Environmental Research Letters

ACCEPTED MANUSCRIPT • OPEN ACCESS Resistance of mound-building termites to anthropogenic land-use change

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1 2 3 1 Resistance of mound-building termites to anthropogenic land-use change 4 5 6 2 7 8 3 Andrew B. Davies1,2, Philip G. Brodrick2, Catherine L. Parr3,4,5 and Gregory P. Asner2 9 10 4 1Department of Organismic and Evolutionary Biology, Harvard University, Cambridge, 11 12 2 13 5 Massachusetts, USA, Center for Global Discovery and Conservation Science, Arizona State 14 3 15 6 University, Tempe, Arizona, USA, School of Environmental Sciences, University of Liverpool, 16 17 7 Liverpool, UK, 4School of Animal, Plant and Environmental Sciences, University of the 18 19 8 Witwatersrand, Johannesburg, South Africa, 5Department of Zoology and Entomology, 20 21 22 9 University of Pretoria, Pretoria, South Africa. 23 24 10 25 26 11 *Corresponding author: Andrew Davies – Department of Organismic and Evolutionary 27 28 29 12 Biology, Harvard University, 22 Divinity Avenue, Cambridge, MA, 02138, USA. E-mail: 30 31 13 [email protected] 32 33 14 34 35 36 15 Running title: Termites resist human land-use change 37 38 16 39 40 17 41 42 18 43 44 45 19 46 47 20 48 49 21 50 51 52 22 53 54 23 55 56 57 58 59 1 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 2 of 32

1 2 3 24 Abstract 4 5 6 25 Humans pose a major threat to many species through land-use change in virtually every habitat. 7 8 26 However, the extent of this threat is largely unknown for invertebrates due to challenges with 9 10 27 investigating their distributions at large scales. This knowledge gap is particularly troublesome 11 12 13 28 for soil macrofauna because of the critical roles many of these organisms perform as ecosystem 14 15 29 engineers. We used a combination of high-resolution airborne Light Detection and Ranging and 16 17 30 deep learning models to map the distribution of the ecologically important termite genus 18 19 31 Macrotermes across a South African savanna land-use gradient, quantifying the effects of land- 20 21 22 32 use change on patterns of mound densities, heights and spatial patterning. Despite significant 23 24 33 anthropogenic alteration to landscapes, termite mounds persisted and shared a number of 25 26 34 similarities to mounds in untransformed areas. Mean mound height was not substantially reduced 27 28 29 35 in transformed landscapes, and over-dispersion of mounds at localized scales was conserved. 30 31 36 However, mound densities were partially reduced, and height distributions in transformed areas 32 33 37 differed to those in protected areas. Our findings suggest that mound-building termites persist 34 35 36 38 even in areas of relatively high human disturbance, but also highlight important differences in 37 38 39 termite distributions that could lead to reductions in ecosystem services provided by termites in 39 40 40 human-modified landscapes. The persistence of at least half of mounds in human-modified 41 42 41 landscapes could serve as starting points for savanna restoration. 43 44 45 42 46 47 43 Key-words: convolutional neural networks, deep learning, LiDAR, Macrotermes, savanna, 48 49 44 South Africa, termite mounds 50 51 52 45 53 54 46 55 56 57 58 59 2 60 Accepted Manuscript Page 3 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 47 Introduction 4 5 6 48 Unabated human land-use change continues worldwide, leading to severe biodiversity loss and 7 8 49 reductions in ecosystem function (Foley et al 2005, Newbold et al 2015, Flynn et al 2009). 9 10 50 These losses increase ecosystem vulnerability and jeopardize ecosystem services and human 11 12 13 51 livelihoods (Chapin III et al 2000, Potts et al 2010, Cardinale et al 2012). However, measuring 14 15 52 changes in ecosystem function is challenging at large scales because biodiversity-ecosystem 16 17 53 function relationships are complex and often result from intra- and/or inter-species interactions 18 19 54 that vary across landscapes. Moreover, alterations to ecosystem function resulting from species 20 21 22 55 loss can take years to manifest, exceeding the lifespan of most studies (Pringle and Tarnita 2017, 23 24 56 Staver 2018). However, some organisms have disproportionate influence on ecosystem function, 25 26 57 and a loss or significant disruption of these species could serve as a proxy for effects of land-use 27 28 29 58 change on ecosystem function. 30 31 59 Globally widespread soil macrofauna such as termites, ants and earthworms play 32 33 60 fundamental roles as ecosystem engineers. Termites are particularly important in savanna 34 35 36 61 environments where their mound-building activities reorganize the physical and chemical 37 38 62 distribution of soils throughout the landscape, creating nutrient and moisture hotspots with 39 40 63 enhanced vegetation growth (Sileshi et al 2010, Seymour et al 2014, Davies et al 2014b). In turn, 41 42 64 these nutrient hotspots have cascading effects on invertebrate and mammalian herbivores, and 43 44 45 65 can influence vegetation and herbivory patterns over as much as 30% of savanna landscapes 46 47 66 (Levick et al 2010b, Davies et al 2016a, 2016b). Moreover, at local scales, termite mounds are 48 49 67 often spatially over-dispersed due to intra-specific competition between neighboring colonies, 50 51 52 68 enhancing termite influence on ecosystem characteristics and function due to the even 53 54 69 distribution of mound benefits across space (Pringle et al 2010). Termites and their mounds, as 55 56 57 58 59 3 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 4 of 32

1 2 3 70 well as the spatial arrangement of colonies, can also increase agricultural yields (Evans et al 4 5 6 71 2011) and may promote ecosystem robustness to climatic change and stress events (Bonachela et 7 8 72 al 2015, Ashton et al 2019). The persistence of keystone termite colonies and their over- 9 10 73 dispersed spatial patterning is therefore essential for maintaining healthy, functioning savanna 11 12 13 74 ecosystems. 14 15 75 Most studies of biodiversity loss resulting from land-use change have focused on plants 16 17 76 and vertebrates (e.g. Pimm and Raven 2000, Ceballos and Ehrlich 2002, Flynn et al. 2009). 18 19 77 Comparatively few studies have investigated changes in invertebrate diversity, despite its wide- 20 21 22 78 ranging impacts on ecosystems, and invertebrate-focused research has largely investigated local- 23 24 79 scale effects in European and North American ecosystems (Sánchez-Bayo and Wyckhuys 2019). 25 26 80 An urgent need exists for large-scale studies of invertebrate responses to human land-use change 27 28 29 81 (Thomas et al 2019), especially studies that investigate insect population and community 30 31 82 changes across environmental gradients. The current lack of large-scale investigations into the 32 33 83 impacts of land-use change on invertebrates is at least partially due to difficulties in observing 34 35 36 84 patterns of invertebrate richness and/or abundance over large extents, caused both by the sheer 37 38 85 abundance of invertebrates and their small size. Mound-building termites, however, provide a 39 40 86 rare case for assessing invertebrate responses to land-use change over large geographic areas due 41 42 87 to the capability of remote-sensing technology to effectively map termite mounds (Levick et al 43 44 45 88 2010a, Davies et al 2014a). However, the impact of human land-use change on termites has not 46 47 89 yet been examined at scales large enough to capture shifts in important mound characteristics 48 49 90 such as height, which is indicative of colony size (Meyer et al 2000), density, or spatial 50 51 52 91 patterning. 53 54 55 56 57 58 59 4 60 Accepted Manuscript Page 5 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 92 Effects of land-use change on termite mound distributions are also difficult to predict. 4 5 6 93 Although termites have been shown to be adversely affected by human disturbance (e.g. 7 8 94 Eggleton et al 1996, Jones et al 2003, Muvengwi et al 2017a, Dosso et al 2013), they are largely 9 10 95 resistant to natural disturbance such as fire and herbivore activity (Davies et al 2012, Lagendijk 11 12 13 96 et al 2016), and could similarly be unperturbed by human activities that do not result in whole- 14 15 97 scale landscape changes or mound destruction, which is often the case in agricultural systems. 16 17 98 Furthermore, human attitudes towards termites, especially in subsistence settings, can be 18 19 99 conflicting and potentially lead to differing outcomes for termite persistence. Many people 20 21 22 100 acknowledge that termites can confer some benefits to agriculture and use termite mound soil in 23 24 101 subsistence agricultural practices (Sileshi et al 2009, Jouquet et al 2018). Humans, particularly in 25 26 102 Africa, also consume termites as part of their diet or for medicinal purposes (Sileshi et al 2009), 27 28 29 103 and could therefore be inclined to conserve termites. Conversely, however, termites are often 30 31 104 viewed as pests that damage buildings and agricultural crops, which can drive efforts to 32 33 105 exterminate them (Rouland-Lefèvre 2011, Su and Scheffrahn 2000, Jouquet et al 2018). 34 35 36 106 Agricultural use of termite mound soil can also be destructive when cultivators disassemble 37 38 107 mounds to distribute the enriched soil in crop fields. 39 40 108 Here, we investigate effects of human activity on termite mounds by combining high- 41 42 109 resolution airborne Light Detection and Ranging (LiDAR) data with deep learning models to 43 44 45 110 map distributions and heights of mounds from the widespread termite genus Macrotermes across 46 47 111 a land-use gradient in South African savanna. We ask whether human land-use change has 48 49 112 altered patterns in Macrotermes mound densities, heights and spatial patterning. The surveyed 50 51 52 113 landscapes consisted of four land-use types, ordered here according to land-use intensity: i) an 53 54 114 untransformed government-managed conservation area, Kruger National Park, that has remained 55 56 57 58 59 5 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 6 of 32

