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Biodiversity and Conservation

, Volume 27, Issue 9, pp 2173–2191 | Cite as

Boots on the ground: in defense of low-tech, inexpensive, and robust survey methods for Africa’s under-funded protected areas

  • Paul SchuetteEmail author
  • Ngawo Namukonde
  • Matthew S. Becker
  • Fred G.R. Watson
  • Scott Creel
  • Clive Chifunte
  • Wigganson Matandiko
  • Paul Millhouser
  • Elias Rosenblatt
  • Carolyn Sanguinetti
Original Paper

Abstract

Protected area managers need reliable information to detect spatial and temporal trends of the species they intend to protect. This information is crucial for population monitoring, understanding ecological processes, and evaluating the effectiveness of management and conservation policies. In under-funded protected areas, managers often prioritize ungulates and carnivores for monitoring given their socio-economic value and sensitivity to human disturbance. Aircraft-based surveys are typically utilized for monitoring ungulates because they can cover large areas regardless of the terrain, but such work is expensive and subject to bias. Recently, unmanned aerial vehicles have shown great promise for ungulate monitoring, but these technologies are not yet widely available and are subject to many of the same analytical challenges associated with traditional aircraft-based surveys. Here, we explore use of inexpensive and robust distance sampling methods in Kafue National Park (KNP) (22,400 km2), carried out by government-employed game scouts. Ground-based surveys spanning 101, 5-km transects resulted in 369 ungulate group detections from 20 species. Using generalized linear models and distance sampling, we determined the environmental and anthropogenic variables influencing ungulate species richness, density, and distribution. Species richness was positively associated with permanent water and percent cover of closed woodland vegetation. Distance to permanent water had the strongest overall effect on ungulate densities, but the magnitude and direction of this effect varied by species. This ground-based approach provided a more cost-effective, unbiased, and repeatable method than aerial surveys in KNP, and could be widely implemented by local personnel across under-funded protected areas in Africa.

Keywords

Protected area management Wildlife monitoring Game scout Distance sampling Detection probability 

Notes

Acknowledgments

We thank the Zambia Department of National Parks and Wildlife for coordinating and carrying out this research. This work was supported by grants from the National Science Foundation [Grant Number IOS 1145749], National Geographic Society Big Cats Initiative, WWF-Netherlands, Painted Dog Conservation Inc., the Wilderness Trust, World Bank and the Norwegian Government. We appreciate the valuable input from two anonymous reviewers whose comments helped improve this manuscript. We thank C. Fenner, P. Bowen, and Treetops School Camp for their logistical support of the Zambian Carnivore Programme.

