SEARCH

SEARCH BY CITATION

References

  • Allan, J.D. (2004) Landscapes and riverscapes: the influence of land use on stream ecosystems. Annual Review of Ecology, Evolution, and Systematics, 35, 257284.
  • Angermeier, P.L. & Schlosser, I.J. (1989) Species-area relationship for stream fishes. Ecology, 70, 14501462.
  • Austin, M. (2007) Species distribution models and ecological theory: a critical assessment and some possible new approaches. Ecological Modelling, 200, 119.
  • Baker, M.E. & King, R.S. (2010) A new method for detecting and interpreting biodiversity and ecological community thresholds. Methods in Ecology and Evolution, 1, 2537.
  • Baker, M.E., Weller, D.E. & Jordan, T.E. (2006) Improved methods for quantifying potential nutrient interception by riparian buffers. Landscape Ecology, 21, 13271345.
  • Borcard, D., Legendre, P. & Drapeau, P. (1992) Partialling out the spatial component of ecological variation. Ecology, 73, 10451055.
  • Breiman, L. (1996) Bagging predictors. Machine Learning, 24, 123140.
  • Breiman, L. (1998) Arcing classifiers (with discussion). The Annals of Statistics, 26, 801849.
  • Breiman, L. (1999) Prediction games and arcing algorithms. Neural Computation, 11, 14931517.
  • Breiman, L. (2001) Random forests. Machine Learning, 45, 532.
  • Bühlmann, P. & Hothorn, T. (2007) Boosting algorithms: regularization, prediction and model fitting (with discussion). Statistical Science, 22, 477522.
  • Bühlmann, P. & Yu, B. (2003) Boosting with the L2 loss: regression and classification. Journal of the American Statistical Association, 98, 324338.
  • Cutler, D.R., Edwards, T.C., Beard, K.H., Cutler, A. & Hess, K.T. (2007) Random forests for classification in ecology. Ecology, 88, 27832792.
  • De’ath, G. (2007) Boosted trees for ecological modeling and prediction. Ecology, 88, 243251.
  • Diggle, P.J. & Ribeiro, P.J. (2007) Model-based Geostatistics. Springer, New York.
  • Elith, J., Leathwick, J.R. & Hastie, T. (2008) A working guide to boosted regression trees. Journal of Animal Ecology, 77, 802813.
  • Everitt, B.S. (2006) The Cambridge Dictionary of Statistics, 3rd edn. Cambridge University Press, Cambridge.
  • Fortin, M.J. 1999. Spatial statistics in landscape ecology. Landscape Ecological Analysis: Issues and Applications (eds J.M. Klopatek & R.H. Gardner), pp. 253279. Springer, New York.
  • Freund, Y. & Schapire, R. 1996. Experiments with a New Boosting Algorithm. Proceedings of the Thirteenth International Conference on Machine Learning Theory. Morgan Kaufmann Publishers Inc, San Francisco.
  • Freund, Y. & Schapire, R. (1997) A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55, 119139.
  • Friedman, J.H. (1991) Multivariate Adaptive Regression Splines (with discussion). Annals of Statistics, 19, 1141.
  • Friedman, J.H. (2001) Greedy function approximation: a gradient boosting machine. The Annals of Statistics, 29, 11891232.
  • Friedman, J.H., Hastie, T. & Tibshirani, R. (2000) Additive logistic regression: a statistical view of boosting (with discussion). The Annals of Statistics, 28, 337407.
  • Harrell, F.E. (2001) Regression Modeling Strategies. Springer, New York.
  • Hastie, T. & Tibshirani, R. (1990) Generalized Additive Models. Chapman & Hall, London.
  • Hastie, T.J. & Tibshirani, R.J. (1993) Varying-coefficient models. Journal of the Royal Statistical Society, Series B, 55, 757796.
  • Hastie, T., Tibshirani, R. & Friedman, J. (2009) The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edn. Springer, New York.
  • Hilbe, J.M. (2007) Negative Binomial Regression. Cambridge University Press, Cambridge.
  • Hoerl, A. & Kennard, R. (1970) Ridge regression: biased estimation for non-orthogonal problems. Technometrics, 12, 5567.
