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Extramural Research

National Research Program on Design-Based/Model-Assisted Survey Methodology for Aquatic Resources

EPA Grant Number: R829096
Center: National Research Program on Design-Based/Model-Assisted Survey Methodology for Aquatic Resources
Center Director: Stevens, Don L.
Title: National Research Program on Design-Based/Model-Assisted Survey Methodology for Aquatic Resources
Investigators: Stevens, Don L. , Breidt, F. Jay , Conquest, Loveday , Courbois, Jean-Yves , Gitelman, Alix I. , Herlihy, Alan T. , Hughes, Robert , Lesser, Virginia , Munoz-Hernandez, Breda , Murtaugh, Paul , Opsomer, Jean , Sifneos, Jean , Smith, Ruben , Urquhart, N. Scott
Current Investigators: Stevens, Don L. , Herlihy, Alan T. , Lesser, Virginia , Urquhart, N. Scott
Institution: Oregon State University , Colorado State University , Iowa State University , University of Washington
Current Institution: Oregon State University , Colorado State University
EPA Project Officer: Smith, Bernice
Project Period: October 15, 2001 through October 14, 2005 (Extended to October 13, 2006)
Project Amount: $2,989,884
RFA: Research Program on Statistical Survey Design and Analysis for Aquatic Resources (2001)
Research Category: Aquatic Ecosystems , Ecological Indicators/Assessment/Restoration , Tribal Environmental Health Research , Water and Watersheds

Description:

Objective:

Our objectives for this Program are to extend existing EMAP survey design and analysis methodology to enhance the use of auxiliary information; to develop design-based model-assisted methodology compatible with EMAP's multi-tiered studies; to implement these techniques on example data sets; to provide expert survey design and analysis assistance to States and Tribes; and to transfer that statistical expertise to States and Tribes through a combination of supervised application of statistical tools and structured distance learning techniques.

Approach:

Our approach will be to initiate statistical research on several well-defined topics in model-assisted estimation: non-parametric model-assisted design-based estimation, estimation and prediction using empirical orthogonal functions on probability survey data, model-assisted imputation, model-assisted survey design, and analysis of multi-year panel studies. We have made preliminary contacts with some State and Tribal agencies who have a connection to EMAP. We will strengthen and expand those contacts to include all identifiable State and Tribal agencies who are using (or plan to use) EMAP, REMAP, or EMAP-like surveys of aquatic resources. We will assist those agencies in applying existing survey design and analysis methods, and, in particular, investigate and extend techniques for incorporating auxiliary information in the design. In collaboration with those agencies and other State and Tribal organizations, we will identify further statistical research needs to be addressed by our statistical methodology research teams.

Three state agencies (Oregon Department of Fish and Wildlife (ODFW), California State Water Resources Control Board (CSWRCB), and the San Francisco Estuary Institute(SFEI)) which have existing or planned aquatic monitoring programs that use EMAP's sampling methodology have a special role. These agencies will identify areas where our statistical expertise will be especially beneficial and act as "laboratories" for testing distance learning methods. They will provide guidance on the areas where they feel they need more knowledge; we will structure the content and delivery method to meet those needs; and they will provide feedback and critique.

The interaction between these three agencies and this Program has the potential to develop into model archetypes for state and local level monitoring programs. The three programs represent a microcosm that spans almost all of the circumstances that state or local aquatic monitoring efforts are likely to encounter. ODFW initiated a survey of Coho salmon in Oregon streams in 1998 using a rotating panel design and EMAP's spatially-balanced sample selection procedure. The sampling is expected to continue indefinitely using the same design, with the objective of estimating both annual escapement of coho salmon, and trends in the escapement. The sample has nested subsamples of juvenile counts and habitat quality, with landscape level information available on several variables, such as vegetation cover, stream order, and basin size. Both spatial and temporal correlation are present. In addition, there is substantial non-response, which may be related to such information as landholder type and size of holding. Thus, the data set provides fertile ground for developing model-assisted, design-based estimation techniques, such as trend estimation, imputation of missing data, small-area estimation, and a variety of model-assisted status estimation techniques. This is a critical, high-visibility issue in the Pacific Northwest because it involves a commercially and recreationally important species that is listed as threatened by the federal government.