1 2 3 115 protected over much of the past century, ii) a privately-owned conservation area that was 4 5 6 116 converted from low density cattle farming to a protected conservation area in the 1960s, and 7 8 117 communal areas comprised of land used either for iii) cattle grazing and/or firewood extraction 9 10 118 or iv) small-scale subsistence agriculture by local communities. Termite mound locations were 11 12 13 119 identified in the LiDAR data using a convolutional neural network (CNN), allowing for the rapid 14 15 120 and consistent classification across land-use types of 47,242 termite mounds over 76,474 ha. We 16 17 121 analyzed differences in mound density, height, and spatial patterning across the land-use types 18 19 122 by bootstrapping equal subsamples across land-use types, and by generating and analyzing 20 21 22 123 Ripley’s K curves of termite mounds within each land-use type. 23 24 124 25 26 125 Methods 27 28 29 126 Study region 30 31 127 Our study took place in the Lowveld region of northeastern South Africa, a low-lying landscape 32 33 128 dominated by savanna vegetation (Fig. 1). Mean annual precipitation is ~630 mm yr−1 and mean 34 35 36 129 annual temperature is ~22° C. The dominant geology is granite, with gabbro intrusions also 37 38 130 present (Venter et al 2003). These gabbro intrusions were excluded from the study due to the low 39 40 131 number of Macrotermes mounds present on this geology (Davies et al 2014a). Three dominant 41 42 132 land tenure systems are present in the region: (i) Kruger National Park (KNP), a large 43 44 45 133 government-owned conservation area, (ii) Sabi-Sand Game Reserve, a privately-owned 46 47 134 conservation area and (iii) state-owned communal areas. The area that is now KNP was officially 48 49 135 conserved between 1898 and 1926 and today comprises almost 2 million ha. Sabi-Sand Game 50 51 52 136 Reserve is a 63,000 ha privately protected area sharing an unfenced boundary (fences were 53 54 137 removed in 1991) with KNP on its southern and eastern limits. The reserve was established in 55 56 57 58 59 6 60 Accepted Manuscript Page 7 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 138 1965 and consists of an association of freehold owners with a strong tourism-based approach to 4 5 6 139 conservation. Prior to being a conservation area, the land was primarily used for low density 7 8 140 commercial cattle farming. State-owned communal areas alongside these conservation areas are 9 10 141 all within the Bushbuckridge municipality and are part of the former self-governing territories or 11 12 13 142 ‘homelands’ established under the apartheid regime. Due to forced removals and relocations to 14 -2 15 143 the region during apartheid, population density (ranging between 150 and 300 people km ) is 16 17 144 high, as are levels of unemployment (Pollard et al 2003). Residents rely mostly on a combination 18 19 145 of subsistence farming, livestock husbandry, and the consumption and trade of natural resources 20 21 22 146 at informal markets (Shackleton et al 2001). Electricity is increasingly available, but firewood 23 24 147 remains a dominant fuel for cooking, resulting in high levels of firewood extraction from 25 26 148 communal rangelands and a subsequent reduction of woody biomass and tree cover (Wessels et 27 28 29 149 al 2013). Rain-fed crops are grown around homesteads or in arable fields in close proximity to 30 31 150 settlements. 32 33 151 34 35 36 152 LiDAR surveys 37 38 153 Discrete-return airborne Light Detection and Ranging (LiDAR) data were collected over the 39 40 154 study landscapes in late March/early April 2012 using the Global Airborne Observatory (GAO) 41 42 155 Alpha system, formerly known as the Carnegie Airborne Observatory (Asner et al 2007). The 43 44 45 156 LiDAR surveys were carried out at the end of the wet season when vegetation was at leaf-on 46 47 157 stage. The aircraft was flown at approximately 2,000 m above ground level, achieving an 48 49 158 average laser shot point spacing of 2 shots m−2. Further details concerning the LiDAR 50 51 52 159 specifications can be found in Asner et al. (2009). LiDAR data enable high-resolution and 53 54 160 accurate measurements of three-dimensional vegetation structure and the underlying terrain over 55 56 57 58 59 7 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 8 of 32

