Final 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. , 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 , Iowa State University , Oregon State University , Southern California Coastal Water Research Project Authority , University of Alaska - Fairbanks
EPA Project Officer: Packard, Benjamin H
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) RFA Text |  Recipients Lists
Research Category: Water , Ecosystems , Aquatic Ecosystems , Ecological Indicators/Assessment/Restoration , Water and Watersheds

Objective:

The objective is the development and implementation of statistical methods for aquatic surveys, including tools need to evaluate landscape covariates.

Summary/Accomplishments (Outputs/Outcomes):

The U.S. Environmental Protection Agency’s (EPA) Science To Achieve Results (STAR) Program announced a competition for Research Programs on Statistical Survey Design and Analysis for Aquatic Resources through a Request for Applications (RFA) on October 1, 2000. The Space-Time Aquatic Resources Modeling and Analysis Program (STARMAP) was conceived, funded, and developed under this governing RFA. That RFA states three major objectives within the context of cooperating with EPA’s Environmental Monitoring and Assessment Program (EMAP):

  • Identify and perform cutting-edge research in environmental statistics that will further the application of statistics to the environmental sciences;
  • Facilitate the professional development of future environmental statisticians; and
  • Develop and extend the expertise on design and analysis to states and tribes.

STARMAP has achieved these three objectives. The RFA clearly anticipated a substantial involvement of landscape ecology in this research, and that was accomplished.

Although STARMAP had four distinct research Projects, each of which made notable contributions, sustained collaboration among all of the Projects produced tools of immediate utility for the analysis of aquatic monitoring data, regardless of what organization collected the data. Water quality indicators gathered at a point in a stream network depend on myriad contributions from the landscape above that collection point, as well as on the same indicator at collection points higher in the network. Three sets of tools are required to appropriately analyze such data: geographic information system (GIS) tools to delineate the upstream area contributing to any collection point; extensions of spatial statistics tools to accommodate the special features of stream networks; and methods for handling missing data. Project 3 (R829095C003) developed, tested, and distributed the needed GIS tools. Projects 1 and 2 (R829095C001 and C002, respectively) have extended relevant statistical tools in two directions. The cooperating Program, Designs and Models for Aquatic Resource Surveys (DAMARS) at Oregon State University, adapted the needed tools for missing data. The tools have been exercised on state-wide and county-wide data from the state of Maryland, gathered using the sort of sampling and collection protocols advanced by EPA’s EMAP. The results have been and are being communicated to diverse audiences (Project 4; R829095C004). Project 5 (R829095C005) provided the necessary administration and coordination to allow the other Projects to function effectively. Results have been and are being published in relevant outlets.

The RFA described the need for two cooperating programs with differing perspectives. The two Programs funded from that solicitation were STARMAP at Colorado State University and DAMARS at Oregon State University (OSU). The first had a model-based perspective, while the second had a design-based perspective, as specified in the governing RFA. The two Programs cooperated extensively, eventually to the extent that they had two joint Projects: the tools of local estimation can be applied in design-based, model-assisted, or model-based ways; STARMAP’s Project 2 on local estimation was implemented as a joint project. Two approaches to outreach were proposed: the development of CD-ROM-based learning materials and direct interaction with members of the client community. Although the development of the learning materials occurred primarily under the guidance of STARMAP’s Project 4, it was funded by both Programs. Likewise even though a substantial part of the direct interaction with the client community was done under the leadership of DAMARS, it was funded by and participated in by participants of both Programs.

STARMAP facilitated the professional development of future environmental statisticians in several ways. The RFA anticipated a substantial involvement of postdoctoral fellows in the research program and their development of interests in environmental statistics. The discipline of statistics does not have a tradition of postdoctoral fellows. Students completing doctorates in statistics are in such short supply that no domestic applicants with real potential for becoming environmental statisticians applied for the available positions. One postdoctoral fellow from Italy spent two periods of time with Project 2 and made substantial contributions, but she returned to Italy. She definitely is advancing the cause of environmental statistics there, as evidenced by her talks, publications, and mentoring of students there. A second postdoctoral fellow spent 9 months conducting research under Project 1 but has returned to Korea.

Once it became clear that the planned postdoctoral fellows would not be a viable path for developing future environmental statisticians, STARMAP, with the concurrence of its Science Advisory Committee, focused some resources on helping early career statisticians develop their environmental interests. This was done for Devin Johnson at the University of Alaska at Fairbanks and Kerry Ritter at the Southern California Coastal Water Research Project (SCCWRP). Other resources were invested in master’s-level students. Overall three students completed doctorates under STARMAP sponsorship; all are engaged in collaboration with the environmental sciences. Six more are well along in their doctoral research as this report is written. Four of these students likely will finish this academic year; one already is employed in the environmental sciences; the other three are seeking employment which will allow them to continue their collaboration with the environmental sciences. Eight students completed master’s degrees under STARMAP sponsorship, and one is nearing completion.

