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

Ulrike E Schlägel1,2, Mark A Lewis3,4

  • 1Department of Mathematical and Statistical Sciences, CAB 632, University of Alberta, Edmonton, AB, T6G 2G1, Canada. ulrike.schlaegel@gmail.com.

Journal of Mathematical Biology
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Understanding animal movement data requires robust models. This study introduces a framework to assess and improve model robustness against varying sampling rates, enhancing the reliability of movement analysis.

Keywords:
Animal movementDiscretizationGPS dataParameter estimationRandom walkSampling rate

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Area of Science:

  • Ecology
  • Movement Ecology
  • Statistical Modeling

Background:

  • Sampling frequency critically impacts animal movement path analysis.
  • Temporal resolution affects derived metrics like distance and sinuosity.
  • Movement model parameter estimates vary with sampling rate, limiting cross-study comparisons.

Purpose of the Study:

  • To investigate models robust to changes in temporal resolution for animal movement data.
  • To formally define and mathematically assess model robustness.
  • To develop methods for enhancing model robustness.

Main Methods:

  • Developed a rigorous mathematical framework to define and assess model robustness.
  • Evaluated the robustness of various random walk models.
  • Investigated the relationship between robustness and other probabilistic concepts.

Main Results:

  • Robustness is a strong condition met by few existing models.
  • Demonstrated how robustness relates to other probabilistic concepts.
  • Proposed a novel method to extend models for improved robustness.

Conclusions:

  • A systematic, mathematically founded approach to sampling rate effects on statistical inference is provided.
  • The findings offer a new perspective on analyzing animal movement data with varying temporal resolutions.
  • The proposed method can improve the reliability and comparability of movement studies.