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Article

Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau

by
Jinkai Gu
1,2,3,
Qiang He
4,
Qingwan Li
1,2,3,
Qinglin Li
5,
Shengjian Xiang
1,2,3,
Wanchi Li
1,2,3,
Aohang Jin
6,
Shunbin Wang
1,
Feipeng Liu
7,* and
Guoyong Tang
1,3,*
1
Institute of Plateau Forestry, Chinese Academy of Forestry Sciences, Kunming 650233, China
2
School of Landscape Architecture, Nanjing Forestry University, Nanjing 210037, China
3
Yuanmou Desert Ecosystem Research Station, National Long-Term Scientific Research Base of Comprehensive Control, Chuxiong 675000, China
4
Institute of Xizang Plateau Ecology, Xizang Agricultural and Animal Husbandry University, Linzhi 860000, China
5
International Rivers and Ecological Security Research Institute, Yunnan University, Kunming 650504, China
6
Resources and Environment College, Xizang Agriculture and Animal Husbandry University, Linzhi 860000, China
7
Southwest Survey and Planning Institute of National Forestry and Grassland Administration, Kunming 650031, China
*
Authors to whom correspondence should be addressed.
Forests 2024, 15(11), 2022; https://doi.org/10.3390/f15112022
Submission received: 9 October 2024 / Revised: 15 November 2024 / Accepted: 15 November 2024 / Published: 16 November 2024
(This article belongs to the Section Forest Ecology and Management)
Browse Figures
Versions Notes

Abstract

:
Global climate change exerts great effects on plant distributions. However, the response of Prunus mira Koehne, one of the most important species for ecological protection in the southeast of the Qinghai–Tibet Plateau, to climate change remains unclear. To explore the ecological factors affecting the distribution of P. mira in the context of global climate change, the MaxENT model is used to predict suitable habitats for P. mira. Our study indicated that the distribution of Prunus mira Koehn is primarily influenced by temperature rather than precipitation, and warming can facilitate the growth of P. mira. When the temperature seasonality (bio4) ranges from 134 to 576 and the mean temperature of the coldest quarter (bio11) ranges from −2.6 °C to 2.7 °C, it is most conducive to the growth of P. mira. Among the four climate scenarios, the optimal habitat for P. mira is predominantly concentrated in river valley areas and is expected to expand into higher altitude regions, particularly in the north and southeast. SSP245 and SSP370 climate pathways are conducive to the growth and spatial expansion of P. mira. Our findings highlight the significant impact of temperature not precipitation on the distribution of P. mira, and this insight is crucial for the stability and conservation of this ecologically significant plant species.

1. Introduction

Since the 20th century, global climate has undergone significant changes, with the United Nations Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report confirming that climate warming is an undeniable fact [1]. This global warming, alongside shifting precipitation patterns, has led to an increase in extreme weather events [2], impacting not only plant growth and geographic distribution [3,4] but also causing the disappearance of specific phytoclimate zones [5], posing major challenges to ecosystem structure and biodiversity. Changes in temperature and precipitation due to climate change will affect species’ environmental tolerance. When these changes surpass a species tolerance threshold, they must either adapt or shift their distribution to find suitable habitats [6]. For species with limited ranges, habitat loss due to future climate change presents a serious threat, heightening their risk of extinction [7]. Understanding the change in species distribution under global climate change is therefore crucial for their conservation.
Prunus mira Koehn is a plant species of the Rosaceae family, native to the Qinghai–Xizang Plateau and also found in the provinces of Sichuan and Yunnan [8]. Compared to other peach varieties, P. mira is distinguished by its smooth pit [9]. As one of the most ancient and widely distributed endemic tree species on the Xizangan plateau, P. mira demonstrates remarkable resilience to cold, drought, barren conditions, and diseases, with a potential lifespan spanning thousands of years [9,10]. It is called the “Living Fossil Group” of peach resources in the world [11]. Prunus mira is of great value for ornamental, culinary, and medicinal purposes [11,12,13]. In recent years, the natural habitat of P. mira has been shrinking due to environmental changes, human activities, and socio-economic development. In traditional Xizangan culture, the fruit is commonly used as livestock feed, making it difficult to preserve for propagation, which has led to the aging of the species [12,13,14]. The rapid decline in species populations has made the protection of P. mira increasingly urgent. Current research on P. mira has primarily emphasized the physiological and genetic aspects [14,15], leaving the suitable distribution area and environmental driving variables under the context of climate change largely unexplored. Understanding the impact of climate change on the suitable habitats of P. mira is crucial for unraveling the mechanisms behind its distribution and for providing a foundation for its sustainable development, utilization, and conservation.
Species Distribution Modeling (SDM) predicts potential species distribution based on the relationship between the ecological environment and climate factors [16]. At present, the most commonly used models for predicting species distributions include the Maximum Entropy Model (MaxEnt) [17], Ecological Niche Factor Analysis Model (ENFA) [18], Genetic Algorithmic Model (GARP) [19], Bioclimate Analysis and Prediction System (BIOCLIM) [20], and Domain Models (DOMAIN) [21]. Among these, the MaxEnt model is widely used due to it is ease of operation and reliable predictions with limited sample data [22,23]. In recent years, the MaxEnt model has been widely used in studies of invasive species, conservation of flora and fauna, disease transmission, and species response to climate change [24,25,26,27,28].
We used the MaxENT model to predict the suitable habitable area of P. mira in Nyingchi in different time periods. The main objectives of our study are as follows: (1) to assess the current spatial distribution pattern of P. mira and identify the key environmental variables that shape its distribution; (2) to forecast the future distribution of P. mira and its response to environmental factors like temperature and elevation under different climate scenarios; and (3) to analyze the expansion patterns of P. mira under the background of climate change. This study aims to provide a scientific reference value for the development, utilization, and protection of P. mira under future climate change.

