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Journal of Insect ConservationAn international journal devoted tothe conservation of insects and relatedinvertebrates ISSN 1366-638XVolume 16Number 5 J Insect Conserv (2012) 16:677-695DOI 10.1007/s10841-011-9449-9

Ants on a mountain: spatial,environmental and habitat associationsalong an altitudinal transect in a centre ofendemism

T. C. Munyai & S. H. Foord

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ORIGINAL PAPER

Ants on a mountain: spatial, environmental and habitatassociations along an altitudinal transect in a centre of endemism

T. C. Munyai • S. H. Foord

Received: 26 March 2011 / Accepted: 12 November 2011 / Published online: 22 November 2011

� Springer Science+Business Media B.V. 2011

Abstract Mountains are biodiversity hotspots and pro-

vide spatially compressed versions of regional and conti-

nental variation. They might be the most cost effective way

to measure the environmental associations of regional

biotic communities and their response to global climate

change. We investigated spatial variation in epigeal ant

diversity along a north–south elevational transect over the

Soutpansberg Mountain in South Africa, to see to what

extent these patterns can be related to spatial (regional) and

environmental (local) variables and how restricted taxa are

to altitudinal zones and vegetation types. A total of 40,294

ants, comprising 78 species were caught. Ant richness

peaked at the lowest elevation of the southern aspect but

had a hump-shaped pattern along the northern slope. Spe-

cies richness, abundance and assemblage structure were

associated with temperature and the proportion of bare

ground. Local environment and spatially structured envi-

ronmental variables comprised more than two-thirds of the

variation explained in species richness, abundance and

assemblage structure, while space alone (regional pro-

cesses) was responsible for\10%. Species on the northern

aspect were more specific to particular vegetation types,

whereas the southern aspect’s species were more general-

ist. Lower elevation species’ distributions were more

restricted. The significance of temperature as an explana-

tory variable of ant diversity across the mountain could

provide a predictive surrogate for future changes. The

effect of CO2-induced bush encroachment on the southern

aspect could have indirect impacts complicating prediction,

but ant species on the northern aspect should move uphill at

a rate proportional to their thermal tolerance and the

regional increases in temperature. Two species are identi-

fied that might be at risk of local extinction.

Keywords Formicidae � Transect � Altitude �Monitoring �Indicator taxa � Elevation � Biosphere Reserve

Introduction

Mountains are hotspots of biodiversity. Their complex

topography, varied climatic conditions and isolation gave

rise to unique and diverse fauna (Lomolino 2001). They

also provide compressed versions of regional and even

continental gradients in diversity with species restricted to

aspects and altitudinal bands resulting in high beta diver-

sity (Jankowski et al. 2009; Pryke and Samways 2010; Wu

et al. 2010). These patterns are often associated with

change in environmental variables such as temperature

(Sanders et al. 2007; Ruggiero and Hawkins 2008), energy,

area (Sanders 2002), soil characteristics and aspect. The

response of species richness and assemblages to elevations

can therefore aid in understanding the mechanisms that

explain diversity patterns with important implication for

conservation (Samways 1990b; Kattan et al. 2006; Wu

et al. 2010). Monitoring across mountains could also pro-

vide the most cost effective and succinct picture of the

response of organisms and biotic assemblages to global

climate change in the tropics (Smith et al. 2002; Hodkinson

2005; Loarie et al. 2009) where baseline data and moni-

toring at a scale that could document range shifts are scarce

(Colwell et al. 2008).

T. C. Munyai � S. H. Foord (&)

Centre for Invasion Biology and Department of Zoology,

University of Venda, Private Bag X5050,

Thohoyandou 0952, South Africa

e-mail: stefan.foord@univen.ac.za

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DOI 10.1007/s10841-011-9449-9

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The physical consequences of climate change are not

uniform across space and time (Pimm 2009). Predicting the

response of biological communities adds a further level of

complexity, confounded by natural variability (Kaspari and

Majer 2000; Colwell et al. 2008) and our limited under-

standing of what organizes these assemblages (Ricklefs

1987; Andersen 1992). Even gradual change in temperature

is complicated by the impact of extreme events. Recent

studies suggest that assemblage responses to climate

change are affected by several indirect factors and histor-

ical influences (Brown et al. 1997; Botes et al. 2007). Long

term monitoring of altitudinal transects can provide a

measure of the natural variability as well as the rate and

extent of these changes, identifying which species are

shifting, which do not and why (Pimm 2009). The applica-

bility of this approach will however depend on the influence

of climatic variables, as opposed to the role of other deter-

minants of species distribution patterns (Samways et al.

2010).

Spatial variation in assemblages are the result of two

mechanisms (Legendre et al. 2009): (1) the environmental

control model (Whittaker 1956) or (2) dispersal limitation

(Hubbell 2001). Mathematical representation of the spatial

relations (spatial variables) between sites could be used to

determine to what extent assemblages are organized in

space (Peres-Neto and Legendre 2010). Mountains are

often sufficiently small scale to allow all species access to

all parts of the gradient minimizing the effect of space

(dispersal limitation) (Longino and Colwell 2011) and

therefore maximizing the role of environmental control.

The extent of this control will however vary between

mountains and taxa.

The Soutpansberg Mountain is an inselberg in north-

eastern South Africa. It contains an intricate mosaic of

habitats and microclimates as a result of its complex

topography, geographical position and macro-climate

(Berger et al. 2003). The mountain has been identified as

one of the 19 centres of plant endemism in Southern Africa

(Van Wyk and Smith 2001) and the region is particularly

diverse at generic (1,066) level, more so than the Cape

Floristic Kingdom (Hahn 2006). The area also harbors 56%

of bird and 60% of mammal species of Southern Africa.

Recent work suggests that the mountain’s spider fauna

mimics the diversity and endemicity of its plant fauna

(Foord et al. 2002, 2008; Huber 2003; Jocque 2008; Had-

dad 2009). Unfortunately very little is known about the

insects of the mountain. This is particularly concerning as

insects form the bulk of all diversity in terrestrial ecosys-

tems and are environmentally sensitive at point localities

(Pryke and Samways 2010). Ants often dominate insect

assemblages and have considerable influence over soil

formation, invertebrate assemblage structure, seed preda-

tion and dispersal (Andersen 1995; Andersen and Sparling

1997). They are increasingly being used in monitoring the

effects of climate change (Botes et al. 2006), habitat

fragmentation and habitat conservation (Mitrovich et al.

2010). Although ants have been the focus of a few studies

in the Kruger National Park (KNP) and surrounds (Swart

et al. 1999; Parr et al. 2004), no work has been done on the

mountain itself or within the Limpopo province outside the

eastern lowlands of the KNP. A further incentive for this

work has been a recent study by Botes et al. (2006, 2007) in

the Cederberg Mountains of the Western Cape (Fynbos

Biome). Theirs was the first long-term site that monitored

invertebrates in South Africa and they found that temper-

ature explained significant amounts of the variation in

species density, abundance and assemblage structure of the

ants across their transect (Botes et al. 2006). Predicted

changes of the Cederberg’s ants were complex, confounded

even further by synergistic effects.

