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Meeting # 942 7359 5552

Zoom Meeting # 942 7359 5552

Abstract:

The spread of pest insect species poses major challenges to agriculture, forestry, and biodiversity. The Food and Agriculture Organization of the United Nations estimates that pests cause 20–40% of global crop losses annually, valued at over $290 billion. To mitigate these losses, understanding where these pests might spread—especially in response to climate change—is crucial for proactive management and food security.

Species distribution modeling (SDM) is a powerful tool for predicting where species are likely to thrive. One widely used method is Maxent, a machine learning-based ecological model. In this talk, I will present a study that predicts suitable habitats for three key pest insect species in the continental United States:

• Brown Marmorated Stink Bug (Halyomorpha halys)
• Corn Earworm (Helicoverpa zea)
• Root Weevil (Diaprepes abbreviatus)

Our models integrate pest occurrence records with crop data, bioclimatic variables, and elevation profiles to estimate current habitat suitability.

We also analyze key factors driving these changes, highlighting the importance of careful model design—particularly the selection of background points, a crucial aspect of Maxent modeling that can influence results.

Our findings emphasize the need to incorporate future climate scenarios into pest management strategies, offering valuable insights to enhance agricultural resilience and ecological sustainability.

Speaker's Bio

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