Integrating Spatially Continuous Environmental Covariates into HMMs for Animal Behavior via Kriging

The advent of high-resolution animal movement tracking technologies has significantly advanced our ability to study animal behavior in natural environments. Hidden Markov Models (HMMs) have emerged as a powerful tool for decoding behavioral states, such as exploratory versus encamped movements, based on telemetry data. However, a common challenge lies in incorporating spatially continuous environmental variables that influence animal behavior, as these covariates are often collected at different times and locations than the movement data. In this study, we present an innovative approach that integrates kriging, a spatial interpolation technique, with HMMs to account for continuous environmental covariates in animal behavior modeling. By first fitting a continuous-time correlated random walk (CTCRW) model to animal tracks, followed by spatial interpolation of environmental data, we predict the necessary covariate values at the regular time intervals required by HMMs. This methodology allows researchers to incorporate complex environmental dynamics, such as changes in temperature or habitat structure, into behavioral state analysis. The proposed framework enhances the precision of behavioral predictions, providing a more nuanced understanding of how animals interact with their environment across both space and time.