Next Article in Journal
Evaluating the Wood Quality of Conifer Species in the Greek Forest Sector Using an Integrated Multi-Criteria Decision Analysis (MCDA) Approach
Previous Article in Journal
Assessing the Impact of Forest Machinery Passage on Soil CO2 Concentration
Previous Article in Special Issue
Utilizing Multi-Source Data and Cloud Computing Platform to Map Short-Rotation Eucalyptus Plantations Distribution and Stand Age in Hainan Island
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Forests
Volume 16
Issue 6
10.3390/f16061026
forests-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

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

MaxEnt Modeling for Predicting the Potential Geographical Distribution of Camellia oleifera Abel Under Climate Change

by
Zhiyin Jiang
1,†,
Yuxin Zhang
2,†,
Qitao Su
2,*,
Qing Gan
1,
Qin Zhou
1,
Yiliu Guo
1,
Zhao Liu
1,
Yanping Zhang
1,
Bing Zhou
2,
Tahani A. Y. Asseri
3 and
Muhammad Umair Hassan
2
1
Ji’an Research Institute of Forestry Science, Ji’an 343009, China
2
Key Laboratory of Jiangxi Province for Biological Invasion and Biosecurity, School of Life Sciences, Jinggangshan University, Ji’an 343009, China
3
Department of Biology, College of Science, King Khalid University, Abha 61413, Saudi Arabia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Forests 2025, 16(6), 1026; https://doi.org/10.3390/f16061026
Submission received: 30 April 2025 / Revised: 12 June 2025 / Accepted: 17 June 2025 / Published: 19 June 2025
(This article belongs to the Special Issue Forest Ecosystem Services: Modelling, Mapping and Valuing)
Browse Figures
Versions Notes

Abstract

Camellia oleifera Abel (C. oleifera) is an evergreen shrub classified under the Camellia genus. It is an important oil species and has great economic benefits. At present, C. oleifera is widely cultivated in the Yangtze River Basin in South China, and its wild species are mainly distributed in the native forests of Hainan Province. Therefore, in the current study, we used the MaxEnt model to predict the suitable habitat for C. oleifera and different environmental factors affecting its current and future distribution. The AUC values exceeded 0.98, showing that the simulation of the model was good, and the TSS values were all above 0.96, indicating that the model was feasible. The results showed that C. oleifera was mainly distributed in Southern China, with a total area of 56.68 × 104 km2. The suitable habitats of Camellia oleifera are affected by the precipitation of the warmest quarter (bio18), human activity, soil available water content (awc_class), and minimum temperature of the coldest month and seasonal temperature (bio04). Furthermore, rainfall in the warmest quarter (bio18) was recognized as a crucial factor impacting its distribution. Under future climate conditions, the suitable habitat area of C. oleifera is projected to expand with a slight northward shift in its distribution center. Therefore, in addition to maintaining the current planting area of C. oleifera, the planting area can be appropriately expanded upward along the current area and along the Yangtze River Basin.

1. Introduction

Climate change is rapidly changing globally, and it significantly affects species distribution [1] and plant growth [2]. Plant species with narrow ecology will be more significantly affected by climate change in the future [3,4]. The changes in temperature and rainfall patterns caused by climate affect plant functioning, growth, reproduction, and species distribution [5]. Besides climate changes, soil production also significantly affects the growth and distribution of species. Soil continuously provides water, nutrients, temperature, and air for normal plant growth and development [6,7]. Therefore, soil factors are interlinked with their effects on plant growth and distribution. Rapid climate change can degrade the habitat of species, and plants having poor adaptability face local extinction [8]. Nevertheless, plants can respond to new conditions by adapting, shifting, and contracting their geographic ranges [9,10]. Therefore, in this context, it is essential to study the impacts of climate change on species distribution for the sustainable utilization of plant resources.
Camellia oleifera Abel (C. oleifera) is an evergreen shrub classified under the Camellia genus in the Theaceae family. It is an important woody oil species rich in squalene, phytosterols, vitamin E, and other nutritional and health-care ingredients. Long-term consumption of C. oleifera is beneficial to human health [11,12,13,14]. C. oleifera seeds contain camellia saponin, camellia polysaccharide, camellia protein, and other substances, which are often used in the chemical, light, food, feed product processing, and other industries. C. oleifera seeds can also be made into furfural and activated carbon [15,16,17]. Camellia oleifera has high economic value, and it plays an imperative role in maintaining biodiversity and protecting the soil [18,19]. The flowers, leaves, and seeds of C. oleifera are also recorded in the ethnomedicines of Hunan and other places [20,21]. At the same time, C. oleifera is a traditional Chinese herbal medicine with a long history of application and a wide range of pharmacological activities. It is a potential resource for the development of drugs or functional products [22]. China is the origin and distribution center of C. oleifera, with rich germplasm resources and a long history of cultivation. C. oleifera is an important cash crop in China, and it is widely planted in the south, which brings economic benefits and employment opportunities to the local area. Therefore, understanding the environmental determinants governing the C. oleifera distribution can help artificially expand its suitable growing areas. This expansion would not only increase plantation coverage but also facilitate the introduction of C. oleifera to other suitable regions, thereby boosting local economic development.
There are currently a variety of models, such as ENFA, BIOCLIM, GRAP, and MaxEnt, that are used to predict domestic and foreign species distribution. Among them, MaxEnt has higher modeling accuracy with an appreciable ability in predicting the species distribution [23]. It synthesizes various environmental parameters in predicting species distribution, and it also determines the different factors impacting the species’ growth [24]. MaxEnt is also more concise, can process continuous and categorical environmental data at the same time, can examine the importance of variables through Jackknife, and can handle low sample sizes [25,26,27]. For instance, the MaxEnt model indicates that the distribution of Yunnan dwarf palm and sandalwood is predominantly influenced by precipitation and temperature [28,29]. We used the MaxEnt model to simulate and analyze the suitable habitat of C. oleifera using environmental factors. The purpose of the current study was to (1) understand the environment suitable for the growth of C. oleifera, (2) understand and analyze the current distribution of C. oleifera, and (3) simulate the future suitable habitat of C. oleifera and expand the planting area to promote local economic development and strengthen the utilization.

