1,539 research outputs found

    Hierarchical models for the anlaysis of species distributions and abundances: development and applications

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    There is a strong need for statistical methods that can maximize the utility of ecological data while providing accurate estimates of species abundances and distributions. This dissertation aims to build on current statistical models using Bayesian hierarchical approaches to advance these methods. Chapters one, two, and three utilize a multi-species modeling framework to estimate species occurrence probabilities. Chapter one presents a model to assess the community response of breeding birds to habitat fragmentation. The results demonstrate the importance of understanding the responses of both individual, and groups of species, to environmental heterogeneity while illustrating the utility of hierarchical models for inference about species richness. Chapter two demonstrates how the multi-species modeling framework can be used to evaluate conservation actions through a component that incorporates species-specific responses to management treatments. In Chapter three, I develop a method for validating predictions generated by the multi-species model that accounts for detection biases in evaluation data. I build competing models using wetland breeding amphibian data and test their abilities to predict occupancy at unsampled locations. Chapters four and five develop count models that are used to estimate population abundances in relation to environmental and climate variables. In Chapter four, I employ a Poisson regression designed to determine how climate affects the annual abundances of migrating monarch butterflies. I incorporate the climate conditions experienced both during a spring migration phase, as well as during summer recruitment. In Chapter five, I analyze sea duck data to characterize the spatial and temporal distributions along the U.S. and Canadian Atlantic coast. I model count data for five species using a zero-inflated negative binomial model that includes latitude, habitat covariates, and the North Atlantic Oscillation. The results from these two chapters demonstrate how Bayesian models can be used to elucidate complicated species-climate relationships. The chapters of this dissertation illustrate creative development and application of advanced statistical methods to complex biological systems. These applications provide a practical framework for dealing with highly aggregated species and uneven species distributions in community analyses, as well as a method for evaluating occurrence estimates that accounts for detection biases. My results highlight the dynamic relationships between population and community structure, habitat, and climate

    Deutsche Bank\u27s TaskMan System and the Job Execution Framework

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    The goal of this project was to aid Deutsche Bank in the migration from a deprecated time based scheduling system to a new service-oriented Job Execution Framework. Throughout the project we identified shortcomings that plagued the old system and designed processes to avoid reinventing these problems in the replacement framework. We also created a system to visually represent the running jobs in the old system, and created a methodology around it for the migration of tasks to their new framework

    A Common Law Court in a Regulatory World

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    Wind Power in Worcester, Massachusetts: Siting and Permitting

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    The goal of this project was to aid the City of Worcester in reaching its commitment to promote renewable energy. First, we analyzed the potential for wind power in Worcester, finding that there is good potential for small wind turbines and limited potential for medium sized turbines. We then recommended a model ordinance allowing the installation of personal wind turbines and described a process for introducing it into the Worcester Zoning Ordinance as an amendment

    Integrated Population Models: Achieving their Potential

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    Precise and accurate estimates of abundance and demographic rates are primary quantities of interest within wildlife conservation and management. Such quantities provide insight into population trends over time and the associated underlying ecological drivers of the systems. This information is fundamental in managing ecosystems, assessing species conservation status and developing and implementing effective conservation policy. Observational monitoring data are typically collected on wildlife populations using an array of different survey protocols, dependent on the primary questions of interest. For each of these survey designs, a range of advanced statistical techniques have been developed which are typically well understood. However, often multiple types of data may exist for the same population under study. Analyzing each data set separately implicitly discards the common information contained in the other data sets. An alternative approach that aims to optimize the shared information contained within multiple data sets is to use a “model-based data integration” approach, or more commonly referred to as an “integrated model.” This integrated modeling approach simultaneously analyzes all the available data within a single, and robust, statistical framework. This paper provides a statistical overview of ecological integrated models, with a focus on integrated population models (IPMs) which include abundance and demographic rates as quantities of interest. Four main challenges within this area are discussed, namely model specification, computational aspects, model assessment and forecasting. This should encourage researchers to explore further and develop new practical tools to ensure that full utility can be made of IPMs for future studies
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