2. Methods and Materials

2.1. Study Area

Nyingchi municipality is located in the southeast of the Qinghai–Tibet Plateau, in the middle and lower reaches of the Yarlung Zangbo River (Figure 1). Geographically, it extends from 92°09′ E to 98°18′ E in longitude and from 27°33′ N to 30°40′ N in latitude. The terrain inclines from northwest to southeast, with an average elevation of 3000 m; the highest point is 7782 m, and the lowest point is 152 m [29]. It is the largest vertical drop area in the world. Nyingchi is situated in a marine monsoon climate zone, with an annual average temperature of 6–17 °C; the coldest average temperature is 0.2 °C, and the hottest average temperature is 20 °C. The Yarlung Zangbo River and Niyang River run through the city, with an average annual rainfall of about 750 mm [30,31]. It is known as “the style Jiangnan”. The optimal growth conditions for P. mira include an average annual temperature of approximately 6–14 °C, with the coldest month (January) averaging −2.7 °C, and the warmest month (July) reaching 18–19 °C. The absolute extreme low temperature can fall below −12 °C, while the absolute extreme high temperature is around 31 °C. The frost-free period lasts 160–180 days, with 1900–2100 h of sunshine annually. Annual precipitation ranges from 500 to 700 mm, and evaporation is approximately 1500–1600 mm per year. The habitat is classified as semi-arid to semi-humid, with certain xerophytic characteristics, typical of alpine shrub communities. The unique climate conditions of Nyingchi make it an ideal region for studying P. mira regia. Furthermore, Nyingchi’s rich biodiversity and relatively undisturbed natural environment make it an ideal area for studying the distribution patterns of P. mira.

2.2. Data Collection and Processing

The boundary of Nyingchi was downloaded from the Department of Natural Resources of the Xizang Autonomous Region (http://zrzyt.xizang.gov.cn/fw/zyxz/ (visited on 22 October 2023)). The natural distribution point data of P. mira were received from the Global Biodiversity Information Network (GBIF: http://www.gbif.org (visited on 2 December 2023)), the Chinese Virtual Herbarium (CVH: http://www.cvh.ac.cn (visited on 2 December 2023)), and field survey data. Data without an exact location in the GBIF were first removed, and then latitude and longitude were acquired based on the geographic location of the species. Next, invalid data and duplicate data were removed using ENMTools. We set up a 1 km × 1 km (30 s) grid buffer, and one geographic point was kept for every grid. The data of 35 effective distribution points were finally obtained and convert distribution point data to CSV format.
According to the growth habit of P. mira [32], this study used 43 environmental variables (Table 1), including climatic, soil factors, terrain, vegetation, and river factors. The current climate factors were acquired from the WorldClim 2.1 version (https://www.worldclim.org/ (visited on 23 December 2023)). Current climate factors are 19 average bioclimatic variables from 1970 to 2000. The future climate factors were acquired from the WorldClim 2.1 version (https://www.worldclim.org/ (visited on 23 December 2023)), which in every scenario included 19 average bioclimatic factors. We used the future climate prediction date from CMIP6 (Coupled Model Intercomparison Project Phase 6) to predict the future distribution of P. mira. Future climate data are bioclimatic variables under different carbon emission scenarios, and shared socio-economic pathways (SSPs) reflect future climate change, including sustainable development path limited to 2 °C (SSP126), moderate development path limited to 3 °C (SSP245), local development path limited to 4.1 °C (SSP370), and conventional development path limited to 5 °C (SSP585). So as to avoid error and uncertainty, we selected four bioclimatic variables (SSP126, SSP245, SSP370, SSP585) under the future climate scenario during 2021–2100. Terrain factors were acquired from the WorldClim 2.1 version (https://www.worldclim.org/ (visited on 23 December 2023)). Soil factors were acquired from the Soil Database (https://www.fao.org/soils-portal/data-hub/soil-maps-and-databases/harmonized-world-soil-database-v20/en/ (visited on 25 December 2023)). NDVI and vegetation type factors were acquired from the Resource and Environment Science and Data Center (http://www.resdc.cn (visited on 13 December 2023)). The dryness index was downloaded from the National Data Centre for the Xizangan Plateau (https://data.tpdc.ac.cn/home (visited on 13 December 2023)). The spatial resolution of each of the 43 environmental variables is 30 s.

