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, integrating livestock with wildlife can provide benefits for the environment and for human well-being in African savannas.

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A summary table containing data used in these analyses is provided in the Supplementary Information (Extended Data Table 7).

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  1. 1.

    Ogutu, J. O. et al. Extreme wildlife declines and concurrent increase in livestock numbers in Kenya: what are the causes? PLoS ONE 11, e0163249 (2016).

  2. 2.

    Terrestrial Protected Areas (% of Total Land Area) (United Nations Environmental Program and the World Conservation Monitoring Centre, 2016); http://data.worldbank.org/indicator/ER.LND.PTLD.ZS

  3. 3.

    Western, D., Russell, S. & Cuthill, I. The status of wildlife in protected areas compared to non-protected areas of Kenya. PLoS ONE 4, e6140 (2009).

  4. 4.

    World Development Indicators (World Bank, 2016); http://databank.worldbank.org/data/reports.aspx?source=2&Topic=1

  5. 5.

    Herrero, M., Thornton, P. K., Gerber, P. & Reid, R. S. Livestock, livelihoods and the environment: understanding the trade-offs. Curr. Opin. Environ. Sustain. 1, 111–120 (2009).

  6. 6.

    de Leeuw, J. et al. Distribution and diversity of wildlife in northern Kenya in relation to livestock and permanent water points. Biol. Conserv. 100, 297–306 (2001).

  7. 7.

    Wildlife Conservation by Sustainable Use (eds H. H. T. Prins et al.) 51–80 (Springer, Dordrecht, 2000).

  8. 8.

    Odadi, W. O., Karachi, M. K., Abdulrazak, S. A. & Young, T. P. African wild ungulates compete with or facilitate cattle depending on season. Science 333, 1753–1755 (2011).

  9. 9.

    Kimuyu, D. M. et al. Influence of cattle on grazing and browsing wildlife varies with rainfall and presence of megaherbivores. Ecol. Appl. 27, 786–798 (2017).

  10. 10.

    Reid, R. S. Savannas of Our Birth: People, Wildlife, and Change in East Africa (Univ. of California Press, Oakland, 2012).

  11. 11.

    Allan, B. F. et al. Can integrating wildlife and livestock enhance ecosystem services in central Kenya? Front. Ecol. Environ. 15, 328–335 (2017).

  12. 12.

    Butt, B. & Turner, M. D. Clarifying competition: the case of wildlife and pastoral livestock in East Africa. Pastoralism 2, 9 (2012).

  13. 13.

    The Contribution of the Rural Economy of Laikipia as the Basis of a Model County (Laikipia Wildlife Forum, 2013); http://go.nature.com/2N4hxDO

  14. 14.

    Economic Survey 2016 (Kenya National Bureau of Statistics, 2016); www.knbs.or.ke/download/economic-survey-2016/

  15. 15.

    Jongejan, F. & Uilenberg, G. The global importance of ticks. Parisitology 129, S3–S14 (2004).

  16. 16.

    Keesing, F., Allan, B. F., Young, T. P. & Ostfeld, R. S. Effects of wildlife and cattle on tick abundance in central Kenya. Ecol. Appl. 23, 1410–1418 (2013).

  17. 17.

    Walker, J. B., Horak, I. G. & Keirans, J. E. The Genus Rhipicephalus (Acari, Ixodidae): a Guide to the Brown Ticks of the World (Cambridge Univ. Press, Cambridge, 2005).

  18. 18.

    Porensky, L. M. & Veblen, K. E. Generation of ecosystem hotspots using short-term cattle corrals in an African savanna. Rangeland Ecol. Manag. 68, 131–141 (2015).

  19. 19.

    Ryan, Z. Establishment and Evaluation of a Livestock Early Warning System for Laikipia, Kenya (Texas A & M University, College Station, 2005).

  20. 20.

    Lengoiboni, M., Bregt, A. K. & van der Molen, P. Pastoralism within land administration in Kenya: the missing link. Land Use Policy 27, 579–588 (2010).

  21. 21.

    Systematic Aerial Sample Survey of Laikipia County (Laikipia Wildlife Forum, 2016); http://go.nature.com/2R6KHFp

  22. 22.

    Augustine, D. J., Veblen, K. E., Goheen, J. R., Riginos, C. & Young, T. P. Pathways for positive cattle-wildlife interactions in semiarid rangelands. Smithson. Contr. Zool. 661, 55–71 (2011).

  23. 23.

    Veblen, K. E. Savanna glade hotspots: plant community development and synergy with large herbivores. J. Arid Environ. 78, 119–127 (2012).

  24. 24.

    Wesangula, D. Two rangers shot dead in Kenya’s Laikipia conservation area. The Guardian (6 June 2017); www.theguardian.com/environment/2017/jun/06/two-rangers-shot-dead-kenya-laikipia-conservation-area

  25. 25.

    Kimuyu, D. M., Sensenig, R. L., Riginos, C., Veblen, K. E. & Young, T. P. Native and domestic browsers and grazers reduce fuels, fire temperatures, and acacia ant mortality in an African savanna. Ecol. Appl. 24, 741–749 (2014).

  26. 26.

    Keesing, F. Ecological Interactions Between Small Mammals, Large Mammals, and Vegetation in a Tropical Savanna of Central Kenya. PhD thesis, Univ. of California, Berkeley (1997).

  27. 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 Wildlife Across Laikipia County (2012); https://doi.org/10.13140/RG.2.1.2498.2246

  28. 28.

    Estes, R. D The Behavior Guide to African Mammals: Including Hoofed Mammals, Carnivores, Primates (Univ. of California, Oakland, 1991).

  29. 29.

