2026-03-06 ワシントン大学（UW）
＜関連情報＞
- https://www.washington.edu/news/2026/03/06/marine-energy-turbines-harris-hydraulics-uw-pnnl/
- https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0338376&utm_source=pr&utm_medium=email&utm_campaign=plos006
小型潮力タービンと海洋動物の相互作用の観察 Observations of marine animal interactions with a small tidal turbine
Emma Cotter,Christopher Bassett,Paul Murphy,Mitchell Scott,Alexa Runyan,Jood M. Almokharrak,Lucy G. Kao,Lillian M. Ovall,Suni A. McMillen
PLOS One Published: January 14, 2026
DOI:https://doi.org/10.1371/journal.pone.0338376
Abstract
The risk of collisions between animals and operating tidal turbines remains a concern in the scientific and regulatory communities. A sensor package including optical cameras was deployed to monitor animal interactions with a small-scale (1 m2) cross-flow tidal turbine. The turbine was deployed in Washington State, USA for 141 days at a site with peak flow speeds of 2.5 m/s. We analyze optical camera imagery spanning 109 days of turbine operation. The analyzed images contain 1044 observations of fish, fish schools, seabirds, or seals in the vicinity of the turbine. No instances of collision with seabirds or seals were observed. Seabirds were only observed during daylight hours and while the turbine was stationary. Both seals and fish were observed during both day and night and while the turbine was stationary and rotating. Four fish were observed colliding with the moving turbine and in all but one case the animals swam away following the collision. Over the same period of time, over fifty times more fish (224 individual fish and 5 fish schools) were observed passing the moving turbine without collision. Fish encounters were likely under counted due to the difficulty in discerning small fish from plant matter in the water column. These observations represent the first optical camera imagery showing fish, bird, and marine mammal interactions with a tidal turbine in North America. In addition to quantitative and qualitative discussion of the implications of our observations for collision risk, we discuss lessons learned on sampling schemes and deployment of machine learning for detection of animals to inform future data collection strategies in future monitoring campaigns.
