Accelerating savanna degradation threatens the Maasai Mara socio-ecological system

Global Environmental Change 60 (2020) 102030

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Accelerating savanna degradation threatens the Maasai Mara socioecological system ⁎

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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 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 Kenya Wildlife Trust, P.O. Box 86-005200, Nairobi, Karen, Kenya e Maasai Mara Wildlife Conservancies Association, P O Box 984, Narok 20500, Kenya b

A R T I C LE I N FO

A B S T R A C T

Keywords: Africa Ecosystem degradation Sustainability Social-ecological systems

Savanna megafauna have become scarce outside of protected areas in Africa, largely because of land conversion for farming (smallholders and agribusiness) and expansion of settlements and other infrastructure. Intensification also isolates protected areas, even affecting natural processes within reserve boundaries. Here, we used satellite imagery from the past 32 years in the iconic Maasai Mara ecosystem to assess the capacity of 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 conservation – 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 unprotected 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 The Greater Maasai Mara Ecosystem in Kenya (henceforth “the Mara”, Fig. 1) is an iconic African savanna ecosystem, a major tourist destination, and contains one of the richest assemblages of wild megafauna (> 45 kg) in the world (Malhi et al., 2016; Mduma and Hopcraft, 2008). However, during the past four decades the Mara has undergone severe ecological degradation, with plummeting large mammal populations (Ogutu et al., 2011; Ogutu et al., 2016) and the loss of seasonal wildlife migrations (Peters et al., 2008). Wildlife populations are declining due to poaching, disease, competition from livestock and habitat fragmentation as a result of fencing and changes in land tenure (Ogutu et al., 2011; Ogutu et al., 2016). Especially fencing and changes in land-use forebode irreversible damage to the Mara

social-ecological system through land degradation (“the persistent decline or loss in biodiversity and ecosystem functions and services that cannot fully recover unaided within decadal time scales”: (IPBES, 2018)). In the Mara, land degradation leads to habitat losses for resident and migratory wildlife, loss of migration corridors and the local extinction of semi-nomadic pastoralism (Reid, 2012). Savannas and drylands across the African continent face similar challenges (Gasparri et al., 2016). Two broad types of land degradation can be identified in the Mara. The most severe and abrupt is conversion of grassy savannas (rangeland) to cropland, which historically has been documented in the northeastern part of the Mara and is strongly driven by agribusiness linked to foreign investors and global markets (Homewood et al., 2001; Serneels and Lambin, 2001b). A second cause of land degradation may

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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 0959-3780/ © 2019 Elsevier Ltd. All rights reserved.

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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.

time and intensity (Thompson et al., 2009). The land-sharing between wildlife, human pastoralists and ecotourism in the conservancies can be seen as an intermediate between the set-aside conservation of the national reserve and the unprotected land that surrounds the conservancies. Several of the land-tenure contracts in the community-based conservation areas of the Mara have recently been extended, suggesting that this conservation model has been beneficial to both parties. However, many contracts are still to be renegotiated in the near future, highlighting the need for up-to-date and high-resolution spatiotemporal information on land cover and habitat quality trajectories under the three contrasting land management strategies, thus allowing evidencebased decision making and conservation planning. Conservation planning and charting the future of the Mara socialecological system is a complex task that is complicated further by ongoing global change, which is likely to affect both social and ecological resilience (Hoag and Svenning, 2017). East Africa is predicted to become substantially hotter throughout the 21st century (1–4 °C, RCP 2.6–8.5), which is likely to reduce grass productivity; especially since rainfall is not expected to keep pace (IPCC, 2013). In addition to total annual rainfall, the complex intra-annual rainfall variability and modality in this region affects vegetation dynamics (Deshmukh, 1984), but these effects are not fully understood and intra-annual rainfall patterns are difficult to forecast (Nicholson, 2017). Finally, grass resources for wildlife and pastoralists could be threatened by the widespread expansion of woody plants in African grassy ecosystems, with evidence suggesting increases in atmospheric CO2 concentrations as a driver (Bond and Midgley, 2012; Buitenwerf et al., 2012; Stevens et al., 2016),

