UMBC ebiquity

ETHAN: the Evolutionary Trees and Natural History Ontology

Authors: Cynthia Parr, Joel Sachs, Andriy Parafiynyk, Taowei Wang, Roger Espinosa

Date: November 01, 2006

Abstract: Large-scale ecological modeling and evolutionary studies often rely on scoring taxon-level characteristics of a wide variety of organisms. Compiling such data is laborious and may involve finding and reformatting data tables in original literature, or personally exchanging spreadsheets or ASCII files with researchers. Compiled taxon-level data is beginning to be shared digitally and efforts to support wide data sharing in ecology and evolution should make even more compiled data available in forms useful to scientists. However, retrieval, integration, transformation, and validation of shared data in traditional archives remain difficult and largely manual processes. Discovery of new insights from such data is therefore delayed if it is even possible. Our interest in natural history information stems from our work on a suite of tools to support invasive species biologists. Though food web structure has been recognized to play a role in the success or failure of potential species invasions, and their impacts few ecosystems have been the subjects of empirical food web studies. Thus response teams are typically unable to get quick answers to questions like "what are likely prey and predator species of the invader in the new environment?" We have developed a food web constructor which currently uses an algorithm relying on taxonomic or phylogenetic relationships to model ecological interactions. Future algorithmic developments will use similarity in life history, natural history, or behavior to inform link predictions.

Type: TechReport

Organization: Computer Science and Electrical Engineering

Institution: University of Maryland, Baltimore County

Series: technical report

Tags: semantic web, ecoinformatics, food web, ontology

Google Scholar: Ehw9373_mKcJ

Number of Google Scholar citations: 1 [show citations]

Number of downloads: 3478


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