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Article

MaxEnt-Based Habitat Suitability Assessment for Vaccinium mandarinorum: Exploring Industrial Cultivation Opportunities

by
Xuxu Bao
1,†,
Peng Zhou
2,†,
Min Zhang
2,
Yanming Fang
1 and
Qiang Zhang
1,*
1
Co-Innovation Center for Sustainable Forestry in Southern China, Key Laboratory of State Forestry and Grassland Administration on Subtropical Forest Biodiversity Conservation, College of Life Sciences, Nanjing Forestry University, Nanjing 210037, China
2
Jiangsu Academy of Forestry, Nanjing 211153, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2024, 15(12), 2254; https://doi.org/10.3390/f15122254
Submission received: 21 November 2024 / Revised: 18 December 2024 / Accepted: 20 December 2024 / Published: 22 December 2024
(This article belongs to the Section Forest Ecology and Management)
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Abstract

:
Vaccinium mandarinorum Diels, a wild blueberry species distributed in the south of the Yangtze River in China, holds significant ecological and commercial value. Understanding its potential distribution and response to climate change is crucial for effective resource utilization and scientific introduction. By using the Maximum Entropy (MaxEnt) model, we evaluated V. mandarinorum’s potential distribution under current (1970–2000) and future climate change scenarios (2041–2060, 2061–2080, and 2081–2100) based on 216 modern distribution records and seven bioclimatic variables. The results showed that the MaxEnt model could effectively simulate the historical distribution and suitability degree of V. mandarinorum. The top two major environmental variables were precipitation of the driest quarter and annual precipitation, considering their contribution rates of 61.3% and 23.4%, respectively. Currently, the high suitability areas were mainly concentrated in central and northern Jiangxi province, central and southern Zhejiang province, southern Anhui province, central and northern Fujian province, and the border areas of Hunan and Guangxi provinces, covering 21.5% of the total suitable area. Future projections indicate that habitat will shift to higher latitudes and altitudes and that habitat quality will decline. Strategies are required to protect current V. mandarinorum populations and their habitats. The study results could provide an important theoretical reference for the optimization of planting distribution and ensure the sustainable production of the blueberry industry.

