Ecological Research

, Volume 21, Issue 4, pp 530–543 | Cite as

A framework for predicting and visualizing the East African wildebeest migration-route patterns in variable climatic conditions using geographic information system and remote sensing

  • Douglas E. Musiega
  • Sanga-Ngoie Kazadi 
  • Kaoru Fukuyama
Original Article


In the Serengeti–Mara ecosystem of East Africa, the migrating wildebeests’ (Connochaetes taurinus) response to food resource distribution and terrain complexity impacts their movement characteristics. This manifests in varying ways such as movement speed, direction, turning frequency, and moving distance. To investigate these characteristics, indices derived from vegetation quantity (normalized difference vegetation index, NDVI) and relief (slope) were used in our previous work to quantify the relationships between migration route location versus vegetation, relief complexity, and their combination. Least cost pathways determined using these indices were representative of approximate migration routes. The simulated routes were shown to be strongly influenced by vegetation during the dry season. However the impact of climatic change (rainfall) on route location was not investigated though known to influence vegetation recovery patterns. This paper specifically addresses the impact of climatic change on route location. The mean monthly rainfall data were used to classify the rainy and dry seasons in the Serengeti, the Western, and the Mara areas as normal, drier, or wetter than normal, over the 1986–1997 period. The classification is based on the mean monthly rainfall variability about the 11-year seasonal mean. Regression analysis showed strong linear relationships between rainfall and mean NDVI for each one of the three areas. The subsequent seasonal classification based on the corresponding habitat vegetation characteristics (NDVI) revealed the relative variation of vegetation with rainfall. Using the derived general categories, migration routes are then predicted for the various categories using a “route attractivity index.” The seasonal migration routes were shown to change depending on the relative abundance of the rainfall during the dry season. Dry season migration routes tended toward areas with better vegetation activity, i.e., those characterized by higher NDVI gradients. Our results showed that during the western trek, wetter dry seasons have the effect of delaying the herds movement northwestward. During the northern trek, wetter dry seasons have the effect of delaying the tendency to move westward. However the variation in rainfall conditions during the rainy and dry season had no significant influence on the southern trek route location. We assume that predicted routes based on average general category conditions for different years are representative of main migration route patterns for similar seasons, therefore they are well suited for approximate route prediction, if the climatic characteristics of the year are known.


Serengeti–Mara Wildebeest Migration Remote sensing GIS 



This research is funded by the Japanese Government through its Mombukasho (Ministry of Education, Science, Sports and Culture) Scholarship program. The authors would like to thank the Kenya Wildlife Services, the East African Tours and Safaris, and the Department of Resource Surveys and Remote Sensing, Nairobi. Last but not least, the support given to us by the members Laboratory of Climate and Ecosystems Dynamics, Mie University, is highly appreciated.


