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Browsing by Author "Hoeting, Jennifer A., advisor"

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    Land use influences on adjacent ecological systems: implications for conservation planning
    (Colorado State University. Libraries, 2009) Wade, Alisa Ann, author; Laituri, Melinda J., advisor; Theobald, David M., advisor; Hoeting, Jennifer A., advisor
    This research investigated the spatial relationships between land uses, primarily urbanization, and adjacent ecological systems. As anthropogenic stressors encroach on protected areas and aquatic systems, the ecological functioning of those systems is reduced, and this has implications for natural resource management and conservation. I conducted three separate studies to address different research questions relating to land use and land cover-ecological system linkages. I assessed the vulnerability of conservation lands throughout the U.S. to adjacent anthropogenic threats and identified protected lands that are likely threatened by human activities as well as unprotected lands that offer opportunities for future conservation action. I also quantified the amount of residential development encroachment surrounding protected lands in the U.S., and I quantified how encroachment has altered the landscape structure around conservation lands nationally from 1970 through 2000, and forecast changes for years 2000 through 2030. Results from these two studies showed that there are a number of protected areas that are vulnerable to neighboring threats and that development has both reduced the buffer surrounding and the connectedness between protected areas. However, results also suggested that there are a number of options for future conservation action, although continued urbanization will limit these options. These studies indicate that conservation planning must consider adjacent land uses. However, the final study presented in this dissertation illustrated that conservation scientists and land managers must recognize the limitations of their approach when modeling the relationships between ecological systems and adjacent land use. I used a conceptual model of how land cover at different upslope scales influences aquatic integrity to show how different modeling approaches can substantially alter resulting inference. Results suggest that a modeling approach that incorporates ecological knowledge may provide more relevant inference for management decisions. A finding applicable to all three studies is that a key conservation strategy will be to work cooperatively with adjacent land owners and mangers to successfully manage both protected areas and aquatic systems.
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    Nonparametric tests of spatial isotropy and a calibration-capture-recapture model
    (Colorado State University. Libraries, 2017) Weller, Zachary D., author; Hoeting, Jennifer A., advisor; Cooley, Dan, committee member; Hooten, Mevin, committee member; Ahola, Jason, committee member
    In this dissertation we present applied, theoretical, and methodological advances in the statistical analysis of spatially-referenced and capture-recapture data. An important step in modeling spatially referenced data is choosing the spatial covariance function. Due to the development of a variety of covariance models, practitioners are faced with a myriad of choices for the covariance function. One of these choices is whether or not the covariance function is isotropic. Isotropy means that the covariance function depends only the distance between observations in space and not their relative direction. Part I of this dissertation focuses on nonparametric hypothesis tests of spatial isotropy. Statisticians have developed diagnostics, including graphical techniques and hypothesis tests, to assist in determining if an assumption of isotropy is adequate. Nonparametric tests of isotropy are one subset of these diagnostic methods, and while the theory for several nonparametric tests has been developed, the efficacy of these methods in practice is less understood. To begin part I of this dissertation, we develop a comprehensive review of nonparametric hypothesis tests of isotropy for spatially-referenced data. Our review provides informative graphics and insight about how nonparametric tests fit into the bigger picture of modeling spatial data and considerations for choosing a test of isotropy. An extensive simulation study offers comparisons of method performance and recommendations for test implementation. Our review also gives rise to a number of open research questions. In the second section of part I, we develop and demonstrate software that implements several of the tests. Because the tests were not available in software, we created the R package spTest, which implements a number of nonparametric tests of isotropy. The package is open source and available on the Comprehensive R Archive Network (CRAN). We provide a detailed demonstration of how to use spTest for testing isotropy on two spatially-referenced data sets. We offer insights into test limitations and how the tests can be used in conjunction with graphical techniques to evaluate isotropy properties. To conclude our work with spatially-referenced data in part I, we develop a new nonparametric test of spatial isotropy using the spectral representation of the spatial covariance function. Our new test overcomes some of the short-comings of other nonparametric tests. We develop theory that describes the distribution of our test statistic and explore the efficacy of our test via simulations and applications. We also note several difficulties in implementing the test, explore remedies to these difficulties, and propose several areas of future work. Finally, in part II of this dissertation, we shift our focus away from spatially-referenced data to capture-recapture data. Our capture-recapture work is motivated by methane concentration data collected by new mobile sensing technology. Because this technology is still in its infancy, there is a need to develop algorithms to extract meaningful information from the data. We develop a new Bayesian hierarchical capture-recapture model which we call the calibration-capture-recapture (CCR) model. We use our model and methane data to estimate the number and emission rate of methane sources within an urban sampling region. We apply our CCR model to methane data collected in two U.S. cities. Our new CCR model provides a framework to draw inference from data collected by mobile sensing technologies. The methodology for our capture-recapture model is useful in other capture-recapture settings, and the results of our model are important for informing climate change and infrastructure discussions.
