Simulating Potential Distribution of Tamarix chinensis in Yellow River Delta by Generalized Additive Models

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• 出版年：2010
• 作者：SONG　Chuangye；HUANG　Chong；LIU　Gaohuan
• 单位1：State Key Laboratory of Vegetation and Environmental Change,Institute of Botany,Chinese Academy of Sciences,
• 出生年：1980
• 学历：PhD
• 语种：中文
• 作者关键词：Yellow River Delta；Tamarix chinensis；Generalized Additive Models
• 起始页：347
• 总页数：7
• 经费资助：Foundation item:Under the auspices of the Project of National Natural Science Foundation of China (No.41001363) and Autonomous Project of State Key Laboratory of Resources and Environmental Information System, Geo-information Tupu Theory and Virtual Geoscience. .
• 刊名：湿地科学
• 是否内版：否
• 刊频：季刊
• 创刊时间：2003
• 主办单位：中国科学院；东北地理与农业生态研究所
• 主编：刘兴土
• 电子信箱：wetlands@neigae.ac.cn
• 卷：8
• 期：4
• 期刊索取号：P224.06141
• 核心期刊：中国科学引文数据库核心库期刊；中国科技核心期刊

摘要

There are typical ecosystems of littoral wetlands in the Yellow River Delta. In order to study the relationshipsbetween Tamarix chinensis and environmental variables and to predict T. chinensis potential distribution in the YellowRiver Delta, 641 vegetation samples and 964 soil samples were collected in the area in October of 2004, 2005 ,2006 and2007. The contents of soil organic matter, total phosphorus, salt, and soluble potassium were determined. Then, theanalyzed data were interpolated into spatial raster data by Kriging interpolation method. Meanwhile, the digital elevationmodel, soil type map and landform unit map of the Yellow River Delta were also collected. Generalized Additive Models(GAMs) were employed to build species-environment model and then simulate the potential distribution of T. chinen-sis. The results indicated that the distribution of T. chinensis was mainly limited by soil salt content, total soil phosphoruscontent, soluble potassium content, soil type, landform unit, and elevation. The distribution probability of T. chinensiswas produced with a lookup table generated by Crasp Module (based on GAMs) in software ArcView CIS 3.2. The AUC(Area Under Curve) value of validation and cross-validation of ROC (Receive Operating Characteristic) were bothhigher than 0.8, which suggested that the established model had a high precision for predicting species distribution.