A two-stage information-theoretic approach to modeling landscape-level attributes and maximum recruitment of chinook salmon in the Columbia River basin [electronic resource]

Abstract

Many anadromous salmonid stocks in the Pacific Northwest are at their lowest recorded levels, which has raised questions regarding their long-term persistence under current conditions. There are a number of factors, such as freshwater spawning and rearing habitat, that could potentially influence their numbers. Therefore, we used the latest advances in information-theoretic methods in a two-stage modeling process to investigate relationships between landscape-level habitat attributes and maximum recruitment of 25 index stocks of chinook salmon (Oncorhynchus tshawytscha) in the Columbia River basin. Our first-stage model selection results indicated that the Ricker-type, stock recruitment model with a constant Ricker a (i.e., recruits-per-spawner at low numbers of fish) across stocks was the only plausible one given these data, which contrasted with previous unpublished findings. Our second-stage results revealed that maximum recruitment of chinook salmon had a strongly negative relationship with percentage of surrounding subwatersheds categorized as predominantly containing U.S. Forest Service and private moderate-high impact managed forest. That is, our model predicted that average maximum recruitment of chinook salmon would decrease by at least 247 fish for every increase of 33% in surrounding subwatersheds categorized as predominantly containing U.S. Forest Service and privately managed forest. Conversely, mean annual air temperature had a positive relationship with salmon maximum recruitment, with an average increase of at least 179 fish for every increase in 2ºC mean annual air temperature.

Citation

Thompson, William L.; Lee, Danny C. 2000. A two-stage information-theoretic approach to modeling landscape-level attributes and maximum recruitment of chinook salmon in the Columbia River basin [electronic resource]. Portland, OR: U.S. Department of Energy, Bonneville Power Administration. BPA Project Number 92-032-00, Contract Number 92AI25866. 48 p