Articles https://doi.org/10.1038/s41893-018-0149-2

Consequences of integrating livestock and wildlife in an African savanna

Felicia Keesing 1*, Richard S. Ostfeld 2, Sharon Okanga3, Steven Huckett3, Brett R. Bayles 4, Rebecca Chaplin-Kramer 5, L. Page Fredericks3, Tyler Hedlund3, Virginia Kowal 5, Heather Tallis6, Charles M. Warui7, Spencer A. Wood5,8 and Brian F. Allan 3

Globally, most wildlife lives outside of protected areas, creating potential conflicts between the needs of wildlife and the needs of humans. East African savannas epitomize this challenge, providing habitat for wildlife such as giraffes and elephants as well as for people and their livestock. Conflicts over land use are common, leading to the assumption of a necessary trade-off between wildlife and livestock management. Here, we show that the integration of livestock and wildlife in a large region of central Kenya can have ecological benefits, reducing the abundance of ticks and improving forage. These ecological benefits can be complemented by economic ones when property owners derive income both from wildlife through tourism and from livestock through meat and dairy production. Our results suggest that under specific ecological, economic and social conditions, integrat- ing livestock with wildlife can provide benefits for the environment and for human well-being in African savannas.

n Kenya, as in most of East Africa, the abundance of large wild (Lycaon pictus). We took advantage of a natural experiment in the animals such as giraffes and zebras is declining1. Though Kenya Laikipia landscape, a socioecological system that currently supports has set aside 12% of its land for national parks and reserves2, a range of management approaches to livestock production, wildlife I 3 two-thirds of the country’s wildlife lives outside of them , making conservation and tourism. We investigated whether trade-offs indeed wildlife conservation on private lands a critical priority. Outside dominate land management outcomes in this system or if co-benefits of protected areas, the human population is growing and so is the of multi-use management occur. amount of land devoted to the agriculture and livestock production We conducted surveys of 23 properties (mean: 14,196 ha needed to support them4. ±​ 2,023 s.e.m.) in Laikipia County to determine several ecologi- Whether wildlife conservation and livestock production are cal consequences of management strategies that emphasize either compatible is a contentious issue. In East Africa, many argue that livestock production, wildlife tourism or a combination of the there is a necessary trade-off5–7 because, for example, livestock and two (Fig. 1a). We estimated the degree of cohabitation of lands wildlife compete for food and water and share parasites and patho- by wildlife and livestock using dung surveys to establish the nat- gens. On the other hand, livestock can facilitate rather than compete ural log of the ratio of wildlife to livestock on each property (see with wildlife under some conditions8–10. If strong trade-offs exist, Supplementary Information). management to promote both livestock production and wildlife Properties varied from almost entirely wildlife to almost entirely conservation is unlikely to be effective, reducing the potential for livestock, with about a third having approximately equal numbers of wildlife conservation outside of national parks and reserves. In livestock and wildlife (Fig. 1b). We sorted properties into three eco- contrast, if co-benefits occur, identifying their nature and mecha- logical classifications: livestock; wildlife; and integrated; the latter nisms might facilitate the development of effective integrated man- consisted of properties with similar densities of livestock and wild- agement11. Studies evaluating the effects of livestock and wildlife at life (Supplementary Information; Extended Data Fig. 1). For all of large spatial scales and incorporating real-world variation have been the properties, we analysed two key pathways by which the needs of identified as critical but rare12. livestock and wildlife are thought to conflict: competition for food Laikipia County in central Kenya exemplifies many of the chal- and parasitism by shared parasites. lenges of conserving wildlife on private lands (Fig. 1a). This semi-arid region hosts 10% of the country’s wildlife biomass, yet none of the Ecological consequences of integration: ticks. Ticks are abundant country’s national parks or preserves. Most people depend on livestock in the Laikipia ecosystem and can compromise animal health directly for income and almost 70% of the land is used for large-scale ranch- through parasitism and indirectly by serving as the vectors of numer- ing or pastoralism13. Wildlife tourism also generates considerable ous infectious agents15. As a result, livestock are routinely treated income and has grown rapidly in recent years (Fig. 1b)13,14. Growth in with acaricides—topical pesticides that kill ticks. Treated livestock tourism is partly due to the presence in the region of charismatic rare act as ecological traps by attracting but then killing ticks that attempt species such as white and black rhinoceroses (Ceratotherium simum, to feed on them16. The presence of treated cattle in the savanna Diceros bicornis), Grevy’s zebras (Equus grevyi) and African wild dogs habitat can reduce overall tick abundance in the environment11,16,

1Bard College, Annandale-on-Hudson, NY, USA. 2Cary Institute of Ecosystem Studies, Millbrook, NY, USA. 3Department of Entomology, University of Illinois at Urbana-Champaign, Urbana, IL, USA. 4Department of Health Sciences, Dominican University of California, San Rafael, CA, USA. 5Natural Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA, USA. 6The Nature Conservancy, Office of the Chief Scientist, Santa Cruz, CA, USA. 7Department of Biological Sciences, Murang’a University of Technology, Murang’a, Kenya. 8School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA. *e-mail: [email protected]

566 Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain NATurE SusTAinAbiliTy Articles

a b longitudinal gradients in mean annual precipitation across the study region (Supplementary Information; Extended Data Fig. 2A,B). Despite receiving significantly less precipitation on average than wildlife properties (Supplementary Information; Extended Data Fig. 2C), ecologically integrated properties did not significantly dif- Laikipia County fer from wildlife properties in total grass biomass (Fig. 2a). Thus, the similarity in total grass quantity between wildlife properties and ecologically integrated properties seems to be the result of the inclusion of livestock grazing in the latter rather than of rainfall pat- terns. However, grass is not all of the same quality for herbivores. Green grass in this ecosystem has twice the crude protein content of brown grass18. The biomass of green grass was negatively correlated c with annual rainfall across ecological classifications (Fig. 2b), and integrated properties had significantly more green grass than live- –1.5 ** ** stock properties (Fig. 2c). Based on these results, ecologically inte- grated properties had higher forage quality than either wildlife or livestock properties, despite receiving less precipitation on average. Ecological + 0.05) classification Competition between livestock and wildlife could also manifest

