The distribution and spatial patterning of mima-like mounds in South Africa suggests genesis through vegetation induced aeolian sediment deposition

Journal of Arid Environments 119 (2015) 16e26

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The distribution and spatial patterning of mima-like mounds in South Africa suggests genesis through vegetation induced aeolian sediment deposition Michael D. Cramer*, Jeremy J. Midgley Department of Biological Sciences, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa

a r t i c l e i n f o

a b s t r a c t

Article history: Received 8 January 2015 Received in revised form 18 March 2015 Accepted 19 March 2015 Available online

Large earthen mima-like mounds, occur over vast areas globally, including south-western Africa, where they are called heuweltjies. Heuweltjies are commonly believed to be produced by fossorial fauna (termites and mammals), although it is also possible that they are the consequence of vegetation stabilising soil against erosion and/or trapping aeolian sediment. We sampled mound presence/absence in South Africa and analysed environmental correlates with mound distribution. Mounds mostly occur in sparse (0.1 < NDVI < 0.5) semi-arid Mediterranean-climate Succulent Karoo vegetation at modest elevations (<1000 m) with relatively high average annual wind speeds. These correlates are consistent with aeolian deposition of sediment in vegetation patches, but not with a termite-induced pattern. We analysed the spatial characteristics of mounds at 23 sites and found that over-dispersion is strongest and the size of mounds smallest closest to neighbouring mounds, indicating competitive interactions between mounds. Large inter-mound distances (ca. 40 m) make it unlikely that spacing is a direct consequence of root-based inter-plant competition. Rather, spacing is likely to be related to the influences of neighbouring mounds on aeolian sediment accretion. We suggest that the mounds arose in the Mediterranean climate zone at a time of greater aridity than currently. © 2015 Elsevier Ltd. All rights reserved.

Keywords: Coppice dunes Heuweltjie Mediterranean climate Nabkha Nebkha Vegetation pattern

1. Introduction Large (4e32 m diameter) regularly dispersed (i.e overdispersed) and non-anthropogenic earth mounds are common in North and South America and Africa (Cramer and Barger, 2014). While these mounds are often considered a mere curiosity (Lovegrove, 1991), they can comprise a large fraction of the land surface (e.g. 14e25% in western South Africa (Lovegrove and Siegfried, 1989); 32% on the Columbia Plateau, USA (Cox, 1990), and host flora distinct from the inter-mound areas (e.g. Silva et al., 2010). In south-western Africa, mounds referred to as heuweltjies (meaning small hills) have deeper and more fertile soils with vegetation distinct from the inter-mound areas that attracts secondary fauna (Kunz et al., 2012), and thus contributes to regional species diversity. The fertile patches associated with mounds are also persistent in agricultural fields where, despite over a century of tillage, the mounds remain identifiable and produce better crop

* Corresponding author. E-mail address: [email protected] (M.D. Cramer). http://dx.doi.org/10.1016/j.jaridenv.2015.03.011 0140-1963/© 2015 Elsevier Ltd. All rights reserved.

and grazing areas (Lovegrove and Siegfried, 1989). Among the intriguing characteristics of mounds around the world is their high degree of over-dispersion (Cramer and Barger, 2014). The over-dispersion of the mounds is generally agreed to be of biotic origin, but the nature of the biotic agent is hotly contested. The preponderance of literature supports the role of diverse fauna worldwide (e.g. gophers in North America and termites in South America and in Africa; reviewed by Cramer and Barger, 2014). For example, the assumption that heuweltjies are the product of termites is so pervasive that many authors refer to them as termite mounds (e.g. Picker et al., 2004; Potts et al., 2009) and heuweltjies have been cited as an example of the extended-phenotype, in this case of the termite Microhodotermes viator (Turner, 2004). Faunal genesis is appealingly simple and direct: a regular dispersion of successive generations of the fauna accumulate sediment to form mounds over long time-periods. The evidence that fauna are capable of constructing such large mounds is less compelling because there is no direct evidence that the identified fauna do construct such large mounds. Instead, Cramer and Barger (2014) argued for the revival of the idea that the mounds are the consequence of vegetation pattern-induced