2. Materials and Methods

2.1. Distribution Data of Species

The distribution data of C. oleifera were collected from three sources: the Global Biodiversity Information Facility (GBIF; https://www.gbif.org/, accessed on 31 March 2025), the China Digital Herbarium (CVH: https://www.cvh.ac.cn/?from=singlemessage, accessed on 31 March 2025) and distribution maps provided on the flora of China (https://www.iplant.cn/, accessed on 31 March 2025). The dataset contained 1727 distribution points of C. oleifera across China, and the longitude and latitude coordinates of the effective distribution points were sorted into CSV format files.

2.2. Environmental Variables

We selected four types of environmental factors, including climate, terrain, soil, and human disturbance factors that affect the suitability of C. oleifera habitats. Climate factors mainly include 19 variables, which were downloaded from the World Climate Database (https://www.worldclim.org/, accessed on 6 April 2025) with a resolution of 2.5 arc min. Terrain parameters were extracted using a 30 m DEM obtained from the Chinese Geospatial Data Cloud repository (https://www.gscloud.cn, accessed on 6 April 2025). From the processed DEM data, we derived three key topographic variables: slope, aspect, and elevation. Further, soil parameters were extracted from the HWSD raster dataset, maintained by the Food and Agriculture Organization (FAO). This included soil moisture content, surface soil pH, soil reference depth, and topsoil texture. Human activity, namely human impact index data, which is an updated map of human impact on the environment in geographical projection. It includes population density, land use infrastructure, and transportation routes, and it was obtained from the International Earth Science Information Network Center (http://ciesin.org, accessed on 6 April 2025) (Table S1). Climate projections were derived from the Coupled Model Intercomparison Project Phase 6 (CMIP6), utilizing Shared Socioeconomic Pathways (SSPs) to represent divergent future scenarios of socioeconomic development and emission levels. Three representative SSP scenarios were selected according to their emission intensities: SSP126 (low emissions), SSP245 (medium emissions), and SSP585 (high emissions) [30]. These descriptions of future climate change are more scientific [31]. The analysis focused on two future periods: 2041–2060 and 2081–2100. Using ArcGIS 10.8, we processed the environmental variables by extracting data using China’s administrative boundaries as a spatial mask and converting them to ASCII format for subsequent modeling and archival purposes.

2.3. Ecological Niche Modeling

We selected the twenty-seven environmental variables; thereafter, records for the occurrence of C. oleifera were loaded into MaxEnt. The species occurrence data were partitioned into training (75%) and validation (25%) subsets for model calibration and evaluation, respectively. We implemented the MaxEnt algorithm with default parameters, converting output projections into raster format using ArcGIS 10.8 (ESRI, Redlands, CA, USA) for spatial analysis. Model performance was assessed through the receiver operating characteristic (ROC) curve, with the area under the curve (AUC) metric ranging from 0 to 1. Higher AUC values (closer to 1) indicate superior predictive accuracy of the used model [32]. The prediction accuracy for the model was divided into five different intervals; excellent (0.9–1), good (0.8–0.9), fair (0.7–0.8), poor (0.6–0.7), and fail (0.5–0.6) [33]. Using the MaxEnt model outputs, we classified the potential distribution of C. oleifera into three habitat suitability tiers through the SDM tool in ArcGIS 10.8 (ESRI): (1) non-suitable habitat (<10th percentile training presence threshold), (2) low-suitability habitat (10th percentile to 0.66 probability), and (3) high-suitability habitat (>0.66 probability). This classification followed established ecological thresholds for interpreting MaxEnt output values [34]. Thereafter, the potential area suitable for distribution of C. oleifera was calculated. The ASCII results file produced by the MaxEnt model operation was imported into ArcGIS 10.8 software. The spatial analyses as well as visualization mapping were performed by a tool to determine the change in the area of the suitable habitat for C. oleifera under both current and future scenarios. A value of −1 demonstrates a newly added suitable habitat, while values of 0 depict that a habitat is not suitable. Further, a value of 1 indicates that a habitat was retained, while a value of 2 demonstrates a decrease in suitable habitats. Furthermore, different thresholds, including suitable and non-suitable habitats, were used to convert the data into binary data. We quantified spatiotemporal shifts in suitable habitat using the SDM Toolbox v2 in ArcGIS 10.8, specifically employing its “Centroid Changes” tool to calculate the location distribution center for the suitable habitats and directions of its migration over time.

2.4. Niche Overlap and Range Overlap Analysis

We assessed niche consistency in C. oleifera using ENMTools [35]. Using ENMTools 1.1.2, we employed MaxEnt to generate current-climate species distribution layers, which were subsequently analyzed to quantify niche overlap using two established metrics: Schoener D [36] and Warren I [37]. Further, by running pseudo-replication dataset 100 times, the expected values for the Schoener D and Warren I distribution frequencies were collected. The Monte Carlo method and non-parametric test were employed to measure the significance between observed and expected values. When observed values of both I and D were significantly lower than the null distribution (p < 0.01), we rejected the niche consistency hypothesis. This statistical evidence indicates significant niche differentiation between the compared populations/species [38,39].

