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Article

Evaluation and Application of the MaxEnt Model to Quantify L. nanum Habitat Distribution Under Current and Future Climate Conditions

by
Fayi Li
1,2,3,
Liangyu Lv
1,2,3,
Shancun Bao
1,2,3,
Zongcheng Cai
1,2,3,
Shouquan Fu
1,2,3 and
Jianjun Shi
1,2,3,*
1
Academy of Animal Science and Veterinary, Qinghai University, Xining 810018, China
2
Qinghai Academy of Animal Science and Veterinary Academy of Animal Science and Veterinary, Xining 810018, China
3
Key Laboratory of Alpine Grassland Ecology in the Three-River-Source Region, Qinghai Provincial Key Laboratory of Adaptive Management of Alpine Grasslands, Ministry of Education, Xining 810018, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(8), 1869; https://doi.org/10.3390/agronomy15081869 (registering DOI)
Submission received: 12 March 2025 / Revised: 29 March 2025 / Accepted: 1 April 2025 / Published: 1 August 2025
(This article belongs to the Section Grassland and Pasture Science)
Browse Figures
Versions Notes

Abstract

Understanding alpine plants’ survival and reproduction is crucial for their conservation in climate change. Based on 423 valid distribution points, this study utilizes the MaxEnt model to predict the potential habitat and distribution dynamics of Leontopodium nanum under both current and future climate scenarios, while clarifying the key factors that influence its distribution. The primary ecological drivers of distribution are altitude (2886.08 m–5576.14 m) and the mean temperature of the driest quarter (−6.60–1.55 °C). Currently, the suitable habitat area is approximately 520.28 × 104 km2, covering about 3.5% of the global land area, concentrated mainly in the Tibetan Plateau, with smaller regions across East and South Asia. Under future climate scenarios, low-emission (SSP126), suitable areas are projected to expand during the 2050s and 2070s. High-emission (SSP585), suitable areas may decrease by 50%, with a 66.07% reduction in highly suitable areas by the 2070s. The greatest losses are expected in the south-eastern Tibetan Plateau. Regarding dynamic habitat changes, by the 2050s, newly suitable areas will account for 51.09% of the current habitat, while 68.26% of existing habitat will become unsuitable. By the 2070s, newly suitable areas will rise to 71.86% of the current total, but the loss of existing areas will exceed these gains, particularly under the high-emission scenario. The centroid of suitable habitats is expected to shift northward, with migration distances ranging from 23.94 km to 342.42 km. The most significant shift is anticipated under the SSP126 scenario by the 2070s. This study offers valuable insights into the distribution dynamics of L. nanum and other alpine species under the context of climate change. From a conservation perspective, it is recommended to prioritize the protection and restoration of vegetation in key habitat patches or potential migration corridors, restrict overgrazing and infrastructure development, and maintain genetic diversity and dispersal capacity through assisted migration and population genetic monitoring when necessary. These measures aim to provide a robust scientific foundation for the comprehensive conservation and sustainable management of the grassland ecosystem on the Qinghai–Tibet Plateau.

