Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area
In the recent past, the Horn of Africa witnessed an upsurge in the desert locust (<i>Schistocerca gregaria</i>) invasion. This has raised major concerns over the massive food insecurity, socioeconomic impacts, and livelihood losses caused by these recurring invasions. This study determin...
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MDPI AG
2023-03-01
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| Series: | Earth |
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| Online Access: | https://www.mdpi.com/2673-4834/4/2/10 |
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| author | Raphael Mongare Elfatih M. Abdel-Rahman Bester Tawona Mudereri Emily Kimathi Simon Onywere Henri E. Z. Tonnang |
| author_facet | Raphael Mongare Elfatih M. Abdel-Rahman Bester Tawona Mudereri Emily Kimathi Simon Onywere Henri E. Z. Tonnang |
| author_sort | Raphael Mongare |
| collection | DOAJ |
| description | In the recent past, the Horn of Africa witnessed an upsurge in the desert locust (<i>Schistocerca gregaria</i>) 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 surveillance, development of control programs, and monitoring of DL invasions at local and regional scales. |
| first_indexed | 2024-03-11T02:33:58Z |
| format | Article |
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| institution | Directory Open Access Journal |
| issn | 2673-4834 |
| language | English |
| last_indexed | 2024-03-11T02:33:58Z |
| publishDate | 2023-03-01 |
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| spelling | doaj.art-b0c8de0c7c1448c2bcd66887b8cb562f2023-11-18T10:04:43ZengMDPI AGEarth2673-48342023-03-014218720810.3390/earth4020010Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge AreaRaphael Mongare0Elfatih M. Abdel-Rahman1Bester Tawona Mudereri2Emily Kimathi3Simon Onywere4Henri E. Z. Tonnang5International Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, KenyaInternational Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, KenyaInternational Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, KenyaInternational Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, KenyaDepartment of Spatial and Environmental Planning, School of Architecture and Built Environment, Kenyatta University, P.O. Box 43844, Nairobi 00100, KenyaInternational Centre of Insect Physiology and Ecology (icipe), P.O. Box 30772, Nairobi 00100, KenyaIn the recent past, the Horn of Africa witnessed an upsurge in the desert locust (<i>Schistocerca gregaria</i>) 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 surveillance, development of control programs, and monitoring of DL invasions at local and regional scales.https://www.mdpi.com/2673-4834/4/2/10food securityinsect pest upsurgeKenyaMaxEntSentinel-2species distribution model |
| spellingShingle | Raphael Mongare Elfatih M. Abdel-Rahman Bester Tawona Mudereri Emily Kimathi Simon Onywere Henri E. Z. Tonnang Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area Earth food security insect pest upsurge Kenya MaxEnt Sentinel-2 species distribution model |
| title | Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area |
| title_full | Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area |
| title_fullStr | Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area |
| title_full_unstemmed | Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area |
| title_short | Desert Locust (<i>Schistocerca gregaria</i>) Invasion Risk and Vegetation Damage in a Key Upsurge Area |
| title_sort | desert locust i schistocerca gregaria i invasion risk and vegetation damage in a key upsurge area |
| topic | food security insect pest upsurge Kenya MaxEnt Sentinel-2 species distribution model |
| url | https://www.mdpi.com/2673-4834/4/2/10 |
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