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Article

Population Structure and Prediction of Potential Suitable Areas of Anemone davidii Franch. (Ranunculaceae) from Southwestern China

by
Yongdong Shen
1,
Xu Zhang
1,
Yuxiao Zhang
1,
Yu Zhang
2,3,*,
Huimin Li
4,
Long Wang
5 and
Yuanqi Chen
6
1
College of Life Science and Health, Hunan University of Science and Technology, Xiangtan 411201, China
2
Institute of International Rivers and Eco-Security, Yunnan University, Kunming 650500, China
3
Hunan Province Key Laboratory of Economic Crops Genetic Improvement and Inte-Grated Utilization, Xiangtan 411201, China
4
Jiangsu Key Laboratory for the Research and Utilization of Plant Resources, Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China
5
Key Laboratory of Plant Resources Conservation and Sustainable Utilization, South China Botanical Garden, Chinese Academy of Sciences, Guangzhou 510650, China
6
School of Earth Science and Spatial Information Engineering, Hunan University of Science and Technology, Xiangtan 411201, China
*
Author to whom correspondence should be addressed.
Forests 2026, 17(2), 207; https://doi.org/10.3390/f17020207
Submission received: 17 December 2025 / Revised: 4 January 2026 / Accepted: 8 January 2026 / Published: 4 February 2026
(This article belongs to the Section Forest Biodiversity)
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Abstract

Anemone davidii Franch. is an herbaceous plant with high ornamental and medicinal value belonging to the Ranunculaceae family. Understanding its genetic diversity and predicting its potential habitat shifts are crucial for its germplasm conservation. In this study, we analyzed the genetic diversity of 164 individuals from A. davidii and its relatives using genotypic sequencing (GBS) technology. The results indicated that the expected heterozygosity (He) of 12 A. davidii populations ranged from 0.074 to 0.095, while the observed heterozygosity (Ho) ranged from 0.105 to 0.127. Phylogenetic, principal component (PCA), and population structure analyses revealed clear genetic separation among A. davidii, A. griffithii, and A. scabriuscula. The 12 A. davidii populations were grouped into three genetic clusters. Six populations—CQ, ES, SNJ, SZ, TR, and WX—of Central China were clustered together. Southwestern region populations were divided into two clusters (DG, PZ, SF and DY, EMS, HY). Low genetic differentiation values (Fst, 0.018–0.053) and high levels of gene flow (Nm, 4.4678–13.639) between populations were observed in this study, indicating that genetic differentiation was lower between adjacent populations. We also used the Maximum Entropy (MaxEnt) model to predict changes in suitable distribution areas of A. davidii across four time periods and two climate scenarios (RCP4.5, RCP8.5). Compared to the Last Glacial Maximum (LGM), the current suitable habitat area has contracted. Future climate projections indicated a progressive range contraction under both scenarios. Therefore, appropriate conservation measures are needed to address its limited genetic diversity and projected habitat loss under climate change. Our findings provide insights into the population genetics of A. davidii and the impact of climate change on plants of Southwestern China.

1. Introduction

Anemone davidii Franch. is a member of the Ranunculaceae family, with cordate-orbicular leaves, bracts with stalks, and white flowers, distributed in southwest China [1,2]. It had high ornamental and medicinal value, and its rhizomes are used medicinally to treat symptoms such as bruises [3]. It contains triterpenoid saponins that have important biological properties, such as antibacterial and anti-inflammatory properties [4]. Within the genus, A. daivdii is closely related to A. griffithii Hook. f. & Thomson and A. scabriuscula W. T. Wang, all belonging to A. sect. Anemonthea [5]. A. griffithii is mainly found in Xizang and Sichuan, in the understory of montane forests or ditch edges at 1650–3000 m. A. scabriuscula is narrowly distributed in northwestern Yunnan, China [6].
Genetic diversity provides the essential heritable variation upon which natural selection acts, serving as the primary driver of evolution [7,8]. A higher level of intraspecific genetic diversity enhances a population’s adaptive capacity, thereby ensuring its ability to cope with environmental changes and secure long-term survival [9]. Genotyping-by-sequencing (GBS) is an effective method for studying the genetic diversity and structure of non-model plant species [10,11,12]. Single-nucleotide polymorphisms (SNPs) can distinguish different species at the population level, aiding in understanding historical evolutionary relationships [13].
Climate change is one of the important factors affecting the suitability of species’ habitats [14]. Some species experience reduced and fragmented suitable habitats, even leading to the extinction of certain species under intensified global climate change [15]. The Maximum Entropy (MaxEnt) model has been widely used to predict species ecological niches under different climate scenarios, such as Aquilegia [16], A. nemorosa L. [17], Ranunculus sardous Crantz [18], and Coptis [19]. These models integrate species occurrence records with environmental variables (e.g., bioclimatic data) to predict current and future suitable habitats [20,21]. Puchałka et al. [17] showed that precipitation in the warmest season was the most significant factor influencing the distribution of A. nemorosa L. and A. ranunculoides L., and the suitable habitat areas for these two species decreased more significantly from 2061 to 2080 based on the Maxent model. Understory spring geophytes are particularly sensitive to climate change due to their specific phenological cues [17]. However, the potential impact of climate change on the distribution of A. davidii remains unexplored.
Here, we obtained single-nucleotide polymorphisms (SNPs) using GBS technologies to characterize the genetic diversity and population structure of A. davidii and used the Maxent model to predict the suitable areas of the species. This study aimed to (i) assess the genetic diversity and population structure of A. davidii and dissect the evolutionary processes, (ii) identify the key environmental factors on its distribution, and (iii) synthesize genetic and distributional data to evaluate its vulnerability to climate change and provide conservation suggestions for wild populations. This study will provide a theoretical basis for understanding its evolutionary potential and developing conservation strategies of A. davidii.

