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B I O D I V E R S I T A S
ISSN: 1412-033X
Volume 21, Number 5, May 2020 E-ISSN: 2085-4722
Pages: 1989-2002 DOI: 10.13057/biodiv/d210526
Floristic analysis of semi-arid mountain ecosystems of the Griqualand
West centre of plant endemism, Northern Cape, South Africa
NANETTE VAN STADEN1,♥, STEFAN JOHN SIEBERT1, DIRK PETRUS CILLIERS1, DIAN WILSENACH1,
ARNOLD WALTER FRISBY2
1Unit for Environmental Sciences and Management, Faculty of Natural and Agricultural Sciences, North-West University. Potchefstroom Campus,
Hoffman Street 11, Potchefstroom 2531, South Africa. Tel.: +27-860-169698. ♥email: nanette.van.staden@gmail.com
2Department of Plant and Soil Sciences, Faculty of Natural and Agricultural Sciences, University of Pretoria. Cnr Lynnwood Road and Roper Street,
Hatfield, Pretoria 0028, South Africa
Manuscript received: 24 February 2020. Revision accepted: 14 April 2020.
Abstract. Van Staden N, Siebert SJ, Cilliers DP, Wilsenach D, Frisby AW. 2020. Floristic analysis of semi-arid mountain ecosystems of
the Griqualand West centre of plant endemism, Northern Cape, South Africa. Biodiversitas 21: 1989-2002. The Griqualand West Centre
(GWC) is one of 13 centres of plant endemism in South Africa. Despite its unique flora, it remains poorly conserved and studied. A
recent study identified an extensive geographical core area for the GWC, but endemic plant species were found to be absent from certain
parts within these borders. To address this, we refined the current GWC borders based on an ecological niche model, which predicted that
endemic species are restricted to four mountain ranges within GWC. Mountain floras within these refined borders were then floristically
compared to assess whether they are hotspots of endemicity. Floristically, the Asteraceae, Fabaceae, Malvaceae, and Poaceae were the
dominant plant families. Mountain ecosystems differed from one another at species level, with indicator species explaining the
compositional differences. Distribution patterns of indicator species were determined by mean annual precipitation, Ca: Mg ratios, soil
pH, cation exchange capacity, iron, and sand content. These environmental factors are possible drivers of niche partitioning,
environmental filtering and habitat specialization in each mountain ecosystem. Limestone and banded ironstone habitats were identified
as conservation priority areas, since they contained the highest numbers of rare and threatened GWC restricted-range species, of which six
were narrow endemics.
Keywords: Asbestos, banded iron formation, Ghaap Plateau, Kuruman, limestone, quartzite
Abbreviations: ANOVA: Analysis of Variance; AUC: Area Under Curve; CCA: Canonical Correspondence Analysis; CEC: Cation
Exchange Capacity; EC: Electrical Conductivity; GWC: Griqualand West Centre of Endemism; GW: Griqualand West; MAP: Mean
Annual Precipitation; MAT: Mean Annual Temperature; MaxEnt: Maximum entropy; NMDS: Non-metric Multi-Dimensional Scaling;
PERMANOVA: Permutational Multivariate Analysis of Variance; QDG: Quarter-Degree Grid; SAWS: South African Weather Service;
XRF: X-ray fluorescence
INTRODUCTION
Mountain ecosystems are characterized by distinct
floras (Harrison et al. 2009) due to habitat heterogeneity
(Noroozi et al. 2018; Chakraborty 2019). Mountains are
therefore considered to function like edaphic islands
(Rajakaruna 2004), with specific microclimates and -
habitats to which plant species are adapted by developing
special traits (Rajakaruna 2004; Rajakaruna 2018),
resulting in speciation and species-rich floras (Kruckeberg
1969). Many unique edaphic floras of mountain ecosystems
have been found to be associated with centres of endemism
(Van Wyk and Smith 2001; Williamson and Balkwill 2015;
Noroozi et al. 2018). Edaphic floras are therefore rich in
endemic, edaphic specialists (Schmiedel and Jürgens 1999;
Siebert et al. 2002). This phenomenon is typical for banded
ironstone (Jacobi et al. 2007; Markey and Dillon 2010),
quartzite (Schmiedel and Jürgens 1999; Curtis et al. 2013),
and carbonate soils (Peñas et al. 2005; Siebert and Siebert
2005; Mota et al. 2008). Mountain floras of GWC are
characterized by these rock types (Frisby et al. 2019), and
has a heterogeneous undulating landscape with diverse
climate and unique vegetation types (Mucina and
Rutherford 2006). Despite GWC's distinct vegetation, and
known endemic flora of 24 endemics and two near-
endemic plant species (Frisby et al. 2019), our
understanding regarding its plant diversity patterns is
limited. Botanical studies in GWC are few (Wilman 1946;
Mostert 1967; Frisby et al. 2019; Van Munster et al. 2019)
and, hence, a descriptive assessment of the endemic
edaphic flora across different mountain geologies of GWC
is required to encourage conservation initiatives (Table 1).
Globally, centres of endemism are inadequately
conserved with some regions not being included within
borders of protected areas (Millar et al. 2017). Hence,
centres of endemism should garner more conservation
attention and it is, therefore, essential to understand the
patterns and drivers of endemism (Noroozi et al. 2018;
Taylor-Smith et al. 2020). Accurate identification of the
floristic borders of centers of endemism is imperative to aid
with designs for effective and strategic biodiversity
conservation and management (Wang et al. 2020).
Accurate demarcation of centres of endemism at a finer
scale is necessary to ensure comprehensive conservation
B I O D I V E R S I T A S
21 (5): 1989-2002, May 2020
1990
and management of species to be protected (Cañadas et al.
2014). Endemic species have the potential to serve as
flagship species, and conservation action will become more
effective by focusing on regions where endemics occur
exclusively (Noroozi et al. 2018; Taylor-Smith et al. 2020).
This seems logical especially when funding for
conservation is limited (Margules and Pressey 2000).