2.3. Selecting of Environmental Variables

Strong correlations between environmental variables can affect the accuracy of species potential distribution and MaxEnt results. The species distribution data and 43 environmental factors used in this study were first imported into the MaxEnt model for simulation; the contribution rate was calculated using the jackknife method in MaxEnt software and screened for environmental factors with percentage contributions ≥ 1% [33]. Next, we calculated the correlation between environmental variables by using SPSS 27.0 software (Figure 2). If the correlation coefficients of both variables were greater than 0.8, only the environmental variables that had a significant impact on the distribution of P. mira were retained [34]. Finally, we selected 14 mutual independence environmental factors to construct a predictive model of the suitable distribution of P. mira. (Table 2).

2.4. Model Parameter Optimization and Establishment

The default parameters of the model run will also lead to over-fitting of the model and affect the authenticity of the prediction results. The accuracy and goodness of fit of MaxEnt are closely related to the regulatory coefficient (regularization multiplier, RM) and the feature combinations (FCs), which are Linear (L), Quadratic (Q), Product (P), Hinge (H), and Threshold (T). By using the kuenm data package of R language to optimize the MaxEnt model, the detailed calibration, parameter selection, model evaluation, and final model establishment of the MaxEnt model were realized. In the optimization process, the RM value was set to be 0.1–4.0, the initial regulation and control frequency was 0.1, the regulation and control frequency was increased by 0.1 in each modeling, and 40 regulation and control frequencies were set in total. At the same time, 29 feature combinations FC of five features, including Linear (L), Quadratic (Q), Product (P), Hinge (H), and Threshold (T), were selected for testing. These specifically included L, Q, P, T, H, LQ, LP, LT, LH, QP, QT, QH, PT, PH, TH, LQP, LQT, LQH, LPT, LPH, QPT, QPH, QTH, PTH, LQPT, LQPH, LQTH, LPTH, and LQPTH. Finally, the kuenm packet was used to test the above 1160 candidate models, and the complexity of the model was determined according to the 5% training omission rate and Delta AICc value of each model. Finally, the model with Delta AICc = 0 was selected as the optimal model [35]. The default parameter RM-3.5 was obtained through optimization, and the delta AICc was equal to 0 when FC-QPT was used. At this time, the model was the optimal model.
Distribution data (CSV format) and environmental data (ASC format) were imported into the MaxEnt model, and the jackknife method was used to determine and construct the response curve. In the MaxEnt model, 75% of the distribution points were used as the training set of the model, and the remaining 25% of the distribution points were used as the test set for validation. To improve the reliability of the results, 10 replicates were performed in this study. In this study, the contribution of each environmental variable was evaluated using the jackknife test. The accuracy of the results was assessed according to the area under the curve (AUC) values of the working characteristics (ROC) of the subjects. AUC values ranged from 0 to 1, and the larger the AUC value, the better the prediction of the MaxEnt model. An AUC value greater than 0.9 indicates perfect prediction [36].

2.5. Classification of Suitable Habitats

The ASC file of the model prediction results was imported into ArcGIS10.8 software, and the suitable areas of P. mira in different periods were visualized. Based on the natural discontinuous classification method and Reclassify tool of ArcGIS10.8 software and the actual distribution of P. mira, the suitable areas were divided into four criteria: Not suitable (p ≤ 0.2), minimally suitable (0.2 < p ≤ 0.4), moderately suitable (0.4 < p ≤ 0.6), and highly suitable (0.6 < p) [36,37]. The area of each suitable area was calculated.

2.6. Analysis of the Dominant Environmental Factors

The importance value of environmental variables was determined using the jackknife method, which could quantitatively analyze the impact of environmental factors on the distribution of P. mira, so as to screen out the dominant environmental factors. In the jackknife method, the higher value of the regularization training gain for “only including this variable” indicates that this environmental factor has a greater impact on species distribution. In this study, the contribution rate and jackknife method were used to evaluate the environmental variables affecting species distribution, and the importance of environmental variables was analyzed. According to the classification standard of suitable area, when the response curve of environmental variables was greater than 0. 6, the range of ecological variables was most suitable for species growth.

2.7. Change in Suitable Habitat Under Different Climatic Conditions

After defining the suitable and unsuitable habitats of P. mira, the spatial pattern of suitable areas under various climate scenarios in the future was analyzed by using the grid processing tool in ArcGIS (10.8). The future changes in suitable habitats were calculated based on current conditions. Under different climate paths in the future, a change from a suitable area to an unsuitable area is considered as a contraction area, a change from an unsuitable area to a suitable area is regarded as an expansion area, and the other changes are regarded as a stability area.