It is clear that STARMAP’s activities have had substantial impact on the career outlook of several Program investigators. Although STARMAP funding has ended, these investigators have developed aquatic environmental interests, which will influence their research and teaching for the rest of their careers.

The following material describes the major accomplishments of STARMAP’s four research Projects (specific details may be found in the Final Reports for R829095C001–R829095C004):

Project 1—Combining Environmental Data Sets (R829095C001)

This project expanded the analysis and interpretation tools available to aquatic scientists, and statisticians who assist them, especially with tools which utilize spatial or temporal models, as well as ones which utilize hierarchical (Bayesian) methods. Specifically this project:

  • Adapted spatial statistical models to accommodate the branching nature of stream networks;
  • Implemented statistical computing tools to support the selection of predictor variables in an aquatic context;
  • Developed hierarchal methods for the analysis of categorical responses, of the sort resulting from macroinvertebrate studies in streams;
  • Developed hierarchal methods for the analysis of ordered categorical responses, of the sort resulting from studies to monitor stream health;
  • Developed and demonstrated hierarchical analysis methods for assigning causes of effects in aquatic systems like stream networks;
  • Produced a textbook that surveys modern methods in computational statistics;
  • Expanded temporal methods for identifying structural breaks;
  • Investigated the uncertainty associated with contour curves developed from spatial statistical models;
  • Developed sampling plans for near-coastal systems;
  • Expanded components of variance tools for characterizing both temporal and spatial variability; and
  • Trained future statisticians, some of whom are already working in environmental fields.

Summary of Work. Project 1 involved a substantial number of researchers working in a large variety of areas. This section summarizes the output of Project 1.

Methodology for Statistical Modeling of Spatially-referenced, Aquatic, and Other Environmental Data. Principal investigator (PI) Hoeting produced a textbook, Computational Statistics, under this cooperative agreement, co-authored by G.H. Givens. This graduate-level textbook surveys a wide variety of topics in modern statistical computing and computational statistics, including optimization, integration (including Markov chain Monte Carlo [MCMC] methods), bootstrapping, and smoothing. The book has been adopted widely for teaching and is being used by statisticians and non-statisticians alike as a reference book on methods for computational statistics. The book includes a Web page with related software and numerous examples and homework problems. Computational Statistics has become a bestselling textbook for Wiley (publisher) and is currently in its fourth printing.

Investigator Gitelman’s “Isomorphic Chain Graphs for Modeling Spatial Dependence in Ecological Data” by Gitelman and Herlihy (published in Environmental and Ecological Statistics) was an important contribution toward developing causal inference models. In this paper, they extend Bayesian belief network models (also called acyclic directed graphs) to accommodate correlation through space.

In conjunction with this work, Investigator Gitelman was invited to serve as the guest associate editor for a special forum on the application of Bayesian belief networks to natural resource management problems organized by the Canadian Journal for Forest Research. She was invited to participate in a section on models for multi-scale analysis at the 2005 Annual Meeting of the Ecological Society of America in Montreal and to review a collection of papers for inclusion in EcoHealth, a Springer publication in environmental science (to appear).

PI Hoeting, PI Davis, STARMAP-funded student Andrew Merton, and S.E. Thompson (Pacific Northwest National Laboratory) developed important methodology for model selection in regression-like spatial models (called geostatistical models). In a paper that appeared in Ecological Applications these co-authors show that ignoring spatial correlation can result in models being selected that are not reflective of the variables that generated the data. This work also produced freely available software that has already been used in several publications to select explanatory variables for geostatistical models.

In closely allied work, Davis and Merton derived the limiting behavior for the maximum likelihood estimator of the range parameter in an exponential covariance function under various scenarios including infill and increasing domain asymptotics. Limit behavior for the case when the sampling strategy within blocks was clustered, regular, or random was also considered. It was shown that for the exponential case in one dimension, all three sampling paradigms produced asymptotically equivalent estimates.

Investigator Gitelman, in collaboration with PI Hoeting and STARMAP-funded student Irvine (at OSU), has submitted a manuscript in which the properties of spatial covariance are examined in situations with varying amount of spatial dependence, under different sampling designs and for different sample sizes. This work demonstrated that large sample sizes are needed to accurately model spatially-dependent data and that sampling pattern can impact the quality of parameter estimates.

Investigator Gitelman and STARMAP-funded student Megan Dailey developed new methodology reported in the paper “Habitat selection models to account for seasonal persistence in radio telemetry data,” published in Environmental and Ecological Statistics. In this work, the authors build a flexible hierarchical model for seasonal persistence and seasonal changes in habitat selection, all fit using Bayesian modeling. The model was applied in an attempt to understand habitat selection behaviors in fish in Oregon streams.