1. Introduction

Global environmental change, especially climate change, has significantly impacted species diversity and distribution patterns [1,2]. As global temperatures rise and climate patterns change, the habitats of many species are changing rapidly, resulting in the expansion, contraction or shift of species’ suitable areas [3,4]. The effects of climate change are particularly pronounced for species in many ecosystems, especially some plants in alpine and alpine regions [5,6]. These species face heightened threats to their survival due to their narrow ecological adaptability and specific growing requirements [7,8]. Climate change not only directly impacts their habitats but also exacerbates ecosystem vulnerabilities by disrupting competitive relationships among biomes and altering ecological processes [9,10]. Therefore, studying the impacts of global environmental changes on species diversity—particularly the distribution of alpine plant species—has become an increasingly important focus in ecological research.
In recent years, with the rapid progress of ecology and geographic information science, Ecological Niche Models (ENMs) and Species Distribution Models (SDMs) have been widely used to predict the potential distribution area of species [11,12]. Prediction of suitable area of species is one of the key research directions in ecology, which is of great value for pest control, protection of rare and endangered species, and optimization of crop introduction and cultivation [13,14]. Currently, the commonly used species distribution models include MaxEnt, Bioclim and GARP, etc. Among them, the MaxEnt model has been widely recognized and applied in the study of the distribution of various species due to its advantages of low sample size requirement, easy operation and high prediction accuracy [15,16]. MaxEnt is a newly introduced technology, which can achieve high-precision prediction and is less affected by other parameters [11,12,13,14,15]. Niche models use existing distribution data and environmental variables to identify potentially suitable areas of species in ecological space, while species distribution models combine geographic information system (GIS), climate and environmental data to build mathematical models to predict the potential distribution range of species [17,18].
Leontopodium nanum is a perennial herb of the genus Leontopodium in the Compositae, also known as Edelweiss, is a typical perennial alpine herb, and is known for its adaptability to extreme habitats, as well as its distinctive appearance [19,20]. The plant has important ecological significance, is a cultural symbol, and has certain value in medicine, ornamental fields and other fields, so it has attracted international attention [19,21]. However, due to the multiple impacts of climate warming, land-use change and human activities, the survival of L. nanum faces severe challenges, especially in the case of continuous climate warming, the ecological environment suitable for its survival may further shrink or shift. Therefore, predicting the suitable areas of L. nanum has become an important basis for formulating effective conservation strategies.
The habitat requirements of L. nanum are relatively harsh, with strict requirements on temperature, precipitation, soil properties and light conditions, and it is mainly suitable for cold and humid alpine areas [20,22]. This adaptation is closely related to physiological properties, such as the characteristic fluff of L. nanum, which can reduce water loss and resist strong ultraviolet radiation [19]. Moreover, previous studies have shown that to adapt to the decreasing temperature with the increase in altitude, L. nanum mainly improves its ability of heat preservation, water retention and mechanical damage resistance by shrinking and thickening its leaves, to use resources more effectively for its growth and development [20]. However, these habitat conditions are highly susceptible to the impact of climate change, which makes the distribution pattern of L. nanum in the natural environment gradually show a trend of fragmentation and marginalization. At present, many scholars have studied the photosynthetic characteristics, medicinal value and leaf traits of L. nanum, but the research on its predicted distribution is rarely reported [19,20,21]. Therefore, it is of practical significance to study the influence of climate factors on the distribution of L. nanum and predict the distribution changes of its suitable areas under future climate scenarios for long-term protection of L. nanum.
To improve the accuracy of their predictions, the researchers used the Intergovernmental Panel on Climate Change (IPCC) to present four climate scenarios and emissions assumptions (SSP126, SSP245, SSP370, SSP585) [23,24]. These scenarios provide a more comprehensive understanding of how various emission pathways may influence species distribution, which is essential for the conservation of Leontopodium nanum. Research has shown that the scenario analysis of climate change elucidates the classification of the IPCC’s various emissions scenarios (SSP1–SSP5) based on differing greenhouse gas concentrations and global mean temperature changes; special attention is given to the numerical definitions of climate scenarios and their role in forecasting future climate change [25]. For example, Zhang utilized four climate scenarios to predict the current and future potential habitat areas of Paeoniaceae [26]; Li employed the MaxEnt model to predict the current distribution and future range shifts of three herbaceous coptis species in China under climate change [27]. Despite the excellent performance of the MaxEnt model in predicting climate factors, the distribution of suitable areas of L. nanum may also be affected by other ecological factors such as soil type, vegetation cover and competition relations. Therefore, future studies can also incorporate these diverse ecological variables to improve the accuracy and comprehensiveness of the model.
This study used the MaxEnt model to predict the potential habitat areas of L. nanum and identify the key environmental factors affecting its growth, which not only helps to reveal the potential threats of climate change to this species but also provides scientific guidance for its conservation and management. Predicting the suitable habitats for L. nanum can help identify potential migration routes and favorable environments, providing strong support for the conservation of existing populations and effective habitat management. Future studies should further explore the combined effects of extreme climatic events, ecological factors and human activities on the distribution of L. nanum, to develop more accurate and comprehensive conservation strategies to ensure the stability of L. nanum population and the continuation of ecosystem diversity.

2. Materials and Methods

This study used the MaxEnt model to predict the potentially suitable distribution area of L. nanum, and the technical route is shown in Figure 1.

2.1. Sources of Species Distribution Data

The distribution data of Leontopodium nanum were obtained from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, accessed on 1 December 2024), retrieved in CSV format on 1 December 2024, National Plant Specimen Resource Database (https://www.cvh.ac.cn/, accessed on 1 December 2024), along with related literature and the author’s site survey results. A total of 480 distribution sites were collected. To avoid over-fitting of prediction results due to excessive concentration of distribution sites, ENM Tools (U.S.A, 1.1.0) was used to filter the distribution sites and load 5 km of environmental factors to ensure that there was only one distribution site in each grid. After screening, 423 distribution points were retained (Figure 2).