This study was conducted to promote conservation
strategies by providing conservation authorities with
detailed information to ensure proper conservation of
GWC, by focusing on priority areas where endemic species
occur at a finer scale. This paper addresses two primary
aims to develop a better understanding of the GWC and its
flora. Firstly, the borders of GWC are refined to establish
which main mountain ranges fall within the centre by using
a MaxEnt spatial model based on geology, climate, and
topography in combination with distribution data of GWC
endemics. Refining the borders of GWC will (i) result in a
smaller geographical region that will allow for focused
botanical studies and, (ii) ensure targeted conservation of
endemic plant species. Secondly, the flora associated with
the main mountain ecosystems within these newly refined
borders will be described. By doing so, knowledge
regarding floristic characteristics of the ecosystems will
depict the distinctness of the mountain floras. Mountain
floras will be described based on (i) dominant plant
families, (ii) common species, (iii) indicator plant species,
(iv) threatened and endemic species, and (v) species
composition.
MATERIALS AND METHODS
Study area
GWC was first proposed and mapped by Van Wyk and
Smith (2001). Recently, borders were described and set by
Frisby et al. (2019). This description was based on QDG’s
of the total distribution of GWC endemic species per se,
which resulted in an extensive area (75 172 km²).
The GWC falls within the Savanna Biome. The
landscape is heterogenous with mountain ranges and/or
ridges trending north-south (Figure 1), with the
intermontane valleys filled with Kalahari sands (Mucina
and Rutherford 2006). In the east, GWC consists of the
dolomitic Ghaap Plateau bordering on an undulating set of
low banded ironstone hills called the Asbestos- and
Kuruman Hills. The landscape becomes more rugged in the
west of GWC due to the quartzitic Langberg. These
mountain systems are each characterized by endemic
vegetation units, i.e. Ghaap Plateau Vaal Bushveld,
Kuruman Mountain Bushveld, and Koranna-Langberg
Mountain Bushveld (Mucina and Rutherford 2006).
Climate
GWC falls within the summer rainfall region of South
Africa. Rainfall is highly erratic, and a semi-arid climate
prevails. From east to west, a gradient of increasing aridity
is evident. In the east, the Ghaap Plateau receives higher
rainfall, whereas the Langberg in the west is a region of
lower, more arid rainfall (Table 1). The Kuruman Hills
receive higher rainfall than the southern Asbestos Hills of
the same geology (Mucina and Rutherford 2006). The
mountains are slightly cooler than the lower-lying areas
(Mucina and Rutherford 2006; Frisby et al. 2019).
Geology and soil
Three subgroups of the Ghaap Group (Griqualand West
Basin) are found in GWC, namely Campbell Rand,
Schmidtsdrif and Asbestos Hills (Van Wyk and Smith
2001; Eriksson et al. 2006). The former two subgroups
dominate on the Ghaap Plateau. Soils are rich in lime due
to the prevalence of dolomite, limestone, and chert (Keyser
1997). Therefore, the soil of the Ghaap Plateau is rich in
both magnesium (Mg) and calcium (Ca) (McCarthy and
Rubidge 2005). On dolomites, soils are sandy and dark
brown to reddish, while shallow, black, turfy and alkaline
soils are found on limestone. Furthermore, the soil is
underlain by weathered rock and slightly leached (Mucina
and Rutherford 2006; AGIS 2007).
The Asbestos Hills subgroup dominates on the
Kuruman- and Asbestos Hills that consist mainly of banded
ironstone. Additionally, jaspillite, chert and riebeckite
asbestos are associated with these two mountain
ecosystems (Keyser 1997; Mucina and Rutherford 2006).
Soils are sandy and shallow with 60-80% of the soil surface
covered with boulders or rocks (Mucina and Rutherford
2006; AGIS 2007).
The geology of the Langberg consists of clastic
sediments such as quartzite (white, pink and green),
greywacke, lavas, conglomerate, and hematite of the
Olifantshoek Supergroup (Keyser 1997; Mucina and
Rutherford 2006). Arenaceous rocks (derived from or
containing sand) of the Volop Group are well exposed in
the landscape with red-brown arenites of the Matsap
Subgroup overlying the Hartley Formation, which is a layer
of conglomerate (Moen 2006). Slopes are steep (mostly 10-
50°) with limited soil cover. Soils are rocky with exposed
boulders and/or rocks covering more than 80% of the
landscape (AGIS 2007). Soil texture varies from sandy
loam to sandy clay loam (AGIS 2007).
Table 1. Summary of environmental conditions associated with each GWC mountain landscape (Mucina and Rutherford 2006)
Mountain
Approx. area (km²)
Altitude
(m.a.s.l.)
MAT
(°C)
MAP
(mm)
Geology
Ghaap Plateau
14 997
1 100-1 500
17.1
370-425
Dolomite, limestone
Kuruman Hills
1 236
1 100-1 800
16.8
355-375
Banded ironstone
Asbestos Hills
2 117
1 100-1 800
16.8
290-360
Banded ironstone
Langberg
1 204
1 000-1 850
16.8
225-295
Quartzite
Note: Abbreviations in the table are as follows: Meters above sea level (m.a.s.l.), Mean annual temperature (MAT) and Mean annual
precipitation (MAP).
VAN STADEN et al. – Floristics of GWC’s mountain ecosystems
1991
Figure 1. Main mountain ranges in GWC in the Greater Griqualand West area, Northern Cape, South Africa. The Langberg extends
ultimately into the Korannaberg in the north. Since the Korannaberg falls outside the center and does not harbor any GWC endemics,
this range was excluded from this study
Data collection and -analysis
Refining the borders
MaxEnt software (Elith et al. 2011; Phillips et al. 2019)
was used to develop an ecological niche model for GWC
based on bioclimatic variables. MaxEnt uses probability of
occurrence to calculate the conditions in which species
occur (Phillips et al. 2009). A total of 95 verified
occurrence records for 24 endemics and two near-endemic
species identified by Frisby et al. (2019) were used as
presence records in the model. A total of 19 bioclimatic
variables obtained from WorldClim version 2 (Fick and
Hijmans 2017) represented environmental conditions (See
supplementary, Table A1). An 80/20 split was applied to
the occurrence records, with 80% of records (n=76) used to
train the model and 20% (n=19) used to test the accuracy of
the model prediction. Default settings were used, except for
the replication number that was set to 100. The AUC score
was used to determine the accuracy of the model (Bean et
al. 2012), where an AUC of 1 would indicate a perfect
prediction and 0.5 a random prediction (Phillips et al.