3. Results

3.1. Main Environment Variables

The human activities (ha), rainfall of the warmest quarter, soil water content, minimum temperature of the coldest month (bio06), and temperature seasonality (bio04), were identified as five important variables with a total contribution of 90.3%. The optimal range of each factor was obtained through the response curve of environmental variables. The precipitation of the warmest quarter was 449.62–788.14 mm, and the range of human activity was 37.54–165.44 (Figure 1). Further, the range of soil available water content was about 1.0768, the minimum temperature of the coldest month was −1.45–6.17 °C, and temperature seasonality was 433.74–895.11.

3.2. Distribution of Suitable Habitat and Model Accuracy Under Current Climate Conditions

The MaxEnt model has a very high prediction accuracy for the distribution of C. oleifera, with a training AUC of 0.98 for repeated runs. The values of TSS were greater than 0.96 (Table 1). This represents the accuracy of the model in predicting species distribution. C. oleifera is currently mainly distributed in the southeast of China, with a main distribution in Guangdong, Hainan, Guangxi, Hunan, Hubei, Jiangxi, Zhejiang, Chongqing, central Sichuan, Jiangsu, and southern Anhui (Figure 2). The total suitable habitat of C. oleifera in these regions is about 56.68 × 104 km2, accounting for about 5.90% of China’s land area, of which the area of highly suitable habitat is 2.94 × 104 km2.

3.3. Range Changes in C. oleifera Under Future Climate Change

Under the 2041s–2081s SSP126 climate scenario, the suitable habitat of C. oleifera did not change much, and its total suitable habitat first decreased by 16.11% and then increased to 58.20 × 104 km2. Among them, the area of highly suitable habitat did not change much. Under the 2041s–2081s SSP245 climate scenario, the total suitable habitat of C. oleifera decreased by 0.98 × 104 km2 and then increased by 5.40 × 104 km2 (Figure 3). Further, higher suitable habitat areas are expected to increase by 12.61 × 104 km2 during 2081 to 2100, while highly suitable habitat is expected in Hunan, Jiangxi, Anhui, Zhejiang, and southern parts of Hubei. Under a different climate scenario (2041s–2081s SSP585), the suitable habitat area for C. oleifera exhibited progressive expansion. Firstly, it was increased by 2.00 × 104 km2, and then by 17.05 × 104 km2 showing that the suitable area was increased by 10 times the current level. Furthermore, in Hubei, Anhui, Zhejiang, Guangdong, Chongqing, and Sichuan Province, the highly suitable habitat area for C. oleifera significantly increased (Table 2).

3.4. Future Changes in the Suitable Habitat and Centroid Migration of C. oleifera

The stable areas were mainly distributed in Guangdong, Hunan, Hubei, and Zhejiang Province (Figure 4). In contrast, habitat loss was mainly concentrated in southwestern Guangxi, southern Yunnan, and eastern Sichuan Province. The habitats in Jiangsu, Henan, and a small part of Shaanxi were expected to expand. The centroid of the C. oleifera suitable habitat under different climate scenarios in different periods is located in Hunan, and compared with the current situation, the centroid has moved northward (Figure 5). The current centroid is located in Qidong County, Hunan Province (111°35′06″ E, 26°55′58.8″ N), and under the 2041s–2081s SSP126 scenario, the centroid first moves northward to Xiangtan County (112°29′34.8″ E, 27°31′34.68″ N) and then moves southward to Shuangfeng County (112°05′45.6″ E, 27°35′44.52″ N). Under the 2041s–2081s SSP245 scenario, the centroid first migrates northward to Ningxiang County (112°22′44.4″ E, 28°07′06.24″ N) and then migrates southward to Lianyuan County (111°44′56.4″ E, 27°40′44.04″ N). Under the 2041s–2081s SSP585 scenario, the centroid first migrates northward to Shuangfeng County (112°11′45.6″ E, 27°29′19.68″ N) and then continues to migrate northward to Yiyang County (112°20′24″ E, 28°25′02.64″ N).

3.5. Niche Comparisons

The niche consistency test shows that the Schoener D niche overlap index under different scenarios at different times is greater than 0.8, and the Warren I value is greater than 0.9. In the future, C. oleifera will partially migrate from the current niche, and its suitable habitat will increase. In the 2041s SSP245 scenario, the Schoener D value was 0.82, showing that the C. oleifera niche will change as a result of future climate change processes, with the largest migration (Table 3).