–2 –2.0 Livestock through an inverse correlation between livestock and wildlife densi- Integrated ties. Livestock properties had significantly higher mean total densi- Wildlife ties of large mammals (livestock +​ wildlife) than wildlife properties, Log(mean number –2.5 with integrated properties having intermediate densities of large of ticks per m mammals (Supplementary Information; Extended Data Fig. 4). This higher total density of grazers may explain the lower quantity –3.0 of grass on livestock properties. However, there was no significant 2014 2015 correlation between the abundance of livestock and the abundance of wildlife across the properties we surveyed (Fig. 3a); densities of Fig. 1 | Tick abundance in relation to management. a, Location of Laikipia large mammals were not significantly different between wildlife and County in Kenya, Africa inset. b, African bush elephants (Loxodonta integrated properties (Supplementary Information; Extended Data africana) on a wildlife-oriented property in Laikipia County. c, Density Fig. 4). In fact, wildlife reached its highest abundances on proper- −2 (ticks m +​ 0.05) in 2014 and 2015 on properties classified as ecologically ties with intermediate densities of livestock (Fig. 3a). Together, these livestock, integrated or wildlife (see main text). P values calculated by data suggest that wildlife can occur at moderate to high levels for Kruskal–Wallis tests; **P <​ 0.025. The black dots represent outliers. Map this region in the presence of moderate densities of livestock. (This of Africa created using mapchart.net; Laikipia map created using map data is based on stocking data provided by property managers during from Google (2018). Photo credit: F. Keesing. our surveys; the mean total density of all grazing livestock, across all of the property types, was 0.25 animals per ha, with a range of potentially to the benefit of human and wildlife health. To assess 0.07–0.43 animals per ha. Cattle densities by property classification ecological variables, including tick abundance, we established mul- are provided in the Supplementary Information.) tiple transects on each property, with the number of transects scaled to the size of the property, and conducted ecological surveys in July– Socio-economic consequences of integration. Wildlife-livestock August 2014 and 2015. Over 94% of the ticks we collected in both integration may not be economically attractive if ecologically inte- years were of a single species, Rhipicephalus pulchellus, an important grated properties are less profitable. To determine the economic vector of pathogens affecting both animals and humans17. As pre- consequences of integration, we surveyed ranch managers to deter- dicted based on prior research11,16, ticks were rare on livestock prop- mine the types and profitabilities of income streams, including those erties and abundant on wildlife properties (Fig. 1c). On ecologically related to livestock and wildlife, on their properties. We defined an integrated properties, tick abundance was 75% lower than on wild- additional classification of the properties—economic integration— life properties, probably due to acaricide treatment of livestock (see based on the relative proportion of income each property received Supplementary Information; Extended Data Fig. 3 for the effects of from activities related to livestock versus wildlife. Properties were grass biomass on tick abundance). considered economically integrated if they received similar levels of income from activities related to both wildlife and livestock. Almost Ecological consequences of integration: vegetation. The ben- all properties had sources of income other than just livestock and efits to wildlife arising from the suppressive effects of livestock on wildlife, but these two sources accounted for an average of over 70% ticks could be counterbalanced by the costs if livestock and wildlife of revenue. Economically integrated properties had a greater total compete for forage. There is considerable debate over the nature diversity of income streams compared to either livestock or wild- of interactions between livestock and wild herbivores for forage, life properties, as measured by the Shannon diversity index (Fig. 4). with evidence for both competition and facilitation (for example, Critically, economically integrated properties were not significantly see Butt and Turner12 and references therein). Based on our veg- less likely to have profitable livestock operations than livestock prop- etation surveys (see Supplementary Information), the total quan- erties were (Supplementary Information; Extended Data Table 3), tity of grass (g m−2) was equally high on integrated properties and suggesting that there is no necessary loss of livestock profitability wildlife properties, and both had significantly more grass than with moderate to high densities of wildlife, at least for the range livestock properties (Fig. 2a). This effect occurred in 2014, which of conditions evaluated in this study. Similarly, economically inte- had lower than average rainfall, and also in 2015, which had aver- grated properties were not significantly less likely to have a profit- age rainfall. (These large-scale results are in partial conflict with able or break-even tourism operation than wildlife properties were evidence from experimental plots in Laikipia, which suggested that (Supplementary Information; Extended Data Table 4), suggesting wildlife facilitated cattle foraging in the wet season but competed that tourists do not avoid visiting properties that harbour livestock with them in the dry season8.) There are strong latitudinal and as well as wildlife.

Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain 567 Articles NATurE SusTAinAbiliTy

a b 200 Ecological classification Livestock 150 Integrated Wildlife

100

200

) 50 ) –2 –2

0 500 700 900 1,100 Mean annual precipitation (mm) c 200 100 Green grass biomass (g m Total grass biomass (g m 150

100

50

0 0 2014 2015 Livestock Integrated W ildlife

Integrated-livestock: P = 0.02 Integrated-livestock: P = 0.05 Wildlife-livestock: P = 0.07 Wildlife-livestock: P = 0.33 Integrated-wildlife: P = 0.98 Integrated-wildlife: P = 0.84

Fig. 2 | Effects of ecological integration of livestock and wildlife on vegetation. a, Ecological classification (livestock, integrated, wildlife) significantly affected total biomass of grass (g m−2) (chi-squared (2) =​ 7.64; P = 0.021),​ with integrated and wildlife properties having more total grass than livestock properties. b, The biomass of green grass (g m−2), which has approximately twice the crude protein content of brown grass, was negatively correlated with mean annual precipitation. c, It also varied as a function of ecological classification (chi-squared (2) =​ 7.64; P = 0.021),​ with ecologically integrated properties having the most green grass. Ecological classifications were based on the ratio of wildlife to livestock on each property (see main text). P values calculated using linear mixed effect models with transformations to meet the assumptions of the tests (see Supplementary Information; ns =​ not significant). The black dot indicates an outlier.