2006). An AUC of 0.979 was obtained suggesting a good
model prediction. To convert the model output to a binary
output usable for delineation purposes, a threshold was
applied. The tenth percentile training presence logistic
threshold (0.2772), that is suitable to (i) use when dealing
with centres of endemism (Escalante et al. 2013) as well as
(ii) studies relying on presence-only data (Callen and
Miller 2015), was used. The binary output was finally
intersected with the boundary delineated by Frisby et al.
(2019) as well as the geology (See supplementary, Table
A2) preferred by endemics in GWC.
Floristic analysis
Historical data
Species lists for GWC were obtained from BODATSA
(Ranwashe 2019). This data was supplemented with
specimen records obtained from herbaria with collections
from the GW region, including PUC, KMG, PRU, KSAN,
NMB, BLFU, and PRE. All distribution data were captured
at species level at QDG resolution. Further distribution data
were supplemented from literature sources (Van Wyk and
Smith 2001; Mucina and Rutherford 2006).
Field sampling
The four mountain ecosystems within the refined border
of the GWC were sampled in the wet season. Total rainfall,
obtained from the SAWS for January to April 2018
(sampling year), ranged between 160.6 mm and 422.4 mm
(west to east). A total of eight Modified-Whittaker plots
(50 m x 20 m) were sampled per mountain system. The
50 m sides of transects were placed parallel to the slope of
the mountain with the 20 m sides perpendicular to the 50 m
sides. Two 1 m² sub-plots were sampled within two
opposite corners of the plot. All rooted herbaceous
individuals within sub-plots were identified up to species
level and counted. Soil samples were collected for each
Modified-Whittaker plot and five soil samples were
randomly collected at a depth of 0-10 cm, depending on
soil depth due to rockiness. From this, a composite sample
was compiled and thoroughly mixed. Macro-and micro-
nutrients of soil samples were analyzed using a portable
XRF analyzer (Koch et al. 2017). Particle size distribution,
soil pH, EC, CEC, percentage clay, and silt were analyzed
according to procedures prescribed by the Non-Affiliated
Soil Analysis Work Committee (1990).
Data analysis
Floristic analysis was conducted on the four mountain
ranges within the refined borders of GWC. Plant lists were
compiled for each mountain system based on historical
distribution records that were obtained from herbarium
specimens and combined with collected field data. A total
B I O D I V E R S I T A S
21 (5): 1989-2002, May 2020
1992
of 44 field-collected plant specimens that could not be
identified below genus level, were excluded from plant
lists. Combined historical and field-collected data in the
1 m² sub-plots were used to identify the 20 largest plant
families of each mountain which were furthermore ranked
based on the number of species. Spearman’s rank
correlation coefficient tests were performed in Statistica
version 13.3 (TIBCO 2017) to assess similarity of plant
family rankings between mountains. This correlation
analysis followed a pairwise comparison between mountain
combinations and Spearman’s rho (ρ), ranging from -1 and
1 (Schober et al. 2018), and was calculated for each
pairwise rank. Significance was determined at p<0.05.
Jaccard similarity coefficients were performed on presence
or absence of collected species data within 1 m² sub-plots
using PAST (Hammer et al. 2001). This analysis was
conducted to establish the degree of similarity between
sampled mountain systems based on presence/absence of
herbaceous species. Plant species sampled in sub-plots of
the Modified-Whittaker plots were ranked based on their
overall abundances to reveal common plant species. NMDS
scatter plot of 1 m² field collected data, using the Bray-
Curtis dissimilarity distance measure, was constructed in
Primer 6 (2012) to compare herbaceous species
composition of mountain ecosystems. To assess whether
clustering in the NMDS was significant, Non-parametric
PERMANOVA analysis was conducted in Primer 6 (2012).
Furthermore, indicator species analysis was performed in
RStudio using the IndVal function under the labdsv
package (Roberts 2016) and significance levels were set at
p<0.05. To correlate abundance of collected indicator plant
species within sub-plots with environmental variables,
CCA was performed in Canoco 5 (Šmilauer and Lepš 2014).
RESULTS AND DISCUSSION
Refined borders of GWC
The niche model had an average AUC of 0.979 for the
100 replicate runs, suggesting high model performance and
a very good prediction (Phillips et al. 2006). Bioclimatic
variables that showed the highest model contribution
included temperature seasonality (annual range in
temperature), precipitation seasonality (annual range in
precipitation) and precipitation of the driest quarter (Table
2). This model output was overlaid onto the geology that is
known to harbor GWC endemics and the core area
boundary proposed by Frisby et al. (2019). The refined area
(Figure 2), where all three layers overlap, is strongly
associated with mountainous habitats with their associated
unique geology and cooler climate, implying that endemic
plant species are absent from the warmer, sand-filled
valleys. Thus, the mountains of GWC are identified as
hotspots within the centre of endemism due to topographic
heterogeneity, geology and climate (Cañadas et al. 2014;
Noroozi et al. 2018; Tordoni et al. 2020). The newly
refined boundaries of GWC covers 24 075 km², a surface
area three times smaller than the core area of 75 172 km² as
proposed by Frisby et al. (2019). The refined boundaries of
GWC are thus identified as conservation priority and
emphasize the need to focus on a finer scale when defining
centres of plant endemism. Focusing conservation efforts
on the endemic rich mountains will ensure that rare species
are protected (Noroozi et al. 2018). However, a systematic
conservation approach (Margules and Pressey 2000) and
development of conservation plans are required (Tordoni et
al. 2020), since identified hotspots of endemism within
GWC lie outside the borders of established protected areas,
i.e. Mokala National Park, Witsand Nature Reserve, and
Tswalu Kalahari Reserve.
Table 2. Estimates of the relative contributions of bioclimatic variables (BIO) to the MaxEnt model for GWC endemics. Values shown
are averages of 100 replicate runs.
