Fine-scale spatial genetic structure and leaf shape variation in five Fagaceae species: insights into conservation and adaptation

Fine-scale spatial genetic structure and leaf shape variation in five Fagaceae species: insights into conservation and adaptation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL Ecology and Evolution This is a preprint and has not been peer reviewed. Data may be preliminary. 5 June 2025 V1 Latest version Share on Fine-scale spatial genetic structure and leaf shape variation in five Fagaceae species: insights into conservation and adaptation Authors : Rongle Wang , Yanjun Luo , Min Qi , Yi Zhang , Jiawen Zhang , Yibo Luo , and Fang Du 0000-0002-7377-5259 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174912593.39850424/v1 Published Ecology and Evolution Version of record Peer review timeline 398 views 315 downloads Contents Abstract Introduction Results Discussion Acknowledgements Data availability statement Supplementary Material References Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Fine-scale spatial genetic structure (FSGS) refers to the pattern of spatial distribution of genetic variation at the local scale, which can indirectly estimate gene flow among individuals and reveal microevolutionary processes in plant populations. Although FSGS is important in explaining dispersal patterns and adaptive variation in plants, few studies have explored its potential application in species conservation strategies. In addition, phenotypic traits, particularly leaf shape, may also exhibit specific spatial variation patterns at fine scales. In this study, we investigated the genetic and leaf shape variation of two genus Quercus species ( Quercus glauca Thunb. and Q. multinervis J. Q. Li) and three genus Castanopsis species ( Castanopsis tibetana Hance, C. faberi Hance, and C. fargesii Franch.) in Wuyishan National Park in southeastern China. Using genetic markers, we found that Quercus species exhibited stronger FSGS and more limited gene flow than Castanopsis species, suggesting greater habitat fragmentation affecting local Quercus species. Leaf morphological analysis revealed inter-generic differences and partial overlap in leaf shape between Quercus and Castanopsis species, with the greatest variation observed in leaf area (LA) and leaf mass (LM). In addition, all five Fagaceae species exhibited significant diminishing returns, with C. fargesii showing the most pronounced effect and possessing the smallest leaves, which may enhance its adaptability to the harsh environments. Despite the leaf shape overlaps blurring species boundaries between Quercus and Castanopsis species, their genetic structure is remained clearly distinct. The observed differences in FSGS intensity and leaf shape variation between the two genera reflect their different environmental adaptability, offering new insights into the integration of genetic and phenotypic data for conservation planning. Introduction Fine-scale spatial genetic structure (FSGS) refers to the non-random spatial distribution of genotypes at the local scale, shaped primarily by limited gene flow (Gamba and Muchhala 2023; Vekemans and Hardy 2004). By quantifying genetic similarity in relation to spatial distance among individuals, researchers can effectively detect patterns and assess the intensity of spatial genetic structure, which in turn provides insights into the dispersal patterns of both pollen and seeds (Angbonda et al. 2021). FSGS is influenced by many factors, including limited gene flow, population density, habitat fragmentation, genetic drift and microhabitat selection (Bizoux et al. 2009). In plants, gene flow mediated by pollen and seed plays a central role in shaping FSGS. Among these, pollen-mediated gene flow is particularly important, as it influences reproductive range, population connectivity, and the degree to which habitat fragmentation affects genetic structure (Ashley et al. 2010). Consequently, restricted pollen dispersal can enhance the intensity of FSGS. Similarly, limited seed dispersal also contributes to stronger FSGS. In many tree species, seeds often fall close to the maternal tree, leading to spatial clustering of related individuals (Berg and Hamrick 1994). This localized seed dispersal results in short dispersal distances and promotes strong FSGS within populations (Buzatti et al. 2012). In addition to genetic factors, phenotypic traits such as leaf shape may also exhibit specific distribution patterns at a fine spatial scale. Leaf shape, closely linked to functional traits, serves as a key feature in plants and plays an important role in plant taxonomy and systematics and also reflects adaptative response to environmental changes (Nicotra et al. 2011; Yang et al. 2022). Meanwhile, genetic structure at a