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Article

Evaluating Habitat Suitability for the EndangeredSinojackia xylocarpa (Styracaceae) in China Under Climate Change Based on Ensemble Modeling and Gap Analysis

Jiangsu Key Laboratory of Biodiversity and Biotechnology, School of Life Sciences, Nanjing Normal University, Wenyuan Road, Nanjing 210023, China
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Author to whom correspondence should be addressed.
Submission received: 7 February 2025 /Revised: 11 March 2025 /Accepted: 16 March 2025 /Published: 17 March 2025
(This article belongs to the SectionConservation Biology and Biodiversity)

Simple Summary

Sinojackia xylocarpa, endemic to China, has high value in landscaping. However, little is known about how it responds to climate change. We built an ensemble model in the Biomod2 package to forecast its potential distribution and evaluated its current protective effectiveness in China. The results showed that the four key influencing environmental factors were precipitation of the driest quarter, mean temperature of the warmest quarter, precipitation of the warmest quarter, and elevation. This species was mainly distributed in southeast China, with an area of 697,200 km2, accounting for 7.26% of China’s total territory. However, only 3.91% was located within national or provincial nature reserves. Under future climates, it would averagely decrease by 10.97% in suitable areas compared to the current, with more fragmented habitats. Therefore, our findings first demonstrate that future climate change may have an adverse effect on its distribution. We recommend conducting a supplementary investigation within the projected suitable range and establishing new conservation sites forS. xylocarpa in China. Moreover, this study provides a general picture for conserving other endangeredSinojackia species under global warming.

Abstract

Sinojackia, endemic to China, comprises five species, and each has restricted distribution but with high value in landscaping. However, how such species respond to climate change remains unclear. We selectedS. xylocarpa as a representative, built an ensemble model in Biomod2 to forecast its potential distribution, identified its key influencing factors, and analyzed its conservation gaps in China’s nature reserves. The four leading factors were precipitation of the driest quarter, mean temperature of the warmest quarter, precipitation of the warmest quarter, and elevation. This species was mainly distributed in southeast China. Its suitable area was 697,200 km2, accounting for 7.26% of China’s total territory. Nevertheless, only 3.91% was located within national or provincial nature reserves. Under future climates, its suitable areas would averagely decrease by 10.97% compared to the current, with intensifying habitat fragmentation. Collectively, its centroid is expected to shift northeastward in the future. Therefore, our findings first demonstrate that future climate change may have an adverse effect on its distribution. We recommend conducting a supplementary investigation within the projected suitable range and establishing new conservation sites forS. xylocarpa in China. Moreover, this study can provide a valuable reference for conserving other endangeredSinojackia species under global warming.

    1. Introduction

    Climate change has a profound effect on plant growth and distribution on a global scale [1,2]. Endangered trees are usually more susceptible to climate change in distribution relative to non-endangered ones. TakeGlyptostrobus pensilis (Staunton ex D. Don) K. Koch as an example. This endangered tree might shrink to varying degrees in suitable habitat under different future climate scenarios [3]. More importantly, these endemic and endangered species usually have restricted distribution ranges and small population sizes. Moreover, their habitats are probably separated, thereby leading to low genetic diversities in most cases. Therefore, future climate change may pose a severe threat to such plants.
    Sinojackia is an oligotypic genus from the Styracaceae family [4]. All species of this genus are endemic to China and endangered. Therefore, they have been protected by the Chinese government since 2021. Due to morphological variation and spontaneous hybridization between different species, there is controversy about interspecific delimitation within this genus. According toFlora of China, theSinojackia genus comprises a total of five species and one variety [5]. BesidesSinojackia xylocarpa Hu, it also includesS. rehderiana Hu,S. henryi (Dümmer) Merr.,S. sarcocarpa L. Q. Luo [6],S. microcarpa Tao Chen bis and G. Y. Li [7] and the variety ofS. xylocarpa—S. xylocarpa var.leshanensis L. Q. Luo [8]. Later on, theSinojackia genus also includedS. dolichocarpa C. J. Qi,S. huangmeiensis J. W. Ge and X. H. Yao, andS. oblongicarpa Tao Chen bis and T. R. Cao. Presently, based on key taxonomic traits and related molecular phylogenetic evidence,S. dolichocarpa has been assigned to a new genus—Changiostyrax Tao Chen [9,10],S. oblongicarpa has been identified as the synonym ofS. sarcocarpa [11], andS. huangmeiensis as the synonym ofS. xylocarpa [12]. As a result, now there are only five species in the genus ofSinojackia in China.
    Currently, there are few studies on the distribution prediction of the genusSinojackia. Yang et al. (2020) used MaxEnt to predict the current range of this genus involving seven species in China, and they pointed out that its suitable distribution would decline in the future (2050s and 2070s) [13]. Conversely, Feng and Zhang (2024) applied MaxEnt to project the suitable distribution ofSinojackia comprising eight species, and they concluded that this genus would expand in the highly suitable distribution in the future (2081–2100) [14]. Such a difference is mainly due to the sampling representativeness like the number of species and their occurrence points. The former used 58 distribution points, while the latter only used 15 ones for this genus in the final model. In addition, both studies contain species that actually do not belong to the genusSinojackia, such asChangiostyrax dolichocarpus. Indeed, genus and species are two different taxonomic categories. The potential distribution of a genus can be modeled based on its species’ occurrence records unless these species have niche conservatism, similar ecological features, and restricted geographical ranges [15,16].
    However, to the best of our knowledge, there are no such studies regarding the potential distribution of any species from theSinojackia genus.S. xylocarpa was designated as the type species of the genus as early as 1929 by a Chinese botanist Hu Hsien-Hsu [4]. According toFlora of China,S. xylocarpa is only found in Nanjing, Jiangsu Province, eastern China [17]. According toFlora of Jiangsu, this species is endemic to Jiangsu Province and is mainly distributed in Nanjing [18]. Its specimen collections show thatS. xylocarpa was once wildly distributed in Mufu Mountain and Laoshan Mountain of Nanjing, and Baohua Mountain of Zhenjiang within southern Jiangsu [19]. However, because of the deterioration of the natural environment and the impact of anthropogenic activities such as quarrying and mining, there are no wild populations ofS. xylocarpa at Mufu Mountain, Nanjing [6]. This species is even considered extinct in the wild in China [20,21]. In recent years, with the extensive investigation of its natural populations,S. xylocarpa has been discovered in the provinces of Anhui and Zhejiang, eastern China, and Hunan Province, central China [12,22]. For example, a wild population ofS. xylocarpa, with more than 200 individuals, was found in 2023 in Majiazui, Yiyang City, Hunan Province (https://lyj.hunan.gov.cn/lyj/xxgk_71167/gzdt/xlkb/xsqxx/202304/t20230418_29316935.html, last accessed on 5 December 2024). In 2024, 11 bushes withS. xylocarpa saplings were found in Huangli Mountain, Chaohu City, Anhui Province (http://www.ahwang.cn/hefei/20241006/2754153.html, last accessed on 5 December 2024). Meanwhile, according to our field survey in the past two years, its wild populations occurred in various sites such as Laoshan, Nanjing City, Jiangsu Province, and Wuwei, Wuhu City, Anhui Province (Figure 1c,d). Additionally, due to the taxonomic revision,S. huangmeiensis has been merged intoS. xylocarpa. In summary, we believe that the distribution ofS. xylocarpa in China is geographically restricted and discontinuous, but its actual distribution range is still unclear.
    Species-distribution models (SDMs) link species presence, absence, or abundance information with environmental variables to predict its potential locations and quantities [23]. At present, it has been widely used in multiple fields like conservation biology, ecological invasion, and habitat suitability assessment [24,25]. For example, SDMs were employed to predict climatically suitable habitats of the endemic and endangeredParrotia subaequalis (H. T. Chang), R. M. Hao, and H. T. Wei in China [26]. More recently, it has been noted that an ensemble model comprising multiple individual models can improve the accuracy of model predictions relative to a single model [27]. Biomod2, including ten species-distribution models, is a program package developed by Wilfried Thuiller et al. for SDM applications [28]. Presently, it is widely applied in potential distribution prediction for endangered species [29].
    In this study, we first collected data on the distribution points ofS. xylocarpa and related environmental variables, then used Biomod2 to screen suitable models to generate an ensemble model, and finally used the ensemble model to predict its potential distribution in China. Specifically, we focused on the following issues. (1) We identified key environmental factors affecting the distribution ofS. xylocarpa; (2) We projected its potential distribution areas under different climate scenarios in the past, current, and future and determined its centroid shift; (3) In addition, we further assessed its conservation status by overlaying the resulting suitable habitats with existing nature reserve layers in China. The purpose of this study is to provide a scientific basis for conservation recommendations for endangeredS. xylocarpa, as well as a conservation reference for other endangeredSinojackia species in China.

