Modeling Landscape Vegetation Pattern in Response to Historic Land-use: A Hypothesis-driven Approach for the North Carolina Piedmont,USA |
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Authors: | Email author" target="_blank">Kristin?TavernaEmail author Dean L?Urban Robert I?McDonald |
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Institution: | (1) Curriculum in Ecology, University of North Carolina, Chapel Hill, NC 27599-3275, USA;(2) Nicholas School of the Environment and Earth Sciences, Duke University, Durham, NC 27708, USA;(3) Virginia Department of Conservation and Recreation, Division of Natural Heritage, 217 Governor Street, Richmond, VA 23219, USA |
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Abstract: | 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. |
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Keywords: | CART Classification tree Generalized linear models GLM Logistic regression Vegetation modeling |
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