Download Climate, vegetation, and predictable gradients in mammal species

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
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P. Andrews and E. M. O'Brien
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Carnivorous
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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
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4
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40
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20
0
0
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15
No. of frugivorous species
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8
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No. of arboreal species
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20
No. of browsing species
25
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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
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P. Andrews and E. M. O'Brien
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Weight 1–10 kg
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Weight >90 kg
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60
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40
20
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20 30
70 75
40
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40
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500
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500
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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). Examining the
sizes of the species concerned shows that nine each were
in the large body size (> 45 kg) classes with an overall
correlation with annual temperature of 0.815. On the
other hand, there is no justi®cation for extending this
relationship either to vegetation change (correlation of
this with large mammals is only 0.211), or to mammals
as a whole, for the species richness of mammals < 45 kg
is only weakly correlated with changes in annual temperature. Early hominids being below the 45 kg
threshold are unlikely to have been affected by changes
in annual temperature or the large mammal turnover.
Acknowledgements
We are grateful to Catherine Badgley, Brian Rosen and
Rob Whittaker for comments on this work at several
stages of progress. We are also glad to acknowledge the
help of Jessica Pearson, Jennifer Scott and Martin
Strawbridge in the data analyses. Two referees made
valuable comments on the text and we are also grateful
to them.
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