Spatial and temporal distribution of cyanobacterial soil crusts in the Kalahari:
implications for soil surface properties
Thomas, A.D.1 and Dougill, A.J.2
– Department of Environmental and Geographical Sciences, Manchester Metropolitan
University, John Dalton Building, Chester Street, Manchester, M1 5GD, U.K. Tel. +44 (0)
161 247 1568. Fax. +44 (0) 161 247 xxxx. Email: a.d.thomas@mmu.ac.uk
Corresponding Author.
1
– School of Earth and Environment, University of Leeds, Leeds, LS2 9JT, UK. Tel. +44 (0)
113 343 6782. Fax. +44 (0) 113 343 6716. Email: adougill@env.leeds.ac.uk
2
Abstract
Keywords
Introduction
Extensive research reviews exist assessing the role of surface vegetation on dryland
geomorphological processes (e.g. Thornes, 1990; Bullard, 1997), in particular on the
role that surface vegetation has in controlling aeolian wind erosion (Tsoar & Møller,
1986; Lancaster & Baas, 1998). Specifically for the Kalahari region of Southern
Africa, research has focused on the link between surface vegetation cover and dune
mobility in the arid southwest of Botswana (Wiggs et al., 1995) and on the controlling
influence of bush cover on nebkha formation in the mixed farming areas in the dry
sub-humid Molopo Basin (Dougill & Thomas, 2002). Much less attention has been
paid to aeolian erosion processes in the more extensive semi-arid savanna rangelands
that typify much of Botswana, Eastern Namibia and Northern South Africa (Thomas,
2002), or to the possible controlling influence of cyanobacteria that colonise
biological soil crusts on the soil surface (Dougill & Thomas, 2004). Biological soil
crusts have been shown in other dryland regions to decrease surface erodibility
significantly by binding sand particles together (e.g. Van der Ancker, 1985; Danin et
al., 1989; Yair, 1990; others???; Belnap et al., 2003; Eldridge & Leys, 2003) and by
providing an enhanced nitrogen source that will increase the surface vegetation cover
(Scholes & Walker, 1993). This paper aims to report on recent research findings from
a range of locations in the semi-arid Kalahari that focus on the impact of biological
soil crusts on spatial and temporal patterns of soil surface properties that regulate
aeolian erosion processes.
Biological soil crusts are present in all arid and semi-arid regions (Belnap & Lange,
2003) and comprise cyanobacteria, algae, lichens, mosses, microfungi and other
bacteria (Belnap, Büdel and Lange, 2003). Fundamental to understanding the
geomorphological significance of cyanobacterial soil crusts is a comprehension of
their spatial distribution. Several factors are recognised as influencing crust
distribution and development, especially substrate, vegetation type and cover, and
disturbance levels (Belnap et al., 2003). It is reported that vegetation and biological
crust cover are inversely proportional due to the effects of competition for light
(Malam Issa et al., 1999) and nutrients (Harper & Belnap, 2001). It is generally
accepted that trampling, as a result of grazing, damages biologically-crusted surfaces
(e.g. Eldridge, 1998), thus in areas of intense grazing such as around boreholes, the
spatial distribution of crusts will be limited. Zaady & Bouskila (2002) describe
disturbance as the key factor in determining biological crust development in areas
where physical conditions are relatively constant. Given the spatial homogeneity of
the Kalahari, in terms of altitude, relief and surface water (Thomas & Shaw, 1993), it
is reasonable to impart a significant role to grazing disturbances in affecting the
distribution of cyanobacterial soil crusts. In this context Berkeley et al. (2005) have
shown that the canopies of woody plants represent quasi-discrete environments, in
which the response of crusts to local disturbance regimes is altered.
Dougill & Thomas (2004) have previously documented a cyanobacterial soil crust
cover of between 19 - 40 % at a range of disturbed sites on Kalahari Sands. They
identified three morphologically distinct crusts: a weakly consolidated crust with no
surface discolouration (type 1); a more consolidated crust with a black or brown
speckled surface (type 2); and a crust with a bumpy surface with an intensely coloured
black/brown surface (type 3). This paper builds on this preliminary analysis by
addressing the following research objectives:
1. To determine the controls on the distribution of soil crusts on Kalahari Sands at a
variety of spatial scales
2. To quantify recovery of cyanobacterial crust cover after disturbance
3. To examine the implications of cyanobacterial crusts for soil surface roughness,
strength, and total nitrogen content.