PI Hoeting worked with ecologists to investigate various factors affecting the accuracy of predicted species distributions in a paper that appeared in Ecological Applications in 2005 (co-authors G. Reese, K. Wilson, and C. Flather). In related work on species distributions, Hoeting served as the discussant for ground-breaking work on Bayesian models for species distributions (conference and journal article in Bayesian Analysis in 2006). In another paper that appeared in Ecological Applications in 2006, PI Hoeting (and co-authors M. Farnsworth, N.T. Hobbs, and M.W. Miller) developed models to examine how animal movement can be linked to the spread of disease. In a manuscript that appeared in Biometrics in 2003, PI Hoeting and STARMAP-funded student D. Johnson developed new models for capture-recapture data. While not directly related to aquatic resource monitoring, the models that appeared in these publications will help ecologists further understand where species live, why they live there, and how diseases impact these organisms. This work, in conjunction with aquatic resource monitoring, will help us further understand entire ecosystems.

STARMAP-funded student D.S. Johnson, PI Hoeting, and N.L. Poff (a researcher on EPA-funded STAR project R828636) developed new models for monitoring aquatic resource data. The new models are for multiple variable, categorical data where researchers are interested in modeling the proportion of observations in each group as well as the relationships between these groups and various explanatory variables. These models were used to investigate the relationship between fish traits and environmental predictors of the presence of fish with these traits, using the EPA EMAP Mid-Atlantic Highlands Assessment (MAHA) data set. This work appeared in a recent book, Bayesian Statistics and its Applications (2006).

Confidence Bounds for Map Contours. Josh French, a graduate student formerly supported by STARMAP, is working with Richard Davis devising confidence bands around level curves for a spatial field. The idea is that from a map produced via kriging one can display level curves of the predicted conditional mean. However, calculation of error bounds for these level curves are not so easy to construct or even to define in a probabilistic sense. Davis and French are developing procedures that allow one to put confidence bands around level curves, displayed as small rectangular boxes that have a preset confidence probability. In other words, these boxes can be constructed so that we are 95 percent confident the process takes the desired threshold value somewhere in the box. The boxes are then linked together to give a “confidence set” for the level curve. This work is still ongoing, illustrating that the effect of STARMAP’s funding from EPA is extending past its funding period.

Structural Breaks in Time Series. PI Davis worked with colleague Thomas Lee and postdoctoral fellow Gabriel Rodriguez-Yam, exploring the problem of detecting structural breaks in a time series. The key idea was to assume that the nonstationary time series can be well represented by piecewise autoregressions (AR). The principle of minimum description length (MDL) was used to assess the quality of fit for various structural break locations and the genetic algorithm was used to find near optimal minima of the MDL. The number of structural breaks, their locations, and the orders of the respective piecewise AR models were assumed unknown. This paradigm seemed to work well in a variety of applications. This research was published in the Journal of the American Statistical Association.

Methodology for Sampling. Investigator Ritter and associates of the SCCWRP developed spatially distributed sampling plans suited to wastewater oceanic outfalls. Their project investigated cost-effective ways to distribute sample points in a near-coastal system to support estimation of the spatial pattern of various analytes and macroinvertebrate indices. The work is summarized in a paper that appeared in Environmental and Ecological Statistics. Ritter applied these methods to produce a design which was implemented by the San Diego Metropolitan Wastewater District.

Some of Director Urquhart’s investigations of components of variance concerned temporal and spatial matters so are reported under this Project. When field visits to aquatic sites are widely distributed in space, and occasionally in time, components of variance can be used to capture most of the spatial and temporal variation but not to model its form. Nevertheless such characterizations have proved useful in evaluating the likely (statistical) power to detect trend. The developed methodology was used to compare temporal or revisit designs relative to their power to detect trend. Some are decidedly better than others, a fact which was communicated to various interested clients.

Training Future Environmental Statisticians

A significant outcome of this project included a large number of students trained in environmental statistics. A number of these students are now working in fields directly related to environmental statistics in the United States. Students involved in Project 1 completed two doctorates and four master’s degrees; two additional doctorates and one master’s are expected from continuing students during 2007, and one additional doctorate, probably in 2008. Most of these students are employed, or are seeking employment, in environmental statistics. A postdoctoral fellow was supported by this project for 6 months and by other funds for 3 months; he produced two submitted manuscripts and one working manuscript.

The investigators on this project were widely scattered throughout the Western United States. The project and the related STARMAP and DAMARS workshops allowed for additional interactions that would not have been possible without EPA funding. These links have and will continue to lead to the development of new methodology to collect and analyze aquatic data.

Project 2—Local Estimation(R829095C002)

This project had an overall goal of developing hierarchical spatio-temporal models for local inferences about aquatic resources. The project was conducted jointly with a DAMARS project on development of nonparametric model-assisted estimators for data obtained in probability surveys of aquatic resources. Accomplishments include:

  • Extension of nonparametric model-assisted and model-based estimators for standard survey problems and for small area estimation problems;
  • Adaptation of deconvolution methods for spatial distribution function estimation;
  • Development of new state-space models and estimation methods for stream networks; and
  • Development of a novel algorithm (spatial least absolute shrinkage and selection operator [Lasso]) for selection of covariates and neighborhoods from GIS data in spatial regression problems.

