Towards a process domain-sensitive substrate habitat model for sea lampreys in Michigan rivers.

Abstract Habitat mapping is a common and often useful tool in the ecological management of rivers. The complex nature of fluvial processes, however, makes it difficult to predict the reach-scale distribution of substrate habitat from landscape-scale covariates. An option is to identify and partition a data set on boundaries of geomorphic process domains, within which the globally complex relationships between landscape, climate, and instream habitat may potentially be approximated by a simpler model. In this study, we used regression trees as a machine learning method for partitioning and identifying useful strata in a geographically extensive substrate habitat model for larvae of the sea lamprey Petromyzon marinus, an invasive and economically harmful species in the Laurentian Great Lakes. We used field survey data from over 5,000 substrate habitat transects collected in 43 watersheds of the Lower Peninsula of Michigan, and we created a geographic database of geographical information systems-derived cova...