"tracking" » "lacking"
Publication Date: 2017.
Collisions of large soaring raptors with wind turbines and other infrastructures represent a growing conservation concern. We describe a way to leverage knowledge about raptor soaring behaviour to forecast the probability that raptors fly in the rotor-swept zone. Soaring raptors are theoretically expected to select energy sources (uplift) optimally, making their flight height dependent on uplift conditions. This approach can be used to forecast collision hazard when planning or operating wind farms. Empirical investigations of the factors influencing flight height have, however, so far been hindered by observation error. We propose a two-pronged approach. First, we fitted state-space models to z-axis GPS tracking data to filter heavy-tailed observation error and estimate the relationship between vertical movement parameters and weather variables describing the energy landscape (thermal and orographic uplift potential). Second, we fitted a mechanistic model of flight height above ground based on aerodynamics and resource selection theories. The approach was replicated for five GPS-tracked Andean condors Vultur gryphus, eight griffon vultures Gyps fulvus, and six golden eagles Aquila chrysaetos. In all individuals, movement parameters correlated with thermal uplift potential in the expected direction. In all species, collision hazard was lowest for high thermal uplift potential values. Species specificities in the presence of a peak in collision hazard for medium values of thermal uplift potential could be explained by differences in wing loading and aspect ratio. Synthesis and applications. Our fitted models convert weather data (thermal uplift potential) into a prediction of collision hazard (probability to fly in the rotor-swept zone), making it possible to prioritize different wind development projects with respect to the relative hazard they would pose to raptors. However, our model should be combined with post-construction monitoring to document, and eventually account for turbine avoidance behaviours in collision rate predictions.
Author affiliation: Péron, Guillaume. Smithsonian Conservation Biology Institute; Estados Unidos
Author affiliation: Fleming, Christen H.. Smithsonian Conservation Biology Institute; Estados Unidos
Author affiliation: Duriez, Olivier. National Research Institute Of Science And Technology-centre de Montpellier; Francia
Author affiliation: Fluhr, Julie. National Research Institute Of Science And Technology-centre de Montpellier; Francia
Author affiliation: Itty, Christian. Université Montpellier II; Francia
Author affiliation: Lambertucci, Sergio Agustin. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Patagonia Norte. Instituto de Investigaciones en Biodiversidad y Medioambiente. Universidad Nacional del Comahue. Centro Regional Universidad Bariloche. Instituto de Investigaciones en Biodiversidad y Medioambiente; Argentina
Author affiliation: Safi, Kamran. Institut Max Planck for Evolutionary Anthropology; Alemania
Author affiliation: Shepard, Emily L. C.. Swansea University; Reino Unido
Author affiliation: Calabrese, Justin. University of Maryland; Estados Unidos
Repository: CONICET Digital (CONICET). Consejo Nacional de Investigaciones Científicas y Técnicas