The extensions of the nonparametric model-assisted methodology allow for a variety of complex designs and for incorporation of the major smoothing techniques in use today (including spline-based regression, additive models and semi-parametric models). The methods were applied to the general problems of estimation of population means, totals, and distribution functions. In many surveys, estimators are desired for small domains within the overall population. Because a survey often is not designed to provide reliable estimators for such small domains, the estimation requires the assumption of a model for the population. Investigators in this Project adapted the nonparametric methodology used in the model-assisted context to this situation and showed how this approach generalizes existing small area estimation methods.

In addition to studying estimation of the distribution function in the design-based setting using nonparametric model-assisted estimators, Project personnel also investigated deconvolution, which is the estimation of the cumulative distribution function (cdf) of a variable given noisy measurements of that variable and distributional information about the measurement noise. They treated this problem as one of constrained Bayes estimation, which they extended to hierarchical Bayesian spatial models and studied under aggregation of small areas.

Because of the natural flow of water in a stream network, characteristics of a downstream reach may depend on characteristics of upstream reaches. The flow of water from reach to reach provides a natural time-like ordering throughout the stream network. Investigators in this Project developed a state-space model to describe the spatial dependence in this tree-like structure with ordering based on flow. The model formulation is flexible, allowing for a variety of spatial and temporal covariance structures in the state and measurement equations. They also derived a variation of the Kalman filter and smoother to allow recursive estimation of unobserved states and prediction of missing observations on the network, as well as computation of the Gaussian likelihood. The state-space formulation is extensible to non-linear and non-Gaussian processes. The Project investigators also developed several models of within-stream dependence, including network analogues of autoregressive-moving average models and of structural models, and fitted those models to real and simulated data.

GIS tools organize spatial data in multiple two-dimensional arrays called layers. In many applications, a response of interest is observed on a set of sites in the landscape, and it is of interest to build a regression model from the GIS layers to predict the response at unsampled sites. Model selection in this context then consists not only of selecting appropriate layers, but also of choosing appropriate neighborhoods within those layers. Project investigators formalized this problem and proposed the use of Lasso to simultaneously select variables, choose neighborhoods, and estimate parameters. They incorporated spatial smoothness in selected coefficients through use of a priori spatial covariance structure, leading to a modification of the Lasso procedure. The least angle regression (LARS) algorithm, which can be used in a fast implementation of Lasso, was also modified to yield a fast implementation of spatial Lasso. The spatial Lasso performed well in numerical examples, including an application to prediction of soil moisture. The work reported in this paragraph was done in cooperation with investigators working under Project 3.

A number of graduate students were involved in this research, including Ji-Yeon Kim and Curtis Miller (Iowa State University); and Alicia Johnson, Siobhan Everson-Stewart, Mark Delorey, and Bill Coar (Colorado State University). Work also involved a postdoctoral fellow, Giovanna Ranalli (Colorado State University), plus two junior researchers (Hsin-Cheng Huang, Academica Sincia, and Nan-Jung Hsu, National Tsing-Hua University).

Project 3—Development and Evaluation of Aquatic Indicators (R829095C003)

This project developed, tested, and distributed GIS-based tools that would facilitate computation of useful watershed metrics for statistical analysis of aquatic indicator variables. Project investigators met these objectives through three key accomplishments by:

  • Developing ArcGIS-based toolsets called Functional Linkage of Waterbasins and Streams (FLoWS), Functional Connectivity Model (FunConn), and Reversed Randomized Quadrant-Recursive Raster (RRQRR);
  • Conducting series of demonstrations and application of those tools to a range of key EPA constituents; and
  • Collaborating with other scientists and training of students.

FLoWS is a set of tools that operate within ArcGIS v9 (written in Python). These tools allow users to rapidly generate a stream network, identify and correct topological errors in a network (fairly common in GIS data), extract watershed characteristics derived from other ancillary data such as topography, land cover, road density, etc. in a way that allows ecologically-relevant processes to be developed. For example, discharge volume (flow volume) is estimated as a function not only of waterbasin area but also of the precipitation regime and the watershed topographic characteristics, including solar insolation and slope. Project investigators also developed a novel approach to identify catchments around stream reaches by identifying water basin boundaries using a cost-weight method, rather than relying on strictly local conditions (slopes) identified in a digital elevation model. The goal was to ensure that these tools work with very large datasets (basins to nationwide) and in a variety of situations.

The goal of the functional connectivity model, whose GIS implementation is named FunConn, is to allow landscape connectivity to be examined from a functional perspective. Functional connectivity recognizes that individuals, species, or processes respond functionally (or behaviorally) to the physical structure of the landscape. From this perspective, landscape connectivity is specific to a landscape and species/individual/process under investigation.

Project investigators also strove to develop metrics and approaches that are more robust to possible data quality issues. For example, a well-known problem with “blue-line” hydrography is that the identification of streamlines can abruptly change at a topographic quadrangle boundary. Traditional metrics that rely on Strahler stream order, for example, are very sensitive to these issues, whereas waterbasin-area computations are more robust.