4. Discussion

Camellia oleifera is widely cultivated from the Yangtze River Basin to South China and is mainly distributed in Yunnan, Fujian, Guangdong, Guangxi, Hainan, etc. The prediction results of the MaxEnt model are aligned with actual distribution, showing that the model’s results were reliable and accurate. Precipitation is the main limiting factor for almost all species [40,41]. The most important factor affecting the distribution of C. oleifera is the precipitation of the warmest quarter, which accounts for 54.3% of the contribution rate, with a range of precipitation of 449.62–788.14 mm, and the influence of soil effective water content (awc_class) on the distribution of C. oleifera accounts for 10.9%. The minimum temperature in the coldest month and the seasonal temperature are also important factors impacting C. oleifera. This was linked with the fact that C. oleifera grows well in warmer and humid conditions [42]; therefore, precipitation may affect the growth and development of C. oleifera. The fruiting period of C. oleifera is September–October of the following year. High precipitation in the warmest season is favorable for plant growth and increased soil moisture content and humidity favor growth and seed quality [43,44]. If the seeds cannot mature, the natural regeneration capacity of the population will be inhibited, reducing the distribution and spread of the species. The lowest temperature in the coldest month indicates that the growth of C. oleifera requires a certain amount of heat, and it may also be related to the flowering period of C. oleifera. Flowering is a key physiological process for plants to transition from vegetative growth to reproductive growth [45]. Normal flower development is essential for the sexual reproduction of plants and crop yields [46]. Suitable temperature is a necessary condition for plants to achieve sexual reproduction, and temperature stress sometimes causes the development of pistils and stamens to be out of sync [47]. Therefore, temperatures that are too low may cause the incomplete development of C. oleifera flowers, thereby hindering the reproduction of C. oleifera. The increase in soil moisture available and suitable soil temperature due to climate change can ensure better seed germination and plant growth [48]. This can increase the suitable habitat area for C. oleifera distribution in the future. The change in land cover and land owing to human activities also affects the species distribution [49,50]. C. oleifera is widely cultivated in southern China as an economic crop. The cultivation range extends from the southern slope of the Qinling Mountains and the Yangtze River basin in the north to Hainan in the south; from Zhoushan, Zhejiang, and Taiwan in the east to Lijiang and Dali in Yunnan in the west. Jiangxi, Hunan, and Guangxi are its core production areas [42]. C. oleifera is an economic crop that can bring economic benefits to the local area. Therefore, humans may expand the planting area of C. oleifera in the future, increasing the area suitable for the growth of C. oleifera. Under future climate conditions with rising carbon dioxide concentrations and temperatures, China’s average annual precipitation is likely to increase. Our study shows that the main factor affecting the distribution of C. oleifera is precipitation. With future climate change-related increases in temperature and precipitation, sufficient soil moisture and suitable soil temperature can ensure better seed germination and plant growth [48]. Rainfalls are expected to increase in the future from the current altitude to higher altitudes, resulting in a shift towards warm and humid conditions [50,51]. The centroid of C. oleifera will shift to higher altitudes, migrate towards the northeast. This aligns with previous findings reporting that the distribution of most species will shift towards higher altitudes and latitudes [52,53]. However, C. oleifera exhibits limited migration capacity, suggesting strong environmental adaptability. While current cultivation areas remain suitable for continuous planting, future expansion could be strategically prioritized in Jiangsu, Anhui, and Henan provinces.
Niche overlap describes similarity between species in their utilization of environmental resources and the relationship of competition or mutual promotion while sharing resources [54,55]. The current niche overlap values between the SSP126, SSP245, and SSP585 climate scenarios are all greater than 0.5, indicating that the utilization of resources and the environment by C. oleifera is relatively similar in different periods and climate scenarios. Therefore, the changes in the suitable habitat of C. oleifera are relatively stable, and the degree of migration is relatively small.
The current study also identified some limitations; first, the reliance solely on recorded occurrence data may introduce prediction biases due to potential incompleteness or sampling bias in the dataset. Second, while we incorporated climatic, topographic, edaphic (soil), and anthropogenic factors into our model, we did not account for species interactions, which may significantly influence species distribution and abundance patterns. These constraints highlight important areas for improvement in future research. Subsequent studies should address these limitations by incorporating more comprehensive data sources and ecological interactions to enhance prediction accuracy.

5. Conclusions

Camellia oleifera is mainly distributed and planted in Guangdong, Hainan, Guangxi, Hunan, Hubei, Jiangxi, Zhejiang, Chongqing, central Sichuan, Jiangsu, and southern Anhui in China. The centroid of C. oleifera will migrate northward, and suitable habitat area will increase in the future. The niche overlap values of C. oleifera are all greater than 0.8, indicating that the migration of C. oleifera is small. Therefore, according to the prediction, the planting area of C. oleifera can be appropriately increased to the north to promote the development of the local economy. Projections indicate that C. oleifera will predominantly expand northeastward from its current, suitable growing regions. Presently, the yield of C. oleifera planting land in China is low due to the lack of artificial management and protection. At the same time, urbanization development, tourism development, and reclamation of wasteland may cause habitat destruction and resource loss of wild C. oleifera. To optimize cultivation potential, we recommend maintaining existing production areas while strategically expanding plantations into adjacent suitable territories. Particular emphasis should be placed on expansion into Jiangsu and Anhui provinces, where environmental conditions appear favorable. This dual approach of conservation and controlled expansion would simultaneously enhance C. oleifera yields and stimulate economic development in mountainous regions.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f16061026/s1, Table S1: Twenty seven different environment variables used in model prediction.

Author Contributions

Z.J.: Data curation, formal analysis, methodology. Y.Z. (Yuxin Zhang): formal analysis, methodology, writing—original draft. Q.S.: methodology, funding acquisition, formal analysis, writing—review and editing. Q.G.: formal analysis. Q.Z.: software. Y.G.: methodology. Z.L.: data curation. Y.Z. (Yanping Zhang): data curation. B.Z.: formal analysis. M.U.H.: conceptualization, project administration, writing—review and editing. T.A.Y.A.: writing—review and editing. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Key Laboratory of Jiangxi Province for Biological Invasion and Biosecurity (2023SSY02111) and Ji’an research institute of forestry science Commissioned Project (2023zkhx0040).

Data Availability Statement

Species occurrence records were obtained from the Global Biodiversity Information Facility database (GBIF: https://www.gbif.org/, accessed on 31 March 2025), the Chinese Virtual Herbarium database (CVH: https://www.cvh.ac.cn/, accessed on 31 March 2025), the National Specimen Information Infrastructure database (NSII: http://nsii.org.cn/2017/home.php, accessed on 31 March 2025) and our field surveys.

