A Markov Model for Planning and Permitting Offshore Wind Energy: A Case Study of Radio-Tracked Terns in the Gulf of Maine, USA

Journal Article

Title: A Markov Model for Planning and Permitting Offshore Wind Energy: A Case Study of Radio-Tracked Terns in the Gulf of Maine, USA
Publication Date:
May 15, 2017
Journal: Journal of Environmental Management
Volume: 193
Pages: 400-409
Publisher: Elsevier
Receptor:

Document Access

Website: External Link

Citation

Cranmer, A.; Smetzer, J.; Welch, L.; Baker, E. (2017). A Markov Model for Planning and Permitting Offshore Wind Energy: A Case Study of Radio-Tracked Terns in the Gulf of Maine, USA. Journal of Environmental Management, 193, 400-409.
Abstract: 

Quantifying and managing the potential adverse wildlife impacts of offshore wind energy is critical for developing offshore wind energy in a sustainable and timely manner, but poses a significant challenge, particularly for small marine birds that are difficult to monitor. We developed a discrete-time Markov model of seabird movement around a colony site parameterized by automated radio telemetry data from common terns (Sterna hirundo) and Arctic terns (S. paradisaea), and derived impact functions that estimate the probability of collision fatality as a function of the distance and bearing of wind turbines from a colony. Our purpose was to develop and demonstrate a new, flexible tool that can be used for specific management and wind-energy planning applications when adequate data are available, rather than inform wind-energy development at this site. We demonstrate how the tool can be used 1) in marine spatial planning exercises to quantitatively identify setback distances under development scenarios given a risk threshold, 2) to examine the ecological and technical trade-offs of development alternatives to facilitate negotiation between objectives, and 3) in the U.S. National Environmental Policy Act (NEPA) process to estimate collision fatality under alternative scenarios. We discuss model limitations and data needs, and highlight opportunities for future model extension and development. We present a highly flexible tool for wind energy planning that can be easily extended to other central place foragers and data sources, and can be updated and improved as new monitoring data arises.

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