Global Environmental Change 60 (2020) 102030

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Global Environmental Change

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Accelerating savanna degradation threatens the socio- ecological system T ⁎ ⁎ Wang Lia,b,c,1, , Robert Buitenwerfa,b, , Michael Munka,b, Irene Amoked,e, Peder Klith Bøchera,b, Jens-Christian Svenninga,b a Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Aarhus University, Ny Munkegade 114, 8000 Aarhus C, Denmark b Section for Ecoinformatics and Biodiversity, Department of Biology, Aarhus University, Ny Munkegade 114, 8000 Aarhus C, Denmark c State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China d Wildlife Trust, P.O. Box 86-005200, Nairobi, Karen, Kenya e Maasai Mara Wildlife Conservancies Association, P O Box 984, Narok 20500, Kenya

ARTICLE INFO ABSTRACT

Keywords: Savanna megafauna have become scarce outside of protected areas in Africa, largely because of land conversion Africa for farming (smallholders and agribusiness) and expansion of settlements and other infrastructure. Ecosystem degradation Intensification also isolates protected areas, even affecting natural processes within reserve boundaries. Here, we Sustainability used satellite imagery from the past 32 years in the iconic Maasai Mara ecosystem to assess the capacity of Social-ecological systems different land tenures to prevent degradation. We compare unprotected land with two types of conservation management: fully protected land without livestock (land sparing) and semi-protected community-based con- servation – protected land with regulated livestock densities (land sharing). On unprotected land (61% of the area), we detected massive and accelerating degradation and fragmentation of natural vegetation, with large losses of woodland (62%) and grassland (56%), resulting in the expansion of bare ground. In contrast, directional change was minimal in both types of protected areas. Vegetation resistance to drought was lowest on un- protected land, intermediate under community-based conservation and highest under full protection. Our results show that the Mara ecosystem is under heavy pressure, but that conservation management counteracts negative trends. Importantly, semi-protected community-based land-sharing conservation offers clear, partial buffering against degradation.

1. Introduction social-ecological system through land degradation (“the persistent de- cline or loss in biodiversity and ecosystem functions and services that The Greater Maasai Mara Ecosystem in Kenya (henceforth “the cannot fully recover unaided within decadal time scales”: Mara”, Fig. 1) is an iconic African savanna ecosystem, a major tourist (IPBES, 2018)). In the Mara, land degradation leads to habitat losses for destination, and contains one of the richest assemblages of wild resident and migratory wildlife, loss of migration corridors and the megafauna (> 45 kg) in the world (Malhi et al., 2016; Mduma and local extinction of semi-nomadic pastoralism (Reid, 2012). Savannas Hopcraft, 2008). However, during the past four decades the Mara has and drylands across the African continent face similar challenges undergone severe ecological degradation, with plummeting large (Gasparri et al., 2016). mammal populations (Ogutu et al., 2011; Ogutu et al., 2016) and the Two broad types of land degradation can be identified in the Mara. loss of seasonal wildlife migrations (Peters et al., 2008). Wildlife po- The most severe and abrupt is conversion of grassy savannas (range- pulations are declining due to poaching, disease, competition from li- land) to cropland, which historically has been documented in the north- vestock and habitat fragmentation as a result of fencing and changes in eastern part of the Mara and is strongly driven by agribusiness linked to land tenure (Ogutu et al., 2011; Ogutu et al., 2016). Especially fencing foreign investors and global markets (Homewood et al., 2001; and changes in land-use forebode irreversible damage to the Mara Serneels and Lambin, 2001b). A second cause of land degradation may

⁎ Correspondence author at: Center for Biodiversity Dynamics in a Changing World (BIOCHANGE), Aarhus University, Ny Munkegade 114, 8000 Aarhus C, Denmark E-mail addresses: [email protected] (W. Li), [email protected] (R. Buitenwerf). 1 W.L and R.B contributed equally to this work. https://doi.org/10.1016/j.gloenvcha.2019.102030 Received 26 July 2019; Received in revised form 16 December 2019; Accepted 30 December 2019 Available online 15 January 2020 0959-3780/ © 2019 Elsevier Ltd. All rights reserved. W. Li, et al. Global Environmental Change 60 (2020) 102030

