{"paper_id":"48868f69-e7c2-48f7-b618-9f896e0e4dff","body_text":"Combining restricted gene flow, local microhabitat, and habitat fragmentation shapes the fine-scale spatial genetic structure of Fagus hayatae Palib. ex Hayata in Micang Mountain | 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 Article Combining restricted gene flow, local microhabitat, and habitat fragmentation shapes the fine-scale spatial genetic structure of Fagus hayatae Palib. ex Hayata in Micang Mountain Jiayu Chen, Gang Xie, Chaoyang Jiang, Xuemei Zhang, Hongyan Han, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4617989/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The beech species Fagus hayatae Palib. ex Hayata is an important relict tree species in subtropical China, which accumulated a wealth of genetic variation during evolution. To revealing its regeneration dynamics, we analyzed the spatial genetic structure and gene flow of Fagus hayatae natural population in Micang Mountain (MCM), China, by using 10 pairs of microsatellite primers. The genetic diversity of F. hayatae MCM population was at the low level among tall trees. The results of Fij and Sp analysis showed that the SGS strength of F. hayatae in MCM were 40 m, the strength of SGS was stronger in saplings compared to adult and old trees. The mean dispersal distance of pollen and seeds were 83.04 m and 30.14 m, respectively. In fine-scale space, F. hayatae population in MCM is poor in genetic variation due to the restricted gene flow and significant SGS, and the strength of SGS and the dispersal distance of gene flow of F. hayatae are influenced by the limited seed dispersal, habitat fragmentation, and microhabitats. During ex situ protection of F. hayatae , the sampling distance between individuals should be greater than 40 m to ensure the most complete genetic efficiency. Biological sciences/Ecology/Biodiversity Biological sciences/Ecology/Conservation Biological sciences/Ecology/Ecological genetics Biological sciences/Genetics/Population genetics Fagus hayatae Palib. ex Hayata Spatial genetic structure Genetic diversity Genetic differentiation Gene flow Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Biodiversity is the basis for human survival and development. However, human activities accelerated the deterioration of the ecological environment, resulting in the rapid decline of biodiversity and the endangerment of a large number of plants [ 1 ][ 2 ] . Thus, the germplasm resource conservation of endangered plants has been widely concerned. Genetic diversity is generally dependent on biological evolution, and the level of genetic diversity is a product of long-term evolution and has a huge impact on the survival and evolution of species [ 3 ] . A thorough understanding of the evolutionary history and regeneration mechanisms of endangered plants is essential for developing practical and feasible conservation and genetic management strategies [ 4 ] . The knowledge of the genetic structure and genetic diversity of the species can provide reasonable and effective genetic parameters for species conservation [ 5 ][ 6 ] . As sessile organisms, plants often exhibit limited dispersal, resulting in a decay of kinship among related individuals over short distances [ 7 ] . Consequently, plant genotypes are distributed non-randomly on a fine scale, which can be defined as a fine-scale spatial genetic structure (FSGS) [ 8 ] . SGS has profound effects on offspring fitness, intraspecific competition, and long-term evolutionary potential of populations in response to changing habitat, by altering the pattern of mating and genotype distribution within populations. The strength of an FSGS is usually determined by the interaction between evolutionary and ecological factors. The key influencing factor is gene flow restriction, which creates patterns of isolation based on the distance between the parents and offspring [ 7 ][ 9 ][ 10 ] . Gene flow usually consists of seed and pollen flow, and the intensity of SGS caused by restricted gene flow is regulated by the mating system of the species [ 11 ][ 12 ] . When pollen dispersal is restricted within a population, offspring are more likely to come from siblings and half-siblings [ 13 ] . In contrast, pollen from wind-borne pollinators is characterized by long-distance and non-directional dispersal, and seed dispersal affects the genetic structure of populations more than pollen movement [ 14 ] . When long-distance pollen movement occurs, restricted seed dispersal might result in the grouping of half-sibs near to their maternal plant [ 15 ] . When both pollen and seed diffusion are confined, populations would form high levels of FSGS and inbreeding, which may be more vulnerable to genetic diversity loss from random events such as genetic interference or drift [ 16 ][ 17 ] . Hence, understanding the FSGS patterns of rare plants contributed to reveal the mode of pollen and seed dispersal that has occurred in the population, as well as the possible limiting factors of pollen and seed flow, so as to formulate conservation strategies and management plans for the species. Fagus hayatae Palib. ex Hayata, a relict species, is a unique tall deciduous tree in the Fagaceae family, which is mainly distributed in Taiwan, Hubei, Sichuan, Gansu, Shaanxi, and Zhejiang in China [ 18 ] . As the only species in Fagus with discontinuous distribution from mainland subtropical mountains to Taiwan island, F. hayatae is of great significance to study the flora connection and the relationship of vegetation type between mainland China and Taiwan [ 19 ] . Growing in 1000 ~ 2300 m, it is an important component of mixed evergreen deciduous broad-leaved forest and mountain deciduous broad-leaved forest, which is of great value to maintaining the local ecosystem [ 20 ] . Owing to its tall and beautiful tree shape and excellent material, F. hayatae is valuable in wood and afforestation [ 18 ] . Recently, due to global climate warming and human disturbance, its habitat fragmentation has intensified, resulting in poor natural regeneration. Thus, it has been listed as the second national key protection of wild plants in China, and also listed as a vulnerable species by IUCN [ 21 ] . The conservation of its germplasm resources has always been valued by ecologists and conservation biologists [ 18 ][ 22 ][ 23 ][ 24 ][ 25 ] . Previous studies showed that level of genetic diversity within F. hayatae populations was relatively lower [ 26 ][ 27 ] . However, the reasons resulting in low-level genetic diversity within populations remain unclear to date. We hypothesize that due to habitat fragmentation, there is limited gene flow mediated by pollen flow and seed flow within the natural F. hayatae population, which in turn leads to low level of genetic diversity. Currently, studies related to the FSGS and gene flow in F. hayatae population have not been reported. Therefore, we applied microsatellite (SSR) markers to analyzed the fine-scale spatial genetic structure and its gene flow of F. hayatae population in the Micang Mountain Natural Reserve (MCM), China. We sought to answer the following questions: (1) Is there a FSGS of F. hayatae population in MCM? If so, how is its strength? (2) What are the dispersal patterns of pollen and seeds in F. hayatae population? Is there gene-flow limitation mediated by pollen or seed within the population? (3)What contributed to the current genetic pattern of F. hayatae in the MCM? 2. Results 2.1 Genetic diversity analysis nSSR primer analysis No null alleles were detected during the amplification of the 10 primers and samples, and the overall mean PIC value (0.622) was greater than 0.5, indicating that these primers have high polymorphism and are suitable for population genetic analysis. A total of 85 alleles were detected using 10 SSR primer pairs. The overall N a , N e , H o , and H e were 8.5, 3.425, 0.438, and 0.639, respectively (Supplementary Table S1 ). Therein, except for TB-140 and TB-142, H o are overall less than H e , suggesting inbreeding within the MCM F. hayatae population. genetic diversity at different age-class At the age-class level within the population, N a ranged from 6.300 (saplings) to 7.200 (old trees), with an average of 6.733; N e ranged from 3.076 (saplings) to 3.878 (old trees), with an average of 3.544; H o ranged from 0.404 (saplings) to 0.457 (adult trees), with an average of 0.434; H e ranged from 0.599 (saplings) to 0.675 (old trees), with an average 0.654 (Table 1 ). Be it the results of H o or H e , all indicates the lowest genetic diversity existing in F. hayatae saplings, compared with adult and old trees. Table 1 Genetic diversity of F. hayatae among age-classes Note: The “Number” denotes the sample size of F. hayatae individuals in current age. The numbers of N a , N e ,H o and H e , are the average values of the corresponding parameters at the age class, and the numbers in brackets are standard errors. Age classes Number N a N e H o H e Saplings 36 6.300(0.943) 3.076(0.445) 0.404(0.107) 0.599(0.070) Adult trees 56 6.700(0.803) 3.708(0.617) 0.457(0.103) 0.660(0.054) Old trees 65 7.200(0.757) 3.878(0.714) 0.442(0.089) 0.675(0.052) Means 52 6.733(0.834) 3.554(0.592) 0.434(0.100) 0.645(0.059) fine-scale genetic diversity At the patch level, N a ranged from 5.900 (CPL) to 6.800 (TBH), with an average of 6.225; N e ranged from 3.193 (CPL) to 3.565 (TBH), with an average of 3.425; H o ranged from 0.418 (LLG) to 0.462 (TBH), with an average of 0.438. The expected heterozygosity value after Nei’s correction of the small population(uH e ) [ 28 ] ranged from 0.621 (CPL) to 0.690 (TBH), with an average of 0.648. The average FI value of the four patches is 0.333. Based on H o , the maximum level of genetic diversity appeared in TBH, followed by ZSB, CPL, and LLG. In addition, the degree of genetic diversity is positively correlated with the growth density of the F. hayatae in each patch (Table 2 ). Table 2 Genetic diversity of F. hayatae among patches Note: The “Number” represents the sample size of F. hayatae , and the D represents the population density (tree/ha), the numbers of N a , N e , H o , uH e and FI, are the average values of the corresponding parameters at the patch, and the numbers in brackets are standard errors. Patch Number D N a N e H o uH e FI LLG 34 12.1 6.100 (0.809) 3.444 (0.649) 0.418 (0.092) 0.652 (0.047) 0.375 (0.139) TBH 38 39.6 6.800 (0.593) 3.565 (0.487) 0.462 (0.096) 0.690 (0.036) 0.344 (0.140) CPL 41 24.4 5.900 (1.027) 3.193 (0.473) 0.422 (0.101) 0.621 (0.074) 0.328 (0.134) ZSB 44 30.6 6.100 (1.005) 3.496 (0.572) 0.449 (0.105) 0.627 (0.079) 0.282 (0.144) means 39.25 26.7 6.225 (0.424) 3.425 (0.265) 0.438 (0.048) 0.648 (0.030) 0.333 (0.067) 2.2 FSGS analysis When the distance between individuals within a population was < 10 m, F(1) was 0.1084, which close to half-sib (0.125). When the distance ranged from 10–40 m, Fij was > 0.02 and < 0.1084 with a decreasing tendency, indicating prominent SGS. When the distance ranged from 40–150 m, Fij was > 0 and < 0.02 with a decreasing tendency, indicating no obvious SGS. When the distance exceeds 150 m, Fij was < 0, indicating a lack of SGS (Fig. 1 ). Based on F(1) and bF , the calculated Sp value was 0.0157 at the population level. At the age-class level, the Sp values were 0.0434 (saplings), 0.0070 (adult trees), and 0.0194 (old trees) (Table 3 ). The F(1) values of saplings and adult trees were close to half-sib (0.125), whereas that of old trees was close to that of their cousins (0.0625)((Table 3 , Fig. 2 ). The Sp of saplings was the largest and was much larger than that of adult trees, whereas the Sp of old trees was close to that at the population level (0.0157) (Table 3 ). Table 3 Sp at age-classe level. Note: The bF refers to the linear regression slope of the genetic relationship to the natural logarithm of the distance level, the F(1) is the average genetic relationship between individuals at the first distance level, the Sp size represent the SGS strength of corresponding age stage. The same as below. Saplings Adult trees Old trees bF -0.0398 -0.0061 -0.0182 F(1) 0.1002 0.1272 0.0620 Sp 0.0434 0.0070 0.0194 The SGS analysis at patch level, TBH was excluded as only one sapling. Faint SGS was tested in the LLG, whereas remarkable SGS within 10 m was detected in the CPL and ZSB (Fig. 3 ), with Sp values of 0.0096 (LLG), 0.0236 (CPL), and 0.0171 (ZSB), respectively. Moreover, the F(1) values of CPL and ZSB were slightly above those of the cousins (0.0625), and those of LLG were far below those of the cousins. Among them, the Sp values of CPL and ZSB were slightly greater than those at the population level (0.0157), whereas the Sp value of LLG was lower (Table 4 ). Table 4 Sp at patch level LLG CPL ZSB bF -0.0095 -0.0216 -0.0158 F(1) 0.0081 0.0826 0.0725 Sp 0.0096 0.0236 0.0171 2.3 Gene flow Among the three patches, the average effective distance of gene diffusion was 66 m and the mean diffusion distances of seeds and pollen were 30.14 m and 83.04 m, respectively. These values were greater in the LLG than in the other two patches (Table 5 ). The maximum diffusion distances of seeds (121.62 m) and pollen (179.92 m) were observed in the LLG. The minimum diffusion distance of seeds (1.57 m) was observed in the CPL, and the minimum diffusion distance of pollen (2.43 m) was detected in the ZSB (Table 6 ). Within the three patches, 93.2% of the seeds in 44 offspring were dispersed within 60 m, and 75% of the pollen was dispersed at a distance of 20–120 m (Fig. 4 ). Table 5 Mean dispersal distance of seed, pollen and gene among patches Patches Mean distance of seed dispersal/m Mean distance of pollen dispersal/m Efficient distance of gene dispersal/m LLG 43.30 122.22 96.66 CPL 23.07 53.14 44.09 ZSB 24.06 73.78 57.45 Mean 30.14 83.04 66.00 Table 6 Maximum and minimum distance of seed and pollen dispersal Patches Max distance of seed dispersal(m) Min distance of seed dispersal(m) Max distance of pollen dispersal(m) Min distance of pollen dispersal(m) LLG 121.62 9.01 179.92 14.91 CPL 53.73 1.57 118.12 12.21 ZSB 45.42 1.82 137.42 2.43 3. Discussion 3.1 Genetic diversity H e is generally considered an important parameter for evaluating genetic diversity and comparing similar species [ 29 ][ 30 ] . In MCM, the H e value of F. hayatae was 0.639, which was quite close to the H e (0.642) detected by Ying et al. [ 27 ] . The genetic diversity of F. hayatae was lower than that of the endangered tree, Dalbergia nigra (H e =0.74), and also lower than that of other trees Himatanthus drasticus (H e = 0.711 ~ 0.743), and Quercus (H e =0.714 ~ 0.854) [ 31 ][ 32 ][ 33 ] . The results showed that the genetic diversity of the F. hayatae population in MCM was at a low level. Among the different age classes, the H e of the old (0.675) and adult trees (0.660) was higher than that of the saplings (0.599), which was consistent with the results obtained for Cryptocarya chinensis [ 34 ] , Glyptostrobus pensilis [ 35 ] , and Rhododendron simsii [ 36 ] . Previous studies have shown that habitat fragmentation is the main cause of weakened genetic diversity of saplings in the population [ 37 ] . In general, the genetic diversity of adult and old trees is less affected by habitat fragmentation; however, habitat fragmentation can affect the gene flow patterns of adult trees, thereby increasing the chance of inbreeding [ 38 ] . This effect acts on the offspring, resulting in a weakened genetic diversity of the saplings. In the MCM, F. hayatae is generally distributed on mountains or ridges at an altitude of 1500–1900 m, and its subpopulations are separated by ridges and streams. In addition, the intensification of human disturbance accelerates the process of habitat fragmentation. Hence, the lower genetic diversity of saplings within the population may be attributed to a combination of the unique topographic structure of the MCM and habitat fragmentation caused by anthropogenic disturbances. Excluding Null alleles by strict SSR primer selection, the fixation index (FI) was greater than 0. The result can usually be explained by the Wahlund effect and the mating system [ 11 ] . The genetic differentiation coefficient (FST) between patches (Supplementary Table S2) indicated that the genetic differentiation among four patches was not closely with distance, suggesting no Wahlund effect. Thus, mating among relatives and/or selfing ocurred in F. hayatae population. Consistently, the average inbreeding coefficient (FIS > 0) (Supplementary Table S3) also suggested inbreeding in F. hayatae population. Continued inbreeding causes depression, resulting in reduced seed fitness and population decline. 