References

  1. Anderson K, Gaston KJ (2013) Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Front Ecol Environ 11:138–146CrossRefGoogle Scholar
  2. Becker M, McRobb R, Watson F et al (2013) Evaluating wire-snare poaching trends and the impacts of by-catch on elephants and large carnivores. Biol Conserv 158:26–36CrossRefGoogle Scholar
  3. Beyer HL (2015) Geospatial modelling environment (Version 0.7.4.0). http://www.spatialecology.com/gme
  4. Borg BL, Arthur SM, Bromen NA et al (2016) Implications of harvest on the boundaries of protected areas for large carnivore viewing opportunities. PLoS ONE 11:e0153808CrossRefPubMedPubMedCentralGoogle Scholar
  5. Brashares JS, Golden CD, Weinbaum KZ et al (2011) Economic and geographic drivers of wildlife consumption in rural Africa. PNAS 108:13931–13936CrossRefPubMedPubMedCentralGoogle Scholar
  6. Buckland ST (2001) Introduction to distance sampling: estimating abundance of biological populations. Oxford University Press, OxfordGoogle Scholar
  7. Buckland ST, Anderson DR, Burnham KP et al (2007) Advanced distance sampling. Oxford University Press, OxfordGoogle Scholar
  8. Burnham KP, Anderson DR (2002) Model selection and multimodel inference: a practical information-theoretic approach. Springer, New YorkGoogle Scholar
  9. Campbell K, Borner M (1995) Population trends and distribution of Serengeti herbivores: implications for management. In: Sinclair ARE, Arcese P (eds) Serengeti II: dynamics, management, and conservation of an ecosystem. University of Chicago Press, ChicagoGoogle Scholar
  10. Caughley G (1974) Bias in aerial survey. J Wildl Manag 38:921–933CrossRefGoogle Scholar
  11. Chandler RB, Royle JA, King DI (2011) Inference about density and temporary emigration in unmarked populations. Ecology 92:1429–1435CrossRefPubMedGoogle Scholar
  12. Craigie ID, Baillie JEM, Balmford A et al (2010) Large mammal population declines in Africa’s protected areas. Biol Conserv 143:2221–2228CrossRefGoogle Scholar
  13. Creel S, Creel NM (2002) The African wild dog: behavior, ecology and conservation. Princeton University Press, PrincetonGoogle Scholar
  14. Creel S, M’soka J, Dröge E et al (2016) Assessing the sustainability of African lion trophy hunting, with recommendations for policy. Ecol Appl 26:2347–2357CrossRefPubMedGoogle Scholar
  15. Danielsen F, Burgess ND, Balmford A (2005) Monitoring matters: examining the potential of locally-based approaches. Biodivers Conserv 14:2507–2542CrossRefGoogle Scholar
  16. Danielsen F, Burgess ND, Balmford A et al (2009) Local participation in natural resource monitoring: a characterization of approaches. Conserv Biol 23:31–42CrossRefPubMedGoogle Scholar
  17. Dröge E, Creel S, Becker MS, M’soka J (2017) Spatial and temporal avoidance of risk within a large carnivore guild. Ecol Evol 7:189–199CrossRefPubMedGoogle Scholar
  18. Estes R (1991) The behavior guide to African mammals. Russell Friedman Books, SunvalleyGoogle Scholar
  19. Estes JA, Terborgh J, Brashares JS et al (2011) Trophic downgrading of planet earth. Science 333:301–306CrossRefPubMedGoogle Scholar
  20. Fiske IJ, Chandler RB (2011) Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J Stat Softw 43:1–23CrossRefGoogle Scholar
  21. Frederick H (2011) Aerial survey: Kafue ecosystem 2011. Zambia Wildlife Authority, ChilangaGoogle Scholar
  22. Geldmann J, Barnes M, Coad L et al (2013) Effectiveness of terrestrial protected areas in reducing habitat loss and population declines. Biol Conserv 161:230–238CrossRefGoogle Scholar
  23. Griffin PC, Lubow BC, Jenkins KJ et al (2013) A hybrid double-observer sightability model for aerial surveys. J Wildl Manag 77:1532–1544CrossRefGoogle Scholar
  24. Grimsdell JJR, Bell RHV (1972) Population growth of red lechwe, Kobus leche leche Gray, in the Busanga Plain, Zambia. Afr J Ecol 10:117–122CrossRefGoogle Scholar
  25. Gros PM, Kelly MJ, Caro TM (1996) Estimating carnivore densities for conservation purposes: indirect methods compared to baseline demographic data. Oikos 77:197–206CrossRefGoogle Scholar
  26. Hayward MW, O’Brien J, Hofmeyr M, Kerley GIH (2006) Prey Preferences of the African wild dog Lycaon pictus (Canidae: Carnivora): ecological requirements for conservation. J Mamm 87:1122–1131CrossRefGoogle Scholar
