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Published on: August 27, 2019
Seasonal patterns of bird and bat collision fatalities at wind turbines
John D Lloyd1, Ryan Butryn1, Schuyler Pearman-Gillman1,2
1Renewable Energy Wildlife Institute, Washington, DC, United States of America.
Abstract:
Information on when birds and bats die from collisions with wind turbines can help refine efforts to minimize fatalities via curtailment of energy productions and can offer insight into the risk factors associated with collision fatalities. Using data pooled from 114 post-construction monitoring studies conducted at wind facilities across the United States, we described seasonal patterns of fatalities among birds and bats. Bat fatalities peaked in the fall. Silver-haired bat (Lasionycteris noctivagans), a long-distance migrant, and Mexican free-tailed bat (Tadarida brasiliensis) both showed maximum fatality counts later in the year-October and November, respectively-than any other bat species. The other common species in our sample-hoary bat (Aeorestes cinereus), Eastern red bat (Lasiurus borealis), and big brown bat (Eptesicus fuscus)-showed broadly overlapping peaks of fatality counts in August. Fatalities of silver-haired bat showed a smaller spring peak in some ecoregions; no other bat species exhibited this pattern. Seasonal patterns of bird fatalities varied among guilds. Woodland birds, many of which were long-distance migrants, showed two peaks in fatalities corresponding to spring and fall migration. Grassland birds and soaring birds, most of which were resident or short-distance migrants, did not exhibit strong seasonal peaks in fatalities. Species in these guilds tend to inhabit regions with extensive wind-energy development year-round, which may explain the more consistent numbers of fatalities that we observed. Our results highlight the value of pooling data to develop science-based solutions to reduce conflicts between wind-energy development and wildlife but also emphasize the need for more extensive data and standardization of post-construction monitoring to support more robust inferences regarding wind-wildlife interactions and collision risk.
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