Next Article in Journal
Study on Salt-Ion Content and Biomass Distribution Characteristic of Different Organs of Poacynum pictum in Different Water–Salt Habitats
Previous Article in Journal
Growth, Productivity, and Nutrient Return of a Mixed Plantation of Fast-Growing Eucalyptus Hybrid and Acacia auriculiformis Trees in Thailand
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 16
Issue 1
10.3390/f16010184
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Climate Change Drives the Adaptive Distribution and Habitat Fragmentation of Betula albosinensis Forests in China

by
Huayong Zhang
1,2,*,
Yue Zhou
1,
Xiande Ji
3,
Zhongyu Wang
1 and
Zhao Liu
2
1
Research Center for Engineering Ecology and Nonlinear Science, North China Electric Power University, Beijing 102206, China
2
Theoretical Ecology and Engineering Ecology Research Group, School of Life Sciences, Shandong University, Qingdao 250100, China
3
Energy Conversion Group, Energy and Sustainability Research Institute Groningen, Faculty of Science and Engineering, University of Groningen, Nijenborgh 6, 9747AG Groningen, The Netherlands
*
Author to whom correspondence should be addressed.
Forests 2025, 16(1), 184; https://doi.org/10.3390/f16010184
Submission received: 6 November 2024 / Revised: 13 January 2025 / Accepted: 17 January 2025 / Published: 19 January 2025
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

:
Betula albosinensis serves as an important constructive and afforestation tree species in mountainous areas. Its suitable habitat and habitat quality are highly vulnerable to the climate. However, few studies have centered on the shrinkage, expansion, and habitat fragmentation of B. albosinensis forests under climate change. In this study, the Random Forest model was employed to predict current and future trends of shrinking and expanding of B. albosinensis, while a composite landscape index was utilized to evaluate the habitat fragmentation in the highly suitable habitats of B. albosinensis. The results indicated that suitable habitats for B. albosinensis were primarily concentrated in the vicinities of the Qinling, Qilian, and Hengduan Mountains, situated in western China. The most influential factor affecting the distribution of B. albosinensis was temperature seasonality (Bio4). In future scenarios, the center of distribution of B. albosinensis was projected to shift towards the west and higher altitudes. The total suitable habitats of B. albosinensis were anticipated to expand under the scenarios of SSP370 and SSP585 in the 2090s, while they were expected to contract under the remaining scenarios. Although these results indicated that the suitable areas of habitat for B. albosinensis were relatively intact on the whole, fragmentation increased with climate change, with the highest degree of fragmentation observed under the SSP585 scenario in the 2090s. The findings of this study provide a foundation for the protection of montane vegetation, the maintenance of montane biodiversity, and the evaluation of species’ habitat fragmentation.

1. Introduction

Climate change has exerted a profound influence on various ecological processes, including species distribution, interspecific relationships, and vegetation productivity. This is particularly prominent in mountainous regions [1,2,3]. Mountains represent a distinctive ecosystem that supports approximately a quarter of the global terrestrial biodiversity, containing 32% of protected areas and nearly half of the world’s biodiversity hotspots [4]. Furthermore, they serve as nurturing habitats and safe refuges for numerous species. Although mountains are renowned for their abundant biodiversity, they are also recognized as being particularly susceptible to the effects of climate change [5]. A growing body of evidence indicates that numerous alpine regions are witnessing considerable increases in temperature and alterations in precipitation patterns, along with an increase in the frequency of extreme weather events [6,7,8]. Climate change represents a significant and irreversible threat to the biodiversity of mountainous regions. Consequently, it is of great significance to explore the potential distribution of mountain species under climate change in order to maintain the local ecological balance and protect the mountain ecosystem [9,10].
Betula albosinensis Burkill (Fagales: Betulaceae), the Chinese red birch, is an endemic species in China, primarily distributed in the middle and high mountain regions of the warm temperate zone and the northern subtropics. It has a broad ecological niche and is adapted to cold and arid climatic conditions. Additionally, B. albosinensis exhibits few symptoms of pests and diseases and is capable of rapid propagation, so it is an important pioneer afforestation species in China’s high-altitude areas and a major forest tree species in high mountains [11]. Due to its vigorous sprouting capability, it can swiftly regenerate following fires, logging, and other disturbances [12], thus playing a crucial role in maintaining the ecological balance of mountainous regions and protecting mountain biodiversity [13]. Although previous studies have revealed, through methods like tree-ring chronology and pot experiments, that factors like the climate and the soil can influence the radial growth and biomass accumulation of B. albosinensis [11,12,14], few studies have predicted the changes in B. albosinensis’s suitable habitats and its habitat fragmentation under future climate conditions. It is imperative to investigate the impact of climate change on the suitable habitats and habitat quality of B. albosinensis to ensure the conservation and management of this species. This research provides the foundation for the conservation of montane vegetation.
The application of the species distribution model and landscape index calculation software provides a foundation for assessing the contraction and expansion of species’ potential habitats and habitat fragmentation [15,16]. Among these models, the Random Forest (RF) model serves as a typical approach for integrating multiple weak classifiers into a single robust classifier. The main advantage of the RF model lies in its possession of a rapid learning process and its effective handling of missing data. It has been widely used due to its high accuracy and robust performance [17,18]. Romero-Sanchez et al. employed three model techniques to predict the potential spatial distribution of Pinus oocarpa Schiede ex Schltdl. (Pinales: Pinaceae) in Mexico; their findings indicated that the RF model exhibited the highest predictive accuracy [19]. Previous studies have demonstrated the efficacy of this model in accurately predicting the distribution of various plants and animals, including the Madeira vine, Anredera cordifolia (Ten.) Steenis (Caryophyllales: Basellaceae), and Boulenger’s spiny frog, Quasipaa boulengeri (Günther, 1889) (Anura: Dicroglossidae) [17,20]. Furthermore, Fragstats 4.2 software is a widely used tool for landscape analysis. It permits the analysis of indices according to the three levels of the landscape pattern, and the types of indices that can be analyzed are more complete [21,22]. The software contains a moving window method that enables the visualization of the landscape index. This is an effective approach for analyzing the spatial variability of the landscape pattern index. The moving window method has already been employed by numerous scholars to assess landscape fragmentation [23,24].
In this study, the RF model and Fragstats 4.2 software were employed to evaluate the shrinkage and expansion of B. albosinensis forests and habitat fragmentation in different time periods. This was achieved by combining species records and environmental data, including the soil, the topography, and the climate. The primary objectives of this study were to (1) analyze the principal environmental factors influencing the distribution of B. albosinensis; (2) model the potential shrinking and expansion of the suitable habitats for B. albosinensis under future scenarios; and (3) assess the fragmentation of B. albosinensis’s habitats in the present day and in the future under different scenarios. The findings of this study will provide theoretical foundations for the management and cultivation of B. albosinensis and for the conservation of mountain ecosystems.

