Integrating climate and trait models to predict the invasiveness of exotic plants in Canada’s Riding Mountain National Park

Abstract

Invasive exotic species threaten the biodiversity and function of native ecosystems. Existing models, attempting to predict successful invaders, often emphasize isolated stages of biological invasions and fail to formalize interactions between exotic species and recipient environments. By integrating the native climatic range and biological traits of exotic vascular plants reported inside and outside Riding Mountain National Park, Manitoba, Canada, we present a model where invasion risk is predicted using the likelihood of establishment and proliferation of exotic species. Exotic vascular plants constituted 11.5% (77/669) of the total vascular flora of Riding Mountain National Park and approximately 14.2% (202/1418) of the flora of Manitoba. Based on their climatic range in Europe, 155 among 174 exotic species absent from the Park, including those found in Manitoba and other natural areas in Canada, were predicted to establish within its boundaries. Among the biological traits of exotic plants, perenniality and vegetative reproduction correctly identified 61% of documented invaders of natural areas in Canada and helped to further define a subset of 40 exotic plants with the highest potential to proliferate within the Park’s natural areas. Among these, 11 are still absent from inside its boundaries and 17 are not yet reported in Manitoba. Our results demonstrate that Riding Mountain National Park and natural areas in western Canada will continue to be impacted by exotic vascular plants. By integrating establishment and proliferation, key stages in the progress of biological invasions, our model offers a synthetic approach to the prediction and management of biological invasions inside natural areas; such integration is critical to the protection of Canada’s endemic biodiversity. Key words: Biological invasions, invasive alien plants, climate-matching model, life history, natural areas, risk assessment