1. Introduction

The relationship between species distribution and climate has long been a focal point in global climate change, plant ecology, and geography research [1,2]. Climate is a critical environmental factor influencing vegetation distribution on both the regional and global scales, and its impact on biodiversity and species distributions has become increasingly pronounced [3]. Global climate change not only involves rising temperatures but also includes more frequent extreme weather events, dramatic fluctuations in precipitation patterns, and heightened seasonal variability [4,5]. These changes are significantly altering the geographic distribution of plants, with far-reaching consequences for ecosystem structure and functioning. As global warming and changes in precipitation patterns intensify, plant habitats and ranges are undergoing substantial transformations [6,7]. For example, extreme weather events such as heatwaves, heavy rainfall, and droughts can disrupt species’ growth and reproductive cycles, leading to habitat shifts or range contractions [8]. Therefore, investigating species’ potential geographic distributions in response to future climate change and predicting these shifts are crucial for developing biodiversity conservation strategies, protecting germplasm resources, and guiding scientific plant introduction efforts.
Currently, models for predicting species’ geographic distributions include Random Forest, Support Vector Machines (SVMs), and the MaxEnt model [9,10,11]. Random forest models, which integrate multiple decision trees, are adept at handling complex data features and robust in feature selection, but they involve high computational complexity and reduced interpretability [12]. SVMs excel at managing high-dimensional data and boundary decisions but face challenges in parameter selection and high computational costs [13]. In contrast, the MaxEnt model, based on the principle of maximum entropy, predicts potential species distributions by combining occurrence data with environmental variables [14,15,16]. MaxEnt handles non-linear relationships, deals with data gaps, and evaluates and optimizes models through techniques such as cross-validation, selecting the optimal model parameters to improve prediction accuracy [17]. As a result, it is widely applied in studies on species conservation, phylogeography, rare species protection, and invasive species management [18,19,20]. With the growing availability of global species distribution data and advancements in spatial analysis techniques, niche models have become widely applied in biodiversity conservation [21,22]. By integrating species distribution data with environmental variables, niche models can infer species’ ecological requirements and predict their actual and potential distributions across spatial and temporal scales [23,24].
Globally, the genus Vaccinium encompasses over 500 species, mostly distributed in the Northern Hemisphere and in mountainous regions of tropical Asia and Central and South America [25]. This genus includes commercially important berry crops such as cranberries, blueberries, and lingonberries, as well as many wild species. Vaccinium species are high-value crops, and their nutritional and health benefits are well-documented [26]. However, their adaptability and resilience to climate change remain poorly understood. The MaxEnt ecological niche model has been successfully applied to other Vaccinium species in previous studies [27,28,29]. For instance, Prevéy used climate envelope and phenology models to predict future changes in suitable habitat and phenology for Vaccinium membranaceum in the coastal regions of northwestern North America [27]. The study found that by the 21st century’s end, the suitable habitat for this species might decline by 5%–40%, particularly at low elevations and in dry areas. Meanwhile, suitable areas could increase by 5%–60% at higher elevations (>3050 m) and further north (e.g., British Columbia). Also, depending on climate change scenarios, flowering and fruit ripening times are expected to shift forward by 23–50 days and 24–52 days, respectively. These changes may affect plant–pollinator interactions and traditional harvest times and locations. Suárez-Seoane applied the MaxEnt model to study the distribution of V. myrtillus in different habitats in the Cantabrian Mountains of northwestern Spain [28]. Hirabayashi utilized the MaxEnt model and CMIP6 (SSP126/SSP585) to predict the climate change responses of four Vaccinium species (V. uliginosum, V. vitis-idaea, V. oxycoccos, and commercially grown V. macrocarpon) during 2041–2060 and 2061–2080. Results showed that wild Vaccinium species’ habitats would expand northward and commercial cultivation areas might change, possibly intensifying competition between them and causing conflicts between agriculture and conservation [29]. Developing new berry varieties may be necessary to sustain existing commercial plantings. In the context of rapid global climate change, particularly with intensified human activities, China is experiencing the challenges of rising temperatures and environmental change [30,31]. This poses new challenges for the cultivation, harvesting, and management of both horticultural and wild crops. Understanding how wild and cultivated berries respond to climate change is critical to coping with this new normality.
V. mandarinorum is a perennial berry plant in the Ericaceae family, belonging to the Vaccinium genus and classified among wild blueberries [32]. It is primarily distributed in regions south of the Yangtze River in China, and grows in the understory of pine and oak forests at altitudes of 400–2000 meters or in mixed wood forests on sunny slopes. The fruit is sweet, rich in vitamins, trace elements, anthocyanin glycosides, flavonoids, and other beneficial components, and can be used in medicine, with spleen and kidney, swelling reduction, and other effects, so it has an important value in food, food processing, and pharmaceutical applications [33]. It is also considered a promising edible, medicinal, and ornamental plant [34,35]. Furthermore, V. mandarinorum exhibits strong adaptability, barrenness tolerance, and low light requirements, thriving in full sunlight or under sparse forest cover. It plays an important ecological role in reducing soil erosion and improving soil fertility, which is essential for maintaining the proper functioning of ecosystems as well as sustainable ecosystems [36,37]. Current research on V. mandarinorum and the broader Ericaceae family primarily focuses on its active components and mechanisms [38,39,40], phylogenetics and diversification history [41,42,43,44], and mycorrhizal fungal diversity and growth-promoting effects [45,46,47], as well as tissue culture and propagation techniques [48,49]. However, significant gaps remain in understanding its geographic distribution, climatic characteristics, and potential distribution under future climate scenarios. These knowledge gaps hinder the conservation, development, and utilization of V. mandarinorum germplasm resources. To address this, our study collected geographic distribution data of V. mandarinorum across China, along with environmental variables under current and four future climate scenarios. Using ArcGIS software and the MaxEnt model, we developed distribution models to project its range under both current and future climate conditions. The objective of this study is to clarify the distribution range and major environmental factors of their suitable habitats and to provide a scientific basis for the development of targeted conservation strategies.

2. Materials and Methods

2.1. Sample Data Collection and Screening

This research focused on China as the study area and collected data on the distribution of specimens of V. mandarinorum in the country. Initially, administrative boundary layers for provinces and cities in China were downloaded from the National Geographic Information Resource Directory Service System (available online: https://www.webmap.cn/, accessed on 11 July 2024) to serve as the base map for analysis. Subsequently, 1432 geographic distribution records of V. mandarinorum were collected from various digital specimen platforms, including the Global Biodiversity Information Facility (available online: https://www.gbif.org/, accessed on 11 July 2024), the Chinese Virtual Herbarium (available online: https://www.cvh.ac.cn/, accessed on 11 July 2024), and the National Science and Technology Museum Specimen Resource Sharing Platform (available online: http://mnh.scu.edu.cn/, accessed on 11 July 2024), as well as from Flora of China, local flora, and related research literature. The geographic coordinates of the specimens were determined by verifying the latitude and longitude coordinates using Google Maps (available online: https://www.google.com/maps/, accessed on 16 July 2024), Baidu’s pickup coordinate system (available online: http://api.map.baidu.com/lbsapi/getpoint/, accessed on 16 July 2024), and Google Earth satellite images as outlined by Li [50]. Then all the distribution records were screened, removing those that were cultivated, ambiguous, or duplicated. Based on this filtering process, we retained only one valid distribution point per 2.5’ × 2.5’ grid cell, resulting in a final dataset of 216 valid geographic distribution records (Table S1).

