• Emma Liu Kent School, Connecticut, United States of America




Understanding animal movement is pivotal in addressing population dynamics. Bayesian statistical techniques have been concentrated in literature to study intricate animal movement, by adapting their analytically manageable likelihoods. With the utilization of Hidden Markov Models (HMMs), the study examines animal tracking data of one elk and highlights step lengths and turning angles across two states. Data is obtained from the work of Morales et al. (2004), titled "Extracting more out of relocation data: building movement models as mixtures of random walks." Collected using tracking systems, the data indicates elk position (longitude and latitude), and the animal’s proximity to water sources along its movement paths. To effectively analyze step length and turning angles on HMMs, Gamma and Von Mises distributions and employed respectively. Results indicate a difference in step length between states 1 and 2, with longer steps observed in state 2 than in state 1. In turning angles, state 1 showcases a uniform distribution, signifying undirected movement in comparison to State 2 which showcases directed movement. The study concludes that movement in state 1 is indicative of foraging, while state 2 signifies traveling between habitat patches and wandering movements, and that the elk grazes closer to water and forages away from water.


Blackwell, P. G. 2003. Bayesian inference for Markov processes with diffusion and discrete components. Biometrika 90:613–627.

Johnson, D. S., J. M. London, M.-A. Lea, and J. W. Durban. 2008. Continuous-time correlated random walk model for animal telemetry data. Ecology 89:1208–1215.

Jonsen, I. D., J. M. Flemming, and R. A. Myers. 2005. Robust state-space modeling of animal movement data. Ecology 86:2874–2880.

Langrock, R., King, R., Matthiopoulos, J., Thomas, L., Fortin, D., and Morales, J. M. (2012). Flexible and practical modeling of animal telemetry data: hidden Markov models and extensions. Ecology, 93(11):2336– 2342.

McClintock, B. T., R. King, L. Thomas, J. Matthiopoulos, B. J.

McConnell, and J. M. Morales. 2012. A general discrete-time modeling framework for animal movement using multi-state random walks. Ecological Monographs.

Michelot, T., Langrock, R., and Patterson, T. A. (2016). moveHMM: an R package for the statistical modelling of animal movement data using hidden Markov models. Methods in Ecology and Evolution, 7(11):1308–1315.

Morales, J. M., Haydon, D. T., Frair, J., Holsinger, K. E., and Fryxell, J. M. (2004). Extracting more out of relocation data: building movement models as mixtures of random walks. Ecology, 85(9):2436–2445.




How to Cite

Liu, E. (2024). EXPLORING ANIMAL MOVEMENT BEHAVIOR WITH SWITCHING STATE SPACE MODELS. LIFE: International Journal of Health and Life-Sciences, 04–15. https://doi.org/10.20319/icrlsh.2024.0415