Stochastic models that predict trout population density or biomass on a mesohabitat scale

Neural networks and multiple linear regression models of the abundance of brown trout (Salmo trutta L.) on the mesohabitat scale were developed from combinations of physical habitat variables in 220 channel morphodynamic 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 observed values of density or biomass were significantly higher for the neural network (r(2) adjusted = 0.83 and r(2) adjusted = 0.92 (p < 0.01) for biomass and density respectively with the neural network, against 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 models and learning of the neural network developed from 165 randomly chosen channel morphodynamic units, was tested on the 55 other channel morphodynamic units. This showed that the biomass and density estimated by 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) for biomass and density respectively) than for the multiple regression model (r(2) adjusted = 0.59 and r(2) adjusted= 0.37 for biomass and density respectively). The present study shows the advantages of the backpropagation procedure with neural networks over multiple linear regression analysis, at least in the field of stochastic salmonid ecology.