Acknowledgments

The authors thank Muhammad Aamer for his suggestions to improve the quality of the manuscript. The authors extend their appreciation to the Deanship of Research and Graduate Studies at King Khalid University for supporting this work through the Large Research Project under grant number RGP 2/325/46.

Conflicts of Interest

The authors declare no conflicts of interest.

Correction Statement

This article has been republished with a minor correction to the existing affiliation information. This change does not affect the scientific content of the article.

References

  1. Dyderski, M.K.; Paź, S.; Frelich, L.E.; Jagodziński, A.M. How much does climate change threaten European forest tree species distributions? Glob. Change Biol. 2018, 24, 1150–1163. [Google Scholar] [CrossRef] [PubMed]
  2. Rovzar, C.; Gillespie, T.W.; Kawelo, K. Landscape to site variations in species distribution models for endangered plants. For. Ecol. Manag. 2016, 369, 20–28. [Google Scholar] [CrossRef]
  3. Ceballos, G.; Ehrlich, P.R.; Barnosky, A.D.; García, A.; Pringle, R.M.; Palmer, T.M. Accelerated modern human–induced species losses: Entering the sixth mass extinction. Sci. Adv. 2015, 1, e1400253. [Google Scholar] [CrossRef]
  4. Sax, D.F.; Early, R.; Bellemare, J. Niche syndromes, species extinction risks, and management under climate change. Trends Ecol. Evol. 2013, 28, 517–523. [Google Scholar] [CrossRef]
  5. Graham, E.M.; Reside, A.E.; Atkinson, I.; Baird, D.; Hodgson, L.; James, C.S.; Vanderwal, J.J. Climate change and biodiversity in Australia: A systematic modelling approach to nationwide species distributions. Australas. J. Environ. Manag. 2019, 26, 112–123. [Google Scholar] [CrossRef]
  6. Ma, T.S.; Deng, X.W.; Chen, L.; Xiang, W.H. The soil properties and their effects on plant diversity in different degrees of rocky desertification. Sci. Total Environ. 2020, 736, 139667. [Google Scholar] [CrossRef] [PubMed]
  7. Yuan, Y.; Xiong, D.H.; Wu, H.; Zhang, S.; Zhang, B.J.; Dahal, N.M.; Liu, L.; Li, W.X.; Zhang, W.D.; Shi, L.T. Spatial variation of soil physical properties and its relationship with plant biomass in degraded slopes in dry-hot valley region of Southwest China. J. Soils Sediments 2020, 20, 2354–2366. [Google Scholar] [CrossRef]
  8. Kouhi, S.M.M.; Erfanian, M. Predicting the Present and Future Distribution of Medusahead and Barbed Goatgrass in Iran. Ecopersia 2020, 8, 41–46. [Google Scholar]
  9. Su, Q.T.; Du, Z.X.; Luo, Y.; Zhou, B.; Xiao, Y.A.; Zou, Z.R. MaxEnt Modeling for Predicting the Potential Geographical Distribution of Hydrocera triflora since the Last Interglacial and under Future Climate Scenarios. Biology 2024, 13, 745. [Google Scholar] [CrossRef]
  10. Su, Q.T.; Du, Z.X.; Xue, Y.X.; Li, H.; Zhang, Y.X.; Zhang, S.J.; Huang, X.Y.; Zhou, B.; Qian, H.; Xiao, Y.A.; et al. Habitat Suitability Modeling of Endemic Genus Chimonanthus in China under Climate Change. Forests 2024, 15, 1625. [Google Scholar] [CrossRef]
  11. Zhang, F.; Zhu, F.; Chen, B.L.; Su, E.R.; Chen, Y.Z.; Cao, F.L. Composition, bioactive substances, extraction technologies and the influences on characteristics of Camellia oleifera oil: A review. Food Res. Int. 2022, 156, 111159. [Google Scholar] [CrossRef]
  12. Yang, L.; Gao, C.; Xie, J.J.; Qiu, J.; Deng, Q.N.; Zhou, Y.C.; Liao, D.S.; Deng, C.Y. Fruit economic characteristics and yields of 40 superior Camellia oleifera Abel plants in the low-hot valley area of Guizhou Province, China. Sci. Rep. 2022, 12, 7068. [Google Scholar] [CrossRef] [PubMed]
  13. Gao, J.; Ma, L.; Ma, J.; Xia, S.T.; Gong, S.M.; Yin, Y.L.; Chen, Y.Z. Camellia (Camellia oleifera Abel.) Seed Oil Regulating of Metabolic Phenotype and Alleviates Dyslipidemia in High Fat-Fed Mice through Serum Branch-Chain Amino Acids. Nutrients 2022, 14, 2424. [Google Scholar] [CrossRef]
  14. Gao, S.; Wang, B.F.; Liu, F.D.; Zhao, J.R.; Yuan, J.; Xiao, S.X.; Masabni, J.; Zou, F.; Yuan, D.Y. Variation in Fruit Morphology and Seed Oil Fatty Acid Composition of Camellia oleifera Collected from Diverse Regions in Southern China. Horticulturae 2022, 8, 818. [Google Scholar] [CrossRef]
  15. Quan, W.X.; Wang, A.P.; Gao, C.; Li, C.C. Applications of Chinese Camellia oleifera and its By-Products: A Review. Front. Chem. 2022, 10, 921246. [Google Scholar] [CrossRef]
  16. Chen, P.J.; Hu, C.S.; Gu, J.; Lin, X.Y.; Yang, C.L.; Leu, S.Y.; Guan, L.T. Pyrolysis characteristics of tea oil camellia (Camellia oleifera Abel.) shells and their chemically pre-treated residues: Kinetics, mechanisms, product evaluation and joint optimization. J. Anal. Appl. Pyrolysis 2022, 164, 105526. [Google Scholar] [CrossRef]
  17. Chaudhary, N.; Bhardwaj, J.; Seo, H.J.; Kim, M.Y.; Shin, T.S.; Kim, J.D. Camellia sinensis fruit peel extract inhibits angiogenesis and ameliorates obesity induced by high-fat diet in rats. J. Funct. Foods 2014, 7, 479–486. [Google Scholar] [CrossRef]
  18. Gao, H.; Ouyang, Z.Y.; Chen, S.B.; van Koppen, C.S.A. Role of culturally protected forests in biodiversity conservation in Southeast China. Biodivers. Conserv. 2013, 22, 531–544. [Google Scholar] [CrossRef]
  19. Zheng, H.; Chen, F.L.; Ouyang, Z.Y.; Tu, N.M.; Xu, W.H.; Wang, X.K.; Miao, H.; Li, X.Q.; Tian, Y.X. Impacts of reforestation approaches on runoff control in the hilly red soil region of Southern China. J. Hydrol. 2008, 356, 174–184. [Google Scholar] [CrossRef]
  20. Yang, S.X.; Wang, X.N.; Zhang, Z.; Chen, L.S.; Wang, R.; Chen, Y.Z. Camellia Resources in Hunan Province. GX For. Sci. 2022, 52, 583–590. [Google Scholar]