2.2. Environment Variable Data and Screening

Using the World Meteorological database WorldClim (Version 2.1, arc-30 s, 1 km resolution, http://www.worldclim.org/, accessed on 10 December 2024) and existing reports [28], different current (1961–1990) and future, 2050s (2041–2060) and 2070s (2061–2080), modes of greenhouse gas emissions (SSP) can be found. One can also find meteorological and topographic data under 126, 245, 370, and 585, including 19 climatic factors and 1 altitude factor. From the world soil database HWSD (v1.2, https://www.fao.org/soils-portal/data-hub/en/, accessed on 10 December 2024), the 17 soil factors can be found. Due to the lack of future soil and altitude data, this study assumed that the future soil and altitude would be consistent with the present [29]. All environmental data are raster data
To avoid overfitting of prediction results caused by autocorrelation of environmental variables, 37 environmental variables and filtered coordinate points are first imported into the ENMtoosls tool, and the correlation tool in ENMtools options is used for correlation analysis of environmental factors. When the absolute value of the correlation coefficient between environmental variables is greater than 0.8, Only the one with the largest contribution rate is retained.

2.3. MaxEnt Model Optimization and Parameter Setting

The predictive performance of the MaxEnt model is influenced by two factors: regulated frequency doubling (RM) and feature combination (FC). The calibration, feature selection, model evaluation, and establishment are performed by optimizing the hyperparameters using Rugin’s kuenm package. In the optimization process, the RM value is set to 0.5~4.0, increasing by 0.5 each time, a total of 8 values. After running, in Calibration_Result’s best_candidate_models_OR_AICc file, select the RM value when delta AICc is 0 (Figure 3).
Import the selected distribution points and environment variables of L. nanum into MaxEnt (Version 3.4.0) software. The random test percentage is 25. To avoid overfitting, the forecast result caused by too-large or too-small value, the Regularization multiplier is adjusted to 2 and Auto features are canceled. Only Linear features and Quadratic features were selected. Select Subsample for Replicated run type, Logitic for Output format. Also check Create responsive curves, Make pictures of predictions and Do jackknife to measure variable importance, with Replicates of 10.

2.4. Suitable Area Division and Area Calculation

ArcGIS reclassification function was used to analyze the prediction results of MaxEnt. Based on habitat suitability index (HSI), the suitability zone of L. nanum was divided into non-suitability zone (0~0.2), low-suitability zone (0.2~0.4), medium-suitability zone (0.4~0.6) and high-suitability zone (0.6~1.0). The number of pixels in different suitable areas was calculated by ArcGIS, and their proportion in the total pixels was calculated. The area of each suitable area was calculated according to the global land area.

2.5. Temporal and Spatial Evolution of Suitable Areas

The forecast ASCII files output by MaxEnt under different GHG emission modes in the future 2050s or 2070s were imported into ArcGIS, merged into one raster file, and the average value was used as the distribution prediction results of 2050s and 2070s. Using “7a. Quick Reclassify to Binary” in the SDM toolbox, the current, 2050s, and 2070s forecast raster files were classified into binary files with a threshold of 0.6. The “Distribution Changes Between Binary SDMs” in the SDM toolbox is used to analyze the growth and decline trend of the suitable area in the future 2050s and 2070s and calculate the area. Additionally, “Centroid Changes (Lines)” tool was used to analyze the centroid migration of suitable areas in the 2050s and 2070s.

3. Results and Analysis

3.1. Species Distribution Sites and Filtering Results of Environmental Variables

Through ENMtools analysis and correlation analysis of environmental variables, 80 species were left after 480 distribution points were filtered, and 18 species were left after 37 environmental variables were filtered (11 climate factors, 6 soil factors, and 1 altitude factor) (Table 1).
Elevation and temperature variables (bio1, bio9) and precipitation variables (bio13, bio15) exhibit a significant negative correlation (p < 0.01), whereas both are significantly positively correlated with bio3 (p < 0.01) (Figure 4).

3.2. MaxEnt Accuracy Evaluation

The area under curve (AUC) value of receiver operation characteristic (ROC) output by the MaxEnt model was used to evaluate the accuracy of prediction results. The closer the AUC value is to 1, the more accurate the prediction. Typically, AUC < 0.7 indicates poor prediction, 0.7 ≤ AUC < 0.8 indicates fair prediction, 0.8 ≤ AUC < 0.9 indicates good prediction, and 0.9 ≤ AUC < 1.0 indicates excellent prediction. The omission rate of the MaxEnt model predicted samples was consistent with that of the test samples (Figure 5a), and the test AUC values were both greater than 0.9 (Figure 5b), indicating that the model predicted results were accurate and reliable and could be used for the prediction of the suitable area of L. nanum.

