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Predicted Impact of Barriers to Migration on the Serengeti Wildebeest Population

Overview of attention for article published in PLOS ONE, January 2011
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Title
Predicted Impact of Barriers to Migration on the Serengeti Wildebeest Population
Published in
PLOS ONE, January 2011
DOI 10.1371/journal.pone.0016370
Pubmed ID
Authors

Ricardo M. Holdo, John M. Fryxell, Anthony R. E. Sinclair, Andrew Dobson, Robert D. Holt

Abstract

The Serengeti wildebeest migration is a rare and spectacular example of a once-common biological phenomenon. A proposed road project threatens to bisect the Serengeti ecosystem and its integrity. The precautionary principle dictates that we consider the possible consequences of a road completely disrupting the migration. We used an existing spatially-explicit simulation model of wildebeest movement and population dynamics to explore how placing a barrier to migration across the proposed route (thus creating two disjoint but mobile subpopulations) might affect the long-term size of the wildebeest population. Our simulation results suggest that a barrier to migration--even without causing habitat loss--could cause the wildebeest population to decline by about a third. The driver of this decline is the effect of habitat fragmentation (even without habitat loss) on the ability of wildebeest to effectively track temporal shifts in high-quality forage resources across the landscape. Given the important role of the wildebeest migration for a number of key ecological processes, these findings have potentially important ramifications for ecosystem biodiversity, structure, and function in the Serengeti.

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Geographical breakdown

Country Count As %
United States 5 2%
Canada 2 <1%
United Kingdom 1 <1%
Germany 1 <1%
Spain 1 <1%
Norway 1 <1%
Unknown 260 96%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 56 21%
Researcher 48 18%
Student > Master 37 14%
Student > Bachelor 35 13%
Student > Doctoral Student 12 4%
Other 40 15%
Unknown 43 16%
Readers by discipline Count As %
Agricultural and Biological Sciences 123 45%
Environmental Science 65 24%
Earth and Planetary Sciences 7 3%
Biochemistry, Genetics and Molecular Biology 6 2%
Economics, Econometrics and Finance 4 1%
Other 18 7%
Unknown 48 18%