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Determining temporal pattern of community dynamics by using unsupervised learning algorithms

Authors
Journal
Ecological Modelling
0304-3800
Publisher
Elsevier
Publication Date
Volume
132
Identifiers
DOI: 10.1016/s0304-3800(00)00312-4
Keywords
  • Community Dynamics
  • Pattern Analysis
  • Temporal Variation
  • Artificial Neural Network
  • Adaptive Resonance Theory
  • Kohonen Network
  • Unsupervised Learning Algorithm
  • Benthic Macroinvertebrate Community
Disciplines
  • Computer Science

Abstract

Abstract Analysis of patterns of temporal variation in community dynamics was conducted by combining two unsupervised artificial neural networks, the Adaptive Resonance Theory (ART) and the Kohonen network. The field data used as input for training represented monthly changes in density and species richness in selected taxa of benthic macroinvertebrates collected in the Suyong River in Korea from September 1993 to October 1994. The sampled data for each month was initially trained by ART, the weights of which preserved conformational characteristics among communities during the process of the training. Subsequently these weights were rearranged sequentially from 2 to 5 months, and were provided as input to the Kohonen network to reveal temporal variations in communities. The network was then able to extract the features of community dynamics in a reduced dimension covering the specified input period.

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