Modeling landscape vegetation pattern in response to historic land-use: a hypothesis-driven approach for the North Carolina Piedmont, USA

Current methods of vegetation analysis often assume species response to environmental gradients is homogeneously monotonic and unimodal. Such an approach can lead to unsatisfactory results, particularly when vegetation pattern is governed by compensatory relationships that yield similar outcomes for various environmental settings. In this paper we investigate the advantages of using classification tree models (CART) to test specific hypotheses of environmental variables effecting dominant vegetation pattern in the Piedmont. This method is free of distributional assumptions and is useful for data structures that contain non-linear relationships and higher-order interactions. We also compare the predictive accuracy of CART models with a parametric generalized linear model (GLM) to determine the relative strength of each predictive approach. For each method, hardwood and pine vegetation is modeled using explanatory topographic and edaphic variables selected based on historic reconstructions of patterns of land use. These include soil quality, potential soil moisture, topographic position, and slope angle. Predictive accuracy was assessed on independent validation data sets. The CART models produced the more accurate predictions, while also emphasizing alternative environmental settings for each vegetation type. For example, relic hardwood stands were found on steep slopes, highly plastic soils, or hydric bottomlands - alternatives not well captured by the homogeneous GLM. Our results illustrate the potential utility of this flexible modeling approach in capturing the heterogeneous patterns typical of many ecological datasets.