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EcologybioRxiv
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Integrating host plants and a natural enemy into MaxEnt improves global suitability predictions for Semanotus bifasciatus

Meiyi YangiD, Zou YaiD, Yuting Zhou, Yuhang Fan, Shixiang ZongiD

Abstract

BACKGROUNDSemanotus bifasciatus is a major conifer pest that causes severe wood damage. The parasitoid Sclerodermus guani is its important natural enemy. However few studies have jointly considered host plants and enemy effects when predicting pest ranges. We applied the optimized MaxEnt model and, on this basis, constructed two models: the "host-pest" model and the "host-pest-enemy" model, to predict the potential global distribution of S. bifasciatus under future scenarios and to explore the effects of climate change and the introduction of biotic interactions. RESULTSResults showed that the climate-only model projected 2.73 x 10{square} km{superscript 2} of suitable area under the historical climate condition, concentrated in Asia, North America and Europe, with expansion toward higher latitudes. The expansion of host plants further enhanced pest habitat suitability, nearly doubling the predicted range (5.46 x 10{square} km{superscript 2}) and increasing the mean suitability. Moreover, the potential distribution of S. guani overlapped extensively with S. bifasciatus, reducing the total suitable area of S. bifasciatus by up to 2.67 x 10{square} km{superscript 2}, and mean suitability declined by nearly 40%, indicating effective suppression of pest risk. Centroid shifts were consistently northward, though magnitude and fragmentation varied among models. CONCLUSIONIntegrating host availability and enemy suppression improves the realism of distribution forecasts for S. bifasciatus. The study highlights the roles of biotic factors in shaping pest suitability and identifies potential future high-risk regions of infestation. These insights provide a solid scientific foundation for targeted monitoring, and the strategic application of biological control in adaptive forest pest management.

PDFDOI

02 /Specification & Classification

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