    2. Materials and Methods

    2.1. Acquisition of Geographic Distribution Data

    S. xylocarpa is a light-demanding tree and grows well in a warm, humid climate. Moreover, it places little emphasis on soil [30]. As a native tree,S. xylocarpa has high ornamental value [6]. In spring, it produces small white delicate flowers (Figure 1a), and in autumn, it bears many conical fruits with long slender pedicels, like a balanced weight set (“Chengtuo” in Chinese) hanging in the tree (Figure 1b). Recent studies have shown thatS. xylocarpa has a low germination rate in the wild, resulting from its physiological seed dormancy, hard seed coat, and high microspore abortion rate in floral organogenesis [31]. Coupled with external factors such as climate change and habitat destruction [6,32,33],S. xylocarpa is on the brink of extinction in China. Therefore, this species was listed as a national secondary protected plant species in 1999. In addition, it has been listed as one of the key protected wild plants of China since 2021 (https://www.forestry.gov.cn/, last accessed on 27 September 2024). In addition, it has been ranked as a “Vulnerable” (VU) species in the IUCN Red List (https://www.iucnredlist.org/, last accessed on 27 September 2024).
    The data regarding the wild distribution ofS. xylocarpa were obtained through several sources. (1) Investigating in the field: From the spring of 2022 to the autumn of 2024, we surveyed the wild population ofS. xylocarpa in Anhui (i.e., Hefei, Wuhu, Xuancheng), Jiangsu (i.e., Changzhou, Nanjing, Zhenjiang), Zhejiang (i.e., Hangzhou, Huzhou, Ningbo), and other provinces in eastern China to determine its distribution. Simultaneously, we located theS. xylocarpa populations with GPS and recorded their geographical coordinates (i.e., latitude and longitude). (2) Browsing related websites: We accessed botanical websites, including the Plant Photo Bank of China (PPBC,http://ppbc.iplant.cn/, last accessed on 5 December 2024), the Chinese Virtual Herbarium (CVH,https://www.cvh.ac.cn/, last accessed on 5 December 2024), and the National Specimen Information Infrastructure (NSII,http://nsii.org.cn/, last accessed on 5 December 2024). (3) Consulting published literature and related reports: We examinedFlora of China, provincial floras, and related checklists that listed the specific name, synonym, and Latin name ofS. xylocarpa. Furthermore, we conducted searches for published literature and pertinent articles [12,34]. Accordingly, we obtained a total of 104 distribution points forS. xylocarpa. After removing error duplicate points, we collected 22 natural distribution records of this species.
    Following preliminary data collation, we employed Spatially Rarefy Occurrence Data for Species-Distribution Models (SDMs) in the SDMs toolbox (version 2.6), ensuring a single occurrence point per 1 km × 1 km grid [35]. Such a filtering approach is particularly useful for species with limited occurrence points, as it maximizes the number of spatially independent localities [36]. Furthermore, finer-scale data are considered to better reflect climatic conditions experienced by species [37]. Finally, we obtained latitude and longitude data for 21 points ofS. xylocarpa (Figure 2;Table S1).

