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Applying Spatial and Temporal Modeling of Statistical Surveys to Aquatic Resources
EPA Grant Number: R829095Center: Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP)
Center Director: Urquhart, N. Scott
Title: Applying Spatial and Temporal Modeling of Statistical Surveys to Aquatic Resources
Investigators: Urquhart, N. Scott , Breidt, F. Jay , Davis, Richard A. , Gitelman, Alix I. , Herlihy, Alan T. , Hoeting, Jennifer A. , Iyer, Hariharan K. , Johnson, Stephen , Loftis, James C. , Reich, Robin M. , Ritter, Kerry J. , Stevens, Don L. , Theobald, David M. , Weisberg, Steven B.
Current Investigators: Urquhart, N. Scott , Breidt, F. Jay , Davis, Richard A. , Gitelman, Alix I. , Hoeting, Jennifer A. , Iyer, Hariharan K. , Johnson, Devin S. , Opsomer, Jean , Ritter, Kerry J. , Stevens, Don L. , Theobald, David M.
Institution: Colorado State University , Oregon State University , Southern California Coastal Water Research Project Authority
Current Institution: Colorado State University , Iowa State University , Oregon State University , Southern California Coastal Water Research Project Authority , University of Alaska Fairbanks
EPA Project Officer: Smith, Bernice
Project Period: October 1, 2001 through September 30, 2006
Project Amount: $2,998,331
RFA: Research Program on Statistical Survey Design and Analysis for Aquatic Resources (2001)
Research Category: Ecological Indicators/Assessment/Restoration , Aquatic Ecosystems
Description:
Objective:This Program will extend existing EMAP survey design and analysis methodology to enhance the use of auxiliary information, to apply recent advances in statistical modeling techniques to aquatic resources, and to develop model-based 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, Tribes and local agencies through a combination of supervised application of statistical tools and structured distance learning techniques.
Approach:The Program proposed here will initiate statistical research on several well-defined topics in model-based inference: Combining environmental data, local prediction of environmental responses, and the development and evaluation of indicators of environmental responses. We have identified some statistical research needs coupled with existing EMAP data sets and appropriate for similar data sets to be gathered by the States and Tribes. We will develop model-based analysis methods to meet those needs. We have established liaisons with some state, tribal and local organizations who are using (or plan to use) EMAP or EMAP-like surveys of aquatic resources. 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 and extend model-based analyses as necessary. Further, we will explore various distance learning techniques with our collaborators, and use their feedback to refine our techniques.
Expected Results:The Program proposed here will substantially improved methodology for assessing the condition of aquatic resources at all levels: national, multiple state regions, state, tribal, and local. The proposed program will also improve the awareness of that methodology by the parties who should use it, and will expand the cadre of statisticians with the experience and expertise to collaborate with aquatic scientists and resource managers on monitoring aquatic resources.
The Program is designed to accomplish five major goals:
- Extend model-based statistical methodology to cover the unique circumstances encountered in EMAP-like situations.
- Extend both existing and newly-developed model-based statistical tools to be more accessible to State, Tribal, and local agency personnel, both the aquatic scientists and managers of aquatic resources.
- Expand the pool of personnel in the States, Tribes, and local agencies who have both understanding of and experience in using these statistical tools.
- Develop and train a cadre of statisticians with the experience and expertise to collaborate on monitoring aquatic resources.
- Develop three archetypes of rigorous probability-based, state or local monitoring programs, along with archetype model-based analyses, incorporating landscape scale features into the analysis. The existence of these archetypes will benefit efforts to build state, tribal, and local infrastructure to monitor the condition of the
- Nation's aquatic resources.