2. Materials and Methods

2.1. Materials and Data Sources

A total of 164 samples were collected from 19 populations of A. davidii and its related species from 2014 to 2021. The 110 leaf samples were collected from 12 populations of A. davidii across its full distribution range. Based on the geographical origin, the sample populations were categorized into two regions: six from Southwestern China (DG, DY, EMS, HY, PZ, SF) and six from Central China (CQ, ES, SNJ, SZ, TR, WX). There were also two populations of A. scabriuscula and three populations of A. griffithii, and one population of A. exigua Maxim. and A. rivularis Buch.-Ham. ex DC., which served as outgroups (Supplementary Table S1). Fresh leaf material was collected from all individuals in the field, rapidly dried using silica gel, and stored at −20 °C until DNA extraction. Total genomic DNA was extracted using the DNeasy Plant Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s instructions [22].

2.2. GBS and SNP Calling

Genomic DNA was extracted from all samples, with a total amount of 1.5 μg per sample, and the DNA concentration was accurately quantified by Qubit. Following sample amplification, selected fragments were utilized for library construction. Whole genome sequencing was performed on the Illumina HiSeq PE150 platform (Santa Clara, CA, USA) [23]. Quality control was implemented through a multi-step process. First, low-quality paired reads were removed based on the following criteria: (1) reads containing ≥ 10% unidentified nucleotides (N); (2) reads with >10 nt adapter alignment (allowing ≤ 10% mismatches); and (3) reads with >50% bases having Phred quality scores < 5. This filtering primarily addressed base-calling repeats and adapter contamination, resulting in 94.55% and 88.14% of bases achieving quality scores of ≥Q20 and ≥Q30, respectively. As there was no full genome as a reference for A. davidii in the databases, we constructed a GBS reference genome for this study. Each sample is clustered using the Ustacks program in Stacks (version 2.4, Catchen Lab, Urbana, IL, USA) software [24] followed by the ASUstacks method to process the clustering results. Subsequent analysis employed the Burrows–Wheeler Aligner (BWA version 0.7.8, Heng Li, Boston, MA, USA) with the command ‘mem -t 4 -k 32 -M’ to map high-quality paired-end reads to the draft reference genome [25]. To minimize PCR amplification artifacts, duplicate reads were removed using SAMtools v0.1.19 [26]. Population-scale SNP calling was performed using a Bayesian approach implemented in SAMtools, with stringent quality filters applied: coverage depth (≥4), RMS mapping quality (≥20), minor allele frequency (maf ≥ 0.01), and missing data rate (≤0.5). Heterozygosity analysis was conducted using VCFtools (version 0.1.14) [27].

2.3. Phylogenetic Tree, Principal Components, and Population Structure Analyses

To visualize the relationships between populations and genetic distances, we used TreeBest-1.9.2 to calculate a distance matrix [28]. A phylogenetic tree of 164 individuals was constructed by the neighbor-joining method. Population genetic structure was assessed using principal component analysis (PCA) implemented in GCTA v1.24.2, with visualization performed using R. The statistical significance of principal components was determined using the Tracy–Widom test. We also analyzed the population structure of 110 individuals of A. davidii using ADMIXTURE v1.23 [14]. The number of assumed genetic clusters K ranged from 2 to 12, with 10,000 iterations for each run. The optimal K value was based on the cross-validation error (CV). Arlequin was used to calculate genetic diversity indicators, including the observed heterozygosity (Ho), expected heterozygosity (He), and population nucleotide diversity (π) [29].

2.4. Genetic Differentiation and Gene Flow Analysis

Population genetic differentiation of A. davidii was calculated using Weir & Cockerham’s Fst index as implemented in VCFtools [30]. The gene flow (Nm) between populations was estimated using the formula (Nm ≈ (1 − Fst)/4 Fst), acknowledging the simplifying assumptions of this model. [31].

