Implications of surveyor accuracy in bird flight height estimation for wind farm collision risk assessment

Abstract The increasing anthropogenic use and modification of the airspace, particularly through the expansion of wind energy, has led to an increase in collision‐related mortality of aerial wildlife. Environmental impact assessments rely on accurate bird flight height estimates to predict collision risk, yet these estimates are typically made by surveyors visually estimating flight height, with limited validation of their accuracy. This study evaluates the accuracy of surveyor flight height estimates against measurements from an unmanned aerial vehicle (UAV) and investigates whether accuracy can be improved by the use of annotated maps (spatial cues) and/or prior survey experience. Results indicate that there was generally a discrepancy between surveyor flight height estimates and the UAV measurements, particularly for inexperienced surveyors. Annotated maps reduced variability in estimates and improved accuracy, with the greatest benefit observed for inexperienced surveyors. There was a discrepancy between the number of model predicted collisions based on surveyor estimates and UAV‐based predictions, but this was reduced with the use of annotated maps and prior experience. Discrepancies in flight height estimation and subsequent discrepancies in collision predictions resulted in underestimating and overestimating mortality and translated into differences in impact assessment classifications, which could influence wind farm planning and mitigation decisions. Practical implication . Overall, our results suggest that while annotated maps are beneficial, particularly for inexperienced surveyors, they are not a substitute for experience. Standardising survey methods through tools like annotated maps can improve data reliability, particularly for less experienced surveyors, ultimately strengthening impact assessments and wind farm management strategies. Beyond the value of spatial cues and surveyor experience, our work underlines the importance of quantifying sources of error in data collection for environmental impact assessments and consequences for subsequent management decisions.