Predicting spatial distribution of key honeybee pests in Kenya using remote sensed and bioclimatic variables: Key honeybee pests distribution models

Presents data on 4 pests - Aethina tumida, Galleria mellonella, Oplostomus haroldi, Varroa destructor

Abstract

Beekeeping is not only essential for overall food production, it also provides an alternate income source (especially in rural underdeveloped African settlements) and an incentive to forest-adjacent communities in Kenya to conserve the forests: Pests and diseases are to blame for the dwindling honey bee colonies around the world, but their real distribution and influences are unknown.

This article presents collected occurrence data on 4 pests (Aethina tumida, Galleria mellonella, Oplostomus haroldi and Varroa destructor) from apiaries within 4 main agroecological regions where over 80% of Kenya’s beekeeping is undertaken (Coast, Mount Kenya, Mwingi and Kakamega), to identify conditions that encourage increase of honey bee pests in the country. The investigators also developed models that could be used to predict their spread. Forecasts reveal the level of risk posed by the honey bee pests to beekeepers in future.

This is an output froom the ‘Geo-Information Unit and African Reference Laboratory (with Satellite Stations) for the Management of Pollinator Bee Diseases and Pests for Food Security’ project. It is partly funded by the UK Department for International Development, a core donor of the International Centre of Insect Physiology and Ecology.

Citation

Makori D., Fombong A., Abdelrahman E., Kiatoko N., Ongus J., Irungu J., Mosomtai G., Makau S., Mutanga O., Odindi J., Raina S. and Landmann T. (2017) Predicting spatial distribution of key honeybee pests in Kenya using remote sensed and bioclimatic variables: Key honeybee pests distribution models. International Journal of Geo-Information 6, 66. doi:10.3390/ijgi6030066.

Predicting spatial distribution of key honeybee pests in Kenya using remote sensed and bioclimatic variables: Key honeybee pests distribution models

Published 1 May 2017