  1. Akama JS (1999) Marginalization of the Maasai in Kenya. Ann Tourism Res 26(3):716–718CrossRefGoogle Scholar
  2. Bian L (2000) The GIS representation of wildlife movements: a framework. Kluwer Academic, Arnheim, The Netherlands, pp 213–228Google Scholar
  3. Boone RB, Coughenour MB, Galvin K, Ellis JE (2002) Addressing management questions for Ngorongoro Conservation Area, Tanzania, using the Savanna modeling system. Afr J Ecol 40:138–150CrossRefGoogle Scholar
  4. Campbell JD, Gichohi H, Mwangi A, Chege L (2000) Land use conflicts in Kajiado District, Kenya. Land Use Policy 17:337–348CrossRefGoogle Scholar
  5. Charlier J (1998) Atlas du Congo. Afrique Editions, Kinshasa, Congo, pp 41–43Google Scholar
  6. Eastman JR (2001) Guide to GIS and image processing, volume 2. Users Guide manual, vol. 2. Clark Labs, WorcesterGoogle Scholar
  7. Etzenhouser MJ, Owens MK, Spalinger D, Murden SB (1998) Foraging behavior of browsing ruminants in a heterogeneous landscape. Landsc Ecol 13(1):55–64CrossRefGoogle Scholar
  8. Griffiths JF (1972) Climates of Africa. Elsevier, Amsterdam, pp 313–332Google Scholar
  9. Gross JE, Zank C, Hobbs NT, Spalinger DE (1995) Movement rules for herbivores in spatially heterogeneous environment: response to small-scale pattern. Landsc Ecol 10(4):209–217CrossRefGoogle Scholar
  10. Haltenorth T, Diller H (1980) A field guide to mammals of Africa including Madagascar. William Collins & Sons, London, pp 84–90Google Scholar
  11. Homewood K, Lambin EF, Coast E, Kikula I, Kivelia J, Said M, Serneels S, Thompson M (2001) Long term changes in Serengeti–Mara wildebeest and land cover: pastoralism, population and policies. PNAS 98(22):12544–12549CrossRefPubMedGoogle Scholar
  12. Johannesen AB, Skonhoft A (2004) Property rights and natural resource conservation. A bio-economic model with numeric examples from Serengeti–Mara ecosystem. Environ Resour Econ 25:469–488Google Scholar
  13. Kumar L, Max R, Frank L, Johan K, Jelte A, John H, Nico R, Leo S, Andrew KS, Herbert HTP (2002) Relationship between vegetation growth rates at the onset of the wet season and soil type in the Sahel of Burkina Faso: implications for resource utilization at large scales. Ecol Modell 149:143–152CrossRefGoogle Scholar
  14. Mduma SAR, Sinclair ARE, Hilborn R (1999) Food regulates the Serengeti wildebeest: a 40 year record. J Anim Ecol 68:1102–1122CrossRefGoogle Scholar
  15. Musiega DE, Sanga-Ngoie K (2004) Simulating the East African wildebeest migration patterns using GIS and remote sensing. Afr J Ecol 2(4):355–362CrossRefGoogle Scholar
  16. Nonomura A, Sanga-Ngoie K, Fukuyama K (2003) Devising a new digital vegetation model for eco-climatic analysis in Africa using GIS and NOAA/AVHRR data. Int J Remote Sens 18:3611–3633Google Scholar
  17. Ottichilo WK (2000) Wildlife dynamics: an analysis of change in the Masai Mara ecosystem of Kenya. PhD Thesis, Wageningen University, WageningenGoogle Scholar
  18. Ottichilo WK, Jan L, Prins HHT (2001) Population trends of resident wildebeest [Connochaetes taurinus hecki (Neumann)] and factors influencing them in the Maasai Mara ecosystem, Kenya. Biol Conserv 97:271–282CrossRefGoogle Scholar
  19. Pennycuick L (1975) Movements of migratory wildebeest population in the Serengeti areas between 1960 and 1973. East Afr J Ecol 13:65–87Google Scholar
  20. Rudolf B, Hauschild H, Rueth W, Schneider U (1994) Terrestrial precipitation analysis: operational method and required density of point measurements. In: Desbois M, Desalmond F (eds) Global precipitations and climate change, NATO ASI Series I, vol. 26. Springer, Berlin Heidelberg New York, pp. 173–186Google Scholar
  21. Sanga-Ngoie K, Musiega DE (2002) Large scale monitoring of herbivores movement patterns and migration routes using GIS and remote sensing: the East African wildebeest. Proceedings of ISPRS 2002 Conference, Hyderabad, India (CD-ROM)Google Scholar
  22. Sanga-Ngoie K, Musiega DE (2003) Wildlife migration monitoring in East Africa using GIS and remote sensing. Proceedings of ASPRS 2003 Conference, Anchorage, Alaska, USA (CD-ROM)Google Scholar
  23. Sanga-Ngoie K, Musiega DE (2004) Proposals for estimating approximate migration route location and patterns in varying climatic conditions for the East African wildebeest using GIS and remote sensing. Proceedings of ASPRS 2004 Conference, Denver, Colorado, USA (CD-ROM)Google Scholar
  24. Serneels S, Lambim EF (2001) Impact of land-use changes on the wildebeest migration in the northern part of the Serengeti–Mara ecosystem. J Biogeogr 28:391–407CrossRefGoogle Scholar
  25. Sinclair ARE, Mduma SAR, Arcene P (2000) What determines the phenology and synchrony of ungulate breeding in Serengeti? J Ecol 81(8):2100–2111CrossRefGoogle Scholar
  26. Stuart C, Stuart T (1997) Field guide to the larger mammals of Africa. Struik, Cape Town, pp 158–162Google Scholar
  27. Wei J, Joske C (2000) Spatial modeling of geographical distribution of wildlife population: a case study in the lower Mississippi river region. Ecol Modell 132:95–104CrossRefGoogle Scholar
  28. Wilmhurst JF, Fryxell JM, Fram BP, Sinclair ARE, Henschel CP (1999) Spatial distribution of Serengeti wildebeest in relation to resources. Can J Zool 77:1223–1232CrossRefGoogle Scholar
  29. Wolanski E, Gereta E (2001) The water quality and quantity as the factors driving the Serengeti ecosystems, Tanzania. Hydrobiologia 458:169–180CrossRefGoogle Scholar
  30. Wolanski E, Gereta E, Borner M, Mduma S (1999) Water, migration and the Serengeti ecosystem: understanding the mechanisms that control the timing of the wildlife migration may prove vital to successful management. Am Sci 87:526–533CrossRefGoogle Scholar

Copyright information

© The Ecological Society of Japan 2006

Authors and Affiliations

  • Douglas E. Musiega
    • 1
  • Sanga-Ngoie Kazadi 
    • 1
  • Kaoru Fukuyama
    • 1
  1. 1.Graduate School of BioresourcesMie UniversityTsuJapan

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