3. Results

3.1. Model Accuracy Evaluation

Through the optimization of parameters, the model accuracy was improved, making it more suitable for modeling across different time periods. The AUC value of P. mira was obtained by simulation training under the parameters of the optimal model. The ROC curve validation results for P. mira showed that the average AUC values from 10 replicates were 0.992, 0.992, 0.991, 0.991, and 0.992 (Table 3), all of which were greater than 0.990 and close to 1.0, indicating that the model had a high prediction accuracy.

3.2. Current Distribution Pattern of P. mira and Key Environment Variables

According to the classification of suitable habitats from the results (Figure 3), the highly suitable area of P. mira was mainly situated in the central and northern parts of Nyingchi, namely, along the river valley. The not suitable area accounted for 62.70% of the total area of Nyingchi, the minimally suitable area accounted for 14.13% of the total area, the moderately suitable area accounted for 12.31%, the highly suitable area for accounted 10.86%, and the area of suitable habits accounted for 37.30% of Nyingchi (Table 4).
The result from the analysis of factor contribution in the MaxEnt model (Table 5) showed that the most critical factor affecting the distribution of P. mira was temperature seasonality (bio4, 30.1%), followed by mean temperature of the coldest quarter (bio11, 25.6%), isothermality (bio3, 12.6%), soil unit symbol (su_sym90, 8.8%), and elevation (ele, 8.2%). The cumulative contribution of these five environmental factors reached 85.3%, with climate variables being the most significant influencing factors and all related to temperature (68.3%), followed by the soil variables (8.8%) and the topography variables (8.2%).
According to the results of regularized training gain with only variable (Figure 4), temperature seasonality (bio4) had the largest training gain, followed by the mean temperature of the coldest quarter (bio11), isothermality (bio3), and elevation (ele), indicating that the temperature seasonality (bio4) was the key variable influencing the geographic distribution of the P. mira. Through the contribution rates of environmental factors and regularized training gain, it is evident that temperature is the primary factor affecting the distribution of P. mira.
Based on the environmental variable response curves (Figure 5), it can be concluded that the suitability of P. mira was negatively associated with temperature seasonality; the substantial fluctuations in temperature throughout the seasons are not favorable for its growth. The highest degree of suitability occurred when the temperature seasonality (bio4) fell within the range of 134–576. The optimal average temperature range for the mean temperature of the coldest quarter (bio11) to P. mira growth was −2.6 °C to 2.7 °C, and the optimum value was 0 °C. The optimal range for the isothermality (bio3) to P. mira growth was 43–57. The elevation (ele) of 2783–4089 m was the most suitable for the growth of P. mira.

3.3. Potential Distribution Areas for P. mira Under Future Climate Conditions

Under the four time periods of the SSP126 pathway, the not suitable area of P. mira was mainly distributed in the southern region of Nyingchi and the area was the largest, followed by the minimally suitable, the moderately suitable, and the highly suitable areas. The highly suitable area would be concentrated in the Yarlung Zangbo Valley, the Niyang Valley, and the southeast region; the minimally and moderately suitable areas would be concentrated in the central and eastern regions (Figure 6). From 2021 to 2100, the unsuitable habitat of P. mira will continue to expand, the area of minimally and highly suitable habitats will continue to decrease, and the area of moderately suitable habitat will first decrease and then increase (Table 6). Compared with the current distribution range (Table 4), the areas of not suitable will be reduced, minimally suitable and moderately suitable areas will be expanded, and the areas of highly suitable will begin to shrink from 2061; the suitable area will reach the minimum in 2081–2100, according for 9.54%. From 2021 to 2100, the suitable area will continue to decrease.
Under the SSP245 pathway, the suitable growth areas of P. mira are mainly concentrated in most areas except the southern part. The proportion of suitable area was followed by the not suitable area, minimally suitable area, moderately suitable area, and highly suitable area (Table 6). From 2021 to 2100, the suitable area decreased first and then increased. Except for the not suitable and the highly suitable areas, the minimally suitable and the moderately suitable areas showed an overall increasing trend; from 2081 to 2100, the area of minimally and moderately suitable areas reached the maximum. Compared with the current distribution range (Table 4), the not suitable areas will be reduced, minimally and moderately suitability will be expanded, and the areas of high suitability will be reduced from 2041 to 2080.
Under the SSP370 pathway, the highly suitable habitat area of P. mira decreased from 2021 to 2080 and increased from 2081 to 2100. The not suitable habitat area reached the maximum from 2061 to 2080, the minimally suitable area decreased, and the moderately suitable habitat area and the highly suitable habitat area decreased first and then increased (Table 6). Compared with the current distribution range (Table 4), the area of the not suitable area will be reduced, and the area of the minimally suitable, moderately suitable, and highly suitable areas will be enlarged.
Under the SSP585 pathway, the not suitable habitat area still accounted for the largest proportion, with the area decreasing and then increasing from 2021 to 2100. The area of moderately suitable habitat reached its minimum during 2081–2100, while suitable habitat area peaked during 2041–2060. Among the four different future climate pathways, the total suitable habitat area for P. mira is maximized under SSP370 conditions, followed by SSP245 conditions. Although the suitable habitat area shows an initial increase followed by a decrease across the four climate pathways, the overall suitable habitat area is expected to increase compared to the current distribution, while the unsuitable habitat area is projected to decrease. In summary, the suitable area of P. mira is mainly concentrated within the river valley, and moderate warming conditions under SSP245 and SSP370 are the most favorable for the growth of P. mira.