2.5. Ecological Models for Different Time Periods

The geographical distribution data of 119 A. davidii accessions were analyzed based on the Chinese Virtual Herbarium (http://www.cvh.ac.cn/, accessed on 7 January 2026) and our field collections. The study focused on analyzing elevation and 19 climate factors. Climate data were obtained from WorldClim (https://www.worldclim.org/, accessed on 7 January 2026) at a 2.5-arcminute resolution for three temporal periods [32]: the Last Glacial Maximum (LGM), current conditions (1970–2000), and future projections (2050s and 2070s) [17,18]. In future scenarios, the bioclimatic data of the 2050s represent the mean values from 2041 to 2060, while the data of the 2070s represent the mean values from 2061 to 2080. For the LGM climate reconstruction, we employed the Community Climate System Model version 4 (CCSM4) [33]. Two Representative Concentration Pathways, including RCP4.5 and RCP8.5 from the IPCC Fifth Assessment Report (IPCC AR5, 2014), were used to represent Future climate projections. These projections were generated using the Beijing Climate Center Climate System Model version 1.1 (BCC-CSM1-1), developed by the National Climate Center of the China Meteorological Administration.
Species distribution modeling was performed using MaxEnt version 3.4.4, incorporating 119 georeferenced occurrence records of A. davidii (in CSV format) and environmental variables (in ASC format). The occurrence data were randomly partitioned, with 75% used for model training and 25% for model validation [34]. Receiver operating characteristic (ROC) curves were created by repeating the modeling process 10 times. The results were imported into ArcMap v 10.5 to generate raster-format outputs for subsequent analysis. Administrative boundary data of China were obtained from the National Fundamental Geographic Information System (https://www.ngcc.cn/, accessed on 7 January 2026.).
After modeling, the area under the curve (AUC) of the ROC was selected to evaluate the prediction accuracy [35,36]. The value of AUC ranges from 0 to 1. A larger value indicates a higher accuracy of the model. It is generally believed that when the AUC value is <0.6, the model prediction fails—0.6–0.7 (poor), 0.7–0.8 (general), 0.8–0.9 (good), and 0.9–1.0 (excellent) [37]. Model outputs in ASC format were converted to raster format using SDMtoolbox 2.5 within ArcMap 10.5 and overlaid with China’s administrative boundaries. The appropriate levels were then classified and visualized using the reclassification tool. The probability results of the suitable distribution areas of A. davidii obtained from the model simulations were classified into four categories: unsuitable area (0–0.2), low suitable area (0.2–0.4), medium suitable area (0.4–0.6), and high suitable area (0.6–1.0) [38]. Area calculations for each suitability class were performed using the field calculator in the attribute table. To quantify range shifts under different climate scenarios, we employed the raster calculator in ArcMap’s Spatial Analyst tool (version 10.5) to calculate expansion and contraction of suitable areas. All spatial calculations were verified through the attribute table.
To analyze spatial distribution shifts, we calculated the centroid of suitable habitats for each climate scenario using the spatial statistics tool in ArcGIS Desktop (version 10.5, ESRI, Redlands, CA, USA) [39], obtaining geographic coordinates (latitude and longitude). Centroid positions from the current period and LGM were merged using the Geoprocessing merge tool. Migration trajectories between these periods were reconstructed using point-to-line conversion in the data management module, with Euclidean distances calculated using spatial analysis tools. Future climate scenarios were processed similarly to project potential migration patterns. All spatial analyses were conducted in ArcMap 10.5, with final migration trajectories visualized using cartographic tools.

3. Results

3.1. Sequencing Data and Genetic Diversity Analysis

Genomic analysis was performed on 164 accessions using GBS technology. The results showed that the sequencing quality was high (Q20 ≥ 96.23%, Q30 ≥ 89.47%) and conformed to the normal GC distribution. The sequencing generated an average of 1,995,236,352 raw reads per sample, with 91.17% of bases achieving Q30 quality, an average GC content of 38.92%, and an average sequencing depth of 18.87×. Initial variant calling identified 485,405 raw SNPs, which were subsequently filtered to yield 15,854 high-quality SNPs for downstream analysis.
For the 12 populations of A. davidii, the values of expected heterozygosity (He) ranged from 0.074 to 0.095, with a mean of 0.087; observed heterozygosity (Ho) values varied from 0.105 to 0.127, and the average was 0.120. The values of nucleotide diversity (π) ranged from 0.086 to 0.111 with an average of 0.097. Our results showed that the TR population had the highest level of genetic diversity among 12 populations, and the DG population was the opposite. Among the two distribution regions, the genetic diversity among the Southwestern region was relatively low (Ho = 0.105–0.121, He = 0.074–0.089), less than that of Central China (Ho = 0.126–0.127, He = 0.081–0.095). The nucleotide diversity (π) in the two regions ranged from 0.091 (Southwestern region) to 0.103 (Central China). In addition, the average values of Ho and He were 0.112 and 0.074 for A. griffithii, and 0.117 and 0.089 for A. scabriuscula, respectively (Table 1).

