Plant communities can predict the distribution of solitarious desert locust Schistocerca gregaria.

Published online
02 Nov 2005
Content type
Journal article
Journal title
Journal of Applied Ecology
DOI
10.1111/j.1365-2664.2005.01073.x

Author(s)
Werf, W. van der & Woldewahid, G. & Huis, A. van & Butrous, M. & Sykora, K.
Contact email(s)
wopke.vanderwerf@wur.nl

Publication language
English
Location
Africa South of Sahara & Sudan

Abstract

The desert locust is a migratory pest whose population development in remote areas must be monitored to prevent outbreaks, upsurges and plagues. Monitoring would be very much facilitated if the area of search could be restricted to sites of likely population increase. The spatial distribution of solitarious desert locusts on the Red Sea coastal plain of Sudan was determined over 3 years from November to March. Additional observations were made on habitat factors, such as plant community, soil texture, soil moisture and land use. Locust densities varied according to the amount and distribution of rainfall and longevity of the annual green vegetation, with virtually no locusts being observed in the driest season. Samples on a grid of 120 sites within a 120-km stretch of coastal plain showed that locusts were prevalent only in the millet-Heliotropium plant community, which is found at sites with a fine sandy soil texture and comparatively high and long-lasting soil moisture in wadi deltas. These sites constitute less than 5% of the area of this part of the plain. An accessory study showed association between desert locust distribution and millet cropping in an area where no Heliotropium was found. Other samples confirmed the association between solitarious desert locust and millet agriculture. Synthesis and applications. The results indicate that surveys for early detection and control of desert locust on the Red Sea coast of Sudan can focus on millet cropping areas. The results suggest that the efficiency of monitoring migratory pest outbreaks in remote areas could be enhanced by using associations between plant communities and herbivorous insects to predict risk areas and target survey efforts.

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