1 2 3 161 large spatial extents by combining laser ranges from the LiDAR with location data from an 4 5 6 162 embedded Global Positioning System-Inertial Measurement Unit (GPS-IMU) (Asner et al 2007). 7 8 163 This combination results in a three dimensional ‘point cloud’ of known locations from each 9 10 164 laser-surface interaction. LiDAR data points were processed to identify which laser pulses 11 12 13 165 penetrated the vegetation canopy and reached the ground surface using the lasground tool 14 15 166 packaged in LAStools software (Rapidlasso, Gilching, Germany). These points were used to 16 17 167 interpolate a digital terrain model (DTM) of the ground surface at a 1 m resolution. 18 19 168 20 21 22 169 Termite mound identification 23 24 170 The LiDAR-derived DTM’s were used to produce a hillshade map, from which 16,025 termite 25 26 171 mounds were manually identified over 23,013 ha. Manual identification of termite mounds from 27 28 29 172 hillshade maps has been shown to be accurate for mounds over 0.5 m in height (Davies et al 30 31 173 2014a). Two separate convolutional neural networks (CNNs) were then trained with different 32 33 174 landscape subset sizes (256 x 256 and 512 x 512 m), and ensembled to generate a final 34 35 36 175 probability. We used a CNN architecture designed for semantic segmentation, structurally 37 38 176 similar to U-Net (Ronneberger et al 2015) but adapted for different input sizes (Brodrick et al 39 40 177 2019). Each contiguous patch of classified pixels was then deemed an independent mound, and 41 42 178 the mound center was selected as the point with the maximum elevation within a 10 m radius of 43 44 45 179 the cluster. Mound height was determined by taking the difference of the mound maximum 46 47 180 elevation and the average elevation of points in a surrounding 10 m radius ring that excluded the 48 49 181 mound itself, following Davies et al. (2014a). Compared with manually identified mounds, we 50 51 52 182 found precision and recall rates of 0.84 and 0.91, respectively (Fig. 2, Appendix S1). In addition 53 54 183 to high accuracy, the use of the CNNs facilitated a consistent treatment of mounds over large 55 56 57 58 59 8 60 Accepted Manuscript Page 9 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 184 areas compared with manual identification. In total, 47,242 termite mounds were identified over 4 5 6 185 76,474 ha of study area. 7 8 186 9 10 187 Characterization of land-use polygons 11 12 13 188 Savanna Macrotermes in this ecosystem (southern African granitic savanna) are known to 14 15 189 restrict mound construction to hillcrests (Davies et al 2014a, Levick et al 2010a), and woody 16 17 190 cover can influence mound densities (Davies et al 2014a). We therefore quantified differences in 18 19 191 topography and vegetation cover within each study polygon (see below) and land-use type. The 20 21 22 192 LiDAR DTM was used to calculate the relative elevation above the nearest stream channel 23 24 193 across each polygon by dividing the elevation above a given channel by the elevation of the 25 26 194 nearest crest above the same channel, producing a relative elevation model (REM). Values in the 27 28 29 195 REM represent proportional distances above streams with streambeds modelled as 0 and 30 31 196 hillcrests as 1. The proportion of each polygon that consisted of stream valleys (REM between 0 32 33 197 and 0.05) and hillcrests (REM >0.9) was then calculated. To calculate tree canopy height, a 34 35 36 198 digital surface model (DSM) was first constructed from the LiDAR data based on interpolations 37 38 199 of all first-return points (i.e. the top of the canopy and, where only ground returns exist, bare 39 40 200 ground). The vertical difference between the DTM and DSM was then measured to generate a 41 42 201 model of canopy height above ground (digital canopy model, DCM) at a 1 m resolution. The 43 44 45 202 proportion of vegetation cover within each polygon was then measured as the proportion of 1 m 46 47 203 cells that contained vegetation taller than 1 m. This height threshold ensured that only woody 48 49 204 vegetation was included in the measurements, with herbaceous cover and bare ground excluded. 50 51 52 205 We further distinguished between the proportion of tree and shrub cover in each polygon by 53 54 55 56 57 58 59 9 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 10 of 32

1 2 3 206 measuring the proportion of 1 m cells that contained vegetation taller than 3 m (tree cover) and 4 5 6 207 the proportion with vegetation between 1 and 3 m in height (shrub cover). 7 8 208 9 10 209 Analysis 11 12 13 210 To divide the study region into the four land-use types (subsistence agriculture, communal 14 15 211 grazing, KNP, and private reserve), we used official boundaries for KNP and Sabi-Sand Private 16 17 212 Reserve, and delineated Subsistence Agriculture and Communal Grazing using Google Earth 18 19 213 images (sourced from DigitalGlobe imagery) corresponding to the 2012 LiDAR survey time 20 21 22 214 period. Subsistence agriculture is clearly distinguishable from other land-uses by the presence of 23 24 215 cleared fields, whereas communal grazing areas are undeveloped, open areas in between 25 26 216 settlements and agricultural fields. Delineated communal grazing areas were also compared to 27 28 29 217 maps in published studies (Wessels et al 2013, Mograbi et al 2015) to ensure that they covered 30 31 218 similar extents. We excluded areas with uncharacteristic terrain (e.g. the Bushbuck Ridge in L8) 32 33 219 or unclear land-use, as well as Andover Nature Reserve in L5 (see Fig. 1). Differences in LiDAR 34 35 36 220 data availability between land-use types (Table 1) resulted in varying numbers of termite mounds 37 38 221 surveyed per land-use. To assess mound densities, we bootstrap sampled 2,000, 100 ha subsets 39 40 222 (sensitivities presented in Appendix S2) within each land-use type and examined the histogram 41 42 223 of mound densities from each set of samples. To compare distributions of mound heights 43 44 45 224 between land-use types, we normalized the generated mound height histograms by the maximum 46 47 225 binned number of mounds within the land-use type. To quantify differences in spatial patterning, 48 49 226 we generated Ripley’s K curves for each land-use type using the R package spatstat (Baddeley 50 51 52 227 and Turner 2005). The Ripley’ s K function is a second-order statistic that uses inter-point 53 54 228 distances to measure deviation from spatial homogeneity. The expected distribution of features 55 56 57 58 59 10 60 Accepted Manuscript Page 11 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 229 under complete spatial randomness is plotted against spatial distance and the observed 4 5 6 230 distribution. When observed values fall below the expected distribution for spatial randomness, 7 8 231 over-dispersion occurs, while values above the expected distribution indicate clustering. Because 9 10 232 not all land-use types were contiguous (Fig. 1), we generated a unique Ripley’s K curve for each 11 12 13 233 polygon that exceeded 300 ha in size (smaller polygons were found to be dominated by 14 15 234 boundary-condition effects and were therefore unreliable – see Appendix S3). To understand the 16 17 235 relationship between termite mound heights and densities, we used the same 2,000 bootstrapped 18 19 236 samples and compared the mean mound height to mound density within each 100 ha sample 20 21 22 237 area. Finally, we examined relationships between mound density and height with woody cover 23 24 238 for each bootstrapped sample, stratifying woody cover by the proportion within each 100 ha 25 26 239 sample area that was under 1m in height, between 1 and 3 m tall, and over 3 m tall. 27 28 29 240 30 31 241 Results 32 33 242 Landscape topography (proportion of each polygon that was either a valley or hillcrest) did not 34 35 36 243 differ substantially between study polygons or land-use types (Table 1). Agricultural areas had 37 38 244 significantly lower vegetation cover, including lower tree and shrub cover, compared with the 39 40 245 other land-use types, which did not differ significantly from one another (Table 1). 41 42 246 Mound densities were lowest in the communal grazing areas (mean = 0.33 mounds ha-1), 43 44 -1 -1 45 247 followed by agricultural fields (0.45 mounds ha ), Kruger National Park (0.62 mounds ha ), and 46 47 248 the private reserve (0.82 mounds ha-1) (Table 1, Fig. 3a). Mound densities varied significantly 48 49 249 among all land-use types (Kolmogorov–Smirnov (K-S) test p < 0.001 for all comparisons). Mean 50 51 52 250 mound height was similar across land-use types (Table 1, Fig. 3b), but the distribution of mound 53 54 251 heights was significantly homogenized in the human-dominated landscapes. There was a 55 56 57 58 59 11 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 12 of 32

