2005 Progress Report: Applying Spatial and Temporal Modeling of Statistical Surveys to Aquatic Resources

EPA Grant Number: R829095
Center: 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. , Hoeting, Jennifer A. , Iyer, Hariharan K. , Theobald, David M.
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
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: Hiscock, Michael
Project Period: October 1, 2001 through September 30, 2006
Project Period Covered by this Report: October 1, 2004 through September 30, 2005
Project Amount: $2,998,331
RFA: Research Program on Statistical Survey Design and Analysis for Aquatic Resources (2001) RFA Text |  Recipients Lists
Research Category: Water , Ecosystems , Aquatic Ecosystems , Ecological Indicators/Assessment/Restoration , Water and Watersheds

Objective:

The objectives of this research project are to: (1) develop and implement statistical methods for aquatic surveys; (2) communicate results to states and tribes; and (3) train future generations of environmental statisticians.

Progress Summary:

Brief Statements Covering Work Status

In broad outline, the Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP) is proceeding very consistent with its proposal, except for its rate of completion of Year 4 work. Approximately 90 percent of the work proposed for the first 4 years has been completed. More than 70 percent of the total project funds have been expended. Limited success in recruiting postdoctoral fellows is the main reason for the delayed rate of completion of work and the slow expenditure of funds. Recruiting efforts for postdoctoral fellows have been pursued vigorously and have been somewhat successful, but alternatives for accomplishing the research are being implemented, as recommended by the program’s science advisory committee. Specifically, we have developed part-time relations through subcontracts with several junior statisticians and have involved them in roles similar to postdoctoral fellows.

Designs and Models for Aquatic Resource Surveys (DAMARS), in cooperation with STARMAP, sponsored the Fourth Annual Conference on Statistical Survey Design and Analysis for Aquatic Resources at Oregon State University, September 7-9, 2005. Last year’s conference at Colorado State University (CSU) included several persons outside of STARMAP and DAMARS but with interests in environmental statistics. This year’s conference expanded to include international participants and had concurrent and joint sessions with user communities. It also included a short course for users. This conference involved major contributions from STARMAP and DAMARS, the programs’ science advisory committee, participants associated with the Oregon plan for salmon and watersheds, personnel from the U.S. Environmental Protection Agency (EPA) and other federal agencies, and other interested participants. Students from both programs had major roles in this conference, presenting talks and posters.

Members of the STARMAP team have been active in publications and presentations; they have completed approximately 25 professional publications, given approximately 60 professional presentations, and have a large number of manuscripts in various stages of development and submission.

During this reporting period, STARMAP personnel have published one book and nine scientific articles. Sixteen articles have been accepted for publication, at least 22 manuscripts are under journal review, and at least 16 other manuscripts are in various stages of preparation. One doctoral dissertation and one masters report have been completed. These personnel made approximately 60 professional presentations (almost all invited) and displayed 3 posters. They have participated in more than 30 professional venues, ranging from departmental seminars to international conferences. All STARMAP students attended at least one professional meeting, and most made at least one oral presentation or displayed a poster. Collectively, they attended three different professional meetings and made seven presentations. One received honorable mention in a student paper competition. Details appear in the individual project reports.

Substantial progress has been made in the direction of local area estimation for eventual use in projecting from a sample of data points to other unobserved locations. New methods for analyzing discrete composition data of the sort resulting from collections of aquatic macroinvertebrate have been developed; their utility will be explored further in collaboration with data resulting from the efforts of other investigators funded by EPA’s Science To Achieve Results (STAR) Program. New methods for selecting predictors for spatial models have been developed and tested in practical situations. Geographic information system (GIS) techniques for accomplishing spatially distributed probability of aquatic resources have been implemented and are now being tested.

Project 1: Combining Environmental Data Sets (Grant No. R829095C001)

Geof Givens and Jennifer Hoeting finished their book, Computational Statistics (Wiley, 2005). The book is serving as a comprehensive text on modern and classical methods of statistical computing and computational statistics with detailed examples and problems drawn from diverse fields.

Andrew Merton, a doctoral student, has developed methods for selecting geostatistical models. Merton, Hoeting, and Davis have presented results of this research at a number of statistical conferences. Megan Dailey and Julia Smith plan to produce papers to appear in the stream ecology and statistical literature. Kathi Georgitis, Oregon State University, is working on parameter estimation for geostatistical models under Gitelman and Hoeting. Urquhart has begun assembling spatially dense aquatic data and has helped several investigators use various aspects of these data. Josh French compiled some of these datasets and displayed a poster about his findings at the recent meeting at Oregon State University.

