Prediction of the Potential Suitable Habitat for Xanthoceras Sorbifolium Bunge Based on MaxEnt Model

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外文摘要：This study integrates MaxEnt ecological niche model with ArcGIS technology to predict the potential suitable habitat for Xanthoceras sorbifolium Bunge in China， based on accurate and effective geographic distribution data。 3 environmental factors， including climate， topography， and soil (comprising 26 environmental variables)， were employed to predict the potential suitable habitats for X。 sorbifolium， classify the suitable habitats， and assess its adaptability。 The results indicate that the potential suitable distribution areas for X。 sorbifolium in China can be categorized into high， medium， and low suitable habitats， covering an area of 189。44×10~4 km~2， 162。05×10~4 km~2， and 252。87×10~4 km~2， respectively。 These areas account for 19。73%， 16。88%， and 26。34% of China's total land area。 The primary environmental factors influencing the distribution of X。 sorbifolium。 ranked from most to least influential， include annual precipitation， soil pH。 average temperature of the coldest quarter， elevation， isothermality， precipitation coefficient of variation， soil organic carbon content， clay mass fraction， percentage of gravel volume， sand content， soil capacity， and maximum temperature of the warmest month。 The results of this study provide a scientific basis for the selection and production planning layout of X。 sorbifolium ecological cultivation bases。

外文关键词：

Xanthoceras sorbifolium BungeMaxEnt modelEnvironmental factorsPotential suitable habitatSuitability assessment

作者：

Dawei Zhang、Shicui Fu、Zhonghui Zhang、Zimo Wang、Lu Liu、Fan Yang、Jingqi Yuan、Zhongliang Yu

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作者单位：

Jilin Provincial Academy of Forestry Sciences (Jilin Province Forestry Biological Control Center Station), Changchun, Jilin, 130033, China

Institute of Forestry Inventory and Planning of Jilin Province, Changchun, Jilin, 130000, China

会议名称：

International Conference on Remote Sensing, Mapping, and Image Processing

会议地点：

Xiamen(CN)

会议母体文献：

International Conference on Remote Sensing, Mapping, and Image Processing

页码：

131671P.1-131671P.9

出版时间：

2024

DOI：

10.1117/12.3029821