Fig. 1. | Study site – (a) Greater Maasai Mara Ecosystem in Kenya, Africa. (b) Boundaries of land-use types overlaid on a digital elevation model. (c) Protection status. (d)-(h) illustrate the different land-cover classes: (d) Woodland; (e) Grassland; (f) Mix of shrubs and bare ground; (g) Mix of grass and bare ground; (h) Bare ground. Photographs were taken by one of the authors. Detailed descriptions of the land cover type are given in Table S1. be livestock herds, which have rapidly expanded over recent decades time and intensity (Thompson et al., 2009). The land-sharing between (Løvschal et al., 2018; Lamprey and Reid, 2004; Ogutu et al., 2011). At wildlife, human pastoralists and ecotourism in the conservancies can be high densities, livestock directly competes for forage with wild grazers, seen as an intermediate between the set-aside conservation of the na- especially in dry years, when resources are scarce (Vetter, 2005). Fur- tional reserve and the unprotected land that surrounds the con- thermore, socio-economic processes such as inter-human conflict over servancies. Several of the land-tenure contracts in the community-based pasture have resulted in increased fencing (Løvschal et al., 2017), conservation areas of the Mara have recently been extended, suggesting which is detrimental to wildlife as it excludes them from habitat and that this conservation model has been beneficial to both parties. potentially obstructs migration routes. At more localised scales wildlife- However, many contracts are still to be renegotiated in the near future, based tourism may contribute to land degradation through the devel- highlighting the need for up-to-date and high-resolution spatiotemporal opment of new infrastructure and widespread off-road driving. To- information on land cover and habitat quality trajectories under the gether, these land-use changes threaten both wildlife and pastoralism three contrasting land management strategies, thus allowing evidence- and thus their sustainable coexistence (Løvschal et al., 2017). based decision making and conservation planning. Nevertheless, recent and up-to-date assessments of land cover Conservation planning and charting the future of the Mara social- change and biodiversity impacts are largely missing. While the ecological system is a complex task that is complicated further by on- -Mara ecosystem in and Kenya has been studied in- going global change, which is likely to affect both social and ecological tensively since the 1970s, most studies have focussed on the larger resilience (Hoag and Svenning, 2017). East Africa is predicted to be- Serengeti (Sinclair et al., 2015; Sinclair et al., 2008; Veldhuis et al., come substantially hotter throughout the 21st century (1–4 °C, RCP 2019). However, the Mara has a unique and highly dynamic social- 2.6–8.5), which is likely to reduce grass productivity; especially since ecological setting (Homewood et al., 2009; Homewood et al., 2001; rainfall is not expected to keep pace (IPCC, 2013). In addition to total Løvschal et al., 2017; Løvschal et al., 2018; Lamprey and Reid, 2004). annual rainfall, the complex intra-annual rainfall variability and mod- Bordering the Maasai Mara National Reserve, where livestock grazing ality in this region affects vegetation dynamics (Deshmukh, 1984), but and other human use other than tourism is prohibited, community- these effects are not fully understood and intra-annual rainfall patterns based conservation areas (locally referred to as conservancies) have are difficult to forecast (Nicholson, 2017). Finally, grass resources for been established since 2005. These conservancies are based on a part- wildlife and pastoralists could be threatened by the widespread ex- nership between Maasai landowners and tourism operators. Land- pansion of woody plants in African grassy ecosystems, with evidence owners are paid a per-area fee for leasing out their land to tourism suggesting increases in atmospheric CO2 concentrations as a driver operators, on the condition that livestock grazing is regulated in space, (Bond and Midgley, 2012; Buitenwerf et al., 2012; Stevens et al., 2016),

