Hierarchical animal movement models for population-level inference

TL;DR

This paper introduces a two-stage hierarchical Bayesian approach for population-level animal movement modeling that simplifies fitting complex models to telemetry data, demonstrated on lynx data.

Contribution

It presents a statistically rigorous, automated two-stage fitting procedure for hierarchical animal movement models, improving scalability and ease of use.

Findings

Effective modeling of telemetry data using the two-stage approach

Successful application to simulated and real lynx data

Enhanced computational efficiency and automation

Abstract

New methods for modeling animal movement based on telemetry data are developed regularly. With advances in telemetry capabilities, animal movement models are becoming increasingly sophisticated. Despite a need for population-level inference, animal movement models are still predominantly developed for individual-level inference. Most efforts to upscale the inference to the population-level are either post hoc or complicated enough that only the developer can implement the model. Hierarchical Bayesian models provide an ideal platform for the development of population-level animal movement models but can be challenging to fit due to computational limitations or extensive tuning required. We propose a two-stage procedure for fitting hierarchical animal movement models to telemetry data. The two-stage approach is statistically rigorous and allows one to fit individual-level movement models separately, then resample them using a secondary MCMC algorithm. The primary advantages of the two-stage approach are that the first stage is easily parallelizable and the second stage is completely unsupervised, allowing for a completely automated fitting procedure in many cases. We demonstrate the two-stage procedure with two applications of animal movement models. The first application involves a spatial point process approach to modeling telemetry data and the second involves a more complicated continuous-time discrete-space animal movement model. We fit these models to simulated data and real telemetry data arising from a population of monitored Canada lynx in Colorado, USA.