3.4. Future Spatial Pattern of P. mira

According to Figure 7 and Table 7, the suitable habitats of P. mira under the four future climate scenarios are in a trend of expansion, and the areas of expansion of P. mira are significantly larger than the areas of contraction. The contraction area is mainly concentrated in the south-central part, and the expansion area is primarily concentrated at high elevations in the north and southeast. Under the SSP126 climate condition, the expansion area is larger than the contraction area. In this climate condition, the P. mira expansion is the smallest in 2081–2100, accounting for 5.71% of the area of Nyingchi, and the expansion area is slightly larger than the contraction area: this indicates a weak expansion towards Nyingchi. Under the 2021–2040 SSP245 and 2021–2040 SSP370 climate scenarios, the P. mira of expansion is the largest, accounting for 13.43% and 12.26% of the Nyingchi area, respectively. Under the 2081–2100 SSP245 and 2021–2040 SSP370 climate scenarios, the contraction area of P. mira is the smallest, accounting for 1.03% and 0.94% of the Nyingchi area, respectively. Under the 2061–2080 SSP370 and 2081–2100 SSP585 climate scenarios, the contraction area of P. mira is the largest, accounting for 5.77% and 5.19% of the Nyingchi area, respectively. On the whole, the SSP245 and SSP370 climate scenarios are more suitable for the expansion of P. mira.