2 W. Li, et al. Global Environmental Change 60 (2020) 102030 a dynamic that is predicted to strengthen in the coming decades (pers.obs.). (Higgins and Scheiter, 2012). Global change may thus put wildlife To aid visual interpretation of pure pixel patches, several false- populations, especially grazers, under further pressure. As the ap- colour images were composited from the Landsat bands to help with proximately 1.3 million affect nearly every aspect of eco- visual interpretation to select pixel patches. For example, we used the system functioning in the Serengeti-Mara (Hopcraft et al., 2015), col- composited image from band 7,5,3 to help with the selection of the bare lapse of this population will thus have far-reaching ramifications for ground patches, and composited images from band 4,3,2 and 7,4,2 to predator populations, fire regimes and local economies, both through select the samples for grassland and vegetation+bare mixed as well as income from tourism and pastoralism. Global change may thus force woodland. In addition to the brightness of the spectral bands, bright- pastoralists to develop new adaptation, risk management and coping ness of NDVI image was also used to help with the visual interpretation, strategies (Bedelian and Ogutu, 2017). but not put into the classification model. The exact procedure to In summary, it is clear that the Mara is under increasing pressure, identify each land cover type varied. For example, woodland can be but it remains unclear how degradation dynamics are unfolding, how recognised by colour and brightness, whereas croplands were identified degradation affects longer-term resilience of the social-ecological using a combination of brightness and geometry. Grassland was iden- system, and whether the community-based conservancies deliver in- tified according to its homogeneous visual texture and colour in the tended ecological and socio-economic outcomes. To address these false colour composited images. questions we use satellite imagery from the past 32 years (1985–2016) After assembling the set of known pure pixels for each land cover to quantify the rate of land degradation on protected, semi-protected class, the data were divided into two groups, about 60% of which were and unprotected land and the capacity of each land management type used for training (900 points in total), and the remaining 40% for va- to maintain ecosystem functioning under drought stress. We ask the lidation (600 points in total) for each single-date image. The number of following specific questions: 1) Has land-degradation accelerated, de- training/validation points for different land cover types per classifica- celerated or remained constant. 2) To what extent has land degradation tion is different, i.e., relative to their abundance in the entire study site fragmented natural and pastoral lands? 3) Do the semi-protected con- via visual estimation. After trial testing, the widely used maximum- servancies buffer against land degradation? 4) Does ecological re- likelihood classifier was employed (Foody et al., 1992) and im- sistance to environmental stress (vegetation functioning during plemented in ArcGIS 10.6 (Environmental Systems Research Institute, drought) increase with level of protection? ESRI). We selected the maximum likelihood method as a classic, ef- fective classification method for similar remote sensing problems 2. Materials and methods (Burai et al., 2015; Mwangi et al., 2018; Yang et al., 2009). Overall accuracy and Kappa coefficients were calculated for 600 independently 2.1. Study site and randomly selected pixels (Table. S2). Finally, a per-pixel land cover transition matrix was calculated to quantify transitions between the six The Greater Maasai Mara ecosystem is located in southwestern main land cover types between 1985 and 2016 using the software Di- Kenya, with an area of over 660,000 ha (Fig.1a and b). It is mainly namica EGO (Soares-Filho et al., 2009). divided into three components according to the land use management type, namely, the fully protected Maasai Mara National Reserve, the 2.3. Fragmentation analysis semi-protected conservancies, and the unprotected land (Fig.1c). The national reserve is managed by the Narok County Government within Fragmentation analysis was conducted based on the four single-date this area (Løvschal et al., 2017). The semi-protected conservancies and land cover classification dataset for the three land use types separately the unprotected pastoral land mainly comprises contrasting land man- using the software FRAGSTATS (McGarigal and Marks, 1994). Before agement areas, including wildlife conservancies, conservation areas and analysis, we re-classified the whole study site into two classes – habitat settlement areas (Løvschal et al., 2017). and non-habitat. The habitat class includes high-value conservation and pastoralist land covers (grassland + woodland). Grasslands are valu- 2.2. Land cover change classification able to wild and domestic grazers. Woodland supports browsers, mixed feeders and arboreal species and allows harvesting of wood and med- Four single-date Landsat were selected to classify the detailed land icinal plants. Woodlands are also essential for grazers, especially in the cover types in 1985, 2003, 2010 and 2016. All images were acquired in dry season because tree leaves protect grass leaves from direct solar the dry season (January and February), when spectral differences be- radiation, allowing grasses to retain green leaves with higher nutri- tween land cover types are greatest and cloud contamination is tional quality later into the dry season than grasses on open grasslands. minimal. To assure that all pixels were exactly aligned, we conducted The remaining land covers (vegetation+bare mixed, bare ground and image-to-image co-registration before the classification in the data pre- water) were classified as non-habitat areas. Two fragmentation metrics processing using the ENVI software (Exelis Visual Information were calculated at class level – patch density (patches/100 ha) and Solutions, Boulder, Colorado). The co-registration had a root mean mean patch size (ha), with an eight-neighbourhood criterion for the square error of less than half a pixel (15 m). definition of patches. Usually, higher patch density and smaller mean To generate a training data set for our supervised classification, we patch size indicate more fragmentation. identified pure pixel patches of each land cover type by visually in- terpreting 1) Landsat images, 2) high-resolution imagery from Google 2.4. Temporal trend analysis Earth and 3) 844 ground-based photographs. Field photographs (N = 844) with precise geographic coordinates had been taken during To quantify vegetation change over time, we used time series of Jan-Mar in 2015. Based on our visual interpretation of field photo- annual NDVI means (NDVI) between 2000 and 2016. This analysis was graphs, texture of pixel patches and brightness of the composited based on MODIS collection (MOD13Q1, 250 m, 16-day intervals) and Landsat imagery from different bands as well as the NDVI values, the temporal trend analysis was performed using the Mann-Kendall training pixels were identified for five main land cover types: wood- (MK) test of monotonic change (Mann, 1945). NDVI was calculated as land, grassland, vegetation+bare mixed, bare ground and water (Fig.1 the mean value of all the 16-day MODIS data in each year. The MK test d–h, Table S1). Cropland was included with the bare ground class as the was used to test the presence of monotonic trend in the time Landsat spectral properties of cropland and bare soil in Mara are very seriesNDVI. It is a rank-based non-parametric test and a useful alter- similar in the dry season. Cropland was limited in the study area and native to linear squares regression with low requirements on assump- restricted to the Trans Mara to the northwest and the northeast Mara tions (Eddy et al., 2017). The MK test is easy to calculate, robust against

