Multivariate regression trees: a new technique for modeling species-environment relationships
Multivariate regression trees: a new technique for modeling species-environment relationships
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DOI：
10.2307/3071917
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发表时间：
2002-04-01
期刊：
影响因子：
4.8
通讯作者：
De'Ath, G
中科院分区：
文献类型：
--
作者：
De'Ath, G
多元回归树（MRT）是一种新的统计技术，可用于探索、描述和预测多物种数据与环境特征之间的关系。 MRT 通过重复分割数据形成站点集群，每个分割由基于环境值的简单规则定义。选择分割是为了最大限度地减少集群内站点的差异。用户可以选择物种差异的度量，因此 MRT 可用于将物种组成的任何方面与环境数据联系起来。聚类及其对环境数据的依赖性由树以图形方式表示。每个簇还代表一个物种组合，其环境值定义了其相关的栖息地。 MRT 可用于分析复杂的生态数据，其中可能包括不平衡、缺失值、变量之间的非线性关系以及高阶相互作用。他们还可以预测仅可获得环境数据的地点的物种组成。使用模拟数据和现场数据集将 MRT 与冗余分析和典型对应分析进行比较。
Multivariate regression trees (MRT) are a new statistical technique that can be used to explore, describe, and predict relationships between multispecies data and environmental characteristics. MRT forms clusters of sites by repeated splitting of the data, with each split defined by a simple rule based on environmental values. The splits are chosen to minimize the dissimilarity of sites within clusters. The measure of species dissimilarity can be selected by the user, and hence MRT can be used to relate any aspect of species composition to environmental data. The clusters and their dependence on the environmental data are represented graphically by a tree. Each cluster also represents a species assemblage, and its environmental values define its associated habitat. MRT can be used to analyze complex ecological data that may include imbalance, missing values, nonlinear relationships between variables, and high-order interactions. They can also predict species composition at sites for which only environmental data are available. MRT is compared with redundancy analysis and canonical correspondence analysis using simulated data and a field data set.