STOCHASTIC-MODELS THAT PREDICT TROUT POPULATION-DENSITY OR BIOMASS ONA MESOHABITAT SCALE

Neural networks and multiple linear regression models of the abundance of brown trout (Salmo trutta L.) on the mesohabitat scale were develo ped from combinations of physical habitat variables in 220 channel mor phodynamic units (pools, riffles, runs, etc.) of 11 different streams in the central Pyrenean mountains. For all the 220 morphodynamic units , the determination coefficients obtained between the estimated and ob served values of density or biomass were significantly higher for the neural network (r(2) adjusted = 0.83 and r(2) adjusted = 0.92 (p < 0.0 1) for biomass and density respectively with the neural network, again st r(2) adjusted = 0.69 (p < 0.01) and r(2) adjusted = 0.54 (p < 0.01) with multiple linear regression). Validation of the multivariate mode ls and learning of the neural network developed from 165 randomly chos en channel morphodynamic units, was tested on the 55 other channel mor phodynamic units. This showed that the biomass and density estimated b y both methods were significantly related to the observed biomass and density. Determination coefficients were significantly higher for the neural network (r(2) adjusted = 0.72 (p < 0.01) and 0.81 (p < 0.01) fo r biomass and density respectively) than for the multiple regression m odel (r(2) adjusted = 0.59 and r(2) adjusted= 0.37 for biomass and den sity respectively). The present study shows the advantages of the back propagation procedure with neural networks over multiple linear regres sion analysis, at least in the field of stochastic salmonid ecology.