3.2. Phylogenetic Relationships, Population Structure, and PCA

The phylogenetic relationships among A. davidii populations and related species were reconstructed using the neighbor-joining method based on 164 samples (Figure 1). Our results revealed 110 accessions of A. davidii were clustered into three clades. Clade I contained all samples of the CQ, ES, SNJ, SZ, TR, and WX populations from Central China. Clade II included the DY, EMS, and HY populations from the southwest region. Clade III comprised three populations from the southwest region, including the DG, SF, and PZ populations. Clades I and II were grouped together into a large clade, indicating that gene exchange and migration likely occurred between some populations within these clades. Furthermore, A. scabriuscula populations (DL and LJ) formed a separate cluster, as did A. griffithii populations (CQ1, CQ2, and CQ3). Clear genetic boundaries were observed between A. davidii, A. scabriuscula, and A. griffithii.
The PCA results also revealed clear genetic differentiation among A. davidii, A. scabriuscula, and A. griffithii (Figure 2). Based on the first two major axes, 110 samples from 12 populations of A. davidii were divided into two groups: the first comprised all individuals of CQ, WX, ES, SNJ, TR, and SZ populations from Central China, as well as DY, EMS, and HY populations from the southwest region; the second contained DG, PZ, and SF populations from the southwest region. The individuals of Central China populations were concentrated and overlapped, suggesting a higher similarity of genetic background between these populations. In contrast, individuals in the DG, PZ, and SF populations exhibited a discretized pattern, revealing a degree of heterogeneity within these groups.
Considering the phylogenetic tree and PCA results, we focused on the population structure of A. davidii with K values of 2 and 3. As the cross-validation error was lowest at K = 2, this value was identified as the most probable number of ancestral genetic clusters (Supplementary Figure S1). At K = 2, the 110 samples from 12 populations were classified into two major genetic clusters (Figure 3). The first cluster (green) contained samples from six Central China populations (CQ, WX, ES, SNJ, TR, and SZ) and three populations of the Southwestern region (DY, EMS, and HY), and another cluster (blue) consisted of three Southwestern China populations (DG, PZ, and SF). As K increased to 3, all individuals from 12 populations were split into three clusters (Figure 3). Cluster I contained the CQ, WX, ES, SNJ, TR, and SZ populations. Cluster II included all individuals of the DY, EMS, and HY populations. Cluster III contained the remaining individuals of the DG, PZ, and SF populations.

3.3. Genetic Differentiation and Gene Flow

The Fst values of A. davidii populations ranged from 0.018 to 0.053 (Table 2), with a mean value of 0.032. The high Fst values occurred between populations from different genetic clusters (DG–CQ, DG–TR, SF–CQ, SF-TR). For example, the greatest genetic differentiation (Fst = 0.053) and lowest gene flow (Nm = 4.467) were found between the SF (Southwestern region) and TR (Central China) populations. Low genetic differentiations (Fst < 0.025) were observed within the Southwestern region (e.g., DY–EMS, PZ–DG) and Central China (e.g., ES–SZ, CQ–TR) groups. The HY and EMS populations from the Southwestern region showed the lowest Fst (0.018) and the highest gene flow (Nm = 13.639). Surprisingly, the low genetic differentiation and high gene flow (Fst = 0.02, Nm = 12.25) between EMS (Southwestern region) and WX (Central China cluster) indicated that they may have historical gene flow.

3.4. Key Environmental Variables

Based on the geographical location of A. davidii and each environmental factor, the mean AUC values of the current period, LGM, and four future climate scenarios were above 0.9 (Figure 4), which indicated that the model accuracy and credibility were exceptionally robust.
The environmental variable analysis revealed that annual precipitation (Bio12), elevation (Elev), and precipitation of the driest month (Bio14) were the primary determinants of A. davidii distribution, contributing 36.85%, 14.57%, and 11.58% respectively (Table S2). Response curve analysis identified optimal ranges for these variables: Bio12 (828.64–1373.92 mm; Figure 5), Elev (805.27–3105.37 m; Figure 5), and Bio14 (8.07–27.16 mm; Figure 5). The probability of occurrence peaked at 0.81 when Bio12 reached 874.72 mm, following an initial increase threshold at 521.44 mm. Maximum occurrence probabilities were observed at 2452.86 m for elevation (0.96) and 18.31 mm for Bio14 (0.84).