be livestock herds, which have rapidly expanded over recent decades (Løvschal et al., 2018; Lamprey and Reid, 2004; Ogutu et al., 2011). At high densities, livestock directly competes for forage with wild grazers, especially in dry years, when resources are scarce (Vetter, 2005). Furthermore, socio-economic processes such as inter-human conflict over pasture have resulted in increased fencing (Løvschal et al., 2017), which is detrimental to wildlife as it excludes them from habitat and potentially obstructs migration routes. At more localised scales wildlifebased tourism may contribute to land degradation through the development of new infrastructure and widespread off-road driving. Together, these land-use changes threaten both wildlife and pastoralism and thus their sustainable coexistence (Løvschal et al., 2017). Nevertheless, recent and up-to-date assessments of land cover change and biodiversity impacts are largely missing. While the Serengeti-Mara ecosystem in Tanzania and Kenya has been studied intensively since the 1970s, most studies have focussed on the larger Serengeti (Sinclair et al., 2015; Sinclair et al., 2008; Veldhuis et al., 2019). However, the Mara has a unique and highly dynamic socialecological setting (Homewood et al., 2009; Homewood et al., 2001; Løvschal et al., 2017; Løvschal et al., 2018; Lamprey and Reid, 2004). Bordering the Maasai Mara National Reserve, where livestock grazing and other human use other than tourism is prohibited, communitybased conservation areas (locally referred to as conservancies) have been established since 2005. These conservancies are based on a partnership between Maasai landowners and tourism operators. Landowners are paid a per-area fee for leasing out their land to tourism operators, on the condition that livestock grazing is regulated in space,

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(pers.obs.). To aid visual interpretation of pure pixel patches, several falsecolour images were composited from the Landsat bands to help with visual interpretation to select pixel patches. For example, we used the composited image from band 7,5,3 to help with the selection of the bare ground patches, and composited images from band 4,3,2 and 7,4,2 to select the samples for grassland and vegetation+bare mixed as well as woodland. In addition to the brightness of the spectral bands, brightness of NDVI image was also used to help with the visual interpretation, but not put into the classification model. The exact procedure to identify each land cover type varied. For example, woodland can be recognised by colour and brightness, whereas croplands were identified using a combination of brightness and geometry. Grassland was identified according to its homogeneous visual texture and colour in the false colour composited images. After assembling the set of known pure pixels for each land cover class, the data were divided into two groups, about 60% of which were used for training (900 points in total), and the remaining 40% for validation (600 points in total) for each single-date image. The number of training/validation points for different land cover types per classification is different, i.e., relative to their abundance in the entire study site via visual estimation. After trial testing, the widely used maximumlikelihood classifier was employed (Foody et al., 1992) and implemented in ArcGIS 10.6 (Environmental Systems Research Institute, ESRI). We selected the maximum likelihood method as a classic, effective classification method for similar remote sensing problems (Burai et al., 2015; Mwangi et al., 2018; Yang et al., 2009). Overall accuracy and Kappa coefficients were calculated for 600 independently and randomly selected pixels (Table. S2). Finally, a per-pixel land cover transition matrix was calculated to quantify transitions between the six main land cover types between 1985 and 2016 using the software Dinamica EGO (Soares-Filho et al., 2009).

