Incorporating periodic variability in hidden Markov models for animal movement.
BACKGROUND
Clustering time-series data into discrete groups can improve prediction and provide insight into the nature of underlying, unobservable states of the system. However, temporal variation in probabilities of group occupancy, or the rates at which individuals move between groups, can obscure such signals. We use finite mixture and hidden Markov models (HMMs), two standard clustering techniques, to model long-term hourly movement data from Florida panthers (Puma concolor coryi). Allowing for temporal heterogeneity in transition probabilities, a straightforward but little-used extension of the standard HMM framework, resolves some shortcomings of current models and clarifies the movement patterns of panthers.


RESULTS
Simulations and analyses of panther data showed that model misspecification (omitting important sources of variation) can lead to overfitting/overestimating the underlying number of movement states. Models incorporating temporal heterogeneity identify fewer underlying states, and can make out-of-sample predictions that capture observed diurnal and autocorrelated movement patterns exhibited by Florida panthers.


CONCLUSIONS
Incorporating temporal heterogeneity improved goodness of fit and predictive capability as well as reducing the selected number of movement states closer to a biologically interpretable level, although there is further room for improvement here. Our results suggest that incorporating additional structure in statistical models of movement can allow more accurate assessment of appropriate model complexity.
Department of Biology, McMaster University, 1280 Main St. West, Hamilton, L8S 4K1 Ontario Canada.
Michael
0000-0001-5150-8662
M
Li
Department of Biology, McMaster University, 1280 Main St. West, Hamilton, L8S 4K1 Ontario Canada.|Department of Mathematics and Statistics, McMaster University, 1280 Main St. West, Hamilton, L8S 4K1 Ontario Canada.
Benjamin M
BM
Bolker
Animal movement; Florida panther; Hidden Markov model; Temporal autocorrelation; Temporal heterogeneity