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An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis

Environmental Modelling and Software

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DOI: 10.1016/S1364-8152(02)00094-4

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Abstract

The biosphere is filled with complex living patterns and important questions about biodiversity and community and ecosystem ecology are concerned with structure and function of multispecies systems that are responsible for those patterns. Cluster analysis identifies discrete groups within multivariate data and is an effective method of coping with these complexities, but often suffers from subjective identification of groups. The bootstrap testing method greatly improves objective significance determination for cluster analysis. The BOOTCLUS program makes cluster analysis that reliably identifies real patterns within a data set more accessible and easier to use than previously available programs. A variety of analysis options and rapid re-analysis provide a means to quickly evaluate several aspects of a data set. Interpretation is influenced by sampling design and a priori designation of samples into replicate groups, and ultimately relies on the researcher's knowledge of the organisms and their environment. However, the BOOTCLUS program provides reliable, objectively determined groupings of multivariate data.

Additional Publication Details

Publication type:
Article
Publication Subtype:
Journal Article
Title:
An enhanced cluster analysis program with bootstrap significance testing for ecological community analysis
Series title:
Environmental Modelling and Software
DOI:
10.1016/S1364-8152(02)00094-4
Volume
18
Issue:
3
Year Published:
2003
Language:
English
Publisher:
Elsevier
Publisher location:
Amsterdam, Netherlands
Contributing office(s):
Great Lakes Science Center
Description:
16 p.
Larger Work Type:
Article
Larger Work Subtype:
Journal Article
First page:
205
Last page:
220
Number of Pages:
16