2. Materials and Methods

2.1. Data Collection

The distribution data for B. albosinensis were obtained from the Vegetation Atlas of China 1:1,000,000, published by the Science Press in 2001 [25]. The Vegetation Atlas of China 1:1,000,000 was spatially aligned, vector digitized, and rasterized based on ArcGIS 10.8 software. A single data point was retained in each 1 × 1 km grid, and, as a result, there were a total of 3259 spatial distribution data points for B. albosinensis. In addition, landscape images of B. albosinensis were obtained from the website (https://baike.so.com/doc/7849525-8123620.html (accessed on 29 December 2024)) (Figure S1).
In this study, a comprehensive set of 37 environmental factors, encompassing the bioclimate, the soil, and the topography, were selected (Table S1) [26]. Nineteen bioclimatic variables corresponding to both current and future scenarios were retrieved from the WorldClim dataset (https://worldclim.org/ (accessed on 10 October 2024))with a spatial resolution of 1 km [27]. The soil variables’ data were sourced from the Harmonized World Soil Database, with a spatial resolution of 1 km (http://www.iiasa.ac.at/web/home/research/researchPrograms/water/HWSD.html (accessed on 10 October 2024)). Additionally, elevation data were also acquired from the WorldClim dataset. The slope and aspect data were extracted using ArcGIS 10.8 software, maintaining a spatial resolution of 1 km.
The future climate change scenarios were derived from the BCC-CSM2-MR model of CMIP6. The CMIP6 model has been significantly improved compared to its predecessor. The findings indicate that it is more consistent with future climate patterns and that it exhibits higher accuracy compared to CMIP5 projections [28,29]. The BCC-CSM2-MR model is a coupled climate model developed by the Beijing Climate Centre [30]. Previous studies have demonstrated that the BCC-CSM2-MR model is more effective in simulating tropospheric temperatures and circulation in East Asia [31,32,33]. In this study, low (SSP126), medium (SSP370), and heavy (SSP585) emission scenarios for the 2050s (averaged over 2041–2060) and the 2090s (averaged over 2081–2100) were selected. A total of six combined scenarios were used to represent future climate conditions. For the projections of future scenarios, it was assumed that the future topography and soils would not change significantly in comparison with the present situation. Given the relative stability of soil and terrain factors, it is reasonable to assume that there will be no change in these factors under future climate conditions [34].

2.2. Identification of the Environmental Drivers

The identification of environmental drivers was conducted using the RF model, provided by the Biomod2 package for the R environment [35]. A total of 3529 species distribution records and 38 environmental variables were incorporated into the model. In order to enhance the predictive capacity of the model, 1500 pseudo-presence points were randomly generated in R. Then, 75% of the sample points were selected to be included in the training set, and 25% were included in the test set [36]. The model was repeated ten times in order to determine the relative importance of each environmental factor. To prevent the adverse effects of autocorrelation and multicollinearity between environmental variables on the model’s process, a correlation analysis was conducted on 38 variables using the ArcGIS “Band Collection Statistics” tool [37]. When two or more of the environmental variables showed a correlation with an absolute value of ≥0.8 [38,39], only the factor that contributed the most was selected. Eventually, a sum of 12 variables was selected for the purposes of modeling (Table 1).

2.3. Calculation of Shrinkage and Expansion of B. albosinensis Forests

The 12 variables and species distribution points were incorporated into the RF model to predict the potential distribution of B. albosinensis under seven climate scenarios. The model parameters were configured in accordance with the process of identifying environmental factors. After ten repetitive runs of each model, the results were outputted. The model output data format was an ASCII layer with a numerical size representing the habitat suitability index (HIS) of B. albosinensis within that raster. This value ranges between 0 and 1 [40,41]. The RF results were imported into ArcGIS 10.8 for reclassification. Based on the HIS, the potential habitats of B. albosinensis were grouped into four classes [42]: unsuitable habitat (HIS ≤ 0.3), generally suitable habitat (0.3 < HSI ≤ 0.5), moderately suitable habitat (0.5 < HSI ≤ 0.7), and highly suitable habitat (0.7 < HSI ≤ 1). Subsequently, the area of each suitable area was calculated, and the tendency for shrinkage and expansion of the suitable area was measured.
The model results were evaluated using two metrics: true skill statistics (TSS) and the area under receiver operating characteristic (ROC) curve (AUC) [43]. The AUC is employed to assess the predictive performance of the model [44]; the value of the AUC lies within the range of [0, 1]. An AUC value closer to 1 indicates better predictive ability of the model [45]. Furthermore, the TSS (TSS = sensitivity + specificity−1) is a straightforward and intuitive statistical value for evaluating the performance of a species distribution model within a range of [−1, 1] [46]. Generally, a model is considered to perform well if the AUC is greater than 0.9 and the TSS is greater than 0.85 [47].

2.4. Calculation of Habitat Fragmentation in Highly Suitable Habitats

Edge effects, segregation effects, and patch size changes are significant characteristics of habitat fragmentation. We can measure them using three different landscape pattern metrics: edge density (ED), patch density (PD), and mean patch area (MPA) [48]. These indicators are calculated as follows:
ED = ∑ k = 1 m e i k A × 10,000
PD = n i A × 10,000 × 100
MPA = mean AREA patch ij
where eik represents the total edge length in meters, ni indicates the number of patches, A stands for the total landscape (with a grid cell being considered a landscape) in square meters, and AREA [patchij] is the area of each patch measured in hectares.
Highly suitable habitats represent the most favorable areas for the distribution of a species. The degree of habitat fragmentation within these areas provides the most precise indication of the species’ habitat condition. In this study, based on the prediction results of the RF model, we selected the highly suitable habitats of B. albosinensis as our research targets. The moving window spatial analysis method in Fragstats 4.2 software was utilized to calculate the spatial distribution patterns of three landscape indices for the highly suitable habitats of B. albosinensis under the current period and future climate scenarios. In order to obtain a comprehensive fragmentation index, the three indicators were normalized by a dimensionless standard using ArcGIS 10.8. This was followed by an extreme deviation standardization process, which was employed to determine the spatial distribution pattern of the fragmentation degree of suitable habitats for B. albosinensis under different climatic scenarios in different periods. Subsequently, the data were divided into five grades according to the isometric classification method: 0–0.2 (lowest fragmentation), 0.2–0.4 (low fragmentation), 0.4–0.6 (moderate fragmentation), 0.6–0.8 (high fragmentation), and 0.8–1 (highest fragmentation).

3. Results

3.1. Drivers of B. albosinensis Forests’ Distribution

The results of the model demonstrated that the main factors influencing the distribution of B. albosinensis included six climatic factors, four soil factors, and two terrain factors. Among these, Bio4 (temperature seasonality) was identified as the most influential factor, with an importance value of 35.6%. Furthermore, the cumulative importance of climatic factors was found to be 87.6% (Figure 1) and thus could be considered as the primary driving factor influencing the distribution of B. albosinensis. Among the six climatic factors, the cumulative importance of the temperature-related factor was 68.3%, which was considerably higher than that of the precipitation-related factor, with a cumulative importance of 19.3% (Figure 1). This indicated that B. albosinensis was more sensitive in its response to temperature than to precipitation. In addition to climatic factors, elevation was also found to exert an influence on the distribution of B. albosinensis, while soil factors had the least influence.

