• Breiman, L. (2001) Statistical modeling: the two cultures. Statistical Science, 16, 199215.
  • Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984) Classification and Regression Trees. Wadsworth International Group, Belmont, CA, USA.
  • Buckland, S.T. & Elston, D.A. (1993) Empirical models for the spatial distribution of wildlife. Journal of Applied Ecology, 30, 478495.
  • Burnham, K.P. & Anderson, D.R. (2002) Model Selection and Inference: A Practical Information-Theoretic Approach, 2nd edn. Springer-Verlag, New York.
  • Clarke, A. & Johnston, N.M. (1999) Scaling of metabolic rate with body mass and temperature in teleost fish. Journal of Animal Ecology, 68, 893905.
  • De’ath, G. (2007) Boosted trees for ecological modeling and prediction. Ecology, 88, 243251.
  • De’ath, G. & Fabricius, K.E. (2000) Classification and regression trees: a powerful yet simple technique for ecological data analysis. Ecology, 81, 31783192.
  • R Development Core Team (2006) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna.
  • Elith, J., Graham, C.H., Anderson, R.P. et al . (2006) Novel methods improve prediction of species’ distributions from occurrence data. Ecography, 29, 129151.
  • Fewster, R.M., Buckland, S.T., Siriwardena, G.M., Baillie, S.R. & Wilson, J.D. (2000) Analysis of population trends for farmland birds using generalized additive models. Ecology, 81, 19701984.
  • Fidler, F., Thomason, N., Cumming, G., Finch, S. & Leeman, J. (2004) Editors can lead researchers to confidence intervals but they can't make them think. Statistical reforms from medicine. Psychological Science, 15, 119126.
    Direct Link:
  • Freund, Y. & Schapire, R.E. (1996) Experiments with a new boosting algorithm. Machine Learning: Proceedings of the Thirteenth International Conference, July 3–6, 1996, Bari Italy. pp. 148–156. Morgan Kaufman, San Francisco, CA, USA.
  • Friedman, J.H. (2001) Greedy function approximation: a gradient boosting machine. Annals of Statistics, 29, 11891232.
  • Friedman, J.H. (2002) Stochastic gradient boosting. Computational Statistics and Data Analysis, 38, 367378.
  • Friedman, J.H. & Meulman, J.J. (2003) Multiple additive regression trees with application in epidemiology. Statistics in Medicine, 22, 13651381.
  • Friedman, J.H., Hastie, T. & Tibshirani, R. (2000) Additive logistic regression: a statistical view of boosting. Annals of Statistics, 28, 337407.
  • Hastie, T. & Tibshirani, R.J. (1990) Generalized Additive Models. Chapman & Hall, London.
  • Hastie, T., Tibshirani, R. & Friedman, J.H. (2001) The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer-Verlag, New York.
  • Kohavi, R. (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: Proceedings of the Fourteenth International Joint Conference on Artificial Intelligence (ed. C.A. San Mateo), pp. 1137–1143. Morgan Kaufmann.
  • Leathwick, J.R., Elith, J., Francis, M.P., Hastie, T. & Taylor, P. (2006) Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology Progress Series, 321, 267281.
  • Leathwick, J.R., Elith, J., Chadderton, W.L., Rowe, D. & Hastie, T. (2008) Dispersal, disturbance, and the contrasting biogeographies of New Zealand's diadromous and non-diadromous fish species. Journal of Biogeography, in press.
  • McCullagh, P. & Nelder, J.A. (1989) Generalized Linear Models, 2nd edn. Chapman & Hall, London.
  • McDowall, R.M. (1993) Implications of diadromy for the structuring and modelling of riverine fish communities in New Zealand. New Zealand Journal of Marine and Freshwater Research, 27, 453462.
  • Miller, A.J. (1990) Subset Selection in Regression. Chapman & Hall, London.
  • Moisen, G.G., Freeman, E.A., Blackard, J.A., Frescino, T.S., Zimmermann, N.E. & Edwards, T.C. (2006) Predicting tree species presence and basal area in Utah: a comparison of stochastic gradient boosting, generalized additive models, and tree-based methods. Ecological Modelling, 199, 176187.
  • Prasad, A.M., Iverson, L.R. & Liaw, A. (2006) Newer classification and regression tree techniques: bagging and random forests for ecological prediction. Ecosystems, 9, 181199.
  • Reineking, B. & Schröder, B. (2006) Constrain to perform: regularization of habitat models. Ecological Modelling, 193, 675690.
  • Ridgeway, G. (2006) Generalized boosted regression models. Documentation on the R Package ‘gbm’, version 1·5–7., accessed March 2008.
  • Schapire, R. (2003) The boosting approach to machine learning – an overview. MSRI Workshop on Nonlinear Estimation and Classification, 2002 (eds D.D.Denison, M. H.Hansen, C.Holmes, B.Mallick & B.Yu). Springer, New York.
  • Segal, M.R. (2004) Machine learning benchmarks and random forest regression. eScholarship Repository. University of California.
  • Whittingham, M.J., Stephens, P.A., Bradbury, R.B. & Freckleton, R.P. (2006) Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75, 11821189.