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 r 2 were 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. IntroductionLakes subject to regular flooding exhibit high functional complexity because of large seasonal changes that result from droughts and floods. This variability causes changes in habitat spatial structure, requiring the biota to adapt to changes in the physical and chemical environments (JUNK et al., 1989).Because of the spatial and temporal complexity of ecological processes occurring in such lakes, ecosystem analysis and prediction by means of linear empirical models is of limited value. Such models commonly reduce the data and simplify relationships between variables in a way that frequently leads to the loss of valuable information and to distortions of ecological truth, by failing to take into account the true complexity of non-linear relationships. From the biological viewpoint, patterns of species existence and abundance of species usually show non-linear complexities in their relations with the habitat spatial heterogeneity and interactions with other species.For these reasons, artificial neural networks (ANNs) can be an attractive alternative as a tool for analyzing and modeling ecological data, since they can take account of specific factors such as non-linearity, adaptation and generalization (SCHLEITER et al., 1999). Most applications of ANNs in ecology have been concerned with formulating predictive models, and with pattern recognition.In aquatic ecology, ANNs have been used extensively for predicting algal growth (RECK- NAGEL et al., 1997;OLDEN, 2000;JEONG et al., 2001) and for studies of the relations between environmental variables and...

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