Ensemble habitat mapping of invasive plant species.
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.
U.S. Geological Survey, Fort Collins Science Center, National Institute of Invasive Species Science, Fort Collins, CO, USA. tom stohlgren@usgs.gov
Thomas J
TJ
Stohlgren
Peter
P
Ma
Sunil
S
Kumar
Monique
M
Rocca
Jeffrey T
JT
Morisette
Catherine S
CS
Jarnevich
Nate
N
Benson
D000413:Alaska; D002140:California; D017753:Ecosystem; D004777:Environment; D016015:Logistic Models; D008954:Models, Biological; D063245:Plant Development; D018515:Plant Leaves; D014197:Trees; D014959:Wyoming