Ecological Informatics

In this scenario, 'ecological informatics' can be regarded as an extremely promising research field, which has the potential to help bridge the gap between data and knowledge. As many emerging disciplines, 'ecological informatics' is still ill-defined, and several different definitions can be found. Most of them, however, agree regarding 'ecological informatics' as a combination of several research fields. It can be summarized as the application of the latest computationally intensive tools to ecological research and the development of novel computational methods inspired by biological and ecological systems.

The purposes of 'ecological informatics' are multiple, but in most cases they involve the development of modeling, data mining, data management, visualization, expert systems, or similar applications in ecological research. Computational techniques such as neural networks (see Artificial Neural Networks), cellular automata (CAs) (see Cellular Automata), or evolutionary algorithms (see Evolutionary Algorithms) are the basis for many successful 'ecological informatics' applications, but any computationally intensive method or information technology may play a role in supporting new applications.

Artificial neural networks (ANNs) have been extensively applied to ecological sciences through supervised and unsu-pervised learning models, and the number of applications has been growing exponentially during the last decade. Multilayer perceptrons (MLPs) (see Multilayer Perceptron) trained with the backpropagation (BP) algorithm are the most popular neural networks in ecological applications and they have been applied to a number of empirical modeling problems. While MLPs are very effective as generalized regression tools, 'self-organizing maps' (SOMs) (see Animal Defense Strategies) may be successfully applied to ordination and classification of ecological data (e.g., in indirect gradient analysis).

Although very popular, neural networks are not the only tools upon which 'ecological informatics' relies. For instance, CAs, although among the earliest artificial life models, are still applied in ecology, and they are certainly also part of'ecological informatics'. CA shows that complex behavior and self-replicating patterns may be obtained from simple rules, when they are applied iteratively. CAs have been applied in many ecological studies, especially when population dynamics or landscape ecology is involved.

Individual-based models (IBMs) are another typical application that can be regarded as a member of the 'ecological informatics' family. They represent plants or animals as individual entities that are programmed to react to environmental stimuli, including interactions with other individual entities. The discrete nature of individual entities in IBMs leads to nonequilibrium systems, and their properties and behavior must be carefully defined in order to obtain useful simulations.

Evolutionary algorithms are certainly the methods that were more directly inspired to biological systems among those in the 'ecological informatics' toolbox. In fact, 'genetic algorithms' (GAs) (see Evolutionary Algorithms) exploit the analogy with biological evolution to solve complex optimization problems. However, the application of evolutionary algorithms should not be regarded as a mere tool for problem solving, because it also stimulated new insight into ecological problems, especially in combination with IBMs.

The list of methods that can be applied in an 'ecological informatics' framework is virtually endless, and it overlaps with the ones of other disciplines, for instance, 'ecological modeling' or 'bioinformatics'. Therefore, a couple of examples are probably more useful than theoretical definitions or comprehensive lists of methods in showing how ecological value can be additionally obtained from the application of appropriate 'ecological informatics' techniques (see Ecological Informatics: Overview).

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