Phenology Data Products to Support Assessment and Forecasting of Phenology on Multiple Spatiotemporal Scales

Katharine Gerst1, Carolyn Enquist2, Alyssa Rosemartin1, Ellen G Denny1, Lee Marsh1, David JP Moore3 and Jake F Weltzin4, (1)USA National Phenology Network, Tucson, AZ, United States, (2)USA National Phenology Network, Tucson, MD, United States, (3)University of Arizona, School of Natural Resources and the Environment, Tucson, AZ, United States, (4)USA National Phenology Network, Fort Collins, CO, United States
Abstract:
The USA National Phenology Network (USA-NPN; www.usanpn.org) serves science and society by promoting a broad understanding of plant and animal phenology and the relationships among phenological patterns and environmental change. The National Phenology Database maintained by USA-NPN now has over 3.7 million records for plants and animals for the period 1954-2014, with the majority of these observations collected since 2008 as part of a broad, national contributory science strategy. These data have been used in a number of science, conservation and resource management applications, including national assessments of historical and potential future trends in phenology, regional assessments of spatio-temporal variation in organismal activity, and local monitoring for invasive species detection.

Customizable data downloads are freely available, and data are accompanied by FGDC-compliant metadata, data-use and data-attribution policies, vetted and documented methodologies and protocols, and version control. While users are free to develop custom algorithms for data cleaning, winnowing and summarization prior to analysis, the National Coordinating Office of USA-NPN is developing a suite of standard data products to facilitate use and application by a diverse set of data users.

This presentation provides a progress report on data product development, including: (1) Quality controlled raw phenophase status data; (2) Derived phenometrics (e.g. onset, duration) at multiple scales; (3) Data visualization tools; (4) Tools to support assessment of species interactions and overlap; (5) Species responsiveness to environmental drivers; (6) Spatially gridded phenoclimatological products; and (7) Algorithms for modeling and forecasting future phenological responses. The prioritization of these data products is a direct response to stakeholder needs related to informing management and policy decisions. We anticipate that these products will contribute to broad understanding of plant and animal phenology across scientific disciplines.

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