A Generic, Computer-assisted Method for Rapid Vegetation Classification and Survey: Tropical and Temperate Case Studies

Standard methods of vegetation classification and survey tend to be either too broad for management purposes or too reliant on local species to support inter-regional comparisons. A new approach to this problem uses species-independent plant functional types with a wide spectrum of environmental sen...

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Bibliographic Details
Main Author: Andrew N. Gillison
Format: Article
Language:English
Published: Resilience Alliance 2002-12-01
Series:Ecology and Society
Subjects:
Online Access:http://www.ecologyandsociety.org/vol6/iss2/art3/
Description
Summary:Standard methods of vegetation classification and survey tend to be either too broad for management purposes or too reliant on local species to support inter-regional comparisons. A new approach to this problem uses species-independent plant functional types with a wide spectrum of environmental sensitivity. By means of a rule set, plant functional types can be constructed according to specific combinations from within a generic set of 35 adaptive, morphological plant functional attributes. Each combination assumes that a vascular plant individual can be described as a "coherent" functional unit. When used together with vegetation structure, plant functional types facilitate rapid vegetation assessment that complements species-based data and makes possible uniform comparisons of vegetation response to environmental change within and between countries. Recently developed user-friendly software (VegClass) facilitates data entry and the analysis of biophysical field records from a standardized, rapid, survey pro forma. Case studies are presented at a variety of spatial scales and for vegetation types ranging from species-poor arctic tundra to intensive, multitaxa, baseline biodiversity assessments in complex, humid tropical forests. These demonstrate how such data can be rapidly acquired, analyzed, and communicated to conservation managers. Sample databases are linked to downloadable software and a training manual.
ISSN:1708-3087