INHABIT: A web-based decision support tool for invasive plant species habitat visualization and assessment across the contiguous United States

Invasive Species Policy Must Embrace a Changing Climate

With increasing impacts of climate change observed across ecosystems, there is an urgent need to consider climate change in all future environmental policy. But existing policy and management might be slow to respond to this challenge, leading to missed opportunities to incorporate climate change into practice. Furthermore, invasive species threats continue to rise and interact with climate change—exacerbating negative impacts. Enabling natural resource managers and individuals to be proactive about climate-driven invasive species threats creates a win–win for conservation. Recommendations include expanding opportunities for information sharing across borders, supporting proactive screening and regulation of high-risk species on the horizon, and incentivizing individual actions that reduce ecological impacts. In addition, invasive species risk should be considered when crafting climate mitigation and adaptation policy to reduce compounding stressors on ecosystems. As we develop much-needed tools to reduce harm, policy and management must consider the combined threats of invasions and climate change.

Regional models do not outperform continental models for invasive species

Aim: Species distribution models can guide invasive species prevention and management by characterizing invasion risk across space. However, extrapolation and transferability issues pose challenges for developing useful models for invasive species. Previous work has emphasized the importance of including all available occurrences in model estimation, but managers attuned to local processes may be skeptical of models based on a broad spatial extent if they suspect the captured responses reflect those of other regions where data are more numerous. We asked whether species distribution models for invasive plants performed better when developed at national versus regional extents.

Location: Continental United States.

Methods: We developed ensembles of species distribution models trained nationally, on sagebrush habitat, or on sagebrush habitat within three ecoregions (Great Basin, eastern sagebrush, and Great Plains) for nine invasive plants of interest for early detection and rapid response at local or regional scales. We compared the performance of national versus regional models using spatially independent withheld test data from each of the three ecoregions.

Results: We found that models trained using a national spatial extent tended to perform better than regionally trained models. Regional models did not outperform national ones even when considerable occurrence data were available for model estimation within the focal region. Information was often unavailable to fit informative regional models precisely in those areas of greatest interest for early detection and rapid response.

Main conclusions: Habitat suitability models for invasive plant species trained at a continental extent can reduce extrapolation while maximizing information on species’ responses to environmental variation. Standard modeling methods can capture spatially varying limiting factors, while regional or hierarchical models may only be advantageous when populations differ in their responses to environmental conditions, a condition expected to be relatively rare at the expanding boundaries of invasive species’ distributions.