Several of the Soutpansberg’s endemic taxa are associ-

ated with the mid and higher elevations ([1,000 m) and are

therefore at risk of extinction due to climate change (Hahn

2006; Jocque 2008; Mostert et al. 2008). The mountain also

forms the focal point of the recently established Vhembe

Biosphere Reserve.1 Management of the biosphere will

have to be informed by the pace and direction of these

changes and there is a real possibility that managerial

responses will not be able to keep up with the effects of

climate change. We report on a transect across the highest

point of the mountain and we firstly tested if ant assem-

blages differ between the major vegetation types and alti-

tudinal zones on the two aspects of the Western

Soutpansberg. Secondly we determined which spatial and

environmental variables underlie differences if they exist

and thirdly we identify indicator taxa and habitat associa-

tions of species across the mountain.

Study area

The Soutpansberg is a quarzite mountain that has an ENE–

WSW orientation which strongly influences its climate

(Fig. 1). The mountain formed as a result of faulting along

three strike-faults, followed by the northwards tilting of the

area (Hahn 2006). The dominantly quartzite and sandstone

derived soils are shallow, acidic with low nutrient content.

The weathering of basalt and diabase intrusions, forms

fine-textured and clayey soils particularly on the southern

side of the mountain (Mostert et al. 2008). The wet seasons

ranges from November to March with considerable varia-

tion between years. The mountain is a barrier for south-

easterly maritime climate influences from the Indian Ocean

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as well as continental climate influences (Hahn 2006). This

together with its topographic diversity results in varying

climatic conditions with rainfall that ranges from 367 mm

annually in the north to 1,874 mm in the south where

orographic and mist precipitation can increase annual

precipitation to 3,233 mm (Hahn 2002).

Mostert et al. (2008) identified five vegetation types

across the western parts of the Soutpansberg, viz. (1)

Soutpansberg Leached Sandveld (LS)—the mid-elevations

(1,000–1,400 m) of the northern aspect are characterized

by shrubland, open woodland and rocky soils on quartzite,

dominated by woodland taxa such as Boscia albitrunca,

Burkea Africana, Cammiphora apiculatum and Elephan-

torrhiza burkei, (2) Soutpansberg Arid North Bushveld

(ANB)—Plant species typical of these lower elevations

(800–1,000 m) include Acacia tortilis, Adansonia digitata,

Grewia flava and Terminalia sericea and consist of mixed

open woodland on deep sandy soils, (3) Soutpansberg

Moist Mountain Thickets (SMMT)—the lower and mid-

altitudes (900–1,000 m) of these sites on the southern

aspect are dominated by closed bushland with red clay-

loamy soils on basalt, (4) Soutpansberg Forest (SF)—the

higher elevations (1,200 m) have forests with woody

species such as Croton sylvaticus and Ekebergia capensis,

and finally, (5) Soutpansberg Cool Mistbelt Vegetation

(CMV)—the highest altitudes on the northern and southern

aspects (1,400–1,700 m) are open sedgeland with shallow

rocky soils on quartzite dominated by Colechloa spp.

Ant sampling

A transect that extends over the highest point of the

Soutpansberg, Lajuma (1,748 m), was set out at 200 m

altitudinal intervals (Fig. 1). It spans an altitudinal range of

900 m in the north from 800 to 1,700 m a.s.l. and descends

another 800 m to the south to 900 m a.s.l., and includes 11

sampling zones in total (Fig. 1).

Ant sampling followed a protocol as set forward in

Parr and Chown (2001)and Botes et al. (2006). Sampling

grids consisted of 10 pitfall traps each (ø 62 mm) that was

laid out in a sampling grid (2 9 5) with 10 m spacing

between traps. Sampling grids within a sampling zone

were replicated four times and were at least 300 m apart

(Fig. 1, ‘‘Appendix 1’’) to avoid pseudoreplication (McKillup

2006).

Fig. 1 Location of the western

Soutpansberg transect in South

Africa and the position of all 44

replicates along the altitudinal

transect projected onto a

vegetation map (Mucina and

Rutherford 2006). Replicates

were at least 300 m apart.

Labels refer to sampling zones

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Pitfall traps were left open for 5 days each during

September 2009 (dry season), January 2010 (wet season),

September 2010 (dry season) and January 2011 (wet

season). Studies have shown that this is sufficiently long

to be representative and not sample excessive amounts of

ants (Parr et al. 2004; Boonzaaier et al. 2007). Distur-

bance of the surface around the traps was avoided and

surface materials were returned to their original state. The

digging-in effect (Greenslade 1973) was assumed negli-

gible (Botes et al. 2006) and traps were set immediately.

Pitfall traps contained 50% solution of propylene glycol

that neither attract nor repel ants (Adis 1979; Abensperg-

Traun and Steven 1995). The ant samples were washed in

the laboratory and stored in 70% alcohol. They were then

identified to morpho-species and where possible, up to

species level by the first author using (Bolton 1984) and

(Taylor 2010). A reference collection is housed in the

insect collection of the Department of Zoology (Univer-

sity of Venda) as well as the Centre for Invasion Biology,

northern hub at the University of Pretoria. Samples from

the four different sampling seasons were combined to

provide a single measure of ant species richness/density

for each site.

Although the aim was to generate comparative rather

than comprehensive data on site species richness, sampling

representivity was still determined by calculating the ratio

of observed species density to three richness estimators in

Estimates 8.2.0 (Colwell 2006), viz. Chao 1, Jack 1 and

MMMmeans. The study distinguishes between species

density, which is the total number of species caught in each

sampling grid over the four sampling periods, and rarefied

species richness which were calculated in R statistical

language (Team 2009), using the package ‘VEGAN’

(Oksanen et al. 2007) to rarify the number of individuals

for each grid to that of the grid where the lowest number of

individuals were caught.

Environmental variables

Soil samples were collected in January 2010 using a soil

auger. A total of ten soil subsamples in each of the 4

replicates of a site were pooled. Soils were dried and then

analyzed for composition (clay, sand, rock and silt), pH,

conductivity, C, K, Na, Ca, Mg, P, NO3 and T value (Botes

et al. 2006), by BemLab (pty) Ltd laboratories, Somerset

West, South Africa.

Two Thermocron iButtons (Semiconductor Corporation,

Dallas/Maxin, TX and USA) were set out at each of the

sites to record soil temperature at 1 h intervals. The iBut-

tons were buried 1 cm below the soil surface at locations

that has direct exposure to sunlight except where canopy

cover was[70%. Temperature sequences from April 2009

to July 2010 were used to calculate mean monthly, mean

monthly minimum, mean monthly maximum, mean

monthly temperature range, absolute maximum and abso-

lute minimum temperature for each altitudinal site.

Distribution (vertical and horizontal) of the vegetation

was determined in September 2009 and January 2010 fol-

lowing methods set forward by (Rotenberry and Wiens

1980; Bestelmeyer and Wiens 1996) with similar protocols

employed by Parr et al. (2004) and Botes et al. (2006).

Horizontal distribution was determined by placing a 1 m2

grid over each pitfall trap and percentage ground covered

by four soil coverage categories, viz. vegetation cover, leaf

litter, exposed rock, and bare ground was estimated based

on digital images taken of each 1 m2 grid. Average ground

cover was calculated for each sampling grid (or replicate).

