Vegetation description and classification are critical for both theoretical and applied science, including conservation planning and ecological restoration. In 2008, the Federal Geographic Data Committee established a US standard for vegetation classification, which mandates that Associations and Alliances recognized in the US National Vegetation Classification (USNVC) be based on quantitative analysis of publicly-available plot data. However, most types have not yet been verified using quantitative data, and few descriptions include the FGDC-mandated detailed summary tables. This project was undertaken to revise the longleaf pine (Pinus palustris) USNVC Groups (G154, G009, G596, G190) using a consistent quantitative framework to document vegetation-environment relationships and provide an example of revision, review, and dissemination of quantitatively-defined vegetation types. We used 891 vegetation plots that capture the range of edaphic conditions that longleaf pine woodlands occupy in NC, SC, GA, and FL. Hierarchical clustering with flexible β using abundance and presence-absence data in combination was used to identify and circumscribe vegetation types. Misclassified plots were reassigned using silhouette width and the optpart function. To identify diagnostic taxa associated with each type as well as environmental variables that influence species richness and composition, we used indicator species analysis and non-metric multidimensional scaling.
Results/Conclusions
Hierarchical clustering divided plots first according to geography rather than environmental context and three major geographic bins emerged from our analysis: the Coastal Plain of North Carolina and northern South Carolina, central South Carolina to the Florida border, and Florida and immediately adjacent Georgia. We recognize and describe the floristics, diagnostic characters and environmental setting of 30 Associations in the first group, 32 Associations in the second, and 27 Associations in Florida. Key environmental variables that explain differences in species richness and composition across vegetation types were soil texture, soil moisture, base cation availability (particularly calcium), and proximity to the coast. Sixty-two of our 89 vegetation types map onto previously recognized USNVC concepts, while 17 types are newly described. Because so little of the longleaf pine ecosystem remains, providing substantial documentation of vegetation units for use as targets for management and restoration is critical. In addition, this work provides a model framework for how to conduct a quantitatively-based classification of a large vegetation unit (Group in the USNVC) with the goal of revising a pre-existing classification hierarchy.