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  • Presentation | B41G: Bridging Remote Sensing, Machine Learning, and Ecological Modeling to Address Forest Health Challenges I Poster
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  • B41G-1947: Predicting future synchrony between tree bud break and egg hatch of a major forest pest in northeastern US
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Author(s):
Cameron Scholl, Cornell University (First Author, Presenting Author)
Xiangtao Xu, Cornell University


Spongy moth periodically reach very high population levels called outbreaks, during which they can eat nearly all of the leaves on a tree. This can have profound impacts on forest as trees will not be able to photosynthesize without their leaves and may die if they experience this damage several years in a row. Previous studies have suggested that pathogens that target spongy moth may not be able to infect caterpillars as efficiently in warm and dry conditions, which are expected to occur more often in the future. As a result, outbreaks may happen more often, and be larger. However, spongy moth also depend on eggs hatching at the same time as nutritious, young leaves emerge in the spring. The timing of leaf emergence is similarly changing as the climate warms. We do not know if spongy moth egg hatch will keep pace with earlier leaf emergence in the future, or if this a greater mismatch will reduce the impact of lower pathogen infection. In this study, we combine existing models of spongy moth and tree life cycles to explore when and where large mismatches will occur in the future.



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