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Browsing by Author "Hooten, Mevin B., advisor"

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    Advances in Bayesian spatial statistics for ecology and environmental science
    (Colorado State University. Libraries, 2024) Wright, Wilson J., author; Hooten, Mevin B., advisor; Cooley, Daniel S., advisor; Keller, Kayleigh P., committee member; Kaplan, Andee, committee member; Ross, Matthew R. V., committee member
    In this dissertation, I develop new Bayesian methods for analyzing spatial data from applications in ecology and environmental science. In particular, I focus on methods for mechanistic spatial models and binary spatial processes. I first consider the distribution of heavy metal pollution from a mining road in Cape Krusenstern, Alaska, USA. I develop a mechanistic spatial model that uses the physical process of atmospheric dispersion to characterize the spatial structure in these data. This approach directly incorporates scientific knowledge about how pollutants spread and provides inferences about this process. To assess how the heavy metal pollution impacts the vegetation community in Cape Krusenstern, I also develop a new model that represents plant cover for multiple species using clipped Gaussian processes. This approach is applicable to multiscale and multivariate binary processes that are observed at point locations — including multispecies plant cover data collected using the point intercept method. By directly analyzing the point-level data, instead of aggregating observations to the plot-level, this model allows for inferences about both large-scale and small-scale spatial dependence in plant cover. Additionally, it also incorporates dependence among different species at the small spatial scale. The third model I develop is motivated by ecological studies of wildlife occupancy. Similar to plant cover, species occurrence can be modeled as a binary spatial process. However, occupancy data are inherently measured at areal survey units. I develop a continuous-space occupancy model that accounts for the change of spatial support between the occurrence process and the observed data. All of these models are implemented using Bayesian methods and I present computationally efficient methods for fitting them. This includes a new surrogate data slice sampler for implementing models with latent nearest neighbor Gaussian processes.
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    Spatial asynchrony and cross-scale climate interactions in populations of a coldwater stream fish
    (Colorado State University. Libraries, 2023) Valentine, George P., author; Kanno, Yoichiro, advisor; Hooten, Mevin B., advisor; Morrison, Ryan R., committee member
    Climate change affects animal and plant populations over broad geographic ranges due to spatially autocorrelated abiotic conditions known as the "Moran Effect". However, populations do not always respond to broad-scale environmental changes synchronously across a landscape. We used a retrospective analysis of time-series count data (5-28 annual samples per site) at 170 stream segments dispersed over nearly 1,000 km to characterize the population structure and scale of spatial population synchrony in a coldwater stream fish (brook trout, Salvelinus fontinalis), which is sensitive to temperature and flow alterations, across its southern native range. Spatial synchrony differed by life stage and geographic region: it was stronger in the juvenile life stage than the adult life stage and in the northern sub-region than the southern sub-region. Spatial synchrony of trout populations extended to 100-150 km but was much weaker than that of climate variables such as temperature, stream flow, and precipitation. Early life stage abundance changed over time due to annual variation in summer temperature and winter and spring stream flow conditions. Climate effects on abundance differed between sub-regions and among local populations, indicating multiple cross-scale interactions where climate interacted with local habitat to generate only a modest pattern of population synchrony over space. We conclude that heterogeneous responses to climate variation lead to only a modest level of spatial synchrony among local trout populations, which leads to varying susceptibility to climate change. This response heterogeneity indicates that some local segments characterized by population asynchrony and resistance to climate variation could represent unique populations of this iconic native coldwater fish that warrant attention in their conservation planning in a changing climate. Identifying such priority populations and incorporating them into landscape-level conservation planning is imperative to their conservation. Our approach is applicable to other widespread aquatic species sensitive to climate change.
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    Statistical models for animal movement and landscape connectivity
    (Colorado State University. Libraries, 2013) Hanks, Ephraim M., author; Hooten, Mevin B., advisor; Hoeting, Jennifer, committee member; Wang, Haonan, committee member; Alldredge, Mat, committee member; Theobald, David, committee member
    This dissertation considers statistical approaches to the study of animal movement behavior and landscape connectivity, with particular attention paid to modeling how movement and connectivity are influenced by landscape characteristics. For animal movement data, a novel continuous-time, discrete-space model of animal movement is proposed. This model yields increased computational efficiency relative to existing discrete-space models for animal movement, and a more flexible modeling framework than existing continuous-space models. In landscape genetic approaches to landscape connectivity, spatially-referenced genetic allele data are used to study landscape effects on gene flow. An explicit link is described between a common circuit-theoretic approach to landscape genetics and variogram fitting for Gaussian Markov random fields. A hierarchical model for landscape genetic data is also proposed, with a multinomial data model and latent spatial random effects to model spatial correlation.
