Gmdh algorithm as a tool for bivalve growth analysis and prediction

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)

Abstract

The question of whether growth in bivalves is predictable in terms of environmental conditions is addressed directly by trying to infer juvenile scallop growth from environmental data within and between two locations in the Baie des Chaleurs, Québec. Using models based on either self-organizing models - the group method of data handling (GMDH.) algorithm - or on multilinear regressions, scallop growth was found to be predictable. GMDH models lead consistently to better predictions than multilinear regressions and could thus be a useful alternative tool in managing scallop fisheries and aquaculture. Temperature and food availability were the most prominent variables included in the GMDH models, emphasizing their importance as physical determinants of scallop growth.

Original languageEnglish
Pages (from-to)439-445
Number of pages7
JournalICES Journal of Marine Science
Volume51
Issue number4
DOIs
Publication statusPublished - Aug 1994
Externally publishedYes

Keywords

  • Environmental effects
  • GMDH
  • Growth
  • Multilinear regression

ASJC Scopus subject areas

  • Oceanography
  • Ecology, Evolution, Behavior and Systematics
  • Aquatic Science
  • Ecology

Cite this