Historical ecological records form an important baseline for understanding past spatial and temporal patterns of biodiversity that can be used to place current and potential future patterns into context, particularly under rapidly changing environments characteristic of the Anthropocene. Recent advances in capacity for modeling, the availability of data at daily time steps and relatively fine spatial resolution, and advanced information science (e.g., visualization tools) enable the conversion of historical data for a variety of contemporary applications at near-real time – aka the “resolution revolution.” 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 (NPDb) maintained by USA-NPN contains almost 6 million in-situ observation records for plants and animals for the period 1954-2015. The National Coordinating Office of USA-NPN is developing a suite of standard data products and tools that leverage on historical data to facilitate use and application by a diverse set of data users.
Results/Conclusions
This presentation outlines a workflow for the development and validation of national spatially gridded phenology products – at daily timesteps and fine spatial resolution – drawing on recent work related to the Spring Indices – originally developed by Prof. Mark D. Schwartz, U Wisconsin-Milwaukee and now curated by the USA-NPN. The basis for the Spring Indices is a historical lilac and honeysuckle dataset – collected by volunteers and citizen scientists – that dates to 1954. The algorithm developed for the Spring Indices was recently restricted to locations with meteorological stations, but we have recently extended the algorithm to the nation using gridded climatological datasets, as well as short-term meteorological forecasts. Thus, we are producing 6-day forecasts of the onset of spring at a national scale, based largely on the availability of that historical lilac dataset. In addition, we discuss how we actively engage observers to collect contemporary observational data to validate model predictions. Preliminary analyses indicate high fidelity between historical in-situ and modeled observations on a national scale, but with considerable variability at the regional scale. Regions with strong differences between expected and observed data are identified and will be the focus of in-situ data collection campaigns using USA-NPN’s Nature’s Notebook on-line user interface (www.nn.usanpn.org).