US-IALE 2018

AUTHORS: Craig Fergus*, Iara Lacher, Bill McShea, Tom Akre – Smithsonian Conservation Biology Institute

ABSTRACT: Non-native invasive plants can aggressively colonize forest habitat, with detrimental impacts to native biodiversity and ecosystem function. Unfortunately, maintaining native forest plant communities in the face of non-native plant invasions is a difficult and costly task, especially for managers with limited resources. The ability to identify areas vulnerable to invasion would enable managers to limit the spread of invasive plants through proactive surveillance and mitigation. To investigate the spread of invasive plants, we created species distribution models (SDMs) for 14 invasive forest plant species, grouped by functional traits, in Northwestern Virginia using physiographic and land use data. An SDM can project potential suitable habitat based on an improved understanding of the relationship between environmental variables and species distributions. However, unlike SDMs for native species, we cannot assume that occurrences for invasive species in the study area capture a broad enough range of environmental variables to precisely estimate their suitable but uncolonized habitat. We could utilize occurrences from native ranges but this approach is resource intensive and contains additional assumptions. Instead, our models account for actively colonizing species by including components for both propagule pressure and limitations on where each species has had the opportunity to disperse. We related occurrence data from publicly available data sets (8770 records) and two seasons of field surveys (5240 records) to habitat and landscape variables including patch size, soil productivity, and surrounding land use. We used the resulting relationships to map the relative vulnerability of forested areas to each invasive species group. By identifying high and low risk areas, our maps aid decision making for land managers and allows them to make proactive decisions by focusing resources on vulnerable high priority regions.