3 W. Li, et al. Global Environmental Change 60 (2020) 102030 non-normality as well as insensitive to missing values (de Jong et al., woodlands were converted to grassland and bare ground, in this case 2011). In the MK test, each NDVI point will be treated as the reference mostly cropland (clearly visible in Trans Mara and northeast Mara, see for the data points in successive time periods after ranking all the NDVI Fig. S1). Other hotspots of bare-ground expansion were along the north- data with reference to time (Neeti and Eastman, 2011). eastern border of the study area, where bare ground replaced grassland, Since the MK test allows for the strength and direction of a trend, again at least partly through cropland expansion (Fig.S1). In the but not for the magnitude, the non-parametric Sen's slope estimator (Q) northern Talek area (Fig. 3), loss of natural vegetation is coupled to was calculated to determine the slope of NDVI trend which is quantified village expansion. Overall accuracy and kappa coefficients for the four as the percentage of change in NDVI from the median value within the single-date classifications (1985, 2003, 2010, 2016) were high: time series dataset (Sen, 1968). The Sen's slope is considered to be a 83.8–86.5% and 80.8–83.6% respectively (Table S2). good approximation to evaluate the net change throughout the studied The expansion of bare ground resulted in habitat fragmentation time range, which can be positive or negative (Martínez and within the unprotected area, where patches of high-quality vegetation Gilabert, 2009). In this study, the Sen's slope for time series NDVI was (grassland + woodland) became smaller, resulting in a higher patch − used to represent vegetation greening rate (hereafter, GR, yr 1, nega- density (i.e. fragmentation, Fig. S5). Both the fully and semi-protected tive values represent browning rate). areas also showed a decrease in the patch size of grassland + wood- In order to cross validate the MODIS greening rate, the MK test was land, but to a smaller degree (Fig. S5). also applied to the annual composited Landsat imagery for the period between 1985 and 2016, and the period between 2000 and 2016. Since 3.2. Ecological resilience we found high consistence between the two satellite dataset (Fig. S3), we only report the MODIS results, considering its higher and homo- NDVI was strongly related to land management type throughout the geneous data availability than Landsat. 2000–2016 period, with the highest NDVI in the fully-protected area, intermediate NDVI in the semi-protected area and the lowest NDVI on 2.5. Ecological resilience unprotected land (Fig. S2). However, such an analysis does not account for differences in annual rainfall between the land management types. The capacity of each land management type to maintain green ve- The nonlinear least-squares regression results suggest that the fully getation under drought stress was quantified by analysing how vege- protected area maintains vegetation functioning (i.e. does not drop into tation greenness (NDVI) responds to rainfall anomalies. This capacity negative NDVI anomalies) until rainfall drops 8.5% below the long- can be interpreted as the resistance component of ecological resilience term average for this land management type (Fig. 4b). In contrast, the (Walker et al., 2004). We fit an asymptotic function using nonlinear unprotected area only maintains vegetation functioning when rainfall is least-squares regression to the relationship between standardised NDVI at or above the long-term average for this land management type. The anomalies and standardised rainfall anomalies for each land manage- semi-protected area has intermediate resistance of vegetation func- ment type between 2000 and 2016. We calculated annual rainfall and tioning to drought stress and maintains vegetation functioning until NDVI anomalies from the 17-year mean for each of the three land rainfall drops 3.5% below its long-term average. management types. We expressed anomalies as the proportional de- viation from the 17-year mean. Rainfall data were taken from the 3.3. Vegetation change CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall product (Funk et al., 2015). Both annual rainfall and NDVI The NDVI time-series between 2000 and 2016 from MODIS showed anomalies were calculated from Nov-Oct, to coincide with the hydro- distinct spatial patterns in pixels with significant vegetation greening logical year in the study region. and browning (Fig. 4a). Most significant browning occurred in un- protected areas where woodland was converted to bare ground. 3. Results Greening occurred in distinct patches throughout all land-use types, with significantly monotonic greening along most of the Siria escarp- 3.1. Land cover change ment slopes.