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sediment erosion/deposition (Barnes, 1879). In arid environments, vegetation spatial patterning is the norm, rather than the exception (Hille Ris Lambers et al., 2001), although vegetation patterning does also occur in mesic and flooded landscapes (reviewed by Cramer and Barger, 2014). Clusters of vegetation induce facilitative feedbacks resulting in the creation of “islands of fertility” that form because trees and shrubs trap sediment as well as locally increase soil moisture and nutrient availability (Schade and Hobbie, 2005). Competitive interactions between vegetation patches are thought to operate over longer distances than facilitative interactions, resulting in scale-dependent feedbacks such as reduced vegetation biomass at intermediate distances between vegetation clumps (Rietkerk and van de Koppel, 2008). The lack of consensus on the floral or faunal influences on mound genesis is partially because there might be diverse mechanisms of formation globally. In addition, the mounds may be ancient. 14C dating of calcretes associated with heuweltjies has yielded minimum ages of between 4.5 ka (Moore and Picker, 1991) and 20e30 ka (Midgley et al., 2002). Such long periods of persistence obfuscate the climatic conditions under which mounds may have formed, and introduce the possibility that the mounds are the product of long periods of biotic activity combined with sediment accretion or erosion and pedogenesis. The antiquity of the mounds and the long periods over which they form make it difficult to design experiments that test the competing theories for mound genesis. The spatial pattern of mounds may provide insight into the processes that have caused them, as has been the case for fairy circles that occur in Namibia (Getzin et al., 2015). Clues to the origins of mounds may also be obtained from the environmental range over which mounds occur. Lovegrove and Siegfried (1986) established the range (an area of 67 309 km2) over which mounds occur in western South Africa using aerial photographs (1:50 000 scale). The broad geographic range over which these authors reported mounds encompass considerable environmental heterogeneity that may yield information pertinent to understanding mound genesis. Here, we examine the environmental correlates of mound distribution and spatial patterning in a larger area of western South Africa (278 961 km2) to establish whether these are consistent with the faunal or floral hypotheses for mound genesis. We also attempt to differentiate between the environmental conditions required for erosional deflation of the landscape, leaving behind islands of soil protected by plant roots (as proposed by Cramer et al., 2012) versus the accretion of mounds predominantly through aeolian sediment accumulation (McAuliffe et al., 2014).

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were scored as having mounds present (n ¼ 681). Some of these included agricultural areas in which mounds are still clearly visible (Fig. 2). Where a degree of uncertainty existed about mound presence or where there were unclear images (e.g. due to clouds or urbanization), sites were excluded from further analysis. Mounds were readily visible across a wide range of vegetation types, including in the sparse arid vegetation within the region (close to the Namibian border, Fig. 1). Sites in which there was no trace of mounds sites were classified as “absent”. We selected 23 sites across the region (Fig. 1) in areas without strong geographic heterogeneities (e.g. rivers, rocky outcrops) in which large numbers of mounds were clearly visible in Google Earth for spatial analysis. We used QGIS version 2.2.0-Valmiera (Quantum GIS Development Team, 2014, qgis.osgeo.org) to trace polygon shapes around all mounds within a given area. Although it is possible that in some sites smaller mounds (i.e. <5 m diameter) might have been inadvertently omitted, average mound size across the region is much larger than this lower threshold (29 ± 2 m in diameter; Lovegrove and Siegfried, 1989). 2.2. Spatial pattern analysis Our spatial analysis was inspired by Getzin et al.'s (2015) approach to interpreting fairy circle origins. Voronoi tessellations were produced for the point patterns formed by the mounds using deldir in R, omitting tessellations adjacent to the border of the plot. The AndersoneDarling test for normal distribution of the frequency distribution of the tessellation was calculated using nortest in R. The densities, areas, perimeters, landscape occupancy (density  mound area), distance between mounds, and the Clark and Evans's (1954) measure of over-dispersion (estimated with Donelly edge correction) were calculated using spatstat in R. The shape index (i.e. compactness, SI) was calculated as a measure of the departure of mound shape from a circular plan, as pffiffiffiffiffiffiffiffiffiffiffiffiffi SI ¼ perimeter=2 parea. To explore mound patterning we used the cumulative distribution function of the nearest-neighbour distance (Gr) estimated using the Gest function in spatstat with 99% simulation envelopes around Poisson null models (i.e. complete spatial randomness) using 100 Monte Carlo simulations. To assess whether the distances between mounds influence the sizes of neighbouring mounds, we used the mark (i.e. mound polygon area) cross-correlation function (kmmr) with a 99% simulation envelope produced using 100 Monte Carlo simulations. 2.3. Spatial environmental data

2. Methods and materials 2.1. Satellite images From our experience it is clear that the regional distribution of mima-like mounds in south-western Africa is more extensive than mapped by Lovegrove and Siegfried (1986). We therefore used the map extents of the Fynbos, Succulent Karoo and Desert Biomes (Mucina and Rutherford, 2006) and inflated this area by 0.5
in all directions (Fig. 1). The Fynbos biome is a nutrient-poor mediterranean-climate shrubland, whereas the Succulent Karoo shrubland is comparatively nutrient rich, but like the Desert biome, an arid mediterranean-climate ecosystem. We used the R (R Development Core Team, 2014, cran.r-project.org) package RgoogleMaps to obtain 7117 images from random locations within this area from Google static maps (scale ¼ 1, zoom ¼ 17, size ¼ 640  640 m, maptype ¼ satellite). These images were examined to determine whether mima-like mounds were in any part of the image. This was done conservatively so that only sites with clearly visible mounds

Climate data averaged for the period 1950e2000 was obtained from www.worldclim.org (accessed Sep 2014) at ca. 1 km2 resolution. Variables included are mean annual temperature (MAT), mean annual precipitation (MAP) and 17 other derived climatic variables. Seasonality of rainfall was expressed as the ratio of winter half-year (ApreSep) to summer (OcteMar) half-year precipitation. Potential evaporation (PET, mm) was obtained from Trabucco and Zomer (2009) and monthly PET subtracted from monthly precipitation to obtain an index of water availability (mm). Average wind speeds (m s1) at 100 m above ground-level were obtained from the numerical wind atlas for South Africa (www.wasaproject.info, accessed Sep 2014). Normalized difference vegetation index (NDVI) data was obtained for southern Africa from eMODIS TERRA (US Geological Survey Earth Resources Observation and Science Center), which is corrected for molecular scattering, ozone absorption and aerosols. The NDVI data from a time series (2001e2010) smoothing at 250 m spatial resolution were averaged to obtain monthly and annual