Using our indices of economic and ecological integration, proper- ask whether grazing agreements influenced relationships in a way ties can be arrayed in a matrix (Fig. 5). Not surprisingly, no prop- that could increase social stability. Based on our surveys, having erty derived income primarily from livestock while being primarily a grazing agreement was not associated with a significantly dif- populated by wildlife; similarly no property derived income primar- ferent number of grievances between property owners and their ily from wildlife while being populated primarily by livestock. Based neighbours from pastoralist communities (P =​ 0.42; Fisher’s exact on our results, properties in two regions of this matrix might ben- test). However, properties with grazing agreements received signifi- efit from adjusting their management strategies. Properties that are cantly more proposals for development projects from neighbours ecologically integrated, but derive their income solely from livestock, (Supplementary Information; Extended Data Fig. 6). This result, could benefit economically by developing sustainably managed though driven by a small number of properties, may indicate a income streams that incorporate wildlife. Properties that are ecologi- higher level of engagement among neighbours, which may be asso- cally and economically focused on wildlife could benefit ecologically ciated with an increased level of trust. Property owners or managers by incorporating some livestock grazing into their management, with grazing agreements for livestock from neighbouring commu- whether or not they derive income from those livestock. Moderate nities were equally likely to report a stable or improving relation- densities of livestock would be expected to reduce tick abundance ship with their neighbours compared to property owners that did and increase forage quality while not reducing overall grass biomass. not allow external livestock (Supplementary Information; Extended Beyond economic concerns, social stability is an increas- Data Table 5, P =​ 0.17). However, no properties with a grazing ingly important consideration across the Laikipia landscape, and agreement reported declining relationships with neighbours and no it is tightly linked to livestock and wildlife management. Grazing property without such an agreement reported improving relation- resources are highly variable in Laikipia County, and vary season- ships (Supplementary Information; Extended Data Table 5). Thus, ally and annually on the basis of rainfall patterns19. Pastoralist herd- there may be social benefits resulting from the use of grazing agree- ers experiencing low forage production on their lands may move ments across the landscape, in addition to potential ecological ones. livestock onto neighbouring or more distant properties20. These Despite some ecological and economic benefits of integration, movements may be undertaken on the basis of negotiated agree- one negative consequence of ecological integration could be the ments, or without such agreements. In the latter case, conflicts often sharing of pathogens between livestock and wildlife, which could arise20, and grazing and livestock-related conflicts are common. introduce risks to livestock production, wildlife conservation and We documented grazing agreements between owners of large human health. We developed a new high-throughput sequencing private properties and members of pastoralist communities to method to detect a diversity of tick-borne bacterial and protozoal

568 Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain NATurE SusTAinAbiliTy Articles

ab

12 P ~ 1 12 P = 0.33 10 10 8 8 P = 0.38 15 6 6 4 4 2 2 Zebra abundance index 10 0 0 02468101214 0246810121416182022

2.0 P = 0.12 2.0 P = 0.05

5 1.5 1.5 Wildlife abundance index

1.0 1.0

0.5 0.5 0

0102030 Giraffe abundance index 0.0 0.0 Livestock abundance index 02468101214 0246810121416182022 Cattle abundance index Sheep and goats abundance index

Fig. 3 | Effects of livestock on wildlife abundance. a, The density of wildlife was not significantly correlated with the density of livestock. P value calculated with a generalized linear model (see Supplementary Information). b, In contrast, zebras and giraffes achieved their highest densities on properties with moderate levels of cattle, but were relatively uncommon with sheep and goats (‘sheep +​ goats’). P values calculated with Fisher’s exact tests (see Supplementary Information for details). Data are from 21 properties in Laikipia County in 2015, using dung density to estimate abundances; two properties that actively exclude wildlife were removed from these analyses.