Variable
Percent contribution (%)
BIO1 = Annual Mean Temperature
0.2
BIO2 = Mean Diurnal Range (Mean of monthly (max temp - min temp))
1
BIO3 = Isothermality (BIO2/BIO7) (* 100)
0
BIO4 = Temperature Seasonality (standard deviation *100)
37.2
BIO5 = Max Temperature of Warmest Month
0.4
BIO6 = Min Temperature of Coldest Month
0.4
BIO7 = Temperature Annual Range (BIO5-BIO6)
2.4
BIO8 = Mean Temperature of Wettest Quarter
0.5
BIO9 = Mean Temperature of Driest Quarter
0.2
BIO10 = Mean Temperature of Warmest Quarter
0
BIO11 = Mean Temperature of Coldest Quarter
0.1
BIO12 = Annual Precipitation
5.5
BIO13 = Precipitation of Wettest Month
0.1
BIO14 = Precipitation of Driest Month
1.6
BIO15 = Precipitation Seasonality (Coefficient of Variation)
27.8
BIO16 = Precipitation of Wettest Quarter
0.1
BIO17 = Precipitation of Driest Quarter
21.4
BIO18 = Precipitation of Warmest Quarter
1.3
BIO19 = Precipitation of Coldest Quarter
0
VAN STADEN et al. – Floristics of GWC’s mountain ecosystems
1993
Figure 2. Refined GWC borders as predicted by overlays of the ecological niche model for endemic plant species with the core area
defined by Frisby et al. (2019) and rock types known to harbor GWC endemics
Soil characteristics
Ca content exceeded 11 000 mg/kg on the Ghaap
Plateau, whilst Mg reached levels above 5 000 mg/kg
(Table 3). Consequently, the Ca: Mg ratio was above 2 and
the soil pH>7. These soil chemical properties of dolomite
and limestone soil are supported by Lee (1999). Iron (Fe)
levels were high (>50 000 mg/kg) on banded ironstone
Asbestos- and Kuruman Hills (Table 3) due to presence of
hematite (Fe2O3) and magnetite (Fe3O4) (Trendall 2013).
Ca: Mg ratios were high due to lower concentrations of Mg
(<3000mg/kg) and higher Ca content (>3000 mg/kg).
Furthermore, the two banded ironstone habitats were
characterized by more acidic soils (pH<7) (Thompson and
Sheehy 2011). This suggests that that banded ironstone
differs from acidic serpentine soils that are usually
associated with higher concentrations of Mg than Ca
(Robinson et al. 1996; Alexander 2011). Aluminum (Al)
levels exceeded 30 000 mg/kg on the Langberg and
Asbestos Hills (Table 3). Despite Al being one of the most
abundant metals in soils, the availability thereof to plants is
dependent on low soil pH (Gupta et al. 2013; Bojórquez-
Quintal et al. 2017). When soil acidity increases, Al can
become available to plants and inhibit plant growth (Abedi
et al. 2013; Bojórquez-Quintal et al. 2017). However, Al
may be beneficial to certain taxa or contribute to the
development of tolerance mechanisms in plants
(Bojórquez-Quintal et al. 2017). In addition, Al levels act
as an environmental filter (Abedi et al. 2013) that
contribute to compositional and structural changes in plant
communities (Mota et al. 2018). Acid and sandy soils,
especially those associated with quartzite, are known to be
rich in Al, low in clay content and all of potassium (K),
sodium (Na), Mg and Ca, and, hence, considered nutrient-
poor (Negreiros et al. 2014; Do Carmo and Jacobi 2016).
EC, an indicator of soil fertility (Fourie 2019), for the dry
Langberg and Asbestos Hills, was below 23 Ms/m and
indicating lower fertility (Table 3). In contrast, EC values
were higher (>30 Ms/m) for the Kuruman Hills and Ghaap
Plateau. A soil fertility gradient, together with a rainfall
gradient, could thus be observed for GWC as indicated by
the dendrogram of Jaccard Similarity based on sampled
species (Figure 3).
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21 (5): 1989-2002, May 2020
1994
Table 3. Mean concentrations with standard deviation of four elements and physical properties of soils associated with each mountain
(n=8). LB=Langberg; AH=Asbestos Hills; KH=Kuruman Hills; GP=Ghaap Plateau.
Ca (mg/kg)
Mg (mg/kg)
Fe (mg/kg)
Al (mg/kg)
Ca: Mg
pH
EC
(Ms/m)
CEC
cmol(+)/kg
LB
1347±921
2643±664
13741±1279
33868±3568
0.5±0.6
4.9±0.4
19.3±11.2
14.9±1.3
AH
4040±1237
3320±651
52437±10021
36318±4703
1.2±0.3
6.0±0.3
22.4±11.9
19.1±2.2
KH
3126±2573
2946±438
61660±19585
27055±5043
1.1±0.8
5.5±0.3
31.8±20.2
20.6±2.7
GP
11844±9241
5296±2790
18876+4721
30142±6981
2.2±3.2
7.6±0.6
32.6±14.2
20.2±3.1
Table 4. Comparison of field-collected (Field) data with historical herbarium records (Hist.) regarding taxa numbers represented in the
flora of each mountain ecosystem of GWC. Unique species are those plant species that are not shared between mountain ranges.
Langberg
Asbestos hills
Kuruman hills
Ghaap plateau
Hist.
Field
Hist.
Field
Hist.
Field
Hist.
Field
Families
65
39
75
40
83
38
73
45
Genera
192
89
252
93
287
89
223
94
Species
325
126
472
114
551
114
410
134
Species: Genus
1.69
1.42
1.87
1.23
1.92
1.28
1.84
1.43
Species/Family
5
3.23
6.29
2.85
6,64
3
5.62
2.98
N Unique Species
102
35
112
14
152
30
103
43
% Unique Species
31.38
27.78
23.73
12.28
27.59
26.32
25.12
32.09
Flora of GWC’s mountain ecosystems
Sampling effort
A plant list of historical data records was compiled for
each mountain system within GWC. As would be expected,
historical data indicated higher taxa numbers than field data
(based on eight Modified-Whittaker plots per system;
Table 4). Restricted sampling effort resulted in certain taxa
not being found (Spyreas 2016). However, each mountain
flora was associated with unique plant species. These
species were restricted to specific habitats and can be
considered habitat specialists within GWC (Anderson and
Ferree 2010; Williamson and Balkwill 2015). Comparing
field data with historical data revealed that unique species
of the Asbestos Hills were more restricted in distribution
and difficult to locate, despite a comparable number of
overall species recorded. The opposite was observed for the
Ghaap Plateau with unique species seemingly widespread
and easily recorded. The latest discovery of a new endemic
plant species Deverra rapaletsa Magee & Zietsman,
restricted to the Ghaap Plateau (Van Munster et al. 2019),
emphasizes the unique flora of the Ghaap Plateau and the
possibility of more species that are yet to be discovered.
