Regionalization of forest pattern metrics for the continental United States using contiguity constrained clustering and partitioning

Abstract

Technological advances have created new opportunities for defining and mapping ecological and biogeographical regions on the basis of quantitative criteria while generating a need for studies that evaluate the sensitivity of ecoregionalizations to clustering methods and approaches. In this study, we used a novel regionalization algorithm, regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP), to identify hierarchical regions based on measures of forest extent, connectivity, and change for 2109 watersheds in the continental U.S. Unlike regionalizations developed using non-spatial clustering techniques, REDCAP directly incorporates a spatial contiguity constraint into a traditional hierarchical clustering method, resulting in contiguous regions that optimize a homogeneity measure. Results of our analyses identified nine- and eighteen-class Forest Pattern Regions that reflected the influence of natural and anthropogenic factors structuring forest extent and fragmentation. Because these regions are defined by the forest pattern metrics themselves, rather than pre-defined political or ecological units, they provide a valuable means for visualizing forest pattern information and quantifying forest patterns across a large, diverse geographic area. In contrast, regionalizations of the same data using two non-spatial methods (k-means clustering and non-spatial average linkage clustering) resulted in more homogeneous classes composed of many discontiguous units. While it should not be viewed as a replacement for non-spatial clustering techniques, REDCAP provides an alternative approach to developing ecological regionalizations by placing greater emphasis on maintaining the spatial contiguity of units, a property that may be desirable in many broad-scale regionalizations because it reduces data complexity and facilitates the visualization and interpretation of ecological or biogeographic data.