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Deforestation and the population decline of the army ant Dorylus wilverthi in western Kenya over the last century

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1. It is important to document changes in the population sizes of species to justify and implement conservation programmes. This is often hampered by the absence of historical population count data, particularly for tropical animal species.

2. Here, we developed a predictive habitat distribution model (PHDM) for the army ant Dorylus wilverthi, a keystone species of Central African rainforests. We applied the model to forest cover data in western Kenya from 1913 to 2003 in order to estimate changes in the distribution and population size over the last century. The model was based on a large data set including abundance data of 51 repeatedly monitored 500 m transects in forest and non-forest habitats.

3. Forest cover within 1400 m of the transect lines explained 58% of the variation in abundance of D. wilverthi. The model predicts that the colony abundance of the army ant exponentially increases with increasing forest cover and that colonies are absent from landscapes with less than 11% forest cover.

4. Applying the PHDM to historical and recent forest cover data predicts a 52% decline in the population size of D. wilverthi (from 2801 to 1346 colonies) between 1913 and 2003. Under a worst-case scenario in which the forest area is reduced to the current national/nature reserves, the population size would decrease to fewer than 300 colonies, which is probably too low to ensure the long-term survival of the species.

5.Synthesis and applications. Our study demonstrates that the application of a PHDM on land-cover change time series data may give valuable insights into changes in the population size of species for which no historical population count data are available. Using this approach, we revealed a significant decline in population size of the army ant D. wilverthi over the last century. High protection status should be applied to all of the remaining forest area to prevent the population becoming regionally threatened and to facilitate long-term conservation of this keystone species in Kenya.
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Keywords: Anomma; army ants; driver ants; keystone species; population size change; predictive habitat distribution models; species distribution models; tropical rain forest change

Document Type: Research Article

Affiliations: 1: Faculty of Geomatics, Karlsruhe University of Applied Sciences, Moltkestrasse 30, D-76133 Karlsruhe, Germany 2: Zoological Research Museum Alexander Koenig, Adenauerallee 160, D-53113 Bonn, Germany

Publication date: June 1, 2011

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