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Browsing by Author "Hooten, Mevin, advisor"

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    Bayesian methods for spatio-temporal ecological processes using imagery data
    (Colorado State University. Libraries, 2021) Lu, Xinyi, author; Hooten, Mevin, advisor; Kaplan, Andee, committee member; Fosdick, Bailey, committee member; Koons, David, committee member
    In this dissertation, I present novel Bayesian hierarchical models to statistically characterize spatio-temporal ecological processes. I am motivated by the volatility of Alaskan ecosystems in the face of global climate change and I demonstrate methods for emerging imagery data as survey technologies advance. For the nearshore marine ecosystem, I developed a model that combines ecological diffusion and logistic growth to quantify colonization dynamics of a population that establishes long-term equilibrium over a heterogeneous environment. I also unified modeling concepts from entity resolution and capture-recapture to identify unique individuals of the population from overlapping images and infer total abundance. For the terrestrial ecosystem, I developed a stochastic state-space model to quantify the impact of climate change on the structural transformation of land cover types. The methods presented in this dissertation provide interpretable inference and employ statistical computing strategies to achieve scalability.
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    Improved estimation and prediction for computationally expensive ecological and paleoclimate models
    (Colorado State University. Libraries, 2016) Tipton, John, author; Hooten, Mevin, advisor; Opsomer, Jean, advisor; Hoeting, Jennifer, committee member; Aldridge, Cameron, committee member
    In this dissertation, we present statistical methods to evaluate estimation and prediction performance for applied ecological problems. We explore a variety of applied problems and, within this context, we investigate how each method performs. We evaluate empirical performance of a model-based estimator of mean percent canopy cover using a representative United States Forest Service Forest Inventory and Analysis dataset. For two paleoclimate reconstructions, we develop novel modeling methodologies and evaluate model performance using both resampling and simulation methods. In each application, we use proper scoring rules while leveraging parallel computing and computational techniques, that allow fitting of complex models in a finite amount of time.
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    Integrated statistical models in ecology
    (Colorado State University. Libraries, 2023) Van Ee, Justin, author; Hooten, Mevin, advisor; Koslovsky, Matthew, advisor; Keller, Kayleigh, committee member; Kaplan, Andee, committee member; Bailey, Larissa, committee member
    The number of endangered and vulnerable species continues to grow globally as a result of habitat destruction, overharvesting, invasive species, and climate change. Understanding the drivers of population decline is pivotal for informing species conservation. Many datasets collected are restricted to a limited portion of the species range, may not include observations of other organisms in the community, or lack temporal breadth. When analyzed independently, these datasets often overlook drivers of population decline, muddle community responses to ecological threats, and poorly predict population trajectories. Over the last decade, thanks to efforts like The Long Term Ecological Research Network and National Ecological Observatory Network, citizen science surveys, and technological advances, ecological datasets that provide insights about collections of organisms or multiple characteristics of the same organism have become prevalent. The conglomerate of datasets has the potential to provide novel insights, improve predictive performance, and disentangle the contributions of confounded factors, but specifying joint models that assimilate all the available data sources is both intellectually daunting and computationally prohibitive. I develop methodology for specifying computationally efficient integrated models. I discuss datasets frequently collected in ecology, objectives common to many analyses, and the methodological challenges associated with specifying joint models in these contexts. I introduce a suite of model building and computational techniques I used to facilitate inference in three applied analyses of ecological data. In a case study of the joint mammalian response to the bark beetle epidemic in Colorado, I describe a restricted regression approach to deconfounding the effects of environmental factors and community structure on species distributions. I highlight that fitting certain joint species distribution models in a restricted parameterization improves sampling efficiency. To improve abundance estimates for a federally protected species, I specify an integrated model for analyzing independent aerial and ground surveys. I use a Markov melding approach to facilitate posterior inference and construct the joint distribution implied by the prior information, assumptions, and data expressed across a chain of submodels. I extend the integrated model by assimilating additional demographic surveys of the species that allow abundance estimates to be linked to annual variability in population vital rates. To reduce computation time, both models are fit using a multi-stage Markov chain Monte Carlo algorithm with parallelization. In each applied analysis, I uncover associations that would have been overlooked had the datasets been analyzed independently and improve predictive performance relative to models fit to individual datasets.
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    Statistical methods for modeling the movement and space-use of carnivores
    (Colorado State University. Libraries, 2017) Buderman, Frances E., author; Hooten, Mevin, advisor; Boone, Randall, committee member; Crooks, Kevin, committee member; Ivan, Jacob, committee member
    Recent advancements in the ability to monitor animal locations through time has led to a rapidly expanding field focused on statistical models for animal movement. However, many of the existing methods are computationally time-consuming to fit, restricting their application to a few individuals, and inaccessible to wildlife management practitioners. In addition, existing movement models were developed for contemporary animal location data. Many previously collected telemetry data sets may provide important information on animal movement, but there may be additional challenges that are not present in data collected explicitly for movement modeling. For example, telemetry data collected for survival studies may have large temporal gaps, and long-term studies may have used multiple data collection methods, resulting in data points with different error structures. My goal is to develop and expand on methods for modeling individual- and population-level animal movement in a flexible and computationally accessible framework. In Chapter 1, I discuss the role of carnivores in natural resource management and the habitat associations and movement ecology of two carnivores native to Colorado, Canada lynx and cougars. I describe the existing data sets, collected by Colorado Parks and Wildlife, that are available for analyzing Canada lynx and cougar movement ecology. I also discuss contemporary statistical methods for analyzing animal telemetry data. Finally, I conclude with my research objectives. Chapter 2 presents a new framework for modeling the unobserved paths of telemetered individuals while accounting for measurement error. Many available telemetry data sets were not collected for the purposes of movement modeling, making the use of existing methods challenging due to large temporal gaps and varying monitoring protocols. In contrast to the more traditional mechanistic movement models that appear in the literature, I propose a phenomenological functional model for animal movement. The movement process is approximated with basis functions (e.g., splines), which are an extremely flexible statistical tool that allows for complex, non-linear movement patterns at different temporal scales. In addition, the observed data contains complicated error structures that vary across telemetry type. I then apply this model to a case-study of two Canada lynx that were reintroduced to Colorado and show that inference about spatio-temporal movement behaviors can be obtained from the unobserved paths. For Chapter 3, I apply a population-level version of the functional movement model, developed in Chapter 1, to 153 Canada lynx that were released in Colorado as part of a state reintroduction program. Twelve offspring of the reintroduced individuals were also included in the analysis. I perform a post hoc analysis of movement paths using spatial visualizations and linear mixed models, allowing the different movement behaviors to vary as a function of season, sex, reproductive status, and reintroduction timeline. This chapter represents one of the most comprehensive analyses of Canada lynx movement in the continental United States. In Chapter 4, I discuss the fine-scale movement of cougars in the Colorado Front Range using a continuous-time discrete-space (CTDS) framework. The CTDS framework is computationally fast, flexible, and easily implemented in standard statistical programs. This chapter focuses on a population-level extension of the CTDS framework that can be used to model the population- and individual-level effect of landscape variables on movement rates and directionality. I use this model to determine potential drivers of cougar movement in the Colorado Front Range, a rapidly urbanizing area in the foothills of the Rocky Mountains. This work also uses the functional model I developed in Chapter 1, but with an error structure more appropriate for small-error GPS data. I conclude with a summary of findings, overarching themes, and potential future research directions in Chapter 5.