2.2. Acquisition and Processing of Bioclimatic Variable Data

The focus of this study was to explore the relationship between climatic factors and the geographical distribution of V. mandarinorum, and therefore non-climatic factors (e.g., soil and land use) were not considered, to reduce the complexity of the model and improve its interpretability. Nineteen bioclimatic variables with a resolution of 2.5 arcminutes (~5 km) were downloaded from WorldClim (available online: https://worldclim.org/, accessed on 11 July 2024) as environmental factors for predicting the potential distribution of V. mandarinorum [51]. These variables cover both contemporary (1970–2000) and future (2041–2060, 2061–2080, and 2081–2100) time periods. To cope with the uncertainty of the future climate, we adopted the mean values of the Beijing Climate Centre Climate System Model (BCC-CSM2-MR, Beijing, China) from the Coupled Model Intercomparison Project Phase 6 (CMIP6) [52]. These projections include four climate scenarios (SSP126, SSP245, SSP370, and SSP585), representing increasing radiative forcing, carbon emissions, and temperature levels up to 2100, thus enabling a more comprehensive exploration of various scenarios of V. mandarinorum that may occur under different climate conditions in the future [53,54].
To avoid the influence of spatial collinearity among bioclimatic variables on the accuracy of the MaxEnt model, we extracted climate data for the distribution points of V. mandarinorum using the raster package in R4.3.3 software [55]. We then used Spearman’s correlation analysis from the Hmisc package to screen the environmental variables, retaining those with low correlation (r < 0.8) and high correlation (r > 0.8) that were either closely related to species distribution or conducive to model interpretation [56]. Ultimately, 7 environmental variables were selected (Table 1). Finally, the 91 environmental layers for the four time periods used in the model predictions were cropped to the study area and converted to ASCII format using ArcGIS 10.8 software.

2.3. Model Construction and Accuracy Analysis

In this study, MaxEnt V3.4.4 software (available online: https://Biodiversityinformatics.amnh.org/open_source/maxent/, accessed on 11 July 2024) was used to predict the potential distribution of V. mandarinorum under current and future climate conditions [57]. We applied a cross-validation method in which 25% of the geographical distribution data were randomly selected as the test set, and the remaining 75% were used as the training set. The model was set to a maximum of 500 iterations and 10,000 background points, with other parameters kept at their default settings. The final output was the average result from 10 iterations. To assess the accuracy of the model predictions, we used the Receiver Operating Characteristic (ROC) curve and its Area Under the Curve (AUC). The AUC value ranges from 0 to 1, with higher values indicating a stronger correlation between species distribution and environmental bioclimatic factors, thus reflecting higher prediction accuracy. Specifically, an AUC value of 0.7 to 0.8 indicates good predictive accuracy, 0.8 to 0.9 indicates very good predictive accuracy, and values above 0.9 indicate excellent predictive accuracy [14].

2.4. Classification and Evaluation of Potential Suitable Habitats

The predictions from the MaxEnt model were converted into raster data to analyze the habitat suitability of V. mandarinorum through Conversion Tools-ASCII to Raster in ArcGIS 10.8 software. Subsequently, the Reclassification tool in Spatial Analyst was employed to categorize habitat suitability into four levels based on the Natural Breaks (Jenks) classification method: non-suitable areas (0–0.11), low-suitability areas (0.11–0.33), moderate-suitability areas (0.33–0.61), and high-suitability areas (0.61–1). The QGIS raster tools were then used to calculate current and future habitat areas suitable for V. mandarinorum under the four climate scenarios. This analysis assessed the extent of habitat range contraction, expansion, and stability, and estimated the magnitude of these changes.

2.5. Assessment of Environmental Variable Importance

To assess the key climate variables affecting the distribution of V. mandarinorum, this study employed a combination of contribution rates, permutation importance, and jackknife tests. The contribution ratio depends on the specific algorithm of the model to find the optimal solution, and indicates the extent to which each environmental variable contributes to the predicted probability of the model by adjusting the coefficients corresponding to each eigenvalue to increase the gain value of the model and allocating the increased gain value to the environmental variables associated with it, and ultimately outputting the contribution of each environmental contributor as a percentage [58]. Permutation importance, independent of the model’s algorithm, assesses variable importance by comparing changes in model performance under different variable permutation scenarios. Jackknife tests involve running the model with each environmental variable individually, sequentially excluding one variable at a time, and using all variables to build the model. This approach allows for the comparison of training gain, test gain, and AUC values to determine which climate variables have a greater influence on the distribution of V. mandarinorum.

3. Results

3.1. Model Accuracy Evaluation

In the MaxEnt model, simulations were conducted based on current environmental factors and the geographic distribution data of V. mandarinorum. The evaluation of the ROC curve across 10 replicated simulations yielded an average AUC value of 0.940 for the training data (Figure 1). This consistently high AUC indicated that the model demonstrates strong predictive accuracy, making it a reliable tool for forecasting the potential distribution of V. mandarinorum.