3.5. Past, Present, and Future Distribution Trends of A. davidii

The changing route of the distribution center and the displacement distance of A. davidii were plotted in this study (Figure 6, Table S3). Historical analysis revealed a 71.16 km northeastward shift from the LGM (105°21′ E, 29°34′ N) to the current distribution center (105°34′ E, 30°14′ N). Under the RCP4.5 scenario, A. davidii moved from 105°34′ E, 30°14′ N to 105°34′ E,29°49′ N, then to 1105°34′ E, 29°49′ N, a total migration displacement of 189.35 km. Under the RCP8.5 scenario, the predicted movement was from 105°34′ E, 30°14′ N to 106°24′ E, 29°52′ N, then to 106°15′ E, 29°40′ N, with a total migration displacement of 116.31 km.
Based on our results presented by the Maxent model, the present low-, medium-, and high-suitability habitats were 23.04 × 104 km2, 16.69 × 104 km2, and 14.90 × 104 km2, respectively, with a total area of 54.63 × 104 km2 (Figure 7A, Table 3). The highly suitable areas were mainly distributed in central Sichuan and northwestern Guizhou, and the border regions of Sichuan, Chongqing, Guizhou, and Hubei. The medium suitable area covers eastern Sichuan, southern Chongqing, and western Guizhou. The low suitable habitat mainly included most of Guizhou, the northwestern part of Hunan, and southwestern Hubei. The total distribution area was decreased by 3.55% from the LGM climatic condition (Figure 7B, Table 3).
Under the RCP4.5 scenario, the total suitable area was projected to decrease by 6.63% (51.01 × 104 km2) compared to the current habitat size by the 2050s (Figure 7C, Table 3). Low suitable habitat covered 18.63 × 104 km2, a decrease of 19.1%. Moderate and high suitable habitats covered 17.44 × 104 km2 and 14.93 × 104 km2, which increased by 4.49% and 0.2%, respectively, from current values. By the 2070s, the total suitable habitat covered 50.03 × 104 km2, a decrease of 8.42%. Low suitable habitat covered 21.27 × 104 km2, moderate suitable habitat covered 15.11 × 104 km2, and high suitable habitat covered 13.65 × 104 km2, representing minimal decreases of 7.68%, 9.46%, and 8.38%, respectively (Figure 7D, Table 3).
Under the RCP8.5 scenario, the total habitat covered 48.39 × 104 km2 by 2050s, which was 8.61% less than that under the contemporary climate (Figure 7E). Low suitable habitat covered 19.49 × 104 km2, moderate suitable habitat covered 15.10 × 104 km2, and highly suitable habitat covered 13.80 × 104 km2; these represent decreases of 15.4%, 9.5%, and 7.38%, respectively. By the 2070s, the total suitable habitat covered 50.63 × 104 km2, a decrease of 4.35% compared with the current value. Low suitable habitat covered 16.89 × 104 km2 and high suitable habitat covered 13.93 × 104 km2, decreasing by 26.6% and 6.5%, respectively, whereas moderate suitable habitat covered 19.81 × 104 km2, an increase of 18.7% (Figure 7F, Table 3).

4. Discussion

4.1. Low Genetic Diversity and Implications for Conservation

Although different analytical methods were employed, our results showed that the genetic diversity of A. davidii was relatively low compared with A. altica Fisch. ex C. A. Mey (He = 0.293) [40], A. coronaria L. (He = 0.124 ± 0.002) [41], A. amurensis (Korsh.) Kom. (He = 0.50~0.91) [42], A. multifida Poir. (He = 0.084~0.135) [43], and A. shikokiana (Makino) Makino (He = 0.1925) [44], as demonstrated in previous studies. Our data confirmed that widespread species A. davidii (He = 0.087) maintains relatively less genetic diversity compared to the narrowly endemic plant A. shikokiana, which was inconsistent with the broader pattern that associates limited species distribution with lower genetic variation [45]. The analysis of genetic diversity not only reveals population genetic structure but also illuminates key aspects of evolutionary history [46]. In this study, the genetic diversity levels of A. davidii populations from Yunnan (DG) and Sichuan (SF, PZ, DY, EMS, and HY) were lower than those from Hunan (SZ), Hubei (SNJ), and Guizhou (TR). This pattern may be attributed to several factors associated with geographic environment, potential impacts of human activities, and extreme weather [47,48]. The increasing climate change has caused a decrease in biodiversity in the mountainous areas of western Sichuan [49,50,51]. It was also reported that plants at high altitudes were in danger of extinction due to increasing human activities and climate change [52,53,54,55]. Low genetic diversity can reduce a population’s adaptive potential due to inbreeding and genetic drift, increasing its vulnerability to environmental changes [9,56]. The lower diversity observed in edge populations (e.g., DG) warrants particular conservation attention.

4.2. Shallow Population Structure and High Gene Flow

Fst and Nm could describe the degree of differentiation between natural populations [57]. Despite covering a broad geographic range, populations of A. davidii exhibit a shallow genetic structure and low genetic differentiation, which differs from other plants from the Yunnan–Guizhou Plateau and its adjacent regions [58,59,60]. This may be because, for subalpine plant species, gene flow mediated by pollen and seeds is greatly influenced by environmental heterogeneity and topographical conditions [61]. Anemone species are often insect-pollinated, white flowers of A. davidii may attract a broad pollinator community during its flowering season (May–June), facilitating pollen-mediated gene flow [40,43]. Our results indicate that populations from Central China (CQ, ES, SNJ, SZ, TR, and WX) and Southwestern China (DY, EMS, HY) maintain relatively short genetic distances, reflecting a history of extensive gene flow that mitigated the effects of geographic distance. However, recent habitat fragmentation, potentiated by the complex topography of the Qinghai–Tibet Plateau (e.g., high mountains and deep valleys), has created effective geographical isolation even over short distances [62,63]. This mechanism is exemplified by the differentiation between populations such as DG–CQ and DG–TR. For instance, significant differentiation is observed between the DG–CQ and DG–TR populations. The observed genetic homogeneity indicates that detrimental genetic effects (e.g., inbreeding depression) could spread more easily if the population size declines. In addition, our results support that the Dali population which was previously misidentified as A. davidii actually belongs to A. scabriuscula. Thus, it is necessary to characterize the population genetic structure and phylogenetic tree for a deep understanding of the relationships between these two species.