Additional areas of research are described below.

Develop New Models and Methodology for the Analysis of Compositional Data, Accounting for Correlation Over Space and Time. This includes the use of landscape variables developed under Project 3. Recent efforts have focused on fish and macroinvertebrate richness and is being done with CSU macroinvertebrate ecologists. Johnson’s STARMAP-funded work at the University of Alaska at Fairbanks continued.

Model Selection for Geostatistical Models. Hoeting, Merton, and Davis continue this work. A paper submitted to Ecological Applications will be published as part of a special issue on the interface of ecology and statistics.

Hierarchical Bayesian Models for Seasonal Radio Telemetry Habitat Data. Initial work on this project included the study of a model proposed by Ramsey and Usner that incorporates a persistence parameter into the analysis of habitat radio telemetry data. The work of Dailey and Gitelman has extended this model by allowing the persistence parameter to change with season.

Geostatistic Modeling. Hoeting, Gitelman, and Georgitis continue to collaborate on geostatistical modeling through weekly conference calls and invited presentations.

Chain Graph Models. A. Gitelman and A. Herlihy continued to develop spatial models using chain graphs and allied model selection tools, and report their results in papers and presentations.

Detecting Changes in Patterns of Variation in Long-Term Time Series. Davis and colleagues have expanded their work, implemented related computer software, and applied these tools to several environmental problems.

Project 2: Local Inferences From Aquatic Studies (Grant No. R829095C002)

Work continues in the use of nonparametric modeling for survey regression estimation, jointly supported by DAMARS and STARMAP. Numerous invited talks have been given and several manuscripts have been submitted and published. The problems of interest in this context combine landscape-level auxiliary data (such as those from GIS coverages) with field observations. The inferential problems range from model-assisted descriptive inferences for aquatic populations to model-based small area estimates. Breidt and Opsomer, together with a postdoctoral fellow, students, and colleagues, extended earlier results on local polynomial survey regression estimation in a number of directions, some of which are described here. The project on cdf estimation was submitted for publication, and the two-dimensional kernel estimators project was extended to the setting of penalized splines. Promising preliminary results on the use of low rank radial basis functions for smoothing data from river networks also were obtained.

A doctoral student is adapting the two-stage local polynomial estimator to other cluster-mean models and other types of auxiliary information. New work was conducted on nonparametric model-assisted estimation using penalized splines and was extended to the case of small area estimation. Penalized spline estimators are being extended further to the setting of two-stage sampling.

Research scientists from Taiwan collaborated with STARMAP researchers from August through November 2004. Work continues on semiparametric modeling for increment-averaged data, such as a rise in soil and sediment core sampling. These methods are being extended to allow for semiparametric small area estimation of profiles, which are infinite-dimensional parameters. Researchers collaborated on autocorrelation diagnostics for increment data using Cholesky autocovariance models. Similar techniques are being used to develop a class of state-space models for stream networks, with corresponding Kalman-like filtering and fixed-network smoothing algorithms, and likelihood-based estimation techniques. Work continues on Bayesian estimation for non-Gaussian noninvertible moving average models, with potential application to spatial data from river networks. Spatio-temporal models are being used to improve the power for trend detection of lake water quality. A new variable selection method for multiple layers of GIS data using Lasso is being developed and is being implemented in collaboration with Project 3. Such a methodology would be applicable immediately to aquatic resource data, for which multiple layers of geospatial data are available as potential regressors.

Project 3: Development and Evaluation of Aquatic Indicators (Grant No. R829095C003)

The STARMAP landscape ecology/GIS efforts have focused on conceptualizing, developing, extending, and implementing GIS tools that will support spatial analyses of aquatic responses whose variation is predictable, at least partly, from the landscape characteristics above the point in the aquatic system where field collections occurred. An automated, robust framework that delineates the watershed above any point (e.g., a sample collection point) is needed to allow relevant, process-based computation of landscaped metrics. The team has developed such a framework, which provides an additional benefit of being flexible to alternative definitions (and weightings) of watershed features utilizing upstream (and downstream) distances.

A set of tools (FLoWS and FunConn) has been designed around this framework that utilize ArcGIS as a platform, extending statistically relevant tools into the GIS community. Substantial progress has been made on these tools. The GIS developments have been critical for landscape analyses comparing the results from various spatial statistical models using data from the Maryland Biological Stream Survey. The FloWS software is available at a Web site listed at the end of this report.