4. Discussion

The MaxENT model has been widely utilized in species distribution modeling due to its unique advantages. However, the model is sensitive to sampling bias, and its default parameter settings can lead to overfitting, negatively impacting the final predictions [38,39]. By employing the ENMeval data package to optimize the combination of various parameters, the prediction accuracy of the model can be significantly enhanced, allowing for a more accurate reflection of the environmental factors influencing species distribution [35]. To address the issue of multicollinearity among environmental variables, correlation analysis combined with the jackknife method is employed to eliminate factors with minimal influence. Additionally, to mitigate overfitting, the model regularization frequency and characteristic parameters are optimized. Following these adjustments, the correlations among environmental factors are low, and the average AUC value of the final model across all periods exceeds 0.99, demonstrating high accuracy and precision in predicting habitat suitability. This study effectively predicts the suitable habitat for P. mira across different time periods.
The current distribution area of P. mira is mainly concentrated in the central and northern parts of Nyingchi, along the Yarlung Zangbo River and Niyang River basins. This result is consistent with previous investigations of P. mira [27,40]. Through the application of the jackknife method and the regularization training gain, it was determined that the main influencing factors are temperature seasonality (bio4), mean temperature of coldest quarter (bio11), isothermality (bio3), and elevation (ele). The first three variables are all related to temperature, indicating that temperature significantly impacts the distribution of P. mira, particularly temperature seasonality (bio4), which has a contribution rate of 30.1%. During the growth period, P. mira is not well-suited to areas with excessively large seasonal temperature fluctuations. The optimal range for temperature seasonality (bio4) is between 134 and 576, suggesting that seasonal temperature changes should remain moderate for optimal growth. Additionally, when the mean temperature of the coldest quarter (bio11) is between −2.6 °C and 2.7 °C, isothermality (bio3) ranges from 43 to 57, and elevation is between 2783 and 4089 m, these conditions are also suitable for the growth of P. mira. Based on the growth characteristics of P. mira [22], the suitable altitude and climate of Nyingchi provide unique growth conditions that favor its growth [29,30,31]. Thus, it can be concluded that the Nyingch area is conducive to the growth of P. mira under the current climate conditions.
Over the past 60 years, the issue of warming in the Qinghai–Xizang Plateau has become increasingly prominent, with temperatures showing a continuous upward trend [41]. In this case, the frequency of extreme weather has increased, climate instability has intensified, and the species composition and community structure of the plateau have changed; this can have an impact on the reorganization and replacement of vegetation communities, but the diversity of regional vegetation does not necessarily decrease [42]. Previous studies have shown that global warming may actually lead to an increased species diversity [43]. In this study, it was found that the suitable area for P. mira will expand under four future climate scenarios compared to the current suitable area. Specifically, under the 2021–2040 SSP370 climate pathway, the suitable habitat area of P. mira is projected to its maximum, accounting for 49.70% of the total area. On the whole, moderate warming is most conducive to the growth of P. mira, while high warming conditions are unsuitable for its growth. Increased seasonal temperature differences due to climate warming in the future may further limit its growth and development.
Compared with the current situation, the suitable growth area for P. mira is expected to expand under future climate warming scenarios, with the area of expansion exceeding that of contraction. Thus, the distribution of P. mira will be significantly influenced by climate change. Under low levels of climate warming, the expansion of P. mira continues to decline, whereas moderate warming conditions are most favorable for its expansion. From a spatial perspective, the primary areas of expansion are the high-altitude regions in the north and south of Nyingchi, which aligns with the observed trend of temperate tree species migrating to higher altitudes in response to climate warming [44]. The Nyingchi region is influenced by the Indian Ocean monsoon and experiences moisture that advances northward along the Yarlung Zangbo River Gorge, resulting in abundant precipitation. Moreover, the region’s numerous rivers and substantial meltwater from snow and ice provide ample water for plant growth. Therefore, water is not a limiting factor for the growth of P. mira in Nyingchi.
In this study, the contribution rate of soil factors reached 10.4%, but their impact on regularization training was not substantial. This may be attributed to the complex terrain and diverse soil types in the Nyingchi area, which likely means that soil requirements for P. mira growth are not as critical. Nonetheless, the relationship between soil factors and their distribution warrants further investigation. Additionally, while this study considered climate, topography, soil, river, and vegetation factors, it did not account for certain factors such as human activities. The operation of the MaxENT model is limited by the data on species existence, and the acquisition of some data in the Nyingchi area is limited due to its unique regional characteristics. In future studies, we should aim to gather more comprehensive and timely data, incorporating more factors and geographical distribution data into model prediction to more accurately forecast the suitable areas for P. mira. Furthermore, integrating considerations of species dispersal ability, land use change, interspecies competition, and human impacts, along with expanding the spatial coverage of data and employing more advanced modeling techniques, will enhance predictive reliability. This approach will help deepen our understanding of the effects of climate change on species distribution and provide new insights for the scientific protection and rational utilization of P. mira.
To protect P. mira in the future, it is essential to proactively survey and safeguard suitable high-altitude habitats based on the predicted expansion trends. Restoration efforts should focus on these areas to facilitate migration under climate warming. Additionally, core habitats, particularly in the Yarlung Zangbo and Niyang River basins in Nyingchi, must be protected from human development and land use changes. Further research on the impacts of climate change, including soil and human activities, is necessary to improve predictive accuracy and enhance the effectiveness of conservation measures. These strategies will ensure the long-term survival and expansion of P. mira under future climate conditions.

5. Conclusions

The results of this study showed that (1) the AUC values of the MaxENT model were greater than 0.99, which accurately predicted the geographical distribution of P. mira; (2) the main environmental factors affecting the potential distribution of P. mira is temperature, not precipitation; (3) under the current and future climate pathways, the suitable area of P. mira was mainly concentrated within the river valley. Under the four different climate pathways in the future, the suitable area of P. mira is increasing, and the SSP245 and SSP370 climate pathways are favorable to the growth and expansion of P. mira; and (4) the case of climate warming in the future is favorable to the spatial expansion of P. mira, particularly at high elevations in the north and southeast. Understanding the future distribution and expansion trends of P. mira under the context of global warming is crucial for ecological sustainability, as well as for the conservation and stability of the species.

Author Contributions

Conceptualization, J.G. and Q.H.; methodology, J.G. and A.J.; software, J.G.; validation, Q.L. (Qingwan Li), Q.L. (Qinlin Li), and J.G.; formal analysis, G.T.; investigation, J.G., S.W., and A.J.; resources, S.W., S.X. and W.L.; data curation, A.J.; writing—original draft preparation, J.G.; writing—review and editing, G.T.; visualization, J.G.; supervision, Q.L. and S.X.; project administration, G.T. and F.L.; funding acquisition, G.T. All authors have read and agreed to the published version of this manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant number 32471714.

