Journal
INSECTS
Volume 12, Issue 4, Pages -Publisher
MDPI
DOI: 10.3390/insects12040273
Keywords
fall armyworm; climate change; pest management; machine-learning algorithm; decision support
Categories
Funding
- Royal Norwegian Embassy in Mali [MLI-17-0008, NER-17-0005]
- Federal Ministry for Economic Cooperation and Development (BMZ/GIZ) [81235252 GA]
- European Union [FOOD/2018/402634]
- CGIAR Trust Fund
- IITA
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The fall armyworm is now a global pest, with biological control being a sustainable and environmentally friendly option. Predicting the habitats of parasitoids in South America offers new insights for controlling the pest.
Simple Summary The fall armyworm (FAW), Spodoptera frugiperda has now become a pest of global importance. Its introduction and detection in Africa in 2016, and subsequent introduction and spread into Asia and Australia, has put several millions of food producers and maize farmers at risk. Not all pest management strategies are sustainable. Biological control with the use of parasitoid wasps is one of the durable and environmentally sound options. The present study was initiated to predict the habitats of high establishment potential of key parasitoids of FAW in South America, which might prove to be effective as classical biological control agents of FAW in regions where it is an invasive species under current and future climate scenarios. The prospective parasitoids are the following: Chelonus insularis, Cotesia marginiventris, Eiphosoma laphygmae, Telenomus remus and Trichogramma pretiosum. The results demonstrate overlapping habitat suitability areas of the pest and the parasitoids, suggesting promises for biological control options for the management of FAW under current and future climate scenarios. The present study is the first modeling effort at a global scale to predict habitat suitability of fall armyworm (FAW), Spodoptera frugiperda and its key parasitoids, namely Chelonus insularis, Cotesia marginiventris, Eiphosoma laphygmae, Telenomus remus and Trichogramma pretiosum, to be considered for biological control. An adjusted procedure of a machine-learning algorithm, the maximum entropy (Maxent), was applied for the modeling experiments. Model predictions showed particularly high establishment potential of the five hymenopteran parasitoids in areas that are heavily affected by FAW (like the coastal belt of West Africa from Cote d'Ivoire (Ivory Coast) to Nigeria, the Congo basin to Eastern Africa, Eastern, Southern and Southeastern Asia and some portions of Eastern Australia) and those of potential invasion risks (western & southern Europe). These habitats can be priority sites for scaling FAW biocontrol efforts. In the context of global warming and the event of accidental FAW introduction, warmer parts of Europe are at high risk. The effect of winter on the survival and life cycle of the pest in Europe and other temperate regions of the world are discussed in this paper. Overall, the models provide pioneering information to guide decision making for biological-based medium and long-term management of FAW across the globe.
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