pathogens of livestock, wildlife and humans in East Africa. Using have suggested that there may be some benefits18,22,23. Our results the nymphal and adult ticks we collected in our surveys, we found demonstrate at a large spatial scale that moderate to high densi- that 16% of ticks were infected with at least one pathogen, including ties of wildlife can co-occur with moderate densities of livestock, the agents of East Coast fever, Q fever and bovine anaplasmosis as particularly cattle, and that wildlife may benefit from reduced par- well as other diseases of humans, wildlife and livestock. Ticks were asitism and higher forage quality when cattle are present. These scarce on livestock-dominated properties so that we could not sam- benefits appear to come without reducing the likelihood of a ple them reliably for pathogen prevalence. There were no differences property’s having profitable livestock or tourism operations, and in the proportion of ticks infected on ecologically integrated versus with the potential benefits of a greater diversity of income sources. wildlife properties (Mann–Whitney U-test, P =​ 0.94; Supplementary There also appear to be potential social benefits of creating graz- Information; Extended Data Fig. 5A). There was also no difference ing agreements among neighbours. For the subset of interactions in the density of infected ticks—a critical risk factor—on integrated we explored, the co-benefits of livestock–wildlife integration properties versus wildlife properties (Supplementary Information; appear to dominate. However, many other interactions were not Extended Data Fig. 5B). These findings suggest that incorporat- explored (for example, the other impacts of acaricide use, social ing moderate numbers of acaricide-treated livestock onto wildlife consequences of grazing agreements, responses of birds or other properties does not introduce a higher risk for tick-borne diseases species to livestock, transmission of non-tick-borne pathogens, of humans, wildlife and livestock. However, the lowest densities and so on), and our research took place during representative but of ticks were found on properties with high densities of acaricide- not extreme conditions. Further research is needed to reveal the treated livestock and comparatively few wildlife (Fig. 1c). full set of social, ecological and economic consequences of wildlife- An important caveat to our findings is that different species of livestock integration. livestock were correlated with profoundly different abundances of The challenges of wildlife conservation on private lands, and of wildlife. Wildlife reached its highest abundances on properties with balancing the needs of wildlife with the growth of human popu- moderate densities of cattle; however, wildlife generally did not co- lations, are prominent and global. Sadly, after our data were col- occur with sheep or goats (Fig. 3b). Across Kenya, cattle abundance lected, events in Laikipia took a tragic turn. Armed raiders invaded has declined 25% since 1977 while the abundance of sheep and many of the properties we surveyed in our study, bringing thou- goats has increased 76%1. In Laikipia, sheep and goats are now the sands of livestock onto these properties without permission and most abundant livestock and they reach their highest densities on killing dozens of people and unknown numbers of wildlife and properties that were not included in our study21. Given this increas- livestock24. While the ultimate causes of these raids continue to ing prominence, the effects of sheep and goats across the landscape be investigated, events were exacerbated by drought and political should be explored further. Sheep and goats are often found on instability. Our research into sustainable levels of economic, social land with a history of overgrazing, although there are many factors and ecological integration in this system was motivated by very affecting their abundance in this landscape. The observed negative real conflicts over resources and access, conflicts that can reach association in this study could reflect the effects of human popula- a level of intensity far beyond the bounds of sustainability. Our tion density, or other geographic or socio-economic factors associ- results suggest that there can be multiple measurable benefits to ated with sheep and goat abundance, that were beyond the scope of careful integration of the needs of people, livestock and wildlife in our study13,21. this complex socioecological system, but the future potential for Historically, livestock and wildlife have been assumed to have integration depends heavily on addressing the current conflicts primarily negative interactions in Laikipia, but recent experiments with the needs of all in mind.

Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain 569 Articles NATurE SusTAinAbiliTy

1.25 *

2 1.00

0.75 Wildlife 0

0.50 Integrated ersity of income sources Ecological classification Div −2 0.25 Livestock

0.00 Livestock Integrated Wildlife Livestock Integrated Wildlife Economic classification Economic classification

Fig. 4 | Diversity of income sources based on economic classification Fig. 5 | Ecological classification (ln wildlife/livestock ratio) versus (relative proportion of income derived from livestock versus wildlife economic classification for each of the 23 properties surveyed. on each property). See main text for details. Almost all properties have The coloured circles and arrows indicate potential management sources of income other than livestock or wildlife. Diversity was calculated recommendations. Properties circled in red are ecologically integrated but using the Shannon index, which combines the number of income sources derive income primarily from livestock. Finding ways to benefit financially and their relative proportions (evenness). P value calculated with the from the wildlife on these properties (red arrow) would increase income diversity and potentially provide greater economic stability. Properties Kruskal–Wallis test (*P <​ 0.05). circled in orange are populated primarily by wildlife and derive income primarily from wildlife, for example, through tourism. These properties Methods could benefit ecologically by allowing cattle to graze periodically, which Study area. Laikipia County, located in central Kenya, is predominantly semi-arid would reduce tick abundance and potentially improve forage quality, savanna in the mountainous region to the west and north-west of Mount Kenya. whether the property received income from cattle (dashed orange arrow) 2 Covering ~9,000 km with an average elevation of ~1,800 m above sea level, or not (solid orange arrow). Laikipia experiences a weak trimodal pattern of rainfall, with a total annual rainfall of ~700 mm25. We selected 23 properties encompassing 3,588 km2, which covered ~40% of dung piles were recorded as unknown. We could not distinguish reliably between the area of Laikipia County. The mean property size was 14,196 ±​ 2,023 ha (s.e.m.). dung piles of domestic cattle and Cape buffalo (Syncerus caffer), so both were Properties were selected to reflect a range of management approaches being used in recorded as ‘bovid dung’. Cape buffalo comprise 1.3–1.6% of bovid numbers in the Laikipia landscape, representing a spectrum from wildlife-oriented to livestock- Laikipia27. Multiple dung piles such as middens were recorded by species and oriented management and the integration of the two. Properties also spanned a treated as one count, as most middens are a form of territorial marking by one range of sources of revenue and types of ownership (private, community-owned, species pair28. For analysis, we calculated the total number of dung piles per species government-owned). Two properties were on the border of Laikipia in Isiolo (or group, for example, ‘bovid’) per transect, and then averaged these sums for all County but were considered part of the Laikipia ecosystem despite the presence of transects on each property; this mean was used for subsequent comparisons among a county border. properties.