4.3. Climatic Niche and Projected Range Contraction

Our MaxEnt models identified A. davidii as a precipitation-sensitive species, which is a common trait of understory herbs in this region. Temperature and precipitation can affect the physiological processes of plants at various stages, especially freezing rain, which could cause damage to plant cells and pollen sterility [64,65]. Previous studies also demonstrated that precipitation played a decisive role in the distribution patterns of plants in Sichuan Province [66].
Global warming is a key driver of changes in species’ habitat and geographic distribution [67,68,69]. These changes are commonly observed as range contractions and shifts toward higher latitudes and elevations [70,71,72]. A. davidii, currently distributed in a subtropical monsoon climate zone, follows this general trend of habitat shrinkage from the Last Glacial Maximum to future climate scenarios, particularly in its highly suitable areas. Specifically, the greatest reduction in total suitable habitat occurred under the RCP8.5–2050s scenario; however, under RCP8.5–2070s, the medium-suitability habitat exhibited an expansion trend. Notably, the projected expansion of medium-suitability areas does not necessarily indicate resilience; it may reflect the fragmentation and degradation of current optimal habitats rather than a viable range expansion. Interestingly, its distribution center is projected to shift southward to the region south of Chongqing, a pattern possibly attributed to the local monsoon climate [73], which contrasts with the commonly expected poleward shift.
This observed contraction aligns with predictions for other species. For instance, Makhkamov et al. [18] predicted a decrease in the suitable area for Ranunculus sardous Crantz in Uzbekistan under RCP8.5-2070. Similarly, Li Jun et al. [19] constructed current and future distributions for three Coptis species, C. chinensis Franch., C. deltoidea C. Y. Cheng & P. G. Xiao, and C. teeta Wall., and indicated that highly suitable habitats for C. chinensis and C. teeta were projected to decrease in the future. However, habitat expansion under future climate change is also possible, as demonstrated for some Fritillaria species [38,74]. These contrasting responses underscore that species may migrate in multiple directions—or not at all—depending on where climatically suitable conditions emerge or disappear [75,76]. Indeed, spatial variation in the net change in habitat suitability—driven by localized gains and losses—determines both the trajectory and extent of a species’ range shift. Consequently, for plant species inhabiting topographically complex regions, migration to higher latitudes or altitudes is not a universal response to climate warming [72,77].
The model projections in the current study were limited by their correlative nature and omission of local biotic interactions. For the understory species like A. davidii, persistence depends not only on macroclimate but on the buffered microclimate and soil conditions provided by the intact forest ecosystem. These factors can create microrefugia, allowing populations to endure in model-projected unsuitable areas. Conversely, disturbance to these local conditions can invalidate model-predicted suitability. Consequently, future research should prioritize integrating these biotic and microhabitat filters. Their omission may lead to a systematic misrepresentation of climate change impacts at the population level, thereby introducing potential bias into the conservation priorities identified.

4.4. An Integrated Vulnerability Assessment for Conservation

Integrating our genetic and ecological findings, A. davidii exhibited limited standing genetic variation, which may impair its adaptive evolutionary response. Concurrently, its climatically suitable habitat is projected to undergo contraction and fragmentation under future climate scenarios. Conservation recommendations should therefore be proactive and multidimensional: (1) core areas in central Sichuan and northwestern Guizhou, predicted to remain highly suitable, should be designated as priority conservation zones; (2) assisted gene flow from diverse populations (e.g., TR) into low-diversity populations (e.g., DG) could boost adaptive potential. Germplasm from all genetic clusters should be collected for seed banks or botanical gardens.

5. Conclusions

In this study, 164 individuals of A. davidii and its affines were analyzed for genetic diversity using GBS technology, and the Maxent model was employed to predict the suitable distribution areas of A. davidii. The results showed that the genetic diversity of the A. davidii populations in Southwestern China was significantly lower than that of the Central population. The low pairwise Fst values and high Nm estimates indicate minimal genetic differentiation and substantial gene flow among populations, particularly between adjacent regions. Phylogenetic tree, PCA, and population structure analysis showed that the 12 populations were clustered into three groups. We revealed a shallow genetic structure and concerningly low diversity of A. davidii, facing significant range contraction under climate change due to its precipitation-dependent niche. The synergy between intrinsic genetic limitations and extrinsic climatic threats underscores their high vulnerability. Our results demonstrate its advantages of combining approaches for conservation prioritization and offer a replicable framework for assessing other endemic species in the biodiverse yet threatened mountains of Southwestern China.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f17020207/s1. Figure S1. Cross-validation (CV) error plot; Table S1. Source information; Table S2. Percent contribution; Table S3. Range shift distances.

