A data-mining approach to determine the spatio-temporal relationship between environmental factors and fish distribution

Fenzhen Su, Chenghu Zhou, Vincent Lyne, Yunyan Du, Wen Zhong Shi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

43 Citations (Scopus)

Abstract

The interaction between environmental factors and the spatiotemporal dynamics of living organism is an important aspect in ecology. We describe here a data-mining approach - the spatiotemporal assignment mining model (STAMM) - to extract the spatiotemporal pattern, or assignment of environmental factors, which control the distribution of a living organism. In STAMM, the spatiotemporal assignment of environmental factors is expressed via neighbourhood rules which will reflect the fuzzy or uncertain prior knowledge about the relationship. The values of cells or points in the neighbourhood and the relationships are used to construct a decision table. Indices expressing the probabilities of the ecological association rules are recursively processed in order to determine the spatiotemporal assignment. These rules are objective assessments of our prior knowledge and they refine our knowledge and understanding of the ecosystem. As a case study, we used this model to study the temperature pattern which controls the assembling of fish in the Dasha area of the Yellow Sea in China.
Original languageEnglish
Pages (from-to)421-431
Number of pages11
JournalEcological Modelling
Volume174
Issue number4
DOIs
Publication statusPublished - 1 Jun 2004

Keywords

  • Ecological association rule
  • Fish assembling
  • Fish distribution
  • Geographical Information System(GIS)
  • Spatiotemporal assignment

ASJC Scopus subject areas

  • Ecological Modelling

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