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J. Zool., Lond. (2000) 251, 205±231 # 2000 The Zoological Society of London Printed in the United Kingdom Climate, vegetation, and predictable gradients in mammal species richness in southern Africa Peter Andrews1 and Eileen M. O'Brien2 1 2 Natural History Museum, London SW7 5BD, U.K. Institute of Ecology, University of Georgia, Athens, GA 30602, U.S.A. (Accepted 26 July 1999) Abstract Many hypotheses have been proposed to account for geographic variations in species diversity. In general these relate to some aspect of climate, particularly climatic variables measuring available or potential energy, but while these relate directly to plant diversity they may only indirectly affect mammal species richness. We have examined these relationships by mapping and correlating mammal species richness in southern Africa (n = 285 species) with 15 climatic variables, two topographic variables, and woody plant species richness (n = 1359 species). The effect of area on richness was held a constant by using an equal-area grid cell matrix superimposed on species range maps, with each grid cell equal to 25 000 km2. We found that variability in the plant species richness alone accounts for 75% of the variability in mammal species richness. Of the climatic variables, only thermal seasonality approaches this ®gure, accounting for 69% of the variability, while annual measures of temperature, precipitation or energy account for only 14±35% of variability. Differences from North American mammal diversity studies, where annual temperature, and hence annual potential evapotranspiration (PET), have been found to be more important, are attributed in part to southern Africa's climate and vegetation being largely temperate to tropical, as opposed to temperate to polar in North America. By distinguishing different types of mammal based on size, spatial and dietary guilds, other differences emerge. Strong correlations with annual temperature exist only for large mammals, accounting for 60±67% of the variability in species richness of large mammals compared with < 20% for small mammals. Small mammals are strongly correlated with other climatic or vegetation parameters, especially plant richness and thermal seasonality; frugivorous and insectivorous mammal richness is correlated with thermal seasonality and minimum monthly PET; and arboreal and aerial species richness is correlated with plant richness, thermal seasonality and minimum monthly PET. Up to 77% of the variability in richness of arboreal, frugivorous and insectivorous species can be explained by woody plant richness, compared with only 38±48% of the variability in terrestrial herbivores. It is clear from this that different kinds of mammals are differentially affected by climatic and environmental factors, and this explains some of the discrepancies found in earlier studies where no distinction was made between different sizes or guilds of mammal. This result has implications both for the conservation of mammalian communities at the present time and for understanding the evolution and structure of mammalian communities in the past. Key words: species diversity, environment, ecology, palaeoecology INTRODUCTION Geographic patterns of mammal species diversity are complex, and several different hypotheses have been put forward to account for them (Pianka, 1966; Begon, Harper & Townsend, 1990; Currie, 1991; Rosenzweig, 1997). Generally it is assumed that climate has some controlling in¯uence, but exactly how is not clear, as mammals are more able to tolerate changes in climate than other animals or sessile plants because they are mobile and warm-blooded. It is also assumed that mammal species richness is related to vegetation (Avery, 1993), and a secondary question, therefore, concerns plant diversity: what is the relationship between plant species richness patterns and vegetation structure, and to what extent are they related to mammal diversity? It is not useful simply to relate mammal species richness to that of plants and/or habitat, for this does not answer the fundamental question about the initial causes of predictable geographic patterns of plant or habitat diversity (e.g. Fraser, 1998; Shepherd, 1998), but it is probable that 206 P. Andrews and E. M. O'Brien 15°E 20° 25° 30° 35° 15°S 15° 20 0 1000 20° 00 10 20° Limpopo 00 10 ve Ri Kalahari r 25° 25° River l 20 0 a Va Orange 30° r ve 00 10 Ri 200 30° ≥ 2000 m Great Escarpment 0 km 35° 15° 20° 25° 30° 500 35° 35° Fig. 1. The geographic extent of the southern African study area with topographic relief, taken from Philips Atlas of Southern Africa. the effects of climate on mammal diversity will be indirect, through its effects on vegetation, the source of food and shelter. This study investigates the geographic distribution of mammal species richness in southern Africa to identify macro-scale patterns of diversity. These are related to climatic and vegetation parameters to determine how much of the variation in mammal diversity can be attributed to present-day variations in climate and/or vegetation. Thus, we also describe how the distribution of mammals might alter with changes in climate and/or vegetation. THE STUDY AREA Southern Africa encompasses 4 104 700 km2 of the African continent south of 158S latitude. It is an area of considerable topographic relief (Fig. 1) with mountains rising to > 2000 m encircling the Kalahari Basin, an uplifted plateau with an average altitude of c. 1000 m. Southern Africa's climate is well known (Tyson, 1986). It is dominated by summer rainfall, with a west to east trend of increasing rainfall that ranges from < 100 mm in the west to > 1000 mm in the east. In the south-east mountains and coastal strip there is year-round rainfall. There is also a narrow zone of winter rainfall along the south-western coast with low annual rainfall (100± 200 mm). Solar radiation decreases latitudinally from north to south, but its interception and conversion to heat is modi®ed by elevation and by cloud cover. Cloud cover is least over the Namib and Kalahari deserts and greatest over the mountains of the eastern Great Escarpment. Thus, the energy and temperature regimes manifest a latitudinal gradient in winter, decreasing polewards, and a more longitudinal gradient in summer, decreasing eastwards as rainfall and cloud cover increase. Frost is common in winter on the interior plateau, especially south of the Limpopo River, and at higher elevations in the mountains of the Great Escarpment, especially in the Drakensberg Mountains. Vegetation The vegetation of southern Africa ranges from desert to evergreen rainforest (Cowling, Richardson et al., 1997). Mammal species diversity in Southern Africa More than half of the identi®ed plant taxa for Africa occur in southern Africa, c. 24 000 out of > 40 000 identi®ed taxa. There is a longitudinal west to east gradient in vegetation across Namibia, Botswana and South Africa (Gibbs Russell, 1985, 1987). In terms of ¯oristic associations and af®nities, there are seven major vegetation zones in southern Africa (White, 1981, 1983), which can be summarized brie¯y. The north and east of the study area (other than the coastal strip) is dominated by woodland to forest, including miombo and mopane woodland, dry evergreen forest in wetter areas, and extensive areas of edaphic grassland. Rainfall ranges from 500 to 1400 mm/year. Frosts are infrequent and localized. In the central and southern portions of the Kalahari basin, wooded grassland is the dominant vegetation type, with woodland and bushland in wetter areas and open grassland on ¯ooded or frosted areas. Rainfall is between 250 and 500 mm, and frosts are widespread and severe. South of the Kalahari basin and along the west coast is desert and bushland where rainfall rarely exceeds 250 mm. Frosts are common during the winter months. The Cape ¯ora (fynbos) occupies the south-west of the study area. It is a characteristic Mediterranean type of low bushland and scrubland, with rainfall con®ned to the winter months and relatively low. Frost is infrequent to absent. The Great Escarpment mountains of the south and east are similar in ¯oristic composition to the tropical montane regions but begin at a lower altitude. Vegetation types are extremely variable because of the topographic relief, and they range from grassland to forest. Climate is also extremely variable, but rainfall usually exceeds 1000 mm and frosts (and snow in places) are frequent. The coastal forest extends along the east coast, and the vegetation ranges from forest to transitional woodlands and edaphic grasslands. It is extremely rich in species further north of the study area, but is relatively species-poor in Mozambique. Rainfall is between 800 and 1000 mm and the region is frost-free. METHODS Taxonomic richness Richness is a measure of diversity that considers only the number of different taxa, not their relative abundance. At the landscape to local scales of analysis, richness varies as a function of the differential distribution of individual members of taxa within their distributional ranges. At the macro-scale, variations in richness are a product of the differential overlap in the distributional ranges of taxa. Whereas ground sampling and ®eld identi®cation can be used to measure richness at the landscape-local scales of analysis, at the macroscale, systematic measurement of richness depends on using distributional range maps. In either case, two conditions are necessary for systematic analysis of richness: ®rst, to avoid the confounding in¯uence of area on the measurement of richness, area should be kept 207 constant through the use of equal-area sampling units; second, richness data should pertain only to ecologically similar taxonomic groups (e.g. only woody plants, or only mammals). If prediction is a goal, all relevant taxa (i.e. all mammals, all woody plants) need to be included. Scale of analysis The appropriate scale for analysing richness is determined by the distance or area required to measure spatial heterogeneity in all variables being analysed. This study is at the macro-scale because measurable heterogeneity in climate, the independent variable, occurs over a minimum distance of 100 km (Grif®ths, 1976), i.e. areas of at least 10 000 km2. Measurable heterogeneity in mammal and plant taxonomic richness also occurs at this scale. Based on the scale, resolution and accuracy of the range maps, the sampling area used here is 25 000 km2, each cell being 1586158 km. Within these 25 000 km2 equal-area sampling units, woody plant richness ranges from 6 to 567 species, from 4 to 297 genera, and from 4 to 85 families (O'Brien, 1993; O'Brien, Whittaker & Field, 1998). Mammal data To allow systematic comparison between climate data, plant richness and mammal richness, the same methodology and sampling strategy employed by O'Brien (1993) to determine woody plant taxonomic richness was used here to determine mammal richness patterns. The same grid matrix of 164 equal-area cells was overlain on mammal species range maps to determine the presence or absence of a mammal species per cell (see O'Brien, 1993: ®g. 4). The presence-absence data were then compiled to determine the total number of species, or species richness, per cell. Only 111 cells are truly equal-area and representative, i.e. fall entirely on land within the study area, and all analyses of richness data are limited to these cells. The layout of the grid is shown in Fig. 2, the numbering and lettering of the rows and columns re¯ecting the fact that this is a continent-wide grid applied here just to southern Africa. The sources of standardized range maps for southern African mammals were: Smithers (1983) and Kingdon (1997), with additional data from Wilson & Reeder (1992), Frandsen (1992) and Stuart & Stuart (1988). These maps describe extant ranges for each mammal species, and no attempt has been made to take account of possible human impact beyond what has been done by the original sources. Exotic species and humans were excluded. There are totals of 285 mammal species (212 excluding bats, which