A key to the investigators’ accomplishment was close interaction and collaboration with a variety of constituents. Two major collaborations were with the Oregon Department of Fish and Wildlife (through collaborations with DAMARS personnel) and the Alaska Department of Fish & Game. Project investigators participated in a variety of workshops and provided technical assistance throughout the STARMAP project.

Two unanticipated products of this Project were the result of synergistic activities. The key to each of these was an informal (initially) exchange of ideas, enthusiasm injected by graduate students involved (esp. Peterson), and the STARMAP Director’s support for risk taking. For example, the new geostatistical method for stream networks developed by ver Hoef, Peterson, and Theobald was the result of informal discussion at workshops, identification of an important research question, and eagerness of a key individual (Peterson) who provided a key trans-disciplinary role. A second example is the development of a robust, spatially-balanced sampling design algorithm implemented in ArcGIS, called the RRQRR tool. This is built fundamentally around Stevens and Olsen’s Generalized Random Tessellation Stratified (GRTS) algorithm, but development within a GIS framework provides the ability to develop a raster of sample locations and extends a tool to a different (and broader) user base.

This project’s investigators collaborated closely with investigators working on Projects 1 and 2. Specifically there was close cooperation between Erin Peterson and Andrew Merton, graduate students funded, respectively, under Projects 3 and 1. Merton, under the guidance of Hoeting (Project 1) and Davis (Project 2), developed computer software used extensively by Peterson and adapted it to several of her special situations. This collaboration produced a jointly authored publication and presentations illustrating how the collaboration functioned. Peterson also collaborated with postdoctoral fellow Ranalli who conducted research under the auspices of Project 2. Another interaction involved Breidt (Project 2), Theobald (Project 3), and international visitors unfunded by STARMAP; this is described in more detail under Project 2. Further, Project 3 investigators developed or assisted in developing covariate data sets for at least five other graduate student projects.

Erin Posten Peterson earned a Ph.D. under the sponsorship of this Project. She now is part way through a postdoctoral fellowship on aquatic monitoring in Queensland, Australia.

Project 4—Extension and Outreach (R829095C004)

The client community for the results of these two research programs consisted of aquatic monitoring scientists in state, tribal, federal, and more local agencies charged with monitoring aquatic resources in compliance with the Clean Water Act. Such aquatic scientists will be assisted by affiliated statisticians and landscape ecologists. Thus the outreach and extension efforts extended to each of these groups of scientists, using a variety of means. Program personnel:

  • Engaged in direct interaction with personnel in relevant state, tribal, and more local environmental agencies.
  • Interacted with EPA personnel who have direct contact with aquatic monitoring personnel in the states and more local agencies.
  • Communicated with diverse members of the client community in a wide variety of conferences and other settings. Program personnel gave more than 200 talks and displayed at least 20 posters. The Program Directors organized and conducted five specialty conferences and made substantial contributions to the organization and execution of several other conferences; most of these conferences included international participants. One of the Directors edited two issues of the Journal of Environmental and Ecological Statistics as part of this communication effort.
  • Presented short courses and tutorials in several settings, some based on a textbook coauthored by one of the Program’s PIs.
  • Developed a set of browser-based learning materials suitable for self-study. EPA is free to utilize those materials as it sees fit.
  • Gave presentations to teachers and students in high school advanced placement statistics courses to interest students in possible careers in environmental statistics.

This Project had three main modes of outreach and extension: Direct implementation of project tools in the design and execution of active state, regional, tribal, and more local monitoring efforts, primarily executed by Stevens (DAMARS) and Theobald (STARMAP); a CD-ROM containing relevant learning materials directed by Urquhart (STARMAP); and communication with various communities by publications, oral presentations, and posters in diverse contexts executed by all members of both Programs.

  • Most of the personnel of both STARMAP and DAMARS participated in interactions with various members of the client community, but major and sustained efforts in this area were made by Don Stevens, the Director of DAMARS, and David Theobald, a STARMAP PI. Don Stevens interacted throughout the Programs’ lives with the San Francisco Estuary Institute and other California agencies and several Oregon state agencies. Toward the end of the Programs, he and, to a lesser extent, David Theobald and collaborators, interacted with the Alaska Department of Fish & Game on the design of monitoring activities related to Pacific salmon. David Theobald, a STARMAP landscape ecologist, developed GIS tools relevant to aquatic monitoring. The nature of each of these tools is documented in the report for Project 3. Theobald and collaborators made these tools available to diverse potential users and gave a number of presentations to the client community on their use; the requesters for these tools span a wide range of aquatic interests, including a surprising number from outside the United States. In the case of Alaska salmon they actually installed the software and demonstrated its use on computers of the Alaska Department of Fish & Game. Scott Urquhart, the STARMAP Director, had continuing interactions with the National Park Service’s Inventory and Monitoring Program and with the San Diego Metropolitan Wastewater treatment activity in collaboration with Kerry Ritter of the SCCWRP.
  • STARMAP results have received international exposure, both by current investigators and former investigators who have gone on to other positions but continue to report on results originating from their work with STARMAP.