Biological data sampling design. On each of the properties, we assessed the Tick surveys. Ticks were collected by dragging a 1 m2 cloth for 100 m on parallel abundance of livestock, wildlife (large herbivorous mammals), herbaceous secondary transects29 5–10 m away from the established transect line and on both vegetation and host-seeking ticks. We conducted annual surveys of ticks, sides of it, for a total of 200 m2 sampled per transect. At each 20 m interval, the drag vegetation and dung of herbivorous mammals in July–August 2014 and 2015. cloth was checked and attached ticks removed from both sides of the drag cloth, Sites for the placement of 100 m sampling transects within each property were and from the clothing of the observer. Ticks of all life stages (that is larvae, nymphs selected by overlaying a 5 ×​ 5 km2 grid over each property and assigning one and adults) were counted and preserved in a 2 ml vial containing 70% ethanol. For transect randomly for every two adjacent grid squares that fell within the property analysis, we pooled nymphs and adults of all species of ticks on each transect, then boundary to achieve a spatially stratified random design. The smallest properties averaged these values for all transects on a property. received a minimum of two transects, while the largest properties had six. Tick, vegetation and dung data were collected simultaneously along transects during Estimating annual rainfall. We obtained annual rainfall at each property each sampling period in 2014 and 2015. All transects were located in savanna centroid from the Famine Early Warning System rainfall estimate (FEWS RFE) habitats with comparable densities of woody vegetation. decadal data set for East Africa, version 2.030. The FEWS RFE data source is provided as a series of rasters at 8 km resolution for the entire region and Vegetation surveys. We sampled the species composition and biomass of grasses is derived from a combination of ground station rain gauges and satellite along transects using a pin frame with ten pins spaced evenly along a 1 m frame. observations. We calculated annual rainfall for each property as the sum of The pin frame was placed at 0, 50 and 100 m along each transect. At each placement decadal rainfall estimates for the FEWS RFE pixel containing the property of the pin frame, and for each pin, we recorded the number of hits for each species centroid, where the property centroid was calculated with the ‘Feature to Point’ of any blade of grass touching the pin, and whether that blade was brown or green. tool in ArcMap version 10.531. The total number of hits for each placement of the pin frame was converted to total plant biomass (g m−2) using data from Keesing26. For analysis, we calculated Tick identification. All ticks were identified to species using a dissecting stereo the total number of hits per species (differentiating green and brown hits of each microscope (Zeiss Stemi 508) and identification guides and manuals17,32,33. The species) per pin frame placement, and then determined the mean number of hits characteristics used for identification included the size of coxal spurs, hypostome for each transect. Estimates for each transect were averaged for all transects on each shape and dentition, shape of basis capituli, shape of adanal plates, shape of genital property and this mean was used for subsequent comparisons among properties. furrow, and the shape and punctuation of the scutum and conscutum. After identification, ticks were separated by collection group, species and life stage and Dung surveys. Abundances of herbivorous wildlife and livestock were assessed placed in vials containing 80% ethanol. by counting dung piles within 1 m on either side of each transect. Dung piles were identified to species where possible, including both wildlife and livestock (that DNA extraction. Individual ticks were rinsed in 70% ethanol, air-dried and is, cattle, camels, donkeys, sheep and goats) dung. Unidentifiable or decomposed homogenized using a TissueLyser II (QIAGEN) in 2 ml deep-well 96-well plates at

570 Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain NATurE SusTAinAbiliTy Articles

25 Hz for 2 ×​ 3 min. Each well contained a 5 ml steel bead (QIAGEN) and 50 μ​l we asked owners/managers to estimate the frequency of grievances and proposals genomic digestion buffer (PureLink Pro 96 kit; Thermo Fisher Scientific). DNA received from communities outside the property. We also asked whether was extracted with Invitrogen’s PureLink Pro 96 kit according to the manufacturer’s relationships with the surrounding community had been stable, improving or protocol and eluted in 70 μ​l elution buffer. Proteinase K digestion was performed declining over the previous 12 months. overnight. DNA was quantified on a Qubit 2.0 fluorometer (Thermo Fisher Scientific) using High Sensitivity DNA Kit. Samples containing sufficient DNA Data analysis. Classifcation of properties. Individual properties varied considerably were sent for high-throughput sequencing. A total of 1,081 ticks were sequenced, in the relative abundances of, and economic return from, wildlife and livestock. consisting of 857 nymphs and 224 adults. To characterize ecological variation, we created an index of the abundance of wildlife relative to livestock, which was the natural log of the ratio of wildlife High-throughput sequencing. Target regions of 31 bacterial and 1 protozoal dung piles to livestock dung piles per property. We created discrete categories gene were amplified using the primer sets in Table S4 and sequenced using the designated as livestock, integrated and wildlife on the basis of natural breaks Illumina HiSeq Rapid SBS Kit v2 on a HiSeq 2500 sequencer (Illumina) at the in a frequency distribution of properties along the ln(wildlife:livestock) axis W.M. Keck Center for Comparative and Functional Genomics, University of (Supplementary Information; Extended Data Fig. 1). To characterize variation in Illinois at Urbana-Champaign as described previously34. Briefly, sample DNA was income derived from the presence of wildlife and livestock, we used data from amplified on the Fluidigm microfluidics PCR platform with appropriate linkers the property owner/manager surveys to determine the relative proportion of and sample barcodes. The final Fluidigm products were pooled in equimolar ratios income derived from activities related to wildlife, livestock or other sources. Our for sequencing, spiked with 10% non-indexed PhiX control library (Illumina) economic integration index was derived by comparing only sources related to and sequenced by 2 ×​ 250 nt paired-end sequencing on the Illumina HiSeq Rapid wildlife and livestock. If the majority of income was derived from wildlife-related system. FASTQ files were generated with the bcl2fastq v2.17.1.14 Conversion activities (>​ 10×​ the income compared to livestock-related activities), a property Software (Illumina). Finally, the raw reads were demultiplexed by primer and was designated economically as wildlife. If the reverse was true, the property was by sample with the Fluidigm demultiplexing pipeline version 2.0. The primers designated economically as livestock. If the diference in income from livestock- designed for this research were created using the Primer3 option in Geneious and wildlife-related activities was less than a factor of ten, the property was version 8 and designed to meet the specifications for Fluidigm PCR (according to designated as economically integrated. Tus, each property was placed into one of the specifications set by Mark Band, Director, Functional Genomics, W.M. Keck three categories of ecological integration and one of three categories of economic Center for Comparative and Functional Genomics). integration (Fig. 5).

