The Use of Artificial Neural Networks (ANNs) in Aquatic Ecology

Abstract

Complex network systems are pervasive in life sciences at all levels, from molecules and genes to organisms and ecosystems. All these systems are characterized by being constituted of numerous components or nodes (molecules, genes, cells, tissues, organisms), which are interconnected by many links in an intricate tangle, just as biological neural networks consist of many interacting neurons (Fig. 1). Apart from its structural complexity, complex networks are inherently difficult to understand because interactions are non-linear, distributed non-randomly, and are adaptive, that is, changing continuously in response to the state of the system itself (Strogatz, 2001; Pascual & Dunne, 2006). Understanding the functioning of these systems consisting of a large number of strongly interacting units represents therefore a major endeavour for biologists and ecologists. As complex networks, ecosystems are non-linear systems constituted by countless interacting pieces, both biotic and abiotic, constituting the entangled web of life. In a world threatened by global environmental problems such as biodiversity loss, climate change, fishing overexploitation or pollution, ecologists are challenged by the need to understand and predict the dynamics of ecosystems as never before. Along with the complexity of ecological systems, ecologists are also faced with a huge amount of information that recent advances in data collection technology such as remote sensing have produced. To cope with the ecosystem complexity and large data sets currently available, ecologists nowadays have the opportunity to use machine-like learning techniques such as the artificial neural networks (ANNs). As their name implies, ANNs are biologically inspired and were initially intended to mimic the neural activity in the human or animal brains (Garson, 1991; Goh, 1995; Stern, 1996). ANNs models are based on the same learning processes as the animal brain, which gathers information from the environment (input data) and gives an answer (output data) after using learned training algorithms. However, given that the architecture and dynamics of the animal brain is exceedingly complex, even the most elaborated ANN models are mere caricatures of the biological brain. Although the original works on ANNs date back to the forties (McCulloch & Pitts, 1943; Pitts & McCulloch, 1947), they not became really popular until the eighties after the work of the physicist John Hopfield. Hopfield (1982) introduced an oversimplified neural network, comprising a set of fully connected binary units, as a metaphor of neural computation. The most remarkable feature of this model was that it could learn by association and was quiet insensitive to noise. This capacity to recognize previously learned patterns, which was thought to be an exclusive property of brains, is precisely what the Hopfield model does (Sole & Goodwin, 2000).