The vertical distribution of vegetation was measured by

determining foliage height profiles, calculating vegetation

heights at four points located at 90� angles on a 1.5-m

radius centred on each pitfall trap. At each of these points

the number of times vegetation touched a 1.5-m rod was

recorded at 25-cm height intervals (0–25, 26–50, 51–75,

76–100, 101–125, 126–150, 151? cm) and averaged for

each replicate. As the study focused on epigeal ants, and

excluded arboreal representatives, this protocol was con-

sidered to be sufficient.

Rosenzweig (1995) highlighted the role of available

geographic area in explaining the latitudinal gradient in

species richness, with climatically similar larger areas

characterized by species with larger geographic range sizes,

lower extinction rates and higher speciation rates (Chown

and Gaston 2000). We identified 5 altitudinal (700–900,

900–1,100, 1,100–1,300, 1,300–1,500, [1,500 m) on both

aspects respectively and considered climatic conditions

within each band to be similar. Available geographic area in

each band was calculated by first creating a 40 km buffer

around the Minimum Convex Polygon of all sampling

grids along the transect. The 5 altitudinal bands were then

extrapolated onto a 3 arc-seconds Shuttle Radar Topog-

raphy Mission (SRTM) digital elevation model (DEM).

These five classes were then split into northern and

southern groups respectively. The north–south divide was

extrapolated using water shed polygons generated through

IDRISI using the same DEM as above. The Soutpansberg-

Blouberg divide (Fig. 1) was bridged by a straight line

rather than following ambiguous watersheds.

The collinearity between the environmental variables (14

soil parameters, 6 temperature, altitude, area, 5 vegetation

parameters) were determined by calculating the Variance

Inflation Factor (VIF) for each of the 27 environmental

variables. A VIF larger than 10 suggests multicollinearity

and these variables were subsequently removed. Collin-

earity was further investigated with Pearson’s product

moment correlations. When variables were significantly

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collinear (r [ 0.5), only the variable that were considered

biologically relevant were retained (‘‘Appendix 2’’).

The collinearity of environmental variables resulted in a

reduced number of 14 variables that was used in further

analyses. The six temperature variables were reduced to

two: absolute minimum temperature and absolute maxi-

mum temperature. Seven soil parameters were chosen for

the analysis of which four were chemical (pH, K, Mg, Na,

C, NO3) and one composition (proportion of sand). Alti-

tude was correlated with available area and excluded. The

five habitat structure variables were also retained.

Statistical analysis

Principle coordinate analysis was used to derive the

spatial variables (Carvalho et al. 2011). The spatial

coordinates of the grids were used to derive spatial

variables based on the initial Euclidian matrix of dis-

tances, this matrix is then truncated at the smallest dis-

tance that connects all grids in one network. Truncated

portions are then filled with an arbitrary large distance

value. The resultant matrix is then subjected to principal

coordinate analysis (PCoA) and eigenvectors associated

with positive eigenvalues retained as spatial variables

(Carvalho et al. 2011). These PCNM variables’s variance

corresponds with their spatial scale. PCoA orders them

in decreasing variance and therefore decreasing spatial

scales. These eigenvectors are also orthogonal and can

therefore be used as independent uncorrelated variables in

regression analysis (Borcard and Legendre 2002; Borcard

et al. 2004). PCNM eigenvectors were created with

‘spacemakeR’ (Dray et al. 2006) for R language (R

Development Core Team 2011).

The environmental and spatial variables that best

explain variation in species richness and total abundance

of ants were analyzed using generalized linear models

(GLM) with a log link function and Poisson error dis-

tribution in R (R Development Core Team 2011),

including only significant terms that were obtained

(McGeoch and Price 2004). If the data was overdispersed

a quasi-likelihood approach that allows parameter esti-

mation without knowing the error distribution of the

response variable was used by defining the error distri-

bution as ‘‘quasipoisson’’ in R (McCullagh and Nelder

1989). Partial linear regression was then conducted where

the PCNM variables and environmental models were

combined. The final model had both spatial and envi-

ronmental terms and was used to partition explained

variance (deviance) in species density and abundance into

four components (Borcard et al. 1992; Legendre et al.

2009): (1) non-environmental spatial (variance explained

by space alone); (2) spatially structured environmental (2)

non-spatial environmental (variance explained by envi-

ronmental variables alone); and (4) unexplained or

residual variation. Similar partitioning of variation of ant

assemblage structure was done in ‘VEGAN’ (Oksanen

et al. 2007).

The geographic structure in the selected environmental

variables was further explored through spatial autocorre-

lation as implemented in PASSAGE version 2 (Rosenberg

and Anderson 2011). This was calculated using Moran’s I

(Legendre and Legendre 1998), a measure of the degree to

which neighbouring sites have similar values for a variable.

Bonferroni approximation calculated the overall signifi-

cance of each correlogram and the significance of Moran’s

I for each distance class.

When analyzing ant assemblage structure, the use of

either linear and unimodal species response models can be

based on the gradient lengths (beta diversity in community

structure) of a Detrended Correspondence Analysis (DCA)

in CANOCO 4.54 (Ter Braak and Smilauer 2008). The

length of the longest axis was less than three and a linear

species response was therefore assumed and redundancy

analysis RDA used (Leps and Smilauer 2003). Species with

less than five individuals in the entire dataset were removed

for this analysis. To identify environmental variables that

explained most of the variation in species composition, a

forward selection of environmental variables, a multivari-

ate equivalent of stepwise regression (Borcard et al. 1992),

was used and their significance tested with Monte Carlo

permutations. The results were shown as a biplot in which

environmental variables are depicted as arrows and sam-

ples as symbols (Leps and Smilauer, 2003). Partial con-

strained redundancy analysis was used to investigate the

unique effect of environmental variables by correcting for

the effect of space.

Species sample relationships were also displayed to

determine which species contributed to differences

between assemblages. Only species with more than 40% of

their variability explained by the biplot were included. The

five vegetation types were included as co-variables in a

subsequent ordination to partition out their confounding

effect.

Characteristic species of these groups in terms of the

degree of specificity (uniqueness to a particular site) and

fidelity (frequency within the vegetation type/aspect) was

identified using the Indicator Value Method (Dufrene and

Legendre 1997). High indicator values (IndVal [ 70) show

that a species is both specific and has a high fidelity for a

particular site. The significance of the IndVal values is then

tested trough a random reallocation of replicates among

groups (McGeoch et al. 2002; Van Rensburg et al. 2002;

Botes et al. 2006).

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Results

A total of 40,294 ants, comprising 78 species in 28 genera

and seven subfamilies were collected over the four sam-

pling periods (‘‘Appendix 3’’). Fourteen of the 78 species

were only collected during January surveys, and none in

September. Myrmicinae, (with 36 species and 11 genera)

was the most diverse subfamily followed by Formicinae

(20 species and 5 genera). Ponerinae (7 genera) had higher

generic diversity than Formicinae. Camponotus (13 spe-

cies), Pheidole (9 species), Pachycondyla (6 species) and

Meranoplus (6 species) were the most diverse genera

(‘‘Appendix 3’’). Six species, Tetramorium sp.01 and sp.02,

Lepisiota capensis, Pheidole sp.01, Technomyrmex sp.01

and Tetramorium quadrispinosum, occurred at all the alti-

tudinal bands (‘‘Appendix 3’’). Eighteen species were

restricted to the southern slope and 11 to the northern slope

while 17 species were restricted to a single altitudinal band.

