We assessed the ability of climatic, environmental, and anthropogenic variables to predict areas of high-risk for plant invasion and consider the relative importance and contribution of these predictor variables by considering two spatial scales in a region of rapidly changing climate. We created predictive distribution models, using Maxent, for three highly invasive plant species (Canada thistle, white sweetclover, and reed canarygrass) in Alaska at both a regional scale and a local scale. Regional scale models encompassed southern coastal Alaska and were developed from topographic and climatic data at a 2 km (1.2 mi) spatial resolution. Models were applied to future climate (2030). Local scale models were spatially nested within the regional area; these models incorporated physiographic and anthropogenic variables at a 30 m (98.4 ft) resolution. Regional and local models performed well (AUC values > 0.7), with the exception of one species at each spatial scale. Regional models predict an increase in area of suitable habitat for all species by 2030 with a general shift to higher elevation areas; however, the distribution of each species was driven by different climate and topographical variables. In contrast local models indicate that distance to right-of-ways and elevation are associated with habitat suitability for all three species at this spatial level. Combining results from regional models, capturing long-term distribution, and local models, capturing near-term establishment and distribution, offers a new and effective tool for highlighting at-risk areas and provides insight on how variables acting at different scales contribute to suitability predictions. The combinations also provides easy comparison, highlighting agreement between the two scales, where long-term distribution factors predict suitability while near-term do not and vice versa.
Nomenclature: Canada thistle, Cirsium arvense (L.) Scop., reed canarygrass, Phalaris arundinacea L, white sweetclover, Melilotus albus Medik.
Management Implications: Effective and proactive management of invasive species requires information on both current and potential future distributions. Alaska, similar to other high latitude areas, is relatively invasion free (Lassuy and Lewis, 2013). The rapidly changing climate in this region, however, is expected to increase the area suitable for establishment for a larger number of invasive species. Here, we present results for habitat suitability models of highly invasive plants in the southern coastal region of Alaska, creating climate driven models at a regional scale and physiographic and anthropogenic models for two local regions. Using these types of models for targeted sampling of invasive plants detected more locations with less effort than nontargeted sampling (Crall et al., 2013). Our local scale models can be thought of as predicting near term establishment and distribution (potential early detection locations for management), while longer term trends in distribution may be driven by climate, especially related to the future climate scenarios at the coastal scale (potential distributions). Locations where models at both scales indicate high habitat suitability values are more appropriate targets for current control and monitoring efforts than locations identified by a model that considers factors operating at a single scale. Additionally, evaluating the areas of future suitable habitat among early invaders can help prioritize which species should be targeted for control first. If two species have similar initial distributions and similar ecological impacts, management efforts should be directed to the species with the largest possible future distribution. These models, when incorporated into an iter
