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Cross-Scale Assessment of Potential Habitat Shifts in a Rapidly Changing Climate

Published online by Cambridge University Press:  20 January 2017

Catherine S. Jarnevich*
U.S. Geological Survey Fort Collins Science Center, 2150 Center Ave Building C Fort Collins, CO 80526
Tracy R. Holcombe
U.S. Geological Survey Fort Collins Science Center, 2150 Center Ave Building C Fort Collins, CO 80526
Elizabeth M. Bella
U.S. Fish and Wildlife Service Kenai National Wildlife Refuge, 1 Ski Hill Road, PO Box 2139, Soldotna, AK 99669
Matthew L. Carlson
UAA Alaska Natural Heritage Program & Biological Sciences Department, 707 A Street, Anchorage, AK 99501
Gino Graziano
UAF Cooperative Extension Service, 1675 C Street, Suite 100, Anchorage, AK 99501
Melinda Lamb
U.S. Forest Service Region 10 Forest Health Protection, 11175 Auke Lake Way. Juneau, AK 99801
Steven S. Seefeldt
UAF Cooperative Extension Service Tanana District, 724 27th Ave., Suite 2 and 3, P.O. Box 758155, Fairbanks, AK 99775-8155
Jeffery Morisette
U.S. Geological Survey, DOI North Central Climate Science Center, Colorado State University, Natural Resource and Ecology Lab, Fort Collins, CO 80523
Corresponding author's E-mail:


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.

Research Article
Copyright © Weed Science Society of America 

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