Testing Geomorphic Controls on Salmonid Spawning Habitat

Abstract:

The physical architecture of a landscape, as recorded in topography, is a major factor driving the spatial distribution of river habitat within a catchment. For this reason, predictive geomorphic models for fluvial characteristics, particularly grain size, have been suggested as possible contributors to salmonid habitat identification efforts. However, to our knowledge, no work has been done to both implement geomorphic predictions of reach-scale grain size and then test those predictions with salmonid habitat use data. We present a physically-based, empirically calibrated approach to predicting grain size distributions from high resolution LiDAR (Light Detection and Ranging)-derived topographic data. This approach builds on previous efforts in that it predicts the full grain size distribution, rather than just median grain size, and incorporates an empirically calibrated shear stress partitioning factor. We use the predicted grain size distributions to calculate the fraction of the bed area movable by salmon of a given size, which we then compare to 7 years of steelhead trout and coho salmon spawning survey data for a 77 km² watershed along the central California Coast. We find that grain size explains the paucity of spawning in the upper reaches of the drainage, but does not explain variation within the mainstem. In order to explain the residuals in spawning within the mainstem, we turn to the spacing of riffle bedforms. Field surveys of riffle spacing explain 64% of the variation in spawning in these reaches, suggesting that spawning is ultimately limited by the availability of riffles. Because riffle spacing varies systematically with channel width, we show that predicting riffle spacing is feasible with LiDAR data. Taken together, these findings highlight both the value and limitations of a grain-size focused approach to habitat prediction, and suggest that such approaches should be used in concert with predictions of channel bed morphology.