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

Area of Science:

• Ecology
• Avian Biology
• Conservation Technology

Background:

• Soaring birds face collision risks with wind turbines due to their flight patterns at low altitudes.
• Understanding fine-scale movement is crucial for mitigating anthropogenic impacts on vulnerable bird populations.

Purpose of the Study:

• To develop a data-driven model predicting golden eagle flight behavior at high resolution.
• To create a tool for assessing and mitigating wind turbine collision risks for eagles.

Main Methods:

• A 1-second resolution Markov model was developed using golden eagle telemetry data.
• Ecological covariates like ground elevation and wind conditions were used.
• The model simulates 3D flight paths to generate collision risk maps.

Main Results:

• An empirical relationship was found between heading changes and orographic updrafts.
• The model accurately predicted soaring flights at rotor-swept altitudes under operational wind conditions.
• The model successfully identified potential turbine collision risks.

Conclusions:

• The developed model provides accurate, real-time predictions of golden eagle flight paths and turbine interaction risks.
• This tool can be integrated with automated detection and curtailment systems for enhanced conservation.
• The findings advance fine-scale movement modeling for soaring bird conservation.