3.2 Fine-scale SGS The F(1) value of F. hayatae in MCM (0.1084) within 10 m was slightly lower than that of half-siblings (0.125) but higher than that of cousins (0.0625), suggesting a close kinship among individuals within the population. In the range of 10–40 m, the value of Fij was > 0.02, and significant SGS was detected within the population; beyond 40 m, Fij was less than 0.02, and no significant SGS was detected. Thus, the effective SGS distance within F. hayatae population in the MCM was 40 m. Sp reflects the level of population SGS. The value of Sp within F. hayatae population was 0.0157, which was higher than the mean values of Sp for outcrossing plants (0.0126), trees (0.0102), wind-pollinated plants (0.0054), and lower than gravity-borne plants (0.0281) [ 7 ] . In addition, the value of Sp within F. hayatae was higher than those of other endangered species such as Ulmus chenmoui (0.0107) [ 39 ] and Pteroceltis tatarinowii (0.0140) [ 40 ] , and lower than Sinojackia huangmeiensis (0.0281) [ 41 ] , Ulmus gaussenii (0.0293) [ 39 ] (He, 2016), and Tetracentron sinense (0.0305) [ 42 ] , indicating an intermediate level. Hardy et al. [ 43 ] found that restricted gene flow is the most important cause of SGS within populations and that the stronger the restriction of gene exchange between individuals, the higher the degree of fine-scale SGS. Ueno et al. [ 44 ] found that the seeds of species diffused by gravity are mostly distributed near their maternal trees; therefore, it is easy to produce an aggregated distribution of related individuals, thereby forming strong SGS within the population. So we hypothesized that the negative effects of restricted seed dispersal are partially offset by abundant gene flow (Nm > 1) (Supplementary Table S3), which might be responsible for intermediate level SGS within the population. In the MCM, significant SGS was detected in the sapling population of F. hayatae within 50 m, which may be attributed to the characteristics of seed dispersal by gravity. In F. hayatae , seed dispersal by gravity resulted in an aggregated distribution near maternal trees of seeds and overlapping seed rain, enabling the maintenance of SGS in the sapling population [ 14 ] . This was confirmed by the low-level genetic diversity of F. hayatae saplings. At the patch level, significant SGS was detected in CPL and ZSB within 10 m, whereas weak SGS was detected in LLG, suggesting a difference in SGS among the patches. Of the three patches, the population densities of CPL (24.4 individuals/ha) and ZSB (30.6 individuals/ha) were significantly greater than that of LLG (12.1 individuals/ha), which is not favorable for pollen and seed dispersal [ 45 ] , resulting in an increase in SGS within patches. In addition, high population densities are often accompanied by accumulated defoliation, which not only creates a physical barrier between seed and soil, but is also accompanied by extinction effects, and microbial pathogen impacts. The potency of these effects increases with the thickness of the humus layer, thus weakening of seed germination rate [ 46 ][ 47 ] . The higher growth destinies were able to be noticed at ZSB and CPL in our study. Therefore, we speculate that this may also contribute to the intensity of SGS. 3.3 Gene flow Gene flow levels were obtained by estimating the pollen and seed flows of F. hayatae in MCM. The mean dispersal distance of pollen and seed were 83.04 m and 30.14 m, respectively. The mean effective dispersal distance of genes within population was calculated to be 66 m (dg = 44.09–99.66m), which was lower than some wind-pollinated trees such as Handeliodendron bodinieri (d g = 200–400 m) [ 48 ] and Neobalanocarpus heimii (d p = 191.2 m) [ 49 ] , but higher than other wind-pollinated trees such as P.tatarinowii (d g = 44.79–77.96 m) [ 40 ] and T.sinense (d g = 21.62–70.37 m) [ 42 ] . The results showed that the effective dispersal distance of F. hayatae gene in MCM was at an intermediate level, and its gene flow was somewhat limited during dispersal. The limiting gene flow of F. hayatae might be attributed to the combination effects of its biological characteristics, topography, and pollution density: (1) F. hayatae is a long-lived tall tree characterized by monoecious, wind-pollination, and heterogamous, facilitating the effect on the exchange of genes in the population. Compared to P. tatarinowii and T. sinense , adult trees of F. hayatae can reach up to 20 m in height with a large crown, which provides an excellent basis for the long-distance dispersal of pollen. However, its seeds are mainly dispersed by gravity, which limits seed dispersal to some extent, resulting in limited gene flow. (2) The F. hayatae population in the MCM was blocked by ridges and streams, which exacerbated habitat fragmentation of the population, making it difficult for pollen and seeds to achieve long-distance migration. Therefore, the unique topography of Micang Mountain partially limits the effective transmission of gene flow. (3) Compared with LLG, the higher population densities of CPL and ZSB caused a greater restriction on the dispersal of pollen and seeds, resulting in restricted gene flow. 3.4 Reasons for fine-scale SGS of F.hayatae The formation of an FSGS is usually determined by multifaceted factors. Limited gene flow is the most dominant factor contributing to FSGS for F.hayatae in MCM. A convincing fact is that the gene flow dispersal distance of LLG was larger than that of ZSB and CPL and only weak SGS was detected, whereas significant SGS was detected in ZSB and CPL. Specifically, limited seed flow is more involved in the formation of FSGS in MCM F. hayatae population. In this population, the effective distance of seed was short (30.14 m), and most seeds dispersed around their maternal plants, resulting in stronger SGS and lower genetic diversity of saplings compared with mature and old trees. Seeds contribute more to fine-scale SGS formation as diploid than haploid pollen because only seeds can germinate, mature, and provide a biological basis for pollen dispersal [ 43 ] . Thus, restricted seed flow is a decisive factor in the formation of fine-scale SGS of F. hayatae population in MCM. In addition, geographic barriers formed by ridges and streams and biological barriers consisting of population density and deadfall in microhabitats prevent dispersal of gene flow and thus promote the formation of FSGS. 3.5 Implication for conservation Because significant SGS were detected in population within 40 m, the sampling distance between individuals should be greater than 40 m to ensure the richness of the germplasm gene pool during genetic sampling or seed collection for ex situ conservation of F. hayatae . Additionally, there is a certain degree of restricted gene flow among patches of F. hayatae , especially the seed flow, seed dispersal between patches can be facilitated by artificial means to weaken SGS within-patches. 4. Conclusion In this study, 10 pairs of microsatellite (SSR) primers were used to analyze the fine-scale spatial genetic structure and gene flow of F. hayatae population in MCM. The results suggest that the genetic diversity of this population was relatively low (He = 0.639) and there was inbreeding or autogamy among individuals of the population; thus, apparent SGS and a certain degree of limited gene flow were detected, which may be attributed to seed dispersal restriction, habitat fragmentation and microhabitats. The SGS varied in different patches owing to population density and habitat fragmentation, whereas the limited seed dispersal of F. hayatae resulted in higher SGS in saplings than in adult and old trees. There was an obvious limitation of gene flow in the population, especially seed flow, most of which was limited to within 60 m. Biological characteristics, topography, and plant population density were the main factors affecting the current pattern of gene dispersal in F. hayatae , and dispersal distance decreased with increasing population density. The spatial genetic structure at a fine scale was 40 m, and individuals within that range were closely related. Therefore, during ex situ protection of F. hayatae , the sampling distance between individuals should be greater than 40 m to ensure complete genetic efficiency. 5. Materials and methods 5.1 Study site MCM is located in the northeast of Wangcang County, China, which belongs to the northeastern edge of Sichuan Basin (N32°29'–32°41', E106°24'–106°39') (Fig. 5 a). MCM is characterized by the relative height difference of 1711 m, annual mean temperature from 13.5–16.5 ℃, the annual frost-free period of 260 d, the annual precipitation from 1100–1400 mm, and the average annual light duration of 1352.52 h. MCM is a transitional area from subtropical to warm temperate zone, wherein plants and habitats are featured by remarkable transition. The vertical band spectrum of vegetation was more evident, including evergreen broad-leaved, evergreen and deciduous broad-leaved mixed, deciduous broad-leaved, evergreen needle-leaved, and coniferous-deciduous-broad-leaved mixed forests [ 50 ] . F. ayatae is mainly distributed in deciduous broad-leaved forests (altitude: 1500–1900 m) [ 23 ] . 5.2 Field investigation and sampling After conducting a comprehensive survey of F. hayatae distribution in the MCM, four patches of F. hayatae with relatively complete age-class structure were determined, with areas of 200 × 140 m (Laolingou, LLG), 120 × 80 m (Tabahe, TBH), 140 × 120 m (Changpingli, CPL), and 160 × 90 m (Zhongshanbao, ZSB) (Fig. 5 b). These four patches are separated by natural ridges or streams, with a geographical distance of greater than 350 m. Specific geographic information for each patch was shown in Table 7 . The geographic and phenological information of all F. hayatae individuals in each patch was recorded (Fig. 6 ), and 4–6 intact young leaves without pests were collected from each individual and placed in a sealed bag containing dry discoloration silica gel to dry over time. Afterwards, these sampling materials were brought back to laboratory and stored in a refrigerator at − 80 ℃. Table 7 Sampling information of F. hayatae individuals Patches Latitude/N Longitude/E Altitude/m Number LLG 32°39.4136′-32°39.5433′ 106°33.38.71′-106°33.4590′ 1750–1825 34 TBH 32°39.7021′-32°39.7425′ 106°33.45.51′-106°33.5160′ 1748–1808 38 CPL 32°39.3246′-32°39.7215′ 106°33.66.20′-106°33.7303′ 1742–1780 41 ZSB 32°39.0000′-32°39.6464′ 106°33.42.24′-106°33.4997′ 1773–1790 43 Following the method of Li et al. [ 51 ] , all individuals in each patch were divided into three age-classes: saplings (DBH < 7.5 cm), adult trees (7.5 cm ≤ DBH < 22.5 cm), and old trees (DBH ≥ 22.5 cm)(Fig. 6 ). 5.3 Extraction of Genomic DNA from F. hayatae Total DNAs was isolated from the leaves using the method described by Li et al. [ 52 ] , and its concentration and purity were detected using a NanoDrop 2000 Microvolume Spectrophotometer. Twenty pairs of primers were synthesized using 20 pairs of SSR sequences of Fagus species reported in the NCBI database ( www.ncbi.nlm.nih.gov/nucleotide?term= txid133895 [Organism]) and 36 pairs of SSR primers from Fagus published by Ju et al. [ 21 ] were used for primer screening. The DNA template of each sample was PCR-amplified with each primer pair, followed by capillary gel electrophoresis to obtain electrophoretic peak maps (Fig. S1 ). Finally, ten pairs of SSR primers with significant absorption peaks were selected for subsequent experiments. 5.4 Genetic diversity analysis nSSR loci were detected using null alleles and polymorphic information content (PIC) from CERVUS3.0 software [ 53 ] . Genetic diversity parameters among populations and among patches within populations were calculated using PopGene 32 software [ 54 ] . The values of allele (N a ), effective allele number (N e ), observed heterozygosity (H o ), unbiased expected heterozygosity (uH e ), and fixation index (FI) of each patch were calculated using GenAlEX6.505 software [ 55 ] . Genetic diversity indicators (Na, Ne, He, Ho, FI), population-level gene flow (Nm) and inbreeding coefficient (FIS, FST) of the populations were also calculated by GenAlEX 6.505 software [ 56 ] . The parameter values for both computational simulations and permutation significance detection were set to 999. FIS=(Mean He-Mean Ho)/Mean He (1) FST=(Ht-Mean He)/Ht (2) Nm=(1-Fst)/4Fst (3) Where the Ht is total expected heterozygosity. 