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    Statistical models for animal telemetry data with applications to harbor seals in the Gulf of Alaska
    (Colorado State University. Libraries, 2017) Brost, Brian M., author; Hooten, Mevin B., advisor; Small, Robert J., committee member; Wittemyer, George, committee member; Boone, Randall B., committee member
    Much is known about the general biology and natural history of harbor seals (Phoca vitulina), but questions remain about the aquatic and terrestrial space use of these marine mammals. This is in large part because methods for examining the spatial ecology of harbor seals are poorly developed. The objective of this dissertation is to pair existing telemetry data with contemporary spatio-temporal modeling to quantify the space use and resource selection of harbor seals in the coastal waters of southern Alaska. Recent extensions to models for analyzing animal telemetry data address complications such as autocorrelation and telemetry measurement error; however, additional challenges remain, especially in the context of analyzing Argos satellite telemetry data collected on marine mammals like harbor seals. For example, existing methods assume elliptical (or circular) patterns of measurement error, even though Argos satellite telemetry devices impose more complicated error structures on the data. Constraints, or barriers, to animal movement present another complication. Harbor seals and other marine mammals are constrained to move within the marine environment, and mechanistic models that do not adhere to movement barriers yield unreliable inference. Therefore, a primary goal of this research is to develop statistical tools that account for these nuances and provide rigorous, ecologically relevant inference. Even though the models presented in this dissertation were specifically developed with Argos satellite telemetry data and harbor seals in mind, the methods are general and can be applied to other species and types of telemetry data. This dissertation consists of five chapters. In Chapter 1, I briefly discuss the general biology of harbor seals, focusing on what is known about their spatial habits in Alaska. I then summarize trends in Alaskan harbor seal abundance, a topic that motivated my research as well as the work of many others. I describe the existing Alaska Department of Fish and Game telemetry data sets that are available for examining harbor seal spatial ecology, commonly-used statistical methods for analyzing animal telemetry data, and conclude with the objectives of my research and an outline for the remainder of the dissertation. In Chapter 2, I propose an approach for obtaining resource selection inference from animal location data that accounts for complicated error structures, movement constraints, and temporally autocorrelated observations. The model consists of two general components: a model for the true, but unobserved, animal locations that reflects prior knowledge about constraints to animal movement, and a model for the observed telemetry locations that is conditional on the true locations. I apply the model to simulated data, showing that it outperforms common ad hoc approaches used when confronted with telemetry measurement error and movement constraints. I then apply the framework to obtain inference concerning aquatic resource selection and space use for harbor seals near Kodiak Island, Alaska. Chapters 3 and 4 shift the focus from inference concerning aquatic space use and resource selection, to inference concerning the use of coastal resources (i.e., haul-out sites) by harbor seals. In Chapter 3, I present a fully model-based approach for estimating the location of central places (e.g., haul-out sites, dens, nests, etc.) from telemetry data that accounts for multiple sources of uncertainty and uses all of the available locational data. The model consists of an observation model to account for large telemetry measurement error and animal movement, and a highly flexible mixture model (a Dirichlet process) to identify the location of central places. Ancillary behavioral data (e.g., harbor seal dive data obtained from the satellite-linked depth recorders) are also incorporated into the modeling framework to obtain inference concerning temporal patterns in central place use. Based on the methods developed in Chapter 3, I present a comprehensive analysis of the spatio-temporal patterns of haul-out use for harbor seals near Kodiak Island in Chapter 4. Chapter 4 also extends previously developed methods to examine the affect of covariates on haul-out site selection and to obtain population-level inference concerning haul-out use. I conclude, in Chapter 5, with some general thoughts about analyzing animal telemetry data, as well as potential future research directions.
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    Statistical models for dependent trajectories with application to animal movement
    (Colorado State University. Libraries, 2017) Scharf, Henry R., author; Hooten, Mevin B., advisor; Cooley, Daniel S., committee member; Fosdick, Bailey K., committee member; Hobbs, N. Thompson, committee member
    In this dissertation, I present novel methodology to study the way animals interact with each other and the landscape they inhabit. I propose two statistical models for dependent trajectories in which depedencies among paths arise from pairwise relationships defined using latent dynamic networks. The first model for dependent trajectories is formulated in a discrete-time framework. The model allows researchers to make inference on a latent social network that describes pairwise connections among actors in the population, as well as parameters that govern the type of behavior induced by the social network. The second model for dependent trajectories is formulated in a continuous-time framework and is motivated primarily by reducing uncertainty in interpolations of the continuous trajectories by leveraging positive dependence among individuals. Both models are used in applications to killer whales. In addition to the two models for multiple trajectories, I introduce a new model for the movement of an individual showing a preference for areas in a landscape near a complex-shaped, dynamic feature. To facilitate estimation, I propose an approximation technique that exploits of locally linear structure in the feature of interest. I demonstrate the model for the movement of an individual responding to a dynamic feature, as well as the approximation technique, in an application to polar bears for which the changing boundary of Arctic sea ice represents the relevant dynamic feature.