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ARS Home » Plains Area » Las Cruces, New Mexico » Range Management Research » Research » Publications at this Location » Publication #316437

Title: Estimating suitable environments for invasive plant species across large landscapes: A remote sensing strategy using Landsat 7 ETM+

item YOUNG, KENDAL - Us Forest Service (FS)
item ABBOTT, LAURIE - New Mexico State University
item CALDWELL, COLLEEN - Department Of Fish And Wildlife
item Schrader, Theodore

Submitted to: Biodiversity and Conservation Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/15/2013
Publication Date: 3/15/2013
Publication URL:
Citation: Young, K., Abbott, L., Caldwell, C., Schrader, T.S. 2013. Estimating suitable environments for invasive plant species across large landscapes: A remote sensing strategy using Landsat 7 ETM+. International Journal of Biodiversity and Conservation. 5(3):122-134.

Interpretive Summary: Invasive species introductions have led resource managers to explore predictive theories of community attributes that increase their susceptibility to invasion. Unfortunately, universal predictive theories are not available because traits associated with invasion potential vary by geographic location, species and habitat. Nevertheless, habitats that are subject to altered disturbance regimes, have a history of prior invasion, provide adequate soil and water resources, or experience high frequency of propagule introductions and are at greater risk to invasive plant incursion.

Technical Abstract: The key to reducing ecological and economic damage caused by invasive plant species is to locate and eradicate new invasions before they threaten native biodiversity and ecological processes. We used Landsat Enhanced Thematic Mapper Plus imagery to estimate suitable environments for four invasive plants in Big Bend National Park, southwest Texas, using a presence-only modeling approach. Giant reed (Arundo donax), Lehmann lovegrass (Eragrostis lehmanniana), horehound (Marrubium vulgare) and buffelgrass (Pennisteum ciliare) were selected for remote sensing spatial analyses. Multiple dates/seasons of imagery were used to account for habitat conditions within the study area and to capture phenological differences among targeted species and the surrounding landscape. Individual species models had high (0.91 to 0.99) discriminative ability to differentiate invasive plant suitable environments from random background locations. Average test area under the receiver operating characteristic curve (AUC) ranged from 0.91 to 0.99, indicating that plant predictive models exhibited high discriminative ability to differentiate suitable environments for invasive plant species from random locations. Omission rates ranged from <1.0 to 18%. We demonstrated that useful models estimating suitable environments for invasive plants may be created with <50 occurrence locations and that reliable modeling using presence-only datasets can be powerful tools for land managers.