5.5 Fine-scale SGS analysis The SPAGeDi1.3 was employed to test the spatial genetic structure and calculate the Nason’s value (Fij) of individuals based on multiloci that represent the relationship coefficient [ 57 ] , where the Fij value indicates that the genotypes of random samples i and j have the same probability. The standard deviation and 95% confidence interval of Fij were calculated by 999 simulations. The method was simple and could reduce the deviation caused by distance grouping [ 57 ] . Statistical effectiveness requires more than 30 pairs of individual data per distance level for analysis [ 58 ] . Hence, the distance level of less than 30 pairs of individuals was abandoned in this analysis [ 7 ] . Based on the Fij value, we calculated Sp to estimate the SGS within the population and patches. The formula is Sp= - bF /(1 − F(1)) (4) where bF refers to the linear regression slope of the genetic relationship to the natural logarithm of the distance level and F(1) is the average genetic relationship between individuals at the first distance level [ 42 ] . 5.6 Parental analysis and gene flow The CERVUS software (version 3.0) was used for gene flow estimation and parental analysis [ 59 ] . Parental analysis in each patch was performed using the saplings within the patch as offspring and adult and old trees as relatives. Because the sex of both parents of F. hayatae is unknown, the combination with the highest and most significant Trio LOD score was considered the best parental combination for parental analysis. Simulation parameters were set as follows: the proportion of candidate parents was 0.9, the average genotyping error rate was 0.01, and the locus mismatch rate was 0.01, with reference to the default 80% confidence level. Subsequently, the geographic distance matrix among individuals in each patch was calculated using GenAlEx6.5. The operative diffusion distance of gene flow, denoted by d g , is calculated as follows: d g 2 =d s 2 + 0.5 d p 2 (5) where d s and d p are the average diffusion distance of seed and pollen, respectively. Declarations Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution J.Y.C. Formal analysis, Writing-original draft, Writing review & editing. G.X. Investigation, Formal analysis. C.Y.J. Investigation, Data curation, Formal analysis. X.M.Z. Data curation, Formal analysis. H.Y.H. Data curation, Funding acquisition. Q.X.Y. Investigation. K.T. Investigation. X.H.G. Conceptualization, Writing review & editing, Funding acquisition, Supervision. All authors reviewed the manuscript. Acknowledgements We thank all students (Fan Duan, Huan Zhang, Yinshu Gong, Xue Wang, Rui Chen) and Xuewu Feng (Micang Mountain National Nature Reserve in Sichuan Province) who help to collect and analyze date. Founding was provided by National Nature Science Foundation of China (No.32070371), the Innovation Team Funds of China West Normal University (KCXTD2022-4), and the Natural Science Foundation of Sichuan Province (No. 23NSFSC1272). Data Availability Data is provided within the manuscript or supplementary information files. All datasets are available from the corresponding author on reasonable request. Additional information The plant materials collected and the plants experiments in this study full complied with relevant institutional, national, and international guidelines and legislation. All field investigation and Fagus hayatae Palib. ex Hayata collected obtained permissions. References Pimm S.L., Jenkins C.N., Abell R., Brooks T.M., Gittleman J.L., Joppa L.N., et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science. 344, 1246752 (2014). Ryan J., Mellish S., Dorrian J., Winefield T., Litchfield C. Effectiveness of biodiversity-conservation marketing. Conversation Biology. 34, 354–367 (2020). Ellegren H. and Galtier N. Determinants of genetic diversity. Nature review genetics . 17, 422 – 33 (2016). Yang Q., Fu Y., Wang Y.Q., Wang Y., Zhang W.H., Li X.Y., et al. Genetic diversity and differentiation in the critically endangered orchid ( Amitostigma hemipilioides ): implications for conservation. Plant Systematic and Evolution, 300, 871–879 (2014). Ennos R.A. and Clegg M.T. Effect of population substruc turing on estimates of outcrossing rate in plant population. Heredity. 48, 283–292 (1982). Young A.G., Boshier D. of referencing in Forest Conservation Genetics: Principles and Practice (ed. Young A.G., Boshier D. and Boyle T.J)123–134 (CSIRO Publishing, Melbourne, 2000). Vekemans, X., Hardy, O.J. New insights from fine-scale spatial genetic structure analyses in plants populations. Molecular Ecology. 13, 921–935 (2004). Chung, M.Y., Nason, J.D., Chung, M.G. Spatial genetic structure in populations of the terrestrial orchid Cephalanthera longibracteata (Orchidaceae). American Journal of Botany. 91, 52–57 (2004). Wright, S. Isolation by distance. Genetics. 28, 114–138 (1943). Hamrick, J.L., Godt, M.J.W. Effects of Life History Traits on Genetic Diversity in Plant Species. The Royal Society. 351, 1291–1298 (1996). De-Lucas, A.I., González-Martínez, S.C., Vendramin, G.G., Hidalgo, E., Heuertz, M. Spatial genetic structure in continuous and fragmented populations of Pinus pinaster Aiton. Molecular Ecology. 18, 4564–4576 (2010). DeSilva R., Dodd R.S. Fragmented and isolated: limited gene flow coupled with weak isolation by environment in the paleoendemic giant sequoia (Sequoiadendron giganteum). American Journal of Botany. 107, 45–55(2020). Angbonda D.M.A., Monthe F.K., Bourland N., Boyemba F., Hardy O.J. Seed and pollen dispersal and fine-scale spatial genetic structure of a threatened tree species: Pericopsis elata (HARMS) Meeuwen (Fabaceae). Tree Genetics & Genomes. 17, 27 (2021). Chung, M.Y., Epperson, B.K., Chung, M.G. Genetic structure of age classes in Camellia japonica (Theaceae). Evolution. 57, 62–73 (2003). Berg E.E and Hamrick J.L. Spatial and genetic structure of two sandhills oaks: Quercus laevis and Quercus margaretta (Fagaceae). American Journal of Botany. 81, 7–14 (1994). Sokal R.R and Wartenberg D. A test of spatial autocorrelation analysis using an isolation-by-distance model. Genetics. 105, 219–237 (1983). Willi, Y., Määttänen, K. The relative importance of factors determining genetic drift: Mating system, spatial genetic structure, habitat and census size in Arabidopsis lyrata . New Phytologist. 189, 1200–1209 (2011). Zhang, X.M. Research Progress of Fagus hayatae : Plant of the Second-class Protection in China. Helongjiang Agricultural Sciences. 5, 148–151 (2017). Shen, Z.H., Fang, J.Y., Chiu, C.A., Chen, T.Y. The geographical distribution and differentiation of Chinese beech forests and the association with Quercus . Applied Vegetation Science. 18, 23–33 (2015). Zhang F.G. The community characteristics of the Taiwan beech forest of Qingliangfeng Mountain in Zhejiang. Journal of Zhejiang University. 27, 403–406 (2001). Ju, L.P., Shih, H.C., Chiang, Y.C. Microsatellite primers for the endangered beech tree, Fagus hayatae (Fagaceae). American Journal of Botany. 99, 453–456 (2012). He, J, Wang, ZX, Lei, Y, Li, ZQ, Zhang, L, Man, J.S. The study on coenological characteristics of Fagus hayatae community in Qizimei mountain natural reserve. Journal of Huazhong Normal University(Nat. Sci). 42, 272–277 (2008). Li, D.D., Xu, X., Shi, Q.M., Chen, J., Zan, X., Wu, D.J., et al. Characteristics of Fagus hayatae Community along Altitudinal Gradient in Micangshan Nature Reserve, Sichuan. Journal of Tropical and Subtropical Plants. 24, 626–634 (2016). Li, J.X., Wu, D.J., Zhang, S.P., He, X.X., Chen, J., Shi, Q.M., et al. Life Table and Dynamic Analysis of Fagus hayatae Population in Micangshan Nature Reserve, Sichuan Province, China. Bulletin of Botanical Research. 36, 68–74 (2016). Zhao Y.F. Conversation genetics of Fagus hayatae Palibin. Master , Jiangxi Agricultural University. (2023). Kato S., Koike T., Lei T.T., Hsieh C.F., Ueda K., Mikami T. Analysis of mitochondria DNA of an endangered beech species, Fagus hayatae Palib. ex Hayata. New Forests. 19, 109–114 (1999). Ying, L.X., Zhang, T.T., Chiu, C.A., Chen, T.Y., Luo, S.J., Chen, X.Y., et al. The phylogeography of Fagus hayatae (Fagaceae): genetic isolation among populations. Ecology and Evolution. 6, 2805–2816 (2016). Nei, M. Genetic distance between populations. Am. Nat. 106, 283–292 (1972). Hamrick, J.L., Godt, M.J.W. Allozyme diversity in plant species. Plant Population Genetics, Breeding and Genetic Resources . 43–63 (1989). Qian, W., Ge, S., Hong, D.Y. Genetic variation within and among populations of a wild rice Oryza granulata from China detected by RAPD and ISSR markers. Theoretical and Applied Genetics. 102, 440–449 (2001). Buzatti R.S.D.O., Ribeiro R.A., Filho J.P.D.L., Lovato M.B. Fine-scale spatial genetic structure of Dalbergia nigra (Fabaceae), a threatened and endemic tree of the Brazilian Atlantic Forest. Genetics and Molecular Biology. 35, 838–846 (2012). Baldauf C., Guillardi M.C., Aguirra T.J., Correˆa C.E., Santos F.A.M.D. Souza A.P.D. Genetic diversity, spatial genetic structure and realised seed and pollen dispersal of Himatanthus drasticus (Apocynaceae) in the Brazilian savanna. Conservation Genetics. 15, 1073–1083 (2014). Curtu A.L., Craciunesc L., Enescu C.M., Vidalis A., Sofletea N. Fine-scale spatial genetic structure in a multi-oak-species ( Quercus spp. ) forest. Biogeosciences and Forestry. 8, 324–332 (2015). Gao, S.H., Wang, Z.F., Zhang, J.L., Tian, S.N. Genetic Diversity of Cryptocarya Chinensis Life Stages in Dinghu Mountain China. Acta Scientiarum Naturalium Universitatis Sunyatseni. 44, 209–212 (2005). Wu, Z.Y., Liu, J.F., Hong, W., Pan, D.M., Zheng, S.Q., He, Z.S. Genetic diversity of different life-stage population of Glyptostrobus pensilis , an endangered plant in China: ISSR analysis. Chinese Journal of Ecology. 31, 1911–1916 (2012). Wang, S.Z., Zhang, L., Yang W., Lou, Y., Zheng, Z., Fang, Y.P., et al. Genetic Diversity of Rhododendron simsii Populations on Dabieshan at Different Life Stages Based on SSR Markers. Forest Research. 31, 125–130 (2018). Czech, B., Krausman, P.R. Distribution and causation of species endangerment in the united states. Science. 277, 1116–1117 (1997). Li, J.H., Jin, Z.X., Li, J.M. RAPD and ISSR analysis on genetic diversity of different life stages in the population of Torreya jackii an endangered plant in China. Journal of Zhejiang University(Science Edition). 37, 104–111 (2010). He, J. Population dynamics and fine-scale spatial genetic structure of Ulmus chenmoui and Ulmus gaussenii , endangered species endemic to China. Master , Nanjing university. (2016). Yang, J. Development of ploymorphic microsatellite loci and study on fine-scale spatial genetic structure of Pteroceltis tatarinowii , an endangered plant endemic to China. Master , Nanjing university. (2016). Ruan, Y.M., Zhang, J.J., Yao, X.H., Ye, Q.G. Genetic diversity and fine-scale spatial genetic structure of different life-history stages in a small, isolated population of Sinojackia huangmeiensis (Styracaceae). Biodiversity Science. 20, 460–469 (2012). Wang, X., Duan, F., Zhang, H., Han, H.Y., Gan, X.H. Fine-scale spatial genetic structure of the endangered plant Tetracentron sinense Oliv. (Trochodendraceae) in Leigong Mountain. Global Ecology and Conservation. 41, e02382 (2023). Hardy, O.J., Maggia, L., Bandou, E., Breyne, P., Caron, H., Chevalier, M.H., et al. Fine-scale genetic structure and gene dispersal inferences in 10 Neotropical tree species. Molecular Ecology. 15, 559–571 (2006). Ueno, S., Tomaru, N., Yoshimaru, H., Manabe, T., Yamamoto, S. Genetic structure of Camellia japonica L. in an old-growth evergreen forest, Tsushima, Japan. Molecular Ecology. 9, 647–656 (2000). Jacquemyn, H., Brys, R., Vandepitte, K., Honnay, O., Roldan-ruiz, I. Fine-scale genetic structure of life history stages in the food-deceptive orchid Orchis purpurea . Molecular Ecology. 15, 2801–2808 (2006). Wang, H.X., Li, G.Z., Yu, D.M., Chen, Y.M. Barrier effect of litter layer on natural regeneration of forest. Chinese Journol of Ecology. 27, 83–88 (2008). Zhu J., Liu J.F., He Z.S., Xin C., Wang X.L., Jiang L. Effects of physical barrier of litter on the seed germination and radicle growth of Castanopsis kawakamii . Acta Ecoligica Sinica. 40, 16, 5630–5637 (2020). He, R.K., Wang, J., Huang, H. Long-distance gene dispersal inferred from spatial genetic structure in Handeliodendron bodinieri , an endangered tree from karst forest in southwest China. Biochemical Systematics and Ecology. 44, 295–302 (2012). Konuma, A., Tsumura, Y., Lee, C.T., Lee, S.L., Okuda, T. Estimation of gene flow in the tropical-rainforest tree Neobalanocarpus heimii (Dipterocarpaceae), inferred from paternity analysis. Molecular Ecology. 9, 1843–1852 (2000). Chen, J. Report of investigation on Fagus of Micangshan Nature Reserve. Chinese Wild Plant Resource, 33, 47–52 (2014). Li, D.D., Dong, T.F., Chen, J., Shi, Q.M., He, X.X., Zhang, S.P., et al. Characteristics of Fagus hayatae Community and Species Diversity in Micangshan Nature Reserve,Sichuan. Acta Botanic Boreali-Occidentalia Sinica. 36, 174–182 (2016). Li, S., Gan, X.H., Han, H.Y., Zhang, X.M., Tian, Z.Q. Low within-population genetic diversity and high genetic differentiation among populations of the endangered plant Tetracentron sinense Oliver revealed by inter-simple sequence repeat analysis. Annals of Forest Science. 75, 74–85 (2018). Kalinowski, S.T., Taper, M.L., Marshall, T.C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology. 16, 1099–1106 (2007). Yeh, F.C., Yang, R.C., Boyle, T.B.J., Ye, Z., Xiyan J.M. PopGene32, Microsoft windows-based freeware forpopulation genetic analysis. Version 1.32. Molecular Biologyand Biotechnology Centre, University of Alberta: Edmonton, Canada. (2000). Peakall, R., Smouse, P.E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics. 28, 2537–2539 (2012). Peng, G., Tang, S. Fine-scale spatial genetic structure and gene flow of Camellia flavida, a shade-tolerant shrub in karst. Acta Ecol. Sin. 37, 7313–7323 (2017). Hardy, O.J., Vekemans, X. Spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes. 2, 618–620 (2002). Peakall, R., Smouse, P.E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes. 6, 288–295 (2006). Sato, T., Isagi, Y., Sakio, H., Osumi, K., Goto, S. Effect of gene flow on spatial genetic structure in the riparian canopy tree Cercidiphyllum japonicum revealed by microsatellite analysis. Heredity. 96, 79–84 (2006). Additional Declarations No competing interests reported. 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01:40:57\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"supplement\",\"size\":226329,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"SupplementaryInformation.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-4617989/v1/8b8f766058a588e03f9d91e4.