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Fig. 1. Sampling locations (7117) at which heuweltjies were scored as being present, absent, not visible in site photograph (e.g. no data due to clouds or developed area) or a site in which mounds may exist but where the image was not of sufficient resolution to be certain (uncertain). The location of sites (n ¼ 23) used for spatial analysis of mound patterning are also indicated. The map also shows the relevant vegetation biomes as defined by Mucina and Rutherford (2006). Projection is Hartebeeshoek94.

Fig. 2. A sample Google static image (31.03812 S, 17.93886 E, 640 m  640 m) showing mounds clearly evident in natural vegetation and in agricultural fields.

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average values. The Harmonized World Soil Database (HWSD, Version 1.2, Batjes, 2012) was produced by harmonizing regional soil databases and the FAO-UNESCO Soil Map of the World (FAO 1971e1981) using a standardized structure imposed on diverse sources of information. HWSD data is available as polygons for soil mapping units, with each polygon associated with textural and chemical characteristics. Each polygon has one or more component soils (‘sequences’). For each “soil mapping unit” (MU) the weighted average of the component ‘sequences’ (SEQ) was calculated based on the proportion (SHARE) of the MU occupied by that sequence (codes refer to variable names used in database) and the database queried for each site where mounds were found. Variables included in the analysis were pH (water extract), gravel-, sand-, silt- and clayfractions (%, w/w), organic carbon (%), soil cation exchange capacity (CEC, cmol kg1), bulk density (kg L1), base saturation (BS, %), total exchangeable base (TEB, cmol kg1), CaCO3 and CaSO4 concentrations (%) and electrical conductivity (ECE, dS m1). All measurements used are for topsoil (0e0.3 m depth). The distance of mounds from the coastline (Natural Earth version 2.0.0, www.naturalearthdata.com, accessed Sep 2014) was estimated using the distance matrix tool in QGIS. Elevation data from the 30 m resolution ASTER Global Digital Elevation model (www.gdem.aster.ersdac.or.jp, accessed Sep 2014) were used to calculate landscape slope, aspect and ruggedness index (quantifying the change in elevation within 3  3 pixel grid) using terrain analysis in QGIS. The spatial distribution of vegetation was obtained from Mucina and Rutherford (2006), but with the Fynbos Biome (note biome names capitalised) separated into strandveld, renosterveld and fynbos (names lowercase). The geological stratigraphic description was obtained from the 1:1 000 000 geological map of South Africa (Council for Geoscience, Pretoria). 2.4. Boosted regression tree analysis Boosted regression tree (BRT) model construction was performed following Elith et al. (2008) using dismo in R. All predictor variables were used in initial Bernoulli model construction to predict mound presence/absence with tree complexity of 8 and learning rate of 0.01 (optimised for the analysis), and a bagging fraction of 0.5. The initial model was simplified and the importance of predictor variables in determining presence/absence of heuweltjies were ranked and used to generate partial dependence plots.

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removed where colinearity existed. The predictors entered into the model were NDVI, precipitation in the warmest quarter, temperature in driest quarter, water availability, wind, sand, ECE, slope and elevation, and their quadratic components (without interaction). The models were constructed in R and simplified using the step function depending on Akaike information criteria (AIC scores). The most important of the multiple regression predictors retained for the three response variables were similar, and thus only the result for landscape occupancy is presented. 3. Results 3.1. Mound distribution analysis The use of satellite imagery available from Google Earth avoids the impracticality of a comprehensive field survey, but does limit the detection of mima-like mounds to recognition of regular vegetation pattern of appropriate scale, although the existence of a mound is also commonly readily discernible (Fig. 2). We sampled a more extensive area of mounds than that documented by Lovegrove and Siegfried (1986) as having mounds (Fig. A.1), but the fact that some sites with mounds lie close to the boundaries of the sampled area indicates a somewhat greater range than documented here, although the area mapped captures the main distribution of mounds. Mounds occur in a variety of vegetation types (Fig. 3), but the main association is with the Succulent Karoo (63%) and Fynbos Biomes, and in the latter (comprising fynbos, renosterveld and strandveld vegetation), renosterveld had the most mounds (11%). The fynbos vegetation units in which mounds were found include 14 different vegetation units of sand, sandstone and quartzite fynbos types (n ¼ 42 sites) with only two fynbos sites on shale-derived soils. The occurrence of mounds was accurately predicted from the boosted regression tree (BRT) models (Fig. 4a). Although the predictive capacity of BRT's is not strongly negatively influenced by correlations between predictor variables, these do complicate interpretation (Elith et al., 2008). NDVI does not have a strong relationship with other variables apart from water availability, for which there is a positive correlation (R2 ¼ 0.80; P < 0.001). Across the sampling points in this analysis, precipitation in the warmest

