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Ensemble Habitat Mapping of Invasive Plant Species
Author(s) -
Stohlgren Thomas J.,
Ma Peter,
Kumar Sunil,
Rocca Monique,
Morisette Jeffrey T.,
Jarnevich Catherine S.,
Benson Nate
Publication year - 2010
Publication title -
risk analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.972
H-Index - 130
eISSN - 1539-6924
pISSN - 0272-4332
DOI - 10.1111/j.1539-6924.2009.01343.x
Subject(s) - multivariate adaptive regression splines , random forest , habitat , species distribution , ensemble forecasting , multivariate statistics , geography , invasive species , regression , logistic regression , environmental niche modelling , ecology , regression analysis , principle of maximum entropy , statistics , ecological niche , computer science , machine learning , mathematics , biology , nonparametric regression , meteorology
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.