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Article

Modeling the Impacts of Climate Change on Potential Distribution of Betula luminifera H. Winkler in China Using MaxEnt

by
Qiong Yang
1,
Yangzhou Xiang
1,
Suhang Li
1,
Ling Zhao
2,
Ying Liu
3,
Yang Luo
1,
Yongjun Long
1,*,
Shuang Yang
1,* and
Xuqiang Luo
1,*
1
Guizhou Provincial Key Laboratory of Geographic State Monitoring of Watershed, School of Geography and Resources, Guizhou Education University, Guiyang 550018, China
2
State Power Investment Corporation Power Station Operation Technology (Beijing) Co., Ltd., Beijing 100032, China
3
School of Biological Sciences, Guizhou Education University, Guiyang 550018, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(9), 1624; https://doi.org/10.3390/f15091624
Submission received: 20 August 2024 / Revised: 8 September 2024 / Accepted: 9 September 2024 / Published: 14 September 2024
(This article belongs to the Section Forest Meteorology and Climate Change)
Browse Figures
Figure 1
<p>Current distribution of <span class="html-italic">B. luminifera</span> in China.</p> "> Figure 2
<p>Correlation analysis of nineteen environmental factors (* and ** indicate significant level at <span class="html-italic">p</span> &lt; 0.05 and <span class="html-italic">p</span> &lt; 0.01, respectively).</p> "> Figure 3
<p>Delta.AICc (<b>a</b>), AUC.DIFF (<b>b</b>), and OR10 (<b>c</b>) for <span class="html-italic">B. luminifera</span> derived from MaxEnt models with diverse parameter configurations. The legends indicate distinct feature classes (L = linear, Q = quadratic, H = hinge, P = product, and T = threshold).</p> "> Figure 4
<p>Receiver Operating Characteristic (ROC) prediction results of MaxEnt model for <span class="html-italic">B. luminifera</span>. (<b>a</b>) original model, (<b>b</b>) optimized model.</p> "> Figure 5
<p>Response curves of the effect of main meteorological factors on occurrence probability of <span class="html-italic">B. luminifera</span>. (<b>a</b>) The contribution rate of the dominant factors, (<b>b</b>) annual precipitation, (<b>c</b>) min temperature of the coldest month, (<b>d</b>) standard deviation of temperature seasonality. The interval between two vertical orange dotted lines represents the optimal suitable range of environmental factors.</p> "> Figure 6
<p>Current potential distribution area of <span class="html-italic">B. luminifera</span> in China.</p> "> Figure 7
<p>Potential distribution of <span class="html-italic">B. luminifera</span> under different future climatic scenarios.</p> "> Figure 8
<p>Changes in the potential geographical distribution of <span class="html-italic">B. luminifera</span> under future climatic scenarios.</p> "> Figure 9
<p>Centroid migration of <span class="html-italic">B. luminifera</span> under different climatic scenarios.</p> ">
Versions Notes

Abstract

:
Betula luminifera H. Winkler, a fast-growing broad-leaved tree species native to China’s subtropical regions, possesses significant ecological and economic value. The species’ adaptability and ornamental characteristics make it a crucial component of forest ecosystems. However, the impacts of global climate change on its geographical distribution are not well understood, necessitating research to predict its potential distribution shifts under future climate scenarios. Our aims were to forecast the impact of climate change on the potential suitable distribution of B. luminifera across China using the MaxEnt model, which is recognized for its high predictive accuracy and low sample data requirement. Geographical coordinate data of B. luminifera distribution points were collected from various databases and verified for redundancy. Nineteen bioclimatic variables were selected and screened for correlation to avoid overfitting in the model. The MaxEnt model was optimized using the ENMeval package, and the model accuracy was evaluated using the Akaike Information Criterion Correction (delta.AICc), Training Omission Rate (OR10), and Area Under the Curve (AUC). The potential distribution of B. luminifera was predicted under current and future climate scenarios based on the Shared Socio-economic Pathways (SSPs). The optimized MaxEnt model demonstrated high predictive accuracy with an AUC value of 0.9. The dominant environmental variables influencing the distribution of B. luminifera were annual precipitation, minimum temperature of the coldest month, and standard deviation of temperature seasonality. The potential suitable habitat area and its geographical location were predicted to change significantly under different future climate scenarios, with complex dynamics of habitat expansion and contraction. The distribution centroid of B. luminifera was also predicted to migrate, indicating a response to changing climatic conditions. Our findings underscore the importance of model optimization in enhancing predictive accuracy and provide valuable insights for the development of conservation strategies and forest management plans to address the challenges posed by climate change.

