Ripple: A Digital- Terrain Based Model for Linking Fish to Channel Networks

River channel networks self-organize into a hierarchical branching structure of downstream increasing drainage area and decreasing slope in which channel scale, bed grain size and morphology, and carrying capacity for biota co-vary. This structure suggests that potential habitat conditions for biota can be crudely predicted based on local slope and drainage area and, if so, species specific models can be devised to estimate their population density. The value of such crude predictions is three fold: 1) in areas of limited data, such model estimates can provide a quick determination of reach to watershed level population potential, 2) limiting factors might be identified, and, conditions could be “gamed” to determine possible manipulations to improve populations, and 3) the models can be used to generate simple testable (and rejectable) hypotheses about channel controls and species abundance that can serve as null hypotheses during field work (guiding field investigations). Here we report the release of the digital-terrain based model “Ripple” for estimating Coho salmon populations in each life stage throughout a watershed. The model exploits the ESRI ArcMap interface, but the essential code is platform independent. We anticipate that the basic structure of the model will prove useful for investigations of other biota as well. The minimum information needed to run the model is a channel network broken into arcs to which is assigned a drainage area and local channel slope. These data are used in several empirical correlations to estimate the area of various channel habitats, from which carrying capacity for each life cycle is estimated. These data are then used in stock-recruitment (typically density dependent) models to estimate populations in each life stage. Application of Ripple to canyon bound rivers of the California Coast Range suggests that the primary bottleneck in Coho populations is commonly a lack of sufficient over-winter habitat.