Land cover changed substantially on unprotected land in the Mara 4. Discussion between 1985 and 2016, with large and accelerating losses of woodland and grassland (Fig. 2c). Dramatically, 62% of the 1985 woodland on We quantified land-cover change in the Mara since 1985 and show unprotected areas was lost, while 56% of the 1985 grassland was lost that, over recent years, bare ground has expanded rapidly on un- (Fig. 2f). Woodland and grassland were replaced by land covers with protected land. This land degradation has fragmented the landscape, substantially more bare ground (bare ground and vegetation+bare with anticipated severe ecological consequences, e.g. for migrating mixed, Fig. 2c and f). Since 61% of land in the Mara is unprotected, this wildlife. Severe land degradation did not occur in fully and semi-pro- equates to an overall loss of 25% of high-quality habitat for wildlife and tected areas, although degradation increased slightly in semi-protected pastoralists. In contrast, within both fully- and semi-protected areas net areas. A more detailed examination of vegetation dynamics using NDVI land-cover change was negligible (Fig. 2a and b). suggests that vegetation in community-based conservation areas may Consistent with these patterns, the greatest proportional transitions be less resistant to drought than in the fully protected area. between land cover classes took place on unprotected land (Fig. 2f). Approximately half of the lost woodland was converted to vegetation 4.1. Land cover change +bare mixed, while a third was converted to grassland. Similarly, more than half of the lost grassland was converted to the vegetation+bare Our results show that protection status affects land-cover and ve- mixed class, while a third was converted to bare ground. In the fully getation dynamics. Land-cover change was most pronounced on un- protected area net transitions were negligible, with an even exchange protected land, where the area of bare ground tripled (271 %) at the between vegetation+bare mixed and grassland (Fig. 2d). A similar expense of grassland and woodland. In general, more severe land de- pattern emerged in semi-protected areas, but here the area transitioning gradation and habitat fragmentation on unprotected land is expected from grassland to vegetation+bare mixed was approximately 50% and well known from previous work (Said et al., 2016). However, un- greater than the reverse (Fig. 2e). protected land constitutes a major part of the Mara ecosystem, and we The loss of woodlands was concentrated in the Trans Mara District, show for the first time that bare-ground expansion in the Mara ac- in the western part of the study area (Fig. 3, Figs.S1 and S4). These celerated sharply since 2003. This suggests that anthropogenic