1. Introduction

One of the hot topics in contemporary ecological research is global climate change, which has profoundly influenced the geographical distribution of species [1,2]. With the rise in global temperatures and changes in precipitation patterns, the suitable habitats for many species have been significantly altered, resulting in the reduction, migration, and even extinction of their distribution ranges [3,4]. In recent years, the potential impacts of climate change on the distribution of plants and animals have been deeply investigated by ecologists using remote sensing technology, Geographic Information Systems (GISs), and Species Distribution Models (SDMs) [5,6]. It has been indicated by relevant studies that climate change may lead to the migration of species distributions towards higher latitudes and altitudes [7,8], and the threats to biodiversity are further exacerbated by competition among species, habitat loss, and changes in the functions of ecosystem services [9,10].
Species Distribution Models (SDMs) are recognized as effective tools for predicting the impacts of climate change on the geographical distribution of species [11]. SDMs are able to forecast the potential suitable habitats of species under future climate scenarios by integrating known distribution data of species with environmental variables. A variety of species distribution models exist, including the Ecological Niche Models, Distance–Area Models, Spatially Explicit Models, Maximum Entropy (MaxEnt) models and so on [12,13,14]. The MaxEnt 3.4.1 model is a software tool that has been designed to predict the geographic range of species based solely on their observed presence data [12]. MaxEnt models, due to their foundation on the principle of maximum entropy, can effectively handle incomplete or uneven datasets, providing probabilistic predictions, and assess the contribution of environmental variables. Compared to Ecological Niche Models, the MaxEnt model offers greater accuracy with limited data; it surpasses Distance-Area Models in predicting distributions in new areas, and it captures spatial heterogeneity through environmental variables, outperforming Spatially Explicit Models in flexibility and predictive power. In recent years, the MaxEnt model has emerged as one of the mainstream methods for studying the impact of climate change on species distribution due to its low sample data requirements and high predictive accuracy [15,16]. The distribution changes of different tree species under future climate scenarios have been predicted by many studies utilizing the MaxEnt model, which have found that climate change may lead to significant alterations in the distribution ranges of tree species [17,18]. Particularly for species with high climatic sensitivity, their suitable habitats may be greatly reduced [19,20]. These studies have provided important scientific evidence for forest management and biodiversity conservation.
Betula luminifera H. Winkler, a deciduous large tree belonging to the family Betulaceae and genus Betula, is an excellent native fast-growing broad-leaved tree species in the subtropical regions of China [21]. The bark of this tree species is smooth, with a silvery-grey or yellowish-white color, and it possesses significant ornamental value. Adaptability is a notable characteristic of B. luminifera, as it can thrive in high-altitude mountainous and hilly areas, with relatively low demands for soil and water. As a pioneer species [22], it plays a crucial role in forest ecosystems by rapidly occupying bare land, preventing soil erosion, and promoting the improvement of soil structure [23,24]. Moreover, the wood of B. luminifera is of high quality and is extensively used in furniture manufacturing, building materials, and the paper industry [25]. In addition, its bark and leaves are utilized in traditional medicine [26,27], adding to its economic value. Given the significant ecological and economic value of Betula luminifera in China’s subtropical regions, understanding its response to climate change is crucial. Previous studies have shown that many species shift their geographical distributions in response to changing climatic conditions, typically moving toward higher latitudes and altitudes. However, research on B. luminifera has been primarily concentrated on aspects such as seed germination, genetic improvement, forest management, ecological functions, and wood resources [28,29,30,31,32], and no research has yet explored the potential distribution changes in B. luminifera under future climate scenarios.
This study intended to utilize ArcGIS 10.8 software and the MaxEnt model to forecast the impact of climate change on the potential suitable distribution of B. luminifera. By comparing the distribution of suitable habitats of B. luminifera under current climatic conditions with those under various future climate scenarios, the spatial pattern changes that may be brought to the distribution of this tree species by climate change will be revealed. The findings of this study contribute to the understanding of the survival challenges faced by B. luminifera in the context of climate change and will offer scientific evidence for the development of forest management strategies and conservation plans. Additionally, the study investigates the trends in potential distribution changes in B. luminifera under different climate scenarios, serving as a reference for the research on other similar species. Based on the known responses of tree species to climate change, we hypothesized that B. luminifera would experience a shift in its potential distribution under future climate scenarios, with suitable habitats contracting in the southern regions of China and expanding northward due to the combined effects of temperature increases and precipitation changes.

2. Materials and Methods

2.1. Species Distribution Data

The geographical coordinate data (longitude and latitude) of the distribution points of B. luminifera are mainly collected from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org, accessed on 10 May 2024), the National Specimen Information Infrastructure of China (NSII, http://www.nsii.org.cn, accessed on 11 May 2024), the Chinese Virtual Herbarium (CVH, http://www.cvh.org.cn, accessed on 12 May 2024), the China National Knowledge Infrastructure (CNKI, https://chn.oversea.cnki.net, accessed on 15 May 2024), and the Google Scholar (https://scholar.google.com, accessed on 16 May 2024). For sampling points with explicit longitude and latitude information, data are directly extracted into the Excel 2016 software. For some samples where the sampling points lack coordinate information but are labeled with place names down to the town or administrative village, longitude and latitude are queried through the Map Location (https://maplocation.sjfkai.com, accessed on 18 May 2024). The geographical coordinate data of the distribution points of B. luminifera from all sources are consolidated in a single sheet, and then duplicate longitudes and latitudes are remigrated using the ‘Remove Duplicates’ command in the Excel software to avoid potential effects of data redundancy on the analysis results. After careful screening and verification, a total of 1207 valid distribution records were collected.
To further optimize data quality and ensure the accuracy of model predictions, we implemented a strict grid processing strategy in the ArcGIS 10.8 software platform. Specifically, we constructed a 2.5′ × 2.5′ grid system, retaining only one distribution point data closest to the center of each grid as the representative sample for that grid area. After this series of screenings and processing, we ultimately obtained information for 1027 valid distribution point coordinates (Figure 1), which laid a solid foundation for predicting the ecological suitability of B. luminifera. To ensure that the data format meets the input requirements of the MaxEnt model, the Excel document in the .xlsx format is saved as a .csv format, which facilitates subsequent data processing.

2.2. China Map

All species distribution maps presented in this study are based on the administrative division map of China (Map Approval Number: GS (2023)2762), which is sourced from the Standard Map Service website (http://bzdt.ch.mnr.gov.cn/, accessed on 2 January 2024). The original map has a scale of 1:10,000,000 with image format. It is converted to a base map in shp format for subsequent analysis and mapping before use.

2.3. Collection of Climatic Variables

Nineteen bioclimatic variables were selected as environmental variables for current and future climate scenarios (Table 1). All climate data were downloaded from the WorldClim database (http://www.worldclim.org, accessed on 18 May 2024), with a spatial resolution of 2.5 min [33]. The current period is from 1970 to 2000, and the future periods include three-time spans: the 2050s (2041–2060), the 2070s (2061–2080), and the 2090s (2081–2100). The general circulation models (GCMs) under the Shared Socio-economic Pathways (SSPs) scenarios, initiated by the Intergovernmental Panel on Climate Change’s (IPCC) Coupled Model Intercomparison Project Phase 6 (CMIP6), were employed to forecast potential climate shifts in the future [34]. We select three Shared Socio-economic Pathways (SSPs) scenarios, namely SSP126, SSP370, and SSP585. Specifically, the SSP126 scenario represents a sustainable development path, with greenhouse gas emissions at a relatively low level, and radiative forcing will rise to 2.6 watts per square meter (W/m2) by the year 2100; the SSP370 scenario represents a moderate development path, with greenhouse gas emissions at a moderate level, and radiative forcing will rise to 7.0 W/m2 by the year 2100; and the SSP585 scenario represents a development path dominated by fossil fuels, with greenhouse gas emissions at a high level, and radiative forcing will rise to 8.5 W/m2 by the year 2100 [35]. The selected climate model is the BCC-CSM2.2-MR, developed by the National Climate Center, which has a high simulation capability in China [36].