Author Contributions

Conceptualization, L.W. and Y.Z. (Yu Zhang); methodology, X.Z. and H.L.; software, Y.S.; validation, Y.S., X.Z., Y.Z. (Yu Zhang), L.W. and H.L.; formal analysis, Y.S.; investigation, X.Z., H.L. and Y.Z. (Yu Zhang); resources, H.L. and L.W.; data curation, Y.S. and X.Z.; writing—original draft preparation, Y.S., Y.Z. (Yuxiao Zhang) and Y.Z. (Yu Zhang); writing—review and editing, Y.S., Y.Z. (Yu Zhang) and Y.C.; visualization, Y.S. and Y.Z. (Yu Zhang); supervision, Y.Z. (Yu Zhang) and Y.C.; project administration, Y.Z. (Yu Zhang); funding acquisition, Y.Z. (Yu Zhang) and Y.C. All authors have read and agreed to the published version of the manuscript.

Funding

This work was funded by the National Natural Science Foundation of China [Grant No. 31600174] and Jiangsu Key Laboratory for Conservation and Utilization of Plant Resources [Grant No. JSPKLB202409].

Data Availability Statement

The original contributions presented in the study are included in the article and Supplementary Material; further inquiries can be directed to the corresponding author.

Acknowledgments

We thank Tian-jing Tong, Jiangping Luo, Wenqun Fei, Youpai Zeng, and Yu Hong for their assistance with the field work.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Neighbor-joining tree of the 19 populations of A. davidii and related species. Different species are color-coded as follows: green (A. davidii), red (A. scabriuscula), purple (A. exigua), blue (A. griffithii), and yellow (A. rivularis).
Figure 1. Neighbor-joining tree of the 19 populations of A. davidii and related species. Different species are color-coded as follows: green (A. davidii), red (A. scabriuscula), purple (A. exigua), blue (A. griffithii), and yellow (A. rivularis).
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Figure 2. Principal component analysis (PCA) plots of 164 individuals from A. davidii and related species. Each point represents an individual colored according to the collection site. The 12 populations of A. davidii (DY, DG, EMS, HY, PZ, SF CQ, WX, ES, SNJ, TR, and SZ populations), two populations of A. scabriuscula (DL and LJ populations), and three populations of A. griffithii (CQ1, CQ2, and CQ3 populations), one population of A. exigua (XGLL population) and A. rivularis (LJ619 population). Circles of different colors represent distinct species: blue (A. davidii), orange (A. scabriuscula), green (A. griffithii), purple (A. exigua), and light blue (A. rivularis). Symbols represent different geographic populations.
Figure 2. Principal component analysis (PCA) plots of 164 individuals from A. davidii and related species. Each point represents an individual colored according to the collection site. The 12 populations of A. davidii (DY, DG, EMS, HY, PZ, SF CQ, WX, ES, SNJ, TR, and SZ populations), two populations of A. scabriuscula (DL and LJ populations), and three populations of A. griffithii (CQ1, CQ2, and CQ3 populations), one population of A. exigua (XGLL population) and A. rivularis (LJ619 population). Circles of different colors represent distinct species: blue (A. davidii), orange (A. scabriuscula), green (A. griffithii), purple (A. exigua), and light blue (A. rivularis). Symbols represent different geographic populations.
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Figure 3. Population structure of 110 A. davidii accessions at K = 2 and 3. The capital letter refers to geographic groups in A. davidii corresponding to DY, DG, EMS, HY, PZ, SF CQ, WX, ES, SNJ, TR, and SZ populations, respectively. Each color represents one cluster.
Figure 3. Population structure of 110 A. davidii accessions at K = 2 and 3. The capital letter refers to geographic groups in A. davidii corresponding to DY, DG, EMS, HY, PZ, SF CQ, WX, ES, SNJ, TR, and SZ populations, respectively. Each color represents one cluster.
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Figure 4. The ROC curve test of the prediction results of A. davidii.
Figure 4. The ROC curve test of the prediction results of A. davidii.
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Figure 5. Response curves of dominant variables. (A) Annual precipitation (mm). (B) Elevation (m). (C) Precipitation of direst month (mm).
Figure 5. Response curves of dominant variables. (A) Annual precipitation (mm). (B) Elevation (m). (C) Precipitation of direst month (mm).
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Figure 6. Potential habitat shifts in A. davidii under different climate scenarios.
Figure 6. Potential habitat shifts in A. davidii under different climate scenarios.