Inventory completion varied between the different esti-

mators but was all more than 70%: 08N (minimum = 85%—

maximum = 96%), 10N (87–99%), 12N (82–92%), 14N

(70–95%), 17N (87–99%), 16S (88–97%), 14S (72–93%),

12S (74–91%), 12S2 (87–98%), 10S (81–95%) and 09S

(80–90%). Sample-based rarefaction curves did asymptote for

some altitudinal bands (‘‘Appendix 4’’). Moist Mountain

Thicket had the highest total species density, the highest total

abundance and mean species density, while Arid Northern

Bushveld had the highest mean abundance (Table 1). The

Cool Misbelt Vegetation had the lowest mean species density

and the Forest had the lowest abundance (Table 1).

The altitudinal bands yielded 21–51 species (Table 2).

Based on the individual-based rarefaction to 270 individ-

uals, species richness of the zones ranged from 4 to 20, 09S

was the most species rich with an average of 18 species,

followed by 12N (16 species). The least diverse site was

the summit (17N), with 10 species on average (Fig. 2a).

Ant species density and richness peaked at mid-elevation

on the northern aspect whereas it peaked at the lowest

altitude on the southern aspect (Fig. 2a). Diversity patterns

along the southern aspect are confounded by the habitat

mosaic (forests, thickets, sedgelands) and edge effects on

this slope, and although there is a general decrease in

richness with elevation, the trend is not monotonic

(Fig. 2a). Richness initially increases from the summit to

the lower elevations of the southern aspect. Diversity then

decreases in the forest only to increase in the thickets

reaching a peak at 09S. Abundance peaked at 08N fol-

lowed by 10S whereas ants were the least abundant at the

summit, 17N. Ant abundance was also correlated with

species density except for 08N and 09S, with 08N having

less species than expected and 09S considerably more

(Fig. 2b).

Table 1 Species density and abundance of ants collected in the

vegetation types

Vegetation type n S Species density

(mean SE)

Abundance

(mean SE

Arid Northern

Bushveld

4 33 23.5 ± 0.5 2,067 ± 239.3

Cool Mistbelt

vegetation

12 39 16.1 ± 1.2 448.3 ± 61.2

Leached Sandveld 12 60 23.7 ± 1 810 ± 118.6

Forest 8 36 16.8 ± 1.1 427.8 ± 73.7

Moist mountain

thicket

12 58 25.5 ± 2 1,687.5 ± 828.6

n: number of sampling grids; S: total species density

Table 2 Observed species

richness and species richness

estimates for all the elevational

zones

ANB: Arid Northern Bushveld,

LS: Leached Sandveld, CMV:

Cool Mistbelt Vegetation, SF:

Soutpansberg Forest, SMMT,

Soutpansberg Moist Mountain

thicket

Elevation

zone

Aspect Vegetation

type

Observed

number

of species

Chao 1 MMMeans Jack 1

08N North ANB 33 35.3 ± 3.4 35.8 38.9 ± 2.3

10N North LS 34 34.2 ± .6 35.2 39 ± 2.2

12N North LS 46 49.8 ± 4.2 49.7 55.9 ± 3.8

14N North LS 37 53 ± 16.5 46.3 45 ± 9.4

17N North CMV 21 21 ± 0.23 21.5 24 ± 1.7

16S South CMV 26 29 ± 1.82 29.4 32 ± 1.9

14S South CMV 32 42.7 ± 10.2 44.4 40.9 ± 3.7

12S South SF 28 36.2 ± 8.3 34.3 37.9 ± 3.3

12S2 South SF 28 28.3 ± 0.9 28.8 32 ± 1.7

10S South SMMT 37 39.1 ± 2.5 42.1 45.9 ± 3.7

09S South SMMT 51 58 ± 6.7 57 63.8 ± 4.3

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The highest mean temperature was found at 10N and the

lowest at 12S, the forest (Fig. 2c). The lowest absolute

minimum temperature was recorded at the summit, 17N,

and the highest at 10N. The highest absolute maximum

temperature was found at 10S and the lowest at 12S

(Fig. 2c). Mean monthly temperature range was the highest

at 17N and the lowest in 12S.

One habitat structure variable (proportion of bare

ground), the proportion sand in the soil, and three spatial

variables (PCNM 5, 15 and 16) remained significant in the

final GLM model and were significantly associated with

species richness (Table 3). The spatial variables represent

medium to small-scale spatial structures. Variation parti-

tioning showed that 69.4% of the variation in species

richness can be explained by the spatial and environmental

variables measured. Most of this (25.3%) corresponds to

pure spatial effects, while 22.1% is pure environmental

effects and spatially structured environmental components

accounts for 22% (Table 3).

Two environmental variables remained significant in the

final model of ant abundance across the mountain

(Table 3). Proportion of sand had a significant positive

term, whereas Magnesium concentration in the soil was

negatively related to abundance (Table 3). Very little var-

iation in ant abundance is assessable by space alone (9.2%)

while the pure environmental component accounted for

25% of the total variation. Fifty-four percent of the varia-

tion in ant abundance along the transect is the result of

spatially structured environmental variables. The high

percentage of variation explained by spatially structured

environmental variables is understood with reference to the

spatial structure of the environmental variables themselves

(Fig. 3).

The first two canonical axes of the redundancy analysis

explained 35.1% (25% by the first axis) and 60% of the

variability in species matrices could be related to the

measured environmental variables (Table 4). The first

canonical axis explained a significant amount of variation

in species composition (F = 16.5, p = 0.005) and the

remaining axes were also significant (F = 1.9, p = 0.01).

There is a clear separation of the ant assemblages of the

northern and southern aspect along the first axis, whereas

the variation along the second axis is largely the result of

differences between assemblages on the southern aspect

where lower elevation sites on the southern aspect had

positive scores and higher elevation sites, negative scores

(Fig. 4).

Four of the 16 environmental variables included con-

tributed significantly to the variance explained in ant

composition (Table 4). This includes absolute minimum

temperature and proportion of bare ground which is posi-

tively correlated with the first axis (Fig. 4, Table 4). This

reflects differences in the development of the vegetation

layer and complexity (more developed in sites on the left

hand side). The first axis therefore represents a temperature

and vegetation cover gradient. Ant communities from open

habitats had positive values along axis one whereas thicket,

forest and cool mistbelt vegetation had negative values.

The second ordination axis correlates negatively with

absolute maximum temperature and positively with the

Fig. 2 a Mean rarefied (710 individuals) species richness (±1 SE),

b mean ant abundance (±1 SE), c mean monthly temperature

(±1 SE) and absolute minimum and maximum temperature for each

of the 11 elevational zone across the Soutpansberg mountain

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Magnesium content of the soil (Table 4). The second axis

also represents a temperature gradient and include the

gradient of sandy (negative) and clayey soils (positive)

found across the mountain. Communities with high abso-

lute maximum temperatures, viz. high altitude cool mist-

belt vegetation and lower elevational sites of the northern

aspect had negative scores along this axis, while the cool

forest replicates had positive scores. The latter sites were

on basaltic clay soils while the sites with sandy soils had

negative scores.

Variation partitioning of species composition suggested

that spatially structured environmental variables accounted

for 21% of the variation, local environment, 15%, and

space alone, 10%. When the multivariate model was cor-

rected for the effect of space through partial constrained

analysis, only two environmental variables, absolute min-

imum and absolute maximum temperatures remained

significant.