2.4. Screening of Environmental Variables

Due to the potential high correlation between environmental factors, there is a risk of overfitting in the MaxEnt model [37]. Therefore, to enhance the reliability of the MaxEnt model’s predictive outcomes, it is necessary to conduct a correlation analysis of environmental variables and to screen for dominant factors. The specific operation is as follows: First, the MaxEnt3.4.1 model (https://github.com/mrmaxent/Maxent, accessed on 19 May 2024) is used to initially simulate the 19 bioclimatic factors along with the longitude and latitude data of the distribution points of B. luminifera, resulting in the contribution rates of the 19 bioclimatic factors to the distribution of B. luminifera (Table 1). Second, the ‘Multi-Value to Point’ tool in ArcGIS 10.8 software is used to extract the 19-environmental-factor data corresponding to the 1027 distribution points of B. luminifera, and then the extracted environmental variables are subjected to Pearson correlation analysis using SPSS 26.0 software (Figure 2). The ‘Conditional Formatting’ tool in Excel software is used to filter out environmental variables with a correlation coefficient |r| value greater than 0.75. Third, when the correlation coefficient |r| value between two environmental variables is greater than 0.75, the factor with the smaller contribution rate is remigrated, and the environmental variable with the larger contribution rate is retained. Fourth, environmental factors with a contribution rate of 0 are remigrated. Moreover, after conducting the correlation analysis to identify and filter out highly correlated environmental variables, we have also considered the ecological relevance of each variable in the context of Betula luminifera H. Winkler’s ecological requirements. We have conducted a thorough literature review and consulted with subject matter experts to assess the impact of each environmental factor on the species’ ecology. For instance, we have given special attention to key ecological factors such as temperature and precipitation, which directly influence the growth and distribution of the species. Even when variables show high correlation, if they have a significant ecological impact on the species’ adaptation, we have retained them in our model. In the final model, we have ensured that the selected variables are not only statistically significant but also ecologically sound, reflecting the true ecological response of Betula luminifera H. Winkler to environmental changes. Finally, all the remaining environmental factors, including standard deviation of temperature seasonality (Bio4), min temperature of coldest month (Bio6), mean temperature of wettest quarter (Bio8), annual precipitation (Bio12), variation of precipitation seasonality (Bio15), and precipitation of warmest quarter (Bio18), are used to simulate the impact of climate change on the potential distribution of B. luminifera.

2.5. MaxEnt Model Optimization and Modeling

2.5.1. Optimization of MaxEnt Model

Unoptimized model predictions may exhibit a significant tendency towards overfitting [38], which could have a misleading impact on the formulation of conservation management strategies for B. luminifera [39]. Therefore, it is very important to appropriately adjust the parameters of the MaxEnt model before analyzing the impact of climate change on the potential distribution of B. luminifera [40]. The R package ‘ENMeval’ has been utilized to refine the MaxEnt model by modifying two pivotal constraint parameters: the Regularization Multiplier (RM) and Feature Combination (FC) [41]. In order to evaluate the MaxEnt model’s capacity for B. luminifera, the 1027 records were randomly divided into a training subset (comprising 75% of the data) and a validation subset (comprising 25% of the data) through k-fold cross-validation. To assess the model efficacy across a range of regularization intensities, eight Regularization Multiplier (RM) parameters were designated: 0.5, 1.0, 1.5, 2.0, 2.5, 3.0, 3.5, and 4.0 [42]. For the FC configurations, the MaxEnt model has the capacity to autonomously adjust to include five unique characteristics: hinge functions (H), linear terms (L), interaction products (P), quadratic terms (Q), and threshold functions (T). Seven FC parameters were defined in this research: H, L, LQH, LQHP, LQHPT, and QHP. The Akaike Information Criterion Correction (delta.AICc) was employed to assess the model’s fit and its level of complexity. Additionally, the 10% Training Omission Rate (OR10) and the divergence in the Area Under the Curve (AUC) scores between the training and testing phases (AUC.DIFF) were used to reduce the likelihood of overfitting [43]. Finally, the parameter set with the smallest increment in delta.AICc was adopted as the optimal configuration for our model construction.

2.5.2. MaxEnt Model Parameter Setting

The longitude and latitude data of the distribution points of B. luminifera and the corresponding key environmental variable data are imported into the MaxEnt 3.4.1 software. The parameters of the MaxEnt model are set as follows: 25% of the distribution points are used as the test set in the model, and 75% of the distribution points are used as the training set. The subsample method is adopted, with the number of replicates set to 10, the replicated run type is set to cross-validate, and the output format is set to logistic values. The optimized Regularization Multiplier (RM) and Feature Combination (FC) parameters from the results of the MaxEnt model optimization are chosen, and other settings are set to default. The final output is the average of ten replicates, in ASCII format. The modeling contribution rate of the environmental factors is used to select the environmental factors that have a dominant effect on the suitable habitat of B. luminifera.

2.5.3. Evaluation of MaxEnt Model Results

The Receiver Operating Characteristic (ROC) curve is utilized to assess the fit accuracy of MaxEnt model [44]. The specific calculation method of the ROC curve is as follows: An ROC curve is plotted with the false positive rate (1-specificity) as the x-axis and the true positive rate (1-miss rate) as the y-axis. The area enclosed by the ROC curve along the x-axis is referred to as the Area Under the Curve (AUC). The range of the AUC value typically lies between [0, 1], with values closer to 1 indicating better predictive accuracy [45]. When the AUC value is within [0.5, 0.6), it indicates that the model construction has failed and the predictive results are invalid; when the AUC value is within [0.6, 0.7), it indicates that the model’s predictive performance is poor; when the AUC value is within [0.7, 0.8), it indicates that the model’s predictive performance is fair; when the AUC value is within [0.8, 0.9), it indicates that the model’s predictive performance is good; and when the AUC value is within [0.9, 1], it indicates that the model’s predictive performance is excellent and it can be considered to accurately reflect the potential distribution of the species [46].

2.6. Classification of the Suitable Habitat Grades for B. luminifera

According to the method by Liu et al. (2024) [47], the average results from the 104simulations of the MaxEnt model are imported into the ArcGIS 10.8 software. Initially, the ‘ASCII to Raster’ tool of the software is utilized to convert the ASCII format files into raster format. Subsequently, the manual classification method within ArcGIS 10.8 software is employed to categorize the potential suitable habitats of B. luminifera for the current period (1970–2000) and the future time spans of 2040–2060, 2061–2080, and 2081–2100 under the three scenarios of SSP126, SSP370, and SSP585. In this study, the suitable habitat of B. luminifera is divided into four grades with thresholds at 0.1, 0.3, and 0.5. Probability values p < 0.1 are classified as unsuitable areas, probability values 0.1 ≤ p < 0.3 are classified as generally suitable areas, probability values 0.3 ≤ p < 0.5 are classified as moderately suitable areas, and probability values p ≥ 0.5 are classified as highly suitable areas. The distribution map of the suitability grades for B. luminifera is drawn using ArcGIS 10.8 software. The area of each suitable habitat is obtained by employing the raster calculation tool of ArcGIS 10.8 software to perform area statistics on the reclassified layers, and then multiplying the weight of each suitable area by the total land area of China.