1 2 3 252 substantially higher proportion of taller mounds in the protected areas, with the tallest mounds 4 5 6 253 found within the private conservation areas (K-S test p < 0.001 for all comparisons, Fig. 3b). 7 8 254 Whereas Ripley’s K analyses revealed that mounds were over-dispersed at local scales in all 9 10 255 land-use types, landscape clustering of mounds was evident in all landscapes other than the 11 12 13 256 agricultural fields where mounds were randomly distributed at broader scales. The human- 14 15 257 modified landscapes (agricultural fields and communal grazing areas) also displayed the largest 16 17 258 variation in spatial patterning between study polygons, demonstrated by the variability in the 18 19 259 degree of clustering and over-dispersion exhibited as well as in the distance over which either 20 21 22 260 pattern persisted. This variability contrasted sharply with the almost identical patterning in each 23 24 261 of the six protected landscape polygons. Over-dispersion of mounds also persisted over larger 25 26 262 distances in the agricultural landscapes (up to ~150 m) relative to all other land-use types (~60- 27 28 29 263 70 m) (Fig. 3c), i.e. mounds remained evenly spaced in these landscapes, but were further apart. 30 31 264 Agricultural fields also displayed the weakest relationship between mound height and 32 33 265 density across the land-use types. While there was a significant (p < 0.001) positive relationship 34 35 36 266 between termite mound height and density in all land-use types, with mound height increasing 37 38 267 linearly with density, this relationship was much stronger (evidenced by the steeper slope) within 39 40 268 the two protected land-use types and was weakest in the agricultural fields (Fig. 4). Neither 41 42 269 mound density nor height was strongly correlated with woody cover in any land-use type, nor for 43 44 45 270 any woody height classification (Fig. S4). 46 47 271 48 49 272 Discussion 50 51 52 273 Airborne mapping and spatial modeling revealed that despite significant anthropogenic alteration 53 54 274 to landscapes over multiple decades, termite colonies persisted in transformed landscapes and 55 56 57 58 59 12 60 Accepted Manuscript Page 13 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 275 exhibited a number of the same spatial patterning characteristics as those in protected areas. 4 5 6 276 Mean termite mound height was not substantially reduced in transformed landscapes (Fig. 3b), 7 8 277 and over-dispersion of mounds was present at localized scales in all land-use types (Fig. 3c). 9 10 278 Moreover, although reduced, mound densities in the transformed areas were still 55% (grazing 11 12 13 279 areas) and 77% (subsistence agriculture) of the densities recorded in Kruger National Park. This 14 15 280 degree of termite resistance to land-use transformation is remarkable considering the scale of 16 17 281 habitat modification that included clearing for agriculture, large-scale and unsustainable 18 19 282 firewood extraction and active attempts by people to destroy and clear mounds (F. Nyathi pers. 20 21 22 283 comm.). That mound densities in the human modified landscapes remained at or above half the 23 24 284 densities of the protected areas suggests a continuation of some termite-mediated ecosystem 25 26 285 functioning in these systems, and the potential use of termite mounds as starting points for 27 28 29 286 savanna restoration given their ability to stabilize ecosystems (Bonachela et al. 2015). 30 31 287 Our findings were also unexpected given that field-based studies have found termites to 32 33 288 be highly sensitive to human disturbance (e.g. Eggleton et al. 1996, Jones et al. 2003, Muvengwi 34 35 36 289 et al. 2017), and suggest that the mound-building termite, Macrotermes, is less susceptible to 37 38 290 disturbance than other genera. Macrotermes are generalist feeders, consuming a wide range of 39 40 291 dead organic matter, and are therefore likely able to switch their diet according to available 41 42 292 feeding substrate. Other generalist feeders within the subfamily Macrotemitinae have been 43 44 45 293 shown to be less sensitive to habitat perturbation compared with other groups, particularly soil- 46 47 294 feeding termites (Davies et al 2012, Muvengwi et al 2017a). Macrotermes is also the largest, and 48 49 295 likely dominant (Evans and Kasseney 2019), termite in these systems, and could exclude other 50 51 52 296 termites from food resources, thereby monopolizing reduced resources in transformed 53 54 297 landscapes. Whether dominance by Macrotermes leads to declines in other termite genera in 55 56 57 58 59 13 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 14 of 32

1 2 3 298 transformed landscapes, with a concomitant reduction in ecosystem services provided by 4 5 6 299 termites, was beyond the scope of our study and requires further investigation. 7 8 300 Notwithstanding their resistance to land-use change, we found important differences in 9 10 301 termite mound characteristics between human-modified and protected areas. Mound densities 11 12 13 302 were lower in transformed landscapes (Fig 3a), consistent with findings of lower abundance for 14 15 303 other taxonomic groups in response to human land-use change (Sánchez-Bayo and Wyckhuys 16 17 304 2019, Flynn et al 2009). However, in contrast to what might be expected, mound densities were 18 19 305 lower in communal grazing areas relative to agricultural fields. Termite abundance has been 20 21 22 306 shown to decline with agricultural clearing elsewhere (Jones et al 2003, Muvengwi et al 2017a), 23 24 307 whereas uncleared grazing areas could be expected to be more similar to natural termite habitat 25 26 308 (see also Hagan et al. 2017 where grazing increased Trinervitermes mound densities). 27 28 29 309 Subsistence agricultural practices in our study area did not include large-scale land clearing 30 31 310 common in industrial farming (Foley et al 2005), and farming practices in the region often result 32 33 311 in additional organic matter inputs to the soil through mulching, excess crop residue after 34 35 36 312 harvesting, and the use of cattle dung for fertilizer, which likely provide increased food resources 37 38 313 for termites. In contrast, grazing areas in the region are subject to large-scale, unsustainable 39 40 314 firewood extraction (Wessels et al 2013), which likely lowers food availability for termites and 41 42 315 could consequently reduce mound density. Future studies quantifying coarse woody debris 43 44 45 316 across land-use types would help determine if firewood extraction is indeed a key mechanism of 46 47 317 reduced mound densities. Over-grazing in these regions can also facilitate woody encroachment 48 49 318 (Mograbi et al 2015), which lowers termite abundance (Leitner et al 2018). However, despite 50 51 52 319 higher mound densities in agricultural areas relative to communal grazing areas, there were 53 54 55 56 57 58 59 14 60 Accepted Manuscript Page 15 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 320 fewer mounds in both these land-use types relative to the protected areas, suggesting that both 4 5 6 321 forms of land use have some negative effect on termite colony survival. 7 8 322 In contrast to clear differences in termite mound densities, mean mound heights were 9 10 323 similar across land-use types (Fig 3b). Mound height is strongly correlated with colony size in 11 12 13 324 Macrotermes (Meyer et al 2000), and the similarities in mean mound height suggest that despite 14 15 325 the presence of fewer colonies in human-modified landscapes, those that do establish are, on 16 17 326 average, of similar size to those inside protected areas, suggesting that individual termite 18 19 327 colonies are able to withstand high levels of human disturbance. Although it is possible that 20 21 22 328 similar mean mound height across the land-use types is a legacy of previously extant colonies 23 24 329 given that termite mounds can persist for exceptionally long periods of time (Erens et al 2015), 25 26 330 abandoned mounds have been observed to remain for only a few years (e.g. 3-10 years in 27 28 29 331 Australia, Wilson & Agnew, 1992), reducing the likelihood of legacy effects driving the 30 31 332 observed pattern. However, determining the proportion of active to inactive mounds in these 32 33 333 landscapes is necessary to better understand effects of land-use change on termite colonies. 34 35 36 334 Despite similar means, the distribution of mound height was significantly truncated in 37 38 335 agricultural fields and communal grazing areas, with most of the tallest mounds found in 39 40 336 protected areas, particularly in privately conserved areas. Termite mound influence on savanna 41 42 337 vegetation increases with mound size (Joseph et al 2013, Muvengwi et al 2017b), and so a higher 43 44 45 338 proportion of tall (and consequently wide, Davies et al. 2014) mounds in protected areas likely 46 47 339 results in stronger mound-induced effects here relative to the human-modified landscapes. Taller 48 49 340 termite mounds in protected areas could also contribute to increased mound densities because 50 51 52 341 larger colonies likely produce more alates capable of initiating new colonies. The increased 53 54 342 frequency of tall mounds in the private conservation areas relative to KNP is intriguing and 55 56 57 58 59 15 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 16 of 32