The major focus of the project is to build better links between watershed parameters and reaches. Tools for accomplishing tessellation-stratified sampling in ArcGIS have been developed and are in final/quality assurance testing stages. We continued to compile data for the Mid-Atlantic Highlands Assessment Program study area but now will focus on building the base data for the Western Pilot study area. We continued to develop basic network modeling tools in GIS. We collaborated with other STAR researchers, especially ones at CSU.

Project 4: Extension of Expertise on Design and Analysis to States and Tribes (Grant No. R829095C004)

Development of browser-based learning materials has continued. A decision to represent all material in pdf format has been made, and video clips of the field training have been incorporated into the materials.

A CSU graduate student attended and videotaped the EMAP training session at EPA’s Western Ecology Division Laboratory in May 2004. This material is being incorporated into Part 4 of the learning materials.

On June 14-15, 2005, Dr. Urquhart participated in a review of the methodology being used for the upcoming National Wetlands Status and Trends Report, to be released in December. This produced other contacts and a wetlands dataset, the investigation of which just started.

Personnel from Minnesota’s Department of Natural Resources (MDNR) are designing an expanded sampling of wetlands over 175 National Wetland Inventory (NWI) plots. Dr. Urquhart has assisted in that design effort. MDNR supplied STARMAP with the areas of 17 wetland classes for Minnesota’s 175 NWI 2-mile square plots and the analogous data for the plots divided into 4 and 16 subplots. The objective of this study will be to incorporate costs into making decisions about optimal size of wetland monitoring plots. Preliminary spatial analysis is interesting.

There are a number of collaborations under this project with other researchers, including scientists at EPA’s Atlantic Ecology Division in Narragansett, Rhode Island; the New Hampshire Department of Environmental Services in Concord, New Hampshire; other STAR grantees.

A manual titled “Ignorable Nonresponse Adjustment Procedures and Algorithms,” has been developed with an accompanying CD-ROM. The manual guides the user through data analysis for probability-based survey data with nonresponse, provides documentation for the weighting adjustment functions, and provides a copy of the R software.

An investigator worked with the San Francisco Estuary Institute (SFEI) from October 18-20, 2004, on the analysis of data resulting from a variable probability survey design and acquainted them with the R software for survey design being developed by Tony Olsen from EPA. A seminar titled, “Environmental Monitoring, Statistics, and the Art of Non-Representation: The Need and Evidence for a Paradigm Shift,” was presented at Eastern Oregon State University in November 2004. Two seminars were presented at the EPA Western Ecology Division Laboratory in November 2004.

At the 2004 Joint Program Meeting, our Science Advisory Committee (SAC) recommended that the two programs undertake and publicize a large case study. The program’s directors, in consultation with program personnel, concluded that a special issue of an environmental statistics journal would be a preferred mechanism. The editor of Environmetrics was contacted and a positive response was received. Papers should be ready for review by early 2006, with a publication target in mid-2006. The concept for the special issue is a series of papers that address the major features of any survey of an aquatic resource from design issues to analysis and presentation issues. Potential topics are listed in the annual report for R829095C004.

Members of the STARMAP team made more than 20 outreach presentations in more than 10 venues to audiences of diverse perspectives.

Project 5: Integration and Coordination for STARMAP (Grant No. R829095C005)

The Director: (1) monitored the progress of Projects 1 - 4, including oversight of their budgets, staffing, and coordination; (2) monitored subcontracts to Oregon State University, the Southern California Coastal Water Research Project, and the University of Alaska at Fairbanks; (3) assembled and submitted quarterly and annual reports; and (4) coordinated various matters with DAMARS, the Oregon State University Program, the Southern California Coastal Water Research Project, and with the program’s EPA Project Officer.

Don Stevens, the Director of the DAMARS Program, with assistance of the STARMAP Director, organized and executed the Fourth Annual Conference on Statistical Survey Design and Analysis for Aquatic Resources. This conference, also advertised as Statistics for Aquatic Resources: Monitoring, Modeling and Management, was held at Oregon State University, Corvallis, Oregon, on September 7-9, 2005. That program is documented at http://oregonstate.edu/dept/statistics/epa_program/meeting.html Exit .