The SFEI is presently concluding a re-design of the San Francisco Estuary Regional Monitoring Program for Trace Substances. While the design has not been finalized, it will almost certainly consist of an EMAP-like probability sample, coupled with more intensive samples at selected sites. SFEI is now initiating the design of a Bay Area Wetlands Regional Monitoring Program. This design effort will provide us with the opportunity to investigate some model-assisted design approaches on a relatively small scale. The regional monitoring conducted by SFEI is an example of a state-supported estuarine monitoring program encompassing the estuary itself and its associated wetlands.

The third archetype program is the Surface Waters Ambient Monitoring Program (SWAMP) being initiated by CSWRCB. This will be a state-wide program with multiple objectives, which include an EMAP-like assessment, e.g., an estimate of the proportion of the resource in nominal or degraded condition. This is the first state-funded project of this scale that may use an EMAP-like sampling design. Its success may encourage other states to follow.

Expected Results:

We expect to accomplish five major goals with the Program. The first is to extend design-based statistical methodology to cover the unique circumstances encountered in EMAP. The second is to make both existing and newly-developed model-assisted design-based statistical tools more accessible to State and Tribe personnel, both the aquatic scientists and managers of aquatic resources. The third is to expand the pool of personnel in the States and Tribes who have both understanding of and experience in using the statistical tools. The fourth is to develop a cadre of statisticians with the experience and expertise to collaborate on monitoring aquatic resources. Finally, we will develop three archetypes of rigorous probability-based, state or local monitoring programs, along with archetype design-based, model-assisted analyses. The existence of these archetypes will benefit EMAP's efforts to build state, tribal, and local infrastructure to monitor the condition of the Nation's aquatic resources.


Journal Articles: 16 Displayed | Download in RIS Format

Other center views: All 142 publications 19 publications in selected types All 16 journal articles

