3.9 Review

An overview of the vegetation classification approach in South Africa

Journal

PHYTOCOENOLOGIA
Volume 48, Issue 2, Pages 163-170

Publisher

GEBRUDER BORNTRAEGER
DOI: 10.1127/phyto/2017/0163

Keywords

Braun-Blanquet; nature conservation; South Africa; survey methods; sustainable utilization; vegetation classification

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The purpose of this paper is to provide an overview of the history, application and challenges of phytosociology in South Africa. There is a high focus on conserving the nature of South Africa to ensure that the different ecosystems and their associated biota are preserved for future generations. Vegetation surveys aimed at identifying and classifying plant communities form the basis of conservation management plans and sustainable resource utilisation. Plot-based vegetation surveys using the Zurich-Montpellier approach have been conducted in South Africa since the mid 1970's. These studies have increased considerably since the 1980's providing vegetation classifications within the different biomes of the country. The studies resulted in the classification of the vegetation on different hierarchical levels. The vegetation data were incorporated into the National Vegetation Database of the country. Although many plant communities were identified, hierarchically classified and described from a rangewide perspective, formal syntaxa such as associations, alliances, orders and classes were rarely formally typified. Although many of these studies are conducted in natural areas with primary vegetation, they are representative of the larger region. A few regional studies, particularly in the grassland and savanna biomes, have been conducted and some have led to formal syntaxonomic classifications. The Zurich-Montpellier approach of total floristic compositions has proved successful in classifying vegetation in South Africa. The continuation of current detailed studies in natural areas is important for scientifically-based nature conservation planning. However, there is an urgent need for syntheses of larger datasets from areas with sufficient data that will lead to formal syntaxonomic treatments.

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