Clustering of ant communities and indicator species analysis using self-organizing maps

Sang Hyun Park, Shingo Hosoishi, Kazuo Ogata, Yuzuru Kuboki

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

To understand the complex relationships that exist between ant assemblages and their habitats, we performed a self-organizing map (SOM) analysis to clarify the interactions among ant diversity, spatial distribution, and land use types in Fukuoka City, Japan. A total of 52 species from 12 study sites with nine land use types were collected from 1998 to 2012. A SOM was used to classify the collected data into three clusters based on the similarities between the ant communities. Consequently, each cluster reflected both the species composition and habitat characteristics in the study area. A detrended correspondence analysis (DCA) corroborated these findings, but removal of unique and duplicate species from the dataset in order to avoid sampling errors had a marked effect on the results; specifically, the clusters produced by DCA before and after the exclusion of specific data points were very different, while the clusters produced by the SOM were consistent. In addition, while the indicator value associated with SOMs clearly illustrated the importance of individual species in each cluster, the DCA scatterplot generated for species was not clear. The results suggested that SOM analysis was better suited for understanding the relationships between ant communities and species and habitat characteristics.

Original languageEnglish
Pages (from-to)545-552
Number of pages8
JournalComptes Rendus - Biologies
Volume337
Issue number9
DOIs
Publication statusPublished - Sep 1 2014

Fingerprint

Ants
Self organizing maps
indicator species
Cluster Analysis
Formicidae
Ecosystem
Land use
land use
habitats
Selection Bias
Spatial distribution
Japan
spatial distribution
Sampling
species diversity
Chemical analysis
correspondence analysis
sampling

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Clustering of ant communities and indicator species analysis using self-organizing maps. / Park, Sang Hyun; Hosoishi, Shingo; Ogata, Kazuo; Kuboki, Yuzuru.

In: Comptes Rendus - Biologies, Vol. 337, No. 9, 01.09.2014, p. 545-552.

Research output: Contribution to journalArticle

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