4 W. Li, et al. Global Environmental Change 60 (2020) 102030

Fig. 2. Land-cover change in the Maasai Mara shows accelerating land degradation in unprotected areas. (a–c) The area (km2) of each land-cover class for the 1985–2016 period. (d–f) Land cover transition, in percentages, between classes for the 1985–2016 period. pressures in unprotected parts of the Mara are accelerating, consistent Effects of pastoralism and increasing livestock densities on wildlife with extremely rapid population growth in the Mara over recent dec- are more difficult to quantify, as these effects are typically less severe, − ades (4.4% yr 1)(Lamprey and Reid, 2004), massive expansion of at least on short time scales, and vary in space and time. Nonetheless, small-holder agriculture in the Trans-Mara District (Golaz and high livestock densities can kill productive perennial grasses, which Médard, 2016) and the ongoing expansion of commercial mechanised reduces herbivore carrying capacity (both livestock and wild grazers) agriculture toward the north-east, which is fuelled by foreign invest- and may lead to shrub expansion and soil erosion (Vetter, 2005). Col- ments and ties to global markets (Serneels and Lambin, 2001a,b; lectively these outcomes have been termed “overgrazing”. Our analysis Serneels et al., 2001). We also note that the accelerated land de- provides two measures of potential overgrazing: bare-ground expansion gradation on unprotected land approximately coincides with the period in areas that were not converted to cropland, and the expansion of of conservancy establishment, which initiated a movement of people shrubs at the cost of grasses. We observed both in unprotected areas. and cattle away from the conservancies, possibly onto the unprotected About one-third of lost grassland was converted to bare ground (e.g. land. Such increasing pressure is illustrative of a broader trend across insets Fig. 3), while the other two-thirds was converted to vegetation savanna regions of Kenya (Ogutu et al., 2016) and Africa (Craigie et al., +bare mixed. The vegetation+bare mixed class includes evergreen 2010; Veldhuis et al., 2019). thickets dominated by Tarchonanthus camphoratus and Croton dicho- The overall consequences of these dynamics in the Mara are a large gamus, fast-growing and unpalatable shrubs that are typical of over- reduction of intact habitat and an increasing fragmentation of the re- grazed land (Coetzee et al., 2008), which may be exacerbated by ele- maining areas. Cropland is fundamentally incompatible with high vated atmospheric CO2, as reported across African savannas densities and diversity of megafauna because of habitat destruction, but (Stevens et al., 2016). also because humans actively discourage herbivores from damaging Consistent with our finding of increased livestock grazing pressure crops, e.g. by fencing fields (Løvschal et al., 2017). In the Mara, the in unprotected parts of the Mara, the density of livestock has risen expansion of agriculture across the Loita plains (the north-eastern part drastically over recent decades, primarily because of growing sheep and of our study area) has resulted in a 75% reduction in the wildebeest goat herds (Ogutu et al., 2011). Consequently, the rate at which local population between the late 1970s and 1990s (Serneels and Maasai fence rangeland to secure dry-season grazing has accelerated Lambin, 2001a). This population uses the Loita plains as wet-season (Løvschal et al., 2017). These fences exclude and obstruct both wildlife calving grounds, before migrating to the Mara National Reserve during and other pastoralists, but are also bound to increase livestock grazing the dry season, where they mingle with the Serengeti wildebeest. pressure on unfenced land during the wet and productive months. This Ecological effects of accelerating cropland expansion since the late may in turn lead to further fencing, resulting in a positive feedback 1990s have not been explicitly tested, but continuous declines of most between increased fencing and overgrazing. The increased competition megafauna species in the region strongly suggest ongoing negative ef- for grazing resources between pastoralists has several complex social- fects that extend beyond the Loita wildebeest migration (Ogutu et al., ecological consequences. For example, it increasingly forces local 2016). Maasai to diversify their livelihood strategies, including crop-based

5 W. Li, et al. Global Environmental Change 60 (2020) 102030

Fig. 3. Land cover classifications for the Maasai Mara show an expansion of bare ground and mixed vegetation-bare ground over time. Panels show land cover in different years: (a) 1985; (b) 2003; (c) 2010 and (d) 2016. Classifications were based on Landsat imagery with a spatial resolution of 30 m.