2.5. Maxent analysis Maxent (3.3.3 k; Phillips et al., 2004) was used to model mound distribution using the presence data to produce “raw” output with 25 replicate runs with cross-validation (other parameters retained at default settings). Regularization was set to 1 after systematically adjusting this without improving predictions. We used all predictors initially, but then simplified the data with only small changes in area under curve (AUC) score. 2.6. Regression and multiple regression analysis Mound density, area and landscape occupancy (density  area) were screened for correlations with predictor variables in R. Multiple regression was conducted on landscape occupancy as a response variable and predictors were selected on the basis of the hypotheses and literature (Picker et al., 2007). Predictor variables were screened for colinearity using the “select07” procedure outlined by Dormann et al. (2013), and less powerful variables

Fig. 3. The proportion of 681 sites with mounds within each of the vegetation types as defined by Mucina and Rutherford (2006), but with the Fynbos Biome separated into fynbos, renosterveld and strandveld vegetation types.

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indicating strong environmental correlates with mound occurrence. Mounds are most common along the west coast of South Africa, but also occur inland of the Cape Fold Belt Mountains (Fig. A.1) and extend well to the east of the range previously documented by Lovegrove and Siegfried (1986). Intriguingly, the area proximal to the southern coast of South Africa, including the Agulhas Plain (Fig. A.1), is essentially devoid of mounds (Figs. 1 and 5), apart from a few isolated occurrences. The BRT partial dependence plots (Fig. 6) show the effect of the six most important variables on the presence/absence of mounds, after accounting for the average effects of all other variables (Fig. 4a) in the model. NDVI, which relates to vegetation leafybiomass, was the strongest predictor of mounds over the range 0.1 < NDVI < 0.5, indicating that mounds do not occur in extremely sparse vegetation (i.e. Desert Biome) or in dense vegetation. Mounds are also most common in areas with low (<50 mm) precipitation in the warmest quarter and thus a strong Mediterranean climate. Mound occurrence is also positively associated with relatively strong winds (>7 m s1) at relatively low elevation sites (<1000 m elevation). Although water availability may operate secondarily through NDVI or precipitation in the warmest quarter, the influence of water availability was weaker than these two, indicating that both the seasonality and the total amount of water available influence mound occurrence. 3.2. Mound spatial patterning

Fig. 4. Relative influence of predictor variables on a) presence/absence of mounds that were retained in a boosted regression tree (BRT) models after simplification and b) presence (only) of mounds in a Maxent model. The influence of each variable is expressed as a proportion (cumulatively 100%). The final BRT model included 4300 trees with area under receiver operating characteristic curve (AUC) cross-validation score of 0.923 ± 0.007 (mean ± SE). The AUC score evaluates the classification accuracy of the model (maximum AUC ¼ 1) and shows that the model was highly accurate. The cross-validation AUC for the Maxent analysis was 0.868 ± 0.005.

quarter is significantly (positively) related to water availability (R2 ¼ 0.69, P < 0.001). Thus water availability may be only secondarily associated (i.e. through NDVI or precipitation in warmest quarter) with mound occurrence. The ranking of the predictor variables in the Maxent analysis (Fig. 4b) (presence data) differed from that of the BRT (presence/absence data). Nevertheless, the six top-ranked Maxent predictors included five of the topranked BRT predictors. The Maxent analysis was used as a check on the BRT analysis of presence/absence data in case some of our absence data were false, i.e. where we scored mounds as missing but they were obscured by land-use or vegetation cover. We conclude that false-absences did not markedly influence our analysis. The Maxent predictions of mound occurrence (Fig. 5) are strongly correlated with the distribution of mounds (Fig. 1)

Overall, we estimate (by combining mound area and the proportion of sites in which mounds occur) that mounds occupy ca. 1.2% of the regional landscape (Table 1). Regionally, mound density, size and inter-mound distance are all highly variable. Any explanation of mound genesis has to be able to account for this variability and also explain the very large areas of some mounds (95th percentile ¼ 802 m2) and large distances (95th percentile ¼ 65 m) separating mounds. The shape index shows that overall the mounds are generally circular (SI ¼ 1.06, where 1 indicates circular; Table 1). Highly over-dispersed patterns have six-sided tessellations approaching hexagonal packing (Getzin et al., 2015), providing evidence that focal points interact with each other in determining their distributions. Mound packing was uniformly close to the hexagonal limit for tessellations (Table 1, Fig. 7). This is partially an artefact of choosing sites in which landscape heterogeneities (e.g. rivers, rocky outcrops) did not disrupt the pattern, and thus relates to the upper limit of mound regularity. The regularity of the pattern is also given by the R-value of Clark and Evans (1954), which shows that for most sites the pattern was significantly over-dispersed. The tessellation areas are 7.2-fold greater than that of the mounds, seldom smaller than ca. 2000 m2, but commonly exceed 5000 m2 (Fig. 8, Table 1). The nearest neighbour analysis shows that mounds did not occur in close proximity to other mounds and were significantly over-dispersed when in close proximity (Fig. 9, Table 1). The markcorrelation function indicates that mound area is initially positively related to inter-mound distance (Fig. 10). This inhibitory effect of mounds on neighbour size (i.e. negative correlation) persisted up to average distances of 41 m (Table 1). 3.3. Environmental correlates with mound size and density We found only a weak negative bilinear correlation between mound density and precipitation in the wettest month (R2 ¼ 0.20, P ¼ 0.038). Mound area was positively correlated with the proportion of sand (R2 ¼ 0.27, P ¼ 0.014), rainfall seasonality (R2 ¼ 0.22, P ¼ 0.029), and temperature in the driest quarter (R2 ¼ 0.19,

