Ranking of simultaneously presented choice options in animal preference experiments

Area of Science:

• Ethology
• Statistics
• Animal Behavior

Background:

• Traditional non-parametric methods in ethology often fail to adequately estimate preference rankings.
• Existing approaches may not fully capture the complexities of repeated measurements in animal choice experiments.

Purpose of the Study:

• To develop and illustrate a statistical model for estimating animal preferences and their rankings from choice experiments.
• To incorporate the structure of repeated measurements using animal-specific random intercepts.

Main Methods:

• Utilized a multinomial logistic model to estimate choice probabilities.
• Incorporated animal-specific random intercepts to account for correlations from repeated measurements.
• Employed a Bayesian approach with Markov chain Monte Carlo (MCMC) sampling for posterior distribution analysis.

Main Results:

• The proposed model successfully estimated preference rankings for animal choices.
• The method effectively handled repeated measurements and provided estimates of ranking variability.
• Illustrated with a pig preference experiment for rooting materials.

Conclusions:

• The multinomial logistic model with random intercepts offers a powerful and flexible approach for analyzing animal preference data.
• This Bayesian method provides a robust framework for estimating and understanding animal preference rankings and their associated uncertainty.