Relevant Websites: http://www.stat.colostate.edu/starmap 
Journal Articles: 48 Displayed | Download in RIS Format
| Other center views: | All 345 publications | 60 publications in selected types | All 48 journal articles |
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Andrews B, Davis RA, Breidt FJ. Maximum likelihood estimation for all-pass time series models. Journal of Multivariate Analysis 2006;97(7):1638-1659. |
R829095 (Final) R829095C002 (2003) R829095C002 (2004) R829096 (2003) |
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Breidt FJ, Hsu NJ. Best mean square prediction for moving averages. Statistica Sinica 2005;15(2):427-446. |
R829095 (Final) R829095C002 (2003) R829095C002 (2004) R829095C002 (2005) R829096 (2003) R829096 (2005) |
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Breidt FJ, Claeskens G, Opsomer JD. Model-assisted estimation for complex surveys using penalised splines. Biometrika2005;92(4):831-846. |
R829095 (Final) R829096 (2004) R829096 (2005) |
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Brockwell PJ, Davis RA, Yang Y. Continuous-time Gaussian autoregression. Statistica Sinica 2007;17(1):63-80. |
R829095 (Final) |
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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. |
R829095 (Final) R829095C003 (2004) R829096 (2003) R829096 (2004) R829096 (2005) |
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Courbois JP, Urquhart NS. Estimating a finite population variance under randomized general random sampling designs. Journal of the Agricultural, Biological and Environmental Statistics. |
R829095C003 (2003) |
not available |
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Dailey M, Gitelman A, Ramsey F, Starcevich S. Habitat selection models to account for seasonal persistence in radio telemetry data. Environmental and Ecological Statistics 2007;14(1):55-68. |
R829095 (Final) R829095C001 (2005) |
not available |
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Davis RA, Dunsmuir WTM, Streett SB. Observation-driven models for Poisson counts. Biometrika 2003;90(4):777-790. |
R829095 (Final) |
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Davis RA, Rodriguez-Yam G. Estimation for state-space models based on a likelihood approximation. Statistica Sinica 2005;15(2):381-406. |
R829095 (Final) R829095C001 (2005) |
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Davis RA, Lee TCM, Rodriguez-Yam GA. Structural break estimation for nonstationary time series models. Journal of the American Statistical Association 2006;101(473):223-239. |
R829095 (Final) |
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Farnsworth ML, Hoeting JA, Hobbs NT, Miller MW. Linking chronic wasting disease to mule deer movement scales:a hierarchical Bayesian approach. Ecological Applications 2006;16(3):1026-1036. |
R829095 (Final) |
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Francisco-Fernandez M, Jurado-Exposito M, Opsomer JD, Lopez-Granados F. A nonparametric analysis of the distribution of convolvulus arvensis in wheat-sunflower rotations. Preprint Series #04-16. Department of Statistics, Iowa State University. Journal of Agricultural, Biological and Environmental Statistics (submitted, 2004). |
R829095C002 (2004) |
not available |
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Francisco-Fernandez M, Opsomer JD. Smoothing parameter selection methods for nonparametric regression with spatially correlated errors. The Canadian Journal of Statistics 2005;33(2):279-295. |
R829095 (Final) R829095C002 (2004) R829095C002 (2005) |
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Georgitis KM, Gitelman AI, Danz NP. Multi-scale analysis: options for modeling bird presence/absence in relation to habitat characteristics at different spatial extents. Ecological and Environmental Statistics (submitted, 2004). |
R829095C001 (2004) |
not available |
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Gitelman AI, Herlihy A. Isomorphic chain graphs for modeling spatial dependence in ecological data. Environmental and Ecological Statistics 2007;14(1):27-40. |
R829095 (Final) R829095C001 (2005) |
not available |
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Hall P, Opsomer JD. Theory for penalised spline regression. Biometrika 2005;92(1):105-118. |
R829095 (Final) R829095C002 (2005) |
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Hoeting JA, Davis RA, Merton AA, Thompson SE. Model selection for geostatistical models. Ecological Applications 2006;16(1):87-98. |
R829095 (Final) R829095C001 (2004) R829095C001 (2005) |
