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The responsiveness of South African fauna to climate change events is poorly documented and not routinely incorporated into regional conservation planning. We model the likely range alterations of a representative suite of 179 animal species to climate change brought about by the doubling of CO2 concentrations. This scenario is expected to cause a mean temperature increase of 2 °C. We applied a multivariate climate envelope approach and evaluated model performance using the most comprehensive bird data set. The results were encouraging, although model performance was inconsistent in the eastern coastal area of the country. The levels of climate change induced impacts on species ranges varied from little impact to local extinction. Some 17% of species expanded their ranges, 78% displayed range contraction (4–98%), 3% showed no response and 2% became locally extinct. The majority of range shifts (41%) were in an easterly direction, reflecting the east–west aridity gradient across the country. Species losses were highest in the west. Substantially smaller westward shifts were present in some eastern species. This may reflect a response to the strong altitudinal gradient in this region, or may be a model artifact. Species range change (composite measure reflecting range contraction and displacement) identified selected species that could act as climate change indicator taxa. Red‐data and vulnerable species showed similar responses but were more likely to display range change (58% vs. 43% for all species). Predictions suggest that the flagship, Kruger National Park conservation area may loose up to 66% of the species included in this analysis. This highlights the extent of the predicted range shifts, and indicates why conflicts between conservation and other land uses are likely to escalate under conditions of climate change.

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In semiarid regions the ratio of annual net primary production to precipitation, rain-use efficiency (RUE), has been used as an index of desertification. In a recent publication (Hein & de Ridder, 2006) it was proposed that an incorrect understanding of the relationship between RUE and rainfall has led to a misinterpretation of the satellite record of desertification in the African Sahel. Here, we examine this suggestion and show that, contrary to Hein and de Ridder's statement, satellite studies of Sahelian RUE have reported increases, decreases, and constant values since 1981. Furthermore, we find that data do not support their proposal that RUE increases with rainfall, even in nondegraded areas. Hence we reject their corollary, that constant RUE is prima facie evidence of desertification. The fundamental difficulty with the use of RUE for detection of desertification remains, that is the difficulty of estimation of the RUE for nondegraded land at a regional scale.

The relationship between multi-year (1989-2003), herbaceous biomass and 1-km 2 Advanced Very High Resolution Radiometer (AVHRR) Normalized Difference Vegetation Index (NDVI) data in Kruger National Park (KNP), South Africa is considered. The objectives were: (1) to analyse the underlying relationship between NDVI summed for the growth season (SNDVI) and herbaceous biomass in field sites (n5533) through time and (2) to investigate the possibility of producing reliable herbaceous biomass maps for each growth season from the satellite SNDVI observations. Landsat Enhanced Thematic Mapper Plus (ETM + ) and Thematic Mapper (TM) data were used to identify highly heterogeneous field sites and exclude them from the analyses. The average R 2 for the SNDVI-biomass relationship at individual sites was 0.42. The growth season mean biomass and SNDVI of most landscape groups were strongly correlated with rainfall and each other. Although measured tree cover and MODIS estimates of tree cover did not have a detectable effect on the SNDVI-biomass relationship, other observations suggest that tree cover should not be ignored. The SNDVI was successful at estimating inter-annual variations in the biomass at single sites, but on an annual basis the relationship derived from all the sites was not strong enough (average R 2 50.36) to produce reliable growth season biomass maps. This was mainly attributed to the fact that the biomass data were sampled from very small field sites that were not fully representative of 1-km 2 AVHRR pixels. Supplementary field surveys that sample a larger area for each field site (e.g. 1 km 2 or larger) should account for the variability in biomass and may improve the strength of SNDVI-biomass relationships observed in a single growth season.