| dc.description.abstract | Predicting the occurrence of economically important demersal fish in a multispecies marine environment can be of considerable value to fisheries management and protection of biodiversity. Here, 2 predictive modelling principles were utilised, artificial neural network (ANN) and discriminant function analysis (DFA), to develop presence/absence models for 3 species (anglerfish Lophius budegassa; hake Merluccius merluccius; red mullet Mullus barbatus) in the Mediterranean Sea. ANN-based models of demersal fish distribution outperformed conventional models and attained better recognition and prediction performance. Results indicated the ability of ANN's to predict presence more accurately than DFA when tested against independent field data. More precisely, sensitivity values obtained using DFA were 62.1 % for anglerfish, 5.8 % for hake and 59.8 % for red mullet whereas using ANN were 75, 71 and 72.9 % respectively. The accuracy of test data was 79.6 % for anglerfish, 49.5 % for hake and 83.3 % for red mullet using DFA and 83.7, 83.3 and 85.6 % respectively using a back-propagation ANN. After learning from a set of selected patterns, the neural network (NN) models displayed a relatively high demersal fish classification accuracy, which was consistent with present understanding of the aggregating effects of the examined variables on these species' distribution. Predicting presence or absence was found to be easier for red mullet and anglerfish than for hake. The present results also suggested that the main processes modulating the occurrence of anglerfish, hake and red mullet in the NE Mediterranean Sea can be approximated by linear functions only to a limited extent. Due to their ability to mimic non-linear systems, ANNs proved far more effective in modelling the distribution of these species in the marine ecosystem. The main results and the ANN potential to predict suitable habitat profiles and structural characteristics of species assemblages are discussed. | en |