Future Activities:

Project 1: Combining Environmental Data Sets

A short course entitled “Statistical Computing: Techniques for Integration and Optimization” will be presented at the Alaska Chapter of the American Statistical Association in Juneau, Alaska, in July 2006, and at the Joint Statistical Meetings in Seattle, Washington, in August 2006. A presentation entitled, “Model Selection and Estimation for Geostatistical Models” will be given at the Joint Statistical Meetings, Seattle, Washington, in August 2006. A presentation also will be given at the Conference on Bayesian Methods in Wildlife Population Monitoring in Fort Collins, Colorado, in 2006.

The integration of STARMAP activities with CSU’s NSF Integrative Graduate Education and Research Traineeship Program for Interdisciplinary Mathematics, Ecology, and Statistics (PRIMES) will continue.

Project 2: Local Inferences From Aquatic Studies

Planned developments include: (1) completing theory and practical methods for the two-stage sampling case (splines and kernels); (2) developing a new theory for semiparametric small area estimation; (3) completing further work on uncertainty estimates for the Spatial Lasso model selection procedure; (4) estimating maximum likelihood via the EM algorithm for network autoregressive models; and (5) determining methods for likelihood estimation and efficient Bayesian inference in network state-space models.

Project 3: Development and Evaluation of Aquatic Indicators

Activities planned for Year 5 of the project include: (1) continued support for working with partners in using FLoWS tools and applications; (2) publication of FLoWS methodologies; (3) publication of a nationwide flow fragmentation analysis; and (4) further development of hydrological distances within reach catchment area. This will allow better incorporation of within catchment heterogeneity of land use changes.

We anticipate extending the FLoWS framework to include classification of geomorphic reach types using stream gradient, valley width, channel type, topographic constraints, and so forth. This would enable us to use understanding of geomorphological processes to better develop predictors of perennial/intermittent, particularly in the face of climate change scenarios. We want to integrate FLoWS tools with USGS National Hydrography Dataset “Plus,” but its projected available date in 9-12 months means we will have to work with draft forms of this dataset. We anticipate examining the development of survey designs that are spatially balanced or spatially autocorrelated at various scales using the RRQRR methodology.

We will continue to develop landscape metrics based on direct measures of stream discharge to develop a regression relationship between watershed conditions and index of biotic integrity.

Project 4: Extension of Expertise on Design and Analysis to States and Tribes

A substantial effort will be invested in learning materials in the coming year. Students in statistics at CSU have been involved with this effort. The STARMAP Director will continue to coordinate outreach activities with the National Water Quality Monitoring Council, the Southern California Coastal Water Research Program, the San Francisco Estuary Institute, Oregon State University, and other agencies. The Director will nurture contacts with various state and tribal entities as opportunities develop. The Director will continue to contact high school teachers of advanced placement statistics to facilitate recruitment of students into undergraduate statistics majors/concentrations, as part of an effort to eventually recruit more graduate students into environmental statistics.

Project 5: Integration and Coordination for STARMAP

The Director will continue to monitor the progress of Projects 1 - 4 and their coordination, as well as the progress of subcontracts to Oregon State University, the Southern California Coastal Water Research Project, and the University of Alaska at Fairbanks. He will coordinate the program’s activities with various parties. He will prepare and submit quarterly and annual reports to the EPA Project Officer and will maintain and expand the STARMAP Web Site. The Director will continue to facilitate development of subcontract relations with young statisticians at other institutions. He also will prepare and submit the STARMAP final report.


