Prediction of breeding regions for the desert locust Schistocerca gregaria in East Africa

Emily Kimathi,Chrysantus M Tanga,Mehari Tesfayohannes,Menale Kassie,Sunday Ekesi,Keith Cressman,Saliou Niassy,Segenet Kelemu,Baldwyn Torto,Sevgan Subramanian,David Mwangi,Henri E Z Tonnang,Thomas Dubois,Elfatih M Abdel-Rahman

doi:10.1038/s41598-020-68895-2

Abstract

Desert locust outbreak in East Africa is threatening livelihoods, food security, environment, and economic development in the region. The current magnitude of the desert locust invasion in East Africa is unprecedented and has not been witnessed for more than 70 years. Identifying the potential breeding sites of the pest is essential to carry out cost-effective and timely preventive measures before it inflicts significant damage. We accessed 9,134 desert locust occurrence records and applied a machine-learning algorithm to predict potential desert locust breeding sites in East Africa using key bio-climatic (temperature and rainfall) and edaphic (sand and moisture contents) factors. Ten days greenness maps from February 2020 to April 2020 were overlaid in model outputs to illustrate the temporal evolution of breeding site locations. This study demonstrated that vast areas of Kenya and Sudan, north eastern regions of Uganda, and south eastern and northern regions of South Sudan are at high risk of providing a conducive breeding environment for the desert locust. Our prediction results suggest that there is need to target these high-risk areas and strengthen ground surveillance to manage the pest in a timely, cost-effective, and environmentally friendly manner.

Highlights

Desert locust outbreak in East Africa is threatening livelihoods, food security, environment, and economic development in the region
The area under the curve (AUC) on the graphs confirmed that all individual models performed well in predicting Morocco (Fig. 1A), Mauritania (Fig. 1B), and Saudi Arabia (Fig. 1C) desert locust breeding areas
The results show that the Morocco model parameters obtained from the maximum entropy (MaxEnt) algorithm (Fig. 2A) performed the best for projecting desert locust breeding sites to other countries as compared to Mauritania and Saudi Arabia models

Summary

Introduction

Desert locust outbreak in East Africa is threatening livelihoods, food security, environment, and economic development in the region. Effective ground and aerial surveillance are constrained by various factors including extensive area of invasion (e.g., 107,000 km[2] in Kenya), inaccessibility of invasion zones due to poor infrastructure, limited resources, lack of human capacity for monitoring and control, and difficulties in predicting suitable areas for breeding and outbreaks Such constraints are typical to the currently invaded zones in Kenya, Uganda, and South Sudan, and to other nearby countries at risk. Previous desert locust outbreaks in the Horn of Africa were observed in 1996–1998, and it affected countries along the Red Sea, with infestations primarily concentrated in Saudi Arabia and, to a lesser extent, in Egypt, Ethiopia, Eritrea, Northern Somalia, Sudan, and Yemen Countries such as Kenya and Uganda have not experienced the current level of desert locust invasion for more than 70 years, and little or no information is available on the suitability of specific sites for desert locust oviposition and b reeding[13]. Such information is urgently needed to strengthen surveillance (ground and aerial) efforts, regional coordination, and preparedness, inform efforts and improve the delivery of preventive measures before the newly emerging hoppers cause damage

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Methods

Results

Conclusion