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

Author:
Young, Kendal E., Abbott, Laurie B., Caldwell, Colleen A., Schrader, T. Scott
Source:
International journal of biodiversity and conservation 2013 v.5 no.3 pp. 122
ISSN:
2141-243X
Subject:
Arundo donax, Cenchrus ciliaris, Eragrostis lehmanniana, Landsat, Marrubium vulgare, biodiversity, data collection, habitats, invasive species, landscapes, managers, models, national parks, remote sensing, Texas
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.
Agid:
60733
Handle:
10113/60733