Combining local‐ and large‐scale models to predict the distributions of invasive plant species

A novel approach combining local and large-scale models reveals significant long-term invasion risk for three invasive plant species within Olympic National Park (ONP). While less than 4% of the park faces high near-term risk, a staggering 67-99% is at moderate or high long-term risk. The study compared logistic regression with presence-only methods and ensemble models to predict distributions, finding that larger-scale climate envelope models, derived from native and invaded ranges, provided more accurate long-term predictions despite lacking finer resolution. This research introduces an index of near- and long-term invasion risk, offering a strategic framework for monitoring and management in ONP. By integrating multi-scale modeling with real-time data, the study proposes a dynamic approach to overcome the limitations of static habitat distribution models.

Jones, C. C., Acker, S. A., & Halpern, C. B. (2010). Combining local‐ and large‐scale models to predict the distributions of invasive plant species. Ecological Applications, 20(2), 311–326. Portico. https://doi.org/10.1890/08-2261.1