Floristic sampling on the Asbestos Hills was hampered due
to poor rangeland conditions (overgrazing), which possibly
favored common species tolerant to disturbance (Table 4).
In contrast, sampling success for edaphic specialists was
greater on the banded ironstone of the Kuruman Hills
where the rangelands were managed responsibly.
Dominant plant families
Combined historical and field data revealed that the
four most species-rich families across the four mountain
landscapes were the Poaceae, Asteraceae, Fabaceae and
Malvaceae in descending order (See supplementary, Table
A4). These plant families are known to be of the largest
and most widespread families, not only in southern Africa,
but on a global scale. Members of these four families are
known to occupy a variety of habitats and persist under
various environmental conditions (Koekemoer et al. 2014).
More specifically, the Asteraceae, Fabaceae, and Poaceae
have been found to dominate plant communities on
limestone and dolomite (Ludwig 1999; Siebert and Siebert
2005), banded ironstone (Jacobi and Do Carmo 2008;
Markey and Dillon 2010; Gibson et al. 2012), as well as
quartzite (Curtis et al. 2013; Neri et al. 2019). Since GWC
is situated in the Savanna Biome, the representation by
members of the Malvaceae can be ascribed to their
preferred association and diversification in savanna
landscapes (Koekemoer et al. 2014; Soares et al. 2015).
The joined fifth most species-rich plant families in GWC,
i.e. Cyperaceae and Scrophulariaceae, is respectively
associated either with the lower rainfall (Langberg and
Asbestos Hills) or higher rainfall mountains (Kuruman
Hills and Ghaap Plateau). The Scrophulariaceae is widely
distributed globally and is common in drier, open savanna-
grasslands, as well as mountainous areas (Fischer 2004;
Koekemoer et al. 2014). Furthermore, some taxa are habitat
specialists since they prefer rocky and dry granitic outcrops
and/or ferricretes and, hence are often drought tolerant
(Clements et al. 2002; Fischer 2004; Koekemoer et al.
2014). Many Scrophulariaceae have also been found to be
metallophytes and, hence able to tolerate heavy metals in
soils especially copper (Cu) and cobalt (Co) in south-
central Africa (Faucon et al. 2009). In contrast, the
Cyperaceae is mostly found in moister habitats in savanna-
grassland regions (Koekemoer et al. 2014). Since the
Ghaap Plateau is underlain by dolomite and limestone,
soils tend to be rich in lime (CaO), alkaline, high in clay
content and poorly drained (Mustart et al. 1994). This
provides a suitable habitat for taxa in the Cyperaceae.
Ludwig (1999), as well as Swadek and Burgess (2012),
conducted studies on North American limestones and
found that the Cyperaceae was respectively the fourth and
fifth most diverse plant family. Both studies recorded 17
VAN STADEN et al. – Floristics of GWC’s mountain ecosystems
1995
taxa within the Cyperaceae, a number that corresponds to
the number of taxa present on the Ghaap Plateau (See
supplementary, Table A4). The Kuruman Hills and Ghaap
Plateau are rocky habitats. Consequently, presence of rock
crevices, drainage lines and shallow depressions where
rainwater can collect, serves as microhabitats for the
Cyperaceae to establish successfully (Porembski and
Barthlott 2000; Jacobi and Do Carmo 2008).
Diversity on family- and species level
Most diverse families of the Asbestos Hills correlated
significantly with the most diverse families of the Kuruman
Hills (Table 5; ρ=0.88; p<0.05). Jaccard similarity, based
on sampled plant species (Table 6), also revealed highest
similarity between these two mountain landscapes (38%).
Similarities between the Kuruman- and Asbestos Hills
could be ascribed to the fact that both these mountains are
characterized by banded ironstone (Van Wyk and Smith
2001) and the same vegetation type, namely the Kuruman
Mountain Bushveld (Mucina and Rutherford 2006).
Dendrograms of Spearman correlation (See supplementary,
Figure B1) and Jaccard similarity (Figure 3) indicated that
the floristic difference between the two banded ironstone
habitats is most likely attributed to rainfall, since the
Kuruman Hills receives higher rainfall than the southern
lying Asbestos Hills (Table 1).
The Langberg family diversity was moderately
correlated (Table 5) with the Asbestos Hills (ρ=0.77;
p<0.05), and the least with the Ghaap Plateau (ρ=0.56;
p<0.05). This is likely attributed to differences in pH
values (low vs. high) since the Langberg has acidic soils
and the Ghaap Plateau is alkaline (Table 3). Jaccard
similarity indicated that the Asbestos Hills and Langberg
share 34% of their species (Table 6), despite differences in
geology. Both these landscapes are characterized by lower
rainfall (Table 1), suggesting a shared drought-tolerant
and/or resistant flora (Kimball et al. 2017).
The Kuruman Hills and Ghaap Plateau mountain
systems revealed similar family diversity (Table 5; ρ=0.72;
p<0.05). This finding could be attributed to the higher
rainfall regime associated with these two systems within
GW (Table 1). Therefore, the most species-rich plant
families shared in high abundances, such as Cyperaceae,
are adapted to these moister systems (See supplementary,
Table A4). However, on species level, the Ghaap Plateau
and Kuruman Hills had the lowest Jaccard similarity (Table
6; 22%) which indicates differing species-specific
colonization on contrasting geological substrates.
Spearman correlation (Table 5) revealed that the Ghaap
Plateau and Asbestos Hills were also similar in their most
diverse plant families (ρ=0.70; p<0.05), but even more so
at species level (Table 6, 34.6%). This was unexpected
since these systems differ in geology and rainfall regimes.