Sequence analysis. A customized HiSeq analysis pipeline was created by the High Statistical analyses. Analyses were performed in the R statistical environment40. Performance Computing in Biology group at the University of Illinois at Urbana- For precipitation statistics, we tested the fit of linear models to data, transforming Champaign. Raw sequences were assessed for quality with FastQC35, trimmed variables as necessary to meet assumptions of heteroscedasticity and normality. with Trimmomatic36, stitched together when needed with PEAR37 and converted For vegetation characteristics, we used the ‘lme4’ package41 to perform linear to FASTA format. Paired reads were not stitched together when the expected size mixed effects analyses of the relationships between vegetation response of the PCR product was either >​ 500 nt or was of variable length, depending on variables (total and green grass biomass) and ecological classification (livestock, pathogen species. In this case, only the trimmed R1 (forward) read was used. integrated, wildlife). As fixed effects, we entered ecological classification and year These sequences were then used as query sequences in comparisons against a (2014/2015) into the model. As random effects, we had intercepts for individual custom database of tick pathogen genes using VSEARCH38 with these parameters: properties. When we detected deviations from homoscedasticity or normality, a minimum of 93% identity and 50% coverage; and a maximum of 50 hits per we transformed values so that these conditions were met. P values were obtained query. Due to the lack of DNA sequence arrays for Kenyan tick-borne pathogens, by likelihood ratio tests of the full model with the effect in question against the we created a custom DNA database using target sequences from expected or closely model without the effect in question, using a null model that included only year related pathogens obtained from published sequences in the National Center as a fixed effect and property as a random effect. When ecological classification for Biotechnology Information Basic Local Alignment Search Tool39, accessed provided a significantly better fit than the null model based on a likelihood ratio in July 2016. Hits were considered positive when an average minimum of 97% test, we then compared the fit of the model with ecological classifications to the identity was achieved. The only exceptions were Coxiella spp., which required an fit of that model with mean annual precipitation as an additional fixed effect. average minimum identity of 99% to distinguish C. burnetii from non-pathogenic We assessed possible collinearity of independent variables in the best-fitting Coxiella endosymbionts, commonly occurring in these ticks. Additionally, with the model with the function ‘vif’ from the package ‘car’42, modified for linear mixed exception of Rickettsia akari and R. felis, species in the genus Rickettsia were not effects models with code available (see here https://jonlefcheck.net/2012/12/28/ distinguishable, even at 100% identity, using the V4_515F_New/V4_806F_New dealing-with-multicollinearity-using-variance-inflation-factors); we used a or 16S8FE3/B_GA1B2 primers due to the conserved sequences in these regions variance inflation factor cut-off value of two. We used the ‘glht’ function in the among many rickettsial species. To distinguish among Rickettsia spp., we used ‘multcomp’43 package to conduct post hoc comparisons of the best-fitting model primers targeted to ompB and the ribosomal RNA 23S–5S intergenic region. using a Tukey’s test. We used the ‘melt’ function in the ‘reshape2’44 package for data manipulation. Social and economic data. Livestock density. All socio-economic data collection For tick abundance, where the data deviated strongly from assumptions of methods were reviewed by the Institutional Review Board of the University linear tests, we first conducted a Spearman’s rank correlation to ask whether of Illinois Urbana-Champaign (application number 13931). To determine the there was a significant correlation between average annual precipitation at the livestock stocking rate, we interviewed the managers/owner of each property centroid of each property and the abundance of nymphal and adult ticks. Because between May and November 2015. Managers/owners were asked to identify the there was no correlation (ρ =​ −​0.18, P =​ 0.41), we then used Kruskal–Wallis numbers of livestock owned, for both the time of the survey and for the period six non-parametric tests to detect differences in tick abundance as a function of the months prior, from a list of species common in the region (that is, cattle, sheep, ecological classification of the properties. We conducted separate tests for both goats, camels, donkeys and chickens). We asked them to distinguish between years, reducing our α level to α =​ 0.025 for multiple comparisons. For comparing their own animals and livestock that grazed on the property but were not owned the proportion of ticks infected with pathogens and the density of infected ticks, we by the property. If the number of animals between the two time periods surveyed used Mann–Whitney U-tests. varied, we calculated the mean to estimate the abundance of each domestic animal For the relationship between the abundances of livestock and wildlife, we were species. Property boundaries were used to calculate property area and livestock interested in whether the abundance of wildlife was inversely correlated with the stocking rates. abundance of livestock, which would suggest competition. To answer this question, we needed to know whether wildlife selected or avoided areas with varying Economic returns from livestock and wildlife. To determine the economic return livestock densities. Thus, we could only consider properties that allowed wildlife to on livestock- and wildlife-related activities, as well as other sources of income, access their properties. Because two of our properties actively excluded wildlife, we we asked what percentage of revenue was derived from each income stream and removed these two properties for the purpose of this analysis. For the remaining whether, for the previous year, each income stream lost or earned money, or broke properties, we used a generalized linear model with the Gaussian family of links to even. The following categories were presented for the responses to each of these determine whether wildlife abundance was linearly related to livestock abundance questions: livestock sales; dairy products; abattoir; tourism; military exercises; on each property. We used a one-way analysis of variance to determine whether agricultural products; minerals/natural resource sales; and other. Livestock sales, the total abundance of large mammals, as measured by livestock plus wildlife dung, dairy products and abattoir were considered sources of revenue derived from was significantly different among ecological classifications (livestock, integrated, livestock production, while tourism was considered a source of revenue derived wildlife), and we conducted post hoc pairwise comparisons among classifications. from wildlife conservation. We used the Shannon diversity index on these For comparing abundances of wildlife as a function of cattle or sheep +​ goat percentages to calculate the diversity (combining both the number of sources and density, we created contingency tables with presence/absence of threshold numbers their relative proportions) of income sources. of wildlife species and livestock. We then analysed these contingency tables with Fisher’s exact tests. For all comparisons of wildlife and livestock densities, we used Social consequences. To evaluate the nature of social relationships between the livestock dung counts to estimate livestock densities rather than the stocking rates residents of each property and residents of communities outside that property, reported by ranch managers.

Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain 571 Articles NATurE SusTAinAbiliTy

For economic and social data, we used Fisher’s exact tests to detect differences 25. Kimuyu, D. M., Sensenig, R. L., Riginos, C., Veblen, K. E. & Young, T. P. in categorical outcomes among the economic classifications of the properties Native and domestic browsers and grazers reduce fuels, fre temperatures, and (livestock, integrated, wildlife). For comparing income diversity versus economic acacia ant mortality in an African savanna. Ecol. Appl. 24, 741–749 (2014). classification, we used a Kruskal–Wallis test on diversity as calculated using the 26. Keesing, F. Ecological Interactions Between Small Mammals, Large Mammals, ‘diversity’ function in the ‘vegan’ package45 in R. and Vegetation in a Tropical Savanna of Central Kenya. PhD thesis, Univ. of Figures were drawn using base R or the ‘ggplot2’ package46in R, with additional California, Berkeley (1997). figure development using the ‘cowplot’47 and ‘popbio’48 packages. 27. Kinnaird, M. F., O’Brien, T. & Strindberg, S. Assessment of Aerial Sample Count Surveys as a Monitoring Method to Track Changes in Livestock and Data availability Wildlife Across Laikipia County (2012); https://doi.org/10.13140/ A summary table containing data used in these analyses is provided in the RG.2.1.2498.2246 Supplementary Information (Extended Data Table 7). 28. Estes, R. D Te Behavior Guide to African Mammals: Including Hoofed Mammals, Carnivores, Primates (Univ. of California, Oakland, 1991). 29. Falco, R. C. & Fish, D. A comparison of methods for sampling the deer tick, Received: 11 December 2017; Accepted: 31 August 2018; Ixodes dammini, in a Lyme disease endemic area. Exp. Appl. Acarol. 14, Published online: 15 October 2018 165–173 (1992). 30. Xie, P. & Arkin, P. A. Global precipitation: a 17-year monthly analysis based References on gauge observations, satellite estimates, and numerical model outputs. Bull. 1. Ogutu, J. O. et al. Extreme wildlife declines and concurrent increase in Am. Meteorol. Soc. 78, 2539–2558 (1997). livestock numbers in Kenya: what are the causes? PLoS ONE 11, 31. ArcGIS Desktop, release 10 (ESRI, 2011); http://desktop.arcgis.com/en/ e0163249 (2016). 32. Walker, A. R. et al. Ticks of Domestic Animals in Africa: A Guide to 2. Terrestrial Protected Areas (% of Total Land Area) (United Nations Identifcation of Species. Bioscience Reports http://www.alanrwalker.com/ Environmental Program and the World Conservation Monitoring Centre, assets/PDF/tickguide-africa.pdf (2003). 2016); http://data.worldbank.org/indicator/ER.LND.PTLD.ZS 33. Okello-Onen, J. Taxonomy of African Ticks: an Identifcation Manual (ICIPE, 3. Western, D., Russell, S. & Cuthill, I. Te status of wildlife in protected Nairobi, Kenya, 1999). areas compared to non-protected areas of Kenya. PLoS ONE 4, 34. Muturi, J. E., Kim, C.-H., Bara, J., Bach, E. M. & Siddappaji, M. Culex pipiens e6140 (2009). and Culex restuans mosquitoes harbor distinct microbiota dominated by few 4. World Development Indicators (World Bank, 2016); http://databank. bacterial taxa. Parasit Vectors 9, 18 (2016). worldbank.org/data/reports.aspx?source=​2&Topic=​1 35. FastQC: a Quality Control Tool for High Troughput Sequence Data 5. Herrero, M., Tornton, P. K., Gerber, P. & Reid, R. S. Livestock, livelihoods (Brabaham Bioinformatics, 2010); https://www.bioinformatics.babraham. and the environment: understanding the trade-ofs. Curr. Opin. Environ. ac.uk/projects/fastqc/ Sustain. 1, 111–120 (2009). 36. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a fexible trimmer for 6. de Leeuw, J. et al. Distribution and diversity of wildlife in northern Illumina sequence data. Bioinformatics 30, 2114–2120 (2014). Kenya in relation to livestock and permanent water points. Biol. Conserv. 100, 37. Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate 297–306 (2001). Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014). 7. Wildlife Conservation by Sustainable Use (eds H. H. T. Prins et al.) 38. Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F.VSEARCH: a 51–80 (Springer, Dordrecht, 2000). versatile open source tool for metagenomics. PeerJ 4, e2584 (2016). 8. Odadi, W. O., Karachi, M. K., Abdulrazak, S. A. & Young, T. P. African wild 39. National Center for Biotechnology Information. Basic Local Alignment Search ungulates compete with or facilitate cattle depending on season. Science 333, Tool (BLAST, accessed July 2016); https://blast.ncbi.nlm.nih.gov/Blast.cgi 1753–1755 (2011). 40. R Development Core Team. R: A Language and Environment for Statistical 9. Kimuyu, D. M. et al. Infuence of cattle on grazing and browsing wildlife Computing (2017); www.gbif.org/tool/81287/r-a-language-and-environment- varies with rainfall and presence of megaherbivores. Ecol. Appl. 27, for-statistical-computing 786–798 (2017). 41. Bates, D., Mächler, M., Bolker, B. & Walker, S. C. Fitting linear mixed-efects 10. Reid, R. S. Savannas of Our Birth: People, Wildlife, and Change in East Africa models using lme4. J. Stat. Sofw. 67, 1–48 (2015). (Univ. of California Press, Oakland, 2012). 