42.75\u0026ndash;47.34%, and the average sequencing depth ranged from 6.316\u0026times; to 13.56\u0026times;. Based on the comparison results, the samples were tested to detect mutations using GATK (Version: 4.6.0.0; Parameters: HaplotypeCaller, CombineGVCFs, and GenotypeGVCFs, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/broadinstitute/gatk\u003c/span\u003e\u003cspan address=\"https://github.com/broadinstitute/gatk\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to detect variants in the samples. The identified single nucleotide polymorphisms (SNPs) and indels were filtered separately using GATK (version 4.6.0.0; parameters: VariantFiltration) according to the hard-filtering criteria officially recommended by GATK. To measure the specific variations in each sample, the filtered results were assessed for variations. Information on base transitions, inversions, inversion transition ratios, pure genotypes, heterozygous genotypes, and heterozygous ratios was obtained for each sample.\u003c/p\u003e\u003cp\u003ePopulation genetic structure was determined using ADMIXTURE (version: 1.3.0; parameter: -cv inputFile K, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dalexander.github.io/admixture/index.html\u003c/span\u003e\u003cspan address=\"https://dalexander.github.io/admixture/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with K values ranging from two to nine.\u003c/p\u003e\u003cp\u003eGenome-wide linkage disequilibrium (LD) analysis was performed using PopLDdecay (version: v3.41; parameters: default parameters, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/BGI-shenzhen/PopLDdecay\u003c/span\u003e\u003cspan address=\"https://github.com/BGI-shenzhen/PopLDdecay\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTreeMix (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bitbucket.org/nygcresearch/treemix/wiki/Home\u003c/span\u003e\u003cspan address=\"https://bitbucket.org/nygcresearch/treemix/wiki/Home\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to infer population differentiation and gene flow from a set of population histories. Moreover, TreeMix 1.3.1 was used to return a maximum likelihood (ML) tree for a population by obtaining allele frequencies from multiple populations and inferring possible hybridization events.\u003c/p\u003e\u003cp\u003eIn this study, we used smc++ (version: latest; base mutation rate: 4e-9; years per generation: 50) to infer the effective population size of the population to which the individual belonged at various times using individual weight sequencing data.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003eSelective sweep analyses\u003c/h2\u003e\u003cp\u003eTo identify key genomic regions subject to selection, this study compared three populations divided according to sequencing results and combined three indices (π-ratio, \u003cem\u003eF\u003c/em\u003e\u003csub\u003est\u003c/sub\u003e, and XPCLR) for selective sweep analysis. The genomes were divided into equal-sized intervals by sliding windows. VCFtools (version: 0.1.16; parameters: -weir-fst-pop, -window-pi, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://vcftools.sourceforge.net/man_latest.html\u003c/span\u003e\u003cspan address=\"https://vcftools.sourceforge.net/man_latest.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to calculate the \u003cem\u003eF\u003c/em\u003e\u003csub\u003est\u003c/sub\u003e values and \u003cem\u003eπ\u003c/em\u003e-ratio between each window of the two compared populations, and the Python version of XPCLR (version: 3.42, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/hardingnj/xpclr\u003c/span\u003e\u003cspan address=\"https://github.com/hardingnj/xpclr\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to calculate the XPCLR values of the two compared populations between each window on each chromosome. Then, the intersection region of the three indices (all top 5%) was identified. Subsequently, for the selected genomic regions, KEGG enrichment analysis was performed to identify sets of functional genes and metabolic regulatory pathways with associations.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003eSeed number determination\u003c/h2\u003e\u003cp\u003eWe collected the fruits of adult and old \u003cem\u003eO. microphylla\u003c/em\u003e trees from October to November between 2019 and 2024 to determine the seed number and viability of different \u003cem\u003eO. microphylla\u003c/em\u003e populations. The plants for fruit collection were selected as described in Section 2.2. Five branches were randomly selected from each individual, and the fruit from each branch was collected. After fruit picking, the seeds were stripped in the laboratory and the total number of seeds was recorded. The average seed number was expressed as the average number of seeds per adult and old \u003cem\u003eO. microphylla\u003c/em\u003e tree.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003eSeed vitality\u003c/h2\u003e\u003cp\u003eWe conducted a sowing test from November 2019 to February 2021 in the Conservation Nursery of Hunan Provincial Botanical Garden to determine the viability of \u003cem\u003eO. microphylla\u003c/em\u003e seeds in terms of germination rate. Owing to the limited number of seeds collected each year, a sowing trial was conducted over two years.