The objective of this study was to identify the patterns of juvenile salmonid distribution and relative abundance in relation to habitat correlates. It is the first dataset of its kind because the entire river was snorkeled by one person in multiple years.

During two consecutive summers, we completed a census of juvenile salmonids and stream habitat across a stream network. We used the data to test the ability of habitat models to explain the distribution of juvenile coho salmon (Oncorhynchus kisutch), young-of-the-year (age 0) steelhead (Oncorhynchus mykiss), and steelhead parr (= age 1) for a network consisting of several different sized streams. Our network-scale models, which included five stream habitat variables, explained 27%, 11%, and 19% of the variation in the density of juvenile coho salmon, age 0 steelhead, and steelhead parr, respectively.

We found weak to strong levels of spatial auto-correlation in the model residuals (Moran's I values ranging from 0.25 - 0.71). Explanatory power of base habitat models increased substantially and the level of spatial auto-correlation decreased with sequential inclusion of variables accounting for stream size, year, stream, and reach ___location. The models for specific streams underscored the variability that was implied in the network-scale models. Associations between juvenile salmonids and individual habitat variables were rarely linear and ranged from negative to positive, and the variable accounting for ___location of the habitat within a stream was often more important than any individual habitat variable. The limited success in predicting the summer distribution and density of juvenile coho salmon and steelhead with our network-scale models was apparently related to variation in the strength and shape of fish-habitat associations across and within streams and years. Summary of statistical analysis of the Calawah Riverscape data. NOAA was not involved and did not pay for the collection of this data. This data represents the statistical analysis carried out by Martin Liermann as a NOAA employee.