Predictive Occurrence Modeling for Three Rare Plants Within the Croatan National Forest
No Thumbnail Available
Date
2023-03
Authors
Advisors
Journal Title
Series/Report No.
Journal ISSN
Volume Title
Publisher
Abstract
Rare plants are valuable indicators of areas of biological significance, and their persistence over
time serves as a measure of ecological health and good land stewardship. Knowledge of the
extent of rare plant distributions and the viability of rare plant populations is valuable
information for assessing the risk of extirpation, future research, and management decisions. I
constructed predictive occurrence models for three rare plants, Platanthera integra, Pinguicula
pumila, and Asclepias pedicellata, using the Presence-only Prediction (MaxEnt) Tool in ArcGIS
Pro 2.9. Explanatory variables used to model rare plant distributions included a LiDAR-based
digital elevation model (DEM) of Croatan National Forest, soil series, and natural community
data. Models were trained with Elemental Occurrence data obtained from the North Carolina
Natural Heritage Program. The tool generated significant models with an area under the receiver-
operator curve (AUC)>0.80 for all three species. The model for Asclepias pedicellata accurately
predicted two new populations. However, observers failed to find individuals at the predicted
sites surveyed for Platanthera integra and Pinguicula pumila. This may have been due to the
timing of surveys or the accuracy of the models. The results of this study demonstrate that the
Presence-only Prediction (MaxEnt) Tool may prove to be a valuable predictor of undiscovered
populations of rare species, especially once the environmental filters and life history traits
driving the distribution patterns of a rare focal species are better understood.
Description
Keywords
Biodiversity, Atlantic Coastal Plain, Croatan National Forest, Carolina Bays, elemental occurrence, rare plants, Presence-only Prediction, MaxEnt, GIS, Spatial Analysis