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

• Author(s) / Creator(s)

  • Schlägel, Ulrike E
  • Lewis, Mark A.

• 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.

• Date created

  2016-01-01

• Subjects / Keywords

  • sampling rate
  • animal movement
  • discretization
  • GPS data
  • parameter estimation
  • random walk

• Type of Item

  Article (Draft / Submitted)

• DOI

  https://doi.org/10.7939/r3-0s4y-g452

• License

  Attribution-NonCommercial 4.0 International