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Absolute direction in organelle movement.

Solveig Plomer1, Annika Meyer1, Philipp Gebhardt2

  • 1Institute of Mathematics Goethe University Frankfurt Frankfurt Germany.

Ecology and Evolution
|August 7, 2024
PubMed
Summary
This summary is machine-generated.

Hidden Markov models (HMMs) for movement analysis can benefit from using absolute angles instead of turning angles. This approach enhances discrimination between movement directions and improves model fit, revealing more about directional changes.

Keywords:
absolute anglebiased random walkcorrelated random walkhidden Markov modelmovement analysisturning angle

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Area of Science:

  • Movement ecology
  • Quantitative biology
  • Animal behavior analysis

Background:

  • Correlated random walk (CRW) models are standard for movement analysis, often using turning angles relative to prior direction.
  • Hidden Markov models (HMMs) segment movements into states based on CRWs, typically differentiating by speed using turning angle and step size distributions.

Purpose of the Study:

  • To introduce and evaluate the use of absolute angles, instead of traditional turning angles, within HMMs for movement analysis.
  • To demonstrate that absolute angles can capture directional information, complementing speed-based discrimination offered by turning angles.

Main Methods:

  • Developed a preprocessing algorithm to enable absolute angle analysis within the existing R package moveHMM.
  • Applied HMMs incorporating absolute angles to a dataset of cell organelle movements.
  • Compared goodness-of-fit and state selection between HMMs using turning angles versus absolute angles.

Main Results:

  • HMMs utilizing absolute angles demonstrated improved goodness-of-fit compared to those using turning angles.
  • Models with absolute angles tended to identify a higher number of states, indicating the significance of directional changes.
  • Analysis of cell organelle movements revealed additional properties captured by absolute angle models.

Conclusions:

  • Absolute angles offer valuable insights into movement patterns, particularly when directional changes are biologically significant.
  • Incorporating absolute angles into HMMs enhances the ability to discriminate between different movement directions and modes.
  • The moveHMM R package is updated to facilitate the analysis of movement patterns using absolute angles.