\u003c/p\u003e\u003cp\u003eThe collected seeds were sown immediately in November 2019, and 30 \u003cem\u003eO. microphylla\u003c/em\u003e seeds from TBC, TD, and CB were randomly selected. The selected seeds were treated for 3 days by soaking in cold water soaking. Then, the seeds were evenly sown on the planting bed, covered with 1 cm of soil, and then covered with film to maintain warmth. During the experiment, seeds were watered with tap water [55]. Seed germination was measured in April 2020.\u003c/p\u003e\u003cp\u003eTo compare the germination of \u003cem\u003eO. microphylla\u003c/em\u003e seeds under different pretreatment methods, we conducted a sowing test in February 2021. The following four methods were applied to pretreat the seeds before sowing: (1) no soaking (CK); (2) soaking in hot water at 45 ℃ for 6 h (T1); (3) scratching the seed coat (T3); and (4) soaking in concentrated sulfuric acid for 3 h (T4). The collected seeds of \u003cem\u003eO. microphylla\u003c/em\u003e were mixed well. For pretreatment, randomly selected seeds were evenly sown on the planting bed, covered with 1 cm of soil, and then covered with a film to maintain warmth. Thirty seeds were sown for each treatment. Seed germination was determined in April 2021.\u003c/p\u003e\u003cp\u003eGermination rate (%) = (number of normal germinated seeds/total number of treated seeds) \u0026times; 100\u003c/p\u003e\u003cdiv id=\"Sec23\" class=\"Section3\"\u003e\u003ch2\u003eDetection of traces of human logging\u003c/h2\u003e\u003cp\u003eWe recorded the number of \u003cem\u003eO. microphylla\u003c/em\u003e logged within each quadrat as an indicator of the degree of anthropogenic disturbance to the \u003cem\u003eO. microphylla\u003c/em\u003e population. As Chinese government regulations do not allow logging in natural forests, the discovery of these stumps also indicates that illegal logging is taking place.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003eSoil composition testing\u003c/h2\u003e\u003cp\u003eWe conducted field sampling in June 2024 at TBC, TD, and CB using the standardized protocol for soil sampling. Three standardized sample plots measuring 20 \u0026times; 20 m were randomly deployed at the three sample sites, and nine sample plots were established. Within each quadrat, we used the five-point mixing method to collect samples, randomly selected five soil cores (diameter 5 cm, soil depth 30\u0026ndash;60 cm), and mixed the soil samples from the three quadrats to form a composite sample. Nine soil samples were collected (three sampling sites \u0026times; three replicates). The soil samples were placed in self-sealing bags and refrigerated for immediate transportation to the laboratory after being sieved through a 2-mm sieve to remove stone particles and plant roots.\u003c/p\u003e\u003cp\u003eThe soil pH, dry matter, organic matter, NO\u003csub\u003e3\u003c/sub\u003e-N, NH\u003csub\u003e4\u003c/sub\u003e-N, available phosphorous, total nitrogen, total carbon, total phosphorus, and humus, including humic acid, fulvic acid, and humin were measured to assess the soil conditions.\u003c/p\u003e\u003cp\u003eSoil pH was determined using the methods of Hu et al. (2021a) [56] with some adjustments. Briefly, soil sample solutions (1:2.5, ratio of soil sample to deionized water) were assessed using a potentiometric method with an acidimeter (Sartorius PB-10). The dry matter content of the soil was determined based on soil dried to a constant weight using the drying and weighing method with an electric blast-drying oven (Boante BAT240-LGF).\u003c/p\u003e\u003cp\u003eThe organic matter content in the soil was determined by the potassium dichromate oxidation-external heating method (the ratio of the soil sample to the solution was 1:40), which was heated by an oil bath at 170\u0026ndash;180 ℃, cooled, washed with water, and titrated with ferrous sulfate solution [57].\u003c/p\u003e\u003cp\u003eThe NO\u003csub\u003e3\u003c/sub\u003e-N and NH\u003csub\u003e4\u003c/sub\u003e-N contents of the soil samples were determined using an ultraviolet spectrophotometer (Shanghai Mepuda UV-1800PC). Fresh soil samples were extracted with a 1 mol/L potassium chloride solution for NO\u003csub\u003e3\u003c/sub\u003e-N and 2 mol/L potassium chloride solution for NH\u003csub\u003e4\u003c/sub\u003e-N [58]. Soil samples were air-dried and sieved to extract the available phosphorus content using ammonium fluoride\u0026ndash;hydrochloric acid leachate (1:10 soil sample-to-leachate ratio). After shaking, centrifugation, and filtration, the available phosphorous content was determined using the molybdenum antimony colorimetric method [59].\u003c/p\u003e\u003cp\u003eThe total carbon and nitrogen contents of the soil were determined by dry combustion using an elemental analyzer (Thermo Fisher Scientific) [60].\u003c/p\u003e\u003cp\u003eThe soil total phosphorus content was determined according to the methods of Pan et al. (2023) [61] with some adjustments. The soil samples were decomposed using sodium hydroxide fusion (1:8 ratio of soil sample to sodium hydroxide solids). Molybdenum antimony anticolorant was added under acidic conditions. The total phosphorus content of the soil samples was determined using an ultraviolet spectrophotometer (Shanghai Mepuda UV-1800PC).