Type Citation Sub Project Document Sources
Journal Article Andrews B, Davis RA, Breidt FJ. Maximum likelihood estimation for all-pass time series models. Journal of Multivariate Analysis 2006;97(7):1638-1659. R829096 (2003)
R829095 (Final)
R829095C002 (2003)
R829095C002 (2004)
  • Full-text: ScienceDirect-Full Text PDF
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  • Abstract: ScienceDirect-Abstract
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  • Journal Article Breidt FJ, Hsu N-J. Best mean square prediction for moving averages. Statistica Sinica 2005;15(2):427-446. R829096 (2003)
    R829096 (2005)
    R829095 (Final)
    R829095C002 (2003)
    R829095C002 (2004)
    R829095C002 (2005)
  • Full-text: StatisticaSinica-Full Text PDF
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  • Abstract: StatisticaSinica-Abstract
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  • Journal Article Breidt FJ, Hsu N-J, Coar W. A diagnostic test for autocorrelation in increment-averaged data with application to soil sampling. Environmental and Ecological Statistics 2008;15(1):15-25. R829096 (2005)
  • Abstract: SpringerLink Abstract
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  • Journal Article Buchanan RA, Conquest LL, Courbois J-Y. A cost analysis of ranked set sampling to estimate a population mean. Environmetrics 2005;16(3):235-256. R829096 (2002)
    R829096 (2003)
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  • Abstract: Wiley Interscience
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  • Journal Article Cooper C. Sampling and variance estimation on continuous domains. Environmetrics 2006;17(6):539-553. R829096 (2005)
  • Abstract: Wiley Interscience Abstract
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  • Journal Article Courbois JP, Urquhart NS. Comparison of survey estimates of the finite population variance. Journal of Agricultural, Biological, and Environmental Statistics 2004;9(2):236-251. R829096 (2003)
    R829096 (2004)
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    R829095 (2004)
    R829095 (2005)
    R829095 (Final)
    R829095C003 (2003)
    R829095C003 (2004)
  • Abstract: SpringerLink
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  • Journal Article Da Silva DN, Opsomer JD. Properties of the weighting cell estimator under a nonparametric response mechanism. Survey Methodology 2004;30(1):45-55. R829096 (2004)
    R829096 (2005)
    R829095 (2003)
    R829095 (2004)
    R829095 (2005)
    R829095 (Final)
    R829095C002 (2004)
    R829095C002 (2005)
  • Full-text: Statistics Canada-Full Text PDF
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  • Abstract: Statistics Canada-Abstract
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  • Journal Article Montanari GE, Ranalli MG. Nonparametric model calibration estimation in survey sampling. Journal of the American Statistical Association 2005;100(472):1429-1442. R829096 (2004)
    R829096 (2005)
    R829095 (Final)
    R829095C002 (2004)
    R829095C002 (2005)
  • Abstract: American Statistical Association-Abstract
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  • Journal Article Munoz B, Lesser VM. Adjustment procedures to account for non-ignorable missing data in environmental surveys. Environmetrics 2006;17(6):653-662. R829096 (2005)
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  • Journal Article Munoz B, Lesser VM, Ramsey FL. Design-based empirical orthogonal function model for environmental monitoring data analysis. Environmetrics 2008;19(8):805-817. R829096 (2003)
    R829096 (2004)
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  • Abstract: Wiley InterScience Abstract
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  • Journal Article Opsomer JD, Botts C, Kim JY. Small area estimation in a watershed erosion assessment survey. Journal of Agricultural, Biological, and Environmental Statistics 2003;8(2):139-152. R829096 (2004)
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    R829095 (2004)
    R829095 (2005)
    R829095 (Final)
    R829095C002 (2004)
  • Full-text: Research Gate-Abstract and Full Text HMTL
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  • Abstract: SpringerLink-Abstract
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  • Journal Article Opsomer JD, Breidt FJ, Moisen GG, Kauermann G. Model-assisted estimation of forest resources with generalized additive models. Journal of the American Statistical Association 2007;102(478):400-409. R829096 (2003)
    R829096 (2004)
    R829096 (2005)
    R829095 (2004)
    R829095 (2005)
    R829095 (Final)
    R829095C002 (2004)
    R829095C002 (2005)
  • Full-text: US Forest Service-Full Text PDF
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  • Abstract: Journal of the American Statistical Association-Abstract
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  • Journal Article Opsomer JD, Claeskens G, Ranalli MG, Kauermann G, Breidt FJ. Non-parametric small area estimation using penalized spline regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 2008;70(1):265-286. R829096 (2005)
    R829095C002 (2005)
  • Abstract: Wiley
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  • Other: Prepublication PDF
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  • Journal Article Stevens Jr. DL, Olsen AR. Variance estimation for spatially balanced samples of environmental resources. Environmetrics 2003;14(6):593-610. R829096 (2003)
    R829096 (2005)
  • Full-text: EPA NHEERL
  • Abstract: Wiley Interscience
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  • Journal Article Stevens Jr. DL, Olsen AR. Spatially-balanced sampling of natural resources. Journal of the American Statistical Association 2004;99(465):262-278 R829096 (2002)
    R829096 (2004)
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    Journal Article Thomas DL, Johnson D, Griffith B. A Bayesian random effects discrete-choice model for resource selection: population-level selection inference. Journal of Wildlife Management 2006;70(2):404-412. R829096 (2005)
    R829095 (Final)
  • Abstract: Wiley-Abstract
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  • Supplemental Keywords:

    public policy, decision making, community-based, monitoring, risk assessment, watersheds, streams, rivers, estuaries,, RFA, Scientific Discipline, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, Aquatic Ecosystem, Environmental Monitoring, EMAP, risk assessment, estuarine research, ecosystem monitoring, statistical survey design, spatial and temporal modeling, aquatic ecosystems, Environmental Monitoring and Assessment Program

    Progress and Final Reports:
    2002 Progress Report
    2003 Progress Report
    2004 Progress Report
    2005 Progress Report

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    The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.

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