Zooplankton Density Prediction in a Flood Lake (Pantanal – Brazil) Using Artificial Neural Networks

Abstract Ecologic relationships are usually non‐linear and highly complex. For this reason, artificial neural networks (ANN) were selected to model zooplankton density groups in the Coqueiro lake in the northern Pantanal of Brazil. The input layer used 11 limnological variables with 13 neurons in the hidden layer; the output layer consisted of three zooplankton groups. Samples were collected monthly between April 2002 and May 2003, at three different points of the lake, two of which were used for training the ANNs and the other for validation. The ANN model performed well at predicting the density of zooplankton groups (coefficients of determination r2were 0.88, 0.50 and 0.82 for rotifers, cladocerans and copepods, respectively). The comparison between models, and the ANN techniques used, demonstrated that zooplankton densities, observed one month previously, did not influence current densities, which were determined by limnological conditions in the lake. It was also shown that the processes that relate zooplankton to their environment remained stable during the study, while a model sensitivity analysis showed that the density dynamics of zooplankton groups in the Coqueiro lake were strongly influenced by availability of food (phytoplankton and detritus) and by variations in water‐level. It can be concluded from the study that ANNs are a powerful tool both for predicting zooplankton densities and for understanding their relationships with the environment. (© 2010 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)