Classification of vegetation of the South African grassland biome

• Main Author: Ellery, William Nolan
• Format: Others
• Language: en
• Published: 2017
• Subjects: Grasslands > South Africa; Vegetation classification > South Africa; Grassland ecology > South Africa; Biotic communities > South Africa; Plant communities > South Africa
• Online Access: Ellery, William Nolan (1992) Classification of vegetation of the South African grassland biome, University of Witwatersrand, Johannesburg, <http://wiredspace.wits.ac.za/handle/10539/22708>; http://hdl.handle.net/10539/22708

A thesis submitted to the Faculty of Science, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Doctor of Philosophy. Johannesburg 1992. === The aim of the study was to develop understanding of the relationships between vegetation types of the grassland biome of South Africa and the environment, with an emphasis on structural and functional characteristics. The grassland biome in South Africa has traditionally been divided into 'pure' grasslands, assumed to be climatically determined, and 'false' grasslands of recent anthropogenic origin. A review of literature from several disciplines including palaeobotany, archaeology, ecology and biogeography indicates that this is not a valid distinction. It is clear that the distribution of the grassland biome as a whole is poorly understood, but the general correlation between the distribution of biomes and climate elsewhere in the world suggests that this warrants more detailed investigation. A water balance approach was used to develop climatic incices that both predict the distribution of grasslands, and are easy to interpret biologically. The indices are the mean. number of days per annum when moisture is available for plant growth, tbe mean temperature on days when moisture is available for plant growth (wet season temperature),. and the mean temperature when moisture is not available for plant growth (dry season temperature). Based on these three.indices the grassland biome in South Africa call be distinguished from neighbouring biomes. The fynbos and succulent karoo biomes have rainfall in winter. The grassland, nama-karoo and savanna biomes have' rainfall in summer. The forest biome experiences rainfall throughout the year. Of the summer rainfall biomes, the quantity of water available in the grassland biome b greater than in the nama-woo, similar to savanna, but less than forest. Grasslands experience cooler dry season temperatures than savannas. The localised distribution of woody plants within the. grassland biome suggests that it is the effect of climate on the fire regime that may be of overriding importance h'l determining the distribution of the biome as a whole. Woody elements are restricted to sites that are either protected from fire, or experience fires of lower intensity than sites that support- grassland, The unifying feature of the grassland biome is its proneness to fire. The presence of a warm, moist season promotes plant production and leads to a high standing crop close to the ground. The prolonged dry season causes vegetation to dry out annually, rendering it flammable. More arid biomes have plants more widely spaced, making it difficult for fire to spread. In more mesic biomes where rainfall is less sea.sonal than in the grasslands or savannas, fuels do not dry out sufficiently to ignite, A number of additional climatic features may promote burning in the grassland biome, It has the highest lightning density of all South Africa's biomes. 'tVarm, dry 'berg' winds desiccate fuels and 1 omote burning in the more mesic grasslands, The 'curing' of the grass sward due to dry season frost and temperature drop is important in establishing early dry season flammability. Savanna trees are fire tolerant, but they appear sensitive to the cold temperatures prevaient in the grassland biome in. the dry season, The relationship between the distribution of functional characters of grassland plants and environmental conditions was investigated. The distincrion between sweetveld, mixed veld and sourveld was recognised as one of the most Important functional features of South Africa's grasslands, The distribution of these vegetation types was examined in detail. Sweetveld occurs In warm, dry areas; sourveld in cool, moist areas. There Is overlap between these tyP.Js that Is dependant on soil nutrient status. Sweetveld that occurs in climatic conditions that would be expected to support mixed veld and sourveld, is on soils derived from basic parent material, including basalt, dolerite, gabbro and norite. Similarly, sourveld that occurs in areas that climatically would be expected to support sweetveld, is on soils derived from acid parent material such as sandstone and quartzite .. Soil nutrients that are most highly correlated to the occurrence of these three veld types are phosphoms availability and an index of nitrogen mineralization potential. 'l'here is an increase in bot; available phosphorus and the index of readily mineralizable nitrogen from sourveld to mixed veld to sweetveld. These features am inc01).10111tedinto a conceptual model that relates the distribution of these grassland types to carbon and nitrogen metabolism, with the role of phosphorus either similar to nitrogen, or else it may act indirectly by affecting the. rate of nitrogen mineralization, Nitrogen mineralization OCcursat lower water availability than carbon assimilation, and its temperature optimum is higher than that of carbon assimilation. Where nitrogen mineralization is favoured ielative to carbon assimilation, sweetveld is likely to (}C(.1\Xr. Where carbon assimilation is. favoured relative to; nitrogen mineralization, sourveld is likely to occur ....Soil texture affects the balance between these two processes in the degree to wm.r;h it protects soil organic matter, and thereforv the size of the nitrogen and ph_QSPllO_rOll.S pools. Changes in the rlj,stribution of South Africa's b~\omesfor a scenario of climate change are predicted using the biome model developed in this study. This illustrates the value of developing predictive models. === MT2017