were analysed separately), 146 genera and 39 families present in the study area. Mammal richness was also examined in terms of various ecological parameters. Following Andrews, Lord & Evans (1979), each species was identi®ed in terms of 3 ecological parameters: body weight (size), 208 P. Andrews and E. M. O'Brien Fig. 2. Mammal species richness values for the 164 equal-area grid cells in the southern Africa study area. Each grid cell is de®ned by letters horizontally and numbers vertically using a system covering the whole continent of Africa. dietary guild (primary diet), and space occupied (Table 1). Each species was assigned to 1 of 8 weight categories, based on the range of weights into which the mode and the greater part of the species range falls. Dietary guild, or primary diet, was based on the morphology of the teeth that shows the clearest adaptation to major food types (Andrews et al., 1979; Janis, 1988; Shepherd, 1998). The space occupied by each species was indicated by the major locomotor adaptation of the species (Andrews, 1996). The different categories for each of the 3 ecological parameters are de®ned in Table 1. They correspond to the principal morphological adaptation of the animals concerned, not necessarily what their behaviour indicates. For example, terrestrial mammals are those that are restricted to the surface of the ground and lack digging, climbing or ¯ying adaptations. The category semi-terrestrial applies to those mammals that are small enough to treat both sub-surface and above-surface spaces as if they were continuous with the surface of the ground, again lacking specialized digging, climbing or ¯ying adaptations but using tunnels below the surface and rocks and trees above the surface to the same extent as the ground surface. Arboreal mammals are those having specialized adaptations to climbing trees by use of gripping feet, distinguished from scansorial mammals which climb trees by hanging on with claws and having reversible feet. Aquatic, fossorial and aerial mammals have specialization in their skeletons for swimming, digging and ¯ying, respectively. Descriptive statistics for the mammal data are given in Table 1. Plant richness data Plant richness data are limited to woody plants and are taken from O'Brien (1993) and O'Brien et al. (1998). Coates Palgrave (1983) provided the species range maps. Only native species of woody plants were considered. With the exception of some aloes and proteas, all species mapped by Coates Palgrave were at least 2.5 m tall. They are all phanerophytes with aboveground stems (except Welwitschsia), having 1 of the following growth habits: tree, shrub, bush, shrublet, palm/cycad, arborescent, liane, suffrutex, or succulent. Climate data Climate station data were available for only 65 of the 111 equal-area cells (see O'Brien, 1993: ®g. 4). Analyses of the climate's relation to mammal or plant richness is limited to the data from these 65 cells. With the exception of the temperature variables, all climate data were taken from Thornthwaite & Mather (1962) because they provide systematic data on the water budget variables (potential evapotranspiration, precipitation (rainfall), de®cit, surplus and actual evapotranspiration) which is based on real not interpolated data. The temperature data were provided by Climate Impacts LINK Project (unpubl. data). The climate data fall into 3 main categories: energy (including temperature), water, and terrain (see Table 2). Temperature is considered here in detail Mammal species diversity in Southern Africa 209 Table 1. Descriptive statistics and de®nitions of taxonomic and ecological variables. nSUM, number of species in each category; n, number of grid cells analysed; X, any ecological category excluding bats n = 65 sample n = 111 sample Variable nSUM Mean sd Min Max Mean sd Min Max Taxonomica PSPECIES PGENUS PFAMILY MSPECIES MSPECX MGENUS MFAMILY 1353 450 110 285 212 146 39 203.0 114.4 47.1 88.1 72.3 69.1 31.7 156.0 79.7 21.0 21.0 11.9 12.6 3.3 27 19 13 55 48 47 25 567 297 83 143 101 102 38 152.7 87.4 38.1 82.7 69.1 65.8 31.0 143.2 75.2 21.3 20.7 12.5 12.7 3.5 6 4 4 51 39 41 21 567 297 85 143 101 102 38 102 65 19 13 75 5 25 45.5 19.4 6.8 4.5 15.6 2.6 3.8 7.9 3.7 3.0 1.1 10.5 1.6 1.0 32 12 2 2 5 0 2 60 27 15 7 42 11 6 45.0 18.0 5.9 4.8 13.5 2.0 3.8 7.7 3.8 3.1 1.0 9.5 1.5 0.9 23 12 1 2 4 0 1 64 27 15 8 42 11 6 144 79 43 35 91 41 29 7 29.2 15.6 11.0 9.0 20.8 10.5 17.0 3.4 11.8 3.8 3.7 2.8 2.9 2.6 2.3 1.2 15 9 3 2 15 4 12 1 61 24 22 17 27 16 22 6 26.3 14.5 10.1 8.4 20.4 9.7 16.5 3.3 11.2 3.8 3.6 2.7 2.8 2.8 2.3 1.3 14 8 3 2 15 3 10 0 61 26 23 18 28 16 22 6 151 86 45 37 34 19 15 4 8 7 196 230 249 34 19 35.1 21.4 10.4 8.4 15.3 11.5 6.3 1.4 2.8 4.8 45.5 60.9 72.4 15.6 9.2 11.9 4.3 2.6 2.3 3.3 2.0 2.1 0.6 1.4 0.9 13.8 16.5 18.0 4.6 2.6 18 12 6 5 9 7 3 1 1 2 25 36 44 9 6 67 33 20 15 24 17 10 3 6 7 87 108 120 24 15 31.8 20.0 9.7 8.0 14.3 10.7 6.3 1.5 3.2 4.7 41.6 55.9 66.7 15.9 9.6 11.6 4.4 2.4 2.0 3.5 2.1 2.2 0.6 1.3 1.1 13.4 16.4 18.0 4.4 2.5 17 12 6 5 6 5 3 1 1 1 24 36 43 7 4 67 34 20 15 24 17 11 4 6 7 87 108 120 27 17 Ecologicalb Space utilization TERR SEMTERR ARB SCANS AER AQ FOSS Dietary guild INSECT INSECTX FRUG FRUGX BROW GRAZ CARN OMNI Body sized A AX B BX C D E F G H AB ABC ABCD EFGH FGH a PSPECIES, woody plant species richness; PGENUS, woody plant genus richness; PFAMILY, woody plant family richness; MSPECIES, mammal species richness; MSPECX, mammal species richness excluding bats; MGENUS, mammal genus richness; MFAMILY, mammal family richness. b T, Terrestrial; B, semi-arboreal; A, arboreal; S, scansorial; Aq, aquatic; F, fossorial; R, aerial. c I, Insectivory; F, frugivory; B, browsing; G, grazing; C, carnivory; O, omnivory. d A, 0±100g; B, 100 g±1 kg; C, 1±10 kg; D, 10±45 kg; E, 45±90 kg; F, 90±180 kg; G, 180±360 kg; H, > 360 kg. because of its common use, especially annual temperature, in faunal analyses and palaeoenvironmental reconstructions (Crowley & North, 1991; Vrba, 1995). Energy variables These are based on Thornthwaite's potential evapotranspiration (PET; Thornthwaite & Mather, 1955) and temperature. PET is a recognized measure of the energy regime. It describes the amount of energy available from insolation (light) and subsequent terrestrial radiation (heat) per day, month or year. PET is empirically derived based on the amount of water required to meet the environmental energy demand for evaporation and biological processes. Because latitude is considered when calculating PET, it also includes the effects of variable day length on the energy regime. Thornthwaite's PET, unlike other measures of PET, is not adjusted to sea level, and thus the effect of elevation on PET is included. 210 P. Andrews and E. M. O'Brien Table 2. Descriptive statistics and acronyms used for climate and terrain variables (n = 65) Variable Energy variables (mm) PET, potential evapotranspiration PEAN, annual PET PEMAX, maximum monthly PET PEMIN, minimum monthly PET DIFPE, range, seasonal variability Temperature variables (8C) TAN, annual TMAX, maximum monthly TMIN, minimum monthly DIFT, range, seasonal variability Moisture variables (mm) PAN, annual precipitation PMAX, maximum monthly precipitation PMIN, minimum monthly precipitation DIFP, range, seasonal variability PMI, Thornthwaite's moisture index AET, actual evapotranspiration AEAN, annual AET RAINS, growing season (months) DRY, dry season (months) Terrain variables (m) ELEV, elevation above sea level TOPOG, topographic relief Mean sd Min Max 938.8 129.2 28.0 101.1 176.4 23.0 11.1 23.6 691.0 94.0 14.0 62.0 1479.0 198.0 60.0 177.0 18.6 23.6 12.2 11.4 2.7 2.2 3.3 2.4 12.4 18.5 5.6 6.9 24.2 27.3 19.9 16.1 610.6 121.6 6.6 115.0 732.6 277.9 59.0 6.4 58.5 33.6 55.0 15.0 0 14.0 796.0 1323.0 298.0 30.0 284.0 48.0 557.8 6.4 216.2 2.3 55.0 0 995.0 11.0 1041.7 796.7 409.9 532.3 19.0 118.0 1972.0 2718.0 When PET is combined with information on the actual amount of water available to meet the environmental demand, it is possible to determine AET, the actual evapotranspiration or the amount of water that can actually be used for evaporation and biological processes at any given place or time. Average minimum and maximum monthly PET (PEMIN and PEMAX), the range between them, or seasonal variability in the energy regime (DIFPE), and average annual PET (PEAN) are examined. Temperature is a partial index of the environmental energy regime. It describes the degree of heat. It is not a measure of day length, nor of the total amount or duration of heat. Thus, monthly and annual temperature values only describe the average daily degree of heat per month or year. Average minimum (TMIN) and maximum (TMAX) monthly temperature, the range between them, or thermal seasonality (DIFT), and average annual temperature (TAN) are examined. Water variables Precipitation is the main water variable considered. It is a primary, albeit partial, measure of the water available to meet the environmental demand for water, soil moisture not being included. Annual, maximum and minimum monthly precipitation, and the range between them (PAN, PMAX, PMIN and DIFP, respectively) are considered, as well as several other water variables. These are: (1) annual AET (AEAN, see above), which in addition to providing information on the effective moisture regime, is a recognized proxy for net primary productivity and biological activity (cf. Rosenzweig, 1968); (2) Thornthwaite's moisture index (PMI), which is an index of the effective moisture regime that is used in the classi®cation of vegetation in southern Africa; (3) the duration of the dry season (DRY), i.e. number of months with zero precipitation; and (4) the duration of the growing season (RAINS), i.e. the number of months with at least 25 mm rainfall (cf. Schulze & McGee, 1977). Terrain variables These are elevation (ELEV) and topographic relief (TOPOG). Both variables are directly related to variability in climate. Changes in elevation cause predictable changes in temperature ( 6.58 per 1 km). Changes in topographic relief cause adiabatic cooling and heating of air forced to rise and fall ( 5±10 8C/km) as variability in elevation changes, and this produces orographic rainfall/cloud cover and/or rainshadows. Topographic relief is de®ned here as the range between the minimum and maximum elevation per cell (> 90 samples per cell). It does not take into account the number of times the elevation changes within a cell, or lesser changes in height, i.e. those changes less than the maximum change. Although elevation is often considered a reasonable proxy for topographic relief, these 2 variables are not signi®cantly correlated with each other at the macro-scale. This is reasonable when we consider that topographic relief can be low at both high and low elevations (e.g. elevated plateaux and coastal plains, respectively). Major variations in elevation and topographic relief for the study area are shown in Fig. 1. Mammal species diversity in Southern Africa 15° (a) 15° 20° 25° 30° 35° 30 40 15° 15° (b) 15° 211 20° 25° 30° 15° 50 45 30 20° 20° 20° 5 10 <1 25° 20 20° 40 20 <1 35° 30 25° 10 25° 35 30 20 15 25 25° 20 30° 30° 30° 30° 35° 35° 0 15° 20° 25° 30° km 500 0 35° 15° 35° 450 20° 25° 30° 500 km 35° 35° 600 (c) South–north transect (d) West–east transect 400 Woody plants 350 500 Total mammals 300 No. of species Woody plants Total mammals 400 250 300 200 150 200 100 100 50 0 Grid cells ii11 hh11 gg11 ff11 ee11 dd11 cc11 bb11 aa11 Z11 Y11 X11 V11 U11 gg13 ff12 ff11 ff10 ff9 ff8 ff7 ff6 ff5 ff4 0 Grid cells Fig. 3. Geographic variation in mammal species in southern Africa. Species richness distribution based on isoclines of numbers of taxa per grid cell for (a) plants and (b) mammals; species numbers are converted into percentages in both cases so that mammal richness (n = 285) can be compared with woody plant species richness (n = 1353). Variability in numbers of species of woody plants and mammals on (c) a south to north transect along 308E longitude, and (d) a west to east transect along 208S latitude. Statistical analyses and model development Statistical analyses were performed using a variety of SAS programs for descriptive statistics correlation, analysis of variance and linear or multiple regression. For model development, SAS regression procedure, RSQUARE, was used to describe the n most powerful 1-, 2-, and 3-variable regression models of mammal species, genus and family richness. The criteria used to select which of the generated models 'best' describe how richness and/or climate relate to mammal richness were: (1) explanatory power (coef®cient of determination r2 ); (2) simplicity (least number of variables); (3) maximum level of precision (low root mean square error, RMSE); (4) parsimony (least number of assumptions). 