    2.2. Selection and Filtering of Environmental Variables

    The environmental data selected for this study are categorized into three groups: climate, terrain, and soil [38,39,40]. They encompass the climatically historical periods (the Last Interglacial period, approximately 12,000–14,000 years ago, and the Middle Holocene, around 5000–7000 years ago), the current, and the future periods (2050s: 2041–2060; 2070s: 2061–2080) [29,41]. Given the considerable temporal separation from the present epoch and the profound transformations Earth’s environment has undergone, only bioclimatic variables from two distinct paleoclimate periods are selected for prediction. In the current and future periods, topographic and soil variables will be used, besides bioclimatic variables. We downloaded 19 bioclimatic factors for the three periods from WorldClim (https://www.worldclim.org/, last accessed on 5 December 2024). Then, we standardized the resolution to 30 s (1 km × 1 km) to ensure accuracy during modeling. Considering inter-model variability and projection uncertainty, we employed an ensemble modeling approach, incorporating multiple climate models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). Future bioclimatic data were derived from three global climate models: BCC-CSM1-1 (the Beijing Climate Center Climate System Model version 1.1), CCSM4 (the Community Climate System Model version 4), and MIROC-ESM (an Earth System Model based on the model for interdisciplinary research on climate) [42]. Additionally, three Representative Concentration Pathways (RCPs) were selected to represent different greenhouse gas emission trajectories: RCP2.6 (representing a moderate emission scenario), RCP4.5 (a medium and stable emission scenario), and RCP8.5 (a high emission scenario).
    Topographic data includes elevation and slope. Since these variables remain essentially unchanged over time, they are added to the models as constant variables [43]. Digital elevation data were obtained from WorldClim, and slope data were downloaded from the National Earth System Science Data Center (https://www.geodata.cn/main/, last accessed on 5 December 2024). Soil characteristics can affect the physiological growth of plants. Species-distribution models with soil data perform significantly better than those without soil information [44]. The China soil dataset (version 1.2) was downloaded from the National Qinghai–Tibet Plateau Scientific Data Center (http://www.tpdc.ac.cn/zh-hans/, last accessed on 5 December 2024), and 16 types of surface soil data were selected from the website for subsequent research.
    The three types of environmental data were standardized using the WGS1984 coordinate system, and the “Extract by Mask and Clip” tool in ArcGIS 10.8 was employed to ensure that the data were confined to China. The data resolution was subsequently adjusted to the 30 s level using resampling tools. Concurrently, Pearson correlation analysis was conducted to mitigate collinearity among related environmental variables, ensuring that redundant information did not compromise the model’s predictions [45]. Environmental variables with a low contribution rate and |r| ≥ 0.8 were excluded from further analysis. Ultimately, we retained 10 bioclimatic variables for the Last Interglacial, 11 bioclimatic variables for the Middle Holocene, and 22 environmental variables for the current and future periods for subsequent modeling (Table 1).

    2.3. Modeling Process

    We used the Biomod2 package to generate an ensemble model to simulate the distribution range ofS. xylocarpa. First, we combined 22 environmental variables with 21 distribution sites ofS. xylocarpa to evaluate the 10 models, respectively, in the Biomod2 package. Then, we obtained the AUC (Area Under the Curve) and TSS (True Skill Statistic) values for each model. Since the AUC value usually ranges from 0 to 1, the closer the value is to 1, the higher the precision. It is classified as failure (0.5–0.6), poor (0.6–0.7), fair (0.7–0.8), good (0.8–0.9), and excellent (0.9–1.0) [46]. The TSS value varies between −1 and +1. The value close to 1 indicates good performance, while the value close to or below 0 indicates poor performance. It can be divided into five groups: excellent (TSS > 0.8), good (0.6–0.8), fair (0.4–0.6), poor (0.2–0.4), and fail (TSS < 0.2) [29].
    At present, there is no consensus on the threshold values of AUC and TSS when evaluating ensemble model performance, so herein we followed the method of ensemble model evaluation forEmmenopterys henryi Oliv., an endangered tree endemic to China [27]. We selected the models with an AUC exceeding 0.8 and a TSS exceeding 0.7 from the 10 models to construct an ensemble model. Consequently, the ensemble model was composed of six individual models: generalized boosted models (GBM), flexible discriminant analysis (FDA), random forest (RF), Maximum entropy model (MaxEnt), artificial neural networks (ANN), and classification tree analysis (CTA). During the modeling process, R4.3.3 randomly generated 1000 pseudo-absence points [47,48]. Simultaneously, we randomly assigned 75% of the occurrence records to the training set and the remaining 25% to the testing set. To ensure the accuracy of the predictive model, we conducted the computation ten times and adopted the average value as the final modeling result.

    2.4. Suitable Habitat Partitions and Centroid Shift

    The results generated by the ensemble model were imported into ArcGIS 10.8 for visualization. Most species modeling techniques produce continuous suitability predictions. However, many practical applications require secondary outputs which need the establishment of thresholds. We employed the maximum sum of specificity and sensitivity (maxSSS) to determine the threshold, which is a promising approach when only limited data are available [49]. Considering thatS. xylocarpa is an endemic and endangered species, we followed the method of Liu et al. (2013) [50]. According to the maxSSS threshold (0.1916), the potential distribution ofS. xylocarpa was categorized into unsuitable (0.0–0.19), low-suitable (0.19–0.46), moderately suitable (0.46–0.73), and highly suitable (0.73–1.00) areas. In this study, we considered both the moderately and highly suitable areas as the suitable habitat forS. xylocarpa [46]. Subsequently, we calculated the suitable area for each category.
    Centroid shifts can provide insights into how species change in distribution under different climate scenarios. We employed the SDMtoolbox in ArcGIS 10.8 to simulate the centroid shift ofS. xylocarpa across various climate scenarios, encompassing both the direction and distance of movement across different periods.