Seven species had more than 40% of their variation

explained by the constrained ordination (Fig. 4). Four of

these, Monomorium damarense, Monomorium sp.03, Ocy-

myrmex fortior, Meranoplus glaber and Camponotus

flavipilosus were positively associated with the first axis

and prefer the warm open habitats of the northern aspect

(Andersen 1995).

Two species, Monomorium sp.02 and Pheidole mega-

cephala had a distinct preference for sites with higher

vegetation cover and lower minimum temperatures with

considerable temperature extremes. Although occurring

across the whole mountain Tetramorium sp.01 preferred

the clayey soils of the forest and thicket sites.

Of the 17 taxa that had significant IndVals [ 70, 12

were characteristic of altitudinal bands and vegetation

types on the northern aspect of the mountain. Only five

species were associated with sites on the southern aspect,

four of these were specific to the Soutpansberg Moist

Mountian Thicket and only one, Nesomyrmex sp.01 was an

indicator of the Forest. The Cold Mistbelt Vegetation had

no indicator species. Each of the altitudinal bands on the

northern aspect had charactistic species, except for 12N,

while 08N had the most, three species. The myrmecochore,

Tetramorium setuliferum, were restricted to the lower ele-

vational sites of 08N–12N.

Discussion

The ant diversity on the mountain compares favorably with

that found along the Greater Cederberg Biodiversity Cor-

ridor (GCBC) study which employed a similar temporal

sampling protocol as this study and collected 72379 indi-

viduals in 85 species along a 162 km transect with 17

altitudinal sites and recorded 24 genera (Botes et al. 2006).

The Soutpansberg (SPB) transect is ten times shorter,

contains 11 sites and although we collected fewer indi-

viduals and marginally less species we did record more

genera. All the altitudinal bands except the two forest

summit grids had less than 30 observed species compared

to the Cederberg’s one altitudinal band that had more than

30 species. The species richness in this study therefore

conforms to that predicted for high energy-savanna envi-

ronments (Samways 1983, 1990a; Swart et al. 1999;

Lindsey and Skinner 2001; Botes et al. 2006). The rela-

tively rich generic and higher taxonomic diversity of the

ant assemblages resembles that found for other taxa,

notably plants (Hahn 1997) and spiders (Foord et al. 2008).

The Soutpansberg is one of the oldest geological forma-

tions in Southern Africa, faulting generated this geomor-

phological feature ca. 160 Ma ago, while erosion over the

last 60 Ma form the present day topographical feature

(Haddon and McCarthy 2005). This in combination with its

biogeographic location could explain the combination of

inflated higher taxonomic diversity and remnant taxa found

across the mountain (Jocque 2008).

Table 3 Generalized linear model (Poisson error distribution, log

link function with quasi-likelihood estimation in the case of

overdispersion) outcomes for the relationships between spatial terms

plus environmental variables on species density and abundance of ant

assemblages across the whole transect

df Deviance Selected environmental terms Spatial terms Percentage deviance

explained

A B C Total

Species

richness

40 65.8 AminT(?0.035) ?Bare(?0.12*)

?Sand(-0.08*)

PCNM4 (0.02) ? PCNM5(0.06*) ?

PCNM15(-0.13*) ?PCNM16(-0.35*)

25.3 22 22.1 69.4

Abundance 39 450.99 AmaxT(?0.17) ? Mg(-

0.3**) ? Sand(?0.29**) ?

NO3(-0.16) ? Area(0.3)

PCNM4(0.02) ? PCNM15(-

0.37*) ? PCNM15(-0.69) ?

PCNM21(-4.1*) ? PCNM22(-7.7)

9.2 54 25 88.4

Estimates of coefficients are given in brackets. Area: available area; AminT: absolute minimum temperature; AmaxT: absolute maximum

temperature; Bare: proportion of bare ground; Mg: Magnesium; NO3–N: Nitrate; Sand: percentage sand in soil

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As observed in Australia (Andersen 1995), North

America (Sanders 2002) and in a global scale study (Dunn

et al. 2009), ant richness and density was the highest in

warm and open habitats, 08N, 12N, 14N and peaked in

09S, all of these habitats were characterised by high pro-

portions of bare ground and high absolute minimum and

maximum temperatures. The most diverse site, 09S, is

dominated by Dichrostachys cinerea, a tree species typi-

cally associated with disturbed sites in the region and

disturbance could therefore play a role the high diversity of

the site. Botes et al. (2006) suggested that variation in

precipitation, an environmental variable that was not

included in their study, could explain the small amount of

variance explained (\40%) in species richness of their

model. There are often no correlation between ant diversity

and rainfall or ant diversity and productivity (Dunn et al.

2009). Although precipitation in this arid region of the

Western Soutpansberg could explain some of the variance,

the large amount of variation (70%) explained by our

model suggests that energy is not a good predictor of ant

richness along the mountain. Several of the sites with the

highest species densities are on the northern aspect with

acidic sandy soil where precipitation (* 300 mm/year) is

considerably less than on the southern aspect (*700 mm/

year). Ants are considered thermophilic with hot, dry

regions of the world having relatively higher species

richness of ground foraging ants (Brown 1973; Holldobler

and Wilson 1990; Kaspari et al. 2000; Dunn et al. 2009).

A large percentage of variation in species richness

(69%), abundance (89%) and assemblage structure to a

lesser extent (46%) were explained by the medium to

small-scale spatial structures (PCNM15, 16 and 21) and

environmental variables (bare ground, sand and Magne-

sium) across the mountain. The fact that the model for ant

abundance explained more of the variation than that of the

richness model conforms to what was found by Botes et al.

(2006). This could be the result of the linear response of ant

abundance to the environment while richness responds in a

non-linear fashion with low richness at both low and high

abundances because of the alternate impacts of stress and

competition (Parr et al. 2005). The large amount of varia-

tion that is explained by spatially structured environmental

variables, particularly for that of ant abundance, could be

explained by the strong spatial gradient in environmental

variables (Fig. 3). The local processes did however

Fig. 3 The spatial structure in species density, abundance, and the

environmental variables that explained significant amounts of vari-

ance in abundance and species density. The correlograms are based on

12 equal frequency classes. Significant Moran’s I values (coefficient

of autocorrelation) are indicated by closed symbols

Table 4 The environmental variables that explained a significant

amount of variation in species composition (R values, ter Braak and

Smilauer 2002) based on redundancy analysis (RDA)

R

Variable Eigenvalues P F Axis 1 Axis 2

Bare 0.12 0.002 5.7 0.55* -0.15

AminT 0.14 0.002 7.9 0.4* 0.56*

Mg 0.08 0.002 4.8 -0.12 0.53*

AmaxT 006 0.002 3.84 0.21 -0.68*

Sand 0.06 0.014 2.34 0.28* -0.26

Sum of all canonical eigenvalues: 0.59; AmaxT: absolute maximum

temperature, AminT: Absolute minimum monthly temperature;

BARE: proportion bare ground

The significance of the R values was determined using Monte Carlo

permutation tests (p significance and F test statistic). Eigenvalues

indicate the additional variance explained by each variable. Axes 1

and 2 are the first two ordination axes of the biplots of samples and

environmental variables. A negative R value reflects gradient direc-

tion in the RDA ordination

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consistently played a larger role in structuring ant assem-

blages across the mountain than did regional processes.

