Item talk:Q259333
From geokb
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"name": "Cross-scale assessment of potential habitat shifts in a rapidly changing climate",
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"value": "10.1614/IPSM-D-13-00071.1",
"url": "https://doi.org/10.1614/IPSM-D-13-00071.1"
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"name": "Invasive Plant Science and Management",
"volumeNumber": "7",
"issueNumber": "3"
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"name": "Invasive Plant Science and Management"
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"datePublished": "2014",
"dateModified": "2015-01-14",
"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\u00a0km (1.2\u00a0mi) 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\u00a0m (98.4\u00a0ft) 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.",
"description": "12 p.",
"publisher": {
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"name": "Weed Science Society of America"
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"name": "Holcombe, Tracy R. holcombet@usgs.gov",
"givenName": "Tracy R.",
"familyName": "Holcombe",
"email": "holcombet@usgs.gov",
"affiliation": [
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"@type": "Organization",
"name": "Fort Collins Science Center",
"url": "https://www.usgs.gov/centers/fort-collins-science-center"
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"name": "Bell, Elizabeth",
"givenName": "Elizabeth",
"familyName": "Bell",
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"value": "0000-0002-1587-8241",
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"name": "Carlson, Matthew L.",
"givenName": "Matthew L.",
"familyName": "Carlson",
"affiliation": [
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"name": "UAA Alaska Natural Heritage Program & Biological Sciences Department"
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"name": "Graziano, Gino",
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"familyName": "Graziano",
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"name": "UAF Cooperative Extension Service"
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"name": "U.S. Forest Service, La Grande, Oregon"
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"name": "Jarnevich, Catherine S. jarnevichc@usgs.gov",
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"name": "North Central Climate Science Center",
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