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Using new tools (boosted regression trees) in predictive biogeography, with extensive spatial 23 distribution data for >19 000 species, we developed predictive models for South African plant species richness patterns. Further, biome level analysis explored possible functional determinants of country-wide regional species richness. Finally, to test model reliability independently, we predicted potential alien invasive plant species richness with an independent dataset. Amongst the different hypotheses generally invoked to explain species 30 diversity (energy, favorableness, topographic heterogeneity, irregularity and seasonality), results revealed topographic heterogeneity as the most powerful single explanatory variable for indigenous South African plant species richness. Some biome-specific responses were observed, i.e. two of the five analyzed biomes (Fynbos and Grassland) had richness best explained by the “species-favorableness” hypothesis, but even in this case, topographic heterogeneity was also a primary predictor. This analysis, the largest conducted on an almost exhaustive species sample in a species-rich region, demonstrates the preeminence of topographic heterogeneity in shaping the spatial pattern of regional plant species richness. Model reliability was confirmed by the considerable predictive power for alien invasive species richness. It thus appears that topographic heterogeneity controls species richness in two main ways: firstly, by providing an abundance of ecological niches in contemporary space (revealed by alien invasive species richness relationships) and secondly, by facilitating the persistence of ecological niches through time. The extraordinary richness of the South African Fynbos biome, a world-renowned hotspot of biodiversity with the steepest environmental gradients in South Africa, may thus have arisen through both mechanisms. Comparisons with similar regions of the world outside South Africa are needed to confirm the generality of topographic heterogeneity and favorableness as predictors of plant richness.
For much of the last century, regional-scale species diversity patterns were studied by biogeographers concerned with faunal and floristic richness in different parts of the globe (Hutchinson 1959). Resulting narrative analyses relied heavily on geographical and historical factors. Over the past three decades, ecologists have sought predictors of regional-scale patterns, using a wide range of explanatory variables, but especially measures of available energy and environmental heterogeneity (Currie 1991, Francis and Currie 1998, Whittaker 1999, Whittaker et al. 2001, Chown et al. 2003, Field et al. 2005, Storch et al. 2005). A new synthesis of diversity theory is now emerging that acknowledges the role of ecology, geography and evolution in determining diversity patterns at all spatial scales (Gaston 2000, Ricklefs 2000, Hubbell 2001, Currie et al. 2004, Ricklefs et al. 2004), and ecological and historical perspectives have been fused.
In this paper we analyze plant regional richness patterns in South Africa. This country is ideal for such analyses due to its high environmental heterogeneity and extraordinarily rich flora (Goldblatt 1978, Cowling et al. 1989). Furthermore, it contains not only some of the most species-rich regions in the world (such as the southwestern part of fynbos biome), but also areas of floristic impoverishment, such as parts of the Upper Karoo and Kalahari Basin (Cowling et al. 1989, Cowling and Hilton-Taylor 1992, Cowling et al. 1997a).
Previous work on regional-scale plant diversity patterns in South Africa provides a somewhat confusing picture on the relative role of climate, available energy and heterogeneity explanatory variables, especially because individual biomes or climate regions have been analysed individually. O'Brien (1993, 1998) demonstrated a curvilinear relationship between energy and species richness of trees in one degree squares; overall, richness was best explained by a simple model based on a linear relationship with annual rainfall and a parabolic relationship with minimum monthly potential evapotranspiration (PET). The analysis excluded the vast majority of South African woody plants (low and dwarf shrubs associated with fynbos and karoo vegetation) limiting the generality of the pattern (Cowling et al. 1997b). Hoffman et al. (1994) showed a negative relationship between richness and available energy (PET) for a full complement of plant species from intensively surveyed sites of roughly equal area across South Africa, interpreted as a consequence of high water deficit impacting negatively on richness in areas of high PET (the Kalahari and Nama-karoo deserts). Both O'Brien (1993) and Hoffman et al. (1994) identified topographic heterogeneity as a relatively unimportant factor in explaining richness, but nonetheless accounting for some otherwise unexplained variance in their models.
Using 12 explanatory variables to reflect heterogeneity, favorableness, energy, seasonality and irregularity, Cowling et al. (1997b) demonstrated that heterogeneity explained plant species richness in the temperate biomes (Fynbos and Karoo), but productivity was the strongest predictor of plants species richness in the subtropical biomes (Grassland and Savanna). They argued that speciation/extinction history, and concomitantly high levels of beta and gamma diversity, was important in the former, whereas ecology was more important in the latter and richness peaked in productive environments as expected for a flora of tropical origins.