The same environmental variables that were significant

in the GLM models of ant abundance and richness,

emerged as significant in the RDA analysis. Ant assem-

blages associated with habitats with a high proportion of

bare ground and sand in the soil, particularly on the

northern aspect, were characterised by high ant abundance

and richness, whereas sites with increased Magnesium

content associated with the forests of the southern aspect

were characterised by lower diversity and abundance.

Magnesium was also significantly positively correlated

with the structural complexity of a habitat, clay content and

pH (‘‘Appendix 2’’). Absolute minimum and maximum

temperature were however associated with sites that had

both high and low species richness and abundance and this

is why these two variables were not significant in the final

GLM model.

The five vegetation types had distinct ant assemblages

i.e. vegetation types explained significant amounts of the

variation in ant assemblages. The largest proportion of

indicator taxa was restricted to the northern aspect. This

suggests that several of the northern slope’s taxa could be

defined as specialists, whereas taxa on the southern slope

were more generalist with wide distribution ranges. In

addition to this, most of the characteristic taxa were also

restricted to the lower altitudinal bands. This is true for

both aspects of the mountain. These specialist species

might therefore also be more sensitive to environmental

change. The narrower distributional ranges of the lower

altitude species also conforms the predictions of Rapa-

port’s rule (Stevens 1992). The highest elevations did

not have a single indicator taxa and the species associated

with sites at these elevations, viz. P. megacephala and

Monomorium sp.02 (Fig. 4) were widely distributed across

the transect. This contrasts with spider and scorpion species

on the mountain where several endemic taxa were restric-

ted to mid- to higher elevations (Foord et al. 2008).

The 6�C temperature gradient over the 900 m altitudinal

range of the northern aspect compared to the 2.6�C gra-

dient of the southern slope where the dense thicket ame-

liorates temperature extremes could explain the contrasting

responses of ants on the different aspects. In addition, the

lack of distinctive taxa associated with the southern slopes

could be the result of recent (\100 years) disappearance of

grasslands on the Soutpansberg because of extensive bush

encroachment (Hahn 2006) partly explained by elevated

CO2 levels (Bond and Midgeley 2000; Bond 2008).

Microclimate, with temperature in particular, plays an

important role in structuring ant communities (Bestelmeyer

2000; Parr et al. 2005). Below ground soil temperature is

affected by soil moisture and the temperature above

ground, while the proportion of bare ground, leaf litter is

affected by vegetation, which itself may change in future.

These changes in habitat structure affect microclimate

and could complicate predictions of the response of ant

assemblages to change.

To a certain extent therefore, the diversity of habitats

across the mountain masks the effect of climate along the

mountain. However, habitat has less relevance on the

northern aspect that comprises of open woodland

throughout, except for the summit itself. Ant assemblages

on this aspect are largely under the control of temperature,

while soil characteristics (leached sandy soils) vary very

little along the aspect. There are several species that are

characteristic to each of the altitudinal bands on this aspect,

Fig. 4 a RDA ordination of environmental variables that explained

significant amounts of variation and b RDA ordination (biplot, sample

scaling) of ant assemblages including species with more than 40% of

their variability explained by the ordination subspace. For abbrevi-

ations of environmental variables see Table 2 and abbreviations of

vegetation types see Table 2, CamF: Camponotus fulvopilosus;

MerG: Meranoplus glaber; MonD: Monomorium damarense,

Monomorium sp.02, Monomorium sp.03; OcyF: Ocymyrmex fortior;

PheM: Pheidole megacephala; Tetm01: Tetramorium sp.01

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e.g. the three species at 08N, two species for 10N and one

for 14N (Table 5). Climate change predictions for the

Limpopo Province suggest increased temperature as well

as an increased rainfall (Davis et al. 2010). Changes in

temperature could shift the assemblages from left to right

in Fig. 5. The rate of these changes will depend on how

close individual species are to their thermal optimums and

tolerances of their fundamental niches (Colwell et al.

2008). Increased rainfall and CO2 levels could result in

further densification of thickets on the southern aspect, a

reduction in ground vegetation cover and temperatures

(reduced light availability) and bare ground (increased leaf

litter) which may translate into lower ant abundance and

diversity, conditions that would favor Nesomyrmex sp.01.

Most higher elevation species in this study had wide dis-

tribution ranges across the mountain as evidenced by the

absence of indicator species for this elevational class.

However, Camponotus sp.03 and Meranoplus sp.01 were

restricted to the highest elevations of 1,600 and 1,700 m

and could be threatened by predicted climate change

scenarios.

Acknowledgments Special thanks to Colin Schoeman and Brigitte

Braschler for verifying identifications. Hendrik Sithole provided

access to the Kruger National Park ant collection and Norbert Hahn

for his various inputs. We are also grateful to Ian and Retha Gaigher,

Oldrich van Schalkwyk and the several volunteers at Lajuma

Research Centre for their hospitality assistance in the field as well as

Dave Dewsnap for access to and support on the farm Goro. This study

was funded by the DST-NRF, Centre of Excellence for Invasion

Biology and the University of Venda.

Table 5 Percentage indicator values (IndVal [ 70%) of ant species

for altitudinal zones and vegetation types along the transect, all

indicator values are significant indicator 0.001

Elevational zone(s)/

vegetation type (s)

Species %IndVal

08N (ANB) Tapinolepis sp.1 97.4

Meranoplus sp.5 73.2

Monomorium sp.4 99.5

08/12N Pheidole sp.4 94.8

10N Camponotus fulvopilosusgroup (Emery, 1920)

92.2

Lepisiota sp.2 81.7

08N–12N Tetramorium setuliferum(Emery, 1895)

70.3

08N–14N Monomorium damarense(Forel, 1910f)

90.8

Ocymyrmex fortior (Santschi,

1911g)

99.5

Monomorium sp.3 95.7

Tapinoma sp.1 89

14N Pheidole sp.7 81.5

12S2/12S (SF) Nesomyrmex sp.1 71

09S/10S (SMMT) Camponotus sp.02 81.7

Myrmicaria natalensis (Smith,

1858)

74.7

Pachycondyla sp.03 75

Polyrhachis sp.01 72.3

ANB: Arid North Bushveld; SMMT: Soutpansberg Moist Mountain

Thicket; SF: Soutpansberg Forest

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Appendix 1

See Table 6.