3.2. Key Environmental Variables Influencing the Distribution of V. mandarinorum

The analysis of contribution rate and permutation importance values identified the dominant climate variables influencing the distribution of V. mandarinorum. The precipitation of the driest quarter (Bio17) emerged as the most significant factor, with a contribution rate of 61.3% and a permutation importance value of 16.9%, indicating its pivotal role. Following this, annual precipitation (Bio12) contributed 23.4% and showed a permutation importance of 28.9%, further highlighting its importance in shaping the species’ distribution. Other environmental variables contributed less than 10% and had permutation importance values below 16.9%. Figure 2 shows that when an individual variable was assessed in isolation, annual precipitation (Bio12) and the precipitation of the driest quarter (Bio17) consistently yielded the highest regularized training gain, test gain, and AUC values, signifying that these variables provide the most crucial information. Conversely, the exclusion of certain variables revealed that temperature variation (Bio4) led to the most substantial reduction in regularized training gain, test gain, and AUC values, suggesting that this variable conveys unique information not captured by other factors. In summary, Bio17 and Bio12 were the most significant climate variables affecting the distribution of V. mandarinorum. Both their contribution rates and permutation importance values emphasize their critical influence in determining suitable habitats. In contrast, other climate variables, such as temperature and seasonal variations, have a lesser impact, but still offer essential information that enhances the model’s predictive capabilities. Ultimately, precipitation-related factors play the most decisive role in shaping the distribution of V. mandarinorum.

3.3. Climate Factor Response Curve Analysis

Key climate factors affecting the distribution of V. mandarinorum were selected to generate univariate response curves. In these curves, the x-axis represents the values of individual environmental variables within the range of actual observed or modeled values of that climate factor in the study area, while the y-axis reflects the predicted probability of favorable conditions based on logistic outputs, which usually ranges from 0 to 1. An upward trend in the curve indicates a positive correlation with habitat suitability, while a downward trend shows a negative correlation. The amplitude of the curve represents the strength of the relationship. Typically, when the logistic value is ≥0.5, the corresponding environmental factor is considered favorable for species survival. As shown in Figure 3, three principal climate factors—annual precipitation (Bio12), precipitation of the driest quarter (Bio17), and average temperature of the wettest quarter (Bio8)—exhibited unimodal response curves. Suitability increases with the value of each variable, reaching a peak before declining, indicating that V. mandarinorum adapts to specific optimal ranges for each factor. These optimal ranges are Annual Precipitation (Bio12): 1244.98–2378.46 mm; Precipitation of the Driest Quarter (Bio17): 114.40–569.21 mm; and Average Temperature of the Wettest Quarter (Bio8): 16.25–24.32 °C. These ranges represent the most suitable environmental conditions for the growth of V. mandarinorum. Understanding these thresholds is essential for guiding the conservation and cultivation strategies, particularly in the context of changing climatic conditions.

3.4. Potential Distribution of V. mandarinorum Under Modern Climatic Conditions

The distribution map of suitable areas (Figure 4) indicated that under current climatic conditions, the optimal habitat for V. mandarinorum was predominantly located in the region south of the Yangtze River in China. The most suitable areas, covering 42.57 × 104 km2, were concentrated in central and northern Jiangxi province, central and southern Zhejiang province, southern Anhui province, central and northern Fujian province, and the border areas of Hunan and Guangxi provinces. The moderately suitable zone extended over 68.57 × 104 km2, covering most of Hunan, Guizhou, and Yunnan provinces, southern Jiangxi province, and southeastern Hubei province. The low-suitability zone spanned 86.66 × 104 km2, distributed across southern Jiangsu province, southeastern Sichuan province, northwestern Yunnan province, southern Hainan province, and most of Guangdong and Guangxi province (Table 2). In contrast, vast areas north of the Yangtze River and western China were deemed unsuitable for the growth of V. mandarinorum due to unfavorable climatic conditions. In summary, the suitable area for V. mandarinorum in southern China amounted to 197.8 × 104 km2, accounting for 20.6% of the national territory. Of this, the most suitable and the moderately suitable zones represented 21.5% and 34.7% of the total suitable area, respectively, indicating that these areas offer favorable conditions for the V. mandarinorum. The remaining 43.8% of the suitable area was classified as low suitability, suggesting weaker but still viable growing conditions. Overall, V. mandarinorum thrives primarily in the warm and humid southern region of southern China, where climatic conditions are most conducive to its growth.

3.5. Changes in the Distribution of V. mandarinorum Under Future Climate Scenarios

Using climate projections for the 2050s, 2070s, and 2090s under four greenhouse gas scenarios (SSP126, SSP245, SSP370, and SSP585), we predicted the future distribution of V. mandarinorum (Figure 5). In the 2050s, under the SSP126 and SSP370 scenarios, the total suitable area for V. mandarinorum was projected to increase by 2.2% (44,400 km2) and 5.0% (98,800 km2), respectively (Table 3 and Figure 6). However, under the SSP245 and SSP585 scenarios, the total suitable area was expected to decrease by 4.3% (84,200 km2) and 7.4% (147,300 km2), respectively. By the 2070s and 2090s, under SSP126, the suitable area was projected to decline by 0.4% (8200 km2) and 4.7% (93,600 km2), respectively, while under SSP370, decreases of 2.1% (41,200 km2) and 3.1% (61,400 km2) were expected. Conversely, under SSP245, a 1.0% (20,600 km2) increase was predicted for the 2070s, rising to 3.8% (74,700 km2) by the 2090s. Under SSP585, the suitable area was expected to grow by 2.2% (44,300 km2) in the 2070s and 2.3% (46,100 km2) by the 2090s.
In summary, across all four climate scenarios, V. mandarinorum was expected to shift towards higher latitudes and altitudes. Although the total suitable habitat may not change dramatically, there will be significant shifts in suitability levels, with many regions transitioning to moderately or low-suitability zones as environmental stress intensifies.