3.3. Key Environmental Variables Affecting the Distribution of L. nanum

As shown in Table 1, the contribution rate of elevation is 61.1%, the cumulative contribution rate of meteorological variables is 29.5%, and the cumulative contribution rate of soil variables is 8.5%. MaxEnt was used for the knife-cutting analysis of environmental variables to determine the important ecological variables affecting L. nanum. As shown in Figure 6, when only one variable was used, the gain value (blue band) of altitude (Wc.2.1_2.5m_elev) was the largest, followed by bio9 (average temperature of driest quarter), indicating that the two variables themselves contained more useful information about the distribution of L. nanum. At the same time, among other variables, altitude (Wc.2.1_2.5m_elev) decreased the most, followed by bio9, indicating that this variable contained more information about the distribution of L. nanum that other variables did not. Therefore, altitude and the average temperature in the driest quarter were the most important environmental variables affecting the distribution of L. nanum, and the cumulative contribution rate of the two variables was 91.4%.
According to the results of the response curve of leading environmental variables (Figure 7), when the probability of suitable growth of L. nanum is greater than 0.6 (high suitability area), the altitude suitable for distribution is 2886.08 m~5576.14 m (Figure 7a), and the average temperature in the driest quarter is −6.60~1.55 °C (Figure 7b).

3.4. Suitable Areas for L. nanum Under Current Climate Conditions

Under the current climate conditions, the suitable habitat of L. nanum is mainly distributed in East Asia, China, Nepal and parts of South Asia, including Tajikistan, Afghanistan, Pakistan and India, with China as the main habitat (Figure 8). The total habitat area of the world is about 520.28 × 104 km2, accounting for 3.5% of the world’s land area (Figure 9). The areas of high- and medium-suitability areas are 106.98 × 104 km2 and 129.63 × 104 km2, respectively. The high-suitability areas of L. nanum are mainly concentrated in the Tibet Plateau, while some of the medium-suitability areas are distributed in the north of the Yellow River basin and the Bohai Sea rim. In addition to the southern and northeast regions of China, most of the northern regions are suitable areas, especially in southern Tibet, southeast Qinghai, southern Gansu, western Sichuan and southern Xinjiang.

3.5. Suitable Area of L. nanum in Different Climate Scenarios in the Future

Under different climate scenarios in the 2050s and 2070s, the global potential suitable areas of L. nanum are roughly the same as the current ones, mainly concentrated in the Tibet Plateau of East Asia, and some medium-to-high-suitability regions are distributed in northern Xinjiang, southern Ningxia, western Shaanxi and southern Shanxi. With the increase in greenhouse gas emission concentration, the total suitable area of L. nanum will decrease in different degrees during the 2050s and 2070s. Especially in the 2070s, under the high greenhouse gas concentration of SSP585, the total suitable area of L. nanum was reduced by about 50% compared with the present, and the high-suitability area was reduced to 70.68 × 104 km2, a decrease of 66.07% compared with the present, and the reduced suitable area was mainly concentrated in the Tibetan Plateau. However, under the low concentration of SSP126, the total area of suitable area in 2050s and 2070s was higher than that under other high concentration conditions, indicating that the increase in greenhouse gas concentration inhibited the growth and reproduction of L. nanum (Figure 10).

3.6. Increasing and Decreasing Trends of Suitable Areas of Leontopodium nanum Under Different Climate Scenarios in the Future

The suitable area of L. nanum will change significantly under the future climate scenario, with the most drastic change in the 2070s (Figure 11).
In the 2050s, about 50% of the suitable area remained stable, with the new area accounting for 51.09% of the current suitable area and the reduced area accounting for 68.26%. Under the low-emission scenario (SSP126), the expansion areas are concentrated in eastern Gansu, central Shaanxi, southern Shanxi and southern Tibet. With the increase in greenhouse gas concentration, the extended area gradually migrated to northern Tibet and western Qinghai in the Tibet Plateau. Under the SSP370 scenario, the expansion is the least and the contraction is the largest.
By the 2070s, the newly added area will increase to 71.86% of the current suitable area, but the contraction area will increase to 1.5 times of the current area, mainly concentrated in the eastern and southern parts of the Tibet Plateau. The expansion area showed a trend of increasing first and then decreasing, and the largest area appeared in the middle-emission scenario (SSP245), with the expansion ratio reaching 25.49% in northern Tibet and central and western Qinghai. Comparatively, under the high-emission scenario (SSP585), the area decreased the most, and 32.38% of the suitable area was lost.
Based on the geometric center of the suitable area of L. nanum, the centroid migration changes under different climate scenarios in the 2050s and 2070s were analyzed in this study (Figure 10). The results show that, under various scenarios, the migration direction and distance of L. nanum were mainly concentrated in the Tibetan Plateau region. The most significant migration occurred in the 2070s under the SSP126 scenario, with a northeastward migration of 342.42 km. Except for SSP585 in the 2070s, SSP585 in the 2050s and SSP370 in the 2050s (28.43 km, 50.74 km and 32.06 km to the southeast, respectively), all the other scenarios showed northward migration with a range of 23.94 km–56.38 km. In conclusion, due to the influence of growth habits, the suitable habitat of L. nanum is still mainly distributed in the high-altitude area of the Tibet Plateau (Figure 12).