212 P. Andrews and E. M. O'Brien Table 3. Taxonomic relations: mammal±plant richness. Pearson's product moment correlations. P < 0.0001. Abbreviations as in Table 1 PSPECIES PGENUS PFAMILY MSPECIES MSPECX MGENUS MFAMILY A (n = 111) PSPECIES PGENUS PFAMILY MSPECIES MSPECX MGENUS MFAMILY 0 0.9936 0.9288 0.8653 0.8216 0.8350 0.7043 0.9936 0 0.9405 0.8772 0.8313 0.8479 0.7110 0.9288 0.9405 0 0.7777 0.7672 0.7625 0.6227 0.8653 0.8772 0.7777 0 0.9546 0.9860 0.8861 0.8216 0.8313 0.7672 0.9546 0 0.9644 0.8975 0.8350 0.8479 0.7625 0.9860 0.9644 0 0.9130 0.7043 0.7110 0.6227 0.8861 0.8975 0.9130 0 B (n = 65) PSPECIES PGENUS PFAMILY MSPECIES MSPECX MGENUS MFAMILY 0 0.9925 0.9216 0.7244 0.7345 0.7030 0.5932 0.9925 0 0.9373 0.7358 0.7488 0.7191 0.5977 0.9216 0.9373 0 0.6047 0.6768 0.5987 0.4788 0.7243 0.7358 0.6047 0 0.9450 0.9869 0.8853 0.7345 0.7488 0.6768 0.9450 0 0.9497 0.8619 0.7030 0.7191 0.5987 0.9869 0.9498 0 0.8993 0.5932 0.5977 0.4788 0.8853 0.8619 0.8993 0 Table 4. Correlations of temperature variables with each other and with other climate variables. Abbreviations as in Table 2. n = 65; P < 0.0001; NS = not signi®cant if P > 0.01 TMAX TMIN DIFT TAN PEMAX PEMIN DIFPE PEAN PMAX PMIN DIFP PAN AEAN PMI RAINS ELEV TOPOG TMAX TMIN DIFT TAN 1.00000 0.0 0.67858 0.0001 0.67858 0.0001 1.00000 0.0 70.73948 0.0001 0.94732 0.0001 NS 70.73948 0.0001 1.00000 0.0 70.49922 0.0001 0.86248 0.0001 0.94732 0.0001 70.49922 0.0001 1.00000 0.0 NS 0.86248 0.0001 0.74249 0.0001 0.34141 0.0054 0.56204 0.0001 0.76532 0.0001 NS 70.41572 0.0006 NS NS NS 70.56975 0.0001 70.34886 0.0044 70.46977 0.0001 70.65118 0.0001 0.38263 0.0017 0.85415 0.0001 NS 0.80696 0.0001 NS 70.84994 0.0001 0.55741 0.0001 70.39717 0.0011 0.48110 0.0001 70.73954 0.0001 NS 0.50212 0.0001 NS 70.72695 0.0001 70.67217 0.0001 70.66907 0.0001 70.45155 0.0002 NS 0.32068 0.0092 NS NS 70.55902 0.0001 NS NS 0.33051 0.0072 NS 0.53041 0.0001 0.68643 0.0001 NS 0.83488 0.0001 NS 70.34261 0.0052 0.34979 0.0043 NS NS NS NS 70.50355 0.0001 70.44591 0.0002 To reduce the potential for multicollinearity amongst the independent variables, only 1 each of the energy, water, or terrain variables was allowed in a model of climate's relation to richness. Models of mammal richness based on vegetation parameters could only combine plant richness with temperature and/or terrain variables (all other variables being inherent in plant richness). No grid cells were removed as outliers, the assumption being that the unexplained variance is more likely to be a function of missing variables than of mis-measurement. The results are presented in the following sequence: (1) mammal taxonomic richness and how it relates to plant taxonomic richness based on the full sample of 111 grid cells, i.e. those cells that do not extend over land or sea boundaries; (2) how temperature relates to other climate variables, and how all climate variables relate to mammal or plant richness, based on the smaller sample of 65 grid cells with climate stations; (3) how ecological subsets of mammal richness relate to each other, to all-mammal and plant richness (n = 111), and to climate (n = 65). The signi®cance level of all statistical results presented in text is P < 0.0001, unless otherwise indicated. RESULTS Geographic patterns in mammal richness in relation to vegetation The variation in numbers of mammals per grid cell across southern Africa is illustrated in Fig. 2, and the pattern of mammal species richness is shown in Fig. 3b as isoclines of species numbers per grid cell. There is only a weak latitudinal gradient of increase in numbers of mammal species from the south of the study area to the north (Fig. 3b,c). By contrast, the woody plant Mammal species diversity in Southern Africa 213 Table 5. Correlations between plant and mammal richness and climate. Abbreviations as in Table 1. n = 65; P < 0.0001, unless otherwise indicated: ** = P < 0.001, * = P < 0.01; NS = not-signi®cant when P > 0.01 PEMAX PEMIN DIFPE PEAN TMAX TMIN DIFT TAN PMAX PMIN DIFP PAN AEAN PMI RAINS ELEV TOPOG PSPECIES PGENUS PFAMILY MSPECIES MSPECX MGENUS MFAMILY 70.356* 0.617 70.639 NS NS 0.413** 70.798 NS 0.750 0.335* 0.719 0.775 0.701 0.662 0.458 NS 0.580 70.343* 0.655 70.644 NS NS 0.428** 70.821 NS 0.761 0.387* 0.723 0.795 0.731 0.669 0.490 NS 0.585 70.502 0.470 70.711 NS 70.475 NS 70.689 NS 0.671 0.486 0.622 0.778 0.711 0.741 0.573 NS 0.678 NS 0.712 70.421 0.375* NS 0.702 70.830 0.530 0.740 NS 0.741 0.593 0.532 0.391* NS NS NS NS 0.669 70.473 0.319* NS 0.627 70.794 0.460 0.737 NS 0.732 0.649 0.601 0.468 NS NS NS NS 0.741 70.418** 0.403** NS 0.714 70.835 0.541 0.725 NS 0.726 0.579 0.539 0.368* NS NS NS NS 0.676 70.377* 0.397** NS 0.715 70.734 0.598 0.584 NS 0.598 0.422** 0.434** NS NS NS NS richness does not show a clear trend latitudinally. From west to east, the dominant pattern is longitudinal, with a trend of increasing richness in both plant and mammal species that is greater for the plants than for the mammals (Fig. 3d). The Namibian coast has the lowest mammal richness extending eastwards into the Kalahari. The highest mammal richness occurs in the Zambezi forests of Zimbabwe, Mozambique and South Africa, along the highlands of the eastern Great Escarpment, as well as the eastern coast, and these are also the areas of highest plant species richness. The geographic pattern of variation in mammal richness is similar at all taxonomic levels. When numbers of genera are plotted in the same way, the distribution is nearly identical to that for species, but because of the comparatively small number of families (39) and their small range of variation (21±38 families per cell), this trend is less obvious. The similarities in mammal distribution are supported by high correlations (Table 3): r = 0.986 between species and genus; r = 0.913 between genus and family; and r = 0.886 between species and family. Similarly strong correlations also occur for the subset of 65 cells with climate station data (Table 3). Correlations between plant species richness and mammal richness range from r = 0.704 for mammal family richness to r = 0.865 for mammal species richness (Table 3). Similar patterns and correlations occur between plant genus/family richness and mammal richness at all taxonomic levels, although the correlations between plant family richness and mammal richness are weaker. In all cases the correlations between plant and mammal richness for the sample of 65 cells that have climate data are weaker. For example, for plant/ mammal at n = 111, r = 0.865 compared with r = 0.724 for n = 65. This suggests that some factor affects plant and mammal richness in the 65 grid cell subset differently from the whole set of 111 equal-area cells, and one such factor could be terrain. How temperature relates to other climate variables The correlations among temperature parameters, and between them and the other climate variables are presented in Table 4. Annual temperature seems to be an ambiguous measure of the climate regime, and the most critical temperature variables are minimum monthly temperature (TMIN) and the difference between maximum and minimum monthly temperatures (DIFT). These are both measures of thermal seasonality, whereas annual temperature can be the same whether the monthly temperature is the same year-round or highly seasonal. Thermal seasonality (DIFT) in southern Africa is most strongly correlated with changes in minimum monthly potential evapotranspiration (PEMIN, r = 0.850) as well as with minimum monthly temperature (TMIN, r =70.740). Thermal seasonality (DIFT) is also the temperature variable most strongly correlated with variations in the water variables (Table 4), especially those shown to be strongly correlated with plant richness (Table 5). The water variables most highly correlated with thermal seasonality are maximum monthly precipitation (PMAX, r =70.739) and the difference between maximum and minimum monthly precipitation (DIFP, r =70.726). These variables are greatest when thermal seasonality is least. By contrast, annual temperature is only weakly correlated with two of the water variables, so that in effect, changes in annual temperature are ambiguous or poor indicators of changes in the rainfall regime. Consistent with how elevation affects air temperature, the terrain variables tend to be weakly and negatively correlated with all temperature variables (Table 4). How climate variables relate to mammal and woody plant richness The climate variables most strongly correlated with 214 P. Andrews and E. M. O'Brien Table 6. Pearson's product moment correlations between ecological and climate variables. n = 65; P < 0.0001 unless otherwise Energy variables PEMAX Ecological categories Space utilization TERR NS SEMTERR 70.47639 ARB NS SCANS NS AER NS AQ NS FOSS NS PEMIN DIFPE 0.68535 70.34859* NS 70.48031 0.70728 70.38496* NS NS 0.66712 NS NS NS 70.42940** NS Temperature PEAN TMAX 0.45184** NS NS 70.47166 0.38779* NS NS 0.38200* 0.39241* NS NS 70.35203* 70.44833* 70.38786* Dietary guilds INSECT NS INSECTX 70.37884* FRUG NS FRUGX NS BROW NS GRAZ NS CARN NS OMNI NS 0.60226 NS 0.72015 0.66965 0.50659 0.54461 0.53925 0.70105 70.40344** 70.48435 70.37068* 70.40024** 70.36065* 70.37487* NS NS NS NS 0.41239** 0.35807* NS NS 0.36670* 0.47526 Body size A AX B BX C D E F G H 0.59722 NS 0.50694 NS 0.59042 0.51713 0.69075 0.66865 0.59017 0.59242 70.42018** 70.48178 NS NS 70.47610 70.40036** NS NS NS NS NS NS NS NS NS NS 0.57295 0.64892 0.51786 0.50743 NS 70.38982* NS NS NS NS NS NS NS NS mammal richness in southern Africa are those describing seasonal variability (Table 5): maximum monthly rainfall (PMAX), rainfall seasonality (DIFP), minimum monthly PET (PEMIN) and the seasonal temperature variables DIFT and TMIN. This is consistent with the study area being dominated by summer rainfall that can last from < 1 month to > 7 months. In effect mammal richness tends to be greatest where seasonal variability in the water regime is least. Here the diversity of food resources (plant richness) and their amount and duration (i.e. productivity or annual AET) are greatest, and as seasonal variability in the water regime increases, the diversity, amount and duration of food resources tend to decrease. The terrain variables are signi®cantly correlated to only plant richness. The weak correlation with mammal richness suggests that changes in elevation or topographic relief are unlikely to affect mammal richness unless this is associated with changes in the vegetation. The weak or insigni®cant correlations between annual measures of the energy regime and mammal/plant richness and the other climate variables (Table 5) further emphasize the importance of climatic seasonality. The energy regime, as de®ned by annual potential evapotranspiration (PET) and annual temperature (TAN) is not signi®cantly correlated with plant richness NS NS NS NS NS NS NS 0.35518* TMIN DIFT 0.74958 NS 0.64677 NS 0.69578 NS 70.52042 70.73027 0.64532 NS NS 70.79422 0.47377 NS 0.35489* 70.76002 0.54113 NS NS 0.35302** 70.50626 0.62821 NS 0.66326 0.60483 0.49282 0.4187** 0.53229 0.70457 NS 0.58896 70.35636* NS NS 0.4483** NS NS NS 0.54898 NS 0.32905* 0.51587 0.81841 0.45471 0.73517 0.45354 70.55808 0.45082** 0.67143 TAN 70.77082 70.52604 70.81213 70.76926 70.58215 70.60668 70.54389 70.63367 0.46380 NS 0.48177 0.43870** 0.36533* NS 0.45310** 0.59420 70.77234 0.44867** 70.51392 NS 70.73578 70.60593 70.64138 70.58411 0.67149 70.50091 0.40957** NS 0.34364* NS 0.38165* NS 0.76999 0.68406 0.39507* 0.63938 (P > 0.01) and is only