Journal Articles: 42 Displayed | Download in RIS Format

Other center views: All 302 publications 54 publications in selected types All 42 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. R829095 (Final)
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  • Full-text: ScienceDirect-Full Text PDF
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  • Abstract: ScienceDirect-Abstract
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  • Journal Article Andrews B, Davis RA, Breidt FJ. Rank-based estimation for all-pass time series models. Annals of Statistics 2007;35(2):844-869. R829095 (2005)
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  • Full-text: Project Euclid-Full Text PDF
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  • Abstract: Project Euclid-Abstract
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  • Other: Cornell University-Full Text PDF
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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. R829095 (Final)
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  • Full-text: StatisticaSinica-Full Text PDF
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  • Abstract: StatisticaSinica-Abstract
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  • Journal Article Breidt FJ, Claeskens G, Opsomer JD. Model-assisted estimation for complex surveys using penalised splines. Biometrika 2005;92(4):831-846. R829095 (Final)
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  • Abstract: Oxford Journals-Abstract
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  • Other: Ku Leuven-Prepublication PDF
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  • Journal Article Breidt FJ, Opsomer JD, Johnson AA, Ranalli MG. Semiparametric model-assisted estimation for natural resource surveys. Survey Methodology 2007;33(1):35-44. R829095 (Final)
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  • Full-text: Statistics Canada-Full Text PDF
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  • Abstract: Statistics Canada-TOC
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  • Journal Article Breidt FJ, Hsu N-J, Ogle S. Semiparametric mixed models for increment-averaged data with application to carbon sequestration in agricultural soils. Journal of the American Statistical Association 2007;102(479):803-812. R829095 (2005)
  • Abstract: Journal of the American Statistical Association-Abstract
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  • Journal Article Brockwell PJ, Davis RA, Yang Y. Continuous-time Gaussian autoregression. Statistica Sinica 2007;17(1):63-80. R829095 (Final)
  • Full-text: Statistica Sinica - Full Text PDF
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  • Abstract: Statistica Sinica - 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. R829095 (2004)
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  • 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. R829095 (2003)
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  • Full-text: Statistics Canada-Full Text PDF
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  • Abstract: Statistics Canada-Abstract
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  • Journal Article Dailey M, Gitelman AI, Ramsey FL, Starcevich S. Habitat selection models to account for seasonal persistence in radio telemetry data. Environmental and Ecological Statistics 2007;14(1):55-68. R829095 (2004)
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  • Abstract: Springer - Abstract
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  • Journal Article Davis RA, Dunsmuir WTM, Streett SB. Observation-driven models for Poisson counts. Biometrika 2003;90(4):777-790. R829095 (Final)
  • Abstract: Biometrika - Abstract
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  • Other: Columbia University-Full Text PDF
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  • Journal Article Davis RA, Rodriguez-Yam G. Estimation for state-space models based on a likelihood approximation. Statistica Sinica 2005;15(2):381-406. R829095 (Final)
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  • Full-text: Institute of Statistical Science Academia Sinica-Full Text PDF
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  • Abstract: Statistica Sinica-Abstract
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  • Journal Article 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)
  • Full-text: University of California-Full Text PDF
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  • Abstract: Taylor & Francis-Abstract
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  • Journal Article 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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  • Abstract: Ecological Applications-Abstract
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  • Journal Article Francisco-Fernandez M, Opsomer JD. Smoothing parameter selection methods for nonparametric regression with spatially correlated errors. Canadian Journal of Statistics 2005;33(2):279-295. R829095 (Final)
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  • Full-text: Universidade da Coruna-Preprint PDF
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  • Abstract: Wiley-Abstract
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  • Journal Article Francisco-Fernandez M, Jurado-Exposito M, Opsomer JD, Lopez-Granados F. A nonparametric analysis of the spatial distribution of Convolvulus arvensis in wheat-sunflower rotations. Environmetrics 2006;17(8):849-860. R829095 (2004)
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  • Full-text: Universidade da Coruna-Prepublication PDF
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  • Abstract: Wiley Interscience Abstract
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  • Journal Article Gitelman AI, Herlihy A. Isomorphic chain graphs for modeling spatial dependence in ecological data. Environmental and Ecological Statistics 2007;14(1):27-40. R829095 (2004)
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  • Journal Article Hall P, Opsomer JD. Theory for penalised spline regression. Biometrika 2005;92(1):105-118. R829095 (Final)
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  • Abstract: Biometrika
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  • Journal Article Hoeting JA, Davis RA, Merton AA, Thompson SE. Model selection for geostatistical models. Ecological Applications 2006;16(1):87-98. R829095 (Final)
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  • Journal Article Hoeting JA. Some perspectives on modeling species distributions. Bayesian Analysis 2006;1(1):93-98. (Comment on article by Gelfand et al.). R829095 (2004)
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  • Full-text: Carnegie Mellon University-Full Text PDF
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  • Abstract: Carnegie Mellon University-Abstract