2.7. Changes in the Spatial Pattern of Suitable Habitats and Shifts in the Centroid of Distribution

Following the method of Xu et al. (2023) [48], the average results from 10 simulations in the MaxEnt 3.4.1 model are imported into ArcGIS 10.8 software for reclassification. The specific steps are as follows: spatial units with a probability value p < 0.1 are assigned 0, and those with a probability value p ≥ 0.1 are assigned 1, thereby establishing a presence/absence (0, 1) matrix between the current and future suitable habitats of B. luminifera. The area changes of suitable habitats for B. luminifera under different future time periods and climate scenarios are all referenced against the area of the potential suitable habitat under current climate conditions, with matrix values of 0→1 indicating areas of gain, 1→0 indicating areas of loss, and 1→1 indicating areas of retention [49]. Distribution maps of the newly added suitable areas, lost suitable areas, and retained suitable areas of B. luminifera under three future scenarios are created in ArcGIS 10.8 software. In addition, areas with a probability threshold set to P ≥ 0.1 are defined as suitable habitats. Using the ‘Zonal Geometry’ tool in ArcGIS 10.8 software, the geometric center of the potential suitable habitat of B. luminifera is assessed for changes under different time periods and conditions. By analyzing the migration paths of these centroids, the evolutionary trends of the potential suitable habitat of B. luminifera under different time periods and conditions can be understood [50].

3. Results

3.1. Optimization and Evaluations of MaxEnt Model

The parameter optimization of the MaxEnt 3.4.1 model using ENMeval indicated that at a Regularization Multiplier (RM) of 0.5 and a Feature Combination (FC) setting of LQHPT, the model attained the minimum delta.AICc, resulting in an AICc value of zero (Figure 3a). A follow-up examination revealed that this model’s AUC.DIFF had increased by 20.10% compared to the model configured with the standard parameters (RM = 1.0 and FC = LQHP) (Figure 3b). Moreover, the OR10 value for this model was raised by 81.63% compared to the default model (Figure 3c). Therefore, the parameters RM = 0.5 and FC = LQHPT were chosen as the optimal setup.
The AUC values were obtained from a 10-fold cross-validation process for both the original and the optimized models (Figure 4). The scores were 0.902 and 0.900, accompanied by standard deviations of 0.0076 and 0.0078, respectively. These outcomes fall between 0.9 and 1, indicating that the predictive model for the suitable habitat of B. luminifera in our study has extremely high accuracy.

3.2. Dominant Environmental Variables and Their Response Curves

Selecting the correct environmental variables is a key step in ensuring the accuracy of the model. Therefore, it is very important to identify which environmental factors have the greatest influence on the model. It can be known from the results of the modeling contribution levels of the environmental factors that the six climate factors are ranked in order of contribution as follows: annual precipitation (64.6%), min temperature of the coldest month (18.8%), standard deviation of temperature seasonality (12.1%), mean temperature of the wettest quarter (2.7%), variation of precipitation seasonality (1.6%), and precipitation of the warmest quarter (0.2%), with the first three factors having a cumulative contribution rate of 95.5% (Figure 5a). These results indicate that precipitation-driven bioclimatic elements constituted 66.4% of the overall contribution, while temperature-driven elements made up 33.6%. Therefore, it is concluded that the dominant factors affecting the potential distribution of B. luminifera are annual precipitation (Bio12), min temperature of the coldest month (Bio6), and standard deviation of temperature seasonality (Bio4).
In order to determine the optimal threshold range of the main environmental factors affecting the geographical distribution of B. luminifera, climate response curves were constructed in the study. With spatial units having a probability value p ≥ 0.5 defined as the most suitable distribution area [51], the range suitable for the growth of B. luminifera for annual precipitation is determined to be 668–2417 mm, min temperature of the coldest month is −12.9–7.5 °C, and standard deviation of temperature seasonality is 521.8–858.2.

3.3. Prediction of Current Potential Distribution Area of B. luminifera in China

Under current climatic conditions, the potential suitable habitat for B. luminifera is mainly located in the southwestern part of China, with a total suitable area of approximately 233.98 × 104 km2, accounting for 24.37% of China’s land area (Figure 6). The area of high suitability for B. luminifera in China is 71.95 × 104 km2, accounting for 17.5% of China’s land area, mainly concentrated in Chongqing Municipality, Guizhou Province, Hunan Province, Hubei Province, Fujian Province, and the northern part of the Guangxi Autonomous Region. The area of moderate suitability for B. luminifera in China is 73.23 × 104 km2, accounting for 7.60% of China’s land area, mainly concentrated in Fujian Province, Zhejiang Province, Guizhou Province, and Chongqing Municipality. The area of low suitability for B. luminifera in China is 88.81 × 104 km2, accounting for 9.25% of China’s land area, mainly distributed in Yunnan Province, Guangxi Autonomous Region, Hunan Province, Hubei Province, and Jiangsu Province. The area where B. luminifera is biologically unsuitable accounts for 75.63% of China’s land area.