Data Availability Statement

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

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Distribution records of P. mira in Nyingchi.
Figure 1. Distribution records of P. mira in Nyingchi.
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Figure 2. Correlation heat map of environment factors.
Figure 2. Correlation heat map of environment factors.
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Figure 3. Current suitable distribution of P. mira.
Figure 3. Current suitable distribution of P. mira.
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Figure 4. Regularized training gain with only variable.
Figure 4. Regularized training gain with only variable.
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Figure 5. Response curves of the main influencing factors of P. mira.
Figure 5. Response curves of the main influencing factors of P. mira.
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Figure 6. Distribution of suitable habits under the 4 climate pathways.
Figure 6. Distribution of suitable habits under the 4 climate pathways.
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Figure 7. Change in the spatial pattern under the 4 climate pathways.
Figure 7. Change in the spatial pattern under the 4 climate pathways.
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Table 1. Environment factors for the model.
Table 1. Environment factors for the model.
CategoryVariableDescriptionUnitCategoryVariableDescriptionUnit
Climate factorsbio1Annual Mean Temperature°C t-CaSO4Topsoil Gypsum%weight
bio2Mean Diurnal Range°C t-cec-clayTopsoil CEC (clay)cmol/kg
bio3Isothermality% t-cec-soilTopsoil CEC (soil)cmol/kg
bio4Temperature Seasonality/ t-clayTopsoil Clay Fraction%wt
bio5Max Temperature of Warmest Month°C t-espTopsoil Sodicity (ESP)%
bio6Min Temperature of Coldest Month°C t-gravelTopsoil Gravel Content%vol
bio7Temperature Annual Range°C t-sandTopsoil Sand Fraction%wt
bio8Mean Temperature of Wettest Quarter°C t-siltTopsoil Silt Fraction%wt
bio9Mean Temperature of Driest Quarter°C t-tebTopsoil TEBcmol/kg
bio10Mean Temperature of Warmest Quarter°C t-usda-tex-clayTopsoil USDA Texture Classification/
bio11Mean Temperature of Coldest Quarter°C t-bsTopsoil Base Saturation%
bio12Annual Precipitationmm t-ref-bulkTopsoil Reference Bulk Densitykg/dm3
bio13Precipitation of Wettest Monthmm t-eceTopsoil Salinity (Elco)dS/m
bio14Precipitation of Driest Monthmm t-ocTopsoil Organic Carbon%wt
bio15Precipitation Seasonality/ t-ph-H2OTopsoil pH (H2O)_log(H+)
bio16Precipitation of Wettest QuartermmTopography factorsEleElevationm
bio17Precipitation of Driest Quartermm AspAspect°
bio18Precipitation of Warmest Quartermm SloSlop°
bio19Precipitation of Coldest QuartermmVegetation factorsNDVINormalized Difference Vegetation Index/
D-indexDryness Index/ V_typeVegetation Type/
Soil factorsSu-sym90Soil Unit Symbol/River factorsD_riverDistance to Riverkm
t-CaCO3Topsoil Calcium Carbonate%weight
Table 2. Select environmental factors for the model.
Table 2. Select environmental factors for the model.
CategoryVariableDescriptionUnit
Climatebio3Isothermality%
bio4Temperature Seasonality/
bio11Mean Temperature of Coldest Quarter°C
bio12Annual Precipitationmm
D-indexDryness Index/
TopographyEleElevationm
SloSlope°
AspAspect°
SoilSu-sym90Soil Unit Symbol/
T-bsTopsoil Base Saturation%
T_siltTopsoil Silt Fraction%wt
VegetationV-typeVegetation Type/
NDVINormalized Difference Vegetation Index/
RiverD-riverDistance to Riverkm
Table 3. The AUC values of the MaxEnt model for P. mira under different climatic backgrounds.
Table 3. The AUC values of the MaxEnt model for P. mira under different climatic backgrounds.
PeriodCurrentSSP126SSP245SSP370SSP585
Average AUC Values0.9920.9920.9910.9910.992
Table 4. Area and proportion of the current suitable distribution for P. mira.
Table 4. Area and proportion of the current suitable distribution for P. mira.
Suitable LevelArea/km2Proportion
Minimally suitable16,206.6114.13%
Moderately suitable14,118.9812.31%
Highly suitable12,450.8410.86%
Suitable42,776.4337.30%
Not suitable71,916.8262.70%
Table 5. Selected environmental factors and their rate of contribution importance.
Table 5. Selected environmental factors and their rate of contribution importance.
CategoryVariableDescriptionUnitPercent Contribution/%
Climatebio3Isothermality%12.6
