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Partialling out the spatial component of ecological variation: questions and propositions in the linear modelling framework

Partialling out the spatial component of ecological variation: questions and propositions in the... First, we formulate some questions posed by the procedure recently proposed by Borcard et al. (1992) and Borcard and Legendre (1994) to partition the ecological variation of a community into different portions related to spatial and environmental descriptors. Working separately on the two steps of this procedure - linear modelling and ordinations on modelled tables - allows us to propose different solutions to these questions. These solutions, which use little-known proper- ties of a linear regression model with two additive factors and no interaction, are also adapted to the case of mixed factors (qualitative and quantitative). These properties are presented in the framework of canonical correlation analysis. In particular, they allow us to propose an alternative to partial regression, which avoids confounding. A detailed illustration is presented. © Rapid Science 1998 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Environmental and Ecological Statistics Springer Journals

Partialling out the spatial component of ecological variation: questions and propositions in the linear modelling framework

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References (64)

Publisher
Springer Journals
Copyright
Copyright © 1998 by Chapman and Hall
Subject
Life Sciences; Ecology; Statistics, general; Mathematical and Computational Biology; Evolutionary Biology
ISSN
1352-8505
eISSN
1573-3009
DOI
10.1023/A:1009693501830
Publisher site
See Article on Publisher Site

Abstract

First, we formulate some questions posed by the procedure recently proposed by Borcard et al. (1992) and Borcard and Legendre (1994) to partition the ecological variation of a community into different portions related to spatial and environmental descriptors. Working separately on the two steps of this procedure - linear modelling and ordinations on modelled tables - allows us to propose different solutions to these questions. These solutions, which use little-known proper- ties of a linear regression model with two additive factors and no interaction, are also adapted to the case of mixed factors (qualitative and quantitative). These properties are presented in the framework of canonical correlation analysis. In particular, they allow us to propose an alternative to partial regression, which avoids confounding. A detailed illustration is presented. © Rapid Science 1998

Journal

Environmental and Ecological StatisticsSpringer Journals

Published: Sep 29, 2004

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