    Falco, R. C. & Fish, D. A comparison of methods for sampling the deer tick, Ixodes dammini, in a Lyme disease endemic area. Exp. Appl. Acarol. 14, 165–173 (1992).

  30. 30.

    Xie, P. & Arkin, P. A. Global precipitation: a 17-year monthly analysis based on gauge observations, satellite estimates, and numerical model outputs. Bull. Am. Meteorol. Soc. 78, 2539–2558 (1997).

  31. 31.

    ArcGIS Desktop, release 10 (ESRI, 2011); http://desktop.arcgis.com/en/

  32. 32.

    Walker, A. R. et al. Ticks of Domestic Animals in Africa: A Guide to Identification of Species. Bioscience Reports http://www.alanrwalker.com/assets/PDF/tickguide-africa.pdf (2003).

  33. 33.

    Okello-Onen, J. Taxonomy of African Ticks: an Identification Manual (ICIPE, Nairobi, Kenya, 1999).

  34. 34.

    Muturi, J. E., Kim, C.-H., Bara, J., Bach, E. M. & Siddappaji, M. Culex pipiens and Culex restuans mosquitoes harbor distinct microbiota dominated by few bacterial taxa. Parasit Vectors 9, 18 (2016).

  35. 35.

    FastQC: a Quality Control Tool for High Throughput Sequence Data (Brabaham Bioinformatics, 2010); https://www.bioinformatics.babraham.ac.uk/projects/fastqc/

  36. 36.

    Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

  37. 37.

    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).

  38. 38.

    Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F.VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).

  39. 39.

    National Center for Biotechnology Information. Basic Local Alignment Search Tool (BLAST, accessed July 2016); https://blast.ncbi.nlm.nih.gov/Blast.cgi

  40. 40.

    R Development Core Team. R: A Language and Environment for Statistical Computing (2017); www.gbif.org/tool/81287/r-a-language-and-environment-for-statistical-computing

  41. 41.

    Bates, D., Mächler, M., Bolker, B. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).

  42. 42.

    Fox, J . & Weisberg, S. An R Companion to Applied Regression (SAGE Publications, Thousand Oaks, 2011).

  43. 43.

    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biom. J. 50, 346–363 (2008).

  44. 44.

    Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).

  45. 45.

    Oksanen, J. Vegan: An Introduction to Ordination (2017); http://cran.r-project.org/web/packages/vegan/vignettes/intro-vegan.pdf

  46. 46.

    Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, Dordrecht, 2009).

  47. 47.

    Wilke, C. O. cowplot: Streamlined Plot Theme and Plot Annotations for ‘ggplot2’ (2017); http://github.com/wilkelab/cowplot

  48. 48.

    Stubben, C. & Milligan, B. Estimating and analyzing demographic models using the popbio package in R. J. Stat. Softw. 22, 1–23 (2007).

  49. 49.

    Berggoetz, M. et al. Tick-borne pathogens in the blood of wild and domestic ungulates in South Africa: interplay of game and livestock. Ticks Tick Borne Dis. 5, 166–175 (2014).

  50. 50.

    Jado, I. et al. Molecular method for identification of Rickettsia species in clinical and environmental samples. J. Clin. Microbiol. 44, 4572–4576 (2006).

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We thank H. Kiai, K. Age, A. Mariki, J. Keesing Ostfeld and numerous enumerators for assistance with data collection, and V. Obiero for assistance with data entry. We extend our great appreciation to the owners, managers, staff members and residents of the properties we surveyed for allowing us to work on their land, and for welcoming and assisting us. We thank the management and staff of Ol Pejeta Conservancy, Lewa Wildlife Conservancy, Mpala Research Centre and Laikipia Wildlife Foundation for their hospitality and for their facilitation of this work. This research was supported by the United States National Science Foundation (Coupled Natural and Human Systems award 1313822).

Author information


  1. Bard College, Annandale-on-Hudson, NY, USA

    • Felicia Keesing
  2. Cary Institute of Ecosystem Studies, Millbrook, NY, USA

    • Richard S. Ostfeld
  3. Department of Entomology, University of Illinois at Urbana-Champaign, Urbana, IL, USA

    • Sharon Okanga
    • , Steven Huckett
    • , L. Page Fredericks
    • , Tyler Hedlund
    •  & Brian F. Allan
  4. Department of Health Sciences, Dominican University of California, San Rafael, CA, USA

    • Brett R. Bayles
  5. Natural Capital Project, Woods Institute for the Environment, Stanford University, Stanford, CA, USA

    • Rebecca Chaplin-Kramer
    • , Virginia Kowal
    •  & Spencer A. Wood
  6. The Nature Conservancy, Office of the Chief Scientist, Santa Cruz, CA, USA

    • Heather Tallis
  7. Department of Biological Sciences, Murang’a University of Technology, Murang’a, Kenya

    • Charles M. Warui
  8. School of Environmental and Forest Sciences, University of Washington, Seattle, WA, USA

    • Spencer A. Wood


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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 the ecological data. H.T. and S.A.W. developed the survey questions for property owners and managers and S.H. conducted the interviews. T.H. assisted in the field and identified ticks. L.P.F. conducted the pathogen identification using a protocol developed by B.F.A. and L.P.F. R.C-K., V.K., C.M.W., B.R.B. and S.A.W. assisted with data preparation and interpretation. F.K. analysed the data and F.K., B.F.A. and R.S.O. wrote the manuscript, which was edited and approved by all authors.

Competing interests

The authors declare no competing interests.

Corresponding author

Correspondence to Felicia Keesing.

Supplementary information

  1. Supplementary Information

    Supplementary Figures 1–6, Supplementary Tables 1–7

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