a dynamic that is predicted to strengthen in the coming decades (Higgins and Scheiter, 2012). Global change may thus put wildlife populations, especially grazers, under further pressure. As the approximately 1.3 million wildebeest affect nearly every aspect of ecosystem functioning in the Serengeti-Mara (Hopcraft et al., 2015), collapse of this population will thus have far-reaching ramifications for predator populations, fire regimes and local economies, both through income from tourism and pastoralism. Global change may thus force pastoralists to develop new adaptation, risk management and coping strategies (Bedelian and Ogutu, 2017). In summary, it is clear that the Mara is under increasing pressure, but it remains unclear how degradation dynamics are unfolding, how degradation affects longer-term resilience of the social-ecological system, and whether the community-based conservancies deliver intended ecological and socio-economic outcomes. To address these questions we use satellite imagery from the past 32 years (1985–2016) to quantify the rate of land degradation on protected, semi-protected and unprotected land and the capacity of each land management type to maintain ecosystem functioning under drought stress. We ask the following specific questions: 1) Has land-degradation accelerated, decelerated or remained constant. 2) To what extent has land degradation fragmented natural and pastoral lands? 3) Do the semi-protected conservancies buffer against land degradation? 4) Does ecological resistance to environmental stress (vegetation functioning during drought) increase with level of protection? 2. Materials and methods 2.1. Study site The Greater Maasai Mara ecosystem is located in southwestern Kenya, with an area of over 660,000 ha (Fig.1a and b). It is mainly divided into three components according to the land use management type, namely, the fully protected Maasai Mara National Reserve, the semi-protected conservancies, and the unprotected land (Fig.1c). The national reserve is managed by the Narok County Government within this area (Løvschal et al., 2017). The semi-protected conservancies and the unprotected pastoral land mainly comprises contrasting land management areas, including wildlife conservancies, conservation areas and settlement areas (Løvschal et al., 2017).

2.3. Fragmentation analysis Fragmentation analysis was conducted based on the four single-date land cover classification dataset for the three land use types separately using the software FRAGSTATS (McGarigal and Marks, 1994). Before analysis, we re-classified the whole study site into two classes – habitat and non-habitat. The habitat class includes high-value conservation and pastoralist land covers (grassland + woodland). Grasslands are valuable to wild and domestic grazers. Woodland supports browsers, mixed feeders and arboreal species and allows harvesting of wood and medicinal plants. Woodlands are also essential for grazers, especially in the dry season because tree leaves protect grass leaves from direct solar radiation, allowing grasses to retain green leaves with higher nutritional quality later into the dry season than grasses on open grasslands. The remaining land covers (vegetation+bare mixed, bare ground and water) were classified as non-habitat areas. Two fragmentation metrics were calculated at class level – patch density (patches/100 ha) and mean patch size (ha), with an eight-neighbourhood criterion for the definition of patches. Usually, higher patch density and smaller mean patch size indicate more fragmentation.

2.2. Land cover change classification Four single-date Landsat were selected to classify the detailed land cover types in 1985, 2003, 2010 and 2016. All images were acquired in the dry season (January and February), when spectral differences between land cover types are greatest and cloud contamination is minimal. To assure that all pixels were exactly aligned, we conducted image-to-image co-registration before the classification in the data preprocessing using the ENVI software (Exelis Visual Information Solutions, Boulder, Colorado). The co-registration had a root mean square error of less than half a pixel (15 m). To generate a training data set for our supervised classification, we identified pure pixel patches of each land cover type by visually interpreting 1) Landsat images, 2) high-resolution imagery from Google Earth and 3) 844 ground-based photographs. Field photographs (N = 844) with precise geographic coordinates had been taken during Jan-Mar in 2015. Based on our visual interpretation of field photographs, texture of pixel patches and brightness of the composited Landsat imagery from different bands as well as the NDVI values, training pixels were identified for five main land cover types: woodland, grassland, vegetation+bare mixed, bare ground and water (Fig.1 d–h, Table S1). Cropland was included with the bare ground class as the Landsat spectral properties of cropland and bare soil in Mara are very similar in the dry season. Cropland was limited in the study area and restricted to the Trans Mara to the northwest and the northeast Mara

2.4. Temporal trend analysis To quantify vegetation change over time, we used time series of annual NDVI means (NDVI ) between 2000 and 2016. This analysis was based on MODIS collection (MOD13Q1, 250 m, 16-day intervals) and the temporal trend analysis was performed using the Mann-Kendall (MK) test of monotonic change (Mann, 1945). NDVI was calculated as the mean value of all the 16-day MODIS data in each year. The MK test was used to test the presence of monotonic trend in the time seriesNDVI . It is a rank-based non-parametric test and a useful alternative to linear squares regression with low requirements on assumptions (Eddy et al., 2017). The MK test is easy to calculate, robust against 3

