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National Aquatic Resource Surveys & Statistics: Role of statistics in the development of a national monitoring program
Citation:
Olsen, Tony. National Aquatic Resource Surveys & Statistics: Role of statistics in the development of a national monitoring program. Presented at Department of Statistics Seminar, Provo, UT, March 08, 2012.
Impact/Purpose:
Objective is to communicate to university statistics students and faculty the opportunities for environmental statistics to have an impact on national environmental programs in the U.S. Environmental Protection Agency.
Description:
The National Aquatic Resource Surveys (NARS) are a series of four statistical surveys conducted by the U.S. Environmental Protection Agency working in collaboration with states, tribal nations and other federal agencies. The surveys are conducted for lakes and reservoirs, streams and rivers, estuaries and Intercoastal waterways, and wetlands. Based on spatial survey designs, the studies provide nationally consistent and scientifically?]defensible assessments of the nation?fs waters and can be used to track changes over time. The NARS are a direct result of research conducted by the Environmental Monitoring and Assessment Program (EMAP). EMAP developed (1) indicators of the ecological condition and stressors to the aquatic resource, (2) assessment methods for reporting on those indicators, and (3) survey design and analysis methods to conduct the monitoring. The focus of the seminar is on the role of statistics in the development of NARS. Some of the statistical developments were (1) spatially?]representative samples using spatially?]balanced survey designs, (2) discrete global grid system for systematic survey designs, (3) local spatial neighborhood variance estimator for spatially?]balanced designs, (4) spsurvey R package for the design and analysis of monitoring programs, (5) panel designs for monitoring status and trends, (6) deconvolution of estimated cumulative distribution functions based on complex survey designs, (7) testing for change in estimated cumulative distribution functions based on complex survey designs, (8) estimating relative risk and attributable risk for categorical indicators of biological condition and stressors, (9) testing for change in status between two survey periods for augmented rotating panel design surveys, (10) estimating probability of site occupancy in presence of imperfect detection when data are from a complex survey design, and (11) linked micromap plots to display statistical information with an organized set of small maps. Pa