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Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction

Prasad, Anantha M., Iverson, Louis R., Liaw, Andy·2006·Ecosystems
DOI 10.1007/s10021-005-0054-1
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Abstract

The task of modeling the distribution of a large number of tree species under future climate scenarios presents unique challenges. First, the model must be robust enough to handle climate data outside the current range without producing unacceptable instability in the output. In addition, the technique should have automatic search mechanisms built in to select the most appropriate values for input model parameters for each species so that minimal effort is required when these parameters are fine-tuned for individual tree species. We evaluated four statistical models--Regression Tree Analysis (RTA), Bagging Trees (BT), Random Forests (RF), and Multivariate Adaptive Regression Splines (MARS)--for predictive vegetation mapping under current and future climate scenarios according to the Canadian Climate Centre global circulation model. To test, we applied these techniques to four tree species common in the eastern United States: loblolly pine (Pinus taeda), sugar maple (Acer saccharum), American beech (Fagus grandifolia), and white oak (Quercus alba). When the four techniques were assessed with Kappa and fuzzy Kappa statistics, RF and BT were superior in reproducing current importance value (a measure of basal area in addition to abundance) distributions for the four tree species, as derived from approximately 100,000 USDA Forest Service's Forest Inventory and Analysis plots. Future estimates of suitable habitat after climate change were visually more reasonable with BT and RF, w…

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Metadata

Authors
Prasad, A. M., Iverson, L. R., & Liaw, A.
Venue
Ecosystems
Publisher
Springer Science and Business Media LLC
Published
2006-03
Volume
9
Issue
2
Pages
181-199
ISSN
  • 1432-9840
  • 1435-0629
DOI
10.1007/s10021-005-0054-1
Cited by
1,987
References
3
Language
en
Licence
www.springer.com/tdm (tdm)

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