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Combining restricted gene flow, local microhabitat, and habitat fragmentation shapes the fine-scale spatial genetic structure of Fagus hayatae Palib. ex Hayata in Micang Mountain\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eBiodiversity is the basis for human survival and development. However, human activities accelerated the deterioration of the ecological environment, resulting in the rapid decline of biodiversity and the endangerment of a large number of plants\\u003csup\\u003e[\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e]\\u003c/sup\\u003e. Thus, the germplasm resource conservation of endangered plants has been widely concerned. Genetic diversity is generally dependent on biological evolution, and the level of genetic diversity is a product of long-term evolution and has a huge impact on the survival and evolution of species\\u003csup\\u003e[\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e]\\u003c/sup\\u003e. A thorough understanding of the evolutionary history and regeneration mechanisms of endangered plants is essential for developing practical and feasible conservation and genetic management strategies\\u003csup\\u003e[\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003cp\\u003eThe knowledge of the genetic structure and genetic diversity of the species can provide reasonable and effective genetic parameters for species conservation\\u003csup\\u003e[\\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e]\\u003c/sup\\u003e. As sessile organisms, plants often exhibit limited dispersal, resulting in a decay of kinship among related individuals over short distances\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. Consequently, plant genotypes are distributed non-randomly on a fine scale, which can be defined as a fine-scale spatial genetic structure (FSGS)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e8\\u003c/span\\u003e]\\u003c/sup\\u003e. SGS has profound effects on offspring fitness, intraspecific competition, and long-term evolutionary potential of populations in response to changing habitat, by altering the pattern of mating and genotype distribution within populations.\\u003c/p\\u003e \\u003cp\\u003eThe strength of an FSGS is usually determined by the interaction between evolutionary and ecological factors. The key influencing factor is gene flow restriction, which creates patterns of isolation based on the distance between the parents and offspring\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e]\\u003c/sup\\u003e. Gene flow usually consists of seed and pollen flow, and the intensity of SGS caused by restricted gene flow is regulated by the mating system of the species\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e]\\u003c/sup\\u003e. When pollen dispersal is restricted within a population, offspring are more likely to come from siblings and half-siblings\\u003csup\\u003e[\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e]\\u003c/sup\\u003e. In contrast, pollen from wind-borne pollinators is characterized by long-distance and non-directional dispersal, and seed dispersal affects the genetic structure of populations more than pollen movement\\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e. When long-distance pollen movement occurs, restricted seed dispersal might result in the grouping of half-sibs near to their maternal plant\\u003csup\\u003e[\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e]\\u003c/sup\\u003e. When both pollen and seed diffusion are confined, populations would form high levels of FSGS and inbreeding, which may be more vulnerable to genetic diversity loss from random events such as genetic interference or drift\\u003csup\\u003e[\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e]\\u003c/sup\\u003e. Hence, understanding the FSGS patterns of rare plants contributed to reveal the mode of pollen and seed dispersal that has occurred in the population, as well as the possible limiting factors of pollen and seed flow, so as to formulate conservation strategies and management plans for the species.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e Palib. ex Hayata, a relict species, is a unique tall deciduous tree in the Fagaceae family, which is mainly distributed in Taiwan, Hubei, Sichuan, Gansu, Shaanxi, and Zhejiang in China\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. As the only species in \\u003cem\\u003eFagus\\u003c/em\\u003e with discontinuous distribution from mainland subtropical mountains to Taiwan island, \\u003cem\\u003eF. hayatae\\u003c/em\\u003e is of great significance to study the flora connection and the relationship of vegetation type between mainland China and Taiwan\\u003csup\\u003e[\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e]\\u003c/sup\\u003e. Growing in 1000\\u0026thinsp;~\\u0026thinsp;2300 m, it is an important component of mixed evergreen deciduous broad-leaved forest and mountain deciduous broad-leaved forest, which is of great value to maintaining the local ecosystem\\u003csup\\u003e[\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e]\\u003c/sup\\u003e. Owing to its tall and beautiful tree shape and excellent material, \\u003cem\\u003eF. hayatae\\u003c/em\\u003e is valuable in wood and afforestation\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e]\\u003c/sup\\u003e. Recently, due to global climate warming and human disturbance, its habitat fragmentation has intensified, resulting in poor natural regeneration. Thus, it has been listed as the second national key protection of wild plants in China, and also listed as a vulnerable species by IUCN\\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e. The conservation of its germplasm resources has always been valued by ecologists and conservation biologists\\u003csup\\u003e[\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e22\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e]\\u003c/sup\\u003e. Previous studies showed that level of genetic diversity within \\u003cem\\u003eF. hayatae\\u003c/em\\u003e populations was relatively lower\\u003csup\\u003e[\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. However, the reasons resulting in low-level genetic diversity within populations remain unclear to date. We hypothesize that due to habitat fragmentation, there is limited gene flow mediated by pollen flow and seed flow within the natural \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population, which in turn leads to low level of genetic diversity. Currently, studies related to the FSGS and gene flow in \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population have not been reported.\\u003c/p\\u003e \\u003cp\\u003eTherefore, we applied microsatellite (SSR) markers to analyzed the fine-scale spatial genetic structure and its gene flow of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in the Micang Mountain Natural Reserve (MCM), China. We sought to answer the following questions: (1) Is there a FSGS of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in MCM? If so, how is its strength? (2) What are the dispersal patterns of pollen and seeds in \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population? Is there gene-flow limitation mediated by pollen or seed within the population? (3)What contributed to the current genetic pattern of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e in the MCM?\\u003c/p\\u003e\"},{\"header\":\"2. Results\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1 Genetic diversity analysis\\u003c/h2\\u003e \\u003cp\\u003e \\u003cem\\u003enSSR primer analysis\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eNo null alleles were detected during the amplification of the 10 primers and samples, and the overall mean PIC value (0.622) was greater than 0.5, indicating that these primers have high polymorphism and are suitable for population genetic analysis. A total of 85 alleles were detected using 10 SSR primer pairs. The overall N\\u003csub\\u003ea\\u003c/sub\\u003e, N\\u003csub\\u003ee\\u003c/sub\\u003e, H\\u003csub\\u003eo\\u003c/sub\\u003e, and H\\u003csub\\u003ee\\u003c/sub\\u003e were 8.5, 3.425, 0.438, and 0.639, respectively (Supplementary Table \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Therein, except for TB-140 and TB-142, H\\u003csub\\u003eo\\u003c/sub\\u003e are overall less than H\\u003csub\\u003ee\\u003c/sub\\u003e, suggesting inbreeding within the MCM \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population.\\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003egenetic diversity at different age-class\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt the age-class level within the population, N\\u003csub\\u003ea\\u003c/sub\\u003e ranged from 6.300 (saplings) to 7.200 (old trees), with an average of 6.733; N\\u003csub\\u003ee\\u003c/sub\\u003e ranged from 3.076 (saplings) to 3.878 (old trees), with an average of 3.544; H\\u003csub\\u003eo\\u003c/sub\\u003e ranged from 0.404 (saplings) to 0.457 (adult trees), with an average of 0.434; H\\u003csub\\u003ee\\u003c/sub\\u003e ranged from 0.599 (saplings) to 0.675 (old trees), with an average 0.654 (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Be it the results of H\\u003csub\\u003eo\\u003c/sub\\u003e or H\\u003csub\\u003ee\\u003c/sub\\u003e, all indicates the lowest genetic diversity existing in \\u003cem\\u003eF. hayatae\\u003c/em\\u003e saplings, compared with adult and old trees.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eGenetic diversity of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e among age-classes Note: The \\u0026ldquo;Number\\u0026rdquo; denotes the sample size of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e individuals in current age. The numbers of N\\u003csub\\u003ea\\u003c/sub\\u003e, N\\u003csub\\u003ee\\u003c/sub\\u003e,H\\u003csub\\u003eo\\u003c/sub\\u003e and H\\u003csub\\u003ee\\u003c/sub\\u003e, are the average values of the corresponding parameters at the age class, and the numbers in brackets are standard errors.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"6\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge classes\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eN\\u003csub\\u003ea\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eN\\u003csub\\u003ee\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eH\\u003csub\\u003eo\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eH\\u003csub\\u003ee\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSaplings\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.300(0.943)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.076(0.445)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.404(0.107)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.599(0.070)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAdult trees\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e56\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.700(0.803)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.708(0.617)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.457(0.103)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.660(0.054)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eOld trees\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e65\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e7.200(0.757)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.878(0.714)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.442(0.089)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.675(0.052)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMeans\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e52\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e6.733(0.834)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.554(0.592)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.434(0.100)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.645(0.059)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cem\\u003efine-scale genetic diversity\\u003c/em\\u003e \\u003c/p\\u003e \\u003cp\\u003eAt the patch level, N\\u003csub\\u003ea\\u003c/sub\\u003e ranged from 5.900 (CPL) to 6.800 (TBH), with an average of 6.225; N\\u003csub\\u003ee\\u003c/sub\\u003e ranged from 3.193 (CPL) to 3.565 (TBH), with an average of 3.425; H\\u003csub\\u003eo\\u003c/sub\\u003e ranged from 0.418 (LLG) to 0.462 (TBH), with an average of 0.438. The expected heterozygosity value after Nei\\u0026rsquo;s correction of the small population(uH\\u003csub\\u003ee\\u003c/sub\\u003e)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e]\\u003c/sup\\u003e ranged from 0.621 (CPL) to 0.690 (TBH), with an average of 0.648. The average FI value of the four patches is 0.333. Based on H\\u003csub\\u003eo\\u003c/sub\\u003e, the maximum level of genetic diversity appeared in TBH, followed by ZSB, CPL, and LLG. In addition, the degree of genetic diversity is positively correlated with the growth density of the \\u003cem\\u003eF. hayatae\\u003c/em\\u003e in each patch (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eGenetic diversity of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e among patches Note: The \\u0026ldquo;Number\\u0026rdquo; represents the sample size of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e, and the D represents the population density (tree/ha), the numbers of N\\u003csub\\u003ea\\u003c/sub\\u003e, N\\u003csub\\u003ee\\u003c/sub\\u003e, H\\u003csub\\u003eo\\u003c/sub\\u003e, uH\\u003csub\\u003ee\\u003c/sub\\u003e and FI, are the average values of the corresponding parameters at the patch, and the numbers in brackets are standard errors.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"8\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePatch\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eD\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eN\\u003csub\\u003ea\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eN\\u003csub\\u003ee\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eH\\u003csub\\u003eo\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003euH\\u003csub\\u003ee\\u003c/sub\\u003e\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eFI\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.100\\u003c/p\\u003e \\u003cp\\u003e(0.809)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.444\\u003c/p\\u003e \\u003cp\\u003e(0.649)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.418 (0.092)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.652\\u003c/p\\u003e \\u003cp\\u003e(0.047)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.375\\u003c/p\\u003e \\u003cp\\u003e(0.139)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTBH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e39.