Gradient forests: Calculating importance gradients on physical predictors

Ecology

Volumn 93, Issue 1, 2012, Pages 156-168

  Ellis, Nick ^a   Smith, Stephen J ^b   Roland Pitcher, C ^a  

^a CSIRO   (Australia)

^b BEDFORD INSTITUTE OF OCEANOGRAPHY   (Canada)

Author keywords

Biodiversity; Community data; Compositional turnover; Great Barrier Reef; Random forests; Variable importance

Indexed keywords

ABUNDANCE; BARRIER REEF; BIODIVERSITY; COMMUNITY COMPOSITION; COMMUNITY STRUCTURE; DATA SET; ENVIRONMENTAL GRADIENT; FOREST ECOSYSTEM; MUD; PROTECTED AREA; TURNOVER;

ARTICLE; BIODIVERSITY; BIOLOGICAL MODEL; COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; PHYSIOLOGY; SPECIES DIFFERENCE; TREE;

BIODIVERSITY; COMPUTER SIMULATION; ENVIRONMENTAL MONITORING; MODELS, BIOLOGICAL; SPECIES SPECIFICITY; TREES;

AUSTRALIA; CORAL SEA; GREAT BARRIER REEF; PACIFIC OCEAN; QUEENSLAND;

EID: 84863338293     PISSN: 00129658     EISSN: None     Source Type: Journal    
DOI: 10.1890/11-0252.1     Document Type: Article

References (33)