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Figure 7. The current (A) and predicted distributions for A. davidii included the past period (B) and future potential period by the 2050s and 2070s according to the climate scenarios RCP 4.5 (C,D) and RCP 8.5 (E,F). Suitability class range: 0–0.2 means unsuitable area (white), 0.2–0.4 means low suitable area (green), 0.4–0.6 means medium suitable area (yellow), and 0.6–1.0 means highly suitable area (red).
Figure 7. The current (A) and predicted distributions for A. davidii included the past period (B) and future potential period by the 2050s and 2070s according to the climate scenarios RCP 4.5 (C,D) and RCP 8.5 (E,F). Suitability class range: 0–0.2 means unsuitable area (white), 0.2–0.4 means low suitable area (green), 0.4–0.6 means medium suitable area (yellow), and 0.6–1.0 means highly suitable area (red).
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Table 1. Genetic diversity parameters of different groups of A. davidii and its affinis.
Table 1. Genetic diversity parameters of different groups of A. davidii and its affinis.
TaxonPopulationObserved
Heterozygosity
(Ho)		Expected
Heterozygosity (He)		Nucleotide Diversity (π)
Anemone davidii Franch.EMS0.1160.0840.091
HY0.1180.0880.094
ES0.1260.0940.102
DG0.1050.0740.086
SNJ0.1270.0810.111
TR0.1260.0950.102
CQ0.1260.0940.101
PZ0.1160.0850.093
DY0.1210.0890.096
SZ0.1270.0940.101
SF0.1090.0750.086
WX0.1270.0930.104
A. exigua Maxim.XGLL0.1040.0760.085
A. griffithii Hook. f. & ThomsonCQ10.1130.0750.097
CQ20.110.0690.096
CQ30.1130.0790.096
A. scabriuscula W. T. WangDL0.1170.0860.095
LJ0.1180.0910.096
Note: EMS, Sichuan, Emei; HY, Sichuan, Hanyuan; ES, Hubei, Enshi; DG, Yunnan, Daguan; SNJ, Hubei, Shennongjia; TR, Guizhou, Tongren; CQ, Chongqing, Nanchuan; PZ, Sichuan, Pengzhou; DY, Sichuan, Dayi; SZ, Hunan, Sangzhi; SF, Sichuan, Shenfang; WX, Chongqing, Wuxi; XGLL, Yunnan, Xianggelila; CQ1, CQ2, CQ3, Chongqing, Nanchuan; DL, Yunnan, Dali; LJ, Yunnan, Lijiang.
Table 2. Genetic differentiation coefficient and gene flow between different populations of A. davidii.
Table 2. Genetic differentiation coefficient and gene flow between different populations of A. davidii.
ESPZSNJCQSZHYDGWXDYSFTREMS
ES-0.0350.0440.0220.0210.0230.0480.0230.0240.0480.0220.023
PZ6.893-0.0210.0380.0390.0370.0350.0320.0350.0330.0390.036
SNJ5.43211.655-0.0470.0390.0310.0350.0460.0230.0290.050.023
CQ11.1146.3295.069-0.0220.0230.050.0240.0250.0510.0210.025
SZ11.6556.166.1611.114-0.0230.0460.0220.0250.0480.0210.024
HY10.626.5077.81510.6210.62-0.0410.0210.0220.0430.0230.018
DG4.9586.8936.8934.755.1855.848-0.0470.0370.0330.0520.035
WX10.627.5635.18510.16711.11411.6555.069-0.0220.0460.0240.02
DY10.1676.89310.629.759.7511.1146.50711.114-0.040.0240.022
SF4.9587.3268.3714.6524.9585.5647.3265.1856-0.0530.037
TR11.1146.164.7511.65511.65510.624.55810.16710.1674.467-0.024
EMS10.626.69410.629.7510.16713.6396.89312.2511.1146.50710.167-
Note. The above-diagonal cells present the interpopulation genetic differentiation coefficient (Fst), and the below-diagonal cells present the interpopulation gene flow.
Table 3. Prediction of the suitable habitat area of A. davidii in different time periods (×104 km2).
Table 3. Prediction of the suitable habitat area of A. davidii in different time periods (×104 km2).
PeriodCurrentLGMRCP4.5RCP8.5
2050s2070s2050s2070s
Low suitable area23.0419.9618.6321.2719.4916.89
Medium suitable area16.6918.9817.4415.1115.1019.81
High suitable area14.9017.7114.9313.6513.8013.93
Total suitable area54.6356.6451.0150.0348.3950.63
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Shen, Y.; Zhang, X.; Zhang, Y.; Zhang, Y.; Li, H.; Wang, L.; Chen, Y. Population Structure and Prediction of Potential Suitable Areas of Anemone davidii Franch. (Ranunculaceae) from Southwestern China. Forests 2026, 17, 207. https://doi.org/10.3390/f17020207

AMA Style

Shen Y, Zhang X, Zhang Y, Zhang Y, Li H, Wang L, Chen Y. Population Structure and Prediction of Potential Suitable Areas of Anemone davidii Franch. (Ranunculaceae) from Southwestern China. Forests. 2026; 17(2):207. https://doi.org/10.3390/f17020207

Chicago/Turabian Style

Shen, Yongdong, Xu Zhang, Yuxiao Zhang, Yu Zhang, Huimin Li, Long Wang, and Yuanqi Chen. 2026. "Population Structure and Prediction of Potential Suitable Areas of Anemone davidii Franch. (Ranunculaceae) from Southwestern China" Forests 17, no. 2: 207. https://doi.org/10.3390/f17020207

APA Style

Shen, Y., Zhang, X., Zhang, Y., Zhang, Y., Li, H., Wang, L., & Chen, Y. (2026). Population Structure and Prediction of Potential Suitable Areas of Anemone davidii Franch. (Ranunculaceae) from Southwestern China. Forests, 17(2), 207. https://doi.org/10.3390/f17020207

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