Range bagging: a new method for ecological niche modelling from presence-only data.
Range bagging: a new method for ecological niche modelling from presence-only data.
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Range bagging：一种根据仅存在数据进行生态位建模的新方法。
DOI：
10.1098/rsif.2015.0086
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发表时间：
2015-06-06
期刊：
影响因子：
--
通讯作者：
Drake JM
中科院分区：
文献类型：
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作者：
Drake JM
生态位是一组环境，其中一个物种的种群可以在没有从其他地方引入个体的情况下持续存在。一个好的数学或计算表示的生态位是一个先决条件，以解决许多问题，在生态学，地理学，进化生物学和保护。生态位建模的一个特别具有挑战性的问题是只存在建模的问题。也就是说，一个生态位可以确定记录只从一组生态位环境没有记录从非生态位环境进行比较？在这里，我介绍了一种新的生态位建模方法，从存在的数据称为范围装袋。范围装袋借鉴了物种的环境范围的概念，但受到其他生态研究领域集成学习算法的经验表现的启发。本文将环境范围的概念扩展到多个维度，并表明即使环境维度的数量很大，范围装袋也是计算上可行的。范围装袋基地学习器的目标是在其生态位的投影的物种的环境耐受性，因此是一个物种的生物需求的生态学解释的属性。范围装袋的计算复杂度是线性的例子的数量，这与主要的替代品，Qhull相比毫不逊色。总之，范围装袋似乎是一个合理的选择利基建模的应用程序中，只存在的方法是理想的，并可能提供一个解决方案，在其他学科的问题，其中一类分类是必需的，如离群值检测和概念学习。
The ecological niche is the set of environments in which a population of a species can persist without introduction of individuals from other locations. A good mathematical or computational representation of the niche is a prerequisite to addressing many questions in ecology, biogeography, evolutionary biology and conservation. A particularly challenging question for ecological niche modelling is the problem of presence-only modelling. That is, can an ecological niche be identified from records drawn only from the set of niche environments without records from non-niche environments for comparison? Here, I introduce a new method for ecological niche modelling from presence-only data called range bagging. Range bagging draws on the concept of a species' environmental range, but was inspired by the empirical performance of ensemble learning algorithms in other areas of ecological research. This paper extends the concept of environmental range to multiple dimensions and shows that range bagging is computationally feasible even when the number of environmental dimensions is large. The target of the range bagging base learner is an environmental tolerance of the species in a projection of its niche and is therefore an ecologically interpretable property of a species' biological requirements. The computational complexity of range bagging is linear in the number of examples, which compares favourably with the main alternative, Qhull. In conclusion, range bagging appears to be a reasonable choice for niche modelling in applications in which a presence-only method is desired and may provide a solution to problems in other disciplines where one-class classification is required, such as outlier detection and concept learning.