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

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.

Type
Research Article
Copyright
Copyright © Weed Science Society of America 

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References

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