4. Discussion

4.1. Evaluation of MaxEnt Model

This study utilized the MaxEnt model, taking advantage of its low sample requirements and high predictive accuracy, to predict the current and future (2050s and 2070s) suitable habitats for L. nanum based on selected environmental variables and species occurrence points [30]. To enhance the accuracy of the predictions and avoid overfitting, the study employed MaxEnt and ENM Tools to exclude environmental variables with 0 contribution or high correlation. Additionally, ENM Tools was used to filter the occurrence points. The results from MaxEnt indicate that, under current climatic conditions, the suitable habitat for L. nanum includes all of its distribution points, with an AUC value greater than 0.9 for model evaluation, demonstrating that the predictions are accurate and reliable.

4.2. Key Environmental Variables Influencing the Distribution of Leontopodium nanum

According to the results of this study, the suitable elevation range for Artemisia frigida is 2886.08 m~5576.14 m, with the average temperature range during the driest quarter being 6.60~1.55 °C. This elevation range is closely related to the growth habits of L. nanum [22]. As a species adapted to alpine grasslands, L. nanum exhibits strong adaptability, particularly in cold, moist, high-altitude regions [20,21]. Wu Tingting found that key factors influencing high-altitude plant species, such as mosses, include elevation and the average temperature during the driest quarter [31], which is consistent with the findings of this study. This elevation range primarily covers the Tibetan Plateau and its surrounding areas, where the climatic conditions—low temperatures, abundant precipitation, and moderate humidity—provide favorable growing conditions for L. nanum [32]. The average temperature range during the driest quarter indicates that L. nanum is capable of growing in cold environments and has a strong adaptation to temperature fluctuations. This temperature range falls within cold climate zones, which is consistent with the species’ growth requirements. Furthermore, this temperature condition somewhat limits the distribution of L. nanum, particularly in warmer regions, where the species’ growth is more severely inhibited. Consequently, temperature increases driven by climate change could exert a substantial negative effect on the suitable habitat of L. nanum, particularly in low-elevation regions.

4.3. Impact of Climate Change on the Suitable Habitat of Leontopodium nanum

Research indicates that the suitable habitat for L. nanum will experience significant change over the next few decades, particularly under high-greenhouse-gas-concentration scenarios, with a substantial reduction in the extent of its suitable range. As temperatures rise, the climate at lower elevations becomes warmer and drier, which poses obstacles to the growth of L. nanum plants adapted to the cold. The warm climate will limit the species growth, especially in the lower elevations of the Tibetan Plateau. However, under lower-greenhouse-gas-emission scenarios (such as SSP126), the suitable areas of L. nanum show an expanding trend, especially in the Tibetan Plateau and its western regions. This indicates that L. nanum has strong adaptation potential under low-greenhouse-gas-concentration scenarios, and it can migrate to higher altitude areas, thus providing more space for its growth. When studying high-altitude species, KC A found that species at lower altitudes that survive in cold climates move to higher altitudes after global warming to adapt to the ever-intensifying climate change situation, which is consistent with the results of this study [33]. The trend of centroid migration in the suitable habitat zones further indicates that, with increasing temperatures, the suitable habitat for L. nanum will primarily shift to higher altitudes, particularly in the western and northern regions of the Tibetan Plateau. In the SSP126 scenario of the 2070s, the centroid of the suitable habitat will migrate about 342.42 km to the northeast, further highlighting the mitigating role of high-altitude regions in climate change. Although climate warming may promote the expansion of L. nanum to higher altitude, this process also faces new ecological challenges, and Rixen concludes that the migration trend of alpine plants to higher altitude may be challenged by soil constraints and biological interactions [34]. However, as the suitable area contracts and shifts, L. nanum may become increasingly vulnerable to the effects of habitat fragmentation. Habitat fragmentation not only diminishes the total extent of available habitats but also impedes gene flow and individual migration, thereby constraining inter-population mating and reducing genetic diversity, which in turn weakens the species’ adaptability to environmental changes [35]. Furthermore, in alpine meadow and grassland ecosystems, the combined influence of topographic heterogeneity and anthropogenic activities often exacerbates the degree of fragmentation and disturbance to community structure. Consequently, it is challenging to delineate the actual distribution of the species within fragmented landscapes based solely on MaxEnt’s predictions of potential distribution on a regional scale. Therefore, subsequent analyses should integrate landscape connectivity assessments to provide a more comprehensive evaluation. Landscape connectivity models, such as CircuitScape (which incorporates graph theory and circuit theory), can assist in identifying critical habitat patches and corridors, quantifying the impact of fragmentation on population dynamics and reproduction, and evaluating barriers under varying climatic or disturbance scenarios [36,37]. By synthesizing species-specific biological traits, topographic data, and habitat models, it becomes possible to further delineate ecological patches and connecting pathways that are essential for sustaining populations and predicting potential fragmentation risks. This approach provides a more targeted scientific foundation for developing conservation strategies and management decisions aimed at preserving alpine ecosystems [38]. Thus, the contraction and redistribution of suitable areas for L. nanum not only affect its growth but may also have far-reaching implications for the stability and biodiversity.