3.4. Prediction of Future Potential Distributions of B. luminifera under Different Climatic Scenarios in China

Under various future climate scenarios, the area and geographical location of B. luminifera’s potential distribution in China will change over time (Table 2, Figure 7). Specifically, under the SSP126 climate scenario, the suitable habitat area for B. luminifera is projected to reach 231.38 × 104 km2 in the 2050s, with the largest area of low suitability mainly distributed in the southern and southwestern regions of China, including Yunnan Province, Guangdong Province, Guangxi Autonomous Region, eastern Hunan Province, northern Jiangxi Province, Hubei Province, Sichuan Province, Gansu Province, and Tibet Autonomous Region, as well as the southern parts of Fujian Province, Jiangsu, Zhejiang Province, and Anhui. The area of moderate suitability is concentrated in Yunnan Province, Sichuan Province, western and southern Hunan Province, and parts of Fujian Province, Zhejiang Province, and Guizhou Province. The area of high suitability is mainly concentrated in Guizhou Province, Sichuan Province, Chongqing Municipality, Hubei Province, and parts of Shaanxi Province, Gansu Province, and Tibet Autonomous Region. Entering the 2070s, the suitable habitat area slightly decreases, but the area of low suitability still accounts for the highest proportion, with an increased distribution ratio in Hunan Province and Jiangxi Province. By the 2090s, the total suitable habitat area rebounds to 243.67 × 104 km2, with further increases in the area of low suitability and changes in the distribution of the areas of moderate and high suitability, demonstrating the adaptability and trends of change in the habitat of B. luminifera.
Under the SSP370 climate scenario, it is projected that the suitable habitat area for B. luminifera will reach 248.04 × 104 km2 in the 2050s, with the area of low suitability, at 106.71 × 104 km2, dominating the distribution, mainly in regions such as Sichuan Province, Hubei Province, southern Henan Province, Jiangsu Province, central Anhui Province, and Tibet Autonomous Region. The area of moderate suitability, at 93.81 × 104 km2, is concentrated in Yunnan Province, Zhejiang Province, Hunan Province, and Jiangxi Province. The area of high suitability, at 47.52 × 104 km2, is primarily focused in Guizhou Province, Sichuan Province, Chongqing Municipality, Hubei Province, as well as parts of Shaanxi Province, Gansu Province, and Tibet Autonomous Region. Entering the 2070s, the total suitable habitat area is slightly reduced to 231.38 × 104 km2, with the area of low suitability increasing to 119.56 × 104 km2, particularly with a significant rise in the proportion of distribution in Hunan Province and Jiangxi Province. The area of moderate suitability decreases to 69.81 × 104 km2, mainly in western Yunnan Province and Hunan Province. The area of high suitability stands at 42.01 × 104 km2, concentrated in Guizhou Province and Sichuan Province. By the 2090s, the total suitable habitat area marginally increases to 238.91 × 104 km2, with the area of low suitability further increasing to 138.08 × 104 km2, especially in the coastal cities of southeastern China. The area of moderate suitability drops to 65.56 × 104 km2, primarily in northern Yunnan Province and western Hunan Province. The area of high suitability is 35.27 × 104 km2, predominantly in Guizhou Province, but also distributed in parts of Yunnan Province, Sichuan Province, Gansu Province, Shaanxi Province, Hubei Province, and Tibet Autonomous Region.
Under the SSP585 climate scenario, it is projected that by the 2050s, the total suitable habitat area for B. luminifera will be 227.07 × 104 km2, where the low suitability habitat area, with a scale of 112.09 × 104 km2, will dominate, mainly concentrated in Sichuan Province, Guizhou Province, Hubei Province, Jiangxi Province, Fujian Province, and Tibet Autonomous Region and also distributed in central Yunnan Province, northern Guangxi Autonomous Region, northern Guangdong Province, Gansu Province, Shaanxi Province, southern Henan, southern Anhui, and Jiangsu. The moderately suitable habitat area is 74.91 × 104 km2, mainly concentrated in Yunnan Province and Hunan Province. The high suitability habitat area is the smallest, with 40.07 × 104 km2, mainly distributed in the southwestern part of China, with some distribution in parts of Gansu Province, Shaanxi Province, and Tibet Autonomous Region. By the 2070s, the total suitable habitat area for B. luminifera is reduced to 209.99 × 104 km2, where the low suitability habitat area increases to 125.37 × 104 km2, especially with an increased proportion in the southeastern region of China. The moderately suitable habitat area is 60.90 × 104 km2, mainly in the southwestern region of China. The high suitability habitat area is reduced to 23.72 × 104 km2, mainly in the southwestern part of China, with some distribution in parts of Gansu Province, Shaanxi Province, and Tibet Autonomous Region, and a significant decrease in the suitable habitat area in Guizhou Province. By the 2090s, the total suitable habitat area for B. luminifera is further reduced to 192.54 × 104 km2, and the low suitability habitat area decreases to 107.01 × 104 km2, especially between Hunan Province, Jiangxi Province, and Hubei Province. The moderately suitable habitat area is 56.58 × 104 km2, and the high suitability habitat area is 28.95 × 104 km2, both showing a downward trend, mainly concentrated in the southwestern region of China.

3.5. Changes in the Area of Suitable Habitats for B. luminifera Suitable Distribution under Different Future Scenarios

During the same period, under different scenarios, as the trend of global warming continues to intensify, the changes in the suitable habitat area of B. luminifera exhibit complex dynamic trends (Figure 8, Table 3). Specifically, under the SSP126 climate scenario, the distribution of the suitable habitat area for B. luminifera in the 2050s shows that the retained suitable area is 248.42 × 104 km2, accounting for 76.15% of the total area, while the expanded and lost suitable areas are 37.29 × 104 km2 and 40.52 × 104 km2, respectively. These areas are mainly concentrated in the southwestern and southeastern parts of China, including provinces such as Yunnan Province, Sichuan Province, Guizhou Province, Chongqing Municipality, Hunan Province, Hubei Province, Jiangxi Province, and Fujian Province. Entering the 2070s, the area of the retained suitable habitat slightly decreases to 246.18 × 104 km2, with the expanded and lost areas being 37.16 × 104 km2 and 42.77 × 104 km2, respectively, and the loss rate rises to 13.12%. By the 2090s, the area of the retained suitable habitat rebounds to 255.80 × 104 km2, the expanded areas increase to 45.20 × 104 km2, and the lost area decreases to 33.16 × 104 km2, with the loss rate falling to 9.92%.
Under the SSP370 climate scenario, in the 2050s, the retained suitable habitat area for B. luminifera is 238.37 × 104 km2, with the expanded and lost areas being 48.02 × 104 km2 and 50.58 × 104 km2, respectively. Compared to the SSP126 scenario, the retention rate decreases to 70.74%, while the expansion and loss rates rise to 14.25% and 15.01%, respectively. By the 2070s, the area of the retained suitable habitat further decreases to 219.13 × 104 km2, expands to 57.83 × 104 km2, and the lost area increases to 69.81 × 104 km2, with the loss rate rising to 20.13%. By the 2090s, the area of the retained suitable habitat significantly decreases to 185.26 × 104 km2, the expanded area slightly increases to 58.94 × 104 km2, and the lost area surges to 103.68 × 104 km2, with the loss rate reaching 29.80%. Under this scenario, the suitable habitat area of B. luminifera generally shows a continuous downward trend.
Under the SSP585 climate scenario, in the 2050s, the retained suitable habitat area for B. luminifera is 234.50 × 104 km2, with the expanded and lost areas being 45.92 × 104 km2 and 54.45 × 104 km2, respectively. Accordingly, the retention rate is 70.03%, the expansion rate is 13.71%, and the loss rate is 16.26%. By the 2070s, the area of the retained suitable habitat significantly decreases to 196.24 × 104 km2, the expanded area increases to 63.06 × 104 km2, and the lost area increases to 92.72 × 104 km2, with the loss rate rising to 26.34%. By the 2090s, the area of the retained suitable habitat further decreases to 171.02 × 104 km2, the expanded area is 66.59 × 104 km2, and the lost area reaches 117.91 × 104 km2, with the loss rate increasing to 33.17%. Under this scenario, the suitable habitat area of B. luminifera continues to decrease, especially showing the most significant decline in the 2070s.