\u003c/p\u003e\u003cp\u003eThe soil humus content, including humic acid, fulvic acid, and humin, was determined as described by Li et al. (2018) [62] with some adjustments according to the actual situation [63]. Humic acid and fulvic acid in the soil samples were extracted using a 0.1 mol/L sodium pyrophosphate\u0026ndash;sodium hydroxide mixture and the ratio of soil sample to mixture was 1:20. The humic and fulvic acid contents were determined by oxidation, precipitation, and filtration with potassium dichromate, and the humin content was determined using difference calculations.\u003c/p\u003e\u003cdiv id=\"Sec25\" class=\"Section3\"\u003e\u003ch2\u003eBird diversity statistics\u003c/h2\u003e\u003cp\u003eBetween April 2022 and May 2023, a bird diversity survey was conducted at three sites: TD, TBC, and CB. The sample line method was used for bird observations, with 3\u0026ndash;5 km long sample lines set up at each site, totaling 312 sample lines. Observations were performed under clear and windless weather at a walking speed of 1\u0026ndash;2 km/h along the set sample lines, and each observation lasted from 07:00 to 09:00 or from 16:00 to 18:00. The names and numbers of birds that appeared and called within 30 m of the left and right sides of the sample line were recorded. The Shannon\u0026ndash;Wiener (H) and Simpson diversity indices (D) were calculated for each of the three sites [64].\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec26\" class=\"Section3\"\u003e\u003ch2\u003eH = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-{\\sum\\:}_{i=1}^{S}\\left({P}_{i}\\text{ln}{P}_{i}\\right)\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\u003cdiv id=\"Sec27\" class=\"Section4\"\u003e\u003ch2\u003eD\u0026thinsp;=\u0026thinsp;1\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:-\\sum\\:_{i=1}^{S}{{P}_{i}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/h2\u003e\u003cp\u003ewhere \u003cem\u003eS\u003c/em\u003e is the species number and \u003cem\u003ePi\u003c/em\u003e is the relative abundance of species i.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec28\" class=\"Section2\"\u003e\u003ch2\u003eData analysis\u003c/h2\u003e\u003cp\u003eThe seven sample plots were clustered according to the community structure of \u003cem\u003eO. microphylla\u003c/em\u003e. Tukey\u0026rsquo;s test was used to test the significance of the variability in \u003cem\u003eO. microphylla\u003c/em\u003e fruiting and seed vitality among the different treatment groups, external risk variables, and bird diversity. A p value of 0.05 was selected as the threshold for significant differences. PCA was performed to assess the population genetics of \u003cem\u003eO. microphylla\u003c/em\u003e within the seven sample plots and the bird species composition in the three representative sample plots. All data processing and plotting were performed using SPSS version 26 (SPSS Inc., Chicago, IL, USA) and Origin (Origin Pro 2025, OriginLab, Northampton, Massachusetts, USA).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e and \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not require ethical approval and did not involve human research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eConsent to Publish declaration\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting \u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eInterest\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interest. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the 2023 Central Finance Second Batch of Subsidized Projects for Conservation of National Key Wildlife and Animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAuthor Contributions\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eX.M.T., B.Y., F.L.H., and H.L. designed the research; C.M., G.F.X., L.Z., and G.F.L. collected the sample; C.L. and X.P.L. performed the research; X.M.T. and B.Y. analyzed the data; and X.M.T., B.Y., F.L.H. and H.L. wrote the paper. All authors read and approved its content.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAvailability of data and material\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe whole-genome sequencing data for \u003cem\u003eO.\u003c/em\u003e \u003cem\u003ehenryi\u003c/em\u003e (No. GWHFICR00000000.1) and DNA sequencing data for the \u003cem\u003eO. microphylla\u003c/em\u003e population (No. PRJCA037388) reported in this paper have been deposited in the Genome Warehouse at the National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences/China National Center for Bioinformation. The accession number is publicly accessible at https://ngdc.cncb.ac.cn/gwh. \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eClinical Trail Number\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHamilton J, Humphries MM, Bennett EM: A mixed method evaluation of ecosystem services and services-to-ecosystems illuminates culturally important trees in a settled landscape. \u003cem\u003eEcosystems and People \u003c/em\u003e2025, 21(1).\u003c/li\u003e\n\u003cli\u003e\u0026Scaron;mejkal M, Kalous L, Auwerx J, Gorule PA, Jarić I, Dočkal O, Fedorč\u0026aacute;k J, Mu\u0026scaron;ka M, Thomas K, Tak\u0026aacute;cs P\u003cem\u003e et al\u003c/em\u003e: Wetland fish in peril: A synergy between habitat loss and biological invasions drives the extinction of neglected native fauna. \u003cem\u003eBiological Conservation \u003c/em\u003e2025, 302.\u003c/li\u003e\n\u003cli\u003eIUCN: Table 1a: Number of species evaluated in relation to the overall number of described species, and number of threatened species by major groups of organisms. \u003cem\u003eIUCN Red List Summary Statistics \u003c/em\u003e2024, 1.