New technological Innovations have allowed researchers to collect a wealth of Information as to the activity (via accelerometers) and location (GPS sensors) of animals. This, combined with advances In Bayesian methods for dependent data Is allowing us to better understand the Individual and collective behavior of animals. Our group and partners have been exploring such data with complex spatial, temporal, and spatio-temporal models. Some recent examples Include:

Schafer, T.L.J., Wikle, C.K., and M.B. Hooten, 2022: Bayesian inverse reinforcement learning for collective animal movement. Annals of Applied Statistics, 16(2): 999-1013.

Laubmeier, A.N., Cazelles, B., Cuddington, K., Erickson, K.D., Fortin, M.-J., Gaoue, O., Ogle, K., Wikle, C.K., Zhang, B., and Zipkin, E., 2020: Ecological dynamics: integrating empirical, statistical, and analytical methods. Trends in Ecology and Evolution, 35, 1090-1099, https://doi.org/10.1016/j.tree.2020.08.006.

Schafer, T.L.J., Wikle, C.K., VonBank, J., Ballard, B.M., Weegman, M.D., 2020: The utility of a Bayesian Markov model with Polya-Gamma sampling for estimating individual behavior transition probabilities from accelerometer classifications, Journal of Agricultural, Biological, and Environmental Statistics, 25, 365382.

McDermott, P.L., Wikle, C.K., and J. Millspaugh, 2017: A hierarchical spatio-temporal analog forecasting model for count data. Ecology and Evolution, 22, 294–312; DOI: 10.1002/ece3.3621.

Wikle, C.K., 2017: Ecological impacts of climate change: the importance of temporal and spatial synchrony. CHANCE, 30, 45–54.

McDermott, P.L., Wikle, C.K., and J. Millspaugh, 2017: Hierarchical nonlinear spatio-temporal agent- based models for collective animal movement. Journal of Agricultural, Biological and Ecological Statistics, doi:0.1007/s13253-017-0289-2.

Rota, C.R., Wikle, C.K., Kays, R.W., Forrester, T.D., McShea, W.J., Parsons, A.W., and Millspaugh, J.J., 2016: A two-species occupancy model accommodating simultaneous spatial and interspecific dependence. Ecology, 97, 48–53.