TICHÝ, Lubomír, Milan CHYTRÝ, Michal HÁJEK, Stephen S. TALBOT and Zoltán BOTTA-DUKÁT. OptimClass: Using species-to-cluster fidelity to determine the optimal partition in classification of ecological communities. Journal of Vegetation Science. Oxford: Wiley-Blackwell, 2010, vol. 21, No 2, p. 287-299. ISSN 1100-9233.
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Basic information
Original name OptimClass: Using species-to-cluster fidelity to determine the optimal partition in classification of ecological communities
Authors TICHÝ, Lubomír (203 Czech Republic, guarantor, belonging to the institution), Milan CHYTRÝ (203 Czech Republic, belonging to the institution), Michal HÁJEK (203 Czech Republic, belonging to the institution), Stephen S. TALBOT (840 United States of America) and Zoltán BOTTA-DUKÁT (348 Hungary).
Edition Journal of Vegetation Science, Oxford, Wiley-Blackwell, 2010, 1100-9233.
Other information
Original language English
Type of outcome Article in a journal
Field of Study 10600 1.6 Biological sciences
Country of publisher Czech Republic
Confidentiality degree is not subject to a state or trade secret
WWW URL
Impact factor Impact factor: 2.457
RIV identification code RIV/00216224:14310/10:00045964
Organization unit Faculty of Science
UT WoS 000274809500008
Keywords in English Cluster analysis; Cover transformation; Dendrogram; Optimal number of clusters; Ordinal clustering; Resemblance measures; Stopping rules; TWINSPAN
Tags International impact, Reviewed
Changed by Changed by: prof. RNDr. Milan Chytrý, Ph.D., učo 871. Changed: 31/1/2011 16:14.
Abstract
Question: Community ecologists are often confronted with multiple possible partitions of a single set of records of species composition and/or abundances from several sites. Different methods of numerical classification produce different results, and the question is which of them, and how many clusters, should be selected for interpretation. We demonstrate a new method for identifying the optimal partition from a series of partitions of the same set of sites, based on number of species with high fidelity to clusters in a partition (faithful species). Methods: The new method, OptimClass, has two variants. OptimClass 1 searches the partition with the maximum number of faithful species across all clusters, while OptimClass 2 searches the partition with the maximum number of clusters that contain at least a preselected minimum number of faithful species. Faithful species are determined based on the P value of the Fisher's exact test, as a measure of fidelity. OptimClass was tested on three vegetation datasets that varied in species richness and internal heterogeneity, using several classification algorithms, resemblance measures and cover transformations. Results: Results from both variants of OptimClass depended on the preselected threshold P value for faithful species: higher P gave higher probability that a partition with more clusters was selected as optimal. Good partitions, in terms of OptimClass criteria, involved flexible beta clustering, and also ordinal clustering. Good partitions were also obtained with TWINSPAN when the required number of clusters was small, or UPGMA when the required number of clusters was large. Poor partitions usually resulted from classifications that used resemblance measures and cover transformations emphasizing differences in species cover; this is not unexpected because OptimClass uses a presence/absence-based fidelity measure. Conclusions: If the aim of a classification is to obtain clusters rich in faithful species, which can be subsequently used as diagnostic species for identification of community types, OptimClass is a suitable method for simultaneous choice of the optimal classification algorithm and optimal number of clusters. It can be computed in the JUICE program.
Links
GA206/09/0329, research and development projectName: Vegetace České republiky: dokončení národního přehledu rostlinných společenstev
Investor: Czech Science Foundation, Vegetation of the Czech Republic: completion of the national survey of plant communities
MSM0021622416, plan (intention)Name: Diverzita biotických společenstev a populací: kauzální analýza variability v prostoru a čase
Investor: Ministry of Education, Youth and Sports of the CR, Diversity of Biotic Communities and Populations: Causal Analysis of variation in space and time
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