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Inference in MCMC step selection models.

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  • 1Centre for Research into Ecological and Environmental Modelling, University of St Andrews, St Andrews, UK.

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Summary
This summary is machine-generated.

This study introduces a new habitat selection model that accounts for animal movement autocorrelation. It enables simultaneous inference of movement and habitat preferences, improving ecological predictions.

Keywords:
MCMC step selectionMarkov chain Monte Carloanimal movementlocal Gibbs samplerresource selection functionstep selection functionutilization distribution

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Area of Science:

  • Ecology
  • Movement Ecology
  • Wildlife Biology

Background:

  • Resource selection functions (RSFs) are widely used to model animal habitat selection.
  • RSFs assume independence of animal locations, which is violated by temporal autocorrelation in movement data.
  • Step selection functions (SSFs) address autocorrelation but do not easily predict steady-state space use.

Purpose of the Study:

  • To develop a novel step selection model with an explicit steady-state distribution.
  • To enable simultaneous inference of animal movement and habitat selection parameters.
  • To provide a flexible framework for analyzing animal space use.

Main Methods:

  • Developed a step selection model by drawing an analogy with Markov chain Monte Carlo (MCMC) algorithms.
  • Utilized maximum likelihood estimation for parameter inference.
  • Introduced a novel rejection-free MCMC scheme, the local Gibbs sampler, for a class of animal movement models.

Main Results:

  • The proposed model successfully incorporates an explicit steady-state distribution.
  • Maximum likelihood estimation allows for simultaneous inference of movement and habitat selection.
  • Simulation studies confirmed the ability of maximum likelihood estimation to recover model parameters.

Conclusions:

  • The new model provides a unified framework for understanding animal movement and habitat selection.
  • It overcomes limitations of traditional RSFs and SSFs by accounting for autocorrelation and predicting steady-state distributions.
  • The method is applicable to real-world data, as demonstrated with zebra movement data.