Premium
A pathway for citizen science data to inform policy: A case study using eBird data for defining low‐risk collision areas for wind energy development
Author(s) -
RuizGutierrez Viviana,
Bjerre Emily R.,
Otto Mark C.,
Zimmerman Guthrie S.,
Millsap Brian A.,
Fink Daniel,
Stuber Erica F.,
StrimasMackey Matthew,
Robinson Orin J.
Publication year - 2021
Publication title -
journal of applied ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.503
H-Index - 181
eISSN - 1365-2664
pISSN - 0021-8901
DOI - 10.1111/1365-2664.13870
Subject(s) - citizen science , scope (computer science) , agency (philosophy) , wildlife , bald eagle , collision , computer science , service (business) , process (computing) , business , data science , environmental resource management , geography , environmental science , ecology , computer security , marketing , physics , biology , philosophy , astronomy , epistemology , programming language , operating system
The research and conservation community has successfully harnessed the wealth of ecological knowledge found in unprecedented volumes of citizen science (CS) data world‐wide. However, few examples exist of the use of CS data to directly inform policy. Current examples of applications of CS data mainly stem from programs that are restricted in scope (e.g. defined protocols, restricted sampling time frame), and the potential use of unrestricted CS data to inform policy remains largely untapped. Here, we make a call for moving beyond questioning the reliability of CS data and present a case study of how the US Fish and Wildlife Service (USFWS) used information from an unrestricted CS program (eBird) to inform levels of exposure to collision risk for wind energy development. Policy implications . The USFWS made the technical recommendation to use eBird abundance estimates for the bald eagle as the only source of information to define low‐risk collision areas as part of the agency's wind energy permitting process. Our study contributes a clear pathway of how to realize the potential of unrestricted CS programs for generating the evidence base needed to inform policy decisions.