Transcript Slide 1
EMAP Monitoring Design & Design Team Ecological Research LTG 1 Poster # 1 Anthony (Tony) R. Olsen (USEPA), N. Scott Urquhart (Colorado State U), & Don L. Stevens (Oregon State U) 1.0 C ontinuous domain w ith no v oids 0.8 0.6 Ex ponentially inc reas ing poly gon s iz e, total perimeter = 43.1 50 100 150 200 Predict likelihood of water-quality impaired stream reaches from probability survey and auxiliary data, e.g., landscape characteristics: relevant to 303(d) 250 point dens ity Maryland Bioglogical Stream Survey (MBSS) Sample Site Locations Improved variance estimation: Better precision for fixed cost. 1.00 • 4 Fellows American Statistical Association sh sv ac cac Kilometers 30 0 Legend nbh MBSS sample sites 1:100,000 National Hydrography Dataset 0.85 Maryland 0 0.0 0.5 1.0 CV Parameter Relative Risk Estimation: The risk of Poor BMI is 1.6 times greater in streams with Poor SED than in streams with OK SED. 1.5 2.0 5,000 Meters ¯ 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 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 Develop methodology using Maryland Biological Stream Survey data srs 0.95 Coverage •Graybill Conference on Spatial Statistics Linearly inc reas ing poly gon s iz e, total perimeter = 84.9 0 •Computational Environmetrics 2004 •Monitoring Science & Technology Symposium: Statistical track, 2004 C ons tant poly gon s iz e, total perimeter = 88.4 0.90 • Conferences organized: Improvement over simple random or systematic sampling 0.4 • Invited monitoring program reviews (e.g., NOAA Mussel Watch, Pacific Rim Salmon monitoring, Everglades restoration, Grand Canyon, Alberta biodiversity, NPS inventory & monitoring) GRTS: Spatially-balanced sampling: 0.2 • Over 250 peer-reviewed publications 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 0.0 • Collaboration among ORD researchers and STAR Grant statistical researchers Small area estimation: Making available data do more •More efficient survey designs •Better statistical analyses polygon area variance ratio Statistical Research 305(b): Status & Trends 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) "Developing statistically-valid and -defensible frameworks to assess status and trends of ecosystem condition at national scales"