The STARMAP Director organized and executed the First and Third Annual Conferences on Statistical Survey Design and Analysis For Aquatic Resources with assistance from Don Stevens, the Director of the DAMARS Program. The Director also assisted Don Stevens in organizing and executing the Second and Fourth Annual Conferences on Statistical Survey Design and Analysis for Aquatic Resources. These conferences were prescribed in the Request For Proposals, which led to funding for STARMAP and DAMARS.

Significance of Accomplishments

Project 1Combining Environmental Data Sets. A major accomplishment of Project 1 was the training of statisticians in environmental problems. As described above, a number of these students are already using the knowledge gained from working on the STARMAP project in various government agencies and private businesses.

The model selection component of the research accomplishments serves as a warning to scientists in selecting covariates in geospatial models. This process needs to be conducted in concert with the modeling of the error term. Often the error term can be used as a proxy for missing covariates or can be used as a correction factor for incorrectly selected covariates.

The asymptotic theory for the exponential covariance function is useful to the scientist for selecting optimal sampling strategies with the goal of producing the most efficient parameter estimates. In the exponential case, it is difficult to beat a uniform sampling plan. This might change for the Matern covariance function, which is a topic of future study.

The structural break detection research has shown great promise in segmenting a time series into stationary segments. The strategy developed, called AutoPARM for Automatic Piecewise AR Modeling, is a general procedure that overcomes many of the limitations and defects of previously proposed procedures. In addition, very few assumptions are made in this framework. We intend to explore versions of AutoPARM that would apply more directly in the geospatial context.

Ritter of the SCCWRP investigated cost-effective ways to distribute sample points in near-coastal systems to support estimation of the spatial pattern of various analytes and macroinvertebrate indices. The resulting design was implemented by the San Diego Metropolitan Wastewater District. This design will likely serve as a prototype for many similar studies along the California coast, developed in collaboration with the SCCWRP.

The work on power to detect trend in aquatic surveys by Urquhart has been and is being used by designers of both aquatic and terrestrial surveys to make effective use of limited resources. This work is widely cited in publications concerned with the design of environmental surveys.

Project 2Local Estimation. This Project developed statistical analysis tools of relevance to aquatic scientists concerned with surveys and spatial-temporal modeling. The client community for the results of this research program consisted of aquatic monitoring scientists in state, tribal, federal, and more local agencies charged with monitoring aquatic resources in compliance with the Clean Water Act. Such aquatic scientists will be assisted by affiliated statisticians and landscape ecologists.

Project 3Development and Evaluation of Aquatic Indicators. The Final Report for this Project lists known adopters of the methodology developed under this Project. The diversity of adopters speaks eloquently to the current significance of the accomplishments of this Project. Adopters ranged from local environmental agencies to national environmental agencies, from governmental agencies to nonprofit agencies to academics, from across the United States to across the world. The acceptance of the products of this Project was due to two factors: the relevancy of the products developed and the scale of the outreach activities associated with them. The specific outreach activities are documented under Project 4, but it needs to be noted here that this Project’s investigators made a major effort in this area.

Project 4Extension and Outreach. This Project has effectively communicated the results of the STARMAP and DAMARS Programs to diverse members of the client community in a variety of effective ways. Most of the communications have been contemporary, but the CD-ROM-based learning materials can be used in several ways, including being available on the Internet.

Stakeholders and Users of Results

Most analyses of aquatic environmental data will involve several professional specialties, ordinarily an environmental scientist familiar with the nature of the aquatic indicators, a statistician, and a landscape ecologist or GIS specialist at a minimum. Thus we need to distinguish between immediate and eventual stakeholders. The immediate stakeholders will be responsible for the analysis and technical interpretation. The eventual stakeholders will be aquatic managers and through them all beneficiaries of our nation’s aquatic resources. Furthermore, some of STARMAP’s research contributions are immediate; others will take longer to be utilized. The results of Project 3 are already being used extensively by diverse stakeholders. Likewise some of the results of Projects 1 and 2 have already been utilized in published research. On the other hand, some of the results of Projects 1 and 2 will form the basis for applications yet to be recognized by the aquatic community but probably will become used in the future. (The field of statistics abounds with examples of things which seemed very abstract and of no immediately recognized utility 20 years ago but are widely used now.)

The results of STARMAP research will be of direct use primarily by the professional specialists mentioned above. Thus this section focuses on those stakeholders.

Project 1Combining Environmental Data Sets. Since Project 1 covered a wide variety of topics, the stakeholders and users of the results also vary widely.

The textbook Computational Statistics has been adopted by a number of universities, including Stanford University, The Ohio State University, University of Minnesota, Bowling Green State University, and others. The book is also sold internationally. It is anticipated that as more universities adopt the textbook, this work will be disseminated throughout the United States and used to educate statisticians, with particular emphasis on the education of future environmental statisticians due to the ecological examples used in the book. This textbook has resulted in four short courses, to date.

