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A Bayesian Markov Model with Pólya-Gamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications

Author

Listed:
  • Toryn L. J. Schafer

    (University of Missouri)

  • Christopher K. Wikle

    (University of Missouri)

  • Jay A. VonBank

    (Texas A&M University-Kingsville)

  • Bart M. Ballard

    (Texas A&M University-Kingsville)

  • Mitch D. Weegman

    (University of Missouri)

Abstract

The use of accelerometers in wildlife tracking provides a fine-scale data source for understanding animal behavior and decision making. Current methods in movement ecology focus on behavior as a driver of movement mechanisms. Our Markov model is a flexible and efficient method for inference related to effects on behavior that considers dependence between current and past behaviors. We applied this model to behavior data from six greater white-fronted geese (Anser albifrons frontalis) during spring migration in mid-continent North America and considered likely drivers of behavior, including habitat, weather and time of day effects. We modeled the transitions between flying, feeding, stationary and walking behavior states using a first-order Bayesian Markov model. We introduced Pólya-Gamma latent variables for automatic sampling of the covariate coefficients from the posterior distribution, and we calculated the odds ratios from the posterior samples. Our model provides a unifying framework for including both acceleration and Global Positioning System data. We found significant differences in behavioral transition rates among habitat types, diurnal behavior and behavioral changes due to weather. Our model provides straightforward inference of behavioral time allocation across used habitats, which is not amenable in activity budget or resource selection frameworks. Supplementary materials accompanying this paper appear online.

Suggested Citation

  • Toryn L. J. Schafer & Christopher K. Wikle & Jay A. VonBank & Bart M. Ballard & Mitch D. Weegman, 2020. "A Bayesian Markov Model with Pólya-Gamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 25(3), pages 365-382, September.
    • Handle: RePEc:spr:jagbes:v:25:y:2020:i:3:d:10.1007_s13253-020-00399-y
    DOI: 10.1007/s13253-020-00399-y
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    References listed on IDEAS

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    1. Nicholas G. Polson & James G. Scott & Jesse Windle, 2013. "Bayesian Inference for Logistic Models Using Pólya--Gamma Latent Variables," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(504), pages 1339-1349, December.
    2. Toby A. Patterson & Alison Parton & Roland Langrock & Paul G. Blackwell & Len Thomas & Ruth King, 2017. "Statistical modelling of individual animal movement: an overview of key methods and a discussion of practical challenges," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 101(4), pages 399-438, October.
    3. Femke Broekhuis & Steffen Grünewälder & John W. McNutt & David W. Macdonald, 2014. "Optimal hunting conditions drive circalunar behavior of a diurnal carnivore," Behavioral Ecology, International Society for Behavioral Ecology, vol. 25(5), pages 1268-1275.
    4. Henry Scharf & Mevin B. Hooten & Devin S. Johnson, 2017. "Imputation Approaches for Animal Movement Modeling," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 335-352, September.
    5. Brett T. McClintock, 2017. "Incorporating Telemetry Error into Hidden Markov Models of Animal Movement Using Multiple Imputation," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 22(3), pages 249-269, September.
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