    2.5. Conservation Gap Analysis

    First, we created a map layer of China’s nature reserves, excluding marine protected areas. This layer includes 464 national nature reserves and 806 provincial nature reserves [51]. The total area of the protected area was 971,800 km2, which was about 10.12% of China’s land area. The data on nature reserves were obtained from the World Database on Protected Areas (http://www.protectedplanet.net/, last accessed on 7 December 2024) and the Ministry of Ecological Environment of China (http://www.mee.gov.cn, last accessed on 7 December 2024). Next, we used ArcGIS 10.8 to overlay the raster model of the predicted distribution ofS. xylocarpa in the current climate with the surface layer of the protected area to obtain the coverage of the nature reserve in theS. xylocarpa’s suitable habitat area and to assess the conservation gaps. TheS. xylocarpa population in a suitable area grid is considered to be protected if the grid falls within a nature preserve [29]. Finally, we calculated the extent and proportion of its suitable areas in national nature reserves, provincial nature reserves, and these two types of reserves.

    3. Results

    3.1. Model Performance

    We used Biomod2 to establish 10 individual models forS. xylocarpa. Following the selection criteria described in the Materials and Methods section, we included models with AUC > 0.8 and TSS > 0.7 in the ensemble model (Table 2).
    Therefore, we selected the six models to establish an ensemble model. Except for ANN and CTA, the AUC values of the other four models were all greater than 0.9, indicating that these models reached an excellent level. Meanwhile, the TSS values of these six models were all greater than 0.7, indicating that they all had high credibility and accuracy. For example, the AUC value of MaxEnt was 0.9690, and the TSS value of CTA was 0.7864. In contrast, the AUC and TSS values of the ensemble model were 0.9960 and 0.9500, respectively, both of which were higher than those of the six individual models. Therefore, the ensemble model’s superior performance likely results from combining predictions from multiple algorithms, which reduces individual model biases and enhances predictive accuracy.

    3.2. Main Environmental Factors

    We used the ensemble model to determine the contribution rate of each environmental variable in different periods (Table 1). Among these variables affecting the distribution ofS. xylocarpa at present, Bio17 (precipitation of driest quarter) was the highest, followed by Bio10 (mean temperature of warmest quarter), Bio18 (precipitation of warmest quarter), and then elevation. Their contribution rates were 61.0%, 9.6%, 8.9%, and 5.4%, respectively, with a cumulative contribution rate reaching 84.9%. Therefore, the top four were identified as the key environmental factors.
    During the Last Interglacial period, the key environmental factors were Bio10 (mean temperature of warmest quarter) (21.7%), Bio17 (19.6%), and Bio4 (temperature seasonality) (19.1%). In the Middle Holocene period, the key factors were Bio15 (precipitation seasonality) (27.5%), Bio17 (23.0%), and Bio11 (mean temperature of coldest quarter) (15.5%), respectively. Notably, Bio17, which is the dominant factor in the current period, ranked second during both the Last Interglacial and Middle Holocene periods, suggesting a shift in climatic drivers of habitat suitability over time. Hence, the contribution of environmental factors varied across different periods, highlighting changes in habitat suitability over time.
    When the presence probability was greater than 0.46, the corresponding areas were considered to be moderately or highly suitable, and we thought that they were conducive to the growth ofS. xylocarpa. Response curves represented the relationship between environmental variables and species presence probability, reflecting the species’ biological tolerance and habitat preferences. In other words, response curves indicate ecological thresholds, where the probability of species presence changes non-linearly in response to key environmental factors.
    When the precipitation of the driest quarter was greater than 90 mm, it was suitable for the survival ofS. xylocarpa. As shown inFigure 3a, the probability ofS. xylocarpa presence increased sharply with precipitation of the driest quarter, stabilized, and then decreased. When the mean temperature of the warmest quarter was greater than 24.6 °C, it was suitable for the survival ofS. xylocarpa. As the mean temperature of the warmest quarter increased, the existence probability ofS. xylocarpa first increased and then remained unchanged (Figure 3b). The suitable precipitation range for the warmest quarter was 417–763 mm. As the precipitation of the warmest quarter increased, the existence probability ofS. xylocarpa first increased considerably and then decreased sharply (Figure 3c).S. xylocarpa was suitable for growth when the elevation was less than 392 m. As the elevation increased, the existence probability ofS. xylocarpa first remained unchanged and then decreased sharply (Figure 3d). Notably, when the elevation exceeded 1000 m, its survival probability decreased to less than 0.3. As a result, the response curves of Bio17 (inFigure 3a) and Bio18 (inFigure 3c) present a unimodal pattern, while the response curves of Bio10 (inFigure 3b) and elevation (inFigure 3d) present an asymptotic pattern.

    3.3. Potential Suitable Habitats in the Current

    At present, the suitable areas forS. xylocarpa are mainly concentrated in southern Anhui, northern Guangxi, eastern Hubei, Hunan, southern Jiangsu, northern Jiangxi, eastern Taiwan, and northern Zhejiang (Figure 4). Some suitable areas were also predicted to be scattered in northern Fujian, southern Guangdong, eastern Guizhou, and other parts of China. Furthermore, its suitable areas were relatively more fragmented than its low ones in the current period (Figure 4). The total suitable area forS. xylocarpa was 697,200 km2, accounting for only 7.26% of China’s total land area, and the highly suitable area was 341,500 km2, accounting for 3.56% (Table 3). Collectively, this species was mainly distributed in the southeast of China, which is largely consistent with the surveyed distribution points.
    Under the current climate, the suitable habitat ofS. xylocarpa within the boundaries of national nature reserves was 12,800 km2, accounting for 1.84%. The suitable habitat ofS. xylocarpa within the boundaries of provincial nature reserves was 15,000 km2, accounting for 2.15%. The coverage ratio of national and provincial nature reserves in the suitable area ofS. xylocarpa was only 3.91%. Therefore, the vast majority of the suitable areas forS. xylocarpa were not effectively protected (Figure 4).

