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A Collision Risk Model to Predict Avian Fatalities at Wind Facilities: An Example Using Golden Eagles, Aquila chrysaetos

Overview of attention for article published in PLOS ONE, July 2015
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (94th percentile)

Mentioned by

news
1 news outlet
blogs
3 blogs
twitter
11 tweeters
facebook
3 Facebook pages

Citations

dimensions_citation
30 Dimensions

Readers on

mendeley
145 Mendeley
citeulike
1 CiteULike
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Title
A Collision Risk Model to Predict Avian Fatalities at Wind Facilities: An Example Using Golden Eagles, Aquila chrysaetos
Published in
PLOS ONE, July 2015
DOI 10.1371/journal.pone.0130978
Pubmed ID
Authors

Leslie New, Emily Bjerre, Brian Millsap, Mark C. Otto, Michael C. Runge

Abstract

Wind power is a major candidate in the search for clean, renewable energy. Beyond the technical and economic challenges of wind energy development are environmental issues that may restrict its growth. Avian fatalities due to collisions with rotating turbine blades are a leading concern and there is considerable uncertainty surrounding avian collision risk at wind facilities. This uncertainty is not reflected in many models currently used to predict the avian fatalities that would result from proposed wind developments. We introduce a method to predict fatalities at wind facilities, based on pre-construction monitoring. Our method can directly incorporate uncertainty into the estimates of avian fatalities and can be updated if information on the true number of fatalities becomes available from post-construction carcass monitoring. Our model considers only three parameters: hazardous footprint, bird exposure to turbines and collision probability. By using a Bayesian analytical framework we account for uncertainties in these values, which are then reflected in our predictions and can be reduced through subsequent data collection. The simplicity of our approach makes it accessible to ecologists concerned with the impact of wind development, as well as to managers, policy makers and industry interested in its implementation in real-world decision contexts. We demonstrate the utility of our method by predicting golden eagle (Aquila chrysaetos) fatalities at a wind installation in the United States. Using pre-construction data, we predicted 7.48 eagle fatalities year-1 (95% CI: (1.1, 19.81)). The U.S. Fish and Wildlife Service uses the 80th quantile (11.0 eagle fatalities year-1) in their permitting process to ensure there is only a 20% chance a wind facility exceeds the authorized fatalities. Once data were available from two-years of post-construction monitoring, we updated the fatality estimate to 4.8 eagle fatalities year-1 (95% CI: (1.76, 9.4); 80th quantile, 6.3). In this case, the increased precision in the fatality prediction lowered the level of authorized take, and thus lowered the required amount of compensatory mitigation.

Twitter Demographics

The data shown below were collected from the profiles of 11 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 145 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 1%
Switzerland 1 <1%
Germany 1 <1%
Chile 1 <1%
Portugal 1 <1%
Unknown 139 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 44 30%
Other 22 15%
Student > Ph. D. Student 20 14%
Student > Master 19 13%
Student > Doctoral Student 8 6%
Other 12 8%
Unknown 20 14%
Readers by discipline Count As %
Agricultural and Biological Sciences 63 43%
Environmental Science 42 29%
Engineering 3 2%
Medicine and Dentistry 2 1%
Energy 2 1%
Other 9 6%
Unknown 24 17%

Attention Score in Context

This research output has an Altmetric Attention Score of 39. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 July 2015.
All research outputs
#595,248
of 16,371,776 outputs
Outputs from PLOS ONE
#9,375
of 160,023 outputs
Outputs of similar age
#9,966
of 234,907 outputs
Outputs of similar age from PLOS ONE
#334
of 6,367 outputs
Altmetric has tracked 16,371,776 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 160,023 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.2. This one has done particularly well, scoring higher than 94% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 234,907 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 6,367 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.