An Integrated Time-Varying Ornstein-Uhlenbeck Process for Jointly Modeling Individual and Population-Level Movement of Golden Eagles

TL;DR

This paper introduces a novel stochastic differential equation model that jointly analyzes individual and population-level movement data of migratory species over a full year, enhancing inference and prediction capabilities.

Contribution

It presents the first integrated time-varying Ornstein-Uhlenbeck process model for full-year animal movement, combining individual telemetry and species distribution data.

Findings

Efficiently models spatio-temporal dynamics of bird populations.

Improves prediction of species distribution from limited data.

Assists in assessing wind project risks for golden eagles.

Abstract

With technological advancements, the quantity and quality of animal movement data have increased greatly. Currently, no movement model can be used to describe full-year data from migratory species by leveraging both individual movement and species distribution data. Herein we propose a full-year stochastic differential equation model for jointly modeling both individual movement and species distribution data. We show that this joint model, under certain assumptions, results in efficient computation of the spatio-temporal dynamics of the entire population, and thus provides straightforward inference on the species distribution data. We illustrate this model by analyzing 215 bird-years of golden eagle movement in western North America jointly with relative abundance data from eBird. We use the results to estimate wind project risk for these eagles and predict where they came from earlier in the year based on a single telemetry observation from later in the year. Our joint model enables additional inference and greater predictive power than afforded by sole use of eBird relative abundance.