Patterning and short-term predictions of benthic macroinvertebrate community dynamics by using a recurrent artificial neural network

Dynamic features of community data were extracted by training with a recurrent artificial neural network. Field data collected monthly from an urbanized stream consisted of densities of selected taxa in benthic macroinvertebrate communities. Sets of time-sequence data for communities were provided as the input for the network. The connectivity of computation nodes was arranged in such a way that the previous community data have recurrent feedback. In concurrence with the input of biological data, corresponding sets of environmental data such as water velocity and depth, sedimented organic matter, and volume of small substrates were also provided for the network. Through the connectivity of the network, environmental data were used as input to produce continuous, independent effects on determining community abundance. A trained pattern effectively represented the effects of habitat types and environmental impact on determining community dynamics. Short-term predictions of changes in the densities of selected taxa were made possible by a trained network after new sets of data were provided to the network.