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Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales

Overview of attention for article published in PLOS ONE, August 2015
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Title
Landscapes for Energy and Wildlife: Conservation Prioritization for Golden Eagles across Large Spatial Scales
Published in
PLOS ONE, August 2015
DOI 10.1371/journal.pone.0134781
Pubmed ID
Authors

Jason D. Tack, Bradley C. Fedy

Abstract

Proactive conservation planning for species requires the identification of important spatial attributes across ecologically relevant scales in a model-based framework. However, it is often difficult to develop predictive models, as the explanatory data required for model development across regional management scales is rarely available. Golden eagles are a large-ranging predator of conservation concern in the United States that may be negatively affected by wind energy development. Thus, identifying landscapes least likely to pose conflict between eagles and wind development via shared space prior to development will be critical for conserving populations in the face of imposing development. We used publically available data on golden eagle nests to generate predictive models of golden eagle nesting sites in Wyoming, USA, using a suite of environmental and anthropogenic variables. By overlaying predictive models of golden eagle nesting habitat with wind energy resource maps, we highlight areas of potential conflict among eagle nesting habitat and wind development. However, our results suggest that wind potential and the relative probability of golden eagle nesting are not necessarily spatially correlated. Indeed, the majority of our sample frame includes areas with disparate predictions between suitable nesting habitat and potential for developing wind energy resources. Map predictions cannot replace on-the-ground monitoring for potential risk of wind turbines on wildlife populations, though they provide industry and managers a useful framework to first assess potential development.

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Geographical breakdown

Country Count As %
Portugal 1 1%
Unknown 85 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 25 29%
Student > Master 14 16%
Student > Ph. D. Student 11 13%
Other 7 8%
Student > Bachelor 5 6%
Other 13 15%
Unknown 11 13%
Readers by discipline Count As %
Agricultural and Biological Sciences 42 49%
Environmental Science 22 26%
Unspecified 1 1%
Chemical Engineering 1 1%
Computer Science 1 1%
Other 3 3%
Unknown 16 19%