3.2. Climate-Change-Driven Shrinkage, Expansion, and Migration of B. albosinensis Forests

The results of the RF model indicated that current suitable habitats for B. albosinensis were mainly concentrated in northwestern and central China, and there was also a relatively small area of suitable habitats in southwest China (Figure 2). The highly suitable habitats, which accounted for 47.7% of the total suitable habitats, were the main part of the suitable habitats (Table S2). These habitats were mainly distributed near the Qilian Mountains and the Qinling Mountains. The moderately suitable habitats occupied a relatively small proportion of the total suitable habitats, and they were mainly distributed in the Qinling Mountains and near the Dalou Mountains. The generally suitable habitats were distributed not only at the edges of the moderately and highly suitable habitats but also in the eastern part of the Tibet Autonomous Region and in the vicinity of the Hengduan Mountains.
In the three scenarios for the 2050s (SSP126, SSP370, and SSP585), the distribution of B. albosinensis’s suitable habitats was predominantly concentrated around the Qilian Mountains and the Qinling Mountains in northwest China and the Hengduan Mountains in southwest China (Figure 3a–c). However, the total suitable habitats of B. albosinensis showed a consistent trend of contraction. There was a reduction of 15.2%, 16.27%, and 18.71% in the total suitable habitats compared to the contemporary period. The highly suitable habitats had the highest rate of contraction, with a decrease of 33.8%, 34.12%, and 46.5%, respectively. Meanwhile, the generally suitable habitats showed a more pronounced expansion trend under the three scenarios (Figure 4a).
In the 2090s, the total suitable habitats of B. albosinensis were contracted by 14.7% in the SSP126 scenario. Meanwhile, in the SSP370 and SSP585 scenarios, the total suitable habitats of B. albosinensis showed an expanding trend, with an increase of 13.48% and 22.13% in the area, respectively. However, the moderately and highly suitable habitats of B. albosinensis in the three scenarios still exhibited a significant contraction trend. Especially in the SSP585 scenario, the moderately suitable habitats decreased by 36.1%, and the highly suitable habitats decreased by 84.1%, respectively (Figure 4b). Furthermore, in response to climate change, the distribution center of B. albosinensis had a tendency to shift its geographic range towards the west and to higher altitudes (Figure S2). This shift was most pronounced in the SSP370 and SSP585 scenarios of the 2090s. Additionally, a considerable area of novel suitable habitat emerged in the southern portion of Qinghai Province and the eastern region of the Tibet Autonomous Region (Figure 3d–f).

3.3. Climate-Change-Driven Fragmentation of B. albosinensis Habitat

The highly suitable habitats of B. albosinensis were successively contracted due to climate change. In order to gain a better understanding of the quality of the habitat, the fragmentation of the highly suitable habitats of B. albosinensis under different scenarios was analyzed.
The results of the spatial distribution of fragmentation showed that the current suitable habitat of B. albosinensis with high fragmentation was mainly concentrated in the edge zone of the suitable habitat (Figure 5). The interior of the suitable habitat was relatively more intact. The area share of five fragmentation degrees in the highly suitable habitats of B. albosinensis was calculated. It was shown that in the current climate scenario, 18.6% of the areas was moderately fragmented, 3.8% was highly fragmented, and 0.3% had the highest fragmentation. This indicated that current highly suitable habitats were less fragmented.
With climate change, the area of the suitable habitat shrank dramatically, and a more obvious fragmentation began to appear. Among the three scenarios in the 2050s, the phenomenon of habitat fragmentation was observed to be more pronounced in the SSP585 scenario (Figure 6a–c). In this scenario, compared to the contemporary period, there was a reduction in the combined areas of low and lowest fragmentation by 4.3%, and the area share of highest fragmentation and high fragmentation remained basically unchanged (Figure 7). In the 2090s, the area share of B. albosinensis’s suitable habitat fragmentation under the SSP126 scenario was essentially the same as in the contemporary period. Under the SSP370 and SSP585 scenarios, the total area shares of low and lowest fragmentation decreased by 8.2% and 16.8%, respectively (Figure 6d–f). Meanwhile, the area shares of moderate fragmentation increased by 7.2% and 14.8%, respectively, and the total area share of high and highest fragmentation also increased by 1.0% and 1.9%, respectively (Figure 7), further increasing the degree of fragmentation in both scenarios.

