Cross-Scale Assessment of Potential Habitat Shifts in a Rapidly Changing Climate. (September 2014)

• Record Type: Journal Article
• Authors: Jarnevich, Catherine S.; Holcombe, Tracy R.; Bella, Elizabeth M.; Carlson, Matthew L.; Graziano, Gino; Lamb, Melinda; Seefeldt, Steven S.; Morisette, Jeffery
• Is Part Of: Invasive plant science and management. Volume 7:Number 3(2014)
• Page Start: 491
• Page End: 502
• Publication Date: 2014-09
• Subjects: Canada thistle, Cirsium arvense (L.) Scop., -- reed canarygrass, Phalaris arundinacea L., -- white sweetclover, Melilotus albus Medik.; Alaska, -- bioclimatic modeling, -- Centaurea stoebe, -- Cirsium arvense, -- climate change, -- Fallopia japonica (Polygonum cuspidatum) complex, -- invasive species, -- Melilotus albus, -- Phalaris arundinacea; Plants, Protection of -- Periodicals; Invasive plants -- Periodicals; 632.5
• DOI: 10.1614/IPSM-D-13-00071.1
• Languages: English
• ISSNs: 1939-7291

Abstract

Abstract: 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.