Firman J.C.,Corvallis Research Office |
Ashley Steel E.,U.S. Department of Agriculture |
Jensen D.W.,National Oceanic and Atmospheric Administration |
Burnett K.M.,U.S. Department of Agriculture |
And 4 more authors.
Transactions of the American Fisheries Society
Salmon occupy large areas over which comprehensive surveys are not feasible owing to the prohibitive expense of surveying thousands of kilometers of streams. Studies of these populations generally rely on sampling a small portion of the distribution of the species. However, managers often need information about areas that have not been visited. The availability of geographical information systems data on landscape features over broad extents makes it possible to develop models to comprehensively predict the distribution of spawning salmon over large areas. In this study, the density of spawning coho salmon Oncorhynchus kisutch was modeled from landscape features at multiple spatial extents to identify regions or conditions needed to conserve populations of threatened fish, identify spatial relationships that might be important in modeling, and evaluate whether seventh-field hydrologic units might serve as a surrogate for delineated catchments.We used geospatial data to quantify landscape characteristics at four spatial extents (a 100-m streamside buffer, a 500-m streamside buffer, all adjacent seventh-field hydrologic units [mean area =18 km2], and the catchment upstream fromthe reach [mean area=17 km2]). Predictions frommodels incorporating land use, land ownership, geology, and climate variables were significantly correlated (r = 0.66-0.75, P < 0.0001) with observed adult coho salmon in the study area. In general, coho salmon densities (peak count of adults per kilometer) were greatest in river reaches within landscapes of undeveloped forest land with little area in weak rock types, areas with low densities of cattle and roads, and areas with a relatively large range in winter temperatures. The ability to predict the spatial distribution of coho salmon spawners from landscape data has great utility in guiding conservation, monitoring, and restoration efforts.© American Fisheries Society 2011. Source