基于GIS的贝叶斯统计推理技术在印度野牛生境概率评价中的应用

目前，ＧＩＳ技术已被广泛应用在野生动物生境研究中。但是，作为空间数据分析和处理工具，ＧＩＳ缺乏进行启发式推理的能力。因此，与擅长于此的贝叶斯统计推理技术相结合则是解决这一问题的重要途径。以西双版纳纳板河流域生物圈保护区为试验区，综合应用ＧＩＳ技术和多元统计技术建立印度野牛生境的两个逻辑斯蒂多元回归模型：趋势表面模型和环境模型，第一个模型的自变量是位置坐标，第二个模型的自变量是一组环境因子，然后应用贝叶斯统计合并这两个模型产生贝叶斯综合模型。结果表明，贝叶斯综合模型优于环境模型，可应用于野生动物生境概率评价。

Application of Bayesian Statistics Inference Techniques Based on GIS to the Evaluation of Habitat Probabilities of Bos Gaurus Readei

At present, GIS has been widely applied to the study of wildlife habitat. However, GIS, which is a tool of spatial data analysis and processing, lacks of the capacity of heuristic reasoning. Therefore, it is an important way to solve this problem by the integration of Bayesian statistics inference with GIS. in this article, the Naban river nature reserve of Xishuangbanna was taken as an experimental area, GIS and multivariate statistical techniques were applied to the development of two logistic multiple regression models for Bos gaurus readei habitat: trend surface model and environmental model. Independent variables were locational coordinates in the first model, and a set of environmental factors in the second model. Bayesian statistics were then used to integrate the two models into a Bayesian integrated model. The results show that the Bayesian integrated model is superior to the environmental model and can be applied to wildlife habitat evaluation.