    3.4. Potential Suitable Habitats in the Past

    During the Last Interglacial, the suitable habitat forS. xylocarpa in China was predominantly found in southern Anhui, southern Hubei, Hunan, southern Jiangsu, northern Jiangxi, and northern Zhejiang (Figure 5a). The species exhibited a more continuous distribution pattern compared to its current fragmented range. The total suitable area amounted to 638,400 km2, showing a decrease of 8.43% from current levels (Table 3).
    During the Middle Holocene, the suitable habitat forS. xylocarpa shifted slightly, mainly concentrating in southern Anhui, northern Fujian, eastern Hubei, Hunan, southern Jiangsu, northern Jiangxi, northern Taiwan and Zhejiang (Figure 5b). Compared with the Last Interglacial, the suitable habitat during the Middle Holocene presented a more fragmented pattern. The total suitable area was estimated at 645,000 km2, indicating a decrease of 7.49% relative to the current (Table 3).
    In a word, the results show a continuous contraction and fragmentation of suitable habitats from the Last Interglacial to the Middle Holocene, indicating increasing environmental constraints forS. xylocarpa over time. For instance, in the Middle Holocene, increased fragmentation suggests reduced connectivity between habitats, which could have impacted species dispersal and survival.

    3.5. Potential Suitable Habitats in the Future

    Future projections indicated that the potential suitable habitat forS. xylocarpa primarily occurred in southern Anhui, southern Hubei, Hunan, southern Jiangsu, northern Jiangxi, northern Taiwan, and Zhejiang. However, model projections revealed varying degrees of suitable habitat contraction across these regions under most future scenarios (Figure 6).
    Under six future climate scenarios, the predicted suitable area was, on average, 620,700 km2, which decreased by 10.97% compared to the current. Except for an increase under RCP 8.5 in the 2070s, the suitable area decreased in the other five scenarios. It was expected that under RCP 8.5 in the 2050s, the suitable area would decrease the most, which was reduced by 16.32% compared to the current. In contrast, under RCP 4.5 in the 2050s, the suitable area was expected to decrease the least, which decreased by 11.20% compared to the current situation. In addition, the highly suitable area was expected to increase in some scenarios and decrease in others. Overall, the average highly suitable area under the six future scenarios was expected to be 362,600 km2, which increased by 6.18% compared to the current condition. However, the average moderately suitable area under future climate was expected to be 258,100 km2, which decreased by 27.44% compared to the current condition. In addition, it was expected that this species would decrease in suitable areas much more in the 2050s than in the 2070s (Table 3).
    Overall, the suitable area forS. xylocarpa under future climate scenarios, with more habitat fragmentation, was mostly smaller than under the current condition. This indicated that future climate might be unfavorable for the survival ofS. xylocarpa.

    3.6. Centroid Shift Under Different Climate Scenarios

    The current centroid ofS. xylocarpa was located at 114.446° E, 27.793° N. From the Last Interglacial (113.216° E, 29.201° N) to the Middle Holocene (112.682° E, 28.955° N) and then to the present, the centroid first shifted 58.70 km toward the southwest and then 215.55 km toward the southeast. Under RCP 2.6, it was expected that in the 2050s, the centroid would shift 147.91 km in the northwest direction to 114.368° E, 29.126° N, and by the 2070s, it would shift 134.57 km in the north direction to 114.446° E, 29.007° N. Under RCP 4.5, it was expected that in the 2050s, the centroid would migrate 52.96 km in the northeast direction to 114.555° E, 28.261° N, and by the 2070s, it would shift 153.22 km in the northeast direction to 115.178° E, 29.015° N. Under RCP 8.5, it was expected that in the 2050s, the centroid would shift 201.67 km in the northeast direction to 115.722° E, 29.221° N, and by the 2070s, it would shift 413.29 km in the northwest direction to 111.412° E, 30.402° N (Figure 7).
    Overall, the centroid exhibited a sinuous changing pattern. The centroid ofS. xylocarpa’s suitable habitat shifted southwest from the Last Interglacial to the Middle Holocene and then southeast to the present. In future projections, the centroid generally moves northeast under all RCP scenarios, indicating a shift toward higher latitudes as the climate warms. This northeastward shift reflects a common response of species to climate change, moving toward cooler regions as temperatures rise. However, such shifts may result in habitat loss if suitable areas become fragmented or unavailable.

    4. Discussion

    4.1. Model Selection and Evaluation

    It is generally believed that ensemble models make better predictions of species distribution than single models [27]. Our ensemble model results confirmed this observation. In this study, we forecast the potential distribution of the endangered tree speciesS. xylocarpa with each of the ten individual models in the Biomod2 platform separately. The results showed that there were six models with the value of AUC > 0.8 and TSS > 0.7. Next, we combined the six models into an ensemble model, whose AUC and TSS were all above 0.9 (Table 2). This indicated that such an integrated model outperformed individual models. Subsequently, we used this model to predict the current distribution ofS. xylocarpa and noticed that the prediction was generally consistent with the known distribution points. This indicated that the ensemble model had demonstrated good predictive accuracy. Thus, we employed this model to project the suitable distribution ofS. xylocarpa under different climate scenarios of past, present, and future.

    4.2. Key Influencing Factors of S. xylocarpa

    The model projections show that the top three factors affecting the current potential distribution ofS. xylocarpa are Bio17, Bio10, and Bio18. The sum of their contribution rates is nearly 80%, which suggests that the main factors limiting the distribution ofS. xylocarpa are bioclimates rather than topography or soil. Among these climatic factors, Bio17 has the largest contribution rate, exceeding 60%, which is much larger than Bio10 (9.6%) and Bio18 (8.9%). This indicates that precipitation-related variables may play a greater role in shaping the distribution ofS. xylocarpa than temperature-related variables. Unlike Zhu et al. (2024), who found temperature-related variables to be dominant, our results emphasize the importance of precipitation, likely due to the larger sample size (21 distribution points vs. 2) [31]. Our results conform with the tree traits that this species prefers to grow in warm and moist conditions [30].
    Our results also show that the contribution rate of elevation is 5.4% among 22 variables, which ranks fourth. This indicates that besides climatic factors, elevation is also one of the important factors limiting the distribution ofS. xylocarpa. Yang et al. (2018) used a self-organizing map (SOM) to analyze the wildS. xylocarpa community in Laoshan mountain of Nanjing, Jiangsu Province, eastern China, and found that elevation was the main factor affecting the growth and distribution ofS. xylocarpa [52]. This is roughly in accord with our results.
    Therefore, our study suggests thatS. xylocarpa prefers to grow in low-altitude areas with a warm and humid climate, which conforms with the phenomenon that most of the knownS. xylocarpa populations concentrate in the subtropical hilly areas of southeastern China (Field observation by corresponding author).