4. Discussion

4.1. Constraints of Climate Factors on the Potential Geographic Distribution of V. mandarinorum

Hydrothermal conditions, particularly climate, play a crucial role in determining the geographical distribution of vegetation [59]. Specifically, temperature influences latitudinal variation from south to north (in the Northern Hemisphere), while moisture conditions shape regional transitions from coastal to inland areas. In our simulation, we examined seven environmental factors, with precipitation emerging as the dominant factor, contributing 86.4% to the model, followed by temperature at 13.5% (Table 1). This finding underscored the importance of precipitation as a possible primary determinant of V. mandarinorum distribution. Further jackknife tests and univariate response curve analyses highlighted precipitation during the driest quarter (Bio17) as the most significant variable, with an optimal range of 114.40–569.21 mm. This was followed by annual precipitation (Bio12, 288.86–2005.11 mm) and the mean temperature of the wettest quarter (Bio8, 16.25–24.32 °C) (Figure 3). These results were consistent with previous studies, which also identified precipitation as the key environmental driver for the distribution and growth of Vaccinium species [60,61]. It is possible that sufficient water is necessary for pollination, seed development, and dispersal of certain plants to ensure the continuation of their populations [29,62,63]. Although precipitation exerts the greatest influence on the potential distribution of V. mandarinorum, temperature variations play an important role in vegetation migration, even when moisture conditions remain stable. Therefore, while precipitation is the primary factor shaping the species’ distribution, temperature should not be overlooked.
As global temperatures continue to rise, water shortages are expected to become more frequent, particularly in the summer. This will extend the growing season, reduce snowpack, and intensify early spring water deficits, leading to prolonged evapotranspiration and exacerbating drought conditions [64]. Research by Selås in northern Norway showed that reduced water availability due to warmer autumn and winter temperature could result in lower berry fruit production in bilberry (Vaccinium myrtillus L.) [65]. Similarly, Krebs found that early spring precipitation in the current and previous two years could serve as a predictor of berry yields [66]. Consequently, it is likely that rising temperatures coupled with decreasing precipitation may negatively impact the survival rate and fruit yield of V. mandarinorum in affected areas.

4.2. Analysis of Suitable Habitats for V. mandarinorum

Under current climatic conditions, the total suitable habitat area for V. mandarinorum in China was approximately 197.8 × 104 km2, accounting for 20.6% of the national land area (Table 2). The most suitable habitats are concentrated in central and northern Jiangxi province, central and southern Zhejiang province, southern Anhui province, central and northern Fujian province, and the border areas of Hunan and Guangxi provinces (Figure 5). These regions are characterized by a subtropical monsoon climate, with hot, rainy summers and mild, humid winters—conditions that align closely with V. mandarinorum’s preference for cooler temperatures and high precipitation. Moderately suitable habitats for V. mandarinorum are primarily located in Hunan, Guizhou, and Yunnan provinces, southern Jiangxi province, and southeastern Hubei province. These regions offer favorable climatic conditions for large-scale introduction and domestication of the species. In contrast, regions such as southern Jiangsu province, southeastern Sichuan province, northwestern Yunnan province, southern Hainan province, and most of Guangdong and Guangxi province represent areas of low suitability.
However, future climate projections based on Shared Socioeconomic Pathway (SSP) models suggested significant changes in habitat suitability. By comparing the current suitable habitat with projections for the 2050s, the study revealed that V. mandarinorum’s suitable habitat area could change by −7.4% to +5.0%, with notable expansion only under the SSP370 scenario, indicating a 5.0% increase. By the 2070s, changes were projected to range from −2.1% to +2.2%, and by the 2090s, from −4.7% to +3.8%, showing a modest expansion trend under the SSP245 and SSP585 scenarios (Table 3). Despite these relatively small changes in total area, most regions currently classified as highly suitable were likely to transition to medium- or low-suitability zones due to rising temperatures and reduced precipitation. These changes were mainly attributed to regional disparities caused by future climate change. Precipitation intensity is expected to increase in mid- to high-latitude regions, while mid- to low-latitude regions are predicted to experience rising temperatures, decreasing precipitation, and prolonged droughts [67,68]. Consequently, suitable habitats are anticipated to gradually shift northward. While new suitable habitats may emerge in northern regions, the overall quality of these habitats is expected to decline compared to the current optimal zones. If this trend continues, the area of optimal habitats may shrink or even disappear altogether [69]. Additionally, this northward shift is not unique to V. mandarinorum. Numerous studies have shown that global climate change prompts similar migration trends in other species, as warming conditions drive them toward higher latitudes and elevations [70]. These shifts are likely driven by functional and phylogenetic similarities among species [71], explaining the significant overlap in their current distributions. In light of these findings, urgent measures are needed to protect the existing V. mandarinorum resources to prevent the loss of valuable germplasm and to mitigate the effects of shifting climate suitability.