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Hoeting JA. Some perspectives on modeling species distributions. Bayesian Analysis 2006;1(1):93-98. |
R829095 (Final) R829095C001 (2005) |
not available |
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Johnson AA, Breidt FJ, Opsomer JD. Estimating distribution functions from survey data using nonparametric regression. Canadian Journal of Statistics (submitted, 2004). |
R829095C002 (2005) |
not available |
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Johnson DS, Hoeting JA. Autoregressive models for capture-recapture data: A Bayesian approach. Biometrics 2003;59(2):341-350. |
R829095 (Final) R829095C001 (2003) |
not available |
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Johnson DS, Hoeting JA. Markov properties of random effects graphical models. The Annals of Statistics (submitted, 2004). |
R829095C001 (2004) |
not available |
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Kahl JS, Stoddard JL, Haeuber R, Paulsen SG, Birnbaum R, Deviney FA, Webb JR, DeWalle DR, Sharpe W, Driscoll CT, Herlihy AT, Kellogg JH, Murdoch PS, Roy K, Webster KE, Urquhart NS. Have U.S. surface waters responded to the 1990 Clean Air Act amendments? Environmental Science & Technology 2004;38(24):484A-490A. |
R829095 (2005) R829095 (Final) |
not available |
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Kauermann G, Opsomer JD. Generalized cross-validation for bandwidth selection of backfitting estimators in generalized additive models. Journal of Computational and Graphical Statistics 2004;13(1):66-89. |
R829095 (Final) R829095C002 (2005) |
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Kim S, Sioutas C, Chang M-C, Gong Jr H. Factors affecting the stability of the performance of ambient fine-particle concentrators. Inhalation Toxicology 2000;12(11, Suppl 4):281-298. |
R829095C004 (2005) R825513C024 (Final) R826708 (2000) R826708 (2001) R826708 (2002) R826708 (Final) R827352 (2004) R827352 (Final) R827352C012 (Final) R827352C014 (Final) R828598C700 (Final) |
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Kincaid TM, Larsen DP, Urquhart NS. The structure of variation and its influence on the estimation of status:Indicators of condition of lakes in Northeast, U.S.A. Environmental Monitoring and Assessment 2004;98(1-3):1-21. |
R829095 (Final) R829095C003 (2003) R829095C003 (2004) |
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Larsen DP, Kincaid TK, Jacobs SE, Urquhart NS. Designs for evaluating local and regional scale trends. Bioscience 2002;51(12):1069-1078. |
R829095 (Final) |
not available |
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Larsen DP, Kaufmann PR, Kincaid TM, Urquhart NS. Detecting persistent change in the habitat of salmon-bearing streams in the Pacific Northwest. Canadian Journal of Fisheries and Aquatic Sciences 2004;61(2):283-291. |
R829095 (Final) R829095C003 (2003) R829095C003 (2004) |
not available |
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Miller CP, Opsomer JD. Bandwidth selection for local polynomial regression estimation. Journal of Nonparametric Statistics (submitted, 2004). |
R829095C002 (2004) |
not available |
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Montanari GE, Ranalli MG. Nonparametric model calibration estimation in survey sampling. Journal of the American Statistical Association 2005;100(472):1429-1442. |
R829095 (Final) R829095C002 (2004) R829095C002 (2005) R829096 (2004) R829096 (2005) |
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Opsomer JD, Breidt FJ, Moisen GG, Kauermann G. Model-assisted estimation of forest resources with generalized additive models. Preprint Series #03-05, Department of Statistics, Iowa State University. Journal of the American Statistical Association (submitted, 2003). |
R829095C002 (2004) R829095C002 (2005) R829096 (2003) |
not available |
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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. |
R829095 (Final) R829095C002 (2004) R829096 (2004) R829096 (2005) |
not available |
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Opsomer JD, Miller CP. Selecting the amount of smoothing in nonparametric regression estimation for complex surveys. Journal of Nonparametric Statistics 2005;17(5):593-611. |
R829095 (Final) R829095C002 (2004) R829095C002 (2005) |
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Peterson EE, Merton AA, Theobald DM, Urquhart NS. Patterns of spatial autocorrelation in stream water chemistry. Environmental Monitoring and Assessment 2006;121(1-3):569-594. |
R829095 (Final) |