− Fig. 4. Temporal vegetation dynamics based on NDVI in the Maasai Mara. (a) Map of NDVI change rate yr 1 between 2000 and 2016. NDVI change rates were estimated using the Theil-Sen estimator, a robust estimator of linear change. (b) Resistance (i.e. the resistance component of resilience sensu (Walker et al., 2004)) of vegetation functioning to drought stress under the three land management types. The lines are 3-parameter asymptotic nonlinear least-squares regressions. The dotted vertical lines indicate the y-intercept and represent the precipitation anomaly down to which vegetation functioning (NDVI anomaly) is maintained. Long- term mean annual rainfall (1999–2016) was 829 mm for unprotected land, 852 mm for semi-protected land and 997 mm for protected land.

6 W. Li, et al. Global Environmental Change 60 (2020) 102030 subsistence agriculture (Homewood et al., 2009; Waithaka, 2004). Also, 4.3. Ecological resilience the increased demand for charcoal and fence-posts is partly satisfied by commercial-scale wood harvesting in the Trans Mara District and Nai- Vegetation greenness was more sensitive to drought conditions in kara where we observed large reductions in woodland. unprotected areas, and less sensitive in the protected area (Fig. 4b). This suggests a loss of vegetation functioning in unprotected areas, potentially due to high livestock grazing pressure. The semi-protected 4.2. Land-use impacts on land-surface greenness conservancies had intermediate resilience, consistent with their status as intermediary between fully and unprotected areas. As droughts have To supplement our land cover change analysis, we performed an become more frequent and intense in East Africa including Mara over additional analysis using NDVI, a measure of vegetation “greenness” recent decades and climate models project further shifts in the rainfall that tends to be strongly related to standing green biomass, in order to regime (Bartzke et al., 2018; Nicholson, 2017), protected areas may characterise more subtle vegetation change. Throughout the second thus become increasingly important to provide buffering against im- half of the study period (2000–2016), vegetation greenness was higher pacts of drought for wildlife and, in the conservancies, livestock. on fully protected land than on semi-protected and unprotected land. In addition to higher rainfall, this can be explained by the absence of li- 4.4. Spatial patterns of land-surface greening vestock and hence a lower grazing pressure on fully protected land, at least between October and June when the migratory wildebeest and Throughout the study area, i.e. regardless of protection status, pixels zebra are further south, in the Serengeti (Hopcraft et al., 2015). with greening or browning tended to form patches. This spatial ag- This pattern may be further amplified in upcoming years, as illegal gregation suggests that local drivers such as land management modify night-time livestock grazing in the protected area (Ogutu et al., 2009; regional patterns (Veldhuis et al., 2019). The browning patches, con- Veldhuis et al., 2019)(Fig 3) has been reduced by recent law enforce- centrated in the western part (Trans Mara District), are caused by non- ment efforts (MM, pers. obs.). Anecdotal evidence from local con- random woodland loss (Figs. 4a and 3). A second striking pattern is servation managers suggest that grass biomass has consequently accu- significant greening along the entire length of the Siria escarpment mulated, which in turn has induced native herbivores to move onto (Fig 4a). This greening might be linked to defaunation and global neighbouring semi-protected land. This is consistent with studies change, as large browsers like giraffe and black rhinoceros have de- showing that before conservancies were established, small and medium clined strongly in the area: -76% since the late 1970s (Ogutu et al., sized herbivores preferred the shorter, more open grasslands of com- 2011) and −95% between 1960s and 1980s (Metzger et al., 2007) munal grazing land over the tall grasslands of the fully protected na- respectively. Increased avoidance by elephants in response to conflict tional reserve in the wet season (Bhola et al., 2012). This pattern has with rapidly expanding crop-farming populations on the highland may been attributed to reduced predation risk (i.e. better visibility), the be another possible explanation (Mukeka et al., 2019). Finally, this higher nutrient concentrations in short, repeatedly grazed grass