Ramona L. Walls 1 , * , John Deck 2 , Robert Guralnick 3 , Steve Baskauf 4 , Reed Beaman 5 , Stanley Blum 6 , Shawn Bowers 7 , Pier Luigi Buttigieg 8 , Neil Davies 9 , Dag Endresen 10 , Maria Alejandra Gandolfo 11 , Robert Hanner 12 , Alyssa Janning 13 , Leonard Krishtalka 14 , Andréa Matsunaga 15 , Peter Midford 16 , Norman Morrison 17 , Éamonn Ó. Tuama 18 , Mark Schildhauer 19 , Barry Smith 20 , Brian J. Stucky 21 , Andrea Thomer 22 , John Wieczorek 23 , Jamie Whitacre 24 , John Wooley 25
3 March 2014
The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.