"Developing statistically-valid and -defensible frameworks to assess status and trends ofecosystem condition at national scales"

"Developing statistically-valid and -defensible frameworks to assess status and trends ofecosystem condition at national scales"

Ecological Research

LTG 1 Poster # 1

Ecological Research

LTG 1 Poster # 1EMAP 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

point density

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0 50 100 150 200 250

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Continuous domain with no voids

Exponentially increasing polygon size, total perimeter = 43.1

Linearly increasing polygon size, total perimeter = 84.9

Constant polygon size, total perimeter = 88.4

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.

0.0 0.5 1.0 1.5 2.0

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CV Parameter

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0 5,000 Meters

Maryland Bioglogical Stream Survey (MBSS) Sample Site Locations

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MBSS sample sites

1:100,000 National Hydrography Dataset

Maryland

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0 30Kilometers

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