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Computational Biogeographic Distribution of the Fall armyworm (Spodoptera frugiperda J.E. Smith) Moth in Eastern Africa

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dc.contributor.author Elfatih M., Abdel-Rahman
dc.contributor.author Kimathi, Emily
dc.contributor.author Mudereri, Bester Tawona
dc.contributor.author Tonnang, Henri E.Z.
dc.contributor.author Mongare, Raphael
dc.contributor.author Saliou, Niassy
dc.contributor.author Subramanian, Sevgan
dc.date.accessioned 2023-10-09T12:08:43Z
dc.date.available 2023-10-09T12:08:43Z
dc.date.issued 2023
dc.identifier.uri http://hdl.handle.net/20.500.12562/1872
dc.description Publication en_US
dc.description.abstract The fall armyworm (FAW), Spodoptera frugiperda J.E. Smith, has caused massive maize losses since its attack on the African continent in 2016, particularly in east Africa. In this study, we predicted the spatial distribution (established habitat) of FAW in five east African countries viz.,Kenya, Tanzania, Rwanda, Uganda, and Ethiopia. We used FAW occurrence observations for three years i.e., 2018, 2019, and 2020, the maximum entropy (MaxEnt) model, and bioclimatic, land surface temperature (LST), solar radiation, wind speed, elevation, and landscape structure data (i.e., land use and land cover and maize harvested area) as explanatory variables. The explanatory variables were used as inputs into a variable selection experiment to select the least correlated ones that were then used to predict FAW establishment, i.e., suitability areas (very low suitability – very high suitability). The shared socio-economic pathways, SSP2-4.5 and SSP5-8.5 for the years 2030 and 2050 were used to predict the effect of future climate scenarios on FAW establishment. The results demonstrated that FAW establishment areas in eastern Africa were based on the model strength and true performance (area under the curve: AUC = 0.87), but not randomly.Moreover, ~27% of eastern Africa is currently at risk of FAW establishment. Predicted FAW risk areas are expected to increase to ~29% (using each of the SSP2-4.5 and SSP5-8.5 scenarios) in the year 2030, and to ~38% (using SSP2-4.5) and ~35% (using SSP5-8.5) in the year 2050 climate scenarios. The LULC, particularly croplands and maize harvested area, together with temperature and precipitation bioclimatic variables provided the highest permutation importance in determining the occurrence and establishment of the pest in eastern Africa. Specifically, the study revealed that FAW was sensitive to isothermality (Bio3) rather than being sensitive to a single temperature value in the year. FAW preference ranges of temperature, precipitation, elevation,and maize harvested area were observed, implying the establishment of a once exotic pest in critical maize production regions in eastern Africa. It is recommended that future studies should thus embed the present study’s modeling results into a dynamic platform that provides near-realtime predictions of FAW spatial occurrence and risk at the farm scale en_US
dc.description.sponsorship European Union (EU) Swedish International Development Cooperation Agency (Sida) Swiss Agency for Development and Cooperation (SDC) Federal Democratic Republic of Ethiopia Government of the Republic of Kenya icipe en_US
dc.publisher Heliyon en_US
dc.rights Attribution-NonCommercial-ShareAlike 3.0 United States *
dc.rights.uri http://creativecommons.org/licenses/by-nc-sa/3.0/us/ *
dc.subject Pest ecology en_US
dc.subject MaxEn en_US
dc.subject Landscape en_US
dc.subject Climate change en_US
dc.subject invasive species en_US
dc.subject Pest management en_US
dc.subject Citizen science en_US
dc.title Computational Biogeographic Distribution of the Fall armyworm (Spodoptera frugiperda J.E. Smith) Moth in Eastern Africa en_US
dc.type Article en_US


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