Movement Models to Predict Low-Altitude Flight of Soaring Birds Using Look-Ahead Environmental Factors

Journal Article · Wed Mar 11 00:00:00 EDT 2026 · Ecology and Evolution

DOI:https://doi.org/10.1002/ece3.73059· OSTI ID:3024207

Sandhu, Rimple ^[1]; Tripp, Charles ^[1]; Quon, Eliot ^[1]; Thedin, Regis ^[1]; Lanzone, Michael ^[2]; Braham, Melissa ^[3]; Miller, Tricia ^[3]; Farmer, Christopher ^[4]; Brandes, David ^[5]; Katzner, Todd ^[6]

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1. National Laboratory of the Rockies, Golden, CO (United States)
2. Cellular Tracking Technologies
3. Conservation Science Global
4. Orsted
5. Lafayette College
6. United States Department of the Interior; West Virginia University

Advances in fine-scale movement modeling of soaring birds can aid efforts to understand and resolve the impacts of anthropogenic activities on such birds. Soaring birds often rely on underlying terrain and low-altitude updrafts to govern their flights at rotor-swept altitudes (<= 200 m above ground level), which puts them at risk of collision with wind turbines. We developed a data-driven Markov model at 1-s resolution that predicts the fine-scale flight behavior of golden eagles (Aquila chrysaetos) as a function of ecological covariates at the current location as well as those within an eagle's line of sight. We only considered ecological covariates that are readily available in real-time (ground elevation and wind conditions). Latent factors (age, sex, species, behavioral intent, migratory status) were intentionally left out of the model. We calibrated the model using golden eagle telemetry data collected in two different ecoregions of the United States. Given a starting location, the calibrated model simulates multiple stochastic 3D paths to produce a time-explicit and spatially explicit risk map of turbine collisions. We discovered an empirical relation between the rate of change of heading and the orographic updraft conditions within an eagle's line of sight. Our model performed most effectively when predicting predominantly-soaring flights at rotor-swept altitudes during wind conditions in which turbines are likely to be operational. The calibrated model could be used in concert with automated eagle detection and turbine curtailment technologies. Specifically, once an eagle is detected by those systems, our model could then provide accurate predictions of turbines the eagle is likely to interact with in the near term.

• Research Organization: National Laboratory of the Rockies (NLR), Golden, CO (United States)
• Sponsoring Organization: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Renewable Power Office. Wind Energy Technologies Office
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 3024207
• Report Number(s): NLR/JA-2C00-87106
• Journal Information: Ecology and Evolution, Journal Name: Ecology and Evolution Journal Issue: 3 Vol. 16
• Country of Publication: United States
• Language: English

Related Subjects

17 WIND ENERGY
97 MATHEMATICS AND COMPUTING
data-driven movement model
golden eagles
raptor conservation
telemetry data
turbine curtailment
wind-wildlife interactions