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Fig. 5. The “raw” Maxent analysis was used to produce a map representation of the likelihood of mounds (warmer colours indicate higher mound probability). Gaps (white) in the prediction layer are due to missing data in one or more variables used in the prediction. Projection is Hartebeeshoek94. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

P ¼ 0.044) and negatively correlated with total exchangeable bases (TEB, R2 ¼ 0.21, P ¼ 0.003) and elevation (R2 ¼ 0.21, P ¼ 0.034). Although landscape occupancy had no significant bilinear

predictors, it combines mound area and density and provides an integrated measure of mound prevalence. The multiple regression model indicated that landscape occupancy had quadratic

Fig. 6. The partial dependence of mound presence/absence on the six most influential predictor variables estimated from the BRT analysis. The Y-axes are on the logit scale and are centered to have zero mean over the data distribution. Rug plots inside the plots show the distribution of sites across that variable in deciles. The broken line represents smoothing of the regression tree output.

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Table 1 Mound and Voronoi tessellation properties (mean ± SE and 5th percentiles median and 95th percentiles) estimated for 23 sites (Fig. 1) at which 44 < n < 146 mounds were measured per site (median ¼ 110). Mound density, area, perimeter, distance between mounds (i.e. empty space), landscape occupancy and Clark and Evans's R (R < 1 for clumped pattern, R ¼ 1 for random pattern and R > 1 for over-dispersed pattern; P indicates significance of this measure) were calculated. The critical maximum r for the nearest neighbour distance represents the distance at which Gr (Fig. 9) crossed the 95% Monte Carlo simulation threshold. The equivalent value for the mark correlation coefficient (kmmr, Fig. 10) is also shown. The AndersoneDarling test for normal distribution was used to determine whether the frequency distribution (Fig. 8) of tessellation areas was normal (i.e. P > 0.05). Measure Mounds Density Area Perimeter Distance between Landscape occupancy Shape index Clark and Evans' R P for Clark and Evans' R Tesselations Number of sides Tesselation area AndersoneDarling P for AndersoneDarling Tesselation: mound area Gr: critical maximum r kmmr: critical maximum r

Unit ha1 m2 m m %

m2

m m

Mean ± SE 3 442 79 41 12.6 1.06 1.27

± ± ± ± ± ± ±

5th (Median) 95th

0.2 40 4 3 1.1 0.00 0.03

1.4 (2.9) 4 239 (458) 802 58 (80) 105 24 (36) 65 5.7 (11.2) 21.7 1.04 (1.06) 1.09 1.05 (1.28) 1.53 0.205 (<0.001) < 0.001

5.8 ± 0.0 3056 ± 247 3.21 ± 0.63

5.8 (5.8) 5.9 1997 (2747) 5520 0.43 (2.61) 8.09 0.309 (<0.001) < 0.001 3.8 (6.9) 12.5 32 (38) 54 25 (36) 83

7.2 ± 0.6 40 ± 1.7 41 ± 4.7

Fig. 7. Voronoi tessellations of an individual sample site (centred on 33.4118 S, 19.9858 E) revealed a dominance of hexagonal spacing structures around each mound indicating optimal packing of mounds in the landscape. The boundaries of each tessellations are the mid-points between two mounds.

relationships with NDVI, temperature in the driest quarter, sand and wind (Table 2). Landscape slope, precipitation in the warmest quarter and elevation were also retained as linear predictors of landscape occupancy. 4. Discussion Mounds are strongly associated with the semi-arid Succulent Karoo Biome shrubland (<1 m tall) that occurs on diverse geologies

Fig. 8. The variation in mound density across Voronoi tessellation areas across all sampled sites (n ¼ 23). The distributions of tessellations areas were generally significantly not normal (Table 1) due to the left-skewing of the distribution resulting from high densities of small tesselations. There were, however, four sites in which the distribution was normal (i.e. AndersoneDarling P > 0.05; Table 1).