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woodlands were converted to grassland and bare ground, in this case mostly cropland (clearly visible in Trans Mara and northeast Mara, see Fig. S1). Other hotspots of bare-ground expansion were along the northeastern border of the study area, where bare ground replaced grassland, again at least partly through cropland expansion (Fig.S1). In the northern Talek area (Fig. 3), loss of natural vegetation is coupled to village expansion. Overall accuracy and kappa coefficients for the four single-date classifications (1985, 2003, 2010, 2016) were high: 83.8–86.5% and 80.8–83.6% respectively (Table S2). The expansion of bare ground resulted in habitat fragmentation within the unprotected area, where patches of high-quality vegetation (grassland + woodland) became smaller, resulting in a higher patch density (i.e. fragmentation, Fig. S5). Both the fully and semi-protected areas also showed a decrease in the patch size of grassland + woodland, but to a smaller degree (Fig. S5).

non-normality as well as insensitive to missing values (de Jong et al., 2011). In the MK test, each NDVI point will be treated as the reference for the data points in successive time periods after ranking all the NDVI data with reference to time (Neeti and Eastman, 2011). Since the MK test allows for the strength and direction of a trend, but not for the magnitude, the non-parametric Sen's slope estimator (Q) was calculated to determine the slope of NDVI trend which is quantified as the percentage of change in NDVI from the median value within the time series dataset (Sen, 1968). The Sen's slope is considered to be a good approximation to evaluate the net change throughout the studied time range, which can be positive or negative (Martínez and Gilabert, 2009). In this study, the Sen's slope for time series NDVI was used to represent vegetation greening rate (hereafter, GR, yr−1, negative values represent browning rate). In order to cross validate the MODIS greening rate, the MK test was also applied to the annual composited Landsat imagery for the period between 1985 and 2016, and the period between 2000 and 2016. Since we found high consistence between the two satellite dataset (Fig. S3), we only report the MODIS results, considering its higher and homogeneous data availability than Landsat.

3.2. Ecological resilience NDVI was strongly related to land management type throughout the 2000–2016 period, with the highest NDVI in the fully-protected area, intermediate NDVI in the semi-protected area and the lowest NDVI on unprotected land (Fig. S2). However, such an analysis does not account for differences in annual rainfall between the land management types. The nonlinear least-squares regression results suggest that the fully protected area maintains vegetation functioning (i.e. does not drop into negative NDVI anomalies) until rainfall drops 8.5% below the longterm average for this land management type (Fig. 4b). In contrast, the unprotected area only maintains vegetation functioning when rainfall is at or above the long-term average for this land management type. The semi-protected area has intermediate resistance of vegetation functioning to drought stress and maintains vegetation functioning until rainfall drops 3.5% below its long-term average.

2.5. Ecological resilience The capacity of each land management type to maintain green vegetation under drought stress was quantified by analysing how vegetation greenness (NDVI) responds to rainfall anomalies. This capacity can be interpreted as the resistance component of ecological resilience (Walker et al., 2004). We fit an asymptotic function using nonlinear least-squares regression to the relationship between standardised NDVI anomalies and standardised rainfall anomalies for each land management type between 2000 and 2016. We calculated annual rainfall and NDVI anomalies from the 17-year mean for each of the three land management types. We expressed anomalies as the proportional deviation from the 17-year mean. Rainfall data were taken from the CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) rainfall product (Funk et al., 2015). Both annual rainfall and NDVI anomalies were calculated from Nov-Oct, to coincide with the hydrological year in the study region.