\u003c/li\u003e\n\u003cli\u003eLi Q, Chen Y, Xu L, Cui X, Xu H, Wang L, You C, Tian X, He X, Liu Y: Loss of plant functional group mediates microbial community assembly in litter decomposition of alpine fir forest. \u003cem\u003eGlobal Ecology and Conservation \u003c/em\u003e2025, 58.\u003c/li\u003e\n\u003cli\u003eLarigauderie A, Prieur-Richard A-H, Mace GM, Lonsdale M, Mooney HA, Brussaard L, Cooper D, Cramer W, Daszak P, D\u0026iacute;az S\u003cem\u003e et al\u003c/em\u003e: Biodiversity and ecosystem services science for a sustainable planet: the DIVERSITAS vision for 2012\u0026ndash;20. \u003cem\u003eCurrent Opinion in Environmental Sustainability \u003c/em\u003e2012, 4(1):101-105.\u003c/li\u003e\n\u003cli\u003eLiu P-P, Yu E-P, Tan Z-J, Sun H-M, Zhu W-G, Wang Z-F, Cao H-L: Genome Assemblies of Two Ormosia Species: Gene Duplication Related to Their Evolutionary Adaptation. \u003cem\u003eAgronomy \u003c/em\u003e2023, 13(7).\u003c/li\u003e\n\u003cli\u003eLiu B, Weng H, Ye X, Zhao Z, Zhan C, Ahmad S, Xu Q, Ding H, Xiao Z, Zhang G, Chen S: Simulation of Potential Geographical Distribution and Migration Pattern with Climate Change of Ormosia microphylla Merr. \u0026amp; H. 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R Package Version 22-1 2015, 2:1-2.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"anthropogenic logging, Ormosia microphylla, plant conservation, population genetics, seed spreader","lastPublishedDoi":"10.21203/rs.3.rs-7231308/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7231308/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe ecologically and economically important species \u003cem\u003eOrmosia microphylla\u003c/em\u003e faces a high extinction risk. However, systematic studies on the multiple threats faced by these populations and mechanisms underlying the interactions among threats remain limited. To formulate effective conservation strategies, we conducted population surveys across seven natural populations within its main distribution range.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOverall, 71% of the studied populations exhibited unstable age structures. Population genomic analyses classified the populations into three genetic clusters that were all characterized by low genetic diversity. The slowest decline rate was predicted for the HN (individuals from Tongdao and Cengbu; lacking young individuals) cluster than for the GX (individuals from Nandan) and HG (individuals from Jianhe, Liping, Jingzhou, and Tangbaocun; lacking mature trees or dominated by senescent individuals) clusters. However, the GX cluster possessed a more stable age structure. Selective sweep analysis further revealed enhanced fatty acid biosynthesis and metabolism pathways in the GX cluster. Further, saplings predominated within the HG cluster. Although all populations produced viable seeds, seed production declined annually and germination was restricted by the seed coat. Illegal logging (tree stumps) evidence was observed in all populations, with populations exhibiting more stumps having fewer mature adult trees. Finally, soil nutrients were not significantly correlation with seedling number, whereas bird diversity was positively correlated with seedling numbers.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eThese results help reconcile the apparent contradictions between field observations and genetic predictions and highlight the critical importance of curbing illegal logging and monitoring bird diversity for the recovery and persistence of \u003cem\u003eO. microphylla\u003c/em\u003e populations.\u003c/p\u003e","manuscriptTitle":"From genes to ecosystems: Analyzing Ormosia microphylla endangerment driven by multiple dimensions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 13:44:00","doi":"10.21203/rs.3.rs-7231308/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-24T05:48:24+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-21T09:57:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278767276480568194949145701957703272777","date":"2026-04-07T08:37:21+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-02T19:58:24+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305362464426371603334805871293683309190","date":"2026-02-27T21:07:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"109489585520025122370787320073838841619","date":"2026-02-09T14:42:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-25T06:22:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-25T06:19:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-22T17:45:44+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-22T13:58:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Plant Biology","date":"2025-08-22T13:55:40+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-plant-biology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pbio","sideBox":"Learn more about [BMC Plant Biology](http://bmcplantbiol.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pbio/default.aspx","title":"BMC Plant Biology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e0ab83ef-08da-4001-a937-2caf1722adad","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-04-24T05:54:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 13:44:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7231308","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7231308","identity":"rs-7231308","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}