  21. Ai, J.X.; Zhen, D.J.; Wu, X. Progress in Germplasm Resources, Chemical Composition and Pharmacological Activities of Camellia oleifera. Food Drug 2025, 27, 209–218. [Google Scholar]
  22. Luan, F.; Zeng, J.S.; Yang, Y.; He, X.R.; Wang, B.J.; Gao, Y.B.; Zeng, N. Recent advances in Camellia oleifera Abel: A review of nutritional constituents, biofunctional properties, and potential industrial applications. J. Funct. Foods 2021, 75, 104242. [Google Scholar] [CrossRef]
  23. Phillips, S.J.; Dudík, M. Modeling of species distributions with Maxent: New extensions and a comprehensive evaluation. Ecography 2008, 31, 161–175. [Google Scholar] [CrossRef]
  24. Zeng, J.R.; Li, C.M.; Liu, J.Z.; Li, Y.Y.; Hu, Z.Z.; He, M.L.; Zhang, H.Y.; Yan, H.J. Ecological assessment of current and future Pogostemon cablin Benth. potential planting regions in China based on MaxEnt and ArcGIS models. J. Appl. Res. Med. Aromat. Plants 2021, 24, 100308. [Google Scholar] [CrossRef]
  25. Phillips, S.J.; Anderson, R.P.; Schapire, R.E. Maximum entropy modeling of species geographic distributions. Ecol. Modell. 2006, 190, 231–259. [Google Scholar] [CrossRef]
  26. Pearson, R.G.; Raxworthy, C.J.; Nakamura, M.; Peterson, A.T. ORIGINAL ARTICLE: Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr. 2007, 34, 102–117. [Google Scholar] [CrossRef]
  27. Elith, J.; Phillips, S.J.; Hastie, T.; Dudík, M.; Chee, Y.E.; Yates, C.J. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 2015, 17, 43–57. [Google Scholar] [CrossRef]
  28. Zhang, X.F.; Nizamani, M.M.; Jiang, C.; Fang, F.Z.; Zhao, K.K. Potential planting regions of Pterocarpus santalinus (Fabaceae) under current and future climate in China based on MaxEnt modeling. Ecol. Evol. 2024, 14, e11409. [Google Scholar] [CrossRef]
  29. Wang, X.F.; Wang, X.H.; Li, Y.; Wu, C.H.; Zhao, B.; Peng, M.C.; Chen, W.; Wang, C.Y. Response of Extremely Small Populations to Climate Change—A Case of Trachycarpus nanus in Yunnan, China. Biology 2024, 13, 240. [Google Scholar] [CrossRef]
  30. Leimbach, M.; Giannousakis, A. Burden sharing of climate change mitigation: Global and regional challenges under shared socio-economic pathways. Clim. Change 2019, 155, 273–291. [Google Scholar] [CrossRef]
  31. Wang, Y.J.; Xie, L.Y.; Zhou, X.Y.; Chen, R.F.; Zhao, G.H.; Zhang, F.G. Prediction of the potentially suitable areas of Leonurus japonicus in China based on future climate change using the optimized MaxEnt model. Ecol. Evol. 2023, 13, e10597. [Google Scholar] [CrossRef] [PubMed]
  32. Ma, D.L.; Lun, X.C.; Li, C.; Zhou, R.B.; Zhao, Z.; Wang, J.; Zhang, Q.F.; Liu, Q.Y. Predicting the Potential Global Distribution of Amblyomma americanum (Acari: Ixodidae) under Near Current and Future Climatic Conditions, Using the Maximum Entropy Model. Biology 2021, 10, 1057. [Google Scholar] [CrossRef]
  33. Beale, C.M.; Lennon, J.J. Incorporating uncertainty in predictive species distribution modelling. Philos. Trans. R. Soc. B 2012, 367, 247–258. [Google Scholar] [CrossRef] [PubMed]
  34. Shi, Y.H.; Ren, Z.X.; Wang, W.J.; Xu, X.; Liu, J.; Zhao, Y.H.; Wang, H. Predicting the spatial distribution of three Astragalusspecies and their pollinating bumblebees in the Sino-Himalayas. Biodivers. Sci. 2021, 29, 759–769. [Google Scholar] [CrossRef]
  35. Warren, D.L.; Glor, R.E.; Turelli, M. ENMTools: A toolbox for comparative studies of environmental niche models. Ecography 2010, 33, 607–611. [Google Scholar] [CrossRef]
  36. Schoener, T.W. The Anolis Lizards of Bimini: Resource Partitioning in a Complex Fauna. Ecology 1968, 49, 704–726. [Google Scholar] [CrossRef]
  37. Warren, D.L.; Glor, R.E.; Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 2008, 62, 2868–2883. [Google Scholar] [CrossRef]
  38. Li, L.; Zhang, J.; Lu, Z.Q.; Zhao, J.L.; Li, Q.J. Genomic data reveal two distinct species from the widespread alpine ginger Roscoea tibetica Batalin (Zingiberaceae). J. Syst. Evol. 2020, 59, 1232–1243. [Google Scholar] [CrossRef]
  39. Zhao, J.L.; Gugger, P.F.; Xia, Y.M.; Li, Q.J. Ecological divergence of two closely related Roscoea species associated with late Quaternary climate change. J. Biogeogr. 2016, 43, 1990–2001. [Google Scholar] [CrossRef]
  40. Marcer, A.; Lloren, S.; Molowny-Horas, R.; Pons, X.; Pino, J. Using species distribution modelling to disentangle realised versus potential distributions for rare species conservation. Biol. Conserv. 2013, 166, 221–230. [Google Scholar] [CrossRef]
  41. Ngarega, B.K.; Masocha, V.F.; Schneider, H. Forecasting the effects of bioclimatic characteristics and climate change on the potential distribution of Colophospermum mopane in southern Africa using Maximum Entropy (Maxent). Ecol. Inform. 2021, 65, 101419. [Google Scholar] [CrossRef]
  42. Wang, H.R.; Xia, J.M.; Sun, Y.J. Effect of short-term low temperature stress on non-structural carbohydrates in Camellia oleifera leaves. J. Fujian Agric. For. Univ. (Nat. Sci. Ed.) 2025, 53, 392–400. [Google Scholar]
  43. Lan, X.H.; Wang, J.L.; Fu, C.; Li, L.M.; Yuan, M.Q.; Tan, T.T.; Du, F.G. Prediction of Suitable Area of Magnolia siboldii in China Based on the Optimized Maxent Model. J. Northwest For. Univ. 2022, 37, 100–106. [Google Scholar]
  44. Leng, X.H.; Xue, L.; Wang, J.; Li, S.; Yang, Z.L.; Ren, H.D.; Yao, X.H.; Wu, Z.Y.; Li, J.Y. Physiological Responses of Handeliodendron bodinieri (Levl.) Rehd. to Exogenous Calcium Supply under Drought Stress. Forests 2020, 11, 69. [Google Scholar] [CrossRef]