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.800\\u003c/p\\u003e \\u003cp\\u003e(0.593)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.565\\u003c/p\\u003e \\u003cp\\u003e(0.487)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.462 (0.096)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.690\\u003c/p\\u003e \\u003cp\\u003e(0.036)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.344\\u003c/p\\u003e \\u003cp\\u003e(0.140)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCPL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e24.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e5.900\\u003c/p\\u003e \\u003cp\\u003e(1.027)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.193\\u003c/p\\u003e \\u003cp\\u003e(0.473)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.422 (0.101)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.621\\u003c/p\\u003e \\u003cp\\u003e(0.074)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.328\\u003c/p\\u003e \\u003cp\\u003e(0.134)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eZSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e44\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e30.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.100\\u003c/p\\u003e \\u003cp\\u003e(1.005)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.496\\u003c/p\\u003e \\u003cp\\u003e(0.572)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.449 (0.105)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.627\\u003c/p\\u003e \\u003cp\\u003e(0.079)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.282\\u003c/p\\u003e \\u003cp\\u003e(0.144)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003emeans\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e39.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e26.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e6.225\\u003c/p\\u003e \\u003cp\\u003e(0.424)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3.425\\u003c/p\\u003e \\u003cp\\u003e(0.265)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.438 (0.048)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.648\\u003c/p\\u003e \\u003cp\\u003e(0.030)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.333\\u003c/p\\u003e \\u003cp\\u003e(0.067)\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2 FSGS analysis\\u003c/h2\\u003e \\u003cp\\u003eWhen the distance between individuals within a population was \\u0026lt;\\u0026thinsp;10 m, F(1) was 0.1084, which close to half-sib (0.125). When the distance ranged from 10\\u0026ndash;40 m, Fij was \\u0026gt;\\u0026thinsp;0.02 and \\u0026lt;\\u0026thinsp;0.1084 with a decreasing tendency, indicating prominent SGS. When the distance ranged from 40\\u0026ndash;150 m, Fij was \\u0026gt;\\u0026thinsp;0 and \\u0026lt;\\u0026thinsp;0.02 with a decreasing tendency, indicating no obvious SGS. When the distance exceeds 150 m, Fij was \\u0026lt;\\u0026thinsp;0, indicating a lack of SGS (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Based on F(1) and \\u003cem\\u003ebF\\u003c/em\\u003e, the calculated Sp value was 0.0157 at the population level.\\u003c/p\\u003e \\u003cp\\u003eAt the age-class level, the Sp values were 0.0434 (saplings), 0.0070 (adult trees), and 0.0194 (old trees) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). The F(1) values of saplings and adult trees were close to half-sib (0.125), whereas that of old trees was close to that of their cousins (0.0625)((Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). The Sp of saplings was the largest and was much larger than that of adult trees, whereas the Sp of old trees was close to that at the population level (0.0157) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSp at age-classe level. Note: The \\u003cem\\u003ebF\\u003c/em\\u003e refers to the linear regression slope of the genetic relationship to the natural logarithm of the distance level, the F(1) is the average genetic relationship between individuals at the first distance level, the Sp size represent the SGS strength of corresponding age stage. The same as below.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eSaplings\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eAdult trees\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eOld trees\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ebF\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.0398\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.0061\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.0182\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eF(1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.1002\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.1272\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0620\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0434\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0070\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0194\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe SGS analysis at patch level, TBH was excluded as only one sapling. Faint SGS was tested in the LLG, whereas remarkable SGS within 10 m was detected in the CPL and ZSB (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e), with Sp values of 0.0096 (LLG), 0.0236 (CPL), and 0.0171 (ZSB), respectively. Moreover, the F(1) values of CPL and ZSB were slightly above those of the cousins (0.0625), and those of LLG were far below those of the cousins. Among them, the Sp values of CPL and ZSB were slightly greater than those at the population level (0.0157), whereas the Sp value of LLG was lower (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSp at patch level\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLLG\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCPL\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eZSB\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cem\\u003ebF\\u003c/em\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.0095\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.0216\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.0158\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eF(1)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0081\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0826\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0725\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSp\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.0096\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.0236\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.0171\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3 Gene flow\\u003c/h2\\u003e \\u003cp\\u003eAmong the three patches, the average effective distance of gene diffusion was 66 m and the mean diffusion distances of seeds and pollen were 30.14 m and 83.04 m, respectively. These values were greater in the LLG than in the other two patches (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). The maximum diffusion distances of seeds (121.62 m) and pollen (179.92 m) were observed in the LLG. The minimum diffusion distance of seeds (1.57 m) was observed in the CPL, and the minimum diffusion distance of pollen (2.43 m) was detected in the ZSB (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Within the three patches, 93.2% of the seeds in 44 offspring were dispersed within 60 m, and 75% of the pollen was dispersed at a distance of 20\\u0026ndash;120 m (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab5\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 5\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMean dispersal distance of seed, pollen and gene among patches\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"4\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePatches\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMean distance of seed dispersal/m\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMean distance of pollen dispersal/m\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eEfficient distance of gene dispersal/m\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e43.30\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e122.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e96.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCPL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23.07\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e53.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e44.09\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eZSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e24.06\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e73.78\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e57.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eMean\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e30.14\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e83.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e66.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab6\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 6\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMaximum and minimum distance of seed and pollen dispersal\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePatches\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMax distance\\u003c/p\\u003e \\u003cp\\u003eof seed dispersal(m)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eMin distance\\u003c/p\\u003e \\u003cp\\u003eof seed dispersal(m)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eMax distance\\u003c/p\\u003e \\u003cp\\u003eof pollen dispersal(m)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eMin distance\\u003c/p\\u003e \\u003cp\\u003eof pollen dispersal(m)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e121.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.01\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e179.92\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e14.91\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCPL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e53.73\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e118.12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12.21\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eZSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e45.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.82\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e137.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2.43\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec7\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1 Genetic diversity\\u003c/h2\\u003e \\u003cp\\u003eH\\u003csub\\u003ee\\u003c/sub\\u003e is generally considered an important parameter for evaluating genetic diversity and comparing similar species\\u003csup\\u003e[\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e]\\u003c/sup\\u003e. In MCM, the H\\u003csub\\u003ee\\u003c/sub\\u003e value of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e was 0.639, which was quite close to the H\\u003csub\\u003ee\\u003c/sub\\u003e (0.642) detected by Ying et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e]\\u003c/sup\\u003e. The genetic diversity of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e was lower than that of the endangered tree, \\u003cem\\u003eDalbergia nigra\\u003c/em\\u003e (H\\u003csub\\u003ee\\u003c/sub\\u003e=0.74), and also lower than that of other trees \\u003cem\\u003eHimatanthus drasticus\\u003c/em\\u003e (H\\u003csub\\u003ee\\u003c/sub\\u003e= 0.711\\u0026thinsp;~\\u0026thinsp;0.743), and \\u003cem\\u003eQuercus\\u003c/em\\u003e (H\\u003csub\\u003ee\\u003c/sub\\u003e=0.714\\u0026thinsp;~\\u0026thinsp;0.854)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e]\\u003c/sup\\u003e. The results showed that the genetic diversity of the \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in MCM was at a low level.\\u003c/p\\u003e \\u003cp\\u003eAmong the different age classes, the H\\u003csub\\u003ee\\u003c/sub\\u003e of the old (0.675) and adult trees (0.660) was higher than that of the saplings (0.599), which was consistent with the results obtained for \\u003cem\\u003eCryptocarya chinensis\\u003c/em\\u003e\\u003csup\\u003e[\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e]\\u003c/sup\\u003e, \\u003cem\\u003eGlyptostrobus pensilis\\u003c/em\\u003e\\u003csup\\u003e[\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e]\\u003c/sup\\u003e, and \\u003cem\\u003eRhododendron simsii\\u003c/em\\u003e\\u003csup\\u003e[\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e]\\u003c/sup\\u003e. Previous studies have shown that habitat fragmentation is the main cause of weakened genetic diversity of saplings in the population\\u003csup\\u003e[\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e]\\u003c/sup\\u003e. In general, the genetic diversity of adult and old trees is less affected by habitat fragmentation; however, habitat fragmentation can affect the gene flow patterns of adult trees, thereby increasing the chance of inbreeding\\u003csup\\u003e[\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e]\\u003c/sup\\u003e. This effect acts on the offspring, resulting in a weakened genetic diversity of the saplings. In the MCM, \\u003cem\\u003eF. hayatae\\u003c/em\\u003e is generally distributed on mountains or ridges at an altitude of 1500\\u0026ndash;1900 m, and its subpopulations are separated by ridges and streams. In addition, the intensification of human disturbance accelerates the process of habitat fragmentation. Hence, the lower genetic diversity of saplings within the population may be attributed to a combination of the unique topographic structure of the MCM and habitat fragmentation caused by anthropogenic disturbances.\\u003c/p\\u003e \\u003cp\\u003eExcluding Null alleles by strict SSR primer selection, the fixation index (FI) was greater than 0. The result can usually be explained by the Wahlund effect and the mating system\\u003csup\\u003e[\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e]\\u003c/sup\\u003e. The genetic differentiation coefficient (FST) between patches (Supplementary Table S2) indicated that the genetic differentiation among four patches was not closely with distance, suggesting no Wahlund effect. Thus, mating among relatives and/or selfing ocurred in \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population. Consistently, the average inbreeding coefficient (FIS\\u0026thinsp;\\u0026gt;\\u0026thinsp;0) (Supplementary Table S3) also suggested inbreeding in \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population. Continued inbreeding causes depression, resulting in reduced seed fitness and population decline.