3.3. Vegetation change The NDVI time-series between 2000 and 2016 from MODIS showed distinct spatial patterns in pixels with significant vegetation greening and browning (Fig. 4a). Most significant browning occurred in unprotected areas where woodland was converted to bare ground. Greening occurred in distinct patches throughout all land-use types, with significantly monotonic greening along most of the Siria escarpment slopes.

3. Results 3.1. Land cover change

4. Discussion

Land cover changed substantially on unprotected land in the Mara between 1985 and 2016, with large and accelerating losses of woodland and grassland (Fig. 2c). Dramatically, 62% of the 1985 woodland on unprotected areas was lost, while 56% of the 1985 grassland was lost (Fig. 2f). Woodland and grassland were replaced by land covers with substantially more bare ground (bare ground and vegetation+bare mixed, Fig. 2c and f). Since 61% of land in the Mara is unprotected, this equates to an overall loss of 25% of high-quality habitat for wildlife and pastoralists. In contrast, within both fully- and semi-protected areas net land-cover change was negligible (Fig. 2a and b). Consistent with these patterns, the greatest proportional transitions between land cover classes took place on unprotected land (Fig. 2f). Approximately half of the lost woodland was converted to vegetation +bare mixed, while a third was converted to grassland. Similarly, more than half of the lost grassland was converted to the vegetation+bare mixed class, while a third was converted to bare ground. In the fully protected area net transitions were negligible, with an even exchange between vegetation+bare mixed and grassland (Fig. 2d). A similar pattern emerged in semi-protected areas, but here the area transitioning from grassland to vegetation+bare mixed was approximately 50% greater than the reverse (Fig. 2e). The loss of woodlands was concentrated in the Trans Mara District, in the western part of the study area (Fig. 3, Figs.S1 and S4). These

We quantified land-cover change in the Mara since 1985 and show that, over recent years, bare ground has expanded rapidly on unprotected land. This land degradation has fragmented the landscape, with anticipated severe ecological consequences, e.g. for migrating wildlife. Severe land degradation did not occur in fully and semi-protected areas, although degradation increased slightly in semi-protected areas. A more detailed examination of vegetation dynamics using NDVI suggests that vegetation in community-based conservation areas may be less resistant to drought than in the fully protected area. 4.1. Land cover change Our results show that protection status affects land-cover and vegetation dynamics. Land-cover change was most pronounced on unprotected land, where the area of bare ground tripled (271 %) at the expense of grassland and woodland. In general, more severe land degradation and habitat fragmentation on unprotected land is expected and well known from previous work (Said et al., 2016). However, unprotected land constitutes a major part of the Mara ecosystem, and we show for the first time that bare-ground expansion in the Mara accelerated sharply since 2003. This suggests that anthropogenic 4

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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.

Effects of pastoralism and increasing livestock densities on wildlife are more difficult to quantify, as these effects are typically less severe, at least on short time scales, and vary in space and time. Nonetheless, high livestock densities can kill productive perennial grasses, which reduces herbivore carrying capacity (both livestock and wild grazers) and may lead to shrub expansion and soil erosion (Vetter, 2005). Collectively these outcomes have been termed “overgrazing”. Our analysis provides two measures of potential overgrazing: bare-ground expansion in areas that were not converted to cropland, and the expansion of shrubs at the cost of grasses. We observed both in unprotected areas. About one-third of lost grassland was converted to bare ground (e.g. insets Fig. 3), while the other two-thirds was converted to vegetation +bare mixed. The vegetation+bare mixed class includes evergreen thickets dominated by Tarchonanthus camphoratus and Croton dichogamus, fast-growing and unpalatable shrubs that are typical of overgrazed land (Coetzee et al., 2008), which may be exacerbated by elevated atmospheric CO2, as reported across African savannas (Stevens et al., 2016). Consistent with our finding of increased livestock grazing pressure in unprotected parts of the Mara, the density of livestock has risen drastically over recent decades, primarily because of growing sheep and goat herds (Ogutu et al., 2011). Consequently, the rate at which local Maasai fence rangeland to secure dry-season grazing has accelerated (Løvschal et al., 2017). These fences exclude and obstruct both wildlife and other pastoralists, but are also bound to increase livestock grazing pressure on unfenced land during the wet and productive months. This may in turn lead to further fencing, resulting in a positive feedback between increased fencing and overgrazing. The increased competition for grazing resources between pastoralists has several complex socialecological consequences. For example, it increasingly forces local Maasai to diversify their livelihood strategies, including crop-based