4.3. Conservation and Introduction Strategies for V. mandarinorum

Exploring the response of species to future climate change is essential not only for understanding ecological risks but also for formulating conservation strategies. With the increasing impact of extreme climate events, the survival of many species, including V. mandarinorum, will be threatened [72]. Thus, developing targeted conservation strategies based on future climate scenarios is critical to mitigate the potential adverse effects on habitat suitability and species survival. One overarching strategy to address climate change is the reduction of greenhouse gas emissions, which is the primary driver of global warming. Rising temperatures lead to higher evapotranspiration rates and faster moisture loss, exacerbating habitat degradation [64]. While emission reduction is a global priority, more localized measures are required to protect species like V. mandarinorum. Since precipitation is identified as a key variable, implementing water resource management in areas where precipitation is likely to decrease (e.g., Guangxi and Guangdong provinces) could mitigate the impact of drought on habitat suitability, and can prioritize the collection of breeding material such as seeds from these marginal populations for gene banking to maintain the genetic diversity of the species. And the regulation of river systems, maintaining groundwater levels, and preventing land-use changes that reduce groundwater retention are critical. Studies have shown that increasing water retention plays a positive role in maintaining biodiversity and species conservation [73,74]. By raising the water table and improving soil moisture, the negative impacts of climate warming on V. mandarinorum habitats can be alleviated, ensuring more stable growing conditions. Additionally, fixed sample plots should be established in key regions for long-term monitoring of natural population dynamics. Continuous monitoring allows for the adaptation of conservation measures based on real-time data. Regions such as central and northern Jiangxi province, central and southern Zhejiang province, southern Anhui province, and central and northern Fujian province are predicted to maintain relatively high-quality habitat under different climate scenarios. Therefore, establishing dedicated reserves in these regions can help maintain habitat integrity and ensure the species’ long-term survival. Moreover, artificial expansion of V. mandarinorum’s habitat through mixed forest cultivation and seedling development is a practical strategy to enhance both ecological and economic benefits. By collecting and selecting germplasm resources, cultivating seedlings, and managing artificial mixed forests, it is possible to expand the species’ distribution beyond its current range, ensuring resilience to future climate conditions.
Based on the predicted shifts in suitable habitats under current and future climate conditions, V. mandarinorum’s habitat can be divided into three zones: conservation zones, contraction zones, and expansion zones. The conservation zones (such as areas in Hunan and Jiangxi provinces) are likely to serve as climate refugia; habitat conditions in these regions will remain favorable under future scenarios. In these areas, focused conservation and management should be prioritized to maintain genetic diversity and habitat integrity. In contraction zones (including southern Guangxi and Guangdong provinces), habitat suitability is projected to decline, and in situ conservation efforts will be critical to reducing genetic resource loss. Research on reproductive biology and artificial cultivation techniques should be prioritized in these regions to support in situ conservation. Lastly, for northern and high-altitude areas that are expected to become suitable habitats in the future (e.g., Shaanxi and Sichuan Provinces), it is recommended that seedling transplantation experiments be actively planned to assess the adaptability of the species, and that protected areas or corridors be set up to safeguard the continuation of V. mandarinorum populations and the integrity of the ecosystems associated with them.

4.4. Limitations of This Study and Directions for Future Exploration

This investigation is informative for understanding geographical elements (longitude and latitude) and climatic variables (temperature and precipitation) affecting the current range of V. mandarinorum. It is also prospective in predicting the future distribution and its driving factors of this tree species. Admittedly, our investigation also has limitations to a certain extent. Only bioclimatic variables were considered in this study, but the distribution and suitability of V. mandarinorum may be related to other variables (e.g., soil bedrock, soil pH value, and mycorrhizal fungi). Edaphic and biological factors linked to the range of V. mandarinorum, especially at the local scale, have not been explored. Firstly, the nature of pedogenic bedrock may be one of the habitats limiting factors. For example, V. uliginosum was considered to be fond of igneous rocks [75]. A possible effect of bedrock (siliceous vs calcareous populations) in shaping genetic patterns was found in V. vitis-daea [76]. Secondly, soil pH value may be another habitat-limiting factor. V. mandarinorum was documented in acidic soil (pH4.7) [37], and the cultivated blueberries were described as suitable for acidic soil (pH 4.0–5.5), with a tolerance threshold of pH 6.5 [77]. pH was believed to regulate growth and development by affecting gene expression in Vaccinium species and impact the physiological activity and molecular patterns of Rhododendron plants [78,79]. Finally, the mycorrhizal fungus–plant interaction may be a critical limiting factor. Vaccinium is characterized by typical ericoid mycorrhizae. Ericoid mycorrhizal Vaccinium may have a nutrient absorption advantage [80], and can suppress the growth and N acquisition of ectomycorrhizal pine [81]. V. membranaceum was reported to have ecological differentiation of ericoid mycorrhizae across an elevation gradient, with communities characterized by Phialocephala fortinii at valley bottoms and communities dominated by Rhizoscyphus ericae at alpine habitats [82]. In addition, climate change was supposed to influence mycorrhizal fungi and mycorrhizal fungal-plant interactions [83,84]. Interactive climate–soil forces may shape the nutrition spatial patterns and agroforest development of V. uliginosum [85]. Vaccinium has evolved diverse habitat adaptability, and its habitats are species-specific; for example, pine–hardwood or managed barrens for V. angustifolium, oak woods or mixed woods for V. corymbosum, and conifer or hardwoods for V. myrtillus [86]. As for V. mandarinoum, its suitable habitat and distribution area may be not only influenced by geographical and climatic factors, but also by soil bedrock, soil pH, and mycorrhizal fungi. Further investigation is needed to reveal the soil characteristics and rhizosphere microorganisms of this tree species, in order to understand the current impact of these factors on its distribution and the future impact under the background of climate change.