  27. Hayward MW, Boitani L, Burrows ND et al (2015) Forum: ecologists need robust survey designs, sampling and analytical methods. J Appl Ecol 52:286–290CrossRefGoogle Scholar
  28. Henson DW, Malpas RC, D’Udine FAC (2016) Wildlife law enforcement in sub-Saharan African protected areas —a review of best practices. IUCN, Cambridge, UK and Gland, SwitzerlandGoogle Scholar
  29. IUCN and World Commission on Protected Areas (2016) IUCN green list of protected and conserved areas: standard. IUCN, GlandGoogle Scholar
  30. Jachmann H (2001) Estimating abundance of African wildlife: an aid to adaptive management. Kluwer Academic Publishers, NorwellCrossRefGoogle Scholar
  31. Jachmann H (2002) Comparison of aerial counts with ground counts for large African herbivores. J Appl Ecol 39:841–852CrossRefGoogle Scholar
  32. Jolly GM (1969) Sampling methods for aerial censuses of wildlife populations. East Afr Agric For J 34:46–49CrossRefGoogle Scholar
  33. Kahler JS, Roloff GJ, Gore ML (2013) Poaching risks in community-based natural resource management. Conserv Biol 27:177–186CrossRefPubMedGoogle Scholar
  34. Kéry M, Royle JA, Schmid H (2005) Modeling avian abundance from replicated counts using binomial mixture models. Ecol Appl 15:1450–1461CrossRefGoogle Scholar
  35. Koh LP, Wich SA (2012) Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop Conserv Sci 5:121–132CrossRefGoogle Scholar
  36. Krüger O (2005) The role of ecotourism in conservation: panacea or Pandora’s box? Biodivers Conserv 14:579–600CrossRefGoogle Scholar
  37. Linchant J, Lisein J, Semeki J et al (2015) Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mamm Rev 45:239–252CrossRefGoogle Scholar
  38. Lindsey PA, Frank LG, Alexander R et al (2007) Trophy hunting and conservation in Africa: problems and one potential solution. Conserv Biol 21:880–883CrossRefPubMedGoogle Scholar
  39. Lindsey PA, Nyirenda V, Barnes J et al (2014) Underperformance of African protected area networks and the case for new conservation models: Insights from Zambia. PLoS ONE 9:e94109CrossRefPubMedPubMedCentralGoogle Scholar
  40. Linkie M, Martyr DJ, Harihar A et al (2015) Safeguarding Sumatran tigers: evaluating effectiveness of law enforcement patrols and local informant networks. J Appl Ecol 52:851–860CrossRefGoogle Scholar
  41. Litoroh M, Omondi P, Kock R, Amin R (2012) Conservation and management strategy for the elephant in Kenya. Kenya Wildlife Service, NairobiGoogle Scholar
  42. M’soka J, Creel S, Becker M, Murdoch J (2017) Ecological and anthropogenic effects on the density of migratory and resident ungulates in a human-inhabited protected area. Afr J Ecol 55:618–631CrossRefGoogle Scholar
  43. Marsh H, Sinclair DF (1989) Correcting for visibility bias in strip transect aerial surveys of aquatic fauna. J Wildl Manag 53:1017–1024CrossRefGoogle Scholar
  44. Mazerolle MJ (2016) AICcmodavg: model selection and multimodel inference based on (Q) AIC (c)[Software]Google Scholar
  45. Midlane N, O’Riain MJ, Balme GA et al (2014) On tracks: a spoor-based occupancy survey of lion Panthera leo distribution in Kafue National Park, Zambia. Biol Conserv 172:101–108CrossRefGoogle Scholar
  46. O’Shaughnessy R, Cain JW, Owen-Smith N (2014) Comparative diet and habitat selection of puku and lechwe in northern Botswana. J Mamm 95:933–942CrossRefGoogle Scholar
  47. Ogada MO, Woodroffe R, Oguge NO, Frank LG (2003) Limiting depredation by African carnivores: the role of livestock husbandry. Conserv Biol 17:1521–1530CrossRefGoogle Scholar
  48. Ogutu JO, Owen-Smith N (2003) ENSO, rainfall and temperature influences on extreme population declines among African savanna ungulates. Ecol Lett 6:412–419CrossRefGoogle Scholar
  49. Ogutu JO, Reid RS, Piepho H-P et al (2014) Large herbivore responses to surface water and land use in an East African savanna: implications for conservation and human-wildlife conflicts. Biodivers Conserv 23:573–596CrossRefGoogle Scholar
  50. Prins HHT (2000) Competition between wildlife and livestock in Africa. In: Prins HHT, Grootenhuis JG, Dolan TT (eds) Wildlife conservation by sustainable use. Springer, Dordrecht, pp 51–80CrossRefGoogle Scholar