4. Discussion

In the context of global warming, exploring the current and future range changes of species is of great importance for the conservation, use, and sustainable management of the species [49]. In this study, we predicted the shrinkage and expansion of B. albosinensis forests based on the species distribution records of B. albosinensis in China using the RF model, combined with climate, soil, and topographic factors. The model as a whole showed excellent performance (AUC > 0.99, TSS > 0.99), which demonstrated that the RF model reliably predicts the suitable habitats, and it can be employed to model trends in the range of B. albosinosis [50].
Numerous studies have shown that climate is a key factor in the distribution, growth, and development of most plants [51,52,53]. The combined model predictions showed that bioclimatic factors had a higher influence on the distribution of B. albosinensis’s adaptive areas than soil and terrain factors. Among them, temperature seasonality (Bio4) had a greater importance (35.6%) than the other factors. Previous studies have yielded similar results regarding the distribution of two maple species in the southeastern Tibetan Plateau and Hengduan Mountains regions [54]. It was found that the effects of temperature seasonality would be more pronounced in areas of greater topographic relief. Furthermore, the cumulative importance of temperature-related climate factors was much higher than that of precipitation-related climate factors, probably because most of the habitable areas of B. albosinensis are located in the humid areas south of the Qinling Mountains, and B. albosinensis is a drought-tolerant species [55], so precipitation is less limiting for it. Temperature, on the other hand, is the key factor influencing the distribution of B. albosinensis. Other research also found that winter temperature was the main limiting factor affecting the radial growth of B. albosinensis in the Qilian Mountains [11]. Betula albosinensis is one of the alpine tree species [13], and the model shows that its suitable habitats are mainly distributed near the Qilian Mountains and the Qinling Mountains in the northwest and also in the Hengduan Mountains in the southwest. In mountainous areas, topographical factors, such as elevation, slope, and aspect, all influence solar radiation, air temperature, precipitation, etc. to some extent and thus further influence the distribution of vegetation [56]. The present study also showed that altitude is one of the important factors influencing the distribution of B. albosinensis. This result is in agreement with the study of vegetation in the Niubieliang Nature Reserve in the Qinling Mountains [57].
Climate change can affect the temperature and precipitation, which will lead to changes in the suitable habitats for species. Especially against the background of global warming, species that are adapted to cold climatic mountain environments are expected to face a high risk of range shrinkage [58,59,60,61]. An assessment of the impact of climate change on 2,632 plant species from all major mountain ranges in Europe indicates that by 2070–2100, 31%–51% of subalpine species and 19%–46% of mountain species will have lost more than 80% of their suitable habitats [62]. Our study also found that the total suitable habitats of B. albosinensis would shrink to varying degrees under the future climate scenarios, which is consistent with the prediction of the suitable habitats of Betula utilis made by researchers using bioclimatic models [63]. However, in the 2090s SSP370 and SSP585 scenarios, the B. albosinensis forests exhibit the most pronounced expansion trend, and there are new suitable habitats in the southeastern part of Tibet and the southern part of Qinghai, which may be attributed to the fact that there will be a significant increase in temperature and precipitation in most areas by the end of the 21st century [64,65,66]. As a result of the contraction and expansion of the distribution area, the center of distribution of B. albosinensis as a whole shifted to the west. In addition, there was also a tendency for B. albosinensis to migrate to higher altitudes as the temperature and precipitation intensity increased. These results are consistent with the speculation that global warming has led to a shift in plant distribution to higher latitudes and altitudes [67]. Thus, on a horizontal gradient, the center of distribution of B. albosinensis shifts towards the west, whereas on a vertical gradient, the center of distribution of B. albosinensis moves towards higher elevations.
Habitat fragmentation is a major cause of biodiversity loss [68], As the impact of human activities on natural ecosystems intensifies and the environment in which species live is altered by climate change, the fragmentation of species’ habitats is becoming increasingly severe. However, researchers have based calculations of most habitat fragmentations on landscape types, and there have been few fragmentation analyses based on the morphology of species’ habitats [69]. In this study, the moving window method was used to combine several landscape indicators to obtain the spatial distribution of fragmentation in B. albosinensis’s highly suitable habitats, which is more representative of the actual degree of habitat fragmentation than the quantitative assessment of individual indicators [48]. The results of the study showed that the highly suitable habitats of contemporary B. albosinensis were relatively intact as a whole, most of the highly suitable habitats were in lowest fragmentation and low fragmentation, and the areas with a high degree of fragmentation were also mainly distributed in the fringe of the suitable habitat. However, with the intensification of climate change, the distribution range of B. albosinensis has changed. The originally continuous habitats have become fragmented because some areas are no longer suitable for survival, which has led to an increased degree of habitat fragmentation. Under the SSP126 scenario, the highly suitable habitats of B. albosinensis were relatively stable. In contrast, under the SSP585 scenario, there was a significant fluctuation in habitat conditions in the highly suitable habitats. Prior research has indicated that high carbon emission scenarios result in a more unstable climate, which may cause extreme weather patterns and, in turn, affect the stability of vegetation habitats [70]. Habitat fragmentation trends were consistent with climate change, especially under the SSP585 scenario in the 2090s, where climate change led to a sharp contraction in the area of the highly suitable habitats of B. albosinensis, a large increase in the area of moderate fragmentation in the habitats, and a more pronounced fragmentation within the habitats. This suggests that habitat contraction will increase habitat fragmentation and that marginal areas of suitable habitats will be more affected by climate change [71].
The fragmentation of habitats has disrupted the continuity of species’ living spaces and posed a significant threat to the biodiversity of woodlands and the genetic diversity of species [72,73]. In addition, the fragmentation of the habitats of B. albosinensis is likely to result in forest degradation, a decrease in water conservation capacity, intensified soil erosion, and a reduction in the stability and service functions of the ecosystem [74,75]. To better protect the habitats of B. albosinensis, we suggest that protected areas should be established in regions where B. albosinensis is densely distributed, such as the Qinling Mountains area. For habitats that have been obviously fragmented, artificial reseeding can be carried out. Meanwhile, ecological corridors should be planned and constructed. Long-term dynamic monitoring is a crucial component to understand the evolution of the habitats of B. albosinensis, as it provides a scientific foundation for formulating precise and effective protection strategies [76]. Advancements in science and technology have enabled the utilization of high-resolution satellite remote sensing images and unmanned aerial vehicle (UAV) aerial photography technology to obtain higher-precision spatial information [77,78]. At the same time, by introducing machine learning and artificial intelligence algorithms, we can better identify the complex correlation mechanisms among the influencing factors and improve the accuracy and reliability of habitat change predictions [79,80].

5. Conclusions

Betula albosinensis is a significant pioneer silvicultural species at high altitudes in the northern part of China. It plays an important role in maintaining the regional climate balance and soil and water conservation. The RF model was employed to forecast the potential habitat of B. albosinensis under three contemporary and prospective scenarios (SSP126, SSP370, and SSP585). Concurrently, the vulnerability of B. albosinensis was evaluated utilizing the landscape fragmentation index. The following conclusions were drawn. (1) B. albosinensis was susceptible to fluctuations in temperature and precipitation. Bio4 was the most significant factor influencing its distribution. (2) The contemporary suitable habitats for B. albosinensis were mainly located near the Qinling, Qilian, and Hengduan mountain ranges in western China. Under projected climate scenarios, the center of distribution of B. albosinensis was expected to shift westward towards higher altitudes. (3) The moderately and highly suitable habitats of B. albosinensis were significantly reduced under the future six scenarios, while generally suitable habitats continued to expand. This finding was also consistent with the characteristics of B. albosinensis as a pioneer species. Except for the scenarios of SSP370 and SSP585 in the 2090s, in which the total suitable habitats of B. albosinensis exhibited an expansion, the total suitable habitats showed a tendency towards contraction in all scenarios. (4) An assessment of habitat fragmentation for B. albosinensis found that the highly suitable habitats of B. albosinensis were relatively intact and less fragmented overall, but fragmentation increased with climate change. The highest degree of fragmentation occurred in the 2090s under the SSP585 scenario. This study provides an understanding of the potential suitable habitats and habitat fragmentation of B. albosinensis, as well as a basis for the management and conservation of the alpine species. We suggest that policymakers integrate these findings into climate adaptation plans.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16010184/s1, Figure S1. (a) Distribution points of B. albosinensis in China ; (b) Landscape of B. albosinensis; Figure S2. Migration of B. albosinensis distribution center. Table S1: All variables and their abbreviations used in our study. Table S2. Areas of suitable habitat for B. albosinensis under different climate scenarios.

Author Contributions

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

Funding

This research was funded by the National Water Pollution Control and Treatment Science and Technology Major Project (2017ZX07101) and the Discipline Construction Program of Huayong Zhang, Distinguished Professor of Shandong University, School of Life Sciences (61200082363001).

