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Article

Distributional Responses of Five Betula (Betulaceae) Species to Future Climate Change in China

by
Zhilong Huang
1,2,†,
Chenlong Fu
1,3,†,
Chenyang Li
1,†,
Xinle Yang
1,
Binyu Shuai
1,
Meng Li
3,
Zefu Wang
1 and
Xiaoyue Yang
1,*
1
State Key Laboratory of Tree Genetics and Breeding, Co-Innovation Center for Sustainable Forestry in Southern China, College of Ecology and Environment, Nanjing Forestry University, Nanjing 210037, China
2
College of Life Sciences, South China Agricultural University, Guangzhou 510614, China
3
College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2025, 16(3), 400; https://doi.org/10.3390/f16030400
Submission received: 30 January 2025 / Revised: 17 February 2025 / Accepted: 20 February 2025 / Published: 24 February 2025
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

Global climate change has the potential to modify the habitats of plant species, thereby exerting a direct impact on biodiversity. Betula species, belonging to the Betulaceae family and commonly known as birch trees, are widely distributed in China. They possess crucial ecological and economic value. However, few studies have examined the potentially suitable distribution of Betula species in China under the influence of climate change. Therefore, it is of great significance to explore the biodiversity patterns of Betula species in China in response to future climate change. In this study, we selected five representative Betula species and collected their distribution records from China. Based on 19 climate variables, the main environmental factors for each species were identified and optimal parameter combinations were determined. The MaxEnt model was employed to construct potentially suitable distribution models for these Betula species, both currently and in the future. The results indicated that the AUC and TSS values for the five species models were greater than 0.903, suggesting a high level of accuracy. The most important climate variable impacting the distribution of Betula species is the temperature seasonality standard deviation. Among the five species, Betula utilis possesses the largest total suitable distribution area, covering 313.42 × 104 km2. Additionally, under future climate warming, the distribution of the studied Betula species will shift toward higher latitudes and altitudes. Species in the southwestern region may migrate toward habitats where the effects of climate change are mitigated, whereas Betula species in the low-latitude southern regions face a substantial threat due to climate change. In the northern areas, under high greenhouse gas emission scenarios, the region experiencing species expansion was much smaller compared to the area of species contraction predicted. Our findings reveal the responses of Betula species to future climate change and provide valuable insights for guiding the future conservation and utilization of Betula forest resources.

1. Introduction

The loss of biodiversity due to global climate change has become a hot topic in the field of ecology [1]. As greenhouse gas emissions continue to rise, global temperatures are gradually increasing [2,3]. By 2100, the average global temperature is projected to increase by a range of 3.6 °C to 5 °C [3,4]. Climate change will impact species’ growth and distribution, thereby accelerating the adaptation process of organisms [5]. In recent years, numerous studies have examined the effects of climate change on the distribution of plants and animals [6,7]. It has been demonstrated that many plant species will migrate to higher latitudes or altitudes in response to the increasing global temperatures [8,9,10,11]. Using current distribution information and climate data, we can reconstruct potentially suitable distribution models for species, which will help predict their responses to future climate change and identify possible migration routes [12,13]. Following this, conservation strategies can be implemented to mitigate the threats that climate change poses to these species [14].
Species distribution models (SDMs) have been widely acknowledged as valuable instruments for forecasting the impacts of climate change on the potential distribution of species [10,15,16]. SDMs can be used to infer both the current and future potential distributions of a species [13,17]. Modeling the future distribution of species provides crucial information about potential habitat changes, which is notable for plant conservation and maintenance of ecological sustainability [18]. Additionally, SDMs play a pivotal role in predicting the habitats of endangered species, invasive species, and marine organisms [19,20,21]. Among the multiple models available, the Maximum Entropy model (MaxEnt) is one of the most widely used for predicting species distribution [22,23,24]. As a machine learning model [25], MaxEnt is capable of predicting the suitable range of a species by integrating environmental variables with species distribution records based on the principle of maximum entropy [26]. Furthermore, it can also effectively delineate the relationship between species distribution and environmental variables [27]. Due to the smaller amount of sample data it requires and its excellent prediction accuracy, the MaxEnt model has been frequently employed to investigate the impacts of climate change on species distribution [28,29,30].
The Betula genus, commonly known as birch trees, belongs to the Betulaceae family [31]. There are approximately 31 Betula species and several varieties in China [32,33]. Betula species originated during the Cretaceous period and are predominantly distributed across cold and temperate regions of the Northern Hemisphere [34]. China is one of the major distribution areas for Betula species. They are continuously distributed throughout China, with southwestern China as the primary distribution center and northeastern China as the secondary distribution center [34]. Southwest China is considered the origin center of Betula species in China, from which it spread and differentiated into surrounding areas. As vital pioneer species and essential forest trees, Betula species are often the first woody species to colonize nutrient-poor regions, playing a crucial role in forest ecosystems [35]. High-quality wood from Betula species is used extensively in the construction, paper manufacturing, and furniture industries [36]. Betula species are also utilized for ornamental purposes and in the production of traditional medicinal products [37,38,39]. Given the considerable ecological and economic value of Betula species, it is crucial to prioritize the conservation of Betula resources and evaluate the potential effects of climate change on these species. Numerous studies have been conducted on Betula species, offering valuable insights into their genetic phylogeny, medicinal properties, photosynthetic mechanisms, and potential distribution patterns of specific Betula species (Betula luminifera) [39,40,41]. However, predictions regarding the potential suitable distribution of other Betula species in China in response to future climate change under different scenarios have not yet been reported.
Predicting the response of Betula species to climate change and their future trends is crucial for their conservation. This not only offers scientific support for the conservation of Betula species in China but also provides valuable insights for the study of other plant groups in comparable latitudinal regions globally. In this study, we selected five representative species (B. chinensis, B. fruticosa, B. insignis, B. potaninii, and B. utilis) to characterize the Betula genus. Their distribution records from China, along with climate environmental variables, were collected. Using the MaxEnt model, we constructed potential suitable distribution models for Betula species in China based on current climate conditions. We predicted the current potential distribution areas and evaluated the associated dominant environmental variables. Moreover, we predicted their potential suitable distributions under various climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) for the 2090s (2080–2100).

