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C. K. Wikle

Publications and source records attributed to C. K. Wikle.

7 recordsLinked to original sources
USGS2010

A general science-based framework for dynamical spatio-temporal models

Spatio-temporal statistical models are increasingly being used across a wide variety of scientific disciplines to describe and predict spatially-explicit processes that evolve over time. Correspondingly, in recent years there has been a significant amount of research on new statistical methodology for such models. Although descriptive models that approach the problem from the second-order (covariance) perspective are important, and innovative work is being done in this regard, many real-world processes are dynamic, and it can be more efficient in some cases to characterize the associated spatio-temporal dependence by the use of dynamical models. The chief challenge with the specification of such dynamical models has been related to the curse of dimensionality. Even in fairly simple linear, first-order Markovian, Gaussian error settings, statistical models are often over parameterized. Hierarchical models have proven invaluable in their ability to deal to some extent with this issue by allowing dependency among groups of parameters. In addition, this framework has allowed for the specification of science based parameterizations (and associated prior distributions) in which classes of deterministic dynamical models (e. g., partial differential equations (PDEs), integro-difference equations (IDEs), matrix models, and agent-based models) are used to guide specific parameterizations. Most of the focus for the application of such models in statistics has been in the linear case. The problems mentioned above with linear dynamic models are compounded in the case of nonlinear models. In this sense, the need for coherent and sensible model parameterizations is not only helpful, it is essential. Here, we present an overview of a framework for incorporating scientific information to motivate dynamical spatio-temporal models. First, we illustrate the methodology with the linear case. We then develop a general nonlinear spatio-temporal framework that we call general quadratic nonlinearity and demonstrate that it accommodates many different classes of scientific-based parameterizations as special cases. The model is presented in a hierarchical Bayesian framework and is illustrated with examples from ecology and oceanography. ?? 2010 Sociedad de Estad??stica e Investigaci??n Operativa.

Test↗
USGS2008

Zero-inflated modeling of fish catch per unit area resulting from multiple gears: Application to channel catfish and shovelnose sturgeon in the Missouri River

Fisheries studies often employ multiple gears that result in large percentages of zero values. We considered a zero-inflated Poisson (ZIP) model with random effects to address these excessive zeros. By employing a Bayesian ZIP model that simultaneously incorporates data from multiple gears to analyze data from the Missouri River, we were able to compare gears and make more year, segment, and macrohabitat comparisons than did the original data analysis. For channel catfish Ictalurus punctatus, our results rank (highest to lowest) the mean catch per unit area (CPUA) for gears (beach seine, benthic trawl, electrofishing, and drifting trammel net); years (1998 and 1997); macrohabitats (tributary mouth, connected secondary channel, nonconnected secondary channel, and bend); and river segment zones (channelized, inter-reservoir, and least-altered). For shovelnose sturgeon Scaphirhynchus platorynchus, the mean CPUA was significantly higher for benthic trawls and drifting trammel nets; 1998 and 1997; tributary mouths, bends, and connected secondary channels; and some channelized or least-altered inter-reservoir segments. One important advantage of our approach is the ability to reliably infer patterns of relative abundance by means of multiple gears without using gear efficiencies. ?? Copyright by the American Fisheries Society 2008.

North American Journal of Fisheries Management↗
USGS2007

Population influences on tornado reports in the United States

The number of tornadoes reported in the United States is believed to be less than the actual incidence of tornadoes, especially prior to the 1990s, because tornadoes may be undetectable by human witnesses in sparsely populated areas and areas in which obstructions limit the line of sight. A hierarchical Bayesian model is used to simultaneously correct for population-based sampling bias and estimate tornado density using historical tornado report data. The expected result is that F2-F5 compared with F0-F1 tornado reports would vary less with population density. The results agree with this hypothesis for the following population centers: Atlanta, Georgia; Champaign, Illinois; and Des Moines, Iowa. However, the results indicated just the opposite in Oklahoma. It is hypothesized that the result is explained by the misclassification of tornadoes that were worthy of F2-F5 rating but were classified as F0-F1 tornadoes, thereby artificially decreasing the number of F2-F5 and increasing the number of F0-F1 reports in rural Oklahoma.

Weather and Forecasting↗
USGS2007

Hierarchical spatiotemporal matrix models for characterizing invasions

The growth and dispersal of biotic organisms is an important subject in ecology. Ecologists are able to accurately describe survival and fecundity in plant and animal populations and have developed quantitative approaches to study the dynamics of dispersal and population size. Of particular interest are the dynamics of invasive species. Such nonindigenous animals and plants can levy significant impacts on native biotic communities. Effective models for relative abundance have been developed; however, a better understanding of the dynamics of actual population size (as opposed to relative abundance) in an invasion would be beneficial to all branches of ecology. In this article, we adopt a hierarchical Bayesian framework for modeling the invasion of such species while addressing the discrete nature of the data and uncertainty associated with the probability of detection. The nonlinear dynamics between discrete time points are intuitively modeled through an embedded deterministic population model with density-dependent growth and dispersal components. Additionally, we illustrate the importance of accommodating spatially varying dispersal rates. The method is applied to the specific case of the Eurasian Collared-Dove, an invasive species at mid-invasion in the United States at the time of this writing. ?? 2006, The International Biometric Society.

Biometrics↗
USGS2005

Dynamic design of ecological monitoring networks for non-Gaussian spatio-temporal data

Many ecological processes exhibit spatial structure that changes over time in a coherent, dynamical fashion. This dynamical component is often ignored in the design of spatial monitoring networks. Furthermore, ecological variables related to processes such as habitat are often non-Gaussian (e.g. Poisson or log-normal). We demonstrate that a simulation-based design approach can be used in settings where the data distribution is from a spatio-temporal exponential family. The key random component in the conditional mean function from this distribution is then a spatio-temporal dynamic process. Given the computational burden of estimating the expected utility of various designs in this setting, we utilize an extended Kalman filter approximation to facilitate implementation. The approach is motivated by, and demonstrated on, the problem of selecting sampling locations to estimate July brood counts in the prairie pothole region of the U.S.

Environmetrics↗
USGS2005

Efficient statistical mapping of avian count data

We develop a spatial modeling framework for count data that is efficient to implement in high-dimensional prediction problems. We consider spectral parameterizations for the spatially varying mean of a Poisson model. The spectral parameterization of the spatial process is very computationally efficient, enabling effective estimation and prediction in large problems using Markov chain Monte Carlo techniques. We apply this model to creating avian relative abundance maps from North American Breeding Bird Survey (BBS) data. Variation in the ability of observers to count birds is modeled as spatially independent noise, resulting in over-dispersion relative to the Poisson assumption. This approach represents an improvement over existing approaches used for spatial modeling of BBS data which are either inefficient for continental scale modeling and prediction or fail to accommodate important distributional features of count data thus leading to inaccurate accounting of prediction uncertainty.

Environmental and Ecological Statistics↗