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

## TL;DR

This paper introduces a model that predicts the flight paths of golden eagles near wind turbines by considering terrain and wind conditions ahead of them, improving collision risk prediction.

## Contribution

The model uses real-time environmental factors ahead of the bird's path to predict flight behavior, improving accuracy by 3× compared to existing methods.

## Key findings

- The model accurately predicts eagle flight paths at rotor-swept altitudes using ground elevation and wind data.
- The rate of change of heading correlates with orographic updrafts in the eagle's line of sight.
- The model performs best during wind conditions when turbines are likely to be operational.

## Abstract

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.

Eagles see ahead. Now our model does too. By modeling how eagles respond to terrain and wind conditions in their line of sight, we can predict where they'll fly in the next 3 min with 3× better accuracy than current methods—protecting eagles while maximizing energy production.