weakly correlated with mammal richness (r < 0.598). Correlations of ecological subsets of mammal species richness Most analyses of mammal species richness consider mammal faunas as a whole (for an exception see Shepherd, 1998), but it is probable that different types of mammal would interrelate with climatic variables to different degrees. We have therefore examined species richness patterns for different categories of mammals according to body size and spatial and dietary guilds. Some of these ecological subsets have few species (< 15 species) with little variation per cell (Table 1), and sometimes we have combined classes of mammals, particularly different size classes. Correlations with climate variables are shown in Table 6, and to investigate these patterns in terms of geographic distributions, we have mapped species numbers as isoclines on the same scale as for total diversity but subdivided into ecological categories. The isoclines are based on percentage numbers of species to make the maps comparable one with another and with Fig. 3. Mammal species diversity in Southern Africa 215 indicated: **P < 0.001, *P < 0.01.) Correlations > 0.7 are in bold; NS, not signi®cant at P > 0.01 Water variables PMAX PMIN DIFP PAN Terrain AEAN PMI RAINS ELEV 0.62560 0.44867** 0.67915 NS 0.65025 NS 70.36780* NS NS NS NS NS NS NS 0.63796 0.43427** 0.66725 NS 0.65907 NS 70.40339** 0.45866 0.51078 0.61309 NS 0.45734 NS NS 0.45894 0.4159** 0.54518 NS 0.39159* NS NS NS 0.58085 0.41925** NS NS NS NS NS NS NS NS NS NS NS 0.70091 0.54933 0.70508 0.71042 0.50215 0.55847 0.46222 0.52177 NS NS NS NS NS NS NS NS 0.70762 0.54232 0.69508 0.70191 0.49959 0.55259 0.46159 0.52273 0.54087 0.54444 0.60055 0.62486 0.38859* 0.53876 0.39706* 0.36144* 0.46440 0.46420 0.56405 0.60921 0.32764* 0.52562 0.43089** 0.31809* 0.37616* 0.51842 0.37876* 0.42009** NS 0.40299** NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS 0.40136** 0.69594 0.48728 0.61470 0.43590** 0.64935 0.49159 0.51702 0.41212** NS 0.39999* NS NS NS NS NS NS NS NS NS NS 0.69752 0.46725 0.60407 0.42943** 0.64197 0.47166 0.54187 0.43306** 0.41644** 0.40858** 0.56131 0.54830 0.51325 0.38347* 0.55499 0.49973 0.32709* NS NS NS 0.47218 0.44117** 0.51068 0.42059** 0.50761 0.40941** 0.34950* NS NS 0.36928* 0.40880** 0.55450 0.33389* NS 0.40628** 0.42249** NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS NS 0.35349* 70.35247* NS 70.40250** Spatial context Four categories of spatial context are shown here. Figure 4a shows the distribution of aerial species, which in southern Africa consist entirely of bats, both insectivorous and frugivorous. Their distribution is strongly correlated with that of woody plant species (Table 7, r = 0.799) and the pattern in Fig. 4a is similar to both the woody plants and mammal species in total (compare Fig. 4a with Fig. 3). Of the climatic variables shown in Table 6, aerial mammals are most strongly correlated with thermal seasonality (DIFT, r =70.760) and to a lesser extent with minimum monthly temperature (TMIN, r = 0.70) and minimum monthly PET (0.667), both also measures of seasonality. The distribution of terrestrial species (Fig. 4b) is slightly different from the total mammal distribution in that there is less of a peak in species richness in the north-east of the study area and less of a dip in the central Kalahari basin. Unlike most other ecological categories, there is a marked increase in richness in the north of the study area, which is the southern part of the Okavanga swamp. This area is rich in large mammals, all but one of which are terrestrial. Terrestrial species richness is less strongly correlated with plant species richness than are the bats (Table 7), but like the NS NS 70.36182* NS NS NS NS TOPOG NS 0.40171** NS NS NS NS NS NS NS NS NS NS NS NS NS NS 0.37648* NS NS NS 0.36588* NS NS NS NS bats they are highly correlated with the same two temperature variables, TMIN (r = 0.749) and thermal seasonality DIFT (r =70.730) (Table 6). Terrestrial mammals also have higher correlations with annual temperature (TAN) and annual PET than any other ecological category, and this will be seen to be signi®cant when body size is considered below. Arboreal and scansorial species (Fig. 4c) have much the same pattern and they are shown here together since scansorial species are not common in southern Africa. Arboreal mammal species distributions are highly correlated with woody plant species (Table 7, r = 0.882) and with mammals in the frugivorous and insectivorous dietary classes (r = 0.853 to 0.865). In terms of climate, they are correlated with thermal seasonality (DIFT, r =70.794), and they are also correlated with minimum potential evapotranspiration (PEMIN, r = 0.707), another measure of seasonality (Table 5). They are poorly correlated with annual temperature but more highly correlated with water variables. The ®nal spatial type shown here are fossorial species (Fig. 4d) and this is of interest because there is little similarity in pattern of species richness variation with the other mammals. The greatest diversity is in the southern and south-eastern region, with a minor peak in southern Mozambique. Fossorial distributions have no 216 (a) 15° P. Andrews and E. M. O'Brien 15° Aerial 20° 25° 40 35 30 20° 25° 30° 10 45 35° 15° 15° 20° Arboreal/scansorial 25° 30° 35° 35° (b) 0 15° 15° Terrestrial 25° 20° 35° 15° 20° 10 25° 20° 40 50 35 15 25° 30 20 30° 20° 30° 60 20° 10 15 15° 25° 50 55 20 5 (c) 15° 30° 25° km 500 30 35° 35° 35° 30° 30° 30° 0 15° 35° (d) 15° 15° 20° 15° Fossorial 25° 20° 25° 30° km 500 35° 30° 35° 15° 10 55 45 40 25° 20° 55 60 20° 30 30° 30° 0 35° 30° 20° 10 25° 5 10 15 15 25° 20 10 15 40 25° 10 15 25° 20° 20 50 45 50 20 15° 20° km 500 35° 30° 30° 35° 35° 0 15° 20° 25° 30° km 500 35° 35° Fig. 4. Isocline maps of distributions of percentage mammal species richness categorized by spatial distribution. Species richness distribution for (a) ¯ying (aerial) mammal species; (b) terrestrial mammal species; (c) tree-living (arboreal and scansorial) mammal species; (d) mammals with fossorial adaptations. Species numbers are converted into percentages of the total number of species in southern Africa (n = 285). signi®cant correlation with overall mammal or woody plant distributions (Table 7), and correlations with all other ecological classes are non-signi®cant (Table 6). Of particular interest, however, is that the fossorial species richness pattern is negatively correlated at low levels of signi®cance with most climate variables with the exception of DIFT (Table 6). Thermal seasonality has been seen above to be strongly negatively correlated with most aspects of plant and mammal richness patterns, but it is positively correlated with fossoriality (Table 6, r = 0.353, P < 0.001). This is a low level of probability compared with most other aspects of this analysis, but it is accepted here as a genuine re¯ection of an important aspect of the fossorial adaptation, namely association with seasonal environments with their greater abundance of geophytes and hemicryptophytes, which provide an important food source for fossorial vegetarians. Other than this, the distribution of fossorial species is probably linked with soil type, which we have not analysed in the present work. Diet Several of the dietary classes show patterns differing from the general mammalian pattern, and they make an interesting comparison with those that are similar. Figures 5a & 5b show the distributions of insectivorous Mammal species diversity in Southern Africa 217 Table 7. Correlations between ecological variables and plant and mammal taxonomic richness for species, genera and families (n = 111). Abbreviations as in Table 1. Correlations > 0.7 are in bold TERR SEMTERR ARB SCANS AER AQ FOSS INSECT INSECTX FRUG FRUGX BROW GRAZ CARN OMNI PSPECIES PGENUS PFAMILY MSPECIES 0.61663 0.62873 0.88252 70.24513 0.79940 0.34764 70.08329 0.85092 0.75683 0.82177 0.73565 0.62352 0.69589 0.43290 0.65911 0.61495 0.63045 0.90226 70.23828 0.81267 0.38057 70.07519 0.85972 0.75545 0.84303 0.75561 0.62063 0.70015 0.45930 0.66808 0.49760 0.72127 0.81512 70.33086 0.68045 0.46981 0.00355 0.76586 0.76610 0.72914 0.66596 0.60642 0.65778 0.39768 0.53540 0.83799 0.61399 0.90597 0.05657 0.92211 0.31082 70.12981 0.96453 0.78873 0.90004 0.83292 0.73177 0.76452 0.69278 0.79408 and frugivorous mammal species, respectively, and these both conform to the general pattern for mammals. Both are strongly correlated with distributions of arboreal and aerial species distributions (Table 6, r = 0.853 to 0.962), and the relationship is shown here in Fig. 6a±c. These combinations of off-the-ground space utilization with frugivory and insectivory are highly dependent on woody plant species richness patterns (Table 7, r = 0.82 to 0.85, although excluding bats r = 0.74 to 0.76). Like the plants also, the distributions of insectivores and frugivores are strongly correlated with thermal seasonality (Table 6, DIFT, r =70.769 to 70.812) and to a slightly lesser degree with moisture variables maximum monthly precipitation (PMAX) and rainfall seasonality (DIFP). It was unexpected to ®nd the distribution of insectivorous mammals so similar to that of woody plants and with the highest correlation of any dietary type (Table 7). Insectivores are secondary consumers at a higher level in the food chain than the insects they are eating, and it would seem that this similarity is the result of both the limited ranges of vegetation types to which the insects are adapted and the limited diversity of insects that the mammalian insectivores are eating. A rather different pattern emerges when carnivorous mammals are analysed separately (Fig. 5c). Carnivores are at least another level up the food chain and have no direct interaction with plants, and not surprisingly their distribution is less tied in to that of plants or other mammals. Rather more surprising is that carnivores have such a poor relationship with their prey, at least in terms of species richness per equal-area grid cell, and this is shown in Fig. 6d, which relates carnivore species richness to that of browsing herbivores. In fact carnivore distribution is more strongly longitudinal than those of other mammals and it ignores the change to winter rainfall in the Cape region. Species richness of carnivores increases rapidly eastwards from the western part of the study area, so that even the Kalahari region has nearly 50% of the southern African carnivore species. Their pattern of distribution is moderately correlated MSPECX 0.88058 0.67096 0.90493 0.10450 0.76556 0.37292 0.02106 0.86508 0.84969 0.86731 0.84283 0.71477 0.85323 0.75347 0.79484 MGENUS MFAMILY 0.86057 0.60579 0.88662 0.08854 0.88058 0.32523 70.08042 0.93161 0.78108 0.89000 0.84019 0.73415 0.78228 0.72771 0.81106 0.89323 0.46539 0.78971 0.16823 0.75342 0.18832 70.02961 0.80724 0.69167 0.79688 0.77492 0.70129 0.73637 0.68597 0.80792 with several climatic variables (Table 6) but not strongly so with any single variable, and the only ecological parameter they are correlated with is terrestrialism (Table 6, r = 0.751). They have the lowest correlation with vegetation of any dietary type (Table 7). Browsing and grazing herbivores have distribution patterns that are both different from each other and different from other mammals (Fig. 5d, e). Even the correlation of browsers with numbers of carnivores per grid cell was not pronounced (Fig. 6d). Over most of the study region, at least 20% of both the 91 browsers and 41 grazers are present, whereas < 20% of insectivores are present over large parts of the western two-thirds of the region. This means that these herbivore species have broader distributional ranges that encompass within them wider ranges of vegetation types than mammals that have insectivorous or frugivorous diets. Like the carnivores, however, the browsers and grazers are only moderately correlated with climate variables and not strongly with any one variable (Table 6), and they are not as strongly correlated with plant species distributions as are frugivores and insectivores, although they are more strongly correlated than the carnivores (Table 