Study Area
Kalahari Sands are ancient wind-blown deposits that cover an area of over 2.5 million
km2 of Southern Africa, including over 80 % of Botswana (Thomas and Shaw, 1991).
Kalahari sand soils typically consist of over 95 % fine sand sized sediments and are
predominantly deep, structureless and nutrient deficient (Dougill et al., 1998). The
Kalahari includes areas of both active sediment movement and stable surfaces, with
much research focusing on the importance of surface vegetation cover (Bullard, 1994;
Wiggs et al., 1994, 1995) and climate (Thomas et al., 1997; Knight et al., 2004) in
affecting sediment mobility. The importance of soil surface characteristics, especially
the development of biogenic crusts, has been largely overlooked. The presence of
crusts has been largely related to its role in affecting nitrogen cycling (Skarpe and
Hendrikson, 1987; Aranibar et al., 2003, 2004; Dougill and Thomas, 2004; Berkeley
et al., 2005). All these studies focused on highly disturbed communal rangeland sites
and have estimated crust covers of between 19 and 40 %. No empirical estimates
exist of crust cover in lightly disturbed sites where crust cover can be expected to be
greater (Belnap et al., 2003).
Land use across the Kalahari is largely pastoral and is made possible by boreholes
which allow access to groundwater. Concentrated grazing pressure close to boreholes
adds a new environmental gradient to an otherwise relatively homogenous
environment (Thomas and Shaw, 1993). This leads to localised sediment mobility in a
spatially confined ‘sacrifice zone’ close to boreholes (Perkins and Thomas, 1993) and
a more extensive ‘bush encroached zone’ where woody shrubs, typically A. mellifera,
dominate. The region has a highly seasonal summer rainfall regime with very high
inter-annual variability that increases with declining mean annual rainfall.
Research Design and Methods
Research was undertaken during July 2003 at five locations and a total of 10 sites on
Kalahari Sands in Kgalagadi District, Botswana (Figure 1). All sites have a mean
annual rainfall of between 300 and 400 mm p.a.(Bhalotra, 1987). Disturbance was
quantified at each site using a disturbance index using cattle track frequency and dung
density (as per Dougill & Thomas, 2004 and Berkeley et al., 2005). At each site, a 30
m x 30 m grid was established. The grid was crossed at 10 m intervals in two
perpendicular directions. Cattle tracks and dung were counted along each of these
gridlines, cattle tracks being defined as well established ‘routes’, and a dung ‘count’
being a single or collection of pats (as opposed to total fragments) laying within an
arbitrary 0.5 m either side of the gridline.
Crust cover data were estimated at three scales. To assess the impact of regional
variations in land use, 10 sites at 5 locations were sampled (Figure 1). These
encompassed a range of grazing intensities from a lightly-grazed National Park,
wildlife management zone to more intensely grazed communal rangelands and
commercial ranches (Table 1). Within each site, a 1 m x 1 m quadrat was sampled at
intervals of 2 m inside 30 m x 30 m grid such a total of 240 quadrats were examined.
Percentage cover was estimated for each cyanobacterial soil crust type (according to
the morphological classification system of Dougill & Thomas, 2004), unconsolidated
soil, litter and cover of each vegetation species within the quadrats. Detailed crust
associations with the two most common shrub species in the Southern Kalahari
(Acacia mellifera and Grewia flava) were also examined. The canopy dimensions of
three shrubs of each species were measured and crust cover estimated in 0.5 m x 0.5
m quadrats, adjacent to one another along a line extending from the bowl to the
canopy edge in a northerly and southerly direction to account for any orientationcontrolled differences in cover.
To assess recovery of crust cover, two 5 m x 5 m plots were established in July 2003
at the commercial ranch site at a severely disturbed site close to the water point where
no crust cover was found. One plot was fenced to exclude all grazing animals and to
compare crust cover characteristics of the ungrazed plot to the neighbouring unfenced
plot. Sixteen 1 m x 1 m quadrats were surveyed for crust type and vegetation cover in
each plot in November 2004.
Properties – OM/TN;
Chlorophyll
Strength and Roughness – Tom
Recall that you have written roughness methods based on Kuipers, 1957; Nash et al., 2002
and links to other Shakesby / Chappell style references!!
Did you give dimensions of bridge used etc.? ie.
