Northwest Fisheries Science Center

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Document Type: Journal Article
Center: NWFSC
Document ID: 1920
Title: Modeling climate change impacts on overwintering bald eagles
Author: C. J. Harvey, Pamela E. Moriarty, Eric P. Salathe
Publication Year: 2012
Journal: Ecology and Evolution
Volume: 2
Issue: 3
Pages: 501-514
DOI: 10.1002/ece3.204
Keywords: bald eagles,salmon,climate change,Puget Sound

Bald eagles (Haliaeetus leucocephalus) are recovering from severe population declines, and are exerting pressure on food resources in some areas. Thousands of bald eagles overwinter near Puget Sound, primarily to feed on chum salmon (Oncorhynchus keta) carcasses. We used modeling techniques to examine how anticipated climate changes will affect energetic demands of overwintering bald eagles. We applied a regional downscaling method to two global climate change models to obtain hourly temperature, precipitation, wind, and longwave radiation estimates at the mouths of three Puget Sound tributaries (the Skagit, Hamma Hamma, and Nisqually rivers) in two decades, the 1970s and the 2050s. Climate data were used to drive bald eagle bioenergetics models from December to February for each river, year, and decade. Bald eagle bioenergetics were insensitive to climate change: despite warmer winters in the 2050s, particularly near the Nisqually River, bald eagle food requirements declined only slightly (<1%). However, the warming climate caused salmon carcasses to decompose more rapidly, resulting in 11% to 14% less annual carcass biomass available to eagles in the 2050s. That estimate is likely conservative, as it does not account for decreased availability of carcasses due to anticipated increases in winter stream flow. Future climate-driven declines in winter food availability, coupled with a growing bald eagle population, may force eagles to seek alternate prey in the Puget Sound area or in more remote ecosystems.

Theme: Recovery, Rebuilding and Sustainability of Marine and Anadromous Species
Foci: Develop methods to use physiological and biological information to predict population-level processes.