Multivariate forecasts of potential distributions of invasive plant species.


The fact that plant invasions are an ongoing process makes generalizations of invasive spread extraordinarily challenging. This is particularly true given the idiosyncratic nature of invasions, in which both historical and local conditions affect establishment success and hinder our ability to generate guidelines for early detection and eradication of invasive species. To overcome these limitations we have implemented a comprehensive approach that examines plant invasions at three spatial scales: regional, landscape, and local levels. At each scale, in combination with the others, we have evaluated the role of key environmental variables such as climate, landscape structure, habitat type, and canopy closure in the spread of three commonly found invasive woody plant species in New England, Berberis thunbergii, Celastrus orbiculatus, and Euonymus alatus. We developed a spatially explicit hierarchical Bayesian model that allowed us to take into account the ongoing nature of the spread of invasive species and to incorporate presence/absence data from the species' native ranges as well as from the invaded regions. Comparisons between predictions from climate-only models with those from the multiscale forecasts emphasize the importance of including landscape structure in our models of invasive species' potential distributions. In addition, predictions generated using only native range data performed substantially worse than those that incorporated data from the target range. This points out important limitations in extrapolating distributional ranges from one region to another.

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@article{Ibez2009MultivariateFO, title={Multivariate forecasts of potential distributions of invasive plant species.}, author={In{\'e}s Ib{\'a}{\~n}ez and John A. Silander and Adam M. Wilson and Nancy Lafleur and Nobuyuki Tanaka and Ikutaro Tsuyama}, journal={Ecological applications : a publication of the Ecological Society of America}, year={2009}, volume={19 2}, pages={359-75} }