Data Availability Statement

All links to input data are reported in the manuscript, and all output data are available upon request to the authors.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Cao, D.; Zhang, J.H.; Xun, L.; Yang, S.S.; Wang, J.W.; Yao, F.M. Spatiotemporal variations of global terrestrial vegetation climate potential productivity under climate change. Sci. Total Environ. 2021, 770, 145320. [Google Scholar] [CrossRef] [PubMed]
  2. Aitken, S.N.; Yeaman, S.; Holliday, J.A.; Wang, T.; Curtis-McLane, S. Adaptation, migration or extirpation: Climate change outcomes for tree populations. Evol. Appl. 2008, 1, 95–111. [Google Scholar] [CrossRef] [PubMed]
  3. Shi, N.; Naudiyal, N.; Wang, J.N.; Gaire, N.P.; Wu, Y.; Wei, Y.Q.; He, J.L.; Wang, C.Y. Assessing the Impact of Climate Change on Potential Distribution of Meconopsis punicea and Its Influence on Ecosystem Services Supply in the Southeastern Margin of Qinghai-Tibet Plateau. Front. Plant Sci. 2022, 12, 830119. [Google Scholar] [CrossRef] [PubMed]
  4. Dobrowski, S.Z.; Parks, S.A. Climate change velocity underestimates climate change exposure in mountainous regions. Nat. Commun. 2016, 7, 12349. [Google Scholar] [CrossRef]
  5. Palombo, C.; Marchetti, M.; Tognetti, R. Mountain vegetation at risk: Current perspectives and research reeds. Plant Biosyst. 2014, 148, 35–41. [Google Scholar] [CrossRef]
  6. Rajczak, J.; Pall, P.; Schär, C. Projections of extreme precipitation events in regional climate simulations for Europe and the Alpine Region. J. Geophys. Res.-Atmos. 2013, 118, 3610–3626. [Google Scholar] [CrossRef]
  7. Kotlarski, S.; Gobiet, A.; Morin, S.; Olefs, M.; Rajczak, J.; Samacoiets, R. 21st Century alpine climate change. Clim. Dyn. 2023, 60, 65–86. [Google Scholar] [CrossRef]
  8. Wang, Y.S.; Sun, W.J.; Huai, B.J.; Wang, Y.Z.; Ji, K.C.; Yang, X.H.; Du, W.T.; Qin, X.; Wang, L. Comparison and evaluation of the performance of reanalysis datasets for compound extreme temperature and precipitation events in the Qilian Mountains. Atmos. Res. 2024, 304, 107375. [Google Scholar] [CrossRef]
  9. He, Y.L.; Xiong, Q.L.; Yu, L.; Yan, W.B.; Qu, X.X. Impact of Climate Change on Potential Distribution Patterns of Alpine Vegetation in the Hengduan Mountains Region, China. Mt. Res. Dev. 2020, 40, R48–R54. [Google Scholar] [CrossRef]
  10. Qian, D.W.; Cao, G.M.; Du, Y.G.; Li, Q.; Guo, X.W. Impacts of climate change and human factors on land cover change in inland mountain protected areas: A case study of the Qilian Mountain National Nature Reserve in China. Environ. Monit. Assess. 2019, 191, 486. [Google Scholar] [CrossRef]
  11. Yan, X.; Li, Q.; Deng, Y.; Gao, L.; Gou, X. Warming-induced radial growth reduction in Betula albosinensis, eastern Qilian Mountains, China. Ecol. Indic. 2021, 120, 106956. [Google Scholar] [CrossRef]
  12. Gradel, A.; Haensch, C.; Ganbaatar, B.; Dovdondemberel, B.; Nadaldorj, O.; Guenther, B. Response of white birch (Betula platyphylla Sukaczev) to temperature and precipitation in the mountain forest steppe and taiga of northern Mongolia. Dendrochronologia 2017, 41, 24–33. [Google Scholar] [CrossRef]
  13. Guo, Y.; Li, G.; Hu, Y.; Kang, D.; Wang, D.; Yang, G. Regeneration of Betula albosinensis in Strip Clearcut and Uncut Forests of the Qinling Mountains in China. PLoS ONE 2013, 8, e59375. [Google Scholar] [CrossRef] [PubMed]
  14. Yin, C.Y.; Palmroth, S.; Pang, X.Y.; Tang, B.; Liu, Q.; Oren, R. Differential responses of Picea asperata and Betula albosinensis to nitrogen supply imposed by water availability. Tree Physiol. 2018, 38, 1694–1705. [Google Scholar] [CrossRef]
  15. Qin, M.; Gao, X.; Feng, M.; Jin, N.; Wang, C.; Cheng, W. Modeling of the potential geographical distribution of naked oat under climate change. Front. Plant Sci. 2023, 13, 1009577. [Google Scholar] [CrossRef]
  16. Cheng, X.L.; Nizamani, M.M.; Jim, C.Y.; Balfour, K.; Da, L.J.; Qureshi, S.; Zhu, Z.X.; Wang, H.F. Using SPOT Data and FRAGSTAS to Analyze the Relationship between Plant Diversity and Green Space Landscape Patterns in the Tropical Coastal City of Zhanjiang, China. Remote Sens. 2020, 12, 3477. [Google Scholar] [CrossRef]
  17. Zhao, Z.Y.; Xiao, N.W.; Shen, M.; Li, J.S. Comparison between optimized MaxEnt and random forest modeling in predicting potential distribution: A case study with Quasipaa boulengeri in China. Sci. Total Environ. 2022, 842, 156867. [Google Scholar] [CrossRef]
  18. Cutler, D.R.; Edwards, T.C.; Beard, K.H.; Cutler, A.; Hess, K.T. Random forests for classification in ecology. Ecology 2007, 88, 2783–2792. [Google Scholar] [CrossRef]
  19. Romero-Sanchez, M.E.; Velasco-Garcia, M.V.; Perez-Miranda, R.; Velasco-Bautista, E.; Gonzalez-Hernandez, A. Different Modelling Approaches to Determine Suitable Areas for Conserving Egg-Cone Pine (Pinus oocarpa Schiede) Plus Trees in the Central Part of Mexico. Forests 2022, 13, 2112. [Google Scholar] [CrossRef]
  20. Zhang, X.; Wei, H.; Zhao, Z.; Liu, J.; Zhang, Q.; Zhang, X.; Gu, W. The Global Potential Distribution of Invasive Plants: Anredera cordifolia under Climate Change and Human Activity Based on Random Forest Models. Sustainability 2020, 12, 1491. [Google Scholar] [CrossRef]
  21. Yao, Y.; Cheng, T.; Sun, Z.H.; Li, L.L.; Chen, D.S.; Chen, Z.H.; Wei, J.L.; Guan, Q.F. VecLI: A framework for calculating vector landscape indices considering landscape fragmentation. Environ. Model. Softw. 2022, 149, 105325. [Google Scholar] [CrossRef]
  22. Kupfer, J.A. Landscape ecology and biogeography: Rethinking landscape metrics in a post-FRAGSTATS landscape. Prog. Phys. Geogr.-Earth Environ. 2012, 36, 400–420. [Google Scholar] [CrossRef]