3.6. Migration of Potential Distribution Centroid of B. luminifera

Under the current climate scenario, the centroid of the potential suitable habitat distribution for B. luminifera is located in Heku Town, Fenghuang County, Hunan Province (109°28′ E, 28°10′ N) (Figure 9). Under the SSP126 scenario, from the present to the 2050s and then to the 2090s, the centroid of B. luminifera shifts 163.36 km towards the northwest, to Fenshui Town, Wuchang County, Guizhou Province (107°60′ E, 28°52′ N). It then migrates 14.66 km northeast to Maotian Town, Wuchang County, Guizhou Province (108°06′ E, 28°58′ N), and finally shifts 47.56 km northeast to Zhufo Town, Pengshui County, Chongqing Municipality (108°29′ E, 29°14′ N). Under the SSP370 climate scenario, from the present to the 2050s and then to the 2090s, the centroid of B. luminifera migrates 202.35 km northwest to Hekou Town, Daozhen County, Guizhou Province (107°37′ E, 28°60′ N). It then migrates 77.60 km northwest to Shi Tan Town, Banan District, Chongqing Municipality (106°52′ E, 29°14′ N), and finally shifts 103.51 km west by southwest to Xianlong Town, Yongchuan District, Chongqing Municipality (105°49′ E, 29°08′ N). Under the SSP585 climate scenario, from the present to the 2050s and then to the 2090s, the centroid of B. luminifera first migrates 200.50 km northwest to Luolong Town, Dao Zhen County, Guizhou Province (107°42′ E, 29°04′ N). It then migrates 141.93 km northwest to Xianfeng Town, Jiangjin District, Chongqing Municipality (106°14′ E, 29°14′ N), and finally shifts 91.71 km northwest to Shinian Town, Longchang City, Sichuan Province (105°18′ E, 29°23′ N).

4. Discussion

4.1. Assessment of MaxEnt Model

The use of the MaxEnt 3.4.1 model in this study has proven effective for predicting the potential distribution of B. luminifera under both current and future climate scenarios [52,53]. Similar to previous research, our study demonstrates that parameter optimization plays a crucial role in improving model performance [19,54,55]. The optimized model configuration, achieved through the ENMeval package, resulted in a higher predictive accuracy compared to the default settings, with an AUC value of 0.902, indicating exceptional model reliability. This aligns with findings from other studies that utilized MaxEnt for tree species distribution modeling, such as the work on Quercus gilva Blume by Shi et al. (2024) [42]. The enhancement of predictive accuracy through model optimization underscores the importance of carefully selecting and adjusting model parameters to improve the precision of habitat suitability predictions [49,56,57]. This is particularly important in ecological studies where accurate predictions are necessary for effective conservation planning and species management. The robustness of the MaxEnt model, as demonstrated in this study, provides a strong foundation for future research on the distribution dynamics of tree species under climate change.

4.2. Effects of the Main Environmental Variables on the Distribution of B. luminifera

Our research identified annual precipitation (Bio12), minimum temperature of the coldest month (Bio6), and the standard deviation of temperature seasonality (Bio4) as the primary environmental variables influencing the potential distribution of B. luminifera. The optimal threshold ranges for these factors indicate the species’ specific climatic requirements, with precipitation between 668 mm and 2417 mm and minimum temperatures ranging from −12.9 °C to 7.5 °C. These findings are consistent with existing research, which similarly highlights the critical role of climatic factors, particularly precipitation and temperature, in determining plant distribution. For instance, studies on Ostrya rehderiana Chun and Pinus densiflora Sieb. et Zucc. have demonstrated that hydrological and thermal conditions are pivotal in shaping species’ geographical ranges [58,59]. The strong dependence of B. luminifera on these variables suggests that its distribution is closely tied to the availability of water and stable temperature conditions, which are crucial for its physiological processes and overall growth. A smaller standard deviation in temperature seasonality may indicate the species’ preference for environmental stability, as observed in Pistacia chinensis, where consistent hydrothermal conditions are key to ecological adaptation [55]. These patterns likely stem from the evolutionary history and ecological niche of B. luminifera, which has adapted to specific climatic conditions within its native range. As climate change progresses, alterations in these critical environmental factors may significantly impact the species’ distribution and habitat quality. Therefore, our findings underscore the necessity of targeted conservation and restoration strategies for B. luminifera to mitigate the effects of climate change and preserve its ecological niche. This research also contributes to a broader understanding of the complex interactions between climate change and plant distribution, emphasizing the need for proactive management in response to shifting environmental conditions.

4.3. Current and Future Potential Suitable Habitat for B. luminifera

The current distribution of B. luminifera’s suitable habitat is predominantly concentrated in the southwestern regions of China, such as Chongqing Municipality, Guizhou Province, Hunan Province, and Hubei Province. This distribution is closely tied to the region’s mild temperatures and high levels of precipitation, which create ideal conditions for the species’ growth. The total area of suitable habitat under current climate conditions spans 233.98 × 104 km2, representing approximately 24.37% of China’s land area. Similar distribution patterns have been observed for other tree species, such as Pinus massoniana Lamb. and Cunninghamia lanceolata (Lamb.) Hook., suggesting that climatic conditions, particularly temperature and precipitation, play a decisive role in defining these areas [34,35,60,61]. Additionally, soil types, fertility, and topography further refine the ecological niche of B. luminifera, while human activities like afforestation and land-use changes can influence its natural distribution [62,63].
As global climate change intensifies [64,65], our projections indicate that the suitable habitat for B. luminifera will undergo significant shifts under different future climate scenarios [66,67]. For instance, under the SSP126 scenario, the total suitable habitat area by the 2050s is expected to reach 231.38 × 104 km2, with high suitability regions concentrated in Guizhou, Sichuan, Chongqing, and Hubei. Although a slight decrease in the total suitable area is anticipated by the 2070s, the proportion of high-suitability regions may increase, particularly in Hunan and Jiangxi provinces. By the 2090s, the total suitable habitat area is projected to rebound to 243.67 × 104 km2, with continued expansion in areas of high suitability.
Under more severe climate scenarios, such as SSP370 and SSP585, the projected changes are more pronounced. In the SSP370 scenario, the suitable habitat area is predicted to expand to 248.04 × 104 km2 by the 2050s, but then gradually decrease, with high-suitability regions contracting to 35.27 × 104 km2 by the 2090s, primarily within Guizhou Province. Similarly, under the SSP585 scenario, the total suitable habitat is expected to decline progressively, reaching 192.54 × 104 km2 by the 2090s, with high-suitability areas shrinking to just 28.95 × 104 km2, concentrated in the southwestern regions of China.
These projections underscore the ecological vulnerability of B. luminifera under future climate change scenarios. The anticipated shifts in habitat suitability highlight the need for targeted conservation strategies to preserve existing habitats and manage the species’ ecological adaptation. However, it is important to acknowledge that these predictions are based on current climate models, which carry inherent uncertainties [68,69]. Therefore, continuous monitoring and research are essential to refine these models and ensure that conservation efforts are both timely and effective [70,71,72,73]. In response to the challenges posed by climate change, enhancing ecosystem resilience and protecting key habitats will be critical strategies for maintaining the long-term survival of B. luminifera.

