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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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A System for Tracking the Dynamics of Social Preference Behavior in Small Rodents
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Flexible hidden Markov models for behaviour-dependent habitat selection.

N J Klappstein1,2, L Thomas3, T Michelot3,4

  • 1School of Mathematics and Statistics, University of St Andrews, St Andrews, UK. nklappstein@dal.ca.

Movement Ecology
|June 3, 2023
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Summary

A new integrated model combines hidden Markov models (HMM) and step selection functions (SSF) to better understand animal behavior and habitat use. This approach accurately models zebra movement and habitat preferences, revealing distinct "encamped" and "exploratory" states.

Keywords:
Animal behaviourAnimal movementHabitat selectionHidden Markov modelsStep selection functions

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Area of Science:

  • Ecology and Evolutionary Biology
  • Quantitative Biology
  • Animal Behavior

Background:

  • Traditional two-stage models for behavior-dependent habitat selection (HMM + SSF) fail to account for behavioral classification uncertainty and state-dependent habitat selection.
  • An integrated Hidden Markov Model-Step Selection Function (HMM-SSF) offers a more robust approach by jointly estimating state switching and habitat selection.

Purpose of the Study:

  • To enhance the efficiency and generality of the HMM-SSF approach for modeling animal movement and habitat selection.
  • To incorporate covariates influencing state switching in the HMM-SSF framework.
  • To demonstrate the utility of the integrated model using plains zebra movement data.

Main Methods:

  • Developed an HMM-SSF model where the SSF defines the observation process, enabling direct application of established HMM inference methods.
  • Extended the model to include covariates on HMM transition probabilities to analyze drivers of state switching.
  • Applied the integrated model to plains zebra data for state estimation and utilization distribution analysis.

Main Results:

  • Identified two distinct behavioral states in plains zebra: 'encamped' and 'exploratory', with differing movement and habitat selection patterns.
  • Zebras exhibited stronger selection for grasslands in the 'exploratory' state compared to the 'encamped' state.
  • A diel cycle was observed, with zebras more active and exploratory in the morning and encamped in the evening.

Conclusions:

  • The integrated HMM-SSF model provides a versatile framework for analyzing behavior-specific habitat selection across diverse species and ecosystems.
  • The model allows for joint inference of animal behavior, habitat selection, and space use, leveraging existing statistical tools for HMMs and SSFs.