Table 6 Geographical co-

ordinates of the 44 replicates

from 11 sampling zones and

their elevation along the

Soutpansberg transect

Sites Replicates Altitude Coordinates

Northern aspect

800 m (08N) 8N1 811 m a.s.l. 22 56.683�S, 29 26.359�E

8N2 808 m a.s.l. 22 56.649�S, 29 26.195�E

8N3 808 m a.s.l. 22 56.633�S, 29 26.015�E

8N4 813 m a.s.l. 22 56.641�S, 29 25.852�E

1,000 m (10N) 10N1 1,022 m a.s.l. 22 58.157�S, 29 24.979�E

10N2 980 m a.s.l. 22 58.028�S, 29 25.086�E

10N3 984 m a.s.l. 22 58.016�S, 29 24.856�E

10N4 1,013 m a.s.l. 22 58.062�S, 29 24.668�E

1,200 m (12N) 12N1 1,215 m a.s.l. 22 59.504�S, 29 25.421�E

12N2 1,199 m a.s.l. 22 59.418�S, 29 25.269�E

12N3 1,198 m a.s.l. 22 59.269�S, 29 25.342�E

12N4 1,189 m a.s.l. 22 59.351�S, 29 25.500�E

1,400 M (14N) 14N1 1,419 m a.s.l. 23 00.018�S, 29 25.545�E

14N2 1,412 m a.s.l. 23 00.018�S, 29 25.715�E

14N3 1,370 m a.s.l. 22 59.893�S, 29 25.831�E

14N4 1,433 m a.s.l. 23 00.202�S, 29 25.482�E

1,700 m (17N) 17N1 1,731 m a.s.l. 23 01.445�S, 29 25.745�E

17N2 1,695 m a.s.l. 23 01.458�S, 29 25.594�E

17N3 1,675 m a.s.l. 23 01.358�S, 29 25.883�E

17N4 1,693 m a.s.l. 23 01.460�S, 29 25.901�E

Southern aspect

900 m (09S) 9S1 903 m a.s.l. 23 03.846�S, 29 29.400�E

9S2 900 m a.s.l. 23 04.030�S, 29 29.628�E

9S3 904 m a.s.l. 23 03.629�S, 29 29.634�E

9S4 907 m a.s.l. 23 03.420�S, 29 29.808�E

1,000 m (10S) 10S1 1,032 m a.s.l. 23 02.961�S, 29 28.680�E

10S2 998 m a.s.l. 23 02.897�S, 29 29.400�E

10S3 1,000 m a.s.l. 23 02.908�S, 29 29.162�E

10S4 1,028 m a.s.l. 23 02.753�S, 29 29.185�E

1,200 m (12S) 12S1 1,206 m a.s.l. 23 02.417�S, 29 27.033�E

12S2 1,203 m a.s.l. 23 02.562�S, 29 26.577�E

12S3 1,221 m a.s.l. 23 02.378�S, 29 27.195�E

12S4 1,207 m a.s.l. 23 02.313�S, 29 28.608�E

1,200 m (12S2) 12S2.1 1,028 m a.s.l. 23 02.661�S, 29 27.869�E

12S2.2 1,206 m a.s.l. 23 02.638�S, 29 27.691�E

12S2.3 1,203 m a.s.l. 23 02.679�S, 29 27.518�E

12S2.4 1,221 m a.s.l. 23 04.035�S, 29 29.631�E

1,400 m (14S) 14S1 1,407 m a.s.l. 23 02.071�S, 29 25.863�E

14S2 1,419 m a.s.l. 23 01.583�S, 29 26.251�E

14S3 1,405 m a.s.l. 23 01.885�S, 29 26.549�E

14S4 1,427 m a.s.l. 23 02.033�S, 29 26.073�E

1,600 m (16S) 16S1 1,591 m a.s.l. 23 01.355�S, 29 26.033�E

16S2 1,623 m a.s.l. 23 01.513�S, 29 25.981�E

16S3 1,523 m a.s.l. 23 01.575�S, 29 25.818�E

16S4 1,587 m a.s.l. 23 01.249�S, 29 26.168�E

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Appendix 2

See Table 7.

Table 7 Pearson’s product-moment correlations of the abiotic and biotic variables

MinT MaxT MeanT Range AminT AmaxT Tothits Rock Bare

MinT

MaxT 0.14

MeanT 0.69 0.77

Range -0.10 0.97 0.61

AminT 0.74 -0.23 0.21 20.41

AmaxT 0.07 0.91 0.65 0.89 20.31

Tothits 0.02 20.58 20.40 20.59 0.16 20.60

Rock 0.53 0.42 0.55 0.29 0.56 0.28 -0.33

Bare 0.24 0.19 0.45 0.14 -0.25 0.21 -0.21 -0.23

Veg 20.75 0.02 20.50 0.20 20.43 0.07 0.03 20.39 20.49

Leaf 0.24 20.64 20.31 20.70 0.25 20.58 0.51 -0.28 -0.06

pH 0.18 20.61 20.36 20.66 0.19 20.49 0.30 20.17 20.08

ExCat 0.07 20.61 20.38 20.63 0.17 20.56 0.24 20.19 20.11

Cond 20.06 0.46 0.38 0.48 20.23 0.37 20.28 20.03 0.34

Ca 0.33 20.67 20.27 20.75 0.33 20.54 0.37 20.12 0.06

Mg 0.22 20.53 20.23 20.58 0.16 20.29 0.33 20.15 0.13

K 0.01 20.06 0.03 20.07 20.12 20.16 20.08 20.07 0.12

Na 20.06 0.08 20.06 0.09 0.13 0.07 20.34 0.42 20.33

Clay 20.16 20.27 20.29 20.23 20.04 0.05 20.02 20.28 0.06

Sand 0.04 0.36 0.30 0.35 20.03 0.02 20.13 0.28 0.00

Silt 0.13 20.40 20.25 20.43 0.13 20.11 0.30 20.23 20.08

C 20.30 20.26 20.47 20.19 20.02 20.24 0.20 0.01 20.47

NO3 0.12 20.32 20.23 20.35 0.22 20.40 0.18 0.05 20.24

H 20.39 0.32 20.09 0.42 20.20 0.28 20.18 0.09 20.29

T value 0.07 20.69 20.50 20.72 0.26 20.50 0.45 20.19 20.19

Alt 20.74 0.12 20.44 0.30 20.38 0.05 20.02 20.12 20.57

Area 0.30 0.23 0.37 0.16 20.33 0.41 20.13 20.19 0.59

CanCov 0.08 20.85 20.55 20.88 0.29 20.69 0.52 20.35 20.11

Veg Leaf pH ExCat Resist Ca Mg K Na

Leaf 20.48

pH 20.30 0.66

ExCat 20.15 0.50 0.80

Resist 0.02 20.33 20.60 20.59

Ca 20.45 0.68 0.89 0.72 20.63

Mg 20.34 0.50 0.72 0.50 20.65 0.84

K 20.02 20.01 0.23 0.55 20.16 0.15 0.07

Na 0.12 20.27 20.09 20.02 20.11 20.18 20.11 0.12

Clay 0.20 20.05 0.28 0.13 20.37 0.34 0.53 20.16 20.03

Sand 20.12 20.12 20.42 20.25 0.45 20.49 20.68 0.14 0.06

Silt 20.01 0.32 0.52 0.36 20.47 0.59 0.73 20.09 20.08

C 0.18 0.19 0.22 0.23 20.45 0.15 0.11 20.24 0.10

NO3–N 20.12 0.35 0.54 0.58 20.54 0.40 0.17 0.18 0.15

H 0.51 20.50 20.54 20.28 20.01 20.54 20.48 20.25 0.16

T value 20.18 0.62 0.79 0.63 20.69 0.80 0.76 20.12 20.11

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Appendix 3

See Table 8.

