Spatial Multi-criteria Decision Modeling for Pinus palustris Mill. (Longleaf Pine) Restoration in the South Carolina Sandhills Wiregrass Gap

Jacob B.W. Murray^1,*, Robert Baldwin¹, Donald L. Hagan¹, and Patrick Hiesl¹

¹Department of Forestry and Environmental Conservation, Clemson University, Clemson, SC 29634. *Corresponding author.

Southeastern Naturalist, Volume 22, Issue 3 (2023): 419–444

Abstract
Currently found in less than 3% of its pre-European range, Pinus palustris (Longleaf Pine) ecosystems are increasingly important for forest-restoration initiatives in the southeastern US. Compared to other areas of the Atlantic Coastal Plain, sites in the Carolina Sandhills often have higher restoration potentials for Longleaf Pine habitat. Distinct sandhill ecology and prevailing biological legacies make Longleaf Pines competitive in this region. Spatial investigations into potential restoration sites are essential preliminary steps to optimize resource allocation for large-scale restoration objectives. This study aimed to illustrate the applicability of various modeling techniques while providing a framework for restoration-parcel identification and selection. We developed multiple weighted spatial multi-criteria decision models to identify Longleaf Pine-restoration sites in the South Carolina Sandhills Wiregrass Gap, an ecologically distinct and understudied region of Longleaf Pine occupancy. Using advanced spatial modeling and analysis tools, we developed a classification system that combined and weighted vegetation cover, land use, and soil property raster data. The models are designed to identify potential sites for restoration, which will ultimately improve resource allocation during the project planning process. We found significant (P < 0.001) spatial clustering of large parcels and 94.7–98.3% pixel similarity between predictive models. An ensemble model, developed by averaging the pixel values of each predictive model, found that Longleaf Pine-restoration potential was highest in large rural parcels primarily east of Kershaw County. Large-parcel clustering and ecological input factors applied to a decision-making model in this study provide a precedent for systematically allocating resources to improve and increase forest restoration and management in South Carolina.

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