Desert Locust (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key Upsurge Area

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dc.contributor.authorMongare, Raphael
dc.contributor.authorAbdel-Rahman, Elfatih M.
dc.contributor.authorMudereri, Bester Tawona
dc.contributor.authorKimathi, Emily
dc.contributor.authorOnywere, Simon
dc.contributor.authorTonnang, Henri E. Z.
dc.date.accessioned2023-06-30T07:03:42Z
dc.date.available2023-06-30T07:03:42Z
dc.date.issued2023-03
dc.descriptionArticleen_US
dc.description.abstractIn the recent past, the Horn of Africa witnessed an upsurge in the desert locust (Schistocerca gregaria) invasion. This has raised major concerns over the massive food insecurity, socioeconomic impacts, and livelihood losses caused by these recurring invasions. This study determined the potential vegetation damage due to desert locusts (DLs) and predicted the suitable habitat at high risk of invasion by the DLs using current and future climate change scenarios in Kenya. The normalized difference vegetation index (NDVI) for the period 2018–2020 was computed using multi-date Sentinel- 2 imagery in the Google Earth Engine platform. This was performed to assess the vegetation changes that occurred between May and July of the year 2020 when northern Kenya was the hotspot of the DL upsurge. The maximum entropy (MaxEnt) algorithm was used together with 646 DL occurrence records and six bioclimatic variables to predict DL habitat suitability. The current (2020) and two future climatic scenarios for the shared socioeconomic pathways SSP2-4.5 and SSP5-8.5 from the model for interdisciplinary research on climate (MIROC6) were utilized to predict the future potential distribution of DLs for the year 2030 (average for 2021–2040). Using Turkana County as a case, the NDVI analysis indicated the highest vegetation damage between May and July 2020. The MaxEnt model produced an area under the curve (AUC) value of 0.87 and a true skill statistic (TSS) of 0.61, while temperature seasonality (Bio4), mean diurnal range (Bio2), and precipitation of the warmest quarter (Bio18) were the most important bioclimatic variables in predicting the DL invasion suitability. Further analysis demonstrated that currently 27% of the total area in Turkana County is highly suitable for DL invasion, and the habitat coverage is predicted to potentially decrease to 20% in the future using the worst-case climate change scenario (SSP5-8.5). These results have demonstrated the potential of remotely sensed data to pinpoint the magnitude and location of vegetation damage caused by the DLs and the potential future risk of invasion in the region due to the available favorable vegetational and climatic conditions. This study provides a scalable approach as well as baseline information useful for surveillancen_US
dc.description.sponsorshipSwedish International Development Cooperation Agency (Sida); the Swiss Agency for Development and Cooperation (SDC); the Australian Centre for International Agricultural Research (ACIAR); the Federal Democratic Republic of Ethiopia; and the Government of the Republic of Kenya.en_US
dc.identifier.citationMongare, R., Abdel-Rahman, E. M., Mudereri, B. T., Kimathi, E., Onywere, S., & Tonnang, H. E. (2023). Desert Locust (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key Upsurge Area. Earth, 4(2), 187-208.en_US
dc.identifier.urihttp://ir-library.ku.ac.ke/handle/123456789/26014
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.subjectfood securityen_US
dc.subjectinsect pest upsurgeen_US
dc.subjectKenyaen_US
dc.subjectMaxEnten_US
dc.subjectSentinel-2en_US
dc.subjectspecies distribution modelen_US
dc.subjectvegetation indexen_US
dc.titleDesert Locust (Schistocerca gregaria) Invasion Risk and Vegetation Damage in a Key Upsurge Areaen_US
dc.typeArticleen_US
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