Use of GIS‐Derived Landscape‐Scale Habitat Features to Explain Spatial Patterns of Fish Density in Michigan Rivers

Abstract

Both site‐ and landscape‐scale processes play important roles in the biological communities of rivers. Understanding the influences of these processes on fish abundance can help direct management and research efforts toward appropriate habitat variables and scales. We used multiple linear regression analysis of a regional fish and habitat database to determine the feasibility of using geographical information systems (GIS)–derived landscape‐scale habitat variables to explain the spatial variation in the density of five sport fish species (Chinook salmon Oncorhynchus tshawytscha, steelhead O. mykiss, brown trout Salmo trutta, brook trout Salvelinus fontinalis, and white sucker Catostomus commersonii) in the rivers of Michigan’s Lower Peninsula. We compared these models with those developed using site‐scale variables traditionally measured in the field. Landscape‐scale riverine habitat variables obtained through GIS analysis and modeling of catchment characteristics accounted for 18–69% of the variation in fish density. Landscape estimates of mean July water temperature were negatively correlated with the density of brook trout, brown trout, and Chinook salmon. Drainage area was negatively correlated with the density of steelhead and white suckers, and 90% exceedence flow yield (a measure of flow stability) was positively correlated with the density of Chinook salmon and steelhead. Site‐scale habitat variables explained less (12–57%) of the variation in fish density than landscape‐scale variables. In the site‐scale models, depth was negatively related to all species’ densities, and the percentage of soft substrates was positively correlated only with white suckers. Although there was still much unexplained variation in density, our models provide insight into key habitat variables that influence fish density patterns on a large scale.