Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification

Webb, J. Angus, Bond, Nicholas R., Wealands, Stephen R., MacNally, Ralph, Quinn, Gerald, Vesk, Peter A. and Grace, Michael R. 2007, Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification, Ecography, vol. 30, no. 4, pp. 526-536, doi: 10.1111/j.0906-7590.2007.05002.x.

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Title Bayesian clustering with AutoClass explicitly recognises uncertainties in landscape classification
Author(s) Webb, J. Angus
Bond, Nicholas R.
Wealands, Stephen R.
MacNally, Ralph
Quinn, GeraldORCID iD for Quinn, Gerald
Vesk, Peter A.
Grace, Michael R.
Journal name Ecography
Volume number 30
Issue number 4
Start page 526
End page 536
Publisher Wiley-Blackwell Munksgaard
Place of publication Copenhagen, Denmark
Publication date 2007
ISSN 0906-7590
Keyword(s) Ecology -- Arctic regions
Summary Clustering of multivariate data is a commonly used technique in ecology, and many approaches to clustering are available. The results from a clustering algorithm are uncertain, but few clustering approaches explicitly acknowledge this uncertainty. One exception is Bayesian mixture modelling, which treats all results probabilistically, and allows comparison of multiple plausible classifications of the same data set. We used this method, implemented in the AutoClass program, to classify catchments (watersheds) in the Murray Darling Basin (MDB), Australia, based on their physiographic characteristics (e.g. slope, rainfall, lithology). The most likely classification found nine classes of catchments. Members of each class were aggregated geographically within the MDB. Rainfall and slope were the two most important variables that defined classes. The second-most likely classification was very similar to the first, but had one fewer class. Increasing the nominal uncertainty of continuous data resulted in a most likely classification with five classes, which were again aggregated geographically. Membership probabilities suggested that a small number of cases could be members of either of two classes. Such cases were located on the edges of groups of catchments that belonged to one class, with a group belonging to the second-most likely class adjacent. A comparison of the Bayesian approach to a distance-based deterministic method showed that the Bayesian mixture model produced solutions that were more spatially cohesive and intuitively appealing. The probabilistic presentation of results from the Bayesian classification allows richer interpretation, including decisions on how to treat cases that are intermediate between two or more classes, and whether to consider more than one classification. The explicit consideration and presentation of uncertainty makes this approach useful for ecological investigations, where both data and expectations are often highly uncertain.
Notes Published Online: 31 Aug 2007
Language eng
DOI 10.1111/j.0906-7590.2007.05002.x
Field of Research 050104 Landscape Ecology
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2007, Ecography
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