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Predicting the spread of all invasive forest pests in the United StatesAuthor(s): Emma J. Hudgins; Andrew M. Liebhold; Brian Leung; Regan Early
Source: Ecology Letters
Publication Series: Scientific Journal (JRNL)
Station: Northern Research Station
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DescriptionWe tested whether a general spread model could capture macroecological patterns across all damaging invasive forest pests in the United States. We showed that a common constant dispersal kernel model, simulated from the discovery date, explained 67.94% of the variation in range size across all pests, and had 68.00% locational accuracy between predicted and observed locational distributions. Further, by making dispersal a function of forest area and human population density, variation explained increased to 75.60%, with 74.30% accuracy. These results indicated that a single general dispersal kernel model was sufficient to predict the majority of variation in extent and locational distribution across pest species and that proxies of propagule pressure and habitat invasibility – wellstudied predictors of establishment – should also be applied to the dispersal stage. This model provides a key element to forecast novel invaders and to extend pathway-level risk analyses to include spread.
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CitationHudgins, Emma J.; Liebhold, Andrew M.; Leung, Brian; Early, Regan. 2017. Predicting the spread of all invasive forest pests in the United States. Ecology Letters. 20(4): 426-435. https://doi.org/10.1111/ele.12741.
KeywordsDispersal kernel, habitat invasibility, macroecology, propagule pressure, spatially explicit
- Representing uncertainty in a spatial invasion model that incorporates human-mediated dispersal
- A dominance-based approach to map risks of ecological invasions in the presence of severe uncertainty
- Human-mediated dispersal in insects
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