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MULTIVARIATE REGRESSION TREES: A NEW TECHNIQUE FOR MODELING SPECIES––ENVIRONMENT RELATIONSHIPS

MULTIVARIATE REGRESSION TREES: A NEW TECHNIQUE FOR MODELING SPECIES––ENVIRONMENT RELATIONSHIPS 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Ecology Ecological Society of America

MULTIVARIATE REGRESSION TREES: A NEW TECHNIQUE FOR MODELING SPECIES––ENVIRONMENT RELATIONSHIPS

De'ath, Glenn
Ecology , Volume 83 (4) – Apr 1, 2002
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Publisher
Ecological Society of America
Copyright
Copyright © 2002 by the Ecological Society of America
Subject
Regular Article
ISSN
0012-9658
DOI
10.1890/0012-9658%282002%29083%5B1105:MRTANT%5D2.0.CO%3B2
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

Ecology – Ecological Society of America

Published: Apr 1, 2002

Keywords: canonical correspondence analysis ; CART ; classification tree ; cluster analysis ; cross-validation ; ecological distance ; gradient analysis ; multivariate regression tree ; ordination ; prediction ; redundancy analysis ; regression tree

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