Comparison of common lilac (Syringa vulgaris) phenology timing between historical data and current Project BudBurst citizen science data: challenges and lessons learned.

Observing the timing of plant phenology provides a way to monitor and predict effects of ecological change on plants. This study compared historical data for common lilac dating from 1956-2003 with recent lilac phenology data collected by Project BudBurst citizen scientists from 2007-2013. Due to the lack of accessible growing degree day data, it was not possible to directly examine climate effects on phenology timing. Instead, we compared geographic distribution patterns between historical and Project BudBurst data to explore what factors might contribute to the timing of phenophase dates between data sets. T-tests were performed on latitude, longitude, and day of year of observation (Julian date) for first flower and first leaf between the two data sets. Differences between latitude were not significant for first flower and first leaf (p = 0.789, p = 0.489, respectively) but there was a difference between longitude for both variables (p\u3c0.001). Mean observation dates for Project BudBurst were 9.5 days earlier for first flower (significantly different, p = 0.0001) and 2.3 days earlier for first leaf (no significant difference, p = 0.063) but the difference in longitude and the small sample size of the Project BudBurst data set makes these findings questionable. Because of the effect of longitude, we suggest future analyses of data by regions. Additional Project BudBurst observations in the western U.S. would allow better comparisons in that region and encouraging observations near historic sites would take advantage of a long, rich data set

Invasive Species Forecasting System: A Decision Support Tool for the U.S. Geological Survey: FY 2005 Benchmarking Report v.1.6

The National Institute of Invasive Species Science (NIISS), through collaboration with NASA's Goddard Space Flight Center (GSFC), recently began incorporating NASA observations and predictive modeling tools to fulfill its mission. These enhancements, labeled collectively as the Invasive Species Forecasting System (ISFS), are now in place in the NIISS in their initial state (V1.0). The ISFS is the primary decision support tool of the NIISS for the management and control of invasive species on Department of Interior and adjacent lands. The ISFS is the backbone for a unique information services line-of-business for the NIISS, and it provides the means for delivering advanced decision support capabilities to a wide range of management applications. This report describes the operational characteristics of the ISFS, a decision support tool of the United States Geological Survey (USGS). Recent enhancements to the performance of the ISFS, attained through the integration of observations, models, and systems engineering from the NASA are benchmarked; i.e., described quantitatively and evaluated in relation to the performance of the USGS system before incorporation of the NASA enhancements. This report benchmarks Version 1.0 of the ISFS