fine spatial scale can influence local adaptation by shaping patterns of gene flow, genetic drift, and selection, thereby affecting the evolutionary potential of populations (Eliades et al. 2018). Therefore, understanding the interplay between FSGS and phenotype variation, such as leaf shape, can reveal how evolutionary processes act on both genetic and phenotypic levels to drive plant adaptation. Integrating FSGS with analyses of leaf shape variation helps identify whether similar phenotypes arise from shared genetic backgrounds or represent independent local adaptations, offering a more comprehensive view of adaptation at a fine spatial scale (Brousseau et al. 2021). Such insights are also critical for designing effective conservation strategies, as they highlight the need to preserve both genetic diversity and phenotypic plasticity within and among populations. In leaf shape research, traditional morphometric methods (TMMs) are commonly used to quantify variation through multivariate statistical analysis of quantitative and qualitative leaf traits, such as the liner distance, angle, and area (Mitteroecker and Gunz 2009; Stephan et al. 2018). However, TMMs studies are often influenced by leaf size, may not effectively capture leaf shape variation in an intuitive way (Mitteroecker and Gunz 2009). Current developed geometric morphometric methods (GMMs) can digitize leaf shape based on Cartesian landmark coordinates, using multivariate statistical analysis to identify leaf shape variation among species to avoid interference (such as leaf size, direction, and location), and can directly reflect leaf shape variation (Du et al. 2022; Klingenberg 2011; Ray 1992; Viscosi and Cardini 2011). Fagaceae (ca. 1000 species across 10 genera) is one of the most important families of temperate and subtropical forests worldwide (Liu et al. 2024; Müller et al. 2019). In China, Fagaceae species inhabit a wide range of environments, from the Himalayas in the west to Taiwan Island in the east (Huang et al. 1999). Fagaceae plants often occur sympatrically at a fine spatial scale, therefore, studying FSGS and leaf shape variation in Fagaceae presents challenges for the effective management and conservation of natural populations. Our study focuses on the FSGS and leaf shape variation of forest tree species located in Wuyishan National Park. Wuyishan National Park represents the most intact and largest mid-subtropical forest ecosystem in southeastern China (Chen et al. 2023), offering an ideal habitat and reproductive environment due to its unique topography habitat diversity (Li et al. 2023). Fagaceae species are dominant or constructive components of the local forest, particularly species of genera Quercus and Castanopsis (Ding et al. 2015). Quercus , the largest genus in the Fagaceae family, is known for its high species richness and strong ecological adaptability (Cavender-Bares 2019; Du et al. 2022; Kremer et al. 2012; Zhang et al. 2025). Castanopsis , the third largest genus in the family, consists of dominant trees in subtropical evergreen broad-leaved forests and tropical seasonal rainforests (Wang et al. 2022). We investigated two genus Quercus species ( Quercus glauca Thunb. and Q . multinervis J. Q. Li) and three genus Castanopsis species ( Castanopsis tibetana Hance, C . faberi Hance, and C . fargesii Franch.) within the region. The region’s diverse topography and favorable climatic conditions contribute to the coexistence of these species in Wuyishan National Park. All five tree species are monoecious plants, wind-pollinated and seeds dispersed by gravity (Chen et al. 2008; Curtu et al. 2015; Liu et al. 2008). Previous studies have investigated either on FSGS ( Q . glauca , Tong et al. 2021), or leaf shape variation ( Q . glauca , Gillani et al. 2023), but few studies have comprehensively explored both FSGS and leaf shape variation to analyze these species. Our study systematically investigates the FSGS and leaf shape variation of five evergreen Fagaceae species in Wuyishan National Park by integrating genetic and leaf morphological analyses, we aim to (1) characterize the fine-scale spatial genetic structure of two Quercus and three Castanopsis species ; (2) explore leaf shape variation within and between genera; (3) access the relationship between leaf shape variation and genetic differentiation at a fine spatial scale; and (4) consider FSGS and leaf shape variation to develop conservation strategies. Combining genetic diversity with leaf shape variation offers valuable insights into the microevolutionary processes shaping the adaptation of Fagaceae species to environmental heterogeneity. This integrative approach contributes to a deeper understanding of how genetic and phenotypic traits interact and respond to ecological factors in