Current research in ecological informatics focuses at four major feature areas:
1. understanding information processing and evolution in ecosystems;
2. computational management of ecological data;
3. computational analysis and synthesis of ecological data; and
Great efforts are undertaken to address feature area (1) by studying both intraspecific population adaptations to changing climate and habitat conditions as well as interspecific population relationships controlled by info-chemicals and allelopathy.
The feature area (2) aims at standardized archiving of highly complex and fragmented ecological data in order to allow ecological data sharing. The ecological metadata language EML (http://knb.ecoinformatics.org/software/ eml/) is an example for developing computational tools based on metadata concepts that will facilitate ecological data warehousing at global scale.
The feature area (3) is being largely stimulated by both the availability of complex ecological data including genomic and phenotypic data, and the development of bioinspired computational techniques. The study of population genomics in their natural habitats without the need for isolation and lab cultivation of individual species has led to the new research area of ecogenomics (also called metagenomics) that promises to determine the impact of environmental and climate changes on biodiversity. Bioinspired computational techniques prove to be superior in unraveling highly complex ecological data, coping with distinct nonlinearities and inducing predictive models by learning from temporal and spatial patterns. The next section illustrates applications of artificial neural networks (ANNs) and evolutionary algorithms for ecological informatics.
Research on hybrid modeling in the feature area (4) promises ecosystem models with improved accuracy and generality. Cao and Recknagel provided a case study for multiobjective optimization of process and parameter representations in process-based ecosystem models by the embodiment of evolutionary algorithms in ordinary differential equations for food web dynamics and nutrient cycles in lakes.
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