MODELACIóN DE LA ESTRUCTURA JERáRQUICA DE MACROINVERTEBRADOS BENTóNICOS A TRAVéS DE REDES NEURONALES ARTIFICIALES

the study of hierarchical structures of ecological communities has been synthesized in an ordinary way by means of multivariated techniques of ordination or clustering. currently, analytical tools of bio-inspired computation belonging to the area of artificial intelligence are available to achieve ecological models with desirable characteristics, such as; flexibility, accuracy, robustness and reliability. in this context, this study employed two computational methods useful in ecoinformatics referring to artificial neural networks (rnar) for the modeling of the hierarchical structure of a benthic macroinvertebrate community in self-organization and prediction terms. the first ann modeling method consisted of a kohonen self-organization map (som), a non-supervised learning tool that classify the species of macroinvertebrates; this som in the input layer of gets the abundance of each ？taxa？ from the data matrix, while in the output layer was visualized the computational results. thus, in the output layer the species are organized in fifteen units and four hierarchical clusters. the second ann method applied consisted of a multilayer feed-forward perceptron net with back-propagation algorithm to predict the three major insect orders; this means, ephemeroptera, coleoptera and trichoptera (ect) richness and abundance using a set of nine physical-chemical variables. this ann architecture included a neuron for each environmental variable, a hidden layer with seven neurons and a neuron in the output layer for ect prediction. the results suggest that both types of ann used, som and perceptron, were correspondingly related to the hierarchical patterns and with the richness and abundance patterns？ predictions, and gave the data analysis and understanding of the dynamic of the macroinvertebrates community, in a correct way.