2. Material and Methods

2.1. Species Distribution Data

The southwestern region of China is characterized by a subtropical monsoon climate [42], with red and brown soils as the dominant soil types in the region. The vegetation distribution is complex, exhibiting a clear vertical stratification [43]. In contrast, the northeastern region spans cold, middle, and warm temperate zones, where biodiversity in transitional areas is relatively low and highly susceptible to climate change [44]. Approximately 31 species of Betula are found in China, primarily distributed across the northeastern, northwestern, and southwestern mountainous regions [34]. However, some species lack sufficient distribution data, making it difficult to construct accurate models [45]. In this study, we selected five representative species based on their distribution areas and collected their distribution data from the Global Biodiversity Information Facility (GBIF, https://www.gbif.org/; accessed 26 December 2024). Spatial autocorrelation was minimized using a thinning approach with the R package “spThin” v.0.1.0, applying a 5 km × 5 km grid resolution to ensure only one occurrence record per grid [46]. After spatial thinning, the following occurrence points were used for modeling: 120 for B. chinensis, 40 for B. fruticosa, 55 for B. insignis, 41 for B. potaninii, and 216 for B. utilis (Table 1 and Figure 1). ArcGIS 10.8 (Esri, Redlands, CA, USA) was used to generate maps of species occurrences (Figure 1).

2.2. Environmental Variable

We downloaded 19 bioclimatic variables at a 2.5-min resolution for the period of 1970–2000 as current environmental variables from the WorldClim 2.1 dataset (https://worldclim.org/; accessed 27 December 2024) (Table S1) [47]. For future climate scenarios, we used the medium-resolution Beijing Climate Center Climate System Model (BCC-CSM2-MR) from WorldClim 2.1, which provides projections under four Shared Socioeconomic Pathways (SSPs): SSP1-2.6 (Sustainable Development), SSP2-4.5 (Moderate Development), SSP3-7.0 (Partial Development), and SSP5-8.5 (General Development). We selected environmental variables for the 2090s (2080–2100) under these four scenarios, also with a resolution of 2.5-min.

2.3. Key Environmental Variable Selection

Multicollinearity and high correlations between environmental variables can lead to overfitting in model predictions [48]. Therefore, it is essential to screen for the main environmental variables. We selected the main environmental variables for each species based on the initial contribution and importance rankings of the environmental variables in the model outputs (Table S2). Pearson correlation analyses (|r| ≤ 0.8) were performed using the R package “corrplot”. Subsequently, variance inflation factors (VIF ≤ 5) were calculated for the key variable groups of each species using the R package “MASS” to assess multicollinearity within these groups (Figure S1 and Table 2). Ultimately, five environmental variables were used for modeling B. chinensis and B. insignis, and four for B. fruticosa, B. potaninii, and B. utilis (Table 2).