  45. Chen, M.; Zhang, T.L.; Hu, C.G.; Zhang, J.Z. The Role of Drought and Temperature Stress in the Regulation of Flowering Time in Annuals and Perennials. Agronomy 2023, 13, 3034. [Google Scholar] [CrossRef]
  46. Liang, J.H.; Wu, Z.; Xu, T.F.; Li, X.F.; Jiang, F.; Wang, H.Q. Overexpression of HANABA TARANU in cultivated strawberry delays flowering and leads to defective flower and fruit development. Plant Sci. 2022, 321, 111307. [Google Scholar] [CrossRef] [PubMed]
  47. Negoro, S.; Hirabayashi, T.; Iwasaki, R.; Torii, K.U.; Uchida, N. EPFL peptide signalling ensures robust self-pollination success under cool temperature stress by aligning the length of the stamen and pistil. Plant Cell Environ. 2022, 46, 451–463. [Google Scholar] [CrossRef]
  48. Wang, M.; Guan, Q.; Xie, Y. Prediction of potential suitable areas for Broussonetia papyrifera in China using the MaxEnt model and CIMP6 data. J. Plant Ecol. 2023, 16, rtad006. [Google Scholar] [CrossRef]
  49. Piao, S.L.; Yin, G.D.; Tan, J.G.; Cheng, L.; Huang, M.T.; Li, Y.; Liu, R.G.; Mao, J.F.; Myneni, R.B.; Peng, S.S.; et al. Detection and attribution of vegetation greening trend in China over the last 30 years. Glob. Change Biol. 2015, 21, 1601–1609. [Google Scholar] [CrossRef]
  50. He, L.M.; Zhang, Y.; Wang, R.; Chen, Y.Z.; Yang, D.Y.; He, Z.L.; Shen, J.C.; Ma, X.F.; Lai, H.G. Phenotypic Genetic Diversity Analysis of 378 Oil-tea Camellia Germplasm Resources. Mol. Plant Breed. 2024, 22, 2732–2749. [Google Scholar]
  51. Jiang, R.P.; Zou, M.; Qin, Y.; Tan, G.D.; Huang, S.P.; Quan, H.G.; Zhou, J.Y.; Liao, H. Modeling of the Potential Geographical Distribution of Three Fritillaria Species Under Climate Change. Front. Plant Sci. 2021, 12, 749838. [Google Scholar] [CrossRef] [PubMed]
  52. Faticov, M.; Abdelfattah, A.; Roslin, T.; Vacher, C.; Hambäck, P.; Blanchet, F.G.; Lindahl, B.D.; Tack, A.J.M. Climate warming dominates over plant genotype in shaping the seasonal trajectory of foliar fungal communities on oak. New Phytol. 2021, 231, 1770–1783. [Google Scholar] [CrossRef] [PubMed]
  53. Yan, X.Y.; Wang, S.C.; Duan, Y.; Han, J.; Huang, D.H.; Zhou, J. Current and future distribution of the deciduous shrub Hydrangea macrophylla in China estimated by MaxEnt. Ecol Evol. 2021, 11, 16099–16112. [Google Scholar] [CrossRef] [PubMed]
  54. Laughlin, D.C.; McGill, B.J. Trees have overlapping potential niches that extend beyond their realized niches. Science 2024, 385, 75–80. [Google Scholar] [CrossRef]
  55. Jacquemyn, H.; De Coensel, B.; Evans, A.; Wang, D.Y.; Merckx, V.S.F.T. The relationship between phylogeny, range size, niche breadth and niche overlap in European orchids (Orchidaceae). J. Biogeogr. 2024, 51, 409–421. [Google Scholar] [CrossRef]
Forests 16 01026 g001
Figure 1. The percentage contributions of the main environment for C. oleifera.
Figure 1. The percentage contributions of the main environment for C. oleifera.
Forests 16 01026 g001
Forests 16 01026 g002
Figure 2. Suitable habitat for C. oleifera in China under the current climate.
Figure 2. Suitable habitat for C. oleifera in China under the current climate.
Forests 16 01026 g002
Forests 16 01026 g003
Figure 3. Changes in the distribution of suitable habitat for C. oleifera under future climate conditions.
Figure 3. Changes in the distribution of suitable habitat for C. oleifera under future climate conditions.
Forests 16 01026 g003
Forests 16 01026 g004
Figure 4. Spatial changes in C. oleifera in China in the future under different climate change scenarios.
Figure 4. Spatial changes in C. oleifera in China in the future under different climate change scenarios.
Forests 16 01026 g004
Forests 16 01026 g005
Figure 5. The migration of the centroid of the suitable area of C. oleifera in different periods and climate scenarios.
Figure 5. The migration of the centroid of the suitable area of C. oleifera in different periods and climate scenarios.
Forests 16 01026 g005
Table 1. The values of the AUC and TSS.
Table 1. The values of the AUC and TSS.
Training AUCTest AUCTSS
current0.9900.9890.967
2041s 1260.9910.9880.960
2041s 2450.9910.9880.961
2041s 5850.9910.9870.960
2081s 1260.9910.9890.964
2081s 2450.9900.9890.968
2081s 5850.9910.9890.962
Table 2. The potential distribution area of C. oleifera under current and future climate scenarios. (×104 km2).
Table 2. The potential distribution area of C. oleifera under current and future climate scenarios. (×104 km2).
Highly Suitable HabitatLow Suitable HabitatUnsuitable Habitat
2041s SSP1262.5145.03913.53
2081s SSP1262.7655.44905.38
2041s SSP2453.8951.80902.39
2081s SSP24512.6148.49902.87
2041s SSP5855.9552.73899.97
2081s SSP58529.5846.14885.34
Table 3. Niche overlap values of C. oleifera under current and future climatic conditions.
Table 3. Niche overlap values of C. oleifera under current and future climatic conditions.
Current2041s1262081s1262041s2452081s2452041s5852081s585
current10.970.970.980.960.970.95
2041s1260.8910.960.970.930.950.92
2081s1260.850.8610.970.960.970.96
2041s2450.840.850.8210.960.970.97
2081s2450.890.900.860.8610.960.97
2041s5850.870.880.830.840.8410.95
2081s5850.940.950.930.980.950.941
Notes: The upper part is the value of the Warren I, and the lower part is the value of the Schoener D.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Jiang, Z.; Zhang, Y.; Su, Q.; Gan, Q.; Zhou, Q.; Guo, Y.; Liu, Z.; Zhang, Y.; Zhou, B.; Asseri, T.A.Y.; et al. MaxEnt Modeling for Predicting the Potential Geographical Distribution of Camellia oleifera Abel Under Climate Change. Forests 2025, 16, 1026. https://doi.org/10.3390/f16061026