bio4Temperature Seasonality/30.1
bio11Mean Temperature of Coldest Quarter°C25.6
bio12Annual Precipitationmm2.4
d-indexDryness Index/0
TopographyeleElevationm8.2
sloSlope°3.3
aspAspect°1.7
Soilsu-sym90Soil Unit Symbol/10.4
t-bsTopsoil Base Saturation%2.2
t-siltTopsoil Silt Fraction%wt0.1
Vegetationv-typeType/0.4
ndviNormalized Difference Vegetation Index/1.2
Riverd-riverDistance to Riverkm1.8
Table 6. Area and proportion of future suitable distributions for P. mira.
Table 6. Area and proportion of future suitable distributions for P. mira.
ClimateYearNot SuitableMinimally SuitableModerately SuitableHighly SuitableSuitable
Area/km2Proportion/%Area/km2Proportion/%Area/km2Proportion/%Area/km2Proportion/%Area/km2Proportion/%
SSP1262021–204060,355.87 52.62%21,459.00 18.71%17,619.33 15.48%15,259.05 13.30%54,337.3847.38%
2041–206063,726.64 55.56%20,349.64 17.74%15,434.33 13.56%15,182.64 13.24%50,966.6144.44%
2061–208067,417.24 58.78%19,799.08 17.26%15,920.47 13.99%11,556.46 10.08%47,276.0141.22%
2081–210070,616.46 61.57%16,611.85 14.48%16,524.21 14.52%10,940.73 9.54%44,076.7938.43%
SSP2452021–204060,013.55 52.33%20,939.16 18.26%16,273.27 14.30%17,467.27 15.23%54,679.747.67%
2041–206066,364.07 57.86%19,202.83 16.74%17,771.39 15.62%11,354.96 9.90%48,329.1842.14%
2061–208066,178.30 57.70%20,149.64 17.57%16,321.22 14.34%12,044.09 10.50%48,514.9542.30%
2081–210060,190.33 52.48%21,723.42 18.94%18,243.29 16.03%14,536.21 12.67%54,502.9247.52%
SSP3702021–204057,695.22 50.30%20,279.98 17.68%17,318.96 15.22%19,399.09 16.91%56,998.0349.70%
2041–206062,555.85 54.54%20,181.85 17.60%15,811.11 13.89%16,144.44 14.08%52,137.445.46%
2061–208068,125.85 59.40%19,475.49 16.98%14,141.46 12.43%12,950.45 11.29%46,567.440.60%
2081–210063,692.18 55.53%18,766.88 16.36%14,052.32 12.35%18,181.87 15.85%51,001.0744.47%
SSP5852021–204065,197.78 56.85%18,650.03 16.26%16,540.69 14.53%14,304.75 12.47%49,495.4743.15%
2041–206060,126.66 52.42%20,230.54 17.64%16,486.01 14.49%17,580.04 15.33%54,296.5947.34%
2061–208066,672.68 58.13%18,730.18 16.33%14,885.26 13.08%14,405.13 12.56%48,020.5741.87%
2081–210069,653.92 60.73%16,880.01 14.72%13,595.39 11.95%14,563.93 12.70%45,039.3339.27%
Table 7. Change in the distribution areas and proportion of P. mira under the 4 climate pathways.
Table 7. Change in the distribution areas and proportion of P. mira under the 4 climate pathways.
ClimateYearStabilityExpansionContraction
Area/km2Proportion/%Area/km2Proportion/%Area/km2Proportion/%
SSP1262021–204041,382.9836.36%13,047.0211.46%1393.991.22%
2041–206039,928.3235.08%11,112.219.76%2841.912.50%
2061–208038,341.8233.69%8979.667.89%4430.653.89%
2081–210037,544.0832.99%6495.80 5.71%5224.654.59%
SSP2452021–204040,839.9135.89%13,948.1312.26%1931.811.70%
2041–206039,245.9334.48%9178.918.07%3524.33.10%
2061–208038,707.3634.01%9850.818.66%4061.373.57%
2081–210041,595.7136.55%12,957.1311.39%1177.511.03%
SSP3702021–204041,707.3236.65%15,288.1913.43%1064.410.94%
2041–206040,685.6135.75%11,517.4510.12%2081.6621.83%
2061–208036,207.0231.81%10,199.878.96%6561.715.77%
2081–210039,394.2434.61%11,560.90 10.16%3375.242.97%
SSP5852021–204039,556.7934.76%9930.968.73%3213.442.82%
2041–206041,362.0136.34%13,267.2411.66%1408.971.24%
2061–208038,257.1833.62%9703.998.53%4511.553.96%
2081–210036,857.20 32.39%8089.037.11%5910.785.19%
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Gu, J.; He, Q.; Li, Q.; Li, Q.; Xiang, S.; Li, W.; Jin, A.; Wang, S.; Liu, F.; Tang, G. Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau. Forests 2024, 15, 2022. https://doi.org/10.3390/f15112022

AMA Style

Gu J, He Q, Li Q, Li Q, Xiang S, Li W, Jin A, Wang S, Liu F, Tang G. Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau. Forests. 2024; 15(11):2022. https://doi.org/10.3390/f15112022

Chicago/Turabian Style

Gu, Jinkai, Qiang He, Qingwan Li, Qinglin Li, Shengjian Xiang, Wanchi Li, Aohang Jin, Shunbin Wang, Feipeng Liu, and Guoyong Tang. 2024. "Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau" Forests 15, no. 11: 2022. https://doi.org/10.3390/f15112022

APA Style

Gu, J., He, Q., Li, Q., Li, Q., Xiang, S., Li, W., Jin, A., Wang, S., Liu, F., & Tang, G. (2024). Global Warming Will Drive Spatial Expansion of Prunus mira Koehne in Alpine Areas, Southeast Qinghai–Tibet Plateau. Forests, 15(11), 2022. https://doi.org/10.3390/f15112022

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