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Ensemble Habitat Mapping of Invasive Plant Species

Authors

  • Thomas J. Stohlgren,

    Corresponding author
    1. U.S. Geological Survey, Fort Collins Science Center, National Institute of Invasive Species Science, Fort Collins, CO, USA.
      *Address correspondence to Thomas J. Stohlgren, U.S. Geological Survey, Fort Collins Science Center, National Institute of Invasive Species Science, Fort Collins, Colorado, USA; tel: 001-970-491-1980; fax: 001-970-491-1965; tom_stohlgren@usgs.gov.
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  • Peter Ma,

    1. NASA Goddard Space Flight Center/Sigma Space, Greenbelt, MD, USA.
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  • Sunil Kumar,

    1. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO, USA.
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  • Monique Rocca,

    1. Department of Forest, Rangeland and Watershed Stewardship, Colorado State University, Fort Collins, CO, USA.
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  • Jeffrey T. Morisette,

    1. U.S. Geological Survey, Fort Collins Science Center, National Institute of Invasive Species Science, Fort Collins, CO, USA.
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  • Catherine S. Jarnevich,

    1. U.S. Geological Survey, Fort Collins Science Center, National Institute of Invasive Species Science, Fort Collins, CO, USA.
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  • Nate Benson

    1. National Interagency Fire Center, National Park Service, Boise, ID, USA.
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*Address correspondence to Thomas J. Stohlgren, U.S. Geological Survey, Fort Collins Science Center, National Institute of Invasive Species Science, Fort Collins, Colorado, USA; tel: 001-970-491-1980; fax: 001-970-491-1965; tom_stohlgren@usgs.gov.

Abstract

Ensemble species distribution models combine the strengths of several species environmental matching models, while minimizing the weakness of any one model. Ensemble models may be particularly useful in risk analysis of recently arrived, harmful invasive species because species may not yet have spread to all suitable habitats, leaving species-environment relationships difficult to determine. We tested five individual models (logistic regression, boosted regression trees, random forest, multivariate adaptive regression splines (MARS), and maximum entropy model or Maxent) and ensemble modeling for selected nonnative plant species in Yellowstone and Grand Teton National Parks, Wyoming; Sequoia and Kings Canyon National Parks, California, and areas of interior Alaska. The models are based on field data provided by the park staffs, combined with topographic, climatic, and vegetation predictors derived from satellite data. For the four invasive plant species tested, ensemble models were the only models that ranked in the top three models for both field validation and test data. Ensemble models may be more robust than individual species-environment matching models for risk analysis.

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