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Peterson EE, Urquhart NS. Predicting water quality impaired stream segments using landscape-scale data and a regional geostatistical model:a case study in Maryland. Environmental Monitoring and Assessment 2006;121(1-3):615-638. |
R829095 (Final) |
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Reese GC, Wilson KR, Hoeting JA, Flather CH. Factors affecting species distribution predictions:A simulation modeling experiment. Ecological Applications 2005;15(2):554-564. |
R829095 (Final) R829095C001 (2005) |
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Ritter KJ, Leecaster MK. Spatial enhancement of grids for the estimation of a variogram in near coastal systems. Environmental and Ecological Statistics (submitted, 2004). |
R829095C001 (2004) |
not available |
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Ritter KJ, Leecaster MK. Multi-lag cluster designs for estimating the semivariogram for sediments affected by effluent discharges offshore in San Diego. Environmental and Ecological Statistics 2007;14(1):41-53. |
R829095 (Final) |
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Theobald DM, Norman J, Peterson EE, Ferraz SB, Deems J. FLOWS: functional linkage of watersheds and streams. Environmental Modeling Software (in preparation, 2004). |
R829095C003 (2005) |
not available |
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Theobald DM, Ferraz SB. Hydrologic-weighting for functional watershed modeling. Journal of American Water Resources Association (in preparation, 2004). |
R829095C003 (2005) |
not available |
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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. |
R829095 (Final) |
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Urquhart NS, Larsen DP, Herlihy AT. Sampling macroinvertebrates: compositing and structure of variance. Canadian Journal of Fisheries and Aquatic Sciences (in preparation, 2004). |
R829095C003 (2004) |
not available |
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Urquhart NS, Moore JC. Statistics in EPA's STAR program: learning materials for surface water monitoring. OPPTS Tribal News 2004;4(3):45-46. |
R829095C004 (2004) |
not available |
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Ver Hoef JM, Peterson E, Theobald D. Spatial statistical models that use flow and stream distance. Environmental and Ecological Statistics 2006;13(4):449-464. |
R829095 (Final) |
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Waite IR, Herlihy AT, Larsen DP, Urquhart NS, Klemm DJ. The effects of macroinvertebrate taxonomic resolution in large landscape bioassessments:an example from the Mid-Atlantic Highlands, U.S.A. Freshwater Biology 2004;49(4):474-489. |
R829095 (Final) R829095C003 (2004) |
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Wang H, Ranalli MG. Low-ranked smoothing spines on complex domains. Biometrika (in preparation, 2004). |
R829095C002 (2005) |
not available |
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Wang H, Ranalli MG. Low-rank smoothing splines on complicated domains. Biometrics 2007;63(1):209-217. |
R829095 (Final) |
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Kim J-Y, Breidt FJ, Opsomer JD. Nonparametric regression estimation of finite population totals under two-stage sampling. Journal of the Royal Statistical Society, Series B (submitted, 2004). |
R829095C002 (2004) |
not available |
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da Silva DN, Opsomer JD. Properties of the weighting cell estimator under a nonparametric response mechanism. Survey Methodology 2004;30(1):45-55. |
R829095 (Final) R829095C002 (2004) R829096 (2005) |
not available |
public policy, decision making, community-based, monitoring, risk assessment, watersheds, surface waters, estuaries, Bayesian, hierarchical modeling, GIS, landscape
,
Ecosystem Protection/Environmental Exposure & Risk, Scientific Discipline, RFA, Aquatic Ecosystems & Estuarine Research, Aquatic Ecosystem, Environmental Monitoring, water quality, EMAP, Environmental Monitoring and Assessment Program, aquatic ecosystems, spatial and temporal modeling, ecosystem monitoring, modeling ecosystems
Progress and Final Reports:
2003 Progress Report
2004 Progress Report
2005 Progress Report
Final Report
Subprojects under this Center:
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R829095C001 Combining Environmental Data Sets
R829095C002 Local Inferences from Aquatic Studies
R829095C003 Development and Evaluation of Aquatic Indicators
R829095C004 Extension of Expertise on Design and Analysis to States and Tribes
R829095C005 Integration and Coordination for STARMAP