Hierarchical spatio-temporal models for ecological processes

The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file.Title from title screen of research.pdf file viewed on (April 26, 2007)Vita.Thesis (Ph.D.) University of Missouri-Columbia 2006.Ecosystems are composed of phenomena that propagate in time and space. Often, ecological processes underlying such phenomena are studied separably in various subdisciplines, while larger scale, interlinking mechanisms are overlooked or only speculated about. As grows the burden of global climate change and human disturbance of natural systems, so grows the need for rigorous statistical methods focused on characterizing and forecasting large scale spatiotemporal environmental and ecological processes in the presence of limited data and multiple sources of uncertainty. Hierarchical models offer a powerful means with which to study complex phenomena in space and time. This dissertation develops and illustrates the utility of spatiotemporal hierarchical models for studying ecological phenomena.Includes bibliographical reference

Monitoring Responses of Bear Foods to Climate Change Evaluating Adaptive Monitoring Designs for Occupancy Studies

Methods for assessing site occupancy while accounting for imperfect detection have quickly become important for ecologists wishing to study the distribution and prevalence of species across landscapes.  Occupancy data are convenient to collect because, while they do require repeated sampling efforts, they do not require the marking of individual organisms. Some guidance on monitoring for occupancy studies has been provided for conventional settings.  However, coupling the data collection and analysis components via an optimal adaptive sampling design may improve precision of estimates and save money. Optimal adaptive sampling designs have not been applied to occupancy models previously. We present a design criterion that facilitates adaptive monitoring for occupancy studies and illustrate its advantages and disadvantages through the use of simulations and real-data scenarios.  Our findings indicate that, depending on the focus of the study in question, monitoring designs can be improved substantially by considering adaptive sampling schemes

Safari Science: assessing the reliability of citizen science data for wildlife surveys

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/140025/1/jpe12921.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/140025/2/jpe12921_am.pd

Source Reconstruction for Spatio-Temporal Physical Statistical Models

In many applications, a signal is deformed by well-understood dynamics before it can be measured. For example, when a pollutant enters a river, it immediately begins dispersing, flowing, settling, and reacting. If the pollutant enters at a single point, its concentration can be measured before it enters the complex dynamics of the river system. However, in the case of a non-point source pollutant, it is not clear how to efficiently measure its source. One possibility is to record concentration measurements in the river, but this signal is masked by the fluid dynamics of the river. Specifically, concentration is governed by the advection-diffusion-reaction PDE, with an unknown source term. We propose a method to statistically reconstruct a source term from these PDE-deformed measurements. Our method is general and applies to any linear PDE. This method has important applications in the study of environmental DNA and non-point source pollution.Comment: 28 pages, 8 figures, 2 table