Prediction of the Future Evolution Trends of Prunus sibirica in China Based on the Key Climate Factors Using MaxEnt Modeling

文献类型： 外文期刊

第一作者： Wang, Jiazhi

作者： Wang, Jiazhi;Peng, Jiuhui;Wang, Jiazhi;Peng, Jiuhui;Wang, Jiazhi;Peng, Jiuhui;Cheng, Jiming;Zhang, Chao;Feng, Yingqun;Jin, Lang;Cheng, Jiming;Yang, Hui;Cao, Ziyu;Luo, Yonghong;Wei, Shuhua

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关键词： climate change; MaxEnt; precipitation; Prunus sibirica; species distribution modeling

期刊名称：BIOLOGY-BASEL （ 影响因子：3.5； 五年影响因子：4.0 ）

ISSN：

年卷期： 2024 年 13 卷 12 期

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收录情况： SCI

摘要： Mountain apricot (Prunus sibirica) is an important fruit tree variety, and has a wide range of planting and application value in China and even the world. However, the current research on the suitable distribution area of P. sibirica is still inconclusive. In this study, we retrieved distribution data for P. sibirica in China from the Global Biodiversity Information Facility (GBIF), and identified six key environmental factors influencing its distribution through cluster analysis. Using these six selected climate factors and P. sibirica distribution points in China, we applied the maximum entropy model (MaxEnt) to evaluate 1160 candidate models for parameter optimization. The final results predict the potential distribution of P. sibirica under the current climate as well as two future climate scenarios (SSPs126 and SSPs585). This study shows that the model optimized with six key climate factors (AUC = 0.897, TSS = 0.658) outperforms the full model using nineteen climate factors (AUC = 0.894, TSS = 0.592). Under the high-emission scenario (SSPs585), the highly suitable habitat for P. sibirica is expected to gradually shrink towards the southeast and northwest, while expanding in the northeast and southwest. After the 2050s, highly suitable habitats are projected to completely disappear in Shandong, while new suitable areas may emerge in Tibet. Additionally, the total area of suitable habitat is projected to increase in the future, with a more significant expansion under the high-emission scenario (SSPs585) compared to the low-emission scenario (SSPs126) (7.33% vs. 0.16%). Seasonal changes in precipitation are identified as the most influential factor in driving the distribution of P. sibirica.

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