1 2 3 343 could be due to land-use practices aimed at increasing wildlife sightings for tourists in the private 4 5 6 344 reserves. These practices, which include an increased number of artificial water points and 7 8 345 vegetation clearing along some roads, could increase food availability for termites through 9 10 346 increased woody debris from vegetation clearing and increased dung availability from more 11 12 13 347 mammalian herbivores attracted to the increased water availability. While similar spatial 14 15 348 patterns, and therefore inter-mound distances, between the private reserves and KNP suggest 16 17 349 consistent termite foraging ranges, increased food supply could enable the persistence of larger 18 19 350 colonies in the private reserves. 20 21 22 351 Termite mound height also increased with mound density in all land-use types, 23 24 352 suggesting that where termite colonies persist in large numbers, they also establish the largest 25 26 353 colonies (see also Davies et al. 2014), likely facilitated by increased food resources. This pattern 27 28 29 354 of increased mound density with height was, however, weaker in the agricultural fields, possibly 30 31 355 because farmers actively clear mounds in a spatially stochastic fashion compared with indirect, 32 33 356 semi-natural regulation of mounds in communal grazing areas where decreased food supply (due 34 35 36 357 to woody biomass extraction) across the landscape leads to a mirrored, yet diminished, pattern of 37 38 358 the natural areas. Farmers in the region also excavate termite mound soil for use as fertilizer (F. 39 40 359 Nyathi, pers. comm.) and sometimes attempt to remove mounds where termites are seen as pests, 41 42 360 altering mound structure and likely reducing height. Such clearing by humans could also lead to 43 44 45 361 an increased number of small mounds in these areas, which may not be detected in the LiDAR 46 47 362 data (Davies et al 2014a), possibly contributing to lower observed mound densities here. 48 49 363 Agricultural fields also displayed the strongest differences in mound spatial patterning 50 51 52 364 across landscapes. Whereas Ripley’s K analyses revealed that mounds were over-dispersed at 53 54 365 local scales in all land-use types, suggesting strong inter-colony competition wherever termite 55 56 57 58 59 16 60 Accepted Manuscript Page 17 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 366 mounds persist, clustering of mounds at the landscape scale was replaced by random patterning 4 5 6 367 in agricultural areas. Landscape clustering arises from abiotic controls over mound distributions, 7 8 368 with factors such as soil moisture content restricting mounds to drier hillcrests (Levick et al 9 10 369 2010a, Davies et al 2014a). While this clustering occurred in grazing areas, it was more variable 11 12 13 370 than in the protected areas, again suggesting that mound density reductions in grazing areas 14 15 371 occurred in a naturally mediated fashion that did not substantively disrupt their spatial patterning. 16 17 372 In contrast, mound density reductions in agricultural areas likely stem from more direct and 18 19 373 spatially random processes that are indifferent to abiotic controls (such as active mound clearing 20 21 22 374 by humans), thereby disrupting landscape clustering. However, local-scale over-dispersion still 23 24 375 persists in these agricultural landscapes, albeit at greater dispersion distances likely due to lower 25 26 376 mound densities, demonstrating the powerful effect inter-colony competition has on mound 27 28 29 377 patterning and the resistance of this natural process to anthropogenic disturbance. Topographic 30 31 378 differences between land-use types were limited (Table 1), suggesting that differences in mound 32 33 379 spatial patterning in agricultural areas were not a result of selection for specific topographic 34 35 36 380 regions for agriculture by people. 37 38 381 Given the disruptions to termite mound densities and spatial patterning in the human 39 40 382 transformed landscapes, what are the likely consequences on ecosystem function? Over- 41 42 383 dispersion of termite colonies at fine spatial scales leads to an even distribution of mound- 43 44 45 384 associated benefits, such as nutrient and moisture enrichment (Pringle et al 2010). Since this 46 47 385 over-dispersed pattern is not severely disrupted in any land-use we studied, the benefits of 48 49 386 mounds remain well distributed where they occur, a remarkable finding given the significant 50 51 52 387 alteration of the land surface by people (see Fig. 2). However, lower mound densities in human- 53 54 388 transformed landscapes, despite similar mean colony size, will lead to reduced termite activity 55 56 57 58 59 17 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 18 of 32

1 2 3 389 between mounds, which is also evidenced by the larger inter-mound distances (up to 150 m). 4 5 6 390 Termite activities, such as foraging for dead organic matter and mound construction, influence 7 8 391 multiple processes that enhance ecosystem function, e.g. water infiltration, nutrient cycling and 9 10 392 decomposition of organic matter (as reviewed in Jouquet et al. 2011). A decrease in termite 11 12 13 393 activity could therefore lower agricultural production (Evans et al 2011) and slow natural 14 15 394 ecosystem processes, leading to cyclical declines in resources such as woody biomass and tall 16 17 395 tree abundance, resources already under pressure in these human-dominated landscapes (Wessels 18 19 396 et al 2013, Mograbi et al 2017). Future studies examining changes in ecosystem function 20 21 22 397 resulting from altered termite populations are essential for uncovering the impacts of termite 23 24 398 declines on both natural ecosystem processes and human livelihoods, especially since it is 25 26 399 unknown how changes in the assemblage composition of ecologically important taxa, such as 27 28 29 400 termites, directly result in changes to the ecosystem functions they mediate (Kagezi et al 2011). 30 31 401 Similarly, mound longevity and enhanced moisture and nutrient content after colony death are 32 33 402 important avenues for future research. Our remote sensing approach could not quantify mound 34 35 36 403 activity, which could vary across land-use types and thus influence the provisioning of 37 38 404 ecosystem services if abandoned mounds disintegrate over short periods of time (Wilson and 39 40 405 Agnew 1992). 41 42 406 There is an urgent need to understand global change impacts on under-studied but critically 43 44 45 407 important groups such as insects, and while recent studies suggest widespread insect declines 46 47 408 (Sánchez-Bayo and Wyckhuys 2019, Lister and Garcia 2018), more robust and large-scale data 48 49 409 are needed before firm conclusions can be made (Thomas et al 2019). There is a particularly 50 51 52 410 important need for more studies investigating insect population and community changes over 53 54 411 large environmental and temporal gradients to better understand the impact of human land-use 55 56 57 58 59 18 60 Accepted Manuscript Page 19 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 412 change on ecosystems and their function. Our results suggest that anthropogenic activities do 4 5 6 413 have clear impacts on termite mounds, but also demonstrate surprising levels of resistance to 7 8 414 land-use change. These findings highlight the need for careful study of changing insect 9 10 415 communities, as well as the impacts these changes might have on ecosystem function, before 11 12 13 416 allowing the rampant expansion of human activities in natural environments. 14 15 417 16 17 418 Author contributions: GPA led the airborne data collection and research funding, ABD and 18 19 419 CLP developed the research questions, ABD and PGB analyzed the data, ABD wrote the first 20 21 22 420 draft of the manuscript, and all authors contributed to revisions. 23 24 421 25 26 422 Data accessibility: Data and code supporting the results will be deposited in Github 27 28 29 423 30 31 424 Acknowledgements 32 33 425 Penelope Mograbi is thanked for helpful comments on an earlier version of the manuscript The 34 35 36 426 Andrew Mellon Foundation, the South African Council for Scientific and Industrial Research 37 38 427 (CSIR) Strategic Research Panel and the South African Government Department of Science and 39 40 428 Technology (DST), funded the LiDAR data collection used in this study. The Global Airborne 41 42 429 Observatory is made possible by support provided by private foundations, visionary individuals, 43 44 45 430 and Arizona State University. 46 47 431 48 49 432 References 50 51 52 433 Ashton L A, Griffiths H M, Parr C L, Evans T A, Didham R K, Hasan F, Teh Y A, Tin H S, 53 54 434 Vairappan C S and Eggleton P 2019 Termites mitigate the effects of drought in tropical 55 56 57 58 59 19 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 20 of 32