2.4. Model Parameter Optimization

The MaxEnt model is constructed using parameters, including the Regularization Multiplier (RM) and Feature Combination (FC). The features include linear features (L), quadratic features (Q), hinge features (H), threshold features (T), and product features (P). The selection of feature combinations and settings for the regularization multiplier may influence the complexity of the model, with excessively high complexity potentially leading to overfitting [49]. In this study, we set the regularization multiplier values from 1 to 4 and used four feature combinations: LQ, LQH, LQHP, and LQHPT, resulting in 16 unique parameter combinations. The R package “ENMeval” [50] was used to analyze and evaluate the complexity of these 16 parameter combinations by calculating indices, including the difference in Delta Akaike Information Criterion corrected (Delta.AICc), Area Under the Curve Difference (AUC.DIFF), and 10% Training Omission Rate (OR10) (Figure S2). Based on these evaluations, we selected the optimal parameter combinations for each species (Table 1) [51,52].

2.5. Model Construction of MaxEnt

We imported species occurrence data for each species, along with their corresponding key environmental variables, into MaxEnt (v.3.4.4) [53,54]. For each species, we selected the appropriate feature combination and regularization multiplier (Table 1). We enabled the options “Create response curves”, “Make picture of predictions”, and “Do jackknife to measure variable importance”. The output format was set to “Logistic”. The output file type was set to “asc”. We randomly selected 75% of the occurrence records for training and 25% for testing, with 20 repetitions and 5000 iterations. The results were averaged across repetitions. In the “Projection layers directory/file” section, we chose a folder containing the environmental variables for future climate projections in order to construct the ecological niche models for future climate scenarios.

2.6. Model Accuracy Evaluation

We assessed the accuracy of the model using two methods: the area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the true skill statistic (TSS) [22,55]. The AUC values ranged from 0 to 1, with higher values indicating better model performance. The AUC evaluation criteria are as follows: 0.5–0.6, ineffective prediction; 0.6–0.7, poor prediction; 0.7–0.8, moderate prediction; 0.8–0.9, good prediction; and 0.9–1, excellent prediction. AUC values exceeding 0.9 indicate highly accurate predictions [56]. The TSS evaluates the overall predictive ability of the model by comparing the true-positive and false-positive rates. The TSS ranges from −1 to +1, with values closer to 1 indicating a higher accuracy. A TSS value greater than 0.75 is considered excellent; 0.75 ≥ TSS ≥ 0.40 indicates a good model, and TSS < 0.40 indicates an ineffective model [56,57].

2.7. Model Output and Data Analysis

The average results of the 20 simulations from the MaxEnt model were imported into ArcGIS 10.8. ASC files were converted to raster format. Using the reclassification tool, the suitability levels were divided into four categories: p values of 0–0.1 were classified as non-suitable areas; p values of 0.1–0.3 as low suitability; p values of 0.3–0.5 as moderate suitability; and p values of 0.5–1 as high suitability [41,58]. ArcGIS v.10.8 was then used to calculate the suitable distribution area for each species at each suitability level and to generate maps of the suitable distribution ranges. Cells with probability values (p) < 0.1 were assigned a value of 0, while cells with probability values (p) ≥ 0.1 were assigned a value of 1. This allowed us to construct a presence/absence (0, 1) matrix for current and future climates, enabling the calculation of area contraction and expansion for each species under different future climate scenarios. Finally, maps were generated to visualize the expansion and contraction of suitable distribution areas for each species from the current to future climates.