    4.3. Current Suitable Area of S. xylocarpa

    The resulting model outcomes show that the current suitable area forS. xylocarpa is 697,200 km2, accounting for only 7.26% of China’s total land area. It is mainly distributed in Anhui, Guangxi, Hubei, Hunan, Jiangsu, Jiangxi, Taiwan, and Zhejiang in China (Figure 4). Given that the fruit ofS. xylocarpa are drupes (Figure 1b) with a hundred-kernel weight of 98.4 g [53], it seems unlikely for its seeds to disperse from the Chinese mainland to Taiwan because Taiwan Strait separates them with a minimum width of 130 km.
    Actually, up to now, there is no record of its wild populations in Taiwan Province [54]. This indicates that the actual geographical range of an endangered tree species depends largely on its suitable habitat, as well as on its origin and evolution, on propagule dispersion, and interaction with different species [25]. Therefore, we think that the suitable distribution area ofS. xylocarpa covers seven provinces except Taiwan in China. However, according toFlora of China, S. xylocarpa occurs only in Jiangsu Province. According to the newly published monographNational Key Protected Wild Plants of China [55], it is distributed in Nanjing in Jiangsu Province, Hangzhou in Zhejiang Province, Shanghai, and Wuhan in Hubei Province, which differs from our results. In fact, as early as 1987,Flora of China recorded thatS. xylocarpa occurred in Nanjing and that it was cultivated in large cities like Hangzhou, Shanghai, and Wuhan [17]. Therefore, it is probably not true about its distribution description by Jin et al. (2023) [55]. More recently, a wild population has been found in the Cixi Mountain area of Ningbo, Zhejiang Province [31]. We also found its wild community in the Wuwei hilly area of Wuhu, Anhui Province, in 2024 (Figure 1d). Therefore, our results indicate thatS. xylocarpa has a much larger suitable habitat in China than it is known.

    4.4. The Change in Suitable Areas in the Past and Future

    According to modeling analysis,S. xylocarpa had a suitable area of 638,400 km2 in the Last Interglacial (LIG) and moderately expanded to 645,000 km2 in the Middle Holocene (MH). Compared with the current climate scenario, their suitable areas decreased by 8.43% and 7.49%, respectively. Furthermore, habitat fragmentation ofS. xylocarpa was increasing from LIG to MH (Figure 4). Overall, there was an increasing trend in suitable habitats forS. xylocarpa over time. This is likely due to global warming since the Holocene, with higher temperatures and more precipitation [56], which favors the expansion ofS. xylocarpa populations. Furthermore, this may also be related to the bottleneck effect ofS. xylocarpa populations after experiencing multiple glacial periods [31].
    Under future climate scenarios, excluding RCP 8.5 in the 2070s, the suitable areas forS. xylocarpa will decrease, ranging from 11.2% to 15.06%, in the five remaining scenarios. Overall, the suitable habitat forS. xylocarpa is expected to be reduced by an average of 10.97% under future scenarios compared to the current (Table 3). Moreover, its habitat fragmentation will be exacerbated under future scenarios compared to the current (Figure 5). This suggests that future climate change may be unfavorable for the growth and distribution ofS. xylocarpa.
    In addition, the shift direction ofS. xylocarpa in the future is largely to the north, especially to the northeast (Figure 6), which is similar to these endangered tree species likeTaxus wallichiana var.Mairei,T. wallichiana var.chinensis [57] andEmmenopterys henryi [27]. This may be related to the tree traits. Just as stated above, the most critical factor influencing the current distribution ofS. xylocarpa is Bio17 (precipitation of driest quarter), followed by Bio10 (mean temperature of warmest quarter) (Table 2). Specifically,S. xylocarpa prefers to grow in relatively warm and humid habitats in China.

    4.5. Conservation Implications for S. xylocarpa

    S. xylocarpa has a suitable area of 697,200 km2, spanning seven provinces, and is mainly distributed in the low-elevation areas of southeastern China. This is quite different from previous views. For example, it is usually assumed that its wild populations only occur in Jiangsu Province, eastern China [18,58], i.e., this species has long been believed to be endemic to Jiangsu. Given thatS. xylocarpa usually forms patchy populations with small deme sizes, it is recommended that supplementary surveys be carried out in suitable areas forS. xylocarpa, especially in highly suitable areas (Figure 4), such as eastern Hunan, northern Jiangxi, and northern Zhejiang in the future.
    In addition, Zhu et al. (2024) pointed out that, like other endangered tree species,S. xylocarpa had a low genome-wide nucleotide diversity [31]. However, their study only sampled two locations: Nanjing in Jiangsu Province and Ningbo in Zhejiang Province. Our results indicate thatS. xylocarpa is distributed across multiple provinces, and its different populations are often isolated from each other (Figure 4). Therefore, it is advised that more samples should be collected from various locations to reveal the genetic diversity and genetic structure ofS. xylocarpa.
    Our analysis of overlaying the suitable distribution ofS. xylocarpa with national and provincial nature reserves reveals that only 1.84% of its suitable area falls within national nature reserves and 2.15% within provincial nature reserves. This highlights that over 90% of its suitable habitat is in a zero-protection status. According to the IUCN Red List, this species is ranked as vulnerable. However, such an assessment is based on the data from 1998 (www.iucnredlist.org/species/32374/9701730, accessed on 3 February 2025). Indeed, it belonged to the “endangered” category in China [59] and “endangered” in Jiangsu Province [60]. Its endangerment can be ascribed to the following reasons. The species is generally distributed in low-altitude areas of southeastern China, in which there are usually intense human activities, such as logging, grazing, road-building, and touring [61]. This inevitably results in habitat fragmentation or habitat destruction. Recent research has confirmed that highly lignified and fibrotic pericarps inhibit the seed germination ofS. xylocarpa [31]. Other researchers hold that its compact endosperm is also a mechanical barrier to embryo germination [53]. Additionally, it is noted that its seeds usually need to be treated with low temperatures in winter after seed maturation in the field. Afterward, its hard seed coat will be decayed and its seeds can germinate in the next spring [53]. Our results also show that the current distribution areas ofS. xylocarpa are isolated from each other, with severe habitat fragmentation. Therefore, we propose conservation strategies forS. xylocarpa, such asin-situ conservation,exsitu conservation, and restoration.In-situ conservation involves expanding existing nature reserves in highly suitable areas, andex-situ conservation focuses on developing seed banks and botanical gardens to maintain genetic diversity. Additionally, restoration strategies for this species highlight reconnecting fragmented habitats through ecological corridors.
    Furthermore,Sinojackia is a monophyletic genus, and currently consists of five species in China. The vast majority ofSinojackia species have small population sizes and narrow distribution areas, and all species are endemic to China. Now, they are all listed as national secondary protected wild plants [55]. This study, takingS. xylocarpa as a representative species, for the first time employs an ensemble model to determine its suitable distribution, identifies the key factors influencing its distribution, and predicts the impact of climate change on its geographical distribution across different periods. Such a study can provide a valuable reference for the conservation of other endangeredSinojackia species in the future. In addition, our study also highlights that for a taxon with restricted or endemic distribution at the local scale, it is more appropriate to forecast the habitat suitability at the level of species than at the level of genus.