  23. Wang, X.H.; Sun, Y.M.; Jia, W.W.; Wang, H.Z.; Zhu, W.C. Coupling of Forest Carbon Densities with Landscape Patterns and Climate Change in the Lesser Khingan Mountains, Northeast China. Sustainability 2023, 15, 14981. [Google Scholar] [CrossRef]
  24. Roy, P.S.; Murthy, M.S.R.; Roy, A.; Kushwaha, S.P.S.; Singh, S.; Jha, C.S.; Behera, M.D.; Joshi, P.K.; Jagannathan, C.; Karnatak, H.C.; et al. Forest fragmentation in India. Curr. Sci. 2013, 105, 774–780. [Google Scholar]
  25. Hou, X.Y. Vegetation Atlas of China. Chinese Academy of Science, the Editorial Board of Vegetation Map of China; Scientific Press: Beijing, China, 2001. [Google Scholar]
  26. Zhu, Q.; Wang, F.; Yi, Q.; Zhang, X.; Chen, S.; Zheng, J.; Li, J.; Xu, T.; Peng, D. Modeling soybean cultivation suitability in China and its future trends in climate change scenarios. J. Environ. Manag. 2023, 345, 118934. [Google Scholar] [CrossRef]
  27. Fick, S.E.; Hijmans, R.J. WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 2017, 37, 4302–4315. [Google Scholar] [CrossRef]
  28. Harvey, B.J.; Cook, P.; Shaffrey, L.C.; Schiemann, R. The Response of the Northern Hemisphere Storm Tracks and Jet Streams to Climate Change in the CMIP3, CMIP5, and CMIP6 Climate Models. J. Geophys. Res.-Atmos. 2020, 125, e2020JD032701. [Google Scholar] [CrossRef]
  29. Tokarska, K.B.; Stolpe, M.B.; Sippel, S.; Fischer, E.M.; Smith, C.J.; Lehner, F.; Knutti, R. Past warming trend constrains future warming in CMIP6 models. Sci. Adv. 2020, 6, eaaz9549. [Google Scholar] [CrossRef]
  30. Wu, T.W.; Lu, Y.X.; Fang, Y.J.; Xin, X.G.; Li, L.; Li, W.P.; Jie, W.H.; Zhang, J.; Liu, Y.M.; Zhang, L.; et al. The Beijing Climate Center Climate System Model (BCC-CSM): The main progress from CMIP5 to CMIP6. Geosci. Model Dev. 2019, 12, 1573–1600. [Google Scholar] [CrossRef]
  31. Jin, H.M.; Qiao, L.; Wang, S.C.; Kong, L.W.; Zhang, J. Performance evaluation of surface air temperature simulated by the Beijing Climate Central Climate Model based on the climate complexity. Clim. Dyn. 2024, 62, 4331–4342. [Google Scholar] [CrossRef]
  32. Rawat, N.; Purohit, S.; Painuly, V.; Negi, G.S.; Bisht, M.P.S. Habitat distribution modeling of endangered medicinal plant Picrorhiza kurroa (Royle ex Benth) under climate change scenarios in Uttarakhand Himalaya, India. Ecol. Inform. 2022, 68, 101550. [Google Scholar] [CrossRef]
  33. Sun, C.; Zuo, J.; Shi, X.; Liu, X.; Liu, H. Diverse Inter-Annual Variations of Winter Siberian High and Link with Eurasian Snow in Observation and BCC-CSM2-MR Coupled Model Simulation. Front. Earth Sci. 2021, 9, 761311. [Google Scholar] [CrossRef]
  34. Zhang, H.; Li, J.; Zou, H.; Wang, Z.; Zhu, X.; Zhang, Y.; Liu, Z. Distribution Pattern of Suitable Areas and Corridor Identification of Endangered Ephedra Species in China. Plants 2024, 13, 890. [Google Scholar] [CrossRef] [PubMed]
  35. Thuiller, W.; Lafourcade, B.; Engler, R.; Araujo, M.B. BIOMOD—A platform for ensemble forecasting of species distributions. Ecography 2009, 32, 369–373. [Google Scholar] [CrossRef]
  36. Rathore, P.; Roy, A.; Karnatak, H. Modelling the vulnerability of Taxus wallichiana to climate change scenarios in South East Asia. Ecol. Indic. 2019, 102, 199–207. [Google Scholar] [CrossRef]
  37. Wu, S.J.; Zhu, T.H.; Qiao, T.M.; Li, S.J.; Shan, H. Prediction of the Potential Distribution of Dendrolimus houi Lajonquiere in Sichuan of China Based on the Species Distribution Model. Appl. Ecol. Environ. Res. 2021, 19, 2227–2240. [Google Scholar] [CrossRef]
  38. Shi, X.; Wang, J.; Zhang, L.; Chen, S.; Zhao, A.; Ning, X.; Fan, G.; Wu, N.; Zhang, L.; Wang, Z. Prediction of the potentially suitable areas of Litsea cubeba in China based on future climate change using the optimized MaxEnt model. Ecol. Indic. 2023, 148, 110093. [Google Scholar] [CrossRef]
  39. Zhang, H.; Sun, P.; Zou, H.; Ji, X.; Wang, Z.; Liu, Z. Adaptive Distribution and Vulnerability Assessment of Endangered Maple Species on the Tibetan Plateau. Forests 2024, 15, 491. [Google Scholar] [CrossRef]
  40. Saranya, K.R.L.; Lakshmi, T.V.; Reddy, C.S. Predicting the potential sites of Chromolaena odorata and Lantana camara in forest landscape of Eastern Ghats using habitat suitability models. Ecol. Inform. 2021, 66, 101455. [Google Scholar] [CrossRef]
  41. Mohapatra, J.; Singh, C.P.; Hamid, M.; Verma, A.; Semwal, S.C.; Gajmer, B.; Khuroo, A.A.; Kumar, A.; Nautiyal, M.C.; Sharma, N.; et al. Modelling Betula utilis distribution in response to climate-warming scenarios in Hindu-Kush Himalaya using random forest. Biodivers. Conserv. 2019, 28, 2295–2317. [Google Scholar] [CrossRef]
  42. Guo, Y.; Wei, H.; Lu, C.; Gao, B.; Gu, W. Predictions of potential geographical distribution and quality of Schisandra sphenanthera under climate change. PeerJ 2016, 4, e2554. [Google Scholar] [CrossRef] [PubMed]
  43. Zhang, H.; Wei, Y.; Yue, J.; Wang, Z.; Zou, H.; Ji, X.; Zhang, S.; Liu, Z. Prediction of Potential Suitable Areas and Priority Protection for Cupressus gigantea on the Tibetan Plateau. Plants 2024, 13, 896. [Google Scholar] [CrossRef] [PubMed]
  44. Webb, G.I.; Ting, K.M. On the application of ROC analysis to predict classification performance under varying class distributions. Mach. Learn. 2005, 58, 25–32. [Google Scholar] [CrossRef]
  45. Kwon, Y.-S.; Bae, M.-J.; Hwang, S.-J.; Kim, S.-H.; Park, Y.-S. Predicting potential impacts of climate change on freshwater fish in Korea. Ecol. Inform. 2015, 29, 156–165. [Google Scholar] [CrossRef]
  46. Mouton, A.M.; De Baets, B.; Goethals, P.L.M. Ecological relevance of performance criteria for species distribution models. Ecol. Model. 2010, 221, 1995–2002. [Google Scholar] [CrossRef]
