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

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.

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Publication Details

Journal
Forests
Published
2025-06-19
DOI
https://doi.org/10.3390/f16061026
Citations
5
Primary Topic
Species Distribution and Climate Change
Type
article
Field-Weighted Citation Impact
9.19

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article

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

Bing Zhou, Quanlin Zhou, Yiduo Guo, Zeping Jiang et al.
5 citations
Forests
Species Distribution and Climate Change
9.19
article

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

Bing Zhou, Quanlin Zhou, Yiduo Guo, Zeping Jiang, Liu Zhao, Quan Gan, Tahani A. Y. Asseri, Qitao Su, Yuxin Zhang, Yanping Zhang, Muhammad Hassan
article en
5 citations

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.

ForestsVol. 16(6)
Jinggangshan University (CN), Forestry Research Institute (UZ), Research Institute of Forestry (CN), King Khalid University (SA)
King Khalid University
Openalex Percentile: Top 2%
Species Distribution and Climate Change
9.19
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