Innovative approach for desert locust Schistocerca gregaria (Forskål) distribution and monitoring in Sudan using ecological niche modelling

Desert locust Schistocerca gregaria (Forskål, 1775) is destructive species due to its ability to swarm and fly across long distances. Desert locust surveillance protocols require consolidation of efforts for detecting and monitoring the pest occurrence and distribution. This study aims to predict spatiotemporal occurrence and habitat distribution of desert locust using innovative geospatial techniques. To achieve this objective, the study collated long-term (i.e. 15 years) historical data on desert locust occurrence between 2003 and 2017 from the Reconnaissance and Monitoring System of the Environment of Schistocerca (RAMSES) database and remotely sensed bioclimatic variables (n = 19) from AfriClim. The maximum entropy (MaxEnt) ecological niche model was used to elucidate current and future (i.e. 2050) desert locust distribution1,2. The results proved the efficiency of MaxEnt model to accurately {area under curve (AUC) ranged between 0.86 and 0.99} predict current and future habitat suitability of desert locust occurrence, breeding sites and swarm formation during recession and outbreak periods in winter and summer seasons. The findings of the current study can be used as advisory tools for desert locust surveillance and monitoring in the traditional and new desert locust breeding and swarm sites in Sudan. Overall, the study provides baseline information, which could explain possible factors behind the 2020 desert locust upsurge in eastern Africa and south Asian countries.