GMDH ALGORITHM AS A TOOL FOR BIVALVE GROWTH ANALYSIS AND PREDICTION

The question of whether growth in bivalves is predictable in terms of environmental conditions is addressed directly by trying to infer juve nile scallop growth from environmental data within and between two loc ations in the Bale des Chaleurs, Quebec. Using models based on either self-organizing models - the group method of data handling (GMDH) algo rithm - 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 avai lability were the most prominent variables included in the GMDH models , emphasizing their importance as physical determinants of scallop gro wth.