Development of maximum entropy model at home and abroad and its applications in different climatic backgrounds and regional scales
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摘要: 物种分布模型(Species Distribution Models, SDMs)作为生物地理学及宏观生态学的重要研究方法,在生命科学及环境科学领域发挥重要作用,在诸多物种分布模型中,最大熵模型(Maximum Entropy model; MaxEnt)因其数据要求较低、预测精确率较高、使用步骤简便等优点,在众多物种分布模型中脱颖而出,也在不断发展过程中得以优化及改进。通过对目前公开发表的MaxEnt模型相关文献及著作进行分析,尝试从MaxEnt模型国内外研究进展、不同气候背景下的应用、区域方面的应用三个角度叙述其发展历程及优化提升。在综合分析基础上,采用扩大预测范围及预测数据样本量建立模型的方法策略,从而解决物种数据量过少或评估调查区域生物多样性空白这一问题。Abstract: As an important research method in biogeography and macroecology, Species Distribution Models(SDMs) play an important role in the fields of life science and environmental science. Among many species distribution models, Maximum Entropy model(MaxEnt) stands out among them because of its advantages such as its low data requirements, high predicted accuracy, easy-to-use operational steps and so on. By reading the related literatures and works about Maximum Entropy model currently published, we tried to describe its development process and optimization from three perspectives: the development of the Maximum Entropy model at home and abroad, its applications in different climatic backgrounds and regional scales. On the basis of comprehensive analysis, we adopt an idea of expanding the prediction range and the sample size of prediction data to build a model to solve the problem of too little data and the investigation of biodiversity in blank areas.
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