In the management of threatened and endangered species, informed population estimates are essential to gage whether or not recovery goals are being met. In the case of Pacific salmonids, this evaluation often involves sampling a small subset of the population and scaling up to estimate larger distinct populations segments. This is made complicated by the fact that fish populations are not evenly distributed along riverscapes but respond to physical and biological stream properties at varying spatial extents. We used rapid assessment survey methods and the River Styles classification to explore fish-habitat relationships at a continuous network scale. Semi-continuous surveys were conducted across nine streams in the upper Middle Fork John Day River watershed and increased the number of sites surveyed eight-fold over other monitoring methods within the watershed. Using this increased sample size and continuous habitat metrics we improved watershed-wide steelhead (Oncorhynchus mykiss) abundance models. We first validated the distinctions among River Styles through a classification analysis using physical metrics measured at the rapid assessment sites. Overall classification accuracy, using a combination of reach and landscape scale metrics, was 88.3% and suggested that River Style classification was identifying variations in physical morphology within the watershed that was quantifiable at the reach scale. Leveraging the continuous River Styles classification of physical habitat and a continuous model of primary production improved the prediction of steelhead abundance across the network. Using random forest regressions, a model that included only habitat metrics resulted in R2 = 0.34, while using the continuous variables improved the model accuracy greatly to R2 = 0.65. Random forest allowed for further investigation into the predictor variables through the analysis of the partial dependence plots and identified a gross primary production threshold, below which production might be limiting steelhead populations. This method also identified the rarest River Style surveyed within the watershed, Confined-Valley Step Cascade, as the morphology that had the largest marginal effect on steelhead. The inherent physical properties and boundary conditions unique to each River Style has the potential to inform fish-habitat relationships across riverscapes and improve abundance estimates on a continuous spatial scale.

The Middle Fork John Day Basin in Northeastern Oregon is prime habitat for spring Chinook salmon and Steelhead trout. In 2008, a major tributary supporting rearing habitat, Big Boulder Creek, was restored to its historic mid-valley channel along a 1 km stretch of stream 800 m upstream of the mouth. Reduction of peak summer stream temperatures was among the goals of the restoration. Using Distributed Temperature Sensing (DTS) Fiber Optic Technology, stream temperature was monitored prior to restoration in June 2008, and after restoration in September 2008, July 2009, and August 2009. Data gathered was used to determine locations of groundwater and hyporheic inflow and to form a stream temperature model of the system. The model was used both to develop an evaluation method to interpret components of model performance, and to better understand the physical processes important to the study reach. A very clear decreasing trend in surface temperature was seen throughout each of the DTS stream temperature datasets in the downstream 500 m of the study reach. Observed reduction in temperature was 0.5°C (±0.10) in June 2008, 0.3°C (±0.37) in September 2008, 0.6°C (±0.25) in July 2009, and 0.2°C (±0.08) in August 2009. Groundwater inflow was calculated to be 3% of the streamflow for July 2009 and 1% during the August 2009 installation. Statistically significant locations of groundwater and hyporheic inflow were also determined. July 2009 data was used to model stream temperature of the 1 km (RMSE 0.28°C). The developed model performance evaluation method measures timelag, offset, and amplitude at a downstream observed or simulated point compared with the boundary condition, rather than evaluating the model based on error. These measures are particularly relevant to small scale models in which error may not be a true reflection of the ability of a model to correctly predict temperature. Breaking down model performance into these three predictive measures was a simple and graphic method to show the model's predictive capability without sorting through large amounts of data. To better understand the model and the stream system, a sensitivity analysis was conducted showing high sensitivity to streamflow, air temperature, groundwater inflow, and relative humidity. Somewhat surprisingly, solar radiation was among the lowest sensitivity. Furthermore, three model scenarios were run: a 25% reduction in water velocity, a 5°C increase in air temperature, and no groundwater inflow. Simulations of removal of groundwater inflows resulted in a 0.5°C increase in average temperature over the modeled time period at the downstream end, further illustrating the importance of groundwater in this stream system to reduce temperatures. / Graduation date: 2012