ETHAN: the Evolutionary Trees and Natural History Ontology

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.
Date: November 01, 2006
Type: TechReport
Series: technical report
Publisher: University of Maryland, Baltimore County
Organization: Computer Science and Electrical Engineering
Google scholar: Ehw9373_mKcJ
Google citations: 1 citations
Downloads: 2463

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  author = "Cynthia Parr and Joel Sachs and Andriy Parafiynyk and Taowei Wang and Roger Espinosa and Tim Finin",
  title = "{ETHAN: the Evolutionary Trees and Natural History Ontology}",
  month = "November",
  year = "2006",
  organization = "Computer Science and Electrical Engineering",
  series = "technical report",
  institution = "University of Maryland, Baltimore County",