5. Conclusions

In this study, the MaxEnt model and seven climate variables were used to predict the potential spatial distribution patterns of V. mandarinorum under both current and future climate scenarios (SSP126, SSP245, SSP370, and SSP585). The results showed that under current climate conditions, V. mandarinorum was predominantly distributed in the subtropical regions of southern and central China, with its distribution primarily driven by precipitation during the driest quarter (Bio17), annual precipitation (Bio12), and mean temperature of the wettest quarter (Bio8). The optimal ranges for these factors were 114.40–569.21 mm, 288.86–2005.11 mm, and 16.25–24.32 °C, respectively. However, future climate projections suggested the suitable habitat for V. mandarinorum will shift towards higher altitudes and latitudes, especially under high radiative forcing scenarios (e.g., SSP370 and SSP585). Although the total area of suitable habitat is expected to remain relatively stable, the significant decline in habitat suitability indicates that most regions will degrade into moderately or marginally suitable areas, threatening the species’ long-term viability. This study emphasizes the critical need for targeted conservation strategies that account for these shifts, aiming to preserve the suitable habitat range and ensure population stability in response to climate change. By identifying key environmental factors and predicting future distribution patterns, these findings offer a scientific basis for conservation efforts and shed light on the broader impacts of climate change on biodiversity. This study did not investigate the effects of soil and mycorrhizal fungi on the range of V. mandarinorum, and subsequent studies need to explore the potential impact of these factors on the habitat and distribution area of the tree species under the context of climate change.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f15122254/s1, Table S1: Detailed occurrence coordinates of the collected specimens of V. mandarinorum in China.

Author Contributions

Conceptualization, Q.Z. and Y.F.; formal analysis, X.B.; investigation, P.Z.; resources, P.Z. and M.Z.; data curation, X.B.; writing—original draft preparation, X.B. and Y.F.; writing—review and editing, M.Z. and Q.Z.; funding acquisition, Q.Z. and M.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Independent Research Projects of Jiangsu Academy of Forestry (ZZKY202202, ZZKY202303) and the Jiangsu Provincial Innovation and Extension Project of Forestry Science and Technology (Su2024TG08).

Data Availability Statement

The datasets are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors declare no conflicts of interests.

