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Bird and bat species' global vulnerability to collision mortality at wind farms revealed through a trait-based assessment.

Chris B ThaxterGraeme M BuchananJamie CarrStuart H M ButchartTim NewboldRhys E GreenJoseph Andrew TobiasWendy B FodenSue O'BrienJames W Pearce-Higgins
Published in: Proceedings. Biological sciences (2018)
Mitigation of anthropogenic climate change involves deployments of renewable energy worldwide, including wind farms, which can pose a significant collision risk to volant animals. Most studies into the collision risk between species and wind turbines, however, have taken place in industrialized countries. Potential effects for many locations and species therefore remain unclear. To redress this gap, we conducted a systematic literature review of recorded collisions between birds and bats and wind turbines within developed countries. We related collision rate to species-level traits and turbine characteristics to quantify the potential vulnerability of 9538 bird and 888 bat species globally. Avian collision rate was affected by migratory strategy, dispersal distance and habitat associations, and bat collision rates were influenced by dispersal distance. For birds and bats, larger turbine capacity (megawatts) increased collision rates; however, deploying a smaller number of large turbines with greater energy output reduced total collision risk per unit energy output, although bat mortality increased again with the largest turbines. Areas with high concentrations of vulnerable species were also identified, including migration corridors. Our results can therefore guide wind farm design and location to reduce the risk of large-scale animal mortality. This is the first quantitative global assessment of the relative collision vulnerability of species groups with wind turbines, providing valuable guidance for minimizing potentially serious negative impacts on biodiversity.
Keyphrases
  • climate change
  • cardiovascular events
  • genetic diversity
  • human health
  • risk factors
  • risk assessment
  • type diabetes
  • genome wide
  • mass spectrometry
  • coronary artery disease