sympatric tree species. Materials and Methods Sampling The sampling was conducted in the evergreen broad-leaved forest at Wuyishan National Park in southeastern China (Figure 1a), where Fagaceae is among the most dominant tree species. We collected a total of 601 adult trees for five species from the genera Quercus and Castanopsis of Fagaceae . For Quercus , we sampled 207 individuals from eight populations of Q. glauca (Figure 1b) and 119 individuals from three populations of Q. multinervis (Figure 1c). For Castanopsis , we sampled 111 individuals from seven populations of C. tibetana (Figure 1d), 102 individuals from seven populations of C. faberi (Figure 1e) and 62 individuals from four populations of C. fargesii (Figure 1f). Within each population, each adult tree sampled at a minimum of 10 m interval, so as to minimize the sampling of close relatives. We collected a total of five to seven mature and intact leaves along the four cardinal directions in the middle layer of the canopy for leaf morphological analysis, and one to two young leaves or new branches for DNA isolation. We dried all leaf samples in silica gel immediately and we recorded the latitude, longitude and altitude of each individual using a 621sc global positioning system (GPS) device (Garmin, Beijing, China). The detail sampling information were listed in Table S1. DNA isolation and microsatellite genotyping We extracted genomic DNA from leaf tissue using a Plant Genomic DNA Extraction Kit (Tiangen, Beijing, China). The DNA quality was initially checked using a 1% agarose gel and then the concentration was measured by an ultramicro-spectrophotometer (Thermo Fisher, USA). We randomly selected two individuals from each of three distant sites of species for pre-amplification experiments with 63 nuclear microsatellite (nSSR) loci developed from other Fagaceae species (Table S2). We excluded loci harboring null alleles as identified by MICRO-CHECKER v.2.2 (Van Oosterhout et al. 2004). We applied ten successfully amplified nSSR loci for genotyping all 601 individuals (Table S2). The PCR conditions followed Du et al. (2017) and we analyzed the PCR products using an ABI PRISM 3730 Genetic Analyzer (Applied Biosystems, USA). Subsequently, we scored the alleles using GENEMARKER v.2.2 (Softgenetics, USA) and checked the genotypes twice. Leaf morphology We scanned five intact and dried leaves of each individual with the abaxial surface uppermost using a CanoScan 5600 F scanner (Canon Inc., Japan) at a resolution of 600 dpi. We then conducted TMMs and GMMs for leaf shape variation. For TMMs, we measured seven leaf traits to study leaf morphological characters, including leaf length (LL, cm), petiole length (PL, cm), leaf width (LW, cm), length of lamina from base to widest point (WP, cm), leaf area (LA, cm 2 ), leaf mass (LM, g), and specific leaf area (SLA, cm 2 ·g -1 ) (Figure 2). For GMMs, we selected 11 landmarks for each leaf, including three landmarks distributed along the middle axis of the leaf (LM1-LM3) and eight landmarks symmetrically distributed on both sides of the leaf (LM4-LM11) (Figure 2) (Jensen 1990; Savriama and Klingenberg 2011; Viscosi et al. 2009). We organized the raw data for all leaf landmark configurations into 11 pairs of Cartesian coordinates ( x , y ) using ImageJ v.1.5 (Abràmoff et al. 2004). Then we imported all the x, y coordinates as input data into MorphoJ for the following analysis (Klingenberg 2011). Data analysis Genetic diversity and differentiation We estimated the number of different alleles ( N A ), number of effective alleles ( N E ), Shannon’s information index ( I ), observed heterozygosity ( H O ), expected heterozygosity ( H E ), and unbiased expected heterozygosity ( uH E ) by GENALEX v.6.5 (Peakall and Smouse 2012). We then used the Kruskal-Wallis H tests in SPSS 26 (SPSS Inc., Chicago, IL, USA) to test the significance of genetic diversity. We performed Bayesian cluster analysis inference of the population structure using STRUCTURE v.2.3 (Pritchard et al. 2000). We performed 20 independent runs for each value of K (1-10) using 200,000 generations for the Markov Chain Monte Carlo cycles (MCMC) and 100,000 generations for the burn-in cycles. We estimated the most likely number of clusters (K) using ΔK and LnP(K) statistics in the STRUCTURE HARVESTER (Earl and Vonholdt 2012; Evanno et al. 2005). In order to further explore possible genetic clusters, we provided STRUCTURE plots for different K values for visual comparison using DISTRUCT (Figure 3a) (Rosenberg 2004). We used admixture coefficient ( Q ) values to determine whether individuals were purebred or hybrid. We selected the threshold Q values of 0.9/0.1 as suggested by other oak studies (Lepais et al. 2009; Peñaloza-Ramírez et al. 2010; Qi et al. 2024). Individuals