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2 Fine-scale SGS\\u003c/h2\\u003e \\u003cp\\u003eThe F(1) value of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e in MCM (0.1084) within 10 m was slightly lower than that of half-siblings (0.125) but higher than that of cousins (0.0625), suggesting a close kinship among individuals within the population. In the range of 10\\u0026ndash;40 m, the value of Fij was \\u0026gt;\\u0026thinsp;0.02, and significant SGS was detected within the population; beyond 40 m, Fij was less than 0.02, and no significant SGS was detected. Thus, the effective SGS distance within \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in the MCM was 40 m.\\u003c/p\\u003e \\u003cp\\u003eSp reflects the level of population SGS. The value of Sp within \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population was 0.0157, which was higher than the mean values of Sp for outcrossing plants (0.0126), trees (0.0102), wind-pollinated plants (0.0054), and lower than gravity-borne plants (0.0281)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. In addition, the value of Sp within \\u003cem\\u003eF. hayatae\\u003c/em\\u003e was higher than those of other endangered species such as \\u003cem\\u003eUlmus chenmoui\\u003c/em\\u003e (0.0107)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e and \\u003cem\\u003ePteroceltis tatarinowii\\u003c/em\\u003e (0.0140)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e, and lower than \\u003cem\\u003eSinojackia huangmeiensis\\u003c/em\\u003e (0.0281)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e]\\u003c/sup\\u003e, \\u003cem\\u003eUlmus gaussenii\\u003c/em\\u003e (0.0293)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e]\\u003c/sup\\u003e (He, 2016), and \\u003cem\\u003eTetracentron sinense\\u003c/em\\u003e (0.0305)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e, indicating an intermediate level. Hardy et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]\\u003c/sup\\u003efound that restricted gene flow is the most important cause of SGS within populations and that the stronger the restriction of gene exchange between individuals, the higher the degree of fine-scale SGS. Ueno et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e44\\u003c/span\\u003e]\\u003c/sup\\u003e found that the seeds of species diffused by gravity are mostly distributed near their maternal trees; therefore, it is easy to produce an aggregated distribution of related individuals, thereby forming strong SGS within the population. So we hypothesized that the negative effects of restricted seed dispersal are partially offset by abundant gene flow (Nm\\u0026thinsp;\\u0026gt;\\u0026thinsp;1) (Supplementary Table S3), which might be responsible for intermediate level SGS within the population.\\u003c/p\\u003e \\u003cp\\u003eIn the MCM, significant SGS was detected in the sapling population of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e within 50 m, which may be attributed to the characteristics of seed dispersal by gravity. In \\u003cem\\u003eF. hayatae\\u003c/em\\u003e, seed dispersal by gravity resulted in an aggregated distribution near maternal trees of seeds and overlapping seed rain, enabling the maintenance of SGS in the sapling population\\u003csup\\u003e[\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e]\\u003c/sup\\u003e. This was confirmed by the low-level genetic diversity of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e saplings.\\u003c/p\\u003e \\u003cp\\u003eAt the patch level, significant SGS was detected in CPL and ZSB within 10 m, whereas weak SGS was detected in LLG, suggesting a difference in SGS among the patches. Of the three patches, the population densities of CPL (24.4 individuals/ha) and ZSB (30.6 individuals/ha) were significantly greater than that of LLG (12.1 individuals/ha), which is not favorable for pollen and seed dispersal\\u003csup\\u003e[\\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e45\\u003c/span\\u003e]\\u003c/sup\\u003e, resulting in an increase in SGS within patches. In addition, high population densities are often accompanied by accumulated defoliation, which not only creates a physical barrier between seed and soil, but is also accompanied by extinction effects, and microbial pathogen impacts. The potency of these effects increases with the thickness of the humus layer, thus weakening of seed germination rate\\u003csup\\u003e[\\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e46\\u003c/span\\u003e][\\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e47\\u003c/span\\u003e]\\u003c/sup\\u003e. The higher growth destinies were able to be noticed at ZSB and CPL in our study. Therefore, we speculate that this may also contribute to the intensity of SGS.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3 Gene flow\\u003c/h2\\u003e \\u003cp\\u003eGene flow levels were obtained by estimating the pollen and seed flows of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e in MCM. The mean dispersal distance of pollen and seed were 83.04 m and 30.14 m, respectively. The mean effective dispersal distance of genes within population was calculated to be 66 m (dg\\u0026thinsp;=\\u0026thinsp;44.09\\u0026ndash;99.66m), which was lower than some wind-pollinated trees such as \\u003cem\\u003eHandeliodendron bodinieri\\u003c/em\\u003e (d\\u003csub\\u003eg\\u003c/sub\\u003e = 200\\u0026ndash;400 m)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e48\\u003c/span\\u003e]\\u003c/sup\\u003e and \\u003cem\\u003eNeobalanocarpus heimii\\u003c/em\\u003e (d\\u003csub\\u003ep\\u003c/sub\\u003e = 191.2 m)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e49\\u003c/span\\u003e]\\u003c/sup\\u003e, but higher than other wind-pollinated trees such as \\u003cem\\u003eP.tatarinowii\\u003c/em\\u003e (d\\u003csub\\u003eg\\u003c/sub\\u003e = 44.79\\u0026ndash;77.96 m)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e]\\u003c/sup\\u003e and \\u003cem\\u003eT.sinense\\u003c/em\\u003e (d\\u003csub\\u003eg\\u003c/sub\\u003e = 21.62\\u0026ndash;70.37 m)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e. The results showed that the effective dispersal distance of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e gene in MCM was at an intermediate level, and its gene flow was somewhat limited during dispersal. The limiting gene flow of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e might be attributed to the combination effects of its biological characteristics, topography, and pollution density: (1) \\u003cem\\u003eF. hayatae\\u003c/em\\u003e is a long-lived tall tree characterized by monoecious, wind-pollination, and heterogamous, facilitating the effect on the exchange of genes in the population. Compared to \\u003cem\\u003eP. tatarinowii\\u003c/em\\u003e and \\u003cem\\u003eT. sinense\\u003c/em\\u003e, adult trees of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e can reach up to 20 m in height with a large crown, which provides an excellent basis for the long-distance dispersal of pollen. However, its seeds are mainly dispersed by gravity, which limits seed dispersal to some extent, resulting in limited gene flow. (2) The \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in the MCM was blocked by ridges and streams, which exacerbated habitat fragmentation of the population, making it difficult for pollen and seeds to achieve long-distance migration. Therefore, the unique topography of Micang Mountain partially limits the effective transmission of gene flow. (3) Compared with LLG, the higher population densities of CPL and ZSB caused a greater restriction on the dispersal of pollen and seeds, resulting in restricted gene flow.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.4 Reasons for fine-scale SGS of F.hayatae\\u003c/h2\\u003e \\u003cp\\u003eThe formation of an FSGS is usually determined by multifaceted factors. Limited gene flow is the most dominant factor contributing to FSGS for \\u003cem\\u003eF.hayatae\\u003c/em\\u003e in MCM. A convincing fact is that the gene flow dispersal distance of LLG was larger than that of ZSB and CPL and only weak SGS was detected, whereas significant SGS was detected in ZSB and CPL. Specifically, limited seed flow is more involved in the formation of FSGS in MCM \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population. In this population, the effective distance of seed was short (30.14 m), and most seeds dispersed around their maternal plants, resulting in stronger SGS and lower genetic diversity of saplings compared with mature and old trees. Seeds contribute more to fine-scale SGS formation as diploid than haploid pollen because only seeds can germinate, mature, and provide a biological basis for pollen dispersal\\u003csup\\u003e[\\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e43\\u003c/span\\u003e]\\u003c/sup\\u003e. Thus, restricted seed flow is a decisive factor in the formation of fine-scale SGS of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in MCM. In addition, geographic barriers formed by ridges and streams and biological barriers consisting of population density and deadfall in microhabitats prevent dispersal of gene flow and thus promote the formation of FSGS.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.5 Implication for conservation\\u003c/h2\\u003e \\u003cp\\u003eBecause significant SGS were detected in population within 40 m, the sampling distance between individuals should be greater than 40 m to ensure the richness of the germplasm gene pool during genetic sampling or seed collection for ex situ conservation of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e. Additionally, there is a certain degree of restricted gene flow among patches of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e, especially the seed flow, seed dispersal between patches can be facilitated by artificial means to weaken SGS within-patches.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Conclusion\",\"content\":\"\\u003cp\\u003eIn this study, 10 pairs of microsatellite (SSR) primers were used to analyze the fine-scale spatial genetic structure and gene flow of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in MCM. The results suggest that the genetic diversity of this population was relatively low (He\\u0026thinsp;=\\u0026thinsp;0.639) and there was inbreeding or autogamy among individuals of the population; thus, apparent SGS and a certain degree of limited gene flow were detected, which may be attributed to seed dispersal restriction, habitat fragmentation and microhabitats. The SGS varied in different patches owing to population density and habitat fragmentation, whereas the limited seed dispersal of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e resulted in higher SGS in saplings than in adult and old trees. There was an obvious limitation of gene flow in the population, especially seed flow, most of which was limited to within 60 m. Biological characteristics, topography, and plant population density were the main factors affecting the current pattern of gene dispersal in \\u003cem\\u003eF. hayatae\\u003c/em\\u003e, and dispersal distance decreased with increasing population density. The spatial genetic structure at a fine scale was 40 m, and individuals within that range were closely related. Therefore, during ex situ protection of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e, the sampling distance between individuals should be greater than 40 m to ensure complete genetic efficiency.\\u003c/p\\u003e\"},{\"header\":\"5. Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.1 Study site\\u003c/h2\\u003e \\u003cp\\u003eMCM is located in the northeast of Wangcang County, China, which belongs to the northeastern edge of Sichuan Basin (N32\\u0026deg;29'\\u0026ndash;32\\u0026deg;41', E106\\u0026deg;24'\\u0026ndash;106\\u0026deg;39') (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003ea). MCM is characterized by the relative height difference of 1711 m, annual mean temperature from 13.5\\u0026ndash;16.5 ℃, the annual frost-free period of 260 d, the annual precipitation from 1100\\u0026ndash;1400 mm, and the average annual light duration of 1352.52 h. MCM is a transitional area from subtropical to warm temperate zone, wherein plants and habitats are featured by remarkable transition. The vertical band spectrum of vegetation was more evident, including evergreen broad-leaved, evergreen and deciduous broad-leaved mixed, deciduous broad-leaved, evergreen needle-leaved, and coniferous-deciduous-broad-leaved mixed forests\\u003csup\\u003e[\\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e50\\u003c/span\\u003e]\\u003c/sup\\u003e. \\u003cem\\u003eF. ayatae\\u003c/em\\u003e is mainly distributed in deciduous broad-leaved forests (altitude: 1500\\u0026ndash;1900 m)\\u003csup\\u003e[\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.2 Field investigation and sampling\\u003c/h2\\u003e \\u003cp\\u003eAfter conducting a comprehensive survey of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e distribution in the MCM, four patches of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e with relatively complete age-class structure were determined, with areas of 200 \\u0026times; 140 m (Laolingou, LLG), 120 \\u0026times; 80 m (Tabahe, TBH), 140 \\u0026times; 120 m (Changpingli, CPL), and 160 \\u0026times; 90 m (Zhongshanbao, ZSB) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003eb). These four patches are separated by natural ridges or streams, with a geographical distance of greater than 350 m. Specific geographic information for each patch was shown in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e. The geographic and phenological information of all \\u003cem\\u003eF. hayatae\\u003c/em\\u003e individuals in each patch was recorded (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e), and 4\\u0026ndash;6 intact young leaves without pests were collected from each individual and placed in a sealed bag containing dry discoloration silica gel to dry over time. Afterwards, these sampling materials were brought back to laboratory and stored in a refrigerator at \\u0026minus;\\u0026thinsp;80 ℃.