4.4. Changes in the Suitable Habitat and Shifts of Distribution Centroid for B. luminifera Induced by Climatic Change

The analysis of B. luminifera’s suitable habitat and shifts in its distribution centroid under various climate scenarios provides valuable insights into the species’ response to climate change. Under the SSP126 scenario, the species’ habitat is predicted to remain relatively stable, with a retention rate of 76.15% in the 2050s and a slight increase to 76.47% by the 2090s. This suggests that under a moderate climate change trajectory, B. luminifera’s habitat may not experience significant disruption. However, more intense climate scenarios, such as SSP370 and SSP585, paint a different picture. By the 2070s, habitat loss rates rise to 20.13% and 26.34%, respectively, indicating that B. luminifera will likely face increased habitat contraction under these scenarios, reflecting the heightened risks associated with more severe climate changes.
The dynamics of habitat expansion and contraction under different scenarios also emphasize the variability of B. luminifera’s response to climate change. While the SSP126 scenario suggests a potential increase in suitable habitat by the 2090s, the more severe SSP370 and SSP585 scenarios predict substantial reductions in suitable areas. This indicates that under higher greenhouse gas emission trajectories, B. luminifera’s habitat will face greater challenges, necessitating adaptive conservation strategies to mitigate these impacts.
The migration of B. luminifera’s distribution centroid further illustrates its response to changing climatic conditions. Under the SSP126 scenario, the centroid shifts to the northwest and northeast, while under the SSP370 and SSP585 scenarios, the migration becomes more pronounced, likely as the species seeks out more favorable, cooler, and more humid environments. This pattern of migration is consistent with other studies, such as the predicted distribution shifts of Sinopodophyllum hexandrum (Royle) T.S.Ying under climate change, where the species is expected to move to higher altitudes to escape warming conditions [74]. However, the specific migration direction and distance of B. luminifera will likely be influenced by a combination of factors, including topography, vegetation types, and human activities, which could either facilitate or hinder its movement [75,76].
Given these findings, conservation strategies for B. luminifera should consider these various factors to ensure the species’ survival and reproduction under changing climatic conditions. This includes the development of artificial forests and other interventions that can help buffer the species against the impacts of climate change. By integrating these considerations into conservation planning, it may be possible to mitigate the risks posed by climate change and support the long-term resilience of B. luminifera populations.
Our results support the initial hypothesis, indicating that the suitable habitat of B. luminifera is likely to shift northward and westward under future climate scenarios. The projected contraction in the southeastern regions and expansion into cooler, more humid areas in the northwest aligns with previous studies on species responses to climate change. However, the complex dynamics of habitat expansion and contraction under different scenarios provide new insights into the species’ ecological adaptability. This study contributes to the growing body of knowledge on species distribution modeling by demonstrating how optimized MaxEnt models can be used to predict potential habitat shifts and inform conservation strategies.

4.5. Study Limitation

It is important to note that this study focused exclusively on bioclimatic variables in modeling the potential distribution of B. luminifera. While climate factors are undoubtedly critical in influencing species distribution, the exclusion of other factors, such as soil properties, topography, and land use, represents a limitation. These variables can significantly impact habitat suitability, species competition, and migration pathways. Additionally, anthropogenic influences, such as deforestation, agricultural expansion, and urbanization, could also modify the realized distribution of B. luminifera beyond what is projected in our climate-based model. Future studies should incorporate these non-climatic variables to offer a more comprehensive understanding of species distribution dynamics and provide more refined guidance for conservation and forest management strategies.

5. Conclusions

This study employs the MaxEnt model to predict the impacts of climate change on the potential geographical distribution of B. luminifera in China. The model, optimized with a Regularization Multiplier (RM) of 0.5 and a Feature Combination (FC) setting of LQHPT, demonstrates high accuracy, as evidenced by an AUC value of 0.900. The analysis reveals that annual precipitation and temperature are the dominant variables influencing B. luminifera distribution. Future climate scenarios predict fluctuating habitat suitability, with a slight decrease in suitable habitat by the 2050s under SSP126, a more pronounced reduction under SSP585, and a mixed pattern of expansion and contraction. Specifically, high-suitability areas are expected to contract in the southeast while expanding in the northwest, with a projected northwestward centroid migration, indicating a shift to cooler and more humid climates.
In response to these changes, adaptive conservation strategies are critical. We propose a two-fold approach: (1) Ex situ conservation, such as seed banking and the establishment of B. luminifera populations in botanical gardens or controlled environments, ensuring genetic diversity is preserved even if natural populations decline. (2) In situ conservation through habitat management in both current and future suitable areas. This includes restoring degraded habitats and implementing assisted migration techniques in northwest regions to facilitate the species’ transition to new habitats. In the current southern range, strengthening protection against habitat fragmentation and promoting climate resilience through sustainable forestry practices will help maintain existing populations. By integrating these strategies, the resilience of B. luminifera against climate change can be enhanced.

Author Contributions

Conceptualization, Y.L. (Yongjun Long) and X.L.; Data curation, Q.Y., S.L., Y.L. (Ying Liu) and Y.L. (Yang Luo); Formal analysis, Y.X.; Funding acquisition, Y.X., Y.L. (Ying Liu) and S.Y.; Investigation, Q.Y. and S.L.; Methodology, Q.Y., L.Z., Y.L. (Ying Liu), Y.L. (Yang Luo), Y.L. (Yongjun Long), S.Y. and X.L.; Project administration, Y.L. (Yongjun Long) and X.L.; Software, Q.Y., Y.X., S.L. and Y.L. (Yongjun Long); Validation, Q.Y.; Visualization, Q.Y., Y.X., S.L. and S.Y.; Writing—original draft, Q.Y., Y.X. and S.Y.; Writing—review and editing, Q.Y., Y.X., S.L. and L.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Fundamental Research Funds for the Guizhou Provincial Science and Technology Projects (QKHJC-ZK [2022] YB335), Guizhou Province Ordinary Colleges and Universities Youth Science and Technology Talent Growth Project (QJHKYZ [2022]304; QJHKYZ [2019]212), Guizhou Province 100-level Talent Project ([2020]6010), and Guizhou Education University Scientific Research Fund Project (2024YB002; 2024BSKQ003).

Data Availability Statement

The original contributions presented in this study are included in this article. Further inquiries can be directed to the corresponding author.