with Q values more than 0.9 or smaller than 0.1 were classified as purebreds, while those with intermediate Q values were considered as hybrids. The analyses were conducted based on pure and all individuals in this study respectively. We also performed principal coordinate analysis (PCoA) of the genetic distance matrix and plotted the first two eigenvectors to visualize genetic proximities of individuals using GENALEX v.6.5 (Peakall and Smouse 2012). We conducted hierarchical analysis of molecular variance (AMOVA) to quantify the degree of genetic differentiation between species and among populations using ARLEQUIN v.3.5 (Excoffier and Lischer 2010). Subsequently, we evaluated the significance of genetic differentiation using 10,000 permutations in ARLEQUIN v.3.5. Inference of fine-scale spatial genetic structure (FSGS) We assessed the FSGS through individual-level spatial autocorrelation analysis using SPAGeDi v.1.5 (Hardy and Vekemans 2002). We first divided the spatial distances of each species into ten distance classes to ensure an equal number of pairwise comparisons for each class. We then estimated the average of the pairwise kinship coefficient ( F ij ) for each distance class using Nason’s estimator, which represents the genetic relatedness between individuals i and j (Loiselle et al. 1995). The significance of F ij at each distance class was tested by the 95% confidence interval derived from 10,000 permutations. Next, we quantified the FSGS intensity using the S p statistic to enable direct comparison of FSGS (Vekemans and Hardy 2004). The S p statistic is calculated as S p = - b /(1 - F 1 ), where b is the slope of the regression of F ij values on the natural logarithm of the spatial distance among individuals, and F 1 is the average of F ij in the first distance class (Vekemans and Hardy 2004). Finally, we visualized FSGS by plotting the relationship between F ij and geographical distance. Leaf morphological analysis For TMMs, we first carried out Shapiro-Wilk test and Levene test (package car, Fox and Weisberg 2019) to explore data distribution and homogeneity respectively. We then conducted a one-way analysis of variance (one-way ANOVA) to test the differences among species for the seven traditional morphological traits (Li et al. 2022). Finally, we calculated the mean and standard deviation (SD) using SPSS 26. We also estimated the coefficient of variation (CV) to compare and quantify the degree of leaf shape variation for each trait among species. Because LA is not always independent to interspecies difference in LM, we used subsequent statistical analyses as log 10 -transformed data (Niklas et al. 2007). We conducted preliminary regression analyses to calculate the standardized major axis slopes and intercepts (α and logβ, respectively) for log-log linear relations between LA and LM for each species. For GMMs analysis, we first conducted generalized procrustes analysis (GPA) to extract leaf shape and leaf size by minimizing the sum of squared distances among corresponding landmarks (Klingenberg et al. 2002; Rohlf and Slice 1990; Viscosi and Cardini 2011). We then removed outliers that significantly deviated from the averages. Next, we separated symmetric (the variation in averages of the original and mirrored configurations) and asymmetric components (the differences between original and mirrored configurations) for the leaf shape data (Klingenberg et al. 2002; Mardia et al. 2000). We created a wireframe for visualizing leaf shape changes. Finally, we created covariance matrices at the tree level for subsequent multivariate statistical analysis. We performed principal component analysis (PCA) on symmetric and asymmetric components to identify the leaf shape variations among species (Klingenberg 2011; Klingenberg et al. 2012). We conducted two-block partial least squares (2B-PLS) analysis on symmetric and asymmetric components to assess allometric patterns of covariation between leaf size and shape (Rohlf and Corti 2000). We performed canonical variate analysis (CVA) to detect differences among species using Mahalanobis distances for permutation tests ( T 2 statistics; 10,000 permutations per test) (Viscosi and Cardini 2011). We performed discriminant analysis (DA) to distinguish the species using cross-validated scores classification tables with T 2 statistics ( P value for tests with 1000 permutations < 0.0001) (Klingenberg 2011). Results Genetic diversity and differentiation The observed heterozygosity ( H O ) ranged from 0.54 to 0.60, and the expected heterozygosity ( H E ) ranged from 0.54 to 0.62 (Table S3 and S4). Bayesian clustering indicated that K equals three as the optimal number of clusters (Figure S1), grouping all individuals into three clusters: one corresponded to Q. glauca , one to