\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab7\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 7\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSampling information of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e individuals\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026minus;\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026minus;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003ePatches\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eLatitude/N\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eLongitude/E\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eAltitude/m\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNumber\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLLG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32\\u0026deg;39.4136\\u0026prime;-32\\u0026deg;39.5433\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e106\\u0026deg;33.38.71\\u0026prime;-106\\u0026deg;33.4590\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1750\\u0026ndash;1825\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e34\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTBH\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32\\u0026deg;39.7021\\u0026prime;-32\\u0026deg;39.7425\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e106\\u0026deg;33.45.51\\u0026prime;-106\\u0026deg;33.5160\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1748\\u0026ndash;1808\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e38\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCPL\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32\\u0026deg;39.3246\\u0026prime;-32\\u0026deg;39.7215\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e106\\u0026deg;33.66.20\\u0026prime;-106\\u0026deg;33.7303\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1742\\u0026ndash;1780\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e41\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eZSB\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e32\\u0026deg;39.0000\\u0026prime;-32\\u0026deg;39.6464\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026minus;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e106\\u0026deg;33.42.24\\u0026prime;-106\\u0026deg;33.4997\\u0026prime;\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e1773\\u0026ndash;1790\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e43\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eFollowing the method of Li et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e51\\u003c/span\\u003e]\\u003c/sup\\u003e, all individuals in each patch were divided into three age-classes: saplings (DBH\\u0026thinsp;\\u0026lt;\\u0026thinsp;7.5 cm), adult trees (7.5 cm\\u0026thinsp;\\u0026le;\\u0026thinsp;DBH\\u0026thinsp;\\u0026lt;\\u0026thinsp;22.5 cm), and old trees (DBH\\u0026thinsp;\\u0026ge;\\u0026thinsp;22.5 cm)(Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.3 Extraction of Genomic DNA from F. hayatae\\u003c/h2\\u003e \\u003cp\\u003eTotal DNAs was isolated from the leaves using the method described by Li et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e52\\u003c/span\\u003e]\\u003c/sup\\u003e, and its concentration and purity were detected using a NanoDrop 2000 Microvolume Spectrophotometer.\\u003c/p\\u003e \\u003cp\\u003eTwenty pairs of primers were synthesized using 20 pairs of SSR sequences of \\u003cem\\u003eFagus\\u003c/em\\u003e species reported in the NCBI database (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ewww.ncbi.nlm.nih.gov/nucleotide?term=\\u003c/span\\u003e\\u003cspan address=\\\"http://www.ncbi.nlm.nih.gov/nucleotide?term=\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e txid133895 [Organism]) and 36 pairs of SSR primers from \\u003cem\\u003eFagus\\u003c/em\\u003e published by Ju et al.\\u003csup\\u003e[\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e]\\u003c/sup\\u003e were used for primer screening. The DNA template of each sample was PCR-amplified with each primer pair, followed by capillary gel electrophoresis to obtain electrophoretic peak maps (Fig. \\u003cspan refid=\\\"MOESM1\\\" class=\\\"InternalRef\\\"\\u003eS1\\u003c/span\\u003e). Finally, ten pairs of SSR primers with significant absorption peaks were selected for subsequent experiments.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.4 Genetic diversity analysis\\u003c/h2\\u003e \\u003cp\\u003enSSR loci were detected using null alleles and polymorphic information content (PIC) from CERVUS3.0 software\\u003csup\\u003e[\\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e53\\u003c/span\\u003e]\\u003c/sup\\u003e. Genetic diversity parameters among populations and among patches within populations were calculated using PopGene 32 software\\u003csup\\u003e[\\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e54\\u003c/span\\u003e]\\u003c/sup\\u003e. The values of allele (N\\u003csub\\u003ea\\u003c/sub\\u003e), effective allele number (N\\u003csub\\u003ee\\u003c/sub\\u003e), observed heterozygosity (H\\u003csub\\u003eo\\u003c/sub\\u003e), unbiased expected heterozygosity (uH\\u003csub\\u003ee\\u003c/sub\\u003e), and fixation index (FI) of each patch were calculated using GenAlEX6.505 software\\u003csup\\u003e[\\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e55\\u003c/span\\u003e]\\u003c/sup\\u003e. Genetic diversity indicators (Na, Ne, He, Ho, FI), population-level gene flow (Nm) and inbreeding coefficient (FIS, FST) of the populations were also calculated by GenAlEX 6.505 software\\u003csup\\u003e[\\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e56\\u003c/span\\u003e]\\u003c/sup\\u003e. The parameter values for both computational simulations and permutation significance detection were set to 999.\\u003c/p\\u003e \\u003cp\\u003eFIS=(Mean He-Mean Ho)/Mean He (1)\\u003c/p\\u003e \\u003cp\\u003eFST=(Ht-Mean He)/Ht (2)\\u003c/p\\u003e \\u003cp\\u003eNm=(1-Fst)/4Fst (3)\\u003c/p\\u003e \\u003cp\\u003eWhere the Ht is total expected heterozygosity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.5 Fine-scale SGS analysis\\u003c/h2\\u003e \\u003cp\\u003eThe SPAGeDi1.3 was employed to test the spatial genetic structure and calculate the Nason\\u0026rsquo;s value (Fij) of individuals based on multiloci that represent the relationship coefficient\\u003csup\\u003e[\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]\\u003c/sup\\u003e, where the Fij value indicates that the genotypes of random samples i and j have the same probability. The standard deviation and 95% confidence interval of Fij were calculated by 999 simulations. The method was simple and could reduce the deviation caused by distance grouping\\u003csup\\u003e[\\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e57\\u003c/span\\u003e]\\u003c/sup\\u003e. Statistical effectiveness requires more than 30 pairs of individual data per distance level for analysis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e58\\u003c/span\\u003e]\\u003c/sup\\u003e. Hence, the distance level of less than 30 pairs of individuals was abandoned in this analysis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e]\\u003c/sup\\u003e. Based on the Fij value, we calculated Sp to estimate the SGS within the population and patches. The formula is\\u003c/p\\u003e \\u003cp\\u003eSp= - \\u003cem\\u003ebF\\u003c/em\\u003e /(1\\u0026thinsp;\\u0026minus;\\u0026thinsp;F(1)) (4)\\u003c/p\\u003e \\u003cp\\u003ewhere \\u003cem\\u003ebF\\u003c/em\\u003e refers to the linear regression slope of the genetic relationship to the natural logarithm of the distance level and F(1) is the average genetic relationship between individuals at the first distance level\\u003csup\\u003e[\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e42\\u003c/span\\u003e]\\u003c/sup\\u003e.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e5.6 Parental analysis and gene flow\\u003c/h2\\u003e \\u003cp\\u003eThe CERVUS software (version 3.0) was used for gene flow estimation and parental analysis\\u003csup\\u003e[\\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e59\\u003c/span\\u003e]\\u003c/sup\\u003e. Parental analysis in each patch was performed using the saplings within the patch as offspring and adult and old trees as relatives. Because the sex of both parents of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e is unknown, the combination with the highest and most significant Trio LOD score was considered the best parental combination for parental analysis. Simulation parameters were set as follows: the proportion of candidate parents was 0.9, the average genotyping error rate was 0.01, and the locus mismatch rate was 0.01, with reference to the default 80% confidence level. Subsequently, the geographic distance matrix among individuals in each patch was calculated using GenAlEx6.5. The operative diffusion distance of gene flow, denoted by d\\u003csub\\u003eg\\u003c/sub\\u003e, is calculated as follows:\\u003c/p\\u003e \\u003cp\\u003ed\\u003csub\\u003eg\\u003c/sub\\u003e \\u003csup\\u003e2\\u003c/sup\\u003e =d\\u003csub\\u003es\\u003c/sub\\u003e \\u003csup\\u003e2\\u003c/sup\\u003e + 0.5 d\\u003csub\\u003ep\\u003c/sub\\u003e \\u003csup\\u003e2\\u003c/sup\\u003e (5)\\u003c/p\\u003e \\u003cp\\u003ewhere d\\u003csub\\u003es\\u003c/sub\\u003e and d\\u003csub\\u003ep\\u003c/sub\\u003e are the average diffusion distance of seed and pollen, respectively.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eDeclaration of Competing Interest\\u003c/h2\\u003e \\u003cp\\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eJ.Y.C. Formal analysis, Writing-original draft, Writing review \\u0026amp; editing. G.X. Investigation, Formal analysis. C.Y.J. Investigation, Data curation, Formal analysis. X.M.Z. Data curation, Formal analysis. H.Y.H. Data curation, Funding acquisition. Q.X.Y. Investigation. K.T. Investigation. X.H.G. Conceptualization, Writing review \\u0026amp; editing, Funding acquisition, Supervision. All authors reviewed the manuscript.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgements\\u003c/h2\\u003e \\u003cp\\u003eWe thank all students (Fan Duan, Huan Zhang, Yinshu Gong, Xue Wang, Rui Chen) and Xuewu Feng (Micang Mountain National Nature Reserve in Sichuan Province) who help to collect and analyze date. Founding was provided by National Nature Science Foundation of China (No.32070371), the Innovation Team Funds of China West Normal University (KCXTD2022-4), and the Natural Science Foundation of Sichuan Province (No. 23NSFSC1272).\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eData is provided within the manuscript or supplementary information files. All datasets are available from the corresponding author on reasonable request.\\u003c/p\\u003e\\u003cp\\u003eAdditional information\\u003c/p\\u003e\\n\\u003cp\\u003eThe plant materials collected and the plants experiments in this study full complied with relevant institutional, national, and international guidelines and legislation.\\u003c/p\\u003e\\n\\u003cp\\u003eAll field investigation and Fagus hayatae Palib. ex Hayata collected obtained permissions.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003ePimm S.L., Jenkins C.N., Abell R., Brooks T.M., Gittleman J.L., Joppa L.N., et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science. 344, 1246752 (2014).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRyan J., Mellish S., Dorrian J., Winefield T., Litchfield C. Effectiveness of biodiversity-conservation marketing. Conversation Biology. 34, 354\\u0026ndash;367 (2020).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEllegren H. and Galtier N. Determinants of genetic diversity. \\u003cem\\u003eNature review genetics\\u003c/em\\u003e. 17, 422\\u0026thinsp;\\u0026ndash;\\u0026thinsp;33 (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang Q., Fu Y., Wang Y.Q., Wang Y., Zhang W.H., Li X.Y., et al. Genetic diversity and differentiation in the critically endangered orchid (\\u003cem\\u003eAmitostigma hemipilioides\\u003c/em\\u003e): implications for conservation. Plant Systematic and Evolution, 300, 871\\u0026ndash;879 (2014).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eEnnos R.A. and Clegg M.T. Effect of population substruc turing on estimates of outcrossing rate in plant population. Heredity. 48, 283\\u0026ndash;292 (1982).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYoung A.G., Boshier D. of referencing in \\u003cem\\u003eForest Conservation Genetics: Principles and Practice\\u003c/em\\u003e (ed. Young A.G., Boshier D. and Boyle T.J)123\\u0026ndash;134 (CSIRO Publishing, Melbourne, 2000).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eVekemans, X., Hardy, O.J. New insights from fine-scale spatial genetic structure analyses in plants populations. Molecular Ecology. 13, 921\\u0026ndash;935 (2004).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChung, M.Y., Nason, J.D., Chung, M.G. Spatial genetic structure in populations of the terrestrial orchid \\u003cem\\u003eCephalanthera longibracteata\\u003c/em\\u003e (Orchidaceae). American Journal of Botany. 91, 52\\u0026ndash;57 (2004).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWright, S. Isolation by distance. Genetics. 28, 114\\u0026ndash;138 (1943).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHamrick, J.L., Godt, M.J.W. Effects of Life History Traits on Genetic Diversity in Plant Species. The Royal Society. 351, 1291\\u0026ndash;1298 (1996).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDe-Lucas, A.I., Gonz\\u0026aacute;lez-Mart\\u0026iacute;nez, S.C., Vendramin, G.G., Hidalgo, E., Heuertz, M. Spatial genetic structure in continuous and fragmented populations of Pinus pinaster Aiton. Molecular Ecology. 18, 4564\\u0026ndash;4576 (2010).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDeSilva R., Dodd R.S. Fragmented and isolated: limited gene flow coupled with weak isolation by environment in the paleoendemic giant sequoia (Sequoiadendron giganteum). American Journal of Botany. 107, 45\\u0026ndash;55(2020).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAngbonda D.M.A., Monthe F.K., Bourland N., Boyemba F., Hardy O.J. Seed and pollen dispersal and fine-scale spatial genetic structure of a threatened tree species: \\u003cem\\u003ePericopsis elata\\u003c/em\\u003e (HARMS) Meeuwen (Fabaceae). Tree Genetics \\u0026amp; Genomes. 17, 27 (2021).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChung, M.Y., Epperson, B.K., Chung, M.G. Genetic structure of age classes in \\u003cem\\u003eCamellia japonica\\u003c/em\\u003e (Theaceae). Evolution. 57, 62\\u0026ndash;73 (2003).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBerg E.E and Hamrick J.L. Spatial and genetic structure of two sandhills oaks: \\u003cem\\u003eQuercus laevis\\u003c/em\\u003e and \\u003cem\\u003eQuercus margaretta\\u003c/em\\u003e(Fagaceae). American Journal of Botany. 81, 7\\u0026ndash;14 (1994).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSokal R.R and Wartenberg D. A test of spatial autocorrelation analysis using an isolation-by-distance model. Genetics. 105, 219\\u0026ndash;237 (1983).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWilli, Y., M\\u0026auml;\\u0026auml;tt\\u0026auml;nen, K. The relative importance of factors determining genetic drift: Mating system, spatial genetic structure, habitat and census size in \\u003cem\\u003eArabidopsis lyrata\\u003c/em\\u003e. New Phytologist. 189, 1200\\u0026ndash;1209 (2011).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang, X.M. Research Progress of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e: Plant of the Second-class Protection in China. Helongjiang Agricultural Sciences. 