Pacific Salmon: Ecology and Management of Western Alaska’s Populations

Data and Modeling Tools for Assessing Landscape-Level Influences on Salmonid Populations: Examples from Western Oregon

Kelly M. Burnett, Christian E. Torgersen, E. Ashley Steel, David P. Larsen, Joseph L. Ebersole, Robert E. Gresswell, Peter W. Lawson, Daniel J. Miller, Jeffery D. Rodgers, and Don L. Stevens, Jr.


Abstract.—Most studies addressing relationships between salmonids, their freshwater habitats, and natural and anthropogenic influences have focused on relatively small areas and short time periods. The limits of knowledge gained at finer spatiotemporal scales have become obvious in attempts to cope with variable and declining abundances of salmon and trout across entire regions. Aggregating fine-scale information from disparate sources does not offer decision makers the means to solve these problems. The Salmon Research and Restoration Plan for the Arctic-Yukon-Kuskokwim Sustainable Salmon Initiative (AYK-SSI) recognizes the need for approaches to characterize determinants of salmon population performance at broader scales. Here we discuss data and modeling tools that have been applied in western Oregon to understand how landscape features and processes may influence salmonids in freshwater. The modeling tools are intended to characterize landscape features and processes (e.g., delivery and routing of wood, sediment, and water) and relate these to fish habitat or abundance. Models that are contributing to salmon conservation in Oregon include: (1) expert-opinion models characterizing habitat conditions, watershed conditions, and habitat potential; (2) statistical models characterizing spatial patterns in and relationships among fish, habitat, and landscape features; and (3) simulation models that propagate disturbances into and through streams and predict effects on fish and habitat across a channel network. The modeling tools vary in many aspects, including input data (probability samples vs. census, reach vs. watershed, and field vs. remote sensing), analytical sophistication, and empirical foundation, and so can accommodate a range of situations. In areas with a history of salmon-related research and monitoring in freshwater, models in the three classes may be developed simultaneously. In areas with less available information, expert-opinion models may be developed first to organize existing knowledge and to generate hypotheses that can guide data collection for statistical and simulation models.