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    Multistate Ornstein-Uhlenbeck space use model reveals sex-specific partitioning of the energy landscape in a soaring bird

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    Eisaguirre_J_2019.pdf
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    Author
    Eisaguirre, Joseph M.
    Chair
    Goddard, Scott
    Committee
    Barry, Ron
    McIntyre, Julie
    Short, Margaret
    Metadata
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    URI
    http://hdl.handle.net/11122/10977
    Abstract
    Understanding animals’ home range dynamics is a frequent motivating question in movement ecology. Descriptive techniques are often applied, but these methods lack predictive ability and cannot capture effects of dynamic environmental patterns, such as weather and features of the energy landscape. Here, we develop a practical approach for statistical inference into the behavioral mechanisms underlying how habitat and the energy landscape shape animal home ranges. We validated this approach by conducting a simulation study, and applied it to a sample of 12 golden eagles Aquila chrysaetos tracked with satellite telemetry. We demonstrate that readily available software can be used to fit a multistate Ornstein-Uhlenbeck space use model to make hierarchical inference of habitat selection parameters and home range dynamics. Additionally, the underlying mathematical properties of the model allow straightforward computation of predicted space use distributions, permitting estimation of home range size and visualization of space use patterns under varying conditions. The application to golden eagles revealed effects of habitat variables that align with eagle biology. Further, we found that males and females partition their home ranges dynamically based on uplift. Specifically, changes in wind and the angle of the sun seemed to be drivers of differential space use between sexes, in particular during late breeding season when both are foraging across large parts of their home range to support nestling growth.
    Description
    Master's Project (M.S.) University of Alaska Fairbanks, 2019
    Date
    2019-12
    Type
    Master's Project
    Collections
    Mathematics and Statistics

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