7). They are correlated with terrestrial space use, and both have distribution peaks in the north of the study area (as has been seen for terrestrial mammals) where it passes into the Okavanga delta region. This is particularly marked in the case of grazing mammals, and it is the only part of southern Africa with a suf®cient variety of resources to support three species of alcelaphine antelope. In general, browsers and grazers lack strong correlations with any one climatic variable (Table 6), and they are less highly correlated with vegetation than are frugivores and insectivores (Table 7). Body size Many of the distributions of ecological classes are known to be strongly size-related. For example insectivorous and aerial species are mostly small, frugivorous 218 (a) 15° P. Andrews and E. M. O'Brien 15° 20° Insectivorous 25° 30° 35° (d) 15° 15° 15° 20° 25° Herbivorous browsing 30° 35° 15° 30 35 40 20° 20° 10 25 25° 10 20 25° 15 20 30° 30° 15 30° 30° 35° 35° 0 15° 15° 10 25° 25° (b) 20° 20° 10 15 20 30 25 20° 15° 20° Frugivorous 25° km 500 30° 25° 35° 35° 30° 0 15° 35° (e) 15° 15° 20° 25° km 500 30° 15° 20° 25° Herbivorous grazing 35° 35° 30° 35° 15° 40 30 25 35 30 20° 45 25 50 20° 25 25° 25° 30 35 10 15 25° 25° 20 30° 30° 30° 30° 35° 35° 0 15° 15° 20° 35 30 20 1015 (c) 40 25 20° 20° 15° 20° Carnivorous 25° km 500 30° 25° 35° 35° 30° 0 15° 20° 25° 30° 35° km 500 35° 35° 15° 60 20° 20° 60 50 25° 20 10 30 40 25° 60 40 30° 30° 0 35° 15° 20° 25° 30° km 500 35° 35° Fig. 5. Isocline maps of distributions of percentage mammal species richness categorized by major dietary guild. Species richness distribution for (a) insectivorous mammal species; (b) frugivorous mammal species; (c) carnivorous mammal species; (d) browsing herbivorous mammal species; (e) grazing herbivorous mammal species. Species numbers are converted into percentages of the total number of species in southern Africa (n = 285). 16 70 (a) Arboreal/frugivores 14 60 No. of insectivorous species 12 No. of arboreal species (c) Arboreal/insectivores 10 8 6 4 50 40 30 20 0 0 5 0 10 15 No. of frugivorous species 20 25 0 25 4 6 8 10 No. of arboreal species 12 20 No. of browsing species 25 14 16 28 (b) Insectivores/frugivores (d) Browsers/carnivores 24 No. of carnivorous species 20 No. of frugivorous species 2 15 10 20 Mammal species diversity in Southern Africa 10 2 16 12 5 0 0 0 10 20 40 30 50 No. of insectivorous species 60 70 10 15 30 219 Fig. 6. Bivariate plots showing the relationships between numbers of mammal species per equal-area grid cell compared between different ecological categories. n = 111. Comparison of numbers of (a) frugivorous species with arboreal species (r = 0.865); (b) frugivorous species with insectivorous species (r = 0.827); (c) insectivorous species with arboreal species (r = 0.853); (d) browsing herbivores with carnivores (r = 0.369). 220 (a) 15° P. Andrews and E. M. O'Brien 15° 20° Weight 0–100 g 25° 30° (d) 35° 15° 20° 20 20° 25° 30° 30° 30° 35° 35° 15° 15° 20° 15° 20° Weight 100–1000 g 25° km 30° 25° 500 20° 70 80 35° (e) 15° 15° 25° 20 30 40 30° 0 15° 35° 30° 35° 15° 50 60 25° 0 30° 40 25° 35° 25° 20° 25 15 (b) 15° 20° Weight 10–45 kg 60 40 35 30 20 15° 20° 25° 15° 20° Weight 45–90 kg 30° 25° km 500 35° 35° 30° 35° 15° 70 20° 25 25 30 40 20° 70 20° 20° 70 20 70 60 15 25° 25° 25 30 50 25° 25° 40 30 30° 35° 0 15° (c) 15° 20° 15° 20° Weight 1–10 kg 25° 30° 25° km 500 30° 30° 35° 35° 35° 30° 30° 0 15° (f) 35° 15° 15° 20° 20° 20° 15° 20° Weight >90 kg 25° 30° 25° km 500 35° 35° 30° 35° 15° 50 70 20° 25° 10 50 30 70 20° 70 60 60 50 40 20 25° 25° 30° 30° 35° 35° 20 30 70 75 40 25° 40 30° 35° 0 15° 20° 25° 30° km 35° 500 30° 0 15° 20° 25° 30° km 35° 500 35° Mammal species diversity in Southern Africa Table 8. Analysis of variance between plant richness and mammal richness by different size categories. Abbreviations as in Table 1. n = 111; P < 0.0001, unless otherwise noted: **P < 0.001; *P < 0.01; NS = not signi®cant, P > 0.01 Plant richness Size Species Genus Family A AX B BX C D E F G H 0±100 g Same excluding bats 100±1000 g Same excluding bats 1±10 kg 10±45 kg 45±90 kg 90±180 kg 180±360 kg > 360 kg 0.747 0.596 0.272 NS 0.669 0.589 0.262 0.084* NS 0.171 0.768 0.611 0.303 NS 0.679 0.595 0.265 0.081* NS 0.175 0.639 0.681 0.233 NS 0.592 0.587 0.155 NS NS 0.089* AB ABX BC BCX CD DE EF GH 0±1 kg Same excluding bats 100 g±10 kg Same excluding bats 11±45 kg 10±90 kg 45±180 kg > 180 kg 0.718 0.481 0.600 0.484 0.728 0.548 0.257 0.125 0.745 0.505 0.628 0.506 0.739 0.554 0.258 0.123** 0.615 0.557 0.525 0.447 0.674 0.449 0.128 NS ABC ABCX CDE DEF EFG FGH 0±10 kg Same excluding bats 1±90 kg 10±180 kg 45±360 kg > 90 kg 0.755 0.644 0.673 0.532 0.188 0.128 0.781 0.666 0.682 0.537 0.186 0.126 0.651 0.667 0.574 0.403 0.063* NS ABCD ABCD EFGH 0±45 kg Excluding bats > 45 kg 0.781 0.700 0.211 0.806 0.721 0.211 0.684 0.719 0.079* and arboreal species are slightly bigger but almost never large, and carnivorous and omnivorous species are intermediate in size, never small or large. Arising from this, we have investigated body size distributions in the study area in some detail (Tables 8 & 9) to see if any one part of the fauna is more strongly linked with vegetation or climate or even with different attributes of these factors. Distributions of species richness for the species in various size categories are shown in Fig. 7. These should be compared with the distributions of species richness for all mammals and plants in Fig. 3. The smallest mammals have a distribution that closely matches the overall distribution of mammals, with the maximum richness in the north-east of the study area, south of the Zambezi river in Mozambique and Zimbabwe in the Zambezian mixed woodlands and evergreen forest. This is shown here for mammals < 100 g body weights, size class A (Fig. 7a). Diversity remains high down the eastern highlands and south-east coast and decreases westwards to lowest values along 221 the Namibian coastal desert. The small mammals in size class B, 100 ±1000 g body weight, have a rather different distribution (Fig. 7b). They are only weakly correlated with vegetation (Table 8), with plant species richness accounting for only 27% of the variability of the species in this size class, and when bats are excluded from the analysis there is no correlation at all. They have a diversity peak in the north-east of the study area, but they have a secondary peak in the Vaal River basin and a marked decline in diversity south of the Orange River in the extension of the Karoo-Namib region into the interior plateau of western South Africa. In this area, category B mammals decline to only six to seven species per grid cell from a maximum of 20 species in the areas of highest diversity. Neither the secondary diversity peak in the Vaal River region nor the low diversity in the interior plateau are seen in any other size group, although the latter is present in the distribution of frugivorous species (Fig. 5b). In both this case and with respect to the frugivorous distribution, this low diversity is the result of the near absence of frugivorous bats, and when the maps are generated excluding bats, this low diversity phenomenon disappears. On the other hand, the diversity peak in the Vaal River region is even more marked when bats are excluded. Large mammal distribution is different from that of the small mammals (Fig. 7e, f ). The three largest size classes have been combined in this map since species numbers are low; n = 19 for all mammals > 90kg. The main trend in the distribution of large mammals is from south to north, with the percentage species richness per grid cell increasing northwards to high values in northern Botswana and into the Okavango region. There is also a peak in southern Mozambique, as for the small mammals and mammals generally, and the Namibian coast has the lowest diversity of large mammals. The longitudinal trend that dominates the mammalian richness patterns generally, and the small mammal trends in particular, are interrupted by this latitudinal trend in the large mammals. This can be related to the observation from Table 5 that mammal species richness is more strongly correlated with minimum monthly temperature (TMIN) than with annual temperature (TAN). Looking at this in more detail (Table 6), it may be seen that the highest correlations between mammal groups and TMIN are for terrestrial species (r = 0.750) and the size groups E (45±90 kg, r = 0.818), F (90±180 kg, r = 0.735) and G (180±360 kg, r = 0.715). At a slightly lower level these same groups are also correlated with annual temperature (TAN, Table 5). The highest correlation of annual temperature with any mammal group is with size class E, 45±90 kg body weight (Table 6, r = 0.77). This size class also has the highest correlation with minimum monthly temperature Fig. 7. Isocline maps of distributions of percentage mammal species richness categorized by body weight classes. Species richness distribution for (a) small mammals 1±100 g (class A); (b) small mammals 100±1000 g (class B); (c) medium-sized mammals, 1±10 kg (class C); (d) medium-sized mammals, 10±45 kg (class D); (e) medium to large mammals, 45±90 kg (class E); (f ) large mammals, > 90 kg (classes F, G, H). Species numbers are converted into percentages of the total number of species in southern Africa (n = 285). 222 P. Andrews and E. M. O'Brien Table 9. Correlations of body size categories with ecological, taxonomic and climatic variables, n = 111 for ecological categories, n = 65 for climatic variables. Body size variables are de®ned in Table 1, climate variables in Table 2, and only those variables with signi®cantly high correlations are included in this Table. Correlations > 0.7 are in bold AB ABC ABCD EFGH FGH MSPECIES TERR SEMTERR ARB AER INSECT FRUG BROW GRAZ CARN OMNI 0.96029 0.67598 0.69286 0.85478 0.94466 0.97913 0.86893 0.72046 0.64722 0.56523 0.65695 0.97995 0.73644 0.69105 0.88304 0.93277 0.97798 0.88884 0.73704 0.69587 0.61422 0.69919 0.98351 0.74515 0.69840 0.89647 0.92309 0.97179 0.88893 0.73834 0.72359 0.63335 0.71563 0.68305 0.89120 0.04023 0.59914 0.56380 0.56642 0.60159 0.41588 0.64590 0.66868 0.81005 0.57293 0.81866 70.07899 0.50190 0.47264 0.45960 0.52854 0.31806 0.56141 0.56429 0.71740 AB ABC ABCD EFGH FGH 1.00000 0.99208 0.98495 0.49315 0.38257 0.99208 1.00000 0.99640 0.53887 0.42982 0.98495 0.99640 1.00000 0.54058 0.42730 0.49315 0.53887 0.54058 1.00000 0.94344 0.38257 0.42982 0.42730 0.94344 1.00000 0.61711 0.04844 0.59851 70.77041 0.42243 0.72380 0.72309 0.58719 0.63409 0.04293 0.61017 70.79134 0.42948 0.73495 0.73286 0.60196 0.64347 0.01966 0.59977 70.79850 0.41145 0.73339 0.72917 0.61188 0.73997 0.55189 0.86701 70.67454 0.81572 0.51645 0.53998 0.32144 0.71141 0.53002 0.82624 70.63908 0.77737 0.46841 0.48870 0.28726 PEMIN TMAX TMIN DIFT TAN PMAX DIFP PAN (TMIN, r = 0.82). The distribution of the size class in southern Africa is similar in many respects to the distribution of mammal species > 90 kg (Fig. 7e), again with a south to north gradient, and a diversity peak in southern Mozambique near the mouth of the Limpopo River, but it differs in that there are three other peaks in the north of the study area, one of which corresponds to the grazing peak shown in Fig. 5e in the Okavanga region. Size class E includes the medium- to large-size carnivores, suids and medium size bovids, both grazers and browsers. The smaller size mammals in size groups C and D, 1±10 kg and 10±45 kg, respectively, have distributions that are to some extent intermediate between the large and small species (Fig. 7c, d). Size group C is similar to the smallest size group in having distributions of mammal species similar to that of the woody vegetation. Several different size combinations are shown in Table 8 in relation to woody plants. Size relationships with ecological and climatic variables are shown in Table 9 for ®ve composite size groups. Small mammals < 1 kg (class AB), < 10 kg (class ABC) and < 45 kg (class ABCD) are shown separately from large mammals > 45 kg (class EFGH) and > 90 kg (class FGH) in Table 9. The small mammals consist mainly of animals with arboreal (mainly rodents) and aerial (bats) life forms and insectivorous, frugivorous and browsing diets, and they are highly correlated with plant distributions, with woody plants accounting for nearly 75% of species < 100 g (Table 9). Also, since small mammals make up most of the mammal species in numbers, they are also highly correlated with total mammal species richness patterns (Table 9). By contrast, larger mammals are most highly correlated with terrestrialism. It is actually class E species (45-90 kg) that show the strongest relationship with terrestrialism (r = 0.848), as they did also with annual temperature, and the larger size classes are less strongly correlated both with terrestrialism and temperature. The distributions of large mammals are only weakly correlated with small mammal distributions and with the distributions of woody plants (Table 9). The strong correlation between minimum monthly temperature and large mammal distributions has been mentioned above, but the correlation with small mammals is lower (Table 9). Small mammal distributions are more strongly correlated with the seasonal difference in temperature (DIFT), and it can be seen from Table 6 that it is mainly the small mammals < 100 g (size class A) that in¯uence this relationship (r =70.772, Table 6). DIFT is also strongly correlated with plant species richness patterns (Table 5, r =70.798), and so it is not surprising to ®nd that small mammals are highly correlated with plant species distributions: for mammals < 100 g, 75% of variability is predicted by woody plants (Table 8). Small mammals are also strongly correlated with seasonal rainfall, as are the woody plants (O'Brien, 1993), and this is indicated by the high correlations of small mammal species distributions with maximum monthly precipitation (PMAX, r = 0.73, Table 9) and the difference in maximum and minimum monthly precipitation (DIFP, r = 0.73, Table 9). Again it is the small Mammal species diversity in Southern Africa 223 Table 10. Comparison of one-, two- and three-variable models of mammal species richness based on multiple regression and analysis of variance (n = 65). r2 values show proportions of mammal richness variance accounted for by each climatic and/or vegetation variable or combination of variables. They are ranked according to r2 values for species. Abbreviations for climate variables are given in Table 2 except VEG, which includes all three plant variables (PSPECIES + PGENUS + PFAMILY) No. of variables Variable Species (All) Species (Xbats) Genus Family 1 DIFT PMAX PGENUS PSPECIES TMIN PFAMILY PAN TAN 0.689 0.547 0.541 0.524 0.494 0.366 0.352 0.281 0.631 0.544 0.561 0.539 0.394 0.458 0.421 0.212 0.698 0.526 0.517 0.494 0.510 0.358 0.335 0.293 0.539 0.341 0.357 0.352 0.512 0.229 0.178 0.357 2 VEG + TMIN PMAX + TMIN PMAX + DIFT DIFT + TOPOG 0.724 0.704 0.724 0.696 0.692 0.641 0.681 ± 0.722 0.700 0.724 0.705 0.626 0.587 ± 0.594 3 VEG + ELEV + TMIN 0.755 0.755 0.766 0.685 mammals < 100 g that are most strongly correlated (class A, Table 6). In summary, small mammal species consisting mainly of non-terrestrial herbivores have distributions that are most highly correlated with plant species distributions, seasonality in temperature and precipitation, and maximum monthly rainfall. Large mammals consisting mainly of terrestrial species have distributions most highly correlated with minimum monthly temperature and annual temperature. They are only weakly correlated with regional variations in plant species richness and the correlates of this. Model development The variables that account for most of the variation in mammal species richness based on one-variable, twovariable and three-variable models are examined here by multiple regression. This analysis is based on the sample of 65 grid cells that have climate station data. Correlation of mammal species richness with that of plants, automatically incorporates the climatic/terrain variables related to plant species richness described earlier, namely annual rainfall, minimum monthly potential evapotranspiration and topography. As we exclude combinations of variables having similar environmental function, these climatic variables cannot be included in a multiple regression model of mammal richness unless it does not include vegetation. For the climate data (n = 65), the best one-variable model for mammal species richness is thermal seasonality (DIFT), accounting for 68.9% of mammal species richness (63.1% excluding bats); 69.8% of mammal genus richness, and 53.9% of mammal family richness (Table 10). The next best models are considerably less powerful and include the variables maximum monthly rainfall (PMAX) and woody plant richness (PGENUS and PSPECIES), which together account for 52.4±54.7% of the variation. In terms of simplicity, i.e. the least number of variables, the best climate model is PMAX because DIFT (and DIFP) are derived from combinations of other climatic variables. Vegetation alone, at either species or genus level, accounts for the same amount of variation as PMAX, which is consistent with the strong relationship between vegetation and rainfall (Table 3). For the larger sample of 111 grid cells, the plant species richness accounts for a larger proportion of mammal species richness; 74.9% for all mammals or 67.5% excluding bats (see Table 3). This difference has been attributed earlier to the more frequent location of climate stations in areas of high relief, therefore as distributions of woody plants are more highly correlated with topography than are mammal distributions, the two distributions are less highly correlated with each other in areas of high relief than in the whole study area. This relationship with plant species richness is shown in Fig. 8, where an anomaly in plant/mammal correlations is apparent in the eastern part of the study area. Values for most grid cells are tightly clustered together, but ®ve of the cells fall outside the cluster, having relatively high plant species numbers compared with mammal species. Both the high peak in plants (Fig. 3d) and the distribution of the cells (inset map, Fig. 8) show that this is the result of high plant diversity rather than low mammal diversity, because the cells follow the line of the eastern highlands, an area of high topographic relief, and the result is a marked increase in species numbers of woody plants with no corresponding increase in mammal diversity. Further analysis reveals similar anomalies for individual dietary guilds, particularly insectivorous and frugivorous species. The best two-variable model is one that combines vegetation and minimum monthly temperature (TMIN) (Table 10). Woody plants are here termed VEG, which includes the plant richness variables at species, genus or family level, and TMIN is an independent climate variable only weakly correlated with plant diversity. Together they account for 72% of the variation in 224 P. Andrews and E. M. O'Brien 160 Mammal species richness 140 120 100 80 60 40 0 100 200 300 400 Woody plant species richness 500 600 Fig. 8. Bivariate plot showing the relationship between numbers of mammal species per grid cell (n = 285) compared with numbers of woody plant species (n = 1353; r = 0.865). Inset, outline map of the study area showing the grid cells where plant diversity is anomalously high. mammal species richness (66.5±71.8% excluding bats). The next best models involve only climate variables, for any relations between VEG and mammal richness automatically include the climatic/terrain variables related to plant species richness. Maximum monthly precipitation (PMAX) and TMIN account for 70% variation, and PMAX and thermal seasonality (DIFT) account for 72% (Table 10). There is only one best three-variable model that meets all criteria: VEG, ELEV and TMIN. These account for 75% of the variation in mammal species richness. The three next-best models involve only climate variables or terrain, and none are simpler or more parsimonious than the two-variable vegetation±climate model. A striking variation of the three±variable multiple regression was observed when it was run on mammal faunas excluding bats. In this analysis, the top 20 solutions had plant family as the sole representative of vegetation, whereas all taxonomic levels were represented for the all-mammal regressions. This is taken to indicate that the exclusion of bats resulted in a more generalized and less species-speci®c relationship between mammals and plants. Annual temperature does not feature as a signi®cant variable in any of the models, accounting for only 28% of mammal species variation (Tables 5 & 10). In addition, it was seen earlier that almost all of that 28% is based on the large mammal element of the mammalian fauna, with small mammals being only weakly correlated with annual temperature (Table 9). With this one exception regarding the large mammals, annual temperature is not a major factor affecting mammal species richness in southern Africa. DISCUSSION Most animal and plant taxa show predictable diversity gradients, and these have been interpreted as either of physico-chemical factors, such as latitude or temperature and their effects on habitat productivity, or as a result of biological interactions such as competition or predation (MacArthur, 1965; Thiery, 1982; Begon et al., 1990). It is our contention that climate, particularly precipitation and energy, account for most of the observed patterns of species richness observed today. This has been demonstrated previously for woody plant species richness (O'Brien, 1993, 1998), which in turn accounts for most of the variation in mammal species richness together with other climate variables. We have found that in the tropical to temperate environments of southern Africa, variations in woody plant richness account for 70 ±77% of the present-day variation in mammal species and genus richness (r2 values, n = 111). Such a close relationship has previously been shown between amphibian species richness and that of tree species richness in North America (Currie, 1991), but in the same study it was concluded that mammal diversity was not functionally related to tree diversity. Currie (1991) suggests that separate analysis of different mammal guilds might shed some light on this issue, and our results show that this is indeed the case. The weak relationship between plants and mammals that Currie found arises because some parts of the mammal fauna are only poorly correlated with woody plant richness. Large mammals over 90 kg, for example, and scansorial, aquatic and fossorial mammals are not signi®cantly correlated with woody plant species Mammal species diversity in Southern Africa 225 100 (a) South–north transect 90 ABC FGH 80 70 60 50 40 30 20 No. of species 10 0 ff3 ff4 ff5 ff6 ff7 ff8 ff9 ff10 ff11 ff12 ff13 100 (b) West–east transect 90 ABC FGH 80 70 60 50 40 30 20 10 0 1 2 3 4 5 6 7 8 Grid cells 9 10 11 12 13 14 15 Fig. 9. Transect of the study area comparing small mammal variation with large mammal by numbers of species per grid cell (a) from south to north along 308E longitude; (b) from the west coast along 208S latitude. ABC = small mammals < 10 kg, n = 230; FGH = large mammals > 90 kg, n = 19. richness (Tables 8 & 9), whereas small-bodied arboreal frugivores and insectivores are strongly correlated (Tables 8 & 9). The pattern of variation in species richness in the former groups of mammal show little change in response to environmental change: e.g. terrestrial (Fig. 10a) and carnivorous (Fig. 10c) species show little change in a series of west to east transects across the study area, whereas small-bodied arboreal frugivores and insectivores tend to mimic the vegetation pattern (Figs 9 & 10b), changing in accord with changes in woody plant richness. This in turn is related to vegetation structure, being least in desert and greatest in evergreen rainforest, in agreement with results from North America (Fleming, 1973; Kerr & Packer, 1997; Fraser, 1998). It has been observed previously that numbers of large and small species are not inter-related (Maiorana, 1990). There is a general relation between body size and population