1. "Developing statistically-valid and -defensible frameworks to assess status and trends of ecosystem condition at national scales" "Developing statistically-valid and -defensible frameworks to assess status and trends of ecosystem condition at national scales" Ecological Research LTG 1 Poster # 1 Ecological Research LTG 1 Poster # 1 EMAP Monitoring Design & Design Team Anthony (Tony) R. Olsen (USEPA), N. Scott Urquhart (Colorado State U), & Don L. Stevens (Oregon State U) Statistical Research Collaboration among ORD researchers and STAR Grant statistical researchers Over 250 peer-reviewed publications Invited monitoring program reviews (e.g., NOAA Mussel Watch, Pacific Rim Salmon monitoring, Everglades restoration, Grand Canyon, Alberta biodiversity, NPS inventory & monitoring) Conferences organized: Computational Environmetrics 2004 Monitoring Science & Technology Symposium: Statistical track, 2004 Graybill Conference on Spatial Statistics 4 Fellows American Statistical Association EMAP Design Team Members from 4 NHEERL Eco-divisions, 2 NERL divisions, Office of Water and EPA Regions Mechanism to transfer statistical research to EPA and state monitoring designs while team works with states Technical Transfer Aquatic Resource Monitoring website: \\www.epa.gov\nheerl\arm Software for site selection and statistical analysis: psurvey.design & psurvey.analysis Monitoring workshops for states and EPA Regions (over 10) Internet meeting training sessions with individual states on monitoring design & analysis 30-40 monitoring designs per year for states, EPA, and other federal agencies (USGS, NPS, NMFS, USFS) Small area estimation: Making available data do more Relative Risk Estimation : The risk of Poor BMI is 1.6 times greater in streams with Poor SED than in streams with OK SED. GRTS: Spatially-balanced sampling: Improvement over simple random or systematic sampling Improved variance estimation: Better precision for fixed cost. 1.A geostatistical model Predict a specific reach scale condition at points that were not sampled Provide a better understanding of the relationship between the landscape and reach scale conditions Give insight into potential sources of water quality degradation Develop landscape indicators Crucial for the rapid and cost efficient monitoring of large areas 2.Better understanding of spatial autocorrelation in stream networks What is the distance within which it occurs? How does that differ between chemical variables? 3. Produce map of study area Shows the likelihood of water quality impairment for each stream segment Based on water quality standards or relative condition (low, medium, high) Future sampling efforts can be concentrated in areas with higher probability of impairment 4. Transfer technology to States and Tribes Predict likelihood of water-quality impaired stream reaches from probability survey and auxiliary data, e.g., landscape characteristics: relevant to 303(d) 305(b): Status & Trends More efficient survey designs Better statistical analyses Use EMAP probability survey data from 557 lakes to estimate average lake ANC for 113 Hydrologic units. Requires auxiliary data and new semi-parametric statistical methods Develop methodology using Maryland Biological Stream Survey data