We expect that the modeling tools that we have developed will be useful to a wide range of users of statistics in the environmental sciences, geosciences, biology, and medical professions. A current example of the structural break work deals with recorded sounds in the National Parks. There is interest in segmenting a long audio stream of recordings obtained from microphones strategically placed in many of the National Parks. One of the goals of this data collection process was to allow the National Park Service to measure and monitor noise pollution in the national parks. One measure of the pollution is the proportion of unnatural (man-made) sound heard in the parks. AutoPARM, software developed by the investigators, can be used to help segment the audio tapes into homogeneous pieces of natural and unnatural (e.g., snow mobile and jet plane noise) sounds. After segmenting the sounds, we attempt to classify the individual pieces into various categories of known sound types. This research is of value to various government agencies, including the National Park Service.

With regards to Ritter’s work, the San Diego Metropolitan Wastewater District used Ritter’s design. Other stakeholders include the people and other living organisms who utilize near-coastal waters and adjacent beaches. Her work shows how to design cost-effective studies of the consequences of oceanic wastewater outfalls.

The immediate users of Urquhart’s work are agency personnel who have to design ecological surveys; these include state-level environmental scientists in many states, including Oregon, Wisconsin, and Maryland. Longer term we all benefit from better and more defensible information being gathered in cost-effective ways.

Project 2Local Estimation. There are many potential users of the methods developed by STARMAP and DAMARS under this joint Project. The two Programs have organized and presented a number of conferences or parts of conferences directed specifically at potential users. Program personnel also have participated in a number of conferences at the invitation of potential users. Some of the conferences are explained in more detail under Project 4, Outreach and Extension.

Project 3Development and Evaluation of Aquatic Indicators. This project has written and made Web-available three sets of GIS tools oriented toward the design and statistical analysis of data resulting from studies in aquatic systems. The tools are programmed in Python and accessible as ArcGIS tools in v9. Each is further documented and available through this Web site: http://www.nrel.colostate.edu/projects/starmap/ Exit

  • FLoWS v1: Functional Linkage of Watersheds and Streams tools for ArcGIS v9: The goal of the functional linkage of watersheds and streams tools is to allow aquatic and terrestrial landscapes to be hydrologically-linked. In this sense, relationships between sites can be represented through functional distance measures. For many hydrological processes (not all!), downstream flow direction is an important ecological process, so that distance is not symmetric. As of December 1, 2006, 32 agencies and organizations requested and received a copy of the FLoWS software. A detailed list appears in the Final Report for Project 3.
  • Functional Connectivity tools (FunConn): There is a large and critical difference between simple hydrological datasets that “look” correct on a map but must have correct topology and attribution to run network-based algorithms correctly. The goal of the functional connectivity model is to allow landscape connectivity to be examined from a functional perspective. As of December 1, 2006, 47 agencies and organizations requested and received a copy of the FunConn software. A detailed list appears in the final report for Project 3.
  • Spatially-balanced sampling using RRQRR: The goal of the RRQRR algorithm is to provide environmental managers a practical, useful GIS tool to generate simple, efficient, and robust survey designs for natural resource applications. RRQRR generates a rigorous probability-based survey design that is spatially-balanced and allows surfaces to be used to specify the inclusion probability. As of December 1, 2006, nine domestic agencies and organizations have requested and received a copy of the RRQRR software. A detailed list appears in the Final Report for Project 3. The relevance of this software is indicated by the fact that Environmental Systems Research Institute (maker of ArcGIS software) is currently implementing the RRQRR algorithm into its core software package.
  • In addition to building tools for statistical analysis of hydrology, this project generated a database (called the FLoWS database) that builds on the U.S. Geological Survey (USGS) National Elevation Data and National Hydrography Dataset (1:100K). This effort provided two important benefits:
    • A nationwide, pre-processed and pre-packaged dataset that will support many types of hydrological analysis and provides a significant “head-start” for EPA clients; and
    • Nationally-consistent, hierarchical, and high-resolution catchment boundaries at a variety of scales—from basins (hydrologic unit code [HUC] 2s) to roughly the HUC 14 level.

Project 4Extension and Outreach. There are many potential users of the methods developed by STARMAP and DAMARS. The two Programs have organized and presented major parts of 10 conferences or parts of conferences directed specifically at potential users. Program personnel also have participated in a number of conferences at the invitation of potential users. Details of these conferences are explained in more detail in the Final Report for STARMAP Project 4.

How Products Will Further Science/Management of Resources

The products developed and disseminated by STARMAP will support the analysis of aquatic responses in diverse contexts but will be especially useful in analyzing aquatic data associated with specific sample points. These tools and demonstrations will support the more accurate and defensible analysis of diverse environmental indicators.

Project 1Combining Environmental Data Sets. The statistical methodology, software, textbook, papers, short courses, and talks all have and will continue to support the design of aquatic environmental studies and the analysis of the resulting data in diverse contexts. These tools and demonstrations will support the more accurate and defensible analysis of diverse environmental variables.

