Abstract
DTBird is an automated detection and audio deterrent system designed to discourage birds from approaching spinning wind turbines. With Golden Eagles (Aquila chrysaetos) the focal species of interest, we evaluated DTBird's performance at two commercial facilities, one situated in a desert landscape in California, USA (Manzana site), and the other in a temperate grassland/scrub landscape on a ridgeline above the Columbia River in Washington, USA (Goodnoe Hills site). To evaluate DTBird's detection and deterrent-triggering functions, we used fixed-wing unmanned aerial vehicles (UAVs) as eagle surrogates in experimental flight trials, involving planned transect arrays that supported evaluating DTBird responses within a 240-m-radius expected maximum, hemispheric detection envelope. We quantified the probability of detection and used logistic regression to evaluate the influence of several predictors. We also built a general linear mixed-effects model (GLMM) to evaluate the influence of several environmental covariates and flight metrics on DTBird detection and deterrent-triggering response distances. The estimated probability of detection was similar at the two sites (64–66%), increased from morning through afternoon (effects of sun positioning), and was highest when the target flew at moderate distances from the turbine through the midsection of the camera viewsheds. The GLMM analysis confirmed modest variation relating to the five distinct UAV models used, possibly mimicking variation that would apply to eagles of variable size and coloration. The analysis also demonstrated that response distances averaged marginally shorter at the Manzana site and increased (suggesting improved detectability) under uniform cloud cover and as the UAV travel rate and exposure of the UAV profile to the cameras increased. Our experience also emphasized that effective use of foam-bodied, fixed-wing UAVs as eagle surrogates at wind facilities can be strongly limited by complicated topography that restricts centralized flight operations; intervening obstacles such as overhead powerlines that restrict automated flight missions; and excessive wind, inclement weather, and rough landing conditions around many turbines that can easily lead to UAV crashes and damage. Using eagle-like UAVs with immobile wings as eagle surrogates also might have constrained the insights generated from the study. Nevertheless, the indicated relationships can help future system users understand the environmental conditions in which DTBird and similar automated systems are likely to perform best and other factors that can substantially influence the targeting accuracy of such systems.