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    Parameter inference and model selection for differential equation models
    (Colorado State University. Libraries, 2015) Sun, Libo, author; Hoeting, Jennifer A., advisor; Lee, Chihoon, advisor; Zhou, Wen, committee member; Hobbs, N. Thompson, committee member
    Firstly, we consider the problem of estimating parameters of stochastic differential equations with discrete-time observations that are either completely or partially observed. The transition density between two observations is generally unknown. We propose an importance sampling approach with an auxiliary parameter when the transition density is unknown. We embed the auxiliary importance sampler in a penalized maximum likelihood framework which produces more accurate and computationally efficient parameter estimates. Simulation studies in three different models illustrate promising improvements of the new penalized simulated maximum likelihood method. The new procedure is designed for the challenging case when some state variables are unobserved and moreover, observed states are sparse over time, which commonly arises in ecological studies. We apply this new approach to two epidemics of chronic wasting disease in mule deer. Next, we consider the problem of selecting deterministic or stochastic models for a biological, ecological, or environmental dynamical process. In most cases, one prefers either deterministic or stochastic models as candidate models based on experience or subjective judgment. Due to the complex or intractable likelihood in most dynamical models, likelihood-based approaches for model selection are not suitable. We use approximate Bayesian computation for parameter estimation and model selection to gain further understanding of the dynamics of two epidemics of chronic wasting disease in mule deer. The main novel contribution of this work is that under a hierarchical model framework we compare three types of dynamical models: ordinary differential equation, continuous time Markov chain, and stochastic differential equation models. To our knowledge model selection between these types of models has not appeared previously. The practice of incorporating dynamical models into data models is becoming more common, the proposed approach may be useful in a variety of applications. Lastly, we consider estimation of parameters in nonlinear ordinary differential equation models with measurement error where closed-form solutions are not available. We propose a new numerical algorithm, the data driven adaptive mesh method, which is a combination of the Euler and 4th order Runge-Kutta methods with different step sizes based on the observation time points. Our results show that both the accuracy in parameter estimation and computational cost of the new algorithm improve over the most widely used numerical algorithm, the 4th Runge-Kutta method. Moreover, the generalized profiling procedure proposed by Ramsay et al. (2007) doesn't have good performance for sparse data in time as compared to the new approach. We illustrate our approach with both simulation studies and ecological data on intestinal microbiota.
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    Statistical modeling and computing for climate data
    (Colorado State University. Libraries, 2019) Hewitt, Joshua, author; Hoeting, Jennifer A., advisor; Cooley, Daniel S., committee member; Wang, Haonan, committee member; Kampf, Stephanie K., committee member
    The motivation for this thesis is to provide improved statistical models and approaches to statistical computing for analyzing climate patterns over short and long distances. In particular, information needs for water managers motivate my research. Statistical models and computing techniques exist in a careful balance because climate data are generated by physical processes that can yield computationally intractable statistical models. Simplified or approximate statistical models are often required for practical data analyses. Critically, model complexity is moderated as much by research needs and available data as it is by computational capabilities. I start by developing a weighted likelihood that improves estimates of high quantiles for extreme precipitation (i.e., return levels) from latent spatial extremes models. In my second project, I develop a geostatistical model that accounts for the influence of remotely observed spatial covariates. The model improves prediction of regional precipitation and related climate variables that are influenced by global-scale processes known as teleconnections. I make the model more accessible by providing an R package that includes visualization, estimation, prediction, and diagnostic tools. The models from my first two projects require estimating large numbers of latent effects, so their implementations rely on computationally efficient methods. My third project proposes a deterministic, quadrature-based computational approach for estimating hierarchical Bayesian models with many hyperparameters, including those from my first two projects. The deterministic method is easily parallelizable and can require substantially less computational effort than common stochastic alternatives, like Monte Carlo methods. Notably, my quadrature-based method can also improve the computational efficiency of other recent, fast, deterministic approaches for estimating hierarchical Bayesian models, such as the integrated nested Laplace approximation (INLA). I also make the quadrature-based method accessible through an R package that provides inference for user-specified hierarchical models. Throughout my thesis, I demonstrate how improved models, more efficient computational methods, and accessible software allow modeling of large, complex climate data.
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    Using community detection on networks to identify migratory bird flyways in North America
    (Colorado State University. Libraries, 2012) Buhnerkempe, Michael G., author; Hoeting, Jennifer A., advisor; Givens, Geof H., committee member; Webb, Colleen T., committee member
    Migratory behavior of waterfowl populations in North America has traditionally been broadly characterized by four north-south flyways, and these flyways have been central to the management of waterfowl populations for more than 80 years. However, recent desires to incorporate uncertainty regarding biological processes into an adaptive harvest management program have underscored the need to re-evaluate the traditional flyway concept and bring uncertainty in flyways themselves into management planning. Here, we use bird band and recovery data to develop a network model of migratory movement for four waterfowl species, mallard (Anas platyrhnchos), northern pintail (A. acuta), American green-winged teal (A. carolinensis), and Canada Goose (Branta Canadensis) in North America. A community detection algorithm is then used to identify migratory flyways. Additionally, we compare flyway structure both across species and through time to determine broad applicability of the previous flyway concept. We also propose a novel metric, the consolidation factor, to describe a node's (i.e., small geographic area) importance in determining flyway structure. The community detection algorithm identified four main flyways for mallards, northern pintails, and American green-winged teal with the flyway structure of Canada geese exhibiting higher complexity. For mallards, flyway structure was relatively consistent through time. However, consolidation factors and cross-community mixing patterns revealed that for mallards and green-winged teal the presumptive Mississippi flyway was potentially a zone of high mixing between flyways. Additionally, interspersed throughout these major flyways were smaller mixing zones that point to added complexity and uncertainty in the four-flyway concept. Not only does the incorporation of this uncertainty due to mixing provide a potential alternative management strategy, but the network approach provides a robust, quantitative approach to flyway identification that fits well with the adaptive harvest management framework currently used in North American waterfowl management.