1. Austin, M. P. 2002. Spatial prediction of species distribution: an interface between ecological theory and statistical modelling. Ecological Modelling 157:101-118. (Pubitemid 35255409)
2. Breiman, L. 2001. Random forests. Machine Learning 45:5-32. (Pubitemid 32933532)
3. Chen, J., K. Yu, A. Hsing, and T. Therneau. 2007. A partially linear tree-based regression model for assessing complex joint gene-gene and gene-environment effects. Genetic Epidemiology 31:238-251. (Pubitemid 46493567)
4. Cutler, D. R., T. C. Edwards, Jr., K. H. Beard, A. Cutler, K. T. Hess, J. Gibson, and J. J. Lawler. 2007. Random forests for classification in ecology. Ecology 88:2783-2792. (Pubitemid 351345081)
5. De'ath, G. 2002. Multivariate regression trees: a new technique for modeling species-environment relationships. Ecology 83:1105-1117. (Pubitemid 36531000)
6. Elith, J., and J. Leathwick. 2009. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution, and Systematics 40:677-697.
7. Evans, J. S., and S. A. Cushman. 2009. Gradient modeling of conifer species using random forests. Landscape Ecology 24:673-683.
8. Ferrier, S., and A. Guisan. 2006. Spatial modelling of biodiversity at the community level. Journal of Applied Ecology 43:393-404. (Pubitemid 43725544)
9. Ferrier, S., G. Manion, J. Elith, and K. Richardson. 2007. Using generalized dissimilarity modelling to analyse and predict patterns of beta diversity in regional biodiversity assessment. Diversity and Distributions 13:252-264. (Pubitemid 46633445)
10. Friedman, J. 1991. Multivariate adaptive regression splines. Annals of Statistics 19:1-67.
11. Grömping, U. 2009. Variable importance assessment in regression: linear regression versus random forest. American Statistician 63:308-319.
12. Guisan, A., and W. Thuiller. 2005. Predicting species distribution: offering more than simple habitat models. Ecology Letters 8:993-1009. (Pubitemid 41247275)
13. Guisan, A., and N. E. Zimmermann. 2000. Predictive habitat distribution models in ecology. Ecological Modelling 135:147-186. (Pubitemid 32102782)
14. Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: data mining, inference, and prediction. Second edition. Springer, New York, New York, USA.
15. Kendall, M. 1938. A new measure of rank correlation. Biometrika 30:81.
16. Knudby, A., A. Brenning, and E. Le Drew. 2010. New approaches to modelling fish-habitat relationships. Ecological Modelling 221:503-511.
17. Lawler, J. J., D. White, R. P. Neilson, and A. R. Blaustein. 2006. Predicting climate-induced range shifts: model differences and model reliability. Global Change Biology 12:1568-1584. (Pubitemid 44028465)
18. Lawler, J. J., Y. F. Wiersma, and F. Huettmann. 2011. Using species distribution models for conservation planning and ecological forecasting. Pages 271-290 in C. A. Drew, Y. F. Wiersma, and F. Huettmann, editors. Predictive species and habitat modeling in landscape ecology. Springer, New York, New York, USA.
19. Leathwick, J., J. Elith, M. Francis, T. Hastie, and P. Taylor. 2006. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology Progress Series 321:267-281. (Pubitemid 44544793)
20. Liaw, A., and M. Wiener. 2002. Classification and regression by random Forest. R News 2:18-22.
21. Murray, K., and M. Conner. 2009. Methods to quantify variable importance: implications for the analysis of noisy ecological data. Ecology 90:348-355.
22. Nicodemus, K. K., J. D. Malley, C. Strobl, and A. Ziegler. 2010. The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics 11:110.
23. Peters, J., B. Baets, N. Verhoest, R. Samson, S. Degroeve, P. Becker, and W. Huybrechts. 2007. Random forests as a tool for ecohydrological distribution modelling. Ecological Modelling 207:304-318. (Pubitemid 47362492)
24. Pitcher, C., et al. 2007. Seabed biodiversity on the continental shelf of the Great Barrier Reef World Heritage Area. Technical report, AIMS/CSIRO/QM/QDPI Final Report to CRC Reef Research Centre. AIMS/CSIRO/QM/QDPI CRC Reef Research Task Final Report. CSIRO Marine and Atmospheric Research, Cleveland, Australia. http://www.reef.crc.org.au/resprogram/programC/seabed/ final-report. htm
25. Pitcher, C., W. Venables, N. Ellis, I. McLeod, M. Cappo, F. Pantus, M. Austin, P. Doherty, and N. Gribble. 2002. GBR seabed biodiversity mapping project: Phase 1 report to CRC-Reef. Technical report, CSIRO/AIMS/QDPI Report. CSIRO Marine and Atmospheric Research, Cleveland, Australia. http://www.reef.crc.org.au/resprogram/programC/seabed/Seabedphase1rpt.htm
26. Poiner, I., J. Glaister, C. Pitcher, C. Burridge, T. Wassenberg, N. Gribble, B. Hill, S. Blaber, D. Milton, D. Brewer, and N. Ellis. 1998. Final report on effects of trawling in the far northern section of the Great Barrier Reef: 1991-1996. CSIRO Division of Marine Research, Cleveland, Australia.
27. Prasad, A. M., L. R. Iverson, and A. Liaw. 2006. Newer classification and regression tree techniques: Bagging and random forests for ecological prediction. Ecosystems 9:181-199.
28. R Development Core Team. 2011. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www. R-project.org
29. Silverman, B. W. 1986. Density estimation. Chapman and Hall, London, UK.
30. Strobl, C., A. Boulesteix, T. Kneib, T. Augustin, and A. Zeileis. 2008. Conditional variable importance for random forests. BMC Bioinformatics 9:307-317.
31. Ter Braak, C. 1994. Canonical community ordination. Part I: Basic theory and linear methods. Ecoscience 1:127-140.
32. Yu, K., W. Wheeler, Q. Li, A. Bergen, N. Caporaso, N. Chatterjee, and J. Chen. 2010. A partially linear tree-based regression model for multivariate outcomes. Biometrics 66:89-96.
33. Zeileis, A., T. Hothorn, and K. Hornik. 2008. Model-based recursive partitioning. Journal of Computational and Graphical Statistics 17:492-514.