density, which decreases with body size (Martin, 1990). Energetic requirements are also related to trophic level, with basal metabolic rates lower in frugivorous or browsing herbivores (McNab, 1990) and in arboreal mammals as a result of less muscle mass (Grand, 1990), thus reducing population metabolic requirement. This may be one of the mechanisms permitting greater species richness in habitats where there is an abundant supply of fruit and browse for feeding and trees for space utilization. It is interesting in this respect that grazing herbivores have considerably higher 226 P. Andrews and E. M. O'Brien 70 (a) 60 50 40 Row 8 Row 9 Row 10 Row 11 Row 12 Row 13 30 20 10 0 16 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 (b) 14 12 No. of species 10 8 Row 8 Row 9 Row 10 Row 11 Row 12 Row 13 6 4 2 0 25 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 (c) 20 15 10 Row 8 Row 9 Row 10 Row 11 Row 12 Row 13 5 0 1 2 3 4 5 6 7 8 9 10 11 12 13 Grid cells from west to east 14 Fig. 10. Transects from the west coast of the study area to the east coast showing numbers of species per grid cell. Row numbers are given in Fig. 2, with the row of grid cells furthest to the north being row 13, running through northern Namibia, northern Botswana, Zimbabwe and Mozambique, and the furthest south row, row 8, runs through southern Namibia, Botswana and Mozambique and northern South Africa. (a) Terrestrial species, n = 102; (b) arboreal species, n = 19; (c) carnivorous species, n = 29. basal metabolic rates (McNab, 1990) than browsers, and this may in part be why we have found that grazers are more highly correlated with woody plant species richness than browsers (Table 7). This is also a result of the greater mixture of habitats occupied by grazers as, with one or two exceptions, open grasslands in tropical Africa are local edaphic phenomena such as ¯ood plains or dambos, which are associated with forest and woodland. Grazers are also more highly correlated with most aspects of climate, particularly the energy, productivity and water variables (Table 6). They are not signi®cantly correlated with annual temperature (TAN) in contrast to the signi®cant correlations of large mammals with TAN. Geographic variations in climate are independent of life, but they control the capacity for biological activity: the amount and duration of primary productivity, and thus the type, distribution, amount and duration of food resources for consumers. Annual precipitation (PAN) or maximum monthly rainfall (PMAX) are the water variables most strongly related to woody plant species richness; and minimum monthly PET is the most strongly related energy variable; and together they account for 79% of the predictable pattern of geographic variation in plant taxonomic richness in southern Africa (O'Brien, 1993). Since slightly over 74% of mammal diversity in turn is predictable from plant diversity, this degree of variation in mammal richness is therefore an indirect function of climate. Climate varies geographically, so that as area increases the amount of climatic and hence habitat variability can increase, the extinction rate of both animal and plant species may decrease (because larger areas can have a greater range of habitats), and speciation rate may increase (more likely to be barriers giving rise to allopatric speciation). Over time these two factors may lead to an increase in numbers of species, particularly small mammals because their restricted habitat range, shorter generation times and higher potential rates of evolution (Mayr, 1963) may lead to higher speciation (Van Valen, 1975) and lower extinction rates (Van Valen, 1973). This is probably a major factor in the dominance of small mammal species numbers globally, but it is not a suf®cient explanation of why there are particular numbers in speci®c habitats or areas (May, 1976, 1978). In our analysis we have taken account of this issue by using an equal-area grid. As with plants, both the energy and water regimes also need to be considered when describing the relation of climate to variations in mammal species richness. This is apparent both from the strong correlation between mammal and plant species richness and between mammal distributions and climatic parameters. In his study of North American animal species richness, Currie (1991) found that annual PET is positively correlated with increases in animal species richness, and he found this parameter to be the best predictor of species richness of all vertebrate classes. We have found, however, that for mammals in southern Africa it is only the large mammals > 45 kg that are signi®cantly correlated with annual PET (Table 6). Small mammals are not signi®cantly correlated with annual PET. In our analysis it is minimum monthly PET that is more highly correlated with mammal distributions in all categories (Tables 5 & 7). It is also strongly correlated with measures of thermal seasonality such as the minimum Mammal species diversity in Southern Africa monthly temperature (TMIN) and with the difference between minimum and maximum monthly temperature (DIFT = TMAX-TMIN). This difference from Currie's (1991) results arises because our study area covers tropical to temperate parts of Africa, where energy is not a limiting factor for food production during the growing season, whereas Currie's North American study area covered temperate to tundra ecosystems, where energy can be limiting. Currie's logistic regression model for North America gave r2 = 0.81 for PET/ mammal richness compared with r2 = 0.14 for our analysis of southern Africa. Our results show that mammal richness is greatest where seasonal variability in the water, temperature and energy regimes is least. Here the diversity of plant species richness is greatest, and here also the amount and duration of food resources and the productivity of the environment is greatest. Net primary productivity as indexed by annual actual evapotranspiration (AET) is highly correlated with most water variables (r = 0.83 to 0.94) but not at all with annual PET or annual temperature (TAN). This is consistent with the study area in southern Africa being dominated by strongly seasonal summer rainfall climates and with the duration of the dry season extending from < 1 month to > 7 months. There is a pronounced difference in the correlations with water variables between small and large mammals. Large mammals are less strongly correlated with maximum monthly precipitation and not correlated at all with annual rainfall (Table 6), whereas small mammals are more highly correlated with both. In a seasonal climate, animals may have to travel long distances to ®nd standing water, and small mammals would be at a disadvantage and could only survive in numbers in wetter areas where water is more generally available. Taking a west to east transect across the study area, in the grid cells along latitude 208S just north of the Limpopo river, small mammal species richness almost doubles with little change in large mammals (Fig. 9b). The increase can be related to habitat heterogeneity owing to greater topographic relief (Fraser, 1998) in grid cells 10±14 (Fig. 9), but habitat heterogeneity provides at best a partial explanation for animal species richness (MacArthur, 1964). The difference is more probably related to climate, especially increasing precipitation, and species richness of woody plants eastwards across the continents, since the eastern and western parts of southern Africa share similar topographic relief. It has been seen earlier, however, that woody plant richness along the same transect increases to a much greater extent than mammal richness (Fig. 3d). This contrasts with evidence from North America, where topographic complexity leads to changes in mammal diversity patterns, with the most extreme rise in species richness correlated with the most rapid topographic change (Simpson, 1964). Temperature has been emphasized in this study because annual temperature has been frequently used as a proxy for climate change in relation to environmental 227 or faunal change. Annual temperature (TAN) has been shown to have little relation to vegetation (Pianka, 1966; Abramsky & Rosenzweig, 1984; Cowling, Rundel et al., 1998), to habitat (Axelrod, 1992), or to mammal diversity (Currie, 1991; Kerr & Packer, 1997; Shepherd, 1998). Temperature is a poor indicator of climatic variability, in particular annual temperature (Axelrod, 1992), except where energy availability limits diversity at high latitudes (Kerr & Packer, 1997). The 0 8C annual temperature line, for instance, passes through a variety of vegetation zones, from glacial environments to boreal forest and temperate mixed forest environments (Axelrod, 1992). Annual temperature is strongly and positively correlated with annual PET in southern Africa, as it is one of the contributing factors to PET, but it is only weakly correlated with water variables, and it is not signi®cantly correlated with woody plant richness (P > 0.01), which is the main determinant of mammal species richness. The energy hypothesis predicts that energy ¯ux per unit area should be the prime determinant of species richness (Wright, 1983): the greater the available energy the broader the resource base permitting more species to coexist. For plants, species richness is greatest where the amount and duration of energy is optimized and where water is not a limited resource, but as energy increases or decreases beyond the optimum, or if water becomes limited, species richness will fall (O'Brien, 1993). Variation in energy accounts largely for the latitudinal gradient in plant species richness, rather than annual temperature, but the impact of energy on species richness of both plants and mammals is different at low and high latitudes. There is also ambiguity in the relationship between mammal species richness and productivity, for it may increase or decline with increased habitat productivity (Rosenzweig, 1997). It is necessary here to distinguish between species richness and species abundance, and distinction must also be made between different food chains in assessing the effects productivity may have. Ecosystems such as grasslands may be less productive than forests, but they may have up to 50% of net production passing through the animal grazing food chain (Odum, 1983), and as the plants grow on an annual cycle, with new resources available on a yearly basis, productivity for mammals may be high even where overall productivity is low. Forests carry a greater plant biomass and higher productivity, but most of it is locked up in long-lived trees, and 90% of net production passes through the detritus food chain (Odum, 1983). The yearly production of leaves and fruit that is available for mammals to eat is only a fraction of total biomass. Thus, animal biomass is greater in open, grassland habitats, because large amounts of plant food are available on an annual basis, but species richness is low because the low variety of plant types and habitat does not provide the niches for species with differing requirements. As a result, mammal species richness is not and should not be expected to be highly correlated with annual net primary productivity. In addition, it has 228 P. Andrews and E. M. O'Brien yet to be explained why higher productivity produces more species as opposed to more individuals of the same species, and here the explanation could be down to biological factors, e.g. increased competition leading to diversi®cation of niche. There is a weak correlation between annual temperature and mammal richness in southern Africa. We have found that it is only the large-sized mammals, especially those between the body weights of 45 and 90 kg, that are correlated with annual temperature (r = 0.77), and smaller mammals are either not signi®cantly correlated or only weakly correlated with annual temperature. This calls into question environmental reconstructions based on annual temperature change, especially for mid to low latitudes. The difference between large and small mammals in their reaction to temperature may seem paradoxical, as large mammals