  • Hothorn, T., Bühlmann, P., Kneib, T., Schmid, M. & Hofner, B. (2010a): mboost: Model-Based Boosting. R package version 2.0-6. https://r-forge.r-project.org/projects/mboost/.
  • Hothorn, T., Bühlmann, P., Kneib, T., Schmid, M. & Hofner, B. (2010b) Model-based boosting 2.0. Journal of Machine Learning Research, 11, 21092113.
  • King, R.S., Baker, M.E., Whigham, D.F., Weller, D.E., Jordan, T.E., Kazyak, P.F. & Hurd, M.K. (2005) Spatial considerations for linking watershed land cover to ecological indicators in streams. Ecological Applications, 15, 137153.
  • Kneib, T., Hothorn, T. & Tutz, G. (2009) Variable selection and model choice in geoadditive regression models. Biometrics, 65, 626634.
  • Kneib, T., Müller, J. & Hothorn, T. (2008) Spatial smoothing techniques for the assessment of habitat suitability. Environmental and Ecological Statistics, 15, 343364.
  • Legendre, P. (1993) Spatial autocorrelation: trouble or new paradigm? Ecology, 74, 16591673.
  • Maloney, K.O., Weller, D.E., Russell, M.J. & Hothorn, T. (2009) Classifying the biological condition of small streams: an example using benthic macroinvertebrates. Journal of the North American Benthological Society, 28, 869884.
  • McCullagh, P. & Nelder, J. (1989) Generalized Linear Models, 2nd edn. Chapman & Hall/CRC, New York.
  • MD DNR. 2007. Maryland Biological Stream Survey, Sampling Manual: Field Protocols. Maryland Department of Natural Resources, Monitoring and Nontidal Assessment Division, Annapolis, Maryland, USA.
  • Meier, L., van de Geer, S. & Bühlmann, P. (2009) High-dimensional additive modeling. The Annals of Statistics, 37, 37793821.
  • Nagelkerke, N.J.D. (1991) A note on a general definition of the coefficient of determination. Biometrika, 78, 691692.
  • O’Hara, R.B. & Sillanpää, M.J. (2009) A review of Bayesian variable selection methods: what, how and which. Bayesian Analysis, 4, 85118.
  • Park, T. & Casella, G. (2008) The Bayesian Lasso. Journal of the American Statistical Association, 103, 681686.
  • Paul, M.J. & Meyer, J.L. (2001) Streams in the urban landscape. Annual Review of Ecology and Systematics, 32, 333365.
  • R Development Core Team. 2010. R: A Language and Environment for Statistical Computing, v. 2.10.1. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org [accessed 30 March 2010].
  • Rigby, R.A. & Stasinopoulos, D.M. (2005) Generalized additive models for location, scale and shape (with discussion). Journal of the Royal Statistical Society, Series C, 54, 507554.
  • Schlosser, I.J. (1982) Fish community structure and function along two habitat gradients in a headwater stream. Ecological Monographs, 52, 395414.
  • Schlosser, I.J. (1990) Environmental variation, life-history attributes, and community structure in stream fishes - implications for environmental management and assessment. Environmental Management, 14, 621628.
  • Tibshirani, R. (1996) Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B, 58, 267288.
  • USEPA. 1999. From the Mountains to the Sea: The State of Maryland’s Freshwater Streams. EPA/903/R-99/023, Office of Research and Development, United States Environmental Protection Agency, Washington, DC.
  • Wang, L. & Lyons, J. (2003) Fish and benthic macroinvertebrate assemblages as indicators of stream degradation in urbanizing watersheds. Biological Response Signatures: Indicator Patterns Using Aquatic Communities (ed. T.P. Simon), pp. 227249. CRC Press, New York.
  • Wood, S.N. (2003) Thin plate regression splines. Journal of the Royal Statistical Society, Series B (Statistical Methodology), 65, 95114.
  • Wood, S.N. (2006) Generalized Additive Models: An Introduction with R. Chapman & Hall/CRC, Boca Raton.
  • Yuan, L.L. & Norton, S.B. (2003) Comparing responses of macroinvertebrate metrics to increasing stress. Journal of the North American Benthological Society, 22, 308322.