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Figure 1. Receiver operating characteristic curve of V. mandarinorum for MaxEnt model.
Figure 1. Receiver operating characteristic curve of V. mandarinorum for MaxEnt model.
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Figure 2. Results of the jackknife test of variables’ contribution in modeling V. mandarinorum’s potential habitat distribution. From left to right, the groups of bars represent AUC, Regularized training gain and Test gain, respectively. Pink, blue, and red bars represent running the MaxEnt model with only the variable, without the variable, and with all variables, respectively.
Figure 2. Results of the jackknife test of variables’ contribution in modeling V. mandarinorum’s potential habitat distribution. From left to right, the groups of bars represent AUC, Regularized training gain and Test gain, respectively. Pink, blue, and red bars represent running the MaxEnt model with only the variable, without the variable, and with all variables, respectively.
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Figure 3. Response curves of three environmental predictor variables used in the MaxEnt model for V. mandarinorum. From left to right: Bio12 (Annual precipitation), Bio17 (Precipitation of the driest quarter), and Bio8 (Mean temperature of the wettest quarter).
Figure 3. Response curves of three environmental predictor variables used in the MaxEnt model for V. mandarinorum. From left to right: Bio12 (Annual precipitation), Bio17 (Precipitation of the driest quarter), and Bio8 (Mean temperature of the wettest quarter).
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Figure 4. Potential distribution of V. mandarinorum in China under the current climate (white dots indicate selected occurrence records) and images of flowers and fruits.
Figure 4. Potential distribution of V. mandarinorum in China under the current climate (white dots indicate selected occurrence records) and images of flowers and fruits.
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Figure 5. Potential distribution of V. mandarinorum in China under future climate scenarios. Panels (a–d) indicate the potential distributions under the four scenarios in the 2050s; panels (e–h) indicate the potential distributions under the four scenarios in the 2070s; panels (i–l) indicate the potential distributions under the four scenarios in the 2090s.
Figure 5. Potential distribution of V. mandarinorum in China under future climate scenarios. Panels (a–d) indicate the potential distributions under the four scenarios in the 2050s; panels (e–h) indicate the potential distributions under the four scenarios in the 2070s; panels (i–l) indicate the potential distributions under the four scenarios in the 2090s.
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Figure 6. Comparison of the distribution pattern of different climate scenarios and contemporary V. mandarinorum suitable regions. (a–d) comparison of the potential distributions under the four scenarios for the 2050s; (e–h) comparison of the potential distributions under the four scenarios for the 2070s; (i–l) comparison of the potential distributions under the four scenarios for the 2090s. Green area indicates persistence (overlap of current and projected climatic suitability); orange, future range contraction; blue, future range expansion; white, absence (unsuitable in both current and projected).
Figure 6. Comparison of the distribution pattern of different climate scenarios and contemporary V. mandarinorum suitable regions. (a–d) comparison of the potential distributions under the four scenarios for the 2050s; (e–h) comparison of the potential distributions under the four scenarios for the 2070s; (i–l) comparison of the potential distributions under the four scenarios for the 2090s. Green area indicates persistence (overlap of current and projected climatic suitability); orange, future range contraction; blue, future range expansion; white, absence (unsuitable in both current and projected).
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Table 1. Contribution rate of environmental variables and importance of replacement.
Table 1. Contribution rate of environmental variables and importance of replacement.
Environmental VariableUnitRelative Contribution (%)Permutation Importance (%)
Precipitation of the Driest Quarter (Bio17)mm61.316.9
Annual Precipitation (Bio12)mm23.428.9
Mean Temperature of the Wettest Quarter (Bio8)°C6.40.02
Annual Mean Temperature (Bio1)°C3.616.0
Temperature Seasonality (Bio4)°C2.716.6
Precipitation of the Warmest Quarter (Bio18)mm1.79.9
Mean Diurnal Range (Bio2)°C0.811.6
Table 2. Prediction of suitable areas for V. mandarinorum under current and future climate scenarios.
Table 2. Prediction of suitable areas for V. mandarinorum under current and future climate scenarios.
ScenariosGenerally Suitable (104 km2)Moderately Suitable (104 km2)Highly Suitable (104 km2)Total Suitable (104 km2)
Current86.6668.5742.57197.8
2050s-SSP12691.0885.0626.10202.24
2050s-SSP24567.2584.5837.55189.38
2050s-SSP37084.2479.6343.81207.68
2050s-SSP58595.6366.5120.93183.07
2070s-SSP12683.2572.1241.61196.98
2070s-SSP24592.2666.6840.92199.86
2070s-SSP37078.0172.2143.46193.68
2070s-SSP58587.8769.1845.18202.23
2090s-SSP12674.6769.3744.40188.44
2090s-SSP24589.8971.6943.69205.27
2090s-SSP37078.5470.5842.54191.66
2090s-SSP58585.2176.5940.61202.41
Table 3. Predicting the habitat suitability areas of V. mandarinorum in China under the current climate and four future climate change scenarios in the 2050s, 2070s, and 2090s.
Table 3. Predicting the habitat suitability areas of V. mandarinorum in China under the current climate and four future climate change scenarios in the 2050s, 2070s, and 2090s.
Climate ScenarioPeriodGenerally Suitable (104 km2)Moderately Suitable (104 km2)Highly Suitable (104 km2)Total Suitable (104 km2)
2050s4.4216.49−16.474.44
SSP1262070s−3.413.55−0.96−0.82
2090s−11.990.81.83−9.36
2050s−19.4116.01−5.02−8.42
SSP2452070s5.6−1.89−1.652.06
2090s3.233.121.127.47
2050s−2.4211.061.249.88
SSP3702070s−8.653.640.89−4.12
2090s−8.122.01−0.03−6.14
2050s8.97−2.06−21.64−14.73
SSP5852070s1.210.612.614.43
2090s−1.458.02−1.964.61
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Bao, X.; Zhou, P.; Zhang, M.; Fang, Y.; Zhang, Q. MaxEnt-Based Habitat Suitability Assessment for Vaccinium mandarinorum: Exploring Industrial Cultivation Opportunities. Forests 2024, 15, 2254. https://doi.org/10.3390/f15122254

AMA Style

Bao X, Zhou P, Zhang M, Fang Y, Zhang Q. MaxEnt-Based Habitat Suitability Assessment for Vaccinium mandarinorum: Exploring Industrial Cultivation Opportunities. Forests. 2024; 15(12):2254. https://doi.org/10.3390/f15122254

Chicago/Turabian Style

Bao, Xuxu, Peng Zhou, Min Zhang, Yanming Fang, and Qiang Zhang. 2024. "MaxEnt-Based Habitat Suitability Assessment for Vaccinium mandarinorum: Exploring Industrial Cultivation Opportunities" Forests 15, no. 12: 2254. https://doi.org/10.3390/f15122254

APA Style

Bao, X., Zhou, P., Zhang, M., Fang, Y., & Zhang, Q. (2024). MaxEnt-Based Habitat Suitability Assessment for Vaccinium mandarinorum: Exploring Industrial Cultivation Opportunities. Forests, 15(12), 2254. https://doi.org/10.3390/f15122254

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