Predictive Modelling of the Potential Distribution of Spodoptera frugiperda (J.E. Smith) in Ecuador Under Changing Climatic Conditions: A Basis for Management Strategies
DOI:
https://doi.org/10.28940/terra.latinam..v44i.2498Keywords:
global warming, phytosanitary management, fall armyworm, ecological niche, pestAbstract
Among the most significant agricultural pests, Spodoptera frugiperda stands out for the significant economic impact it causes. In Ecuador, this species represents a persistent threat, and monitoring and management strategies within its distribution area need to be strengthened. This study evaluated changes in the potential geographic distribution of S. frugiperda using ecological niche models, considering alterations associated with climate change. Based on 211 records of presence in mainland Ecuador a species distribution model based on climatic suitability was developed to characterize the current and potential distribution of the species in two future climate scenarios. The optimal niche is defined by rainfall in the driest month between 0-20 mm, annual temperature ranges of 10.5-13 °C, and elevations < 500 m of altitude. Excessive rainfall is associated with areas of low favorability. Approximately 78 151 km² of territory contain favorable climatic conditions for S. frugiperda. The coast is home to 62.5% of the favorable area in the provinces of Los Ríos, Guayas, Manabí, Santa Elena, and El Oro. The highlands contain 36.5% of the favorable area, and the Amazon is the least vulnerable due to its excessive rainfall. Projections under the conservative scenario indicate relative stability. In contrast, the extreme scenario projects a progressive expansion to 89 482 km² by 2100. The alarming change will be in the Amazon region, which would experience a potential expansion from the current 527 km² to 9680 km² in 2100. This complex dynamic of spatial redistribution implies that some areas that are currently hotspots for infestation will become less favorable, while regions historically free of the pest will become high-risk areas. This change requires a fundamental reconfiguration of monitoring and control strategies. This information will enable a consolidated diagnosis and the implementation of ef fective management measures.
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