Q. multinervis and the other to the three species of Castanopsis (Figure 3a). When K equals five, each species was classified into a distinct cluster (Figure 3a). Based on a threshold Q value of 0.9/0.1, 522 individuals were assigned as pure individuals while 79 individuals were identified as admixtures (Figure 3a). The PCoA analysis was consistent with the STRUCTURE analysis, showing five clusters corresponding to the five Fagaceae species (Figure 3b; Figure S2). The results of AMOVA indicated a high level of genetic differentiation among species (Table 1; Table S5). The intraspecies analysis revealed that Q. glauca exhibited the highest genetic differentiation, while C. fargesii showed the lowest genetic differentiation (Table 1; Table S5). The majority of the genetic variation occurred within populations (Table 1; Table S5). The results of genetic diversity and differentiation for all individuals were similar to those for pure individuals. Fine-scale spatial genetic structure (FSGS) We detected significant FSGS in both Quercus and Castanopsis species ( P < 0.001) (Figure 4; Figure S3). For the Quercus species, Q. glauca presented a stronger FSGS with significant F ij extending up to 2.91 km, while Q. multinervis displayed significant F ij up to 0.63 km. Among the Castanopsis species, C. faberi exhibited significant F ij up to 2.53 km, C. fargesii up to 0.65 km and C. tibetana up to 0.53 km. For all species, F ij peaked at the first distance class and decreased with the increased distance. Q. glauca had the highest F 1 value at the first distance class of 0.89 km. The S p statistic, indicating the intensity of FSGS, was highest in Q. glauca and lowest in C. faberi . The FSGS results for all individuals were similar to those for pure individuals. Leaf shape variation One-way ANOVA showed significant differences in the seven traditional morphological traits among species (Table S6 and S7). The seven traditional morphological traits showed that the CV ranged from 17.80% to 96.81%, LM and LA had higher variability than other traits (Table 2; Table S8). C . tibetana had the largest leaves, while C . fargesii had the smallest leaves (Table 2; Table S8). Statistically significant scaling relationships between LA and LM were observed. LA and LM were correlated, with LA generally scaling less than one-to-one ratio with increasing LM (Table 3; Table S9). C. fargesii had the highest scaling exponents, while the scaling exponents of other species were similar (Table 3; Table S9). For GMMs analysis, the results of PCA for symmetric components indicated partial separation of Q. glauca , C. tibetana and C. faberi with PC1 accounted for 45% and PC2 accounted for 28% (Figure 5a; Figure S4a). The leaf shape variations were associated with the relative length of the petiole, the upper part of the leaf, and the leaf base and apex (Figure 5a; Figure S4a). PCA for asymmetric components indicated fully and densely overlap among species, and the leaf shape variation showed no regular pattern (Figure S5a and S6a). 2B-PLS analysis showed significant allometric patterns in the symmetric components (Figure 5b; Figure S4b), while not in the asymmetric components (Figure S5b and S6b). As leaf size increased, the relative length of the petiole decreased, the leaf shape changed from subelliptical to lanceolate, and overall narrowed (Figure 5b; Figure S4b). CVA indicated the five species formed five groups along CV axis, with the first two CVs accounted for 85% of the total leaf shape variation (Figure 5c; Figure S4c). CVA showed that Q. glauca had relative longer petioles and narrower leaf base compared to C. faberi (Figure 5c; Figure S4c). DA showed high accuracy in leaf species identification, ranging from 93% to 100%, with the lowest discrimination rate between Q. glauca and Q. multinervis at 94.65% and 92.59% (Figure S7 and S8). The results of leaf shape variation for all individuals were similar to those for pure individuals. Discussion In this study, we investigated both FSGS and leaf shape variation in five Fagaceae species from the genera Quercus and Castanopsis in Wuyishan National Park. Our results revealed that Quercus species exhibit stronger FSGS than Castanopsis species. Leaf morphological analysis shows that Quercus and Castanopsis species differ in leaf shape, LA and LM display the most significant interspecific differences. All five species show a pattern of diminishing returns in leaf trait scaling. Species delimitation based on molecular markers was more distinct than that based on morphological traits. Given the habitat fragmentation in this region, our findings underscore the importance of conserving FSGS and considering leaf shape variation in the design of effective conservation strategies. Patterns of fine-scale spatial genetic structure