The only meaningful explanation would be that the Ghaap
Plateau and Asbestos Hills are in close proximity to one
another (less than 50 km). Therefore, plant species may
easily migrate between the two systems especially when
species have wide niche breadths and are generalist plant
species (Sklenář et al. 2014; Zhang et al. 2016).
Species composition
NMDS analysis of floristic sub-plot data revealed
clustering according to mountain ecosystems (Figure 4).
PERMANOVA analysis revealed that the clustering was
significant (See supplementary, Table A5; Pseudo-
F=9.138; p<0.001). Although Langberg plots were
dispersed without a clear cluster, herbaceous assemblages
differed significantly between mountains (See
supplementary, Table A5). These findings are in
accordance with studies which have found that plant
communities underlay by banded ironstone (Jacobi et al.
2007; Gibson et al. 2012), dolomite and limestone (Siebert
and Siebert 2005; Zietsman and Bredenkamp 2007; Mota et
al. 2008) and quartzites (Wild et al. 1963; Schmiedel and
Jürgens 2004) are distinct. In semi-arid savannas, soil
nutrients and rainfall are of the most important factors
determining vegetation dynamics, especially for the
herbaceous layer (Siebert and Dreber 2019). Despite
similar geologies, the separate clustering displayed by the
Kuruman Hills and Asbestos Hills emphasizes the
important role of rainfall in driving compositional
differences, since both have the same rock type, but the
latter is a drier system.
Each mountain system was characterized by certain
commonly occurring species (See supplementary, Table
A6) as indicated by the top 20 highest ranked taxa based on
abundance values per se. Despite certain of these taxa
being shared between different mountain systems, their
relative frequency differed per system. For example,
Eragrostis nindensis Ficalho & Hiern was shared between
the Langberg, Asbestos Hills and Kuruman Hills, yet this
grass species had the highest abundance values on the
Langberg (See supplementary, Table A6). Indicator plant
species (Table 7) are characterized by high relative
frequency of occurrence in a specific mountain habitat
(specificity) and thus were primarily found in that habitat
in high numbers (Dufrêne and Legendre 1997). In contrast
to common species, indicator plant species provide
valuable ecological information on various species groups
of different plant communities (Dufrêne and Legendre
1997), especially with respect to their habitat preferences
and adaptations to persist under certain environmental
conditions (Siebert et al. 2010).
The effects of rainfall and soil properties on indicator
plant species were confirmed by CCA analysis (Figure 5).
Explanatory variables accounted for 47.7% of the total
variation. The first canonical axis explained 70.3% of total
variation and the second axis 41% of the variance. The
Ghaap Plateau was positively correlated with Ca: Mg ratio,
soil pH and negatively correlated with Fe content (Figure
5), with a clear separation between the plots of the Ghaap
Plateau and banded ironstone hills. Thus, indicator plant
species of the Ghaap Plateau preferred alkaline soil with
high Ca: Mg ratios. In contrast, those of the Kuruman Hills
are adapted to more acidic soils with high Fe content. A
study conducted by Li et al. (2015) in subtropical China,
revealed separation of indicator plant species across a pH
gradient in combination with other environmental
variables. Therefore, soil chemical characteristics can be
considered as one of the most significant factors driving
B I O D I V E R S I T A S
21 (5): 1989-2002, May 2020
1996
floristic composition across mountain ecosystems
(Boneschans et al. 2015) and explain the preferences of
indicator plant species associated with each mountain
(Soares et al. 2015).
The Langberg was positively correlated with higher soil
sand content. The Ghaap Plateau, as well as Kuruman
Hills, were positively correlated with MAP and CEC
(Figure 5). Indicator plant species of the Langberg, with its
low MAP, high sand content and low CEC values, were
separated clearly from other wetter and more nutrient-rich
mountain systems (Figure 5). Plots of the Asbestos Hills
were clustered intermediately between those of the Ghaap
Plateau and Kuruman Hills. There is thus evidence of niche
partitioning (Naaf and Wulf 2012) and a filtering effect
(Franklin et al. 2013) for herbaceous indicator plant species
across the nutrient- and rainfall gradient. The two drier and
nutrient-poor systems (Langberg and Asbestos Hills) were
predominantly characterized by perennial species of only
two life form types (dwarf shrubs and grasses; Table 7). In
contrast, the regions of higher rainfall and nutrients
(Kuruman Hills and Ghaap Plateau), consisted of both
perennial and annual indicator plant species comprising
four life form types (dwarf shrubs, forbs, grasses, and
sedges; Table 7).
Table 5. Spearman rank correlation (ρ) test of the highest-ranked
and most diverse plant families. All correlations were significant
(p<0.05). ** highest correlation; * lowest correlation.
Asbestos
Hills
Langberg
Kuruman
Hills
Langberg
0.77
Kuruman Hills
0.88**
0.66
Ghaap Plateau
0.70
0.56*
0.72
Table 6. Jaccard similarity coefficient measuring the degree of
similarity of plant species between sampled mountain systems.
Values are expressed as percentages. ** highest similarity; *
lowest similarity.
Langberg
Kuruman
Hills
Asbestos
Hills
Kuruman Hills
31.2
-
Asbestos Hills
34.4
38.2**
-
Ghaap Plateau
24.3
21.7*
34.6
Figure 3. Dendrogram of Jaccard Similarity indicating the
relatedness of each mountain ecosystem across the pH-, nutrient-
and rainfall gradient. Clusters were based on presence and
absence of sampled plant species.
Figure 4. NMDS ordination of sampled sub-plots representing herbaceous species assemblages of the four mountain ecosystems.