4.4. Elevation and Temperature Suitability and Ecological Adaptation

The suitability of plants is not only affected by altitude and temperature but also closely related to other ecological factors [39,40]. First, the low-temperature environment at high altitudes provides less competitive pressure for L. nanum, which somewhat promotes the growth and reproduction of L. nanum. On the other hand, plants in warmer regions have longer growth cycles and greater competitive pressure [41], which is not good for L. nanum. Therefore, with the increase in temperature, the competitive ability of L. nanum in low-altitude regions may be compromised, leading to a contraction of its distribution range. Additionally, the suitable temperature range for L. nanum during the driest quarter is between −6.60 and 1.55 °C, indicating its strong cold tolerance and adaptation to cool, humid environments. With climate warming, the rising temperatures will increasingly limit its habitat, particularly in low-altitude regions, potentially exceeding its growth thresholds. This trend is particularly pronounced under the SSP585 scenario, where the contraction of the suitable habitat is most severe. Similarly, Telwala notes that many plant species in the Himalayas are migrating to higher altitudes due to climate warming, which is consistent with the findings of this study [42]. L. nanum’s survival and diffusion potential under future climate conditions will influence its adaptability across both spatial and temporal scales. Primarily, reproductive ecology and genetic diversity are key determinants of its dispersal capacity. However, an over-reliance on a specific pollinator, coupled with the relative scarcity of this vector within its habitat, may constrain its distribution. On the other hand, if L. nanum exhibits a strong ecological affinity with other plant species, it may alleviate its population bottleneck [43]. Additionally, the spatiotemporal heterogeneity characteristic of alpine regions significantly influences the migration patterns of L. nanum. The predicted distribution of L. nanum in this study reveals habitat contraction in the southern regions and an expansion of potentially suitable areas in the northern regions or at higher elevations. Additionally, it highlights substantial changes in factors such as sunshine duration and temperature seasonality. Assuming a positive correlation between the phenology of L. nanum and these climatic factors, its northward or upward dispersal may be limited by variations in day and night length as well as shorter growth cycles [44].
Although model projections suggest that this species may continue to inhabit the Tibetan Plateau under future climate scenarios, its actual capacity for migration could be constrained by intrinsic biological characteristics, such as seed dispersal rates, freeze–thaw cycle requirements for seedling colonization, and soil moisture demands. Additionally, habitat fragmentation in alpine environments, including geographic isolation caused by glacial retreat or grazing activities, may further impede its expansion. Consequently, even if the overall spatial extent of the future suitability area remains stable, the disruption of gene flow between populations might still jeopardize the long-term survival of the species [35]. This is consistent with the “migration lag” result proposed by Hoegh-Guldberg et al., in which climate change leads to rapid migration of suitable areas but slower natural migration of plants [25]. For L. nanum, assisted migration may be the best way to maintain genetic diversity, but its effectiveness depends on the resistance of the native community to invasion and the adaptability of the ecological functions of the destination [45]. At the same time, Ackerly et al. pointed out that the success of plants under climate change depends on whether they are dependent on the original ecological environment and whether the migration rate of species is consistent with the rate of environmental change [46]. At high altitudes, due to steep terrain and significant vertical gradients, natural migration is often limited by geomorphological and habitat fragmentation, which makes it difficult for species to spread to the potential suitable areas predicted by the model on time [43]. To alleviate this contradiction, assisted migration can be used to achieve the purpose of expansion, and artificially assist vulnerable species to enter more ideal habitats, thereby reducing the risk of local extinction brought about by climate warming [47]. However, this method may disturb the community structure and cause biological invasion, so it needs to be determined by combining multiple factors such as species reproduction characteristics, interaction relationships and genetic diversity [46]. Although the results of the distribution model in this study provide a spatial reference for future suitable areas, spatial prediction alone is not sufficient to evaluate the feasibility of actual migration, and a more comprehensive analysis of the long-term survival and diffusion potential of L. nanum in fragmented environments is still needed in combination with assisted migration tests, landscape connectivity, and population genetics studies.