1 2 3 435 rainforest Science (80-. ). 363 174–7 Online: 4 5 6 436 http://science.sciencemag.org/content/363/6423/174.abstract 7 8 437 Asner G P, Knapp D E, Kennedy-Bowdoin T, Jones M O, Martin R E, Boardman J and Field C 9 10 438 B 2007 Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and 11 12 13 439 waveform light detection and ranging for three-dimensional studies of ecosystems J. Appl. 14 15 440 Remote Sens. 1 13536 Online: http://dx.doi.org/10.1117/1.2794018 16 17 441 Asner G P, Levick S R, Kennedy-Bowdoin T, Knapp D E, Emerson R, Jacobson J, Colgan M S 18 19 442 and Martin R E 2009 Large-scale impacts of herbivores on the structural diversity of 20 21 22 443 African savannas Proc. Natl. Acad. Sci. USA 106 4947–52 23 24 444 Baddeley A and Turner R 2005 spatstat: An R Package for Analyzing Spatial Point Patterns. R- 25 26 445 package version: 1.42-2 J. Stat. Softw. 12 1–42 Online: 27 28 29 446 https://user2015.math.aau.dk/docs/tutorials/spatstat.pdf 30 31 447 Bonachela J A, Pringle R M, Sheffer E, Coverdale T C, Guyton J A, Caylor K K, Levin S A and 32 33 448 Tarnita C E 2015 Termite mounds can increase the robustness of dryland ecosystems to 34 35 36 449 climatic change Science (80-. ). 347 651 LP – 655 Online: 37 38 450 http://science.sciencemag.org/content/347/6222/651.abstract 39 40 451 Brodrick P G, Davies A B and Asner G P 2019 Uncovering Ecological Patterns with 41 42 452 Convolutional Neural Networks Trends Ecol. Evol. 34 734–45 Online: 43 44 45 453 http://www.ncbi.nlm.nih.gov/pubmed/31078331 46 47 454 Cardinale B J, Duffy J E, Gonzalez A, Hooper D U, Perrings C, Venail P, Narwani A, Mace G 48 49 455 M, Tilman D, Wardle D A, Kinzig A P, Daily G C, Loreau M, Grace J B, Larigauderie A, 50 51 52 456 Srivastava D S and Naeem S 2012 Biodiversity loss and its impact on humanity Nature 486 53 54 457 59–67 Online: http://www.nature.com/articles/nature11148 55 56 57 58 59 20 60 Accepted Manuscript Page 21 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 458 Ceballos G and Ehrlich P R 2002 Mammal Population Losses and the Extinction Crisis Science 4 5 6 459 (80-. ). 296 904 LP – 907 Online: 7 8 460 http://science.sciencemag.org/content/296/5569/904.abstract 9 10 461 Chapin III F S, Zavaleta E S, Eviner V T, Naylor R L, Vitousek P M, Reynolds H L, Hooper D 11 12 13 462 U, Lavorel S, Sala O E, Hobbie S E, Mack M C and Díaz S 2000 Consequences of changing 14 15 463 biodiversity Nature 405 234 Online: https://doi.org/10.1038/35012241 16 17 464 Davies A B, Baldeck C A and Asner G P 2016a Termite mounds alter the spatial distribution of 18 19 465 African savanna tree species J. Biogeogr. 43 301–13 Online: 20 21 22 466 http://dx.doi.org/10.1111/jbi.12633 23 24 467 Davies A B, Eggleton P, van Rensburg B J and Parr C L 2012 The pyrodiversity–biodiversity 25 26 468 hypothesis: a test with savanna termite assemblages J. Appl. Ecol. 49 422–30 Online: 27 28 29 469 http://dx.doi.org/10.1111/j.1365-2664.2012.02107.x 30 31 470 Davies A B, Levick S R, Asner G P, Robertson M P, van Rensburg B J and Parr C L 2014a 32 33 471 Spatial variability and abiotic determinants of termite mounds throughout a savanna 34 35 36 472 catchment Ecography (Cop.). 37 852–62 37 38 473 Davies A B, Levick S R, Robertson M P, van Rensburg B J, Asner G P and Parr C L 2016b 39 40 474 Termite mounds differ in their importance for herbivores across savanna types, seasons and 41 42 475 spatial scales Oikos 125 726–34 43 44 45 476 Davies A B, Robertson M P, Levick S R, Asner G P, van Rensburg B J and Parr C L 2014b 46 47 477 Variable effects of termite mounds on African savanna grass communities across a rainfall 48 49 478 gradient ed F Gilliam J. Veg. Sci. 25 1405–16 Online: 50 51 52 479 http://doi.wiley.com/10.1111/jvs.12200 53 54 480 Dosso K, Deligne J, Yéo K, Konaté S and Linsenmair K E 2013 Changes in the termite 55 56 57 58 59 21 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 22 of 32

1 2 3 481 assemblage across a sequence of land-use systems in the rural area around Lamto Reserve 4 5 6 482 in central Côte d’Ivoire J. Insect Conserv. 17 1047–57 7 8 483 Eggleton P, Bignell D E, Sands W A, Mawdsley N A, Lawton J H, Wood T G and Bignell N C 9 10 484 1996 The diversity, abundance and biomass of termites under differeing levels of 11 12 13 485 disturbance in the Mbalmayo Forest Reserve, southern Cameroon Philos. Trans. R. Soc. 14 15 486 London B 351 51–68 16 17 487 Erens H, Boudin M, Mees F, Mujinya B B, Baert G, Van Strydonck M, Boeckx P and Van Ranst 18 19 488 E 2015 The age of large termite mounds—radiocarbon dating of Macrotermes falciger 20 21 22 489 mounds of the Miombo woodland of Katanga, DR Congo Palaeogeogr. Palaeoclimatol. 23 24 490 Palaeoecol. 435 265–71 25 26 491 Evans T A, Dawes T Z, Ward P R and Lo N 2011 Ants and termites increase crop yield in a dry 27 28 29 492 climate Nat Commun 2 262 Online: http://dx.doi.org/10.1038/ncomms1257 30 31 493 Evans T A and Kasseney B D 2019 The dominance hierarchy of wood-eating termites from 32 33 494 China Insects 10 210 Online: https://www.mdpi.com/2075-4450/10/7/210 34 35 36 495 Flynn D F B, Gogol-Prokurat M, Nogeire T, Molinari N, Richers B T, Lin B B, Simpson N, 37 38 496 Mayfield M M and DeClerck F 2009 Loss of functional diversity under land use 39 40 497 intensification across multiple taxa Ecol. Lett. 12 22–33 Online: 41 42 498 https://doi.org/10.1111/j.1461-0248.2008.01255.x 43 44 45 499 Foley J A, DeFries R, Asner G P, Barford C, Bonan G, Carpenter S R, Chapin F S, Coe M T, 46 47 500 Daily G C, Gibbs H K, Helkowski J H, Holloway T, Howard E A, Kucharik C J, Monfreda 48 49 501 C, Patz J A, Prentice I C, Ramankutty N and Snyder P K 2005 Global Consequences of 50 51 52 502 Land Use Science (80-. ). 309 570 LP – 574 Online: 53 54 503 http://science.sciencemag.org/content/309/5734/570.abstract 55 56 57 58 59 22 60 Accepted Manuscript Page 23 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 504 Hagan J G, du Toit J C and Cramer M D 2017 Long-term livestock grazing increases the 4 5 6 505 recruitment success of epigeal termites: insights from a >75-year grazing experiment in the 7 8 506 Karoo, South Africa African J. Range Forage Sci. 34 123–32 Online: 9 10 507 https://www.tandfonline.com/doi/full/10.2989/10220119.2017.1314981 11 12 13 508 Jones D T, Susilo F X, Bignell D E, Hardiwinoto S, Gillison A N and Eggleton P 2003 Termite 14 15 509 assemblage collapse along a land-use intensification gradient in lowland central Sumatra, 16 17 510 Indonesia J. Appl. Ecol. 40 380–91 Online: http://doi.wiley.com/10.1046/j.1365- 18 19 511 2664.2003.00794.x 20 21 22 512 Joseph G S, Seymour C L, Cumming G S, Cumming D H M and Mahlangu Z 2013 Termite 23 24 513 mounds as islands: woody plant assemblages relative to termitarium size and soil properties 25 26 514 J. Veg. Sci. 24 702–11 Online: http://dx.doi.org/10.1111/j.1654-1103.2012.01489.x 27 28 29 515 Jouquet P, Chaudhary E and Kumar A R V 2018 Sustainable use of termite activity in agro- 30 31 516 ecosystems with reference to earthworms. A review Agron. Sustain. Dev. 38 3 Online: 32 33 517 http://link.springer.com/10.1007/s13593-017-0483-1 34 35 36 518 Jouquet P, Traoré S, Choosai C, Hartmann C and Bignell D 2011 Influence of termites on 37 38 519 ecosystem functioning. Ecosystem services provided by termites Eur. J. Soil Biol. 47 215– 39 40 520 22 Online: http://www.sciencedirect.com/science/article/pii/S1164556311000422 41 42 521 Kagezi G H, Kaib M, Nyeko P, Bakuneeta C, Schädler M and Brandl R 2011 Decomposition of 43 44 45 522 tissue baits and termite density along a gradient of human land-use intensification in 46 47 523 Western Kenya Afr. J. Ecol. no-no Online: http://dx.doi.org/10.1111/j.1365- 48 49 524 2028.2011.01263.x 50 51 52 525 Lagendijk D D G, Davies A B, Eggleton P and Slotow R 2016 No evidence for an elephant- 53 54 526 termite feedback loop in Sand Forest, South Africa Biol. Conserv. 203 55 56 57 58 59 23 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 24 of 32