Table 7 continued

Veg Leaf pH ExCat Resist Ca Mg K Na

Altitude 0.75 20.34 20.43 20.35 0.21 20.62 20.59 20.20 0.25

Area 20.41 0.18 0.30 0.09 20.06 0.33 0.48 0.11 20.25

CanCov 20.19 0.73 0.72 0.60 20.55 0.82 0.70 20.03 20.24

Clay Sand Silt C NO3–N H T value Altitude Area

Sand 20.93

Silt 0.65 20.88

C 20.01 0.01 0.00

NO3–N 20.09 20.02 0.17 0.43

H 20.09 0.20 20.30 0.56 0.05

T value 0.46 20.60 0.66 0.55 0.48 20.18

Altitude 20.23 0.32 20.38 0.37 20.10 0.59 20.30

Area 0.25 20.34 0.39 20.26 20.09 20.36 0.14 20.62

CanCov 0.46 20.58 0.63 0.19 0.29 20.42 0.81 20.39 0.09

Significant values are indicated in bold. MinT: mean minimum monthly temperature; MaxT: mean maximum monthly temperature; MeanT:

mean monthly temperature; Range: mean monthly temperature range; AminT: absolute minimum monthly temperature; AmaxT: absolute

maximum monthly temperature; Tothits: vertical complexity of the vegetation; Rock: proportion of exposed rock; Bare: proportion of bare

ground; Veg: proportion of vegetation cover; Cond: conductivity; ExCat: Exchangeable cations; Alt: Altitude; CanCov: Canopy Cover

Table 8 Subfamilies and ant

species collected during four

sampling surveys (September

2009, January 2010, September

2010 and January 2011) in

different vegetation type

Subfamily and species Abundance Vegetation type

Aenictinae

Aenictus rotundatus (mayr, 1901) 517 ANB; LS; SMMT

Dorylinae

Dorylus helvolus (Emery, 1985j) 651 CMV; SMMT; SF

Dolichoderinae

Tapinoma sp.01 223 ANB; CMV; LS; SMMT

Technomyrmex sp.01 418 ANB; CMV; LS; SF; SMMT

Technomyrmex sp.02 27 CMV; LS; SF

Formicinae

Anoplolepsis (Zealleylla) refuscens (Santschi, 1917) 33 LS; SMMT

Camponotus fulvopilosus group (Emery, 1920) 167 ANB; LS

Camponotus sp.02 86 CMV; SMMT

Camponotus sp.03 13 CMV

Camponotus sp.04 66 ANB; CMV; LS; SF, SMMT

Camponotus sp.05 37 ANB; CMV; LS; SMMT

Camponotus maculatus (Fabricius, 1782) 33 CMV; SMMT

Camponotus sp.09 34 LS; SMMT

Camponotus tanaemyrmex group (Ashmead, 1905b) 1 SMMT

Camponotus sp.11 19 LS; SMMT

Camponotus sp.12 15 LS; SMMT

Camponotus cf. Mayr (Forel, 1916) 3 LS

Camponotus dofleini (Forel, 1911) 2 CMV; LS

Lepisiota capensis (Mayr, 1862) 446 ANB; CMV; LS; SF, SMMT

Lepisiota sp.02 43 LS; ANB

Lepisiota sp.04 12 LS; CMV

Lepisiota sp.05 2 SF; SMMT

Lepisiota sp.06 49 LS; CMV; SMMT

Polyrhachis schistacea (Gerstaecker, 1859) 44 LS; SMMT

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Table 8 continued

The abundance of the species collected

is given. LS: Leached Sandveld;

SMMT: Soutpansberg Moist Mountain

Thickets; ANB: Arid North Busheveld;

CMV: Cool Mistbelt Vegetation; SF:

Soutpansberg Forest

Subfamily and species Abundance Vegetation type

Tapinolepis sp.01 1,452 ANB; LS; SMMT

Myrmicinae

Crematogaster sp.01 18 LS; SF; SMMT

Crematogaster sp.02 40 LS; CMV; SF; SMMT

Crematogaster sp.03 33 LS; SMMT

Crematogaster sp.5 84 ANB; CMV; LS

Nesomyrmex sp.01 164 ANB; CMV; LS; SF; SMMT

Nesomyrmex sp.02 41 LS; SMMT

Meranoplus sp.01 87 CMV; LS; SF

Meranoplus glaber (Arnold, 1926) 93 ANB; LS; SMMT

Meranoplus sp.03 4 LS; SMMT

Meranoplus sp.04 14 ANB; LS; SMMT

Meranoplus sp.05 35 ANB; LS

Meranoplus sp.06 1 SMMT

Monomorium damarense (Forel, 1910f) 4,187 ANB, CMV; LS; SF; SMMT

Monomorium sp.02 1,855 CMV; LS; SMMT

Monomorium sp.03 1,341 ANB; CMV; LS; SF; SMMT

Monomorium sp.04 831 ANB; LS; CMV; SMMT

Monomorium sp.05 152 LS; SF; SMMT

Pheidole sp.01 2,418 ANB; CMV; LS; SF; SMMT

Pheidole rugaticeps group (Emery, 1877) 25 LS; CMV; SMMT

Pheidole sp.03 652 ANB; LS; CMV; SF; SMMT

Pheidole sp.04 1,695 ANB; CMV; LS; SF; SMMT

Pheidole sp.05 275 ANB; LS; CMV; SF; SMMT

Pheidole sp.06 300 LS; CMV; SMMT

Pheidole sp.07 429 ANB; LS

Pheidole megacephala (Fabricius, 1793) 14,478 ANB; CMV; LS; SF; SMMT

Pheidole sp.09 1,076 LS; SMMT

Pyramica sp.01 1 LS

Myrmicaria natalensis (Smith, 1858) 634 CMV; LS; SF; SMMT

Ocymyrmex fortior (Santschi, 1911 g) 1,391 ANB; LS; SMMT

Solenopsis sp.01 160 ANB; LS; CMV; SF; SMMT

Solenopsis sp.02 28 SMMT

Tetramorium sp.01 1,562 ANB; CMV; LS; SF; SMMT

Tetramorium sp.02 246 ANB; CMV; LS; SF; SMMT

Tetramorium setuliferum (Emery, 1895) 139 ANB; CMV; LS

Tetramorium sp.04 448 ANB; CMV; LS; SMMT

Tetramorium quadrispinosum (Emery, 1886) 712 ANB; CMV; LS; SF; SMMT

Anochectus traegaordhi (Mayr, 1940b) 5 LS; SMMT

Hypoponera sp.01 3 LS; CMV; SF

Hypoponera sp.02 4 CMV; SMMT

Hypoponera hlavac (Santschi, 1935b) 5 SMMT

Hypoponera sp.04 4 LS; SMMT

Odontomachus traglodytes (Santschi, 1914) 14 ANB; LS; SMMT

Pachycondyla sp.01 35 LS

Pachycondyla sp.02 47 SMMT

Pachycondyla sp.03 20 SMMT

Pachycondyla sp.04 10 SF; SMMT

Pachycondyla sp.05 6 SF; SMMT

Pachycondyla sp.06 3 SF

Platythyrea sp.01 13 LS; SMMT

Plectroctena mandibularis (Smith, 1858) 5 LS; SMMT

Leptogenys sp.01 13 LS; SMMT

Leptogenys sp.02 59 CMV; SMMT

Psedomyrmicinae

Tetraponera sp.01 2 ANB

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Appendix 4

See Fig. 5.

Fig. 5 Sample-based

rarefaction curves for 11

elevational zone of the northern

aspect a 08N, b10N, c 12N,

d 14N, e 17N, f 16S, g 14S,

h12S, i 12S2, j10S, k 09S

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