  51. R Core Team (2016) R: A language and environment for statistical computing. R Foundation for Statistical Computing, ViennaGoogle Scholar
  52. Ransom JI, Kaczensky P, Lubow BC et al (2012) A collaborative approach for estimating terrestrial wildlife abundance. Biol Conserv 153:219–226CrossRefGoogle Scholar
  53. Republic of Zambia (2011) 2010 census of population and housing. Central Statistical Office, LusakaGoogle Scholar
  54. Ripple WJ, Estes JA, Beschta RL et al (2014) Status and ecological effects of the world’s largest carnivores. Science 343:1241484CrossRefPubMedGoogle Scholar
  55. Ripple WJ, Newsome TM, Wolf C et al (2015) Collapse of the world’s largest herbivores. Sci Adv 1:e1400103CrossRefPubMedPubMedCentralGoogle Scholar
  56. Rosenblatt E, Becker MS, Creel S et al (2014) Detecting declines of apex carnivores and evaluating their causes: an example with Zambian lions. Biol Conserv 180:176–186CrossRefGoogle Scholar
  57. Rosenblatt E, Creel S, Becker MS et al (2016) Effects of a protection gradient on carnivore density and survival: an example with leopards in the Luangwa valley, Zambia. Ecol Evol 6:3772–3785CrossRefPubMedPubMedCentralGoogle Scholar
  58. Schiffman R (2014) Drones flying high as new tool for field biologists. Science 344:459CrossRefPubMedGoogle Scholar
  59. Schuette P, Creel S, Christianson D (2016) Ungulate distributions in a rangeland with competitors, predators and pastoralists. J Appl Ecol 53:1066–1077CrossRefGoogle Scholar
  60. Steinhorst RK, Samuel MD (1989) Sightability adjustment methods for aerial surveys of wildlife populations. Biometrics 45:415–425CrossRefGoogle Scholar
  61. Stoner C, Caro TM, Mduma SAR et al (2007) Assessment of effectiveness of protection strategies in Tanzania based on a decade of survey data for large herbivores. Conserv Biol 21:635–646CrossRefPubMedGoogle Scholar
  62. Thaker M, Vanak A, Owen C et al (2011) Minimizing predation risk in a landscape of multiple predators: effects on the spatial distribution of African ungulates. Ecology 92:398–407CrossRefPubMedGoogle Scholar
  63. UNEP-WCMC and IUCN (2016) Protected planet report 2016. UNEP-WCMC and IUCN, CambridgeGoogle Scholar
  64. Venables WN, Ripley BD (2002) Modern applied statistics with S, Fourth. Springer, New YorkCrossRefGoogle Scholar
  65. Watson F, Becker MS, McRobb R, Kanyembo B (2013) Spatial patterns of wire-snare poaching: Implications for community conservation in buffer zones around National Parks. Biol Conserv 168:1–9CrossRefGoogle Scholar
  66. Watson FGR, Becker MS, Milanzi J, Nyirenda M (2015) Human encroachment into protected area networks in Zambia: implications for large carnivore conservation. Reg Environ Change 15:415–429CrossRefGoogle Scholar
  67. Winnie J, Creel S (2017) The many effects of carnivores on their prey and their implications for trophic cascades, and ecosystem structure and function. Food Webs 12:88–94CrossRefGoogle Scholar
  68. Zuur AF, Ieno EN, Walker NJ et al (2009) Mixed effects models and extensions in ecology with R. Springer, New YorkCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V., part of Springer Nature 2018

Authors and Affiliations

  • Paul Schuette
    • 1
    • 2
    Email author
  • Ngawo Namukonde
    • 3
  • Matthew S. Becker
    • 2
    • 4
  • Fred G.R. Watson
    • 5
  • Scott Creel
    • 4
  • Clive Chifunte
    • 6
  • Wigganson Matandiko
    • 4
  • Paul Millhouser
    • 7
  • Elias Rosenblatt
    • 2
    • 8
  • Carolyn Sanguinetti
    • 2
  1. 1.Alaska Center for Conservation ScienceUniversity of Alaska AnchorageAnchorageUSA
  2. 2.Zambian Carnivore ProgrammeMfuweZambia
  3. 3.Department of Zoology and Aquatic SciencesCopperbelt UniversityKitweZambia
  4. 4.Department of EcologyMontana State UniversityBozemanUSA
  5. 5.Division of Science and Environmental PolicyCalifornia State University Monterey Bay, Chapman Science CenterSeasideUSA
  6. 6.Department of National Parks and Wildlife, Department of ResearchChilangaZambia
  7. 7.Independent GIS ConsultantDenverUSA
  8. 8.Rubenstein School of Environment and Natural ResourcesUniversity of Vermont, Aiken CenterBurlingtonUSA

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