    5. Conclusions

    Here, we developed an ensemble model consisting of six models to project the potential distribution of the endangered treeS. xylocarpa endemic to China across different climate scenarios. The outcomes indicate that climate change may have an adverse effect on its suitable area and habitat integrity. This study is the first to demonstrate that this species is mainly distributed in southeast China, with a suitable area of 697,200 km2, which is larger than known. Nevertheless, more than 90% of the suitable areas are outside national or provincial nature reserves in China. Therefore, our study contributes to the conservation, management, and cultivation ofS. xylocarpa and can also provide useful information for other endangeredSinojackia species in China.

    Supplementary Materials

    The following supporting information can be downloaded at:www.mdpi.com/article/10.3390/biology14030304/s1, Table S1: Latitude and longitude coordinates of 21 occurrence records of the endangeredSinojackia xylocarpa in China.

    Author Contributions

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

    Funding

    This research was financially supported by the investigation and assessment of key protected wild plants in Jiangsu Province from the Jiangsu Forestry Bureau (No. 2023053SMnull0162).

    Institutional Review Board Statement

    Not applicable.

    Informed Consent Statement

    Not applicable.

    Data Availability Statement

    Data are contained within the article andSupplementary Materials.

    Acknowledgments

    We are very grateful to Hanwei Cai, Weiyi Hang, Yanrong Zhou, Ze Lan, and Yansong Chen for their assistance in fieldwork. We thank Haoran Wang and Ting Liu for their help in data processing.

    Conflicts of Interest

    The authors declare no conflicts of interest.

