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

Abraham A, Steinberg D (2001) MARS: Still an alien planet in soft computing? In: Alexandrov VN, Dongarra JJ, Juliano BA, Renner RS, Tan CJK (eds) Lecture notes in computer science 2074. Springer, Berlin Heidelberg New York, p 235–244

Baker FA (1993) Classification and regression tree analysis for assessing hazard of pine mortality caused by Heterobasidion annosum. Plant Dis 77:136–9

Boer GJ, Flato GM, Ramsden D (2000) A transient climate change simulation with historical and projected greenhouse gas and aerosol forcing: projected climate for the 21st century. Clim Dyn 16:427–51

Breiman L (1996a) Bagging predictors. Mach Learn 24:123–40

Breiman L. 1996b. Out-of-bag estimation. Technical report, Department of Statistics: University of California, Berkeley

Breiman L (2001) Random forests. Mach Learn 45:5–32

Breiman L (2002) Using models to infer mechanisms. IMS Wald Lecture 2. [online] URL: http://www.oz.berkeley.edu/users/breiman/wald2002-2.pdf

Breiman L, Cutler A. 2003. Setting up, using, and understanding Random Forests v4.0. [online] URL: http://www.stat.berkeley.edu/users/breiman/rf.html

Breiman L, Freidman J, Olshen R, Stone C(1984) Classification and regression trees. Wadsworth, Belmont (CA), p 358

Buhlmann P, Yu B (2002) Analyzing bagging. Ann Stat 30:927–61

Chambers JM (1998) Programming with data: a guide to the S language. Springer, Berlin Heidelberg New York, p 469

Chambers JM, Hastie TJ (1993) Statistical models in New York; S. Chapman & Hall, 608 p

Chan JCW, Huang C, DeFries R (2001) Enhanced algorithm performance for land cover classification using bootstrap aggregating (bagging). IEEE Trans Geosci Remote Sens 39(3):693–5

Clark JS (1998) Why trees migrate so fast: confronting theory with dispersal biology and the paleorecord. Am Nat 152:204–24

Clark LA, Pregibon D (1992) Tree-based models. In: Chambers JM, Hastie TJ (eds) Statistical models S. Pacific Grove (CA): Wadsworth, p 377–419

Davis MB (1989) Lags in vegetation response to greenhouse warming. Clim Change 15:75–82

De’ath G, Fabricius KE (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology 81:3178–192

Dobbertin M, Biging GS (1998) Using the non-parametric classifier CART to model forest tree mortality. For Sci 44(4):507–516

Environmental Systems Research Institute. 2001. Arc ver. 8.1.2. Environmental Systems Research Institute, Redlands (CA)

Franklin J (1995) Predictive vegetation mapping: geographic modeling of biospatial patterns in relation to environmental gradients. Prog Phys Geogr 19:494–519

Franklin J (1998) Predicting the distribution of shrub species in southern California from climate and terrain-derived variables. J Veg Sci 9:733–48

Freidman JH (1991) Multivariate adaptive regression splines. Ann Stat 19:1–141

Freund Y (1995) Boosting a weak learning algorithm by majority. Inf Comput 121:256–85

Furlanello C, Neteler M, Merler S, Menegon S, Fontanari S, Donini A, Rizzoli A, Chemini C. 2003. GIS and the Random Forests predictor: integration in R for tick-borne disease risk assessment. In: Hornik K, Leisch F, Zeileis A, Eds. Proceedings of the 3rd international workshop on distributed statistical computing. Vienna, Austria, p 1–11

Hagen A. 2002. Technical report: comparison of maps containing nominal data. RIVM project: MAP-SOR S/550002/01/RO, order no. 143699. Maastricht (The Netherlands): Research Institute for Knowledge Systems

Hagen A (2003) Fuzzy set approach to assessing similarity of categorical maps. Int J Geog Inf Sci 17(3):235–49

Hansen M, Dubayah R, Defries R (1996) Classification trees: an alternative to traditional land cover classifiers. Int J Remote Sens 17(5):1075–81

Hansen MH, Frieswyk T, Glover JF, Kelly JF (1992) The Eastwide forest inventory data base: users manual. General technical report NC-151. St. Paul (MM): US Department of Agriculture, Forest Service, North Central Forest Experiment Station, 48 p

Hawkins DM, Musser BJ (1999) One tree or a forest? Alternative dendrographic models. Comput Sci Stat 30:534–42

Hernandez JE, Epstein LD, Rodriguez MH, Rodriguez AD, Rejmankova E, Roberts DR (1997) Use of generalized regression tree models to characterize vegetation favoring Anopheles albimanus breeding. J Am Mosq Control Assoc 13(1):28–34

Higgins SI, Lavorel S, Revilla EE (2003) Estimating plant migration rates under habitat loss and fragmentation. Oikos 101:354–66

Hobbs RJ (1994) Dynamics of vegetation mosaics: can we predict responses to global change? Ecoscience 1(4):346–56

Hothorn T, Lausen B, Benner A, Radespiel-Troger M (2004) Bagging survival trees. Stat Med 23:77–91

Iverson LR, Prasad AM (1998) Predicting abundance of 80 tree species following climate change in the eastern United States. Ecol Mono 68:465–85

Iverson LR, Prasad AM (2002) Potential redistribution of tree species habitat under five climate change scenarios in the eastern US. For Ecol Manage 155(1–3):205–22

Iverson LR, Prasad AM, Hale BJ, Sutherland EK 1999a. An atlas of current and potential future distributions of common trees of the eastern United States. General technical report NE-265. Northeastern Research Station, USDA Forest Service, 245 p