FSGS intensity ( S p ) across the five species ranged from 0.016 to 0.031(Figure 4), placing them in the upper-medium range compared with previous studies in Fagaceae species (e.g., Q . ilex , S p = 0.004; Q . suber , S p = 0.023; Soto et al. 2007). FSGS intensity is affected by pollen and seed dispersal abilities (Born et al. 2008; Mosca et al. 2018) and is positively correlated with the degree of limited gene flow (Curtu et al. 2015). We found that Quercus species exhibit higher FSGS intensity than Castanopsis species, consistent with more limited gene flow. Studies show that Quercus species possess typically limited pollen dispersal distances approximately 65 m ( Q. lobata , Sork et al. 2002), and most seeds dispersal constrained within 20 m ( Q. liaotungensis , Li and Zhang 2003). In contrast, Castanopsis species appear to rely more on high potential for long-distance dispersal of pollen ( C. sieboldii , Nakanishi et al. 2012), and most seeds dispersal distances might exceed 100 m ( C. chinensis , Wang et al. 2014). These dispersal strategies have direct consequences for genetic diversity and spatial genetic patterns (Islam et al. 2014). Limited dispersal in Quercus species can lead to stronger local genetic clustering and increased genetic drift, potentially reducing within-population genetic diversity over time (Montalvo et al. 1997). In contrast, greater gene flow in Castanopsis species may buffer against genetic erosion and promote higher heterozygosity (Wu et al. 2024). Leaf shape variation associated with ecological adaptation Leaf shape variation often reflects the adaptability of plant species to their environment (Blue and Jensen 1988; Wang et al. 2022). The seven traditional morphological traits exhibited significant variations among five Fagaceae species (Table S6), with an average CV of 49.24% (Table 2). Compared with other woody plants (e.g., Eucommia ulmoides , 20.77%, Gong et al. 2023; Phoebe chekiangensis , 41.43%, Lu et al. 2018), the five Fagaceae species showed greater variation in leaf shape. Notably, LA and LM exhibited the highest CV values among the five Fagaceae species, suggesting that these two traits may be more responsive to the species differentiation. Further investigation of the scaling relationships between LA and LM revealed significant diminishing returns in five Fagaceae species (Table 3), with similar diminishing returns also observed in other Fagaceae studies (e.g., Q. glauca , Guo et al. 2022; Q. serrata , Qi et al. 2025; Q. multinervis , Zhu et al. 2019). The concept of diminishing returns indicates a faster increase in LM than LA, with increased investment in dry mass of per unit leaf area reflecting higher investment to inert mass components (which increase leaf size but contribute minimally to photosynthetic capacity), such as cellulose, lignin, and sclerenchyma (Niklas et al. 2007). Among the five species, C. fargesii not only exhibited the strongest diminishing returns (Table 3), but also had the smallest leaves (i.e. smallest LL, PL, and LA) (Table 2), which may reduce transpiration water loss through a smaller surface area (Casper et al. 2001; Qin et al. 2018). This combination of strongest diminishing returns and smallest leaves may enhance the species’ competitiveness in resource-limited environments (Goud et al. 2023; Qi et al. 2025 Wright et al. 2004). GMMs results revealed that symmetric components of leaf shape more effectively captured morphological variation across the five species than asymmetric components (Figure 5; Figure S5), a phenomenon also found in other oak studies, such as Q. dentata (Yang et al., 2022), Q. cerris and Q. frainneto (Jovanović et al. 2022), and Q. aquifolioides (Li et al. 2021). Leaf morphology between the genera Quercus and Castanopsis species showed a clear separation in symmetric components, with partial overlap (Figure 5). Quercus species exhibited a greater range of leaf shape variation, characterized by longer petioles and narrower leaf base compared to Castanopsis species (Figure 5). One plausible explanation for this difference is their potential adaptation to varying water availability. Some studies indicated that Quercus species exhibit strong adaptation to drought conditions (e.g., Q. velutina , Kusi and Karsai 2019; Q. robur , Nosenko et al. 2025), while Castanopsis species are more adapted to humid environments and tend to be less drought-resistant (e.g., C. hystrix , Shen et al. 2023). Alternatively, differences in light adaptation may also contribute. Light-demanding Quercus species might enhance light capture through longer petioles (e.g., Q. velutina , Kusi and Karsai 2019) while Castanopsis species ( C. faberi and C. fargesii ) might be better suited to closed-canopy environments due to their shorter petioles (e.g. C. fargesii , Cornelissen et al. 1993). Conservation implications Integrating studies of FSGS intensity and leaf shape variation is crucial for developing species conservation strategies. The five species exhibited significant FSGS, likely reflecting local habitat fragmentation (Harata et al. 2012), which might drive loss of genetic diversity (Moreira et al. 2009) and thereby reduce species’ adaptability to environmental change (Du 2023). Moreover, variation in leaf traits represents phenotypic responses of plants to environmental pressures (Wang et al. 2022; Zhang et al. 2025). High FSGS intensity and low genetic diversity as observed in Q. glauca and Q. multinervis , indicating limited adaptive capacity. These species also exhibit weak diminishing returns, suggesting reduced competitiveness under resource-limited conditions. However, their broad range of leaf shape variation and long petioles suggest adaptative advantages in drought-prone, high-light environments. For these species, ex situ conservation strategies in drought, sufficient light environments are recommended. In contrast, C. tibetana , C. faberi and C. fargesii exhibit low FSGS intensity and high genetic diversity, indicating greater adaptive potential. These species show strong diminishing returns, which align with a resource-conservative strategy that may be advantageous in competitive or resource-limited environments. Their narrow leaf shape variation and short petioles suggest adaptation to humid, shaded habitats. In situ conservation, combined with effort to maintain higher humid and reduce light exposure, would be appropriate for these species. Overall, our findings advocate for prioritizing genetic factors in conservation planning, while considering phenotypic traits, such as leaf shape variation, as supplementary indicators of adaptive capacity. Author contributions FD designed the research; RLW, YJL and JWZ performed the experiments; RLW, YJL performed the analyses under the help of MQ and YZ; RLW, FD, YJL, MQ and YZ wrote the manuscript and all authors contributed to its revision. Acknowledgements We thank Wuyishan National Park and Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences for their support in field. FD acknowledges Dr. Saneyoshi Ueno for hosting her during her sabbatical at the Forestry and Forest Products Research Institute (FFPRI), Japan where she prepared the draft of this manuscript. Funding information This research was supported by the National Key Programme of Research and Development, the Ministry of Science and Technology (2022YFF1301401) to YBL, the Special Program for the Institute of National Parks, Chinese Academy of Sciences (KFJ-STS-ZDTP-2022-001) and National Science Foundation of China (No. 42071060) to FD. Conflict of interest statement The authors declare no competing interests. Data availability statement Genotyping data can be found in https://doi.org/10.6084/m9.figshare.29126033, leaf traditional morphological data can be obtained in https://doi.org/10.6084/m9.figshare.29124053, and leaf geometric morphological data can be obtained in https://doi.org/10.6084/m9.figshare.29124047. Photographs of sampling sites can be obtained at https://www.oakofchina.org/photo-of-sampling/. Source of variation df SS VC V% F ST Q. glauca Among populations 7 138.99 0.37 12.26 0.12 Within populations 366 976.97 2.67 87.74 Q. multinervis Among populations 2 37.05 0.25 7.42 0.07 Within populations 213 677.08 3.18 92.58 C. tibetana Among populations 6 35.06 0.14 5.29 0.05 Within populations 167 411.65 2.46 94.71 C. faberi Among populations 6 27.16 0.09 3.69 0.04 Within populations 165 387.71 2.35 96.31 C. fargesii Among populations 3 9.96 0.04 1.43 0.01 Within populations 104 251.74 2.42 98.57 All species Among species 4 856.85 1.00 24.12 Among populations within species 24 279.52 0.26 6.23 0.30 Within populations 1015 2936.17 2.89 69.65 Note: df , degrees of freedom; SS, sum of squares; VC, variance component; V%, percentage of variation; F ST , differentiation among populations. Significance tests (1,000 permutations) showed all fixation indices were significant ( P < 0.001). Table 2 Means and standard deviations (SD) of seven traditional morphological traits for pure individuals of the five Fagaceae species and coefficient of variation (CV) for each trait. Traditional morphological traits Q. glauca Q. multinervis C. tibetana C. faberi C. fargesii CV (%) Leaf length (LL) (cm) 12.31 ± 1.85 13.45 ± 1.86 22.16 ± 3.71 11.33 ± 1.72 10.17 ± 1.58 32.24 Petiole length (PL) (cm) 2.14 ± 0.50 1.74 ± 0.40 2.15 ± 0.43 0.78 ± 0.28 0.78 ± 0.20 41.62 Leaf width (LW) (cm) 2.47 ± 0.45 2.42 ± 0.39 4.41 ± 0.77 1.93 ± 0.30 1.58 ± 0.24 37.72 Length of lamina from base to widest point (WP) (cm) 6.10 ± 1.19 6.46 ± 1.21 11.43 ± 2.64 4.88 ± 1.06 4.68 ± 0.79 