Fig. 9. The variation in nearest neighbour distance for a typical point in the mound pattern (Gr, solid line) with distance (r) tested against the Poison null model of complete spatial randomness (broken line) for an individual site (centred on 33.4118 S, 19.9858 E). The shaded band represents 99% simulation envelopes around Poisson null model using 100 Monte Carlo simulations. Critical r values (Table 1) representing the maximum distance at which mounds were over-dispersed were obtained by determining where the pattern (Gr) crossed the lower threshold of the 99% envelope. Where Gr is below the envelope the pattern is significantly over-dispersed.

and is relatively nutrient rich compared to the Fynbos Biome (Mucina and Rutherford, 2006). The occurrence of mounds in a variety of present vegetation types, however, suggests that either the mounds are not a unique outcome of a particular flora, or that the mounds are relics of a time when vegetation distributions were different to those of the current vegetation. Since these vegetation types are tightly associated with particular soils (reviewed in Cramer et al., 2014) it is unlikely that the vegetation was dramatically different from present when the mounds formed, unless the climate was significantly different. While climate-induced

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Fig. 10. The variation in the mark-correlation function (kmmr, solid line) for the areas of heuweltjies at an individual site (centred on 33.4118 S, 19.9858 E) with distance (r), tested against the Poison null model. The shaded band represents 99% simulation envelopes around Poisson null model using 100 Monte Carlo simulations. The mark correlation function reported here tests the effects of distance between mounds on the size (area) of the mounds. If kmmr falls in the shaded band there is no association of the area of the mounds with distance between mounds. When kmmr is less than the shaded band, the areas of the mounds are negatively correlated at distance r (i.e. proximity of mounds is correlated with smaller sizes of mounds). Critical r values (Table 1) representing the maximum distance at which mound areas were dependent on the proximity of neighbouring mounds were obtained by determining where the pattern (kmmr) crossed the lower threshold of the 99% envelope.

variations in vegetation are possible (e.g. in South America; Silva, 2014), the regional climate is thought to have been relatively stable over a long period (Chase et al., 2009) and the vegetation exhibits a high degree of edemism (Bradshaw and Cowling, 2014), possibly indicating long-term association with conditions similar to those currently. The commonly proposed faunal agent for mound genesis in the region (M. viator), avoids fynbos vegetation and sandstone-derived soils and has a more restricted distribution (Picker et al., 2004) than the mounds (Fig. 1), raising the question of how this termite might have generated mounds in all of these diverse vegetation zones. Overall, the BRT and Maxent analyses indicate that mounds occur at low-moderate elevations (<1000 m) in sparse winterrainfall vegetation that experiences summer drought and that is

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subjected to relatively strong winds. Picker et al. (2007) found a positive relationship between mound density and NDVI (range 0.05e0.3) and attributed this to higher termite abundance with increasing productivity. However, they excluded sites with NDVI >0.3 because, in their analysis, these were in fynbos or renosterveld, which they suggested were atypical. If NDVI influences mound density through termite abundance, one might expect mound occurrence to be positively related to NDVI in the Succulent Karoo Biome alone. In contrast, there was a decrease in the partial dependence of mound occurrence at NDVI >0.25 when the BRT analysis was run with only Succulent Karoo Biome sites (data not shown). Here, we also found no support for bilinear correlations between mound density with MAP or NDVI. In contrast, there was a weak negative relationship between mound density and precipitation in the wettest month, consistent with mound occurrence being favoured at the lowest precipitation in the warmest quarter (Fig. 6). Furthermore, multiple regression correlates with landscape occupancy suggest that mounds occupy the largest proportion of the landscape in sparsely vegetated areas with sandy soils, relatively strong winds and summer droughts. In summary, results presented here are at odds with the hypothesis that the mound genesis is associated with M. viator for four reasons: 1) mounds do occur in fynbos where M. viator tends not to occur; 2) mounds do not increase in density with increasing vegetation productivity; 3) mounds do occur in sandy sites, which tends to preclude mound building by this species of termite; and 4) it is unlikely that centralfeeding termites such as M. viator could generate the enormous sizes of the tesselations and the mounds. The association of mound occurrence and landscape occupancy with sparsely vegetated semi-arid areas is also at odds with the hypothesis that mounds are mainly the product of vegetation pattern combined with erosion of inter-mound spaces by overland flow (Cain, 1974; Cramer et al., 2012). Formation of mounds over long-time periods is, however, likely to involve both simultaneous erosion and deposition (e.g. Cramer and Barger, 2014; Fig. A.2), although which process dominates is important. Because they found large rocks on and in mounds, Cramer et al. (2012) concluded that the mounds were the largely consequence of vegetation pattern protecting soil from erosion, rather than as a consequence of vegetation trapping of aeolian sediment. This was consistent with proposals for the origin of mima-like mounds in South America (de Arujo Neto et al., 1986; Silva et al., 2010). In suggesting an aeolian origin of heuweltjies, McAuliffe et al. (2014) pointed out that bioturbation by powerful fossorial mammals (e.g. aardvark and porcupines) could elevate quite large rocks in the soil profile. Nabkha-like mounds (i.e. coppice dunes or nebkha) generally occur in arid climates where wind blown sediment is

Table 2 Multiple regression analysis of the predictors of mound landscape occupancy. The model was simplified stepwise using Akaike information criteria (AIC scores) resulting in a final AIC ¼ 121. The predictors retained were linear and/or quadratic (2). The final model had an adjusted R2 ¼ 0.60 (P ¼ 0.020). The coefficients are ranked in order of decreasing absolute values of the standardised coefficients. Predictor

Coefficient

P

Standardised coefficients

NDVI2 NDVI Temperature driest quarter2 Temperature driest quarter Sand Sand2 Wind2 Wind Slope Precipitation warmest quarter Elevation Intercept

