Relationships between Environmental Characteristics and the Density of Age-0 Eurasian Perch Perca fluviatilis in the Littoral Zone of a Lake: A Nonlinear Approach

Abstract

We studied the spatial distribution of age-0 Eurasian perch Perca fluviatilis in the littoral area of a large lake (Lake Pareloup, France) using eight environmental variables as habitat descriptors. Nonparametric locally weighted scatterplot smother (Lowess) functions were used to visualize the relationships between spatial distribution and the habitat descriptors. The highest abundance was observed in the transition area between shallow water with dense vegetation cover and unvegetated open water. Habitat use depended on a combination of environmental variables, such as depth, distance from the bank, vegetation cover, and slope of the bank, with abundance exhibiting nonlinear responses to each variable. We hypothesized that these complex responses resulted from a trade-off between searching for food and avoiding predators. We then attempted to build a predictive model of age-0 perch abundance based on the environmental descriptors using an artificial neural network (ANN). The predictive quality of the model was high (r ² = 0.78 between the observed and estimated perch densities) compared with that of the more classical linear modeling technique (i.e., multiple linear regression; r ² = 0.20) and another nonlinear modeling technique (a generalized additive model; r ² = 0.33). Finally, ANN sensitivity analyses of the environmental variables in the models confirmed the results obtained with the Lowess approach, which considered the influence of each variable on perch habitat use. In light of these results, ANN and Lowess methods have considerable potential in the prediction and explanation of ecological relationships.