A framework for analyzing the robustness of movement models to variable step discretization

  • Author(s) / Creator(s)
  • When sampling animal movement paths, the frequency at which location
    measurements are attempted is a critical feature for data analysis. Important quantities
    derived from raw data, e.g. travel distance or sinuosity, can differ largely based
    on the temporal resolution of the data. Likewise, when movement models are fitted to
    data, parameter estimates have been demonstrated to vary with sampling rate. Thus,
    biological statements derived from such analyses can only be made with respect to the
    resolution of the underlying data, limiting extrapolation of results and comparison between
    studies. To address this problem, we investigate whether there are models that
    are robust against changes in temporal resolution. First, we propose a mathematically
    rigorous framework, in which we formally define robustness as a model property.We
    then use the framework for a thorough assessment of a range of basic random walk
    models, in which we also show how robustness relates to other probabilistic concepts.
    While we found robustness to be a strong condition met by few models only,
    we suggest a new method to extend models so as to make them robust. Our framework
    provides a new systematic, mathematically founded approach to the question if,
    and how, sampling rate of movement paths affects statistical inference.

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  • Type of Item
    Article (Draft / Submitted)
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  • License
    Attribution-NonCommercial 4.0 International