Iverson LR, Prasad AM, Schwartz MW (1999b) Modeling potential future individual tree-species distributions in the Eastern United States under a climate change scenario: a case study with Pinus virginiana. Ecol Mod 115:77–93

Kittel TGF, Rosenbloom NA, Kaufman C, Royle JA, Daly C, Fisher HH, and others. 2000. VEMAP phase 2 historical and future scenario climate database. Oak Ridge (TN): ORNL Distributed Active Archive Center, Oak Ridge National Laboratory. [online] URL: http://www.daac.ornl.gov/

Lees BG, Ritman K (1991) Decision-tree and rule-induction approach to integration of remotely sensed and GIS data in mapping vegetation in disturbed or hilly environments. Environ Manage 15:823–31

Liaw A, Wiener M. 2002. Classification and regression by Random Forests. R News, 2/3:18–22. [online] URL http://www.CRAN.R-project.org/doc/Rnews/

Little EL. 1971. Atlas of United States trees; vol 1. Conifers and important hardwoods. Miscellaneous publication 1146. Washington (DC), US Department of Agriculture, Forest Service, 200 p

Little EL. 1977. Atlas of United States Trees; vol 4. Minor eastern hardwoods. Miscellaneous publication 1342. Washington (DC): US Department of Agriculture, Forest Service, 230 p

Malcolm JR, Markham A, Neilson RP, Garaci M (2002) Estimated migration rates under scenarios of global climate change. J Biogeogr 29:835–49

Map Comparison Kit. 2003. Research Institute for Knowledge Systems, Netherlands. http://www.riks.nl

Meyer D, Leisch F, Hornik K (2003) The support vector machine under test. Neurocomputing 55:59–71

Michaelsen J, Schimel DS, Friedl MA, Davis FW, Dubayah RC (1994) Regression tree analysis of satellite and terrain data to guide vegetation sampling and surveys. J Veg Sci 5:673–86

Miller JR, Turner MG, Smithwick EAH, Dent CL, Stanley EH (2004. Spatial extrapolation: the science of predicting ecological patterns and processes. BioScience 54(4):310–20

Moisen GG, Frescino T (2002) Comparing five modelling techniques for predicting forest characteristics. Ecol Model 157:209–25

Monserud RA, Leemans R (1992) Comparing global vegetation maps with the Kappa statistic. Ecol Model 62:275–93

Moore DE, Lees BG, Davey SM (1991) A new method for predicting vegetation distributions using decision tree analysis in a geographic information system. J Environ Manage 15:59–71

Munoz J, Felicisimo AM (2004) Comparison of statistical methods commonly used in predictive modelling. J Veg Sci 15:285–92

Peters A, Hothorn T, Lausen B. 2002. ipred: Improved predictors. R News, 2(2):22–6 [online] URL http://www.CRAN.R-project.org/doc/Rnews/

Pitelka LF, Plant Migration Workshop Group. 1997. Plant migration and climate change. Am Sci 85:464–73

Pontius RG Jr (2000) Quantification error versus location error in comparison of categorical maps. Photogram Eng Remote Sens 66(8):1011–16

Power C, Simms A (2001) Hierarchical fuzzy pattern matching for regional comparison of land use maps. Int J Geogr Inf Sci 15(1):77–100

Prasad AM, Iverson LR. 2000a. A climate change atlas for 80 forest tree species of the eastern United States [database]. [online] URL: http://www.fs.fed.us/ne/delaware/atlas

Prasad AM, Iverson LR. 2000b. Predictive vegetation mapping using a custom built model-chooser: comparison of regression tree analysis and multivariate adaptive regression splines. In: Proceedings CD-ROM. 4th International Conference on Integrating GIS and Environmental Modeling: Problems, Prospects and Research Needs. Banff, Alberta, Canada. [online] URL: http://www.colorado.edu/research/cires/banff/upload/159/index.html

Prasad AM, Iverson LR. 2003. Little’s range and FIA importance value database for 135 eastern US tree species. Northeastern Research Station, USDA Forest Service, Delaware, Ohio. [online] URL: http://www.fs.fed.us/ne/delaware/4153/global/littlefia/index.html

R Development Core Team. 2004. R: a language and environment for statistical computing. Vienna (Austria): R Foundation for Statistical Computing. [online] URL: http://www. R-project.org

Reichard SH, Hamilton CW (1997) Predicting invasion of woody plants introduced into North America. Conserv Biol 11:193–203

Schapire RE, Freund Y, Barlett P, Lee W (1998) Boosting the margin: a new explanation for the effectiveness of voting methods. Ann Stat 26(5):1651–86

Schwartz MW, Iverson LR, Prasad AM (2001) Predicting the potential future distribution of four tree species in Ohio, USA, using current habitat availability and climatic forcing. Ecosystems 4:568–81

Skurichina M, Duin RPW (2002) Bagging, boosting and the random subspace method for linear classifiers. Pattern Anal Appl 5:121–35

Steinberg D, Colla PL, Martin K (1999) MARS user guide. Salford Systems, San Diego (CA)

Stoppiana D, Gregoire J-M, Pereira JMC (2003) The use of SPOT VEGETATION data in a classification tree appproach for burnt area mapping in Australian savanna. Int J Remote Sens 24:2131–51

Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random Forests: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947–58

Therneau TM, Atkinson EJ (1997) An introduction to recursive partitioning using the RPART routines. Technical report no. 61. Mayo Clinic, Rochester (MM) p 52

Verbyla DL (1987) Classification trees: a new discrimination tool. Can J For Res 17:1150–52