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    Figure 1. Photos of endangeredSinojackia xylocarpa in the field. (a) Individuals in Nanjing, Jiangsu Province; (b) Individuals in Wuwei, Anhui Province; (c) Blooming flowers; (d) Ovoid fruit (drupes). The photos were taken by Guangfu Zhang.
    Figure 1. Photos of endangeredSinojackia xylocarpa in the field. (a) Individuals in Nanjing, Jiangsu Province; (b) Individuals in Wuwei, Anhui Province; (c) Blooming flowers; (d) Ovoid fruit (drupes). The photos were taken by Guangfu Zhang.
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    Figure 2. Distribution records of endangeredS. xylocarpa in China.
    Figure 2. Distribution records of endangeredS. xylocarpa in China.
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    Figure 3. Response curves ofS. xylocarpa to crucial environmental variables. (a) Precipitation of driest quarter (Bio17, mm); (b) Mean temperature of warmest quarter (Bio10, °C); (c) Precipitation of warmest quarter (Bio18, mm); (d) Elevation (m).
    Figure 3. Response curves ofS. xylocarpa to crucial environmental variables. (a) Precipitation of driest quarter (Bio17, mm); (b) Mean temperature of warmest quarter (Bio10, °C); (c) Precipitation of warmest quarter (Bio18, mm); (d) Elevation (m).
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    Figure 4. The current suitable habitat ofS. xylocarpa overlaps with national and provincial nature reserves in China.
    Figure 4. The current suitable habitat ofS. xylocarpa overlaps with national and provincial nature reserves in China.
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    Figure 5. Spatial distribution change in suitable habitats under different past scenarios. (a) Last Interglacial (LIG); (b) Middle Holocene (MH).
    Figure 5. Spatial distribution change in suitable habitats under different past scenarios. (a) Last Interglacial (LIG); (b) Middle Holocene (MH).
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    Figure 6. Spatial distribution change in suitable habitats under six future scenarios.
    Figure 6. Spatial distribution change in suitable habitats under six future scenarios.
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    Figure 7. Centroid shift of suitable habitats forS. xylocarpa in China under different climate scenarios.
    Figure 7. Centroid shift of suitable habitats forS. xylocarpa in China under different climate scenarios.
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    Table 1. Environmental variables and percent contribution of variables (in bold font) used in the final ensemble model across different climate scenarios. Note: LIG and MH mean the Last Interglacial and the Middle Holocene, respectively.
    Table 1. Environmental variables and percent contribution of variables (in bold font) used in the final ensemble model across different climate scenarios. Note: LIG and MH mean the Last Interglacial and the Middle Holocene, respectively.
    CategoryVariableDescriptionUnitPercent Contribution (%)
    LIGMHCurrent
    BioclimateBio1Annual mean temperature°C
    Bio2Mean diurnal range (mean of monthly (max temp–min temp))°C5.10.5
    Bio3Isothermality ((Bio2/Bio7) × 100)%0.42.82.2
    Bio4Temperature seasonality
    (standard deviation × 100)    		-19.11.5
    Bio5Max temperature of warmest month°C1.2
    Bio6Min temperature of coldest month°C0.6
    Bio7Temperature annual range (Bio5–Bio6)°C2.1
    Bio8Mean temperature of wettest quarter°C3.17.3
    Bio9Mean temperature of driest quarter°C1.92.7
    Bio10Mean temperature of warmest quarter°C21.79.6
    Bio11Mean temperature of coldest quarter°C16.615.5
    Bio12Annual precipitationmm4.4
    Bio13Precipitation of wettest monthmm3.78.7
    Bio14Precipitation of driest monthmm
    Bio15Precipitation seasonality (coefficient of variation)-6.527.50.9
    Bio16Precipitation of wettest quartermm
    Bio17Precipitation of driest quartermm19.623.061.0
    Bio18Precipitation of warmest quartermm4.29.58.9
    Bio19Precipitation of coldest quartermm
    TopographyElevation-m5.4
    Slope-°0.2
    SoilT-BSTopsoil Base Saturation%0.5
    T-CaCO3Topsoil Calcium Carbonate%0.1
    T-CEC-CLAYTopsoil CEC (clay)-0.7
    T-CEC-SOILTopsoil CEC (soil)-0.1
    T-CLAYTopsoil Clay Fraction%
    T-ECETopsoil Salinity (Elco)S/m
    T-ESPTopsoil Sodicity (ESP)-0.1
    T-GRAVELTopsoil Gravel Content%0.6
    T-OCTopsoil Organic Carbon%0.1
    T-PH-H2OTopsoil pH (H2O)-
    T-REF-BULKTopsoil Reference Bulk Densitykg/m30.1
    T-SANDTopsoil Sand Fraction%
    T-SILTTopsoil Silt Fraction%0.1
    T-TEBTopsoil TEB-
    T-TEXTURETopsoil TEXTURE-
    T-USDA-TEXTopsoil USDA Texture Classification-0.2
    Table 2. The mean value (± SD) of the area under the curve (AUC) and true skill statistic (TSS) for various model algorithms.
    Table 2. The mean value (± SD) of the area under the curve (AUC) and true skill statistic (TSS) for various model algorithms.
    Model NameModel CodeAUCTSS
    Artificial neural networks modelANN0.8632 ± 0.17190.7324 ± 0.1882
    Classification tree analysis modelCTA0.8930 ± 0.07220.7864 ± 0.1436
    Flexible discriminant analysis modelFDA0.9274 ± 0.04290.7376 ± 0.0938
    Generalized additive modelGAM0.7692 ± 0.17090.6320 ± 0.2128
    Generalized boosting modelGBM0.9376 ± 0.03900.7089 ± 0.0548
    Generalized linear modelGLM0.8468 ± 0.08170.6936 ± 0.1635
    Maximum entropy modelMaxEnt0.9690 ± 0.02090.8918 ± 0.0398
    Multivariate adaptive regression splines modelMARS0.8342 ± 0.11350.6704 ± 0.2276
    Random forest modelRF0.9499 ± 0.02970.7130 ± 0.0867
    Surface range envelope modelSRE0.5352 ± 0.04980.1920 ± 0.0056
    Ensemble model0.9960 ±0.06410.9500 ±0.0610
    Table 3. Changes in the suitable habitats forS. xylocarpa in percent (compared to the current) under different climate scenarios. Up arrow (↑) denotes the increased case; down arrow (↓) denotes the decreased case.
    Table 3. Changes in the suitable habitats forS. xylocarpa in percent (compared to the current) under different climate scenarios. Up arrow (↑) denotes the increased case; down arrow (↓) denotes the decreased case.
    ScenariosLow
    Suitable Area    		Moderately
    Suitable Area    		Highly
    Suitable Area    		Suitable Area
    (Moderately and Highly)
    Area
    (×104 km2)    		Trend (%)Area
    (×104 km2)    		Trend (%)Area
    (×104 km2)    		Trend (%)Area
    (×104 km2)    		Trend (%)
    Last Interglacial97.56↑97.8120.38↓42.7043.46↑27.2663.84↓8.43
    Middle Holocene59.24↑20.1126.30↓26.0638.20↑11.8664.50↓7.49
    Current49.32-35.57-34.15-69.72-
    2050sRCP2.657.36↑16.3030.98↓12.9028.83↓15.5859.81↓14.21
    RCP4.538.31↓22.3224.66↓30.6737.25↑9.0861.91↓11.20
    RCP8.522.59↓54.2016.24↓54.3442.10↑23.2858.34↓16.32
    2070sRCP2.647.90↓2.8821.48↓39.6138.86↑13.7960.34↓13.45
    RCP4.575.56↑53.2030.06↓15.4929.16↓14.6159.22↓15.06
    RCP8.564.34↑30.4531.44↓11.6141.33↑21.0272.77↑4.37
    The mean value of six future climate scenarios51.01↑3.4325.81↓27.4436.26↑6.1862.07↓10.97
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    Hu, C.; Wu, H.; Zhang, G. Evaluating Habitat Suitability for the EndangeredSinojackia xylocarpa (Styracaceae) in China Under Climate Change Based on Ensemble Modeling and Gap Analysis.Biology2025,14, 304. https://doi.org/10.3390/biology14030304

    AMA Style

    Hu C, Wu H, Zhang G. Evaluating Habitat Suitability for the EndangeredSinojackia xylocarpa (Styracaceae) in China Under Climate Change Based on Ensemble Modeling and Gap Analysis.Biology. 2025; 14(3):304. https://doi.org/10.3390/biology14030304

    Chicago/Turabian Style

    Hu, Chenye, Hang Wu, and Guangfu Zhang. 2025. "Evaluating Habitat Suitability for the EndangeredSinojackia xylocarpa (Styracaceae) in China Under Climate Change Based on Ensemble Modeling and Gap Analysis"Biology 14, no. 3: 304. https://doi.org/10.3390/biology14030304

    APA Style

    Hu, C., Wu, H., & Zhang, G. (2025). Evaluating Habitat Suitability for the EndangeredSinojackia xylocarpa (Styracaceae) in China Under Climate Change Based on Ensemble Modeling and Gap Analysis.Biology,14(3), 304. https://doi.org/10.3390/biology14030304

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