Bayesian State-Space Modeling Framework for Understanding and Predicting Golden Eagle Movements Using Telemetry Data

Rimple Sandhu, Charles Tripp, David Brandes, Regis Thedin, Eliot Quon, Lindy Williams, Michael Lawson, Todd Katzner, Chris Farmer, Tricia Miller, Adam Duerr, Missy Braham

Research output: NRELPresentation


Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.
Original languageAmerican English
Number of pages14
StatePublished - 2021

Publication series

NamePresented at the Raptor Research Foundation 2021 Annual Conference, 8-14 October 2021

NREL Publication Number

  • NREL/PR-5000-81236


  • Bayesian modeling
  • golden eagles
  • raptor conservation
  • telemetry data
  • turbine collisions
  • wind-wildlife interaction


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