Variation in the composition and structure of tropical savannas as a function of rainfall and soil texture along a large-scale climatic gradient in the Northern Territory, Australia
Williams, RJ and Duff, GA and Bowman, DMJS and Cook, GD, Variation in the composition and structure of tropical savannas as a function of rainfall and soil texture along a large-scale climatic gradient in the Northern Territory, Australia, Journal of Biogeography, 23, (6) pp. 747-756. ISSN 0305-0270 (1996) [Refereed Article]
Variation in structural and compositional attributes of tropical savannas are described in relation to variation in annual rainfall and soil texture along a subcontinental-scale gradient of rainfall in the wet-dry tropics of the Northern Territory, Australia. Rainfall varies along the gradient from over 1500 mm p.a. in the Darwin region (c. 12° S) to less than 500 mm in the Tennant Creek region (c. 18° S). Soils are patchy, and sands, loams and clays may occur in all major districts within the region. We utilized a large data set (1657 quadrats x 291 woody species; with numerous measured and derived sample variables) covering an area of 0.5 million km2. Correlations between floristic composition of woody species and environmental variables were assessed using DCA ordination and vector fitting of environmental variables. Vectors of annual rainfall and soil texture were highly correlated with variation in species composition. Multiple regression analyses incorporating linear and quadratic components of mean annual rainfall and topsoil clay content were performed on three structural attributes (tree height, tree cover, tree basal area) and two compositional attributes (woody species richness, deciduous tree species richness). Tree height declined with decreasing rainfall; cover, basal area, woody species richness and deciduous species richness all declined with decreasing rainfall and increasing soil clay content. Regression models accounted for between 17% and 45% of the variation in the data sets. Variation in other factors such as soil depth, landscape position and recent land-use practices (for which there were no data on an individual quadrat basis) are likely to have contributed to the large residual variation in the data set.