4.5. Limitations and Prospects of the Study

Although this study provides an important prediction for the change of suitable planting area of L. nanum under future climate change, there are still limitations. First, although the MaxEnt model can accurately predict the appropriate areas of species, it may have prediction errors in some special ecosystems or ecological processes (such as the interaction of species with vegetation dynamics). Secondly, this study did not fully consider the impact of local extreme weather conditions, land-use change and human activities, which may further restrict or change the distribution of suitable areas of L. nanum. Future studies should further combine ecological process models, conduct field investigations, and comprehensively consider the interaction of climate change, human activities and ecological factors to more accurately predict the change of the suitable area of L. nanum under different climate scenarios. At the same time, it is necessary to strengthen the research on the growth habit, adaptation mechanism and the relationship between L. nanum and other species to further understand its ecological adaptability in the context of climate change.

5. Conclusions

(1) In this study, the MaxEnt analysis revealed that the primary factors influencing the change in the suitable area for L. nanum were altitude (2886.08 m–5576.14 m) and temperature (−6.60–1.55 °C).
(2) Currently, the suitable habitat of L. nanum area is approximately 520.28 × 104 km2, concentrated mainly in the Tibetan Plateau.
(3) This study demonstrates that under low emission concentrations, the area of suitable habitat increases. However, as emission concentrations rise, the suitable habitat decreases, and dwarf tinder grass exhibits an altitudinal migration pattern, shifting from lower to higher elevations.

Author Contributions

Conceptualization, F.L. and L.L.; Methodology, F.L. and Z.C.; Software, F.L. and Z.C.; Validation, F.L., J.S. and L.L.; Formal analysis, F.L. and S.F.; Investigation, S.B. and S.F.; Resources, S.B. and F.L.; Data curation, F.L.; Writing—original draft preparation, F.L.; Writing—review and editing, J.S.; Visualization, F.L. and S.F.; Supervision, J.S.; Project administration, J.S.; Funding acquisition, J.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data in this study are from the following sources: The distribution data of Leontopodium nanum were obtained from the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, accessed on 1 December 2024), retrieved in CSV format on December 1, 2024. National Plant Specimen Resource Database (https://www.cvh.ac.cn/, accessed on 10 December 2024), related literature and author’s site survey results. From the World Meteorological database WorldClim (Version 2.1, arc-30 s, 1 km resolution, http://www.worldclim.org/, accessed on 10 December 2024) and existing reports [28] for different current (1961–1990) and future, 2050s (2041–2060) and 2070s (2061–2080), modes of greenhouse gas emissions (SSP).

Acknowledgments

The author would like to express sincere gratitude to Shi Jianjun from the Academy of Animal Husbandry and Veterinary Sciences of Qinghai Province for his invaluable guidance on this thesis.

Conflicts of Interest

The authors declare no conflicts of interest.