The erosion bridge consisted of a 61 cm long metal pipe with holes drilled at 5 mm intervals
along the central 30 cm of the pipe. Solid metal pins (0.5 mm in diameter) were inserted
through each of the holes in the pipe. The height of each pin above the first reference pin was
recorded and was adjusted for plot slope by using the first and last pins as zero reference. The
ends of the pipe were fastened into vertical end stands that were driven into the soil at varying
depths until the erosion bridge pipe was level, as indicated by a spirit-level secured on top of
the pipe.
Need to add statement on analysis methods used for this data?? (I have attempted this
explanation below).
Indices of both microtopography (based on analysis of mounds or depressions above a
threshold value) and of roughness based on deviations of all measured pin heights from
erosion bridge transects were calculated. Distinct microtopography features were designated
when the height of a mound, or depth of a depression was greater than 2 cm from the slope
adjusted ‘0’ reference level. A threshold value of 2 cm was chosen, due to the small number
of sites at which any microtopography features above the 3 cm threshold adopted by Nash et
al.(2002) were found (Type 1 crusts - 0 features of over 3 cm in 76 transects; Type 2 crusts 2 of 64 transects; Type 3 crusts – 5 of 60 transects). The high fine sand content of Kalahari
Sand soils (typically over 98 % fine sand) and the lack of algal or moss formation in Kalahari
soil crusts (Skarpe & Henrikkson, 1987; Dougill & Thomas, 2004) explains why such larger
mounds or depressions on crusts are rare compared to other dryland areas where crusts have
been more extensively studied. Three different indices of microtopography were calculated in
relation to their relative occurrence on each of the four surface classifications (unconsolidated
soil, Type 1 crust, Type 2 crust, Type 3 crust). The indices are: (1) the sum of the absolute
value of the depressions and mounds, (2) the frequency of depressions and mounds, and (3)
the sum of the depressions and the sum of mounds. For each index, values are presented as
an average for a single 30 cm transect to account for the different sample sizes of transects
from the different surface classifications. In addition, as a measure of roughness for all the
transects measured the mean heights of all deviations from the ‘0’ reference level and the
associated standard deviations were calculated for all transects and used to statistically
compare the mean roughness of each soil surface class using ANOVA applied using a posthoc Scheffe test (This is the Figure you have drawn up from Angela’s results already).
Results and Analysis
For roughness needs something along the lines of –
The microtopography indices for the four different surface classifications are shown in Table
xx. All indices have a consistent pattern showing that type 1 crusts are typified by
significantly smaller number and size of microtopography features compared to either the
better developed type 2 and 3 crusts, or disturbed unconsolidated soil that is typified by wider
mounds and depressions typical of sandy soils that are regularly churned by livestock hooves.
Type 2 and 3 crusts have a similar magnitude of total depressions / mounds over the 2 cm
threshold. However, it is notable that there are a greater number of smaller depressions /
mounds recorded on Type 3 crusts indicative of the distinct microtopography typical of well
developed biological soil crusts (Belnap & Gillette, 1997), rather than larger (smoother)
disturbances that can typify disturbed sandy surfaces that are then colonised (and
consolidated) by crusts. (TOM – I sense a better index for getting this rough compared to
smooth ‘roughness’ message across would be based on sum of difference to the previous
value on the transect rather than to a ‘0’ reference value as per Nash et al. method –
However, this would take me a while to practicalise in excel – I’ll see if I get chance to do this
pre-Tuesday – otherwise I think the Nash et al., indices do at least get message across).
Table xx. Microtopography Indices for Each Classified Soil Surface Type (mean per transect
values calculated as per method of Nash et al. , 2002).
Soil Type
Microtopography Index
∑ depressions / mounds
No. of depressions / mounds
∑depressions - ∑mounds
Unconsolidated
21.94
1.67
12.01
Crust Type 1
0.14
0.05
0.08
Crust Type 2
5.92
0.41
5.06
Crust Type 3
5.67
0.68
4.11
The surface roughness index based on mean deviation from the ‘0’ reference point of all
measured pins for each of the surface types is shown in Fig xx (diagram you have already
done). Statistical analyses using ANOVA shows the significant smoothing effect of type 1
crusts (compared to unconsolidated soils; p = 0.) and that a significant increase in surface
roughness occurs as crusts develop from type 1 into type 2 and 3 crust surfaces (p = 0. and p
= 0. respectively). (TOM – my job for tonight is to get p values to support this, though error
bars on Figure show clearly that this is the case).
Discussion and Conclusions
Acknowledgements
References
Tables
Figures