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Artificial neural networks (ANNs) are non-linear mapping structures based on the function of the human brain. They have been shown to be universal and highly flexible function approximators for any data. These make powerful tools for models, especially when the underlying data relationships are unknown. In this reason, the international workshop on the(More)
Parasite species with global distributions and complex life cycles offer a rare opportunity to study alternative mechanisms of speciation and evolution in a single model. Here, genealogy and genetic structure, with respect to geography and fish host preference, have been analyzed for Ligula intestinalis, a tapeworm affecting freshwater fish. The data(More)
The method of neural networks was tested for its ability to assign individuals on the basis of their multilocus genotypes, using a data collection of 430 honeybees and 8 microsatellite loci. This data set includes various taxonomical levels (populations within the same subspecies, various subspecies belonging to the same evolutionary lineage, and the 3(More)
The present work describes a comparison of the ability of multiple linear regression (MLR) and artificial neural networks (ANN) to predict fish spatial occupancy and abundance in a mesotrophic reservoir. Models were run and tested with 306 observations obtained by the sampling point abundance method using electrofishing. For each of the 306 samples, the(More)
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(More)
The impact of agricultural land use on the composition and structure of aquatic insect assemblages (i.e., taxa of Ephemeroptera, Plecoptera, Trichoptera, and Coleoptera (EPTC)) was investigated in tributary streams of the Garonne river basin, southern France. The self-organizing map (SOM) method was applied to compare both instream environmental conditions(More)
* ORSTOM, Laboratoire d’Hydrobiologie Marine et Continentale, UMR CNRS 5556, Station Méditerranéenne de l’Environnement Littoral de l’Université de Montpellier II, 1 quai de la Daurade, F-34200 Sète, France † CNRS-UMR 5576, CESAC, Université Paul Sabatier, Bât. IVR3, 118 route de Narbonne, F-31062 Toulouse cedex, France ‡ Muséum National d’Histoire(More)
In most applications of the multilayer perceptron (MLP) the main objective is to maximize the generalization ability of the network. We show that this ability is related to the sensitivity of the output of the MLP to small input changes. Several criteria have been proposed for the evaluation of the sensitivity. We propose a new index and present a way for(More)
The effect of environmental conditions on river macrobenthic communities was studied using a dataset consisting of 343 sediment samples from unnavigable watercourses in Flanders, Belgium. Artificial neural network models were used to analyse the relation among river characteristics and macrobenthic communities. The dataset included presence or absence of(More)
Identifying the processes maintaining genetic variability in wild populations is a major concern in conservation and evolutionary biology. Parasite-mediated selection may strongly affect genetic variability in wild populations. The inbreeding depression theory predicts that directional selection imposed by parasites should act against the most inbred hosts,(More)