  47. Zhang, X.; Wei, H.; Zhang, X.; Liu, J.; Zhang, Q.; Gu, W. Non-Pessimistic Predictions of the Distributions and Suitability of Metasequoia glyptostroboides under Climate Change Using a Random Forest Model. Forests 2020, 11, 62. [Google Scholar] [CrossRef]
  48. Ma, J.; Li, J.; Wu, W.; Liu, J. Global forest fragmentation change from 2000 to 2020. Nat. Commun. 2023, 14, 3752. [Google Scholar] [CrossRef]
  49. Bao, R.; Li, X.L.; Zheng, J.H. Feature tuning improves MAXENT predictions of the potential distribution of Pedicularis longiflora Rudolph and its variant. PeerJ 2022, 10, 13337. [Google Scholar] [CrossRef]
  50. Swets, J.A. Measuring the Accuracy of Diagnostic Systems. Science 1988, 240, 1285–1293. [Google Scholar] [CrossRef]
  51. Zhang, P.; Kong, X.; Bakker, E.S.; Xu, J.; Zhang, M. Temperature affects carbon and nitrogen stable isotopic signatures of aquatic plants. Aquat. Sci. 2021, 83, 39. [Google Scholar] [CrossRef]
  52. Wang, C.J.; Liu, C.Z.; Wan, J.Z.; Zhang, Z.X. Climate change may threaten habitat suitability of threatened plant species within Chinese nature reserves. PeerJ 2016, 4, e2091. [Google Scholar] [CrossRef] [PubMed]
  53. Pugnaire, F.I.; Morillo, J.A.; Peñuelas, J.; Reich, P.B.; Bardgett, R.D.; Gaxiola, A.; Wardle, D.A.; van der Putten, W.H. Climate change effects on plant-soil feedbacks and consequences for biodiversity and functioning of terrestrial ecosystems. Sci. Adv. 2019, 5, eaaz1834. [Google Scholar] [CrossRef] [PubMed]
  54. Liu, D.T.; Chen, J.Y.; Sun, W.B. Distributional responses to climate change of two maple species in southern China. Ecol. Evol. 2023, 13, e10490. [Google Scholar] [CrossRef] [PubMed]
  55. Guo, Y.; Zhao, P.; Yue, M. Canopy disturbance and gap partitioning promote the persistence of a pioneer tree population in a near-climax temperate forest of the Qinling Mountains, China. Ecol. Evol. 2019, 9, 7676–7687. [Google Scholar] [CrossRef]
  56. Parmesan, C.; Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 2003, 421, 37–42. [Google Scholar] [CrossRef]
  57. Deng, C.; Ma, X.; Xie, M.; Bai, H. Effect of Altitude and Topography on Vegetation Phenological Changes in the Niubeiliang Nature Reserve of Qinling Mountains, China. Forests 2022, 13, 1229. [Google Scholar] [CrossRef]
  58. Thomas, C.D.; Cameron, A.; Green, R.E.; Bakkenes, M.; Beaumont, L.J.; Collingham, Y.C.; Erasmus, B.F.N.; de Siqueira, M.F.; Grainger, A.; Hannah, L.; et al. Extinction risk from climate change. Nature 2004, 427, 145–148. [Google Scholar] [CrossRef]
  59. Wang, W.J.; Thompson, F.R., III; He, H.S.; Fraser, J.S.; Dijak, W.D.; Jones-Farrand, T. Climate change and tree harvest interact to affect future tree species distribution changes. J. Ecol. 2019, 107, 1901–1917. [Google Scholar] [CrossRef]
  60. Román-Palacios, C.; Wiens, J.J. Recent responses to climate change reveal the drivers of species extinction and survival. Proc. Natl. Acad. Sci. USA 2020, 117, 4211–4217. [Google Scholar] [CrossRef]
  61. Braunisch, V.; Coppes, J.; Arlettaz, R.; Suchant, R.; Zellweger, F.; Bollmann, K. Temperate Mountain Forest Biodiversity under Climate Change: Compensating Negative Effects by Increasing Structural Complexity. PLoS ONE 2014, 9, e97718. [Google Scholar] [CrossRef]
  62. Engler, R.; Randin, C.F.; Thuiller, W.; Dullinger, S.; Zimmermann, N.E.; Araújo, M.B.; Pearman, P.B.; Le Lay, G.; Piedallu, C.; Albert, C.H.; et al. 21st century climate change threatens mountain flora unequally across Europe. Glob. Change Biol. 2011, 17, 2330–2341. [Google Scholar] [CrossRef]
  63. Song, M.H.; Zhou, C.P.; Ouyang, H. Distributions of dominant tree species on the Tibetan Plateau under current and future climate scenarios. Mt. Res. Dev. 2004, 24, 166–173. [Google Scholar] [CrossRef]
  64. He, X.; Burgess, K.S.; Yang, X.-F.; Ahrends, A.; Gao, L.-M.; Li, D.-Z. Upward elevation and northwest range shifts for alpine Meconopsis species in the Himalaya-Hengduan Mountains region. Ecol. Evol. 2019, 9, 4055–4064. [Google Scholar] [CrossRef]
  65. Zhang, F.X.; Yang, L.H.; Wang, C.J.; Zhang, C.H.; Wan, J.Z. Distribution and Conservation of Plants in the Northeastern Qinghai-Tibet Plateau under Climate Change. Diversity 2022, 14, 956. [Google Scholar] [CrossRef]
  66. Ni, J. Impacts of climate change on Chinese ecosystems: Key vulnerable regions and potential thresholds. Reg. Environ. Change 2011, 11, S49–S64. [Google Scholar] [CrossRef]
  67. Takahashi, K.; Okuhara, I. Forecasting the effects of global warming on radial growth of subalpine trees at the upper and lower distribution limits in central Japan. Clim. Change 2013, 117, 273–287. [Google Scholar] [CrossRef]
  68. Wilson, M.C.; Chen, X.Y.; Corlett, R.T.; Didham, R.K.; Ding, P.; Holt, R.D.; Holyoak, M.; Hu, G.; Hughes, A.C.; Jiang, L.; et al. Habitat fragmentation and biodiversity conservation: Key findings and future challenges. Landsc. Ecol. 2016, 31, 219–227. [Google Scholar] [CrossRef]
  69. Bruna, E.M.; Fiske, I.J.; Trager, M.D. Habitat fragmentation and plant populations: Is what we know demographically irrelevant? J. Veg. Sci. 2009, 20, 569–576. [Google Scholar] [CrossRef]
  70. Lemordant, L.; Gentine, P. Vegetation Response to Rising CO2 Impacts Extreme Temperatures. Geophys. Res. Lett. 2019, 46, 1383–1392. [Google Scholar] [CrossRef]
  71. Masuda, K.; Setoguchi, H.; Nagasawa, K.; Ishihara, M.I.; Sawa, K.; Horie, K.; Tsuboi, H.; Fukumoto, S.; Tango, T.; Sakaguchi, S. Rear-edge daylily populations show legacies of habitat fragmentation due to the Holocene climate warming. J. Biogeogr. 2023, 50, 551–563. [Google Scholar] [CrossRef]
  72. Yu, X.D.; Luo, T.H.; Zhou, H.Z. Distribution of carabid beetles among 40-year-old regenerating plantations and 100-year-old naturally regenerated forests in Southwestern China. For. Ecol. Manag. 2008, 255, 2617–2625. [Google Scholar] [CrossRef]