3. Results

3.1. Model Accuracy and Key Environmental Variables

Modeling the current distribution of Betula species in China yielded strong evaluation scores, with AUC and TSS values greater than 0.903 (Table 1), indicating that the MaxEnt model was highly effective in predicting their potential distribution areas in China. After screening, four to five main environmental factors were chosen and retained for each species for model construction (Table 2). Among them, the seasonal temperature standard deviation (bio04) was the common and most crucial environmental variable for all species, contributing an average of 38.56% and having the greatest impact on the potential distribution of Betula species (Table 2). This suggests that bio04 contains crucial information that is not encompassed by other variables in the model. Additionally, annual precipitation (bio12) and precipitation of the coldest quarter (bio19) both impacted the possible distribution of the three species. When analyzing individual species, precipitation in the wettest month (bio13) was found to be the most crucial environmental variable influencing the potential distribution of B. chinensis, with a contribution of 32.3% (Table 2). Similarly, the annual temperature range (bio07) was the most important factor affecting the potential distribution of B. insignis, contributing 50.4% to the model (Table 2).
Response curves illustrating the main environmental variables that affect the potential suitability distribution areas of Betula species are shown in Figure S3. By combining the environmental variables for each of the five studied species and reviewing their thresholds corresponding to the highest probability of suitability for each species (Table 2), temperature and precipitation variables were found to have a relatively balanced impact on the distribution of each species (Table 2). There were slight differences in precipitation requirements among species and across different times for individual species. Meanwhile, most of the studied Betula species tend to thrive in cooler regions, and temperature variables play an important role in affecting their potential distribution. The range of bio04 varied among different species (Table 2). When exploring the impact of the bio04 factor for each species, we found that B. fruticosa had the highest bio04 value among the five species, with the most suitable range being 1310.77–1961.68 (Figure 2). B. chinensis was most suitable for distribution when bio04 ranged from 917.36–1233.91. In contrast, B. utilis, B. insignis, and B. potaninii exhibited smaller bio04 ranges, specifically 459.88–738.72, 557.57–788.92, and 61.82–690.20, respectively (Figure 2). This indicates that the five Betula species may be influenced by environmental variables in distinct ways due to variations in their geographical distribution patterns.

3.2. Predicted Potential Suitable Distribution of the Betula Species

The potential suitable distribution areas of Betula species under both current and future climatic scenarios are illustrated in Figure 3, which categorizes the suitability into four levels: high, medium, low, and non-suitable zones. Under current climate conditions, there is a notable geographical distinction in the distribution ranges of these five species. B. insignis, B. potaninii, and B. utilis are primarily found in the southwestern regions at lower latitudes, while B. fruticosa is predominantly distributed in the northeastern regions at higher latitudes. B. chinensis occupies the transitional area between the southwest and northeast regions (Figure 3). Among these species, B. utilis possesses the largest total suitable distribution area, accounting for 32.51%, whereas B. fruticosa exhibits the largest area of high suitability, accounting for 11.03% (Table 3). Under different future climatic scenarios, the low greenhouse gas emission scenario, SSP1-2.6, favors the expansion of the potential suitable distribution for most Betula species (Table 3). However, under the SSP5-8.5 scenario, the suitable distribution areas of B. fruticosa and B. chinensis are severely constrained. The high suitability area for B. chinensis decreases by 71.4%, and its total suitable area decreases by 19.5%. The high suitability area for B. fruticosa decreases by 33.8% (Table S3). In contrast, the high suitability areas for B. insignis, B. potaninii, and B. utilis increase by 138.9%, 58.5%, and 23.8%, respectively (Table S3). Overall, rising temperature exerts a suppressive effect on the suitable distribution of species at higher latitudes, such as B. chinensis and B. fruticosa, whereas it promotes the expansion of the suitable distribution of species at lower latitudes, including B. insignis, B. potaninii, and B. utilis (Figure 3 and Table 3).

3.3. The Expansion and Contraction of the Distribution Area of Betula Species

We analyzed the expansion and contraction of suitable distribution areas for each Betula species across different climate scenarios, spanning from the present to the future. We further investigated the migration trends under future climate change (Figure 4). It is evident that climate change will prompt Betula species, particularly those in the southwestern low latitudes, to migrate toward higher latitudes or higher altitudes (Figure 4). Due to geographical constraints, B. fruticosa within China is unable to migrate further north and instead shows a tendency to spread westward toward higher altitudes (Figure 4). For most of the studied Betula species, the area of expansion surpasses the area lost. For example, under the SSP1-2.6 scenario, the suitable distribution area of B. chinensis contracts by 20.90% but expands by 49.43% relative to the current period, while B. fruticosa experiences a contraction of 8.03% and an expansion of 17.66% (Table 4). However, under the SSP8-8.5 scenario, the expansion areas of B. fruticosa and B. chinensis are less than the areas they have lost (Table 4). In contrast, for B. utilis, its expansion area is smaller than the area lost across all scenarios. It is observed that climate change has led to a substantial reduction in its distribution in low-latitude regions, whereas its expansion toward the higher-altitude areas of Xizang has been relatively slow (Figure 4).

4. Discussion

4.1. Assessment of MaxEnt Model

This study effectively predicts the current and future potential suitable distributions of Betula species using species distribution models, a method that has been previously validated [41]. Following the research methodologies of previous studies [59,60], we selected the main environmental variables corresponding to each species and performed parameter optimization for the MaxEnt model to enhance the accuracy of the species distribution models. The final distribution models for the five species exhibited AUC and TSS values greater than 0.903 (Table 1), indicating that the distribution models for Betula species have a very high predictive capacity. This result is consistent with that of Geng et al., who predicted the potentially suitable distribution of B. platyphylla using MaxEnt, achieving an AUC value of 0.88. Similarly, Yang et al. employed MaxEnt to model the distribution of B. luminifera and obtained an excellent AUC score of 0.902 [41,61]. Moreover, our research demonstrates that selecting the main environmental variables and optimizing the parameters can obviously improve the model’s prediction [62,63].

4.2. Key Environmental Variables Influencing the Potential Distribution of Betula Species

Plant growth and reproduction are notably influenced by climate change [64]. The reactions of the main environmental variables differ among diverse plant groups. For example, the main environmental variable influencing the suitable distribution of Strobilanthes cusia is temperature seasonality (bio04), while for Ensete glaucum, it is the precipitation of the warmest quarter (bio18) [65,66]. In this study, the main environmental variables that influence the maximum suitability probability vary among the five studied Betula species, likely due to differences in life habits. Our results indicate that the most crucial environmental variable impacting the potentially suitable distribution of Betula species is seasonal temperature variability (bio04), followed by annual precipitation (bio12) and precipitation during the coldest season (bio19). These findings are consistent with those of previous research conducted on another Betula species, B. luminifera [41]. Furthermore, the results suggest that both temperature and precipitation serve as limiting factors that impact the distribution of Betula species. Temperature can exert an influence on plant photosynthesis, whereas precipitation regulates carbon sequestration and transpiration by altering soil moisture [67]. Recent studies have revealed that plants undergo physiological changes, including chlorophyll degradation, growth cessation, and senescence, under high temperatures and drought stress [68,69], thereby further emphasizing the crucial role of temperature and precipitation in determining species distribution [70,71]. In this study, most of the studied Betula species occupy suitable distribution areas characterized by lower temperatures and higher precipitation requirements (Table 2), consistent with previous research findings [41]. Additionally, the results for seasonal temperature variability (bio04) among the five selected Betula species reveal two different patterns: species distributed in the northeast exhibit higher bio04 values compared to those in the southwest, suggesting that Betula species from different regions have varying preferences for environmental stability. With climate change, alterations in key climatic variables may notably influence the potentially suitable distribution of Betula species in the future.

4.3. Current and Future Potential Distribution Range of the Betula Species

The potentially suitable distribution of the five Betula species, under the current climate conditions, is predominantly concentrated in the southwestern and northeastern regions. This is closely related to the climatic conditions of the regions to which the species have adapted [72]. As climate change intensifies, we analyzed shifts in the possible suitable distribution areas of Betula species under different future scenarios (2090s). The models indicate that the high suitability areas for B. insignis, B. potaninii, and B. utilis in the southwestern regions are positively correlated with greenhouse gas emission scenarios (Table 3), consistent with previous studies on species in the southwestern regions, including Cyananthus and Primula [9]. For species belonging to these genera, their distribution areas tend to expand as greenhouse gas emission levels rise [9]. However, for B. fruticosa, which is distributed in the northeastern regions, and B. chinensis, located in the transitional area between the southwest and northeast, the high suitability areas exhibit a slight increase under the SSP1-2.6 scenario, followed by a sharp decline as greenhouse gas emission levels increase (Table 3 and Figure 3). This could be attributed to intensified warming caused by higher greenhouse gas emissions, which stabilize temperature fluctuations but subsequently lead to a decrease in bio04 values. These conditions are unfavorable for the survival of Betula species distributed in the north.