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  • Journal Article Johnson AA, Breidt FJ, Opsomer JD. Estimating distribution functions from survey data using nonparametric regression. Journal of Statistical Theory and Practice 2008;2(3):419-431. R829095 (2004)
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  • Abstract: Taylor&Francis-Abstract
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  • Journal Article Johnson DS, Hoeting JA. Autoregressive models for capture-recapture data:a Bayesian approach. Biometrics 2003;59(2):341-350. R829095 (2004)
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  • Journal Article Johnson DS, Hoeting JA. Bayesian multimodel inference for geostatistical regression models. PLoS ONE 2011;6(11):e25677. R829095C001 (2005)
  • Full-text: PLoS ONE-Full Text HTML & PDF Link
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  • Journal Article 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. Peer Reviewed: Have U.S. surface waters responded to the 1990 Clean Air Act Amendments? Environmental Science & Technology 2004;38(24):484A-490A. R829095 (2004)
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  • Abstract: ES&T-Abstract
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  • Journal Article Kauermann G, Opsomer JD. Generalized cross-validation for bandwidth selection of backfitting estimates in generalized additive models. Journal of Computational & Graphical Statistics 2004;13(1):66-89. R829095 (Final)
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  • Full-text: Research Gate-Abstract and Full Text HTML
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  • Journal Article 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)
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  • Journal Article Larsen DP, Kincaid TM, Jacobs SE, Urquhart NS. Designs for evaluating local and regional scale trends. Bioscience 2001;51(12):1069-1078. R829095 (2004)
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  • Abstract: BioOne Abstract
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  • Journal Article 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 (2004)
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  • Full-text: Instituto de Ciencias Biologicas-Full Text PDF
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  • Abstract: NRC Research Press 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. R829095 (Final)
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  • Abstract: American Statistical Association-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. R829095 (2004)
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  • Full-text: Research Gate-Abstract and Full Text HMTL
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  • Abstract: SpringerLink-Abstract
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  • Journal Article 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)
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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. R829095 (2004)
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  • 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. R829095C002 (2005)
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  • Abstract: Wiley
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  • Other: Prepublication PDF
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  • Journal Article Opsomer JD, Francisco-Fernandez M. Finding local departures from a parametric model using nonparametric regression. Statistical Papers 2010;51(1):69-84. R829095C002 (2005)
  • Abstract: SpringerLink-Abstract
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  • Journal Article Peterson EE, Merton AA, Theobald DM, Urquhart NS. Patterns of spatial autocorrelation in stream water chemistry. Environmental Monitoring and Assessment 2006;121(1-3):571-596. R829095 (Final)
  • Abstract from PubMed
  • Full-text: Research Gate-Full Text PDF
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  • Abstract: SpringerLink-Abstract
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  • Journal Article 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 (2005)
    R829095 (Final)
  • Abstract from PubMed
  • Full-text: Research Gate-Abstract and Full Text HTML
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  • Abstract: SpringerLink-Abstract
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  • Journal Article 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)
  • Full-text: U.S. Forest Service-Full Text PDF
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  • Abstract: Ecological Society of America-Abstract
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  • Journal Article 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)
  • Abstract: Springer - Abstract
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  • Other: Southern California Coastal Water Research Project-Report PDF
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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. R829095 (Final)
    R829096 (2005)
  • Abstract: Wiley-Abstract
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  • Journal Article 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)
  • Full-text: University of Nebrask-Lincoln-Full Text PDF
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  • Abstract: SpringerLink
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  • Journal Article 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)
    R829498 (2003)
    R829498 (Final)
  • Abstract: Wiley - Abstract
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  • Journal Article Wang H, Ranalli MG. Low-rank smoothing splines on complicated domains. Biometrics 2007;63(1):209-217. R829095 (2004)
    R829095 (2005)
    R829095 (Final)
    R829095C002 (2005)
  • Abstract from PubMed
  • Abstract: Wiley Interscience-Abstract
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  • Other: BNU-PrepublicationFull Text PDF
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  • Supplemental Keywords:

    latent processes, matern covariance function, model selection, remote sensing, sampling design, path analysis, kernel regression, thin plate splines, small area estimation, GIS, tessellation stratified sampling, water quality, land cover, land use, accuracy, precision, outreach, distance learning, web-based learning, needs-based instruction, accommodating cultural differences, management, efficiency,, RFA, Scientific Discipline, Air, Ecosystem Protection/Environmental Exposure & Risk, Aquatic Ecosystems & Estuarine Research, climate change, Air Pollution Effects, Aquatic Ecosystem, Environmental Monitoring, Atmosphere, EMAP, ecosystem monitoring, statistical survey design, spatial and temporal modeling, aquatic ecosystems, water quality, Environmental Monitoring and Assessment Program

    Relevant Websites:

    http://www.stat.colostate.edu/starmap Exit
    http://www.nrel.colostate.edu/projects/starmap/ Exit

    Progress and Final Reports:

    Original Abstract
  • 2002
  • 2003 Progress Report
  • 2004 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