42. Fox, J . & Weisberg, S. An R Companion to Applied Regression (SAGE 11. Allan, B. F. et al. Can integrating wildlife and livestock enhance ecosystem Publications, Tousand Oaks, 2011). services in central Kenya? Front. Ecol. Environ. 15, 328–335 (2017). 43. Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general 12. Butt, B. & Turner, M. D. Clarifying competition: the case of wildlife and parametric models. Biom. J. 50, 346–363 (2008). pastoral livestock in East Africa. Pastoralism 2, 9 (2012). 44. Wickham, H. Reshaping data with the reshape package. J. Stat. Sofw. 21, 13. Te Contribution of the Rural Economy of Laikipia as the Basis of a Model 1–20 (2007). County (Laikipia Wildlife Forum, 2013); http://go.nature.com/2N4hxDO 45. Oksanen, J. Vegan: An Introduction to Ordination (2017); http://cran.r-project. 14. Economic Survey 2016 (Kenya National Bureau of Statistics, 2016); www.knbs. org/web/packages/vegan/vignettes/intro-vegan.pdf or.ke/download/economic-survey-2016/ 46. Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 15. Jongejan, F. & Uilenberg, G. Te global importance of ticks. Parisitology 129, Dordrecht, 2009). S3–S14 (2004). 47. Wilke, C. O. cowplot: Streamlined Plot Teme and Plot Annotations for 16. Keesing, F., Allan, B. F., Young, T. P. & Ostfeld, R. S. Efects of wildlife ‘ggplot2’ (2017); http://github.com/wilkelab/cowplot and cattle on tick abundance in central Kenya. Ecol. Appl. 23, 48. Stubben, C. & Milligan, B. Estimating and analyzing demographic models 1410–1418 (2013). using the popbio package in R. J. Stat. Sofw. 22, 1–23 (2007). 17. Walker, J. B., Horak, I. G. & Keirans, J. E. Te Genus Rhipicephalus (Acari, 49. Berggoetz, M. et al. Tick-borne pathogens in the blood of wild and domestic Ixodidae): a Guide to the Brown Ticks of the World (Cambridge Univ. Press, ungulates in South Africa: interplay of game and livestock. Ticks Tick Borne Cambridge, 2005). Dis. 5, 166–175 (2014). 18. Porensky, L. M. & Veblen, K. E. Generation of ecosystem hotspots using 50. Jado, I. et al. Molecular method for identifcation of Rickettsia species short-term cattle corrals in an African savanna. Rangeland Ecol. Manag. 68, in clinical and environmental samples. J. Clin. Microbiol. 44, 131–141 (2015). 4572–4576 (2006). 19. Ryan, Z. Establishment and Evaluation of a Livestock Early Warning System for Laikipia, Kenya (Texas A & M University, College Station, 2005). Acknowledgements 20. Lengoiboni, M., Bregt, A. K. & van der Molen, P. Pastoralism within We thank H. Kiai, K. Age, A. Mariki, J. Keesing Ostfeld and numerous enumerators land administration in Kenya: the missing link. Land Use Policy 27, for assistance with data collection, and V. Obiero for assistance with data entry. We 579–588 (2010). extend our great appreciation to the owners, managers, staff members and residents of 21. Systematic Aerial Sample Survey of Laikipia County (Laikipia Wildlife Forum, the properties we surveyed for allowing us to work on their land, and for welcoming 2016); http://go.nature.com/2R6KHFp and assisting us. We thank the management and staff of Ol Pejeta Conservancy, Lewa 22. Augustine, D. J., Veblen, K. E., Goheen, J. R., Riginos, C. & Young, T. P. Wildlife Conservancy, Mpala Research Centre and Laikipia Wildlife Foundation for Pathways for positive cattle-wildlife interactions in semiarid rangelands. their hospitality and for their facilitation of this work. This research was supported by Smithson. Contr. Zool. 661, 55–71 (2011). the United States National Science Foundation (Coupled Natural and Human Systems 23. Veblen, K. E. Savanna glade hotspots: plant community development award 1313822). and synergy with large herbivores. J. Arid Environ. 78, 119–127 (2012). 24. Wesangula, D. Two rangers shot dead in Kenya’s Laikipia conservation area. Author contributions Te Guardian (6 June 2017); www.theguardian.com/environment/2017/ B.F.A., F.K., R.S.O., R.C-K. and H.T. designed the study. S.O., B.F.A. and F.K. collected jun/06/two-rangers-shot-dead-kenya-laikipia-conservation-area the ecological data. H.T. and S.A.W. developed the survey questions for property owners

572 Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain NATurE SusTAinAbiliTy Articles and managers and S.H. conducted the interviews. T.H. assisted in the field and identified Additional information ticks. L.P.F. conducted the pathogen identification using a protocol developed by B.F.A. Supplementary information is available for this paper at https://doi.org/10.1038/ and L.P.F. R.C-K., V.K., C.M.W., B.R.B. and S.A.W. assisted with data preparation and s41893-018-0149-2. interpretation. F.K. analysed the data and F.K., B.F.A. and R.S.O. wrote the manuscript, Reprints and permissions information is available at www.nature.com/reprints. which was edited and approved by all authors. Correspondence and requests for materials should be addressed to F.K. Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in Competing interests published maps and institutional affiliations. The authors declare no competing interests. © The Author(s), under exclusive licence to Springer Nature Limited 2018

Nature Sustainability | VOL 1 | OCTOBER 2018 | 566–573 | www.nature.com/natsustain 573