A quantitative method for delineating regions: An example for the western Corn Belt plains ecoregion of the USA

A method was developed to systematically delineate boundaries for ecological classification of regions. The process entailed the use of small-scale digital data to quantify spatial concordance among environmental attribute data sets. The data sets were grouped into spatially related themes using cluster analysis and multidimensional scaling. Selected data sets were then used either individually or collectively to divide the study area into subregions that exhibited different environmental attributes. The method was applied to a previously defined ecological unit, the western Corn Belt of the central United States. The results showed that the portion of the study area with intensive corn and soybean production was identifiable using each of the three input data sets selected for partitioning (soil associations; AVHRR remote-sensing imagery; and a combined data set of landform, forest, and soils data). The classification of other portions of the study area was highly dependent on the type and scale of the input data. The systematic methodology used here offers advantages over other methods for identifying ecological regions in that the results from the systematic approach can be reproduced, the boundaries between ecological units can be revised based on new or more accurate data, important ecological processes are explicitly chosen to delineate boundaries, and transition zones between regions can be quantified.