The training of statisticians versed in methods for modeling aquatic data has already resulted in new statisticians working in positions related to ecology and aquatic resources.

The design results of Ritter’s work will allow better management of wastewater outfalls. The associated current work of French demonstrates the uncertainty associated with the sorts of contours often computed by GIS applications, and used in regulatory statements. The latter eventually will be improved by ongoing work in setting confidence intervals for map contours.

Project 2Local Estimation. The statistical analysis tools (products) developed and disseminated by this Project provide aquatic scientists and affiliated statisticians with expanded and more defensible ways to draw inferences to local concerns from wide-area surveys than were available prior to this Project. These tools extend previously available spatial-temporal methods to accommodate the branching nature of streams and rivers.

Project 3Development and Evaluation of Aquatic Indicators. These products will support the analysis of aquatic responses in diverse contexts but will be especially useful in developing landscape indicators associated with specific aquatic sample points. These tools and demonstrations will support the more accurate and defensible analysis of diverse environmental variables.

Project 4Extension and Outreach. The products of this project are both tangible (the manuscripts, presentations, software, and learning tools) and intangible (the insight and knowledge of environmental sampling that has been passed on to non-statistician environmental scientists and managers). The tangible products developed by STARMAP and DAMARS provide an expanded tool kit for designing, monitoring, and analyzing the data resulting from studies of aquatic resources at a variety of levels, from national surveys to studies near a single oceanic sewage outfall. The intangible products have the potential for greater impact on how the resources of this Nation are managed. DAMARS and STARMAP have demonstrated the utility of rigorous statistical design and analysis of environmental monitoring programs to diverse parts of the client community for which these tools were developed and evaluated. Thus, we have not only provided tools, but we have provided the tools to people who are in a position to apply them and given them the knowledge to do so. There is substantial evidence that the use of the tool kit is spreading…more states, tribes, federal agencies, even other countries, are recognizing its utility.

Software that was developed includes:

Theobald DM, Norman JB, Sherburne MR. FunConn v1: functional connectivity tools for ArcGIS v9. Natural Resource Ecology Lab, Colorado State University, 2006.

Theobald Norman DM. Spatially-balanced Sampling (RRQRR). Natural resource ecology lab, Colorado State University, 2006.

Theobald DM, Norman JB, Peterson EE, Ferraz SB. FLoWS v1: functional linkage of watersheds and streams tools for ArcGIS v9. Natural Resource Ecology Lab, Colorado State University, 2005.

Merton AA, Hoeting JA, Davis RA. Model Selection for geostatistical models. Software to compute AIC and MDL for geostatistical models for R, 2004.


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)
    R829095 (Final)
    R829095C002 (2003)
    R829095C002 (2004)
  • 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)
    R829095C002 (2003)
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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)
    R829095C002 (2003)
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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)
    R829095C002 (2003)
    R829095C002 (2004)
  • 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)
    R829095C001 (2005)
  • 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)
  • Abstract from PubMed
  • Full-text: Colorado State University-Full Text PDF
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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)
    R829095C002 (2004)
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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)
    R829095 (2005)
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    R829095C001 (2005)
  • Abstract: SpringerLink Abstract
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  • Journal Article Hall P, Opsomer JD. Theory for penalised spline regression. Biometrika 2005;92(1):105-118. R829095 (Final)
    R829095C002 (2005)
  • 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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  • Abstract from PubMed
  • Full-text: Colorado State University-Full Text PDF
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  • Abstract: ESA-Abstract
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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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  • Other: Journal Issue Contents
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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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  • Abstract from PubMed
  • Full-text: Colorado State University-Full Text PDF
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  • Abstract: Wiley-Abstract
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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 from PubMed
  • Full-text: University of Virginia-Full Text PDF
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  • Abstract: ES&T-Abstract
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  • Other: Research Gate-Full Text PDF
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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)
    R829095C002 (2005)
  • Full-text: Research Gate-Abstract and Full Text HTML
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  • Abstract: Taylor & Francis - Abstract
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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)
    R829095C003 (2003)
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  • Abstract: Springer-Abstract
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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)
    R829095C002 (2004)
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  • Abstract: Taylor & Francis-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. 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)
    R829096 (2005)
  • 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)
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  • 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)
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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)
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  • 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)
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  • Abstract from PubMed
  • Abstract: Wiley Interscience-Abstract
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  • Other: BNU-PrepublicationFull Text PDF
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  • Supplemental Keywords:

    geostatistical modeling, computational statistics, latent processes, spatial covariance functions, model selection, sampling design, remote sensing, 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, 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.stat.colostate.edu/~jah/software/geo_Model_Selection.R Exit
    http://www.nrel.colostate.edu/projects/starmap/ Exit

    Progress and Final Reports:

    Original Abstract
  • 2002
  • 2003 Progress Report
  • 2004 Progress Report
  • 2005 Progress 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