4.4. The Future Prospects for Betula Species

The responses of Betula species from different distribution regions to future climate change vary. Our models predict that Betula species will tend to migrate toward higher altitudes and higher latitudes in the future (Figure 4). This may be due to increasing temperatures, which result in the loss of their southern habitats, thereby driving them to migrate toward higher northern latitudes and altitudes in search of environments comparable to their previous habitats. This trend has been validated in previous studies. For example, species such as yews and wild soybeans are predicted to migrate northward in response to future temperature increases [73,74]. Plants that currently occupy high altitudes in the Himalaya-Hengduan Mountains (HHM) are expected to move to even higher altitudes in the future [55]. In our study, B. insignis and B. potaninii, which are distributed in the southwestern high mountains, exhibit a more pronounced trend of expansion compared to the contraction under future climate warming scenarios (Table 4). This is consistent with findings from research on the majority of plants in the HHM, which indicate an expansion of their distribution areas in response to future warming [55,75,76].
In contrast, under the SSP5-8.5 scenario, B. fruticosa and B. chinensis display a trend of contraction that is more pronounced than expansion. This may be attributed to the lack of suitable habitats in the north, coupled with the fact that the distribution of B. fruticosa has already reached the northern boundary of China [9]. Moreover, the suitable distribution area of B. utilis shows a more obvious contraction than expansion, possibly because of the loss of large regions of suitable growing areas in the southern low-altitude regions [41]. In conclusion, our predictive results indicate that although Betula species may have suitable habitats for migration under climate warming scenarios, the low-latitude southern regions within their suitable distribution areas will undergo notable losses. Especially, under high greenhouse gas emission scenarios, the impact on Betula species in the northern regions is more pronounced.

4.5. Limitations of This Study

The Betula genus in China comprises approximately 31 species with a broad distribution range. Due to the limited research background on the genus Betula, the available distribution records for many species are insufficient to fully support the modeling requirements. Our study focused on five representative Betula species, which are primarily distributed in southwestern and northeastern China. However, the geographical scope of these areas does not encompass the entire distribution range of Betula species in China. Therefore, our conclusions are applicable only to Betula species located in the southwestern and northeastern regions. The interpretation of Betula species in other regions is more limited. This study also did not explore the impact of climate factors on the viability of Betula species in depth. Future research should include a broader range of Betula species, conduct in-depth studies on the mechanisms by which climate factors affect the survival of Betula species, and conduct field surveys under future climate change scenarios.

5. Conclusions

This study employed the MaxEnt model to predict the potentially suitable distributions of five Betula species under both current and future climate scenarios. The results indicate that the potentially suitable distribution ranges of Betula species are notably influenced by climate change, with seasonal temperature standard deviation (bio04) being the most crucial environmental variable. The bio04 values of Betula species differ notably between regions, with those in Northeast China being notably higher than those in Southwest China, likely reflecting the species’ regional preferences. Betula species are expected to migrate toward higher latitudes and altitudes as climate warming intensifies. Species in the southwestern highlands are predicted to move westward to higher altitudes, thereby expanding their distribution range. In contrast, Betula species in the northeastern regions of China face limitations in their northward migration and are more likely to seek suitable habitats further west. The southern distribution range of Betula species is expected to experience notable losses under climate warming scenarios. Therefore, special attention should be paid to the protection of Betula species in southern and low-latitude regions. Additionally, the response mechanisms of Betula species to climate change, along with those of other understudied species, merit further investigation. Overall, our study provides scientific evidence for the future conservation and utilization of Betula species, offering new insights into the study of other forest trees in China and other plant groups in comparable latitudinal regions globally.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16030400/s1, Table S1: Nineteen bioclimatic variables used in this study; Table S2: Contribution of initial environmental variables for Betula species; Table S3: Suitable distribution areas for Betula species under current and future climate scenarios; Table S4: Changes in the suitable area of distribution of Betula species under different future scenarios; Figure S1: Results of screening for key environmental variables for Betula species; Figure S2: Optimization results of MaxEnt model parameters for Betula species; Figure S3: Response curves of the main environmental variables affecting the potential distribution of Betula species.

Author Contributions

X.Y. (Xiaoyue Yang) and Z.W. designed and led the project. Z.H. and C.L. collected data and conducted analyses. X.Y. (Xinle Yang), B.S. and M.L. provided technical support. Z.H. and C.F. drafted the manuscript. X.Y. (Xiaoyue Yang) and Z.W. revised the manuscript. All the authors have read and approved the final version of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported equally by grants from the National Natural Science Foundation of China (NSFC) (32422053 and 32301411) and the Natural Science Foundation of Jiangsu Province, China (BK20230394).