References

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Figure 1. MaxEnt predicts the technical route of suitable areas for L. nanum.
Figure 1. MaxEnt predicts the technical route of suitable areas for L. nanum.
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Figure 2. Distribution points of Leontopodium nanum in the world.
Figure 2. Distribution points of Leontopodium nanum in the world.
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Figure 3. MaxEnt model running parameter calibration.
Figure 3. MaxEnt model running parameter calibration.
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Figure 4. The correlation between environmental factors affecting the distribution of Leontopodium nanum.
Figure 4. The correlation between environmental factors affecting the distribution of Leontopodium nanum.
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Figure 5. (a) Omission rate for accuracy analysis and (b) ROC curve of MaxEnt under the current climate conditions.
Figure 5. (a) Omission rate for accuracy analysis and (b) ROC curve of MaxEnt under the current climate conditions.
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Figure 6. The importance of environmental variables was tested by the knife-cut method.
Figure 6. The importance of environmental variables was tested by the knife-cut method.
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Figure 7. Response curves of dominant environmental variables. (a) Response curve of bio09; (b) Response curve of Wc.2.1_2.5m_elev.
Figure 7. Response curves of dominant environmental variables. (a) Response curve of bio09; (b) Response curve of Wc.2.1_2.5m_elev.
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Figure 8. Global suitability zones of Leontopodium nanum under current climate conditions. Note: The figure is based on the standard map No. Gs-jing (2022) 1061 downloaded from the standard map service website of the National Administration of Surveying, Mapping and Geographic Information. The base map is not modified.
Figure 8. Global suitability zones of Leontopodium nanum under current climate conditions. Note: The figure is based on the standard map No. Gs-jing (2022) 1061 downloaded from the standard map service website of the National Administration of Surveying, Mapping and Geographic Information. The base map is not modified.
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Figure 9. Global habitat area (km2) of Leontopodium nanum under different climate scenarios.
Figure 9. Global habitat area (km2) of Leontopodium nanum under different climate scenarios.
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Figure 10. Potential distribution of Leontopodium nanum under different future Climate Scenarios. Note: This map was derived from the unaltered standard map No. Gs-Jing (2022) 1061, which was obtained from the official website of the Ministry of Natural Resources’ standard map service.
Figure 10. Potential distribution of Leontopodium nanum under different future Climate Scenarios. Note: This map was derived from the unaltered standard map No. Gs-Jing (2022) 1061, which was obtained from the official website of the Ministry of Natural Resources’ standard map service.
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Figure 11. Temporal and spatial evolution trend of Leontopodium nanum distribution under the influence of climate change (Map No.: GS Jing (2022) 1061). Note: This map was derived from the unaltered standard map No. Gs-Jing (2022) 1061, which was obtained from the official website of the Ministry of Natural Resources standard map service.
Figure 11. Temporal and spatial evolution trend of Leontopodium nanum distribution under the influence of climate change (Map No.: GS Jing (2022) 1061). Note: This map was derived from the unaltered standard map No. Gs-Jing (2022) 1061, which was obtained from the official website of the Ministry of Natural Resources standard map service.
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Figure 12. Change of centroid migration of Leontopodium nanum under the influence of climate change. Red dot: The coordinates of migration; Dotted line arrow: migration distance; Continuous line arrow: migration coordinate points under different climatic conditions.
Figure 12. Change of centroid migration of Leontopodium nanum under the influence of climate change. Red dot: The coordinates of migration; Dotted line arrow: migration distance; Continuous line arrow: migration coordinate points under different climatic conditions.
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Table 1. Environmental variables involved in MaxEnt model construction and their contribution rates.
Table 1. Environmental variables involved in MaxEnt model construction and their contribution rates.
VariableNamePercent Contribution
W_elev wc2.1_2. 5Altitude61.1
bio9Mean temperature of driest quarter9.9
bio1Annual mean temperature9
t_bsTopsoil base saturation8.5
bio3Isothermality (BIO2/BIO7) (×100)4
bio14Precipitation of driest month (mm)4
bio15Precipitation seasonality (mm)1.5
bio18Precipitation of warmest quarter (mm)0.8
t_gravelTopsoil gravel content0.4
t_ref_bulk_densityTopsoil reference bulk density0.3
t_usda_tex_classTopsoil USDA soil texture classification0.1
t_textureTopsoil texture0.1
bio5Maximum temperature of warmest month (°C)0.1
bio17Precipitation of driest quarter (mm)0.1
bio10Mean temperature of warmest quarter (°C)0.1
t_caco3Topsoil calcium carbonate content0.1
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Li, F.; Lv, L.; Bao, S.; Cai, Z.; Fu, S.; Shi, J. Evaluation and Application of the MaxEnt Model to Quantify L. nanum Habitat Distribution Under Current and Future Climate Conditions. Agronomy 2025, 15, 1869. https://doi.org/10.3390/agronomy15081869

AMA Style

Li F, Lv L, Bao S, Cai Z, Fu S, Shi J. Evaluation and Application of the MaxEnt Model to Quantify L. nanum Habitat Distribution Under Current and Future Climate Conditions. Agronomy. 2025; 15(8):1869. https://doi.org/10.3390/agronomy15081869

Chicago/Turabian Style

Li, Fayi, Liangyu Lv, Shancun Bao, Zongcheng Cai, Shouquan Fu, and Jianjun Shi. 2025. "Evaluation and Application of the MaxEnt Model to Quantify L. nanum Habitat Distribution Under Current and Future Climate Conditions" Agronomy 15, no. 8: 1869. https://doi.org/10.3390/agronomy15081869

APA Style

Li, F., Lv, L., Bao, S., Cai, Z., Fu, S., & Shi, J. (2025). Evaluation and Application of the MaxEnt Model to Quantify L. nanum Habitat Distribution Under Current and Future Climate Conditions. Agronomy, 15(8), 1869. https://doi.org/10.3390/agronomy15081869

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