Substantial effort has focused on explaining the high richness in the Fynbos biome. While topographic and climatic heterogeneity are good predictors of species richness at the regional scale within the western, strongly winter-rainfall part of the biome (Linder 1991), other studies suggest that historical factors are pre-eminent. Using species-area analysis of regional-scale data sets, and analysis of covariance, Cowling and Lombard (2002) showed a 95 clear geographic diversity pattern in the Fynbos biome: western sites with high winter rainfall have more than double the number of species as eastern sites, a difference which is not explicable by heterogeneity or available energy. Cowling and Lombard (2002) invoked historical factors, arguing that higher Pleistocene climatic stability in the west had resulted in elevated speciation rates and depressed extinction rates there, and hence higher regional richness than in the less climatically stable east, further supported by the higher incidence of natural rare species (both neoendemics and relictual endemics) in the west. A similar climate-stability explanation has been posited to explain the west-east gradient in regional richness in the karroid biomes (Cowling et al. 1998). There is now considerable evidence that climatically stable Pleistocene environments are associated with high richness and endemism across many taxa (Fjeldså and Lovett 1997, Midgley et al. 2001, Cowling et al. 2004, Dynesius et al. 2004).
No general explanation for patterns of regional-scale plant diversity in South Africa is accepted, and the wide range of diversity values recorded here within several phylogenetically distinct floras and six regionally extensive biomes offers great potential for exploring determinants of regional plant richness patterns. Such a study could also overcome problems of previous studies since a) we have region-wide data on >19 000 plant species at a fixed spatial scale, therefore avoiding the pitfalls of variably sized sites and the attendant species-area analyses required to overcome these; and b) new tools in predictive biogeography are now available. Hence, we now have an opportunity to gain a predictive understanding of richness across an entire megadiverse country.
To assess the many deterministic explanations of regional richness (Table 1), we have used a wide array of explanatory variables reflecting contemporary environmental conditions, namely topographic heterogeneity, energy, bioclimatic suitability, seasonality and irregularity 120 (Table 2). We do not address the role historic drivers such as climate history in this analysis, though topographic heterogeneity can be interpreted in this light through providing climatic refugia. Finally, as an independent test of model reliability, we predict invasive plant diversity in South Africa and compare this with observed patterns.
Table 1. Common hypotheses invoked to explain species richness pattern in southern Africa.
|General hypothesis||Result||Southern Africa study||Scale|
|Available energy||Curvilinear relationship between tree species richness and potential evapotranspiration.||O'Brien 1993, 1998||Southern Africa|
| ||Negative and linear relationship between species richness and potential evapotranspiration in arid and semi-arid South Africa.||Hoffman et al. 1994||Semi arid South Africa|
|Topographic heterogeneity||Positive relationship for Fynbos and the two Karroid biomes.||Cowling et al. 1997b||South Africa (plots)|
|Favourableness||Positive relationship between woody species richness and rainfall.||O'Brien 1993, 1998||Southern Africa|
| ||Weak relationship between plant species and rainfall in semi-arid South Africa.||Hoffman et al. 1994||Regional|
|Climate seasonality and irregularity||Plant species richness negatively related to rainfall reliability.||Cowling et al. 1994, 1997b||South Africa (plots)|
Table 2. Explanatory variables used to model pattern of plant species richness at the regional (QDS) scale in South African biomes. The variables are grouped according to hypotheses invoked to explain patterns of regional richness. Abbreviations used throughout the text.
|Topographic heterogeneity|| |
|AHI||StD altitude: standard deviation of all the grid altitude values at 200×200 m in a QDS pixel.|
|MAP1||Mean annual precipitation (mm)|
|MTC1||Mean temperature of the coldest month (°C)|
|AET/PET3||Index of humidity: ratio actual to potential evapotranspiration|
|MAT1||Mean annual temperature (°C)|
|NPP4||Net primary productivity (ton ha−1)|
|PPI1||Plant productivity index: number of months per year receiving more rainfall (mm) than twice the mean annual temperature (°C)|
|PCV1||Coefficient of variation of annual rainfall|