IN31C-3737:
Integrating Phenological, Trait and Environmental Data For Continental Scale Analysis: A Community Approach

Wednesday, 17 December 2014
Ramona Walls1, Jake F Weltzin2, Robert P Guralnick3, Alyssa Rosemartin2, John Deck4 and Lindsay A Powers5, (1)University of Arizona, Tucson, AZ, United States, (2)USA National Phenology Network, Tucson, AZ, United States, (3)University of Colorado at Boulder, Boulder, CO, United States, (4)University of California Berkeley, Berkeley, CA, United States, (5)NEON, Boulder, CO, United States
Abstract:
There is a wealth of biodiversity and environmental data that can provide the basis for addressing global scale questions of societal concern. However, our ability to discover, access and integrate these data for use in broader analyses is hampered by the lack of standardized languages and systems. New tools (e.g. ontologies, data standards, integration tools, unique identifiers) are being developed that enable establishment of a framework for linked and open data. Relative to other domains, these tools are nascent in biodiversity and environmental sciences and will require effort to develop, though work can capitalize on lessons learned from previous efforts. Here we discuss needed next steps to provide consistently described and formatted ecological data for immediate application in ecological analysis, focusing on integrating phenology, trait and environmental data to understand local to continental-scale biophysical processes and inform natural resource management practices. As more sources of data become available at finer spatial and temporal resolution, e.g., from national standardized earth observing systems (e.g., NEON, LTER and LTAR Networks, USA NPN), these challenges will become more acute. Here we provide an overview of the standards and ontology development landscape specifically related to phenological and trait data, and identify requirements to overcome current challenges. Second, we outline a workflow for formatting and integrating existing datasets to address key scientific and resource management questions such as: “What traits determine differential phenological responses to changing environmental conditions?” or “What is the role of granularity of observation, and of spatiotemporal scale, in controlling phenological responses to different driving variables?” Third, we discuss methods to semantically annotate datasets to greatly decrease time needed to assemble heterogeneous data for use in ecological analyses on varying spatial scales. We close by making a call to interested community members for a working group to model phenology, trait and environmental data products from continental-scale efforts (e.g. NEON, USA-NPN and others) focusing on ways to assure discoverability and interoperability.