5, 148\\u0026ndash;151 (2017).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eShen, Z.H., Fang, J.Y., Chiu, C.A., Chen, T.Y. The geographical distribution and differentiation of Chinese beech forests and the association with \\u003cem\\u003eQuercus\\u003c/em\\u003e. Applied Vegetation Science. 18, 23\\u0026ndash;33 (2015).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhang F.G. The community characteristics of the Taiwan beech forest of Qingliangfeng Mountain in Zhejiang. Journal of Zhejiang University. 27, 403\\u0026ndash;406 (2001).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJu, L.P., Shih, H.C., Chiang, Y.C. Microsatellite primers for the endangered beech tree, \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e (Fagaceae). American Journal of Botany. 99, 453\\u0026ndash;456 (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe, J, Wang, ZX, Lei, Y, Li, ZQ, Zhang, L, Man, J.S. The study on coenological characteristics of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e community in Qizimei mountain natural reserve. Journal of Huazhong Normal University(Nat. Sci). 42, 272\\u0026ndash;277 (2008).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi, D.D., Xu, X., Shi, Q.M., Chen, J., Zan, X., Wu, D.J., et al. Characteristics of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e Community along Altitudinal Gradient in Micangshan Nature Reserve, Sichuan. Journal of Tropical and Subtropical Plants. 24, 626\\u0026ndash;634 (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi, J.X., Wu, D.J., Zhang, S.P., He, X.X., Chen, J., Shi, Q.M., et al. Life Table and Dynamic Analysis of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e Population in Micangshan Nature Reserve, Sichuan Province, China. Bulletin of Botanical Research. 36, 68\\u0026ndash;74 (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhao Y.F. Conversation genetics of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e Palibin. \\u003cem\\u003eMaster\\u003c/em\\u003e, Jiangxi Agricultural University. (2023).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKato S., Koike T., Lei T.T., Hsieh C.F., Ueda K., Mikami T. Analysis of mitochondria DNA of an endangered beech species, \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e Palib. ex Hayata. New Forests. 19, 109\\u0026ndash;114 (1999).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYing, L.X., Zhang, T.T., Chiu, C.A., Chen, T.Y., Luo, S.J., Chen, X.Y., et al. The phylogeography of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e (Fagaceae): genetic isolation among populations. Ecology and Evolution. 6, 2805\\u0026ndash;2816 (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNei, M. Genetic distance between populations. Am. Nat. 106, 283\\u0026ndash;292 (1972).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHamrick, J.L., Godt, M.J.W. Allozyme diversity in plant species. \\u003cem\\u003ePlant Population Genetics, Breeding and Genetic Resources\\u003c/em\\u003e. 43\\u0026ndash;63 (1989).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eQian, W., Ge, S., Hong, D.Y. Genetic variation within and among populations of a wild rice \\u003cem\\u003eOryza granulata\\u003c/em\\u003e from China detected by RAPD and ISSR markers. Theoretical and Applied Genetics. 102, 440\\u0026ndash;449 (2001).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBuzatti R.S.D.O., Ribeiro R.A., Filho J.P.D.L., Lovato M.B. Fine-scale spatial genetic structure of \\u003cem\\u003eDalbergia nigra\\u003c/em\\u003e (Fabaceae), a threatened and endemic tree of the Brazilian Atlantic Forest. Genetics and Molecular Biology. 35, 838\\u0026ndash;846 (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBaldauf C., Guillardi M.C., Aguirra T.J., Correˆa C.E., Santos F.A.M.D. Souza A.P.D. Genetic diversity, spatial genetic structure and realised seed and pollen dispersal of \\u003cem\\u003eHimatanthus drasticus\\u003c/em\\u003e (Apocynaceae) in the Brazilian savanna. Conservation Genetics. 15, 1073\\u0026ndash;1083 (2014).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCurtu A.L., Craciunesc L., Enescu C.M., Vidalis A., Sofletea N. Fine-scale spatial genetic structure in a multi-oak-species (\\u003cem\\u003eQuercus spp.\\u003c/em\\u003e) forest. Biogeosciences and Forestry. 8, 324\\u0026ndash;332 (2015).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eGao, S.H., Wang, Z.F., Zhang, J.L., Tian, S.N. Genetic Diversity of \\u003cem\\u003eCryptocarya Chinensis\\u003c/em\\u003e Life Stages in Dinghu Mountain China. Acta Scientiarum Naturalium Universitatis Sunyatseni. 44, 209\\u0026ndash;212 (2005).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWu, Z.Y., Liu, J.F., Hong, W., Pan, D.M., Zheng, S.Q., He, Z.S. Genetic diversity of different life-stage population of \\u003cem\\u003eGlyptostrobus pensilis\\u003c/em\\u003e, an endangered plant in China: ISSR analysis. Chinese Journal of Ecology. 31, 1911\\u0026ndash;1916 (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang, S.Z., Zhang, L., Yang W., Lou, Y., Zheng, Z., Fang, Y.P., et al. Genetic Diversity of \\u003cem\\u003eRhododendron simsii\\u003c/em\\u003e Populations on Dabieshan at Different Life Stages Based on SSR Markers. Forest Research. 31, 125\\u0026ndash;130 (2018).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eCzech, B., Krausman, P.R. Distribution and causation of species endangerment in the united states. Science. 277, 1116\\u0026ndash;1117 (1997).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi, J.H., Jin, Z.X., Li, J.M. RAPD and ISSR analysis on genetic diversity of different life stages in the population of \\u003cem\\u003eTorreya jackii\\u003c/em\\u003e an endangered plant in China. Journal of Zhejiang University(Science Edition). 37, 104\\u0026ndash;111 (2010).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe, J. Population dynamics and fine-scale spatial genetic structure of \\u003cem\\u003eUlmus chenmoui\\u003c/em\\u003e and \\u003cem\\u003eUlmus gaussenii\\u003c/em\\u003e, endangered species endemic to China. \\u003cem\\u003eMaster\\u003c/em\\u003e, Nanjing university. (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYang, J. Development of ploymorphic microsatellite loci and study on fine-scale spatial genetic structure of \\u003cem\\u003ePteroceltis tatarinowii\\u003c/em\\u003e, an endangered plant endemic to China. \\u003cem\\u003eMaster\\u003c/em\\u003e, Nanjing university. (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRuan, Y.M., Zhang, J.J., Yao, X.H., Ye, Q.G. Genetic diversity and fine-scale spatial genetic structure of different life-history stages in a small, isolated population of \\u003cem\\u003eSinojackia huangmeiensis\\u003c/em\\u003e (Styracaceae). Biodiversity Science. 20, 460\\u0026ndash;469 (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang, X., Duan, F., Zhang, H., Han, H.Y., Gan, X.H. Fine-scale spatial genetic structure of the endangered plant \\u003cem\\u003eTetracentron sinense\\u003c/em\\u003e Oliv. (Trochodendraceae) in Leigong Mountain. Global Ecology and Conservation. 41, e02382 (2023).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHardy, O.J., Maggia, L., Bandou, E., Breyne, P., Caron, H., Chevalier, M.H., et al. Fine-scale genetic structure and gene dispersal inferences in 10 Neotropical tree species. Molecular Ecology. 15, 559\\u0026ndash;571 (2006).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eUeno, S., Tomaru, N., Yoshimaru, H., Manabe, T., Yamamoto, S. Genetic structure of \\u003cem\\u003eCamellia japonica\\u003c/em\\u003e L. in an old-growth evergreen forest, Tsushima, Japan. Molecular Ecology. 9, 647\\u0026ndash;656 (2000).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eJacquemyn, H., Brys, R., Vandepitte, K., Honnay, O., Roldan-ruiz, I. Fine-scale genetic structure of life history stages in the food-deceptive orchid \\u003cem\\u003eOrchis purpurea\\u003c/em\\u003e. Molecular Ecology. 15, 2801\\u0026ndash;2808 (2006).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eWang, H.X., Li, G.Z., Yu, D.M., Chen, Y.M. Barrier effect of litter layer on natural regeneration of forest. Chinese Journol of Ecology. 27, 83\\u0026ndash;88 (2008).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eZhu J., Liu J.F., He Z.S., Xin C., Wang X.L., Jiang L. Effects of physical barrier of litter on the seed germination and radicle growth of \\u003cem\\u003eCastanopsis kawakamii\\u003c/em\\u003e. Acta Ecoligica Sinica. 40, 16, 5630\\u0026ndash;5637 (2020).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHe, R.K., Wang, J., Huang, H. Long-distance gene dispersal inferred from spatial genetic structure in \\u003cem\\u003eHandeliodendron bodinieri\\u003c/em\\u003e, an endangered tree from karst forest in southwest China. Biochemical Systematics and Ecology. 44, 295\\u0026ndash;302 (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKonuma, A., Tsumura, Y., Lee, C.T., Lee, S.L., Okuda, T. Estimation of gene flow in the tropical-rainforest tree \\u003cem\\u003eNeobalanocarpus heimii\\u003c/em\\u003e (Dipterocarpaceae), inferred from paternity analysis. Molecular Ecology. 9, 1843\\u0026ndash;1852 (2000).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChen, J. Report of investigation on Fagus of Micangshan Nature Reserve. Chinese Wild Plant Resource, 33, 47\\u0026ndash;52 (2014).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi, D.D., Dong, T.F., Chen, J., Shi, Q.M., He, X.X., Zhang, S.P., et al. Characteristics of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e Community and Species Diversity in Micangshan Nature Reserve,Sichuan. Acta Botanic Boreali-Occidentalia Sinica. 36, 174\\u0026ndash;182 (2016).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLi, S., Gan, X.H., Han, H.Y., Zhang, X.M., Tian, Z.Q. Low within-population genetic diversity and high genetic differentiation among populations of the endangered plant \\u003cem\\u003eTetracentron sinense\\u003c/em\\u003e Oliver revealed by inter-simple sequence repeat analysis. Annals of Forest Science. 75, 74\\u0026ndash;85 (2018).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKalinowski, S.T., Taper, M.L., Marshall, T.C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Molecular Ecology. 16, 1099\\u0026ndash;1106 (2007).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eYeh, F.C., Yang, R.C., Boyle, T.B.J., Ye, Z., Xiyan J.M. PopGene32, Microsoft windows-based freeware forpopulation genetic analysis. Version 1.32. Molecular Biologyand Biotechnology Centre, University of Alberta: Edmonton, Canada. (2000).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePeakall, R., Smouse, P.E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research\\u0026ndash;an update. Bioinformatics. 28, 2537\\u0026ndash;2539 (2012).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePeng, G., Tang, S. Fine-scale spatial genetic structure and gene flow of Camellia flavida, a shade-tolerant shrub in karst. Acta Ecol. Sin. 37, 7313\\u0026ndash;7323 (2017).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHardy, O.J., Vekemans, X. Spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Molecular Ecology Notes. 2, 618\\u0026ndash;620 (2002).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePeakall, R., Smouse, P.E. GENALEX 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes. 6, 288\\u0026ndash;295 (2006).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSato, T., Isagi, Y., Sakio, H., Osumi, K., Goto, S. Effect of gene flow on spatial genetic structure in the riparian canopy tree \\u003cem\\u003eCercidiphyllum japonicum\\u003c/em\\u003e revealed by microsatellite analysis. Heredity. 96, 79\\u0026ndash;84 (2006).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Fagus hayatae Palib. ex Hayata, Spatial genetic structure, Genetic diversity, Genetic differentiation, Gene flow\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-4617989/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-4617989/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eThe beech species \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e Palib. ex Hayata is an important relict tree species in subtropical China, which accumulated a wealth of genetic variation during evolution. To revealing its regeneration dynamics, we analyzed the spatial genetic structure and gene flow of \\u003cem\\u003eFagus hayatae\\u003c/em\\u003e natural population in Micang Mountain (MCM), China, by using 10 pairs of microsatellite primers. The genetic diversity of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e MCM population was at the low level among tall trees. The results of Fij and Sp analysis showed that the SGS strength of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e in MCM were 40 m, the strength of SGS was stronger in saplings compared to adult and old trees. The mean dispersal distance of pollen and seeds were 83.04 m and 30.14 m, respectively. In fine-scale space, \\u003cem\\u003eF. hayatae\\u003c/em\\u003e population in MCM is poor in genetic variation due to the restricted gene flow and significant SGS, and the strength of SGS and the dispersal distance of gene flow of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e are influenced by the limited seed dispersal, habitat fragmentation, and microhabitats. During ex situ protection of \\u003cem\\u003eF. hayatae\\u003c/em\\u003e, the sampling distance between individuals should be greater than 40 m to ensure the most complete genetic efficiency.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Combining restricted gene flow, local microhabitat, and habitat fragmentation shapes the fine-scale spatial genetic structure of Fagus hayatae Palib. ex Hayata in Micang Mountain\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2024-07-19 01:32:52\",\"doi\":\"10.21203/rs.3.rs-4617989/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"9b98dc9e-8a2c-4219-b1c8-2260952bda78\",\"owner\":[],\"postedDate\":\"July 19th, 2024\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[{\"id\":34585929,\"name\":\"Biological sciences/Ecology/Biodiversity\"},{\"id\":34585930,\"name\":\"Biological sciences/Ecology/Conservation\"},{\"id\":34585931,\"name\":\"Biological sciences/Ecology/Ecological genetics\"},{\"id\":34585932,\"name\":\"Biological sciences/Genetics/Population genetics\"}],\"tags\":[],\"updatedAt\":\"2024-08-06T09:09:08+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2024-07-19 01:32:52\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-4617989\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-4617989\",\"identity\":\"rs-4617989\",\"version\":[\"v1\"]},\"buildId\":\"qtupq5eGEP_6zYnWcrvyt\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}