VAN STADEN et al. – Floristics of GWC’s mountain ecosystems
1997
Despite this study not following a trait-based approach,
the larger variety of life forms and life history
characteristics of indicator species associated with the
Kuruman Hills and Ghaap Plateau, suggests that niches
increase along a soil fertility and precipitation gradient
(Schellenberger Costa et al. 2017). Since indicator species
of wetter and more nutrient-rich habitats have more traits
(i.e., different life forms and life histories), it can be
ascribed to niche partitioning (Naaf and Wulf 2012). In
contrast, nutrient-poor and drier mountains have indicator
plant species with fewer traits (Wright et al. 2002; Shovon
et al. 2020). Therefore, reduction of trait richness of
indicator plant species in the drier Langberg and Asbestos
Hills are ascribed to environmental filtering. Thus, these
dominant traits provide species with competitive vigor and
stress tolerance to persist in the associated extreme
environmental conditions (Negreiros et al. 2014). This
suggests habitat specialization of indicator plant species (Li
et al. 2015).
Table 7. List of indicator plant species associated with each mountain as determined by indicator species analysis (Indval function of the
labdsv package in RStudio).
Mountain
Family
Species
Indval
p-value
Frequency
Life history
Life form
Langberg
Poaceae
Brachiaria nigropedata
0.48
0.001
13
Perennial
Grass
Convolvulaceae
Evolvulus alsinoides
0.44
0.001
7
Perennial
Dwarf shrub
Poaceae
Eragrostis nindensis
0.32
0.019
14
Perennial
Grass
Poaceae
Eragrostis chloromelas
0.2
0.037
6
Perennial
Grass
Asbestos Hills
Acanthaceae
Glossochilus burchellii
0.69
0.001
11
Perennial
Dwarf shrub
Poaceae
Tragus koelerioides
0.59
0.001
21
Perennial
Grass
Poaceae
Aristida diffusa
0.57
0.001
32
Perennial
Grass
Poaceae
Cymbopogon pospischilii
0.54
0.001
28
Perennial
Grass
Verbenaceae
Chascanum pinnatifidum
0.35
0.003
15
Perennial
Dwarf shrub
Malvaceae
Sida chrysantha
0.32
0.007
11
Perennial
Dwarf shrub
Malvaceae
Corchorus aspelinifolius
0.25
0.034
12
Perennial
Forb
Lamiaceaae
Leucas capensis
0.21
0.028
5
Perennial
Dwarf shrub
Kuruman Hills
Poaceae
Diheteropogon amplectens
0.81
0.001
13
Perennial
Grass
Cyperaceae
Bulbostylis hispidula
0.67
0,001
15
Annual
Sedge
Poaceae
Cymbopogon caesius
0.5
0.002
8
Perennial
Grass
Euphorbiaceae
Phyllanthus parvulus
0.46
0.002
35
Perennial
Dwarf shrub
Poaceae
Brachiaria serrata
0.45
0.001
14
Perennial
Grass
Poaceae
Elionurus muticus
0.44
0.001
10
Perennial
Grass
Asteraceae
Pegolettia retrofracta
0.32
0.003
7
Perennial
Dwarf shrub
Verbenaceae
Chascanuma denostachyum
0.31
0.005
5
Perennial
Dwarf shrub
Poaceae
Anthephora pubescens
0.31
0.008
17
Perennial
Grass
Ebenaceae
Euclea undulata
0.19
0.046
3
Perennial
Dwarf shrub
Polygalaceae
Polygala hottentotta
0.19
0.048
3
Perennial
Dwarf shrub
Ghaap Plateau
Cyperaceae
Bulbostylis humilis
0.3
0.001
25
Annual
Sedge
Poaceae
Enneapogon desvauxii
0.69
0.001
11
Perennial
Grass
Poaceae
Fingerhuthia africana
0.5
0.001
18
Perennial
Grass
Oxalidaceae
Oxalis depressa
0.44
0.001
7
Perennial
Forb
Poaceae
Tragus racemosa
0.44
0.001
7
Annual
Grass
Euphorbiaceae
Euphorbia inaequilatera
0.42
0.002
14
Perennial
Forb
Molluginaceae
Limeum fenestratum
0.38
0.002
6
Annual
Dwarf shrub
Molluginaceae
Limeum argute-carinatum
0.34
0.003
7
Annual
Dwarf shrub
Poaceae
Eragrostis lehmanniana
0.33
0.019
23
Perennial
Grass
Cyperaceae
Cyperus bellus
0.31
0.002
5
Perennial
Forb
Poaceae
Oropetium capense
0.25
0.008
4
Perennial
Grass
Poaceae
Eragrostis trichophora
0.24
0.019
8
Perennial
Grass
Poaceae
Enneapogon scoparius
0.19
0.045
3
Perennial
Grass
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21 (5): 1989-2002, May 2020
1998
Figure 5. CCA biplot of indicator plant species per plot and associated environmental variables within each mountain system. Species
included in the analysis are listed in Table 7.
Threatened and endemic species
The Ghaap Plateau hosted the highest number of GWC
endemic plant species followed by the ironstone hills (See
supplementary, Table A7). These findings are in
accordance with several studies which revealed that
limestone and dolomite (Ludwig 1999; Zietsman and
Bredenkamp 2007; Mota et al. 2008), as well as banded
ironstone (Gibson et al. 2012; Robinson et al. 2019) harbor
high numbers of endemics. Six GWC endemic plant
species (See supplementary, Table A7) can be considered
narrow endemics since they are restricted to a single
mountain range (Wild et al. 1963). Three of these restricted
endemics were associated with the Ghaap Plateau that
included a recently added species, Nerine hesseoides
L.Bolus (See supplementary, Table A8), after an outlying
locality was confirmed to be a different species. Two other
endemics were associated with seasonal pans of the Ghaap
Plateau. The ironstone hills harbor one narrow endemic,
while the Langberg with its deeper, sandy soils harbors two
species. Therefore, these plant species show an edaphic
preference (Mason 1946; Rajakaruna 2004) and can be
considered as rare (Stebbins 1942; Gaston 1997). Edaphic
restricted endemic plant species may be prone to extinction
(Harrison et al. 2009) due to low genetic variability
(Stebbins 1942). Despite low genetic diversity, narrow
resource use abilities and narrowed niche range (Gaston
and Kunin 1997), these plants are highly specialized and
thus edaphic specialists (Mason 1946; Anderson and Ferree
2010).