AMA Style

Jiang Z, Zhang Y, Su Q, Gan Q, Zhou Q, Guo Y, Liu Z, Zhang Y, Zhou B, Asseri TAY, et al. MaxEnt Modeling for Predicting the Potential Geographical Distribution of Camellia oleifera Abel Under Climate Change. Forests. 2025; 16(6):1026. https://doi.org/10.3390/f16061026

Chicago/Turabian Style

Jiang, Zhiyin, Yuxin Zhang, Qitao Su, Qing Gan, Qin Zhou, Yiliu Guo, Zhao Liu, Yanping Zhang, Bing Zhou, Tahani A. Y. Asseri, and et al. 2025. "MaxEnt Modeling for Predicting the Potential Geographical Distribution of Camellia oleifera Abel Under Climate Change" Forests 16, no. 6: 1026. https://doi.org/10.3390/f16061026

APA Style

Jiang, Z., Zhang, Y., Su, Q., Gan, Q., Zhou, Q., Guo, Y., Liu, Z., Zhang, Y., Zhou, B., Asseri, T. A. Y., & Hassan, M. U. (2025). MaxEnt Modeling for Predicting the Potential Geographical Distribution of Camellia oleifera Abel Under Climate Change. Forests, 16(6), 1026. https://doi.org/10.3390/f16061026

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

Article Metrics

Back to TopTop