0.07 17.8 0.005 1.74 3.19 0.023 2.26 24.1 6 0.18 0.019 1201

0.001 0.001 <0.001 <0.001 0.024 0.033 0.006 0.007 0.002 0.002 0.013 0.001

30.2 29.7 15.5 13.8 8.5 7.5 6.0 5.8 1.8 1.2 0.9

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plentiful. We suggest that the strong link between mound occurrence and current summer aridity and sparse vegetation is more consistent with aeolian sediment deposition driving mound formation, as suggested by McAuliffe et al. (2014), and is also consistent with the fact that terrestrial vegetation patterning is most common in arid environments (e.g. Hille Ris Lambers et al., 2001), although it does also occur in wetlands (reviewed by Cramer and Barger, 2014). Nabkhas result from aeolian deposition of sediment around vegetation clumps (Quets et al., 2013) and are common features in some semi-arid regions worldwide (Cramer et al., 2014). Variations in the sizes, density and over-dispersion of nabkhas depends on factors governing rates of aeolian sedimentation and erosion, including wind characteristics, sediment loads (King et al., 2006) and vegetation properties (Nickling and Wolfe, 1994). Although nabkhas are commonly circular (Seifert et al., 2009) they may depart from circular plans due to leeward deposition and windward erosion (Nickling and Wolfe, 1994) with coarser sand being found on the windward flank (Khalaf and Al-Awadhi, 2012). We suggest that the overall circularity of the mounds studied here is partially the consequence of long-term and seasonal variations in wind directions (Kruger et al., 2010). Furthermore, since the calcrete in heuweltjies is thousands of years old, the mounds are the product of long periods of time in which sediment erosion (Fig. A.2) and accretion have interacted with each other to dictate the shape and size of the mounds. The association of mound occurrence with current aridity does not necessarily imply that the mounds are currently forming in the areas in which they occur. For example, it could have been wetter at the time of mound origin. The Mediterranean climate of the study region has, however, persisted for ca. 10 Ma, albeit with some relatively minor regional locational displacement (Chase et al., 2009) due to a series of alternating glacial and interglacial climates, particularly over the last 2 Ma (Bradshaw and Cowling, 2014). There is evidence of several periods of wetter conditions interspersed with more arid conditions than current between 11.7 ka and today, with aridity being particularly marked 3.5e0.3 ka (Chase et al., 2009). Despite this variability, the seasonality and location of the Mediterranean climate is unlikely to have varied strongly for a prolonged period (Chase and Meadows, 2007). Large mounds across a range of prairie ecosystems in the USA have also been suggested to originate as nabkhas (e.g. Barnes, 1879; Seifert et al., 2009), but Seifert et al. (2009) concluded that these nabkha-like mounds originated during a multi-decadal drought in the mid-Holocene and are currently stabilised by distinct (generally prairie) vegetation. Thus, although it is possible that the mounds formed by erosion under wetter conditions than current (e.g. Silva et al., 2010; Cramer et al., 2012), we suggest that for heuweltjies it was during periods of greater aridity than current that the mounds formed, consistent with both the association with aridity (Fig. 6) and mound pedology (McAuliffe et al., 2014). The distributions of many non-anthropogenic earth mounds around the world are over-dispersed (Cramer et al., 2014). Our Clark and Evans' R value is considerably smaller (1.27) than that reported by Lovegrove and Siegfried (1989) for the region (1.7). Nevertheless, the over-dispersion is consistent with the near-hexagonal tessellations. More informatively, the nearest neighbour analysis indicate that over-dispersion is strongest in close proximity to the mounds where on average inter-mound distance <40 m, but that mounds become randomly distributed at greater distances. Thus low values of Clark and Evans' R occur because this is estimated across all points in the per-site analysis. This is important for understanding mound genesis, because it implies that the inhibitory influence of mounds on neighbouring mound occurrence operates over distances of <40 m. Thus any explanation of mound over-dispersion is

