| Summary: | Master's Project (M.S.) University of Alaska Fairbanks, 2023 In movement ecology, one frequently encounters situations in which a test statistic is easy to define but its distribution is difficult or impossible to compute in closed form. As such, it is of interest to find methods for simulating data which can be used to approximate the null distribution of such test statistics. In this paper, we describe a motivating scenario for simulating data involving Canadian lynx collared by researchers in the Alaskan arctic. We initially use a hidden Markov model (HMM) to model the behavioral patterns of these animals, and use kernel density estimation to describe their usage distributions. We then describe a novel method for simulating animal tracks based on these telemetry data, which closely preserves the HMM and kernel density estimate (KDE) while removing any causal dependency between them. Finally, we apply this method to identify relationships between an individual’s behavioral state and location within its home range.
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