Data Availability Statement

Data are contained within the article.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution records of the five Betula species used in the modeling.
Figure 1. Distribution records of the five Betula species used in the modeling.
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Figure 2. Response curves of temperature seasonal standard deviation (bio04) affecting the distribution of five Betula species. The range of logistic greater than 0.5 is shown in the figure.
Figure 2. Response curves of temperature seasonal standard deviation (bio04) affecting the distribution of five Betula species. The range of logistic greater than 0.5 is shown in the figure.
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Figure 3. Potential suitable distributions map of five Betula species under the current climate and four future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Suitability levels are represented by different colors: gray for unsuitable ranges, green for low suitability, yellow for medium suitability, and red for high suitability.
Figure 3. Potential suitable distributions map of five Betula species under the current climate and four future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Suitability levels are represented by different colors: gray for unsuitable ranges, green for low suitability, yellow for medium suitability, and red for high suitability.
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Figure 4. Distribution changes of five Betula species from the current climate to four future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Gray represents unsuitable ranges, green indicates contraction ranges, blue represents stable ranges, and red indicates expansion ranges.
Figure 4. Distribution changes of five Betula species from the current climate to four future climate scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). Gray represents unsuitable ranges, green indicates contraction ranges, blue represents stable ranges, and red indicates expansion ranges.
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Table 1. Distribution data, MaxEnt model parameters, and model accuracy estimates for Betula species.
Table 1. Distribution data, MaxEnt model parameters, and model accuracy estimates for Betula species.
SpeciesDistribution PointsParameterAUC (SD)TSS (SD)
FCRM
B. chinensis120LQHPT20.936 (0.011)0.912 (0.036)
B. fruticosa40LQHPT20.930 (0.011)0.930 (0.011)
B. insignis55LQ10.948 (0.015)0.924 (0.046)
B. potaninii41LQ20.948 (0.017)0.943 (0.035)
B. utilis216LQHPT30.916 (0.011)0.903 (0.025)
SD = Standard deviation, FC = feature combination, RM = regularization multiplier, L = linear features, Q = quadratic features, H = hinge features, T = threshold features, P = product features.
Table 2. Contributions and ranges of values of the main environmental variables for Betula species.
Table 2. Contributions and ranges of values of the main environmental variables for Betula species.
SpeciesVariablePercent ContributionLogistic > 0.5Logistic MaxVIF
B. chinensisbio1332.3121.16–198.73152.671.89
bio0431.2917.39–1233.911147.592.62
bio0312.627.80–31.6328.821.80
bio19127.71–50.8129.783.11
bio0811.921.08–25.5723.181.09
B. fruticosabio0457.81310.77–1961.681812.173.12
bio1833.9261.78–430.863142.12
bio014.7−2.10–5.922.612.85
bio173.78.86–34.6018.821.76
B. insignisbio0750.423.55–30.6827.063.93
bio0927.51.70–9.295.493.88
bio0410557.57–788.92673.243.32
bio127.5891.48–1530.561211.023.78
bio194.639.18–169.88104.093.34
B. potaniniibio0448.361.82–690.2061.821.02
bio1140.7−3.87–6.271.20 2.21
bio1910.40–76.1736.162.78
bio120.5592.58–1538.101060.362.80
B. utilisbio0445.5459.88–738.72558.711.83
bio1226.2544.41–1162.35664.852.31
bio1024.78.17–18.9211.132.21
bio153.760.29–96.9991.811.17