1 2 3 527 Leitner M, Davies A B, Parr C L, Eggleton P and Robertson M P 2018 Woody encroachment 4 5 6 528 slows decomposition and termite activity in an African savanna Glob. Chang. Biol. 24 7 8 529 2597–606 Online: http://doi.wiley.com/10.1111/gcb.14118 9 10 530 Levick S R, Asner G P, Chadwick O A, Khomo L M, Rogers K H, Hartshorn A S, Kennedy- 11 12 13 531 Bowdoin T and Knapp D E 2010a Regional insight into savanna hydrogeomorphology from 14 15 532 termite mounds Nat. Commun. 1 65 16 17 533 Levick S R, Asner G P, Kennedy-Bowdoin T and Knapp D E 2010b The spatial extent of termite 18 19 534 influences on herbivore browsing in an African savanna Biol. Conserv. 143 2462–7 Online: 20 21 22 535 http://www.sciencedirect.com/science/article/B6V5X-50G0F5S- 23 24 536 5/2/0f304adf1b2cee4e168627dc74b6033c 25 26 537 Lister B C and Garcia A 2018 Climate-driven declines in arthropod abundance restructure a 27 28 29 538 rainforest food web. Proc. Natl. Acad. Sci. U. S. A. 115 E10397–406 Online: 30 31 539 http://www.ncbi.nlm.nih.gov/pubmed/30322922 32 33 540 Meyer V W, Crewe R M, Braack L E O, Groeneveld H T and van der Linde M J 2000 34 35 36 541 Intracolonial demography of the mound-building termite Macrotermes natalensis (Haviland) 37 38 542 (Isoptera, Termitidae) in the northern Kruger National Park, South Africa Insectes Sociaex 39 40 543 47 390–7 41 42 544 Mograbi P J, Asner G P, Witkowski E T F, Erasmus B F N, Wessels K J, Mathieu R and Vaughn 43 44 45 545 N R 2017 Humans and elephants as treefall drivers in African savannas Ecography (Cop.). 46 47 546 40 1274–84 Online: http://doi.wiley.com/10.1111/ecog.02549 48 49 547 Mograbi P J, Erasmus B F N, Witkowski E T F, Asner G P, Wessels K J, Mathieu R, Knapp D E, 50 51 52 548 Martin R E and Main R 2015 Biomass Increases Go under Cover: Woody Vegetation 53 54 549 Dynamics in South African Rangelands PLoS One 10 e0127093 Online: 55 56 57 58 59 24 60 Accepted Manuscript Page 25 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 550 https://doi.org/10.1371/journal.pone.0127093 4 5 6 551 Muvengwi J, Mbiba M, Ndagurwa H G T, Nyamadzawo G and Nhokovedzo P 2017a Termite 7 8 552 diversity along a land use intensification gradient in a semi-arid savanna J. Insect Conserv. 9 10 553 21 801–12 Online: http://link.springer.com/10.1007/s10841-017-0019-7 11 12 13 554 Muvengwi J, Witkowski E T F, Davies A B and Parrini F 2017b Termite mounds vary in their 14 15 555 importance as sources of vegetation heterogeneity across savanna landscapes J. Veg. Sci. 28 16 17 556 1008–17 18 19 557 Newbold T, Hudson L N, Hill S L L, Contu S, Lysenko I, Senior R A, Börger L, Bennett D J, 20 21 22 558 Choimes A, Collen B, Day J, De Palma A, Díaz S, Echeverria-Londoño S, Edgar M J, 23 24 559 Feldman A, Garon M, Harrison M L K, Alhusseini T, Ingram D J, Itescu Y, Kattge J, Kemp 25 26 560 V, Kirkpatrick L, Kleyer M, Correia D L P, Martin C D, Meiri S, Novosolov M, Pan Y, 27 28 29 561 Phillips H R P, Purves D W, Robinson A, Simpson J, Tuck S L, Weiher E, White H J, 30 31 562 Ewers R M, Mace G M, Scharlemann J P W and Purvis A 2015 Global effects of land use 32 33 563 on local terrestrial biodiversity Nature 520 45 Online: https://doi.org/10.1038/nature14324 34 35 36 564 Pimm S L and Raven P 2000 Biodiversity: Extinction by numbers Nature 403 843–5 37 38 565 Pollard S, Shackleton C M and Carruthers J 2003 Beyond the fence: people and the lowveld 39 40 566 landscape The Kruger Experience: Ecology and Management of Savanna Heterogeneity ed 41 42 567 J T Du Toit, K H Rogers and H C Biggs (Washington, DC: Island Press) pp 422–46 43 44 45 568 Potts S G, Biesmeijer J C, Kremen C, Neumann P, Schweiger O and Kunin W E 2010 Global 46 47 569 pollinator declines: Trends, impacts and drivers Trends Ecol. Evol. 25 345–53 Online: 48 49 570 https://www.sciencedirect.com/science/article/pii/S0169534710000364 50 51 52 571 Pringle R M, Doak D F, Brody A K, Jocqué R and Palmer T M 2010 Spatial pattern enhances 53 54 572 ecosystem functioning in an African savanna PLoS Biol 8 e1000377 Online: 55 56 57 58 59 25 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 26 of 32