pressures in unprotected parts of the Mara are accelerating, consistent with extremely rapid population growth in the Mara over recent decades (4.4% yr−1) (Lamprey and Reid, 2004), massive expansion of small-holder agriculture in the Trans-Mara District (Golaz and Médard, 2016) and the ongoing expansion of commercial mechanised agriculture toward the north-east, which is fuelled by foreign investments and ties to global markets (Serneels and Lambin, 2001a, b; Serneels et al., 2001). We also note that the accelerated land degradation on unprotected land approximately coincides with the period of conservancy establishment, which initiated a movement of people and cattle away from the conservancies, possibly onto the unprotected land. Such increasing pressure is illustrative of a broader trend across savanna regions of Kenya (Ogutu et al., 2016) and Africa (Craigie et al., 2010; Veldhuis et al., 2019). The overall consequences of these dynamics in the Mara are a large reduction of intact habitat and an increasing fragmentation of the remaining areas. Cropland is fundamentally incompatible with high densities and diversity of megafauna because of habitat destruction, but also because humans actively discourage herbivores from damaging crops, e.g. by fencing fields (Løvschal et al., 2017). In the Mara, the expansion of agriculture across the Loita plains (the north-eastern part of our study area) has resulted in a 75% reduction in the wildebeest population between the late 1970s and 1990s (Serneels and Lambin, 2001a). This population uses the Loita plains as wet-season calving grounds, before migrating to the Mara National Reserve during the dry season, where they mingle with the Serengeti wildebeest. Ecological effects of accelerating cropland expansion since the late 1990s have not been explicitly tested, but continuous declines of most megafauna species in the region strongly suggest ongoing negative effects that extend beyond the Loita wildebeest migration (Ogutu et al., 2016). 5

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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. Longterm mean annual rainfall (1999–2016) was 829 mm for unprotected land, 852 mm for semi-protected land and 997 mm for protected land. 6

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4.3. Ecological resilience

subsistence agriculture (Homewood et al., 2009; Waithaka, 2004). Also, the increased demand for charcoal and fence-posts is partly satisfied by commercial-scale wood harvesting in the Trans Mara District and Naikara where we observed large reductions in woodland.

Vegetation greenness was more sensitive to drought conditions in 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 conservancies had intermediate resilience, consistent with their status as intermediary between fully and unprotected areas. As droughts have become more frequent and intense in East Africa including Mara over recent decades and climate models project further shifts in the rainfall regime (Bartzke et al., 2018; Nicholson, 2017), protected areas may thus become increasingly important to provide buffering against impacts of drought for wildlife and, in the conservancies, livestock.

