WK 7 - Vegetation Analysis in Support of the National Vegetation Classification

Sunday, August 7, 2011: 8:30 AM-4:30 PM
Trinity Street Lobby Field Trip Pick Up, Austin Convention Center
Organizer: David Roberts
The National Vegetation Classification (NVC) is a collaborative effort among vegetation scientists, the ESA, NatureServe, and federal natural resource agencies to develop a comprehensive, data-driven classification of the vegetation of the United States. The NVC requires supporting vegetation analysis for all proposed vegetation types. This workshop is a hands-on approach to exploring the range of multivariate analyses for vegetation analysis, including specifically ordination, classification, and indicator species analysis, with a primary focus on the Alliance and Association level. Workshop attendees will work at computer workstations on vegetation data from the Balcones Canyonlands National Wildlife Refuge and other nearby areas provided by the organizers in parallel with the associated ESA/NVC field trip, and will explore the broad range of analyses available in the open source statistical package R. The R statistical package provides state-of-the-art analytical routines and graphics. We will focus on (1) vegetation dataset visualization (heterogeneity, dominance/diversity, species richness, and species distributions), (2) graphical analysis of environmental variability (empirical cumulative density functions, boxplots, scatterplots, and distribution functions), (3) hierarchical and non-hierarchical cluster analysis, cluster validity assessment through geometric evaluators and non-geometric indicator species assessments, and (4) ordination analysis of vegetation clusters and environmental drivers using non-metric multidimensional scaling (NMDS), fuzzy set ordination (FSO) and canonical correspondence analysis (CCA). Workshop attendees will gain working knowledge on the use of a broad range of multivariate analyses useful in community ecology. Attendees will be given copies of the software for future use on their own computers and data.
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