greening is consistent with (partly) CO2-driven woody expansion across (McNaughton, 1985; Olff et al., 2002), and maximising the intake rate African savannas (Stevens et al., 2016). of digestible energy (Ogutu et al., 2010). Populations of all native grazers in Narok county and the Mara have declined by 40–90% since 4.5. Implications for social-ecological sustainability 1977 (Ogutu et al., 2011; Ogutu et al., 2016) and it is likely that these declines have reduce wildlife grazing pressure in the national reserve to In summary, we show accelerating degradation and fragmentation such an extent that remaining resident wildlife grazers cannot maintain on unprotected land, which represents 61% of the Greater Maasai Mara the grass in a palatable state. More stringent enforcement of the laws Ecosystem. As a consequence, the extent and quality of woodland and prohibiting livestock grazing, as has been attempted in recent years, grassland is declining sharply, threatening the unique Maasai pastor- may have exacerbated this process. alist culture and the largest remaining large-herbivore migration. These If the above processes hold and grass biomass increases, two eco- findings are consistent with reports of sharply declining wildlife po- logically plausible scenarios may unfold. First, fire may replace herbi- pulations (Ogutu et al., 2016). Remaining wildlife, migratory routes vores as the dominant disturbance in fully protected land, keeping the and savanna habitat on these unprotected lands are thus disappearing system open, but selecting for fire-adapted rather than grazing/ at an accelerating rate in the Mara, as they do in other parts of Africa browsing-adapted vegetation (Bond, 2008; Eby et al., 2015; (Craigie et al., 2010; IPBES, 2018). Sinclair et al., 2007). At the same time, high grass biomass would also In contrast, both fully and semi-protected areas effectively pre- allow more animals to stay in the Mara for longer during the great vented severe land degradation, suggesting that these forms of protec- migration, potentially buffering the impact of fire. Second, if fire does tion benefit taxa that depend on functioning savanna ecosystems not replace herbivores and herbivore numbers keep declining, large- (Geldmann et al., 2013; Gray et al., 2016). However, vegetation func- scale expansion of woody plants should be expected as the climate and tioning in the semi-protected conservancies appeared less resistant to soils can support much higher woody cover than is currently present drought than in the fully protected area, suggesting that community- ( Sankaran et al., 2005). This pattern has been seen in other defaunated based conservation is not a panacea despite its clear buffering effect on savannas (Daskin et al., 2016; Sinclair et al., 2007) and would nega- land degradation. Socio-economically, income from wildlife tourism is tively impact the number and residence time of animals during the a welcome addition to the livelihoods of households that receive these great migration in the Mara. The extent to which potential tree ex- benefits, but it also inflates inequality between and within households pansion could be inhibited by increasing elephant numbers in the Mara (Homewood et al., 2012; Keane et al., 2016; Thompson et al., 2009). remains an open question. This example illustrates the complexity of Importantly, the majority of profit from tourism-related services in the interactions between human land-use, social constructs such as law Mara continues to flow out of the Mara (Norton-Griffiths et al., 2008). enforcement, animal behaviour, animal metabolic demands, climate Sound conservation planning and implementation in the Mara is and fire in driving savanna vegetation dynamics. It identifies a strong urgent, as evidenced by the accelerating land degradation on un- need to improve predictive models on the sustainability of extracted protected land. This is challenging, as rapid human population expan- ecosystem services, in this case grass, which requires investments in sion across African savannas compounded by a future with increasing improving the ecological understanding of the Mara social-ecological pressures from livestock expansion, conversion to cropland driven by system. (foreign) commercial interests, globalisation of trade and climate in- stability is likely to increase strain on conservation efforts (Hoag and Svenning, 2017). Nonetheless, our study highlights the ability of

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