Environment Canada at Canadian Rivers Institute, Department of Biology, University of New Brunswick, P.O. Box 45111, Fredericton, New Brunswick E3B6E1, Canada.
Integr Environ Assess Manag. 2011 Apr;7(2):209-15. doi: 10.1002/ieam.129. Epub 2010 Nov 2.
The trait approach has already indicated significant potential as a tool in understanding natural variation among species in sensitivity to contaminants in the process of ecological risk assessment. However, to realize its full potential, a defined nomenclature for traits is urgently required, and significant effort is required to populate databases of species-trait relationships. Recently, there have been significant advances in the area of information management and discovery in the area of the semantic web. Combined with continuing progress in biological trait knowledge, these suggest that the time is right for a reevaluation of how trait information from divergent research traditions is collated and made available for end users in the field of environmental management. Although there has already been a great deal of work on traits, the information is scattered throughout databases, literature, and undiscovered sources. Further progress will require better leverage of this existing data and research to fill in the gaps. We review and discuss a number of technical and social challenges to bringing together existing information and moving toward a new, collaborative approach. Finally, we outline a path toward enhanced knowledge discovery within the traits domain space, showing that, by linking knowledge management infrastructure, semantic metadata (trait ontologies), and Web 2.0 and 3.0 technologies, we can begin to construct a dedicated platform for TERA science.
特质方法已经显示出作为一种工具的巨大潜力,可以在生态风险评估过程中理解物种对污染物敏感性的自然变异。然而,为了充分发挥其潜力,迫切需要为特质制定明确的命名法,并需要大量努力来填充物种特质关系数据库。最近,语义网领域在信息管理和发现方面取得了重大进展。结合生物特质知识的持续进步,这表明现在是重新评估如何从不同研究传统中整理特质信息并将其提供给环境管理领域的最终用户的时机。尽管已经有很多关于特质的研究,但这些信息分散在数据库、文献和未被发现的来源中。进一步的进展将需要更好地利用这些现有数据和研究来填补空白。我们审查和讨论了将现有信息汇集在一起并朝着新的协作方法发展所面临的一些技术和社会挑战。最后,我们概述了在特质领域内进行增强型知识发现的途径,表明通过链接知识管理基础设施、语义元数据(特质本体)以及 Web 2.0 和 3.0 技术,我们可以开始构建一个专门的 TERA 科学平台。