||In the central Piedmont of North Carolina, prairies and savannas were noted by European
settlers to have covered a significant portion of the landscape. Piedmont prairie
is valued for its extraordinary biodiversity; at least 277 plant species, some endemic,
are associated with this unique area. Rich prairie ecosystems in the Piedmont were
maintained by both naturally-occurring and human-ignited fires, which created open
fields or patches of prairie within oak-pine-hickory or Piedmont longleaf pine forests.
Anthropogenic changes to fire regimes and land use have fragmented the Piedmont prairie
ecosystem, such that several of its plant species are now federally endangered. Effective
conservation of this native ecosystem in our rapidly developing state depends on a
solid understanding of its science. Just as importantly, it necessitates the ability
for conservation agencies to act efficiently to protect and maintain areas of intact
prairie, while quickly identifying and protecting other areas with restoration potential.
This masters project compares the suitability of two multivariate modeling tools,
CART (Classification and Regression Tree) and Maxent (Maximum entropy), for predicting
the potential geographic distribution of the Piedmont prairie ecosystem in nine Piedmont
counties of North Carolina. Natural Heritage “Element Occurrence” point location data
of four prairie species were the basis for the models, which considered environmental
variables such as elevation, topographic relative moisture index (TRMI), slope, relative
aspect, soil clay content, and soil effective cation exchange capacity (ECEC) in the
prediction of potential prairie extent. Further, a basic prioritization of the resulting
prairie “habitat” patches mapped in GIS highlights areas adjacent to existing protected
areas in which to focus conservation and restoration efforts.
The results indicated that the habitat model of prairie created by Maxent reasonably
predicts known prairie species occurrences without over-generalizing the possible
distribution of prairie in the study area. Maxent also highlights that ECEC is the
most important predictor variable of prairie distribution, followed by clay content.
The CART technique resulted in similar accuracy and explanatory variables, but when
mapped, “habitat” covered a large proportion of the study area, less useful for targeting
regions for further study. The preliminary prioritization suggests that several zones
around Charlotte, NC and in Davidson County warrant further investigation for prairie
remnants. With sufficient additional information about current land use and cover,
the prioritization can be further refined to reduce the effort needed to find suitable
sites for the restoration and conservation of Piedmont prairie and its associated
forest cover types.