  73. Pálsson, S.; Wasowicz, P.; Heiomarsson, S.; Magnússon, K.P. Population structure and genetic variation of fragmented mountain birch forests in Iceland. J. Hered. 2023, 114, 165–174. [Google Scholar] [CrossRef] [PubMed]
  74. Chhetri, P.K.; Gaddis, K.D.; Cairns, D.M. Predicting the Suitable Habitat of Treeline Species in the Nepalese Himalayas Under Climate Change. Mt. Res. Dev. 2018, 38, 153–163. [Google Scholar] [CrossRef]
  75. Liu, J.J.; Wilson, M.; Hu, G.; Liu, J.L.; Wu, J.G.; Yu, M.J. How does habitat fragmentation affect the biodiversity and ecosystem functioning relationship? Landsc. Ecol. 2018, 33, 341–352. [Google Scholar] [CrossRef]
  76. Rawat, B.; Gairola, S.; Tewari, L.M.; Rawal, R.S. Long-term forest vegetation dynamics in Nanda Devi Biosphere Reserve, Indian west Himalaya: Evidence from repeat studies on compositional patterns. Environ. Monit. Assess. 2021, 193, 459. [Google Scholar] [CrossRef] [PubMed]
  77. Praticò, S.; Solano, F.; Di Fazio, S.; Modica, G. Machine Learning Classification of Mediterranean Forest Habitats in Google Earth Engine Based on Seasonal Sentinel-2 Time-Series and Input Image Composition Optimisation. Remote Sens. 2021, 13, 586. [Google Scholar] [CrossRef]
  78. Zheng, J.P.; Yuan, S.; Wu, W.Z.; Li, W.J.; Yu, L.; Fu, H.H.; Coomes, D. Surveying coconut trees using high-resolution satellite imagery in remote atolls of the Pacific Ocean. Remote Sens. Environ. 2023, 287, 113485. [Google Scholar] [CrossRef]
  79. Zhang, K.L.; Yao, L.J.; Meng, J.S.; Tao, J. Maxent modeling for predicting the potential geographical distribution of two peony species under climate change. Sci. Total Environ. 2018, 634, 1326–1334. [Google Scholar] [CrossRef]
  80. Sandfort, R.; Uhlhorn, B.; Geissler, G.; Lyhne, I.; Jiricka-Pürrer, A. AI will change EA practice—But are we ready for it? A call for discussion based on developments in collecting and processing biodiversity data. Impact Assess. Proj. Apprais. 2024, 42, 200–208. [Google Scholar] [CrossRef]
Forests 16 00184 g001
Figure 1. The contribution rate of environmental variables in the RF model.
Figure 1. The contribution rate of environmental variables in the RF model.
Forests 16 00184 g001
Forests 16 00184 g002
Figure 2. Potential suitable habitat of B. albosinensis under the current climate in China.
Figure 2. Potential suitable habitat of B. albosinensis under the current climate in China.
Forests 16 00184 g002
Forests 16 00184 g003
Figure 3. Distribution of B. albosinensis forests under the SSP126 (a,d), SSP370 (b,e), and SSP585 (c,f) scenarios in the 2050s (a–c) and 2090s (d–f) in China.
Figure 3. Distribution of B. albosinensis forests under the SSP126 (a,d), SSP370 (b,e), and SSP585 (c,f) scenarios in the 2050s (a–c) and 2090s (d–f) in China.
Forests 16 00184 g003
Forests 16 00184 g004
Figure 4. Shrinkage and expansion of B. albosinensis’s suitable habitat in the 2050s (a) and 2090s (b).
Figure 4. Shrinkage and expansion of B. albosinensis’s suitable habitat in the 2050s (a) and 2090s (b).
Forests 16 00184 g004
Forests 16 00184 g005
Figure 5. Spatial distribution of fragmentation in highly suitable habitats of B. albosinensis under current climate in China.
Figure 5. Spatial distribution of fragmentation in highly suitable habitats of B. albosinensis under current climate in China.
Forests 16 00184 g005
Forests 16 00184 g006
Figure 6. Spatial distribution of fragmentation in highly suitable habitat of B. albosinensis under the SSP126 (a,d), SSP370 (b,e), and SSP585 (c,f) scenarios in the 2050s (a–c) and 2090s (d–f) in China.
Figure 6. Spatial distribution of fragmentation in highly suitable habitat of B. albosinensis under the SSP126 (a,d), SSP370 (b,e), and SSP585 (c,f) scenarios in the 2050s (a–c) and 2090s (d–f) in China.
Forests 16 00184 g006
Forests 16 00184 g007
Figure 7. Percentage of fragmentation at each level under different scenarios.
Figure 7. Percentage of fragmentation at each level under different scenarios.
Forests 16 00184 g007
Table 1. Environmental factors involved in modeling species.
Table 1. Environmental factors involved in modeling species.
CategoryAbbreviationsEnvironmental Variables
TemperatureBio1Annual mean temperature
Bio2Mean temperature, diurnal range
Bio3Isothermality
Bio4Temperature seasonality
PrecipitationBio12Annual precipitation
Bio15Precipitation seasonality
SoilT_pH_H2OTopsoil pH
T_CLAYPercentage of the clay in the topsoil
T_REF_BULKTopsoil reference bulk density
T_pH_H2OTopsoil pH
TerrainALTElevation
SLOSlope
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Zhang, H.; Zhou, Y.; Ji, X.; Wang, Z.; Liu, Z. Climate Change Drives the Adaptive Distribution and Habitat Fragmentation of Betula albosinensis Forests in China. Forests 2025, 16, 184. https://doi.org/10.3390/f16010184

AMA Style

Zhang H, Zhou Y, Ji X, Wang Z, Liu Z. Climate Change Drives the Adaptive Distribution and Habitat Fragmentation of Betula albosinensis Forests in China. Forests. 2025; 16(1):184. https://doi.org/10.3390/f16010184

Chicago/Turabian Style

Zhang, Huayong, Yue Zhou, Xiande Ji, Zhongyu Wang, and Zhao Liu. 2025. "Climate Change Drives the Adaptive Distribution and Habitat Fragmentation of Betula albosinensis Forests in China" Forests 16, no. 1: 184. https://doi.org/10.3390/f16010184

APA Style

Zhang, H., Zhou, Y., Ji, X., Wang, Z., & Liu, Z. (2025). Climate Change Drives the Adaptive Distribution and Habitat Fragmentation of Betula albosinensis Forests in China. Forests, 16(1), 184. https://doi.org/10.3390/f16010184

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop