物种分布模型(SDM) -预测保护目标物种的适宜生境的战略工具

IF 1.3 Q4 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS International Journal of Agricultural and Environmental Information Systems Pub Date : 2019-01-01 DOI:10.4018/978-1-5225-7033-2.ch023
Balaguru Balakrishnan, Nagamurugan Nandakumar, Soosairaj Sebastin, Khaleel Ahamed Abdul Kareem
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引用次数: 0

摘要

利用地理信息系统(GIS)工具生成和整合来自不同来源的空间数据,通过物种分布模型(SDM)了解物种的空间分布模式及其生态连通性,从而实现物种在原生景观中的保护。SDM是一种生态/空间模型,通过将各种算法连接在一起,将目标物种发生的数据集和地图及其地理和环境变量结合在一起,用于识别或预测满足设定条件的区域。本文的重点是对空间数据需求、统计算法和用于生成sdm的软件的全面回顾。本章还包括了一个案例研究,利用最大熵(MaxEnt)物种分布模型,在生物气候和海拔等环境变量的输入下,预测了特有和脆弱的植物物种黄麻的适宜栖息地分布。
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Species Distribution Models (SDM) – A Strategic Tool for Predicting Suitable Habitats for Conserving the Target Species
Conservation of the species in their native landscapes required understanding patterns of spatial distribution of species and their ecological connectivity through Species Distribution Models (SDM) by generation and integration of spatial data from different sources using Geographical Information System (GIS) tools. SDM is an ecological/spatial model which combines datasets and maps of occurrence of target species and their geographical and environmental variables by linking various algorithms together, that has been applied to either identify or predict the regions fulfilling the set conditions. This article is focused on comprehensive review of spatial data requirements, statistical algorithms and softwares used to generate the SDMs. This chapter also includes a case study predicting the suitable habitat distribution of Gnetum ula, an endemic and vulnerable plant species using maximum entropy (MaxEnt) species distribution model for species occurrences with inputs from environmental variables such as bioclimate and elevation.
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来源期刊
International Journal of Agricultural and Environmental Information Systems
International Journal of Agricultural and Environmental Information Systems COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-
CiteScore
6.70
自引率
0.00%
发文量
10
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