Microsatellites and artificial neural networks: tools for the discrimination between natural and hatchery brown trout (Salmo trutta, L.) in Atlantic populations

Artificial Neural Networks (ANN) were applied to microsatellite data (highly variable genetic markers) to separate genetically differentiated forms of brown trout (Salmo trutta) in south-western France. A classic feed-forward network with one hidden layer was used. Training was performed using a back-propagation algorithm and reference samples representing the different genetic types. The hold-out and the leave-one-out procedures were used to test the validity of the network. They were chosen according to the populations and the questions analysed. The informative content of the different variables used for the distinction (the alleles of the different loci) was also evaluated using the Garson–Goh algorithm. The results of learning gave high percentages of well-classified individuals (up to 95% for the test with the hold-out analysis). This confirms that ANNs are suitable for such genetic analyses of populations. From a biological point of view, the study enabled evaluation of the genetic composition and differentiation of different river populations and of the impact of stocking.