1 2 3 573 http://dx.doi.org/10.1371%2Fjournal.pbio.1000377 4 5 6 574 Pringle R M and Tarnita C E 2017 Spatial Self-Organization of Ecosystems: Integrating Multiple 7 8 575 Mechanisms of Regular-Pattern Formation Annu. Rev. Entomol. 62 359–77 Online: 9 10 576 http://www.annualreviews.org/doi/10.1146/annurev-ento-031616-035413 11 12 13 577 Ronneberger O, Fischer P and Brox T 2015 U-net: Convolutional networks for biomedical image 14 15 578 segmentation Lecture Notes in Computer Science (including subseries Lecture Notes in 16 17 579 Artificial Intelligence and Lecture Notes in Bioinformatics) vol 9351 (Springer, Cham) pp 18 19 580 234–41 Online: http://link.springer.com/10.1007/978-3-319-24574-4_28 20 21 22 581 Rouland-Lefèvre C 2011 Termites as Pests of Agriculture BT - Biology of Termites: a Modern 23 24 582 Synthesis ed D E Bignell, Y Roisin and N Lo (Dordrecht: Springer Netherlands) pp 499– 25 26 583 517 Online: https://doi.org/10.1007/978-90-481-3977-4_18 27 28 29 584 Sánchez-Bayo F and Wyckhuys K A G 2019 Worldwide decline of the entomofauna: A review 30 31 585 of its drivers Biol. Conserv. 232 8–27 Online: 32 33 586 https://www.sciencedirect.com/science/article/pii/S0006320718313636 34 35 36 587 Seymour C L, Milewski A V, Mills A J, Joseph G S, Cumming G S, Cumming D H M and 37 38 588 Mahlangu Z 2014 Do the large termite mounds of Macrotermes concentrate micronutrients 39 40 589 in addition to macronutrients in nutrient-poor African savannas? Soil Biol. Biochem. 68 95– 41 42 590 105 Online: http://www.sciencedirect.com/science/article/pii/S0038071713003234 43 44 45 591 Shackleton C M, Shackleton S E and Cousins B 2001 The role of land-based strategies in rural 46 47 592 livelihoods: The contribution of arable production, animal husbandry and natural resource 48 49 593 harvesting in communal areas in South Africa Dev. South. Afr. 18 581–604 Online: 50 51 52 594 https://www.tandfonline.com/doi/full/10.1080/03768350120097441 53 54 595 Sileshi G W, Arshad M A, Konaté S and Nkunika P O Y 2010 Termite-induced heterogeneity in 55 56 57 58 59 26 60 Accepted Manuscript Page 27 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 596 African savanna vegetation: mechanisms and patterns J. Veg. Sci. 21 923–37 Online: 4 5 6 597 http://dx.doi.org/10.1111/j.1654-1103.2010.01197.x 7 8 598 Sileshi G W, Nyeko P, Nkunika P O Y, Sekematte B M, Akinnifesi F K and Ajayi O C 2009 9 10 599 Integrating Ethno-Ecological and Scientific Knowledge of Termites for Sustainable Termite 11 12 13 600 Management and Human Welfare in Africa Ecol. Soc. 14 Online: 14 15 601 http://www.ecologyandsociety.org/vol14/iss1/art48/ 16 17 602 Staver A C 2018 Prediction and scale in savanna ecosystems New Phytol. 219 52–7 Online: 18 19 603 http://doi.wiley.com/10.1111/nph.14829 20 21 22 604 Su N-Y and Scheffrahn R H 2000 Termites as Pests of Buildings BT - Termites: Evolution, 23 24 605 Sociality, Symbioses, Ecology ed T Abe, D E Bignell and M Higashi (Dordrecht: Springer 25 26 606 Netherlands) pp 437–53 Online: https://doi.org/10.1007/978-94-017-3223-9_20 27 28 29 607 Thomas C D, Jones T H and Hartley S E 2019 “Insectageddon”: a call for more robust data and 30 31 608 rigorous analyses Glob. Chang. Biol. Online: http://doi.wiley.com/10.1111/gcb.14608 32 33 609 Venter F J, Scholes R J and Eckhardt H C 2003 The abiotic template and its associated 34 35 36 610 vegetation pattern The Kruger Experience: Ecology and Management of Savanna 37 38 611 Heterogeneity ed J T Du Toit, H C Biggs and K H Rogers (Washington DC, USA: Island 39 40 612 Press) 41 42 613 Wessels K J, Colgan M S, Erasmus B F N, Asner G P, Twine W C, Mathieu R, van Aardt J A N, 43 44 45 614 Fisher J T and Smit I P J 2013 Unsustainable fuelwood extraction from South African 46 47 615 savannas Environ. Res. Lett. 8 014007 Online: http://stacks.iop.org/1748- 48 49 616 9326/8/i=1/a=014007?key=crossref.056731292c319cf2898a2f3b76e7b6f1 50 51 52 617 Wilson J B and Agnew A D Q 1992 Positive-feedback Switches in Plant Communities Adv. 53 54 618 Ecol. Res. 23 263–336 55 56 57 58 59 27 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 28 of 32

1 2 3 619 Table 1: Termite mound, topographic and vegetation cover characteristics for each of the four land-use types. 4 5 Bootstrapped Bootstrapped Tree cover Shrub cover 6 mound density std. dev. of Mound Mean Std. dev. Proportion Proportion Proportion (proportion of (proportion of 7 Land-use No. of (mean mound density density mound of mound valley (REM crest (REM vegetation vegetation vegetation cover 8 type Area (ha) mounds mounds/ha) (mounds/ha) (mounds/ha) height (m) height (m) < 0.05) > 0.9) cover > 1 m cover > 3 m) > 1 & < 3 m) 9 10 Agriculture 2328.89 1039 0.48 0.10 0.45 1.19 0.41 0.09 0.12 0.11 0.05 0.06 11 Grazing 9341.57 3082 0.34 0.14 0.33 1.15 0.39 0.09 0.11 0.31 0.12 0.20 12 13 Kruger 51196.92 31994 0.62 0.27 0.62 1.27 0.45 0.11 0.08 0.27 0.14 0.13 14 15 Private 13606.57 11127 0.78 0.30 0.82 1.37 0.50 0.11 0.10 0.31 0.16 0.16 16 17 620 18 19 621 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 28 45 Accepted Manuscript 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 Page 29 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 4 5 6 7 South Africa 8 5 9 10 11 12 6 13 14 2 15 16 17 18 19 8 20 7 21 22 4 1 23 24 Kruger National Park 25 Sabi-Sand Game Reserve 26 LiDAR coverage 27 Gabbro geology 28 29 Settlements 30 Study polygons 31 32 Kruger NP 3 33 Private reserve 34 Subsistence agriculture 35 Communal grazing 36 ¯ 37 Km 0 5 10 20 38 39 40 Figure 1: Study area in the Lowveld region of northeastern South Africa (see inset map) 41 42 43 spanning (from east to west) Kruger National Park, the privately owned Sabi-Sand Game 44 45 Reserve, and communal areas within the Bushbuckridge municipality. Landscapes for which 46 47 airborne LiDAR data were available are delineated 1–8. Gabbro intrusions, which were excluded 48 49 50 from analysis, are mapped within the predominantly granitic landscape. Human settlements in 51 52 proximity to the study polygons are also mapped. 53 54 55 56 57 58 59 29 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 30 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 Figure 2: Example hillshade images showing termite mound distributions, with and without 46 47 mounds identified by the convolutional neural network (CNN) model (depicted by red circles in 48 49 the right-hand panels). Hillshades were produced from airborne LiDAR-derived digital elevation 50 51 models, and highlight the differences between Kruger National Park (a-b, subset of Landscape 52 53 54 3), communal grazing land (c-d, subset of Landscape 7), and agricultural fields (e-f, subset of 55 56 Landscape 7). See Fig. 1 for landscape locations. 57 58 59 30 60 Accepted Manuscript Page 31 of 32 AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 Figure 3: Distribution of termite mound heights (a) and densities (b) measured with airborne 31 32 33 LiDAR across areas of varying land-use in northeastern South Africa. The height distributions 34 35 were generated from all measured mounds per land-use, whereas density distributions were 36 37 generated by sampling 2000 100 ha blocks with replacement from each land-use type. c) 38 39 40 Ripley’s K (L transformation) function for termite mounds across the land-use types. The red 41 42 dashed line represents the distribution of mounds under complete spatial randomness, whereas 43 44 the black solid lines represent the observed mound distribution per land-use polygon (> 300 ha in 45 46 47 extent) in each land-use type. Values below the red dashed line indicate over-dispersion (even 48 49 spacing) while values above the dashed line indicate clustering. Private Reserve represent areas 50 51 within Sabi-Sand Game Reserve. 52 53 54 55 56 57 58 59 31 60 Accepted Manuscript AUTHOR SUBMITTED MANUSCRIPT - ERL-108787.R1 Page 32 of 32

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 Figure 4: Termite mound heights vs densities. Points represent the mean height and density in 38 39 each of the 2000 100 ha blocks from each land-use type. The lines represent linear regressions 40 41 (with associated R2 values in the legend) per land-use type. 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 32 60 Accepted Manuscript