4.2. Land-use impacts on land-surface greenness To supplement our land cover change analysis, we performed an additional analysis using NDVI, a measure of vegetation “greenness” that tends to be strongly related to standing green biomass, in order to characterise more subtle vegetation change. Throughout the second half of the study period (2000–2016), vegetation greenness was higher on fully protected land than on semi-protected and unprotected land. In addition to higher rainfall, this can be explained by the absence of livestock and hence a lower grazing pressure on fully protected land, at least between October and June when the migratory wildebeest and zebra are further south, in the Serengeti (Hopcraft et al., 2015). This pattern may be further amplified in upcoming years, as illegal night-time livestock grazing in the protected area (Ogutu et al., 2009; Veldhuis et al., 2019)(Fig 3) has been reduced by recent law enforcement efforts (MM, pers. obs.). Anecdotal evidence from local conservation managers suggest that grass biomass has consequently accumulated, which in turn has induced native herbivores to move onto neighbouring semi-protected land. This is consistent with studies showing that before conservancies were established, small and medium sized herbivores preferred the shorter, more open grasslands of communal grazing land over the tall grasslands of the fully protected national reserve in the wet season (Bhola et al., 2012). This pattern has been attributed to reduced predation risk (i.e. better visibility), the higher nutrient concentrations in short, repeatedly grazed grass (McNaughton, 1985; Olff et al., 2002), and maximising the intake rate of digestible energy (Ogutu et al., 2010). Populations of all native grazers in Narok county and the Mara have declined by 40–90% since 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 such an extent that remaining resident wildlife grazers cannot maintain the grass in a palatable state. More stringent enforcement of the laws prohibiting livestock grazing, as has been attempted in recent years, may have exacerbated this process. If the above processes hold and grass biomass increases, two ecologically plausible scenarios may unfold. First, fire may replace herbivores as the dominant disturbance in fully protected land, keeping the system open, but selecting for fire-adapted rather than grazing/ browsing-adapted vegetation (Bond, 2008; Eby et al., 2015; Sinclair et al., 2007). At the same time, high grass biomass would also allow more animals to stay in the Mara for longer during the great migration, potentially buffering the impact of fire. Second, if fire does not replace herbivores and herbivore numbers keep declining, largescale expansion of woody plants should be expected as the climate and soils can support much higher woody cover than is currently present (Sankaran et al., 2005). This pattern has been seen in other defaunated savannas (Daskin et al., 2016; Sinclair et al., 2007) and would negatively impact the number and residence time of animals during the great migration in the Mara. The extent to which potential tree expansion could be inhibited by increasing elephant numbers in the Mara remains an open question. This example illustrates the complexity of interactions between human land-use, social constructs such as law enforcement, animal behaviour, animal metabolic demands, climate and fire in driving savanna vegetation dynamics. It identifies a strong need to improve predictive models on the sustainability of extracted ecosystem services, in this case grass, which requires investments in improving the ecological understanding of the Mara social-ecological system.

4.4. Spatial patterns of land-surface greening Throughout the study area, i.e. regardless of protection status, pixels with greening or browning tended to form patches. This spatial aggregation suggests that local drivers such as land management modify regional patterns (Veldhuis et al., 2019). The browning patches, concentrated in the western part (Trans Mara District), are caused by nonrandom woodland loss (Figs. 4a and 3). A second striking pattern is significant greening along the entire length of the Siria escarpment (Fig 4a). This greening might be linked to defaunation and global change, as large browsers like giraffe and black rhinoceros have declined strongly in the area: -76% since the late 1970s (Ogutu et al., 2011) and −95% between 1960s and 1980s (Metzger et al., 2007) respectively. Increased avoidance by elephants in response to conflict with rapidly expanding crop-farming populations on the highland may be another possible explanation (Mukeka et al., 2019). Finally, this greening is consistent with (partly) CO2-driven woody expansion across African savannas (Stevens et al., 2016). 4.5. Implications for social-ecological sustainability In summary, we show accelerating degradation and fragmentation on unprotected land, which represents 61% of the Greater Maasai Mara Ecosystem. As a consequence, the extent and quality of woodland and grassland is declining sharply, threatening the unique Maasai pastoralist culture and the largest remaining large-herbivore migration. These findings are consistent with reports of sharply declining wildlife populations (Ogutu et al., 2016). Remaining wildlife, migratory routes and savanna habitat on these unprotected lands are thus disappearing at an accelerating rate in the Mara, as they do in other parts of Africa (Craigie et al., 2010; IPBES, 2018). In contrast, both fully and semi-protected areas effectively prevented severe land degradation, suggesting that these forms of protection benefit taxa that depend on functioning savanna ecosystems (Geldmann et al., 2013; Gray et al., 2016). However, vegetation functioning in the semi-protected conservancies appeared less resistant to drought than in the fully protected area, suggesting that communitybased conservation is not a panacea despite its clear buffering effect on land degradation. Socio-economically, income from wildlife tourism is a welcome addition to the livelihoods of households that receive these benefits, but it also inflates inequality between and within households (Homewood et al., 2012; Keane et al., 2016; Thompson et al., 2009). Importantly, the majority of profit from tourism-related services in the Mara continues to flow out of the Mara (Norton-Griffiths et al., 2008). Sound conservation planning and implementation in the Mara is urgent, as evidenced by the accelerating land degradation on unprotected land. This is challenging, as rapid human population expansion across African savannas compounded by a future with increasing pressures from livestock expansion, conversion to cropland driven by (foreign) commercial interests, globalisation of trade and climate instability is likely to increase strain on conservation efforts (Hoag and Svenning, 2017). Nonetheless, our study highlights the ability of 7

