Topics in constrained and unconstrained ordination

In this paper, we reflect on a number of aspects of ordination methods: how should absences be treated in ordination and how do model-based methods, including Gaussian ordination and methods using generalized linear models, relate to the usual least-squares (eigenvector) methods based on (log–) tran...

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Bibliographic Details
Published in:Plant ecology Vol. 216; no. 5; pp. 683 - 696
Main Authors: ter Braak, Cajo J. F., Šmilauer, Petr
Format: Journal Article
Language:English
Published: Dordrecht Springer 01.05.2015
Springer Netherlands
Springer Nature B.V
Subjects:
abundance data
Applied Ecology
Biodiversity
Biomedical and Life Sciences
canonical correspondence-analysis
Community & Population Ecology
Community ecology
correspondence analysis
detrended correspondence-analysis
Ecology
environment relationships
environmental factors
gaussian ordination
least squares
Life Sciences
linear mixed models
linear models
Multivariate analysis
Ordination
Plant Ecology
putting things
redundancy analysis
response model
Statistical analysis
Terrestial Ecology
Variance analysis
zero inflation
Constrained ordination
Experimental design
Ordination
Interaction
Data transformation
Principal response curves
Multivariate analysis
ISSN:1385-0237, 1573-5052
Online Access:Get full text
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Summary:In this paper, we reflect on a number of aspects of ordination methods: how should absences be treated in ordination and how do model-based methods, including Gaussian ordination and methods using generalized linear models, relate to the usual least-squares (eigenvector) methods based on (log–) transformed data. We defend detrended correspondence analysis by theoretical arguments and by reanalyzing data that previously gave bad results. We show by examples that constrained ordination can yield more informative views on effects of interest compared to unconstrained ordination (where such effects can be invisible) and show how constrained axes can be interpreted. Constrained ordination uses an ANOVA/regression approach to enable the user to focus on particular aspects of species community data, in particular the effects of qualitative and quantitative environmental variables. We close with an analysis examining the interaction effects between two factors, and we demonstrate how principal response curves can help in their visualisation. Example data and Canoco 5 projects are provided as Supplementary Material.
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ISSN:1385-0237
1573-5052
DOI:10.1007/s11258-014-0356-5