Ecological Archives M077-018-A2

A. Guisan, N. E. Zimmermann, J. Elith, C. H. Graham, S. Phillips, and A. T. Peterson. 2007. What matters for predicting the occurrences of trees: techniques, data, or species' characteristics? Ecological Monographs 77:615–630.

Appendix B. A summary table of predictive techniques.

TABLE A2. Summary table of predictive techniques. Modeling techniques used for predicting tree-species distribution in Switzrland. Edited extract from Elith et al. (2006).

No

Method

Class of model, and explanation

Data1

Software

Key reference

Modeler

1

BRUTO

regression, a fast implementation of a GAM

pa

R and S+, mda package

Hastie et al. (2001), Leathwick et al. in press-b

J. Elith

2

BRT

boosted regression trees

pa

R, GBM package

Hastie et al. (2001), Leathwick et al. in press-a

J. Elith

3

BIOCLIM

envelope model

p

DIVA-GIS

Busby (1991), Hijmans et al. (2001)

C. Graham

4

DOMAIN

multivariate distance

p

DIVA-GIS

Carpenter et al. (1993), Hijmans et al. (2001)

C. Graham

5

GAM

regression: generalized additive model

pa

S+, GRASP add-on

Hastie and Tibshirani (1986), Yee and Mitchell (1991)

A. Guisan

6

OM-GARP

New version of GARP

p or pa

NewDesktopGarp (www. cria.org.br)

Stockwell and Peters (1999), Anderson et al. (2002)

A.T. Peterson

7

GLM

regression; generalized linear model

pa

S-Plus, GRASP add-on

Austin et al. (1983), McCullagh and Nelder (1989)

A. Guisan

8

MAXENT

maximum entropy, implemented with thresholds features

pe

MAXENT

Phillips et al. (2006)

S. Phillips

9

MARS

regression; multivariate adaptive regression splines

pa

R, mda package
+ codes for binomial responses

Friedman (1991), Moisen and Frescino (2002)

J. Elith

10

GDMSS

generalized dissimilarity modelling – single-species implementation

pa

Custom codes

Ferrier et al. (2002)

J. Elith

LITERATURE CITED

Anderson, R. P., M. Gómez-Laverde, and A. T. Peterson. 2002. Geographical distributions of spiny pocket mice in South America: insights from predictive models. Global Ecology and Biogeography 11:131–141.

Austin, M. P., R. B. Cunningham, and R. B. Good. 1983. Altitudinal distribution in relation to other environmental factors of several Eucalypt species in southern New South Wales. Australian Journal of Ecology 8:169–180.

Busby, J. R. 1991. BIOCLIM. A bioclimate analysis and prediction system. Pages 64–68 in C. R. Margules and M. P. Austin, editors. Nature conservation: cost effective biological surveys and data analysis. CSIRO, Melbourne, Australia.

Carpenter, G., A. N. Gillison, and J. Winter. 1993. DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals. Biodiversity and Conservation 2:667–680.

Ferrier, S., M. Drielsma, G. Manion, and G. Watson. 2002. Extended statistical approaches to modelling spatial pattern in biodiversity in northeast New South Wales. II. Community-level modelling. Biodiversity and Conservation 11:2309–2338.

Friedman, J. 1991. Multivariate adaptive regression splines. Annals of Statistics 19:1–141.

Hastie, T., and R. Tibshirani. 1986. Generalized additive models. Statistical Sciences 1:297–318.

Hastie, T., R. Tibshirani, and J. Friedman. 2001. The elements of statistical learning. Springer Series in Statistics.

Hijmans, R. J., L. Guarino, M. Cruz, and E. Rojas. 2001. Computer tools for spatial analysis of plant genetic resources data: 1. DIVA-GIS. Plant Genetic Resources Newsletter 127:15–19.

Leathwick, J. R., J. Elith, M. P. Francis, T. Hastie, and P. Taylor. In press-a. Variation in demersal fish species richness in the oceans surrounding New Zealand: an analysis using boosted regression trees. Marine Ecology Progress Series.

Leathwick, J. R., J. Elith, and T. Hastie. In press-b. Comparative performance of generalized additive models and multivariate adaptive regression splines for statistical modelling of species distribution. Ecological Modelling.

McCullagh, P., and J. A. Nelder. 1989. Generalized linear models. Second edition. Chapman and Hall, London, UK.

Moisen, G. G., and T. S. Frescino. 2002. Comparing five modelling techniques for predicting forest characteristics. Ecological Modelling 157:209–225.

Phillips, S. J., R. P. Anderson, and R. E. Schapire. 2006. Maximum entropy modeling of species geographic distributions. Ecological Modelling 190:231–259.

Stockwell, D., and D. Peters. 1999. The GARP modelling system: problems and solutions to automated spatial prediction. International Journal of Geographical Information Science 13:143–158.

Yee, T. W., and N. D. Mitchell. 1991. Generalized additive models in plant ecology. Journal of vegetation Science 2:587–602.



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