Eleven of the 24 GWC endemics were recorded during
the field surveys. More commonly sampled endemics
included Blepharis marginata (Nees) C.B.Clarke, Calobota
cuspidosa (Burch.) Boatwr. & B.-E.vanWyk, Glossochilus
burchellii Nees, Searsia tridactyla (Burch.) Moffett and
Tarchonanthus obovatus DC. (Table 8). These endemic
plant species are also associated with a wider distribution
range within GWC and can be considered regional
endemics (Cowling et al. 1994) with wider ecological
niches compared to narrow endemics (Gaston and Kunin
1997). The random sampling approach of this study did not
allow the targeting of rare species with patchy distributions
and strict habitat specificity (Stohlgren et al. 2005). It is
suggested that future studies must determine optimal
sampling effort, sampling time and plot size to ensure more
comprehensive data capturing of endemic species in GWC,
especially at landscape scale (Zhang et al. 2014). By doing
so, conservation efforts of endemic plant species can be
promoted since all 24 endemic plant species are of
conservation concern, irrespective that none of the
endemics are currently regarded as endangered (South
African National Biodiversity Institute 2019).
VAN STADEN et al. – Floristics of GWC’s mountain ecosystems
1999
Table 8. List of GWC endemic plant species, number of individuals recorded during Modified-Whittaker plot surveys, mountains where
it is known to occur, number herbarium QDG records and Red List category. AH-Asbestos Hills; GP-Ghaap Plateau; KH-Kuruman
Hills; LB-Langberg.
Family
Taxon
Individuals
recorded
Mountains
QDG
records
Category
Acanthaceae
Barleria media
-
GP, KH
4
Vulnerable
Blepharis marginata
51
AH, GP, KH, LB
12
Least concern
Glossochilus burchellii
56
AH, GP, KH
10
Least concern
Justicia puberula
4
GP, KH, LB
30
Least concern
Aizoaceae
Antimima lawsonii
-
AH, GP, KH
5
Rare
Hereroa wilmaniae
3
AH, GP, KH, LB
15
Data deficient
Lithops aucampiae subsp. euniceae
-
AH
2
Vulnerable
Lithops bromfieldii
-
LB
4
Least concern
Lithops lesliei subsp. burchellii
-
AH, GP
3
Near threatened
Prepodesma orpenii
-
AH, GP, KH, LB
23
Least concern
Amaryllidaceae
Nerine hesseoides
-
GP
4
Least concern
Anacardiaceae
Searsia tridactyla
30
AH, GP, KH, LB
63
Least concern
Apiaceae
Deverra rapaletsa
-
GP
2
Not yet assessed
Asteraceae
Amphiglossa tecta
1
AH, GP, LB
3
Critically Rare
Cineraria exilis
-
GP, KH
1
Data deficient
Dicoma kurumanii
4
GP, KH
1
Least concern
Eriocephalus ericoides subsp. griquensis
17
AH, GP, KH, LB
26
Least concern
Gnaphalium englerianum
-
GP, KH
2
Least concern
Pentzia stellata
-
GP
11
Near threatened
Tarchonanthus obovatus
40
AH, GP, KH, LB
53
Least concern
Celastraceae
Maytenus ilicina
-
AH, GP, KH, LB
11
Least concern
Putterlickia saxatilis
3
AH, GP, KH, LB
28
Least concern
Fabaceae
Calobota cuspidosa
23
AH, GP, KH, LB
45
Least concern
Poaceae
Brachiaria dura var. pilosa
-
LB
4
Data deficient
Note: QDG’s were obtained from BODATSA (Ranwashe 2019). Categories for threats were based on the National Red List (South
African National Biodiversity Institute 2019).
In conclusion, this study refined the borders of GWC
which enabled the floristic description of the four,
endemic-rich mountain landscapes within these new
borders. These borders were based on an ecological model,
which was constructed using presence and absence records
of GWC endemics and environmental parameters.
Distribution patterns of endemics were restricted to certain
mountain landscapes and geologies which allowed for
refinement of the model.
A clear soil fertility and rainfall gradient was identified
for the GWC and, subsequently, each mountain flora was
associated with different family- and species diversity, and
composition. All four mountain landscapes were dominated
by the Asteraceae, Fabaceae Malvaceae and Poaceae.
Furthermore, the Scrophulariaceae dominated on the
mountains of lower rainfall that are nutrient-poor
(Langberg and Asbestos Hills), whereas the Cyperaceae
were prominent on the two mountain systems of higher
rainfall and which are more nutrient rich (Kuruman Hills
and Ghaap Plateau). Indicator plant species explained the
compositional differences since each mountain ecosystem
was characterized by habitat specialists adapted to
prevailing edaphic and climatic conditions. Primary drivers
of the distribution of indicator species were soil pH, Ca:
Mg ratios and rainfall. These drivers contributed to niche
B I O D I V E R S I T A S
21 (5): 1989-2002, May 2020
2000
partitioning and environmental filtering (dry and nutrient
poor vs. wet and nutrient rich).
From a conservation perspective, future botanical
studies, and conservation and management strategies,
should focus within the refined borders of GWC. The
mountains are hotspots of endemics in GWC and should be
considered as conservation priority areas. Especially the
Ghaap Plateau and the ironstone hills since these systems
harbour most of the GWC endemics. Special attention
should be given to narrow endemic plant species with
restricted distributions within GWC’s borders as well as
those species having a category of threat.
ACKNOWLEDGEMENTS
The first author would like to thank the landowners for
providing access to their properties and for their warm
welcome. Many thanks to everyone who assisted with
fieldwork. Also, to Marié du Toit for her assistance with
the maps and Ricart Boneschans for his help with XRF soil
analysis. We thank the curators of various herbaria in
central South Africa for providing access to their
collections. Additional data were kindly provided by
Proffs. Braam van Wyk, University of Pretoria, and
Norbert Hahn, University of Venda. Analysis of the soil
samples was conducted by the Eco Analytica Laboratory,
North-West University. We also thank the SAWS for the
provision of climate data. The financial assistance of the
National Research Foundation (Grant UID: 103370), South
Africa, as well as the Deutscher Akademischer
Austauschdienst (DAAD) (Grant UID: 109741) towards
this research is hereby acknowledged. Opinions expressed
and conclusions arrived at, are those of the author and are
not necessarily to be attributed to the NRF.
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