Bold values indicate key environmental variables common to all five species. bio1 = annual mean temperature (°C), bio3 = isothermality (°C), bio4 = temperature seasonality, bio7 = temperature annual range (°C), bio8 = mean temperature of wettest quarter (°C), bio9 = mean temperature of driest quarter (°C), bio10 = mean temperature of warmest quarter (°C), bio11 = mean temperature of coldest quarter (°C), bio12 = annual precipitation (mm), bio13 = precipitation of wettest month (mm), bio15 = precipitation seasonality, bio17 = precipitation of driest quarter (mm), bio18 = precipitation of warmest quarter (mm), bio19 = precipitation of coldest quarter (mm), VIF = variance inflation factors.
Table 3. Percentage of suitable distribution area for Betula species under current and future climate scenarios relative to China’s total land area.
Table 3. Percentage of suitable distribution area for Betula species under current and future climate scenarios relative to China’s total land area.
SpeciesPeriodHighMediumLowTotal
B. chinensiscurrent6.99%4.47%10.35%21.82%
SSP1-2.67.82%4.50%15.71%28.03%
SSP2-4.56.21%4.31%14.31%24.84%
SSP3-7.03.43%4.83%16.40%24.65%
SSP5-8.52.00%3.51%12.05%17.57%
B. fruticosacurrent11.03%6.00%12.06%29.09%
SSP1-2.611.10%6.40%14.40%31.89%
SSP2-4.511.01%6.33%11.95%29.29%
SSP3-7.08.29%7.12%15.41%30.82%
SSP5-8.57.30%6.93%13.75%27.97%
B. insigniscurrent5.88%4.21%12.13%22.23%
SSP1-2.610.48%4.50%9.59%24.57%
SSP2-4.513.41%6.42%11.49%31.32%
SSP3-7.013.61%3.46%7.78%24.85%
SSP5-8.514.04%3.71%8.20%25.95%
B. potaniniicurrent4.36%5.50%13.30%23.16%
SSP1-2.65.21%5.08%14.22%24.51%
SSP2-4.56.03%4.61%15.65%26.29%
SSP3-7.06.99%4.05%13.16%24.21%
SSP5-8.56.91%4.13%13.54%24.59%
B. utiliscurrent7.11%9.21%16.19%32.51%
SSP1-2.65.39%7.10%11.89%24.38%
SSP2-4.56.99%7.60%10.66%25.26%
SSP3-7.08.01%7.09%7.98%23.08%
SSP5-8.58.81%6.60%6.73%22.14%
Table 4. Percentage of contraction and expansion in the suitable distribution area of Betula species under different future scenarios relative to the current climate.
Table 4. Percentage of contraction and expansion in the suitable distribution area of Betula species under different future scenarios relative to the current climate.
SpeciesPeriodContractionExpansion
B. chinensisCurrent-SSP1-2.620.90%49.43%
Current-SSP2-4.525.04%38.94%
Current-SSP3-7.028.20%41.26%
Current-SSP5-8.558.64%39.16%
B. fruticosaCurrent-SSP1-2.68.03%17.66%
Current-SSP2-4.514.41%15.11%
Current-SSP3-7.015.71%21.66%
Current-SSP5-8.516.97%13.09%
B. insignisCurrent-SSP1-2.619.17%29.76%
Current-SSP2-4.56.71%47.66%
Current-SSP3-7.022.35%34.19%
Current-SSP5-8.521.73%38.49%
B. potaniniiCurrent-SSP1-2.69.91%15.70%
Current-SSP2-4.516.42%16.42%
Current-SSP3-7.030.62%35.06%
Current-SSP5-8.535.63%41.74%
B. utilisCurrent-SSP1-2.638.06%13.03%
Current-SSP2-4.541.12%18.81%
Current-SSP3-7.048.59%19.57%
Current-SSP5-8.552.21%20.30%
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Huang, Z.; Fu, C.; Li, C.; Yang, X.; Shuai, B.; Li, M.; Wang, Z.; Yang, X. Distributional Responses of Five Betula (Betulaceae) Species to Future Climate Change in China. Forests 2025, 16, 400. https://doi.org/10.3390/f16030400

AMA Style

Huang Z, Fu C, Li C, Yang X, Shuai B, Li M, Wang Z, Yang X. Distributional Responses of Five Betula (Betulaceae) Species to Future Climate Change in China. Forests. 2025; 16(3):400. https://doi.org/10.3390/f16030400

Chicago/Turabian Style

Huang, Zhilong, Chenlong Fu, Chenyang Li, Xinle Yang, Binyu Shuai, Meng Li, Zefu Wang, and Xiaoyue Yang. 2025. "Distributional Responses of Five Betula (Betulaceae) Species to Future Climate Change in China" Forests 16, no. 3: 400. https://doi.org/10.3390/f16030400

APA Style

Huang, Z., Fu, C., Li, C., Yang, X., Shuai, B., Li, M., Wang, Z., & Yang, X. (2025). Distributional Responses of Five Betula (Betulaceae) Species to Future Climate Change in China. Forests, 16(3), 400. https://doi.org/10.3390/f16030400

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