only required to explain how mounds influence each other up to this distance. Spatial vegetation patterning theory suggests that the overdispersion of vegetation requires competitive feedbacks between vegetation patches. Since the vegetation of all sites where mounds are found in this region is generally <1 m tall, it is unlikely that vegetation patches could experience direct root competition over long distances. Although semi-shrubs (<1 m tall) and succulents have been reported to have spectacular lateral root spreads up to 10 and 8 m, respectively, the averages for these plant forms are <2 m (Schenk and Jackson, 2002). Thus the distance between mounds is too great to result from direct root competition from the extant flora. Considering the association with aridity, it also seems unlikely that the on-mound flora could have been significantly larger in the past than the current vegetation. Vegetation patterning does not, however, require direct root competition. For example, vegetation patches may harvest overland flow, resulting in competition for this resource between vegetation patches (Meron, 2012). Furthermore, if mound formation requires aeolian deposition of sediment, the degree of deposition will be negatively influenced by the presence of surrounding mounds that accumulate sediment and accelerate winds in the inter-mound areas, promoting erosion of the inter-mound area and concentration of sediments on mounds (Tengberg, 1995). In addition to inhibition of mound formation in close proximity to other mounds, mounds that are closely spaced are also smaller. This also indicates that mounds may compete for resources, whether as a consequence of faunal competition, vegetation competition, or because the mounds themselves inadvertently compete for aeolian sediment. Preferential sediment deposition on vegetation patches, subsequent increase in vegetation patch size and negative influence on sediment deposition in sparsely vegetated interstices (Tengberg, 1995) adds a potentially powerful competitive component to explaining mound spatial characteristics. The positive feedback resulting from the passive influence of above ground vegetation of aerial trapping of sediment is an abiotic addition to a biotic pattern based on below-ground root-competition. The absence of small mounds suggests that significant sediment will fail to be accumulated if neighbouring mounds are too close and possibly that the “birth” of mounds is rare, and therefore that the pattern is mostly a relic of the past. The ratio of tessellation:mound areas (7.2, Table 1) may be used to infer the degree to which resources could be concentrated on the mounds from the inter-mound areas. Calcium carbonate rich soil horizons commonly occur in heuweltjies (Potts et al., 2009; Midgley et al., 2013) and mima-mounds (Ross et al., 1968), but are absent from inter-mound areas. In heuweltjies this layer is generally elevated above the inter-mound area (Lovegrove, 1991) indicating that it formed after the mounds. Islands of fertility that focus nutrients in the vegetation clumps are also associated with caliche (Shreve and Mallery, 1933; Schlesinger and Pilmanis, 1998), the occurrence of which is a component of the general accumulation of nutrients in mounds. For example, the concentration ratios of nutrients between on-mound and off-mound soils in the Worcester area were N ¼ 1.4, P ¼ 4.5, K ¼ 3.4, Ca ¼ 13.1 (Midgley and Musil, 1990) indicating concentration of the nutrients onmound. While sediment relocation from inter-mound areas to mounds may increase nutrient content, it does not necessarily increase soil nutrient concentrations. The vertical distributions of nutrients in soils are, however, strongly determined by the interplay between plant nutrient cycling, leaching, weathering, disso
gy and Jackson, 2001). lution, and atmospheric deposition (Jobba Thus plants concentrate nutrients in the topsoil, possibly accounting for the formation of caliche. Based on work with strontium isotopes, the Ca in heuweltjie calcrete at Clanwilliam is of

M.D. Cramer, J.J. Midgley / Journal of Arid Environments 119 (2015) 16e26

25

Table 3 Evaluation of evidence for hypotheses for mima-like mound origins. Mound characteristics

Hypotheses Termite

Vegetation-water erosion

Vegetation-aeolian accretion

Large sizes Large empty distances

Only with multiple generations Depends on foraging distances of termites

Circular shape

Consistent

Over-dispersion

No evidence that M. viator colonies are over-dispersed Only explicable through secondary mammalian bioturbation

Consistent Depends on vegetation and runoff Expect downslope slumping Consistent

Consistent Depends on vegetation, wind and sediment loads Circularity requires variable wind directions Consistent

Consistent

Only explicable through secondary mammalian bioturbation Consistent

Stone/rocks (>0.05 m diameter)

Diverse vegetation types

Association with winter rainfall

Contrary, unlikely for termites to occur on very sandy soils (Picker et al., 2004) Contrary, unlikely without substantial climate change; termite density lower with aridity Contrary, termites should be more common at higher NDVI No reason for association, except via vegetation

Association with high wind

No reason for association

Association with aridity Restriction to low NDVI

marine origin (Midgley et al., 2013). Interception of atmospheric nutrients by vegetation is not unusual (e.g. Martorell and Ezcurra, 2007) and thus, in addition to vegetation trapping aeolian sediment to form the mound and concentrating nutrients, plants may additionally intercept nutrient aerosols, consequently enriching the soil over prolonged periods. 5. Conclusion We have summarised our arguments for and against the three hypotheses in Table 3. Consistent with aeolian deposition and the requirements for vegetation pattern formation under arid conditions, mound distributions in South Africa are associated with sparse vegetation that experiences summer droughts. We thus concur with McAuliffe et al. (2014) that the large mounds are probably aeolian in origin. We find no evidence, however, that the pattern of mound formation is initiated by termites. We conclude that the correlative data presented here is consistent with a previous vegetation pattern initiating the mounds, which then sets up a positive feedback in which larger plants continue to trap aeolian sediment and aerosols resulting in large, ancient, nutrient-rich mounds with distinctive flora and secondary fauna that are selfsustaining. Key to fully determining the scale of the pattern we have detected will be an understanding of facilitative and inhibitory patterns of aerosol and sediment trapping in relation to wind and vegetation properties, and how this results in relatively circular mounds. Acknowledgements We thank the 2014 “Vegetable Wars” BSc honours students for participation in initial data analysis. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.jaridenv.2015.03.011. References Barnes, G.W., 1879. The hillocks or mound-formations of San Diego, California. Am. Nat. 13, 565e571.

Consistent Contrary without substantial climate change Contrary without substantial climate change Contrary without substantial climate change Contrary without substantial climate change

Consistent Consistent Consistent Consistent

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