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community-based conservancies to reduce severe land degradation, suggesting that this form of payment for ecosystem services has potential. However, in order to maintain wildlife populations, this investment will need to be extended to safeguarding important seasonal grazing areas outside protected parts of the Mara and the migration routes that lead there. In a world where natural resources are aspired to be exploited sustainably, this would highlight the need for further regulation of the use and revenue from ecosystem services such as grazing resources and wildlife-based tourism.

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Code availability Two codes are used in the text: one to pre-process the time series Landsat and MODIS imagery in Google Earth Engine, and one to perform the temporal trend analysis in Matlab. Code is available directly from the authors upon request. CRediT authorship contribution statement Wang Li: Conceptualization, Formal analysis, Writing - original draft, Writing - review & editing. Robert Buitenwerf: Conceptualization, Formal analysis, Writing - original draft, Writing review & editing. Michael Munk: Visualization, Validation. Irene Amoke: Validation. Peder Klith Bøcher: Resources, Validation. JensChristian Svenning: Conceptualization, Resources, Writing - review & editing, Project administration, Funding acquisition. Data availability The time series satellite data used in this study are open-access in Google Earth Engine (https://earthengine.google.com/datasets/). The land cover and use classification products are available from the corresponding author upon reasonable request. Declaration of Competing Interest The authors declare no competing financial interests. Acknowledgements This work was supported by the European Research Council (ERC2012-StG-310886-HISTFUNC, to J.C.S), Carlsberg Foundation (Semper Ardens project MegaPast2Future, grant CF16-000, to J.C.S), VILLUM FONDEN (VILLUM Investigator project, grant 16549, to J.C.S). China Natural Science Foundation (grant 41730107, 41701392, to W.L), the Youth Innovation Promotion Association Chinese Academy of Sciences (grant 2018084 to W.L). Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.gloenvcha.2019.102030. References Bartzke, G.S., Ogutu, J.O., Mukhopadhyay, S., Mtui, D., Dublin, H.T., Piepho, H.-P., 2018. Rainfall trends and variation in the Maasai Mara ecosystem and their implications for animal population and biodiversity dynamics. PLoS One 13, e0202814. Bedelian, C., Ogutu, J.O., 2017. Trade-offs for climate-resilient pastoral livelihoods in wildlife conservancies in the Mara ecosystem, Kenya. Pastoralism 7, 10. Bhola, N., Ogutu, J.O., Said, M.Y., Piepho, H.P., Olff, H., 2012. The distribution of large herbivore hotspots in relation to environmental and anthropogenic correlates in the Mara region of Kenya. J. Anim. Ecol. 81, 1268–1287. Bond, W.J., 2008. What limits trees in C4 grasslands and savannas? Annu. Rev. Ecol. Evol. Syst. 39, 641–659. Bond, W.J., Midgley, G.F., 2012. Carbon dioxide and the uneasy interactions of trees and savannah grasses. Philos. Trans. R. Soc. B 367, 601–612. Buitenwerf, R., Bond, W.J., Stevens, N., Trollope, W.S.W., 2012. Increased tree densities in South African savannas: >50 years of data suggests CO2 as a driver. Glob. Change

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