Advanced statistical methods for uncovering complex latent processes in animal movement

Telemetry data offer unprecedented opportunities to study wildlife behaviour, but extracting ecological insights from these complex processes requires advanced statistical methods. Using narwhal (Monodon monoceros) movement data as a case study, my PhD develops novel statistical methods to address three key challenges in animal movement analysis. First, while hidden Markov models (HMMs) provide a natural and powerful framework for inferring latent behavioural states from movement data, selecting the number of hidden states in such models is a notoriously difficult task. Common information criteria perform poorly in selecting the number of states under model misspecifications. I build upon a double penalized maximum likelihood estimator (DPMLE) for simultaneous estimation of the number of states and parameters of non-stationary HMMs. Through simulations and the narwhal case study, I show that the DPMLE outperforms traditional methods under misspecifications and enables more realistic modelling of movement data. Second, as human activities expand across wildlife habitats, quantifying behavioural responses to disturbances is crucial for conservation. I introduce a lasso-penalized threshold HMM that jointly estimates the distance at which animals react to a stimulus and ensures this distance threshold corresponds to a meaningful behavioural shift. Results suggest that narwhal react to vessels up to 4 km away, reducing movement persistence and spending more time in deeper waters. To my knowledge, this is the first model-based estimate of a disturbance threshold in movement ecology. Third, understanding habitat selection requires methods robust to location error inherent in animal tracking data. I extend the Langevin diffusion habitat selection model to accommodate error-prone observations, using automatic differentiation and the Laplace approximation for efficient maximum-likelihood estimation, providing the first Template Model Builder (TMB) implementation capable of handling covariates depending on latent variables. Simulations indicate that the proposed method performs better than conventional two-step procedures, which tend to produce estimates biased towards zero. Application to narwhal data reveals a stronger selection signal towards deeper water under my approach. Together, the methods developed in my dissertation advance the statistical toolkit for movement ecology and beyond, as the frameworks developed here are broadly applicable to time series analysis across a wide range of fields.

To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.