Date of this Version
An international symposium in October 1999 demonstrated the state of the art in modeling species occurrences (Scott et al. 2001). One clear message from the symposium was the broad diversity of approaches that constitute the state of the art. No single method excels, largely because of the very particular and local nature of the problem. Organisms both influence and respond to their local environment; thus, the same species may key in on different resources in different landscapes. Furthermore, modeling methods vary widely in their "transparency," which can inhibit transportability or robustness.
In order to provide an analytical modeling framework that is transparent and durable, we have chosen to use recursive partitioning methods to develop "objective" semi-empirical models of wildlife-habitat relationships for the Nebraska Gap Analysis Project. Recursive partitioning aims to predict membership of individual cases (here, species occurrences) in classes of a categorical dependent variable from measurements of one or several independent variables (here, land cover, soils, climate, etc.). The motivation for using this strategy is twofold: (1) the resulting trees of decision points and values that form the models are readily understandable, debatable, and tunable; and (2) its non-parametric modeling handles the multimodality likely to be found in species occurrence data.