39.60 Leaf mass (LM) (g) 0.68 ± 0.24 0.62 ± 0.19 2.59 ± 0.96 0.43 ± 0.14 0.30 ± 0.09 96.81 Leaf area (LA) (cm 2 ) 41.00 ± 12.33 43.61 ± 11.63 135.07 ± 42.88 30.09 ± 8.44 22.33 ± 6.05 78.89 Specific leaf area (SLA) (cm 2 ·g -1 ) 61.61 ± 8.28 72.14 ± 10.45 53.54 ± 8.00 70.80 ± 9.36 75.28 ± 9.96 17.80 Table 3 Statistical parameters of the log-log linear relations between leaf area (LA) and leaf mass (LM) for pure individuals of the five Fagaceae species. Q. glauca Q. multinervis C. tibetana C. faberi C. fargesii α 0.80 0.80 0.80 0.81 0.83 log β 1.75 1.80 1.80 1.77 1.78 95% CI (0.78, 0.82) (0.78, 0.83) (0.76, 0.83) (0.77, 0.85) (0.78, 0.88) R 2 0.84 0.80 0.84 0.79 0.81 P <0.001 <0.001 <0.001 <0.001 <0.001 Note: α, the slope of the log 10 -transformed LA vs. LM regression curve; β, the elevation of the log 10 -transformed LA vs. LM regression curve; CI, confidence intervals; R 2 , coefficient of determination. Figure legends Figure 1. Sampling locations for five Fagaceae species: Q. glauca , Q. multinervis , C. tibetana , C. faberi and C. fargesii in the Wuyishan National Park. (a) Sampling sites of each Fagaceae species. The red area in the upper left corner indicated the sampling range. (b-f) Sampling locations for each individual of the five Fagaceae species in the study. Population codes and site description are given in Table S1. Figure 2. Leaf configuration of Q. glauca , showing four traditional morphological traits and locations of the 11 features used as landmarks (LMs) with descriptions of the LMs on the right. Figure 3. Genetic assignment and differentiation of the five Fagaceae species based on nSSR data. (a) Histogram assignments of all individuals, each bar represents a single individual, with portions of the bar colored depending on the ancestry proportions estimated. Each species is indicated on the top and population codes below the histogram. (b) Principal coordinates analysis (PCoA) for pure individuals based on nSSR data. Figure 4. Fine-scale spatial genetic structure (FSGS) for pure individuals of the five Fagaceae species based on nSSR data. The pairwise kinship coefficient ( F ij ) plotted against geographical distances (km). The dotted lines indicate the 95% confidence interval for the pairwise kinship coefficient ( F ij ) values (shown by solid lines). S p statistic represent the FSGS intensity for each species. Figure 5. Leaf geometric morphometric analysis at the tree level for pure individuals of the five Fagaceae species. (a) Scatter plot of principal component analysis (PCA) for symmetric components with 90% confidence ellipses. Transformation grid below shows extreme leaf shapes along PCs. (b) Scatter plot of two-block partial least squares (2B-PLS) analysis for symmetric components. Transformation grid shows leaf shapes along PLS axis. (c) Scatter plot of canonical variate analysis (CVA) with 90% confidence ellipses. Transformation grid below shows extreme leaf shapes along CVs. Supplementary Material File (figure_4.tif) Download 6.63 MB References 1. Abràmoff, M. D., Magalhães, P. J., and Ram, S. J. (2004). Image processing with ImageJ. Biophotonics International , 11 (7), 36-42. https://doi.org/10.3233/ISU-1991-115-601 Angbonda, D. M. A., Monthe, F. K., Bourland, N., Boyemba, F., and Hardy, O. J. (2021). 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Forests , 10 (12), 1138. https://doi.org/10.3390/f10121138 Table 1 Analysis of molecular variance (AMOVA) for pure individuals of the five Fagaceae species based on nSSR data. Crossref Google Scholar Information & Authors Information Version history V1 Version 1 05 June 2025 Peer review timeline Published Ecology and Evolution Version of Record 18 Feb 2026 Published Copyright This work is licensed under a Non Exclusive No Reuse License. Collection Ecology and Evolution Keywords comparative genetics molecular genetics plants sequencing statistical terrestrial Authors Affiliations Rongle Wang Beijing Forestry University View all articles by this author Yanjun Luo Beijing Forestry University View all articles by this author Min Qi Beijing Forestry University View all articles by this author Yi Zhang Beijing Forestry University View all articles by this author Jiawen Zhang Beijing Forestry University View all articles by this author Yibo Luo Chinese Acad Sci View all articles by this author Fang Du 0000-0002-7377-5259 [email protected] Beijing Forestry University View all articles by this author Metrics & Citations Metrics Article Usage 398 views 315 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Rongle Wang, Yanjun Luo, Min Qi, et al. Fine-scale spatial genetic structure and leaf shape variation in five Fagaceae species: insights into conservation and adaptation. Authorea . 05 June 2025. 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