Joint optimization of cluster number and abundance transformation for obtaining effective vegetation classifications

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F18%3A00106674" target="_blank" >RIV/00216224:14310/18:00106674 - isvavai.cz</a>

Result on the web

<a href="http://dx.doi.org/10.1111/jvs.12604" target="_blank" >http://dx.doi.org/10.1111/jvs.12604</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1111/jvs.12604" target="_blank" >10.1111/jvs.12604</a>

Result language

angličtina

Original language name

Joint optimization of cluster number and abundance transformation for obtaining effective vegetation classifications

Original language description

Question: Is it possible to determine which combination of cluster number and taxon abundance transformation would produce the most effective classification of vegetation data? What is the effect of changing cluster number and taxon abundance weighting (applied simultaneously) on the stability and biological interpretation of vegetation classifications? Locality: Europe, Western Australia, simulated data. Methods: Real data sets representing Hungarian sub-montane grasslands, European wetlands, and Western Australian kwongan vegetation, as well as simulated data sets were used. The data sets were classified using the partitioning around medoids method. We generated classification solutions by gradually changing the transformation exponent applied to the species projected covers and the number of clusters. The effectiveness of each classification was assessed with a stability index. This index is based on bootstrap resampling of the original data set with subsequent elimination of duplicates. The vegetation types delimited by the most stable classification were compared with other classifications obtained at local maxima of the stability values. The effect of changing the transformation power exponent on the number of clusters, indexed according to their stability, was evaluated. Results: The optimal number of clusters varied with the power exponent in all cases, both with real and simulated data sets. With the real data sets, optimal cluster numbers obtained with different data transformations recovered interpretable biological patterns. Using the simulated data, the optima of stability values identified the simulated number of clusters correctly in most cases. Conclusions: With changing the settings of data transformation and the number of clusters, classifications of different stability can be produced. Highly stable classifications can be obtained from different settings for cluster number and data transformation. Despite similarly high stability, such classifications may reveal contrasting biological patterns, thus suggesting different interpretations. We suggest testing a wide range of available combinations to find the parameters resulting in the most effective classifications.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10611 - Plant sciences, botany

Project

—

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Journal of Vegetation Science

ISSN

1100-9233

e-ISSN

—

Volume of the periodical

29

Issue of the periodical within the volume

2

Country of publishing house

US - UNITED STATES

Number of pages

12

Pages from-to

336-347

UT code for WoS article

000431503000021

EID of the result in the Scopus database

2-s2.0-85042097350