Running on empty: Recharge dynamics from animal movement data

TL;DR

This paper introduces a new modeling framework that links animal movement data with physiological recharge dynamics, enabling direct inference of energetic processes influencing movement in heterogeneous environments.

Contribution

It presents a flexible, mechanistic modeling approach that explicitly incorporates physiological processes into telemetry data analysis, extending current methods to infer recharge dynamics.

Findings

Successfully inferred recharge dynamics for mountain lions and African buffalo.

Demonstrated the model's ability to connect movement patterns with physiological states.

Extended the framework to incorporate auxiliary data when available.

Abstract

Vital rates such as survival and recruitment have always been important in the study of population and community ecology. At the individual level, physiological processes such as energetics are critical in understanding biomechanics and movement ecology and also scale up to influence food webs and trophic cascades. Although vital rates and population-level characteristics are tied with individual-level animal movement, most statistical models for telemetry data are not equipped to provide inference about these relationships because they lack the explicit, mechanistic connection to physiological dynamics. We present a framework for modeling telemetry data that explicitly includes an aggregated physiological process associated with decision making and movement in heterogeneous environments. Our framework accommodates a wide range of movement and physiological process specifications. We illustrate a specific model formulation in continuous-time to provide direct inference about gains and losses associated with physiological processes based on movement. Our approach can also be extended to accommodate auxiliary data when available. We demonstrate our model to infer mountain lion (in Colorado, USA) and African buffalo (in Kruger National Park, South Africa) recharge dynamics.