are better buffered physiologically against extremes of temperature. Mammals < 45 kg are not correlated with annual temperature but are strongly correlated with vegetation, thermal seasonality and moisture seasonality. Mammals > 45 kg, on the other hand, are weakly correlated with vegetation and moisture variables but are strongly correlated with annual temperature and minimum monthly PET and temperature. It is probably the greater mobility in larger mammals that accounts for this discrepancy, resulting in the greater latitudinal gradient of large mammals (see Fig. 6f ) and their strong correlation with minimum monthly temperature. The lower correlations of small mammals with temperature may be a result of the greater protection from extremes of temperature afforded small mammals by thick ground vegetation or by their ability to burrow underground. Many small mammals live underground even in the absence of fossorial adaptations, and it is the smallest size class A (0±100 g) that is most strongly correlated with vegetation (Table 8), followed by size classes C and D (1±45 kg). Size class B, on the other hand, is weakly correlated with vegetation, and this is probably because of the high proportions in this size class of scansorial and fossorial species, which are weakly and negatively correlated with vegetation (Table 7). In general, seasonal variability in climate is strongly correlated with variations in mammal richness in all size categories. In large mammals, the most strongly correlated seasonal variable is minimum monthly PET (Table 6), followed by minimum monthly temperature. Small mammal richness displays a different pattern. It is most strongly correlated with thermal seasonality (Table 6) and maximum monthly rainfall. In effect, large mammal richness is best described as a function of the temperature/energy regime, especially minimum temperatures. Small mammal richness is best described as a function of decreases in seasonal variability in the thermal, energy and precipitation regimes. This is consistent with how these climate variables relate to plant richness, and thus geographic variations in vegetation. Time Whatever the factors underlying predictable changes in species richness, time is required for them to operate. Movements of species into or out of geographic regions as a result of climatic or physical ¯uctuations take time to have any effect, particularly with the limitations imposed by geographic barriers. Similarly, the accumulation of species as a result of the differential effects of speciation and extinction also take time to affect species richness patterns (Rosen, 1981, 1984). For example, the South African Cape is remarkable for its species richness of plants, with about 7000 species, more than half of which are endemic (White, 1983). The Cape centre of endemism has been isolated for a long period, allowing time for species to accumulate, and during the past the area of winter rainfall in southern Africa has periodically been greater (Lee-Thorp & Beaumont, 1995). Whole continents can be isolated in this way, for example with the absence of murid rodents from the North American fauna and the near absence of placental mammals from Australia, and the result is that speciation is restricted to those groups already present in the area. In general, species numbers can only be built upon ancestral populations that are already present in or are contiguous with particular geographic regions. Correlations of mammal faunas excluding bats Most mammal faunas in the fossil record do not include bats for various reasons. Their life style is different, their manner of death and preservation are different, and their bones are more fragile than most other mammal bones. It is only in cave deposits that bats are commonly found, and their mode of preservation is likely to be different even there (Andrews, 1990). For this reason we have extended our analyses to exclude bats to see if their absence changes the correlations with vegetation and climatic factors. Correlations with plant species richness are still strong, but slightly lower than when bats are included (r2 = 68% compared with 75%, Table 3). Correlations with temperature and PET climatic variables are also slightly lower, but correlations with annual rainfall and AET are actually higher when bats are excluded (Table 5). This seems to be mainly the product of the frugivorous dietary guild, which has higher correlations with PAN and AET when bats are excluded, whereas there is little difference in the correlations of the insectivorous guild (Table 6). The multiple regression models for these subsets of mammals have the same variables as for the complete mammal faunas but they account for 4±5% less variance. Multiple regression of different ecological guilds against the total mammalian fauna shows a distinct difference when bats are excluded. Over 93% of species richness variability is accounted for in the southern African mammalian faunas by insectivorous species, the great majority of which are bats, and 85% is accounted Mammal species diversity in Southern Africa for by bats alone. Arboreal species account for 82% of variability in mammal species richness, and when bats are excluded these remain as the best predictor of variability. In the two-variable model, insectivorous and terrestrial species account for 97% of mammal species richness, but when bats are excluded it is the combination of terrestrial and semi-terrestrial species that account for the highest variability at 95%. In the threevariable model, terrestrial and semi-terrestrial species and bats account for nearly 99% of variability, whereas when bats are excluded, arboreal species replace bats with a similar r2 value. Considering just the dietary guilds, frugivores account for the highest variability of mammal species richness when bats are excluded from the analysis, accounting for 75% compared with 81% when the small number of southern African frugivorous bats are included. Inclusion of bats adds greatly to the discriminatory power of mammalian faunas, but their absence from most fossil faunas would be unlikely to alter their interpretation; multiple regression values between mammals and woody plants decrease by only about 7% when bats are excluded, by 10% between mammals and separate dietary and spatial guilds, while correlations with many climatic variables are virtually unchanged. Correlations at higher taxonomic levels Variation in woody plant species richness accounts for 77% of the variation in mammal species richness and nearly as much for mammal genus richness (70%), but it accounts for only 35±50% of the variation in mammal family richness. This discrepancy at the family level may in part be a function of taxonomic classi®cation. However, a more likely possibility is that at higher taxonomic levels the distributional ranges of mammal taxa increase absolutely, and include the distributional ranges of all subordinate taxa, thus encompassing a wider range of climatic, vegetation (plant richness) and topographic conditions. The same applies to the ecological and physiological characteristics of the taxa themselves. Such blending tends to homogenize the variations in climate, vegetation and terrain and should result in lower correlations between mammal richness and these parameters at higher levels. This only seems to be a signi®cant factor at the family level of analysis for mammals, and it should caution against the use of family level identi®cations for palaeoecological interpretation in the fossil record. Multiple regression of separate ecological guilds against mammalian genus richness is almost the same as for species (see previous section). At the genus level, however, insectivores account for only 87% and frugivores for 79% of genus variability, 3±6% less than the species level analysis. In the two-variable regression analysis terrestrial and insectivorous species account for 94% of variability. At the family level, the patterns show some differences from the species and genus analyses. 229 Nearly 80% of family variability is accounted for by terrestrial species, with omnivores, insectivores and frugivores only accounting for 63±65%. In the twovariable regression, terrestrial and insectivorous species account for 85% of family variability. Again, these differences should caution against reconstructions based at the family level. Applications to palaeoecology The climate of the earth has changed over time as a function of long-term and gradual environmental trends and processes (e.g. continental drift, orogeny, orbital forcing; Shackleton & Kennett, 1975; Ruddiman, McIntyre & Raymo, 1986; deMenocal & Bloemendal, 1995; Shackleton, 1995). Since the Eocene, for example, the tendency appears to be one of global cooling. Climate has also changed as a function of catastrophic events (meteorite impacts). Regardless of cause, these changes are generally expressed as raising or lowering global annual temperatures, and estimates of change are based primarily on marine sources ± for instance, on oxygen isotope records (Shackleton, 1995) or on changes in the amount of wind-blown dust in marine cores (deMenocal & Bloemendal, 1995). Changes in the annual temperature of the ocean are then translated into terrestrial environmental change, e.g. in the spread of forests or deserts as temperatures rise or fall. At a gross, mega-scale level of analysis there is probably some justi®cation for this, particularly at higher latitudes (cf. Kerr & Packer, 1997; Shepherd, 1998). Our results show that from the mid to low latitudes, there is little support for changes in annual temperature being a reasonable indicator of environmental change, especially of changes in climate, vegetation or mammal diversity. Instead, the critical variable appears to be seasonal variability, whether considering just temperature, or energy, or available water. Thermal seasonality when combined with a rainfall variable such as maximum monthly rainfall accounts for 72.4% of the variation in mammal species richness. Given the results of this study, it would be equally true that observed changes in the mammal fossil record could be used directly to reconstruct past changes in terrestrial temperature and rainfall regimes. The small mammals would provide information on changes in the rainfall regime and vegetation structure; the large mammals, changes in the temperature regime. Note that all changes in climate and vegetation and thus mammal diversity, can be local, regional or global in their cause or impact (e.g. local changes in elevation, orogeny). It has been shown that mammal species respond separately to climate change rather than as whole communities (Faunmap, 1996). Changes in mammal species distributions since the end of the last ice age in North America came about by differential expansion of species on a one by one basis and not by the expansion of whole communities. Species expanded at different rates 230 P. Andrews and E. M. O'Brien and in different directions, but in terms of their ecological niche structure, it is probable that community structure would be maintained even though the individual species contributing to the overall community varied. Our results support the individual effect, but we have also found that climatic effects are rather more subtle than the gross levels of the north±south temperature change and the east±west moisture gradient postulated (Faunmap, 1996). One quali®cation to this conclusion about annual temperature is that large mammals in southern Africa, particularly those in the intermediate/large size classes, have been found here to be more highly correlated with temperature than are mammals < 45 kg in body weight. Linking faunal turnover to past temperature ¯uctuations has to take this into account. For example, the relationship shown between speciation/extinction rates of southern African bovids and a lowering of global (annual) temperature is based on 14 bovid extinctions and the 15 apparent speciations occurring at around the 2.5 Ma climatic event (Vrba, 1985, 1995). 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