Conflicts of Interest

Author Ling Zhao was employed by the company State Power Investment Corporation Power Station Operation Technology (Beijing) Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Figure 1. Current distribution of B. luminifera in China.
Figure 1. Current distribution of B. luminifera in China.
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Figure 2. Correlation analysis of nineteen environmental factors (* and ** indicate significant level at p < 0.05 and p < 0.01, respectively).
Figure 2. Correlation analysis of nineteen environmental factors (* and ** indicate significant level at p < 0.05 and p < 0.01, respectively).
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Figure 3. Delta.AICc (a), AUC.DIFF (b), and OR10 (c) for B. luminifera derived from MaxEnt models with diverse parameter configurations. The legends indicate distinct feature classes (L = linear, Q = quadratic, H = hinge, P = product, and T = threshold).
Figure 3. Delta.AICc (a), AUC.DIFF (b), and OR10 (c) for B. luminifera derived from MaxEnt models with diverse parameter configurations. The legends indicate distinct feature classes (L = linear, Q = quadratic, H = hinge, P = product, and T = threshold).
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Figure 4. Receiver Operating Characteristic (ROC) prediction results of MaxEnt model for B. luminifera. (a) original model, (b) optimized model.
Figure 4. Receiver Operating Characteristic (ROC) prediction results of MaxEnt model for B. luminifera. (a) original model, (b) optimized model.
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Figure 5. Response curves of the effect of main meteorological factors on occurrence probability of B. luminifera. (a) The contribution rate of the dominant factors, (b) annual precipitation, (c) min temperature of the coldest month, (d) standard deviation of temperature seasonality. The interval between two vertical orange dotted lines represents the optimal suitable range of environmental factors.
Figure 5. Response curves of the effect of main meteorological factors on occurrence probability of B. luminifera. (a) The contribution rate of the dominant factors, (b) annual precipitation, (c) min temperature of the coldest month, (d) standard deviation of temperature seasonality. The interval between two vertical orange dotted lines represents the optimal suitable range of environmental factors.
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Figure 6. Current potential distribution area of B. luminifera in China.
Figure 6. Current potential distribution area of B. luminifera in China.
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Figure 7. Potential distribution of B. luminifera under different future climatic scenarios.
Figure 7. Potential distribution of B. luminifera under different future climatic scenarios.
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Figure 8. Changes in the potential geographical distribution of B. luminifera under future climatic scenarios.
Figure 8. Changes in the potential geographical distribution of B. luminifera under future climatic scenarios.
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Figure 9. Centroid migration of B. luminifera under different climatic scenarios.
Figure 9. Centroid migration of B. luminifera under different climatic scenarios.
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Table 1. Nineteen bioclimatic variables used in this study.
Table 1. Nineteen bioclimatic variables used in this study.
VariablesDescriptionUnitsRangeContribution Rate (%)
Bio1Annual mean temperature°C−0.56–23.080.1
Bio2Mean diurnal range (Mean of monthly)°C5.95–13.880.4
Bio3Isothermality (Bio2/Bio7) (×100) 22.46–52.971.0
Bio4Standard deviation of temperature seasonality 342.10–925.146.8
Bio5Max temperature of warmest month°C12.40–34.280.7
Bio6Min temperature of coldest month°C−17.59–10.6810.2
Bio7Temperature annual range (Bio5-Bio6)°C19.16–36.214.3
Bio8Mean temperature of wettest quarter°C6.66–28.891.8
Bio9Mean temperature of driest quarter°C−8.31–19.800.5
Bio10Mean temperature of warmest quarter°C6.66–28.892.0
Bio11Mean temperature of coldest quartermm−8.31–18.251.8
Bio12Annual precipitationmm553.00–2225.0055.2
Bio13Precipitation of wettest monthmm106.00–411.000.0
Bio14Precipitation of driest monthmm1.00–56.0013.3
Bio15Variation of precipitation seasonality 43.05–100.091.2
Bio16Precipitation of wettest quartermm290.00–964.000.1
Bio17Precipitation of driest quartermm8.00–227.000.1
Bio18Precipitation of warmest quartermm278.00–951.000.0
Bio19Precipitation of coldest quartermm8.00–254.000.7
Table 2. Potential suitable distribution area of B. luminifera in different periods. (units: 104 km2).
Table 2. Potential suitable distribution area of B. luminifera in different periods. (units: 104 km2).
PeriodUnsuitable AreaGenerally
Suitable Area		Moderately
Suitable Area		Highly
Suitable Area		Total
Suitable Area
Current726.0288.8173.2371.95233.98
2050s-SSP126728.62106.3580.9344.10231.38
2070s-SSP126730.63111.7077.1240.55229.38
2090s-SSP126716.33123.7075.4844.49243.67
2050s-SSP370711.96106.7193.8147.52248.04
2070s-SSP370728.62119.5669.8142.01231.38
2090s-SSP370721.09138.0865.5635.27238.91
2050s-SSP585732.93112.0974.9140.07227.07
2070s-SSP585750.01125.3760.9023.72209.99
2090s-SSP585767.46107.0156.5828.95192.54
Table 3. Suitable area changes of B. luminifera under future climate change.
Table 3. Suitable area changes of B. luminifera under future climate change.
Periods-ScenariosArea (104 km2)Rate of Change (%)
StabilityExpansionContractionStabilityExpansionContraction
2050s-SSP126248.4237.2940.5276.1511.4312.42
2070s-SSP126246.1837.1642.7775.4911.3913.12
2090s-SSP126255.8045.2033.1676.5513.539.92
2050s-SSP370238.3748.0250.5870.7414.2515.01
2070s-SSP370219.1357.8369.8163.1916.6820.13
2090s-SSP370185.2658.94103.6853.2516.9429.80
2050s-SSP585234.5045.9254.4570.0313.7116.26
2070s-SSP585196.2463.0692.7255.7517.9126.34
2090s-SSP585171.0266.59117.9148.1018.7333.17
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Yang, Q.; Xiang, Y.; Li, S.; Zhao, L.; Liu, Y.; Luo, Y.; Long, Y.; Yang, S.; Luo, X. Modeling the Impacts of Climate Change on Potential Distribution of Betula luminifera H. Winkler in China Using MaxEnt. Forests 2024, 15, 1624. https://doi.org/10.3390/f15091624

AMA Style

Yang Q, Xiang Y, Li S, Zhao L, Liu Y, Luo Y, Long Y, Yang S, Luo X. Modeling the Impacts of Climate Change on Potential Distribution of Betula luminifera H. Winkler in China Using MaxEnt. Forests. 2024; 15(9):1624. https://doi.org/10.3390/f15091624

Chicago/Turabian Style

Yang, Qiong, Yangzhou Xiang, Suhang Li, Ling Zhao, Ying Liu, Yang Luo, Yongjun Long, Shuang Yang, and Xuqiang Luo. 2024. "Modeling the Impacts of Climate Change on Potential Distribution of Betula luminifera H. Winkler in China Using MaxEnt" Forests 15, no. 9: 1624. https://doi.org/10.3390/f15091624

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