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Predicting and mapping potential Whooping Crane stopover habitat to guide site selection for wind energy projects
J Amy Belaire1, Betty J Kreakie, Timothy Keitt
1Department of Biological Sciences, University of Illinois at Chicago, 845 W. Taylor St (MC 066), Chicago, IL, 60607, U.S.A.. jbelai2@uic.edu.
Identifying critical stopover habitats for migratory birds, like the endangered Whooping Crane, can minimize conflicts with renewable energy development. This approach helps guide conservation and site selection for wind power projects.
Area of Science:
- Conservation Biology
- Avian Ecology
- Renewable Energy Planning
Background:
- Migratory stopover habitats are crucial for avian survival but often overlooked in land-use planning.
- Wind energy development is expanding, potentially impacting critical migratory flyways and species.
Purpose of the Study:
- To identify and map potential stopover habitats for the endangered Whooping Crane (Grus americana).
- To integrate habitat data into the site-selection process for wind energy development, minimizing ecological conflicts.
- To inform conservation planning and incidental take permit considerations for at-risk species.
Main Methods:
- Utilized random forests modeling to predict Whooping Crane stopover habitat distribution.
- Mapped habitat suitability within a key migratory flyway in Nebraska.
- Identified areas with low potential for conflict between Whooping Crane stopover use and wind power development.
Main Results:
- Predicted that up to 54% of the study area is unsuitable as Whooping Crane stopover habitat.
- Identified low-risk areas for wind energy development, minimizing potential impacts on Whooping Cranes.
- Demonstrated the utility of predictive habitat modeling in guiding land-use decisions.
Conclusions:
- Integrating predictive habitat models into conservation and development planning is essential.
- This approach can proactively minimize conflicts between endangered species and renewable energy infrastructure.
- Field surveys are crucial for validating model predictions and understanding baseline ecological conditions before development.
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